modeling the survival of salmonella in low-moisture foods

MODELING THE SURVIVAL OF SALMONELLA IN LOW-MOISTURE FOODS

by

SOFIA MARIA SANTILLANA FARAKOS

(Under the Direction of JOSEPH F. FRANK)

ABSTRACT

Salmonella can survive in low-moisture foods (aw<0.7) for long periods of time. The

interaction of cells with water is often related to aw. Little is known about the role of water

mobility. The aim of this study was to determine how the physical state of water in low-moisture

foods influenced the survival of Salmonella and to use this information to develop mathematical

models that predict the behavior of Salmonella in these foods. Whey protein powder of differing

water mobilities was produced and equilibrated to various aw levels (<0.6). Powders were

inoculated with a four-strain cocktail of Salmonella and stored at temperatures ranging from 21

°C to 80 °C. Survival data was fitted to primary inactivation models. Secondary linear models

relating the time required for first decimal reduction (δ) and shape factor values (β) to

temperature, aw and water mobility were fit using multiple linear regression. The models were

validated in dry non-fat dairy and grain products, as well as low-fat peanut and cocoa products.

The Weibull model provided the best description of survival kinetics for Salmonella. Water

activity significantly influenced the survival of Salmonella at all temperatures, survival

increasing with decreasing aw. Water mobility did not significantly influence survival

independent of aw. Secondary models were useful in predicting the survival of Salmonella in

various low-moisture foods, providing more accurate predictions for survival in non-fat food.

When tested against published literature data, the secondary models were fail-safe and provided

acceptable prediction performances. Serotype and product composition showed to be global

influencing factors on Salmonella survival. The presence of NaCl did not influence the kinetic

parameters for Salmonella in whey protein powder. Significant differences in prevalence were

found among Salmonella serotypes surviving storage treatments. The models can quantitatively

support a hazard analysis and critical control point system and provide an accurate quantification

of the risk of Salmonella in low-moisture foods. Future studies should incorporate compositional

factors such as fat content as well as the Salmonella serotype to improve the developed

predictive models.

INDEX WORDS: Foodborne microorganism, Predictive Models, Inactivation kinetics, Dry food,

Persistence, Challenges, Control, Water activity, Water mobility, Nuclear Magnetic Resonance

(NMR), Temperature, Salinity, Serotype Prevalence, Global Influencing Factors


by


B.S., Universidad Autónoma de Madrid, Spain, 2006

M.S., Wageningen Universiteit, the Netherlands, 2009

A Dissertation Submitted to the Graduate Faculty of The University of Georgia in Partial

Fulfillment of the Requirements for the Degree

DOCTOR OF PHILOSOPHY

ATHENS, GEORGIA

2013

© 2013

Sofia Maria Santillana Farakos

All Rights Reserved


by


Major Professor: Joseph F. Frank

Committee: Donald W. Schaffner

William L. Kerr

Mary A. Smith

Electronic Version Approved:

Maureen Grasso

Dean of the Graduate School

The University of Georgia

August 2013

iv

DEDICATION

To my parents

v

ACKNOWLEDGEMENTS

I would like to thank everyone who has been part of this compelling project. Dr. Frank, it

was a pleasure to work with you and an honor to have you as my mentor during these years.

Your infinite knowledge in food science and particularly food microbiology together with your

critical thinking has made me grow as a researcher and round up my education. Thank you for

believing in me. Dr. Schaffner, I admire your promptness in decision making and outstanding

expertise in quantitative food microbiology. Thank you for being a devoted part of our project.

Dr. Kerr and Dr. Smith, thank you for being part of my Ph.D. committee. I would also like to

thank Dr. Marcel Zwietering, who is the person that got me started in quantitative food

microbiology from my years in Wageningen. It is with the knowledge and tools that you gave me

that I have been able to succeed in Georgia. I would also like to give a special thanks to all the

people in Dr. Frank's lab including the student workers without whom this could not have been

done. Last but not least, I want to thank my friends and family for their support throughout these

years. Thank you for making my years in Athens one of the most memorable times in my life.

vi

TABLE OF CONTENTS

Page

ACKNOWLEDGEMENTS............................................................................................. v

LIST OF TABLES ......................................................................................................... ix

LIST OF FIGURES ........................................................................................................ x

CHAPTER

1 INTRODUCTION AND LITERATURE REVIEW ....................................... 1

2 CHALLENGES IN THE CONTROL OF FOOD BORNE PATHOGENS

IN LOW-MOISTURE FOODS AND SPICES ............................................... 7

ABSTRACT ............................................................................................ 8

INTRODUCTION ................................................................................... 9

PATHOGENS OF CONCERN IN LOW-MOISTURE FOODS

AND SPICES ........................................................................................ 10

ORIGIN OF PATHOGEN CONTAMINATION IN LOW-MOISTURE

FOODS .................................................................................................. 14

PRACTICES TO REDUCE PATHOGEN CONTAMINATION

IN LOW-MOISTURE FOODS .............................................................. 22

CONCLUSIONS ................................................................................... 25

vii

3 MODELING THE INFLUENCE OF TEMPERATURE, WATER ACTIVITY

AND WATER MOBILITY ON THE PERSISTENCE OF SALMONELLA IN

LOW-MOISTURE FOODS ......................................................................... 41

ABSTRACT .......................................................................................... 42

INTRODUCTION ................................................................................. 44

MATERIALS AND METHODS............................................................ 45

RESULTS AND DISCUSSION ............................................................. 54

CONCLUSIONS ................................................................................... 65

4 SURVIVAL OF SALMONELLA IN LOW-MOISTURE FOODS: A META-

ANALYSIS OF THE PUBLISHED LITERATURE DATA ........................ 83

ABSTRACT .......................................................................................... 84

INTRODUCTION ................................................................................. 85

MATERIALS AND METHODS............................................................ 86

RESULTS AND DISCUSSION ............................................................. 91

CONCLUSIONS ................................................................................... 99

5 HEAT RESISTANCE OF SALMONELLA IN LOW-MOISTURE WHEY

PROTEIN POWDER AS AFFECTED BY SALT CONTENT ................... 117

ABSTRACT ........................................................................................ 118

INTRODUCTION ............................................................................... 119

MATERIALS AND METHODS.......................................................... 120

RESULTS AND DISCUSSION ........................................................... 122

CONCLUSIONS ................................................................................. 123

viii

6 RELATIVE SURVIVAL OF FOUR SEROTYPES OF SALMONELLA

ENTERICA IN LOW-MOISTURE WHEY PROTEIN POWDER HELD

AT 36 °C AND 70 °C AT VARIOUS WATER ACTIVITY LEVELS ....... 130

ABSTRACT ........................................................................................ 131

INTRODUCTION ............................................................................... 132

MATERIALS AND METHODS.......................................................... 133

RESULTS AND DISCUSSION ........................................................... 135

7 CONCLUSIONS ....................................................................................... 141

ix

LIST OF TABLES

Page

Table 2.1: Outbreaks associated with contamination of low-moisture foods…….............37

Table 2.2: U.S. recalls associated with low-moisture foods not given a lethality step…...39

Table 2.3: U.S. recalls of processed low-moisture foods from 2007-2012 (FDA, 2012)...40

Table 3.1: Water mobility (T2*) values for whey protein powder………………………..76

Table 3.2: Statistical parameter fit results of the log-linear, Baranyi, Weibull…………...77

Table 3.3: δ and β values of the Weibull model fit for Salmonella inactivation………….80

Table 3.4: Correlation, discrepancy and bias between predicted and observed…………82

Table 4.1: Fitted and estimated log β-, log δ- and log D-values for Salmonella…………109

Table 4.2: Root mean square error, correlation coefficient, discrepancy and bias……...114

Table 4.3: Discrepancy and bias percentage between predicted and observed counts….115

Table 5.1: δ and β values of the Weibull model fit for Salmonella inactivation………...127

Table 6.1: Serotype prevalence (%) for Salmonella survival in low moisture protein….140

x

LIST OF FIGURES

Page

Figure 3.1: Survival of Salmonella at 36 °C during 168 days of storage......................... 70

Figure 3.2: Survival of Salmonella at 50 °C during 30 days of storage .......................... 71

Figure 3.3: Survival of Salmonella at 70 °C during 2880 min (48 hours) of storage…....72

Figure 3.4: Survival of Salmonella at 80 °C during 60 min of storage............................ 73

Figure 3.5: Salmonella inactivation experiments at 6 T (°C), 5 water activities (aw) ....... 74

Figure 3.6: Observed versus predicted Salmonella counts (log CFU/g) for validation .... 75

Figure 4.1: Plots of R2

adj values against temperature (a) and aw (b) for Salmonella ...... 106

Figure 4.2: Log δ-values of Salmonella in various food products are plotted ………….107

Figure 4.3: Log β-values for Salmonella serotypes in various food products..................108

Figure 5.1: Survival of Salmonella at 70 °C during 48 hours of storage..........................128

Figure 5.2: Survival of Salmonella at 80 °C during 60 minutes of storage ................... 129

1

CHAPTER 1

INTRODUCTION AND LITERATURE REVIEW

Salmonella spp. are facultative anaerobic pathogenic bacteria which belong to the

Enterobacteriaceae family (D'Aoust and Maurer, 2007, ICMSF, 1996). The genus consists of

two species: S. enterica (with 2,519 serovars) and S. bongori (22 serovars). Salmonella enterica

additionally has six subspecies, of which subspecies enterica (S. enterica enterica) includes the

highest number of serovars (1,504) (D'Aoust and Maurer, 2007). Salmonella is a worldwide

zoonotic agent which resides in the gastrointestinal tract of infected animals (ICMSF, 1996).

Foods of animal origin as well as those exposed to sewage or other environmental contaminants

are vehicles for transmitting the disease (ICMSF, 1996). Infections with Salmonella can lead to

several clinical conditions including gastroenteritis (diarrhea and fever) and septicemia by non-

typhoid microorganisms as well as enteric fever by typhoid and paratyphoid Salmonella

(D'Aoust and Maurer, 2007, ICMSF, 1996).

A risk ranking of the foodborne pathogens of greatest concern in the United States found

Salmonella enterica to be the leading cause of health burdens based on public health data (Batz

et al., 2012). In the study of Batz et al. (2012) it was estimated that Salmonella is annually

responsible for 1,027,561 illnesses and 378 deaths in the U.S. associated with poultry, eggs and

complex foods (Batz et al., 2012). Salmonella is very resilient to environmental stresses and has

a prominent ability to adapt and persist in hostile environments (D'Aoust and Maurer, 2007,

ICMSF, 1996). An example of this ability is its outstanding capacity to survive in low-moisture

foods for very long periods of time (Beuchat et al., 2013). Low-moisture foods are characterized

2

by having water activity levels below 0.7 (Blessington et al., 2012). Water activity (aw) is a basic

chemical property of all foods and is defined as equilibrium relative humidity of the product

divided by 100. Water activity is a major influence on microbial survival and growth in foods.

Although Salmonella is not able to grow at water activity levels below 0.94 (ICMSF, 1996), it

has been known to survive for weeks, months and even years in low-moisture foods (Beuchat et

al., 2013, Podolak et al., 2010, ICMSF, 1996). In a study by Beuchat and Scouten (2002),

Salmonella survived on inoculated alfalfa seeds for over a year at 5 °C and 21 °C at 0.2 and 0.4

aw. In this study, increasing resistance of the pathogen was observed at decreasing water activity

and temperature. Similarly, in a study by Burnett et al. (2000), Salmonella survived in peanut

butter spreads after 24 weeks at 5 °C and 21 °C at aw levels ranging from 0.2 to 0.3. In addition

to its extreme persistence at low storage temperatures (<35 °C), Salmonella has greater heat

resistance in low-moisture foods than in liquid products (Archer et al., 1998). In a study by Ma et

al. (2009) Salmonella was detected after 50 minutes of heat treatment at 90 °C in peanut butter

(aw =0.45). Additionally, Jung and Beuchat (1999) found that heating Salmonella in egg powders

of different moisture contents (aw<0.7) at 82 °C for 8 hours was not enough to decrease the

population by 5 log CFU. The widespread presence of Salmonella in nature together with its

ability to persist in low-moisture foods even when subject to high heat has led to the pathogen

being responsible for 94% of all U.S. low-moisture food recalls and 53% of low-moisture food

outbreaks worldwide (Santillana Farakos et al., 2013).

The extent of Salmonella survival in low-moisture foods differs among foods (Beuchat et

al., 2013). There is a lack of information as to what causes these differences, but it is believed

that the interaction of Salmonella cells with food components influences its survival (Podolak et

al., 2010). Water, as a component of foods, is considered a key factor (Podolak et al., 2010). The

3

interaction of cells with water is often related to aw, as it reflects the intensity with which water

associates with non-aqueous components at a macroscopic level (Fennema, 1996). Water

mobility is an additional measure of the translocation of water molecules in the food (Schmidt,

2007), possibly determining the ability with which water molecules interact with the bacterial

cell at the molecular level. While reduced aw protects the pathogen against inactivation, little is

known about the role of water mobility in influencing the survival of Salmonella in low-moisture

foods. Moreover, the usefulness of aw as a predictor of Salmonella survival is not known. The

goal of this project was to determine how the physical state of water in low-moisture foods

influences the survival of Salmonella and to use this information to develop mathematical

models that will predict the behavior of Salmonella in these foods. The specific objectives were:

1. To determine the survival kinetics of Salmonella in non-fat food powders exhibiting

different molecular water mobilities, water activities (<0.7) and temperatures (20 °C-80

°C). This work employed whey protein concentrate, as it can be easily manipulated to

obtain various degrees of water mobility.

2. To apply the developed kinetic models to additional low-moisture foods. Hypotheses on

the relationship between aw, water mobility and Salmonella inactivation were developed

based on the data from objective 1. These hypotheses were tested using additional low-

moisture foods including wheat flour, oat flour, non-fat dry milk, whey protein, low-fat

peanut flour and low-fat cocoa powder.

3. To validate the developed models with available literature data and determine global

influencing factors on survival of Salmonella in low-moisture foods.

This dissertation is divided into seven chapters. Chapter 1 provides background information

on Salmonella spp. as well as the research rationale on which the dissertation is based, including

4

specific objectives. In Chapter 2, an overview of the challenges faced in the control of foodborne

microorganisms in low-moisture foods is presented, with particular emphasis on available

practices to prevent pre- and post-process contamination. Chapter 3 presents a study on the

influence of temperature, aw and water mobility on the survival of Salmonella, including the

development of the first predictive models for survival of Salmonella in low-moisture foods at

any water activity (<0.6) and temperature (20 °C-80 °C). The results of the quantitative analysis

of literature data on survival of Salmonella in low-moisture foods to validate the developed

models and establish global influencing factors are discussed in Chapter 4. The results in Chapter

4 show that food composition and Salmonella serotype were global influencing factors on the

survival kinetics of the pathogen in low-moisture foods. In line with this, Chapter 5 evaluates the

influence of sodium chloride on the survival of Salmonella in low-moisture whey protein powder

at 70 °C and 80 °C at various water activity levels. Chapter 6 demonstrates differences in the

influence of temperature and water activity on the survival kinetics among four different

serotypes of Salmonella enterica. Finally, Chapter 7 outlines the overall conclusions of the

research carried out for this project.

5

References

Archer J., Jervis, E.T., Bird, J., Gaze, J.E. 1998. Heat resistance of Salmonella

weltevreden in low-moisture environments. Journal of Food Protection 61:969-973

Batz, M.B., Hoffmann, S., Morris, J.G.Jr. 2012. Ranking the Disease Burden of 14

Pathogens in Food Sources in the United States Using Attribution Data from Outbreak

Investigations and Expert Elicitation. Journal of Food Protection 75: 1278–1291

Beuchat, L.R., E. Komitopoulou, H. Beckers, R.P. Betts, F. Bourdichon, et al. 2013. Low

water activity foods: increased concern as vehicles of foodborne pathogens. Journal of Food

Protection 76:150-72

Beuchat, L., Scouten, A. 2002. Combined effects of water activity, temperature and

chemical treatments on the survival of Salmonella and Escherichia coli O157:H7 on alfalfa

seeds. Journal of Applied Microbiology 92: 382-395

Burnett, S.,E. Gehm, W. Weissinger, L. Beuchat. 2000. Survival of Salmonella in peanut

butter and peanut butter spread. Journal of Applied Microbiology 89:472-7

Blessington, T., Theofel, C. G., Harris, L. J. 2012. A dry-inoculation method for nut

kernels. Food Microbiology 33:292-297

D’Aoust, J-Y., Maurer, J. 2007. Salmonella species. In: Doyle, M.P., Beuchat, L. (eds)

Food microbiology: Fundamentals and frontiers 3rd edn. ASM Press, Washington. D.C., p 187-

236

Fennema, O. R. 1996. Food chemistry (3rd ed.). New York: M. Dekker.

International Commission on Microbiological Specifications for Foods (ICMSF). 1996.

Microorganisms in foods 5: Characteristics of Microbial Pathogens, Blackie Academy and

Professional, London

6

Jung, Y.S., and L.R. Beuchat. 1999. Survival of multidrug-resistant Salmonella

typhimurium DT104 in egg powders as affected by water activity and temperature. International

Journal of Food Microbiology 49:1-8

Ma, L., G. Zhang, P. Gerner-Smidt, V. Mantripragada, I. Ezeoke, and M.P. Doyle. 2009.

Thermal inactivation of Salmonella in peanut butter. Journal of Food Protection 72:1596-601

Podolak, R., Enache, E., Stone, W., Black, D. G., Elliot, P. H. 2010. Sources and risk

factors for contamination, survival, persistence, and heat resistance of Salmonella in low-

moisture foods. Journal of Food Protection 73:1919-1936

Santillana Farakos, S.M., J.F. Frank, and D.W. Schaffner. 2013. Modeling the influence

of temperature, water activity and water mobility on the persistence of Salmonella in low-

moisture foods. International Journal of Food Microbiology. doi:

10.1016/j.ijfoodmicro.2013.07.007

Schmidt, D., 2007. Water mobility in foods. In: Water Activity in Foods: Fundamentals

and Applications. Barbosa-Canovas, et al. (ed.). IFT Press, Blackwell Publishing. Oxford, UK.

pp 47-108

7

CHAPTER 2

CHALLENGES IN THE CONTROL OF FOOD-BORNE PATHOGENS IN LOW-MOISTURE

FOODS AND SPICES1

1 Santillana Farakos, S.M. and J.F. Frank. Submitted to: “Microbiological Safety of Low Water

Activity Foods and Spices”, 03/22/13.

8

ABSTRACT

Vegetative cells of foodborne pathogens as well as bacterial spores are able to survive in

dry processing environments and low-moisture foods for lengthy periods. Between 2007 and

2012, there were 7,315 cases of infection and 63 deaths due to contaminated low-moisture foods

worldwide. Salmonella is the pathogen of greatest concern having been responsible for 94% of

U.S. low-moisture food recalls and 53% of outbreaks worldwide from 2007-2012. In this

chapter, an overview of the challenges faced in the control of foodborne microorganisms in low-

moisture foods is presented, including practices for preventing pre- and post-process

contamination. Potential sources of bacterial contamination in the supply chain include

incoming raw materials, the external environment (pests, water and air), inadequate hygienic

facility and equipment design, inadequate sanitation practices, and lack of process control. Major

areas of control include the sourcing of raw commodities and ingredients, controlling cross-

contamination from harvest to post-process, controlling the entry of water into dry processing

areas, employing effective dry cleaning and sanitation processes, and implementing validated

lethal processes.

KEYWORDS: Foodborne microorganisms, Dry food, Persistence, Challenges, Control, Water

activity

9

INTRODUCTION

Low-moisture foods are those with water activity (aw) levels lower than 0.70 (Blessington

et al., 2012). Such foods do not support the growth of either vegetative or sporeforming bacteria.

However, in the United States, 5,141 low-moisture food products were recalled from 2007 to

2012 because of bacterial pathogen contamination (FDA, 2012). In 2009, three major recalls in

the U.S. that involved pistachio nuts (662 products), dry milk (286 products) and peanut butter

(3,918 products) indicated the existence of serious health and economic burdens due to pathogen

contamination of low-moisture foods. In 2012, peanut butter contaminated with Salmonella

Bredeney resulted in 42 cases of infection and a large number of recalled products (CDC 2012a,

FDA, 2012). From 2007-2012, a total of 41 outbreaks (world-wide reports) involving low-

moisture foods resulted in 7,315 infection cases leading to 536 hospitalizations and 63 deaths

(CDC, 2012a, EFSA, 2009, EFSA, 2010, EFSA, 2011, Harris et al., 2012, Rodriguez-Urrego et

al., 2008, SFI, 2012). The presence, survival, and heat resistance of bacterial pathogens in low-

moisture foods combine to provide a continuing challenge to the food industry (Podolak et al.,

2010). Low-moisture foods include products which may or may not be subjected to a pathogen

inactivation step. These foods may also contain ingredients that are added after the inactivation

step (Scott et al., 2009). Reduction of risk for human illness can be achieved by controlling

points of potential contamination in the field, during harvesting, processing and distribution, as

well as in retail markets, food-service facilities, or the home. The effective application of such

controls requires a coordinated effort between production, processing and distribution. This

chapter presents an overview of the challenges faced in the control of foodborne microorganisms

in low-moisture foods, with particular emphasis on available practices to prevent pre- and post-

process contamination. Mycotoxygenic molds and their toxins will not be discussed.

10

PATHOGENS OF CONCERN IN LOW-MOISTURE FOODS AND SPICES

Vegetative cells and endospores of many foodborne pathogens are able to survive in dry

processing environments and low-moisture foods for lengthy periods (Beuchat et al., 2013).

Disease outbreaks reported by various countries that were associated with low-moisture foods

from 2007 to 2012 are presented in Table 2.1. In Tables 2.2 and 2.3, low-moisture food recalls in

the U.S. from 2007 to 2012 are listed. Table 2.2 includes recalls of food products at the pre-

processing stage (pre- and post-harvest). Table 2.3 lists recalls of processed foods some of which

may not have undergone a lethal processing step. The bacterial pathogens which have been

associated with outbreaks and/or recalls involving low-moisture foods during 2007 to 2012 are

Bacillus cereus, Clostridium botulinum, Clostridium perfringens, enterohemorrhagic Escherichia

coli, Listeria monocytogenes, Salmonella enterica, and Staphylococcus aureus (Tables 2.1-2.3).

Cronobacter sakazakii has not been the cause of reported outbreaks since 2001 (Beuchat et al.,

2013). This pathogen has also not been involved in any reported low-moisture food product

recalls in the U.S. from 2007 to 2012 (Tables 2.2 and 2.3). However, Cronobacter may be

present and survive for long periods of time in dry foods. Thus, it is a pathogen of concern in

these foods.

B. cereus is widespread in nature (Granum, 2007). This pathogen has been involved in

four outbreaks from 2007 to 2012 that were associated with consumption of cooked rice served

in restaurants, banquets, prison or in the home (Table 2.1). It has also been the source of two

outbreaks in the EU involving cereals as well as herbs and spices (Table 2.1). The presence of B.

cereus has not been the cause of low-moisture product recalls from 2007-2012 in the U.S.

(Tables 2.2 and 2.3). B. cereus is frequently found in raw materials like vegetables, starch and

spices due to its presence in soil and growing plants, in food ingredients such as flours, and in

11

dry foods such as milk powders and cereals. However, B. cereus must grow in a food to high

numbers to cause illness. Spores of B. cereus can survive in dry foods and dry processing

environments for long periods of time. These spores can then germinate and grow in

reconstituted products that are not properly processed and/or stored (Beuchat et al., 2013).

Attention needs to be placed on the temperature and moisture conditions in which processed food

is stored to prevent spore germination and outgrowth.

Similarly, C. botulinum spores are commonly present in the soil (Johnson, 2007). The

intestinal tract of animals is an additional reservoir (Johnson, 2007). Botulism can result from the

ingestion of the botulinum neurotoxin preformed in foods. C. botulinum spores can also grow

and produce the toxin in the gastrointestinal (GI) tract. Outbreaks of botulinum intoxication in

the last five years included peanut butter (Canada, 2008) and dried tofu (Taiwan, 2010) as food

vehicles (Table 2.1). In 2003, the presence of C. botulinum spores in honey led to a severe case

of infant botulism in the U.S. (Barash et al., 2005). Isolates from the honey reflected those found

in local soil (Beuchat et al., 2013).

Like C. botulinum, C. perfringens is also present throughout the natural environment,

including soil, foods, dust, and the intestinal tract of animals (McClane, 2007). Spores of C.

perfringens survive well in dust and on surfaces and are resistant to routine cooking temperatures

(McClane, 2007). C. perfringens produces toxins and exhibits rapid growth in many foods

(McClane, 2007). C. perfringens was responsible for an outbreak involving cooked rice in the

U.S. (2008) and one involving herbs and spices in the EU (2007) (Table 2.1). Spores of C.

perfringens have also been found in powdered infant formula (Beuchat et al., 2013) where the

organism would be of concern if rehydrated formula was subject to temperature abuse.

12

Enteric pathogens such as enterohemorrhagic E. coli and Salmonella can contaminate raw

agricultural commodities through vehicles such as raw or improperly composted manure,

irrigation water containing untreated sewage, or contaminated wash water (Beuchat and Ryu,

1997). Contact with animals and unpasteurized products of animal origin are additional

contamination routes (Beuchat and Ryu, 1997). Contact surfaces including human hands

represent potential points of contamination (Beuchat and Ryu, 1997). C. sakazakii, also a

member of the Enterobacteriaceae family, has been isolated from dry milk. However, the natural

habitat of this pathogen remains unknown (Pagotto et al., 2007). Growth of enteric pathogens in

the food is not necessary for them to cause illness.

Enterohemorrhagic E. coli has been associated with several outbreaks involving low-

moisture foods (Table 2.1). A major outbreak took place in Germany in 2011 due to fenugreek

seeds contaminated with E. coli O104:H4 that were imported from Egypt. Also in 2011, raw

shelled walnuts and hazelnuts (which do not undergo a lethal processing step) were the vehicle

for two E. coli O157:H7 outbreaks in North America. Raw cookie dough, not intended to be

consumed raw, caused a major outbreak occurring in the U.S. in 2009. The specific ingredient in

the cookie dough that was contaminated has still not been identified (Beuchat et al., 2013).

Enterohemorrhagic E. coli is widely present in and able to colonize farm environments, but it

does not survive well in low-moisture environments (Beuchat et al., 2013). This is why its

involvement in outbreaks and recalls of low-moisture foods is more limited than that of

Salmonella.

Salmonella has caused the vast majority of outbreaks (Table 2.1) and U.S. recalls (Tables

2.2 and 2.3) with regards to low-moisture foods in the last five years (2007-2012). Salmonella is

wide spread in nature and survives in dry foods for weeks, months or even years (Chang et al.,

13

2013, Beuchat and Mann, 2010a, Komitopoulou and Penaloza, 2009, Beuchat and Scouten,

2002, Burnett et al., 2000, Archer et al., 1998). Food composition, water activity, and

temperature influence its survival in foods (He et al., 2011, Mattick et al., 2001, Corry, 1974,

Dega et al., 1972, Moats et al., 1971). The pathogen exhibits increasing resistance at decreasing

water activity during heat treatment (Beuchat and Scouten, 2002, Doyle and Mazzotta, 2000,

Archer et al., 1998). The presence of fat in the food offers additional protective effect against

inactivation (Ma et al., 2009, Shachar and Yaron, 2006, van Asselt and Zwietering, 2006, Juneja

and Eblen, 2001). In an extensive review conducted by Podolak et al. (2010) on the sources and

risk factors of Salmonella in low-moisture foods, its presence was linked to cross-contamination

through poor sanitation practices, substandard facility and equipment design, improper

maintenance, poor operational practices and manufacturing practices (GMPs) as well as

inadequate ingredient control and pest control. A review by Carrasco et al. (2012) corroborates

that cross-contamination plays a major role in the contamination of low-moisture foods with

Salmonella.

Similarly to Salmonella, C. sakazakii is able to survive in low-moisture foods for long

periods of time (Caubilla Barron and Forsythe, 2007, Gurtler and Beuchat, 2007, Lin and

Beuchat, 2007). Neither Salmonella nor C. sakazakii will survive typical pasteurization

treatments in foods having high water activity. Thus, as with Salmonella, contamination of

processed low-moisture foods with C. sakazakii usually occurs as a result of poor process control

or by post-process contamination (Breeuwer et al., 2003). Although not involved in as many

low-moisture outbreaks and recalls as Salmonella, Staphylococcus aureus is also well adapted to

survival in low-moisture environments. Humans and animals are the main reservoirs of this toxin

producer. Once it contaminates dry food, it is able to survive for months (Beuchat et al., 2013,

14

Scott, 1958). Contamination of food can occur by direct human contact, indirectly by skin

fragments or though respiratory tract droplet nuclei (Seok and Bohach, 2007). Sanitation,

hygienic and manufacturing practices play a crucial role in preventing food contamination.

L. monocytogenes has not been involved in low-moisture food associated outbreaks

reported from 2007-2012 but it has been part of several U.S. product recalls of these foods

(Table 2.3). L. monocytogenes can be present in soil, water and feces (Swaminathan et al., 2007).

Because of its widespread distribution, it can take advantage of many routes for food

contamination.

ORIGIN OF PATHOGEN CONTAMINATION IN LOW-MOISTURE FOODS

The origin of pathogen contamination in low-moisture foods depends on the history and

use of the food. A number of raw materials and ingredients of primary agricultural origin are

consumed without processing or included as an added ingredient to previously processed food

(i.e. nuts, herbs, spices). Other low-moisture raw agricultural products are used to manufacture

processed low-moisture foods (i.e. peanuts in peanut butter). Others are marketed to be further

processed in the kitchen (i.e. wheat flour, oats). A discussion of potential sources of

contamination in the supply chain during pre-harvest, post-harvest, processing and post-

processing follows.

Pre and post-harvest contamination (pre-processing)

The likelihood of bacterial pathogens being present in a raw food at pre-processing is

associated with the pathogens being present in the production environment. Many low-moisture

foods derive from raw materials of primary agricultural origin. The type and amount of

microorganisms on nuts, seeds, cereal grains, herbs and spices, is mainly determined by the soil

and plant microflora. Nuts come in contact with the soil during harvest (i.e. hazelnuts, almonds,

15

walnuts) or growth (i.e. peanuts). Seeds (i.e. cacao beans), herbs and spices (ICMSF, 2005) may

also be in contact with the soil during harvest and/or growth. A variety of bacteria can grow on

cereal crops (i.e. bacilli, enteric bacteria), and cereal grains are subject to microbiological

contamination while growing in the field (Gilbert et al., 2010). For example, a Salmonella

Agona outbreak in 2002 involving herbal tea in Germany was traced back to contaminated

aniseeds imported from Turkey. The source of the contamination was attributed to the use of

manure as a natural fertilizer (Koch et al., 2005). Again in 2011, in Germany, E. coli O104:H4

caused an outbreak involving 4321 cases of infection (Table 2.1) with the source of the

contamination traced back to imported seeds. The imported fenugreek seeds were processed by a

single German producer into sprouts (EFSA, 2011). The European Food Safety Authority

(EFSA) found no evidence of environmental contamination at the producer or signs of employee

cross-contamination. Thus, the contamination was attributed to the importer, and the source of

the contamination speculated to be the use of natural fertilizer or contaminated irrigation water

(EFSA, 2011). Although soils are partially removed from nuts, plants, cereals and seeds during

harvest, significant amounts may still remain and thus brought into storage and processing

facilities (Danyluk et al., 2007). Kokal and Thorpe (1969) observed E. coli contamination on

almonds taken from both the trees and from the ground where they were left to dry. In another

study by Marcus and Amling (1973), the proportion of pecan samples contaminated with E. coli

collected from cattle grazed orchards was six-fold higher than that of samples from non-grazed

orchards. Sources of soil contamination in the field include bacteria in the farm environment,

animal waste, contaminated manure/compost, biological solids from human waste-water and

contaminated irrigation water (Jacobsen and Bech, 2012, Critzer and Doyle, 2010, Beuchat and

Mann, 2010b, Isaacs et al., 2005). Water is, in general, a major source of microbial

16

contamination in food commodities that are eventually marketed as low-moisture foods. Possible

pre-harvest sources are run-off from nearby animal pastures and irrigation from a contaminated

source (FDA, 1998). The type of irrigation also plays a role. There is a lower likelihood of

transmitting pathogen from contaminated water through drip irrigation versus a higher likelihood

through overhead sprinkler systems (Berger et al., 2010, FDA, 1998). The use of water in post-

harvest processing is also a risk factor. The addition of water to dust or dried raw materials can

result in rapid outgrowth of the bacteria present (Danyluk et al., 2007). Control over water in

storage and processing environments is one of the most critical factors in reducing the risk of

pathogen contamination in dry foods. Water can also present suitable conditions for production

of heat stable microbial toxins by, for example, B. cereus (Beuchat et al., 2013). Dust also

increases the risk of cross-contamination by accumulating microorganisms that can be spread by

airflow (Podolak et al., 2010, Doan and Davidson, 2000). For example, when hulls and shells of

almonds and macadamia nuts are mechanically removed from the kernel under dry conditions,

large volumes of fine particulate matter are generated. This dust, composed of soil, hulls and

shells, is difficult to eliminate from the processing environment and can contribute significantly

to cross-contamination of the kernels (Danyluk et al., 2007). The first U.S. reported outbreak of

salmonellosis traced to chocolate occurred in 1973, where 119 cases of infection with S.

Eastbourne were linked to contaminated raw cocoa beans (Craven et al., 1975). Salmonella was

also detected in accumulated dust at the bean-processing areas (Craven et al., 1975).

Pathogen contaminated dust is often associated with product contamination and the

unexpected addition of water to this dust creates exceptionally high risk conditions due to the

logarithmic increase in pathogen numbers that may occur.

17

In addition to natural microflora, soil, water and dust, birds, rodents and insects all

influence the risk of pathogen contamination of nuts, seeds, herbs, cereals and spices (Danyluk et

al., 2007, Doan and Davidson, 2000). Worker hygiene and sanitation can also be important

(FDA, 1998, Podolak et al., 2010).

Similarly, sources of microbial contamination on raw potatoes and derived low-moisture

products include their natural microflora, soil, dust, water, human handling and animals (Doan

and Davidson, 2000). Contamination of raw milk and dairy products also results from direct

contact with contaminated sources in the farm environment (soil, water, equipment, etc.) and

excretion from the udder of an infected animal (Oliver et al., 2005). However, potatoes are rarely

consumed raw and raw milk is required to undergo a pasteurization process (ICMSF, 2005).

Thus, the presence of pathogenic microorganisms in low-moisture dairy as well as potato

products is usually associated with the lack of process control or post-process contamination.

Contamination during processing and post-process

Contamination of low-moisture foods with pathogenic bacteria during processing and

post-process is mainly due to cross-contamination (Carrasco et al., 2012, Podolak et al., 2010).

Foods processed using pathogen-lethal treatments were responsible for 54% of U.S. recalls and

63% of outbreaks (reported world-wide) in low-moisture foods in the period from 2007 to 2012

(Tables 2.1 and 2.3). Minimally processed foods (which do not undergo a lethal step) have been

responsible for a lower number of U.S. recalls (35%) and outbreaks globally (12%). Of the

pathogens involved, Salmonella has been responsible for 94% of all product recalls and 53% of

total outbreaks in low-moisture foods from 2007-2012. Therefore, Salmonella is the pathogen of

greatest concern in processed and minimally processed low-moisture food. Various sources and

18

practices contribute to cross-contamination in the processing facility. Raw materials (Scott et al.,

2009) are a major source of pathogens. Lack of effective prerequisite programs at the supplier

can result in pathogens being introduced into the facility through raw materials. In some cases

the presence of pathogens in raw materials cannot be prevented by currently acceptable growing,

harvesting, and pre-processing practices. In addition to directly contaminating the end product,

pathogen-containing raw materials can contaminate equipment, employees and other product

lines. Raw materials which are not further processed can also contaminate final products if they

are used as an added ingredient. For example, in 2007, an outbreak in the U.S involving S.

Wandsworth, S. Typhimurium, S. Mbandaka and S. Haifa resulted in 75 cases of infection

associated with consumption of a contaminated rice snack (Table 2.1). The seasoning mix, used

as an added ingredient after a lethal process was applied to the snack, was the source of the

contamination. The seasonings were obtained from domestic as well as international suppliers

(Sotir et al., 2009). No positive environmental samples were found in the snack processing

facility, and the source of the contamination remained unknown (Sotir et al., 2009). The 2010

outbreak in the U.S involving S. Montevideo resulted in 272 cases of infection due to

consumption of contaminated salami (Table 2.1). The source of the contamination was red and

black pepper added after the lethal process was applied. The pepper was produced in Asia,

distributed by a U.S. spice company, and finally delivered to the salami processing facility

(CDC, 2012b). S. Montevideo was isolated from samples of the crushed red and black pepper.

These results underline the risk of cross-contamination from raw materials, highlighting the need

for strict supplier control when raw materials will be used as an added ingredient to processed

food.

19

A second major source of pathogen cross-contamination in dry foods is the environment.

This includes personnel, equipment, pests, dust, water and air (Beuchat et al., 2013). Employees

carrying pathogens via shoes or clothing worn outside the plant can lead to cross-contamination

if not adhering to good manufacturing practices (GMPs) (Podolak et al., 2010, Scott et al., 2009).

Pests may also carry pathogens through openings in buildings that require repair (including the

roofs, floors, doors). For instance, birds’ nests can shed pathogens into the facility (Scott et al.,

2009). Contaminated equipment entering the facility can also introduce pathogens (Scott et al.,

2009). Ventilation units connected to the outside air without proper filtration as well as

unsanitary vents are an additional source of contamination from the external environment (Scott

et al., 2009). The 2008-09 foodborne illness outbreak linked to Salmonella in processed peanuts

(Tables 2.1 and 2.3) resulted in one of the largest recalls in U.S. history (Wittenberger and

Dohlman, 2010). A joint FDA and Georgia Department of Agriculture investigation at the

production facility found multiple possibilities for Salmonella contamination including evidence

of rain and other water leakage into storage areas used for roasted peanuts, as well as practices

that allowed for cross-contamination between raw and roasted peanuts (Cavallaro et al., 2011).

The FDA investigation of the 2012 peanut butter outbreak (S. Bredeney) found raw in-shell

peanuts outside the plant in uncovered trailers and exposed to rain (FDA, 2013a). Birds were

also observed landing on these trailers (FDA, 2013a). Inside the warehouse, facility doors were

open to the outside, allowing pests to enter the premises (FDA, 2013a). The combination of

pathogens in the processing environment and lack of water control has often led to product

contamination because of the high populations of pathogens that will result.

A third major source of cross-contamination is the lack of proper facility and equipment

design including deficiencies in GMPs (Beuchat et al., 2013, Carrasco et al., 2012, Podolak et

20

al., 2010). An inspection of the production facility after the 2012 outbreak of S. Infantis

involving dry dog food in the U.S., found the processing facility did not have hygienic

equipment and facility design (CDC, 2012c). There were no hand washing and sanitizing

facilities where needed. Equipment was found to have gouges and cuts, with attempted repairs

using duct tape and cardboard (CDC, 2012c). Equipment was observed to have accumulated

food residue and dust in areas with poor access to cleaning (CDC, 2012c). Similarly, the

investigation of the 2012 peanut butter outbreak found there were no hand-washing sinks in the

peanut production or packaging areas (FDA, 2013a). There were also no documented cleaning

records (FDA, 2013a). There was a leaking sink in a washroom which resulted in water

accumulation on the floor (FDA, 2013a). During the inspection, employees had bare-handed

contact with ready-to-package peanuts and were found to improperly handle equipment and tools

(FDA, 2013a). Separate hygiene zones are generally established by compartmentalization of the

facility. Zones should be established based on an evaluation of risk (Beuchat et al., 2013, Chen et

al., 2009a). The lack of proper control measures in each zone including transition areas could

result in cross-contamination from the low hygiene to the high hygiene zone (Chen et al., 2009a).

This includes a lack of adequate physical separation, control of traffic, and air flow, as well as

cleaning and the use of water (Beuchat et al., 2013, Chen et al., 2009a). Indeed, the use of water

in processing of low-moisture foods is one of the major risk factors for pathogen contamination

(Beuchat et al., 2013, Chen et al., 2009a). Different areas of a processing facility have different

water requirements. Thus, much of the microbiological control in a dry processing facility is

centered on keeping areas dry to avoid resuscitation and growth of dormant microorganisms

(Beuchat et al., 2013, Chen et al., 2009a). One of the most difficult challenges is to design and

operate a processing facility that can be properly cleaned and sanitized while maintaining

21

separation of dry and wet areas of the facility. Lack of proper hygiene practices and controls can

ultimately lead to pathogens finding a growth niche in the facility (Chen et al., 2009a). Bacteria

attached to surfaces in the presence of water are less sensitive to disinfection, especially when

forming a biofilm (Aviles et al., 2013, Møretrø et al., 2012). Thus, an environment that may

appear marginally dustier after dry cleaning may be much lower risk than a wet-cleaned

environment without visual dust. This is because wet cleaning introduces moisture into cracks

and areas that are difficult to clean and may not fully dry before production start-up (Chen et al.,

2009a). This can ultimately lead to concentration of microorganisms in hidden growth niches.

Finally, in low-moisture foods subjected to a lethal process, a major source of pathogen

contamination is lack of adequate process control (Podolak et al., 2012, Chen et al., 2009b). This

may be the result of using invalidated or inadequately controlled processing operations. Generic

processing standards should not be used for low-moisture foods as they may result in inadequate

pathogen inactivation (Podolak et al., 2010). For example, in the Salmonella outbreak

investigation of Georgia processed peanuts in 2008-09, there was uncertainty as to whether the

peanut roaster routinely reached a temperature sufficient to kill Salmonella (Cavallaro et al.,

2011).

Current challenges in the control of foodborne pathogens in low-moisture foods

The control of foodborne pathogens in low-moisture foods is a continued challenge. Raw

materials may be produced under conditions in which the entry of pathogens into the food are not

or cannot be controlled. Dry foods may be contaminated with pathogens during harvest, storage

and through pre-processing environments. Control of pests, dust, and water is essential for

pathogen control in these foods, yet facility and equipment design may be inadequate to control

these factors and conventional cleaning/sanitation practices may promote pathogen growth and

22

survival by not controlling water. As many low-moisture food products receive no further

cooking and have a long shelf life, the production process should meet high standards of

hygiene. Process parameters for inactivation of pathogens in dry foods are often not well-defined

or validated. As such, many products have no tradition of being subject to validated pathogen-

killing processes. The fact that pathogens survive during storage of these foods for long periods

of time and that these foods often have a long shelf life, substantially increases the public health

risk.

PRACTICES TO REDUCE PATHOGEN CONTAMINATION IN LOW-MOISTURE

FOODS

Because potential sources of pathogen contamination in low-moisture foods and

ingredients are numerous and varied, multiple practices specific for the product are usually

necessary for reducing the risk of contamination. The Grocery Manufacturers Association

(GMA) has developed guidelines to control the presence of Salmonella in processing facilities

and assure the microbial safety of low-moisture foods (GMA, 2009). An industry handbook for

safe processing of nuts was also developed (GMA, 2010a) as well as equipment (GMA, 2010b)

and facility (GMA, 2010c) design checklists to self-evaluate compliance with GMA sanitary

design principles. Additionally, in light of the burden of foodborne illnesses in the USA, the

FDA has issued the Food Safety Modernization Act (FSMA) rule which was signed into law in

January 2011. This law shifts the focus of regulators from response to prevention, using a risk-

based approach (FDA, 2013b). Guidance for industry and rules related to the FDA FSMA

include produce food safety standards, preventive controls for human food, foreign supplier

verification program, preventive controls for animal food and accredited third party certification

(FDA, 2013b). Guidance documents advise food processors to assess the risk that ingredients

23

may contain pathogens. For this, a series of questions such as the nature of the product, the type

of treatment placed on the product, the use of the product, whether the process was validated,

and the cooking instructions for consumers must be addressed (Beuchat et al., 2013). Good

agricultural, manufacturing and hygienic practices need to be employed at every step of the

chain. In the processing facility, segregated hygiene areas must be established based on the need

for moisture control and exposure of product to the environment, and environmental monitoring

conducted. Hygienic principles of equipment design and installation need to consider the need

for water control and dry and wet cleaning. An overview of available practices during pre-

harvest, post-harvest, processing and post-processing follows.

Practices to reduce contamination at pre- and post-harvest (pre-processing)

Pre-harvest and harvest control of pathogen contamination is critical for low-moisture

foods that do not undergo a lethal processing treatment. To avoid the presence of pathogens in

the soil, growers should consider measures to ensure that animal waste from adjacent fields or

waste storage facilities does not contaminate the production sites (FDA, 1998, Simonne and

Treadwell, 2008, Gilbert et al., 2010). This includes barriers to secure manure storage and

treatment areas and good agricultural practices to minimize leachate (FDA, 1998, Simonne and

Treadwell, 2008). Farm, wildlife and domestic animals should be excluded from the fields (FDA,

1998, Simonne and Treadwell, 2008). Additionally, the sources, distribution and quality of water

should be identified and the risk of pathogens from such sources assessed (FDA, 1998). Wells

should be maintained in proper working conditions, water should be tested for agricultural

quality periodically (cleanliness) and good manufacturing practices should be followed (FDA,

1998, Gilbert et al., 2010). Good agricultural practices with regards to fertilization and irrigation

should be in place (Simonne and Treadwell, 2008, Gilbert et al., 2010). Best practices for each

24

product should be individually assessed and training programs in worker health and hygiene

established (FDA, 1998, Simonne and Treadwell, 2008). Toilet facilities should be accessible,

properly located and clean (with hand washing stations and proper sewage disposal) (FDA, 1998,

Simonne and Treadwell, 2008). Storage facilities should be cleaned prior to use (FDA, 1998,

Gilbert et al., 2010). Harvesting and packaging equipment should be used appropriately and kept

clean (FDA, 1998, Gilbert et al., 2010). As much dirt and mud should be removed from the

product before it leaves the field (FDA, 1998). Ensuring the raw agricultural product is quickly

and thoroughly dried is crucial (Beuchat et al., 2011).

Reducing contamination during processing and post-process (including minimally

processed low-moisture food)

The first step in reducing contamination at processing is to control the quality of

incoming raw materials and ingredients. Knowledge about ingredient suppliers and verifying the

effectiveness of their control programs is important. For this, a supplier approval program should

be in place (Scott et al., 2009). The supplier control program is of particular importance for raw

or processed ingredients which are added to the final product after the latter has gone through an

inactivation step (i.e. spices, nuts, etc.). Following a sampling plan such as that established by

the ICMSF case 15 (n=60, c=0, m=0, n=25) is useful, realizing sampling alone does not result in

the absence of pathogens (Scott et al., 2009).

Ensuring there is no post-process re-contamination of the product is the next step to

reduce contamination (Beuchat et al., 2011). For this, compartmentation of the facility into

processing lines, wet and dry zones as well as hygiene zones needs to be established by physical

means (Beuchat et al., 2013). Wet processing lines should be constructed as UHT lines, cleaning

being conducted in a separate room from the main line (Beuchat et al., 2011). Within the dry

25

zones, different hygiene zones must be established as well as control of traffic within different

hygiene zones (Beuchat et al., 2011). Dry zones should be kept dry and cleaned using a dry

process (Beuchat et al., 2011, Gilbert et al., 2010). Monitoring programs targeting the external

environment, employee sanitation, operational and manufacturing practices, presence of pests,

hygiene design of building and equipment must also be established (Chen et al., 2009a, Gilbert et

al., 2010).

Another major target for pathogen control efforts is process control and validation

(Beuchat et al., 2011, Chen et al., 2009b). Food processors should individually determine the

process parameters that result in an appropriate level of protection (ALOP) for each product.

Validation of the chosen process parameters is crucial (Beuchat et al., 2013, Chen et al., 2009b).

Finally, verification that the above control measures have been implemented, that

standard operating procedures as well as hygiene practices are in place (environmental

monitoring) and that overall the final product adheres to a pre-defined safety criteria assures the

effectiveness of the food safety management system in place and drives continuous improvement

(Chen et al., 2009b).

CONCLUSIONS

Major challenges for pathogen control in low-moisture foods include sourcing of raw

commodities and ingredients, controlling cross-contamination from harvest to post-process and

implementing validated lethal processes. The ability of pathogens to survive for long periods of

time in dry foods, ingredients and environments greatly increases the difficulty of meeting these

challenges. Food processors should assess the risk for pathogen contamination of the product and

ingredients. Effective control of incoming raw materials and ingredients should be in place as

well as monitoring programs targeting the external environment, employee sanitation,

26

operational and manufacturing practices, presence of pests, and hygiene design of building and

equipment. Process control followed by validation and verification of control measures will

increase effectiveness of food safety management systems and reduce risks to public health.

27

References

Archer, J., Jervis, E.T., Bird, J., Gaze, J.E. 1998. Heat resistance of Salmonella


Aviles, B., Klotz, C., Eifert, J., Williams, R., Ponder, M. 2013. Biofilms promote survival

and virulence of Salmonella enterica sv. Tennessee during prolonged dry storage and after

passage through an in vitro digestion system. International Journal Food Microbiology 162:252-

259

Barash, J.R., Tang, T.W., Arnon, S.S. 2005. First case of infant botulism caused by

Clostridium baratii type F in California. Journal of Clinical Microbiology 43:4280-2

Berger, C.N., Sodha, S.V., Shaw, R.K., Griffin, P.M., Pink, D., Hand, P., et al. 2010.

Fresh fruit and vegetables as vehicles for the transmission of human pathogens. Environmental

Microbiology 12:2385-97

Beuchat, L.R., Mann, D.A. 2010a. Factors affecting infiltration and survival of

Salmonella on in-shell pecans and pecan nutmeats. Journal of Food Protection 73:1257-68

Beuchat, L.R., Mann, D.A. 2010b. Survival and growth of Salmonella in high-moisture

pecan nutmeats, in-shell pecans, inedible nut components, and orchard soil. Journal of Food


Beuchat, L.R., Ryu, J.H. 1997. Produce handling and processing practices. Emerging

Infectious Diseases 3: 459-465

Beuchat, L.R., Scouten, A. 2002. Combined effects of water activity, temperature and


seeds. Journal of Applied Microbiology 92:382-395

28

Beuchat, L.R., Komitopoulou, E., Beckers, H., Betts, R.P., Bourdichon, F., Fanning, S.,

et al. 2013. Low-water activity foods: increased concern as vehicles of foodborne pathogens.

Journal of Food Protection 76: 150-172

Beuchat, L., Komitopoulou, E., Betts, R., Beckers, H., Bourdichon, F., Fanning, S., et al.

2011. Persistence and survival of pathogens in dry foods and dry food processing environments.

International Life Sciences Institute—Europe Report Series 2011:1–48.

http://www.ilsi.org/Europe/Documents/Persistence%20and%20survival%20report.pdf Accessed

August 2012

Blessington, T., Theofel, C.G., Harris, L.J. 2013. A dry-inoculation method for nut


Breeuwer, P., Lardeau, A., Peterz, M., Joosten. H. 2003. Desiccation and heat tolerance

of Enterobacter sakazakii. Journal of Applied Microbiology 95:967-973

Burnett, S., Gehm, E., Weissinger, W., Beuchat, L. 2000. Survival of Salmonella in

peanut butter and peanut butter spread. Journal of Applied Microbiology 89:472-477

Carrasco, E., Morales-Rueda, A., Garcia-Gimeno R.M. 2012. Cross-contamination and

recontamination by Salmonella in foods: A review. Food Research International 45:545-56

Caubilla Barron, J., Forsythe, S.J. 2007. Dry stress and survival time of Enterobacter

sakazakii and other Enterobacteriaceae in dehydrated powdered infant formula. Journal of Food


Cavallaro, E., Date, K., Medus, C., Meyer, S., Miller, B., Kim, C., Nowicki, S., et al.

2011. Salmonella Typhimurium infections associated with peanut products. New England

Journal of Medicine 365:601-610

29

CDC (Centers for Disease Control and Prevention). 2012a. Foodborne outbreak online

database. Accessed August 2012. http://wwwn.cdc.gov/foodborneoutbreaks/

CDC (Centers for Disease Control and Prevention). 2012b. Salmonella Montevideo

infections associated with salami products made with contaminated imported black and red

pepper --- United States, July 2009--April 2010 Morbidity and Mortality Weekly Report

(MMWR) http://www.cdc.gov/mmwr/preview/mmwrhtml/mm5950a3.htm?s_cid=mm5950a3_w

Accessed January 2013

CDC (Centers for Disease Control and Prevention). 2012c. Multistate outbreak of human

Salmonella Infantis infections linked to dry dog food (Final Update)

http://www.cdc.gov/salmonella/dog-food-05-12/index.html Accessed January 2013

Chang, A.S., Sreedharan, A., Schneider K.R. 2013. Peanut and peanut products: A food

safety perspective. Food Control 32:296-303

Chen, Y.H., Scott, V.N., Freier, T.A., Kuehm, J., Moorman, M., Meyer, J., et al. 2009a.

Control of Salmonella in low-moisture foods II: Hygiene practices to minimize Salmonella

contamination and growth. Food Protection Trends 29: 435-445

Chen, Y.H., Scott, V.N., Freier, T.A., Kuehm, J., Moorman, M., Meyer, J., et al. 2009b.

Control of Salmonella in low-moisture foods III: Process validation and environmental

monitoring - part three of a three-part series. Food Protection Trends 29: 493-508

Corry, J.E.L. 1974. The effect of sugars and polyols on the heat resistance of

Salmonellae. Journal of Applied Microbiology 37:31-43

Craven, P., Baine, W., Mackel, D., Barker, W., Gangarosa, E., Goldfield, M., et al. 1975.

International outbreak of Salmonella eastbourne infection traced to contaminated chocolate. The

Lancet 305:788-92

30

Critzer, F.J., Doyle M.P. 2010. Microbial ecology of foodborne pathogens associated

with produce. Current Opinion in Biotechnology 21:125-130

Danyluk, M.D., Harris, L.J., Sperber, W.H. 2007. Nuts and cereals. In: Doyle, M.P.,

Beuchat, L. (eds) Food microbiology: Fundamentals and frontiers 3rd edn. ASM Press,

Washington. D. C., p 171-183

Dega, C.A., Goepfert, J.M., Amundson, C.H. 1972. Heat Resistance of Salmonellae in

concentrated milk. Applied Microbiology 23:415-20

Doan, C.H., Davidson, P.M. 2000. Microbiology of potatoes and potato products: A

review. Journal of Food Protection 63:668-83

Doyle, M.E., Mazzotta, A.S. 2000. Review of studies on the thermal resistance of

Salmonellae. Journal of Food Protection 63:779-795

EFSA (European Food Safety Authority). 2011. Shiga toxin-producing E. coli (STEC)

O104:H4 2011 outbreaks in Europe: Taking Stock. EFSA Journal 9:2390

http://www.efsa.europa.eu/en/efsajournal/doc/2390.pdf Accessed August 2012

EFSA (European Food Safety Authority). 2010. The Community summary report on

trends and sources of zoonoses, zoonotic agents and food-borne outbreaks in the European Union

in 2008. EFSA Journal 2010 8:1496 http://www.efsa.europa.eu/en/efsajournal/pub/1496.htm

Accessed August 2012


food-borne outbreaks in the European Union in 2007

http://www.efsa.europa.eu/en/efsajournal/pub/271r.htm Accessed August 2012

31

Food and Drug Administration (FDA). 1998. Guide to minimize microbial food safety

hazards for fresh fruits and vegetables

http://www.fda.gov/downloads/Food/GuidanceComplianceRegulatoryInformation/GuidanceDoc

uments/ProduceandPlanProducts/UCM169112.pdf. Accessed August 2012

Food and Drug Administration (FDA). 2012. Archive for recalls, market withdrawals and

safety alerts. http://www.fda.gov/Safety/Recalls/ArchiveRecalls/default.htm Accessed December

2012

Food and Drug Administration (FDA). 2013a. FDA Investigation summary: Multistate

outbreak of Salmonella Bredeney infections linked to peanut butter made by Sunland Inc.

http://www.fda.gov/food/foodsafety/corenetwork/ucm320413.htm#problem Accessed February

2013

Food and Drug Administration (FDA). 2013b. The new FDA Food Safety Modernization

Act (FSMA) http://www.fda.gov/Food/FoodSafety/FSMA/default.htm Accessed February 2013

Gilbert, S., Lake, R., Cressey, P., King, N. 2010. Risk profile: Salmonella (non typhoidal)

in cereal grains. Prepared for New Zealand Food Safety Authority under project MRP/09/01

http://www.foodsafety.govt.nz/elibrary/industry/salmonella-in-cereals.pdf Accesed December

2012

Granum, E. 2007. Bacillus cereus. In: Doyle, M.P., Beuchat, L. (eds) Food microbiology:

Fundamentals and frontiers 3rd edn. ASM Press, Washington. D. C., p 445-455

Grocery Manufacturers Association (GMA). 2009. Control of Salmonella in low-

moisture foods http://www.gmaonline.org/downloads/technical-guidance-and-

tools/SalmonellaControlGuidance.pdf Accessed August 2012

32

Grocery Manufacturers Association (GMA). 2010a. Industry handbook for safe

processing of nuts http://www.gmaonline.org/downloads/technical-guidance-and-

tools/Industry_Handbook_for_Safe_Processing_of_Nuts_1st_Edition_22Feb10.pdf. Accessed

August 2012

Grocery Manufacturers Association (GMA). 2010b. Equipment design checklist for low-

moisture foods http://www.gmaonline.org/resources/research-tools/technical-guidance-and-

tools/Accessed August 2012

Grocery Manufacturers Association (GMA). 2010c. Facility design checklist

http://www.gmaonline.org/resources/research-tools/technical-guidance-and-tools/Accessed

August 2012

Gurtler, J.B., Beuchat, L.R. 2007. Survival of Enterobacter sakazakii in powdered infant

formula as affected by composition, water activity, and temperature. Journal of Food Protection

70:1579-86.

Harris, L.J., Beuchat, L.R., Danyluk, M.D., Palumbo, M. 2012. USDA NIFSI, 2009-

01951 Accessed December 2012 http://ucfoodsafety.ucdavis.edu/files/132896.pdf

He, Y.S., Guo, D.J., Yang, J.Y., Tortorello, M.L., Zhang, W. 2011. Survival and heat

resistance of Salmonella enterica and Escherichia coli O157:H7 in peanut butter. Applied and

Environmental Microbiology 77:8434-8


Microorganisms in foods 6: Microbial ecology of food commodities, Kluwer, NY

Isaacs, S., Aramini, J., Ciebin, B., Farrar, J.A., Ahmed, R., Middleton, D., et al. 2005. An

International outbreak of Salmonellosis associated with raw almonds contaminated with a rare

phage type of Salmonella Enteritidis. Journal of Food Protection 68:191-8

33

Jacobsen, C.S., Bech, T.B. 2012. Soil survival of Salmonella and transfer to fresh water

and fresh produce. Food Research International 45: 557-566

Johnson, E. 2007. Clostridium botulinum. In: Doyle, M.P., Beuchat, L. (eds) Food

microbiology: Fundamentals and frontiers 3rd edn. ASM Press, Washington. D. C., p 401-422

Juneja, V.K., Eblen, B.S. 2000. Heat inactivation of Salmonella typhimurium DT104 in

beef as affected by fat content. Letters in Applied Microbiology 30:461-467

Koch, J., Schrauder, A., Alpers, K., Werber, D., Frank, C., Prager, R., et al. 2005.

Salmonella Agona outbreak from contaminated aniseed, Germany. Emerging Infectious Diseases

11:1124-7

Kokal, D., Thorpe, D.W. 1969. Occurrence of Escherichia coli in almonds of nonpareil

variety. Food Technology 23:93–98

Komitopoulou, E., Peñaloza, W. 2009. Fate of Salmonella in dry confectionery raw

materials. Journal of Applied Microbiology 106:1892-900

Lin, L-C., Beuchat, L.R. 2007. Survival of Enterobacter sakazakii in infant cereal as

affected by composition, water activity, and temperature. Food Microbiology 24:767-77

Ma, L., Zhang, G., Gerner-Smidt, P., Mantripragada, V., Ezeoke, I., Doyle, M.P. 2009.


McClane, B. 2007. Clostridium perfringens. In: Doyle, M.P., Beuchat, L. (eds) Food

microbiology: Fundamentals and frontiers 3rd edn. ASM Press, Washington. D. C., p 423-444

Marcus, K.A., Amling, H.J. 1973. Escherichia coli field contamination of pecan nuts.

Applied Microbiology 26:279–281

34

Mattick, K.L., Jorgensen, F., Wang, P., Pound, J., Vandeven, M.H., Ward, L.R., et al.

2001. Effect of challenge temperature and solute type on heat tolerance of Salmonella serovars at

low water activity. Applied and Environmental Microbiology 67:4128-36

Moats, W.A., Dabbah, R., Edwards, V.M. 1971. Survival of Salmonella anatum heated in

various media. Applied Microbiology 21:476-81

Møretrø, T., Heir, E., Nesse, L.L., Vestby, L,K., Langsrud, S. 2012. Control of

Salmonella in food related environments by chemical disinfection. Food Research International

45:532-44

Oliver, S.P., Jayarao, B.M., Almeida, R.A. 2005. Foodborne pathogens in milk and the

dairy farm environment: food safety and public health implications. Foodborne Pathogen

Diseases 2:115-29

Pagotto, F.J., Lenati, R.F., Farber, J.M. 2007. Enterobacter sakazakii. In: Doyle, M.P.,

Beuchat, L. (eds) Food microbiology: Fundamentals and frontiers 3rd edn. ASM Press,

Washington. D. C., p 271-291

Podolak, R., Enache, E., Stone, W., Black, D.G., Elliott, P.H. 2010. Sources and risk



Rodriguez-Urrego, J., Herrera-León, S., Echeita-Sarriondia, A., Soler, P., Simon, F.,

Mateo, S. 2008. Nationwide outbreak of Salmonella serotype Kedougou associated with infant

formula, Spain, 2008. Euro Surveillance 15:19582

Safe Food International (SFI). 2012. Food/water borne illness outbreaks

http://regionalnews.safefoodinternational.org/page/Food%2FWater+Borne+Illness+Outbreaks


35

Shachar, D., Yaron, S. 2006. Heat tolerance of Salmonella enterica Serovars Agona,

Enteritidis, and Typhimurium in peanut butter. Journal of Food Protection 69:2687-91

Scott, W.J. 1958. The effect of residual water on the survival of dried bacteria during

storage. Journal of General Microbiology 19:624-33

Scott, V., Chen, Y., Freier, T., Kuehm, J., Moorman, M., Meyer, J., et al. 2009. Control

of Salmonella in low-moisture foods I: Minimizing entry of Salmonella into a processing facility.

Food Protection Trends 29:342-353

Seok Seo, K., Bohach, G.A. 2007. Staphylococcus aureus. In: Doyle, M.P., Beuchat, L.

(eds) Food microbiology: Fundamentals and frontiers 3rd edn. ASM Press, Washington. D. C., p

493-518

Simonne, A., Treadwell, D. 2008. Minimizing food safety hazards for organic growers.

Department of Family, Youth, and Community Sciences, Florida Cooperative Extension Service,

Institute of Food and Agricultural Sciences, University of Florida.

http://edis.ifas.ufl.edu/pdffiles/FY/FY106200.pdf. Accessed August 2012

Sotir, M.J., Ewald, G., Kimura, A.C., Higa, J.I., Sheth, A., Troppy, S., et al. 2009.

Outbreak of Salmonella Wandsworth and Typhimurium infections in infants and toddlers traced

to a commercial vegetable-coated snack food. The Pediatric Infectious Disease Journal 28: 1041-

6

Swaminathan, B., Cabanes, D., Zhang, W., Cossart., P. 2007. Listeria monocytogenes In:

Doyle, M.P., Beuchat, L. (eds) Food microbiology: Fundamentals and frontiers 3rd edn. ASM

Press, Washington. D. C., p 457-492

36

van Asselt, E.D., Zwietering M.H. 2006. A systematic approach to determine global

thermal inactivation parameters for various food pathogens. International Journal Food


Wittenberger, K., Dohlman, E. 2010. Peanut outlook: Impacts of the 2008-09 foodborne

illness outbreak linked to Salmonella in peanuts. Outlook No. (OCS-10a-01)

http://www.ers.usda.gov/media/146487/ocs10a01_1_.pdf Accessed January 2013

37

Table 2.1. Outbreaks associated with contamination of low-moisture foods from 2007-2012 worldwide

Stagea Year Microorganism Product Cases Location Reference

_b

2008 Salmonella spp. Rice, seeds, nuts and

almonds 2

EU

EFSA, 2010

2007

B. cereus

Rice, seeds, nuts and almonds

62

EFSA, 2009

E. coli VTEC 45

Salmonella spp. 34

S. aureus 26

Staphylococcus spp. 120

B. cereus

Herbs and spices

149

C. perfringens 19

Salmonella spp. 3

Pre-processing

2011 E. coli O104:H4 Fenugreek seeds 4321 Germany EFSA, 2011

2009 E.coli O157:H7 Raw cookie dough 77 U.S. (30 states) CDC, 2012a

2007 S.Weltevreden Alfalfa seeds 25 EU (Scandinavia) SFI, 2012

No lethality 2011

E.coli O157:H7 Raw shelled walnuts 11 Canada

CDC, 2012a E.coli O157:H7 In shell hazelnuts 8 U.S. (3 states)

2008 S. Rissen White ground pepper 87 U.S. (Multistate)

Ingredient in processed

food

2011 S. Enteritidis Turkish pine nuts 43 U.S. (5 states) CDC, 2012a

2010 S. Montevideo Black pepper in salami 272 U.S. (44 states)

Processed food

2012

S. Bredeney Peanut butter 42 U.S. (20 states) CDC, 2012a

S. Infantis Dry dog food 49 U.S. (20 states)

S. Oranienburg Dry milk 16 Russia SFI, 2012

2010 B. cereus Rice

103 U.S. (Florida)

CDC, 2012a 6 U.S.

(Pennsylvania)

22 U.S. (Tennessee)

C. botulinum Dried tofu NA Taiwan SFI, 2012

2009

B. cereus Rice 13 U.S. (Alabama)

CDC, 2012a 15 U.S. (Washington)

S. Montevideo Pistachios roasted NA U.S.

S. Enteritidis Fried rice 11 U.S. (Virginia)

2008 B. cereus Rice 10 U.S. (Georgia) CDC, 2012a

38

astage of the food chain where contamination took place; binformation is not specified at the source

C. perfringens Spanish rice 2 U.S. (Colorado)

S. Kedougo Infant formula 42 Spain Rodríguez-Urrego

et al., 2008

S. Agona Rice cereals 28 U.S. (15 states) CDC, 2012a

S. Enteritidis Rice 82 U.S. (Washington)

Salmonella spp. Sweets and chocolate 4 EU EFSA, 2010

Salmonella spp. Infant formula 8 France SFI, 2012

S. Typhimurium Peanut butter 714 U.S. (46 states) CDC, 2012a

C. botulinum Peanut butter 5 Canada Harris et al., 2012

2007

B. cereus Fried rice 8 U.S. (Washington) CDC, 2012a

S. Schwarzengrund Dry pet food 62 U.S. (18 states)

Salmonella spp. Sweets and chocolate 242 EU EFSA, 2009

Salmonella spp. Imperial rice 27 U.S. (Florida)

CDC, 2012a S. Tenessee Peanut butter 425 U.S. (44 states)

S. Wandsworth Rice snack 75 U.S. (20 states)

39

Table 2.2. U.S. recalls associated with low-moisture foods not given a lethality step or at pre-

processing from 2007-2012 (FDA, 2012)

Stagea Year Microorganism Product Implicated

Pre-processing 2010

Salmonella spp.

Black pepper

Garlic powder

2009 Hazelnut

No lethality

2012 E.coli O157:H7 Organic cacao nibs

2011

E.coli O157:H7 Hazelnut and mixed nut products

Salmonella spp.

Bulk turkish pine nuts

Organic celery seed

Pine nuts

Peppermint organic tea

Soybean flour and soy meal

2010 Salmonella spp.

Black pepper

Black peppercorns

Nutmeg

Nuts

Seasonings

Sesame seeds

Shelled walnuts

Spice packages

Pistachio kernel products

Seasoning salt,

Gravy Mix,

Onion dip Mix

Seasoning mixes

Dip mix

Soup mix

Sauce mix

Soy grits and flour

2009 Salmonella spp.

Hazelnut

Hazelnut kernels

Raw shelled hazelnut kernels

Red pepper

Shelled hazelnuts and shelled

organic hazelnuts

White and black pepper

Curry spice

2007 Salmonella spp. Parsley powder dehydrated bulk

Four cheese risotto mix

Pre-processing/No

lethality b

2010

Salmonella spp.

Spices, Spice blends, Rub,

Seasoning

Walnuts

Whole raw pistachios and kernels

2009 Shelled hazelnuts

Raw hazelnut kernels astage of the food chain where contamination took place

brecall included products which were given no lethality and

products that were contaminated at pre-processing

40

Table 2.3. U.S. recalls of processed low-moisture foods from 2007-2012 (FDA, 2012)

Microorganism Year Product Implicated

C. botulinum 2008 Chai concentrate

L. monocytogenes

2012 Popcorn with select flavors

2010 Peanut butter, dips, spreads, cheese, salsa

2007 Italian sausage hoagie

Salmonella spp.

2012 Crush roasted Thai red pepper

Crackers

Prebiotic powder formula

Dog food

Flake fish food

2011 Dry cat food

Natural peanut butter chunky

Peanut butter

Powdered protein products

Snack products with chili

2010 Cat food

Corn chips

Pet food

Egg noodle

Pancake, cake and cookie mix

Potato chips

Potato crisps

Pretzels

Spreads

Batter mix

Cheese ball mix

Mixes containing pretzels

Snack mix

Snack mix and cashews

Barbecue potato chips

2009 Dry roasted hazelnut kernels

Chocolate-covered peanuts

Instant oatmeal variety pack

Maple and brown sugar instant oatmeal

Variety pack instant oatmeal

Dry roasted hazelnut kernels

Instant nonfat dried milk, whey protein (286

products)

Peanut butter and paste (3918 products)

2008 Pancake and waffle mix

Pet food

Puffed rice wheat cereals

Dry pet food

2007 Peanut butter

Roasted sesame tahini

Topping, chocolate and peanut butter flavor

Corn sticks

Sweet dairy whey powder White chocolate baking squares

41

CHAPTER 3

MODELING THE INFLUENCE OF TEMPERATURE, WATER ACTIVITY AND WATER

MOBILITY ON THE PERSISTENCE OF SALMONELLA IN LOW-MOISTURE FOODS 2

2Santillana Farakos, S.M., J.F. Frank and D.W. Schaffner. Accepted by International Journal of

Food Microbiology.

Reprinted here with permission of the publisher.

42

ABSTRACT

Salmonella can survive in low-moisture foods for long periods of time. Reduced

microbial inactivation during heating is believed to be due to the interaction of cells and water,

and is thought to be related to water activity (aw). Little is known about the role of water mobility

in influencing the survival of Salmonella in low-moisture foods. The aim of this study was to

determine how the physical state of water in low-moisture foods influences the survival of

Salmonella and to use this information to develop mathematical models that predict the behavior

of Salmonella in these foods. Whey protein powder of differing water mobilities was produced

by pH adjustment and heat denaturation, and then equilibrated to aw levels between 0.19±0.03

and 0.54±0.02. Water mobility was determined by wide-line proton-NMR. Powders were

inoculated with a four-strain cocktail of Salmonella, vacuum-sealed and stored at 21, 36, 50, 60,

70 and 80 °C. Survival data was fitted to the log-linear, the Geeraerd-tail, the Weibull, the

biphasic-linear and the Baranyi models. The model with the best ability to describe the data over

all temperatures, water activities and water mobilities (ftest<Ftable) was selected for secondary

modeling. The Weibull model provided the best description of survival kinetics for Salmonella.

The influence of temperature, aw and water mobility on the survival of Salmonella was evaluated

using multiple linear regression. Secondary models were developed and then validated in dry

non-fat dairy and grain, and low-fat peanut and cocoa products within the range of the modeled

data. Water activity significantly influenced the survival of Salmonella at all temperatures,

survival increasing with decreasing aw. Water mobility did not significantly influence survival

independent of aw. Secondary models were useful in predicting the survival of Salmonella in

various low-moisture foods providing a correlation of R=0.94 and an acceptable prediction

performance of 81 %. The % bias and % discrepancy results showed that the models were more

43

accurate in predicting survival in non-fat food systems as compared to foods containing low-fat

levels (12 % fat). The models developed in this study represent the first predictive models for

survival of Salmonella in low-moisture foods. These models provide baseline information to be

used for research on risk mitigation strategies for low-moisture foods.

KEYWORDS: Dry food, Kinetics, Predictive models, Moisture, NMR

44

INTRODUCTION

Low-moisture foods are those with water activity (aw) levels lower than 0.70 (Blessington

et al., 2012). Such foods include products which have undergone a lethality step, those that are

not subjected to an inactivation step, and those in which ingredients are added after an

inactivation step. A review of recall records of low-moisture foods on the Centers for Disease

Control and Prevention (CDC) website showed that in the U.S., from 2007 to 2012, there were

119 recalls (5,010 entries) involving pet food, powdered infant formula, peanut butter, spices,

dry nuts, dry milk, seeds, etc. (CDC, 2012). From 2007 to 2012, 22 reported Salmonella

outbreaks caused by low-moisture foods occurred globally, resulting in 2,293 cases of infection

and 9 deaths (CDC 2012, EFSA 2009, EFSA 2010, Rodriguez-Urrego et al 2010, SFI 2012). The

consumption of only one Salmonella cell in a food product may be sufficient to cause illness

(D’Aoust and Maurer, 2007), and most low-moisture food products require no further cooking

and have a long shelf life. Hence, the presence of Salmonella in low-moisture foods can cause

extended outbreaks which impact large numbers of people.

Salmonella is able to survive in low-moisture foods for long periods of time. Increased

heat resistance in low-moisture foods is believed to be the result of the interaction of Salmonella

cells with food components (Podolak et al., 2010). Water, as a component of food, is considered

a key factor in microbial inactivation (Podolak et al., 2010). The interaction of cells with water is

often related to aw, as it reflects the intensity with which water associates with non-aqueous

components at a macroscopic level. Several studies have shown reduced aw protects against the

inactivation of Salmonella in low-moisture foods (Beuchat and Scouten 2002, Doyle and

Mazzotta 2000, Archer et al 1998). However, different D- and z- values have been observed for

different products under similar moisture conditions (Podolak et al., 2010). Water mobility is a

45

measure of the translocation of water molecules in the food, with the possibility of determining

the ability at which water molecules interact with the bacterial cell at a molecular level. At

present, little is known about the role of water mobility in influencing the survival of Salmonella

in low-moisture foods. The aim of this study was to determine how the physical state of water in

low-moisture foods influenced the survival of Salmonella, and to use this information to develop

mathematical models that predict the behavior of Salmonella in these foods.

MATERIALS AND METHODS

Preparation of modified whey protein powder

The ability of whey protein (beta-lactoglobulin) to immobilize water was modified by

changing the secondary and tertiary structure of the protein through pH adjustment and heat.

Whey protein powder (95 % protein) was obtained from Davisco Foods International (Le Sueur,

MN). The pH of three 1.5 liter solutions of 40 g/L whey protein was adjusted to 2, 5 and 7, with

36.5 % HCl (J.T. Baker, Phillipsburg, NJ). The protein was then denatured by heating the

solution to 80 ºC. The solutions were rapidly cooled under cold water and refrigerated overnight.

This process stabilized the modified protein structures, but the resulting product contained

sufficient bacterial spores to interfere with Salmonella analysis. Therefore, the protein

suspensions were further pasteurized at 80 °C for 30 min after adjusting pH levels to 2.0. After

cooling to room temperature, the pH of all the solutions was re-adjusted to 7 by using 10N

NaOH (J.T. Baker, Phillipsburg, NJ). The solutions were then poured into sterile aluminum pans

and frozen to -40 °C overnight in a freeze drier (Freezemobile 25SL Unitop 600L, Virtis

Company, Gardiner, NY). The vacuum of the freeze drier was started once the samples reached -

40 °C, and the temperature of the freeze drier was gradually increased from -40 °C to 0 °C every

24 hr for a total of 96 hr (-20, -10, 0). Once freeze dried, the modified whey protein powder of

46

each structure type (denatured at pH 2, 5 and 7) was broken down to homogenous particles by

crushing it with a rolling pin. The powders were stored in the dark under N2 atmosphere with

silica gel packets to avoid oxidation and moisture absorption. Protein powders denatured at pH 2,

5 and 7 are referred to as protein configuration 1, 2 and 3, respectively.

Water activity equilibration of protein powders

Protein powders were adjusted to the various aw values in vacuum desiccators by

absorption at 21°C. Target aw levels were: 0.11 (Lithium Chloride, Fisher scientific, Pittsburgh,

PA), 0.23 (Potassium Acetate, Sigma Aldrich, St. Louis, MO), 0.33 (Magnesium Chloride

Hexahydrate, Fisher scientific, Pittsburgh, PA), 0.43 (Potassium Carbonate, Anhydrous,

Granular, J.T. Baker, Phillipsburg, NJ) and 0.58 (Sodium Bromide Crystal, J.T. Baker,

Phillipsburg, NJ). Water activity was determined using a bench top water activity meter

(AquaLab Series 4TEV, Decagon Devices Inc., Pullman, WA) of ±0.003 precision.

Water mobility determination

A Varian Inova 500 MHz spectrometer (Complex Carbohydrate Research Center, The

University of Georgia, Athens, GA) was used to obtain the wide line H-NMR spectra for protein

powders. Approximately 200 g of sample was packed into a 5mm ASTM Type 1 Class B glass

NMR tube (Norrell Inc., Landisville, NJ). All measurements were obtained in triplicate at 25 ºC.

The spectral width used was 300 kHz. The methodology used was based on that of Kou et al.,

2000.

Effective spin-spin relaxation time (T2*)

A 90° 1H pulse with a pre-acquisition delay time of 2.5 s was used to obtain the H-NMR

spectra of each aw equilibrated sample. These spectra have a broad component of the peak

corresponding to the immobile protons and a narrow component of the peak corresponding to the

47

mobile protons. The spectrum of each sample was decomposed into broad and narrow

components, each fitted to a Lorentzian line shape using MestRenova 7 software (Mestrelab

Research, S.L., Santiago de Compostela, Spain). The areas of the broad and narrow components

and the line width at half-height of each component were measured by using MestRenova 7.

Effective spin-spin relaxation time (T2*) values were obtained using Equation 3.1.

( )

( ) (3.1)

where T2* represents the effective spin-spin relaxation time and v1/2 represents the line width at

half-height

Significant differences in water mobility (T2*) at different water activities and for

different protein configurations were analyzed by ANOVA using the General Linear Model

procedure with Tukey's test at p <0.05 (IBM SPSS Statistics for Windows, Version 21.0, IBM

Corp. Armonk, NY). Water mobility has units of milliseconds (ms).

Sample inoculation and packaging for survival experiments

Four Salmonella serovars previously involved in outbreaks in dry foods were used in this

study: S. Typhimurium (peanut), S. Tennessee (peanut), S. Agona (dry cereal) and S. Montevideo

(pistachios and others). The cultures were stored in cryovials containing beads suspended in

phosphate buffered saline, glycerol and peptone (Cryobank, Copan Diagnostics Inc., CA) and

kept at -80 ºC. They were prepared for use by consecutive culturing in 9 ml of Tryptic Soy Broth

(TSB, Becton, Dickinson and Company, Sparks, MD) at 37 ºC for 24 hr. Following the second

culture, a final transfer of 3ml to 225 ml of TSB was made, followed by incubation for 24 hr at

37 ºC. Cells from the final culture were collected by centrifugation (3,363 g, 30 min), the

supernatant fluid was removed, and the pellet was re-suspended in 2 ml of 1 % bacto-peptone

(Becton, Dickinson and Company, Sparks, MD). The cell suspension was then dried in a vacuum

48

desiccator over anhydrous calcium sulfate for a minimum of three days to obtain aw levels below

0.1. The dried cells were pooled and manually crushed into a powder. The dried inoculum (0.05

g) was mixed with 0.95 g of moisture equilibrated test protein powder to provide a 1 g sample.

This inoculation method led to starting concentrations of 109 CFU/g. Re-equilibration of samples

to the target aw was not necessary when using this procedure. Inoculated and control samples

were packaged in retort pouches under vacuum to minimize moisture transfer to head space

during survival studies. Samples were placed into standard retort pouches (Stock America, Inc.,

Grafton, WI). Retort pouches were then placed in FoodSaver Quart Bags, and the FoodSaver

equipment (FoodSaver Silver, model FSGSSL0300-000, Sunbeam Products, Inc., Boca Raton,

FL) was used for pulling a vacuum and sealing. After initial sealing of the FoodSaver bag, a

second seal was applied to the retort pouch using an impulse sealer. The vacuum-sealed

inoculated samples were stored at different temperatures (21±0.6 °C, 36±0.3 °C, 50±0.5 °C,

60±0.5 °C, 70±0.5 °C and 80±0.5 °C). For the six-month storage experiments (21 °C and 36

°C), the retort pouches were stored in desiccators at their corresponding relative humidity in

controlled temperature incubators. Samples were stored in a circulating water bath (Lauda,

Lauda-Konigshofen, Germany) for the high temperature experiments (50 °C, 60 °C, 70 °C and

80 °C). The water bath was equipped with custom-designed racks that kept the samples

submerged and allowed for water circulation between pouches.

Experimental plan

Each survival experiment was replicated three times. Samples in six-month storage

experiments at 21°C and 36 °C were taken at: 0, 7, 14, 21, 28, 42, 56, 84,112,140, and 168 days.

Samples in one-month storage experiments at 50 °C and 60 °C were taken at: 0, 2, 6, 12, 24, 48,

96, 168, 336, 504, and 672 hours. Samples in 48 hour experiments at 70 °C and 80 °C were taken

49

at: 0, 0.5, 4, 10, 30, 60, 240, 480, 960, 1440 and 2880 min. Time 0 corresponds to the time after

come-up-time (the time needed to raise the temperature to reach a target level).

Uninoculated controls were analyzed for background microflora and aw at three sampling

times throughout each experiment. Salmonella were recovered on non-selective and selective

differential media. The non-selective medium consisted of Tryptic Soy Agar (TSA, Becton,

Dickinson and Company, Sparks, MD) (40.0 g/L), ferric ammonium citrate (Sigma-Aldrich Co.,

St Louis, MO) (0.8 g/L), yeast extract (Becton, Dickinson and Company, Sparks, MD) (3.0 g/L)

and sodium thiosulfate (J.T. Baker, Phillipsburg, NJ) (6.8 g/L). The selective medium contained

the same ingredients with the addition of sodium desoxycholate (Becton, Dickinson and

Company, Sparks, MD) (2.5 g/L) as the selective agent. The proportion of injured cells was

calculated according to Boziaris et al. (1998) and Heddleson et al. (1994) using Equation 3.2.

(3.2)

where A represents the counts (CFU/g) on non-selective differential media and B represents the

counts on selective differential media (CFU/g)

Development of predictive models

Model fitting and selection

The following inactivation models were fit to the survival data.

Log-linear model (Bigelow and Esty, 1920)

( ) (3.3)

where Nt is the population at time t (CFU/g), No is the population at time 0 (CFU/g), kmaxB is the

maximum specific inactivation rate (min¹), t is the time (min) and

50

Geeraerd-tail model (Geeraerd et al., 2000)

( ) ( ) (3.4)

where Nt, No and t are defined as above, Nres is the heat resistant population and kmaxG is the

maximum specific inactivation rate (min¹)

Weibull model (Mafart et al., 2002)

(

)

(3.5)

where Nt, No and t are defined as above, δ is the time required for first decimal reduction (min)

and β is a fitting parameter that defines the shape of the curve

Biphasic-linear model (Cerf, 1977)

( ( ) ( ) ( )) (3.6)

where Nt, No and t are defined as above, f and (1- f) are the heat resistant and heat sensitive

fraction of the population, respectively. kmax1 and kmax2 (min¹) are the maximum specific

inactivation rates of the heat resistant and heat sensitive populations, respectively

Baranyi growth model as a mirror of inactivation (Baranyi and Roberts, 1994) with m=1,

lag time=0 (min) and ν=μ

( )

( ) (

⌈ ⌉

⌊ ⌋

) (3.7)

where Nt, No and t are defined as above, Nf is the final population (log10 CFU/g), μ is the

maximum specific growth rate (min¹)

51

Data was fitted to the Baranyi model using DMFit Version 2.0 (Baranyi and Le Marc,

Institute of Food Research, Norwich, UK). GInaFiT Version 1.6 (Geeraerd et al., 2005,

Katholieke Universiteit Leuven, Leuven, Belgium) was used to fit data to the remaining models.

To determine which of the models best described the data, the f value (ftest), the root mean square

error (RMSE) and the adjusted coefficient of determination ( ) were calculated using Excel

2007 (Microsoft, Redmond, WA) according to the equations given below (3.8-3.14) (den Besten

et al., 2006).

( )( )

(3.8)

where ∑( ̅̅ ̅̅ ̅̅ ̅̅ ̅̅ ̅̅ ̅̅ )

∑( ̅̅ ̅̅ ̅̅ ̅̅ ̅̅ ̅̅ ̅̅ ) ∑( )

∑( ) (3.9)

∑( ) (3.10)

√

(3.11)

(3.12)

(3.13)

(3.14)

where is the residual sum of squares of the data (sum of the squared differences between

the observed values and the average values), is the residual sum of squares of the

model (sum of the squared differences between the observed values and the predicted values)

52

and is degrees of freedom where dfmodel= n-p and dfdata=n-m (n is the total number of

observations at all time points, p is the number of parameters in the model and m is the number

of time points).

The ftest value was tested against Ftable (95 % confidence). If the ftest value was lower than

the Ftable (dfmodel/dfdata), the ftest was judged to provide an acceptable fit of the data (den Besten et

al., 2006). The primary criterion used to choose the best model to describe the survival data was

the capacity of the model to describe the data well for all temperature, aw and water mobility

conditions (ftest<Ftable). If more than one model fitted the data well for all conditions, the model

with best statistical parameter fits was chosen (highest , lowest RMSE). If these first two

criteria were equally met, the number of parameters of the model and the biological meaning of

the model parameters were considered (den Besten et al., 2006).

Secondary model development

The influence of temperature, aw and water mobility on the survival of Salmonella was

evaluated using Multiple Linear Regression (IBM SPSS Statistics for Windows, Version 21.0,

IBM Corp.), where aw, water mobility and temperature represent the dependant variables of the

secondary models. A ttest was used to assess the significance of each factor on the survival of

Salmonella. Secondary models were developed based on parameter significance. If the

significance of the test was lower than the level of confidence (p<0.05), the parameter was

judged significant and included in the secondary model. Normal probability plots were visually

evaluated for a linear relationship (where linearity indicates normality). Uniform variance was

verified using residual plots. If the plots of the residuals against log CFU/g values clustered

around zero, variances were considered constant.

53

Validation of the secondary models

The secondary models were validated by obtaining Salmonella survival data (in

duplicate) in whole wheat flour, low-fat peanut meal (12 % fat), non-fat dry milk, whey protein

and low-fat cocoa powder (12 % fat) at various temperatures (from 22 °C to 80 °C), aw levels

(0.20±0.03 to 0.55±0.06) and storage times (from 0 to 6 months) within the range of the modeled

data. The bias factor (Bf) expressed as % bias (Equation 3.15) and accuracy factor (Af) expressed

as % discrepancy (Equation 3.16) were used to measure model performance (Baranyi et al.,

1999). Residuals (r) were calculated using Equation 3.17 and the acceptable residual zone was

established to be from -1 log CFU (fail safe) to 0.5 log CFU (fail dangerous) (Oscar, 2009). The

percentage of residuals in the acceptable zone was used as a model performance measure (Oscar,

2009). A model was considered validated and the model performance acceptable with a residual

percentage ≥ 70 % (Oscar, 2009). Visual inspection of the data including the correlation

coefficient values (R) (Equation 3.18) for the plots of the predicted against experimental survival

data were also used for model evaluation.

( ) ( | | ) (3.15)

where: f 0 [∑ log(

log o ellog ⁄ )n

n]

; ( ) (

)

( ) (3.16)

where: 0 [∑ | log(

log o ellog ob er e ⁄ )|n

n]

54

ob er e

re icte (3.17)

( ) ∑( ̅)( ̅)

√∑( ̅) ∑( ̅)

(3.18)

RESULTS AND DISCUSSION

Water mobility of modified whey protein powder

The T2* values for mobile and immobile protons from the H-NMR spectra analyses are

presented in Table 3.1. The NMR spectra (not shown) for samples of different aw indicated that

sorbed water produced an increase in the relative intensity of the narrow component of the peak

(representing mobilized water) and a decrease in the relative intensity of the broad component of

the peak (representing immobile water). A progressive decrease in line width was observed for

both the broad component and the narrow component as aw increased. Statistical analyses

indicated that the T2* values for mobile protons (Table 3.1, column 3) increased with increasing

aw (p<0.001). This indicated that molecular mobility successively increased with an increasing

bulk water phase. Similarly, T2* values for immobile protons (Table 3.1, column 4) significantly

increased with increasing aw (p<0.001). Proton exchange in low-moisture conditions is slow, so

the increasing mobility of immobile protons as aw increased was not the result of proton

exchange but indicated that water was causing an increase in protein mobility (Kou et al., 2000).

T2* values for mobile protons at the lower aw levels (0.16-0.28) did not significantly

differ for the three protein configurations (p=0.908), but there were significant differences in

water mobility for samples at the higher aw levels (0.37-0.59) (p=0.021). Specifically, samples

with configuration 2 showed greater mobility than samples of configuration 3 (p=0.023) in this

aw range. No significant differences were observed in water mobility for immobile protons at the

3 protein configurations (p>0.05).

55

Survival of Salmonella in samples held at 21 to 80 °C

Data corresponding to the survival of Salmonella at various temperatures in low-moisture

protein powder are presented in Figures 3.1 through 3.4. Model fit statistics for the log-linear,

Baranyi, Geeraerd-tail, Weibull and biphasic-linear models for all experimental conditions under

study are presented in Table 3.2, where the best statistical parameter fits are shown in bold. The

Geeraerd-tail, Weibull and biphasic-linear models were not suitable for describing the 21°C data

because survival numbers were maintained throughout the experiment. .

The Salmonella counts used for the data analyses, model development and model

validation, were obtained using non-selective differential media. A comparison of non-selective

with selective counts indicated that the proportion of injured cells (Equation 3.2, data not shown)

was not significantly influenced by temperature (p=0.228), aw (p=0.371) or water mobility

(p=0.411). Storage time just significantly influenced the proportion of injured cells (p=0.044), as

longer storage times led to increasing proportions of injured cells. These results do not support a

hypothesis that the mechanism of inactivation changed from membrane damage at lower

temperatures (≤50 °C) to ribosomal degradation at higher temperatures (>50 °C) as suggested by

Aljarallah and Adams (2007). Heating cells to temperatures just above their maximum growth

temperature causes damage to the cytoplasmic membrane, which in enteric bacteria can be

detected by plating the cells on non-selective media and media containing bile salts. If cells are

treated at sufficiently high temperatures, death results from ribosome degradation, and there will

be a small or no difference in the ability of the survivors to grow on selective and non-selective

media. Aljarallah and Adams (2007) observed these effects using Salmonella treated at 53 °C

and 60 °C at water activities of 0.99 and 0.94. Results in the present study indicated there were

no significant differences in the proportion of injured cells among those exposed to different

56

water activities and temperatures. However, one major difference in our study is that we

investigated lower water activities (<0.6) over a wider temperature range (21 °C -80 °C).

Salmonella survival data at 21 °C during 168 days (6 months) of storage (results not

shown) showed populations were maintained under these conditions, with log reduction values

of 0.001, 0.003, 0.002, 0.003 and 0.005 log CFU/day at aw levels of 0.16±0.01, 0.26±0.002,

0.34±0.009, 0.41±0.01 and 0.53±0.05, respectively. These data indicated a significantly better

survival of Salmonella at lower aw levels (0.16 and 0.26) as compared to higher ones (0.34 to

0.53) (p<0.001). Significant differences in survival were also observed between the two highest

aw levels (0.41 and 0.53). However, no significant differences in survival were found between aw

levels of 0.16 and 0.26 (p=0.541), 0.34 and 0.41 (p=0.730) or 0.34 and 0.53 (p=0.074). No

influence of water mobility at the same aw level was observed (p=0.917). Because the survival

rates were essentially linear at 21 °C, the Geeraerd-tail model, the Weibull model (with β≠ in

Equation 3.5) and the biphasic-linear model were not suitable for describing the data. The

Baranyi and the log-linear models were appropriate in describing the data for all conditions

(ftest<Ftable) and showed similar statistical fit parameter values (Table 3.2).

Figure 3.1 presents data on Salmonella survival at 36 °C during 168 days (6 months) of

storage. Survival increased with decreasing aw (p<0.001). As with the observations at 21 °C, no

influence of water mobility independent of aw level was observed (p=0.507). Average log

reduction values of 0.003, 0.005, 0.008, 0.01 and 0.02 log CFU/day were observed at aw levels of

0.17, 0.26, 0.33, 0.42 and 0.52, respectively. At the lower aw levels (0.17 and 0.26), there was a

slight decline in Salmonella population (Figure 3.1) which resembled that seen at 21 °C. Greater

inactivation was seen at the higher aw levels (0.33-0.52), with an initial decline followed by a tail

starting at around 50 days of storage (Figure 3.1). The model fit statistics corresponding to 36 °C

57

survival data are presented in Table 3.2. Unlike the results at 21 °C, the survival data at 36°C

could be described by all models (ftest<Ftable) with the exception of the log-linear model, which

did not fit the data at the highest aw (0.52) (Table 3.2). Salmonella survival in protein powder

held at aw level of 0.52 showed tailing after approximately 50 days of storage. The log-linear

model did not describe such tailing behavior as indicated by an ftest which was higher than the

Ftable. The biphasic-linear model produced the best fit statistics at aw level of 0.52 (Table 3.2).

This model may represent samples containing two populations with differing survival rates, and

therefore their fitness may be associated with using a multistrain cocktail. The highest

values for survival data at 36 °C were found when fit to the Geeraerd-tail model followed by the

biphasic-linear and Weibull models (Table 3.2).

Survival data at 50 °C showed increased heat resistance of Salmonella associated with

decreasing aw (p<0.001) (Figure 3.2). Even at temperatures as high as 50 °C, Salmonella

continued to inactivate slowly at the lowest aw level (0.22). Average log reduction values of 0.06,

0.09, 0.13, 0.16 and 0.22 log CFU/day were observed at aw levels of 0.22, 0.33, 0.39, 0.46 and

0.58, respectively. No significant differences in resistance were associated with water mobilities

at the same aw level (p=0.418). All models adequately described the inactivation data at the

lower aw levels (0.22 and 0.33) (Table 3.2). However, at the higher aw levels (0.39-0.58), the best

fits were found when using the Weibull model followed by the biphasic-linear model and the

Geeraerd-tail model (Table 3.2). The log-linear and Baranyi models showed poorer fits at the

higher aw (0.39-0.58) because under these conditions Salmonella produced a non-log-linear

inactivation rate (Figure 3.2).

Data on survival of Salmonella at 60 °C showed increased survival with decreasing aw

(p<0.001) (results not shown). Average log reduction values of 0.2, 0.4, 0.6, 0.6 and 0.8 log

58

CFU/day were observed at aw levels of 0.22±0.002, 0.34±0.0003, 0.39±0.006, 0.46±0.005 and

0.57±0.002, respectively. Salmonella was not detected after 2 weeks (336 hours) of storage at the

higher aw levels (0.46, 0.57). At the intermediate aw levels (0.34 and 0.39) Salmonella was not

detected in samples after 504 hours (3 weeks). When Salmonella was held at 60 °C at aw level of

0.22, samples contained detectable Salmonella even after 4 weeks of storage. No significant

differences in resistance were found for survival in the different water mobilities at the same aw

level (p=0.880). The survival data were well described by all the models except for the log-linear

model which did not describe survival well at the highest aw level (0.57) (ftest>Ftable) (Table 3.2).

The highest values were found when using the Weibull model followed by the biphasic-

linear and the Geeraerd-tail models. As the storage temperature increased to 70 °C, survival

kinetics became non-linear, as the inactivation curves had a non-linear mid-phase and

pronounced tails (Figure 3.3). Average log reduction values of 1.6, 2.5, 3.0, 3.0 and 3.0 log

CFU/day were obtained at aw levels of 0.19, 0.28, 0.36, 0.43 and 0.56, respectively. After 48

hours of treatment, an average 6 log CFU reduction was observed for Salmonella at the higher aw

levels (0.36-0.56). Average log reduction values of 3 and 5 log CFU after 48 hours of treatment

were observed at aw levels 0.19 and 0.28, respectively. Water activity significantly influenced the

survival of Salmonella at this temperature (p<0.001) while water mobility had no influence when

the aw level was constant (p=0.781). The non-linear behavior of the pathogen at this temperature

(Figure 3.3) made the log-linear model unsuitable for describing this data (Table 3.2). Similarly,

the Baranyi model produced poor fit results and unacceptable ftest results in more than 50 % of

the conditions (Table 3.2). The best fit statistics were for the Weibull model, followed by the

biphasic-linear and Geeraerd-tail models. The highest values were obtained when fitting the

data to the Weibull and biphasic-linear models.

59

As with the results at 70 °C, survival of Salmonella at 80 °C (Figure 3.4) produced

inactivation curves with pronounced tails (tails are not shown on Figure 3.4). Data during the

first 60 min of storage indicated non-linear inactivation kinetics at every aw level. Water activity

significantly influenced the survival of Salmonella at 80 °C (p<0.001). Generally 2-3 log CFU

reduction numbers were observed at the lower aw levels (0.18 and 0.29) during the first 60 min of

storage followed by an additional 4-5 log CFU reduction from 60 to 1440 min (results not

shown). The 80 °C treatment produced average log reduction values of 0.7, 1.3, 1.3, 1.4 and 1.5

log CFU/hr at aw levels of 0.18, 0.29, 0.36, 0.42 and 0.52, respectively. At the higher aw levels

(0.36-0.52), 2-4 log reduction values were seen after 60 min of treatment (Figure 3.4). After

1440 min (24hr), Salmonella was only detected in the samples with the lowest aw level (0.18).

The pathogen was not detected in any samples after 24 hr of treatment. Water mobility did not

have a significant effect on microbial death at 80 °C independent of aw (p=0.912). When fitting

the survival data at 80 °C to the models, similar statistical fit results were found with the

Geeraerd-tail model, the Weibull model and the biphasic-linear model (Table 3.2). Model fit

statistics showed (Table 3.2) that the log-linear model did not describe the survival behavior of

Salmonella in half of the conditions. The Baranyi model provided a better fit as compared to the

log-linear model, but did not adequately describe the data at the lowest aw (0.18). The highest

values at 80 °C were found when using the biphasic-linear and the Weibull models, which

is in line with the results seen at 50°, 60° and 70 °C. Consequently, the best description of

Salmonella inactivation in low-moisture foods at high temperatures (T> 50 °C) requires a model

that includes a non-constant inactivation rate at the mid-phase and the ability to incorporate

tailing.

60

Survival data at temperatures ranging from 21 to 80 °C demonstrate the highly adaptive

capacity of Salmonella to survive in low-moisture foods for long periods of time, even when

subject to high heat. Results also indicate aw significantly influences the survival of Salmonella

at all temperatures, with survival increasing with decreasing aw. These results are consistent with

previous studies showing the protective effect of aw against the inactivation of Salmonella in

low-moisture foods (Beuchat and Scouten, 2002, Archer et al., 1998, Mattick et al., 2001). In

contrast to that found by Hills et al. (1997), water mobility has shown to have no effect on

survival of Salmonella independent of aw.

Increased tailing was associated with increased inactivation temperature for any given aw-

water mobility condition (Figures 3.1-3.4). Similarly, at the same inactivation temperature,

increasing aw (and thus water mobility) led to curves with a more pronounced downward

concavity while different water mobilities at the same aw showed no effect on curve shape

(Figures 3.1-3.4). The Ftest results indicated the log-linear and Baranyi models did not describe

the data well for several storage conditions (ftest>Ftable), except as previously noted for survival at

21 °C. Therefore, these models were not selected for further analyses. The statistical parameters

presented in Table 3.2 indicated that the Weibull model was the best of those under study for

describing the survival of Salmonella at five temperatures from 36 °C to 80 °C, five aw and three

water mobility levels at each aw. The Weibull model provided suitable fits for the inactivation

data under all experimental conditions except one (T= 70 °C, aw=0.36, water mobility= 0.121

milliseconds), and generally gave the highest statistical fit parameters (Table 3.2). The biphasic-

linear model was the second best model under study as it provided suitable data fits under almost

all conditions and had statistical fit parameters which approximated those of the Weibull model

(Table 3.2). The Geeraerd-tail model had lower goodness of fit as compared to the Weibull and

61

biphasic-linear models (Table 3.2). The Geeraerd-tail model described the data well in only 69 of

the 75 conditions and generally showed poorer statistical fits (Table 3.2). These results are

consistent with previous studies showing non-linear models, particularly the Weibull model,

describe the thermal resistance of Salmonella in low-moisture foods more accurately as

compared to log-linear ones (Podolak et al., 2010).

Based on the previous analysis, the Weibull model was selected for secondary modeling.

The Weibull model satisfactorily described the greatest number of conditions and statistical

parameters indicated the best fit. Moreover, the Weibull model could also produce linear fits

(with β=1 in Equation 3.5) and thus also described linear inactivation kinetics as obtained at 21

°C. Table 3.3 presents δ and β parameter values (Equation 3.5) for the fits of the Weibull model

for all conditions under study. Because δ values for data at distinct temperatures differed by

several orders of magnitude, these values were transformed to log scale. The log δ (log min) are

presented in Table 3.3.

Linear models relating the time required for first decimal reduction (log δ) and shape

factor values (log β) to temperature, aw and water mobility were fit using multiple linear

regression. The β values were log transformed to normalize the data. The analysis indicated that

temperature was a significant factor influencing the time required for first decimal reduction and

the shape of the inactivation curve (p<0.001). Water activity was also a significant factor in the

model that related the time required for first decimal reduction to temperature (p<0.001). Water

activity did not significantly influence the shape of the inactivation curve (p=0.279). Water

mobility did not significantly influence the time required for first decimal reduction or the shape

of the inactivation curve (p>0.05).

62

The secondary models developed for Salmonella spp. survival in low-moisture foods are

presented in Equations 3.19 and 3.20.

(3.19)

(3.20)

In Equation 3.19, the standard error (s.e.) of log δ was 0.35, that of the temperature

parameter (T) was 0.003, that of the water activity parameter (aw) was 0.52 and that of the

constant was 0.26. In Equation 3.20, the s.e. of log β was 0.22 and that of T was 0.001.

Model validation

Thirteen δ (time required for first decimal reduction) and β (shape factor) values for

Salmonella survival were obtained from 151 CFU measurements. These correspond to survival

in low-fat cocoa powder at 22 °C for 168 days (aw=0.35), 35 °C for 168 days (aw=0.32 and

aw=0.34) and 70 °C for 24 hr (aw=0.33 and aw=0.35), low-fat peanut meal at 60 °C for 672 hr

(aw=0.21 and aw=0.35), non-fat dry milk at 50 °C for 96 hr (aw=0.28 and aw=0.41), wheat flour at

36 °C for 84 days (aw=0.20 and aw=0.55) and whey protein at 80 °C for 60 min (aw=0.21 and

aw=0.42).Because δ values for data at distinct temperatures differed by several orders of

magnitude, log δ were calculated. A plot of observed versus predicted log δ values (Figure 3.5a)

as well as a plot of observed versus predicted β values (Figure 3.5b) including their

corresponding correlation coefficients is presented in Figure 3.5. Observed versus predicted

Salmonella count values for all data are presented in Figure 3.6. Additionally, Table 3.4 shows

the correlation (R), % discrepancy (% Df) and % bias (% Bf) values for predicted versus observed

time required for first decimal reduction (δ), shape factor values (β) and Salmonella counts in the

different food products used.

63

Data presented in Figure 3.5a and the results in Table 3.4 (all data) indicate that the

secondary model (Equation 3.19) provides a high correlation between observed versus predicted

times required for first decimal reductions (R=0.97, p<0.001). The correlation of observed versus

predicted shape factor values was not as satisfactory (R=0.03, p=0.915), with Equation 3.20 both

over and under predicting β values (Figure 3.5b). Still, as seen in Figure 3.6 and Table 3.4, a

significant correlation (R=0.94, p<0.001) of observed versus predicted CFU values was obtained

when using the developed secondary models to predict the survival of Salmonella in all tested

food types. The degrees of discrepancy and bias found between the secondary predictive models

and the data used to develop these models was found to be 16 % discrepancy and -2 % bias. A

negative percent bias is indicative of a tendency of the models to underestimate survival numbers

(even when using the data that derived the model). This underestimation followed from the

degree to which the shape parameter (in Equation 3.20) deviated from the observed values and

was more prominent at the lower CFU values. The extent to which the models underestimated

the survival of Salmonella in the validation data is illustrated in Figure 3.6. Data points which

appear below the equivalence line are CFU values that have been underestimated and are

consistent with the shape factor results in Figure 3.5b. As seen in Table 3.4, the % bias and %

accuracy factors showed a discrepancy of 41 % and a bias of -7 % for all validation data

collected. These discrepancy and bias values differ from those inherent to the models (16 % and

-2 %). However, the data collected in non-fat products including wheat flour, non-fat dry milk

and whey protein powder (Table 3.4) gave 12 % discrepancy and -3 % bias. The bias and

accuracy percentage results in non-fat food are within the error margin inherent to the models,

and are an example of the consistency of the models in predicting survival data in non-fat foods.

The higher discrepancy and bias percentages obtained for the whole dataset are the result of the

64

higher discrepancy and bias percentages found for data in low-fat food products (which contain

12 % fat). Table 3.4 shows low-fat products to have 50 % discrepancy and -9 % bias. These

increased discrepancy and bias values seen in food containing low levels of fat are most probably

due to the greater resistance of Salmonella in the presence of fat (Podolak et al., 2010). Despite

the higher discrepancy and bias percentages seen when predicting Salmonella survival in

products containing fat, the models still showed an overall acceptable prediction performance of

81 % (for both non-fat and low-fat food). The prediction performance of the models when only

data from non-fat food products was included increased by 8 %. Both prediction performances

(81 % for all data and 88 % for only non-fat data) showed a high percentage of the residuals

were within the acceptable fail safe and dangerous zone (-1 to 0.5 log CFU). In fact, even the

prediction performance for low-fat food products showed an acceptable prediction rate of 79 %.

The previously discussed results demonstrate the validity of the secondary models

developed in this study to predict the survival of Salmonella in low-moisture foods at any given

temperature and aw within the data range evaluated. To the authors’ knowledge, previously

developed models for survival of Salmonella in low-moisture foods are those by Lambertini et

al. (2012) and Danyluk et al. (2006) for use in risk assessment of Salmonella in almonds. These

are models that assumed log-linear declines of Salmonella in almonds at three temperatures (-20,

4 and 24 °C). The models developed in this study represent the first predictive models developed

for survival of Salmonella in low-moisture foods that are validated for temperature (21-80 °C)

and water activity (aw<0.6). Because the data used to derive the models were collected by

simulating how food may be contaminated and stored, the models are useful and credible for use

in a wide range of products (Jaykus et al., 2006). The models will be useful for providing

quantitative support for a hazard analysis and critical control point system (HACCP) (Zwietering

65

and Nauta, 2007). The models can also be used in quantitative microbiological risk assessment to

provide a more accurate risk quantification of Salmonella in low-moisture foods (Jaykus et al.,

2006, Zwietering and Nauta, 2007). This will aid in developing policies for protecting the safety

of consumers (Jaykus et al., 2006). It will also serve for confirmation of product adherence to a

food safety objective (FSO) (Zwietering and Nauta, 2007). However, model predictions are not

absolute, and decisions should not be based only on modeling (Zwietering and Nauta, 2007). In

addition to quantitative data, qualitative and knowledge based information should be considered

for an optimal risk management decision support system (McMeekin et al., 2006). The predictive

models developed in this study will aid in the selection of appropriate strategies to decrease the

risk of Salmonella in low-moisture foods.

CONCLUSIONS

Water activity significantly influenced the survival of Salmonella in low-moisture foods

(aw<0.6) at temperatures ranging from 21 to 80 °C, while water mobility had no effect

independent of aw. The Weibull model provided the best description of survival kinetics for

Salmonella survival in low-moisture foods. Secondary models were developed which predicted

the time required for first decimal reduction (δ) and shape factor values (β) as influenced by

temperature and aw. These models were useful in predicting the survival of Salmonella in several

tested low-moisture foods providing acceptable prediction performances. The models were more

accurate in predicting the survival of Salmonella in non-fat food systems as compared to foods

containing low-fat levels. These models provide baseline information to be used for research on

risk mitigation strategies for low-moisture foods.

66

References

Aljarallah K., Adams M. 2007. Mechanisms of heat inactivation in Salmonella serotype

Typhimurium as affected by low water activity at different temperatures. Journal of Applied


Archer J., Jervis, E.T., Bird, J., Gaze, J.E. 1998. Heat resistance of Salmonella


Baranyi, J., Pin, C., Ross, T. 1999. Validating and comparing predictive models.

International Journal of Food Microbiology 48: 159-166

Baranyi, J., Roberts, T.A. 1994. A dynamic approach to predicting bacterial growth in

food. International Journal of Food Microbiology 23: 277-294

Beuchat, L., Scouten, A. 2002. Combined effects of water activity, temperature and


seeds. Journal of Applied Microbiology 92: 382-395

Bigelow, W. D., Esty, J. R. 1920. The thermal death point in relation to typical

thermophylic organisms. Journal of Infectious Diseases 27:602-617

Blessington, T., Theofel, C. G., Harris, L. J. 2012. A dry-inoculation method for nut


Boziaris, I. S., Humpheson, L., Adams, M. R. 1998. Effect of nisin on heat injury and

inactivation of Salmonella enteritidis PT4. International Journal of Food Microbiology 43:7-13

CDC (Centers for Disease Control and Prevention). 2012. Foodborne Outbreak Online

Database (FOOD) http://wwwn.cdc.gov/foodborneoutbreaks/ Accessed August 2012

Cerf, O. 1977. Tailing of survival curves of bacterial spores. Journal of Applied

Bacteriology 42:1-19

67

D’Aoust, J-Y., Maurer, J. 2007. Salmonella species. In: Doyle, M.P., Beuchat, L. (eds)

Food microbiology: Fundamentals and frontiers 3rd edn. ASM Press, Washington. D.C., p 187-

236.

Danyluk, M.D., Harris, L.J., Schaffner, D.W. 2006. Monte Carlo simulations assessing

the risk of salmonellosis from consumption of almonds. Journal of Food Protection 69:1594-

1599

den Besten, H.M.W., Mataragas, M., Moezelaar, R., Abee, T., Zwietering, M.H. 2006.

Quantification of the effects of salt stress and physiological state on thermotolerance of Bacillus

cereus ATCC 10987 and ATCC 14579. Applied and Environmental Microbiology 72:5884-5894

Doyle, M.E., Mazzotta, A.S. 2000. Review of studies on the thermal resistance of

Salmonellae. Journal of Food Protection 63:779-795


food-borne outbreaks in the European Union in 2007.

http://www.efsa.europa.eu/en/efsajournal/pub/271r.htm Accessed August 2012.

EFSA (European Food Safety Authority). 2010. Trends and sources of zoonoses and

zoonotic agents and food-borne outbreaks in the European Union in 2008.

http://www.efsa.europa.eu/en/efsajournal/pub/1496.htm Accessed August 2012.

Geeraerd, A.H., Herremans, C.H., Van Impe, J.F. 2000. Structural model requirements to

describe microbial inactivation during a mild heat treatment. International Journal of Food


Geeraerd, A.H., Valdramidis, V.P., Van Impe, J.F. 2005. GInaFiT, a freeware tool to

assess non log-linear microbial survivor curves. International Journal of Food Microbiology

102:95-105

68

Heddleson, R.A., Doores, S. 1994. Injury of Salmonella species heated by microwave

energy. Journal of Food Protection 57:1068-1073

Hills, B.P., Manning, C.E., Ridge, Y., Brocklenhurst, T. 1997. Water availability and the

survival of Salmonella typhimurium in porous systems. International Journal of Food

Microbiology 36: 187-198

Jaykus, L., Dennis, S., Bernard, D., Claycamp, H. G., Gallagher, D., Miller, A. J., Potter,

M. et al. 2006. Using risk analysis to inform microbial food safety decisions. Issue Paper 31.

CAST, Ames, Iowa.

Kou, Y., Dickinson, L.C., Chinachoti, P. 2000. Mobility characterization of waxy corn

starch using wide-Line H nuclear magnetic resonance. Journal of Agricultural Food Chemistry

48:5489-5495

Lambertini, E., Danyluk, M.D., Schaffner, D.W., Winter, C.K., Harris, L.J. 2012. Risk of

salmonellosis from consumption of almonds in the North American market. Food Research

International 45:1166–1174

Mafart, P., Couvert, O., Gaillard, S., Leguerinel, I. 2002. On calculating sterility in

thermal preservation methods: application of the Weibull frequency distribution model.

International Journal of Food Microbiology 72:107-113

Mattick, K.L., Jørgensen, F., Wang, P., Pound, J., Vandeven, M.H., Ward, L.R., Legan,

J.D., et al. 2001. Effect of challenge temperature and solute type on heat tolerance of Salmonella

serovars at low water activity. Applied and Environmental Microbiology 67:4128-4136

McMeekin, T.A., Baranyi, J., Bowman, J., Dalgaard, P., Kirk, M., Ross, T. et al. 2006.

Information systems in food safety management. International Journal of Food Microbiology,

112:181–194

69

Oscar, T.P. 2009. General regression neural network and Monte Carlo simulation model

for survival and growth of Salmonella on raw chicken skin as a function of serotype,

temperature, and time for use in risk assessment. Journal of Food Protection 72:2078-2087

Podolak, R., Enache, E., Stone, W., Black, D. G., Elliot, P. H. 2010. Sources and risk



Rodríguez-Urrego, J., Herrera-León, S., Echeita-Sarriondia, A., Soler, P., Simon, F.,

Mateo, S. 2010. Nationwide outbreak of Salmonella serotype Kedougou associated with infant

formula, Spain, 2008. Euro Surveillance 15:19582.

Ross, T. 1996. Indices for performance evaluation of predictive models in food

microbiology. Journal of Applied Microbiology 81:501-508

Safe Food International (SFI). 2012. Food/Water borne illness outbreaks.

http://regionalnews.safefoodinternational.org/page/Food%2FWater+Borne+Illness+Outbreaks


Zwietering, M.H., Nauta, M.J. 2007. Predictive models in microbiological risk

assessment. In: Brul, S., van Gerwen, S., Zwietering, M.H. (Eds.), Modeling Microorganisms in

Food. CRC press, Boca Raton, FL, p. 110–125.

70

Figure 3.1. Survival of Salmonella at 36 °C during 168 days of storage in low-moisture whey

protein powder at 5 water activities (aw) and 3 water mobilities (T2*, where mobility is measured

in milliseconds) at each aw. Error bars represent the ±standard deviation of the average of three

replicas for each "aw-water mobility sample combination".

71

Figure 3.2. Survival of Salmonella at 50 °C during 30 days of storage in low-moisture whey




72

Figure 3.3. Survival of Salmonella at 70 °C during 2880 min (48 hours) of storage in low-

moisture whey protein powder at 5 water activities (aw) and 3 water mobilities (T2*, where

mobility is measured in milliseconds) at each aw. Error bars represent the ±standard deviation of

the average of three replicas for each "aw-water mobility sample combination".

73

Figure 3.4. Survival of Salmonella at 80 °C during 60 min of storage in low-moisture whey




74

Figure 3.5. Salmonella inactivation experiments at 6 T (°C), 5 water activities (aw), 3 water

mobilities (T2*, where mobility is measured in milliseconds) at each aw, in 5 food products: ( )

low-fat cocoa powder, (□) low-fat peanut meal, (○) non-fat dry milk, (◊) wheat flour, (-) whey

protein; (a) Observed versus predicted time required for first decimal reduction (log δ); (b)

Observed versus predicted shape factor values (β).

75

Figure 3.6. Observed versus predicted Salmonella counts (log CFU/g) for validation experiments

carried at 6 T (°C) and 5 water activities (aw) in 5 food products.

76

Table 3.1. Water mobility (T2*) values for whey protein powder of 3

different configurations and equilibrated to 5 water activity (aw) levels

Measured awa Configuration

T2* Mobile Protons

(ms)b

T2* Immobile Protons

(ms)

0.19±0.03 1c 0.075±0.01 0.0071±0.0005

2d 0.076±0.01 0.0069±0.0007

3e 0.076±0.009 0.0069±0.0001

0.29±0.03 1 0.093±0.02 0.0070±0.0001

2 0.092±0.009 0.0072±0.0003

3 0.098±0.03 0.0072±0.0004

0.36±0.03 1 0.096±0.001 0.0075±0.0002

2 0.121±0.03 0.0079±0.0008

3 0.094±0.007 0.0073±0.0007

0.43±0.02 1 0.101±0.003 0.0075±0.0003

2 0.108±0.006 0.0074±0.0001

3 0.094±0.005 0.0075±0.0002

0.54±0.02 1 0.129±0.008 0.0122±0.0002

2 0.132±0.002 0.0093±0.003

3 0.106±0.004 0.0095±0.0007 aaverage measured water activity ± sd of three replicates; baverage measured water

mobility ± sd of three replicates (where mobility is measured in milliseconds); cprotein

denatured at pH 2; dprotein denatured at pH 5; eprotein denatured at pH 7

77

Table 3.2. Statistical parameter fit results of the log-linear, Baranyi, Weibull, biphasic-linear and Geeraerd-tail models for Salmonella

inactivation in powders by adjusted R2 (R

2adj) and Root Mean Square Error (RMSE), where best values are shown in bold for

experiments at 6 T (°C), 5 water activities (aw) and 3 water mobilities (T2*) at each aw

Water

activitya

Water

mobilityb (ms)

T

°C

Log-

linearc

Baranyid Weibull

e Biphasic

f Geeraerd

g

Log-

linear Baranyi Weibull Biphasic Geeraerd

values RMSE values

0.19±0.03 0.075±0.011 21 -0.02 -0.11 -h - - 0.26 0.27 - - -

36 0.01 0.29 0.26 0.33 0.36 0.39 0.19 0.33 0.32 0.31

50 0.60 0.61 0.64 0.63 0.64 0.46 0.44 0.43 0.44 0.43

60 0.94 0.94 0.94 0.94 0.95 0.45 0.43 0.44 0.43 0.42

70 0.78 0.83 0.89 0.88 0.86 0.53 0.44 0.37 0.39 0.43

80 - 0.82 0.91 0.90 0.84 - 0.56 0.41 0.44 0.55

0.076±0.009 21 0.04 -0.07 - - - 0.19 0.20 - - -

36 0.13 0.32 0.31 0.36 0.38 0.29 0.16 0.26 0.25 0.25

50 0.73 0.75 0.76 0.76 0.77 0.38 0.35 0.35 0.35 0.35

60 0.94 0.93 0.94 0.94 0.94 0.39 0.41 0.39 0.39 0.39

70 - 0.75 0.84 0.84 0.77 - 0.60 0.50 0.50 0.59

80 - - 0.94 0.93 0.90 - - 0.45 0.47 0.56

0.076±0.010 21 -0.03 -0.11 - - - 0.34 0.35 - - -

36 0.19 0.35 0.32 0.38 0.40 0.29 0.13 0.27 0.25 0.25

50 0.70 0.69 0.79 0.80 0.73 0.32 0.32 0.27 0.27 0.30

60 0.84 0.83 0.84 0.79 0.85 0.70 0.73 0.71 0.82 0.68

70 0.61 0.76 0.79 0.79 0.79 0.71 0.53 0.52 0.52 0.52

80 0.71 - 0.87 0.86 0.79 0.75 - 0.50 0.52 0.63

0.29±0.03 0.092±0.009 21 0.01 -0.09 - - - 0.47 0.48 - - -

36 0.33 0.43 0.50 0.50 0.51 0.28 0.10 0.24 0.24 0.24

50 0.78 0.80 0.86 0.85 0.82 0.41 0.38 0.32 0.33 0.37

60 0.66 0.73 0.83 0.82 0.77 0.56 0.48 0.39 0.41 0.46

70 0.70 - 0.87 0.86 0.82 0.82 - 0.53 0.57 0.64

80 0.75 0.76 0.87 0.91 0.79 0.71 0.67 0.52 0.42 0.65

0.093±0.021 21 0.06 -0.04 - - - 0.20 0.21 - - -

36 0.32 0.50 0.49 0.53 0.54 0.31 0.16 0.26 0.26 0.25

50 0.81 0.81 0.85 0.84 0.83 0.39 0.38 0.35 0.37 0.37

60 0.78 0.74 0.83 0.85 0.77 0.60 0.63 0.53 0.49 0.61

78

70 - - 0.95 0.94 0.88 - - 0.29 0.32 0.45

80 - 0.82 0.92 0.94 0.84 - 0.56 0.39 0.32 0.55

0.098±0.028 21 0.30 0.22 - - - 0.17 0.18 - - -

36 0.27 0.50 0.51 0.54 0.56 0.29 0.24 0.24 0.23 0.23

50 0.88 0.88 0.92 0.92 0.90 0.31 0.29 0.26 0.26 0.28

60 0.77 0.73 0.78 0.79 0.76 0.77 0.81 0.76 0.74 0.79

70 0.69 0.84 0.85 0.86 0.85 0.87 0.61 0.60 0.60 0.60

80 0.58 0.57 0.70 0.72 0.64 0.74 0.70 0.62 0.60 0.68

0.36±0.03 0.094±0.007 21 0.07 -0.02 - - - 0.25 0.25 - - -

36 0.49 0.59 0.60 0.62 0.63 0.33 0.29 0.30 0.29 0.29

50 - - 0.82 0.78 NA - - 0.47 0.53 -

60 0.57 0.88 0.94 0.95 0.89 0.73 0.62 0.47 0.53 0.59

70 - - 0.96 0.93 - - - 0.28 0.36 -

80 0.67 0.72 0.78 0.77 0.77 0.58 0.50 0.47 0.48 0.48

0.096±0.001 21 0.14 0.05 - - - 0.26 0.27 -- - -

36 0.67 0.71 0.72 0.73 0.74 0.29 0.27 0.27 0.27 0.26

50 0.81 0.78 0.89 0.91 0.80 0.49 0.51 0.37 0.34 0.50

60 0.69 0.65 0.84 0.83 0.70 0.77 0.81 0.56 0.58 0.77

70 - - 0.95 0.93 0.90 - - 0.34 0.41 0.50

80 0.78 0.79 0.89 0.89 0.83 0.68 0.63 0.49 0.49 0.61

0.121±0.027 21 0.13 0.04 - - - 0.23 0.24 - - -

36 0.68 0.74 0.76 0.75 0.75 0.26 0.23 0.23 0.24 0.23

50 0.81 0.78 0.86 0.86 0.80 0.49 0.49 0.40 0.40 0.48

60 0.81 0.78 0.83 0.88 0.80 0.64 0.67 0.60 0.51 0.66

70 - - - - - - - - - -

80 - 0.84 0.93 0.73 0.87 - 0.48 0.94 0.94 0.47

0.43±0.02 0.094±0.005 21 0.49 0.43 - - - 0.21 0.22 - - -

36 0.75 0.82 0.83 0.84 0.85 0.36 0.29 0.30 0.29 0.29

50 - 0.91 0.95 0.93 0.92 - 0.46 0.37 0.42 0.45

60 0.53 0.77 0.76 0.80 0.81 1.10 0.72 0.78 0.71 0.69

70 - 0.79 0.91 0.86 0.82 - 0.69 0.47 0.57 0.65

80 - 0.79 0.93 0.93 0.83 - 0.45 0.29 0.27 0.36

0.101±0.003 21 0.40 0.34 - - - 0.16 0.17 - - -

36 0.75 0.81 0.84 0.85 0.84 0.36 0.30 0.29 0.28 0.29

50 0.83 0.86 0.93 0.92 0.87 0.65 0.58 0.43 0.45 0.57

60 0.54 0.71 0.83 0.77 0.76 0.79 0.59 0.48 0.55 0.57

70 - - 0.94 0.90 0.88 - - 0.35 0.46 0.51

80 - 0.82 0.89 0.91 0.85 - 0.63 0.52 0.49 0.61

79

0.108±0.006 21 0.12 0.03 - - - 0.26 0.27 - - -

36 0.76 0.78 0.81 0.80 0.81 0.37 0.34 0.33 0.34 0.33

50 0.83 0.83 0.89 0.89 0.85 0.64 0.62 0.53 0.52 0.61

60 0.45 0.69 0.76 0.72 0.74 0.91 0.65 0.60 0.65 0.63

70 - - 0.93 0.88 0.81 - - 0.35 0.46 0.59

80 - 0.82 0.84 0.85 0.85 - 0.43 0.43 0.41 0.42

0.54±0.02 0.106±0.004 21 0.29 0.22 - - - 0.31 0.32 - - -

36 - 0.87 0.85 0.89 0.89 - 0.37 0.42 0.36 0.36

50 - 0.72 0.89 0.90 0.77 - 0.58 0.38 0.37 0.56

60 0.39 0.69 0.80 0.76 0.76 1.32 0.87 0.76 0.83 0.83

70 - - 0.94 - - - - 0.33 - -

80 0.68 0.62 0.81 0.81 0.73 0.49 0.26 0.41 0.43 0.49

0.129±0.008 21 0.34 0.27 - - - 0.31 0.32 - - -

36 0.78 0.79 0.83 0.82 0.81 0.51 0.48 0.45 0.46 0.47

50 - - 0.89 - - - - 0.57 - -

60 - 0.73 0.92 0.82 0.78 - 0.71 0.41 0.62 0.68

70 - 0.78 0.91 0.88 0.84 - 0.63 0.43 0.48 0.55

80 0.74 0.84 0.87 0.89 0.89 0.55 0.38 0.39 0.36 0.36

0.132±0.002 21 0.51 0.46 - - - 0.31 0.32 - - -

36 - 0.76 0.84 0.86 0.80 - 0.48 0.41 0.38 0.45

50 - 0.78 0.86 0.85 0.82 - 0.46 0.38 0.40 0.44

60 0.35 0.74 0.74 0.76 0.79 1.56 0.91 0.98 0.94 0.88

70 - - 0.93 0.93 - - - 0.38 0.38 -

80 - 0.72 0.87 0.83 0.77 - 0.76 0.56 0.63 0.73 aaverage measured water activity ±sd of 3 replicates at all T; baverage measured water mobility± sd of 3 replicates at all T (milliseconds); cBigelow and Esty

(1920); dBaranyi and Roberts (1994); eMafart et al. (2002); fCerf (1977); gGeeraerd, Herremans and van Impe (2005); hftest>Ftable thus model does not describe

data well

80

Table 3.3. δ and β values of the Weibull model fit for Salmonella inactivation experiments

at 6 T (°C), 5 water activities (aw) and 3 water mobilities (T2*) at each aw

Water

activitya Water mobility

(ms)

b T °C log δ

c(log min) log se δ

d β

e se β

f

0.19±0.03 0.075±0.011 21 6.40 7.36 1.00 3.92 36 6.48 7.09 0.09 0.09

50 4.11 3.88 0.47 0.18

60 3.68 3.07 0.81 0.09

70 2.24 1.92 0.42 0.07

80 1.38 1.14 0.38 0.05

0.076±0.009 21 -g - - -

36 6.17 6.34 0.21 0.12

50 4.12 3.74 0.55 0.16

60 3.75 3.08 0.89 0.10

70 2.11 1.92 0.41 0.08

80 1.08 0.83 0.39 0.05

0.076±0.010 21 - - - - 36 5.94 5.95 0.28 0.15

50 4.03 3.72 0.37 0.09

60 3.79 3.36 0.88 0.18

70 2.02 1.95 0.36 0.09

80 1.39 1.20 0.41 0.07

0.29±0.03 0.092±0.009 21 6.29 7.30 0.04 0.09

36 5.57 5.38 0.26 0.10

50 3.60 3.33 0.39 0.07

60 2.72 2.56 0.35 0.07

70 1.59 1.47 0.35 0.06

80 0.81 0.68 0.43 0.08

0.093±0.021 21 6.03 6.35 1.08 1.57 36 5.43 5.22 0.28 0.10

50 3.81 3.48 0.49 0.11

60 3.29 2.98 0.63 0.14

70 1.68 1.33 0.35 0.04

80 0.76 0.47 0.41 0.05

0.098±0.028 21 5.95 5.87 0.78 0.51

36 5.52 5.40 0.22 0.08

50 3.88 3.34 0.55 0.08

60 3.26 3.03 0.69 0.18

70 1.73 1.64 0.38 0.08

80 0.83 0.87 0.38 0.11

0.36±0.03 0.094±0.007 21 7.88 8.69 0.18 0.20 36 5.12 4.80 0.39 0.12

50 3.49 3.04 0.47 0.06

60 2.22 2.24 0.29 0.07

70 1.15 0.85 0.30 0.03

80 0.74 0.64 0.40 0.11

0.096±0.001 21 6.35 6.59 0.35 0.26

36 5.08 4.59 0.53 0.13

50 3.30 3.05 0.40 0.07

60 2.45 2.37 0.36 0.07

70 1.27 1.01 0.32 0.03

80 0.71 0.60 0.40 0.07

0.121±0.027 21 6.11 6.33 0.71 0.74

81

36 5.06 4.57 0.45 0.10

50 3.49 3.25 0.43 0.08

60 3.26 2.93 0.67 0.14

70 0.99 0.85 0.29 0.04

80 0.76 0.44 0.43 0.05

0.43±0.02 0.094±0.005 21 5.51 4.76 1.59 0.74

36 4.69 4.28 0.50 0.09 50 3.64 3.15 0.67 0.09

60 2.29 2.38 0.37 0.11

70 0.89 0.83 0.28 0.04

80 0.52 0.24 0.40 0.06

0.101±0.003 21 5.98 5.80 0.59 0.29

36 4.82 4.35 0.56 0.11

50 3.38 3.07 0.53 0.08

60 2.04 2.02 0.29 0.06

70 1.13 0.91 0.30 0.03

80 0.39 0.35 0.35 0.06

0.108±0.006 21 6.18 6.41 0.56 0.56

36 4.72 4.32 0.53 0.10 50 3.63 3.31 0.65 0.13

60 1.63 1.83 0.25 0.06

70 0.31 0.23 0.22 0.02

80 -0.21 -0.16 0.25 0.05

0.54±0.02 0.106±0.004 21 - - - -

36 4.14 3.89 0.43 0.07

50 2.47 2.26 0.36 0.06

60 1.37 1.60 0.26 0.07

70 0.19 0.09 0.22 0.02

80 0.76 0.64 0.43 0.13

0.129±0.008 21 5.53 5.02 1.00 0.62

36 4.41 4.10 0.52 0.10 50 2.39 2.31 0.34 0.05

60 1.53 1.46 0.28 0.04

70 0.71 0.66 0.26 0.04

80 0.61 0.43 0.44 0.10

0.132±0.002 21 5.38 4.35 1.81 0.74

36 4.29 4.01 0.45 0.08

50 2.87 2.57 0.48 0.09

60 1.28 1.53 0.28 0.08

70 0.79 0.62 0.30 0.04

80 0.07 0.05 0.32 0.06 aaverage measured water activity ±sd of 3 replicates at all T; baverage measured water mobility± sd of 3

replicates at all T (measured in milliseconds); ctime required for first decimal reduction (measured in

minutes); dstandard error of δ parameter value; efitting parameter that defines the shape of the curve;

fstandard error of β parameter value; glog-linear regression gives a positive slope

82

Table 3.4. Correlation, discrepancy and bias between predicted and observed: time required

for first decimal reduction values (δ), shape factor values (β) and Salmonella cfu validation

counts according to product type

Food product Ra p-value

b

%

Df c

%

Bf d

All

(peanut meal, cocoa powder, wheat flour, whey

protein, non-fat dry milk)

Pred vs obsf 0.94 <0.001 41 -7

δg 0.97 <0.001 - e -

βh 0.03 0.915 - -

Low-fat*

(peanut meal, cocoa powder)

Pred vs obsf 0.95 <0.001 50 -9

δ 0.98 <0.001 - -

β -0.74 0.058 - -

Non-fat

(wheat flour, whey protein, non-fat dry milk)

Pred vs obsf 0.91 <0.001 12 -3

δ 1.00 <0.001 - -

β 0.60 0.208 - - acalculated correlation statistic; bsignificance of the correlation test; cpercent discrepancy; dpercent bias; enot

applicable; fpredicted versus observed bacterial count values; gtime required for first decimal reduction

(Equation 3.19); hshape factor (Equation 3.20); *the product contains 12 % fat

83

CHAPTER 4

SURVIVAL OF SALMONELLA IN LOW-MOISTURE FOODS: A META-ANALYSIS OF

THE LITERATURE DATA3

3 Santillana Farakos, S.M., D.W. Schaffner and J.F. Frank. To be submitted to Applied and

Environmental Microbiology.

84

ABSTRACT

Factors such as temperature, water activity, substrate, culture media, serotype and strain

have sown to influence the survival of Salmonella in low-moisture foods. Using the Weibull

model as a primary model, predictive models for Salmonella survival in low-moisture foods at

temperatures ranging from 21-80 °C and water activities below 0.6 have been developed. In the

present study, a quantitative analysis of literature data on survival of Salmonella in low-moisture

foods was conducted to validate the developed predictive models and to determine global

influencing factors. Results showed the Weibull model provided suitable fits and gave higher

statistical fit parameters in 75% of the curves as compared to traditional log-linear kinetics. The

secondary models predicting the time required for decimal reduction (log δ) and shape factor

(log β) values were useful in predicting the survival of Salmonella. Statistical analysis indicated

an overall fail-safe model with 88% of the residuals in the acceptable and safe zones (<0.5 log

CFU). A high variability in log δ- and log β-values was observed, emphasizing the importance of

experimental design. Factors of significant influence on the times required for first decimal

reduction included temperature, water activity, product and serotype. Log β- values were

significantly influenced by serotype, the type of inoculum (wet or dry) and whether the recovery

media was selective or not. The results of this meta-analysis serve as a general overview of

survival kinetics of Salmonella in low-moisture foods and its influencing factors.

KEYWORDS: Temperature, Water activity, Influencing factors, Kinetics, Weibull, D-values

85

INTRODUCTION

The presence, survival, and heat resistance of Salmonella in low-moisture foods combine

to provide a continuing challenge to the food industry (Podolak et al., 2010). In the United

States, Salmonella has caused the vast majority of outbreaks and recalls with regards to low-

moisture foods in the last several years (CDC, 2012). Numerous studies in two extensive reviews

conducted by Beuchat et al. (2013) and Podolak et al. (2010) have shown that Salmonella is able

to survive in low-moisture foods for weeks, months or even years. Heat can be applied to

inactivate Salmonella and, assuming log linear kinetics, survival numbers can be estimated using

the traditional D/z concept (Equation 4.1). In such cases, a D-value is defined as the time

necessary to reduce the population by 1 log.

(4.1)

where Nt is the concentration at time t, No is the concentration at time 0, t is the time (min), and

D is the decimal reduction time (min).

However, survivor curves of Salmonella in low-moisture foods often do not follow log-

linear kinetics and show significant asymptotic tails (Abd et al., 2012, Ma et al., 2012). The

Weibull model (Mafart el al., 2002) (Equation 4.2) has been shown to be the most accurate

model in describing Salmonella survival in low-moisture whey protein powder at temperatures

ranging from 21 °C to 80 °C and aw below 0.6 (Santillana Farakos et al., 2013).

(

)

(4.2)

where Nt, No and t are defined as above, δ is the time required for first decimal reduction (min)

and β is a fitting parameter that defines the shape of the curve.

86

Using the Weibull model as the primary model, our research group has recently

developed the first predictive models for survival of Salmonella in low-moisture foods at

temperature from 21 °C-80 °C and aw below 0.6 (Santillana Farakos et al., 2013). These

secondary models were useful in predicting the survival of Salmonella in the several low-

moisture foods we tested. We observed that factors food composition, water activity (aw), and

temperature influenced the survival of Salmonella. The pathogen exhibited increasing

persistence at decreasing aw, and the presence of fat protected against inactivation (Santillana

Farakos et al., 2013). Additional factors, including the addition of solutes to the matrix, acidity,

growth medium, stage of growth of the cells, stress prior to heating, species and strain, also

influence Salmonella survival in low-moisture foods (Podolak et al., 2010).

In this study, an extensive quantitative analysis of the low-moisture food Salmonella

survival data available in the literature was conducted. The objectives were to validate our

developed linear secondary models with literature data and to determine global influencing

factors on survival of Salmonella in low-moisture foods.


Selection of data

The literature was searched for data on the survival of Salmonella in low-moisture foods

primarily based on references cited in the reviews by Beuchat et al. (2013) and Podolak et al.

(2010). A database was created in Excel 2010 (Microsoft, Redmond, WA) detailing the

substrate, species, serovar, strain, packaging method, inoculum preparation method, growth

medium, recovery medium, temperature (°C), aw and time (min). Low-moisture foods are

characterized by having aw levels below 0.7 (Blessington et al., 2012). Thus, survival data were

excluded if the conditions used in the study included aw levels higher than 0.7 (i.e. Kieboom et

87

al., 2006). Studies were also excluded if survival numbers were reported as the time to achieve a

certain log-reduction (i.e. Abd et al., 2012, Ma et al., 2012, Mattick et., 2001, Dega et al., 1972,

Goepfert and Biggie, 1968) or as % recovery (i.e. Hills et al., 1997) due to the inability to fit this

data to primary models. Data from experiments with less than three data points were also

excluded (i.e. Van Cauwenberge et al., 1981) as model parameters cannot reliably be identified

on less than three data points (Geeraerd et al., 2005). A small number of data points (6%, n = 69)

for which the fitting of the primary models resulted in parameter estimates with very high

standard errors were removed from the data set and not considered in further analyses. Studies

which reported the moisture content of the substrates instead of their aw values (i.e.

Komitopoulou and Penaloza, 2009, Shachar and Yaron, 2006, Lee et al., 2006, McDonough and

Hargrove, 1968, Rayman et al., 1979) were included by using Appendix E in Schmidt and

Fontana (2008), where aw values for select food products are listed together with their

corresponding moisture content at a certain temperature. In one case (Liu et al., 1969) meat and

bone meal, 10% moisture) the moisture absorption curve published in Garcia et al. (2004) was

used for conversion, as meat and bone meal was not included in Appendix E of Schmidt and

Fontana (2008).

Due to the large variability in substrates, each substrate was grouped into one of the

following 11 categories: nuts (almond kernels, almonds, hazelnuts, in-shell pecans, pecan halves,

pecan pieces and walnut kernels), peanut butter (different product compositions such as % fat, %

protein, % carbohydrates, % sodium and % sugar), dairy (non-fat dry milk), seeds (alfalfa),

chocolate (bitter chocolate, cocoa beans, crushed cocoa shells and milk chocolate), halva, eggs

(egg white powder, egg yolk powder and whole egg powder), cereals (dried pasta and wheat

flour), meat (beef jerky), dry mixes (onion soup) and feed (dry feedstuff, meat and bone meal

88

and poultry feed). A final database of 1,064 data points was created.

Inactivation models

Salmonella survival data was fit to the log-linear model (Equation 4.1) and the Weibull

model (Equation 4.2) using GInaFiT Version 1.6 (Geeraerd et al., 2005, Katholieke Universiteit

Leuven, Leuven, Belgium). Decimal reduction times (log D-values) as well as the times required

for first decimal reduction (log δ) and shape factor values (log β) were obtained by fitting the

data to the log-linear and Weibull models, respectively. To determine which of the models best

described the data, the root mean square error (RMSE) and the adjusted coefficient of

determination (R2

adj) were calculated using Excel 2010 (Microsoft, Redmond, WA) according to

Equations 4.3 and 4.4 below (den Besten et al., 2006).

√

(4.3)

( )( )

(4.4)

where ∑( ̅̅ ̅̅ ̅̅ ̅̅ ̅̅ ̅̅ ̅̅ )

∑( ̅̅ ̅̅ ̅̅ ̅̅ ̅̅ ̅̅ ̅̅ ) ∑( )

where is the residual sum of squares of the model (sum of the squared differences

between the predicted and the observed values) and df is degrees of freedom where dfmodel= n-p

and dfdata=n-m (n is the total number of observations, p is the number of parameters in the model

and m is the number of time points).

At the same time, D-values were estimated for specific temperatures based on the method

described by Van Asselt and Zwietering (2006). Once the D-value (log Dref) at a certain

reference temperature (Tref) is known, one can obtain a D-value for any desired temperature

89

using Equation 4.5. Log Dref was calculated as the intercept of the linear fit between the log D-

values obtained by fitting the data to the log-linear model and temperature (Van Asselt and

Zwietering, 2006).

( ) (4.5)

og intercept (log ) (4.6)

(slope ( ) (4.7)

where log is the logarithm of the D-value (log min) as obtained by fitting the data to the log-

linear model, is the log D-value at a reference temperature (°C), T is temperature and z

is the temperature increase (°C) needed to reduce the D-value by a factor of 10. Tref was

calculated as the average of all temperatures included in the database (27.1 °C).

Similarly, log δ- and log β-values were estimated for any temperature and aw by using the

linear models developed previously (Santillana Farakos et al., 2013) (Equations 4.8 and 4.9).

log (4.8)

log (4.9)

where δ, β and T are defined as above and aw represents water activity.

To measure secondary model performance, the bias factor (Bf) expressed as % bias

(Equation 4.10) and accuracy factor (Af) expressed as % discrepancy (Equation 4.11) were

calculated (Baranyi et al., 1999). Residuals (r) were obtained using Equation 4.12 and the

acceptable residual zone was established to be from -1 log (fail safe) to 0.5 log (fail dangerous)

(Oscar, 2009). The percentage of residuals in the acceptable zone was used as an additional

model performance measurement (Oscar, 2009). The RMSE (Equation 4.4) and the correlation

90

coefficient values (R) (Equation 4.13) for the plots of the predicted against experimental survival

data were also used for model evaluation.

( ) ( | | ) (4.10)

where: f= 0

[∑ log(

log o ellog ob er e ⁄ )n

n]

( )=(

)

( ) (4.11)

where: = 0 [∑ | log(

log o ellog ob er e ⁄ )|n

n]

ob er e

o el (4.12)

( ) ∑( ̅)( ̅)

√∑( ̅) ∑( ̅)

(4.13)

where n is the total number of observations and p is the number of parameters in the model.

Statistical analyses

IBM SPSS Statistics for Windows, Version 21.0, IBM Corp was used to analyze the

data. Log D-values, log δ- and log β-values were plotted against temperature and aw to

determine visual differences among strains, food products and other factors. Multiple linear

regression was used to determine whether a factor was of significant influence to log D, log δ

and log β (with a ttest) using a significance level of 5 %. Normal probability plots were visually

91

evaluated for a linear relationship (where linearity indicates normality). Uniform variance was

verified using residual plots. If the plots of the residuals against log CFU values clustered around

zero, variances were considered constant.


Inactivation models

A total of 180 fitted and predicted log δ-, log β- and log D-values were obtained for

Salmonella survival data (Table 4.1). Statistical analysis showed the Weibull model provided the

best description of survival kinetics for Salmonella. The Weibull model showed lower RMSE and

higher R2

adj values for 75 % of the fitted curves (Table 4.1). In Figure 4.1, R2adj values for

survival data fitted with the Weibull and the log-linear models are plotted against temperature

(4.1a) and aw (4.1b). The negative R2

adj values in Figure 4.1 and Table 4.1 should not be

interpreted as poor model fits. These negative R2

adj values are representative of Salmonella

population numbers that are constant in time and are characteristic of survival studies done at

lower temperatures (≤ 40 °C) and during longer storage times (ranging from 56 days to more

than one year). Survival curves in these studies have a slope close to zero and thus have R2

values near zero. When these R2 values are adjusted for the degrees of freedom of the model, the

resulting R2

adj values become a negative number. Products for which R2

adj values were found to

be negative when fitting the data to both the Weibull and log-linear models include certain nuts

and chocolate products (Table 4.1). Additionally, some peanut butter products, seeds, dry mixes,

egg powder, halva and cereal products were found to have negative R2

adj values when using the

log-linear model to fit the survival data (Table 4.1). Thus, the conditions where the primary

models had negative R2

adj values were associated with higher Salmonella survival rates (Table

4.1). Both the Weibull model and the log-linear model showed increasingly better R2adj values

92

for survival studies at higher temperatures (> 40 °C) (p<0.001) (Figure 4.1a). Similarly, survival

data from studies at higher aw levels (>0.3) resulted in higher R2

adj values when fitting the data to

the Weibull model (p=0.042) (Figure 4.1b). No significant differences were found for R2

adj

values of the log-linear model curves at the different water activity levels (p=0.459) (Figure

4.1b). Salmonella survival curves in low-moisture environments generally show increased tailing

associated with increased inactivation temperature for any given aw (Santillana Farakos et al.,

2013). Similarly, at the same inactivation temperature, increasing aw results in curves with a

more pronounced downward concavity (Santillana Farakos et al., 2013). The outperformance of

the Weibull model in fitting the survival data in this meta-analysis stems from its ability to model

asymptotic curves with tails. Moreover, the Weibull model is able to produce linear fits (with

β=1 in Equation 4.2) and thus can also describe linear inactivation kinetics as obtained at lower

storage temperatures (< 40 °C). Similar to the results seen in this meta-analysis, previous studies

have shown Salmonella kinetics in low-moisture foods are best described by the Weibull model

(Abd et al., 2012, Mattick et al., 2001, Ma et al., 2009).

The statistical performance results of the secondary models are presented in Table 4.2.

The secondary models of the Weibull model (Equations 4.8 and 4.9) result in better prediction

performances as compared to the secondary model (Equation 4.5), which corresponds to the log-

linear model. The RMSE using Equations 4.8 and 4.9 is almost ten times lower than that obtained

when using Equation 4.5 (Table 4.2). Moreover, the correlation coefficient of predicted versus

observed survival counts (CFU) was considerably higher (R= 0.59) as compared to its log-linear

counterpart (R=0.37) (Table 4.2). These lower RMSE and increased correlation coefficients when

using the Weibull model highlight the need for non-linear kinetics to successfully describe the

survival of Salmonella in low-moisture foods. Additionally, Table 4.2 shows the percentage of

93

residuals in the acceptable zone and the discrepancy and bias percentages obtained when using

the secondary models of the non-linear and log-linear primary models. The percentages of

residuals in the acceptable zone are similar when using the models developed by Santillana

Farakos et al. (2013) (55%) and Equation 4.3 (52%) (Table 4.2). In addition, the discrepancy and

bias percentages when using Equations 4.8 and 4.9 are similar to those obtained from traditional

log D-values (Table 4.2). However, using Equation 4.3 resulted in a higher correlation between

predicted and observed log D-values (R=81%) as compared to using Equations 4.8 and 4.9 to

predict log δ- and log β- values, respectively (Table 4.2). Even though the log D-value

correlation was high, the prediction performance of Equation 4.3 for bacterial survivor counts

was poor (R=37%). When using the secondary models developed previously (Santillana Farakos

et al., 2013), a significant correlation of bacterial counts (R=59%) as well as log δ- (R=50%)

values was observed (p<0.001). However, the correlation of log β (R=-0.10) was poor (p=0.185).

As the Weibull model provided the best description of survival kinetics and its secondary models

a better prediction performance of bacterial survival numbers, the models developed by

Santillana Farakos et al. (2013) were chosen for further analysis. In this regard, Table 4.3

summarizes the discrepancy and bias percentages between predicted and observed survivor

counts as well as the percentage of residuals in the acceptable zone for Equations 4.8 and 4.9 by

substrate. Prediction performances that stand out for their deviation from the 16 % discrepancy

and -2 % bias inherent to the models are presented in bold in Table 4.3. Differences appear in the

prediction performance of the secondary models for the different products under study.

Examples of products for which large deviations exist in prediction performance include

chocolate, eggs, feed, nuts and peanut butter, all products with a high fat component (Table 4.3).

However, the deviations in secondary model statistical performance are not only the result of

94

differences in survival rates for Salmonella in the different products. They are also a result of the

product condition and data collection method. For instance, in the peanut butter category, half of

the predictions are well within the error margins of the model while the other half greatly deviate

(shown in bold). Peanut butter data showing great deviations in bias and accuracy percentages

correspond to the study of Burnett et al. (2000). In that study, peanut butter containing

Salmonella was stored for up to 6 months at 5 °C and 21 °C (Table 4.1). Peanut butter data

showing bias and discrepancy values in line with those inherent to the models corresponded to

the studies of Shachar and Yaron (2006) and Park et al. (2008). In the former study, Salmonella

was subject to high temperatures for short times, while in the latter study Salmonella was stored

at 4 °C and 22 °C for 2 weeks. A similar case is that of non-fat dry milk, where poor prediction

performances were seen in two of the studies, while data from a third study showed prediction

performances well within the error margins of the models (Table 4.3). The predictive models

developed by Santillana Farakos et al. (2013) were developed under controlled relative humidity

conditions by packaging the substrate under vacuum in retort pouches. However, most data

included in this meta-analysis comes from studies where relative humidity was not well

controlled. Water activity is a significant influencing factor on survival of Salmonella and lower

aw levels offer a protective effect (Beuchat et al., 2013, Podolak et al., 2010). If water activity

during storage is not controlled, the water activity of the substrate will equilibrate to the relative

humidity in the ambient air. Thus, collecting data in environments with humidity higher than that

established for the product will result in lower survival rates estimated for Salmonella.

Consequently, the predictive models developed by Santillana Farakos et al. (2013) were shown

to be fail-safe, with 55% of the residuals being in the acceptable residual zone (-1 to 0.5 log ) and

33% of the residuals being in the safe zone (< -1 log). This leaves 12% of the predictions to be

95

fail-dangerous (Table 4.3). These results show that there is a high degree of variability in the

survival of Salmonella under distinct substrate, temperature and aw conditions. Moreover, the

results in Table 4.3 indicate that though treatment conditions may be similar, different

experimental designs can lead to large variations in estimated survival numbers.

Factors influencing Salmonella survival

A high variation exists in log δ- and log β-values for Salmonella serotypes in different

substrates under distinct low-moisture conditions (Table 4.1). Figure 4.2 shows the times

required for first decimal reduction (log δ) plotted against temperature (top) and water activity

(bottom) for the distinct product categories under study. Data for product categories

characterized by having fat in their product composition (eggs, meat, chocolate, feed, halva,

mixes, nuts, peanut butter) are bolded in Figure 2. Despite the high variability in log δ-values

found, temperature (p<0.001), aw (p=0.025) and product category (p<0.001) had a significant

influence on log δ. However, possibly due to most product categories including fat in their

composition, no significant differences in log δ-values were found between product categories

containing fat and those which are considered non-fat (p=0.098). These results are in line with

those presented in Tables 4.1, 4.3 and Figure 4.1, where temperature and water activity influence

log δ-values producing different model performance. As temperature increases, there is a

decrease in log δ-values, which is associated with a decrease in Salmonella resistance at higher

temperatures (Figure 4.2). As seen in Figure 4.2 (top), Salmonella in seeds and nuts stands out

for its persistence at temperatures around 20 °C. In fact, log δ-values in seeds and nuts are even

higher than those found for chocolate and peanut butter at the same temperature (Figure 4.2, top).

The results for temperature are strikingly similar to those for water activity. As water activity

increases, log δ-values tend to decrease, indicating a lower heat resistance of Salmonella at

96

higher water activities (Figure 4.2, bottom). Moreover, seeds and nuts again stand out for their

ability to protect Salmonella from inactivation at higher water activity levels (0.4 < aw< 0.6)

(Figure 4.2, bottom). The greater protective ability of seeds and nuts could partly explain the

higher bias and discrepancy percentages seen for this product category (Table 4.3). Consistent

with the results of others (Podolak et al., 2010), increasing temperature and water activity

resulted in decreasing resistance of Salmonella. Medium composition also significantly

influenced the survival of Salmonella, with nuts and seeds associated with the greatest

persistence. This is in line with the results of other studies in which substrate significantly

influenced survival (Moats et al., 1971, Hiramatsu et al., 2005, Dega et al., 1972, Goepfert et al.,

1970).

In addition to temperature, water activity and product category, the type of inoculum (wet

or dry) and the Salmonella serotype used resulted in significant differences in log δ-values

(p<0.001). Greater survival rates were found for Salmonella when using a liquid as opposed to

dry inoculum. This is in contrast to other studies that found that drying the inoculum used in

survival studies produces data showing less inactivation (1, S.M. Santillana Farakos, J.F. Frank,

and D.W Schaffner, unpublished data). Because 92% of the data included in this meta-analysis

came from studies in which the Salmonella inoculum was not previously dried, the greater

survival observed when using a liquid inoculum may be biased. Moreover, significantly different

log δ-values were obtained for different Salmonella serotypes. This could be an additional

explanatory variable for the striking differences in persistence found in peanut butter of similar

composition at similar temperatures and water activities (Figure 4.2). This could also explain the

differences seen in the secondary model statistical performance shown in Table 4.3. As seen in

Table 4.1, peanut butter data at refrigeration (4 °C - 5 °C) and room temperature (22 °C - 25 °C)

97

and average aw levels of 0.22±0.04 were taken from Burnett et al. (2000) and Park et al. (2008).

The major difference between these two studies is the Salmonella serotype used. The study of

Burnett et al. (2000) used a cocktail of Salmonella serotypes including S. Agona, S. Enteritidis,

S. Michigan, S. Montevideo and S. Typhimurium while the study of Park et al. (2008) used only

S. Tennessee. It is the S. Tennessee data that showed greater survival of Salmonella in peanut

butter. It is also the data of Park et al. (2008) that produced greater model performance with the

secondary models of Santillana Farakos et al. (2013) (Table 4. 3). Thus, S. Tennessee in peanut

butter at temperatures below 25 °C and water activities of 0.22±0.04 survived better than S.

Agona, S. Enteritidis, S. Michigan, S. Montevideo and S. Typhimurium. Other factors, such as

strain (p=0.081), whether the product was packaged under vacuum or not (p=0.099), whether the

growth media was agar or broth (p=0.480) and whether the recovery media was selective or non-

selective (p=0.102) did not significantly influence log δ-values. This is in contrast to other

studies which have shown culturing and harvest methods of the inoculum as well as recovery

methodology affect Salmonella survival numbers (Komitopoulou and Penaloza, 2009, Uesugi et

al., 2006). However, it is in line with results that showed that using different culture media (agar

or broth) to grow Salmonella did not influence the statistical performance of the secondary

models (S.M. Santillana Farakos, J.F. Frank, and D.W Schaffner, unpublished data).

The influence of various factors on the shape of the inactivation curve (log β) was also

studied, with very different results from those seen for log δ-values. Temperature (p= 0.920),

water activity (p=0.147) and product category (p=0.236) did not significantly influence the shape

of the survivor curve (log β). The results also indicated that strain (p=0.271), whether the

product was or was not vacuum-packed (p=0.435) or whether the growth media was agar or

broth (p=0.464) did not significantly influence the shape of the survival curve. Log β was

98

significantly influenced by serotype (p=0.040), the type of inoculum (wet or dry) (p=0.016) and

whether the recovery media was selective or not (p=0.005). Log β-values are plotted against

temperature for all Salmonella serotypes included in the study (Figure 4.3). These data show

high variability in log β-values for the different serotypes, negative log β-values representing

asymptotic tails, log β-values close to zero representing linear inactivation curves and log β-

values higher than zero representing curves with shoulders. Decreasing levels of log β represent

curves characterized by a sharp initial decrease in Salmonella numbers followed by a

characteristic survivor tail. An example of such behavior is that of S. Enteritidis in crushed

hazelnut shells stored for 3 weeks at 5 °C and 0.24 aw (Komitopoulou and Penaloza, 2009).

Under these conditions, S. Enteritidis produced a log-β value that is the lowest of the whole

dataset (Figure 4.3). This is in line with the results presented by Komitopoulou and Penaloza

(2009), where S. Enteritidis showed increased survival when compared to other serotypes.

Similarly, as seen in Figure 4.3, at 80 °C and 90 °C, negative log β-values were observed for a

cocktail of Salmonella serotypes (S. Agona, S. Enteritidis and S. Typhimurium) in peanut butter

(Shachar and Yaron, 2006). A different cocktail of Salmonella serotypes (S. Senftenberg, S.

Typhimurium and S. New Brunswick) in non-fat dry milk at 77 °C also resulted in negative log

β-values (-0.33). However, the same Salmonella serotypes in the same product at 60 °C produced

the highest log β-value of the whole dataset (0.49). Moreover, S. seftenberg in feed at 77 °C

produced an inactivation curve close to linearity (log β= -0.12). With regards to the type of

inoculum, data using wet Salmonella cells gave negative and lower log β-values as compared to

studies based on dried cells. However, since only 8% of the data came from studies using a dry

inoculum, these results are not conclusive. Additionally, studies in which a selective recovery

media was used showed average log β-values that were negative and lower than those obtained

99

with non-selective media. Cells with an injured cytoplasmic membrane are not able to grow in

selective media. The faster decline in Salmonella numbers seen in studies using selective media

(represented by negative log β-values) may result from the lower recovery rates when using that

type of media.

CONCLUSIONS

In conclusion, while the results of any meta-analysis should be interpreted cautiously, our

results show that temperature and water activity, along with medium composition and serotype,

play a crucial role in the survival kinetics of Salmonella in low-moisture foods. Although the

secondary models developed by Santillana Farakos et al. (2013) demonstrated acceptable

prediction performances, the models should not be applied to specific food systems without

further validation. The Santillana Farakos et al. (2013) models can be improved by adding

additional factors such as serotype and product matrix, as predictive factors for log δ- and log β-

values. Large variations in log δ- and log β-values were observed in the various data sources,

emphasizing the importance of experimental design and the limits of generalizations. The results

of this meta-analysis serve as a general overview of survival kinetics of Salmonella in low-

moisture foods and its influencing factors.

100

References

Abd, S.J., K.L. McCarthy, and L.J. Harris. 2012. Impact of storage time and temperature

on thermal inactivation of Salmonella Enteritidis PT 30 on oil-roasted almonds. Journal of Food

Science 77:M42-M7

Archer, J., E.T. Jervis, J. Bird, J.E. Gaze. 1998. Heat resistance of Salmonella


Baranyi, J., C. Pin, and T. Ross. 1999. Validating and comparing predictive models.

International Journal of Food Microbiology 48: 159-166

Beuchat, L. and A. Scouten. 2002. Combined effects of water activity, temperature and


seeds. Journal of Applied Microbiology 92:382-95

Beuchat, L.R., and E.K. Heaton. 1975. Salmonella survival on pecans as influenced by

processing and storage conditions. Appled and Environmental Microbiology 29:795-801

Beuchat, L.R., E. Komitopoulou, H. Beckers, R.P. Betts, F. Bourdichon, et al. 2013. Low

water activity foods: increased concern as vehicles of foodborne pathogens. Journal of Food

Protection 76:150-72.

Beuchat, L.R., and D.A. Mann. 2010. Factors affecting infiltration and survival of

Salmonella on in-shell pecans and pecan nutmeats. Journal of Food Protection 73:1257-68

Bigelow, W.D., and J. R. Esty. 1920. The thermal death point in relation to typical

thermophylic organisms. Journal of Infectious Diseases 27: 602-617

Blessington, T., E.J. Mitcham, L.J. Harris. 2012. Survival of Salmonella enterica,

Escherichia coli O157:H7, and Listeria monocytogenes on inoculated walnut kernels during

storage. Journal of Food Protection 75:245-54

101

Burnett, S.,E. Gehm, W. Weissinger, L. Beuchat. 2000. Survival of Salmonella in peanut

butter and peanut butter spread. Journal of Applied Microbiology 89:472-7

Calicioglu, M., J.N. Sofos, P.A. Kendall, G.C. Smith. 2003. Effects of acid adaptation

and modified marinades on survival of post-drying Salmonella contamination on beef jerky

during storage. Journal of Food Protection 66:396-402

CDC (Centers for Disease Control and Prevention), 2012. Foodborne Outbreak Online

Database (FOOD) http://wwwn.cdc.gov/foodborneoutbreaks/ Accessed August 2012

Christian, J. H. B., and B. J. Stewart. 1973. Survival of Staphylococcus aureus and

Salmonella newport in dried foods, as influenced by water activity and oxygen, p. 107-119. In

B.C. Hobbs and J.H.B. Christian (ed.), The microbiological safety of foods. Academic Press,

London.

de Rezende, C. L. E., E. T. Mallinson, A. Gupta, and S. W. Joseph. 2001. Salmonella

spp. are affected by different levels of water activity in closed microcosmos. Journal of Industrial

Microbiology and Biotchnology 26:222-225

Dega, C.A., J.M. Goepfert, C.H. Amundson. 1972. Heat resistance of Salmonellae in

concentrated milk. Journal of Applied Microbiology 23:415-20

den Besten, H.M.W., M. Mataragas, R. Moezelaar, T. Abee, and M.H. Zwietering. 2006.

Quantification of the effects of salt stress and physiological state on thermotolerance of Bacillus

cereus ATCC 10987 and ATCC 14579. Applied and Environmental Microbiology 72: 5884-

5894

Garcia, R.A., C.I. Onwulata, and R.D. Ashby. 2004. Water plasticization of extruded

material made from meat and bone meal and sodium caseinate. Journal of Agricultural Food

Chemistry 52: 3776-9

102

Geeraerd, A.H., V.P. Valdramidis, and J.F. Van Impe. 2005. GInaFiT, a freeware tool to

assess non log-linear microbial survivor curves. International Journal of Food Microbiology 102:

95-105

Goepfert, J. M., and R. A. Biggie. 1968. Heat resistance of Salmonella senftenberg 775W

in milk chocolate. Journal of Applied Microbiology 16:1939–1940

Goepfert, J. M., I.K. Iskander, and C.H. Amundson. 1970. Relation of the heat resistance

of salmonellae to the water activity of the environment. Journal of Applied Microbiology

19:429–4331970

Hills, B.P., C.E. Manning, Y. Ridge, and T. Brocklenhurst. 1997. Water availability and

the survival of Salmonella typhimurium in porous systems. International Journal of Food


Hiramatsu, R., M. Matsumoto, K. Sakae, Y. Miyazaki. 2005. Ability of shiga toxin-

producing Escherichia coli and Salmonella spp. to survive in a desiccation model system and in

dry foods. Applied and Environmental Microbiology 71:6657-63

Jung, Y.S., and L.R. Beuchat. 1999. Survival of multidrug-resistant Salmonella

typhimurium DT104 in egg powders as affected by water activity and temperature. International

Journal of Food Microbiology 49:1-8

Juven, B.J., N.A. Cox, J.S. Bailey, J.E. Thomson, O.W. Charles and J.V. Shutze. 1984.

Survival of Salmonella in dry food and feed. Journal of Food Protection 47:445-448

Kieboom, J., K.D. Harshi, M.H. Tempelaars, W.C. Hazeleger, T. Abee, and R.R.

Beumer. 2006. Survival, elongation, and elevated tolerance of Salmonella enterica serovar

Enteritidis at reduced water activity. Journal of Food Protection 69:2681–2686

103

Komitopoulou, E., and W. Peñaloza. 2009. Fate of Salmonella in dry confectionery raw


Kotzekidou, P. 1998. Microbial stability and fate of Salmonella Enteritidis in halva, a

low-moisture confection. Journal of Food Protection 61:181-5

Lee, S-Y, S-W Oh, H-J Chung, J.I. Reyes-De-Corcuera, J.R. Powers,and D-H Kang.

2006. Reduction of Salmonella enterica serovar Enteritidis on the surface of raw shelled almonds

by exposure to steam. Journal of Food Protection 69:591-5

Liu, T.S., G.H. Snoeyenbos, and V.L. Carlson. 1969. Thermal resistance of Salmonella

senftenberg 775W in dry animal feeds. Avian Diseases 13:611-31

Ma, L., G. Zhang, P. Gerner-Smidt, V. Mantripragada, I. Ezeoke, and M.P. Doyle. 2009.


Mafart, P., O. Couvert, , S. Gaillard, I. Leguerinel. 2002. On calculating sterility in



Mattick, K.L., F. Jørgensen, P. Wang, J. Pound, M.H. Vandeven, L.R. Ward, J.D. Legan,

H.M. Lappin-Scott, and T.J. Humphrey. 2001. Effect of challenge temperature and solute type on

heat tolerance of Salmonella serovars at low water activity. Applied and Environmental


McDonough, F.E., R.E. Hargrove. 1968. Heat Resistance of Salmonella in dried milk.

Journal of Dairy Science 51:1587-91

Moats, W.A., R. Dabbah, and V.M. Edwards. 1971. Survival of Salmonella Anatum

heated in various media. Journal of Applied Microbiology 21: 476–4811

104

Oscar, T.P., 2009. General regression neural network and Monte Carlo simulation model

for survival and growth of Salmonella on raw chicken skin as a function of serotype,

temperature, and time for use in risk assessment. Journal of Food Protection 72: 2078-2087

Park, E.J., D.H. Kang, and S.W. Oh. 2008. Fate of Salmonella Tennessee in peanut butter

at 4 and 22 °C. Journal of Food Science 73:m82-m6

Podolak, R., E. Enache, W. Stone, D.G. Black, P.H. Elliot. 2010. Sources and risk factors

for contamination, survival, persistence, and heat resistance of Salmonella in low-moisture

foods. Journal of Food Protection 73:1919-1936

Rayman M.K., J.Y. D'Aoust, B. Aris, C. Maishment, and R. Wasik. 1979. Survival of

microorganisms in stored pasta. Journal of Food Protection 42:330-4

Santillana Farakos, S.M., J.F. Frank, and D.W. Schaffner. 2013. Modeling the influence

of temperature, water activity and water mobility on the persistence of Salmonella in low-

moisture foods. International Journal of Food Microbiology. doi:

10.1016/j.ijfoodmicro.2013.07.007

Schmidt, S.J. and A.J. Fontana. 2007. Water activity values of select food ingredients and

products. In Water activity in foods: fundamentals and applications Gustavo V. Barbosa-

Cánovas, Anthony J. Fontana, Shelly J. Schmidt, Theodore P. Labuza (Eds)

Shachar, D., and S. Yaron. 2006. Heat Tolerance of Salmonella enterica serovars Agona,


Tamminga, S.K., R.R. Beumer, E.H. Kampelmacher, F.M. van Leusden. Survival of

Salmonella eastbourne and Salmonella typhimurium in chocolate. Epidemiology and Infection

76:41-7

105

Uesugi, A.R., L.J. Harris, and M.D. Danyluk. 2006. Survival of Salmonella Enteritidis

phage type 30 on inoculated almonds stored at -20, 4, 23, and 35 °C. Journal of Food Protection

69:1851-7

Van Cauwenberge, J.E., R.J. Bothast, and W.F. Kwolek. 1981. Thermal inactivation of

eight Salmonella serotypes on dry corn flour. Applied and Environmental Microbiology 42:688-

91

Van Asselt, E., and M. H. Zwietering. 2006. A systematic approach to determine global

thermal inactivation parameters for various food pathogens. International Journal of Food

Microbiology 107:73–82

106

Figure 4.1. Plots of R2

adj values against temperature (a) and aw (b) for literature data on

Salmonella survival fitted with the Weibull (○) and the log-linear (◊) models using GInaFiT.

107

Figure 4.2. Log δ-values of Salmonella in various food products are plotted against temperature

(top) and water activity (bottom).

108

Figure 4.3. Log β-values for Salmonella serotypes in various food products plotted against

temperature.

109

Table 4.1. Fitted and estimated log β-, log δ- and log D-values for Salmonella survival in different substrates at distinct T and aw

conditions

Weibulle Log-linear

h

Substrate Serotypea T °C aw

b RMSE

c R

2adj

d

Log β±

s.e.f

Log δ±

s.e.g

RMSE R2

adj Log D±

s.e.i

Reference

Cereal

Infantis 22 0.40 0.33 0.78 -0.30±-0.65 5.16±5.04 0.46 0.57 5.36±6.02

Rayman et al., 1979 0.25 0.73 -0.55±-0.86 5.39±5.40 0.57 -0.36 5.47±5.92

Typhimurium 22 0.40

0.22 0.96 -0.04±-0.76 5.19±4.58 0.36 0.89 5.17±6.12

Rayman et al., 1979 0.32 0.69 -0.71±-0.95 4.43±4.69 0.64 -0.24 5.15±5.56

0.33 0.87 0.58±0.46 5.61±4.88 0.52 0.68 5.43±5.97

Wetevreden 70 0.40 0.51 0.88 -0.39±-0.92 0.56±0.63 0.79 0.72 1.59±2.23 Archer et al., 1998

Chocolate

Eastbourne 20

0.38 0.89 0.59 -0.26±-0.39 4.73±4.96 1.60 -0.33 4.91±5.34

Tamminga et al., 1976 0.41 0.70 0.59 -0.46±-0.62 4.27±4.61 1.36 -0.56 4.90±5.23

0.44 0.99 0.73 -0.59±-0.81 2.08±2.67 1.55 0.33 4.24±4.51

0.51 0.71 0.88 -0.66±-1.19 1.82±2.24 2.53 -0.53 4.59±4.96

Enteritidis 21

0.24 0.16 0.74 -0.65±-0.50 5.06±5.47 0.26 0.30 4.76±4.94 Komitopoulou and

Penaloza, 2009 0.69 0.26 0.98 -0.13±-0.91 3.71±3.27 0.44 0.94 3.95±4.86

0.22 0.82 -0.35±-0.57 4.39±4.14 0.26 0.75 4.54±5.09

Montevideo 5 0.24

0.19 0.95 -0.60±-0.80 3.41±3.58 0.53 0.64 4.23±4.63 Komitopoulou and

Penaloza, 2009 0.34 0.84 -0.27±-0.46 3.96±3.88 0.36 0.82 4.21±4.80

21 0.24 0.39 0.90 -0.29±-0.57 3.60±3.63 0.51 0.83 4.05±4.65

Napoli 5 0.24 0.35 0.71 0.16±0.04 4.38±3.88 0.26 0.84 4.33±4.94 Komitopoulou and

Penaloza, 2009 21 0.24 0.05 1.00 -0.04±-1.17 4.10±2.98 0.06 1.00 4.14±5.56

Oranienburg

5 0.24 0.20 0.88 -0.37±-0.55 4.24±3.99 0.25 0.80 4.39±4.95

Komitopoulou and Penaloza, 2009

1.69 0.50 0.04±0.05 3.83±4.04 1.20 0.75 3.78±4.27

21

0.24

0.09 0.95 0.14±-0.39 4.50±3.46 0.09 0.95 4.51±5.40

1.27 0.65 -0.01±-0.09 3.79±3.94 0.90 0.82 3.81±4.40

0.19 -0.02 0.11±0.34 4.84±4.99 0.14 0.48 4.93±5.22

0.69 0.53 0.93 -0.20±-0.70 3.48±3.41 0.66 0.89 3.92±4.69

0.33 0.71 -0.32±-0.44 4.26±4.17 0.32 0.74 4.46±5.00

Poona

5

0.24

0.64 0.82 -0.20±-0.38 3.64±3.75 0.59 0.85 3.96±4.58


0.31 0.84 -0.84±-0.56 3.08±3.83 0.56 0.48 4.32±4.61

21 0.34 0.95 -0.49±-0.80 2.78±3.05 0.83 0.73 3.96±4.43

0.34 0.91 -1.38±-0.71 -4.22±-2.25 0.86 0.43 4.16±4.42

Seftenberg 5

0.24


Penaloza, 2009 0.39 0.17 0.42±0.64 4.52±3.92 0.37 0.26 4.63±4.79

21 0.04 1.00 -0.04±-1.45 3.97±2.66 0.06 1.00 4.01±5.54

Typhimurium 5 0.24 0.39 0.29 -0.56±-0.22 4.45±4.70 0.34 0.48 4.54±4.83 Komitopoulou and

Penaloza, 2009

110

20

0.37 0.26 0.97 -0.86±-1.49 0.07±0.46 1.30 0.34 4.23±4.51

Tamminga et al., 1976 0.40 0.46 0.93 -0.61±-1.24 2.13±2.36 1.10 0.59 4.49±5.00

0.42 0.12 1.00 -0.84±-1.71 -0.19±-0.05 1.57 0.36 3.76±3.97

0.48 0.25 0.99 -0.75±-1.77 0.14±0.19 1.78 0.48 4.37±4.79

21 0.24 0.04 1.00 -0.08±-1.17 4.24±3.03 0.06 0.96 4.64±5.57 Komitopoulou and

Penaloza, 2009

Dairy

Cocktailj

4

0.20

0.47 0.19 -0.46±-0.40 5.09±5.30 0.47 0.18 5.19±5.43

McDonough et al., 1968

15 0.19 0.89 -0.14±-0.69 4.89±4.35 0.19 0.88 4.96±5.81

27 0.31 0.91 -0.30±-0.90 4.22±4.03 0.49 0.77 4.72±5.42

38 0.53 0.68 -0.34±-0.52 4.50±4.63 0.67 0.49 4.75±5.28

43 0.19 0.98 -0.40±-1.24 3.26±2.98 0.63 0.83 4.21±4.91

50 0.36 0.97 -0.35±-1.03 3.18±3.08 0.73 0.86 4.10±4.85

60 0.12 0.97 0.49±-0.12 2.68±1.47 0.30 0.83 2.48±3.25

77 0.31 0.93 -0.33±-1.01 1.58±1.40 0.54 0.78 2.28±2.98

85 0.36 0.94 -0.24±-0.88 1.39±1.19 0.47 0.90 1.89±2.77

115 0.01 1.00 0.15±-2.20 1.25±-1.09 0.30 0.97 1.09±2.05

Heidelberg 25 0.43 0.38 0.92 -0.02±-0.43 4.34±4.06 0.27 0.96 4.36±5.30

Juven et al., 1984 0.52 0.42 0.93 -0.15±-0.61 4.07±3.91 0.38 0.94 4.31±5.15

Montevideo 25 0.43 0.31 0.95 -0.06±-0.57 4.29±3.94 0.24 0.97 4.36±5.36

Juven et al., 1984 0.52 0.58 0.86 -0.21±-0.52 3.96±4.00 0.52 0.88 4.32±5.01

Eggs Typhimurium

13

0.33

0.27 0.95 -0.52±-1.06 3.32±3.35 0.83 0.49 4.55±4.89

Jung and Beuchat, 1999

0.16 0.98 -0.92±-1.39 1.14±1.57 1.00 0.26 4.61±4.80

0.18 0.97 -0.62±-1.22 3.13±3.14 0.80 0.47 4.57±4.90

0.19 0.97 -0.80±-1.28 2.05±2.38 0.92 0.33 4.61±4.84

0.57

0.35 0.93 -0.73±-1.05 2.19±2.65 0.95 0.44 4.51±4.83

0.18 0.97 -0.68±-1.22 3.01±3.10 0.70 0.48 4.62±4.96

0.10 0.99 -0.45±-1.42 3.64±3.16 0.58 0.70 4.53±5.04

0.29 0.93 -0.47±-0.98 3.56±3.16 0.67 0.63 4.53±4.98

0.11 0.99 -0.84±-1.47 2.10±2.25 0.82 0.36 4.63±4.89

37

0.33

0.20 0.99 -1.25±-1.42 -5.28±-4.09 1.79 -0.04 4.57±4.54

1.21 0.54 -0.63±-0.24 2.83±3.81 1.54 0.24 4.14±4.29

1.31 0.52 -0.43±-0.26 3.18±3.86 1.61 0.27 4.10±4.27

0.46 0.94 -1.24±-0.88 -5.34±-3.63 1.73 0.11 4.17±4.24

0.57

1.83 0.12 0.00±-0.97 -5.35±-3.76 1.83 0.12 4.14±4.21

0.39 0.96 -0.73±-0.94 1.28±1.94 1.54 0.38 4.06±4.29

2.12 0.13 -0.58±-0.04 2.81±3.95 2.17 0.08 4.09±4.14

Feed

Heidelberg 25 0.43 0.41 0.95 -0.14±-0.69 3.97±3.77 0.46 0.94 4.23±5.07

Juven et al., 1984 0.52 0.14 0.99 -0.35±-1.34 3.42±2.96 0.81 0.81 4.25±4.82

Montevideo 25 0.43 0.29 0.98 -0.23±-0.94 3.75±3.47 0.55 0.92 4.23±5.00 Juven et al., 1984

0.52 0.12 1.00 -0.39±-1.45 3.30±2.80 0.87 0.77 4.27±4.79

Seftenberg

54

0.60

0.07 0.98 -0.18±-1.14 2.04±1.12 0.13 0.95 2.17±3.23

Liu et al., 1969 57 0.03 1.00 -0.22±-1.55 1.72±0.53 0.18 0.94 1.94±2.90

60 0.05 1.00 -0.22±-1.50 1.55±0.53 0.18 0.95 1.84±2.94

111

63 0.04 1.00 -0.19±-1.52 1.24±0.08 0.16 0.96 1.45±2.51

66 0.06 0.99 -0.14±-1.33 1.04±0.02 0.14 0.97 1.21±2.40

66 0.10 0.98 -0.39±-1.24 1.79±1.24 0.30 0.81 2.30±3.01

71 0.15 0.98 -0.25±-1.10 1.55±1.02 0.30 0.91 1.94±2.85

74 0.18 0.98 -0.15±-0.98 1.45±0.88 0.26 0.95 1.67±2.68

77 0.22 0.98 -0.12±-1.13 1.21±0.59 0.31 0.97 1.46±2.65

79 0.08 1.00 -0.17±-1.50 0.83±-0.13 0.28 0.97 1.15±2.27

Halva Enteritidis 19 0.18 0.31 0.53 -0.58±-0.76 5.47±5.62 0.52 -0.30 5.41±5.86 Kotzekidou, 1998

Meat

Heidelberg 25 0.43 0.55 0.91 -0.45±-0.82 3.05±3.31 1.12 0.64 4.28±4.68

Juven et al., 1984 0.52 0.19 0.99 -0.45±-1.31 2.95±2.74 1.10 0.70 4.24±4.69

Montevideo 25 0.52 0.30 0.98 -0.47±-1.14 2.85±2.85 1.15 0.68 4.24±4.67 Juven et al., 1984

Typhimurium 25 0.66 0.21 0.97 0.01±-0.64 4.50±3.92 0.15 0.98 4.49±5.62 Calicioglu et al., 2003

Mixes Newport 25

0.11 0.16 0.68 -0.83±-1.06 5.59±5.74 0.35 -0.64 5.50±5.84

Christian and Stewart,

1973

0.22 0.05 0.98 -0.57±-1.40 5.46±4.84 0.32 0.23 5.38±5.88

0.33 0.25 0.92 -0.29±-0.89 4.65±4.39 0.48 0.71 4.97±5.71

0.43 0.20 0.98 -0.19±-1.04 4.54±4.05 0.48 0.88 4.81±5.71

0.53 0.40 0.95 -0.32±-1.03 3.94±3.80 1.01 0.71 4.67±5.39

Nuts

Anatum 21 0.70 1.50 0.25 -0.19±-0.01 4.72±5.16 2.17 -0.55 4.64±4.98

Beuchat and Heaton, 1975 0.51 0.88 -0.01±-0.40 4.95±4.71 0.51 0.88 4.89±5.77

Cocktail

-20

0.47 0.19 0.10 -0.87±-1.04 6.19±6.47 0.35 -2.00 6.03±6.34

Beuchat and Mann, 2010

0.57

0.18 0.35 -0.22±-0.25 6.24±6.30 0.17 0.36 5.99±6.45

0.37 0.80 -0.52±-0.91 4.50±4.63 0.85 -0.04 5.22±5.76

0.31 0.10 -0.37±-0.20 6.39±6.80 0.29 0.19 5.92±6.22

0.44 0.64 -0.32±-0.44 5.18±5.21 0.74 -0.01 5.13±5.64

4 0.57

0.12 0.72 -0.09±-0.34 6.07±5.78 0.12 0.71 6.07±6.62

0.31 0.89 -0.20±-0.71 5.06±4.80 0.47 0.75 5.22±6.02

0.14 0.61 -0.16±-0.30 6.15±6.04 0.13 0.69 5.97±6.58

0.23 0.88 -0.29±-0.80 5.18±4.89 0.36 0.70 5.40±6.13

21

0.47

0.08 0.98 0.31±-0.57 5.45±4.07 0.25 0.83 5.42±6.11

0.28 0.91 -0.16±-0.73 5.29±4.92 0.34 0.86 5.44±6.34

0.22 0.76 -0.54±-0.84 5.78±5.72 0.53 -0.38 5.61±6.15

0.57

0.44 0.68 -0.34±-0.60 5.05±5.09 0.56 0.48 5.37±5.94

0.19 0.97 -0.51±-1.34 4.00±3.81 1.08 0.06 5.07±5.66

0.68 0.13 -0.53±-0.22 5.93±6.44 1.09 -0.76 5.18±5.65

0.19 0.47 -0.10±-0.08 6.15±6.19 0.20 0.39 6.12±6.38

0.21 0.59 0.09±0.08 5.91±5.62 0.29 0.24 6.07±6.19

25 0.35 0.20 0.10 0.00±0.14 5.67±5.85 0.20 0.13 5.84±5.97 Blessington et al., 2012

37

0.47

0.49 0.74 0.23±-0.03 5.34±4.83 0.54 0.68 5.10±5.77

Beuchat and Mann, 2010

0.84 0.61 -0.07±-0.19 5.38±5.38 1.12 0.32 5.21±5.83

0.25 0.93 -0.49±-1.18 4.45±4.35 0.92 0.05 5.32±5.91

0.57

0.54 0.62 -0.28±-0.35 5.32±5.38 1.04 -0.39 5.17±5.67

0.73 0.58 -0.23±-0.30 5.15±5.31 1.43 -0.62 5.04±5.53

0.34 0.83 -0.48±-0.85 4.80±4.87 1.09 -0.76 5.18±5.65

112

0.28 0.62 -0.48±-0.68 5.57±5.55 0.44 0.07 5.55±6.05

0.64 0.86 -0.29±-0.71 4.30±4.39 1.59 0.13 4.76±5.31

0.21 0.87 -0.32±-0.83 5.26±4.96 0.36 0.59 5.46±6.09

Enteritidis

5 0.24 0.40 -0.10 -1.46±-0.27 4.76±6.09 0.42 -0.22 4.90±4.73

Komitopoulou and Penaloza, 2009 21

0.18 0.20 -0.16 -0.22±-0.05 5.26±5.66 0.17 0.09 5.19±5.26

0.15 0.09 -0.03±-0.01 5.03±5.05 0.11 0.45 5.13±5.45

0.24 0.17 0.55 -0.18±-0.27 4.71±4.53 0.13 0.72 4.76±5.23

93 0.48

0.19 0.99 -0.08±-1.15 -0.89±-1.60 0.24 0.99 -0.73±0.64

Lee et al., 2006 0.19 0.98 -0.02±-0.94 -0.61±-1.33 0.18 0.99 -0.58±0.77

0.21 0.99 -0.18±-1.19 -1.10±-1.66 0.39 0.96 -0.69±0.42

0.26 0.97 -0.09±-0.87 -0.73±-1.22 0.27 0.97 -0.57±0.58

Montevideo

5 0.24 0.36 0.72 -0.03±-0.15 4.29±3.98 0.26 0.86 4.31±4.95


0.24 0.94 -0.66±-0.71 3.26±3.64 0.62 0.60 4.19±4.56

21 0.24 0.03 0.99 -0.07±-0.98 4.68±3.59 0.03 0.99 4.65±5.86

0.12 0.97 -0.39±-0.85 4.05±3.64 0.25 0.87 4.28±4.95

Napoli

5 0.24 0.50 -1.89 -0.30±0.16 4.83±5.34 0.23 0.38 4.76±4.98

Komitopoulou and

Penaloza, 2009

0.07 0.24 0.03±0.21 5.28±5.72 0.05 0.60 5.29±5.65

21 0.24 0.12 0.97 -0.40±-0.90 3.95±3.56 0.32 0.80 4.28±4.84

0.74 0.42 0.06±0.14 4.24±4.17 0.51 0.73 4.17±4.65

Oranienburg

5 0.24 0.32 0.13 -2.06±-0.4 4.72±6.5 0.34 0.03 4.80±4.82

Komitopoulou and

Penaloza, 2009

0.61 0.65 -0.01±-0.08 4.13±4.03 0.43 0.82 4.13±4.72

21

0.18 0.23 -0.11 0.04±0.30 5.00±5.23 0.19 0.26 5.03±5.22

0.24 -0.18 -0.38±-0.3 4.92±5.1 0.20 0.14 5.09±5.20

0.24 0.29 0.86 0.10±-0.19 4.29±3.77 0.23 0.91 4.24±4.99

0.60 -1.21 -0.34±0.25 4.94±5.67 0.44 -0.18 4.85±4.71

Poona 5

0.24


Penaloza, 2009 0.03 1.00 -0.46±-1.67 3.54±2.66 0.44 0.78 4.18±4.71

21 0.85 0.56 -0.24±-0.20 3.72±4.02 0.71 0.70 4.06±4.50

Seftenberg 5 0.24 0.03 0.26 0.01±0.17 5.58±6.14 0.02 0.62 5.57±5.96


21 0.70 0.57 0.94 -0.15±-0.72 4.30±4.16 0.68 0.91 4.60±5.49 Beuchat and Heaton, 1975

Typhimurium

5 0.24 0.37 0.67 -0.28±-0.22 4.33±4.25 0.32 0.74 4.35±4.84 Komitopoulou and

Penaloza, 2009

21

0.24 0.31 0.53 0.11±0.12 4.50±4.03 0.23 0.74 4.51±4.99 Komitopoulou and

Penaloza, 2009 0.01 1.00 -0.16±-1.39 4.69±3.34 0.06 0.96 4.64±5.57

0.70 1.02 0.62 -0.13±-0.20 4.82±5.00 1.60 0.06 4.75±5.27

Beuchat and Heaton, 1975 0.61 0.92 -0.05±-0.55 4.55±4.36 0.57 0.93 4.63±5.56

Peanut Butter

Cocktail

5

0.22 0.83 0.43 -0.33±-0.21 4.90±5.26 1.20 -0.20 4.90±5.22

Burnett et al., 2000

0.25 0.14 0.99 -0.99±-1.67 0.34±0.70 1.73 -0.76 4.84±5.07

0.29 0.11 0.99 -0.90±-1.68 1.70±1.83 1.43 -0.67 4.91±5.15

0.33 0.99 0.24 -0.93±-0.35 4.93±5.91 1.56 -0.89 4.91±5.11

21 0.20 0.44 0.96 -1.09±-1.11 -3.40±-2.17 2.00 0.18 4.25±4.36

0.22 0.29 0.97 -0.75±-1.42 1.75±2.01 1.86 -0.29 4.72±5.03

113

0.02 1.00 -0.58±-2.45 2.94±1.84 1.50 0.14 4.73±5.13

70

0.40

0.23 0.93 -0.64±-1.27 -0.33±-0.26 0.64 0.49 1.40±1.87

Shachar and Yaron, 2006 80 0.17 0.97 -0.92±-1.51 -2.36±-1.98 0.79 0.31 1.46±1.78

90 0.18 0.97 -1.06±-1.51 -3.96±-3.30 0.89 0.22 1.48±1.73

Tennessee

4

0.17 0.12 0.56 -0.09±-0.27 4.71±4.55 0.11 0.64 4.69±5.19

Park et al., 2008

0.21 0.04 0.61 -0.04±-0.22 5.13±5.22 0.04 0.70 5.08±5.63

0.23 0.04 0.89 0.01±-0.47 4.75±4.29 0.04 0.92 4.76±5.64

0.25 0.08 0.60 -0.11±-0.36 4.89±4.79 0.07 0.63 4.87±5.35

22

0.17 0.07 0.98 -0.14±-0.97 4.15±3.22 0.10 0.95 4.21±5.21

0.18 0.16 0.33 -0.38±-0.47 4.96±5.14 0.16 0.29 4.76±5.00

0.21 0.07 0.75 -0.07±-0.41 4.77±4.49 0.06 0.79 4.76±5.41

0.23 0.16 0.00 0.07±0.26 4.80±5.05 0.14 0.25 4.87±5.08

0.25 0.13 0.91 -0.21±-0.75 4.20±3.59 0.14 0.89 4.26±5.06

Seeds Cocktail

5

0.21 0.18 0.13 -0.15±0.02 6.36±6.70 0.16 0.30 6.23±6.48

Beuchat and Scouten, 2002

0.40 0.09 0.29 0.00±0.10 6.42±6.73 0.08 0.34 6.33±6.76

0.60 0.14 0.23 -0.04±0.09 6.30±6.57 0.12 0.42 6.28±6.61

25

0.21 0.20 0.86 -0.13±-0.47 5.62±5.08 0.21 0.84 5.57±6.38

0.40 0.30 0.65 -0.58±-0.70 5.64±5.76 0.54 -0.17 5.55±5.97

0.60 0.28 0.97 -0.02±-0.74 5.07±4.56 0.28 0.97 5.08±6.26

37 0.21 0.20 0.96 -0.14±-0.82 5.06±4.57 0.27 0.92 5.20±6.13

0.40 0.44 0.93 -0.03±-0.57 4.94±4.64 0.64 0.86 4.91±5.75 a Salmonella enterica serotype; bwater activity; cRoot Mean Square Error (Equation 4.3); dcoefficient of determination adjusted for degrees of freedom

(Equation 4.4); eMafart et al. (2002);

fFitted shape factor values ± s.e. of the fit (Equation 4.2); gFitted time required for first decimal reduction ±

standard error of the fit (Equation 4.2); hBigelow and Esty (1920); iFitted decimal reduction time values ± s.e. of the fit (Equation 4.1); ja cocktail of

serotypes was used

114

Table 4.2. Root mean square error, correlation coefficient, discrepancy and bias between

predicted and observed Salmonella counts, decimal reduction times (D-value), time required

for first decimal reduction (δ) and shape factor (β) values

Secondary Model* RMSE

g R

h p-value

i

% Df

j

% Bf

k

% residuals acceptable zone

l

Equation 4.5 a Pred vs obsc 11.5 0.37 <0.001 42.5 22.3 52

D-valued - m 0.81 <0.001 - - -

Equations 4.8 and 4.9b

Pred vs obs 2.2 0.59 <0.001 42.2 22.4 55

δe - 0.50 <0.001 - - -

βf - -0.10 0.185 - - -

*Refers to the secondary model used to fit Salmonella survival data; aBigelow and Esty, 1920; bMafart et al.,

2002; cpredicted versus observed bacterial count values; ddecimal reduction time (Equation 4.5); etime required

for first decimal reduction (Equation 4.8); fshape factor (Equation 4.9); groot mean square error (Equation 4.3);

hcalculated correlation statistic (Equation 4.13); isignificance of the correlation test; jpercent discrepancy

(Equation 4.11); kpercent bias (Equation 4.10); lpercentage of residuals (Equation 4.12) between -1 to 0.5 log;

mnot applicable

115

Table 4.3. Discrepancy and bias percentage between predicted and observed counts and

percentage of residuals in acceptable zone summarized by product substrate and

characteristics

Substratea Characteristics

b % Df

c % Bf

d

% Acceptable

Residualse

Cereal 25.7 24.0 70

Dried egg pasta Durum semolina (wheat), 5% whole egg powder 21.2 18.2 88 Dried pasta Durum semolina (wheat) 34.0 34.0 67

Plain wheat flour - f 24.1 24.0 25

Chocolate 58.5 58.3 42

Bitter Chocolate

49.4% sucrose, 10.5% cocoa butter, 39.5% cocoa

mass, 0.6% lecithin 191.3 191.3 19

Cocoa beans - 32.7 32.7 55

Crushed cocoa shells - 28.4 28.2 54

Milk Chocolate

40.6% sucrose, 25% cocoa butter, 9.1% cocoa

mass, 0.5% lecithin, 9.9% skim milk, 14.9%

whole milk 84.7 84.7 20

Dairy 28.6 24.1 55

Non-fat dry milk -

35.9 35.9 43

13.0 1.4 85 39.0 39.0 38

Eggs 78.9 78.9 26

Egg white powder - 103.2 103.2 35

Egg yolk powder - 63.4 103.2 20

Whole egg powder - 83.0 83.0 26

Whole egg powder Added corn syrup and salt (1.9 %) 49.2 49.2 20

Feed 59.1 12.6 50

Dry feedstuff - 49.7 48.8 59

Meat and bone meal - 101.1 101.1 25

Poultry feed - 77.5 77.5 25

Halva 22.7 22.7 56

Halva 49.5% sugars, 32% fat, 15% protein, 1.7% ash 22.7 22.7 56

Meat 18.7 15.6 100

Beef jerky High protein, low fat (3%) 18.7 15.6 100

Mixes 32.5 32.5 67

Onion soup - 32.5 32.5 67

Nuts 40.3 16.0 57 Almond kernel - 3.3 3.3 100

Almonds - 36.1 30.8 50

Hazelnut shells - 14.2 14.2 68

In-shell pecans - 55.8 16.4 61

In-shell pecans - 215.9 215.9 15

Pecan halves - 68.7 13.7 51

Pecan pieces - 23.8 6.4 25

Pecan halves - 37.9 37.7 74

Walnut kernels - 15.8 9.5 100

Peanut Butter 26.4 21.9 64

Peanut butter

- 16.4 0.1 56

22% sugars, 50% fat, 25% protein, 0.5% sodium 6.8 6.8 92


116




Natural (no stabilizers added) 207.2 207.2 25

Reduced sugar, reduced sodium 57.7 57.7 20

No sugar, no sodium 73.3 73.3 20

Reduced fat 66.8 66.8 20 Traditional (Regular) 53.8 53.8 20

Traditional (no monoglycerides, higher peanut

oil) 108.8 108.8 20

Seeds 22.8 13.4 84

Alfalfa seed - 22.8 13.4 84

aProduct category and product; bproduct formulation (when available); cpercent discrepancy (Equation 4.11);

dpercent bias (Equation 4.10); epercent of residuals in the acceptable residual zone (-1 to 0.5 log ) (Equation

4.12); fnot available

117

CHAPTER 5

HEAT RESISTANCE OF SALMONELLA IN LOW-MOISTURE WHEY PROTEIN POWDER

AS AFFECTED BY SALT CONTENT4

4 Santillana Farakos, S.M., Hicks, J.W. and J.F. Frank. To be submitted to Journal of Food

Protection.

118

ABSTRACT

Salmonella can survive in low-moisture foods for long periods of time. Water activity

(aw) and the presence of solutes affect its survival during heating. Reduced aw protects against

the inactivation, but there is conflicting information on the role of sodium chloride (NaCl). The

aim of this study was to determine if NaCl influenced the survival of Salmonella in low-moisture

whey protein powder independent of aw at 70 °C and 80 °C. Whey protein powders of differing

NaCl concentrations (0%, 8% and 17% w/w) were equilibrated to average aw levels of 0.22, 0.38

and 0.56. Powders were inoculated with Salmonella, vacuum-sealed and stored at 70 °C and 80

°C for 48 hours. Survival data were fit to the Weibull model and first decimal reduction times (δ)

and shape factor values (β) were estimated. Data was analyzed using multiple linear regression.

Results showed aw significantly influenced the survival of Salmonella at both temperatures,

increasing resistance at decreasing aw. Sodium chloride did not provide additional protection or

destruction of Salmonella at any temperature beyond that attributed to aw. At 70 °C, Salmonella

showed log-reduction values of 0.1, 0.2 and 0.3 log CFU/hour at aw levels of 0.19, 0.36 and 0.54,

respectively. At 80 °C, log reductions of 0.9, 1.4 and 1.6 log CFU/hour were observed at aw

levels of 0.25, 0.41 and 0.58, respectively. The Weibull model described the survival kinetics of

Salmonella well. Temperature and aw influenced the times required for first decimal reduction

(p<0.05) whereas no significant differences in δ-values were found between 70 °C and 80 °C

among the different salt concentrations (p>0.05). β-values were not significantly influenced by

temperature, aw or NaCl (p>0.05). This study indicates that information on salt content in food

may not improve predictions on the behavior of Salmonella in low-moisture protein systems

within the aw and temperature values under study.

KEYWORDS: Water activity, Temperature, NaCl, Survival, Kinetics

119

INTRODUCTION

Low-moisture foods have aw levels below 0.7 (Blessington et al., 2012). Salmonella is

able to survive in low-moisture foods for very long periods of time even when subject to high

heat (Podolak et al., 2010, Beuchat et al., 2013). As most low-moisture food products require no

further cooking and have a long shelf life, the presence of Salmonella causes lengthy disease

outbreaks which impact large numbers of people. The extent of Salmonella survival differs

among foods, depending on factors such as water activity (aw), fat content, and the addition of

solutes to the food matrix, which have been shown to influence the pathogen’s survival during

heating (Podolak et al., 2010). Reduced aw has been shown to protect Salmonella against

inactivation in low-moisture foods (Beuchat et al., 2013, Podolak et al., 2010). The presence of

fat in the food matrix offers an additional protective effect (Podolak et al., 2010). Moreover, the

presence of sucrose in the food matrix has been shown to substantially increase survival among

dried Salmonella cells (Hiramatsu et al., 2005). The influence of sodium chloride (NaCl) on the

survival of Salmonella in low-moisture foods during heating has not been previously determined.

Sodium chloride significantly influences the survival of Salmonella in an aqueous system

(Blackburn et al., 1997). In this case, optimal survival was observed at NaCl concentrations

between 5% and 7% w/w as compared to lower (0.5% w/w) and higher (19% w/w)

concentrations. However, the heat resistance of Salmonella in a liquid phase is not applicable to

that in a low-moisture system (Podolak et al., 2010). The aim of this study was to determine the

effect of NaCl on the survival kinetics of Salmonella in low-moisture whey protein powder

during heating at 70 °C and 80 °C over 48 hours.

120


Preparation of whey protein powders of different NaCl concentrations

Whey protein powder (95 % protein) was obtained from Davisco Foods International (Le

Sueur, MN). Solutions of 40 g/L whey protein were adjusted to three different NaCl

concentrations: 0% w/w, 8% w/w, 17% w/w (Fisher Scientific, Fair Lawn, NJ). The pH of the

solutions was approximately 7. The solutions were then pasteurized at 80 ºC for 30 min, cooled

and poured into sterile aluminum pans and frozen to -40 °C overnight in a freeze drier

(Freezemobile 25SL Unitop 600L, Virtis Company, Gardiner, NY). The vacuum of the freeze

drier was started once the samples reached -40 °C, and the temperature of the freeze drier was

gradually increased from -40 °C to 0 °C every 24 hr for a total of 96 hr (-20, -10, 0). Once freeze

dried, the whey protein powders of different salt content were broken down into homogeneous

particles by crushing them with a rolling pin. The protein powders were stored in the dark under

N2 atmosphere with silica gel packets to avoid oxidation and moisture absorption.

Water activity equilibration of protein powders

Protein powders were equilibrated to three different aw values in vacuum desiccators by

absorption at 21°C. Average aw levels were 0.22±0.01 (Lithium Chloride, Fisher scientific,

Pittsburgh, PA), 0.38±0.01 (Magnesium Chloride Hexahydrate, Fisher scientific, Pittsburgh,

PA), and 0.56±0.01 (Sodium Bromide Crystal, J.T. Baker, Phillipsburg, NJ). Water activity was

determined using a water activity meter (AquaLab Series 4TEV, Decagon Devices Inc., Pullman,

WA) of ±0.003 precision.

Sample inoculation and packaging

Four Salmonella serovars were used: S. Typhimurium, S. Tennessee, S. Agona and S.

Montevideo. The cultures were kept in cryobeads at -80 ºC and prepared by consecutive

121

culturing in 9 ml of Tryptic Soy Broth (TSB, Becton, Dickinson and Company, Sparks, MD) at

37 ºC for 24 hr. Following the second culture, a final transfer of 3ml to 225 ml of TSB was

made, followed by incubation for 24 hr at 37 ºC. Cells from the final culture were collected by

centrifugation (3,363 g, 30 min), the supernatant fluid was removed, and the pellet was re-

suspended in 2 ml of 1 % bacto-peptone (Becton, Dickinson and Company, Sparks, MD). The

cell suspension was dried in a vacuum desiccator over anhydrous calcium sulfate for a minimum

of three days to obtain aw levels below 0.1. The dried cells were crushed into a powder. The

dried inoculum (0.05 g) was mixed with 0.95 g of moisture equilibrated test protein powder

providing a 1 g sample. Samples were vacuum packaged in standard retort pouches (Stock

America, Inc., Grafton, WI) using Food Saver equipment (FoodSaver Silver, model

FSGSSL0300-000, Sunbeam Products, Inc., Boca Raton, FL). The vacuum-sealed inoculated

samples were stored at 70±0.5 °C and 80±0.5 °C for 48 hr.

Experimental plan

Each survival experiment was replicated three times. Samples were taken at: 0, 0.5, 4, 10,

30, 60, 240, 480, 1440 and 2880 min. Time 0 corresponds to the time after come-up-time (the

time needed to raise the temperature to reach a target level). The average come-up-times for the

70 °C and 80 °C temperature experiments were 20.2±4.4 seconds and 16.5±3.4 seconds,

respectively. Uninoculated controls were analyzed for background microflora and aw at the start

and end of each experiment. Salmonella cells were recovered on non-selective differential media.

The non-selective medium consisted of Tryptic Soy Agar (TSA, Becton, Dickinson and

Company, Sparks, MD) (40.0 g/L), ferric ammonium citrate (Sigma-Aldrich Co., St Louis, MO)

(0.8 g/L), yeast extract (Becton, Dickinson and Company, Sparks, MD) (3.0 g/L) and sodium

thiosulfate (J.T. Baker, Phillipsburg, NJ) (6.8 g/L).

122

Inactivation model

Salmonella survival data was fit to the Weibull model (Mafart et al., 2002, Equation 5.1)

using GInaFiT Version 1.6 (Geeraerd et al., 2005, Katholieke Universiteit Leuven, Leuven,

Belgium).

(

)

(5.1)

The times required for first decimal reduction (δ) and shape factor values (β) were

obtained by fitting the data to the Weibull model. The root mean square error (RMSE) and the

adjusted coefficient of determination (R2

adj) of the fits were given by GInaFiT. The influence of

temperature, aw and NaCl concentration on the survival of Salmonella (log CFU) was evaluated

using Multiple Linear Regression (IBM SPSS Statistics for Windows, Version 21.0, IBM Corp.).


Data corresponding to the survival of Salmonella at three water activities (aw) and three

salt concentrations (% w/w NaCl) at each aw during heating at 70 °C and 80 °C is shown in

Figures 5.1 and 5.2, respectively. Additionally, in Table 5.1, the times for first decimal reduction

(δ) and shape factor values (β) for each of the survival curves is presented together with their

corresponding standard error. The RMSE and the adjusted R2 values for each of the fitted curves

are also shown in Table 5.1.

As seen by the RMSE and R2

adj values reported in Table 5.1, survival kinetics of

Salmonella in low-moisture whey protein powder at different temperatures, aw, and NaCl

concentrations were well described by the Weibull model. Salmonella survival in low-moisture

whey protein powder of different salinity does not follow log-linear kinetics, showing curves

with significant asymptotic tails (Figures 5.1 and 5.2). Populations of Salmonella in whey protein

powders had starting concentrations of around 109 CFU/g (Figures 5.1 and 5.2). Increasing

123

resistance to heat was observed at decreasing aw at both 70 °C and 80 °C (p<0.001) (Figures 5.1

and 5.2). Similarly, times for first decimal reduction (δ) were shown to be influenced by aw at all

temperatures (p=0.017) (Table 5.1). These results are in line with studies which show Salmonella

has an increasing resistance at decreasing aw in low-moisture foods (Beuchat et al., 2013,

Podolak et al., 2010). The present study even demonstrated aw was a significant factor on δ-

values whereas the different temperatures and salt concentrations resulted in similar times for

first decimal reduction (p>0.05). The results also showed that β-values were not influenced by

temperature (p=0.781), aw (p=0.211) or NaCl concentration (p=0.318).

At 70 °C, Salmonella inactivated slowly with log-reduction numbers of 0.1, 0.2 and 0.3

log CFU per hour at aw levels of 0.19, 0.36 and 0.54, respectively (Figure 5.1). Statistical

analysis indicated that NaCl concentration was not an influencing factor on log CFU at any aw in

experiments carried out at 70 °C (p=0.875). In line with the results at 70 °C, Salmonella survival

numbers at 80 °C were very similar in whey protein powders of comparable aw but different

NaCl concentrations (Figure 5.2). Log reductions of 0.9, 1.4 and 1.6 log CFU per hour were

found at aw levels of 0.25, 0.41 and 0.58, respectively (Figure 5.2). No significant differences

were found for survival of Salmonella at the different NaCl concentrations at 80 °C (p=0.922).

Additionally, statistical analysis demonstrated that the interaction between aw and NaCl

concentration did not significantly influence survival at 70 °C (p=0.954) or 80 °C (p=0.782).

CONCLUSIONS

This study clearly shows the potential for Salmonella to survive in low-moisture whey

protein powders for long periods of time when subject to high heat. Given the pathogen’s

survival kinetics, traditional thermal processing conditions applied to eliminate Salmonella will

not be successful in these low-moisture conditions. The presence of salt at the concentrations

124

under study did not significantly influence the kinetic parameters used to describe the survival of

the pathogen. These results may derive from the fact that, during the drying process, Salmonella

cells acquire a higher resistance to heat as well as other stresses (i.e. NaCl) (Mattick et al., 2001,

Podolak et al., 2010, Gruzdev et al. 2012). In a study by Hiramatsu et al. (2005), increasing NaCl

concentrations during drying of Salmonella enterica cells led to a significantly decreased

resistance of cells to desiccation. However, in a study by Gruzdev et al. (2011), no significant

differences in survival of S. enterica desiccated cells were found at increasing NaCl

concentrations (from 0.5M to 1M). Survival of Salmonella in dry foods at lower temperatures is

much slower than that observed in this study. Thus, the influence of salt independent of aw at

temperatures lower than those used in this study is also unlikely to be significant. Although

additional validation is required, these data indicate that predictive models for survival of

Salmonella in dry foods as a function of aw may not need to consider the salt content of the food

product. Future research studies should evaluate whether significant differences in resistance

exist for Salmonella cells subjected to heat after undergoing a drying process in environments

with different salt concentrations. This will enable better understanding of survival kinetics in

low-moisture foods of distinct salt content. It will also aid in simulating an actual contamination

scenario where Salmonella encounters both dehydration and NaCl stressors simultaneously.

125

References

Beuchat, L.R., Komitopoulou, E., Beckers, H., Betts, R.P., Bourdichon, F. et al. 2013.

Low water activity foods: increased concern as vehicles of foodborne pathogens. Journal of Food


Blackburn, C.W., Curtis, L.M., Humpheson, L., Billon, C., McClure, P.J. 1997.

Development of thermal inactivation models for Salmonella enteritidis and Escherichia coli

O157:H7 with temperature, pH and NaCl as controlling factors. International Journal of Food

Microbiology 38:31–44

Blessington, T., Theofel, C.G., Harris, L.J. 2012. A dry-inoculation method for nut


Geeraerd, A.H., Valdramidis, V.P., Van Impe, J.F., 2005. GInaFiT, a freeware tool to

assess non log-linear microbial survivor curves. International Journal of Food Microbiology

102:95-105

Gruzdev, N., McClelland, M., Porwollik, S., Ofaim, S., Pinto, R., Saldinger-Sela, S.

2012. Global transcriptional analysis of dehydrated Salmonella enterica serovar Typhimurium.

Applied and Environmental Microbiology 78:7866–7875

Gruzdev, N., Pinto, R., Sela, S. 2011. Effect of desiccation on tolerance of Salmonella

enterica to multiple stresses. Applied and Environmental Microbiology 77:1667–1673

Hiramatsu, R., Matsumoto, M., Sakae, K., Miyazaki, Y. 2005. Ability of shiga toxin-

producing Escherichia coli and Salmonella spp. to survive in a desiccation model system and in

dry foods. Applied and Environmental Microbiology 71:6657-6663

126

Mafart, P., Couvert, O., Gaillard, S., Leguerinel, I. 2002. On calculating sterility in



Mattick, K.L., Jorgensen, F., Wang, P., Pound, J., Vandeven, M.H., Ward, L.R., et al.

2001. Effect of challenge temperature and solute type on heat tolerance of Salmonella serovars at

low water activity. Applied and Environmental Microbiology 67:4128-4136

Podolak, R., Enache, E., Stone, W., Black, D.G., Elliott, P.H. 2010. Sources and risk



127

Table 5.1. δ and β values of the Weibull model fit for Salmonella inactivation experiments

at 2 T (°C), 3 water activities (aw) and 3 salt contents (% w/w) at each aw

T

(°C) aw

a

% NaClb

(w/w)

δc

(min) s.e. δ

d β

e s.e. β

f RMSE

g R

2adj

h

70

0.19±0.01

0 754.8 212.2 1.1 0.2 0.59 0.77

8 374.3 145.04 0.6 0.1 0.38 0.88

17 304.5 62.0 0.7 0.1 0.27 0.96

0.36±0.02

0 13.6 9.2 0.3 0.04 0.40 0.95

8 21.3 8.1 0.4 0.03 0.27 0.97

17 32.6 25.1 0.4 0.1 0.65 0.87

0.54±0.02

0 12.4 5.8 0.4 0.1 0.32 0.96

8 1.6 1.7 0.3 0.04 0.54 0.93

17 4.1 2.8 0.4 0.05 0.40 0.95

80

0.25±0.02

0 17.8 10.5 0.4 0.1 0.46 0.89

8 32.4 14.8 0.6 0.1 0.48 0.92

17 9.2 6.5 0.4 0.1 0.55 0.89

0.41±0.01

0 1.1 1.0 0.3 0.04 0.44 0.90

8 3.1 1.6 0.4 0.04 0.37 0.95

17 0.5 0.3 0.3 0.03 0.37 0.95

0.58±0.003

0 6.1 7.1 0.7 0.5 0.94 0.68

8 14.5 16.4 0.8 0.6 1.07 0.59

17 0.2 0.2 0.2 0.04 0.60 0.89 aaverage measured water activity ±sd of 3 replicates;bweight per weight percentage of NaCl; ctime required

for first decimal reduction (measured in minutes); dstandard error of δ parameter value; efitting parameter

that defines the shape of the curve; fstandard error of β parameter value; groot mean square error; hcoefficient

of determination adjusted for the degrees of freedom

128

Figure 5.1. Survival of Salmonella at 70 °C during 48 hours of storage in low-moisture whey

protein powder at 3 water activities (aw) and 3 salt contents (% w/w NaCl) at each aw. Error bars

represent the ±standard deviation of the average of three replicas for each "aw-salt content".

129

Figure 5.2. Survival of Salmonella at 80 °C during 60 minutes of storage in low-moisture whey

protein powder at 3 water activities (aw) and 3 salt contents (% w/w NaCl) at each aw. Error bars

represent the ±standard deviation of the average of three replicas for each "aw-salt content".

130

CHAPTER 6

RELATIVE SURVIVAL OF FOUR SEROTYPES OF SALMONELLA IN LOW-MOISTURE

WHEY PROTEIN POWDER HELD AT 36 °C AND 70 °C AT VARIOUS WATER ACTIVITY

LEVELS5

5 Santillana Farakos, S.M., Hicks, J.W., Frye, J.G., and J.F. Frank. To be submitted to Journal of

Food Protection.

131

ABSTRACT

Salmonella enterica is the leading cause of health burdens in the United States. Although

the pathogen is not able to grow at aw levels below 0.94, it can survive in low-moisture foods for

long periods of time. Temperature, aw, substrate and serotype affect its persistence. The aim of

this study was to evaluate the influence of temperature and aw on the relative persistence among

four serotypes of Salmonella enterica in low-moisture whey protein powder. Whey protein

powder was equilibrated to aw 0.18±0.02 and 0.54±0.03, inoculated with a cocktail of Salmonella

(S. Agona, S. Tennessee, S. Montevideo and S. Typhimurium), vacuum-sealed and stored at 36

°C for six months and 70 °C for 48 hours. Salmonella colonies (30-32) were randomly picked

from each plate at the end of each survival study. Multiplex PCR was used to amplify the DNA

and the resulting fluorescently labeled amplicons were separated by capillary electrophoresis.

Genemapper software was used to analyze the size of the amplicons and using the Salmonella

Multiplex Assay for Rapid Typing code, the serotypes of the isolates were determined. A Chi-

square test for independence was used to test for significant differences in serotype frequency

distribution. Results showed significant differences in prevalence exist among Salmonella

serotypes in low-moisture whey protein powder of distinct aw and stored at different

temperatures (p<0.001). S. Agona was more persistent at the lower water activity (0.18) at both

36 °C and 70 °C. At higher water activity (0.54) and 70 °C, S. Tennessee was the most

predominant serovar. S. Montevideo and S. Typhimurium were the serovars with the lowest

persistence. These results should be considered when developing predictive models for survival

of Salmonella in low-moisture foods.

KEYWORDS: Water activity, Temperature, Prevalence, Chi-Square, Dry Food

132

INTRODUCTION

A risk ranking of the foodborne pathogens of greatest concern in the United States found

Salmonella enterica to be the leading cause of health burdens based on public health data (Batz

et al., 2012). From 2007 to 2012, 14 reported Salmonella outbreaks caused by low-moisture

foods occurred in the U.S., resulting in 1,842 cases of infection and 9 deaths (CDC, 2012). Three

major outbreaks were responsible for the vast majority of cases during this period of time. In

2007, the contamination of peanut butter with S. Tennessee lead to 425 cases of infection (CDC,

2012). Again, in 2008, a multistate outbreak infecting 725 individuals resulted from the

contamination of peanuts with S. Tyhphimurium (CDC, 2012). Moreover, in 2010, a S.

Montevideo outbreak caused by contamination of black pepper added to salami resulted in 272

cases (CDC, 2012). Other Salmonella serotypes that have been involved in major low-moisture

food outbreaks in recent years include S. Agona (2008) in puffed cereal and S. Enteritidis (2011)

in pine nuts (Beuchat et al., 2013). Reduced water activity (aw) is used to control microbial

growth in foods (Jung and Beuchat, 1999). Although Salmonella is not able to grow at aw levels

below 0.94 (ICMSF, 1996), it can survive for weeks, months and even years in low-moisture

foods (aw<0.7) (Beuchat et al., 2013, Podolak et al., 2010, ICMSF, 1996). The extent of

Salmonella survival differs among foods, as different factors affect its resistance (Podolak et al.,

2010). Temperature, aw, substrate and serotype influence the survival of Salmonella (Podolak et

al., 2010). The pathogen exhibits increasing persistence at decreasing aw and temperature, and the

presence of fat protects against inactivation (Santillana Farakos et al., 2013). Moreover, the

results of an extensive quantitative analysis of literature data on the survival of Salmonella in

low-moisture food showed that survival kinetics differ among serotypes (S.M. Santillana

Farakos, D.W Schaffner and J.F. Frank, in preparation). The aim of this study was to evaluate the

133

influence of temperature and aw on the relative persistence of four different serotypes of

Salmonella enterica in low-moisture whey protein powder.


Salmonella survival data

In a previous investigation (Santillana Farakos et al., 2013), whey protein powder

(Davisco Foods International, Le Sueur, MN) was equilibrated to aw levels 0.18±0.02 (Lithium

Chloride, Fisher scientific, Pittsburgh, PA) and 0.54±0.03 (Sodium Bromide Crystal, J.T. Baker,

Phillipsburg, NJ). The pH of the whey protein powder was approximately 7. Water activity was

determined using a bench top water activity meter (AquaLab Series 4TEV, Decagon Devices

Inc., Pullman, WA) of ±0.003 precision.

Protein powders were inoculated with a previously dried four-serotype cocktail of Salmonella,

vacuum-sealed and stored at 36 °C for 6 months and 70 °C for 48 hours (Santillana Farakos et

al., 2013). Salmonella serovars previously associated with outbreaks in low-moisture foods were

selected for the study. This included S. Typhimurium (peanut), S. Tennessee (peanut), S.

Montevideo (pistachios), and S. Agona (cereal). Cells were recovered at different times on non-

selective differential media. The non-selective medium consisted of Tryptic Soy Agar (TSA,

Becton, Dickinson and Company, Sparks, MD) (40.0 g/L), ferric ammonium citrate (Sigma-

Aldrich Co., St Louis, MO) (0.8 g/L), yeast extract (Becton, Dickinson and Company, Sparks,

MD) (3.0 g/L) and sodium thiosulfate (J.T. Baker, Phillipsburg, NJ) (6.8 g/L) (Santillana Farakos

et al., 2013).

134

Preparation of samples for serotyping

Presumptive colonies of Salmonella (30-32 per plate) were randomly picked at the end of

each temperature and aw survival study. Colonies were taken from two replicate experiments. A

total of 261 colonies from survival experiments were serotyped. Additionally, 45 colonies

randomly picked from samples of the dried inoculum were serotyped. The colonies selected for

serotyping were transferred to Tryptic Soy Broth (TSB, Becton, Dickinson and Company,

Sparks, MD) and grown at 37 °C for 24 hours. The cultures were stored in cryovials containing

beads suspended in phosphate buffered saline, glycerol and peptone (Cryobank, Copan

Diagnostics Inc., CA) and kept at -80 °C. Cultures for serotyping were streaked on Lennox Broth

agar (LB, Becton, Dickinson and Company, Sparks, MD) and incubated at 37°C 18-20 hours.

One well isolated colony from the plate was streaked to a second LB agar plate. After incubation

at 37 °C for 18-20 hours, a well isolated colony was transferred to a sterile 0.5 ml micro

centrifuge tube containing 100µl of sterile distilled de-ionized water. Samples were then stored

at -20 °C until further use.

Determination of Salmonella enterica serovars

The serotyping of the selected colonies was based on the molecular assay developed by

Leader et al. (2009). Briefly, primers for 16 Salmonella gene targets were used in a single

multiplex PCR. The resulting fluorescently labeled amplicons were separated by capillary

electrophoresis in an ABI 3130XL gene analyzer. Sterile distilled de-ionized water was used as a

negative control. S. Typhimurium LT2, S. Typhi CT18, and two S. Enteritidis strains (98104 and

21027) were used as positive controls. Genemapper software v3.5 (Applied Biosystems, Foster

City, CA) was used to analyze the size of the resulting PCR products, and using the Salmonella

135

Multiplex Assay for Rapid Typing code (SMART) generated by Leader et al. (2009), the

serotypes of the isolates were determined.

Statistical analysis

A Chi-square test for independence (IBM SPSS Statistics for Windows, Version 21.0,

IBM Corp.) was used to test for significant differences in frequency distribution of Salmonella

serotypes at the different aw levels and temperatures of the survival studies.


Prevalence percentages among S. Agona, S. Montevideo S. Tennessee and S.

Typhimurium, after sample storage at 36 °C and 70 °C and at higher (0.54) and lower (0.18) aw

are presented in Table 6.1. Also in Table 6.1, the Chi square statistic for the frequency

distribution at the different temperatures and water activities is presented together with its

corresponding significance. Salmonella Agona and S. Tennessee were the most prevalent

serotypes in samples from survival studies, followed in decreasing order by S. Montevideo and S.

Typhimurium (Table 6.1). The serotyping results of samples representing randomly picked

colonies from the dried inoculum (results not presented) showed equivalent prevalence

percentages among the four Salmonella serovars (p=0.127). Significant differences in prevalence

were found among Salmonella serotypes in low-moisture whey protein powder of distinct aw and

stored at different temperatures (p<0.001) (Table 6.1). Salmonella Agona was more persistent at

the lower aw (0.18) at both temperatures (36 °C and 70 °C) as well as at 36 °C and 0.54 aw,

indicating greater ability to survive in low-moisture environments. At 70 °C and 0.54 aw, S.

Tennessee was the most predominant serovar (Table 6.1). S. Montevideo at the lower

temperature (36 °C) and aw (0.18) condition shows a slightly higher prevalence as compared to

S. Tennessee (Table 6.1), but the prevalence of S. Montevideo in the rest of the experimental

136

conditions under study is low as compared to S. Tennessee and S. Agona (Table 6.1). S.

Typhimurium shows a slight presence at lower temperatures (36 °C) and aw (0.18), but its overall

prevalence is very low as compared to the other serotypes under study (Table 6.1).

To the authors’ knowledge, the relative persistence of different Salmonella serovars in

low-moisture foods by using a cocktail of serotypes as an inoculum and further serotyping a

sufficiently large number of surviving colonies (>300) to obtain statistical significance has not

been determined to date. Previous studies on the persistence of different Salmonella serovars in

low-moisture foods have generally determined the persistence of each serovar independently and

then compared survival kinetics (Sachar and Yaron, 2006, Ma et al., 2009, Komitopoulou and

Penaloza, 2009, Van Cauwenberge et al., 1981). Ma et al. (2009) and Komitopoulou and

Penaloza (2009) found that outbreak-associated strains were amongst the best surviving serovars

of Salmonella in low-moisture foods. However, the authors highlighted that differences in

persistence observed for the outbreak strains was not serotype specific (Ma et al., 2009,

Komitopoulou and Penaloza, 2009). Similarly, in the study of Shachar and Yaron (2006), no

significant differences in persistence were found among different Salmonella serovars when

stored in peanut butter at 5 °C and 21 °C. In line with the results found in the present study, Van

Cauwenberge et al. (1981) found S. Tennessee had a significantly greater persistence to heating

at 49 °C in dried corn flour as compared to S. Typhimurium. Similarly, in an extensive review

conducted by Podolak et al. (2010), the Salmonella serotype used was recognized as an

influencing factor on the survival kinetics of the pathogen in low-moisture foods.

In this study, significant differences in prevalence exist among Salmonella serotypes in

low-moisture whey protein powder of distinct aw and stored at different temperatures (p<0.001).

137

Future studies should consider incorporating the Salmonella serotype used when designing

models to predict the behavior of the pathogen in low-moisture foods.

138

References

Batz, M.B., Hoffmann, S., Morris, J.G.Jr. 2012. Ranking the Disease Burden of 14

Pathogens in Food Sources in the United States Using Attribution Data from Outbreak

Investigations and Expert Elicitation. Journal of Food Protection 75: 1278–1291

Beuchat, L.R., Komitopoulou, E., Beckers, H., Betts, R.P., Bourdichon, F. et al. 2013.

Low water activity foods: increased concern as vehicles of foodborne pathogens. Journal of Food


CDC (Centers for Disease Control and Prevention). 2012. Foodborne Outbreak Online

Database. Accessed August 2012. http://wwwn.cdc.gov/foodborneoutbreaks/


Microorganisms in foods 5: Characteristics of Microbial Pathogens, Blackie Academy and

Professional, London

Jung, Y.S., Beuchat, L.R. 1999. Survival of multidrug-resistant Salmonella typhimurium

DT104 in egg powders as affected by water activity and temperature. International Journal of

Food Microbiology 49:1-8

Komitopoulou, E., Peñaloza, W. 2009. Fate of Salmonella in dry confectionery raw


Leader, B.T., Frye, J.G., Hu, J., Fedorka-Cray, P.J. Boyle, D.S. 2009. High-throughput

molecular determination of Salmonella enterica serovars by use of multiplex PCR and capillary

electrophoresis analysis. Journal of Clinical Microbiology 47: 1290-1299

Ma, L., Zhang, G., Gerner-Smidt, P., Mantripragada, V., Ezeoke, I., Doyle, M.P. 2009.


139

Podolak, R., Enache, E., Stone, W., Black, D.G., Elliot, P. H. 2010. Sources and risk



Santillana Farakos, S.M., Schaffner, D.W., Frank, J.F. 2013. Survival of Salmonella in

low-moisture foods: a meta-analysis of the published literature data. To be submitted to Applied

and Environmental Microbiology

Santillana Farakos, S.M., Frank, J.F., Schaffner, D.W. 2013. Modeling the influence of

temperature, water activity and water mobility on the persistence of Salmonella in low-moisture

foods. International Journal of Food Microbiology. doi: 10.1016/j.ijfoodmicro.2013.07.007

Shachar, D., Yaron, S. 2006. Heat Tolerance of Salmonella enterica serovars Agona,


Van Cauwenberge, J.E., Bothast, R.J., Kwolek, W.F. 1981. Thermal inactivation of eight

Salmonella serotypes on dry corn flour. Applied and Environmental Microbiology 42:688-91

140

Table 6.1. Serotype prevalence (%) for Salmonella survival in low moisture protein powder stored

at 36 °C for 6 months and 70 °C for 48 hours at water activities of 0.18 and 0.54 together with the

corresponding Chi Square statistic (Χ2)

T (°C)a aw

b

Salmonella serotype Χ

2c Significance

d

Agona Montevideo Tennessee Typhimurium

36±0.3 0.18±0.02 44 25 24 7

76.7 <0.0001 36±0.3 0.54±0.03 62 0 37 1

70±0.5 0.18±0.02 50 6 43 1

70±0.5 0.54±0.03 16 2 82 0 aRepresents the average temperature ± standard deviation;

bRepresents the average water activity ±

standard deviation; cRepresents the Chi Square test statistic of the test for independence in

frequency distribution of Salmonella serotypes at the different temperatures and water activities;

dRepresents the significance of the Chi Square test statistic

141

CHAPTER 7

CONCLUSIONS

In this research, how the physical state of water in low-moisture foods influenced the

survival of Salmonella was determined and this information was used to develop mathematical

models to predict the behavior of Salmonella in these foods. The models where developed using

whey protein powder, applied to additional low-moisture foods and further validated with

literature data. Global influencing factors on survival of Salmonella in low-moisture foods were

established. The influence of sodium chloride on the survival of Salmonella at 70 °C and 80 °C

at various water activity levels (<0.6) was assessed. Furthermore, the influence of temperature

(36 °C and 70 °C) and water activity (<0.6) on the survival kinetics among four different

serotypes of Salmonella enterica was evaluated.

Salmonella holds great potential to survive in low-moisture whey protein powder

(aw<0.6) for long periods of time even when subject to high heat. Conventional thermal

processing conditions applied to eliminate Salmonella will not be successful in these low-

moisture conditions. Water activity significantly influenced the survival of Salmonella in low-

moisture foods at temperatures ranging from 21°C to 80 °C while water mobility had no effect

independent of aw. The Weibull model provided the best description of survival kinetics.

Secondary models were developed which predicted the times required for first decimal reduction

(δ) and shape factor values (β) as influenced by temperature and aw. The secondary models were

useful in predicting the survival of Salmonella in several tested low-moisture foods, providing a

more accurate prediction in non-fat food systems (non-fat dry milk, oat flour and wheat flour) as

142

compared to food containing low-fat levels (12% fat peanut meal and cocoa powder). The

models demonstrated acceptable prediction performances when used to validate literature data.

Temperature and aw along with medium composition and serotype showed to be global

influencing factors on the survival kinetics of Salmonella in low-moisture foods. The presence of

NaCl at the concentrations under study (8%, 17%) did not significantly influence the kinetic

parameters used to describe the survival of Salmonella at 70 °C and 80 °C. Significant

differences in prevalence existed among the four Salmonella serotypes (S. Montevideo, S.

Agona, S. Typhimurium and S. Tennessee). S. Agona and S. Tennessee had the highest overall

prevalence. S. Agona was more persistent at the lower temperatures (36 °C) at both lower (0.18)

and higher aw (0.54), while at 70 °C and higher aw (0.54), S. Tennessee was the predominant

serotype.

The models developed in this study represent the first predictive models for survival of

Salmonella in low-moisture foods validated for temperature (21 °C - 80 °C) and aw (<0.6). As the

data used to derive the models were collected by simulating the contamination and storage of

low-moisture foods, the models are credible for use in a wide range of products and can

quantitatively support a hazard analysis and critical control point system (HACCP). By

providing a more accurate quantification of the risk of Salmonella in low-moisture foods and its

impact on public health, the models can also contribute to a risk analysis framework of

Salmonella. As such, they will help to develop policies and select appropriate strategies to

decrease the risk of Salmonella in low-moisture foods, thus enhancing consumer safety. The

models developed in this study are baseline models for prediction of the behavior of Salmonella

in low-moisture foods. However, the models should not be applied to specific food systems

without further validation. The models can be improved by adding additional factors such as

143

serotype and product composition as predictive factors for δ and β. As such, the models should

be expanded to include fat and other food components (sugar) which may affect the survival of

Salmonella. Future studies should also consider incorporating the Salmonella serotype used

when designing models to predict the behavior of the pathogen in low-moisture foods.