The potential of end-product metabolites on The potential of end-product metabolites on predicting the shelf life of minced beef stored under predicting the shelf life of minced beef stored under aerobic and modified atmospheres with or without aerobic and modified atmospheres with or without

the effect of essential oilthe effect of essential oil

Washington D.C., USAWashington D.C., USASeptember 8-12, 2009September 8-12, 2009

1 Laboratory of Microbiology and Biotechnology of Foods, Dept of Food Science and Technology, Agricultural University of Athens, Greece

2 Laboratory of Applied Microbiology, Cranfield Health, Cranfield University, UK3 Laboratory of Bioanalytical Spectroscopy, School of Chemistry, University of

Manchester, UK

A.A. Argyri1,2, E.Z. Panagou1, R. Jarvis3, R. Goodacre3, G.-J.E. Nychas1

• The relationship between microbial growth and chemical The relationship between microbial growth and chemical changes occurring during meat storage has been changes occurring during meat storage has been continuously recognized as a potential means to reveal continuously recognized as a potential means to reveal indicators that may be useful for quantifying beef quality indicators that may be useful for quantifying beef quality or freshness (Nychas or freshness (Nychas et al.,et al., 2008). 2008).

• The imposed different storage conditions and preservatives The imposed different storage conditions and preservatives could influence the production of these potential could influence the production of these potential indicators, through the establishment of a transient indicators, through the establishment of a transient microbial association defined as the ‘Ephemeral spoilage microbial association defined as the ‘Ephemeral spoilage micro-organisms’ - ESO (Nychas and Skandamis, 2005). micro-organisms’ - ESO (Nychas and Skandamis, 2005).

Background and RationaleBackground and Rationale

Nychas, G.-J.E., Skandamis, P., Tassou, C.C., Koutsoumanis, K. (2008) Meat spoilage during distribution. Nychas, G.-J.E., Skandamis, P., Tassou, C.C., Koutsoumanis, K. (2008) Meat spoilage during distribution. Meast Meast Science, Science, 78: 77-89.78: 77-89.

Nychas, G.-J.E., Skandamis, P. (2005) Fresh meat spoilage and modified atmosphere packaging. Nychas, G.-J.E., Skandamis, P. (2005) Fresh meat spoilage and modified atmosphere packaging. In In J.N. Sofos J.N. Sofos (Ed.), Improving the Safety of Fresh Meat, CRC/Woodhead Publishing Ltd, Cambridge, UK.(Ed.), Improving the Safety of Fresh Meat, CRC/Woodhead Publishing Ltd, Cambridge, UK.

There is need for a holistic approach in introducing shelf-life There is need for a holistic approach in introducing shelf-life indicators that could be applied irrespective of storage indicators that could be applied irrespective of storage temperature or packaging system and be eligible to the temperature or packaging system and be eligible to the income of new technologies. income of new technologies.

ThisThis approach is based on the mining of qualitative and approach is based on the mining of qualitative and quantitative data of metabolomics e.g. indigenous or quantitative data of metabolomics e.g. indigenous or metabolic compounds associated with meat spoilage, due to metabolic compounds associated with meat spoilage, due to interaction of ESO with nutrients existing in meat (Ellis interaction of ESO with nutrients existing in meat (Ellis at al.,at al., 2001). 2001).

The use of HPLC to monitor changes in the organic acid The use of HPLC to monitor changes in the organic acid profile from food models systems, poultry, fish stored under profile from food models systems, poultry, fish stored under different storage conditions, has been considered as a different storage conditions, has been considered as a relatively simple and promising method.relatively simple and promising method.

Background and RationaleBackground and Rationale

Ellis, D.I., Goodacre, R. (2001) Rapid and quantitative detection of the microbial spoilage of muscle foods: Ellis, D.I., Goodacre, R. (2001) Rapid and quantitative detection of the microbial spoilage of muscle foods: Current status and future applications. Current status and future applications. Trends in Food Science and Technology, Trends in Food Science and Technology, 12: 414-424.12: 414-424.

Objectives of the workObjectives of the work

The aim of the present work was to investigate the potential of The aim of the present work was to investigate the potential of HPLC spectral data of organic acids, as a quick analytical HPLC spectral data of organic acids, as a quick analytical method, in combination with an appropriate data analysis method, in combination with an appropriate data analysis strategy to:strategy to:

1.1. Discriminate among different quality classes of minced beef Discriminate among different quality classes of minced beef samples during storage at different temperatures (0, 5, 10, samples during storage at different temperatures (0, 5, 10, 15°C) and packaging conditions (aerobic, MAP, MAP+EO).15°C) and packaging conditions (aerobic, MAP, MAP+EO).

2.2. Correlate the microbial load of different microbial groups at Correlate the microbial load of different microbial groups at different temperatures, packaging conditions and storage different temperatures, packaging conditions and storage times with spectral data, in an effort to predict microbial times with spectral data, in an effort to predict microbial population directly from HPLC measurements.population directly from HPLC measurements.

Materials & MethodsMaterials & MethodsProduct : Minced beef Minced beef

PackagingPackaging :: Aerobic, Aerobic, MAP (MAP (40% CO40% CO22, 30% O, 30% O22, 30% N, 30% N22),),

MAP + oregano essential oil volatile compoundsMAP + oregano essential oil volatile compounds

StorageStorage temperaturetemperature :: 0, 5, 10, 15 0, 5, 10, 15CC

Microbiological analysisMicrobiological analysis :: Total viable counts, Pseudomonads, Enterobacteriaceae, lactic acid bacteria, Brochotrix thermosphacta,

and yeasts and moulds

Organoleptic assessmentOrganoleptic assessment :: Spoilage detection based on changes in Spoilage detection based on changes in colour, colour, odour and taste based on a five member taste panel (Score odour and taste based on a five member taste panel (Score range 1-3; range 1-3; 1=Fresh, 1.5 Semi-Fresh, 2-3 Spoiled).1=Fresh, 1.5 Semi-Fresh, 2-3 Spoiled).

HPLC analysis of organic acidsHPLC analysis of organic acids :: Collection of spectral data from the Collection of spectral data from the HPLC HPLC (areas under peaks)(areas under peaks) to monitor biochemical changes in meat to monitor biochemical changes in meat during during storage.storage.

Materials & MethodsMaterials & Methods

HPLC analysis of organic acidsHPLC analysis of organic acids

Sample preparation: Sample preparation: 2g meat + 4mL dH2O + 1%TFA

Organic Acid Standards: Organic Acid Standards: oxalic, citric, malic, lactic, acetic, formic,

tartaric, succinic and propionic Apparatus:Apparatus: Jasco,

Model PU-980 Inteligent pump, Model LG-980-02 ternary gradient unit, MD-910 multiwavelength detector at 210 nm

Data analysisData analysisCollection of the Collection of the HPLC HPLC spectral data spectral data (areas under (areas under

peaks) peaks)

11stst Principal components analysis (PCA) Principal components analysis (PCA)

(Investigation of the peaks that significantly fluctuate during storage)(Investigation of the peaks that significantly fluctuate during storage)

22ndnd PCA PCA

Factorial Discriminant Factorial Discriminant Analysis (FDA)Analysis (FDA)

predict the spoilage status of a predict the spoilage status of a sample; fresh, semi-fresh, and spoiledsample; fresh, semi-fresh, and spoiled

Regression ModelsRegression Models

predict the counts of the different predict the counts of the different microbial groupsmicrobial groups

Partial least squares regressionPartial least squares regression(PLS-R)(PLS-R)

Support vector machines regression Support vector machines regression (SVR)(SVR)

Data mean centered and Data mean centered and standardizedstandardized

Multivariate calibrationMultivariate calibration

Building calibration modelsBuilding calibration models

We start off with a data matrix, and a corresponding output We start off with a data matrix, and a corresponding output vector which indicates the value associated with each sample.vector which indicates the value associated with each sample.

(Meat)(Meat)

(HPLC areas under (HPLC areas under peak)peak)

(Bacterial counts)(Bacterial counts)

We build a calibration model that relates the matrix to the vector.We build a calibration model that relates the matrix to the vector.

Using calibration modelsUsing calibration models

(New meat sample)(New meat sample)

(HPLC area under peak)(HPLC area under peak)

(Predicted bacterial count)(Predicted bacterial count)

The developed model on The developed model on known data, can be then known data, can be then

applied to unknown samplesapplied to unknown samples

Multivariate calibration approachesMultivariate calibration approaches

There are two mainThere are two main pattern recognition approaches based pattern recognition approaches based on:on:

Multivariate statisticsMultivariate statistics

• Multiple Linear Regression (MLR)Multiple Linear Regression (MLR)

• Principal Components Regression (PCR)Principal Components Regression (PCR)

• Partial Least Squares Regression (PLS-R)Partial Least Squares Regression (PLS-R)

Machine learningMachine learning

• Artificial neural networksArtificial neural networks

• Support Vector Machines (SVM)Support Vector Machines (SVM)

SVM underlying principleSVM underlying principle**

Li, H., Liang, Y., Xu, Q. (2009) Support vector machines and its applications in chemistry. Li, H., Liang, Y., Xu, Q. (2009) Support vector machines and its applications in chemistry. Chemometrics and Intelligent Laboratory Systems, Chemometrics and Intelligent Laboratory Systems, 95: 188-198.95: 188-198.

• The idea behind SVMs is to project The idea behind SVMs is to project the original data from a low the original data from a low dimensional input space to a higher dimensional input space to a higher dimensional feature space.dimensional feature space.

• This operation is called feature This operation is called feature mapping and it is a key element in mapping and it is a key element in SVM building.SVM building.

• Dimension superiority plays a vital Dimension superiority plays a vital role in SVMs.role in SVMs.

• The data contain more information The data contain more information as the dimension increases. as the dimension increases.

SVM underlying principleSVM underlying principle**

Li, H., Liang, Y., Xu, Q. (2009) Support vector machines and its applications in chemistry. Li, H., Liang, Y., Xu, Q. (2009) Support vector machines and its applications in chemistry. Chemometrics and Intelligent Laboratory Systems, Chemometrics and Intelligent Laboratory Systems, 95: 188-198.95: 188-198.

SVM underlying principleSVM underlying principle

Li, H., Liang, Y., Xu, Q. (2009) Support vector machines and its applications in chemistry. Li, H., Liang, Y., Xu, Q. (2009) Support vector machines and its applications in chemistry. Chemometrics and Intelligent Laboratory Systems, Chemometrics and Intelligent Laboratory Systems, 95: 188-198.95: 188-198.

• Data projection into a higher Data projection into a higher dimensional space is carried out by a dimensional space is carried out by a kernel functionkernel function that serves as a that serves as a dimension increasing technique and dimension increasing technique and further transforms the linearly further transforms the linearly inseparable data into linearly separable inseparable data into linearly separable one.one.

• There are number of kernels that can There are number of kernels that can be used in Support Vector Machines be used in Support Vector Machines models. These include linear, models. These include linear, polynomial, radial basis function (RBF) polynomial, radial basis function (RBF) and sigmoidand sigmoid..

Pre-spoilage

Post-spoilage

Post-spoilagePost-spoilage

15°C Air

15°C MAP+OEO

15°C MAP

ResultsResults

17 pure peaks were selected for analysis ; RT of 6.2, 6.9 (citric acid), 7.0, 7.9, 8.3, 9.7, 10.9 (lactic acid),

11.9 (formic acid), 12.9 (acetic acid), 14.9, 15.1 (propionic acid), 16.1, 17.8, 18.6, 20.5, 24.6 and 28.1.

0 h0 h 48 h48 h

60 h60 h54 h54 h

Aerobic storage, Storage under MAP, Storage under MAP + OEO

Lactic acidLactic acid0C

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ResultsResults

ResultsResultsQualitative classification of the samplesQualitative classification of the samples

Observations (axes F1 and F2: 100.00 %)

0h

0A4

0M4

0M6

0M8

0O4

0O6

0O8

5A3

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0O14

5A75A8

5A115A14

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10A11

10A14

10A17

10M8

10M11

10M1410M17

10O11

10O14

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15A1115A13

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-5 -4 -3 -2 -1 0 1 2 3 4 5

F1 (95.81 %)

F2

(4

.19

%)

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S

SF

After the end of

shelf life

Before the end of shelf life

Discriminant analysis similarity map determined by discriminant factors 1 (F1) and 2 Discriminant analysis similarity map determined by discriminant factors 1 (F1) and 2 (F2) for HPLC spectral data of the 3 different beef fillets freshness groups:(F2) for HPLC spectral data of the 3 different beef fillets freshness groups:

Fresh (F),Fresh (F), Semi-fresh (SF),Semi-fresh (SF), and and Spoiled (S).Spoiled (S).

Confusion matrix for the cross-validation results of DFAConfusion matrix for the cross-validation results of DFA

True classTrue classPredicted classPredicted class

Sensitivity Sensitivity (%)(%)

FreshFresh Semi-freshSemi-fresh SpoiledSpoiled

FreshFresh

(n (n = 26= 26))2323 22 11 88.4688.46

Semi-freshSemi-fresh

(n (n = 11= 11))00 1010 11 90.9190.91

SpoiledSpoiled

(n (n = 38= 38))22 22 3434 89.4789.47

Overall correct classification (accuracy): Overall correct classification (accuracy): 89.33%89.33%

ResultsResults

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ResultsResultsPrediction of the microbial population – Performance of regression models – Performance of regression models

PLS-R Linear SVR

Radial SVR

Sigmoid SVR

ResultsResultsCalculation of performance indices (Bias and Accuracy factors)Calculation of performance indices (Bias and Accuracy factors)

PLS-R Linear SVR Radial basis SVR Sigmoidal SVR

Microbial group

Bf Af Bf Af Bf Af Bf Af

TVC 0.99 1.10 0.99 1.10 1.00 1.10 1.00 1.10

Pseudomonas spp 1.02 1.17 1.35 1.39 1.00 1.15 1.02 1.19

Br. thermosphacta 1.00 1.18 0.99 1.18 1.01 1.17 1.00 1.18

LAB 1.00 1.09 1.01 1.09 1.01 1.08 1.02 1.08

Enterobacteriaceae 0.99 1.16 1.00 1.14 1.00 1.14 0.98 1.14

Yeasts & Molds 0.99 1.15 1.00 1.15 0.99 1.11 1.02 1.14

RMSE

0.00

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Pseudo

mon

ads

Br. th

erm

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cta LAB

Enter

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teria

ceae

Yeasts

-Mold

s

PLS-R

Linear SVR

Radial SVR

Sigmoid SVR

ResultsResultsPrediction of the microbial loads - Regression models’ Performance - Regression models’ Performance

Good correlation of the sensorial evaluation of spoilage Good correlation of the sensorial evaluation of spoilage with the dynamic changes of the chromatographic areas of with the dynamic changes of the chromatographic areas of organic acids at different time intervals.organic acids at different time intervals.

In general the In general the PLS-R, radial basis SVR and sigmoid SVRPLS-R, radial basis SVR and sigmoid SVR exhibited slightly better performance than the exhibited slightly better performance than the Linear SVRLinear SVR whereas twhereas the models that described the estimates of the he models that described the estimates of the TVCTVC, as well as the , as well as the LABLAB, had better performance, regardless , had better performance, regardless of the type of the model builtof the type of the model built..

HPLC analysis of organic acids can be proved as a HPLC analysis of organic acids can be proved as a potential technique for meat analysis in predicting the potential technique for meat analysis in predicting the spoilage status and the microbial load of a meat sample spoilage status and the microbial load of a meat sample regardless of the storage conditions.regardless of the storage conditions.

Concluding remarksConcluding remarks

AcknowledgementsAcknowledgements

This work was supported by the EU projects Symbiosis [7th Framework Programme (Con. No 21638)] and ProSafeBeef [6th Framework Programme (ref. Food-CT-2006-36241)].

Thank you for your attentionThank you for your attention