# CHAPTER IV # 91

CHAPTER IV

Optimization of enzyme production by fermentation

technology

# CHAPTER IV # 92

4.1. INTRODUCTION

To meet the demand of industries, low-cost medium is required for the production of α-

amylase. Both solid state fermentation (SSF) and submerged fermentation (SmF) could

be used for the production of amylases, although traditionally these have been obtained

from submerged cultures because of ease of handling and greater control of

environmental factors such as temperature and pH. Mostly synthetic media have been

used for the production of bacterial amylase through SmF (Haddaoui et al., 1999;

McTigue et al., 1995; Haq et al., 1997; Hamilton et al., 1999b). The contents of synthetic

media such as nutrient broth, soluble starch, as well as other components are very

expensive and these could be replaced with cheaper agricultural by-products for the

reduction of the cost of the medium. SSF resembles natural microbiological processes

such as composting and ensiling, which can be utilized in a controlled way to produce a

desired product. SSF has been used for long to convert moist agricultural polymeric

substrates to such products including industrial enzymes (Rahardjo et al., 2005). SSF is

generally defined as the growth of microorganisms on moist solid substrates with

negligible free water. The solid substrate may provide only support or both support and

nutrition. SSF constitutes an interesting alternative since the metabolites so produced are

concentrated and purification procedures are less costly (Pandey et al., 2000; Pandey,

1992; Nigam and Singh, 1995; Chaddha et al., 1997). SSF is preferred to SmF because of

(1) simple technique, (2) low capital investment, (3) lower levels of catabolite repression

(4) end-product inhibition, (5) low waste water output, (6) better product recovery, and

(7) high quality production (Lonsane et al., 1985). Among the different substrates used

for SSF, wheat bran has been reported to produce promising results (Kunamneni et al.,

# CHAPTER IV # 93

2005; Mulimani et al., 2000; Nandakumar et al., 1996; Haq et al., 2003). Other substrates

such as sunflower meal, rice husk, cottonseed meal, soybean meal, and pearl millet and

rice bran have been tried for SSF (Haq et al., 2003; Baysal et al., 2003). SSF technique is

generally confined to the processes involving fungi. However, successful bacterial

growth in SSF is known in much natural fermentation (Lonsane and Ramesh, 1990;

Ramesh and Lonsane, 1991). The production of α-amylase by SSF is limited to the genus

Bacillus. B. subtilis, B. polymyxa, B. mesentericus, B. vulgarus, B. coagulans, B.

megaterium and B. licheniformis (Babu and Satyanarayana, 1995). The production of

bacterial α-amylase using the SSF technique requires less fermentation time (Ramesh and

Lonsane, 1987), which leads to considerable reduction in the capital and recurring

expenditure. Research on the selection of suitable substrates for SSF has mainly been

centered on agro-industrial residues due to their potential advantages for filamentous

fungi, which are capable of penetrating into the hardest of these solid substrates, aided by

the presence of turgor pressure at the tip of the mycelium (Ramachandran et al., 2004).

In addition, the utilization of these agro-industrial wastes, on one hand, provides

alternative substrates and, on the other, helps in solving pollution problems, which

otherwise may cause their disposal (Pandey et al., 1999). Optimization of various

parameters and manipulation of media are one of the most important techniques used for

the overproduction of enzymes in large quantities to meet industrial demands (Tanyildizi

et al., 2005). Production of α-amylase in fungi is known to depend on both morphological

and metabolic state of the culture. Growth of mycelium is crucial for extracellular

enzymes like α-amylase (Carlsen et al., 1996). Various physical and chemical factors

have been known to affect the production of α-amylase such as temperature, pH, period

# CHAPTER IV # 94

of incubation, carbon sources acting as inducers, surfactants, nitrogen sources, phosphate,

different metal ions, moisture and agitation with regards to both SSF and SmF.

Interactions of these parameters are reported to have a significant influence on the

production of the enzyme (Sivaramakrishnan et al., 2006).

The influence of temperature on amylase production is related to the growth of

the organism. Hence, the optimum temperature depends on whether the culture is

mesophilic, psychrophilic or thermophilic. pH is one of the important factors that

determine the growth and morphology of microorganisms as they are sensitive to the

concentration of hydrogen ions present in the medium. Earlier studies have revealed that

fungi required slightly acidic pH and bacteria required neutral pH for optimum growth.

pH is known to affect the synthesis and secretion of α-amylase just like its stability

(Fogarty, 1983). Supplementation of carbon and nitrogen sources, salts of certain metal

ions provided good growth of microorganisms and thereby better enzyme production (as

most α-amylases are known to be metalloenzymes). Moisture is one of the most

important parameters in SSF that influences the growth of the organism and thereby

enzyme production. Low and high moisture levels of the substrate affect the growth of

the microorganism. High moisture content leads to reduction in substrate porosity,

changes in the structure of substrate particles and reduction of gas volume. Bacteria are

generally known to require initial moisture of 70–80%. Significant decrease in enzyme

production was observed with high increase in moisture content, which was due to the

decrease in the rate of oxygen transfer. Studies indicated that enzyme titers could be

increased significantly by agitation of the medium with high moisture content (Ramesh

and Lonsane, 1990). In SSF, particle size of the substrate affects growth of the organism

# CHAPTER IV # 95

and thereby influences the enzyme production. The adherence and penetration of

microorganisms as well as the enzyme action on the substrate clearly depend upon the

physical properties of the substrate such as crystalline or amorphous nature, the

accessible area, surface area, porosity, particle size, etc. In all the above parameters,

particle size plays a major role because all these factors depend on it (Nandakumar et al.,

1996; Pandey, 1991). Smaller substrate particles have greater surface area for growth but

inter-particle porosity is lower. For larger particle sizes, the porosity is greater but the

saturated surface area is smaller. Hence, determination of particle size corresponding to

optimum growth and enzyme production is necessary (Pandey, 1991).

For production of α-amylase in SSF, sugarcane Bagasse holds its own importance

among various possible agro-substrates at industrial level, as it is a byproduct of juice

extraction in sugar industries and being highly biodegradable; its disposal is a serious

problem. Bagasse is a heterogeneous mixture and it is a rich source of carbohydrates,

acids, fibers, vitamins and minerals. Its disposal as a waste in the environment is a huge

loss of precious natural resources. However, its nitrogen deficient nature makes it

inadequate as an animal feed. Therefore, its utilization in one or other forms is the

immediate necessity from the economic and environmental protection point of view.

In comparison to traditional method, i.e. “one-variable-at-a-time” for production

of enzyme, statistically based experimental designs like Plackett-Burman design and

response surface methodology are more efficient in experimental biology, as variables are

tested simultaneously and they need fewer experiments, which are more efficient and can

move through the experimental domain. Moreover, the interactions between different

variables can be estimated. Response surface methodology (RSM) consists of a group of

# CHAPTER IV # 96

empirical techniques used for evaluation of relationship between cluster of controlled

experimental factors and measured response. A prior knowledge with understanding of

the related bioprocesses is necessary for a realistic modeling approach.

# CHAPTER IV # 97

4.2. MATERIALS AND METHODS

4.2.1. Optimization of α-amylase production

The methods used to study the optimization of growth parameter for α-amylase

production aimed to evaluate the effect of a single parameter at a time and later

manifesting it as standardized condition before optimizing the next parameter. For each

step, enzyme activity was assayed to know optimal yield. The experiments were

conducted in duplicate and the results are the average of three independent experiments.

4.2.1.1. Incubation time

To investigate the optimum time duration for production of cold-adapted α-

amylase, 100 ml of amylase producing broth media was inoculated at 1% (v/v) with 48

hour old (OD, 0.6) cultures and incubated at 15±2oC in shaking condition. The samples

were withdrawn aseptically at different time intervals (24, 48, 72, 96, 120, 144 and 168

hours) and the enzyme activity was assayed. The cell density was also measured by

using following method.

Cell growth measurement:

For the determination of culture turbidity, culture broths were appropriately

diluted with distilled water, if needed, and the optical densities were measured at 660 nm

using a spectrophotometer. The uninoculated media was used as a blank.

4.2.1.2. Incubation temperature

To find out the optimum temperature required for maximum α-amylase

production, the amylase producing broth media was inoculated with 48 hour old

# CHAPTER IV # 98

cultures and incubated at different temperatures (4, 10, 20, 27, 37, 45 and 50°C) for

optimum time duration.

4.2.1.3. pH of broth media

Optimum pH condition for maximum production of α-amylase was ascertained

by inoculating the amylase producing broth media having different pH (pH 3.0, 5.0, 7.0,

9.0, 10.0 and 12.0) with 48 hour old cultures. After incubation for appropriate time and

temperature the amylase production was measured using standard assay method.

4.2.1.4. Agitation/static condition

To optimize the best condition for fermentation, the broth media was inoculated

with 48 hour old cultures and incubated at 20ºC for 48 hours, in static condition and in a

rotary shaker at 120 rpm.

4.2.1.5. Effect of different Carbon and Nitrogen sources

The effect of various carbon and nitrogen sources as additional supplement in

media was studied to maximize the α-amylase production. Different carbon sources (1%)

such as lactose, maltose, glucose, sucrose, and glycerol and different nitrogen sources

(1%) viz. casein, glycine, yeast extract, ammonium sulfate and ammonium acetate were

tried to maximize the enzyme production in optimized conditions.

4.2.1.6. Utilization of Heavy metals

To evaluate the impact of heavy metals on cold-adapted α-amylase production,

broth media was supplemented with maximal tolerance level of different metal ions for

individual isolates and incubated under optimal conditions for 48 hours at 20ºC. The

# CHAPTER IV # 99

heavy metals used were Ca2+, Cu2+, Zn2+, Fe2+, Mg2+ and Hg2+. The enzyme production

was measured as per standard protocol.

4.2.2. Statistical Optimization of α-amylase production in solid-state

fermentation

4.2.2.1. Maintenance and growth of microorganism

M. foliorum GA2 was used in this present study instead of GA6 due to higher

enzyme production in optimized conditions. The culture was maintained on starch agar

slants. The slants were incubated at 20°C for 4 days and stored at 4°C. The stains were

subcultured every 6-7 weeks.

4.2.2.2. Raw material characterization in solid-state fermentation

Initial enzyme production was checked individually using wheat bran and rice

husk procured from local market of Lucknow, (U.P) and agro-industrial wastes of

sugarcane industry and from carpentry (in Lucknow) i.e. sugarcane Bagasse and saw

dust, respectively. These four wastes were screened for maximum production of α-

amylase and further optimization of process parameters was studied using the substrate

giving maximum activity with M. foliorum GA2 amylase at low temperature in solid-

state fermentation. Further, lactose as amylase inducer (0.002M) was supplemented as

individual component to the production media to check their effect on enzyme production

(Sivaramakrishnan et al., 2007; Kelly et al., 1995).

# CHAPTER IV # 100

4.2.2.3. Development of the inoculums, enzyme production and extraction

For the development of inoculum, culture was transferred from stock to 100 ml

nutrient broth and the inoculated flasks were incubated overnight at 20±2°C and 150 rpm.

Cells were harvested from the broth and their absorbance (A) was checked at 660 nm.

Accordingly, cells with inoculums size of A660=0.5 [10% inoculum (volume per mass)]

per 5 g of substrate were harvested, washed and moistened with sterile distilled water in

the ratio 1:1.5 (w/w). Production media contained 5 g of solid substrate and 10 ml of

starch agar media (Appendix 1) in 250 ml Erlenmeyer flasks and were inoculated with

the above inoculum. Inoculated production media were incubated under static conditions

at 20°C and enzyme production was checked after 120 hours. Enzyme was extracted in

50 ml of 0.1M phosphate buffer (pH 6.0) on a rotary shaker at 250 rpm for 30 min. The

content was filtered through muslin cloth, filtrate was centrifuged at 10,000×g for 10

minutes and clear supernatant was used as the enzyme source (Anto and Trivedi, 2006).

Aplha-amylase assay was performed by the method of Swain et al. (2006). All the

activity measurements were made in triplicates and experiments were repeated twice.

4.2.2.4. Experimental design and data analysis

4.2.2.4.1. Plackett-Burman design

The purpose of the first optimization step is to identify which ingredients of the

medium have significant effect on α-amylase production. The Plackett-Burman statistical

experimental design is very useful in screening the most significant factors. This design

does not consider the interaction effects between the variables and is used to screen the

# CHAPTER IV # 101

important variables affecting α-amylase production. The design matrix was developed

according to Plackett-Burman (1946). The total number of experiments to be carried out

according to Plackett-Burman is N+1, where N is the number of variables (medium

components and environmental factors). Each variable is represented at two levels,

namely a high level denoted by ‘+’ and a low level denoted by ‘–’ (Table 4.1). The high

level of each variable is far enough from the low level so that a significant effect, if

exists, is likely to be detected.

Table 4.4 shows the Plackett-Burman design with the seven factors under

investigation as well as the levels of the various factors used in the experimental design,

based on the first-order polynomial model as follows:

Y = β0 ∑ βi Χi (1)

Where Y is the response (growth of microorganisms), β0 is the model intercepts, βi is the

linear coefficient and Χi is the level of the independent variable. The rows in the Table

4.4 represent the eight different experiments and each column represents a different

variable. For each experimental variable, high (+) and low (-) levels are tested. All

experiments were performed in duplicate and the average of the maximal α-amylase

enzymatic activity was taken as the response.

# CHAPTER IV # 102

Table 4.1. Experimental variables at various levels used in the production of Cold-

active α-amylase by M.foliorum GA2 using the Plackett-Burman design

Variables Symbol code Experimental values

Lower (-) Higher (+)

pH X1 6.0 8.0

Bagasse (%) X2 25 55

KCl (g/L) X3 0.5 1

Yeast Extract (g/L) X4 0.25 1

MgSO4 (g/L)* X5 0.5 1

Lactose (M) X6 0.002 0.004

Peptone (g/L) X7 6 12

*X5 and X7 were dummy variables.

The Plackett-Burman design was analyzed using Statistica Version 7.0 software

(StatSoft, USA) to estimate the significant factors. The Pareto chart of standardized

effects was drawn to detect the most significant variables inside the experiment (Siva

Kiran et al., 2010). The Pareto chart analysis is a simple but powerful way of identifying

the significant variables. It amounts to construct a histogram of variables highlighting the

significant variables by crossing the p-value line (0.05 level of significance). The p-

values were calculated by performing analysis of variance (ANOVA).

# CHAPTER IV # 103

4.2.2.4.2. Optimization of significant variables using response surface methodology

The experimental design using Central Composite Design (CCD) was used to

estimate the coefficients in a mathematical model, to predict the response and to check

the applicability of the model. The factor, temperature was investigated with the three

variable medium components (pH, Bagasse, lactose) obtained after selection by the

Plackett-Burman design for the production of cold-active α-amylase. These four

independent variables were studied at five different levels and their minimum, maximum

and centre investigated values are listed in Table 4.2. The CCD contained an imbedded

factorial or fractional factorial matrix with centre points and star points around the centre

points that allowed the estimation of curvature. The distance from the centre of the design

space to a factorial point was ±1 unit for each factor and the distance from the centre of

the design space to a star point was ±α, where |α| > 1. The precise value of ‘α’ dependent

on certain properties needed for the design and on the number of factors used (in this case

α=2). Similarly, the number of centre point runs that the design must contain also

depends on certain properties required for the design. The CCD always contains twice as

many star points as factors in the design. The star points represent new extreme values

(low and high) for each factor on the design. To maintain rotability, the value of ‘α’

depends on the number of experimental runs in the factorial portion of the CCD.

Upon the completion of experiments, the average maximum α-amylase activity

was taken as the response (Y). A multiple regression analysis of the data was carried out

for obtaining an empirical model that relates the response measured to the independent

variables.

# CHAPTER IV # 104

A second-order polynomial equation is,

Y = β0 + ∑ βi Χi + ∑ βii Χi2 + ∑ βij Χi Χj (2)

where Y represents the response variable, β0 is the interception coefficient, βi is the

coefficient for the linear effects, βii is the coefficient for the quadratic effect, βij are

interaction coefficient and Χi Χj are coded independent variables that influence the

response variable Y. The response variable in each trial was the average of three

replicates. In this experimental design, the Statistica Version 7.0 software (StatSoft,

USA) was used for design of the experiments, analysis of the experimental data

(ANOVA) and the generation of 3D-contour plots.

Temperature is the most essential condition for the production of cold-active α-

amylase enzyme (as was also observed by optimization results) and thereby a range of

temperature between 15-30°C with the boundary of 10-50°C for ±α was selected in this

experimental design with a subsequent range of pH, Bagasse and lactose. It included a

total of 27 runs with three trials of centre points.

Table 4.2. Experimental codes, ranges and levels of independent variables in the

Response-Surface Methodology Experiment

Variables Symbol code Levels

Low Centre High

pH X1 6.0 8.0 10.0

Bagasse (%) X2 10 40 70

Lactose (M) X6 0.001 0.003 0.005

Temperature (°C) X8 10 20 30

# CHAPTER IV # 105

4.3. RESULTS AND DISCUSSION

4.3.1. Optimization of α-amylase production

4.3.1.1. Effect of Incubation time on growth and α-amylase production

The growth pattern of GA2 and GA6 and amylase production was observed for

168 hours in amylase production media at 15±2ºC and pH 7.0 in 250 ml Erlenmeyer flask

(Figure 4.1 and 4.2). GA2 grew very fast within 48 hours and shows maximum growth at

72 hours, after that it becomes constant. The production of amylase started from 48 hour

of the growth and reached maximum in 120 hours (4090 units) during stationary growth

phase after that rapid decline has been observed. Gupta et al. (2008) also reported same

time duration for maximum α-amylase production in case of Aspergillus niger. At 144

and 168 hours of incubation, 3256 and 2408 units of enzyme were produced. It was found

that, at 24, 48, 72 and 96 hours of incubation 323, 1493, 2581 and 2877 units of enzyme

were produced, respectively. Likewise, GA6 also showed gradual increase in cell growth

but attained maxima at 120 hours, after that it becomes constant. The production of

amylase was started from 24 hours of growth and reached maximum in 96 hours (4364

units) in logarithmic phase of growth. After 120, 144 and 168 hours of incubation the

enzyme production was 4314, 4119, 3840 units, respectively. The isolate also produced

good amount of enzyme at 24, 48 and 72 hours that is 2259, 2538 and 2904 units,

respectively. The result suggested that enzyme production has direct relationship with

cell growth. In reference to growth phase of the cells, Wijbenga et al. (1991) also

reported that Bacillus sp. produced maximum amylase into its late logarithmic growth

phase and continued to secrete well into the stationary phase.

# CHAPTER IV # 106

Figure 4.1. Effect of incubation time on growth and α-amylase production from

GA2

Figure 4.2. Effect of incubation time on growth and α-amylase production from

GA6

0

0.5

1

1.5

2

2.5

0

500

1000

1500

2000

2500

3000

3500

4000

4500

24 48 72 96 120 144 168

Cel

l mas

s (6

60nm

)

Enzy

me

activ

ity (U

nits

)

Incubation time (hrs)

Enzyme activity Cell mass

0

0.5

1

1.5

2

2.5

0500

100015002000250030003500400045005000

24 48 72 96 120 144 168

Cel

l mas

s (6

60nm

)

Enzy

me

activ

ity (U

nits

)

Incubation time (hrs)

Enzyme activity Cell mass

# CHAPTER IV # 107

4.3.1.2. Effect of Incubation temperature

Production of enzyme by microorganisms depends on the ability to thrive at

temperatures which requires a vast array of adaptations to maintain the metabolic rates

and sustained growth. In order to determine the optimum temperature for amylase

production, the cells were incubated at 4 to 50ºC in α-amylase production media. The

production of amylase was found to be maximal at 20ºC (4200 and 4662 units for GA2

and GA6, respectively) after incubation of 120 hours for GA2 and 96 hours for GA6 at

pH 7.0 (Figure 4.3). According to the most widely accepted definition given by Morita

(1975), it was found that the strains were psychrotrophs. There was sharp continuous

decline in enzyme production with increase in incubation temperature above 20ºC and it

was totally inhibited at 50ºC. At 10º, 27º, and 37ºC, the enzyme production from GA2

was 2674, 3043 and 606 units; however from GA6 it was 2895, 3695 and 1818 units

respectively. GA2 and GA6 produced 152 units and 280 units of enzyme at 4ºC

respectively.

Similar result was obtained by Lu et al. (2010) where a novel cold-adapted

amylase-producing bacteria, Pseudoalteromonas arctica GS230 isolated from seawater

collected from Gaogong island of Jiangsu Province, China attained maximum activity

when was cultured at 20°C, pH 8.0 for 24 hour. Michael et al. (2005) isolated

Arthrobacter psychrolactophilus ATCC 700733 from Pennsylvania soil which showed

maximum enzyme production at 22ºC but can grow up to 0ºC. Whereas optimum

temperature for Micrococcus antarcticus was 12ºC, isolated from Antarctica as reported

by Fan et al. (2009). For production of cold-active amylase, highest activity was observed

at 35ºC for both Lactobacillus plantarum at pH 7.0 and Bacillus sp. A-001 at pH 7.5 as

# CHAPTER IV # 108

reported by Smita et al. (2008) and Lealem and Gashe (1994), respectively. Morita et al.

(1997) observed maximum amylase activity at 30ºC from Flavobacterium balustinum

A201 strain isolated from cold soil (snow-covered) of Ishikawa Prefecture, Japan. This

strain also showed 80% of its relative activity at 20°C. All of the above isolates were

proved to be cold-adapted amylase producing strains on the basis of their optimum

temperature.

Figure 4.3. Effect of temperature on production of α-amylase (incubation of 120

hours for GA2 and 96 hours for GA6 at pH 7.0)

# CHAPTER IV # 109

4.3.1.3. Effect of pH

The pH of the culture media strongly affects many enzymatic reactions and

transport of compounds across the cell membrane as they are sensitive to the

concentration of hydrogen ions present in the medium. The pH is also known to affect the

synthesis and secretion of α-amylase (Fogarty, 1983). The effect of pH on the production

of amylase was studied in a pH range of 3.0-12.0. As alkalinity increases, production of

amylase increases and it was found that GA2 produced maximum amylase (4600 units) at

pH 9.0 after 120 hours of incubation at 20ºC. Whereas pH 10.0 was found to be optimum

for GA6 (4732 units) when was incubated for 96 hours at 20ºC. Production yield from

GA2 was 4010, 4110 and 4052 units at pH 7.0, 10.0 and 12.0, respectively. Whereas for

GA6, it was 3103, 3824 and 3551 units at pH 7.0, 9.0 and 12.0, respectively. However,

GA2 (3400 units) produces more amylase at pH 5.0 as compared to GA6 (1379 units).

Figure 4.4. Effect of pH on production of amylase (incubation of 120 hours for GA2

and 96 hours for GA6 at 20ºC)

# CHAPTER IV # 110

Fan et al. (2009) reported the maximum production of amylase, at pH 8.0 from

Micrococcus antarcticus, isolated from Antarctica. Optimum pH was 7.5 for Bacillus sp.

A-001 as reported by Lealem and Gashe (1994). But a similar result was obtained by

Poornima et al. (2008) where pH 9.0 was optimum for amylase production from

Actinomycete strain AE-19. On the contrary of my results, Abou-Elela et al. (2009)

reported cold-active acidic strain, Nocardiopsis aegyptia (optimum pH, 5.0) for amylase

production which was isolated from Abu Qir Bay, Alexandria, Egypt. It has been

observed that my both investigated strains were alkaliphilic in nature, producing α-

amylase at alkaline pH. These pH tolerant α-amylases from microbes are commercially

important for detergent industry and can also be successfully used for bioremediation of

polluted soils and waste water.

4.3.1.4. Effect of Agitation

The supply of oxygen is very essential for the aerobic fermentation. The oxygen

dissolved in the medium becomes available to the organism for growth. The consequence

of agitation on the production of amylase was studied at 120 rpm. The cells were

subjected to agitation in triplicate at 20ºC for incubation of 48 hours. It was observed that

both the isolate GA2 and GA6 produced more amylase during shaking condition in

comparison to stagnant condition (Figure 4.5). The enzyme production from GA2 was

increased from 2594 to 4210 units and in case of GA6, from 1204 to 4662 units, i.e. ~1.5

times & ~4 times more amylase production was noticed in shaking condition as

compared to unruffled condition for GA2 and GA6, respectively. Aeration is also

necessary factor for the growth of bacterium Pseudoalteromonas arctica GS230 as

# CHAPTER IV # 111

reported by Lu et al. (2010) where production of enzyme increases when carried out in

presence of air. Similar result was also reported by Haq et al. (2010) where they found

that the production of alpha-amylase from mutant strain of Bacillus amyloliquefaciens

was maximal (96.5 U/ml/min) when agitated for 48 hour at 37ºC, pH 7.0 as compared to

static condition.

Figure 4.5. Effect of agitation on production of amylase (20±2ºC and 48 hour

incubation)

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

GA2 GA6

Enzy

me

Activ

ity (U

)

Stagnant condition Agitation

# CHAPTER IV # 112

4.3.1.5. Effect of different carbon and nitrogen sources

Carbon and nitrogen sources are necessary for the proper growth and metabolism

of microorganisms. The use of cheap sources of C and N are important as these can

significantly reduce the cost of production of amylase. The effect of carbon and nitrogen

sources as additional supplement in media was studied to maximize the enzyme

production. Therefore, different carbon sources (1%) such as lactose, maltose, glucose,

sucrose, and glycerol were tried to maximize the enzyme production in optimized

conditions. From Figure 4.6, it was clear that the isolate GA2 produced maximum

amylase (5862 units) when supplemented with lactose followed by maltose (4137 units)

and glucose (3448 units), while glycerol has an inhibitory effect (344.8 units). Whereas in

case of GA6, exactly clashing results were obtained. Maximum production was obtained

with glycerol (4744 units) followed by sucrose and maltose (1952 units each) whereas

lactose serves as an inhibitor (1238 units). Hamilton et al. (1999b) also reported lactose

as a superior C-source for amylase production by Bacillus sp. Among the tested N

sources, GA2 production increases only with yeast extract (5870 units) while others

proved to be inhibitory like ammonium acetate (1443 units) and ammonium sulphate

(1634 units). However in case of GA6, ammonium acetate acts as best nitrogen source for

amylase production (4746 units) while glycine (317.4 units) and ammonium sulphate

(476.1 units) worsened the production. The result was in agreement with Dettori-Campus

et al. (1992) and Narayana and Vijayalakshmi (2008), who reported yeast extract as a

best N-source for B. stearothermophilus and S. albidoflavus, respectively.

# CHAPTER IV # 113

Figure 4.6. Effect of carbon source on production of α-amylase by GA2 and GA6

(20±2ºC and 48 hour incubation with agitation)

Soluble starch and beef extract were the most promising carbon and nitrogen sources,

respectively for Pseudoalteromonas arctica GS230, as reported by Lu et al. (2010). In

contrast, Pedersen and Nielson (2000) reported casein hydrolysate as a best nitrogen

source for A. oryzae and Gurudeeban et al. (2011) reported maximum amylase production

by B. megaterium with peptone as nitrogen source (Figure 4.6 and 4.7).

# CHAPTER IV # 114

Figure 4.7. Effect of nitrogen source on production of α-amylase by GA2 and GA6

(20±2ºC and 48 hour incubation with agitation)

4.3.1.6. Effect of heavy metals

Heavy metals present in surroundings play an important role in the growth of

bacteria. Supplementation of salts of certain heavy metal ions provided good growth of

microorganisms and thereby better enzyme production (as most α-amylases are known to

be metalloenzymes). Impact of heavy metals on production of cold-adapted amylase was

evaluated with maximum tolerance level of metals to the organism. Figure 4.8 showed

that enzyme production by GA2 was enhanced (208%) in presence of Mg2+ (6250 units)

in comparison to control (3000 units), whereas Cu2+, Fe2+, Zn2+ and Hg2+ worsened the

production as producing quite less than 3000 units of enzyme. However, Ca2+ (3000

# CHAPTER IV # 115

units) have no significant effect. Whereas in case of GA6, production was enhanced by

Ca2+ (145%) and Mg2+ (107%) in comparison to control (3150 units), whereas Cu2+, Fe2+,

Zn2+ and Hg2+ inhibit the production as with GA2. A similar result was also reported by

Vishwanathan and Surlikar (2001) where increased production of amylase occurs with

Ca2+. Lu et al. (2010) also found that Ca2+ had a significant effect on maintaining the

activity of the amylase enzyme isolated from Pseudoalteromonas arctica GS230.

Calcium is necessary for production and stability of amylase of many Bacillus sp. as

mentioned by Tonkova (1991).

Figure 4.8. Effect of heavy metals on α-amylase production by GA2 and GA6

(20±2ºC and 48 hour incubation with agitation)

# CHAPTER IV # 116

The result suggested that GA6 amylase is metalloenzyme requiring metal ion (Ca2+) for

its growth but GA2 produces Ca2+ independent amylase, whose merits are in starch

liquefaction, especially in manufacturing of fructose syrup, where Ca2+ is a known

inhibitor of glucose isomerase as reported by Tonkova (2006). The fluctuation in enzyme

production may be due to either utilization of metals by organism or due to binding of

metal ions to the enzyme that may increase or decrease enzyme activity. It can be

concluded that heavy metal rich soil was not good for proper growth of these microbes

and α-amylase production from them.

4.3.2. Statistical optimization of α-amylase production in solid-state

fermentation

4.3.2.1. Raw material screening for SSF

Among the four tested agro-substrates, maximum enzyme production was

observed with sugarcane Bagasse (2211 units) at 20°C followed by rice-husk while

enzyme production was not satisfactory with wheat bran and saw dust (Table 4.3). In

contrast, Gangadharan et al. (2006) found that among different screened agro-residues,

wheat bran increased α-amylase production from Bacillus amyloliquefaciens ATCC

23842 and gave maximum enzyme titer (62470 U/g) at 37°C after 72 hours of

fermentation. Supplementation of α-amylase inducer in the form of lactose resulted in

twice increase in α-amylase production by M. foliorum GA2 during solid-state

fermentation using Bagasse. Higher activity of enzyme (4090 units) was noticed in

presence of 0.002M lactose as compared to 2211 units without lactose (Table 4.3). Effect

of inducer on α-amylase production was almost nil with wheat-bran and saw-dust.

# CHAPTER IV # 117

Table 4.3. Production of α-amylase by M. foliorum GA2 on different agro-substrates

by solid-state fermentation (incubation at 20°C for 120 hours)

Agro-substrate α-amylase activity (units)

(without inducer)

α-amylase activity (units)

(with inducer, lactose: 0.002M)

Bagasse 2211 4090

Rice-husk 2000 2884

Wheat-bran 1684 1760

Saw-dust 1507 1510

4.3.2.2. Screening of significant medium constituents for cold-active α-amylase

production

A total of seven variables that influenced cold-active α-amylase production were

analyzed using the Plackett-Burman design at two concentration levels (Table 4.4). The

average of maximum α-amylase activity was taken as response Y. To examine the fitting

quality of the model, the proximate correlation coefficient (R2) to 1 indicated better

fitting of the predicted values from the equation to the experimental values. The value of

R2 was 0.9533, which can be interpreted as 95.33% of the variability in the response

(Table 4.5). The magnitude and direction of the factor coefficient in the equation

explained the influence of the eight medium components on the cold-active α-amylase

production from M. foliorum GA2. The greater magnitude of the coefficient indicated a

# CHAPTER IV # 118

large effect on the response. The corresponding response of the α-amylase production

was expressed in terms of the following regression equation:

Y = X1 + X2 + X3 + X4 + X5 + X6 + X7

Y = 5.535 + 0.557X1 + 0.40X2 + 0.12X3 -0.18X4 + 0.004X5+ 0.44X6 + 0.33X7 (3)

The Pareto chart of standardization histogram graph (Figure 4.9) showed that pH (X1),

Bagasse (X2) and Lactose (X6) with significance level (p<0.05), crosses the p-line and

were considered to significantly influence cold-active α-amylase production by M.

foliorum GA2. Whereas X4 variable (yeast extract) was far from the p-line, confirming

the insignificance of the variable inside the experiment. Importance of Plackett-Burman

experimental design to optimize culture conditions and evaluate the most significant

variables affecting α-amylase production was also performed by Abou-Elela et al. (2009).

Here, potassium nitrate concentration (1.5 g/l) and inoculum size (1.5 ml/50 ml medium)

were found most significant and thereby production was increased up to 1.12 fold at

25°C.

# CHAPTER IV # 119

Table 4.4. Plackett-Burman experimental design matrix for screening of seven

medium components for α-amylase production by M. foliorum GA2

Variable level Response

Run no.

X1 X2 X3 X4 X5 X6 X7 Enzyme activity

(units)

1 + + + - + - + 1300

2 - + + + - + - 2500

3 - - + + + - + 1350

4 + - - + + + - 1210

5 - + - - + + + 2245

6 + - + - - + + 1200

7 + + - + - - + 1300

8 - - - - - - - 1500

# CHAPTER IV # 120

Table 4.5. Analysis of variance (ANOVA) for seven variables by Plackett-Burman

design experiment

Sum of

squares

Degree of

freedom

Mean

squares

F-value Coefficient

factor

R2 p-value

Bagasse*(X2) 2.28 1 2.28 275.77 0.55 0.97 0.03

Lactose* (X6) 2.32 1 2.32 249.25 0.40 0.95 0.04

KCl (X3) 0.62 1 0.62 52.62 0.12 0.44 0.76

Yeast extract (X4) 0.78 1 0.78 35.40 -0.18 0.35 0.92

MgSO4 (X5) 0.26 1 0.26 45.88 0.004 0.38 0.83

pH* (X1) 2.25 1 2.25 270.11 0.44 0.94 0.04

Peptone (X7) 3.35 1 3.35 185.52 0.33 0.65 0.11

Error 0.006 1 0.006

Total SS 8.08 8

*Factors with p<0.05 are significant

R2 (mean coefficient of determination) = 0.9533

F = variance

# CHAPTER IV # 121

p= 0.05

Figure 4.9. Pareto chart standardized effects of seven factors screening design for the

production of α-amylase

4.3.2.3. Optimization of significant variables using Response Surface methodology

for cold-active α-amylase production

On regression analysis of the experimental data, the CCD generated a quadratic

equation (eq. 4) for α-amylase production as follows:

Y (α-amylase activity) = + X1 + X2 + X6 + X8 + X12 + X2

2 + X62 + X8

2 + X1X2 +

X1X6 + X1X8 + X2X6 + X2X8 + X6X8 (4)

27 runs were performed for optimization production of M. foliorum GA2 to obtain

maximum amount of cold-active α-amylase for 5-day cultivation period. As evident from

Table 4.6, run orders of 11, 14 and 25 with different combinations of pH, Bagasse,

# CHAPTER IV # 122

lactose and temperature levels enhanced α-amylase activity to production levels of 6570,

6610 and 6400 units, respectively. The predicted results are also shown in Table 4.6. The

predicted values from the regression equation closely agreed with that obtained from

experimental values. Validation of the experimental model was tested by carrying out the

experiment under optimal operation conditions in SSF. Three repeated experiments were

performed, and the results were compared. The α-amylase activity obtained from

experiments was very close to the actual response predicted by the regression model,

which proved the validity of the model. At these optimized conditions, the maximum α-

amylase activity was found to be 6610 units. Thereby, it can be concluded, that a linear

model, response surface method and the numerical optimization showed three-fold

improvement in cold-adapted α-amylase production by M. foliorum GA2 under the

optimized conditions of SSF than that obtained in the un-optimized reference medium at

20°C.

Exactly similar result was obtained by Liu et al. (2011a) where Plackett-Burman

design with response surface methodology was proved better for optimization to increase

cold-adapted amylase production by marine bacterium Wangia sp. C52. Ten-fold higher

amylase production was obtained than that of the control in shake-flask experiments. The

optimized cultivation conditions for amylase production were pH 7.18, a temperature of

20°C, and a shaking speed of 180 rpm. Cotarlet et al. (2011) also mentioned the

importance of statistical designs over “one-variable-at-a-time” conventional approach for

optimization production of cold-adapted α-amylase from Streptomyces 4 Alga but under

submerged fermentation. Here, 1.71-fold improvement in α-amylase production occurred

after 24 hour. Same finding was even reported by Shabbiri et al. (2012) where alpha-

# CHAPTER IV # 123

amylase yield by Brevibacterium linens DSM 20158 was found to be two-fold higher

when was done using a statistical approach (Plackett-Burman design with CCD) than that

obtained in the unoptimized reference medium but at 35°C. Another report where

production of α-amylase by Aspergillus oryzae As 3951 in solid state fermentation using

spent brewing grains as substrate, found much potential (17.5% increase in enzyme yield

as compared to normal method) when carried out using response surface methodology

(RSM) based on Plackett–Burman design (PBD) and Box–Behnken design (BBD), as

was reported by Xu et al. (2008). Significance of statistical designs for production of

enzymes was also in agreement with the results of Kammoun et al. (2008), where yield of

alpha-amylase by Aspergillus oryzae CBS 819.72 grown on gruel (wheat grinding by-

product) was 72.7% more when was performed with statistical methodology based on

three experimental designs viz Plackett-Burman design, Box-Behnken design and

Taguchi experimental design.

# CHAPTER IV # 124

Table 4.6. Experimental design obtained by applying the CCD matrix for four

factors and predicted responses for amylase production

Experimental value (Coded value) α-amylase activity

(units)

Run

order

pH Bagasse

(%)

Lactose

(M)

Temp.

(°C)

Observed Predicted

1 9(-1.0) 25(-1.0) 0.002(-1.0) 15(-1.0) 3410 3200 2 8(-1.0) 10(-1.0) 0.003(-1.0) 20(1.0) 3590 3860 3 7 (1.0) 25(-1.0) 0.002(-1.0) 15(-1.0) 3580 3580 4 8(1.0) 40(-1.0) 0.005(-1.0) 20(1.0) 3370 3212 5 7(-1.0) 55(-1.0) 0.004(1.0) 25(-1.0) 3990 4000 6 9(-1.0) 55(-1.0) 0.002(1.0) 25(1.0) 6040 6050 7 9(1.0) 55(-1.0) 0.004(1.0) 25(-1.0) 3740 3500 8 7(1.0) 25(-1.0) 0.004(1.0) 25(1.0) 6050 6100 9 9(-1.0) 55(1.0) 0.002(-1.0) 15(-1.0) 3400 3400

10 7(-1.0) 25(1.0) 0.004(-1.0) 15(1.0) 3780 3668 11 8(1.0) 40(1.0) 0.003(-1.0) 20(-1.0) 6550 6570 12 8(1.0) 40(1.0) 0.001(-1.0) 20(1.0) 1900 1985 13 7(-1.0) 55(1.0) 0.002(1.0) 15(-1.0) 3810 3990 14 8(-1.0) 40(1.0) 0.003(1.0) 20(1.0) 6600 6610 15 9(1.0) 25(1.0) 0.002(1.0) 25(-1.0) 3350 3254 16 8(1.0) 70(1.0) 0.004(1.0) 20(1.0) 3910 3890 17 7(0.0) 55(-2.0) 0.002(0.0) 25(0.0) 6070 6000 18 8(0.0) 40(2.0) 0.003(0.0) 30(0.0) 1360 1300 19 9(0.0) 25(0.0) 0.004(-2.0) 25(0.0) 3370 3450 20 6(0.0) 40(0.0) 0.003(2.0) 20(0.0) 3500 3590 21 8(-2.0) 40(0.0) 0.003(0.0) 10(0.0) 1950 1900 22 9(2.0) 55(0.0) 0.004(0.0) 15(0.0) 3970 3890 23 7(0.0) 55(0.0) 0.004(0.0) 15(-2.0) 3960 3821 24 7(0.0) 25(0.0) 0.002(0.0) 25(2.0) 6020 6150 25 8(0.0) 40(0.0) 0.003(0.0) 20(0.0) 6510 6400 26 10(0.0) 40(0.0) 0.003(0.0) 20(0.0) 3870 3820 27 9(1.0) 40(0.0) 0.003(0.0) 20(0.0) 3600 3685

# CHAPTER IV # 125

These results suggest that M. foliorum GA2 produced maximum α-amylase at low

temperature (20°C) under alkaline condition using less amount of agricultural waste

product. The quadratic model in equation 4 was further simplified, corresponding to the

p-value in the model terms. p-value of less than 0.05 indicated significant model terms

and values higher than 0.05 value indicated insignificant model terms. The second-order

regression equation provided the levels of α-amylase activity as the function of pH,

Bagasse, lactose, and temperature which can be presented in terms of coded factors as in

the following equation:

Y = 16811.72 - 239.59 X1 - 16259.03 X2 - 20 X6 - 37.1 X8 + 7.34 X12 + 18.08 X2

2 +

31.02 X62 + 44.2 X8

2 - 15 X1X2 -66.2 X1X6 + 5.33 X1X8 + 6.43 X2X6 - 51.2

X2X8 - 11.2 X6X8, (5)

where Y was the α-amylase activity.

ANOVA for the response surface is shown in Table 4.7. In this present work, square

effects of X6 (lactose) and X8 (temperature) were significant for α-amylase production as

p-value were 0.016 and 0.003, respectively. The Pareto chart of standardization

histogram graph (Figure 4.10) also showed that only temperature (X82) and lactose (X6

2),

with significance level (p<0.05), crosses the p-line and was considered to significantly

influence α-amylase production by M. foliorum GA2. These results confirmed the

importance of temperature for secretion of enzyme by bacteria and here, 20°C was found

best.

# CHAPTER IV # 126

Pareto Chart of Standardized Effects; Variable: Var54 factors, 1 Blocks, 27 Runs; MS Residual=1558911.

DV: Var5

.0376778

-.087348

-.146197

.3991089

-.758994

-.842238

-.952997

.9631019

-1.01531

1.388248

-1.70799

-1.82199

-2.79043

-3.69432

p=.05

Standardized Effect Estimate (Absolute Value)

X2*X8

X1*X6

X6

X2

X2*X6

X1*X8

X6*X8

X1*X2

X1

X8

X2*X2

X1*X1

X6*X6

X8*X8

Figure 4.10: Pareto chart of standardized effects of fourteen interactive factors affecting

production optimization of cold-adapted M. foliorum GA2

High positive values of coefficient factor (31.02 for X62 and 44.2 for X8

2) for these two

variables also suggest its significance. The coefficient of determination (R2) for α-

amylase activity was calculated as 0.9754, which is very close to 1 and can explain up to

97.54% variability of the response. The predicted R2 value of 92.26% was in reasonable

agreement with the adjusted R2 value of 96.28%.

# CHAPTER IV # 127

Table 4.7. Analysis of variance (ANOVA) for the parameters of Response surface

methodology fitted to quadratic equation

Source Sum of

squares

Degree of

freedom

Coefficient

factor

F-ratio p-Value > F

X1 16,070,06 1 -239.59 1.030 0.329

X1*X1 51,750,12 1 7.34 3.319 0.093

X2 24,8316 1 -16259.03 0.159 0.696

X2*X2 45,477,23 1 18.08 2.917 0.113

X6 33,320 1 -20 0.021 0.886

X6*X6 12,138,502 1 31.02 7.786 0.016

X8 30,043,84 1 -37.1 1.927 0.190

X8*X8 21,276,023 1 44.2 13.65 0.003

X1*X2 14,459,92 1 -15 0.927 0.354

X1*X6 11,894 1 -66.2 0.007 0.931

X1*X8 11,05836 1 5.33 0.709 0.416

X2*X6 89,8046 1 6.43 0.576 0.462

X2*X8 2213 1 -51.2 0.001 0.970

X6*X8 14,158,08 1 -11.2 0.908 0.359

Residual 14,043,80 12

Lack of fit 13, 277,45 10 8.6480 0.002

Error 18,706,933 2

Total SS 54,341,496 26

R2 = 97.54%; CV = 12.78%; Adj R2 = 96.28%; Pred R2 = 92.26%

# CHAPTER IV # 128

The interaction effects of variables on cold-active α-amylase production were also

studied by plotting 3D-contour plots against any two independent variables, while

keeping another variable at its central (0) level. These plots (described by the regression

model) were drawn to illustrate the effects of independent variables, and the combined

effect of each independent variable, upon the response variable are shown in Figures 4.11

to 4.16. The optimal values obtained from the contour plots were almost equal to the

results obtained by optimizing the regression equation. To test the goodness of fit of the

regression equation, the determination coefficient, R2 were evaluated which indicates a

good agreement between the experimental and predicted values (Xu et al. 2008).

Figure 4.11 shows the dependency of cold-active α-amylase on pH and Bagasse.

The α-amylase activity increases with increase in pH up to 8.0 with an amount of 40%

Bagasse and thereafter activity decreases with further increase/decrease in values of these

two independent variables. The same trend of pH was observed in Figures 4.12 and 4.13,

but with an optimum concentration of lactose, i.e. 0.003M and at a temperature of 20°C,

respectively.

# CHAPTER IV # 129

Var5 = 9089.6173-232.2432*x-155.5062*y-55.7448*x*x+21.7416*x*y-0.138*y*y

6000 5000 4000 3000 2000 1000

Figure 4.11. 3D-Contour plot showing the effect of pH and Bagasse on the

production of cold-active α-amylase enzyme.

# CHAPTER IV # 130

Var5 = -4733.9931+1732.9167*x+2.224E6*y-125.1042*x*x+11250*x*y-3.876E8*y*y

4000 3000 2000 1000

Figure 4.12. 3D-Contour plot showing the effect of pH and lactose on the production

of cold-active α-amylase enzyme.

# CHAPTER IV # 131

Var5 = -27328.8889+3966.6667*x+1664.3333*y-186.3542*x*x-61*x*y-27.7542*y*y

5000 4000 3000 2000 1000 0 -1000 -2000

Figure 4.13. 3D-Contour plot showing the effect of pH and temperature on the

production of cold-active α-amylase enzyme.

Figures 4.14 and 4.15 shows the dependency of α-amylase activity on Bagasse with

lactose and temperature, respectively. The effect of variables on α-amylase activity was

similar to other variables. Interaction effects of temperature and lactose on α-amylase

activity was shown in Figure 4.16. Quadratic equation (eq. 6) for these two square effects

(X62 and X8

2) which were found most significant in ANOVA as follows;

# CHAPTER IV # 132

Y = -16897.53 + 4.435 X6 + 1485.33 X8 - 5.1448 X62 - 68000 X6X8 - 30.3792 X8

2

(6)

(Y = α-amylase activity)

These positive and significant interaction results indicated that using optimum values of

all these variables, maximum production of cold-active α-amylase from M. foliorum GA2

could be achieved. The optimum conditions were 40% Bagasse with 0.003M lactose at

pH 8.0 and at a temperature of 20°C when experiment was performed in SSF for 5 days

of incubation period.

Var5 = -2034.1793+94.1759*x+3.0644E6*y-0.3667*x*x-19978.8504*x*y-3.7849E8*y*y

4000 3000 2000 1000 0

Figure 4.14. 3D-Contour plot showing the effect of Bagasse and lactose on the

production of cold-active α-amylase enzyme.

# CHAPTER IV # 133

Var5 = -8746.225+58.5932*x+1161.9695*y-0.7029*x*x+0.2251*x*y-27.5693*y*y

4000 3000 2000 1000 0 -1000

Figure 4.15. 3D-Contour plot showing the effect of Bagasse and temperature on the

production of cold-active α-amylase enzyme.

# CHAPTER IV # 134

Var5 = -16897.5347+4.4352E6*x+1485.3333*y-5.1448E8*x*x-68000*x*y-30.3792*y*y

4000 2000 0 -2000 -4000

Figure 4.16. 3D-Contour plot showing the effect of lactose and temperature on the

production of cold-active α-amylase enzyme.

# CHAPTER IV # 135

4.3.3. Conclusion

Novel psychrotrophic bacterial strains, M. foliorum GA2 and B. cereus GA6 were

producing cold-active α-amylase in alkaline medium (pH 9.0–10.0), so the enzyme could

be successfully applied to remove starchy stains from clothes and used in detergent

industry for cold washing that protect the color of fabrics and will be beneficial to save

energy as they work at lower temperatures. The starch-digesting characteristics of these

organisms at low temperature and their additional growth capability in various carbon

and nitrogen sources may be useful in bioremediation of polluted soils and waste waters

in cold regions as they are stable over a broad pH range and resistant to various metal

ions.

The Plackett-Burman design and Response Surface methodology were employed

to enhance the production of cold-active α-amylase by psychro-tolerant M. foliorum

GA2. Bagasse is a rich source of carbohydrates, acids, fibers, vitamins and minerals and

is considered to be a cost-effective agricultural raw waste material for Uttar Pradesh

(highest sugar producing state in India) which can be utilized in one or other forms by

applying the action of α-amylase at low temperature. It is the immediate necessity from

the economic and environmental protection point of view. Interactions between the

independent variables and the response were clearly evident. Plackett-Burman design was

used to select three medium components that exerted maximum influence on amylase

production by establishing a linear model. The second-order quadratic model generated

by CCD was also used to stimulate the optimal conditions for maximum yield. The

optimum conditions for cold-active α-amylase production in solid-state fermentation

medium were 40% Bagasse with 0.003M lactose at pH 8.0, 20°C when incubated for 5

# CHAPTER IV # 136

days. The α-amylase activity (6610 units) obtained with the statistically optimized

medium was three-fold higher than the α-amylase production obtained with basal

medium in submerged fermentation by the “one-variable-at-a-time” methodology.

Thereby application of an experimental design approach, the cost of production of cold-

active α-amylase can be dramatically reduced and can give maximum yield of

ubiquitously used α-amylase in low temperature conditions. Few studies on the

production of α-amylase by solid-state fermentation have been recently reported and this

is perhaps the first report for production of cold-active α-amylase by M. foliorum GA2 at

low temperature by response surface methodology in SSF.