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# 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.