- Email: [email protected]
Use of response surface methodology for optimizing process parameters for the production of α-amylase by Aspergillus oryzae
Biochemical Engineering Journal 15 (2003) 107–115
Use of response surface methodology for optimizing process parameters for the production of ␣-amylase by Aspergillus oryzae Febe Francis a,b , Abdulhameed Sabu a , K. Madhavan Nampoothiri a , Sumitra Ramachandran a , Sanjoy Ghosh b , George Szakacs c , Ashok Pandey a,∗ a
c
Biotechnology Division, Regional Research Laboratory, CSIR, Trivandrum, Kerala 695019, India b Centre for Biotechnology, Anna University, Chennai 600025, India Department of Agricultural Chemical Technology, Budapest University of Technology and Economics, 1111 Budapest, Gellerter 4, Hungary Received 7 May 2002; accepted after revision 20 November 2002
Abstract Optimization of three parameters (incubation temperature, initial substrate moisture and inoculum size) was attempted by using a Box–Behnken design under the response surface methodology for the optimal production of ␣-amylase by Aspergillus oryzae NRRL 6270 in solid-state fermentation (SSF). Spent brewing grains (SBG) was used as sole carbon source. The experimental data was fitted into a polynomial model for the yield of enzyme and an optimum level was arrived at which nutrient supplements were optimized. A Plackett–Burman design was employed to screen nineteen nutrient components for their influence on ␣-amylase production by the fungal culture. Three components (soybean meal, calcium chloride and magnesium sulphate) were selected based on their positive influence on enzyme formation. A Box–Behnken design was employed to optimize their composition, which showed that an incubation temperature of 30 ◦ C, an initial moisture of 70% and an inoculum rate of 1 × 107 spores/g dry substrate were the best conditions to produce ␣-amylase with A. oryzae NRRL 6270 on SBG. Under optimized conditions of SSF, about 20% increase in enzyme yield was observed. © 2002 Elsevier Science B.V. All rights reserved. Keywords: ␣-Amylase; Aspergillus oryzae; Spent brewing grains; Response surface methodology; Solid-state fermentation
1. Introduction Large quantities of agricultural and agro-industrial residues are generated round the year from diverse agricultural and industrial practices. These residues represent one of the most energy-rich resources on the planet [7]. They are in fact, one of the best reservoirs of fixed carbon in nature. Utilization of such resources has been in the frontier of industries practicing solid-state fermentation (SSF) [9,11–14,16]. Such resources are particularly attractive as they provide an inexpensive industrial substrate; moreover because, it offers elimination of large-scale accumulation of biomass. Spent brewing grains (SBG) is one such residue that has gained attention as a substrate for the production of enzymes under SSF [18]. The amylase family of enzymes finds potential application in a number of industrial processes especially in food, textiles and paper industries. ␣-Amylases (endo-1,4-␣-d-glucan ∗ Corresponding author. Tel.: +91-471-251-52-79; fax: +91-471-249-17-12. E-mail address: [email protected] (A. Pandey).
glucohydrolase EC 3.2.1.1) are extra-cellular enzymes that randomly cleave the 1,4-␣-d-glucosidic linkages between adjacent glucose units in the linear amylose chain. ␣-Amylase is secreted as a primary metabolite and its production is reported to be growth associated [19,20]. The optimization of fermentation conditions, particularly physical and chemical parameters are of primary importance in the development of any fermentation process owing to their impact on the economy and practicability of the process. The diversity of combinatorial interactions of medium components with the metabolism of the cells as well as the large number of medium constituents necessary for cell growth and production do not permit satisfactory detailed modeling. The one-dimensional search with successive variation in variables is still employed, even though it is well accepted that it is practically impossible for the one-dimensional search to achieve an appropriate optimum in a finite number of experiments. Single variable optimization methods are not only tedious, but also can lead to misinterpretation of results, especially because the interaction between different factors is overlooked [24].
1369-703X/02/$ – see front matter © 2002 Elsevier Science B.V. All rights reserved. PII: S 1 3 6 9 - 7 0 3 X ( 0 2 ) 0 0 1 9 2 - 4
108
F. Francis et al. / Biochemical Engineering Journal 15 (2003) 107–115
Statistical experimental designs have been in use for several decades [2,17]. These experimental layouts can be adopted at various phases of an optimization process, such as for screening experiments or for finding the optimal conditions for targeted results. Of late, the results analyzed by a statistically planned experiment are better acknowledged than those carried out by the traditional single variable experiments. Some of the popular choices in statistical design includes the Plackett–Burman design [22], the central composite design [4,21,23], the Box–Behnken design [22] and the Graeco–Latin square design [5]. Response surface methodology has by now been established as a convenient method for developing optimum processes with precise conditions and has also minimized the cost of production of many a process with efficient screening of process parameters [23].
Response surface methodology has been adopted here as a tool to obtain best process conditions.
1.1. Response surface methodology, the Box–Behnken design
2.3. Inoculum preparation
Box–Behnken designs [1] are response surface designs, especially made to require only three levels, coded as −1, 0, and +1. They are formed by combining two-level factorial designs with incomplete block designs. This procedure creates designs with desirable statistical properties but, most importantly, with only a fraction of the experiments required for a three-level factorial. Because there are only three levels, the quadratic model is appropriate. The coefficients of the quadratic model may be calculated using standard regression techniques. The present study employed this design twice. At first, it was used to study the interaction of significant physical parameters and later to find the optimal composition of selected nutrient components screened using the Plackett–Burman design. 1.2. The Plackett–Burman design The Plackett–Burman experimental design assumes that there are no interactions between the different media constituents, xi in the range of variables under consideration [17]. A linear approach is considered to be sufficient for screening. Y = β0 + βi xi
(i = 1, . . . , k)
where Y is the estimated target function and βi are the regression coefficients. The Plackett–Burman experimental design is a fractional factorial design and the main effect (the contrast coefficient) of such a design may be simply calculated as the difference between the average of measurements made at the high level (+1) of the factor and the average of measurements at the low level (−1). The experience of safe commercial use of Aspergillus oryzae for the production of ␣-amylase is well established. This reputation of A. oryzae is put to use in the development of an appropriate bioprocess by utilizing SBG for the production of ␣-amylase under SSF in the present study.
2. Materials and methods 2.1. Microorganism A strain of A. oryzae NRRL 6270 was used for the present study. It was maintained on potato–dextrose–agar (Hi-Media, Bombay). Subculturing was carried out once in every 3 weeks and culture was stored at 4 ◦ C. 2.2. Spent brewing grains (SBG) SBG was used as the substrate. It was obtained from a brewery in Budapest, Hungary.
Spores of A. oryzae NRRL 6270 from 7 days old PDA slants were dislodged into 10 ml sterile distilled water containing 0.1% Tween-80. This spore suspension was used as the master suspension, which was appropriately diluted for the required density of spores. The number of viable spores in the inoculum was determined by the pour plate count technique. 2.4. Substrate preparation Five grams of SBG was weighed into a 250 ml Erlenmeyer flask and to this a supplementing salt solution was added to the desired moisture level. The composition of the salt solution was as follows (in percentage (g/g) of dry substrate), NH4 NO3 : 1; KH2 PO4 : 1; NaCl: 0.2; MgSO4 7H2 O: 0.2. The contents were thoroughly mixed and autoclaved at 121 ◦ C (15 psi) for 20 min. 2.5. Solid-state fermentation (SSF) The sterilized solid substrate was inoculated with 1 ml of inoculum. The contents were mixed thoroughly and incubated at the appropriate temperature. Samples as whole flasks in duplicate, were withdrawn after 96 h of incubation. Experiments were conducted according to the statistical design. Variations in the process parameters were maintained according to the design. 2.6. Enzyme extraction The crude enzyme from the fermented material was extracted by simple contact method. For this, the fermented substrate was mixed thoroughly with distilled water containing 0.1% Tween-80, to a total extract volume amounts of 100 ml. Contents were mixed thoroughly by shaking for 1 h at room temperature in a rotary shaker (Certomat, B. Braun Biotech) at 150 rpm. At the end of the extraction, the suspension was centrifuged at 7000 rpm for 10 min (C-24 Cooling
F. Francis et al. / Biochemical Engineering Journal 15 (2003) 107–115
Centrifuge, REMI) and the supernatant was collected and used as the crude enzyme for further analysis. 2.7. α-Amylase assay α-Amylase activity was determined as described by Okolo et al. [8]. The reaction mixture consisted of 1.25 ml 1% (w/v) soluble starch (Merck) solution, 0.25 ml, 0.1 M sodium acetate buffer (pH 5.0), 0.25 ml of distilled water, and 0.25 ml of properly diluted crude enzyme extract (10–320×). After 10 min of incubation at 50 ◦ C, the liberated reducing sugars (glucose equivalents) were estimated by the dinitrosalicylic acid method [6]. Appropriate blanks were used. One unit (U) of ␣-amylase is defined as the amount of enzyme releasing 1 mol glucose equivalent per minute under the assay conditions.
109
Table 2 Experimental design (Box–Behnken) used to optimize the physical parameters for the production of ␣-amylase on SBG by A. oryzae NRRL 6270 Medium code
Temperature Moisture (◦ C) content (%)
Log (number of spores)
␣-amylase activity (U/g dry substrate)
A B C D E F G H I J K L M
25 35 25 35 25 35 25 35 30 30 30 30 30
6.5 6.5 6.5 6.5 5.5 5.5 7.5 7.5 5.5 5.5 7.5 7.5 6.5
5100 3801 4417 6523 4634 4651 5048 5335 2734 4100 5048 5990 6356
68 68 72 72 70 70 70 70 68 72 68 72 70
3. Results and discussion 3.1. Optimization of physical parameters Temperature, initial moisture content of substrate and inoculum size were identified as most influential among physical parameters for the production of ␣-amylase on SBG. A Box–Behnken design was employed to analyze the interactive effect of these parameters and to arrive at an optimum. The base points for the design were selected from a single-parameter study (data not shown). A summary of the variables and their variation levels is given in Table 1. SSF was carried out according to the design for 96 h. The fermented samples were extracted and assayed for ␣-amylase activity and soluble protein contents. The results were analyzed on a PC running under Windows OS, using Design expert 6.0 (StatEase Inc., Minneapolis, USA) statistical software and the response surface generated using STATISTICA (StatSoft Inc., Tulsa, USA). The design and results of experiments carried out with the Box–Behnken design are given in Table 2. The analysis of variance (ANOVA) was employed (data not shown) for the Table 1 Summary of variables for the Box–Behnken design for the optimization of physical parameters Factor
Basic level
Variation interval
Value of the factor
Coded value
Temperature (◦ C)
30
5
25 30 35
− 0 +
Moisture content (%)
70
2
68 70 72
− 0 +
6
1
Log (number of spores)
5.5 6.5 7.5
− 0 +
determination of Significant parameters. ANOVA consists of classifying and cross classifying statistical results and testing whether the means of a specified classification differ significantly. This was carried by Fisher’s statistical test for the analysis of variance. The F-value is the ratio of the mean square due to regression to the mean square due to error and indicates the influence (significance) of each controlled factor on the tested model. The model equation fitted by regression analysis is given by Y = −1, 048, 272 − 4883T − 19T 2 + 30, 346M −231M 2 + 16, 521S − 966S 2 + 85TM +14TS − 53MS where Y is the ␣-amylase activity (U/g dry substrate), T the temperature (◦ C), M the moisture content (% (w/w)) and S the log10 (spores/g dry substrate). The Model F-value of 11.44 implied that the model was significant. There was only a 0.2% chance that a “Model F-value” this large could occur due to noise. Values of “Probability > F ” less than 0.0500 indicated that model terms were significant. In this case the model terms M, S, M2 , S2 and TM were found significant. The model determination coefficient R2 (0.9363) suggested that the fitted model could explain 93.63% of the total variation. This implies a satisfactory representation of the process by the model. The fitted response surface for the production of ␣-amylase by the above model was generated using STATISTICA and is given in Figs. 1–3. Fig. 1 shows the effect of interaction of incubation temperature and initial moisture content of substrate on the production of ␣-amylase. A decrease in production was observed at temperatures outside the mesophilic range, which was in good agreement to the fact that A. oryzae belonged to mesophilic group. The contours were slightly inclined to the horizontal showing that there was a significant interaction
110
F. Francis et al. / Biochemical Engineering Journal 15 (2003) 107–115
Fig. 1. Effect of incubation temperature and initial moisture on production of ␣-amylase by A. oryzae on SBG.
between the two parameters. A single parameter study would overlook this entity. An interaction of temperature and moisture content was obvious as temperature was a factor that influenced humidity and water activity, which in turn was a governing factor of the transport phenomena across cellular membranes [16]. In SSF, during fermentation, there is a gen-
eral increase in the temperature of the fermenting substrate due to respiration [9,10]. However, these problems are generally encountered during the scale-up of SSF. In laboratory studies using flasks, no such difficulty was noticed. Fig. 2 depicts the interaction of moisture content and inoculum size. The contours were parallel to the two axes
Fig. 2. Effect of incubation temperature and inoculum size on production of ␣-amylase by A. oryzae on SBG.
F. Francis et al. / Biochemical Engineering Journal 15 (2003) 107–115
111
Fig. 3. Effect of inoculum size and initial moisture on production of ␣-amylase by A. oryzae on SBG.
suggesting that the two parameters were quite independent of each-other. Fig. 3 shows a similar trend between inoculum size and temperature. Age and size of the inoculum is usually found to relate with time-course of incubation rather than temperature or moisture content.
4. Optimization of nutrient supplements A total of 19 components were analyzed for their effect on ␣-amylase production when supplemented to SBG using the Plackett–Burman design. These included the four compounds used in previous experiments (section–substrate preparation), as well as some organic and inorganic nitrogen sources. A few metallic salts were also included. Table 3 shows the different levels of each of the constituents used in the Plackett–Burman design. Codes A to S were used to designate each of the components. The comparison of ␣-amylase production in different media is given in Table 4 along with the design of the experiment. Codes ‘1’ to ‘20’ were used to designate the media supplementation to SBG in each trial. The analysis showed that SBG supplemented with medium ‘15’ gave the maximum yield followed by medium ‘13’ and medium ‘7’. Corn steep liquor, soybean meal and CaCl2 were present in higher titres in these three media. The analysis of the contrast coefficient (b) showed that soybean meal, CaCl2 and MgSO4 7H2 O had pronounced influence on the production of ␣-amylase. The effect of Ca2+ and Cl− ions on the stability of ␣-amylase have been well documented [3,15,22]. The organic nitrogen sources, such as corn steep liquor and soybean meal have an edge over
Table 3 Nutrient supplements for screening using Plackett–Burman design Nutrient code
Compound
(+) Level (%) (g/g dry substrate)
(−) Level (%) (g/g dry substrate)
A B C D E F G H I J K L M N O P Q R S
NH4 NO3 KH2 PO4 NaCl MgSO4 Yeast extract Corn steep liquor Peptone Casein Soybean meal Urea NH4 H2 PO4 (NH4 )2 SO4 NH4 Cl NaNO3 CaCl2 ZnCl2 FeCl3 MnSO4 KCl
0.2 0.2 0.1 0.1 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.1 0.1 0.1 0.1 0.1
0.1 0.1 0.05 0.05 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.05 0.05 0.05 0.05 0.05
inorganic sources as they also have trace mineral and ions that could enhance the production of the enzyme. The other components were neglected and optimum combinations of these three were further analyzed by a Box–Behnken design. 5. Optimization of nutrient supplements Experiments were carried out in duplicates to arrive at an optimum combination of the three nutrients using a
112
Medium code
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
Activity (U/g dry substrate) at 96 h
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 b
+ + − − + + + + − + − + − − − − + + − − −307
− + + − − + + + + − + − + − − − − + + − 203
+ − + + − − + + + + − + − + − − − − + − −511
+ + − + + − − + + + + − + − + − − − − − 1124
− + + − + + − − + + + + − + − + − − − − 502
− − + + − + + − − + + + + − + − + − − − 338
− − − + + − + + − − + + + + − + − + − − 488
− − − − + + − + + − − + + + + − + − + − 142
+ − − − − + + − + + − − + + + + − + − − 1972
− + − − − − + + − + + − − + + + + − + − 555
+ − + − − − − + + − + + − − + + + + − − −129
− + − + − − − − + + − + + − − + + + + − −1174
+ − + − + − − − − + + − + + − − + + + − −893
+ + − + − + − − − − + + − + + − − + + − −288
+ + + − + − + − − − − + + − + + − − + − 2206
+ + + + − + − + − − − − + + − + + − − − 210
− + + + + − + − + − − − − + + − + + − − 596
− − + + + + − + − + − − − − + + − + + − −1571
+ − − + + + + − + − + − − − − + + − + − −1132
6057 7436 4757 1885 4963 3876 8216 4012 5400 3841 4575 5074 8425 6581 8753 5735 2012 2702 1342 3806
F. Francis et al. / Biochemical Engineering Journal 15 (2003) 107–115
Table 4 Experimental design using Plackett–Burman method for screening of nutrients
F. Francis et al. / Biochemical Engineering Journal 15 (2003) 107–115 Table 5 Composition (% (w/w)) of supplementing nutrients added to the substrate Nutrient
−
0
+
Soybean meal CaCl2 MgSO4
0 0 0
0.5 0.1 0.1
1 0.2 0.2
Table 6 Box–Behnken design for optimizing supplementation of SBG with nutrients Medium code
Soybean meal (%)
CaCl2 (%)
MgSO4 (%)
␣-Amylase activity (U/g dry substrate)
A B C D E F G H I J K L M
0 1 0 1 0 1 0 1 0.5 0.5 0.5 0.5 0.5
0 0 0.2 0.2 0.1 0.1 0.1 0.1 0 0.2 0 0.2 0.1
0.1 0.1 0.1 0.1 0 0 0.2 0.2 0 0 0.2 0.2 0.1
62 4252 68 6410 75 4605 1348 5185 4719 3905 4691 3750 4285
Box–Behnken design. Table 5 gives the variation levels at which these components were supplemented to SBG. Table 6 gives the design and results of experiments carried out by the Box–Behnken design. The results obtained were sub-
113
mitted to analysis of variance on Design expert 6.0 and the regression model is given as Y = 452 + 10, 095.25S − 3311.25C + 3272.5M −6100S 2 − 6200C2 + 4325M + 10, 760SC −3465SM − 3175CM where S is the concentration (%) of soybean meal, C that of CaCl2 and M MgSO4 7H2 O, respectively. A Model F-value of 14.45 was observed and implied the model to be significant. There was only a 0.10% chance that a “Model F-value” this large could occur due to noise. The analysis showed that S and S2 were significant model terms. Hence, soybean meal could be considered as to have the most effect among the three nutrients studied. The value of the determination coefficient R2 (0.9489) suggested that the fitted model could explain 94.89% of the total variation. The fitted response for the above regression model is plotted in Figs. 4–6. Fig. 4 depicts the variation in enzyme production with addition of soybean meal and CaCl2 as supplements to SBG. The yield showed a quadratic dependence on the concentration of soybean meal. However, increase in production was only marginal with the addition of CaCl2 . A slight increase was observed at higher concentrations. A similar observation can be made from Fig. 5 on the influence of soybean meal on enzyme production. However, with the increase in MgSO4 concentration, ␣-amylase production was not observed to vary much. Fig. 6 showed that the presence of CaCl2 had a better impact on ␣-amylase production than
Fig. 4. Effect of supplementation of SBG with soybean meal and CaCl2 on production of ␣-amylase by A. oryzae (with 0.1% supplement of MgSO4 7H2 O).
114
F. Francis et al. / Biochemical Engineering Journal 15 (2003) 107–115
Fig. 5. Effect of supplementation of SBG with soybean meal and MgSO4 7H2 O on production of ␣-amylase by A. oryzae (with 0.1% supplement of CaCl2 ).
MgSO4 . The optimum composition of the components were selected based on the significant parameters; the analysis showed only soybean meal to be significant, however, CaCl2 was also incorporated as its stabilizing effect on ␣-amylase
has been well-documented [3,22]. The optimum combination was found to be soybean meal: 1.0%, CaCl2 : 0.2% and MgSO4 7H2 O: 0.0% (w/w of dry substrate). The model showed that MgSO4 7H2 O was not essential for ␣-amylase
Fig. 6. Effect of supplementation of SBG with CaCl2 and MgSO4 7H2 O on production of ␣-amylase by A. oryzae (with 0.5% supplement of soybean meal).
F. Francis et al. / Biochemical Engineering Journal 15 (2003) 107–115 Table 7 Comparison between the original and optimized media
Original medium
Optimized medium
Nutrient supplement
Composition (%) (w/w of dry SBG)
Observed yield (U/g dry substrate)
NH4 NO3
1.00
5464
KH2 PO4 NaCl MgSO4 7H2 O
1.00 0.20 0.20
Soybean meal
1.00
CaCl2
0.20
6583
production along with a nutrient source such as soybean meal. Table 7 gives the comparison between the yield of ␣-amylase from original and optimized media under the experimental conditions. The increase in yield (6583 U/g dry substrate) is noteworthy. Use of Spent brewing grains has been beneficiary, as the yield of ␣-amylase has been high in comparison to ␣-amylase production reported under SSF using other substrates, such as amaranthus grains [22].
6. Conclusion Spent brewing grains (SBG) was found to be a good substrate for the production of ␣-amylase by filamentous fungi under solid-state fermentation. Statistical analysis proved to be a useful and powerful tool in developing optimum fermentation conditions. The statistical analysis based on a Box–Behnken design showed that an incubation temperature of 30 ◦ C, an initial moisture of 70% and an inoculum of 1 × 107 spores/g dry substrate were the best conditions to produce ␣-amylase with A. oryzae NRRL 6270 on SBG. Incorporation of most suitable conditions and supplements to the SSF medium resulted about 20% increase in enzyme yield. References [1] G.E.P. Box, D.W. Behnken, Some new three level designs for the study of quantitative variables, Technometrics 2 (1960) 455–475. [2] G.E.P. Box, J.S. Hunter, Multi-factorial designs for exploring response surfaces, Ann. Math. Stat. 28 (1957) 195–241. [3] J.P. Chessa, G. Feller, C. Gerday, Purification and characterization of the heat-labile ␣-amylase secreted by the psychrophilic bacterium TAC 240B, Can. J. Microbiol. 45 (6) (1999) 452–457. [4] G. Dey, A. Mitra, R. Banerjee, B.R. Maiti, 2001. Enhanced production of amylase by optimization of nutritional constituents using response surface methodology, Biochem. Eng. J., pp. 227–231.
115
[5] D. Haltrich, B. Laussamayer, W. Steiner, Xylanase formation by Sclerotium rolfii: effect of growth substrates and development of a culture medium using statistically designed experiments, Appl. Microbiol. Biotechnol. 42 (1994) 522–530. [6] G.L. Miller, Use of dinitrosalicylic acid reagent for determination of reducing sugar, Anal. Chem. 31 (1959) 426–429. [7] P. Nigam, D. Sing, A. Pandey, Utilization of agricultural and food waste and by-products by biotechnology, Agro Food Ind. Hi-Tech/AG Biotech 12 (3) (2001) 26–29. [8] B.N. Okolo, L.I. Ezeogu, C.N. Mba, Production of raw starch digesting amylase by Aspergillus niger grown on native starch sources, J. Sci. Food Agric. 69 (1995) 109–115. [9] A. Pandey, Recent developments in solid-state fermentation, Proc. Biochem. 27 (1992) 109–116. [10] A. Pandey, S. Radhakrishnan, Packed bed column bioreactor for production of enzymes, Enzyme Microb. Technol. 14 (1992) 486– 488. [11] A. Pandey, C.R. Soccol, D.A. Mitchell, New developments in solid-state fermentation. Part I. Bioprocesses and products, Proc. Biochem. 35 (2000a) 1158–1169. [12] A. Pandey, C.R. Soccol, P. Nigam, V.T. Soccol, Biotechnological potential of agro-industrial residues. Part I. Sugarcane bagasse, Bioresour. Technol. 81 (2000b) 71–80. [13] A. Pandey, C.R. Soccol, P. Nigam, L.P.S. Vandenberghe, R. Mohan, Biotechnological potential of agro-industrial residues. Part II. Cassava bagasse, Bioresour. Technol. 81 (2000c) 81–87. [14] A. Pandey, C.R. Soccol, P. Nigam, D. Brand, R. Mohan, S. Roussos, Biotechnological potential of coffee pulp and coffee husk for bioprocesses, Biochem. Eng. J. 6 (2) (2000d) 153–162. [15] A. Pandey, P. Nigam, C.R. Soccol, D. Singh, V.T. Soccol, R. Mohan, Advances in microbial amylases, Biotechnol. Appl. Biochem. 31 (2000e) 135–152. [16] A. Pandey, C.R. Soccol, J. Rodriguez-Leon, P. Nigam, 2001. Solid State Fermentation in Biotechnology, Asiatech Publishers, Delhi, p. 221. [17] R.L. Plackett, J.P. Burman, The design of optimum multifactorial experiments, Biometrika 33 (1946) 305–325. [18] T.S. Sim, J.C.S. Oh, Spent brewery grains as substrate for the production of cellulase by Trichoderma reesei qm9414, J. Ind. Microbiol. 5 (1990) 153–158. [19] A. Spohr, M. Carlsen, J. Nielsen, J. Villadsen, ␣-Amylase production in recombinant Aspergillus oryzae during Fed-batch and continuos cultivation, J. Fermentation Bioeng. 86 (1) (1998) 49–56. [20] S. Sudo, T. Ishikawa, K. Sato, T. Oba, Comparison of acid stable ␣-amylase production by Aspergillus kawachii in solid-state and submerged cultures, J. Fermentation Bioeng. 77 (5) (1994) 483– 489. [21] K. Sunitha, T.-K. Lee Jung-Kee, Optimization of medium components for phytase production by E. coli using response surface methodology, Bioproc. Eng. 21 (1999) 477–481. [22] P. Viswanathan, N.R. Surlikar, Production of ␣-amylase with Aspergillus flavus on amaranthus grains by solid-state fermentation, J. Basic Microbiol. 41 (1) (2001) 57–64. [23] A. Vohra, T. Satyanarayana, Statistical optimization of medium components by response surface methodology to enhance phytase production by Pichia anomala, Proc. Biochem. 37 (2002) 999–1004. [24] D. Wenster-Botz, Experimental design for fermentation media development: Statistical design or Global random search? J. Biosci. Bioeng. 90 (5) (2000) 473–483.
Use of response surface methodology for optimizing process parameters for the production of ␣-amylase by Aspergillus oryzae Febe Francis a,b , Abdulhameed Sabu a , K. Madhavan Nampoothiri a , Sumitra Ramachandran a , Sanjoy Ghosh b , George Szakacs c , Ashok Pandey a,∗ a
c
Biotechnology Division, Regional Research Laboratory, CSIR, Trivandrum, Kerala 695019, India b Centre for Biotechnology, Anna University, Chennai 600025, India Department of Agricultural Chemical Technology, Budapest University of Technology and Economics, 1111 Budapest, Gellerter 4, Hungary Received 7 May 2002; accepted after revision 20 November 2002
Abstract Optimization of three parameters (incubation temperature, initial substrate moisture and inoculum size) was attempted by using a Box–Behnken design under the response surface methodology for the optimal production of ␣-amylase by Aspergillus oryzae NRRL 6270 in solid-state fermentation (SSF). Spent brewing grains (SBG) was used as sole carbon source. The experimental data was fitted into a polynomial model for the yield of enzyme and an optimum level was arrived at which nutrient supplements were optimized. A Plackett–Burman design was employed to screen nineteen nutrient components for their influence on ␣-amylase production by the fungal culture. Three components (soybean meal, calcium chloride and magnesium sulphate) were selected based on their positive influence on enzyme formation. A Box–Behnken design was employed to optimize their composition, which showed that an incubation temperature of 30 ◦ C, an initial moisture of 70% and an inoculum rate of 1 × 107 spores/g dry substrate were the best conditions to produce ␣-amylase with A. oryzae NRRL 6270 on SBG. Under optimized conditions of SSF, about 20% increase in enzyme yield was observed. © 2002 Elsevier Science B.V. All rights reserved. Keywords: ␣-Amylase; Aspergillus oryzae; Spent brewing grains; Response surface methodology; Solid-state fermentation
1. Introduction Large quantities of agricultural and agro-industrial residues are generated round the year from diverse agricultural and industrial practices. These residues represent one of the most energy-rich resources on the planet [7]. They are in fact, one of the best reservoirs of fixed carbon in nature. Utilization of such resources has been in the frontier of industries practicing solid-state fermentation (SSF) [9,11–14,16]. Such resources are particularly attractive as they provide an inexpensive industrial substrate; moreover because, it offers elimination of large-scale accumulation of biomass. Spent brewing grains (SBG) is one such residue that has gained attention as a substrate for the production of enzymes under SSF [18]. The amylase family of enzymes finds potential application in a number of industrial processes especially in food, textiles and paper industries. ␣-Amylases (endo-1,4-␣-d-glucan ∗ Corresponding author. Tel.: +91-471-251-52-79; fax: +91-471-249-17-12. E-mail address: [email protected] (A. Pandey).
glucohydrolase EC 3.2.1.1) are extra-cellular enzymes that randomly cleave the 1,4-␣-d-glucosidic linkages between adjacent glucose units in the linear amylose chain. ␣-Amylase is secreted as a primary metabolite and its production is reported to be growth associated [19,20]. The optimization of fermentation conditions, particularly physical and chemical parameters are of primary importance in the development of any fermentation process owing to their impact on the economy and practicability of the process. The diversity of combinatorial interactions of medium components with the metabolism of the cells as well as the large number of medium constituents necessary for cell growth and production do not permit satisfactory detailed modeling. The one-dimensional search with successive variation in variables is still employed, even though it is well accepted that it is practically impossible for the one-dimensional search to achieve an appropriate optimum in a finite number of experiments. Single variable optimization methods are not only tedious, but also can lead to misinterpretation of results, especially because the interaction between different factors is overlooked [24].
1369-703X/02/$ – see front matter © 2002 Elsevier Science B.V. All rights reserved. PII: S 1 3 6 9 - 7 0 3 X ( 0 2 ) 0 0 1 9 2 - 4
108
F. Francis et al. / Biochemical Engineering Journal 15 (2003) 107–115
Statistical experimental designs have been in use for several decades [2,17]. These experimental layouts can be adopted at various phases of an optimization process, such as for screening experiments or for finding the optimal conditions for targeted results. Of late, the results analyzed by a statistically planned experiment are better acknowledged than those carried out by the traditional single variable experiments. Some of the popular choices in statistical design includes the Plackett–Burman design [22], the central composite design [4,21,23], the Box–Behnken design [22] and the Graeco–Latin square design [5]. Response surface methodology has by now been established as a convenient method for developing optimum processes with precise conditions and has also minimized the cost of production of many a process with efficient screening of process parameters [23].
Response surface methodology has been adopted here as a tool to obtain best process conditions.
1.1. Response surface methodology, the Box–Behnken design
2.3. Inoculum preparation
Box–Behnken designs [1] are response surface designs, especially made to require only three levels, coded as −1, 0, and +1. They are formed by combining two-level factorial designs with incomplete block designs. This procedure creates designs with desirable statistical properties but, most importantly, with only a fraction of the experiments required for a three-level factorial. Because there are only three levels, the quadratic model is appropriate. The coefficients of the quadratic model may be calculated using standard regression techniques. The present study employed this design twice. At first, it was used to study the interaction of significant physical parameters and later to find the optimal composition of selected nutrient components screened using the Plackett–Burman design. 1.2. The Plackett–Burman design The Plackett–Burman experimental design assumes that there are no interactions between the different media constituents, xi in the range of variables under consideration [17]. A linear approach is considered to be sufficient for screening. Y = β0 + βi xi
(i = 1, . . . , k)
where Y is the estimated target function and βi are the regression coefficients. The Plackett–Burman experimental design is a fractional factorial design and the main effect (the contrast coefficient) of such a design may be simply calculated as the difference between the average of measurements made at the high level (+1) of the factor and the average of measurements at the low level (−1). The experience of safe commercial use of Aspergillus oryzae for the production of ␣-amylase is well established. This reputation of A. oryzae is put to use in the development of an appropriate bioprocess by utilizing SBG for the production of ␣-amylase under SSF in the present study.
2. Materials and methods 2.1. Microorganism A strain of A. oryzae NRRL 6270 was used for the present study. It was maintained on potato–dextrose–agar (Hi-Media, Bombay). Subculturing was carried out once in every 3 weeks and culture was stored at 4 ◦ C. 2.2. Spent brewing grains (SBG) SBG was used as the substrate. It was obtained from a brewery in Budapest, Hungary.
Spores of A. oryzae NRRL 6270 from 7 days old PDA slants were dislodged into 10 ml sterile distilled water containing 0.1% Tween-80. This spore suspension was used as the master suspension, which was appropriately diluted for the required density of spores. The number of viable spores in the inoculum was determined by the pour plate count technique. 2.4. Substrate preparation Five grams of SBG was weighed into a 250 ml Erlenmeyer flask and to this a supplementing salt solution was added to the desired moisture level. The composition of the salt solution was as follows (in percentage (g/g) of dry substrate), NH4 NO3 : 1; KH2 PO4 : 1; NaCl: 0.2; MgSO4 7H2 O: 0.2. The contents were thoroughly mixed and autoclaved at 121 ◦ C (15 psi) for 20 min. 2.5. Solid-state fermentation (SSF) The sterilized solid substrate was inoculated with 1 ml of inoculum. The contents were mixed thoroughly and incubated at the appropriate temperature. Samples as whole flasks in duplicate, were withdrawn after 96 h of incubation. Experiments were conducted according to the statistical design. Variations in the process parameters were maintained according to the design. 2.6. Enzyme extraction The crude enzyme from the fermented material was extracted by simple contact method. For this, the fermented substrate was mixed thoroughly with distilled water containing 0.1% Tween-80, to a total extract volume amounts of 100 ml. Contents were mixed thoroughly by shaking for 1 h at room temperature in a rotary shaker (Certomat, B. Braun Biotech) at 150 rpm. At the end of the extraction, the suspension was centrifuged at 7000 rpm for 10 min (C-24 Cooling
F. Francis et al. / Biochemical Engineering Journal 15 (2003) 107–115
Centrifuge, REMI) and the supernatant was collected and used as the crude enzyme for further analysis. 2.7. α-Amylase assay α-Amylase activity was determined as described by Okolo et al. [8]. The reaction mixture consisted of 1.25 ml 1% (w/v) soluble starch (Merck) solution, 0.25 ml, 0.1 M sodium acetate buffer (pH 5.0), 0.25 ml of distilled water, and 0.25 ml of properly diluted crude enzyme extract (10–320×). After 10 min of incubation at 50 ◦ C, the liberated reducing sugars (glucose equivalents) were estimated by the dinitrosalicylic acid method [6]. Appropriate blanks were used. One unit (U) of ␣-amylase is defined as the amount of enzyme releasing 1 mol glucose equivalent per minute under the assay conditions.
109
Table 2 Experimental design (Box–Behnken) used to optimize the physical parameters for the production of ␣-amylase on SBG by A. oryzae NRRL 6270 Medium code
Temperature Moisture (◦ C) content (%)
Log (number of spores)
␣-amylase activity (U/g dry substrate)
A B C D E F G H I J K L M
25 35 25 35 25 35 25 35 30 30 30 30 30
6.5 6.5 6.5 6.5 5.5 5.5 7.5 7.5 5.5 5.5 7.5 7.5 6.5
5100 3801 4417 6523 4634 4651 5048 5335 2734 4100 5048 5990 6356
68 68 72 72 70 70 70 70 68 72 68 72 70
3. Results and discussion 3.1. Optimization of physical parameters Temperature, initial moisture content of substrate and inoculum size were identified as most influential among physical parameters for the production of ␣-amylase on SBG. A Box–Behnken design was employed to analyze the interactive effect of these parameters and to arrive at an optimum. The base points for the design were selected from a single-parameter study (data not shown). A summary of the variables and their variation levels is given in Table 1. SSF was carried out according to the design for 96 h. The fermented samples were extracted and assayed for ␣-amylase activity and soluble protein contents. The results were analyzed on a PC running under Windows OS, using Design expert 6.0 (StatEase Inc., Minneapolis, USA) statistical software and the response surface generated using STATISTICA (StatSoft Inc., Tulsa, USA). The design and results of experiments carried out with the Box–Behnken design are given in Table 2. The analysis of variance (ANOVA) was employed (data not shown) for the Table 1 Summary of variables for the Box–Behnken design for the optimization of physical parameters Factor
Basic level
Variation interval
Value of the factor
Coded value
Temperature (◦ C)
30
5
25 30 35
− 0 +
Moisture content (%)
70
2
68 70 72
− 0 +
6
1
Log (number of spores)
5.5 6.5 7.5
− 0 +
determination of Significant parameters. ANOVA consists of classifying and cross classifying statistical results and testing whether the means of a specified classification differ significantly. This was carried by Fisher’s statistical test for the analysis of variance. The F-value is the ratio of the mean square due to regression to the mean square due to error and indicates the influence (significance) of each controlled factor on the tested model. The model equation fitted by regression analysis is given by Y = −1, 048, 272 − 4883T − 19T 2 + 30, 346M −231M 2 + 16, 521S − 966S 2 + 85TM +14TS − 53MS where Y is the ␣-amylase activity (U/g dry substrate), T the temperature (◦ C), M the moisture content (% (w/w)) and S the log10 (spores/g dry substrate). The Model F-value of 11.44 implied that the model was significant. There was only a 0.2% chance that a “Model F-value” this large could occur due to noise. Values of “Probability > F ” less than 0.0500 indicated that model terms were significant. In this case the model terms M, S, M2 , S2 and TM were found significant. The model determination coefficient R2 (0.9363) suggested that the fitted model could explain 93.63% of the total variation. This implies a satisfactory representation of the process by the model. The fitted response surface for the production of ␣-amylase by the above model was generated using STATISTICA and is given in Figs. 1–3. Fig. 1 shows the effect of interaction of incubation temperature and initial moisture content of substrate on the production of ␣-amylase. A decrease in production was observed at temperatures outside the mesophilic range, which was in good agreement to the fact that A. oryzae belonged to mesophilic group. The contours were slightly inclined to the horizontal showing that there was a significant interaction
110
F. Francis et al. / Biochemical Engineering Journal 15 (2003) 107–115
Fig. 1. Effect of incubation temperature and initial moisture on production of ␣-amylase by A. oryzae on SBG.
between the two parameters. A single parameter study would overlook this entity. An interaction of temperature and moisture content was obvious as temperature was a factor that influenced humidity and water activity, which in turn was a governing factor of the transport phenomena across cellular membranes [16]. In SSF, during fermentation, there is a gen-
eral increase in the temperature of the fermenting substrate due to respiration [9,10]. However, these problems are generally encountered during the scale-up of SSF. In laboratory studies using flasks, no such difficulty was noticed. Fig. 2 depicts the interaction of moisture content and inoculum size. The contours were parallel to the two axes
Fig. 2. Effect of incubation temperature and inoculum size on production of ␣-amylase by A. oryzae on SBG.
F. Francis et al. / Biochemical Engineering Journal 15 (2003) 107–115
111
Fig. 3. Effect of inoculum size and initial moisture on production of ␣-amylase by A. oryzae on SBG.
suggesting that the two parameters were quite independent of each-other. Fig. 3 shows a similar trend between inoculum size and temperature. Age and size of the inoculum is usually found to relate with time-course of incubation rather than temperature or moisture content.
4. Optimization of nutrient supplements A total of 19 components were analyzed for their effect on ␣-amylase production when supplemented to SBG using the Plackett–Burman design. These included the four compounds used in previous experiments (section–substrate preparation), as well as some organic and inorganic nitrogen sources. A few metallic salts were also included. Table 3 shows the different levels of each of the constituents used in the Plackett–Burman design. Codes A to S were used to designate each of the components. The comparison of ␣-amylase production in different media is given in Table 4 along with the design of the experiment. Codes ‘1’ to ‘20’ were used to designate the media supplementation to SBG in each trial. The analysis showed that SBG supplemented with medium ‘15’ gave the maximum yield followed by medium ‘13’ and medium ‘7’. Corn steep liquor, soybean meal and CaCl2 were present in higher titres in these three media. The analysis of the contrast coefficient (b) showed that soybean meal, CaCl2 and MgSO4 7H2 O had pronounced influence on the production of ␣-amylase. The effect of Ca2+ and Cl− ions on the stability of ␣-amylase have been well documented [3,15,22]. The organic nitrogen sources, such as corn steep liquor and soybean meal have an edge over
Table 3 Nutrient supplements for screening using Plackett–Burman design Nutrient code
Compound
(+) Level (%) (g/g dry substrate)
(−) Level (%) (g/g dry substrate)
A B C D E F G H I J K L M N O P Q R S
NH4 NO3 KH2 PO4 NaCl MgSO4 Yeast extract Corn steep liquor Peptone Casein Soybean meal Urea NH4 H2 PO4 (NH4 )2 SO4 NH4 Cl NaNO3 CaCl2 ZnCl2 FeCl3 MnSO4 KCl
0.2 0.2 0.1 0.1 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.1 0.1 0.1 0.1 0.1
0.1 0.1 0.05 0.05 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.05 0.05 0.05 0.05 0.05
inorganic sources as they also have trace mineral and ions that could enhance the production of the enzyme. The other components were neglected and optimum combinations of these three were further analyzed by a Box–Behnken design. 5. Optimization of nutrient supplements Experiments were carried out in duplicates to arrive at an optimum combination of the three nutrients using a
112
Medium code
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
Activity (U/g dry substrate) at 96 h
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 b
+ + − − + + + + − + − + − − − − + + − − −307
− + + − − + + + + − + − + − − − − + + − 203
+ − + + − − + + + + − + − + − − − − + − −511
+ + − + + − − + + + + − + − + − − − − − 1124
− + + − + + − − + + + + − + − + − − − − 502
− − + + − + + − − + + + + − + − + − − − 338
− − − + + − + + − − + + + + − + − + − − 488
− − − − + + − + + − − + + + + − + − + − 142
+ − − − − + + − + + − − + + + + − + − − 1972
− + − − − − + + − + + − − + + + + − + − 555
+ − + − − − − + + − + + − − + + + + − − −129
− + − + − − − − + + − + + − − + + + + − −1174
+ − + − + − − − − + + − + + − − + + + − −893
+ + − + − + − − − − + + − + + − − + + − −288
+ + + − + − + − − − − + + − + + − − + − 2206
+ + + + − + − + − − − − + + − + + − − − 210
− + + + + − + − + − − − − + + − + + − − 596
− − + + + + − + − + − − − − + + − + + − −1571
+ − − + + + + − + − + − − − − + + − + − −1132
6057 7436 4757 1885 4963 3876 8216 4012 5400 3841 4575 5074 8425 6581 8753 5735 2012 2702 1342 3806
F. Francis et al. / Biochemical Engineering Journal 15 (2003) 107–115
Table 4 Experimental design using Plackett–Burman method for screening of nutrients
F. Francis et al. / Biochemical Engineering Journal 15 (2003) 107–115 Table 5 Composition (% (w/w)) of supplementing nutrients added to the substrate Nutrient
−
0
+
Soybean meal CaCl2 MgSO4
0 0 0
0.5 0.1 0.1
1 0.2 0.2
Table 6 Box–Behnken design for optimizing supplementation of SBG with nutrients Medium code
Soybean meal (%)
CaCl2 (%)
MgSO4 (%)
␣-Amylase activity (U/g dry substrate)
A B C D E F G H I J K L M
0 1 0 1 0 1 0 1 0.5 0.5 0.5 0.5 0.5
0 0 0.2 0.2 0.1 0.1 0.1 0.1 0 0.2 0 0.2 0.1
0.1 0.1 0.1 0.1 0 0 0.2 0.2 0 0 0.2 0.2 0.1
62 4252 68 6410 75 4605 1348 5185 4719 3905 4691 3750 4285
Box–Behnken design. Table 5 gives the variation levels at which these components were supplemented to SBG. Table 6 gives the design and results of experiments carried out by the Box–Behnken design. The results obtained were sub-
113
mitted to analysis of variance on Design expert 6.0 and the regression model is given as Y = 452 + 10, 095.25S − 3311.25C + 3272.5M −6100S 2 − 6200C2 + 4325M + 10, 760SC −3465SM − 3175CM where S is the concentration (%) of soybean meal, C that of CaCl2 and M MgSO4 7H2 O, respectively. A Model F-value of 14.45 was observed and implied the model to be significant. There was only a 0.10% chance that a “Model F-value” this large could occur due to noise. The analysis showed that S and S2 were significant model terms. Hence, soybean meal could be considered as to have the most effect among the three nutrients studied. The value of the determination coefficient R2 (0.9489) suggested that the fitted model could explain 94.89% of the total variation. The fitted response for the above regression model is plotted in Figs. 4–6. Fig. 4 depicts the variation in enzyme production with addition of soybean meal and CaCl2 as supplements to SBG. The yield showed a quadratic dependence on the concentration of soybean meal. However, increase in production was only marginal with the addition of CaCl2 . A slight increase was observed at higher concentrations. A similar observation can be made from Fig. 5 on the influence of soybean meal on enzyme production. However, with the increase in MgSO4 concentration, ␣-amylase production was not observed to vary much. Fig. 6 showed that the presence of CaCl2 had a better impact on ␣-amylase production than
Fig. 4. Effect of supplementation of SBG with soybean meal and CaCl2 on production of ␣-amylase by A. oryzae (with 0.1% supplement of MgSO4 7H2 O).
114
F. Francis et al. / Biochemical Engineering Journal 15 (2003) 107–115
Fig. 5. Effect of supplementation of SBG with soybean meal and MgSO4 7H2 O on production of ␣-amylase by A. oryzae (with 0.1% supplement of CaCl2 ).
MgSO4 . The optimum composition of the components were selected based on the significant parameters; the analysis showed only soybean meal to be significant, however, CaCl2 was also incorporated as its stabilizing effect on ␣-amylase
has been well-documented [3,22]. The optimum combination was found to be soybean meal: 1.0%, CaCl2 : 0.2% and MgSO4 7H2 O: 0.0% (w/w of dry substrate). The model showed that MgSO4 7H2 O was not essential for ␣-amylase
Fig. 6. Effect of supplementation of SBG with CaCl2 and MgSO4 7H2 O on production of ␣-amylase by A. oryzae (with 0.5% supplement of soybean meal).
F. Francis et al. / Biochemical Engineering Journal 15 (2003) 107–115 Table 7 Comparison between the original and optimized media
Original medium
Optimized medium
Nutrient supplement
Composition (%) (w/w of dry SBG)
Observed yield (U/g dry substrate)
NH4 NO3
1.00
5464
KH2 PO4 NaCl MgSO4 7H2 O
1.00 0.20 0.20
Soybean meal
1.00
CaCl2
0.20
6583
production along with a nutrient source such as soybean meal. Table 7 gives the comparison between the yield of ␣-amylase from original and optimized media under the experimental conditions. The increase in yield (6583 U/g dry substrate) is noteworthy. Use of Spent brewing grains has been beneficiary, as the yield of ␣-amylase has been high in comparison to ␣-amylase production reported under SSF using other substrates, such as amaranthus grains [22].
6. Conclusion Spent brewing grains (SBG) was found to be a good substrate for the production of ␣-amylase by filamentous fungi under solid-state fermentation. Statistical analysis proved to be a useful and powerful tool in developing optimum fermentation conditions. The statistical analysis based on a Box–Behnken design showed that an incubation temperature of 30 ◦ C, an initial moisture of 70% and an inoculum of 1 × 107 spores/g dry substrate were the best conditions to produce ␣-amylase with A. oryzae NRRL 6270 on SBG. Incorporation of most suitable conditions and supplements to the SSF medium resulted about 20% increase in enzyme yield. References [1] G.E.P. Box, D.W. Behnken, Some new three level designs for the study of quantitative variables, Technometrics 2 (1960) 455–475. [2] G.E.P. Box, J.S. Hunter, Multi-factorial designs for exploring response surfaces, Ann. Math. Stat. 28 (1957) 195–241. [3] J.P. Chessa, G. Feller, C. Gerday, Purification and characterization of the heat-labile ␣-amylase secreted by the psychrophilic bacterium TAC 240B, Can. J. Microbiol. 45 (6) (1999) 452–457. [4] G. Dey, A. Mitra, R. Banerjee, B.R. Maiti, 2001. Enhanced production of amylase by optimization of nutritional constituents using response surface methodology, Biochem. Eng. J., pp. 227–231.
115
[5] D. Haltrich, B. Laussamayer, W. Steiner, Xylanase formation by Sclerotium rolfii: effect of growth substrates and development of a culture medium using statistically designed experiments, Appl. Microbiol. Biotechnol. 42 (1994) 522–530. [6] G.L. Miller, Use of dinitrosalicylic acid reagent for determination of reducing sugar, Anal. Chem. 31 (1959) 426–429. [7] P. Nigam, D. Sing, A. Pandey, Utilization of agricultural and food waste and by-products by biotechnology, Agro Food Ind. Hi-Tech/AG Biotech 12 (3) (2001) 26–29. [8] B.N. Okolo, L.I. Ezeogu, C.N. Mba, Production of raw starch digesting amylase by Aspergillus niger grown on native starch sources, J. Sci. Food Agric. 69 (1995) 109–115. [9] A. Pandey, Recent developments in solid-state fermentation, Proc. Biochem. 27 (1992) 109–116. [10] A. Pandey, S. Radhakrishnan, Packed bed column bioreactor for production of enzymes, Enzyme Microb. Technol. 14 (1992) 486– 488. [11] A. Pandey, C.R. Soccol, D.A. Mitchell, New developments in solid-state fermentation. Part I. Bioprocesses and products, Proc. Biochem. 35 (2000a) 1158–1169. [12] A. Pandey, C.R. Soccol, P. Nigam, V.T. Soccol, Biotechnological potential of agro-industrial residues. Part I. Sugarcane bagasse, Bioresour. Technol. 81 (2000b) 71–80. [13] A. Pandey, C.R. Soccol, P. Nigam, L.P.S. Vandenberghe, R. Mohan, Biotechnological potential of agro-industrial residues. Part II. Cassava bagasse, Bioresour. Technol. 81 (2000c) 81–87. [14] A. Pandey, C.R. Soccol, P. Nigam, D. Brand, R. Mohan, S. Roussos, Biotechnological potential of coffee pulp and coffee husk for bioprocesses, Biochem. Eng. J. 6 (2) (2000d) 153–162. [15] A. Pandey, P. Nigam, C.R. Soccol, D. Singh, V.T. Soccol, R. Mohan, Advances in microbial amylases, Biotechnol. Appl. Biochem. 31 (2000e) 135–152. [16] A. Pandey, C.R. Soccol, J. Rodriguez-Leon, P. Nigam, 2001. Solid State Fermentation in Biotechnology, Asiatech Publishers, Delhi, p. 221. [17] R.L. Plackett, J.P. Burman, The design of optimum multifactorial experiments, Biometrika 33 (1946) 305–325. [18] T.S. Sim, J.C.S. Oh, Spent brewery grains as substrate for the production of cellulase by Trichoderma reesei qm9414, J. Ind. Microbiol. 5 (1990) 153–158. [19] A. Spohr, M. Carlsen, J. Nielsen, J. Villadsen, ␣-Amylase production in recombinant Aspergillus oryzae during Fed-batch and continuos cultivation, J. Fermentation Bioeng. 86 (1) (1998) 49–56. [20] S. Sudo, T. Ishikawa, K. Sato, T. Oba, Comparison of acid stable ␣-amylase production by Aspergillus kawachii in solid-state and submerged cultures, J. Fermentation Bioeng. 77 (5) (1994) 483– 489. [21] K. Sunitha, T.-K. Lee Jung-Kee, Optimization of medium components for phytase production by E. coli using response surface methodology, Bioproc. Eng. 21 (1999) 477–481. [22] P. Viswanathan, N.R. Surlikar, Production of ␣-amylase with Aspergillus flavus on amaranthus grains by solid-state fermentation, J. Basic Microbiol. 41 (1) (2001) 57–64. [23] A. Vohra, T. Satyanarayana, Statistical optimization of medium components by response surface methodology to enhance phytase production by Pichia anomala, Proc. Biochem. 37 (2002) 999–1004. [24] D. Wenster-Botz, Experimental design for fermentation media development: Statistical design or Global random search? J. Biosci. Bioeng. 90 (5) (2000) 473–483.