Bioprocess Biosyst Eng (2013) 36:1115–1123 DOI 10.1007/s00449-012-0865-3
ORIGINAL PAPER
Production, statistical optimization and application of endoglucanase from Rhizopus stolonifer utilizing coffee husk P. N. Navya • S. Murthy Pushpa
Received: 28 June 2012 / Accepted: 19 November 2012 / Published online: 7 December 2012 Ó Springer-Verlag Berlin Heidelberg 2012
Abstract Coffee cherry husk (CH) is one of the major by-products obtained from coffee processing industry and accounts to 43 ± 5.9 % of cellulose. Screening of fungal organism for cellulase production was carried out and the potential organism was identified as Rhizopus stolonifer by internal transcribed spacer’s (ITS)—5.8S rDNA analysis. A systematic study with response surface methodology (RSM) based on CCRD was used to study the interactions among the variables such as pH (3–7), moisture (40–80 %) and progression duration (72–168 h) of the fermentation process to maximize the enzyme production. Under the optimized cultivation condition, R. stolonifer synthesized 22,109 U/gds. Model validations at optimum operating conditions showed excellent agreement between the experimental results and the predicted responses with a confidence level of 95 %. Endoglucanase thus produced was utilized for ethanol production by simultaneous saccharification and fermentation and maximum of 65.5 g/L of ethanol was obtained. This fungal cellulase has also reported to be efficient detergent additives and promising for commercial use. The present study demonstrates coffee husk as a significant bioprocess substrate. Statistical optimization with major parameters for cellulase production can be highly applicable for industrial scale. Furthermore, value addition to coffee husk with sustainable waste management leading to environment conservation can be achieved.
P. N. Navya S. M. Pushpa (&) Plantation Products Spices and Flavour Technology Department, Central Food Technological Research Institute, Council of Scientific and Industrial Research, New Delhi, India e-mail:
[email protected];
[email protected] P. N. Navya e-mail:
[email protected]
Keywords Coffee husk (CH) Detergent compatibility Endoglucanase Response surface methodology (RSM) Simultaneous saccharification and fermentation (ssf)
Introduction Coffee husk, the by-product of coffee processing industries contains 43 % of cellulose, 22.8 % of sugars and 11.2 % of crude protein. In recent years, there has been an increasing biotechnological interest on the utilization of these residues as substrates in biotechnological processes. They have been suitable as a carbon source, and reported to be good substrate for enzyme production using fungal species in solid-state fermentation (SSF) [1]. SSF is defined as a fermentation process in which microorganisms grow on solid materials without the presence of free liquid and the moisture necessary for microbial growth exists in adsorbed state with solid matrix [2]. Fungal solid-state fermentation of coffee husk for cellulolytic enzyme production is an affirmative value addition. A cellulolytic enzyme system is a group of enzymes that work synergistically to hydrolyze lignocellulosic biomass. It is composed of endoglucanase, exoglucanase and b-glucosidase [3]. Endo-b-1, 4-glucanase (Endoglucanase or CMCase) randomly hydrolyzes internal b-1, 4-D-glycosidic bonds in cellulose producing oligos and reducing polymer length, while exo-b-1, 4-glucanase (cellobiohydrolase) cleave cellobiosyl residues from the nonreducing end of cellulose chain. Subsequently, cellobiose is hydrolyzed by b-glucosidase to yield two glucose units. These enzymes are produced by several microorganisms, mainly by bacteria and fungi. Although a large number of microorganisms are capable of degrading cellulose, only few of these produce significant quantities of cell free enzyme cellulase capable of completely hydrolyzing
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crystalline cellulose in vitro. Fungi are the main cellulase producing microorganisms compared to bacteria and actinomycetes. Cellulases have a wide range of industrial applications. The main application of these enzymes in textile, paper and pulp, food and animal feed, fuel and chemical industry, demand highly stable enzymes, able to excel at extreme conditions of pH and temperatures [4]. The intensity of enzyme activity produced by organisms from the natural environment is often low and needs to be elevated for industrial production. Increase in enzyme levels is often achieved by optimizing growth conditions using statistical tools. Response surface methodology (RSM) have been utilized as reliable techniques for studies on the influence of multiple factors on production of enzymes [5] and other bioactive metabolites with biotechnological interest. With this background, the study describes the screening, identification of the fungi by molecular studies, effects of fermentation conditions on production of endoglucanase enzyme with statistical techniques (RSM) to analyze the optimal parameters so as to maximize the endoglucanase activity, application of enzyme for ethanol production by simultaneous saccharification and fermentation (ssf) and to check the compatibility of the enzyme with detergents.
Materials and methods Media and analytical chemicals All chemicals used in the study were of analytical grade and were procured from Hi-Media, India, Sigma-Aldrich, USA. Substrate and proximate analysis Coffee husk was obtained from the local coffee curing works, Mysore, India and was utilized as the substrate for solid-state fermentation. It was proximally analyzed under standard experimental conditions for determination of moisture, sugars, protein, lignin, fiber and cellulose content to investigate chemical composition of substrate. Isolation, screening and identification of fungal organisms The fungal organisms were isolated from soil and coffee byproducts collected from coffee growing areas in Coorg, India. Screening of the isolates for cellulase production was carried out on selective agar plates containing (w/v): 1 % carboxymethyl cellulose (CMC), 0.2 % NaNO3, 0.1 % K2HPO4, 0.05 % MgSO4, 0.05 % KCl, 0.02 % peptone and 3 % agar. Point inoculation was done on agar plates with
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pure cultures and was incubated at 30 °C for 72 h. The plates were flooded with Gram’s iodine solution and the clearing zone around the colony was measured so as to opt for the best cellulose degrading organism. Morphology of the colony and the growth rate was observed [6] and further identification was carried out by molecular methods. The fungal organisms were cultured in flasks containing 200 mL potato dextrose broth at 28 °C for 72 h. After cultivation, the mycelium was separated from the liquid medium. The DNA was extracted by a Fungal Genomic DNA Extraction Kit (Chromous Biotech Pvt Ltd, Bangalore). The extract was used as a template DNA for PCR and Internal Transcribed Spacer’s (ITS)5.8S rDNA amplification was carried out. The ITS region of fungal DNA was amplified using the fungal specific primer set: ITS F (GGAAGTAAAAGTCGTAACAAGG) and ITS R (TCCTCCGCTTATTGATATGC). PCR was performed in a total volume of 50 lL and the reaction mixture contained 5 lL of 10X PCR buffer, 4 lL of dNTP mixture, 0.8 lL of each primer, and 2 units of Taq DNA polymerase (Sigma-Aldrich, USA) and diluted to a final volume of 50 ll with de-ionized distilled water. PCR was conducted using a thermal cycler GeneAmp PCR System 9700 (Perkin-Elmer, USA) to subject the reaction mix to 35 cycles of the following reaction conditions: denaturation at 94 °C for 30 s, primer annealing at 55 °C for 30 s and extension at 72 °C for 1 min, followed by final extension for 7 min at 72 °C to ensure full extension of the products [7]. The amplified PCR products were then analyzed in 1.0 % (w/v) agarose gel. Further, the PCR product was purified with a PCR Products Purification Kit (Sigma-GenElute PCR clean-up kit) and was sequenced by Chromous Biotech Pvt Ltd, Bangalore. In order to analyze the Rhizopus CFR 307, DNA sequence of the ITS-5.8S rDNA, related sequences were obtained from the GenBank. The sequence was compared by the BLAST program from the National Center for Biotechnological Information (NCBI) for the identification of the species. The organism was identified based on the highest homology of the sequence with sequence available in the database. The evolutionary history was inferred by the maximum likelihood method based on the Tamura 3-parameter model and phylogenetic trees were constructed [8]. The robustness of tree topology was tested using bootstrap analysis with 1,000 replicates. The MEGA5 package was used for all analyses [9]. Preparation of inoculum and solid-substrate fermentation The fungal organism CFR 307 was cultured in 100 mL Erlenmeyer flasks containing 50 mL of potato dextrose
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broth inoculated with 5 mL of 48 h culture. The flasks were incubated at 30 °C for 72 h and the inoculum thus obtained was used for the inoculation of the solid-substrate medium. Coffee husk was pre-treated with steam explosion at 121 °C for 15 min before using as substrate in the culture media. The modulation of the pH value was adjusted with Mandel’s solution [10]. The contents were sterilized at 121 °C, 1.1 kg/cm2 for 30 min. After sterilization, the flasks were cooled and inoculated with the 72 h old culture and incubated at 30 °C for 96 h.
Table 1 Definition and coded levels for CCRDa matrix
Cellulase extraction and analytical methods
Y ¼ b0 þ b1A þ b2B þ b3C þ b11A2 þ b22B2
Cellulase was extracted by suspending the fermented substrate in 5 times citrate buffer (50 mM, pH 4.8) using orbital shaker for 1 h at 150 rpm. The fungal biomass were separated by filtering through a Whatman no 1 filter paper and then by centrifugation at 10,000 rpm for 15 min. The clarified supernatant was used as the crude enzyme. Endoglucanase activity was measured by a reactive mixture containing 0.5 ml of 1 % (w/v) carboxymethyl cellulose (CMC) in 0.05 M citrate buffer (pH 4.8) and 0.1 mL of culture supernatant [11]. The liberated reducing sugar was estimated with DNS reagent after incubating the reactive mixture for 30 min at 50 °C [12]. One unit of enzyme activity expressed as the amount of enzyme required to release 1 lmol reducing sugar/mL under the standard assay conditions. Experimental design and statistical analysis Evaluation and optimization of culture conditions were carried out using RSM based on three factors and five level central composite rotatable designs consisting of 14 experimental runs including 6 replicates at the center point to allow a better estimate of the experimental error [13]. The design variables were pH (A), moisture (B, %) and fermentation duration (C, hour) while the response variable was endoglucanase (Y, U/gds). The range and the levels of the independent variables, i.e., pH, moisture and fermentation duration chosen for the current study are shown in Table 1. Table 3 shows the definitions of the three dependent variables and the experimental and predicted responses. Each experiment was performed in duplicates and the average yield of endoglucanase was taken as the response variable, Y. A second-order polynomial equation was then fitted to the data by a multiple regression procedure. This resulted in an empirical model that related the responses measured to the independent variables of the experiment. For a three-factor system, the model equation was taken to be
Independent variables
Symbol
Coded levels -a
-1
0
?1
?a
pH
A
3.32
4
5
6
6.68
Moisture (%)
B
43.18
50
60
70
76.82
Fermentation duration (h)
C
79.64
96
120
144
160.36
a
Central composite rotatable design
þ b33C2 þ b12AB þ b23BC þ b13AC
ð1Þ
where Y is the predicted response, b0 is the intercept, b1, b2, b3 are linear coefficients, b11, b22, b33 are squared coefficients and b12, b23, b13 interaction coefficients and A denoted pH, B was moisture (%) and C was the fermentation duration (h). The responses of the CCRD design were fitted with a second-order polynomial equation. Statistical analysis of the data was performed by design Package Design-Expert version 8.0.6.1, from Stat Ease, Inc., Minneapolis, USA to evaluate the analysis of variance (ANOVA), to determine the significance of each term in the equations fitted and to estimate the goodness of fit in each case. The fitted polynomial equation was then expressed in the form of three-dimensional response surface plots to illustrate the main and interactive effects of the independent variables on the dependent ones. Validation of the model was carried out to check the adequacy of the model. Applications of endoglucanase Simultaneous saccharification and fermentation (ssf) Simultaneous saccharification and fermentation experiments were carried out in 250 mL Erlenmeyer flasks, each containing 100 mL of fermentation medium in 0.05 M citrate buffer (pH 4.8) at 30 °C on a rotary incubator shaker at 120 rpm for 72 h and at 10 % (w/v) coffee husk. The fermentation medium contained 1 g/L yeast extract, 1 g/L peptone at pH 5. The production medium was supplemented with 2.0 mL of the crude cellulase to hydrolyze the substrate. Flasks were inoculated with dried baker’s yeast with a concentration of 3 g/L. The samples were taken every 24 h and analyzed for ethanol production. Ethanol content was estimated by cell-free supernatant by dichromate method. During heating, ethanol was converted to acid on reaction with dichromate and resulted in color change from orange to green which can be analyzed spectrophotometrically at 600 nm [14]. Ethanol was used as standard.
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Cellulase as detergent additive and its effect
Table 2 The clearance zone of fungi isolates used in screening study for endoglucanase production
For the possible commercial exploitation of cellulase in detergent industry, the endoglucanase was tested for its compatibility with five locally available detergent brands (Surf excel, Ariel, Wheel, Tide and Rin) under normal conditions. Detergent solutions were prepared as per directions labeled on their respective sachet. Carboxymethyl cellulose solution (1 %) was used as substrate and prepared in sodium citrate buffer of pH 4.8. A reaction mixture comprising 3 mL of substrate solution, 1 mL detergent solution and 1 mL enzyme was incubated at 55 °C for 10–15 min followed by normal enzyme assay as described earlier. A control sample without enzyme was also incubated in parallel to reaction mixture solution.
Organism
of fungal specificity grouped Rhizopus CFR 307 with R. stolonifer (96 supports in ML values) (Fig. 1) and hence the isolated fungus was identified as R. stolonifer.
Results and discussion
Endoglucanase production by SSF on coffee husk using R. stolonifer
Chemical composition of coffee husk Knowledge about the physico-chemical composition of coffee by-product is important; as it plays a key role during the optimization of process. The analysis of coffee husk resulted to contain 43 % of cellulose followed by 22.8 % of sugars, 11 % of protein, 9 % lignin, 13 % moisture and 24 % of fiber. The nutrient composition of coffee husk elucidates its application as solid substrate for bioprocess. The composition of the husk may vary depending on the selection/varieties and geographical locations of the coffee grown, methods chosen to prepare them, on the conditions and resources in each production region; on the species grown, on the type of the process adopted. Screening and identification of fungal organism for endoglucanase production Selection of potent strain for fermentation process is prime necessary, since the efficiency of the organisms to release secondary metabolite utilizing the substrate for their growth and to secrete enzyme is interrelated. Rhizopus CFR 307 was the best producer of endoglucanase and exhibited maximum clearing zone around the colony degrading the cellulose (Table 2). Compared to the reported literature on degradation of cellulose, our organism is potential degrader of cellulose with largest clearing zone of 90 mm [15]. The growth rate of the fungal organisms was rapid, cottony texture. Microscopic illustrations revealed, nonseptate hyphae with diameter ranging from 6 to 13 lm, rhizoids, sporangia, and sporangiophores. The identification by molecular characterization targeting ITS regions
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Diameter of clearance zone (mm)
Rhizopus CFR 307
90 ± 4
Penicillium CFR 303
65 ± 3
Penicillium CFR 304 Neurospora CFR 308
33 ± 6 52 ± 5
Aspergillus CFR 301
30 ± 3
Aspergillus CFR 302
29 ± 4
Aspergillus CFR 305
30 ± 7
Microorganisms are considered as model system for hydrolysis of agricultural residues for industrial applications. Their activities are high when cultivated on medium containing complex natural substrates, especially agricultural wastes. Endoglucanase was produced by solid-state fermentation using R. stolonifer on pre-treated coffee husk. The enzyme activity of 5,619.9 U/gds was obtained at 96 h of fermentation at 30 °C. Substrate concentration is a dynamic influencing feature that affects the yield and initial hydrolysis rate of cellulose. This result relates application of R. stolonifer in industries which could be more economical for synthesis of cellulase. Evaluation and process optimization of solid-state fermentation A central composite rotatable design was used to develop a correlation between pH, moisture and fermentation duration to improve the endoglucanase production by R. stolonifer. The complete design matrix together with the value of response was obtained from the experimental work. By multiple regression analysis, the polynomial equation with the coefficient of the full regression model equation and their statistical significance was determined. The final model for the endoglucanase response in terms of coded value is: Y ¼ þ16;378:50 3;468:83B 582:69C þ 1;105:06AB 1;634:28AC 646:69A2 897:72B2 þ 1;890:07C 2 ð2Þ where Y is endoglucanase and A, B and C were the coded variables for the pH, moisture (%) and fermentation
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Fig. 1 Phylogenetic tree based on the ITS-5.8S rDNA sequence which shows the positions of the Rhizopus CFR 307 and related strains
duration (h), respectively. Based on the above equation, the coefficients with one factor represented the effect of that particular factor while the coefficients with two factors and those with second-order terms represented the interaction between the two factors and quadratic effect, respectively. The positive sign infront of the terms indicated synergistic effect, while the negative sign indicated antagonistic effect [16]. The above equation was used to predict the endoglucanase yield presented in Table 3. ANOVA is a statistical technique that subdivides the total variation of a set of data into component associated to specific sources of variation and is important in determining the adequacy and significance of the quadratic model. The analysis of variance (ANOVA) for the CCRD is presented in Table 4. To test the good fit of the model, the regression equation and regression coefficient R2 was calculated. The model represented a high R2 value (R2 = 0.9688) indicating about 96.88 % variability in responses. The rest (3.12 %) of the total variation was not explained by the model. The adjusted R2 is 0.9407 and the predicted R2 is 0.8017, which indicate that the model is good. For a good statistical model, the R2 should be in the range of 0–1.0 and nearer to 1.0 the value is, the more fit the model is deemed to be. The adequate precision value of the model is 21.922. The lower value of the coefficient of variation (CV % = 5.70) showed that the experiments were precise and reliable. The Model F-value of 33.05 implies the model is significant. The P value of the model was \0.0001 (P \ 0.05) and insignificant lack of fit suggested good fit. According to the P value, (the value, in case of below 0.05, indicated significance level) B, C, AB, AC, A2, B2, C2 were significant (Table 4). The response surface plots described by the regression model were drawn to illustrate the effects of the independent variables and effects of interactions of each independent variable on the response variables. The response surface plots showing the interaction effect of pH and moisture, pH and fermentation duration and moisture and fermentation duration is depicted in Fig. 2a, b, c, respectively. Figure 2a indicates that when the moisture was increased from 50 to 80 %, the endoglucanase production was reduced and was very low between 70 and 80 % moisture. Higher moisture levels can cause a reduction in enzyme yield due to steric hindrance of the growth of the producer strain by reduction in porosity (inter particle
Table 3 Experimental design matrix and the response of the endoglucanase activitya Run
pH
Moisture (%)
Incubation (h)
Endoglucanase activity (U/gds) Experimental
Predicted
1
4
50
96
21,678.3
20,764
2
4
50
144
21,813.4
21,746.6
3
4
70
96
11,443.4
10,495.7
4
4
70
144
13,634.2
13,719.5
5
6
50
96
22,727.2
21,907.9
6
6
50
144
16,139.8
16,353.4
7
6
70
96
16,727.1
16,059.8
8
6
70
144
12,566.2
12,746.4
9
3.32
60
120
13,735.4
14,481.6
10
6.68
60
120
14,325.3
14,625.0
11
5
43.18
120
19,083.6
19,673.3
12
5
76.82
120
7,556.99
8,004.2
13
5
60
79.64
21,067.2
22,703.5
14 15
5 5
60 60
160.36 120
21,343.5 16,871
20,743.7 16,378.5
16
5
60
120
16,825
16,378.5
17
5
60
120
15,463
16,378.5
18
5
60
120
16,728
16,378.5
19
5
60
120
15,995
16,378.5
20
5
60
120
16,923
16,378.5
a
Experimental endoglucanase activity was average of duplicates
spaces) of the solid matrix, thus interfering oxygen transfer [17]. When pH of the substrate was varied from 3 to 7, significant difference in the endoglucanase production was not observed. Figure 2b relates the interaction between pH and fermentation duration. The activity increased significantly when pH was increased, whereas only a slight increase in enzyme activity was observed with increase in fermentation duration. This is because fungi could have entered exponential phase of the growth. Figure 2c indicates the interaction between moisture and fermentation duration. With increase in the moisture to mid range, the surface is ascending indicating enhancement of enzyme activity and beyond the midrange the surface curvature is declining. Lower moisture level gives a lower degree of swelling and
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Table 4 Analysis of variance for endoglucanase production Source
Sum of squares
Model
2.791E ? 008
A B
df
Mean square
F-value
P value Prob [ F
9
3.101E ? 007
34.49
\0.0001
24,890.83
1
24,890.83
1.643E ? 008
1
1.643E ? 008
0.028 182.76
0.8712 \0.0001
C
4.637E ? 006
1
4.637E ? 006
5.16
0.0465
AB
9.769E ? 006
1
9.769E ? 006
10.86
0.0081
AC
2.137E ? 007
1
2.137E ? 007
23.76
0.0006
BC A2
2.511E ? 006 6.410E ? 006
1 1
2.511E ? 006 6.410E ? 006
2.79 7.13
0.1256 0.0235
B2
1.214E ? 007
1
1.214E ? 007
13.50
0.0043
C2
5.039E ? 007
1
5.039E ? 007
56.04
\0.0001
Residual
8.992E ? 006
10
9.386E ? 006
Lack of fit
7.193E ? 006
5
7.172E ? 006
4.00
0.0772
Pure error
1.798E ? 006
5
2.214E ? 006
Cor total
2.881E ? 008
19
2.886E ? 008
R2 0.9688, adj R2 0.9407, pred R2 0.8017, adeq precission 21.922, CV % 5.70 Fig. 2 Response surfaces showing the effects of a pH and moisture, b pH and fermentation duration, c moisture and fermentation duration
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higher water tension and then reduces the solubility of nutrients. Higher moisture level decreases porosity, changes particle structure, promotes development of stickiness, decreases diffusion, lowers oxygen transfer or increases formation of aerial hyphae [18]. Only a slight increase in the CMCase activity was observed when the fermentation duration was increased. Comparison of CMCase activities in SSF on various lignocelluloses using various fungal strains is presented in Table 5. The results obtained in the present study with cheap industrial waste coffee husk are much higher than those found in the literature using high cost substrates. Higher CMCase activity of 22,109 U/gds was achieved through solid-state fermentation after 96 h incubation period. The higher production of carboxymethyl cellulase can be attributed to the induction caused by the presence of appreciable amounts of cellulose content in coffee husk. This argument can be validated by the fact that cellulose induces cellulase production, as the cellulase regulator ACEII influences cellulase production [23]. In the present study, the utilization of low cost agro-industrial waste, coffee husk and rapid production of carboxymethyl
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Table 5 Comparison of carboxymethyl cellulase activities in solidstate fermentation on various lignocelluloses using various fungal strains Microorganism
Substrate
Enzyme activity (U/gds)
Trichoderma reesei RUT C30
WBa
299.55
Penicillium roqueforti
WBa
59.9
Aspergillus niger
RSb
Trichoderma viride
Incubation time (h) 96
References
[19]
Applications of endoglucanase 120
[20]
402.13
96
[20]
BPc
104.8
72
[21]
Latrunculia corticata
SBd ? CCe
450
168
[22]
Rhizopus stolonifer
CHf
22,109
a
Wheat bran
b
Rice straw Banana peel
c
the model. The experimental values (21,082 U/gds) were found to be close to the predicted values (22,124 U/gds) and hence, the model was successfully validated. The close agreement of observed results with the model prediction indicated that our model was accurate and reliable for predicting the production of CMCase.
d
Sugarcane bagasse
e
Corn cob
f
Coffee husk
96
Present study
Simultaneous saccharification and fermentation (ssf) Simultaneous saccharification and fermentation was performed on coffee husk at 30 °C for 72 h. High alcohol yield at 24 h of fermentation with 65.5 g/L ethanol was obtained. The results obtained in the present study proved coffee husk as excellent potential for residue-based ethanol production. Furthermore, there are various possibilities for improving ethanol production based on this residue, including the addition of pre-treatment steps (acid and/or enzymatic saccharification), use of other micro-organisms and optimization of fermentation conditions. It was observed that the production of ethanol by ssf of coffee husks was rational to that of the reported findings carried out using various residues [24]. Cellulase as detergent additive and its effect
cellulase in 96 h provides advantages over the previously reported studies. The conventional utilization of high cost substrates increases the production cost of enzymes, which is a major hurdle in commercialization of bioethanol production from lignocellulosic wastes. Numerical optimization of independent variables to maximize endoglucanase activity Using Design-Expert 8.0.6.1, numerical optimization sub routine design space was explored with fitted quadratic model to arrive at optimum pH, moisture and fermentation duration that maximize endoglucanase activity. In this task, goals were set to achieve maximum activity of endoglucanase by setting independent variables within the range of upper and lower limit. The optimized variables were found using desirability objective function that assigns relative importance to the responses. Solutions with higher desirability gave optimum pH 5.43, moisture 50 % and fermentation duration of 96 h (Fig. 3). Verification experiments Verification of the optimum conditions for endoglucanase production by R. stolonifer was done by carrying out the experiments in shake flasks under conditions predicted by
For the commercial exploitation of CMCase in detergent industry, the enzyme was tested for its compatibility with five different detergents. The enzyme incubated at 55 °C with detergent solution revealed maximum compatibility with Tide followed by Surf Excel (Fig. 4). Their suitable controls were also run and their activities were found very low as compared to those supplemented with cellulase. This revealed that the cellulase with local detergents is latent as suitable additive to detergents and improved washing.
Conclusion The results designate significant potential of abundantly available coffee husk as solid substrate for production of endoglucanase in a SSF system for the first time. RSM was efficient to maximize yield based on the data from few experiments, in which all the factors varied within chosen ranges. The enzyme obtained through solid-state fermentation was used in the production of ethanol utilizing coffee husk as a substrate through SSF and a maximum of 65.5 g/L of ethanol was obtained. The enzyme was also compatible as a detergent additive for improved washing. Bioconversion of cellulose to fermentable sugars and
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Fig. 3 Optimal condition and desirability for endoglucanase activity by Rhizopus stolonifer using coffee husk
Fig. 4 Detergent compatibility of endoglucanase with five local detergent brands
bioalcohol could be accomplished by R. stolonifer with coffee husk and contributes to efficient solid-waste management conserving environment. Acknowledgments The authors are thankful to the Director, Central Food Technological Research Institute, Mysore and Head, Plantation products, Spices and Flavor Technology Department for providing the facilities and support to carry out this study. The authors also wish to acknowledge the Council of Scientific and Industrial Research, New Delhi for funding this project.
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