Solid State Fermentation of Apple Pomace for Production of Fungal Inulinase using Mucor circinelloides BGPUP-9.
Punjabi University, Patiala
Inulinase extraction is an important step involved in production of inulinase. In this study, apple pomace was used as substrate for production of inulinase. RSM was used for optimization of solid state fermentation and effects of moisture content, incubation time and pH was studied on inulinase production. The moisture content of 80%, pH 6.0 and incubation time of 6 days was found optimal for obtaining maximum inulinase production (405.2 IU/gds).
KEYWORDS: Solid state fermentation, Inulinase production, Response surface methodology
Inulinases (1-β-D-fructan fructanhydrolase) are important class of industrial enzymes which act on β- 2, 1 linkages of inulin to produce fructose or fructooligosaccharides. On the basis of their mode of action, inulinases are categorized as exo- (EC 220.127.116.11) and endoinulinase (EC 18.104.22.168). Inulinases have been reported from a number of microbial groups like bacteria, fungi, yeast and actinomycetes. Xanthomonas sp., Pseudomonas sp., Clostridium sp. are some potent bacterial sources reported for inulinase production (Singh et al., 2017). Whereas amongst fungi and yeast, Penicillium sp., Aspergillus sp., Kluyveromyces sp., Cryptococcus sp., etc. have been reported as efficient inulinase producers (Singh & Chauhan, 2018). Presently, the use of fungal sources for inulinase production has gained tremendous attention due to its various advantageous features such as xerophytic nature, ability to grow in cost-effective substrate, lesser chances of mutational occurrence, etc. Inulin is considered as a best substrate for inulinase production. Inulin is a polyfructan, containing β- (2, 1) linked repetitive units of fructose and a sucrose type linkage (α- 1, 2) at the end of the chain.
Various inulin-rich plant materials such as chicory, Jerusalem artichoke, dahlia, etc. have been employed as potent substrate for inulinase production. Inulinases are significantly used for the production of high fructose syrup (HFS) and fructooligosaccharides (FOSs). Additionally, citric acid, lactic acid, single cell oil, single cell proteins, bioethanol, sorbitol production, etc. are some other industrial applications of inulinases (Singh & Chauhan, 2018).
Inulinases can be produced either by solid-state fermentation (SSF) or submerged fermentation (SmF). However, SSF presently is considered far better than SmF for various metabolites production, because of its various advantages like high productivity, simple technique, low production cost, low energy requirement and better product recovery (Singhania et al., 2009). SSF is a process in which solid-substrate act as carbon/energy source for metabolite production in the absence or near-absence of free water. For SSF, filamentous fungi is considered as most perpetual choice, owing to their ability to tolerate low water ability (Aw), easy growth in high osmotic conditions and their physiological-biochemical properties. In SSF, generally agro-industrial wastes which are rich in sugars, proteins, minerals and water are used for a metabolite production. Sugarcane baggase, wheat bran, rice bran, soyabean bran, orange rind, press mud, Jerusalem artichoke leaves, chicory leaves, etc. are various agro-industrial residues reported for inulinase production under SSF. Amongst various agro-industrial residues, apple pomace is also an important perishable agro-industrial residue. After processing of apples (milling and pressing), 75% of fresh weight of apple is removed as juice and 25% is utilized as pomace (Kaushal et al., 1995). Apple pomace typically contains 66.4-78.2% moisture, 9.5%-22.0% carbohydrates, 4.0% proteins, 3.6% of fermentable sugars, 6.8% cellulose, 0.38% ash, 0.42% acid and calcium (Vasil'ev et al., 1976; Sun et al., 2007). Considering the applicability of apple pomace as a suitable substrate for inulinase production, the present research work was carried out to achieve the following objectives:
1. To determine the suitability of apple pomace for the production of inulinase from Mucor circinelloides BGPUP-9 under SSF.
2. Optimization of SSF for inulinase production from Mucor circinelloides BGPUP-9 using response surface methodology.
MATERIALS AND METHODS
Fungal culture and its maintenance
Mucor circinelloides BGPUP-9, an isolate of our laboratory was used in the present study (Singh et al., 2018c). The culture was maintained on potato dextrose agar (PDA) slants containing (%, w/v): potato extract (20), dextrose (2) and agar (2.5). The culture was subcultured every fortnight and stored at 4˚C, until further use.
PDA plates were prepared and a loopful of fungal stock culture was transferred aspectically onto them. Thereafter, each plate was incubated at 30˚C for 5 days. After incubation, four agar discs (10 mm) uniformly covered with fungal mycelium, were used as inoculum to inoculate Erlenmeyer flasks containing fermentation medium.
Preparation of substrate
The combination of dahlia tubers and apple pomace was used as sole substrate for inulinase production. Dahlia tubers were procured from Botanic Gardens, Punjabi University, Patiala, while apple pomace was collected as residue from apple juice processing industry. Both the substrates were dried at 40˚C and grounded to a fine uniform powder. After grinding, powder form of each substrate was also sieved through a pore size of 150 μm to get more uniform particle size powder.
Solid state fermentation
Solid state fermentation was carried out using combination of apple pomace (7g) and dahlia (3g). The solid substrate added into Erlenmeyer flask (250 mL) was further supplemented with other media constituents (%, w/v) NH4H2PO4 (0.3), KH2PO4 (0.2) and KCl (0.1). Three variables for SSF were optimized using response surface methodology (RSM) were: A: moisture content (70-90%), B: incubation time (3.0-8.0 days) and C: pH (5-7), whereas above mentioned medium constituents were kept constant throughout the study. Each flask was sterilized and inoculated with 4 agar discs of fungal culture and kept at 30˚C under stationary conditions. Each flask was shaked thrice a day for proper availability of substrate to the growing fungal mycelium.
Statistical optimization of inulinase production from apple pomace using Mucor circinelloides BGPUP-9
Central composite rotatable design (CCRD) of response surface methodology (RSM) was used for the optimization of three independent variables for inulinase production from Mucor circinelloides BGPUP-9. Each independent variable was studied at five coded levels: -1.414, -1, 0, 1, 1.414 (Table 3.1). A total 15 runs of different combinations of each variable consisting four factorial, six axial points and five replicates at the centre point were studied (Table 3.2). The experimental runs 11-15 at the centre point were used to determine the experimental and manual error. Furthermore, to verify the model's accuracy, validation of experimental runs was also performed to compare predicted results with experimental values.
Design expert version 7.0 software package developed by State- Ease Inc., USA was used for the present statistical analysis. A low order polynomial equation was constructed by approximating the response function. Then, the second order model was constructed which was used in examining the true response of incubation time, moisture content and pH on inulinase production. Second order polynomial equation used in the model is as given below:
Y = β0 + ∑ βiXi + ∑βiiXi2 + ∑ βijXiXj (1)
Where, Y is measured response, β0 is the intercept term, βi is the linear coefficient, βii is the quadratic coefficient, βij is an interaction coefficient and Xi, Xj represents the coded independent variables. The statistical significance of the model was justified using analysis of variance (ANOVA). Furthermore, analysis of variance (ANOVA) method was also employed to predict if addition of an unimportant variable added to the model can increase the mean square error which decreases the usefulness of the model. Student's t-test and Fischer's F test were also employed to establish statistical importance of regression coefficients, second-order model equation and model terms. Besides, least regression coefficient method was used to estimate the parameters involved in second-order polynomial equation. Multiple regression model was generated to test the usefulness of the model. Thereafter, the goodness of fit of the model was also tested by employing Lack-of-Fit, multiple correlation coefficient (R²), adjusted R² and predicted R². Besides, 3D-surface plots were also examined to determine the effect of interactions between variables on inulinase production.
*A, B & C are same as given in Table 3.1
Extraction of enzyme
Sodium acetate buffer (100 mL, 0.1M, pH 5.0) was added to each Erlenmeyer flask containing fermented solid substrate for the extraction of enzyme. Thereafter, each flask was kept under agitation (150 rpm) at 30˚C for 2h. After incubation, extract was filtered using Whatman filter paper and followed by centrifugation at 10,000 rpm for 10 min at 4˚C. Later, supernatant was assayed for inulinase activity.
Inulinase activity was determined by mixing crude enzyme solution (100 μL) with 900 μL of inulin substrate solution (2% prepared in 0.1M sodium acetate buffer, pH 5.0). The reaction mixture was incubated at 55˚C for 10 min. Thereafter, the reaction was stopped by incubating the mixture at 100˚C for 10 min. The resultant hydrolysate was analysed by dinitrosalicylic acid method (Miller, 1959). One unit of inulinase activity is the amount of enzyme that produces one micromole of fructose per unit, under standard assay conditions.
RESULTS AND DISCUSSION
Statistical optimization of significant variables using CCRD and ANOVA
For statistical optimization of SSF for inulinase production, three significant variables used were moisture content, incubation time and pH. The experimental design matrix was calculated using CCRD (Table 3.1). Second order model was constructed and analyzed for the best fitted model for generating linear regression equation of the data collected. Quadratic model was evaluated on the basis of sequential sum of squares and ANOVA (Table 3.2). The model's higher F-value for inulinase production validates the significance of the present model. Besides, the good agreement between experimental and predicted values further authenticates the validity of the present polynomial model (Table 3.1). The expression relating the response variables in terms of coded values for predicting inulinase production, regardless of coefficient's significance is given below:
Inulinase production = 404.5 + 67.6*A + 18.4*B – 2.4*C – 25.7*A*B + 58.7*A*C + 41.7*B*C – 72.7A*A – 106.2B*B - 71.4C*C (2)
Where A, B and C are the coded values of the test variables, moisture content (%), incubation time (days) and pH, respectively.
*A, B & C are same as given in Table 3.1
The statistical significance of Eq.2 for selected quadratic model was confirmed using ANOVA. Besides, Prob˃F demonstrates the significance of corresponding coefficient model terms. The Prob˃F values less than 0.05 indicates the significance of each coefficient, while values greater than 0.05 indicates the insignificant model terms. In the present model, B, C, AB, AC, A2 and B2 were significant model terms. Fischer's F test and Student t-test also evidences the significance of the present model. As larger the magnitude of F-value, more significant is the model. In the present quadratic model higher F-value (94051.9) indicates the authentification of the experimental data collected and the model also (Table 4.3).
*A, B & C are same as given in Table 3.1.
Goodness of fit of the model was also determined using determination of coefficient (R²). The coefficient of determination (R²) was calculated to be 1.00. This shows that more than 99% of experimental data was compatible with the data predicted by the model and there was only less than 1% chance of such large value due to noise. Higher and close to one value of R² for inulinase production showed good agreement between experimental and predicted values. Besides, adjusted R² and predicted R² value was 1.00 and 0.99, respectively for inulinase production, shows the reasonable agreement between two values. The coefficient of variation (CV) was also very low (0.17), which indicates the lowest deviations between experimental and predicted values. Additionally, higher adequate precision (762.75) for inulinase production also defines an adequate signal of the present model and predicts the navigability of designed space by the present quadratic model.
Effect of independent variables and their interactions on inulinase production
The effects of different combinations of three variables were also interpreted using 3D surface graphs (Fig. 3.1 A, B and C). 3D graphs specify the role played by each factor and the effect of interactions between different variables on inulinase production. Maximum inulinase production (405.2 IU/gds) was obtained at 80% moisture content, thereafter reduction in inulinase production was observed. Moisture content lesser and higher than 80%, observed to support low inulinase production. Requirement of moisture content varies from species to species. However, generally minimum 20% of moisture level is necessary for supporting fungal growth, nutrient absorption and metabolic function. Higher moisture content inhibits conidial induction, hence declining metabolite production. Whereas, lower moisture content would not be sufficient to initiate cells growth and metabolite production (Nuñez-Gaona et al., 2010). Incubation period also plays pivotal role in metabolite production. Maximum inulinase production was obtained after 6.0 days of incubation. After that, declination in inulinase yield was observed. Decrease in inulinase production after 6.0 days of incubation can be attributed to the secretion of proteases into medium which hydrolyzes peptide bonds between amino acids and denature enzyme. Inulinase production increased with the increase in pH up to 6.0. Thereafter, reduction in inulinase synthesis was observed.
Besides, pH of the medium get more acidic with increase in incubation period. Hence, it can be inferred that the three factors showed summative effect on inulinase production from M. circinelloides BGPUP-9. Fungal species have been reported to show good inulinase production at slightly acidic environment (Singh & Chauhan, 2018). K. marxianus (Makino et al., 2009), P. gulliermondii (Guo et al., 2009) and P. oxalicum (Singh & Chauhan, 2017) have also been reported efficient inulinase producers at slightly acidic pH. Therefore, it can be inferred that all the three independent variables on interaction with each other at an optimal level supported maximum inulinase production.
Validation of experimental mode
To examine the validation of the quadratic model for predicting inulinase production from Mucor circinelloides BGPUP-9, experimental trials were conducted in triplicates using optimized conditions (moisture content 80%, incubation time 6.0 days and pH 6.0). The maximum inulinase production obtained was 405.2 IU/gds. On comparing experimental data with predicted values of the regression model, validation of the present model for inulinase production from Mucor circinelloides BGPUP-9 under SSF can be easily assessed. Furthermore, R² value close to one justifies the good agreement between actual and predicted results, which also again authenticates the validity of the present polynomial model.
1. The moisture content of 80%, pH 6.0 and incubation time of 6 days was found optimal for obtaining maximum inulinase production (405.2 IU/gds).
2. The lower values of the coefficient of variation for inulinase production (CV%=0.17) showed an improved precision and reliability on the present model.
3. The second order polynomial equation shown the significant effect of moisture content, pH and incubation time on inulinase production in solid state fermentation.
4. Response surface methodology was found to be a suitable technique for the optimization of inulinase production from apple pomace in solid state fermentation.
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