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Enhanced pectinase production by optimizing fermentation conditions of Bacillus subtilis growing on hazelnut shell hydrolyzate
Journal of Molecular Catalysis B: Enzymatic 113 (2015) 62–67
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Enhanced pectinase production by optimizing fermentation conditions of Bacillus subtilis growing on hazelnut shell hydrolyzate Sibel Uzuner a,b , Deniz Cekmecelioglu a,∗ a b
Department of Food Engineering, Faculty of Engineering, Middle East Technical University, 06800 Ankara, Turkey Department of Food Engineering, Faculty of Engineering and Architecture, Abant Izzet Baysal University, 14280 Bolu, Turkey
a r t i c l e
i n f o
Article history: Received 21 July 2014 Received in revised form 1 January 2015 Accepted 10 January 2015 Available online 17 January 2015 Keywords: Hazelnut shell hydrolyzate Pectinases Plackett–Burman Design Polygalacturonase activity Response surface optimization
a b s t r a c t This study describes optimization of polygalacturonase (PG) production using Bacillus subtilis in submerged fermentation by Plackett–Burman (PB) design and response surface methodology (RSM). Five variables (pH, time, temperature, yeast extract concentration and K2 HPO4 ), which were determined to be significant by the PB analysis, were further optimized using Box–Behnken response surface method. The optimization results indicated that a maximal PG activity of 5.60 U mL−1 was achieved at pH 7.0, 72 h, and 30 ◦ C using 0.5% (w/v) yeast extract and 0.02% (w/v) K2 HPO4 in the fermentation medium. The results implied a 2.7-fold increase in PG activity of B. subtilis under the optimized conditions. Thus, it was concluded that hazelnut shell hydrolyzate have remarkable potential for low cost commercial PG production. © 2015 Elsevier B.V. All rights reserved.
1. Introduction Pectinase is an enzyme group, which hydrolyzes pectic substances present in agricultural and food commodities [1]. Pectinases are grouped as acidic or alkaline enzymes and find diverse applications in extraction and clarification of fruit juices [2–4], bleaching of paper pulp [5], degumming of fibers [6], oil extraction [7], and coffee and tea fermentation [8,9]. Novel use of pectinases in DNA extraction from plants [10] and production of pectic oligosaccharides as functional/prebiotic food components has also emerged [11]. Thus, studies on pectinase production with low cost and high activity are still needed to meet the increasing demands. Bacterial and fungal pectinase productions have been reported under both submerged (SmF) [12–14] and solid state (SSF) fermentation conditions [15,16]. As SSF simulates the actual living conditions of fungi, it provides higher enzyme yields than the SmF method [17]. However, industrial application of SSF still suffers from the complicated product purification resulting from heterogeneity of fermentation medium, difficulty in scale up, and losses of enzyme in the solid residues [16,17]. Besides, SSF requires long fermentation periods (e.g. 5–6 days) [15]. The SmF method, on the other hand, is easier to control at large scale production and has been successfully used for production of various metabolites since 1940s.
∗ Corresponding author. Tel.: +90 312 210 5631; fax: +90 312 210 2767. E-mail address: [email protected] (D. Cekmecelioglu). http://dx.doi.org/10.1016/j.molcatb.2015.01.003 1381-1177/© 2015 Elsevier B.V. All rights reserved.
Rangarajan et al. [18] compared the pectinase production by Aspergillus niger by SmF and SSF methods at shake flask and reactor levels, using orange peel as the carbon source and varying amounts of organic and inorganic nitrogen sources. A maximal exo-pectinase activity of 5128 U g−1 and endo-pectinase activity of 793 U g−1 were reported with 4% soybean meal in the SSF method, whereas maximal exo-pectinase activity of 5834 U g−1 and endopectinase activity of 951 U g−1 were achieved with the SmF method using 4% peptone and 3% soybean meal, respectively. A similar trend was also reported for the reactor trials. The fungus A. niger was used as the pectinase producer in the SmF system run for 24–120 h by others [13,19], who reported promising results. The use of Bacillus species for pectinase production by the SmF method provided successful results as reported by Sharma and Satyanarayana [20], Ahlawat et al. [12], and Joshi et al. [14]. However, production of alkaline pectinase by SmF using bacterial culture is limited compared to fungal culture. Thus, in this study Bacillus subtilis was used as the pectinase producer by SmF under various conditions. Also for SmF method to be commercially viable, pectinases should be produced on low cost carbon sources such as citrus limetta peel [14], orange peel extract [18], mix of apple pulp and corn flour [13], wheat bran [12], pumpkin oil cake [21] and other agricultural wastes, rather than using synthetic liquid carbon sources [22]. The selection of appropriate sources of carbon, nitrogen and other nutrients is one of the most critical stages in the development of an efficient and economic enzyme production process, where the solid agricultural substances act as the source of carbon,
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nitrogen, and minerals [6]. It is also known that 30–40% of the enzyme production cost is attributed to the fermentation medium [15]. Hazelnut shells account for the majority of the by-products of hazelnuts, as the kernel occupies less than 50% of the total nut weight [23]. The shells contain 24.2% cellulose, 28.2% hemicellulose, 34.6% lignin, 9.7% moisture, and 1.1% ash [24] and are disposed of as a low-value heat source or used as the raw material for furfural production [25]. The annual generation of the shells is in the range of 250,000–400,000 tons in the Black-Sea region of Turkey [26]. Thus, it is important to utilize hazelnuts shells in bioprocessing and their use in pectinase production can improve the production cost. To the best of our knowledge, no work has been reported on the pectinase production using the hazelnut shells hydrolyzate as the carbon source by B. subtilis. This study was undertaken to investigate the effects of fermentation medium composition (pectin, yeast extract, MgSO4 , K2 HPO4 ) and culture conditions (pH, time, temperature, inoculum amount) on the polygalacturonase (PG) activity during submerged fermentation in shake-flask bioreactors. Initial screening of the factors was performed with the Plackett–Burman Design (PBD) to screen the important factors affecting the PG production and subsequently the Box–Behnken Design (BBD) technique was used to determine the optimal PG production using the selected factors. In all trials, the sugar rich medium obtained after enzymatic saccharification of the acid-pretreated hazelnut shells was used as the carbon source. The findings of this study are believed to be useful to enzyme, food, textile, and paper and pulp industry. 2. Materials and methods 2.1. Materials Hazelnut shells were provided by a local plant in Ordu, province of Turkey, and dried at 70 ◦ C in a convection oven for 24 h on arrival. The shells were then ground to pass through a 1 mm sieve and stored in plastic bags at room temperature until use.
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2.3. Polygalacturonase (PG) production by B. subtilis under submerged fermentation (SmF) The ground hazelnut shells were treated with 3.42% dilute acid at 130 ◦ C for 31.7 min and subsequently saccharified using Viscozyme L (Sigma–Aldrich, Denmark) (cellulases and xylanases) at 200 units per gram (U g−1 ) for 20 h according to the results of a previous study [27]. One Viscozyme L unit was defined as the amount of enzyme that liberates 1 mol of reducing sugars equivalent to glucose per min under the standard assay conditions (50 ◦ C, pH 5.0 and 30 min). The enzyme activity was then expressed in units of activity per milliliter (U mL−1 ). The resultant activity value was divided by substrate concentration to obtain U g−1 . A 500 mL Erlenmeyer flask containing 100 mL of fermentation medium similar to the growth medium but only amended with 5% hazelnut shell hydrolyzate instead of glucose was used in the fermentation. Before inoculation, the flasks and medium were sterilized by autoclaving. Sterile fermentation medium was inoculated with 1 mL of overnight grown bacterial culture containing 106 CFU mL−1 and incubated under agitation at 130 rpm. After fermentation, the biomass was separated by centrifugation at 2000 × g for 20 min and the supernatant was used as the crude enzyme source in the pectinase activity assay. 2.4. Analytical methods The composition of ground hazelnut shells including moisture, ash, protein, fat, crude fiber, cellulose, hemicellulose and lignin content was determined. Moisture content was determined by drying at 105 ◦ C until constant weight. The dried samples were burned in an oven for 24 h at 550 ◦ C, for ash content determination [28]. The total fat content was determined by hexane extraction method [29]. For crude fiber determination, samples were hydrolyzed by sulfuric acid and then calcinated by potassium hydroxide and burned at 550 ◦ C until constant weight. The cellulose and hemicelulose contents of the extractive-free (ethanol–hexane) samples were determined according to the study of Browning [30]. Acid-insoluble lignin was calculated by the TAPPI standard method (T2220S-74) [31]. 2.5. Assay of polygalacturonase activity
2.2. Micro-organism and culture conditions The strain B. subtilis NRRL B-4219 was kindly provided by the ARS culture collection, Northern Regional Research Laboratory (NRRL), Peoria, IL, USA. The stock culture was activated in medium containing (g L−1 ) yeast extract, 1.0; glucose, 10.0; K2 HPO4 , 0.4; KH2 PO4 , 0.2; MgSO4 ·7H2 O, 0.4; and citrus pectin, 2.0 at a temperature of 30 ◦ C and pH of 6.5 in an incubator shaker adjusted to 130 rpm for 24 h.
Polygalacturonase (PG) activity was assayed by measuring the release of polygalacturonic acid using the 3,5-dinitrosalicylic acid (DNS) reagent [32]. The 3,5-dinitrosalicylic acid is an aromatic compound that reacts to form 3-amino-5-nitrosalicylic acid, which absorbs light strongly at 575 nm. Galacturonic acid monohydrate was used as the standard. PG activity was evaluated by mixing 0.5 mL of crude enzyme and 0.5 mL of polygalacturonic acid solution (1%, w/v polygalacturonic acid in 0.05 M acetate buffer at pH
Table 1 Plackett–Burman Design for screening the major factors affecting the PG production. Serial
pH
Time (h)
Temperature (◦ C)
Inoculum volume (%, v/v)
Pectin (%, w/v)
Yeast extract (%, w/v)
MgSO4 (%, w/v)
K2 HPO4 (%, w/v)
PG activity (U mL−1 ) B. subtilis
1 2 3 4 5 6 7 8 9 10 11 12
+1 +1 −1 −1 −1 −1 +1 −1 −1 +1 +1 +1
−1 +1 +1 −1 −1 +1 +1 +1 −1 −1 +1 −1
+1 −1 +1 +1 −1 −1 −1 +1 −1 −1 +1 +1
−1 +1 −1 +1 +1 −1 +1 +1 −1 −1 −1 +1
−1 −1 +1 +1 +1 −1 +1 −1 −1 +1 +1 −1
−1 −1 −1 −1 +1 +1 −1 +1 −1 +1 +1 +1
+1 −1 −1 +1 −1 +1 +1 +1 −1 +1 −1 −1
+1 +1 −1 +1 +1 +1 −1 −1 −1 −1 +1 −1
3.26 3.40 3.96 3.40 2.20 2.11 3.57 3.52 3.04 3.16 3.31 3.46
± ± ± ± ± ± ± ± ± ± ± ±
0.07 0.04 0.06 0.13 0.10 0.09 0.09 0.09 0.22 0.07 0.08 0.07
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7.0). Samples were incubated at 50 ◦ C for 30 min [33] and the reducing sugar was determined by the DNS method. One unit of PG activity (U) was defined as the amount of enzyme which releases 1 mol of galacturonic acid per minute under assay conditions. The PG activity was then expressed in units of activity per milliliter (U mL−1 ). 2.6. Experimental design For screening and optimization purposes, the fermentation experiments were performed at shake flask level (100 mL working volume) using the hazelnut hydrolyzate inoculated with 1 mL of overnight grown bacterial culture containing 106 CFU mL−1 supplemented with various nutrients and conditions according to the experimental design. The physical parameters such as pH, fermentation time, temperature, inoculum volume (%, v/v) and medium components such as pectin, yeast extract, magnesium sulphate [MgSO4 ], and dipotassium hydrogen phosphate [K2 HPO4 ] were evaluated as per the design specifications (Table 1). All experiments were performed in two replicates and the results are reported as mean values. The experimental design and statistical analyses were carried out using MINITAB 16.0 (Minitab Inc. State College, PA, USA)
Table 2 The factors and their coded levels used in the PBD and BBD methods. Serial number
Variable
Low (−1)
Center point (0)
High (+1)
1 2 3
pH Fermentation time (h) Fermentation temperature (◦ C) Inoculum volume (%, v/v) Pectin (%, w/v) Yeast extract (%, w/v) MgSO4 ·7H2 O (%, w/v) K2 HPO4 (%, w/v)
5 24 30
7 48 35
9 72 40
4 5 6 7 8
1 0.2 0.1 0.02 0.02
3 0.35 0.3 0.05 0.03
5 0.5 0.5 0.08 0.04
with ˛ = 0.05 (95% level of confidence) for Plackett–Burman Design (PBD) and Box–Behnken response surface methods. 2.6.1. Plackett–Burman Design (PBD) A two level PBD experimental matrix was set up to identify the significant factors of pectinase production. The PB design provides a linear model where only main effects are taken into account. Selection of appropriate carbon, nitrogen and other nutrients is
Table 3 Experimental design for optimization of polygalacturonase production using Box–Behnken response surface method (RSM). Run order
pH (A)
Temperature (B)
Time (h) (C)
Yeast extract (%, w/v) (D)
K2 HPO4 (%, w/v) (E)
Experimental response pectinase activity (U mL−1 )
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46
0 0 0 0 0 +1 0 +1 0 +1 +1 0 0 +1 0 −1 −1 0 −1 0 0 0 0 0 0 0 0 −1 0 0 +1 0 0 0 +1 0 0 −1 −1 0 +1 −1 −1 0 0 0
0 +1 +1 0 −1 0 0 0 +1 +1 0 −1 0 0 0 0 0 +1 0 0 0 +1 −1 −1 0 0 +1 0 0 0 −1 0 −1 0 0 0 0 +1 0 0 0 0 −1 0 −1 0
+1 0 0 +1 0 +1 0 0 +1 0 0 0 0 −1 −1 +1 −1 0 0 +1 0 0 0 +1 0 0 −1 0 0 0 0 −1 0 +1 0 0 −1 0 0 0 0 0 0 −1 −1 0
−1 0 −1 0 0 0 0 +1 0 0 0 +1 0 0 0 0 0 +1 0 0 0 0 0 0 0 0 0 +1 −1 +1 0 0 −1 +1 −1 −1 +1 0 −1 +1 0 0 0 −1 0 0
0 +1 0 −1 −1 0 0 0 0 0 +1 0 0 0 +1 0 0 0 −1 +1 0 −1 +1 0 0 0 0 0 −1 +1 0 −1 0 0 0 +1 0 0 0 −1 −1 +1 0 0 0 0
3.35 4.02 3.70 4.04 4.41 3.98 4.24 3.68 3.46 3.65 3.72 4.41 4.28 3.84 4.28 4.15 3.55 3.49 3.69 4.39 4.23 3.51 4.04 4.58 4.34 4.35 3.65 4.08 3.39 3.79 3.84 3.69 3.75 4.77 3.54 4.46 3.52 3.69 3.56 4.24 4.24 4.69 4.28 4.02 3.58 4.32
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
0.03 0.03 0.03 0.01 0.04 0.04 0.04 0.04 0.03 0.03 0.03 0.05 0.04 0.02 0.04 0.03 0.06 0.03 0.04 0.03 0.02 0.05 0.02 0.04 0.16 0.07 0.03 0.03 0.03 0.04 0.03 0.02 0.05 0.03 0.03 0.03 0.01 0.03 0.03 0.04 0.04 0.04 0.03 0.04 0.04 0.05
Model predicted response (U mL−1 ) 3.40 4.18 3.64 4.14 4.37 3.86 4.29 3.77 3.51 3.61 3.75 4.37 4.29 3.76 4.09 4.24 3.69 3.47 3.62 4.42 4.29 3.47 4.20 4.57 4.29 4.29 3.78 4.10 3.37 3.92 3.89 3.82 3.67 4.63 3.68 4.41 3.35 3.57 3.64 4.41 4.23 4.65 4.23 4.03 3.65 4.29
S. Uzuner, D. Cekmecelioglu / Journal of Molecular Catalysis B: Enzymatic 113 (2015) 62–67 Table 4 Regression analysis results for the Plackett–Burman Design. Term Intercept Block pH Fermentation time Temperature % inoculum (v/v) Pectin Yeast extract Magnesium sulphate Di-potassium hydrogen phosphate
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Table 5 Model regression results for the BBD model.
Effect
Coefficient
T value
P value
Source of variation
0.319 0.225 0.571 0.119 0.134 −0.481 −0.056 −0.504
0.320 −0.066 0.159 0.113 0.286 0.060 0.067 −0.241 −0.028 −0.252
91.88 −1.88 4.58 3.24 8.20 1.71 1.93 −6.91 −0.80 −7.24
0.000 0.081 0.000 0.006 0.000 0.109 0.074 0.000 0.438 0.000
Model Linear effects Interaction effects Quadratic effects Residual Lack of fit Pure error Total
one of the most critical parameter in the development of an efficient and economic process. In this study, 8 independent variables were selected, namely pH, fermentation time, temperature, inoculum volume (%, v/v) and medium components of pectin, yeast extract, magnesium sulphate [MgSO4 ], and dipotassium hydrogen phosphate [K2 HPO4 ]. The concentration range of each nutrient was based on the various studies in the literature. Table 1 illustrates the design matrix of various components with coded values; low (−1) and high (+1), while Table 2 represents their actual values. 2.6.2. BBD design and optimization BBD technique is a statistical tool used to develop a quadratic model including the major factors and their interactions to estimate the PG production [34]. The BBD matrix was constructed for the five significant factors (pH, fermentation time, fermentation temperature, yeast extract and dipotassium hydrogen phosphate [K2 HPO4 ]) each having 3 levels (−1, 0 and 1) as shown in Table 3. A second order predictive model was used to represent linear, interaction and quadratic effects of variables on the pectinase production (Eq. (1)).
Sum of squares
Mean squares
F ratio
P value
5 9 4 72 62 10 91
12.0890 5.8354 2.3081 0.8201 0.7559 0.0643 12.9214
0.67161 0.64838 0.57703 0.01139 0.01219 0.00643
69.27 56.92 50.66
0.000 0.000 0.000
1.90
0.135
the pectinase production by Bacillus pumilus dcsr1 when 11 variables were analyzed by the PB method. The variability in the initial amount of yeast extract was not found as significant, and the time and temperature change were not studied in their study. 3.2. Optimization of the culture conditions for polygalacturonase production The experimental plan and the results of experimental vs. predicted PG production for various combinations of pH (X1 ), temperature (X2 ), time (X3 ), yeast extract (X4 ) and dipotassium hydrogen phosphate [K2 HPO4 ] (X5 ) are shown in Table 3. In order to fit the experimental data to a second order predictive model, regression analysis was performed and the resultant model was evaluated by ANOVA (Table 5). The derived polynomial equation was found adequate in representing the experimental data (R2 = 0.94). According to the ANOVA results, the insignificant terms were excluded and the final equation is given in coded values as: Y = 4.27 − 0.08A − 0.23B + 0.16C + 0.14D + 0.14E − 0.22A2 − 0.23B2 − 0.17C 2 − 0.26D2 + 0.09AB − 0.11AC − 0.09AD − 0.38AE − 0.30BC − 0.22BD + 0.22BE + 0.48CD − 0.38DE
Y = b0 + b1 X1 + b2 X2 + b3 X3 + b4 X4 + b5 X5 + b11 X12 + b22 X22
(2)
+ b33 X32 + b44 X42 + b55 X52 + b12 X1 X2 + b13 X1 X3 + b14 X1 X4 + b15 X1 X5 + b23 X2 X3 + b24 X2 X4 + b25 X2 X5 + b34 X3 X4 + b35 X3 X5 + b45 X4 X5
Degrees of freedom
(1)
where Y is the response (PG activity), b0 , b1 , b2 , b3 , b4 and b5 are regression coefficients, and X1 , X2 , X3 , X4 , X5 are pH, fermentation time, fermentation temperature, yeast extract and dipotassium hydrogen phosphate [K2 HPO4 ], respectively. The analysis of variance (ANOVA) and regression analysis were performed to define the coefficients of the predictive model and significant terms using MINITAB 16.0. The optimal conditions of pectinase production were determined by the response optimizer tool in MINITAB. 2.6.3. Model validation To validate the derived model, additional trials at the optimal fermentation conditions were carried out in triplicate. 3. Results and discussion 3.1. Evaluation of variables affecting the pectinase production In order to obtain industrially feasible results, the hazelnut shell hydrolysate with optimum blend of nutrients and physical parameters was evaluated for its potential of high PG activity. The variability in five factors (pH, fermentation time, temperature, yeast extract and K2 HPO4 ) was found significant (P < 0.05) by PB analysis (Table 4). These factors were further optimized by the BBD response surface method. Sharma and Satyanarayana [20] reported that C:N ratio, K2 HPO4 and pH affected significantly
where Y is the predicted PG activity, X1 , X2 , X3 , X4 , X5 are the coded values of pH, fermentation time, temperature, yeast extract and [K2 HPO4 ], respectively. The insignificant lack of fit (P = 0.135 > 0.05) also showed that the model fit the experimental data well. The qual2 ) and predicted R2 ity of fit was estimated by the adjusted R2 (Radj 2 (Rpred ) values and found to be 0.92 and 0.89 respectively, which are fairly high and close values to 1.0. These values indicated that about 8% variation in the response could not be suitably explained by the model. It was also observed that the five major factors and almost all interactions significantly affected the PG production with low P values (P < 0.05) (Table 6). The most important factors impacting the pectinase activity (U mL−1 ) were temperature with highest coefficient (0.23) followed by time (0.16), [K2 HPO4 ] (0.14), yeast extract (0.14) and pH (0.08) (Table 6). Three dimensional (3D) response surface plots were plotted to observe the interaction effect of variables in pairs on PG production prior to determining the optimal conditions (Fig. 1a–j). The remaining three factors were held constant at center levels (i.e. pH 7, time 48 h, temperature 35 ◦ C, yeast extract 0.3 (%, w/v) and K2 HPO4 0.03 (%, w/v) respectively). An increase in PG production was observed when yeast extract was increased up to its midvalue and stayed constant thereafter, whereas a slight increase was observed with K2 HPO4 (Fig. 1a). Joshi et al. [14] also reported high pectinase production with 2.5% yeast extract in the fermentation medium. A similar trend was reported for K2 HPO4 [20], where a maximum increase in pectinase was attained at 0.5% K2 HPO4 . Fig. 1b shows that PG activity increased with increase in K2 HPO4 concentration, and started to decrease beyond 50 h. Pectinase production is directly proportional to the microbial growth and is high
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Fig. 1. Response surface plots showing the effect of (a) [K2 HPO4 ] vs. yeast extract concentration, (b) [K2 HPO4 ] vs. time, (c) [K2 HPO4 ] vs. pH, (d) [K2 HPO4 ] vs. temperature, (e) yeast extract concentration vs. time, (f) yeast extract concentration vs. temperature, (g) yeast extract concentration vs. pH, (h) time vs. temperature, (i) time vs. pH and (j) temperature vs. pH on PG production. Table 6 Revised ANOVA results for the BBD model (A: pH; B: temperature; C: fermentation time (h); D: yeast extract (%, w/v); E: K2 HPO4 concentration (%, w/v)). Term
Coefficient
Standard error coefficient
T value
P value
Constant A B C D E A*B A*C A*D A*E B*C B*D B*E C*D D*E A2 B2 C2 D2
4.271 −0.075 −0.233 0.162 0.137 0.137 0.096 −0.111 −0.093 −0.379 −0.296 −0.218 0.218 0.478 −0.381 −0.221 −0.231 −0.166 −0.258
0.025 0.019 0.019 0.019 0.019 0.019 0.038 0.038 0.038 0.038 0.038 0.038 0.038 0.038 0.038 0.025 0.025 0.025 0.025
173.769 −3.960 −12.323 8.561 7.252 7.278 2.537 −2.941 −2.460 −10.031 −7.846 −5.763 5.765 12.674 −10.094 −8.990 −9.390 −6.755 −10.498
0.000 0.000 0.000 0.000 0.000 0.000 0.013 0.004 0.016 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
in the late exponential phase of growth and decreases thereafter [14]. Varying optimal fermentation periods in the range of 24 h to 6 days have been observed for B. subtilis [12,14,34]. An increase in PG activity was observed as pH was increased to about 7.0, but a slight decrease occurred beyond pH 8 (Fig. 1c, g, i, and j). A similar positive effect of pH was reported by others [20]. The high pH values account typically for PG and pectate lyase production [14].
The fermentation temperature exerted a profound effect on the PG production, giving high enzyme yields around 30–35 ◦ C and decreasing at higher ends (Fig. 1d, f, and h). Similar mild temperatures were reported elsewhere [12,14]. The influence of temperature is associated with the growth of the organism [35]. It was observed that PG activity increased when K2 HPO4 increased and temperature decreased (Fig. 1d). An increase in yeast extract concentration led to an increase in the PG activity but this increase was limited by the time variable (Fig. 1e). Yeast extract contains vitamins, minerals and amino acids, which are necessary for bacterial growth and enzyme production [36]. Fig. 1f–j shows that as the levels of the variables increased, the PG activity increased nonlinearly. Fig. 1f shows that the PG activity initially increased and then decreased with increase in concentration of yeast extract at the constant temperature. A similar nonlinear effect of yeast extract and pH on the PG activity was observed in Fig. 1g. The nonlinearity for different combinations of temperature, time and pH also existed in Fig. 1h–j. The high temperature (35 ◦ C) increases the solubility and diffusivity of the proteins and no protein loss is indicated through thermal denaturation [37]. The enzyme activity decreased gradually as the temperature was raised to 40 ◦ C. Thus, we can conclude that the PG production was affected significantly (P < 0.05) by all factors and their interactions, except for time vs. K2 HPO4 and the quadratic term for K2 HPO4 . To determine the optimal PG production conditions, the response optimizer tool in MINITAB® 16.0 was used. The optimum conditions giving the maximal PG activity (5.60 U mL−1 ) were identified as 0.5% (w/v) yeast extract, pH 7.0, 72 h of fermentation
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time, 30 ◦ C of temperature and 0.02% (w/v) K2 HPO4 . Thus, a 2.7fold (or 165%) increase in the pectinase production of B. subtilis was achieved in the shake flasks by PB and RSM optimization compared to the unoptimized culture conditions. A 3.4-fold increase in the PG production by RSM optimization using B. subtilis RCK was reported by others [16]. A 48% increase in enzyme activity of Aspergillus sojae ATCC 20235 was also reported after optimizing the culture conditions by RSM [15]. Similarly, a 1.5-fold increase in pectinase secretion of Kluyveromyces wickerhamii by RSM was reported in the study of Moyo et al. [38]. Soares et al. [39] selected six strains of Bacillus sp. as good PG producers, whose activities varied from 0.3 to 4.0 U mL−1 . Galiotou-Panayotou and Kapantai [40] achieved 3.0 U mL−1 of PG activity by submerged fermentation of A. niger in a medium composed of pectin from citrus. Comparing the results of our work with those reported in the literature, we can conclude that promising pectinase activity was achieved in this study. The slight variation in the enzyme activity is inevitable due to the differences in the nature of the organism cultivated, varying culture conditions tested in the PBD and RSM, and raw material used as carbon or nitrogen sources. 3.3. Validation of the RSM model To validate the model, additional runs were carried out for pectinase production at the optimal conditions predicted by the RSM. The results indicated that the experimental PG activity of 4.84 U mL−1 was slightly lower than the predicted value of 5.60 U mL−1 . However, a low value of coefficient of variation, determined to be 10.6%, satisfies the adequacy of the model. 4. Conclusions Hazelnut shells, inexpensive agro-residues, were shown to serve as an appropriate source for production of PG using B. subtilis after optimizing the submerged fermentation medium composition and conditions by the PB and RSM tools. A 2.7-fold enhancement in the enzyme production was achieved compared to the unoptimized fermentation conditions. B. subtilis produced the PG enzyme at promising level at neutral pH; hence, it can be used to increase the extraction yield of banana, carrot, grape, or apple juice. Further research is being carried out in our laboratory to apply the crude enzyme to the clarification of carrot juice as model vegetable juice. Acknowledgements Funding for this study was provided by the METU Scientific Research Council (project number is BAP-03-14-2011-002) and the Graduate School of Natural and Applied Sciences at the Middle East Technical University, Turkey. References [1] R.S. Jayani, S. Saxena, R. Gupta, Process Biochem. 40 (2005) 2931–2944.
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[2] W.C. Lee, S. Yusof, N.S.A. Hamid, B.S. Baharin, J. Food Eng. 73 (2006) 55–63. [3] H.N. Sin, S. Yusof, N.S.A. Hamid, R.A. Rahman, J. Food Eng. 73 (2006) 313–319. [4] I.G. Sandri, R.C. Fontana, D.M. Barfknecht, M.M. Da Silveira, Food Sci. Technol. 44 (2011) 2217–2222. [5] S. Ahlawat, R.P. Mandhan, S.S. Dhiman, R. Kumar, J. Sharma, Appl. Biochem. Biotechnol. 149 (2008) 287–293. [6] D.C. Sharma, T. Satyanarayana, Enzyme Res. (2012), http://dx.doi.org/10.1155/ 2012/281384. [7] L. Najafian, A. Ghodsvali, M.H.H. Khodaparast, L.L. Diosady, Food Res. Int. 42 (2009) 171–175. [8] D.R. Kashyap, P.K. Vohra, S. Chopra, R. Tewari, Bioresour. Technol. 77 (2001) 215–227. [9] P.S. Murthy, M.M. Naidu, Eur. J. Appl. Sci. 3 (2011) 130–139. [10] A.M.M. Combo, M. Aguedo, D. Goffin, B. Wathelet, M. Paquot, Food Bioprod. Process. 90 (2012) 588–596. [11] M.M. Sabajanes, R. Yanez, J.L. Alonso, J.C. Parajo, Int. J. Food Sci. Technol. 47 (2012) 747–754. [12] S. Ahlawat, S.S. Dhiman, B. Battan, R.P. Mandhan, J. Sharma, Process Biochem. 44 (2009) 521–526. [13] K. Mojsov, Appl. Technol. Innov. 2 (2010) 40–46. [14] M. Joshi, M. Nerurkar, R. Adivarekar, Innov. Roman. Food Biotechnol. 12 (2013) 75–83. [15] F.I. Ustok, C. Tari, N. Gogus, J. Biotechnol. 127 (2007) 322–334. [16] S. Gupta, M. Kapoor, K.K. Sharma, L.M. Nair, R.C. Kuhad, Bioresour. Technol. 99 (2008) 937–945. [17] D.B. Pedrolli, A.C. Monteiro, E. Gomes, E.C. Carmona, Open Biotechnol. J. 3 (2009) 9–18. [18] V. Rangarajan, M. Rajasekharan, R. Ravichandran, K. Sriganesh, V. Vaitheeswaran, Int. J. Biotechnol. Biochem. 6 (2010) 445–453. [19] J. Zeni, K. Cence, C.E. Grando, L. Tiggermann, R. Colet, L.A. Lerin, R.L. Cansian, G. Toniazzo, D. De Oliveira, E. Valduga, Appl. Biochem. Biotechnol. 163 (2011) 383–392. [20] D.C. Sharma, T. Satyanarayana, Bioresour. Technol. 97 (2006) 727–733. – ˇ ´ R.M. Skrinjar, [21] D.M. Periˇcin, S.Z. Madarev, L.M. Radulovic, Proc. Natl. Sci. Matica Srpska Novi Sad 113 (2007) 313–320. [22] E. Namasivayam, J.D. Ravindar, K. Mariappan, A. Jiji, M. Kumar, R.L. Jayaraj, J. Bioanal. Biomed. 3 (2011) 070–075. [23] Y. Xu, E.N. Sismour, J. Parry, M.A. Hanna, H. Li, Int. J. Food Sci. Technol. 47 (2012) 940–946. [24] S. Uzuner, D. Cekmecelioglu, Int. Proc. Chem. Biol. Environ. Eng. 50 (2013) 30–33. [25] A. Demirbas, Energy Sources 24 (2002) 891–897. [26] M. Dogru, C.R. Howarth, G. Akay, B. Keskinler, A.A. Malik, Energy 27 (2002) 415–427. [27] S. Uzuner, D. Cekmecelioglu, Int. J. Food Eng. (2014), http://dx.doi.org/10.1515/ ijfe-2014-0158.2014. [28] AOAC, Official Methods of Analysis, Association of Official Analytical Chemists, Washington, DC, 1984. [29] A. Hara, N.S. Radin, Anal. Biochem. 90 (1978) 420–426. [30] B.L. Browning, Methods of Wood Chemistry: Determination of Sugars, InterScience, New York, 1967, pp. 589–590. [31] TAPPI Test Method T222 om-88, Acid-Insoluble Lignin in Wood and Pulp, in: Tappi Test Methods, Technical Association of the Pulp and Paper Industry, Atlanta, GA, 1989. [32] G.L. Miller, Anal. Chem. 31 (1959) 426–428. [33] M. Kapoor, Q.K. Beg, B. Bhushan, K.S. Dadhich, G.S. Hoondal, Process Biochem. 36 (2000) 467–473. [34] G.E.P. Box, D.W. Behnken, Technometrics 2 (1960) 455–475. [35] M.R. Swain, S. Kar, R.C. Ray, Braz. J. Microbiol. 40 (2009) 636–648. [36] H.U. Rehman, S.A. Qader, A. Aman, Carbohydr. Polym. 90 (2012) 387–391. [37] L.R. Castilho, R.A. Medronho, T.L.M. Alves, Bioresour. Technol. 71 (2000) 45–50. [38] S. Moyo, B.A. Gashe, E.K. Collison, S. Mpuchane, Int. J. Food Microbiol. 15 (2003) 87–100. [39] M.M.C.N. Soares, R. da Silva, E.C. Carmona, E. Gomes, Annu. Rev. Microbiol. 30 (1999) 299–303. [40] M.P.R. Galiotou-Panayotou, M. Kapantai, Lett. Appl. Microbiol. 17 (1993) 145–148.
Contents lists available at ScienceDirect
Journal of Molecular Catalysis B: Enzymatic journal homepage: www.elsevier.com/locate/molcatb
Enhanced pectinase production by optimizing fermentation conditions of Bacillus subtilis growing on hazelnut shell hydrolyzate Sibel Uzuner a,b , Deniz Cekmecelioglu a,∗ a b
Department of Food Engineering, Faculty of Engineering, Middle East Technical University, 06800 Ankara, Turkey Department of Food Engineering, Faculty of Engineering and Architecture, Abant Izzet Baysal University, 14280 Bolu, Turkey
a r t i c l e
i n f o
Article history: Received 21 July 2014 Received in revised form 1 January 2015 Accepted 10 January 2015 Available online 17 January 2015 Keywords: Hazelnut shell hydrolyzate Pectinases Plackett–Burman Design Polygalacturonase activity Response surface optimization
a b s t r a c t This study describes optimization of polygalacturonase (PG) production using Bacillus subtilis in submerged fermentation by Plackett–Burman (PB) design and response surface methodology (RSM). Five variables (pH, time, temperature, yeast extract concentration and K2 HPO4 ), which were determined to be significant by the PB analysis, were further optimized using Box–Behnken response surface method. The optimization results indicated that a maximal PG activity of 5.60 U mL−1 was achieved at pH 7.0, 72 h, and 30 ◦ C using 0.5% (w/v) yeast extract and 0.02% (w/v) K2 HPO4 in the fermentation medium. The results implied a 2.7-fold increase in PG activity of B. subtilis under the optimized conditions. Thus, it was concluded that hazelnut shell hydrolyzate have remarkable potential for low cost commercial PG production. © 2015 Elsevier B.V. All rights reserved.
1. Introduction Pectinase is an enzyme group, which hydrolyzes pectic substances present in agricultural and food commodities [1]. Pectinases are grouped as acidic or alkaline enzymes and find diverse applications in extraction and clarification of fruit juices [2–4], bleaching of paper pulp [5], degumming of fibers [6], oil extraction [7], and coffee and tea fermentation [8,9]. Novel use of pectinases in DNA extraction from plants [10] and production of pectic oligosaccharides as functional/prebiotic food components has also emerged [11]. Thus, studies on pectinase production with low cost and high activity are still needed to meet the increasing demands. Bacterial and fungal pectinase productions have been reported under both submerged (SmF) [12–14] and solid state (SSF) fermentation conditions [15,16]. As SSF simulates the actual living conditions of fungi, it provides higher enzyme yields than the SmF method [17]. However, industrial application of SSF still suffers from the complicated product purification resulting from heterogeneity of fermentation medium, difficulty in scale up, and losses of enzyme in the solid residues [16,17]. Besides, SSF requires long fermentation periods (e.g. 5–6 days) [15]. The SmF method, on the other hand, is easier to control at large scale production and has been successfully used for production of various metabolites since 1940s.
∗ Corresponding author. Tel.: +90 312 210 5631; fax: +90 312 210 2767. E-mail address: [email protected] (D. Cekmecelioglu). http://dx.doi.org/10.1016/j.molcatb.2015.01.003 1381-1177/© 2015 Elsevier B.V. All rights reserved.
Rangarajan et al. [18] compared the pectinase production by Aspergillus niger by SmF and SSF methods at shake flask and reactor levels, using orange peel as the carbon source and varying amounts of organic and inorganic nitrogen sources. A maximal exo-pectinase activity of 5128 U g−1 and endo-pectinase activity of 793 U g−1 were reported with 4% soybean meal in the SSF method, whereas maximal exo-pectinase activity of 5834 U g−1 and endopectinase activity of 951 U g−1 were achieved with the SmF method using 4% peptone and 3% soybean meal, respectively. A similar trend was also reported for the reactor trials. The fungus A. niger was used as the pectinase producer in the SmF system run for 24–120 h by others [13,19], who reported promising results. The use of Bacillus species for pectinase production by the SmF method provided successful results as reported by Sharma and Satyanarayana [20], Ahlawat et al. [12], and Joshi et al. [14]. However, production of alkaline pectinase by SmF using bacterial culture is limited compared to fungal culture. Thus, in this study Bacillus subtilis was used as the pectinase producer by SmF under various conditions. Also for SmF method to be commercially viable, pectinases should be produced on low cost carbon sources such as citrus limetta peel [14], orange peel extract [18], mix of apple pulp and corn flour [13], wheat bran [12], pumpkin oil cake [21] and other agricultural wastes, rather than using synthetic liquid carbon sources [22]. The selection of appropriate sources of carbon, nitrogen and other nutrients is one of the most critical stages in the development of an efficient and economic enzyme production process, where the solid agricultural substances act as the source of carbon,
S. Uzuner, D. Cekmecelioglu / Journal of Molecular Catalysis B: Enzymatic 113 (2015) 62–67
nitrogen, and minerals [6]. It is also known that 30–40% of the enzyme production cost is attributed to the fermentation medium [15]. Hazelnut shells account for the majority of the by-products of hazelnuts, as the kernel occupies less than 50% of the total nut weight [23]. The shells contain 24.2% cellulose, 28.2% hemicellulose, 34.6% lignin, 9.7% moisture, and 1.1% ash [24] and are disposed of as a low-value heat source or used as the raw material for furfural production [25]. The annual generation of the shells is in the range of 250,000–400,000 tons in the Black-Sea region of Turkey [26]. Thus, it is important to utilize hazelnuts shells in bioprocessing and their use in pectinase production can improve the production cost. To the best of our knowledge, no work has been reported on the pectinase production using the hazelnut shells hydrolyzate as the carbon source by B. subtilis. This study was undertaken to investigate the effects of fermentation medium composition (pectin, yeast extract, MgSO4 , K2 HPO4 ) and culture conditions (pH, time, temperature, inoculum amount) on the polygalacturonase (PG) activity during submerged fermentation in shake-flask bioreactors. Initial screening of the factors was performed with the Plackett–Burman Design (PBD) to screen the important factors affecting the PG production and subsequently the Box–Behnken Design (BBD) technique was used to determine the optimal PG production using the selected factors. In all trials, the sugar rich medium obtained after enzymatic saccharification of the acid-pretreated hazelnut shells was used as the carbon source. The findings of this study are believed to be useful to enzyme, food, textile, and paper and pulp industry. 2. Materials and methods 2.1. Materials Hazelnut shells were provided by a local plant in Ordu, province of Turkey, and dried at 70 ◦ C in a convection oven for 24 h on arrival. The shells were then ground to pass through a 1 mm sieve and stored in plastic bags at room temperature until use.
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2.3. Polygalacturonase (PG) production by B. subtilis under submerged fermentation (SmF) The ground hazelnut shells were treated with 3.42% dilute acid at 130 ◦ C for 31.7 min and subsequently saccharified using Viscozyme L (Sigma–Aldrich, Denmark) (cellulases and xylanases) at 200 units per gram (U g−1 ) for 20 h according to the results of a previous study [27]. One Viscozyme L unit was defined as the amount of enzyme that liberates 1 mol of reducing sugars equivalent to glucose per min under the standard assay conditions (50 ◦ C, pH 5.0 and 30 min). The enzyme activity was then expressed in units of activity per milliliter (U mL−1 ). The resultant activity value was divided by substrate concentration to obtain U g−1 . A 500 mL Erlenmeyer flask containing 100 mL of fermentation medium similar to the growth medium but only amended with 5% hazelnut shell hydrolyzate instead of glucose was used in the fermentation. Before inoculation, the flasks and medium were sterilized by autoclaving. Sterile fermentation medium was inoculated with 1 mL of overnight grown bacterial culture containing 106 CFU mL−1 and incubated under agitation at 130 rpm. After fermentation, the biomass was separated by centrifugation at 2000 × g for 20 min and the supernatant was used as the crude enzyme source in the pectinase activity assay. 2.4. Analytical methods The composition of ground hazelnut shells including moisture, ash, protein, fat, crude fiber, cellulose, hemicellulose and lignin content was determined. Moisture content was determined by drying at 105 ◦ C until constant weight. The dried samples were burned in an oven for 24 h at 550 ◦ C, for ash content determination [28]. The total fat content was determined by hexane extraction method [29]. For crude fiber determination, samples were hydrolyzed by sulfuric acid and then calcinated by potassium hydroxide and burned at 550 ◦ C until constant weight. The cellulose and hemicelulose contents of the extractive-free (ethanol–hexane) samples were determined according to the study of Browning [30]. Acid-insoluble lignin was calculated by the TAPPI standard method (T2220S-74) [31]. 2.5. Assay of polygalacturonase activity
2.2. Micro-organism and culture conditions The strain B. subtilis NRRL B-4219 was kindly provided by the ARS culture collection, Northern Regional Research Laboratory (NRRL), Peoria, IL, USA. The stock culture was activated in medium containing (g L−1 ) yeast extract, 1.0; glucose, 10.0; K2 HPO4 , 0.4; KH2 PO4 , 0.2; MgSO4 ·7H2 O, 0.4; and citrus pectin, 2.0 at a temperature of 30 ◦ C and pH of 6.5 in an incubator shaker adjusted to 130 rpm for 24 h.
Polygalacturonase (PG) activity was assayed by measuring the release of polygalacturonic acid using the 3,5-dinitrosalicylic acid (DNS) reagent [32]. The 3,5-dinitrosalicylic acid is an aromatic compound that reacts to form 3-amino-5-nitrosalicylic acid, which absorbs light strongly at 575 nm. Galacturonic acid monohydrate was used as the standard. PG activity was evaluated by mixing 0.5 mL of crude enzyme and 0.5 mL of polygalacturonic acid solution (1%, w/v polygalacturonic acid in 0.05 M acetate buffer at pH
Table 1 Plackett–Burman Design for screening the major factors affecting the PG production. Serial
pH
Time (h)
Temperature (◦ C)
Inoculum volume (%, v/v)
Pectin (%, w/v)
Yeast extract (%, w/v)
MgSO4 (%, w/v)
K2 HPO4 (%, w/v)
PG activity (U mL−1 ) B. subtilis
1 2 3 4 5 6 7 8 9 10 11 12
+1 +1 −1 −1 −1 −1 +1 −1 −1 +1 +1 +1
−1 +1 +1 −1 −1 +1 +1 +1 −1 −1 +1 −1
+1 −1 +1 +1 −1 −1 −1 +1 −1 −1 +1 +1
−1 +1 −1 +1 +1 −1 +1 +1 −1 −1 −1 +1
−1 −1 +1 +1 +1 −1 +1 −1 −1 +1 +1 −1
−1 −1 −1 −1 +1 +1 −1 +1 −1 +1 +1 +1
+1 −1 −1 +1 −1 +1 +1 +1 −1 +1 −1 −1
+1 +1 −1 +1 +1 +1 −1 −1 −1 −1 +1 −1
3.26 3.40 3.96 3.40 2.20 2.11 3.57 3.52 3.04 3.16 3.31 3.46
± ± ± ± ± ± ± ± ± ± ± ±
0.07 0.04 0.06 0.13 0.10 0.09 0.09 0.09 0.22 0.07 0.08 0.07
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7.0). Samples were incubated at 50 ◦ C for 30 min [33] and the reducing sugar was determined by the DNS method. One unit of PG activity (U) was defined as the amount of enzyme which releases 1 mol of galacturonic acid per minute under assay conditions. The PG activity was then expressed in units of activity per milliliter (U mL−1 ). 2.6. Experimental design For screening and optimization purposes, the fermentation experiments were performed at shake flask level (100 mL working volume) using the hazelnut hydrolyzate inoculated with 1 mL of overnight grown bacterial culture containing 106 CFU mL−1 supplemented with various nutrients and conditions according to the experimental design. The physical parameters such as pH, fermentation time, temperature, inoculum volume (%, v/v) and medium components such as pectin, yeast extract, magnesium sulphate [MgSO4 ], and dipotassium hydrogen phosphate [K2 HPO4 ] were evaluated as per the design specifications (Table 1). All experiments were performed in two replicates and the results are reported as mean values. The experimental design and statistical analyses were carried out using MINITAB 16.0 (Minitab Inc. State College, PA, USA)
Table 2 The factors and their coded levels used in the PBD and BBD methods. Serial number
Variable
Low (−1)
Center point (0)
High (+1)
1 2 3
pH Fermentation time (h) Fermentation temperature (◦ C) Inoculum volume (%, v/v) Pectin (%, w/v) Yeast extract (%, w/v) MgSO4 ·7H2 O (%, w/v) K2 HPO4 (%, w/v)
5 24 30
7 48 35
9 72 40
4 5 6 7 8
1 0.2 0.1 0.02 0.02
3 0.35 0.3 0.05 0.03
5 0.5 0.5 0.08 0.04
with ˛ = 0.05 (95% level of confidence) for Plackett–Burman Design (PBD) and Box–Behnken response surface methods. 2.6.1. Plackett–Burman Design (PBD) A two level PBD experimental matrix was set up to identify the significant factors of pectinase production. The PB design provides a linear model where only main effects are taken into account. Selection of appropriate carbon, nitrogen and other nutrients is
Table 3 Experimental design for optimization of polygalacturonase production using Box–Behnken response surface method (RSM). Run order
pH (A)
Temperature (B)
Time (h) (C)
Yeast extract (%, w/v) (D)
K2 HPO4 (%, w/v) (E)
Experimental response pectinase activity (U mL−1 )
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46
0 0 0 0 0 +1 0 +1 0 +1 +1 0 0 +1 0 −1 −1 0 −1 0 0 0 0 0 0 0 0 −1 0 0 +1 0 0 0 +1 0 0 −1 −1 0 +1 −1 −1 0 0 0
0 +1 +1 0 −1 0 0 0 +1 +1 0 −1 0 0 0 0 0 +1 0 0 0 +1 −1 −1 0 0 +1 0 0 0 −1 0 −1 0 0 0 0 +1 0 0 0 0 −1 0 −1 0
+1 0 0 +1 0 +1 0 0 +1 0 0 0 0 −1 −1 +1 −1 0 0 +1 0 0 0 +1 0 0 −1 0 0 0 0 −1 0 +1 0 0 −1 0 0 0 0 0 0 −1 −1 0
−1 0 −1 0 0 0 0 +1 0 0 0 +1 0 0 0 0 0 +1 0 0 0 0 0 0 0 0 0 +1 −1 +1 0 0 −1 +1 −1 −1 +1 0 −1 +1 0 0 0 −1 0 0
0 +1 0 −1 −1 0 0 0 0 0 +1 0 0 0 +1 0 0 0 −1 +1 0 −1 +1 0 0 0 0 0 −1 +1 0 −1 0 0 0 +1 0 0 0 −1 −1 +1 0 0 0 0
3.35 4.02 3.70 4.04 4.41 3.98 4.24 3.68 3.46 3.65 3.72 4.41 4.28 3.84 4.28 4.15 3.55 3.49 3.69 4.39 4.23 3.51 4.04 4.58 4.34 4.35 3.65 4.08 3.39 3.79 3.84 3.69 3.75 4.77 3.54 4.46 3.52 3.69 3.56 4.24 4.24 4.69 4.28 4.02 3.58 4.32
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
0.03 0.03 0.03 0.01 0.04 0.04 0.04 0.04 0.03 0.03 0.03 0.05 0.04 0.02 0.04 0.03 0.06 0.03 0.04 0.03 0.02 0.05 0.02 0.04 0.16 0.07 0.03 0.03 0.03 0.04 0.03 0.02 0.05 0.03 0.03 0.03 0.01 0.03 0.03 0.04 0.04 0.04 0.03 0.04 0.04 0.05
Model predicted response (U mL−1 ) 3.40 4.18 3.64 4.14 4.37 3.86 4.29 3.77 3.51 3.61 3.75 4.37 4.29 3.76 4.09 4.24 3.69 3.47 3.62 4.42 4.29 3.47 4.20 4.57 4.29 4.29 3.78 4.10 3.37 3.92 3.89 3.82 3.67 4.63 3.68 4.41 3.35 3.57 3.64 4.41 4.23 4.65 4.23 4.03 3.65 4.29
S. Uzuner, D. Cekmecelioglu / Journal of Molecular Catalysis B: Enzymatic 113 (2015) 62–67 Table 4 Regression analysis results for the Plackett–Burman Design. Term Intercept Block pH Fermentation time Temperature % inoculum (v/v) Pectin Yeast extract Magnesium sulphate Di-potassium hydrogen phosphate
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Table 5 Model regression results for the BBD model.
Effect
Coefficient
T value
P value
Source of variation
0.319 0.225 0.571 0.119 0.134 −0.481 −0.056 −0.504
0.320 −0.066 0.159 0.113 0.286 0.060 0.067 −0.241 −0.028 −0.252
91.88 −1.88 4.58 3.24 8.20 1.71 1.93 −6.91 −0.80 −7.24
0.000 0.081 0.000 0.006 0.000 0.109 0.074 0.000 0.438 0.000
Model Linear effects Interaction effects Quadratic effects Residual Lack of fit Pure error Total
one of the most critical parameter in the development of an efficient and economic process. In this study, 8 independent variables were selected, namely pH, fermentation time, temperature, inoculum volume (%, v/v) and medium components of pectin, yeast extract, magnesium sulphate [MgSO4 ], and dipotassium hydrogen phosphate [K2 HPO4 ]. The concentration range of each nutrient was based on the various studies in the literature. Table 1 illustrates the design matrix of various components with coded values; low (−1) and high (+1), while Table 2 represents their actual values. 2.6.2. BBD design and optimization BBD technique is a statistical tool used to develop a quadratic model including the major factors and their interactions to estimate the PG production [34]. The BBD matrix was constructed for the five significant factors (pH, fermentation time, fermentation temperature, yeast extract and dipotassium hydrogen phosphate [K2 HPO4 ]) each having 3 levels (−1, 0 and 1) as shown in Table 3. A second order predictive model was used to represent linear, interaction and quadratic effects of variables on the pectinase production (Eq. (1)).
Sum of squares
Mean squares
F ratio
P value
5 9 4 72 62 10 91
12.0890 5.8354 2.3081 0.8201 0.7559 0.0643 12.9214
0.67161 0.64838 0.57703 0.01139 0.01219 0.00643
69.27 56.92 50.66
0.000 0.000 0.000
1.90
0.135
the pectinase production by Bacillus pumilus dcsr1 when 11 variables were analyzed by the PB method. The variability in the initial amount of yeast extract was not found as significant, and the time and temperature change were not studied in their study. 3.2. Optimization of the culture conditions for polygalacturonase production The experimental plan and the results of experimental vs. predicted PG production for various combinations of pH (X1 ), temperature (X2 ), time (X3 ), yeast extract (X4 ) and dipotassium hydrogen phosphate [K2 HPO4 ] (X5 ) are shown in Table 3. In order to fit the experimental data to a second order predictive model, regression analysis was performed and the resultant model was evaluated by ANOVA (Table 5). The derived polynomial equation was found adequate in representing the experimental data (R2 = 0.94). According to the ANOVA results, the insignificant terms were excluded and the final equation is given in coded values as: Y = 4.27 − 0.08A − 0.23B + 0.16C + 0.14D + 0.14E − 0.22A2 − 0.23B2 − 0.17C 2 − 0.26D2 + 0.09AB − 0.11AC − 0.09AD − 0.38AE − 0.30BC − 0.22BD + 0.22BE + 0.48CD − 0.38DE
Y = b0 + b1 X1 + b2 X2 + b3 X3 + b4 X4 + b5 X5 + b11 X12 + b22 X22
(2)
+ b33 X32 + b44 X42 + b55 X52 + b12 X1 X2 + b13 X1 X3 + b14 X1 X4 + b15 X1 X5 + b23 X2 X3 + b24 X2 X4 + b25 X2 X5 + b34 X3 X4 + b35 X3 X5 + b45 X4 X5
Degrees of freedom
(1)
where Y is the response (PG activity), b0 , b1 , b2 , b3 , b4 and b5 are regression coefficients, and X1 , X2 , X3 , X4 , X5 are pH, fermentation time, fermentation temperature, yeast extract and dipotassium hydrogen phosphate [K2 HPO4 ], respectively. The analysis of variance (ANOVA) and regression analysis were performed to define the coefficients of the predictive model and significant terms using MINITAB 16.0. The optimal conditions of pectinase production were determined by the response optimizer tool in MINITAB. 2.6.3. Model validation To validate the derived model, additional trials at the optimal fermentation conditions were carried out in triplicate. 3. Results and discussion 3.1. Evaluation of variables affecting the pectinase production In order to obtain industrially feasible results, the hazelnut shell hydrolysate with optimum blend of nutrients and physical parameters was evaluated for its potential of high PG activity. The variability in five factors (pH, fermentation time, temperature, yeast extract and K2 HPO4 ) was found significant (P < 0.05) by PB analysis (Table 4). These factors were further optimized by the BBD response surface method. Sharma and Satyanarayana [20] reported that C:N ratio, K2 HPO4 and pH affected significantly
where Y is the predicted PG activity, X1 , X2 , X3 , X4 , X5 are the coded values of pH, fermentation time, temperature, yeast extract and [K2 HPO4 ], respectively. The insignificant lack of fit (P = 0.135 > 0.05) also showed that the model fit the experimental data well. The qual2 ) and predicted R2 ity of fit was estimated by the adjusted R2 (Radj 2 (Rpred ) values and found to be 0.92 and 0.89 respectively, which are fairly high and close values to 1.0. These values indicated that about 8% variation in the response could not be suitably explained by the model. It was also observed that the five major factors and almost all interactions significantly affected the PG production with low P values (P < 0.05) (Table 6). The most important factors impacting the pectinase activity (U mL−1 ) were temperature with highest coefficient (0.23) followed by time (0.16), [K2 HPO4 ] (0.14), yeast extract (0.14) and pH (0.08) (Table 6). Three dimensional (3D) response surface plots were plotted to observe the interaction effect of variables in pairs on PG production prior to determining the optimal conditions (Fig. 1a–j). The remaining three factors were held constant at center levels (i.e. pH 7, time 48 h, temperature 35 ◦ C, yeast extract 0.3 (%, w/v) and K2 HPO4 0.03 (%, w/v) respectively). An increase in PG production was observed when yeast extract was increased up to its midvalue and stayed constant thereafter, whereas a slight increase was observed with K2 HPO4 (Fig. 1a). Joshi et al. [14] also reported high pectinase production with 2.5% yeast extract in the fermentation medium. A similar trend was reported for K2 HPO4 [20], where a maximum increase in pectinase was attained at 0.5% K2 HPO4 . Fig. 1b shows that PG activity increased with increase in K2 HPO4 concentration, and started to decrease beyond 50 h. Pectinase production is directly proportional to the microbial growth and is high
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Fig. 1. Response surface plots showing the effect of (a) [K2 HPO4 ] vs. yeast extract concentration, (b) [K2 HPO4 ] vs. time, (c) [K2 HPO4 ] vs. pH, (d) [K2 HPO4 ] vs. temperature, (e) yeast extract concentration vs. time, (f) yeast extract concentration vs. temperature, (g) yeast extract concentration vs. pH, (h) time vs. temperature, (i) time vs. pH and (j) temperature vs. pH on PG production. Table 6 Revised ANOVA results for the BBD model (A: pH; B: temperature; C: fermentation time (h); D: yeast extract (%, w/v); E: K2 HPO4 concentration (%, w/v)). Term
Coefficient
Standard error coefficient
T value
P value
Constant A B C D E A*B A*C A*D A*E B*C B*D B*E C*D D*E A2 B2 C2 D2
4.271 −0.075 −0.233 0.162 0.137 0.137 0.096 −0.111 −0.093 −0.379 −0.296 −0.218 0.218 0.478 −0.381 −0.221 −0.231 −0.166 −0.258
0.025 0.019 0.019 0.019 0.019 0.019 0.038 0.038 0.038 0.038 0.038 0.038 0.038 0.038 0.038 0.025 0.025 0.025 0.025
173.769 −3.960 −12.323 8.561 7.252 7.278 2.537 −2.941 −2.460 −10.031 −7.846 −5.763 5.765 12.674 −10.094 −8.990 −9.390 −6.755 −10.498
0.000 0.000 0.000 0.000 0.000 0.000 0.013 0.004 0.016 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
in the late exponential phase of growth and decreases thereafter [14]. Varying optimal fermentation periods in the range of 24 h to 6 days have been observed for B. subtilis [12,14,34]. An increase in PG activity was observed as pH was increased to about 7.0, but a slight decrease occurred beyond pH 8 (Fig. 1c, g, i, and j). A similar positive effect of pH was reported by others [20]. The high pH values account typically for PG and pectate lyase production [14].
The fermentation temperature exerted a profound effect on the PG production, giving high enzyme yields around 30–35 ◦ C and decreasing at higher ends (Fig. 1d, f, and h). Similar mild temperatures were reported elsewhere [12,14]. The influence of temperature is associated with the growth of the organism [35]. It was observed that PG activity increased when K2 HPO4 increased and temperature decreased (Fig. 1d). An increase in yeast extract concentration led to an increase in the PG activity but this increase was limited by the time variable (Fig. 1e). Yeast extract contains vitamins, minerals and amino acids, which are necessary for bacterial growth and enzyme production [36]. Fig. 1f–j shows that as the levels of the variables increased, the PG activity increased nonlinearly. Fig. 1f shows that the PG activity initially increased and then decreased with increase in concentration of yeast extract at the constant temperature. A similar nonlinear effect of yeast extract and pH on the PG activity was observed in Fig. 1g. The nonlinearity for different combinations of temperature, time and pH also existed in Fig. 1h–j. The high temperature (35 ◦ C) increases the solubility and diffusivity of the proteins and no protein loss is indicated through thermal denaturation [37]. The enzyme activity decreased gradually as the temperature was raised to 40 ◦ C. Thus, we can conclude that the PG production was affected significantly (P < 0.05) by all factors and their interactions, except for time vs. K2 HPO4 and the quadratic term for K2 HPO4 . To determine the optimal PG production conditions, the response optimizer tool in MINITAB® 16.0 was used. The optimum conditions giving the maximal PG activity (5.60 U mL−1 ) were identified as 0.5% (w/v) yeast extract, pH 7.0, 72 h of fermentation
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time, 30 ◦ C of temperature and 0.02% (w/v) K2 HPO4 . Thus, a 2.7fold (or 165%) increase in the pectinase production of B. subtilis was achieved in the shake flasks by PB and RSM optimization compared to the unoptimized culture conditions. A 3.4-fold increase in the PG production by RSM optimization using B. subtilis RCK was reported by others [16]. A 48% increase in enzyme activity of Aspergillus sojae ATCC 20235 was also reported after optimizing the culture conditions by RSM [15]. Similarly, a 1.5-fold increase in pectinase secretion of Kluyveromyces wickerhamii by RSM was reported in the study of Moyo et al. [38]. Soares et al. [39] selected six strains of Bacillus sp. as good PG producers, whose activities varied from 0.3 to 4.0 U mL−1 . Galiotou-Panayotou and Kapantai [40] achieved 3.0 U mL−1 of PG activity by submerged fermentation of A. niger in a medium composed of pectin from citrus. Comparing the results of our work with those reported in the literature, we can conclude that promising pectinase activity was achieved in this study. The slight variation in the enzyme activity is inevitable due to the differences in the nature of the organism cultivated, varying culture conditions tested in the PBD and RSM, and raw material used as carbon or nitrogen sources. 3.3. Validation of the RSM model To validate the model, additional runs were carried out for pectinase production at the optimal conditions predicted by the RSM. The results indicated that the experimental PG activity of 4.84 U mL−1 was slightly lower than the predicted value of 5.60 U mL−1 . However, a low value of coefficient of variation, determined to be 10.6%, satisfies the adequacy of the model. 4. Conclusions Hazelnut shells, inexpensive agro-residues, were shown to serve as an appropriate source for production of PG using B. subtilis after optimizing the submerged fermentation medium composition and conditions by the PB and RSM tools. A 2.7-fold enhancement in the enzyme production was achieved compared to the unoptimized fermentation conditions. B. subtilis produced the PG enzyme at promising level at neutral pH; hence, it can be used to increase the extraction yield of banana, carrot, grape, or apple juice. Further research is being carried out in our laboratory to apply the crude enzyme to the clarification of carrot juice as model vegetable juice. Acknowledgements Funding for this study was provided by the METU Scientific Research Council (project number is BAP-03-14-2011-002) and the Graduate School of Natural and Applied Sciences at the Middle East Technical University, Turkey. References [1] R.S. Jayani, S. Saxena, R. Gupta, Process Biochem. 40 (2005) 2931–2944.
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[2] W.C. Lee, S. Yusof, N.S.A. Hamid, B.S. Baharin, J. Food Eng. 73 (2006) 55–63. [3] H.N. Sin, S. Yusof, N.S.A. Hamid, R.A. Rahman, J. Food Eng. 73 (2006) 313–319. [4] I.G. Sandri, R.C. Fontana, D.M. Barfknecht, M.M. Da Silveira, Food Sci. Technol. 44 (2011) 2217–2222. [5] S. Ahlawat, R.P. Mandhan, S.S. Dhiman, R. Kumar, J. Sharma, Appl. Biochem. Biotechnol. 149 (2008) 287–293. [6] D.C. Sharma, T. Satyanarayana, Enzyme Res. (2012), http://dx.doi.org/10.1155/ 2012/281384. [7] L. Najafian, A. Ghodsvali, M.H.H. Khodaparast, L.L. Diosady, Food Res. Int. 42 (2009) 171–175. [8] D.R. Kashyap, P.K. Vohra, S. Chopra, R. Tewari, Bioresour. Technol. 77 (2001) 215–227. [9] P.S. Murthy, M.M. Naidu, Eur. J. Appl. Sci. 3 (2011) 130–139. [10] A.M.M. Combo, M. Aguedo, D. Goffin, B. Wathelet, M. Paquot, Food Bioprod. Process. 90 (2012) 588–596. [11] M.M. Sabajanes, R. Yanez, J.L. Alonso, J.C. Parajo, Int. J. Food Sci. Technol. 47 (2012) 747–754. [12] S. Ahlawat, S.S. Dhiman, B. Battan, R.P. Mandhan, J. Sharma, Process Biochem. 44 (2009) 521–526. [13] K. Mojsov, Appl. Technol. Innov. 2 (2010) 40–46. [14] M. Joshi, M. Nerurkar, R. Adivarekar, Innov. Roman. Food Biotechnol. 12 (2013) 75–83. [15] F.I. Ustok, C. Tari, N. Gogus, J. Biotechnol. 127 (2007) 322–334. [16] S. Gupta, M. Kapoor, K.K. Sharma, L.M. Nair, R.C. Kuhad, Bioresour. Technol. 99 (2008) 937–945. [17] D.B. Pedrolli, A.C. Monteiro, E. Gomes, E.C. Carmona, Open Biotechnol. J. 3 (2009) 9–18. [18] V. Rangarajan, M. Rajasekharan, R. Ravichandran, K. Sriganesh, V. Vaitheeswaran, Int. J. Biotechnol. Biochem. 6 (2010) 445–453. [19] J. Zeni, K. Cence, C.E. Grando, L. Tiggermann, R. Colet, L.A. Lerin, R.L. Cansian, G. Toniazzo, D. De Oliveira, E. Valduga, Appl. Biochem. Biotechnol. 163 (2011) 383–392. [20] D.C. Sharma, T. Satyanarayana, Bioresour. Technol. 97 (2006) 727–733. – ˇ ´ R.M. Skrinjar, [21] D.M. Periˇcin, S.Z. Madarev, L.M. Radulovic, Proc. Natl. Sci. Matica Srpska Novi Sad 113 (2007) 313–320. [22] E. Namasivayam, J.D. Ravindar, K. Mariappan, A. Jiji, M. Kumar, R.L. Jayaraj, J. Bioanal. Biomed. 3 (2011) 070–075. [23] Y. Xu, E.N. Sismour, J. Parry, M.A. Hanna, H. Li, Int. J. Food Sci. Technol. 47 (2012) 940–946. [24] S. Uzuner, D. Cekmecelioglu, Int. Proc. Chem. Biol. Environ. Eng. 50 (2013) 30–33. [25] A. Demirbas, Energy Sources 24 (2002) 891–897. [26] M. Dogru, C.R. Howarth, G. Akay, B. Keskinler, A.A. Malik, Energy 27 (2002) 415–427. [27] S. Uzuner, D. Cekmecelioglu, Int. J. Food Eng. (2014), http://dx.doi.org/10.1515/ ijfe-2014-0158.2014. [28] AOAC, Official Methods of Analysis, Association of Official Analytical Chemists, Washington, DC, 1984. [29] A. Hara, N.S. Radin, Anal. Biochem. 90 (1978) 420–426. [30] B.L. Browning, Methods of Wood Chemistry: Determination of Sugars, InterScience, New York, 1967, pp. 589–590. [31] TAPPI Test Method T222 om-88, Acid-Insoluble Lignin in Wood and Pulp, in: Tappi Test Methods, Technical Association of the Pulp and Paper Industry, Atlanta, GA, 1989. [32] G.L. Miller, Anal. Chem. 31 (1959) 426–428. [33] M. Kapoor, Q.K. Beg, B. Bhushan, K.S. Dadhich, G.S. Hoondal, Process Biochem. 36 (2000) 467–473. [34] G.E.P. Box, D.W. Behnken, Technometrics 2 (1960) 455–475. [35] M.R. Swain, S. Kar, R.C. Ray, Braz. J. Microbiol. 40 (2009) 636–648. [36] H.U. Rehman, S.A. Qader, A. Aman, Carbohydr. Polym. 90 (2012) 387–391. [37] L.R. Castilho, R.A. Medronho, T.L.M. Alves, Bioresour. Technol. 71 (2000) 45–50. [38] S. Moyo, B.A. Gashe, E.K. Collison, S. Mpuchane, Int. J. Food Microbiol. 15 (2003) 87–100. [39] M.M.C.N. Soares, R. da Silva, E.C. Carmona, E. Gomes, Annu. Rev. Microbiol. 30 (1999) 299–303. [40] M.P.R. Galiotou-Panayotou, M. Kapantai, Lett. Appl. Microbiol. 17 (1993) 145–148.