Multiple Parameter Optimization for Maximization of Pectinase Production by Rhizopus sp. C4 under Solid State Fermentation

Abstract

A novel strain Rhizopus sp. C4 was isolated from compost for the production of pectinase. Cultivation of Rhizopus sp. C4 on orange peel substrate under various solid-state fermentation (SSF) conditions was evaluated for pectinase yield along with the enzyme activity profile as a potential, low-cost alternative to submerged-liquid fermentation. Response surface methodology (RSM) was employed to optimize various environmental parameters for pectinase production. Various parameters, namely temperature, moisture and incubation days, were studied statistically for a total of 20 runs using central composite design. The highest yield of the enzyme, i.e., 11.63 IU/mL, was obtained from 1:3.5 moisture ratios in 7 days at 30°C. The study demonstrated that optimization through RSM could improve the enzymatic characteristics and yield of the enzyme.

1. Introduction

Annually, about 34 million tons of citrus residues (peel, seeds) are produced from the processing of citrus fruits [1,2,3]. Although a part of these residues is applied in the diet of monogastric animals, its accumulation in the biosphere causes several environmental problems. At the same time, citrus peel can be an important and economical raw material to produce enzymes, mainly pectinase, by fermentative processes. This material contains almost all nutrients needed for microorganism growth, and several processes have been studied for the production of many important metabolites including enzymes [4]. Pectinases are enzymes that catalyze the hydrolysis of pectin (polygalacturonic acid) to galacturonic acid residues with extensive applications in extraction, clarification, and cloud stabilization of fruit juices, in degumming and retting of natural fibers. Pectinolytic enzymes that hydrolyze pectins find applications in various industrial processes. These enzymes are extensively used in the food and drink industries, chiefly in juice clarification because they are capable of reducing the viscosity of liquors during the clarification process [5,6].

The production of PG by SSF employing agro-industrial residues and different microorganisms has been previously studied [7,8,9,10,11,12,13]. In SSF, it is necessary to consider that the produced metabolites remain in a solid matrix and, therefore, must be extracted by solid-liquid extraction or leaching. Therefore, this is the first step in any recovery and purification process that is intended for any desirable metabolite produced by SSF [14,15].

To reduce the cost of production, orange peel (pectic waste) as a substrate is used instead of synthetic cellulose due to their reasonable cost, high enzyme production capacity etc. The reduction in cost leads to an economically easy methodfor pectinase production. It is an important issue to deal with the residue, both for the comprehensive utilization of pectic resources and for the prevention of environmental pollution.

The conventional method for optimizing enzyme production via a“one variable at a time” approach involves varying a single independent variable while maintaining the others at a constant level. This one-dimensional approach is laborious and time-consuming especially for a large number of variables, and does not consider interactions among variables [16]. An alternative and more efficient approach is the use of statistical methods. Response surface methodology (RSM) involves afull factorial search by examining simultaneous, systematic and efficient variation of all components [17]. Statistical optimization not only allows forquick screening of alarge experimental domain, but also reflects the role of each of the components. RSM has already been successfully applied for the optimization of the media and the culture conditions in cultivation processes for the production of primary and secondary metabolites, i.e., amino acids, ethanol and enzymes [18].

In view of the potential industrial applications of pectinases, the present investigation was carried out to enhance the production of a pectinase from Rhizopus sp. C4 using citrus waste as acarbon source under SSF. Central composite design and RSM were employed to identify critical variables and optimize them for maximizing enzyme production in submerged fermentation.

2. Materials and Methods

2.1. Substrates and Chemicals

Various agro-waste residue of orange peels were purchased from the market of Nauni, Solan, Himachal Pradesh, India. Substrates were dried in the hot air oven at 60 °C up to 48 h, ground to fine powder, sieved and kept in sterile containers until used. All chemicals used in this study were of analytical reagent (AR) grade and purchased from Sigma, Hi-Media Limited, SRL Pvt. Limited and Merck India Limited (Mumbai, India).

2.2. Source of Fungal Inoculum

A pectinase producing Rhizopus sp. C4 was isolated from pectic rich compost on Malt extract agar and the culture was maintained at 4 °C.

2.3. Solid State Fermentation

Dried citrus peel (75% of the peel had a particle size ranging from 0.8 mm to 2 mm, and the other 25% had a particle size between 2 mm and 3 mm) was used and supplemented with the following substances in dry basis: NH₄NO₃ 0.43%; Na₂SO₄ 0.021%; MgSO₄ 7H₂O 0.077%; ZnSO₄ 7H₂O 0.042%; KCl 0.162%; and Ca(OH)₂ 0.011%. Water was added to obtain 60% of initial humidity. Inoculation was made in a 1:10 (v/w) ratio in 10 g of wet solid media. Fermentations were carried out in a 250 mL wide-necked Erlenmeyer flask [19]. The cultures were incubated without agitation. All experiments were completed in triplicate.

2.4. Polygalacturonase (PG) Activity

PG was assayed by measuring the release of reducing sugars fromthe di-nitro-salicylic (DNS) method [20]. In this case, galacturonic acid (Sigma) was considered asimple sugar formed after pectin degradation. In a test tube, 0.9 mL of 0.5% (w/v) pectin (Sigma) in 0.1 M citrate buffer (pH 4.0) was added to 0.1 mL of the diluted enzyme solution. After incubation for 15 min at 50°C, the reaction was stopped by the addition of 1 mL DNS, and the mixture was heated in boiling water for 5 min. Distilled water (5 mL) was then added to each sample. Absorbances of the samples were read at 540 nm. One unit of PG activity was defined as the amount of enzyme that liberates 1 μmol of d-galacturonic acid per minute at 50°C and pH 4.0 [19].

2.5. Experimental Design for Optimization

RSM consists of a group of empirical techniques devoted to the evaluation of relations existing between a cluster of controlled experimental factors and the measured responses, according to one or more selected criteria. Prior knowledge and understanding of the process variables under investigation is necessary to achieve a realistic model. A three-factor CCD consisting of 20 experimental runs with three replications at the central point (

Table 1. Levels of factors chosen for the experimental design.

Factors	Unit	Symbols	Actual Level Coded Factor
			−1	1
Temperature	°C	A	20	40
Moisture	%	B	1:1	1:5
Incubation Time	Days	C	4	12

) was used to optimize the independent variables, i.e., temperature, moisture and incubation days. A 20-factorial design was used in order to study the effect of temperature, moisture and incubation days. The statistical analysis of the results was performed using Design Expert^® version 7.0 statistical software (Stat-Ease Inc, Minneapolis, MN, USA). Pectinase activity was analyzed using the analysis of variance (ANOVA) combined with the Fischer test to evaluate if a given term has a significant effect (p ≤ 0.05). The optimum levels of the variables were obtained by graphical and numerical analysis using Design Expert program.

2.6. Model Validation

The mathematical model generated during RSM implementation was validated by conducting check point studies. The experimentally obtained data were compared with the predicted resultand the prediction error was calculated.

3. Results and Discussion

3.1. Cultural Characteristics of Fungal Isolate

C4 showed the aseptate whitish mycelium in conidia were rough globular and tightly packed [20]. The organism was identified as Rhizopus sp. (Zygomycota: mucormycotina) based on its morphological and physiological characteristics. Figure 1 shows the microscopic examination of isolate C4 under animaging compound microscope.

3.2. Quantitative Estimation of Pectinase

The quantitative estimation of pectinase was done by growing the culture of Rhizopus sp. C4 in media containing 1% pectin pectin (Sigma-Aldrich Co., St. Louis, MO, USA) using the standard protocol fromMiller [21] and itwas found to produce 4.12 IU/mL of enzyme. Pectinase production from Rhizopus sp. C4 was further optimized using thestatistical method.

3.3. Optimization of Culture Conditions Using Response Surface Methodology

The applied optimization approach is based on a central composite design and RSM. This method is one of the most important experimental designs used in process optimization studies [22]. The characterization of different factors for pectinase production was optimized by applying RSM. The statistical model was obtained using Central Composite Design (CCD) with three independent variables (i.e., temperature, moisture ratio, incubation days). A set of 20 experiments was performed. Pectinase production by Rhizopus sp. C4 is presented along with the predicted and observed responses in

Table 2. Central composite design for the experimental design and predicted results for pectinase activity.

Run	A:Temperature	B:moisture	C:Incubation Time	Enzyme Units (IU/mL)
				Experimental Values	Predicted Value
1	40.00	1:5.00	10.00	2.90	2.62
2	30.00	1: 3.50	7.00	11.63	11.61
3	30.00	1:0.98	7.00	3.40	3.43
4	30.00	1:3.50	12.05	1.93	2.60
5	20.00	1:2.00	10.00	2.11	2.00
6	40.00	1:2.00	4.00	3.50	3.75
7	30.00	1:3.50	7.00	11.63	11.61
8	13.18	1:3.50	7.00	2.33	2.43
9	30.00	1:3.50	7.00	11.63	11.61
10	30.00	1:6.02	7.00	5.07	5.60
11	20.00	1:5.00	10.00	5.81	5.16
12	40.00	1:2.00	10.00	0.90	0.47
13	40.00	1:5.00	4.00	3.49	3.18
14	20.00	1:2.00	4.00	2.11	2.02
15	30.00	1:3.50	7.00	11.63	11.61
16	46.82	1:3.50	7.00	1.70	1.96
17	30.00	1:3.50	7.00	11.63	11.61
18	20.00	1:5.00	4.00	2.40	2.43
19	30.00	1:3.50	1.95	3.17	3.06

.

Solid-state fermentation has been advocated assuperior tosubmerged culture for the processing of agricultural and agro-based industrial wastes for generating higher enzyme yield and regulating the biosynthesis of particular composition of enzyme mixtures [23]. Solid state fermentation (SSF) is generally preferred because highly concentrated crude enzymes are obtained at low costs [24,25]. BesideSmF, SSF offers distinct advantages including economy of space needed for fermentation, simplicity of fermentation media, andno requirement for complex machinery, equipment orcontrol systems. Since the culture conditions in SSF are much more similar to the natural habitat of filamentous fungi, these are able to grow well and excrete large quantities of enzymes. Additionally, these processes are of special interest for countries like India with an abundance of agro-industrial wastes which can be used as cheap raw materials and it allows the utilization of wastes to produce useful products [26].

3.4. Validation of the Model

Moisture content is a crucial factor in any SSF process because this variable influences growth and biosynthesis of the microbe, as well as secretion of different metabolites such as enzymes. Low or high water content beyond its optimal content may affect enzyme production [27]. Optimization of moisture content could be employed for the management and the modification of microorganism metabolic processes [28]. In any SSF method, a vital factor is the incubation time because it has a largeeffect on growth and biosynthesis of the fungus, as well as the production of various primary and secondary metabolites, such as enzymes [29,30]. For optimum production of various enzymes by fungus, the temperatures optima of 25 to 37 °C range have been reported [31,32].

ANOVA for the refined model is summarized in

Table 3. Analysis of variance (ANOVA) for theresponse surface quadratic model obtained from experimental designs.

Source	Sum of Squares	DF	Mean Value	F Square	Prob> F
Model	300.42	9	33.38	170.45	<0.0001 *
A	0.53	1	0.53	2.72	0.1332
B	5.66	1	5.66	28.88	0.0004 *
C	0.25	1	0.25	1.30	0.2835
A2	148.07	1	148.07	756.08	<0.0001 *
B2	85.90	1	85.90	438.65	<0.0001 *
C2	131.55	1	131.55	671.73	<0.0001 *
AB	0.50	1	0.50	2.55	0.1445
AC	5.44	1	5.44	27.80	0.0005 *
BC	3.67	1	3.67	18.75	0.0019 *
Residual	1.76	9	0.20
Lack of Fit	1.76	5	0.35
Pure Error	0.000	4	0.000
Cor Total	302.18	18

* Significant at p > 0.05, Predicted R² = 0.9545, Adjusted R² = 0.9883.

. The regression model for pectinase production was highly significant (p < 0.01) with a satisfactory value of determination co-efficient (R² = 0.9883) indicating 98.83% variability in the response could be explained by second order polynomial equation given below.

The fitted equation (in terms of coded values) for pectinase production (Y) is expressed as:

Y = +11.61 − 0.20A + 0.64B − 0.14C − 3.29A² − 2.51B² − 3.10C² − 0.25AB − 0.82AC + 0.68BC

where Y is enzyme production, A is temperature, B is moisture ratio and C is incubation time (days). The R² value is always between 0 and 1. The closer the R² is to 1.0, the stronger the model and better it predicts the response [33].

The justification of the variability in observed response values by the experimental factors (variables) and their interactions was measured by R² (coefficient of determination). The predicted R² value of 0.9545 by the model in close agreement with actual R² value 0.9883 implied that the fitted linear, interaction and quadratic terms could elucidate 98.83% of variation, showing satisfactory representation of the process model. The model F-value and probality value (P_model> F) of 170.45 and <0.0001 respectively for pectinase production indicate that the applied model is highly significant. Values of Prob>F less than 0.05 indicate model terms are significant. For pectinase production B, A², B², C², AC, BC are significant models. The precisionand reliability of the conducted experiments were confirmed by the lower value of coefficient of variation (CV) of 8.50. The measured signal-to-noise ratio indicated adequate precision. A desirable ratio should be greaterthan 4. The model ratio of 34.689 was found to be greater than 4, indicating an adequate signal that can be used for design space navigation. The standard deviation value 0.44 indicated that the model showed strong compliance with the predicted response.This model can be used to navigate the design space. The optimum experimental conditions were obtained by the differentiation of the quadratic model to achieve maximum enzyme production. The optimum conditions were A = 30 °C, B = 1:3.5 and C = 7 days. The predicted enzyme production corresponding to these levels was 11.63 IU/mL. A 3-fold increase in the enzyme activity was observed after optimizing the factors using RSM and it was found tobea valid model for the optimization processes.

Similar to our results, a 1.5-fold increase in pectinolytic enzymes secretion by Kluyveromyces wickerhamii was attained when pH, temperature and incubation period were optimized by RSM [34]. A 70% increase in a-amylase production by Geobacillusthermoleovorans was achieved using RSM [35].

Comparative effects of any two variables were explained by contour plots while holding the other factors fixed at their central point values. From response surface (3D) and contour plots (2D), the interactive effects of experimental factors on pectinase synthesis were determined. Figure 2 shows athree-dimensional diagram and contour plot of calculated response surface from the interaction between various variables.

Temperature, moisture content and incubation days showed quadratic effects on the production of pectinase. The interaction effects of three independent variables were significant through interaction of all of these three factors for pectinase production. The changes brought about by physical characteristics are an important design tool for process development of the SSF. By changing the physical attributes, the expression level of theenzyme can be varied such that physiochemical characteristics become the reading force in directing synthesis of pectinase enzyme system in SSF. Figure 3 shows thenormal probability withinternally studentized residuals for Rhizopus sp. C4.

4. Conclusions

Rhizopus sp. C4 is a promising organism for production of pectinase, and statistical design like RSM can be used for optimization of process parameters and their interactions. The research results indicated that RSM not only helps us locate the optimum conditions of the process variables in order to enhance the maximum pectinase enzyme production, but also proves to be well suited to evaluating the main effectsand interaction effects of the process variables on pectinase production from waste agricultural residues. Citrus waste was found tobea promising waste for pectinase production, whichhelps in decreasing both pollution and the cost of enzyme production.