An Improved Method of Optimizing the Extraction of Polyphenol Oxidase from Potato ( Solanum tuberosum L.) Peel
Suprabhat MUKHERJEE
1*,Bidyut BANDYOPADHAYAY
2,Bikram BASAK
3,Nilrudra MANDAL
1,Apurba DEY
3,Biswanath MONDAL
11CSIR-Central Mechanical Engineering Research Institute, Centre for Advanced Materials Processing, Durgapur-713209, India; [email protected] (*corresponding author)
2Oriental Institute of Science and Technology, Department of Biotechnology, Burdwan, India
3National Institute of Technology, Department of Biotechnology, Durgapur, Mahatma Gandhi Avenue, Durgapur-713209, India
Abstract
The present study has an objective to optimize the extraction of Polyphenol Oxidase (PPO) from potato (Solanum tuberosum L.) peel. Response surface methodology (RSM) was used to design experiments and study the effect of six influential extraction parameters: extraction buffer concentration (100-500 mM), pH of extraction buffer (4.5-8.5), time (1-12 hours), temperature (4-40°C), concentration of PMSF (1-5 mM) and volume of extraction buffer (200-1000 ml) on the extraction of PPO. The dependent variable was considered as response function which was specific activity (SA) of extracted PPO. ANOVA was performed to obtain the regression equation that could predict the responses within given range. From RSM generated model, the optimum conditions for the maximum extraction of PPO were phosphate buffer concentration of 100 mm, buffer pHof 4.5, extraction time of 1 hour, 40°C temperature, PMSF concentration of 5 mM and buffer volume of 200 ml. Finally, this study illustrates a cost effective and less time consuming method to maximize the extraction of PPO from a vegetable waste.
Keywords: ANOVA, PPO regression equation, RSM, specific activity
List of abbreviation: ANOVA: Analysis of varience; BC: Buffer concentration; C.V.: Coefficient of variation; d.f.: Degrees of freedom;
EA: Enzyme Activity; ET: Extraction time; ETM: Extraction temperature; F: Fisher variance ratio; p: Probability; PB: pH of extraction buffer; PC: PMSF concentration; PPO:Polyphenol oxidase; RSM: Response Surface Methodology; PMSF: Phenyl Methyl Sulfonyl Fluoride; R:Coefficient of variation; R2: Coefficient of determination; SA: Specific Activity; VB: Volume of extraction buffer
Introduction
PPO is a copper-containing enzyme that catalyses both molecular oxygen-dependent hydroxylation of monophe- nols to their corresponding o-diphenols and oxidation of o-diphenols such as L-DOPA to their cognate o-quinones (Ni Eidhin et al., 2010; Palma-Orozco et al., 2011). Im- portant properties of PPO viz. wider substrate specificity, ability of catalyzing reaction in wider range of pH and temperature (Seo et al., 2003), protease activity (Gomez- Lopez, 2002) etc have been utilized for various purposes.
The ability of oxidizing large group of phenolic com- pounds has been utilized in removal of phenolic contami- nants from waste water, effluents and contaminated soil (Klibanov et al., 1980; López-Molina et al., 2003; Torres et al., 2003). The same property of PPO has also been uti- lized for removal of reactive textile dyes. Researchers have reported the use of both soluble and immobilized PPO in the biotransformation of phenolic contaminants but later provide better performance in terms of reusability and ca- talysis (Amjad et al., 2009; Duran et al., 2002; Niladevi and Prema, 2008). Immobilization of PPO onto porous
and conducting surface has led to the development of bio- sensor for monitoring of aqueous phenolic components.
Construction of biosensor using immobilized PPO on Carbon Nanotube (Mohammadi et al., 2009) and calcium carbonate nanoparticles (Shan et al., 2007) are the better examples of this fact. Since the enzymatic browning by PPO causes decrease of nutritional quality and affects the appearance of food, inactivation of PPO is desirable for preservation of foods (Langdon, 1987; Lee et al., 2007).
All these mentioned purposes require PPO either in crude or purified form. Therefore the need, demand and market value of PPO in its different fields of application is quite high. However, high production cost has limited the feasibility of its uses in industries. The cost of the sources of PPO is also a matter of concern. Keeping in view of the usefulness and cost effectiveness of PPO for various indus- trial purposes, it is very important to develop some ana- lytical methods for the extraction of this enzyme in such a way that will be of high yield and cost effective. Usually, the method for determining optimal conditions in extrac- tion processes is varying one parameter while keeping oth- ers at a constant level. This is a time consuming and cost Received 15 November 2011; accepted 29 January 2012
periments were designed using central composite design (CCD) with a quadratic model in order to study the com- bined and individual effects of six influential experimen- tal parameters on the extraction of PPO. These variables were A: concentration of the extraction buffer (BC), B:
pH of extraction Buffer (PB), C: extraction time (ET), D:
extraction temperature (ETM), E: PMSF concentration in extraction buffer (PC) and F: volume of extraction buf- fer (VB). Each parameter had two levels which were-1 and +1, shown in Tab. 1. A total of 74 sets of experiments were performed to determine significant factors for the extrac- tion of PPO.
Specific activity (SA) of extracted PPO was considered as the only dependent variable or response function. As the objective of this study was to optimize the yield of PPO in the extract, the response, SA has been considered as key factor because optimization of SA will ensure maximum activity of PPO per mg of protein in the extract. Relation- ships between six parameters (BC, PB, ET, ETM, PC and VB) and process responses (SA) for the extraction of PPO were analyzed using RSM. All the experimental condi- tions and value of the corresponding response studied are depicted in Tab. 2.
Extraction of PPO
One hundred g of potato peel was blended with phos- phate buffer of different concentration and pH under dif- ferent experimental conditions based on the combinations programmed by RSM (as per Tab. 2). 1% Triton X-114 was also added to the extraction medium. The extraction was performed under continuously stirring using mag- netic stirrer. After extraction each mixture was centrifuged at 18,000 rpm for 20 minutes using a compufuge (Remi, India) and the supernatant was filtered through Whatman no. 4 filter paper. The filtrate was taken as crude enzyme extract and it was stored at-20°C.
Assay of PPO
PPO activity was assayed using the procedure of Oktay et al. (1995) with some modification. Briefly, 0.1 ml en- ineffective method that does not include interaction ef-
fects among variables (Ranjan et al., 2009). Optimization employing central composite design (CCD) and response surface methodology (RSM) can overcome such draw- backs (Bas and Boyaci, 2007; Vohra and Satyanarayana, 2002). Researchers have reported the utility of CCD in optimization of experimental determinants (Ebrahimpour et al., 2008; Hameed et al., 2009) and RSM for process development as it provides all the information regarding the combinatorial effects of variables, regression modeling and optimization of the variables to maximize the desired product (Chen et al., 2002; Gaur et al., 2008; Manohar and Divakar, 2004; Sztajer et al., 1988).
Although considerable research works have been car- ried out on extraction, purification and characterization of PPO from various fruits and vegetables (Aydemir, 2010;
Marri et al., 2003; Sener and Ümit Ünal, 2011; Yang et al., 2001). There was no evidence on the improvement of extraction of PPO from potato peel through statistical optimization. In this paper, RSM was conducted to study the effects of different influential extraction parameters to maximize the yield of PPO from potato peels. The opti- mum extraction conditions (environmental, process and solution parameters) were predicted and validated using statistical methodologies.
Materials and methods Plant materials
Potato (Solanum tuberosum) peels were collected from the hotels near National Institute of Technology, Dur- gapur. These peels were washed several times with double distilled water and used for study. The reasons behind the selection of potato peels as experimental material were firstly, researchers have reported the presence of PPO in potato(Do-Yoon and Woo-Yean, 1996; Thygesen et al., 1995). Secondly, potato tuber is mainly used as the source of dietary carbohydrate whereas peels are mainly consid- ered as waste and easily available. Therefore it is an eco- nomic source of PPO.
Chemicals
Di-sodium hydrogen phosphate (Na2HPO4), Sodium di-hydrogen phosphate (NaH2PO4), Sodium Hydroxide (NaOH) and Catechol (Pyrocatechol) were purchased from Merck (Germany). Triton X-114 (Sigma-Aldrich, USA) and PMSF (Sisco research laboratory, India) were used in this study. All the chemicals used were of analytical grade commercially available in India.
Development of suitable design matrix
RSM was employed to find the optimum experimental condition with definite values of key experimental deter- minants for the maximum yield of PPO in the potato peel extract. The experimental design and statistical analysis was performed using design expert software. All the ex-
Tab. 1. Independent variables and their coded levels used in RSM studies
Level Factors Units Low (-1) High
(1) A: Buffer Concentration of
Extraction Buffer (BC) mM 100 500
B: Buffer Concentration of
Extraction Buffer (BC) 4.5 8.5
C: Extraction Time (ET) Hours 1 12 D: Extraction Temperature (ETM) ºC 4 40
E: PMSF Concentration (PC) mM 1 5
F: Volume of Extraction Buffer (VB) ml 200 1000
Tab. 2. CCD design for six variables showing observed values of SA of PPO
Run A: BC (mM) B: PB C: ET (Hour) D: ETM (°C) E: PC (mM) F: VB (ml) Response:
SA Unit/mg protein
1 300 6.5 6.5 22 3 600 3235
2 300 6.5 6.5 22 3 600 3235
3 500 4.5 12 4 5 200 3105
4 500 4.5 12 40 1 1000 3112
5 100 4.5 1 4 5 200 3383
6 100 4.5 12 4 1 200 3309
7 500 8.5 12 4 5 1000 3007
8 300 6.5 6.5 22 3 600 3235
9 500 4.5 1 40 5 200 3107
10 100 8.5 12 40 1 1000 3381
11 500 8.5 1 40 1 1000 3017
12 100 8.5 12 40 5 200 3511
13 500 8.5 1 40 1 200 3047
14 500 4.5 12 40 5 200 3171
15 500 8.5 12 4 5 200 3103
16 500 4.5 1 4 1 1000 3043
17 500 4.5 12 4 1 200 3103
18 500 8.5 1 4 5 200 3077
19 300 6.5 6.5 22 3 600 3235
20 100 8.5 1 4 5 200 3305
21 500 4.5 12 40 5 1000 3157
22 500 4.5 12 40 1 200 3142
23 500 8.5 12 4 1 200 3077
24 100 8.5 12 4 1 200 3304
25 500 8.5 12 40 1 200 3103
26 100 8.5 1 40 5 1000 3357
27 500 4.5 12 4 1 1000 3077
28 100 8.5 1 40 1 200 3307
29 100 8.5 1 40 5 200 3441
30 100 4.5 12 4 5 200 3422
31 300 6.5 6.5 22 3 600 3235
32 500 8.5 1 4 5 1000 3043
33 100 8.5 12 4 5 200 3357
34 100 4.5 1 40 5 200 3493
35 100 4.5 1 40 5 1000 3422
36 500 4.5 1 4 1 200 3077
37 100 8.5 1 4 1 200 3269
38 500 4.5 1 4 5 200 3103
39 500 8.5 1 4 1 1000 3000
40 300 6.5 6.5 22 3 600 3235
41 100 4.5 12 40 1 1000 3441
42 100 8.5 1 4 1 1000 3263
43 100 4.5 1 40 1 1000 3333
44 300 6.5 6.5 22 3 600 3235
45 100 4.5 1 4 5 1000 3306
46 500 8.5 12 4 1 1000 3043
47 500 4.5 1 40 1 200 3103
48 100 4.5 12 40 1 200 3478
49 500 8.5 12 40 5 1000 3142
50 500 8.5 12 40 1 1000 3103
51 500 4.5 12 4 5 1000 3103
and interaction terms, respectively, and Xi, and Xj are the independent variables. For coded independent variables (A, B, C, D, E and F), the selected polynomial equation could be expressed as:
Y = β0 + β1A + β2B + β3C + β4D + β5E + β6F + β12AB + β13AC + β14AD + β15AE + β16AF + β23BC + β34CD + β45DE + β56EF + β11A2 (2)
The design expert software was used to generate re- sponse surfaces and three dimensional (3D) plots. The adequacy and significance of the regression model was tested using ANOVA method. Test for significance on in- dividual model coefficients and test for lack-of-fit was also estimated.
Determination of optimum extraction and validation of the final model
Optimum condition for the possible maximum extrac- tion of PPO from potato peel depends on all the six pa- rameters were obtained using the predictive equation of RSM. The software design expert was applied to search the optimum desirability of the response which is maximum SA of PPO. The verification of the validity and adequacy of the predictive extraction model with respect to all the six variables within the design space was done by perform- ing a random set of 6 experimental combinations to study specific activity. Three verification run experiments were previously and remaining three experiments were those which have not been used but are within the range of the zyme extract was added to 2.9 ml of Catechol (100 mM)
in 0.1 M phosphate buffer (pH-6.5) solution and change in absorbance at 420 nm was measured using a dual beam UV-Visible spectrophotometer (UV 3600, Shimadzu, Ja- pan) against reference (3.0 ml catechol). Change in absor- bance was recorded every 1 second for 3 minutes. One unit of PPO activity was defined as the change in absorbance of 0.001 per minute per milliliter of enzyme. Finally, enzyme activity was expressed in units/ml. Activity measurements were carried out in triplicate.
Measurement of specific activity of PPO
Protein quantity was estimated from the supernatant of each extract by the method of Lowry et al. (1951) and protein quantity was expressed in mg/ml using bovine se- rum albumin as standard. Specific activity (SA) of PPO was expressed in Units/mg of protein.
Mathematical modeling
Analysis of variance (ANOVA) was performed for the independent and dependent values to obtain regression equations that could predict the responses within a given range. The generalized second order regression equation used in the response surface study was as follows:
∑ ∑ ∑
= = =
+ +
+
= 6
1 i
6 1 j i,
6 1 i
2i ii j i ij i i
0 βX β XX β X
β
Y (1)
Where Y is the predicted response, β0, βi, βii, and βij are the regression coefficients for intercept, linear, quadratic
Tab. 2. CCD design for six variables showing observed values of SA of PPO (cont.)
Run A: BC (mM) B: PB C: ET (Hour) D: ETM (°C) E: PC (mM) F: VB (ml) Response:
SA Unit/mg protein
52 100 8.5 12 4 5 1000 3307
53 100 8.5 1 40 1 1000 3305
54 500 4.5 1 4 5 1000 3077
55 300 6.5 6.5 22 3 600 3235
56 500 4.5 1 40 5 1000 3125
57 500 8.5 1 40 5 200 3142
58 100 8.5 12 40 5 1000 3461
59 500 4.5 1 40 1 1000 3103
60 500 8.5 1 40 5 1000 3103
61 500 8.5 12 40 5 200 3157
62 100 4.5 12 40 5 1000 3550
63 100 4.5 12 40 5 200 3616
64 300 6.5 6.5 22 3 600 3235
65 300 6.5 6.5 22 3 600 3235
66 500 8.5 1 4 1 200 3043
67 100 4.5 12 4 1 1000 3305
68 100 4.5 1 4 1 1000 3271
69 100 4.5 1 40 1 200 3381
70 100 4.5 12 4 5 1000 3381
71 100 8.5 12 4 1 1000 3273
72 100 8.5 1 4 5 1000 3273
73 100 4.5 1 4 1 200 3304
74 100 8.5 12 40 1 200 3401
ficient of variation (C.V %) is 0.59 and this lower value of C.V % designates a better reliability of the model (Khuri and Cornell, 1987). A corelation coefficient (R2) of 0.9861 was obtained indicating high degree of corelation between the experimental parameters and response (SA of PPO in Unit/mg of protein).
The experimental results of the CCD design were fit- ted with a second order polynomial equation. The Eq. 3 depicts the empirical relationship between specific activity of extracted PPO (SA) and the six independent variables in coded units obtained by applying RSM.
SA = 3232.09-140.55 × A-21.58 × B + 24.67 × C + 40.61 × D + 27.95 × E-16.52 × F + 5.92 × A × B-9.20 × A × C-17.08 × A × D-11.42 × A × E + 10.95 × C × D +
9.48 × D × E-4.70 × E × F (3)
While, the final empirical relationship between response:SA and the six independent process variables in actual units obtained by the applyication of RSM is given by Eq.4:
SA (U mg of protein-1) = 3395.26878-0.55455 × BC-15.23047 × PB + 4.56171 × ET + 2.16974 × ETM + 20.2743 × PC-0.023652 × VB + 0.014805 × BC × PB-0.00836648 × BC × ET-0.00474392 × BC × ETM- 0.028555 × BC × PC + 0.11064 × ET× ETM + 0.26345
× ETM × PC-0.00587891 × PC × VB (4) The normal probability described in Fig. 1. which shows some scatters along the line which indicates that the residuals follow a normal distribution. This designates that the model satisfies the assumptions of the ANOVA levels defined previously. The experimental and predictive
values of SA were compared to validate the model.
Results and discussion ANOVA analysis
As summarized in Tab. 3, the ANOVA analysis of response 1: SA, the model F-value of 328.96 implies the model is significant. There is only a 0.01% chance that a
“Model F-Value” could be large which may occur due to noise. Values of “Prob > F” less than 0.0500 indicate mod- el terms are significant. In this case A, B, C, D, E, F, AB, AC, AD, AE, CD, DE are significant model terms. The insignificant model terms can be eliminated to improve the model. In this study, backward elimination procedure was used to reduced the insignificant terms . The predicted R2 of 0.9775 is in reasonable agreement with the adjusted R2 of 0.9832. The adjusted R2 value corrects the R2 value for the sample size and for number of terms used in the model. The high adjusted R2 value (0.9832) obtained from ANOVA analysis indicating that the developed model is highly significant (Akhnazarova and Kafarov, 1982; Box et al., 1978). “Adeq Precision” measures the signal to noise ratio. A ratio greater than 4 is desirable. From this study, the obtained ratio of 65.547 indicates an adequate signal.
This model can be used to navigate the design space.
The model shows standard deviation (SD), mean, and predicted residual sum of squares (PRESS) value of 19.07, 3232.09 and 35513.75. Here, the calculated value of coef-
Tab. 3. ANOVA table for response surface quadratic model (Response: SA of PPO)
Source Sum of Squares df Mean Square F Value p-value
Prob > F
Model 1555515.953 13 119655.0733 328.9579291 < 0.0001 Significant
A-BC 1264219.141 1 1264219.141 3475.614522 < 0.0001
B-PB 29799.39063 1 29799.39063 81.92503299 < 0.0001
C-ET 38956.89063 1 38956.89063 107.1010005 < 0.0001
D-ETM 105543.7656 1 105543.7656 290.1628624 < 0.0001
E-PC 50008.14062 1 50008.14062 137.4833003 < 0.0001
F-VB 17457.01563 1 17457.01563 47.99314855 < 0.0001
AB 2244.390625 1 2244.390625 6.170320001 0.0158
AC 5420.640625 1 5420.640625 14.90252494 0.0003
AD 18666.39063 1 18666.39063 51.31798455 < 0.0001
AE 8349.390625 1 8349.390625 22.95429833 < 0.0001
CD 7678.140625 1 7678.140625 21.10888548 < 0.0001
DE 5757.015625 1 5757.015625 15.82729328 0.0002
EF 1415.640625 1 1415.640625 3.891905253 0.0531
Residual 21824.38471 60 363.7397452
Lack of Fit 21824.38471 51 427.929112
Pure Error 0 9 0
Cor Total 1577340.338 73
Std. Dev. 19.07196228 R-Squared 0.986163807
Mean 3232.094595 Adj R-Squared 0.983165966
C.V. % 0.590080572 Pred R-Squared 0.97748504
PRESS 35513.75454 Adeq Precision 65.54743972
was the most significant factor that contributed to the ex- traction of PPO and had the most pronounced quadratic effect.
The 3D response surface plots obtained from RSM study shows the interaction or combined effect of the vari- ables on SA. As presented in Fig. 3(A), sharp increase in SA was observed with the decrease of both BC and PB when other parameters were kept as constant. Similarly, the value of SA gradually increases with the increase of PC and decrease of VB (Fig. 3B). Therefore lowering of volume and concentration of extraction buffer (Phosphate buffer) facilitates the extraction of PPO as value of SA increases.
The reason may be that lowering of buffer concentration may facilitate the interaction between potato peel and extraction media whereas lowering volume of extraction buffer concentrates the enzyme. PMSF is serine protease which depicting the accuracy and applicability of RSM in
optimizing all six parameters to maximize the extraction of PPO.
Effect of experimental parameters on PPO extraction The perturbation plot describes the comparative effect of all the parameters at the midpoint (coded 0.00) in the design space shown in Fig. 2. The most influential factor is characterized by a steep slope or curvature in a perturba- tion plot which shows that the response is very sensitive to that factor. In this study, perturbation plot suggests that all variables exerted different degree of quadratic effects. But the curve with the most significant change was the per- turbation curve of variable A i.e. BC compared to those of the other factors fixed at their maximum levels. Thus, it is obvious that BC i.e. concentration of phosphate buffer
Fig.1. Normal plot of residuals for SA (Unit/ mg of protein)
Fig. 2. Perturbation plot of independent process variables
mational change in the 3D structure of PPO which may results to expose the active site of PPO. Though this fact needs experimental proof, researchers have reported the allosteric behavior of PPO (Ricquebourg et al., 1996) and it has also been reported that PPO activity increases with increase in acidity (Valero et al., 1992).
Extraction of PPO is greatly influenced by time and temperature (Fig. 3(C)) because increase of extraction time prolongs the interaction between potato peels ho- mogenate and extraction media. But, longer extraction inhibitor and increase concentration of this compound in
the extraction buffer positively influence SA as it prevents the proteolysis of PPO by irreversibly blocking the serine residue of protease present in its active site (Gauillard and Richard-Forguet, 1997; Staszczak et al., 2000).
Significant increase in SA is observed with the increase of both ET and ETM (Fig. 3(C)). From Fig. 3(A), it is evident that extraction of PPO is positively influenced by acidic pH as SA increases with the decrease with pH. It may be due to the fact that acidic pH may induce confor-
Fig. 3A. Three dimensional plots for the interaction effect of volume of ex- traction buffer (VB) and PMSF concentration (PC) on SA
Fig. 3B. Three dimensional plots for the interaction effect of extraction time (ET) and temperature (ETM) on SA
Fig. 3C. Three dimensional plots for the interaction effect of concentration of extraction buffer (BC) and pH of extraction buffer (PB) on SA
ity which designates the accuracy of the developed meth- od. It also describes that the developed model satisfy the variance requirement and these also reflect applicability and accuracy of RSM for improved extraction of PPO.
The developed model was further validated by perform- ing six additional experiments which constitutes three ex- perimental combinations from the design and remaining three experiments were those which have not been used previously. As summarized in Tab. 4, experimental values were reasonably close to the predicted values confirming the validity and adequacy of the proposed model. More- over, the validation experiments also proved that the pre- dicted values of SA could be satisfactorily achieved within 0.80% of predicted error of experimental values.
Optimization of extraction condition
In the present study, desirability function optimization of the RSM has been employed for the optimization of the extraction of PPO by means of the response; SA. The op- timization module searches a combination of factor levels that simultaneously satisfies the requirements placed on period increases process economics and also chances of
proteolysis. Rise in temperature increases SA of PPO by increasing the activity of PPO. So, the magnitude of all these parameters should be optimized to maximize the ex- traction of PPO.
Validation of developed model
Plot of experimental or actual value vs. predicted values of SA (given in Fig. 4) represents a high degree of similar- Tab. 4. Validation of the final reduced quadratic model
Parameters Run 1 Run 2 Run 3 Run 4 Run 5 Run 6
Buffer Concentration (BC) 50 200 400 450 550 600
pH of Buffer (PB) 4 5 5.5 7 9 9.5
Extraction time (ET) 0.5 2 4 6 8 13
Extraction temperature (ETM) 2 6 12 24 36 42
PMSF Concentration (PC) 0.5 1.5 2 4 5.5 6
Volume of Buffer (VB) 50 100 250 500 700 1100
Predicted error (%)a) 0.07 0.09 0.146 0.304 0.90 0.058
Specific activity (SA) in Units/mg of protein
Predicted 3323.5 3254.09 3139.19 3090.4 2978.26 2919
Actual 3320 3260 3130 3080 2970 2940
Predicted error (%)a) 0.10 0.18 0.29 0.33 0.26 0.72
Predicted error = (actual value-predicted value)× 100/predicted value
Tab.5. Constraints for optimization of extraction conditions Constraints
Name Goal Lower Limit UpperLimit
A:BC Minimize 100 500
B:PB Is in range 4.5 8.5
C:ET Minimize 1 12
D:ETM Is in range 4 40
E:PC Is in range 1 5
F:VB Minimize 200 1000
SA Maximize 3000 3616
Fig. 4. Plot of predicted versus actual values of response (SA in Units/mg of protein)
purification, characterization and immobilization of PPO from potato peel are currently under way.
Acknowledgements
The authors are grateful to Council of Scientific and Industrial Research (CSIR), Govt. of India for providing financial grant. The help and rendered by the scientific and technical staffs of Centre for Advanced Materials Process- ing, CSIR-CMERI, Durgapur is acknowledged.
References
Akhnazarova S, Kafarov V (1982). Experiment optimization in chemistry and chemical engineering. Mir Publishers, Moscow and Chicago 312 p.
Amjad AK, Suhail A, Qayyum H (2009). Direct immobilization of polyphenol oxidases on Celite 545 from ammonium sulphate fractioned proteins of potato (Solanum tuberosum).
J Mol Catal B-Enzym 40:58-63.
Aydemir T (2010). Selected kinetic properties of polyphenol Oxidase extracted from Rosmarinus officinalis L. Int J Food Prop 13:475-485.
Bas D, Boyaci IH (2007). Modeling and optimization I: Usability of response surface methodology. J Food Eng 78:836-845.
Box GEP, Hunter WG, Hunter JS (1978). Empirical Model Building and Response Surfaces, 291-334 p. In: Statistics for Experiments, Wiley, New York.
Chen CS, Liu KJ, Lou YH, Shieh CJ (2002). Optimization of kojic acid monolaurate synthesis PS from Pseudomonas cepacia. J Sci Food Agric 82:601-605.
Do-Yoon K, Woo-Yean K (1996). Purification of glycosylated polyphenol oxidase from potato. J Biochem Mol Biol 29:163-168.
Duran N, Rosa MA, D’Annibale A, Gianfreda L (2002).
Applications of laccases and tyrosinases (phenoloxidases) immobilized on different supports: a review. Enzyme Microb each of the responses and factors in an attempt to establish
the appropriate model. The aim of this optimization pro- cess was to find the optimum values of extraction param- eters in order to maximize the value of SA in the extract.
The constraints used during optimization process are sum- marized in Tab. 5. As one of the major aims of this study was to reduce process economics, the minimum level of two parameters viz. concentration and volume of extrac- tion media (phosphate buffer) were used. Minimum value of extraction time was also taken to make the method less time consuming. The optimum experimental conditions required for maximum extraction of PPO from of potato peels are phosphate buffer concentration of 100 mm, buf- fer pH of 4.5, extraction time of 1 hour, 40°C temperature, PMSF concentration of 5 mM and buffer volume of 200 ml. The desirability of this optimization model is 94.10%
(Tab. 6.) which is very much acceptable.
Conclusions
The CCD employed in this study proved to be an ef- fective tool for the optimization of six influential process parameters to maximize the extraction of PPO. The best models were achieved by modified response surface model using backward elimination and these models provide good quality predictions for the six independent variables in terms of the extraction of PPO. The results of the pres- ent studies revealed that several factors influence the ex- traction of PPO and its activity. From the response surface analysis, BC had the most significant effect on SA among the six parameters studied. The results of ANOVA analysis which demonstrated optimal experimental conditions for extraction of PPO with maximum SA (3572.74 Units/mg of protein) were BC of 100 mm, PBof 4.5, ET of 1 hour, ETM of 40°C, PC of 5 mM and VB of 200 ml. This extrac- tion model is cost effective and time saving as it requires low instrumental support. Further experiments including Tab. 6. Optimization result
Solutions No. BC PB ET ETM PC VB SA Desirability
1 100.00 4.50 1.00 40.00 5.00 200.00 3483.08 0.94104-Selected
2 100.00 4.57 1.01 40.00 5.00 200.57 3482.12 0.94017
3 100.03 4.50 1.15 40.00 4.98 211.58 3483.16 0.93443
4 100.02 4.50 1.00 40.00 4.71 211.60 3474.64 0.93348
5 101.55 4.50 1.00 37.48 5.00 209.51 3473.45 0.93262
6 100.11 4.50 1.09 39.71 4.27 200.11 3463.41 0.92928
7 100.01 6.13 1.00 40.00 4.66 200.12 3451.55 0.92525
8 100.06 6.86 1.03 39.70 5.00 200.03 3449.89 0.92372
9 101.87 4.50 1.13 30.00 4.99 200.43 3451.04 0.92115
10 101.02 4.50 1.02 32.66 3.77 200.11 3428.98 0.91238
11 100.00 4.96 2.41 40.00 5.00 200.00 3488.23 0.9118
12 100.00 7.98 1.00 39.18 4.73 201.25 3425.48 0.91129
13 100.00 7.86 1.00 38.82 4.67 200.00 3424.55 0.91114
14 100.00 4.67 1.00 32.53 3.49 200.00 3419.91 0.90865
15 100.03 4.50 1.00 28.83 3.75 200.02 3418.35 0.90775
cv ‘Rooster’). J Food Biochem 34:13-30.
Niladevi KN, Prema P (2008). Immobilization of laccase from Streptomyces psammoticus and its application in phenol removal using packed bed reactor. World J Microb Biot 24:1215-1222.
Oktay M, Küfreviolu I, Kocaçaliskan I, Sakirolu H (1995).
Polyphenoloxidase from ‘Amasya’ apple. J Food Sci 60:494- 496.
Palma-Orozco G, Ortiz-Moreno A, Dorantes-Álvarez L, Sampedro JG, Nájera H (2011). Purification and partial biochemical characterization of polyphenol oxidase from mamey (Pouteria sapota). Phytochemistry 72:82-88.
Ranjan D, Srivastava P, Talat M, Hasan SH (2009). Biosorption of Arsenic from aqueous solution using agricultural residue
‘‘Rice Polish’’. J Hazard Mater 166:1050-1059.
Ricquebourg SL, Da Silva CR, Rouch CC, Cadet FR (1996).
Theoretical support for a conformational change of polyphenol oxidase induced by metabisulfite. J Agric Food Chem 44:3457-3460.
Sener A, Ümit Ünal M (2011). Purification and characterization of polyphenol oxidase from Akko XIII Loquat (Eriobotrya japonica cv ‘Akko XIII’). Food Biotechnol 25:30-42.
Seo SY, Sharma VK, Sharma N (2003). Mushroom tyrosinase:
recent prospects. J Agric Food Chem 51:2837-2853.
Shan D, Zhu M, Han E, Xue H, Cosnier S (2007). Calcium carbonate nanoparticles: A host matrix for the construction of highly sensitive amperometric phenol biosensor. Biosens Bioelectron 23:648-654.
Staszczak M, Zdunek E, Leonowicz A (2000). Studies on the role of proteases in the white-rot fungus Trametes versicolor:
effect of PMSF and chloroquine on ligninolytic enzymes activity. J Basic Microbiol 40:51-63.
Sztajer H, Maliszewska I, Wieczorek J (1988). Production of exogenous lipases by bacteria, fungi, and actinomycetes.
Enzyme Microb Tech 10:492-497.
Thygesen WP, Dry IB, Robinson SP (1995). Polyphenol oxidase in potato’ a multigene family that exhibits differential expression patterns. Plant Physiol 109:525-531.
Torres E, Bustos-Jaimes I, Le Borgne S (2003). Potential use of oxidative enzymes for the detoxification of organic pollutants. Appl Catal B: Environ 46:1-15.
Valero E, Garcia-Carmona F (1992). pH-induced kinetic co-operativity of a thylakoid-bound polyphenol oxidase.
Biochem J 286:623-626.
Vohra A, Satyanarayana T (2002). Statistical optimization of the medium components by response surface methodology to enhance phytase production, Pichia anomala. Process Biochem 37:999-1004.
Yang C, Fujita S, Kohno K, Kusubayashi A, Ashrafuzzaman MD, Hayashi N (2001). Partial purification and characterization of polyphenol oxidase from banana (Musa sapientum L.) Peel. J Agric Food Chem 49:1446-1449.
Tech 31:907-931.
Ebrahimpour A, Rahman RNZRA, Ean Ch’ng DH, Basri M, Salleh AB (2008). A modeling study by response surface methodology and artificial neural network on culture parameters optimization for thermostable lipase production from a newly isolated thermophilic Geobacillus sp. strain ARM. BMC Biotechnol 8:96.
Gauillard F, Richard-Forguet F (1997). Polyphenoloxidases from ‘Williams’ pear (Pyrus communis L., cv ‘Williams’):
activation, purification and some properties. J Sci Food Agric 74:49-56.
Gaur R, Gupta A, Khare SK (2008). Lipase from solvent tolerant Pseudomonas aeruginosa strain: Production optimization by response surface methodology and application. Bioresour Technol 99:4796-4802.
Gomez-Lopez VM (2002). Some biochemical properties of polyphenol oxidase from two varieties of avocado. Food Chem 77:163-169.
Hameed BH, Tan IAW, Ahmad AL (2009). Preparation of oil palm empty fruit bunch-based activated carbon for removal of 2,4,6-trichlorophenol: Optimization using response surface methodology. J Hazardous Mater 164:1316-1324.
Khuri AI, Cornell JA (1987). Response Surfaces: Design and Analysis. Marcel Dekker, New York.
Klibanov AM, Alberti BN, Morris ED, Felshin LM (1980).
Enzymatic removal of toxic phenols and anilines from waste waters. Appl Biochem 2:414-421.
Langdon TT (1987). Preventing browning in freshly prepared potatoes without the use of sulfating agents. Food Tech 41:64-67.
Lee MY, Lee MK, Park I (2007). Inhibitory effect of onion extract on polyphenol oxidase and enzymatic browning of taro (Colocasia antiquorum var. esculenta). Food Chem 105:528-532.
López-Molina D, Hiner ANP, Tudela J, Garcìa-Cánovas F, López JNR (2003). Enzymatic removal of phenols from aqueous solution by artichoke (Cynara scolymus L.) extracts.
Enzyme Microb Tech 33:738-742.
Lowry HO, Rosebrough NJ, Farr AL, Randall RJ (1951).
Protein measurement with the Folin phenol reagent. J Biol Chem 193:265-275.
Manohar B, Divakar S (2004). Applications of surface plots and statistical design to selected lipase-catalyzed esterification reactions. Process Biochem 39:847-853.
Marri C, Frazzoli A, Hochkoeppler A, Poggi V (2003).
Purification of a polyphenol oxidase isoform from potato (Solanum tuberosum) tubers. Phytochem 63:745-752.
Mohammadi A, Moghaddam AB, Dinarvand R, Rezaei-Zarchi S (2009). Direct electron transfer of polyphenol oxidase on carbon nanotube surfaces: application in biosensing. Int J Electrochem Sci 4:895-905.
Ni Eidhin D, Degn P, O’beirne D (2010). Characterization of polyphenol oxidase from rooster potato (Solanum tuberosum
