2016-10-31T16:37:31+05:302016-10-31T16:37:12+05:302016-10-31T16:37:31+05:30Acrobat PDFMaker 11 for Worduuid:bb25db4c-9063-4ba7-84b4-a809ba1c6d1buuid:6bff64f8-5ab7-4296-bfca-8b151dbc1e708xmlOptimization of Thermal Degradation of Chlorophyll in Amaranth Leaves Puree Using Response Surface Methodology (RSM)hpAdobe PDF Library 11.0D:20161023095256Hewlett-Packard

International Journal of ChemTech Research CODEN (USA): IJCRGG, ISSN: 0974-4290, ISSN(Online):2455-9555 Vol.9, No.09 pp 532-542, 2016

Optimization of Thermal Degradation of Chlorophyll in Amaranth Leaves Puree Using Response Surface Methodology (RSM) Richa Gopal1, Amit Keshav*1, A.B. Soni1 1Department of Chemical Engineering, National Institute of Technology, Raipur (C.G.) India Abstract : Amaranth (Amaranthus tricolor) is highly nutritious leafy vegetables containing higher amount of vitamins and minerals. The color of green leafy vegetables is due to the presence of chlorophyll molecule which gets affected during heat treatment. In the present wok, kinetics of thermal degradation of chlorophyll a (Cha) and chlorophyll b (Chb) in Amaranth leaf puree were studied at pH values of 5.6, 6.6 and 7.6 and temperatures of 70 to 90oC for time 0 to 60 min. Nonlinear regression equation based on response surface method (RSM) has been developed for determining the concentration of chlorophyll a and chlorophyll b and comparison has been made with the experimental data for different parameters that describes the thermal preservation studies on Amaranth leaf puree considering the influence of temperature, pH and time. The adequacy of the proposed equation has been performed with normal probability plot of the residuals for Cha and Chb and coefficient of regression (R2). Keywords : Amaranth, Chlorophyll, Nonlinear Regression Equation, Response Surface Method, Thermal Degradation. Introduction Amaranth comes under Amaranthus plant family having multi-purpose use of crop which can supply grains and leafy vegetables of high nutritional quality as a food and animal feed and also cultivated as an ornamental plant1. It is popularly grown leafy vegetable in tropical regions of the world like India, Africa, Bangladesh, Sri lanka and the Caribbean2. Amaranth leaves having rich source of protein, vitamins, minerals3,4 and is used as herbal remedies effective in stopping diarrhea and hemorrhagic problems. Color of green vegetables is due to the presence of chlorophyll which is of many types, though the formation of chlorophyll a and chlorophyll b are typically found in higher plants in an approximate ratio of 3:15. Color of chlorophyll a appears blue-green while for chlorophyll b having yellow-green6. Thermal stabilities and structure of chlorophyll a and b were found to be different and chlorophyll a was reported to be thermally less stable than chlorophyll b7-9. Since color is a major, sensory characteristic in determining the product acceptability, it is of critical importance to the food industry to prevent or minimize pigments degradation during processing10. Thermally processed green vegetables exhibit inferior color as compared to fresh ones. Several researchers7,11 have reported the color changes (from bright green to olive-brown) during processing and attributed it mostly to conversion of chlorophylls to pheophytins8. Various methods for preservation of green color of vegetables have been proposed. Investigations have been carried out to maintain pH at high levels during heat treatment and storage. This procedure was beneficial after processing but eventually during storage the conversion of chlorophyll to pheophytin occurs, causing drastic color changeschlorophyll to pheophytin occurs, causing drastic color changeschlorophyll to pheophytin occurs, causing drastic color changeschlorophyll to pheophytin occurs, causing drastic color changeschlorophyll to pheophytin occurs, causing drastic color changeschlorophyll to pheophytin occurs, causing drastic color changeschlorophyll to pheophytin occurs, causing drastic color changesColor degradation of green leafy vegetables can be predicted from kinetic models; usually first order and a variety of different models have been developed13-15 while for the appearance of browning can be evaluated by first and zero order kinetic models16,17. Several researchers have studied the rate of conversion of chlorophyll extent18,19 and others were investigated the stability of chlorophyll at various pH levels using high temperature short time process (HTST)20-22. In continuation of this work nonlinear equation has been developed on the frame work of response surface method (RSM) for concentration of Cha and Chb. RSM is the combinations of three different parameters such as design of experiment (DoE), regression modelling techniques and optimizing process23242526 . Experimental designs in combination with RSM help to visualize the relationship between responses (output) and factor levels for locating highest response values. Even having complex interactions, it is helpful in evaluating the relative significance of several affecting factors. RSM is an approach applied in the field of production and optimization of various products equivalent to chemical and enzymes industries. This technique requires minimum time of experimentation and was found more effective than conventional methods. The objective of the study was to evaluate the kinetics of thermal degradation at various temperatures (70 to 90°C) & pH (5.6 to 7.6) of Amaranth leave puree. An attempt has been made to develop a nonlinear regression equation based on RSM for concentration of Cha and Chb and a comparision is made between the results obtained using modeled equations and experimental. The adequacy of nonlinear equations has been checked by normal probability plot and coefficient of regression. Experimental All chemicals were of AR grade purchased from Fisher Scientific Ltd, India. Fresh Amaranth leaves were obtained from a local market in Raipur. The leaves were de-stemmed, washed, drained and blanched in hot water at 85 ± 5ºC for 3.5 min. Sample were taken out and cooled immediately in chilled water. Chilled leaves after removing excess amount of water were comminuted in wet grinder afterwards the puree was sieved (14 mesh aperture size) and was stored in deep freezer (-70C) until further use. The pH of the puree (5.6, 6.6 and 7.6) was measured using pH meter. Adjustment of pH was done using buffered solution by combining 0.1 M citric with 0.2 M disodium hydrogen phosphate (Mcllvaine’s buffer) to the desired pH ±0.0522. Thermal treatment Kinetics of thermal degradation of amaranth leaves was studied using mild thermal treatment under pasteurization condition of 70, 80 and 90ºC using water bath for a residence time of 0 to 60 min for different pH ranging from (5.6-7.6). The puree sample of approximately 1.5-2 gm were sealed in vials and immersed in a water bath for times (0, 10, 20, 30, 40 and 60 min). Time taken by the sample to reach the desired temperature (come up time) was considered when the exact temperature of the water bath was reached. Come-up time was determined by inserting thermocouple in the vials. The samples were transferred in a container containing chilled water just immediately after the thermal treatment to stop the degradation rate. Chlorophyll estimation 2±0.5 gm of puree was taken for chlorophyll extraction with 80% acetone (V/V) using centrifuge (Remi Lab Equipments, India). 30 ml supernatant extract was collected in 50 ml volumetric flask. 10 ml by volume of this sample was taken in three different flasks of 25 ml. In the first flask, volume was made up to 25 mL by 80 % acetone. In the second flask, 1 mL of 0.5 M oxalic acid in acetone was added and mixed. It was then allowed to stand for 90 min. The flasks volume was then made up to 25 mL with 80 % acetone. In the third flask, 0.32 mL of 12 N HCL was added and the flask was allowed to stand for 30 min, following which 0.23 mL of ethanolamine was added and volume was made up with 80% acetone. Sample was filtered through Whatman filter paper no. 1(125mm). Percent absorbance of clear pigment solution was measured at both 664 and 646.5 nm using spectrophotometer (Systronic, India Ltd., India). Concentrations of chlorophyll a, chlorophyll b and total chlorophyll were calculated by the method of Jones et al.total chlorophyll were calculated by the method of Jones et al.total chlorophyll were calculated by the method of Jones et al.Response Surface Method RSM is a collection of statistical and mathematical techniques useful for developing, improving, and optimizing process25. For the practical application of response surface method (RSM), it is necessary to develop an approximating model for the true response surface. A second order nonlinear response surface model (for three input variables), obtained based on regression analysis is given by (1)
εββββββββββ++++++++++=1393282172362252143322110xxxxxxxxxxxxyiHere, yi is the observed value of concentration of chlorophyll a and b, the term nonlinear is used because equation (1) represents nonlinear relationship between dependent and independent variables and it gives better approximation with the experimental results. are regression coefficients and are coded variables usually defined to be dimensionless with mean zero and standard deviation. The natural variables (time, pH and temperature) are converted into coded variables using the following relationship:
nββββ.......,,210
321,,xxx (2)
2/)]min()[max(2/)]min()[max(111111iiiiiixξξξξξ−++=where ξi1 represents natural variables with maximum and minimum values. The method of least squares is used to estimate the regression coefficients in a multilinear regression model. Myers and Montgomery25, presented the multilinear regression equation in the form of matrix: (3) And the least squares estimators b are given by,
εβ+=Xy (4)
yXXXb')'(1−=Where X is the coded matrix corresponding to the variable parameters (time, pH and temperature), X’ is the transpose matrix of X and y is the experimental observed value of Cha and Chb. Residual analysis Analysis of residuals is conducted to examine if the regression equations provide an adequate approximation to the true system and verify that none of the least square regression assumptions is violated. The residuals is defined by (5)
iiiyye∧−=i = 1,2,3…..n where, yis the predicted value from regression model. The residuals play an important role in judging model adequacy. If the residual plot falls along a straight line, then the normality assumption is satisfied. Further, computed values of the coefficient of regressionand the adjusted coefficient of regression also give description of adequacy of fitted model. Regression coefficients are calculated by the equations given below: i is the concentration of chlorophyll a and b obtained using experimental procedure (observed value) and
iy∧
)(2R
)(2adjR (6)
nyyeRiniiniini2121212)(1===∑−∑∑−= (7)
)1(1122RpnnRadj−−−−=where n is the total number of sample points and p is the number of model parameters. The value of R2 varies from 0 to 1 and value close to 1 indicates good agreement with the fitted model explained by the regression equations. Results and discussion Chlorophyll Estimation The degradation of chlorophyll is higher at lower pH values and higher temperatures. It was found that in the temperature range of 70 to 90 °C there has been uniform decrease in k values of both Cha and Chb (Table 1). Though it is recommended to apply strong thermal treatment to achieve commercial sterility, it is noticed that this leads to higher color degradation. However, it can be lowered by altering the pH of the puree (at higher pH values lower thermal degradation was found). Table 1. Experimental rate constant value (k) for degradation of Cha and Chb of Amaranth leave puree at different temperature and pH

Temperature (ºC)

pH

k (Cha) (min-1)

k (Chb) (min-1)

70 80 90

5.6

0.012 0.016 0.027

0.010 0.014 0.024

70 80 90

6.6

0.008 0.014 0.016

0.007 0.012 0.014

70 80 90

7.6

0.007 0.011 0.014

0.006 0.009 0.011

Arrhenius equation27 describes temperature dependence by relating the reaction rate constant k to the reciprocal of absolute temperature as (9)
⎟⎠⎞⎜⎝⎛−=RTEkkaoexpEa is activation energy (kJ/mol), R is the universal gas constant (8.314 J/mol.K), k is reaction rate constant at T (absolute temperature) and ko is frequency factor. The logarithms of rate constants (k) were plotted against 1/T (where T is temperature in K) (Fig 1 and 2). Kinetic parameters (k,Ea) are useful to predict the quantity change that occur during thermal processing. Lower the value of k better is the stability of color. Higher is the Ea value, greater is the heat sensitivity of visual color degradation during thermal processing. It has been found that the activation energies of both Cha and Chb are comparable. Activation energies for Cha are 41.84, 36.07, and 31.77 kJ/mol at pH of 5.6, 6.6 and 7.6 respectively; while for Chb were 40.83, 31.85 and 27.30 kJ/mol at pH of 5.6, 6.6 and 7.6, respectively. Figure 1. Arrhenius plot of Cha degradation of Amaranth leaves puree under different pH conditions Figure 2. Arrhenius plot of Chb degradation of Amaranth leaves puree under different pH conditions Lower Ea value at higher pH signifies that the puree is more heat stable at higher pH. The values are in agreement by the work by Koca et al.22, studied the effect of pH (5.5-7.5) and temperature (70-100 oC) on chlorophyll degradation and color loss in blanched green peas. Activation energies in this case were found to be for –a value ranged from 8.13 to 12.0 kcal/mol, and for -a/b values ranged from 8.77 to 12.0 kcal/mol with varying pH values at 70, 80, 90 and 100°C. Esther et al.28 used curry leaf paste for the study of thermal degradation kinetics of color at temperature ranges from 50 to 90 ºC up to 60 min. Thermal degradation of color obeys first order reaction kinetics and corresponding activation energies were found to be 18.44±3.2 and 17.203±5.9 kJ/mol. Lixia et al.29 studied the kinetics of color degradation of chestnut kernel during thermal treatment at 50 to 70 ºC for 0 to 160 min at 3 ºC of storage for five months using computer vision system (CVS). First order reaction model showed a good fit for L* and b* values and R2 ranges from 0.923 to 0.977. The activation energies during thermal treatment of chestnut were found to be 287.16 and 347.48 kJ/mol while during storage were found 89.18 and 78.47 kJ/mol respectively. Color degradation (L*, a*& b* value) of African cowpea leaves during thermal treatment (55-80 ºC per 90 min) were investigated30. Thermal degradation of the green color in the leaves is of first order reaction kinetics followed by Arrhenius equation having activation energy of 88.78±3.21 kJ/mol. Regression equations Above study suggests that degradation of chlorophyll due to thermal effects is the function of time, pH and temperature. The variation of Cha and Chb as a function of different parameters such as time, pH and temperature is presented in equation (11) and (12). The values of Cha and Chb calculated from the above equations and presented in Table 2 and 3. Table 2. Typical predicted values of chlorophyll concentration from regression model, values obtained through experiments and residuals concentration values for Cha

time (min)

pH

temperature (°C)

Cha (yi) (observed value)

Cha
∧)(iy(predicted value)

Residuals (ei) y i-
∧iy

0

5.6

70

2.81

2.85

-0.04

10

5.6

70

2.59

2.55

0.04

20

5.6

70

2.32

2.26

0.06

30

5.6

70

1.97

1.99

-0.02

40

5.6

70

1.61

1.74

-0.13

60

5.6

70

1.28

1.30

-0.02

0

5.6

80

2.32

2.31

0.01

10

5.6

80

2.06

2.01

0.05

20

5.6

80

1.79

1.72

0.07

30

5.6

80

1.48

1.45

0.03

40

5.6

80

1.14

1.20

-0.06

60

5.6

80

0.85

0.75

0.10

0

5.6

90

1.92

1.87

0.05

10

5.6

90

1.59

1.56

0.03

20

5.6

90

1.26

1.27

-0.01

30

5.6

90

0.95

1.00

-0.05

40

5.6

90

0.63

0.75

-0.12

60

5.6

90

0.34

0.30

0.04

0

6.6

70

3.18

3.18

0.00

10

6.6

70

2.93

2.90

0.03

20

6.6

70

2.75

2.64

0.11

30

6.6

70

2.48

2.39

0.09

40

6.6

70

2.17

2.16

0.01

60

6.6

70

1.88

1.76

0.12

0

6.6

80

2.65

2.69

-0.04

10

6.6

80

2.31

2.41

-0.10

20

6.6

80

2.06

2.14

-0.08

30

6.6

80

1.87

1.89

-0.02

40

6.6

80

1.46

1.66

-0.20

60

6.6

80

1.11

1.26

-0.15

0

6.6

90

2.27

2.29

-0.02

10

6.6

90

1.98

2.00

-0.02

20

6.6

90

1.71

1.74

-0.03

30

6.6

90

1.56

1.49

0.07

40

6.6

90

1.32

1.26

0.06

60

6.6

90

1.03

0.85

0.18

0

7.6

70

3.61

3.65

-0.04

10

7.6

70

3.37

3.39

-0.02

20

7.6

70

3.13

3.15

-0.02

30

7.6

70

2.89

2.93

-0.04

40

7.6

70

2.68

2.72

-0.04

60

7.6

70

2.31

2.36

-0.05

0

7.6

80

3.11

3.20

-0.09

10

7.6

80

3.01

2.94

0.07

20

7.6

80

2.83

2.70

0.13

30

7.6

80

2.61

2.47

0.14

40

7.6

80

2.39

2.27

0.12

60

7.6

80

1.95

1.90

0.05

0

7.6

90

2.88

2.85

0.03

10

7.6

90

2.62

2.58

0.04

20

7.6

90

2.34

2.34

0.00

30

7.6

90

2.05

2.11

-0.06

40

7.6

90

1.85

1.90

-0.05

60

7.6

90

1.4

1.53

-0.13

Table 3. Typical predicted values of chlorophyll concentration from regression model, values obtained through experiments and residuals concentration values for Chb

time (min)

pH

temperature (°C)

Chb (yi) (observed value)

Chb
∧)(iy(predicted value)

Residuals (ei) y i-
∧iy

0

5.6

70

1.18

1.19

-0.01

10

5.6

70

1.12

1.07

0.05

20

5.6

70

0.98

0.97

0.01

30

5.6

70

0.85

0.88

-0.03

40

5.6

70

0.75

0.79

-0.04

60

5.6

70

0.63

0.65

-0.02

0

5.6

80

1.08

1.01

0.07

10

5.6

80

0.96

0.89

0.07

20

5.6

80

0.84

0.77

0.07

30

5.6

80

0.68

0.67

0.01

40

5.6

80

0.59

0.57

0.02

60

5.6

80

0.47

0.41

0.06

0

5.6

90

0.84

0.87

-0.03

10

5.6

90

0.72

0.74

-0.02

20

5.6

90

0.62

0.62

0.00

30

5.6

90

0.41

0.50

-0.09

40

5.6

90

0.31

0.40

-0.09

60

5.6

90

0.18

0.21

-0.03

0

6.6

70

1.39

1.46

-0.07

10

6.6

70

1.25

1.33

-0.08

20

6.6

70

1.19

1.22

-0.03

30

6.6

70

1.11

1.11

0.00

40

6.6

70

0.98

1.01

-0.03

60

6.6

70

0.88

0.84

0.04

0

6.6

80

1.19

1.23

-0.04

10

6.6

80

1.06

1.10

-0.04

20

6.6

80

0.96

0.97

-0.01

30

6.6

80

0.88

0.85

0.03

40

6.6

80

0.68

0.75

-0.07

60

6.6

80

0.55

0.55

0.00

0

6.6

90

1.04

1.05

-0.01

10

6.6

90

0.93

0.90

0.03

20

6.6

90

0.81

0.76

0.05

30

6.6

90

0.71

0.64

0.07

40

6.6

90

0.6

0.52

0.08

60

6.6

90

0.39

0.31

0.08

0

7.6

70

2.10

2.09

0.01

10

7.6

70

1.98

1.95

0.03

20

7.6

70

1.88

1.82

0.06

30

7.6

70

1.74

1.70

0.04

40

7.6

70

1.64

1.59

0.05

60

7.6

70

1.38

1.39

-0.01

0

7.6

80

1.81

1.81

0.00

10

7.6

80

1.68

1.66

0.02

20

7.6

80

1.52

1.53

-0.01

30

7.6

80

1.34

1.39

-0.05

40

7.6

80

1.19

1.27

-0.08

60

7.6

80

1.00

1.05

-0.05

0

7.6

90

1.56

1.57

-0.01

10

7.6

90

1.43

1.42

0.01

20

7.6

90

1.27

1.27

0.00

30

7.6

90

1.11

1.13

-0.02

40

7.6

90

0.98

0.99

-0.01

60

7.6

90

0.75

0.76

-0.01

The predicted values from regression equations and experimental results are compared and predicted values are in good agreement with experimental values. The response surface equation for the concentration of Cha and Chb obtained from nonlinear regression are as follows: (11)
1332212322213210064.00433.00654.00461.00694.00802.04524.05106.07158.08941.1xxxxxxxxxxxxyCha−+++++−+−= (12)
1332212322213210299.00497.00389.00204.01774.00396.02359.03615.03396.08534.0xxxxxxxxxxxxyChb−−−+++−+−=With reference to the above equations (11) and (12) the predicted values for concentration of Cha and Chb are used to calculate rate constant (k) (Table 4). In Fig.1 and Fig.2 the line represents the model plots for Cha and Chb at different pH and temperature. Table 4. Model rate constant value (k) for degradation of Cha and Chb of Amaranth leave puree at different temperature and pH

Temperature (ºC)

pH

k (Cha) (min-1)

k (Chb) (min-1)

70 80 90

5.6

0.012 0.017 0.023

0.010 0.014 0.021

70 80 90

6.6

0.009 0.014 0.015

0.008 0.012 0.014

70 80 90

7.6

0.007 0.010 0.013

0.006 0.008 0.011

The slope of Arrenhius plot is multiplied with gas constant (R) to get activation energy of Cha and Chb. The activation energy for Cha were observed 38.35, 32.07, and 29.07 kJ/mol for different pH of 5.6, 6.6 and 7.6 and for Chb were 33.68, 31.34 and 26.61 kJ/mol respectively. The predicted activation energy of Cha and Chb are compared with the experimental values. The adequacy of proposed nonlinear response surface equations is evaluated from residual analysis and coefficients of regression (R2) are discussed in subsequent section. Analysis of residuals and regression In order to examine the adequacy of proposed response surface equations, normal probability versus residuals plot is obtained for Cha and Chb. The residuals defined in equation (5) play an important role in judging model adequacy. A check of the normality assumption made by constructing a normal probability plot of the residuals and if the residuals plot fall along a straight line, signifies satisfactory assumption25. Concentration of chlorophyll a and b obtained from response surface equations and compared with experimental results and results are found in good agreement. Tables 2 and 3 represent typical results of the concentration of chlorophyll a and b of experimental and predicted values and residuals for Cha and Chb respectively. It is observed that the experimental values corresponding to Cha varies (max. & min) from 3.61 to 0.34 µmol/lt and 2.098 to 0.18 µmol/lt for Chb whereas the predicted values for Cha varies from 3.65 to 0.29 µmol/lt and 2.086 to 0.21 µmol/lt for Chb respectively. The values from predictions using regression equation for Cha and Chb are quite close and comparable. In addition, a check of the normality assumptions is made by the construction of normal probability plot of the residuals and typical results for Cha and Chb are presented in Figures 3 and 4. It is observed that the residual plot falls along a straight line indicating satisfactory assumptions of normality. In addition, computed values of coefficients of regression (R2) and adjusted R2 also gives description of adequacy of fitted model. For a good model value of R2 and adjusted R2 should be close to 1. The calculated values of Cha and Chb are in the range of 0.988 indicating adequacy of the regression equations. Figure 3. Normal probability plot of the residuals for Cha Figure 4. Normal probability plot of the residuals for Chb Conclusion In this paper an approach to use nonlinear regression equation based on response surface method was suggested. Considering three different modes of parameters (time, pH and temperature) in the evaluation of concentration of Cha and Chb, predicted value was compared with the experimental values. Thermal degradation kinetic was studied at different temperatures and pH values. First order degradation was found and lower degradation was found at higher pH (7.6) and lower temperature (70°C). Degradation of chlorophyll a was found higher than chlorophyll b. Activation energy was computed by Arrhenius equation and was found to be higher at lower pH for both chlorophyll molecules. The concentrations, reaction rate constant (k) and activation energy (ENomenclature: Cha = Chlorophyll a (µmol/l) Chb = Chlorophyll b (µmol/l) Ea = Activation Energy (kJ/mol) k = Rate constant (min-1) t = time (min) T = temperature (K) R2 = Regression coefficient M = Molarity N = Normality Acknowledgement The authors thank the reviewers for the critical review of the manuscript which have been very useful. The authors also acknowledge Dr. Sandeep Kumar Chouksey, Department of Civil Engineering; National Institute of Technology-Raipur, for helping in optimization technique using RSM. This work was supported by the MOFPI, SERB New Delhi, India. References

1. Mlakar SG, Turinex M, Jakop M, Bavec M, Bavec F. Nutrition value and use of grain amaranth: potential future application in bread making. Agricultura., 2009, 6; 43-53.

2. Fasuyi AO, Dairo FAS, Adeniji AO. Tropical vegetable (Amaranthhus Cruentus) leaf meal as alternative protein supplement in broiler starter diets: Bionutritional evaluation. J Cent Eur Agri., 2008, 9; 23-34.

3. Makus DJ, Davis DR. A mid-summer crop for fresh greens or canning vegetable amaranth. Ark Farm Res., 1984, May-June.

4. Igbokwe PE, Tiwari SC, Collins JB, Tartt JB, Russell LC. Amaranth-A potential crop for south western Mississippi. Res. Report 13 No. 10. Mississippi Agric. and Forestry Expt. Sta, Mississippi State Univ, Mississippi State. 1998.

5. Belitz HD, Grosch W. Vegetables and their products. Food Chem., 1987, 549-584.

6. Schwartz SJ, Lorenzo TV. Chlorophylls in foods. Crit Rev Food Sci Nutri., 1990, 29; 1-17.

7. Tan CT, Francis FJ. Effect of processing temperatures on pigments and color of spinach. J Food Sci., 1962, 27; 232-240.

8. Schwartz SJ, Von Elbe JH. Kinetics of chlorophyll degradation to pyropheophytin in vegetables. J Food Sci., 1983, 48; 1303-1306.

9. Canjura FL, Schwartz SJ, Nunes RV. (1991). Degradation kinetics of chlorophylls and chlorophyllides. J Food Sci., 56, 1639-1643.

10. Alexander J, Jayanthi G, Lakshmipathy R, Kulasekaran A, Andal V. Color removal studies on treatment of textile dyeing effluent by chitosan modified watermelon rind composite (CWR). Int J ChemTech Res., 2015, 8(5); 10-15.

11. Blair JS, Ayres TB. Protection of natural green pigment in the canning of peas. Ind Eng Chem., 1943, 35; 85-95.

12. Jones ID, White RC, Gibbs E, Butler LS, Nelson LA. Experimental formation of zinc and copper complexes of chlorophyll derivatives in vegetable tissue by thermal processing. J Agri Food Chem., 1977, 25; 149-153.

13. Gupte SM, El-Bisi HM, Francis FJ. Kinetics of thermal degradation of chlorophyll in spinach puree. J Food Sci., 1964, 29; 379-382.

14. Nisha P, Singhal RS, Pandit AB. A study on the degradation kinetics of visual green color in spinach (Spinacea pleracea L.) and the effect of salt therein. J Food Emer Tech., 2004, 64; 135-142.

15. Dadali G, Demirhan E, Ozbek B. Microwave heat treatment of spinach: Drying kinetics and effective moisture diffusivity. Dry Tech., 2007, 25; 1703-1712.

16. Ibarz A, Bermeijo ML. Effect of storage temperature on concentrated pear juices. Rev de Agro Tecnol Alimen., 1991, 31; 195-204.

17. Stamp JA, Labuza TP. Kinetics of the Maillard reaction between aspartame and glucose in solution at high temperature. J Food Sci., 1983, 48; 543-544, 547.

18. Shin S, Bhowmik SR. Thermal kinetics of color changes in pea puree. J Food Eng., 1996, 24; 77-86.

19. Steet JA, Tong CH. Degradation kinetics of green color and chlorophylls in peas by colorimetry and HPLC. J Food Sci., 1996, 61(5); 924-928.

20. Clydesdale FM, Francis FJ. Chlorophyll changes in thermally processed spinach as influenced by enzyme conversion and pH adjustment. Food Tech., 1968, 22; 793-796.

21. Schwartz SJ, Lorenzo TV. Chlorophyll stability during continuous aseptic processing and storage. J Food Sci., 1991, 56; 1059-1062.

22. Koca N, Karadeniz F, Burdurlu HS. Effect of pH on chlorophyll degradation and color loss in blanched green peas. J Food Chem., 2007, 100(2); 609-615.

23. Kamali H, Aminimoghadamfarouj N, Nematollahi A. Optimization of pressurized hot water extraction of Lavandin essential oils via central composite design. Int J ChemTech Res., 2014, 6(11); 4853-4861.

24. Montgomery DC. Design and Analysis of Experiments, John Wiley and Sons: New York, 1991, pp. (1-2, 175-228, 241-284, 465-510).

25. Myers RH, Montgomery DC. RSM process and product optimization using designed experiments, second ed. John Wiley and Sons: New York. 2002.

26. Adalarasan R, Santhanakumar M. Response Surface Methodology and Desirability Analysis for optimizing µWEDM Parameters for Al6351/20%Al2O3 composite. Int J ChemTech Res., 2014-2015, 7(6); 2625-2631.

27. Levenspiel O. Interpretation of batch reactor data in chemical reaction engineering second ed. Wiley, New York. 1972.

28. Esther Magdalene Sharon M, Kavitha Abirami CV, Alagusundaram K. Modeling thermal degradation kinetics of color in curry leaf paste. Asi J Sci Tech., 2014, 5(3); 272-275.

29. Lixia H, Bo L, Shaojin W. Kinetics of color degradation of chestnut kernel during thermal treatment and storage. Int J Agri Bio Eng., 2015, 8(4); 106-115.

30. Wawire M, Oey I, Mathooko FM, Njoroge CK, Shitanda D, Hendrickx M. Kinetics of thermal inactivation of peroxidase and color degradation of African cowpea (Vigna unguiculata) leaves. J Food Sci., 2016, 81(1); E56-E64.