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Optimization of Fractional Freezing Process for Orange Juice Concentration
Available online at www.sciencedirect.com
ScienceDirect Materials Today: Proceedings 19 (2019) 1591–1598
www.materialstoday.com/proceedings
ICCSE 2018
Optimization of Fractional Freezing Process for Orange Juice Concentration Nor Zanariah Safiei*, Nur Farah Najian Danuri, Maisarah Khalisah Rosly, Shahrulzaman Shaharuddin Food Technology Section, Universiti Kuala Lumpur, Malaysian Institute of Chemical and Bioengineering Technology, Melaka, Malaysia
Abstract In this research, fractional freezing (FF) was introduced to replace the conventional evaporation to concentrate fruit juice which usually require a higher energy and cost demand. Response surface methodology (RSM) was applied to optimize the process, as well as to investigate the effect of coolant temperature and freezing time towards the efficiency of the concentration process to concentrate orange juice which is represented by Vitamin C increment and Effective Partition Constant (K). Subsequently, a validation experiment was conducted to validate the predicted optimum conditions given by RSM. From the result, the optimum value of Vitamin C increment (45%) and K value (0.78) were found at coolant temperature of -8°C and 30 minutes of freezing time, with error for both responses less than 10% from prediction.
© 2019 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Conference on Chemical Sciences and Engineering: Advance and New Materials, ICCSE 2018. Keywords: Fractional Freezing; Concentration; Effective Partition Constant
* Corresponding author. Tel.: +606-5512000 E-mail address: [email protected] 2214-7853 © 2019 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Conference on Chemical Sciences and Engineering: Advance and New Materials, ICCSE 2018.
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N.Z. Safiei et al. / Materials Today: Proceedings 19 (2019) 1591–1598
1. Introduction The orange or Citrus X sinensis (L.) Osbeck is one of the most popular of citrus fruits consumed globally. Orange trees originate in India, with some varieties being found in the south east of the country. For production of orange, Brazil is the world’s leading orange producer, with an output of 17 million tonnes, followed by China, India and the United States as the four major producers. Besides, orange or citrus production in Japan, Korea and Taiwan are difficult to growth because cold-season temperature. While orange or citrus production also difficult in Malaysia, which has not only a tropical climate, but one which is humid throughout most of the year. As compared to the other concentration methods, fractional freezing is believed to be the best option since evaporation requires higher amount of energy to boil the target solution for the purpose of removing it. Freeze concentration (FC) technology or fractional freezing (FF) is a process where water could be separated from a solution by freezing or cooling [1]. This technique’s low temperature range can be used to prevent the loss of quality in liquid foods, including fruit juices and dairy products [2,3]. There are three categories of FC: progressive freeze concentration (PFC), block freeze concentration (BFC) and suspension freeze concentration (SFC). SFC is deemed as the most traditional approach of FC where ice is formed in a chilled solution and it comprises of nucleation and crystal growth by using recrystallizer and a scraped-surface heat exchanger (SSHE) [4]. 2. Materials and methods 2.1 Materials Orange juice was used as a sample solution. The orange bought from supermarket at Tampin, Negeri Sembilan based on the consistency on the shape, freshness and colour. The juice solution was stored at 4 ⁰C prior to the tests. 2.2 Freeze Concentration method The orange juice of initial concentration of 7 ⁰Brix was kept first at near freezing point by keeping it in a freezer. The coolant solution in the portable chiller was set at desired temperature value and circulated it in the double jacket crystallizer. The coolant solution was cooled down first below 0 ⁰C because it takes approximately 30 minutes to cool down to the desired value. After the temperature reached the desired value, 1.6L of orange juice was poured in a stainless steel jacketed vessel, which also called as crystallizer. The stirring rate at constant speed of 300 rpm was applied to provide movement of the target solution inside the crystallizer. The sample solution was left for freezing for 20 minutes in the crystallizer assisted with stirring continuously in order to facilitate the nucleation process to occur. After the crystallization process finish, the concentrated solution was poured to separate the concentrated solution from the ice crystal. For analysis purpose, the volume, Vitamin C and solute concentration of the melted ice crystal and concentrated solution were analyzed. The thickness of the ice crystal on the inside wall of crystallizer was also measured immediately after the process before it melts. Figure 1 shows the experimental setup for fractional freezing.
Figure 1: Fractional Freezing Set-Up with Crystallizer
N.Z. Safiei et al. / Materials Today: Proceedings 19 (2019) 1591–1598
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2.3 Experimental Design In this paper, statistical analysis of Vitamin C increment and K value in orange juice concentration process was performed using Minitab software. The central composite design (CCD) was used to study the interaction of process variables and to predict the optimum process condition for coolant temperature and freezing time by applying RSM. The range and coded level of the concentration process variables studied are listed in Table 1. Two variables involve are coolant temperature (X1) and freezing time (X2). Total of 13 experiments were run to optimize the designed system. The substitution of the selected parameters into the model enabled a calculation for a predicted response. Equation (1) shows the calculation for the predicted response. Table 1: Range and coded level Range and Levels -α -1 0 Coolant Temperature, X1 0 -2 -4 (°C) Operation time, X2 11.8 15.0 22.5 (min) Parameter
4
4
j =1
j =1
Y = β 0 + β j X j + β jj X 2j + β ij X i X j
+1 -6
+α -8
30
33.1
(1)
i< j
where Y is the predicted response value, β is the regression coefficient which is a weighting factor that been calculated by the statistical program to fit the experimental data and X is an experimental factor that influence the process. 2.4 Evaluation of Process Efficiency First analysis is vitamin C analysis for Vitamin C Increment calculation. The solution is 1% starch indicator, iodine, vitamin C standard solution and standardizing solution. For preparation of iodine solution, 5.00 g potassium iodide (KI) and 0.268 g potassium iodate (KIO3) were dissolved in 200 ml of distilled water. Then, 30 ml of 3 M sulfuric acid was added to the solution. This solution is then poured into a 500 ml cylinder and final volume of 500 ml diluted with distilled water. Lastly, the solution was transferred to a 600 ml beaker before titrating the orange juice samples, which 25 ml of orange juice sample was added to a 125 ml Erlenmeyer flask. It is titrated until the endpoint is reached.
VitCf − VitCi x100 VitCi
(2)
Effective partition constant (K) has been used prominently in assessing the performance of freeze crystallization process from several aspects of application [5]. K value is stand for a ratio of solute in ice and liquid phase, as indicated in the following Equation (2).
C V (1 − K ) log L = log o V C o L
(3)
where Co is defined as the initial component concentration in liquid mixture (mgL-1), CL is concentration of purified liquid (mgL-1), VL is volume of purified liquid (mL) and lastly Vo is defined as volume of the liquid mixture.
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3.0 Result and Discussion 3.1 Model Adequacy Check Based on the CCD tabulated, all 13 of the designed experiments and results of response Vitamin C increment and K value for each run are obtained and tabulated in Table 2. Table 2. Design of experiment and response for Vitamin C Increment and K value Manipulated Variables Vitamin C Run K value preservation (%) X1 (°C) X2 (min) -6.00000 15.0000 9.0909 0.96 1 2
-4.00000
22.5000
7.2727
0.89
3
-4.00000
33.1066
9.0909
0.90
4
-4.00000
22.5000
7.2727
0.89
5
-4.00000
22.5000
7.2727
0.89
6
-6.00000
30.0000
45.455
0.78
7
-1.17157
22.5000
0.0000
0.95
8
-2.00000
15.0000
0.0000
0.97
9
-6.82843
22.5000
27.273
0.88
10
-4.00000
22.5000
7.2727
0.89
11
-2.00000
30.0000
9.0909
0.98
12
-4.00000
22.5000
7.2727
0.89
13
-4.00000
11.8934
4.5455
0.88
The response, Vitamin C increment and K value were correlated with two independent variables studied by using multiple regression analysis with second order polynomial. The empirical mathematical model of the predicted Vitamin C increment (Y1) and K (Y2) as a function of X1 and X2 and their interaction using linear and quadratic regression coefficient of main factors and linear-by-linear regression coefficients of interaction was derived and represented in the following equations. Eq. (4) and Eq. (5) represent the predicted Vitamin C increment and K value.
Y1 = 7.2727 − 11.6395X 1 + 5.3490X 2 + 4.0342X 12 + 0.6250X 22 − 9.0910X 1 X 2
(4)
Y2 = 0.8900 + 0.0386X1 − 0.0177X 2 + 0.0175X12 + 0.005X 22 + 0.0475X1 X 2
(5)
The coefficients with one factor represent the effect of the factor itself while the coefficients with two factors indicate the effect and interaction between the two factors. The quadratic effect of the factor can be seen in coefficients with second order terms. The positive and negative signs in the equations signify parallel and adverse effect of the factors to the responses respectively. The coefficient of determination (R2) had been analyzed to evaluate the adequacy of the fit model. R2 represent the validity of the model generated. For the percentage vitamin C increment, the R-squared for the response that explain the parameter is 91.31 percent. This value is considered as moderate to validate the fit, which might lead to large variation in the Vitamin C increment predicted from this
N.Z. Safiei et al. / Materials Today: Proceedings 19 (2019) 1591–1598
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model (Tan et al., 2008). This value indicates that 91.31 percent of the sample variation for Vitamin C increment could be attributed to the independent factors. Hence in this case, the obtained value of R2 indicates that there was a good agreement between the observed and the predicted value of response. Table 3 and Table 4 show the predicted value for each responses obtained from the regression model and the observed value from the experimental data for Vitamin C increment and K value, respectively. Table 3. Observed and Predicted Vitamin C increment for each run Observed Vitamin Predicted Vitamin C Run Residual C increment increment 9.0909 9.131 -0.040 1 7.2727 7.273 0.000 2 9.0909 16.087 -6.997 3 7.2727 7.273 0.000 4 7.2727 7.273 0.000 5 45.455 38.011 7.444 6 0.0000 -1.120 1.120 7 0.0000 4.034 -4.034 8 27.273 31.802 -4.529 9 7.2727 7.273 0.000 10 0.0000 -3.450 3.450 11 7.2727 7.273 0.000 12 4.5455 0.958 3.587 13
Table 4. Observed and Predicted K value for each run Run 1 2 3 4 5 6 7 8 9 10 11 12 13
Observed K value 0.96 0.89 0.90 0.89 0.89 0.78 0.95 0.97 0.88 0.89 0.98 0.89 0.88
Predicted K value 0.939 0.890 0.875 0.890 0.890 0.809 0.980 0.921 0.870 0.890 0.981 0.890 0.925
Residual 0.021 0.000 0.025 0.000 0.000 -0.029 -0.030 0.049 0.010 0.000 -0.001 0.000 -0.045
3.2 Analysis of variance (ANOVA) The appropriateness of the generated prediction models was further evaluated by using the analysis of variance (ANOVA) method. Table 5 and Table 6 show the ANOVA analysis of the quadratic model for response Vitamin C increment and K value respectively. The method requires one to observe the F-value which portrays the ratio of
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mean square regression to the mean square residual. The tabulated F-values for Vitamin C increment (14.71) has exceeded the tabulated F-value for 95% confidence level (6.2561). Hence, the prediction mathematical model is considered to have a good or satisfactory agreement to the experimental. Table 5: ANOVA for prediction model of percentage Vitamin C increment
Regression
Sum squares of error (SSE) 1756.59
Degree of freedom (df) 5
Mean square (MS) 351.32
Residual
167.17
7
23.88
Total
1923.75
12
Source
Sum squares of
Degree of
Mean square
error (SSE)
freedom (df)
(MS)
Regression
0.025653
5
0.005131
Residual
0.007255
7
0.001036
Total
0.032908
12
Source
F 14.71
F 4.95
Then, the variables which significantly affect the selected responses were then identified. Table 6 and Table 7 shows the arranged multiple regression results, which later would be used to assess the significance of each factor in the model. Table 6: Regression analysis for Vitamin C increment Factor X2 X1X1 X2X2 X1X2 X1
Coefficient 5.3490 4.0342 0.6250 -9.0910 -11.6395
Standard Error 1.728 1.853 1.853 2.443 1.728
F 3.096 2.177 0.337 -3.721 -6.737
P 0.017 0.066 0.746 0.007 0.000
F 3.393 -1.556 1.434 0.410 2.951
P 0.012 0.164 0.195 0.694 0.021
Table 7: Regression analysis for K value Term X1 X2 X1X1 X2X2 X1X2
Coefficient 0.038624 -0.017714 0.017500 0.005000 0.047500
Standard Error 0.01138 0.01138 0.01221 0.01221 0.01610
3.3 Response surface contour plot analysis The effects of the two process variables or factors on the response of Vitamin C Increment and K value were also observed through three-dimensional (3D) surface plot of the response against the two selected factors., a two dimensional (2D) fitted response profile known as contour plot was observed. Figure 2 (a) and Figure2 (b) show the 3D and 2D surface plots of vitamin C increment as a function of coolant temperature and freezing time.
N.Z. Safiei et al. / Materials Today: Proceedings 19 (2019) 1591–1598
Figure 2 (a): 3D surface plot of percentage Vitamin C increment as a function of temperature and time
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Figure 2 (b): 2D contour plot of percentage Vitamin C increment as a function of temperature and time
It can be seen from the Figure 2, Vitamin C Increment increases as the coolant temperature decreases at longer range of time. Generally longer time could provide longer time for the crystallization to occur in the crystallizer, as the ice grows thicker. Higher temperature difference between crystallizer wall and solution temperature also affected the ice growth rate. The rate of ice crystal growth was higher and the velocity of the ice front formation is adequate for the impurities to move outward. Hence the vitamin C in the solution phase is higher as the purer ice crystal was obtained. Figure 3(a) and Figure 3(b) show the surface plot for K value. When the freezing time in the range from 15 to 30 minutes, with the lower temperature, the value of K will increase. While for coolant temperature, when the temperature decreases, the effective partition constant (K) will also decreases. Surface Plot of Effective Partition Constant, K vs Time, Temperature
Contour Plot of Effective Partition Constant, K vs Time, Temperature Effectiv e Partition C onstant, K < 0.80 0.80 – 0.85 0.85 – 0.90 0.90 – 0.95 0.95 – 1.00 1.00 – 1.05 > 1.05
30
1.1
Time
25
1.0 Effective Partition Constant, K
0.9
20
0.8 -2 T emperature
-4
-6
30
25
15 20 T ime
15
-6
Figure 3 (a): 3D surface plot of percentage K value as a function of temperature and time
-5
-4 -3 Temperature
-2
Figure 3 (b): 2D contour plot of percentage K value as a function of temperature and time
From the figure, K value decreases as the coolant temperature decreases, which is the desired situation in this study. It indicates that the ice crystal formed at low temperature range is purer and most of the impurities were not trapped in the ice formed.
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4.0 Conclusion In conclusion, this study has proved that progressive freeze concentration (PFC) process has a good potential to be applied to concentrate fruit juice. It was recommended that the low range of coolant temperature and longer freezing time will result in a higher concentration efficiency. References [1] N. Z. Safiei, N. Ngadi, A. Johari, Z. Y. Zakaria, M. Jusoh, J Food Process. Preserv. 2017, 41 (1), E12910. Doi: 10.1111/Jfpp.12910 [2] Jusoh, M. 2008. Effect of flowrate and Coolant Temperature on Efficiency of Progressive Freeze Concentration On Simulated Waste Water. J. Technol. 42, 69. [3] Sanchez, J., Ruiz, Y., Raventos, M., Auleda, J.M. And Hernandez, E. 2010. Progressive Freeze Concentration of Orange Juice in A Pilot Plant Falling film. Innov. Food Sci. Emerg. Technol. 11, 644–651. [4] Miyawaki, O., Liu, L., Shirai, Y., Sakashita, S. And Kagitani, K. 2005. Tubular Ice System for Scale-Up of Progressive Freeze-Concentration. J. Food Eng. 69, 107–113. [5] O. Miyawaki, L. Liu, Y. Shirai, S. Sakashita, K. Kagitani, J. Food Eng. 2005, 69 (1), 107-113. Doi: 10.1016/J.Jfoodeng.2004.07.016
ScienceDirect Materials Today: Proceedings 19 (2019) 1591–1598
www.materialstoday.com/proceedings
ICCSE 2018
Optimization of Fractional Freezing Process for Orange Juice Concentration Nor Zanariah Safiei*, Nur Farah Najian Danuri, Maisarah Khalisah Rosly, Shahrulzaman Shaharuddin Food Technology Section, Universiti Kuala Lumpur, Malaysian Institute of Chemical and Bioengineering Technology, Melaka, Malaysia
Abstract In this research, fractional freezing (FF) was introduced to replace the conventional evaporation to concentrate fruit juice which usually require a higher energy and cost demand. Response surface methodology (RSM) was applied to optimize the process, as well as to investigate the effect of coolant temperature and freezing time towards the efficiency of the concentration process to concentrate orange juice which is represented by Vitamin C increment and Effective Partition Constant (K). Subsequently, a validation experiment was conducted to validate the predicted optimum conditions given by RSM. From the result, the optimum value of Vitamin C increment (45%) and K value (0.78) were found at coolant temperature of -8°C and 30 minutes of freezing time, with error for both responses less than 10% from prediction.
© 2019 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Conference on Chemical Sciences and Engineering: Advance and New Materials, ICCSE 2018. Keywords: Fractional Freezing; Concentration; Effective Partition Constant
* Corresponding author. Tel.: +606-5512000 E-mail address: [email protected] 2214-7853 © 2019 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Conference on Chemical Sciences and Engineering: Advance and New Materials, ICCSE 2018.
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N.Z. Safiei et al. / Materials Today: Proceedings 19 (2019) 1591–1598
1. Introduction The orange or Citrus X sinensis (L.) Osbeck is one of the most popular of citrus fruits consumed globally. Orange trees originate in India, with some varieties being found in the south east of the country. For production of orange, Brazil is the world’s leading orange producer, with an output of 17 million tonnes, followed by China, India and the United States as the four major producers. Besides, orange or citrus production in Japan, Korea and Taiwan are difficult to growth because cold-season temperature. While orange or citrus production also difficult in Malaysia, which has not only a tropical climate, but one which is humid throughout most of the year. As compared to the other concentration methods, fractional freezing is believed to be the best option since evaporation requires higher amount of energy to boil the target solution for the purpose of removing it. Freeze concentration (FC) technology or fractional freezing (FF) is a process where water could be separated from a solution by freezing or cooling [1]. This technique’s low temperature range can be used to prevent the loss of quality in liquid foods, including fruit juices and dairy products [2,3]. There are three categories of FC: progressive freeze concentration (PFC), block freeze concentration (BFC) and suspension freeze concentration (SFC). SFC is deemed as the most traditional approach of FC where ice is formed in a chilled solution and it comprises of nucleation and crystal growth by using recrystallizer and a scraped-surface heat exchanger (SSHE) [4]. 2. Materials and methods 2.1 Materials Orange juice was used as a sample solution. The orange bought from supermarket at Tampin, Negeri Sembilan based on the consistency on the shape, freshness and colour. The juice solution was stored at 4 ⁰C prior to the tests. 2.2 Freeze Concentration method The orange juice of initial concentration of 7 ⁰Brix was kept first at near freezing point by keeping it in a freezer. The coolant solution in the portable chiller was set at desired temperature value and circulated it in the double jacket crystallizer. The coolant solution was cooled down first below 0 ⁰C because it takes approximately 30 minutes to cool down to the desired value. After the temperature reached the desired value, 1.6L of orange juice was poured in a stainless steel jacketed vessel, which also called as crystallizer. The stirring rate at constant speed of 300 rpm was applied to provide movement of the target solution inside the crystallizer. The sample solution was left for freezing for 20 minutes in the crystallizer assisted with stirring continuously in order to facilitate the nucleation process to occur. After the crystallization process finish, the concentrated solution was poured to separate the concentrated solution from the ice crystal. For analysis purpose, the volume, Vitamin C and solute concentration of the melted ice crystal and concentrated solution were analyzed. The thickness of the ice crystal on the inside wall of crystallizer was also measured immediately after the process before it melts. Figure 1 shows the experimental setup for fractional freezing.
Figure 1: Fractional Freezing Set-Up with Crystallizer
N.Z. Safiei et al. / Materials Today: Proceedings 19 (2019) 1591–1598
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2.3 Experimental Design In this paper, statistical analysis of Vitamin C increment and K value in orange juice concentration process was performed using Minitab software. The central composite design (CCD) was used to study the interaction of process variables and to predict the optimum process condition for coolant temperature and freezing time by applying RSM. The range and coded level of the concentration process variables studied are listed in Table 1. Two variables involve are coolant temperature (X1) and freezing time (X2). Total of 13 experiments were run to optimize the designed system. The substitution of the selected parameters into the model enabled a calculation for a predicted response. Equation (1) shows the calculation for the predicted response. Table 1: Range and coded level Range and Levels -α -1 0 Coolant Temperature, X1 0 -2 -4 (°C) Operation time, X2 11.8 15.0 22.5 (min) Parameter
4
4
j =1
j =1
Y = β 0 + β j X j + β jj X 2j + β ij X i X j
+1 -6
+α -8
30
33.1
(1)
i< j
where Y is the predicted response value, β is the regression coefficient which is a weighting factor that been calculated by the statistical program to fit the experimental data and X is an experimental factor that influence the process. 2.4 Evaluation of Process Efficiency First analysis is vitamin C analysis for Vitamin C Increment calculation. The solution is 1% starch indicator, iodine, vitamin C standard solution and standardizing solution. For preparation of iodine solution, 5.00 g potassium iodide (KI) and 0.268 g potassium iodate (KIO3) were dissolved in 200 ml of distilled water. Then, 30 ml of 3 M sulfuric acid was added to the solution. This solution is then poured into a 500 ml cylinder and final volume of 500 ml diluted with distilled water. Lastly, the solution was transferred to a 600 ml beaker before titrating the orange juice samples, which 25 ml of orange juice sample was added to a 125 ml Erlenmeyer flask. It is titrated until the endpoint is reached.
VitCf − VitCi x100 VitCi
(2)
Effective partition constant (K) has been used prominently in assessing the performance of freeze crystallization process from several aspects of application [5]. K value is stand for a ratio of solute in ice and liquid phase, as indicated in the following Equation (2).
C V (1 − K ) log L = log o V C o L
(3)
where Co is defined as the initial component concentration in liquid mixture (mgL-1), CL is concentration of purified liquid (mgL-1), VL is volume of purified liquid (mL) and lastly Vo is defined as volume of the liquid mixture.
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3.0 Result and Discussion 3.1 Model Adequacy Check Based on the CCD tabulated, all 13 of the designed experiments and results of response Vitamin C increment and K value for each run are obtained and tabulated in Table 2. Table 2. Design of experiment and response for Vitamin C Increment and K value Manipulated Variables Vitamin C Run K value preservation (%) X1 (°C) X2 (min) -6.00000 15.0000 9.0909 0.96 1 2
-4.00000
22.5000
7.2727
0.89
3
-4.00000
33.1066
9.0909
0.90
4
-4.00000
22.5000
7.2727
0.89
5
-4.00000
22.5000
7.2727
0.89
6
-6.00000
30.0000
45.455
0.78
7
-1.17157
22.5000
0.0000
0.95
8
-2.00000
15.0000
0.0000
0.97
9
-6.82843
22.5000
27.273
0.88
10
-4.00000
22.5000
7.2727
0.89
11
-2.00000
30.0000
9.0909
0.98
12
-4.00000
22.5000
7.2727
0.89
13
-4.00000
11.8934
4.5455
0.88
The response, Vitamin C increment and K value were correlated with two independent variables studied by using multiple regression analysis with second order polynomial. The empirical mathematical model of the predicted Vitamin C increment (Y1) and K (Y2) as a function of X1 and X2 and their interaction using linear and quadratic regression coefficient of main factors and linear-by-linear regression coefficients of interaction was derived and represented in the following equations. Eq. (4) and Eq. (5) represent the predicted Vitamin C increment and K value.
Y1 = 7.2727 − 11.6395X 1 + 5.3490X 2 + 4.0342X 12 + 0.6250X 22 − 9.0910X 1 X 2
(4)
Y2 = 0.8900 + 0.0386X1 − 0.0177X 2 + 0.0175X12 + 0.005X 22 + 0.0475X1 X 2
(5)
The coefficients with one factor represent the effect of the factor itself while the coefficients with two factors indicate the effect and interaction between the two factors. The quadratic effect of the factor can be seen in coefficients with second order terms. The positive and negative signs in the equations signify parallel and adverse effect of the factors to the responses respectively. The coefficient of determination (R2) had been analyzed to evaluate the adequacy of the fit model. R2 represent the validity of the model generated. For the percentage vitamin C increment, the R-squared for the response that explain the parameter is 91.31 percent. This value is considered as moderate to validate the fit, which might lead to large variation in the Vitamin C increment predicted from this
N.Z. Safiei et al. / Materials Today: Proceedings 19 (2019) 1591–1598
1595
model (Tan et al., 2008). This value indicates that 91.31 percent of the sample variation for Vitamin C increment could be attributed to the independent factors. Hence in this case, the obtained value of R2 indicates that there was a good agreement between the observed and the predicted value of response. Table 3 and Table 4 show the predicted value for each responses obtained from the regression model and the observed value from the experimental data for Vitamin C increment and K value, respectively. Table 3. Observed and Predicted Vitamin C increment for each run Observed Vitamin Predicted Vitamin C Run Residual C increment increment 9.0909 9.131 -0.040 1 7.2727 7.273 0.000 2 9.0909 16.087 -6.997 3 7.2727 7.273 0.000 4 7.2727 7.273 0.000 5 45.455 38.011 7.444 6 0.0000 -1.120 1.120 7 0.0000 4.034 -4.034 8 27.273 31.802 -4.529 9 7.2727 7.273 0.000 10 0.0000 -3.450 3.450 11 7.2727 7.273 0.000 12 4.5455 0.958 3.587 13
Table 4. Observed and Predicted K value for each run Run 1 2 3 4 5 6 7 8 9 10 11 12 13
Observed K value 0.96 0.89 0.90 0.89 0.89 0.78 0.95 0.97 0.88 0.89 0.98 0.89 0.88
Predicted K value 0.939 0.890 0.875 0.890 0.890 0.809 0.980 0.921 0.870 0.890 0.981 0.890 0.925
Residual 0.021 0.000 0.025 0.000 0.000 -0.029 -0.030 0.049 0.010 0.000 -0.001 0.000 -0.045
3.2 Analysis of variance (ANOVA) The appropriateness of the generated prediction models was further evaluated by using the analysis of variance (ANOVA) method. Table 5 and Table 6 show the ANOVA analysis of the quadratic model for response Vitamin C increment and K value respectively. The method requires one to observe the F-value which portrays the ratio of
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mean square regression to the mean square residual. The tabulated F-values for Vitamin C increment (14.71) has exceeded the tabulated F-value for 95% confidence level (6.2561). Hence, the prediction mathematical model is considered to have a good or satisfactory agreement to the experimental. Table 5: ANOVA for prediction model of percentage Vitamin C increment
Regression
Sum squares of error (SSE) 1756.59
Degree of freedom (df) 5
Mean square (MS) 351.32
Residual
167.17
7
23.88
Total
1923.75
12
Source
Sum squares of
Degree of
Mean square
error (SSE)
freedom (df)
(MS)
Regression
0.025653
5
0.005131
Residual
0.007255
7
0.001036
Total
0.032908
12
Source
F 14.71
F 4.95
Then, the variables which significantly affect the selected responses were then identified. Table 6 and Table 7 shows the arranged multiple regression results, which later would be used to assess the significance of each factor in the model. Table 6: Regression analysis for Vitamin C increment Factor X2 X1X1 X2X2 X1X2 X1
Coefficient 5.3490 4.0342 0.6250 -9.0910 -11.6395
Standard Error 1.728 1.853 1.853 2.443 1.728
F 3.096 2.177 0.337 -3.721 -6.737
P 0.017 0.066 0.746 0.007 0.000
F 3.393 -1.556 1.434 0.410 2.951
P 0.012 0.164 0.195 0.694 0.021
Table 7: Regression analysis for K value Term X1 X2 X1X1 X2X2 X1X2
Coefficient 0.038624 -0.017714 0.017500 0.005000 0.047500
Standard Error 0.01138 0.01138 0.01221 0.01221 0.01610
3.3 Response surface contour plot analysis The effects of the two process variables or factors on the response of Vitamin C Increment and K value were also observed through three-dimensional (3D) surface plot of the response against the two selected factors., a two dimensional (2D) fitted response profile known as contour plot was observed. Figure 2 (a) and Figure2 (b) show the 3D and 2D surface plots of vitamin C increment as a function of coolant temperature and freezing time.
N.Z. Safiei et al. / Materials Today: Proceedings 19 (2019) 1591–1598
Figure 2 (a): 3D surface plot of percentage Vitamin C increment as a function of temperature and time
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Figure 2 (b): 2D contour plot of percentage Vitamin C increment as a function of temperature and time
It can be seen from the Figure 2, Vitamin C Increment increases as the coolant temperature decreases at longer range of time. Generally longer time could provide longer time for the crystallization to occur in the crystallizer, as the ice grows thicker. Higher temperature difference between crystallizer wall and solution temperature also affected the ice growth rate. The rate of ice crystal growth was higher and the velocity of the ice front formation is adequate for the impurities to move outward. Hence the vitamin C in the solution phase is higher as the purer ice crystal was obtained. Figure 3(a) and Figure 3(b) show the surface plot for K value. When the freezing time in the range from 15 to 30 minutes, with the lower temperature, the value of K will increase. While for coolant temperature, when the temperature decreases, the effective partition constant (K) will also decreases. Surface Plot of Effective Partition Constant, K vs Time, Temperature
Contour Plot of Effective Partition Constant, K vs Time, Temperature Effectiv e Partition C onstant, K < 0.80 0.80 – 0.85 0.85 – 0.90 0.90 – 0.95 0.95 – 1.00 1.00 – 1.05 > 1.05
30
1.1
Time
25
1.0 Effective Partition Constant, K
0.9
20
0.8 -2 T emperature
-4
-6
30
25
15 20 T ime
15
-6
Figure 3 (a): 3D surface plot of percentage K value as a function of temperature and time
-5
-4 -3 Temperature
-2
Figure 3 (b): 2D contour plot of percentage K value as a function of temperature and time
From the figure, K value decreases as the coolant temperature decreases, which is the desired situation in this study. It indicates that the ice crystal formed at low temperature range is purer and most of the impurities were not trapped in the ice formed.
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4.0 Conclusion In conclusion, this study has proved that progressive freeze concentration (PFC) process has a good potential to be applied to concentrate fruit juice. It was recommended that the low range of coolant temperature and longer freezing time will result in a higher concentration efficiency. References [1] N. Z. Safiei, N. Ngadi, A. Johari, Z. Y. Zakaria, M. Jusoh, J Food Process. Preserv. 2017, 41 (1), E12910. Doi: 10.1111/Jfpp.12910 [2] Jusoh, M. 2008. Effect of flowrate and Coolant Temperature on Efficiency of Progressive Freeze Concentration On Simulated Waste Water. J. Technol. 42, 69. [3] Sanchez, J., Ruiz, Y., Raventos, M., Auleda, J.M. And Hernandez, E. 2010. Progressive Freeze Concentration of Orange Juice in A Pilot Plant Falling film. Innov. Food Sci. Emerg. Technol. 11, 644–651. [4] Miyawaki, O., Liu, L., Shirai, Y., Sakashita, S. And Kagitani, K. 2005. Tubular Ice System for Scale-Up of Progressive Freeze-Concentration. J. Food Eng. 69, 107–113. [5] O. Miyawaki, L. Liu, Y. Shirai, S. Sakashita, K. Kagitani, J. Food Eng. 2005, 69 (1), 107-113. Doi: 10.1016/J.Jfoodeng.2004.07.016