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Research Article

Optimization of Pectinase Assisted Extraction of Chironji (Buchanania Lanzan) Fruit Juice Using Response Surface Methodology and Artificial Neural Network

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Figures & data

Table 1. Independent variables with their levels for rotatable central composite design

Table 2. RCCD experimental design with experimental, RSM predicted, and ANN predicted results of juice yield

Table 3. Analysis of variance (ANOVA) for the quadratic model

Table 4. Validation of RSM and ANN-GA model

Table 5. Physicochemical properties of the untreated juice (control) and pectinase treated juice at optimized conditions (RSM and ANN-GA)

Figure 1. RSM predicted vs. Experimental value of juice yield (%)

Figure 1. RSM predicted vs. Experimental value of juice yield (%)

Figure 2. Influence of interaction of independent variables on juice yield (%) (a) concentration of pectinase (% w/w) and temperature for incubation (°C), (b) concentration of pectinase (% w/w) and time of incubation (minutes), (c) temperature for incubation (°C) and time of incubation (minutes)

Figure 2. Influence of interaction of independent variables on juice yield (%) (a) concentration of pectinase (% w/w) and temperature for incubation (°C), (b) concentration of pectinase (% w/w) and time of incubation (minutes), (c) temperature for incubation (°C) and time of incubation (minutes)

Figure 3. ANN predicted (all data set) vs. Experimental value of juice yield (%)

Figure 3. ANN predicted (all data set) vs. Experimental value of juice yield (%)

Figure 4. Change in fitness value with the change in the number of generations during GA optimization

Figure 4. Change in fitness value with the change in the number of generations during GA optimization

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