Modeling and Optimization of Ellagic Acid from Chebulae Fructus Using Response Surface Methodology Coupled with Artificial Neural Network
Abstract
:1. Introduction
2. Results and Discussion
2.1. Experimental Ranges from Screening Study
2.2. Response Surface Methodology Statistical Analysis and Model Fitting
0.1150X2X4 + 16.47X3X4 − 25.23X12 − 18.44X22 − 23.29X32 − 15.80X42
2.3. Effects of Processing Parameters on Extraction
2.4. ANN Modeling and ANN Coupled with GA Optimization
2.5. Comparative Analysis of RSM and ANN
3. Materials and Methods
3.1. Materials and Chemicals
3.2. EA Extraction
3.3. EA Content Determination
3.4. Experimental Design and Statistical Analysis of RSM
3.5. Artificial Neural Network Model with Genetic Algorithm
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Run | Independent Variables | The Yield of Ellagic Acid (mg g−1) | |||||
---|---|---|---|---|---|---|---|
X1 | X2 | X3 | X4 | Actual Values | RSM Predicted | ANN Predicted | |
1 | 40 | 70 | 25 | 90 | 80.05 | 80.12 | 84.78 |
2 | 40 | 90 | 25 | 90 | 87.39 | 81.54 | 88.02 |
3 | 80 | 70 | 25 | 90 | 72.95 | 71.99 | 72.95 |
4 | 80 | 90 | 25 | 90 | 84.64 | 77.75 | 84.23 |
5 | 60 | 80 | 20 | 60 | 89.92 | 79.11 | 90.23 |
6 | 60 | 80 | 30 | 60 | 71.11 | 72.19 | 72.68 |
7 | 60 | 80 | 20 | 120 | 67.61 | 59.72 | 69.70 |
8 | 60 | 80 | 30 | 120 | 114.69 | 118.69 | 114.82 |
9 | 60 | 70 | 25 | 60 | 78.68 | 76.96 | 78.92 |
10 | 60 | 90 | 25 | 60 | 69.51 | 70.46 | 72.41 |
11 | 60 | 70 | 25 | 120 | 78.68 | 80.42 | 81.99 |
12 | 60 | 90 | 25 | 120 | 89.69 | 94.10 | 92.77 |
13 | 40 | 80 | 20 | 90 | 59.01 | 68.56 | 59.54 |
14 | 80 | 80 | 20 | 90 | 63.82 | 65.00 | 63.82 |
15 | 40 | 80 | 30 | 90 | 95.48 | 96.99 | 94.54 |
16 | 80 | 80 | 30 | 90 | 95.48 | 88.62 | 100.85 |
17 | 60 | 70 | 20 | 90 | 56.09 | 58.45 | 58.75 |
18 | 60 | 90 | 20 | 90 | 55.91 | 61.52 | 56.74 |
19 | 60 | 70 | 30 | 90 | 85.45 | 83.96 | 85.66 |
20 | 60 | 90 | 30 | 90 | 86.31 | 88.07 | 87.78 |
21 | 40 | 80 | 25 | 60 | 82.81 | 83.36 | 83.89 |
22 | 80 | 80 | 25 | 60 | 67.67 | 77.63 | 69.01 |
23 | 40 | 80 | 25 | 120 | 102.99 | 97.15 | 106.77 |
24 | 80 | 80 | 25 | 120 | 87.39 | 90.96 | 87.54 |
25 | 60 | 80 | 25 | 90 | 125.46 | 121.52 | 123.39 |
26 | 60 | 80 | 25 | 90 | 123.4 | 121.52 | 123.39 |
27 | 60 | 80 | 25 | 90 | 118.58 | 121.52 | 123.39 |
28 | 60 | 80 | 25 | 90 | 116.52 | 121.52 | 123.39 |
29 | 60 | 80 | 25 | 90 | 123.63 | 121.52 | 123.39 |
Source | Sum of Squares | df | Mean SQUARE | F-Value | p-Value | |
---|---|---|---|---|---|---|
Model | 11,462.56 | 14 | 818.75 | 16.54 | <0.0001 | Significant |
X1 | 38.7 | 1 | 38.7 | 0.7817 | 0.3916 | |
X2 | 106.68 | 1 | 106.68 | 2.15 | 0.1642 | |
X3 | 2032.16 | 1 | 2032.16 | 41.05 | <0.0001 | |
X4 | 551.49 | 1 | 551.49 | 11.14 | 0.0049 | |
X1X2 | 4.73 | 1 | 4.73 | 0.0956 | 0.7618 | |
X1X3 | 0.2704 | 1 | 0.2704 | 0.0055 | 0.9421 | |
X1X4 | 101.81 | 1 | 101.81 | 2.06 | 0.1735 | |
X2X3 | 5.78 | 1 | 5.78 | 0.1168 | 0.7376 | |
X2X4 | 0.0529 | 1 | 0.0529 | 0.0011 | 0.9744 | |
X3X4 | 1085.37 | 1 | 1085.37 | 21.92 | 0.0004 | |
X12 | 4128.66 | 1 | 4128.66 | 83.39 | <0.0001 | |
X22 | 2205.09 | 1 | 2205.09 | 44.54 | <0.0001 | |
X32 | 3517.75 | 1 | 3517.75 | 71.05 | <0.0001 | |
X42 | 1620.11 | 1 | 1620.11 | 32.72 | <0.0001 | |
Residual | 693.13 | 14 | 49.51 | |||
Lack of Fit | 635.98 | 10 | 63.6 | 4.45 | 0.0816 | Not significant |
Pure Error | 57.15 | 4 | 14.29 | |||
Cor Total | 12,155.69 | 28 |
Optimum Condition | Extraction Yield (mg g−1) | Relative Error | |||||
---|---|---|---|---|---|---|---|
X1 | X2 | X3 | X4 | Actual Value | Predicted Value | ||
RSM ANN | 60 61 | 78 77 | 27 26 | 101 103 | 128.31 130.21 | 125.46 131 | 2.85 0.79 |
Experimental Parameters | Unit | Symbols (Xi) | Coded Values | ||
---|---|---|---|---|---|
Low (−1) | Medium (0) | High (+1) | |||
Ethanol concentration | % | X1 | 40 | 60 | 80 |
Extraction temperature | °C | X2 | 70 | 80 | 90 |
liquid-solid ratio | mL g−1 | X3 | 20 | 25 | 30 |
Extraction time | min | X4 | 60 | 90 | 120 |
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Wu, J.; Yang, F.; Guo, L.; Sheng, Z. Modeling and Optimization of Ellagic Acid from Chebulae Fructus Using Response Surface Methodology Coupled with Artificial Neural Network. Molecules 2024, 29, 3953. https://doi.org/10.3390/molecules29163953
Wu J, Yang F, Guo L, Sheng Z. Modeling and Optimization of Ellagic Acid from Chebulae Fructus Using Response Surface Methodology Coupled with Artificial Neural Network. Molecules. 2024; 29(16):3953. https://doi.org/10.3390/molecules29163953
Chicago/Turabian StyleWu, Junkai, Fan Yang, Liyang Guo, and Zunlai Sheng. 2024. "Modeling and Optimization of Ellagic Acid from Chebulae Fructus Using Response Surface Methodology Coupled with Artificial Neural Network" Molecules 29, no. 16: 3953. https://doi.org/10.3390/molecules29163953