Statistical Design of Experimental and Bootstrap Neural Network Modelling Approach for Thermoseparating Aqueous Two-Phase Extraction of Polyhydroxyalkanoates
Abstract
:1. Introduction
2. Materials and Methods
2.1. Materials
2.2. Production of PHAs
2.3. Experimental Design and Statistical Analysis
2.4. Partitioning of PHAs in Thermoseparating ATPE
2.5. Quantification of PHAs by Gas Chromatography (GC) Analysis
2.6. Partitioning Behaviors of PHAs
2.7. Neural Network Methodology
3. Results and Discussion
3.1. Statistical Experimental Result
3.1.1. Effect on the “Yield”
3.1.2. Effect on the “Partition Coefficient”
3.1.3. Effect on the “Purification Factor”
3.2. Feed Forward Neural Network (FFNN) Model Results
4. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Run | X1 (wt %) | X2 (wt %) | X3 | X4 (mM) | Kpa | Yield (%) | PF (fold) |
---|---|---|---|---|---|---|---|
1 | 8 | 10 | 10 | 0 | 0.814 | 24.6 | 0.93 |
2 | 8 | 10 | 8 | 0 | 0.646 | 21.4 | 0.93 |
3 | 15 | 10 | 10 | 0 | 3.371 | 57.4 | 1.35 |
4 | 8 | 18 | 8 | 0 | 0.431 | 25.5 | 1.07 |
5 | 8 | 18 | 8 | 100 | 0.402 | 24.8 | 1.04 |
6 | 15 | 18 | 10 | 0 | 16.547 | 93.9 | 1.46 |
7 | 15 | 18 | 10 | 100 | 5.585 | 85.5 | 1.48 |
8 | 15 | 10 | 8 | 0 | 1.554 | 38.3 | 0.95 |
9 | 8 | 10 | 8 | 100 | 0.525 | 16.8 | 0.67 |
10 | 15 | 18 | 8 | 100 | 12.291 | 92.5 | 1.54 |
11 | 15 | 18 | 8 | 0 | 8.047 | 86.8 | 1.52 |
12 | 15 | 10 | 8 | 100 | 1.314 | 32.9 | 1.22 |
13 | 8 | 18 | 10 | 0 | 0.512 | 30.1 | 0.96 |
14 | 15 | 10 | 10 | 100 | 2.322 | 45.5 | 1.04 |
15 | 8 | 18 | 10 | 100 | 0.369 | 22.7 | 1.04 |
16 | 8 | 10 | 10 | 100 | 0.817 | 24.6 | 1.21 |
Source | Sum of squares | Degree of freedom | Mean square | F value | Prob>F |
---|---|---|---|---|---|
Model | 12070.8 | 14 | 862.2 | 1007.687 | 0.0247 |
X1 | 7323.1 | 1 | 7323.1 | 8558.750 | 0.0069 |
X2 | 2507.5 | 1 | 2507.5 | 2930.613 | 0.0118 |
X3 | 128.3 | 1 | 128.3 | 149.897 | 0.0519 |
X4 | 66.8 | 1 | 66.8 | 78.107 | 0.0717 |
X1X2 | 1783.0 | 1 | 1783.0 | 2083.799 | 0.0139 |
X1X3 | 20.9 | 1 | 20.9 | 24.462 | 0.1270 |
X1X4 | 3.3 | 1 | 3.3 | 3.893 | 0.2986 |
X2X3 | 100.5 | 1 | 100.5 | 117.459 | 0.0586 |
X2X4 | 7.7 | 1 | 7.7 | 9.000 | 0.2048 |
X3X4 | 32.2 | 1 | 32.2 | 37.640 | 0.1029 |
X1X2X3 | 33.4 | 1 | 33.4 | 38.978 | 0.1011 |
X1X2X4 | 20.5 | 1 | 20.5 | 23.931 | 0.1284 |
X1X3X4 | 21.4 | 1 | 21.4 | 25.000 | 0.1257 |
X2X3X4 | 22.3 | 1 | 22.3 | 26.093 | 0.1231 |
Residual | 0.9 | 1 | 0.9 | ||
Cor Total | 12071.7 | 15 | |||
R2 | 0.9999 | ||||
R2adj | 0.999 | ||||
R2pred | 0.982 |
Source | Sum of squares | Degree of freedom | Mean square | F value | Prob>F |
---|---|---|---|---|---|
Model | 3.568 | 9 | 0.396 | 221.5 | <0.0001 |
X1 | 2.891 | 1 | 2.891 | 1615.4 | <0.0001 |
X3 | 0.052 | 1 | 0.0516 | 28.8 | 0.0015 |
X4 | 0.033 | 1 | 0.0332 | 18.5 | 0.0057 |
X1X2 | 0.516 | 1 | 0.516 | 288.5 | <0.0001 |
X2X3 | 0.046 | 1 | 0.0460 | 25.7 | 0.0022 |
X2X3X4 | 0.018 | 1 | 0.0179 | 10.0 | 0.0265 |
Residual | 0.011 | 6 | 0.0018 | ||
Cor Total | 3.579 | 15 | |||
R2 | 0.997 | ||||
R2adj | 0.992 | ||||
R2pred | 0.979 |
Source | Sum of squares | Degree of freedom | Mean square | F value | Prob>F |
---|---|---|---|---|---|
Model | 0.955 | 6 | 0.159 | 42.6 | <0.0001 |
X1 | 0.459 | 1 | 0.459 | 122.9 | <0.0001 |
X2 | 0.205 | 1 | 0.205 | 54.8 | <0.0001 |
X1X2 | 0.072 | 1 | 0.071556 | 19.2 | 0.0018 |
X2X3 | 0.061 | 1 | 0.061256 | 16.4 | 0.0029 |
X1X3X4 | 0.095 | 1 | 0.094556 | 25.3 | 0.0007 |
X1X2X3X4 | 0.064 | 1 | 0.063756 | 17.1 | 0.0026 |
Residual | 0.034 | 9 | 0.003734 | ||
Cor Total | 0.988 | 15 | |||
R2 | 0.966 | ||||
R2adj | 0.943 | ||||
R2pred | 0.893 |
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Leong, Y.K.; Chang, C.-K.; Arumugasamy, S.K.; Lan, J.C.-W.; Loh, H.-S.; Muhammad, D.; Show, P.L. Statistical Design of Experimental and Bootstrap Neural Network Modelling Approach for Thermoseparating Aqueous Two-Phase Extraction of Polyhydroxyalkanoates. Polymers 2018, 10, 132. https://doi.org/10.3390/polym10020132
Leong YK, Chang C-K, Arumugasamy SK, Lan JC-W, Loh H-S, Muhammad D, Show PL. Statistical Design of Experimental and Bootstrap Neural Network Modelling Approach for Thermoseparating Aqueous Two-Phase Extraction of Polyhydroxyalkanoates. Polymers. 2018; 10(2):132. https://doi.org/10.3390/polym10020132
Chicago/Turabian StyleLeong, Yoong Kit, Chih-Kai Chang, Senthil Kumar Arumugasamy, John Chi-Wei Lan, Hwei-San Loh, Dinie Muhammad, and Pau Loke Show. 2018. "Statistical Design of Experimental and Bootstrap Neural Network Modelling Approach for Thermoseparating Aqueous Two-Phase Extraction of Polyhydroxyalkanoates" Polymers 10, no. 2: 132. https://doi.org/10.3390/polym10020132
APA StyleLeong, Y. K., Chang, C.-K., Arumugasamy, S. K., Lan, J. C.-W., Loh, H.-S., Muhammad, D., & Show, P. L. (2018). Statistical Design of Experimental and Bootstrap Neural Network Modelling Approach for Thermoseparating Aqueous Two-Phase Extraction of Polyhydroxyalkanoates. Polymers, 10(2), 132. https://doi.org/10.3390/polym10020132