The joint effect of ultrasound and magnetic Fe3O4 nanoparticles on the yield of 2,6-dimethoxy-ρ-benzoquinone from fermented wheat germ: Comparison of evolutionary algorithms and interactive analysis of paired-factors
•PSO-ACO was the best training algorithm of the ANN in present work.•The synergistic enhancement effect of Fe3O4 nanoparticles and ultrasound was found.•The optimal solution was confirmed by comparison of 6 optimization algorithms. 2,6-Dimethoxy-ρ-benzoquinone (DMBQ) is a potential anti-tumor substa...
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| Published in | Food chemistry Vol. 302; p. 125275 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.01.2020
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0308-8146 1873-7072 1873-7072 |
| DOI | 10.1016/j.foodchem.2019.125275 |
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| Abstract | •PSO-ACO was the best training algorithm of the ANN in present work.•The synergistic enhancement effect of Fe3O4 nanoparticles and ultrasound was found.•The optimal solution was confirmed by comparison of 6 optimization algorithms.
2,6-Dimethoxy-ρ-benzoquinone (DMBQ) is a potential anti-tumor substance found in the fermented wheat germ. In this study, ultrasound and Fe3O4 nanoparticles were used to improve the DMBQ yield. An artificial neural network (ANN) embedded separately with the back-propagation algorithm (BP), genetic algorithm (GA), particle swarm optimized algorithm (PSO), ant colony optimized algorithm (ACO), GA-ACO, GA-PSO and PSO-ACO, were used to establish the relationship between 11 factors and DMBQ yield. The robustness and generalization of PSO-ACO-ANN, which gave the minimum mean squared error and mean absolute percentage error for the training and test dataset, was superior to the others. Next, a modified Garson’s algorithm and mixed partial derivatives algorithm indicated that the most influential paired-parameters were ultrasonic power and concentration of nanoparticles. Finally, the factors were optimized by six optimization algorithms, and confirmatory experimental results indicated that the optimum DMBQ yield was 0.213 ± 0.007 mg/g, which was 161.2% higher than the control. |
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| AbstractList | 2,6-Dimethoxy-ρ-benzoquinone (DMBQ) is a potential anti-tumor substance found in the fermented wheat germ. In this study, ultrasound and Fe3O4 nanoparticles were used to improve the DMBQ yield. An artificial neural network (ANN) embedded separately with the back-propagation algorithm (BP), genetic algorithm (GA), particle swarm optimized algorithm (PSO), ant colony optimized algorithm (ACO), GA-ACO, GA-PSO and PSO-ACO, were used to establish the relationship between 11 factors and DMBQ yield. The robustness and generalization of PSO-ACO-ANN, which gave the minimum mean squared error and mean absolute percentage error for the training and test dataset, was superior to the others. Next, a modified Garson’s algorithm and mixed partial derivatives algorithm indicated that the most influential paired-parameters were ultrasonic power and concentration of nanoparticles. Finally, the factors were optimized by six optimization algorithms, and confirmatory experimental results indicated that the optimum DMBQ yield was 0.213±0.007 mg/g, which was 161.2% higher than the control. 2,6-Dimethoxy-ρ-benzoquinone (DMBQ) is a potential anti-tumor substance found in the fermented wheat germ. In this study, ultrasound and Fe3O4 nanoparticles were used to improve the DMBQ yield. An artificial neural network (ANN) embedded separately with the back-propagation algorithm (BP), genetic algorithm (GA), particle swarm optimized algorithm (PSO), ant colony optimized algorithm (ACO), GA-ACO, GA-PSO and PSO-ACO, were used to establish the relationship between 11 factors and DMBQ yield. The robustness and generalization of PSO-ACO-ANN, which gave the minimum mean squared error and mean absolute percentage error for the training and test dataset, was superior to the others. Next, a modified Garson's algorithm and mixed partial derivatives algorithm indicated that the most influential paired-parameters were ultrasonic power and concentration of nanoparticles. Finally, the factors were optimized by six optimization algorithms, and confirmatory experimental results indicated that the optimum DMBQ yield was 0.213 ± 0.007 mg/g, which was 161.2% higher than the control.2,6-Dimethoxy-ρ-benzoquinone (DMBQ) is a potential anti-tumor substance found in the fermented wheat germ. In this study, ultrasound and Fe3O4 nanoparticles were used to improve the DMBQ yield. An artificial neural network (ANN) embedded separately with the back-propagation algorithm (BP), genetic algorithm (GA), particle swarm optimized algorithm (PSO), ant colony optimized algorithm (ACO), GA-ACO, GA-PSO and PSO-ACO, were used to establish the relationship between 11 factors and DMBQ yield. The robustness and generalization of PSO-ACO-ANN, which gave the minimum mean squared error and mean absolute percentage error for the training and test dataset, was superior to the others. Next, a modified Garson's algorithm and mixed partial derivatives algorithm indicated that the most influential paired-parameters were ultrasonic power and concentration of nanoparticles. Finally, the factors were optimized by six optimization algorithms, and confirmatory experimental results indicated that the optimum DMBQ yield was 0.213 ± 0.007 mg/g, which was 161.2% higher than the control. •PSO-ACO was the best training algorithm of the ANN in present work.•The synergistic enhancement effect of Fe3O4 nanoparticles and ultrasound was found.•The optimal solution was confirmed by comparison of 6 optimization algorithms. 2,6-Dimethoxy-ρ-benzoquinone (DMBQ) is a potential anti-tumor substance found in the fermented wheat germ. In this study, ultrasound and Fe3O4 nanoparticles were used to improve the DMBQ yield. An artificial neural network (ANN) embedded separately with the back-propagation algorithm (BP), genetic algorithm (GA), particle swarm optimized algorithm (PSO), ant colony optimized algorithm (ACO), GA-ACO, GA-PSO and PSO-ACO, were used to establish the relationship between 11 factors and DMBQ yield. The robustness and generalization of PSO-ACO-ANN, which gave the minimum mean squared error and mean absolute percentage error for the training and test dataset, was superior to the others. Next, a modified Garson’s algorithm and mixed partial derivatives algorithm indicated that the most influential paired-parameters were ultrasonic power and concentration of nanoparticles. Finally, the factors were optimized by six optimization algorithms, and confirmatory experimental results indicated that the optimum DMBQ yield was 0.213 ± 0.007 mg/g, which was 161.2% higher than the control. |
| ArticleNumber | 125275 |
| Author | Li, Lin Zheng, Zi-Yi Xie, Guo Liu, Wen-Li |
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| Snippet | •PSO-ACO was the best training algorithm of the ANN in present work.•The synergistic enhancement effect of Fe3O4 nanoparticles and ultrasound was found.•The... 2,6-Dimethoxy-ρ-benzoquinone (DMBQ) is a potential anti-tumor substance found in the fermented wheat germ. In this study, ultrasound and Fe3O4 nanoparticles... |
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| SubjectTerms | algorithms ant colonies Artificial neural network benzoquinones data collection Evolutionary algorithm iron oxides magnetism Nanoparticles neural networks ultrasonics Ultrasound wheat germ |
| Title | The joint effect of ultrasound and magnetic Fe3O4 nanoparticles on the yield of 2,6-dimethoxy-ρ-benzoquinone from fermented wheat germ: Comparison of evolutionary algorithms and interactive analysis of paired-factors |
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