Ecofriendly Extraction of Polyphenols from <i>Ampelopsis grossedentata</i> Leaves Coupled with Response Surface Methodology and Artificial Neural Network–Genetic Algorithm
This study aimed to optimize a novel deep eutectic solvents (DESs)-assisted extraction process for polyphenols in the leaves of <i>Ampelopsis grossedentata</i> (AGPL) with response surface methodology (RSM) and a genetic algorithm–artificial neural network (GA-ANN). Under the influence o...
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2025-05-01
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| author | Xubo Huang Chen Li Yanbin Wang Jinrong Jiang Weizhi Wu Shifeng Wang Ming Lin Liang He |
| author_facet | Xubo Huang Chen Li Yanbin Wang Jinrong Jiang Weizhi Wu Shifeng Wang Ming Lin Liang He |
| author_sort | Xubo Huang |
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| description | This study aimed to optimize a novel deep eutectic solvents (DESs)-assisted extraction process for polyphenols in the leaves of <i>Ampelopsis grossedentata</i> (AGPL) with response surface methodology (RSM) and a genetic algorithm–artificial neural network (GA-ANN). Under the influence of ultrasonic excitation, the L-carnitine-1,4-butanediol system was selected for the phenolics extraction process. The ideal conditions for AGPL extraction were the following: liquid to solid ratio of 35.5 mL/g, ultrasonic power of 697 W and extraction duration of 46 min. Under those conditions, the actual AGPL yield was 15.32% ± 0.12%. The statistical analysis showed that both models could predict AGPL yield well and GA-ANN had relatively higher accuracy in the prediction of AGPL output, supported by the coefficient of determination (R<sup>2</sup> = 0.9809) in GA-based ANN compared to R<sup>2</sup> = 0.9145 in RSM, as well as lower values for mean squared error (MSE = 0.0279), root mean squared error (RMSE = 0.1669) and absolute average deviation (AAD = 0.1336) in the GA-ANN model. Moreover, the extracted polyphenols were determined by HPLC-MS to have 20 phenolic compounds corresponding to some bioactive acids such as nonadecanoic acid and neochlorogenic acid. The in vitro ORAC assay revealed that Carn-Bu4 assisted AGPL extract exhibited a notable antioxidant capacity of 275.3 ± 0.64 μmol TE/g. |
| format | Article |
| id | doaj-art-c04b1b462d0d4445bc410f335d421fa5 |
| institution | OA Journals |
| issn | 1420-3049 |
| language | English |
| publishDate | 2025-05-01 |
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| series | Molecules |
| spelling | doaj-art-c04b1b462d0d4445bc410f335d421fa52025-08-20T02:23:00ZengMDPI AGMolecules1420-30492025-05-013011235410.3390/molecules30112354Ecofriendly Extraction of Polyphenols from <i>Ampelopsis grossedentata</i> Leaves Coupled with Response Surface Methodology and Artificial Neural Network–Genetic AlgorithmXubo Huang0Chen Li1Yanbin Wang2Jinrong Jiang3Weizhi Wu4Shifeng Wang5Ming Lin6Liang He7Key Laboratory of State Forest Food Resources Utilization and Quality Control, Zhejiang Academy of Forestry, Hangzhou 310023, ChinaKey Laboratory of State Forest Food Resources Utilization and Quality Control, Zhejiang Academy of Forestry, Hangzhou 310023, ChinaKey Laboratory of State Forest Food Resources Utilization and Quality Control, Zhejiang Academy of Forestry, Hangzhou 310023, ChinaForestry Technology Extension Station, Qingtian County Forestry Bureau, Lishui 323999, ChinaKey Laboratory of State Forest Food Resources Utilization and Quality Control, Zhejiang Academy of Forestry, Hangzhou 310023, ChinaForestry Technology Extension Station, Qingtian County Forestry Bureau, Lishui 323999, ChinaKey Laboratory of State Forest Food Resources Utilization and Quality Control, Zhejiang Academy of Forestry, Hangzhou 310023, ChinaKey Laboratory of State Forest Food Resources Utilization and Quality Control, Zhejiang Academy of Forestry, Hangzhou 310023, ChinaThis study aimed to optimize a novel deep eutectic solvents (DESs)-assisted extraction process for polyphenols in the leaves of <i>Ampelopsis grossedentata</i> (AGPL) with response surface methodology (RSM) and a genetic algorithm–artificial neural network (GA-ANN). Under the influence of ultrasonic excitation, the L-carnitine-1,4-butanediol system was selected for the phenolics extraction process. The ideal conditions for AGPL extraction were the following: liquid to solid ratio of 35.5 mL/g, ultrasonic power of 697 W and extraction duration of 46 min. Under those conditions, the actual AGPL yield was 15.32% ± 0.12%. The statistical analysis showed that both models could predict AGPL yield well and GA-ANN had relatively higher accuracy in the prediction of AGPL output, supported by the coefficient of determination (R<sup>2</sup> = 0.9809) in GA-based ANN compared to R<sup>2</sup> = 0.9145 in RSM, as well as lower values for mean squared error (MSE = 0.0279), root mean squared error (RMSE = 0.1669) and absolute average deviation (AAD = 0.1336) in the GA-ANN model. Moreover, the extracted polyphenols were determined by HPLC-MS to have 20 phenolic compounds corresponding to some bioactive acids such as nonadecanoic acid and neochlorogenic acid. The in vitro ORAC assay revealed that Carn-Bu4 assisted AGPL extract exhibited a notable antioxidant capacity of 275.3 ± 0.64 μmol TE/g.https://www.mdpi.com/1420-3049/30/11/2354<i>Ampelopsis grossedentata</i> leavesdeep eutectic solventextractionresponse surface methodologyartificial neural network |
| spellingShingle | Xubo Huang Chen Li Yanbin Wang Jinrong Jiang Weizhi Wu Shifeng Wang Ming Lin Liang He Ecofriendly Extraction of Polyphenols from <i>Ampelopsis grossedentata</i> Leaves Coupled with Response Surface Methodology and Artificial Neural Network–Genetic Algorithm Molecules <i>Ampelopsis grossedentata</i> leaves deep eutectic solvent extraction response surface methodology artificial neural network |
| title | Ecofriendly Extraction of Polyphenols from <i>Ampelopsis grossedentata</i> Leaves Coupled with Response Surface Methodology and Artificial Neural Network–Genetic Algorithm |
| title_full | Ecofriendly Extraction of Polyphenols from <i>Ampelopsis grossedentata</i> Leaves Coupled with Response Surface Methodology and Artificial Neural Network–Genetic Algorithm |
| title_fullStr | Ecofriendly Extraction of Polyphenols from <i>Ampelopsis grossedentata</i> Leaves Coupled with Response Surface Methodology and Artificial Neural Network–Genetic Algorithm |
| title_full_unstemmed | Ecofriendly Extraction of Polyphenols from <i>Ampelopsis grossedentata</i> Leaves Coupled with Response Surface Methodology and Artificial Neural Network–Genetic Algorithm |
| title_short | Ecofriendly Extraction of Polyphenols from <i>Ampelopsis grossedentata</i> Leaves Coupled with Response Surface Methodology and Artificial Neural Network–Genetic Algorithm |
| title_sort | ecofriendly extraction of polyphenols from i ampelopsis grossedentata i leaves coupled with response surface methodology and artificial neural network genetic algorithm |
| topic | <i>Ampelopsis grossedentata</i> leaves deep eutectic solvent extraction response surface methodology artificial neural network |
| url | https://www.mdpi.com/1420-3049/30/11/2354 |
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