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人工智能辅助超声提取. 总黄酮

Artificial Intelligence Assisted Ultrasonic Extraction of Total Flavonoids from .

机构信息

Key Laboratory of Plant Physiology and Developmental Regulation of Guizhou Province, College of Life Sciences, Guizhou Normal University, Guiyang 550025, China.

Guizhou Provincial Key Laboratory for Information Systems of Mountainous Areas and Protection of Ecological Environment, Guizhou Normal University, Guiyang 550001, China.

出版信息

Molecules. 2021 Jun 23;26(13):3835. doi: 10.3390/molecules26133835.

DOI:10.3390/molecules26133835
PMID:34201870
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8270336/
Abstract

Flavonoids in were studied. The flavonoids in were extracted by ultrasonic method, and the extraction conditions were modeled and optimized by response the surface methodology and the artificial intelligence method. The results show that the ultrasonic method can effectively extract total flavonoids, and the extraction rate is close to the prediction value of ANN-GA algorithm, which proves the rationality of the model. The order of the effects of the parameters on the experiment was material liquid ratio > extraction power > extraction time > ethanol concentration. In addition, the scavenging effects of flavonoids on DPPH, O· and ·OH were also determined, and these indicated that flavonoids have strong antioxidant activities. The kinetics of the extraction process was studied by using the data of the extraction process, and it was found that the extraction process conformed to Fick's first law.

摘要

对 中的类黄酮进行了研究。采用超声法提取 中的类黄酮,并用响应面法和人工智能法对提取条件进行了建模和优化。结果表明,超声法能有效地提取总黄酮,且提取率接近 ANN-GA 算法的预测值,证明了模型的合理性。各参数对实验的影响顺序为料液比>提取功率>提取时间>乙醇浓度。此外,还测定了类黄酮对 DPPH、O·和·OH 的清除作用,表明类黄酮具有较强的抗氧化活性。通过提取过程的数据对提取过程的动力学进行了研究,发现提取过程符合菲克第一定律。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c30b/8270336/23f65fb12ab8/molecules-26-03835-g012.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c30b/8270336/77deaaf4a52c/molecules-26-03835-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c30b/8270336/937fcf6ffce0/molecules-26-03835-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c30b/8270336/d61bbed0bdb5/molecules-26-03835-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c30b/8270336/2af352808c69/molecules-26-03835-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c30b/8270336/6fd1acad058c/molecules-26-03835-g008a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c30b/8270336/93d0572b7c54/molecules-26-03835-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c30b/8270336/e7dbe9435b2d/molecules-26-03835-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c30b/8270336/d2ec5e53124e/molecules-26-03835-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c30b/8270336/23f65fb12ab8/molecules-26-03835-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c30b/8270336/3e912e254e23/molecules-26-03835-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c30b/8270336/734342ca9995/molecules-26-03835-g002a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c30b/8270336/3f1600b532f3/molecules-26-03835-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c30b/8270336/77deaaf4a52c/molecules-26-03835-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c30b/8270336/937fcf6ffce0/molecules-26-03835-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c30b/8270336/d61bbed0bdb5/molecules-26-03835-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c30b/8270336/2af352808c69/molecules-26-03835-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c30b/8270336/6fd1acad058c/molecules-26-03835-g008a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c30b/8270336/93d0572b7c54/molecules-26-03835-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c30b/8270336/e7dbe9435b2d/molecules-26-03835-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c30b/8270336/d2ec5e53124e/molecules-26-03835-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c30b/8270336/23f65fb12ab8/molecules-26-03835-g012.jpg

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