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Artificial Intelligence Assisted Ultrasonic Extraction of Total Flavonoids from Rosa sterilis

descriptionPublicationkeyboard_double_arrow_right Article , Other literature type , Journal 23 Jun 2021Publisher:MDPI AGJournal:Molecules, volume 26, page 3,835 (eissn: 1420-3049,

Authors: Jing Liu; Chaochan Li; Guijie Ding; Wenxuan Quan;

doi: 10.3390/molecules26133835

pmid: 34201870

pmc: PMC8270336

Artificial Intelligence Assisted Ultrasonic Extraction of Total Flavonoids from Rosa sterilis

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Abstract

Flavonoids in Rosa sterilis were studied. The flavonoids in Rosa sterilis 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, O2−· 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.

Related Organizations

Guizhou University
China (People's Republic of)
Guizhou Normal University
China (People's Republic of)
Guizhou Normal University
China (People's Republic of)
Guizhou University
China (People's Republic of)

Keywords

ultrasonic, Organic chemistry, Rosa, Article, Antioxidants, <i>Rosa sterilis</i>, QD241-441, Picrates, Artificial Intelligence, Ultrasonics, Flavonoids, Ethanol, Plant Extracts, Biphenyl Compounds, Free Radical Scavengers, free radical scavenging, artificial intelligence, Kinetics, Ultrasonic Waves, kinetics, extraction, Neural Networks, Computer, Algorithms

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	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%