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Multi-index optimization of extraction process of the body of Angelica sinensis (Oliv.) Diels by BP neural network combined with entropy weight method*

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  • 1. Gansu Provincial Hospital of TCM, Lanzhou 730050, China;
    2. Lanzhou City Chengguan District, Baiyin Road Street Community Sanitary Service Center, Lanzhou 730030, China;
    3. Research Center of Processing Technology for Chinese Materia Medica, Zhejiang Chinese Medical University, Hangzhou 311401, China

Revised date: 2022-12-26

  Online published: 2024-06-24

Abstract

Objective: To optimize the extraction process of the body of Angelica sinensis (Oliv.) Diels BP neural network combined with orthogonal experiment. Methods: The extraction temperature, the liquid amount and the extraction time were taken as factors. Entropy weight method was used to calculate the comprehensive scores of the multi-indicators of the content and four active components of chlorogenic acid, ferulic acid, imperatorin and butenylphthalide. using comprehensive score as an evaluation indicator.The BP neural network model was established by orthogonal experiment design, and the optimal extraction process of the body of Angelica sinensis (Oliv.) Diels was predicted through network training. Results: The optimized extraction process of the body of Angelica sinensis (Oliv.) Diels was carried out by adding 12 times of 70% methanol, extracting 80 minute at 87 ℃. The relative error between the network predicted value and the actual measured value of the test sample was 0.776 4%. Conclusion: The established mathematical model can analyze and predict the extraction process of the body of Angelica sinensis (Oliv.) Diels. The obtained process is stable and feasible, and can effectively extract the active ingredients in the body of Angelica sinensis (Oliv.) Diels.

Cite this article

XU Zhi-wei, WANG Bao-cai, DAN Xiao-san, BI Ying-yan, LI Ji-wen, BIAN Na, DU Wei-feng . Multi-index optimization of extraction process of the body of Angelica sinensis (Oliv.) Diels by BP neural network combined with entropy weight method*[J]. Chinese Journal of Pharmaceutical Analysis, 2023 , 43(2) : 341 -347 . DOI: 10.16155/j.0254-1793.2023.02.18

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