Optimization of water extraction technology of Xiangqin jiere granules by orthogonal design based on G1-entropy weight compared with BP neural network
- VernacularTitle:基于G1-熵权法的正交实验设计对比BP神经网络优化香芩解热颗粒水提工艺
- Author:
Bingduo CHENG
1
,
2
,
3
;
Liqin LUO
1
,
2
;
Yuanzeng LI
1
,
2
,
3
;
Jie JIANG
1
,
2
,
3
;
Yiying CHEN
1
,
2
,
3
;
Ji ZHAO
1
,
2
;
Rui XUE
1
,
2
;
Yunshu MA
1
,
2
Author Information
1. School of TCM，Yunnan University of Chinese Medicine，Kunming 650500，China
2. Yunnan Key Lab of Dai and Yi Medicine，Kunming 650500，China
3. Key Laboratory of External Drug Delivery System and Preparation Technology Research in Universities of Yunnan Province/Yunnan Provincial Key Laboratory of Sustainable Utilization of Southern Medicine/Engineering Research Center for Medicine and Food Homologous Beverage of Yunnan Province，Kunming 650500，China
- Publication Type:Journal Article
- Keywords:
Xiangqin jiere granules;
water extraction technology;
G1-entropy weight;
orthogonal test;
BP neural network
- From:
China Pharmacy
2024;35(1):27-32
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE Optimizing the water extraction technology of Xiangqin jiere granules. METHODS The orthogonal test of 3 factors and 3 levels was designed， and comprehensive scoring was conducted for the above indexes by using G1-entropy weight to obtain the optimized water extraction technology of Xiangqin jiere granules with water addition ratio， extraction time and extraction times as factors， using the contents of forsythoside A， baicalin， phillyrin， oroxylin A-7-O-β-D-glycoside， wogonoside， baicalein and wogonin， and extraction rate as evaluation indexes. BP neural network modeling was used to optimize the network model and water extraction process using the results of 9 groups of orthogonal tests as test and training data， the water addition multiple， decocting time and extraction times as input nodes， and the comprehensive score as output nodes. Then the two analysis methods were compared by verification test to find the best water extraction process parameters. RESULTS The water extraction technology optimized by the orthogonal test was 8-fold water， extracting 3 times， extracting for 1 h each time. Comprehensive score was 96.84 （RSD＝0.90%）. The optimal water extraction technology obtained by BP neural network modeling included 12-fold water， extracting 4 times， extracting for 0.5 h each time. The comprehensive score was 92.72 （RSD＝0.77%）， which was slightly lower than that of the orthogonal test. CONCLUSIONS The water extraction technology of Xiangqin jiere granules is optimized successfully in the study， which includes adding 8-fold water， extracting 3 times， and extracting for 1 hour each time.
