Application of near infrared spectroscopy to predict contents of various lactones in chromatographic process of Ginkgo Folium.
10.19540/j.cnki.cjcmm.20211206.202
- Author:
Yan-Qin HE
1
;
Chu-Hong ZONG
2
;
Jun WANG
3
;
Qian LI
2
;
Jun WANG
4
;
Yong-Jiang WU
2
;
Yong CHEN
2
;
Xue-Song LIU
1
Author Information
1. College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058, China Innovation Institute for Artificial Intelligence in Medicine, Zhejiang University Hangzhou 310018, China.
2. College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058, China.
3. Shanghai Shangyao Xingling Technology Pharmaceutical Co., Ltd. Shanghai 201703, China.
4. Suzhou Zeda Xingbang Pharmaceutical Technology Co., Ltd. Suzhou 215000, China.
- Publication Type:Journal Article
- Keywords:
chromatographic process;
genetic algorithm joint extreme learning machine(GA-ELM);
ginkgolide;
near infrared spectroscopy(NIRS);
rapid determination
- MeSH:
Chromatography, High Pressure Liquid;
Ginkgo biloba;
Lactones/analysis*;
Least-Squares Analysis;
Spectroscopy, Near-Infrared/methods*
- From:
China Journal of Chinese Materia Medica
2022;47(5):1293-1299
- CountryChina
- Language:Chinese
-
Abstract:
This study established a method for rapid quantification of terpene lactone, bilobalide, ginkgolide C, ginkgolide A and ginkgolide B in the chromatographic process of Ginkgo Folium based on near infrared spectroscopy(NIRS). The effects of competitive adaptive reweighting sampling(CARS), random frog(RF), and synergy interval partial least squares(siPLS) on the performance of partial least squares regression(PLSR) model were compared to the reference values measured by HPLC. Among them, the correlation coefficients of prediction(Rp) of validation sets of terpene lactone, bilobalide, and ginkgolide C were all higher than 0.98, and the relative standard errors of prediction(RSEPs) were 5.87%, 6.90% and 6.63%, respectively. Aiming at ginkgolide A and ginkgolide B with relatively low content, the genetic algorithm joint extreme learning machine(GA-ELM) was used to establish the optimized quantitative analysis model. Compared with CARS-PLSR model, the CARS-GA-ELM models of ginkgolide A and ginkgolide B exhibited a reduction in RSEP from 15.65% to 8.52% and from 21.28% to 10.84%, respectively, which met the needs of quantitative ana-lysis. It has been proved that NIRS can be used for the rapid detection of various lactone components in the chromatographic process of Ginkgo Folium.
