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| Variable Selection Method in the NIR Quantitative Analysis Model of Total Saponins in Red Ginseng Extract |
| AN Si-yu1, 2, ZHANG Lei1, SHANG Xian-zhao1, YUE Hong-shui1, LIU Wen-yuan2*, JU Ai-chun1* |
1. Tianjin Tasly Pride Pharmaceutical Co., Ltd., Tianjin Key Laboratory of Safety Evaluation Enterprise of TCM Injections, Tianjin 300402, China
2. Key Laboratory of Drug Quality Control and Pharmacovigilance, Ministry of Education, China Pharmaceutical University, Nanjing 210009, China |
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Abstract Yiqi Fumai Lyophilized Injectionis a new type of freeze-dried powder injection made of red ginseng, ophiopogon japonicus and schisandrachinensis. The total saponin content of red ginseng extract is an important quality control index in the production process of Yiqi Fumai lyophilized injection. The results of traditional analysis methods lag far behind, which cannot feedback the quality information of the production process timely, it is necessary to establish a rapid method for the determination of total saponin. As a process monitoring tool, near-infrared spectroscopy (NIR) has been widely used in the quality control of traditional Chinese medicine. How to extract the effective information from the spectrum with weak absorption and serious overlapping of spectral regions is the key to improve the monitoring veracity. Model population analysis (MPA) provides a new idea for variable selection method. In this study, the near-infrared spectrum data of 55 batches red ginseng extracts were collected. Themulti scattering correction (MSC) method was used to preprocess the spectrum data, the variable screening methods derived from MPA, such as random frog (RF), competitive adaptive reweighted sampling (CARS), variable combination population analysis (VCPA), VCPA combined with IRIV (iterative retaining information variables) and the variable selection method of OPUS were respectively used in the establishment of PLS quantitative analysis model. The results showed that the Rc of model established by OPUS, CARS-PLS and RF-PLS were only 0.601 3, 0.565 3 and 0.644 0, respectively. The Rc of model established by VCPA-PLS was 0.951 2, which was the highest, but this model did not present good robustness. The model established by VCPA-IRIV-PLS had the best prediction effects; its Rc was 0.928 , RSEP% was 7.99%.
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Received: 2019-12-06
Accepted: 2020-04-19
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Corresponding Authors:
LIU Wen-yuan, JU Ai-chun
E-mail: juach@tasly.com；liuwenyuan@cpu.edu.cn
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[1] Li W, Prasad S, Fowler J E. IEEE Transactions on Geoscience & Remote Sensing, 2012, 50(4): 1185.
[2] HUAN Ke-wei, LIU Xiao-xi, ZHENG Feng(宦克为，刘小溪，郑 峰). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2013, 29(4): 266.
[3] Duan F, Fu X, Jiang J J, et al. Spectrochimica Acta Part B: Atomic Spectroscopy，2018，143: 12.
[4] Nespeca M G, Pavini W D, de Oliveira J E. Vibrational Spectroscopy, 2019，102: 97.
[5] Chen H Z, Tang G Q, Song Q Q, et al. Analytical Letters, 2013, 46(13): 2060.
[6] Tan C, Wu T, Xu Z H, et al. Vibrational Spectroscopy, 2012，58: 44.
[7] Theocharis John B, Tsakiridis Nikolaos L, Tziolas Nikolaos V, et al. European Journal of Soil Science, 2019, 70(3): 578.
[8] YUN Yong-huan, DENG Bai-chuan, LIANG Yi-ceng(云永欢，邓百川，梁逸曾). Chinese Journal of Analytical Chemistry(分析化学), 2015, 43(11): 1638.
[9] Yao X Q, Yang W, Li M Z, et al. IFAC-Papers OnLine, 2018, 51(17): 660.
[10] CHEN Li-dan, ZHAO Yan-ru(陈立旦，赵艳茹). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2014, 30(8): 168.
[11] Jiang H, Xu W, Chen Q. Spectrochimica Acta-Part A：Molecular and Biomolecular Spectroscopy, 2019，214: 366.
[12] Wang W, Jiang H, Liu G H, et al. Chinese Journal of Analytical Chemistry, 2017, 45(8): 1137.
[13] Yun Y H, Wang W T, Deng B C, et al. Analytica Chimica Acta, 2015，862: 14.
[14] Zhao H, Huan K W, Shi X G. Chinese Journal of Analytical Chemistry, 2018, 46(1): 136.
[15] Yun Y H, Wang W T, Tan M L, et al. Analytica Chimica Acta, 2014，807: 36.
[16] YU Lei, ZHANG Tao, ZHU Ya-xing, et al(于 雷，章 涛，朱亚星，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2018, 34(16): 148.
[17] Yun Y H, Bin J, Liu D L, et al. Analytica Chimica Acta, 2019，1058: 58. |
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