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Variable Selection Based on Gray Wolf Optimization Algorithm for the Prediction of Saponin Contents in Xuesaitong Dropping Pills Using NIR Spectroscopy

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Abstract

Purpose 

A rapid quantification method for the five saponins was developed based on near infrared (NIR) spectroscopy as a quality control strategy for the Xuesaitong dropping pills (XDP).

Methods

In this study, NIR spectroscopy coupled with partial least squares regression (PLSR) was used to predict the contents of the five saponins in XDPs, named Notoginsenoside R1 (R1), Ginsenoside Rg1 (Rg1), Ginsenoside (Re), Ginsenoside Rb1 (Rb1) and Ginsenoside Rd (Rd). In order to obtain more accurate and robust prediction models, the gray wolf optimizer (GWO) algorithm, a new swarm intelligence algorithm, combined with PLSR algorithm were used to select the NIR spectral feature of the XDPs samples. In addition, six variable selection methods, i.e. stability competitive adaptive reweighted sampling (sCARS), genetic algorithm (GA), Monte Carlo-uninformative variable elimination (MCUVE), successive projections algorithm (SPA), bootstrapping soft shrinkage (BOSS), and variable combination population analysis (VCPA), were compared with GWO algorithm.

Results 

The results showed that the GWO algorithm significantly improved the model prediction performance when compared with other variable selection methods. Finally, the stability of the calibration models were used as an index to evaluate the prediction performance of the models, and the results showed that the performance of the GWO was stable under 20 runs.

Conclusion

In summary, for the five saponins in XDPs, GWO, as an effective feature extraction algorithm for the NIR spectra, can significantly improve the prediction performance of the quantitative calibration model, so as to realize the rapid and accurate quantitative analysis of the saponins in XDPs.

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Funding

This work was financially supported by the National Nature Science Foundation of China (No. 82074276), Tianjin Science and technology project (No. 20ZYJDJC00090), and National S&T Major Project of China (No. 2018ZX09201011). Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine. (No. ZYYCXTD-D-202002).

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Authors and Affiliations

  1. College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China

    Yizhe Hou, Xin Gao, Shunan Li, Wenlong Li & Zheng Li

  2. State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China

    Yizhe Hou, Xin Gao, Shunan Li, Wenlong Li & Zheng Li

  3. Langtian Pharmaceutical (Hubei) Co., Ltd, Huangshi, 435000, China

    Xiang Cai & Pian Li

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  1. Yizhe Hou
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  2. Xin Gao
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Correspondence to Wenlong Li or Zheng Li.

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Hou, Y., Gao, X., Li, S. et al. Variable Selection Based on Gray Wolf Optimization Algorithm for the Prediction of Saponin Contents in Xuesaitong Dropping Pills Using NIR Spectroscopy. J Pharm Innov 18, 43–59 (2023). https://doi.org/10.1007/s12247-022-09620-6

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