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An integrated strategy of spectrum–effect relationship and near-infrared spectroscopy rapid evaluation based on back propagation neural network for quality control of Paeoniae Radix Alba

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Abstract

The quantitative analysis of near-infrared spectroscopy in traditional Chinese medicine has still deficiencies in the selection of the measured indexes. Then Paeoniae Radix Alba is one of the famous "Eight Flavors of Zhejiang" herbs, however, it lacks the pharmacodynamic support, and cannot reflect the quality of Paeoniae Radix Alba accurately and reasonably. In this study, the spectrum–effect relationship of the anti-inflammatory activity of Paeoniae Radix Alba was established. Then based on the obtained bioactive component groups, the genetic algorithm, back propagation neural network, was combined with near-infrared spectroscopy to establish calibration models for the content of the bioactive components of Paeoniae Radix Alba. Finally, three bioactive components, paeoniflorin, 1,2,3,4,6-O-pentagalloylglucose, and benzoyl paeoniflorin, were successfully obtained. Their near-infrared spectroscopy content models were also established separately, and the validation sets results showed the coefficient of determination (R2 > 0.85), indicating that good calibration statistics were obtained for the prediction of key pharmacodynamic components. As a result, an integrated analytical method of spectrum–effect relationship combined with near-infrared spectroscopy and deep learning algorithm was first proposed to assess and control the quality of traditional Chinese medicine, which is the future development trend for the rapid inspection of traditional Chinese medicine.

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Data availability statement

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors gratefully acknowledge the experimental support from the Public Platform of Pharmaceutical Research Center, Academy of Chinese Medical Science, Zhejiang Chinese Medical University. This work was supported by the National Natural Science Foundation of China (No. 82073956), Zhejiang Provincial Natural Science Foundation of China (LQ22H270004, LGC20B050010), Zhejiang Provincial Medicine and Health Science and Technology Plan Project (2022490995), Zhejiang Province Traditional Chinese Medicine Science and Technology Project (2023ZF087), Zhejiang Chinese Medical University Research Foundation (2021ZR08, 2020ZG23), and Zhejiang students’ technology and innovation program (2022R410A025).

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Author notes

  1. Qi Wang and Huaqiang Li contributed equally to this work and are therefore considered co-first authors.

Authors and Affiliations

  1. School of Pharmaceutical Sciences, Traditional Chinese Medicine Resources and Quality Evaluation Ressearch, Zhejiang Chinese Medical University, Sphingolipid Metabolomics, Hangzhou, 310053, China

    Qi Wang, Huaqiang Li, Jinling You, Binjun Yan, Weifeng Jin, Menglan Shen, Yunjie Sheng, Xinrui Wang, Xiongyu Meng & Luping Qin

  2. Academy of Chinese Medical Science, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, 548 Binwen Road, Binjiang District310053, Hangzhou, Zhejiang Province, People’s Republic of China

    Bingqian He

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  1. Qi Wang
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Contributions

All authors contributed to the ideas of the whole study. Conceptualization: P.L.Q., X.Y.M., H.Q.L.; writing—original draft preparation: Q.W.; data collection, and analysis: Q.W., M.L.S., W.F.J., J.L.Y., X.R.W.; writing—review and editing: X.Y.M., B.J.Y., H.Q.L., B.Q.H., Y.J.S.; funding acquisition B.J.Y., X.Y.M, B.Q.H, X.R.W.

Corresponding authors

Correspondence to Xiongyu Meng or Luping Qin.

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The authors have no competing interests to declare that are relevant to the content of this article.

Ethics approval

All animal operations were performed in accordance with the Guide for the Care and Use of Laboratory Animals (Publication No. 80-23, revised 1996) and approved by the Animal Ethics Committee of Zhejiang University of Traditional Chinese Medicine, China (Laboratory Animal Ethics No. 11766).

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Wang, Q., Li, H., You, J. et al. An integrated strategy of spectrum–effect relationship and near-infrared spectroscopy rapid evaluation based on back propagation neural network for quality control of Paeoniae Radix Alba. ANAL. SCI. 39, 1233–1247 (2023). https://doi.org/10.1007/s44211-023-00334-4

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