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Rapid evaluation of Radix Paeoniae Alba and its processed products by near-infrared spectroscopy combined with multivariate algorithms

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Abstract

It is well known that the processing method of herbal medicine has a complex impact on the active components and clinical efficacy, which is difficult to measure. As a representative herb medicine with diverse processing methods, Radix Paeoniae Alba (RPA) and its processed products differ greatly in clinical efficacy. However, in some cases, different processed products are confused for use in clinical practice. Therefore, it is necessary to strictly control the quality of RPA and its processed products. Giving that the time-consuming and laborious operation of traditional quality control methods, a comprehensive strategy of near-infrared (NIR) spectroscopy combined with multivariate algorithms was proposed. This strategy has the advantages of being rapid and non-destructive, not only qualitatively distinguishing RPA and various processed products but also enabling quantitative prediction of five bioactive components. Qualitatively, the subspace clustering algorithm successfully differentiated RPA and three processed products, with an accuracy rate of 97.1%; quantitatively, interval combination optimization (ICO), competitive adaptive reweighted sampling (CARS), and competitive adaptive reweighted sampling combined with successive projections algorithm (CARS-SPA) were used to optimize the PLS model, and satisfactory results were obtained in terms of wavelength selection. In conclusion, it is feasible to use NIR spectroscopy to rapidly evaluate the effect of processing methods on the quality of RPA, which provides a meaningful reference for quality control of other herbal medicines with numerous processing methods.

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Abbreviations

BRPA:

Bran-fried Radix Paeoniae Alba

CARS:

Competitive adaptive reweighted sampling

CARS-SPA:

Competitive adaptive reweighted sampling combined with successive projections algorithm

FDR:

False discovery rate

FNR:

False negative rate

ICO:

Interval combination optimization

NIR:

Near-infrared

PLS:

Partial least squares

PPV:

Positive predictive value

ROC:

Receiver operating characteristic

RPA:

Radix Paeoniae Alba

RSDs:

Relative standard deviations

SD:

Standard deviation

SRPA:

Stir-fried Radix Paeoniae Alba

TPR:

True positive rate

WRPA:

Wine-fried Radix Paeoniae Alba

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Funding

This research study was supported by the National Key R&D Program of China (Grant No. 2019YFC1711500) and the National Key R&D Program of China (Grant No. 2018YFC1707000).

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Author notes

  1. Jiuba Zhang and Yu Li contributed equally to this work.

Authors and Affiliations

  1. College of Pharmacy, Nanjing University of Chinese Medicine, 138 Xianlin Rd, Nanjing, 210023, People’s Republic of China

    Jiuba Zhang, Yu Li, Bin Wang, Jiantao Song, Mingxuan Li, Peng Chen, Zheyuan Shen, Yi Wu, Chunqin Mao, Xiachang Wang, Wei Zhang & Tulin Lu

  2. Research Center for Traditional Chinese Medicine of Lingnan (Southern China), Jinan University, Guangzhou, 510632, China

    Hui Cao

  3. College of Pharmacy, Anhui University of Chinese Medicine, Hefei, 230038, China

    Wei Zhang

  4. Anhui Province Key Laboratory of Traditional Chinese Medicine Decoction Pieces of New Manufacturing Technology, Hefei, 230038, China

    Wei Zhang

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  1. Jiuba Zhang
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Contributions

Jiuba Zhang: conceptualization, methodology, software, writing—original draft. Yu Li: visualization, investigation, methodology, formal analysis. Bin Wang, Jiantao Song, Mingxuan Li, Peng Chen, Zheyuan Shen, and Yi Wu: project administration, writing—review and editing. Chunqin Mao and Hui Cao: data curation, formal analysis. Xiachang Wang: data curation, funding acquisition. Wei Zhang and Tulin Lu: writing—review and editing, software.

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Correspondence to Wei Zhang or Tulin Lu.

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Zhang, J., Li, Y., Wang, B. et al. Rapid evaluation of Radix Paeoniae Alba and its processed products by near-infrared spectroscopy combined with multivariate algorithms. Anal Bioanal Chem 415, 1719–1732 (2023). https://doi.org/10.1007/s00216-023-04570-5

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