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In this study, a classification model was established based on near-infrared spectroscopy and random forest method to accurately distinguish three samples of Schisandra chinensis from different habitats. At the same time, the feasibility of fast and effective prediction of polysaccharide contents in Schisandra chinensis by near-infrared spectroscopy combined with chemometrics was evaluated. In this paper, phenol sulfuric acid method was used to determine the content of total polysaccharides in samples, and partial least squares regression algorithm was used to link the spectral information with the reference value. Different spectral pretreatment methods were used to optimize the model to improve its predictability and stability. The results showed that random forest could distinguish these samples accurately, with an accuracy of 97.47%. In the established prediction model, the RMSEC of the optimal model calibration set is 0.0012, and the coefficient of determination R is 0.9976. The RMSEP of prediction set is 0.0024, the coefficient of determination R is 0.9922, and the RPD is 11.36. In general, the method has good stability and applicability, which provides a new analytical method for the identification of Schisandra chinensis origin and quality evaluation. Copyright © 2021 Elsevier B.V. All rights reserved.


Lun Wu, Yue Gao, Wen-Chen Ren, Yang Su, Jing Li, Ya-Qi Du, Qiu-Hong Wang, Hai-Xue Kuang. Rapid determination and origin identification of total polysaccharides contents in Schisandra chinensis by near-infrared spectroscopy. Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy. 2022 Jan 05;264:120327

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PMID: 34474220

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