Rapid determination and origin identification of total polysaccharides contents in Schisandra chinensis by near-infrared spectroscopy

In this study, a classification model was established based on near-infrared spectroscopy and random for -est 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 chinen-sis by near-infrared spectroscopy combined with chemometrics was evaluated. In this paper, phenol sul-furic 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 stabil -ity. 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 Ris 0.9976. The RMSEP of prediction set is 0.0024, the coefficient of determination Ris 0.9922, and the RPD is 11.36. In general, the method has good stability and applica-bility, which provides a new analytical method for the identification of Schisandra chinensis origin and quality evaluation. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

In this study, a classification model based on near-infrared spectroscopy and random forest method accurately distinguished three samples of Schisandra chinensis from different habitats and predicted polysaccharide contents effectively. The method showed high accuracy, stability, and applicability in identifying the origin of Schisandra chinensis and evaluating its quality. The results demonstrate the potential for near-infrared spectroscopy combined with chemometrics in fast and reliable analysis of herbal products.