Prediction performance and reliability evaluation of three ginsenosides in Panax ginseng using hyperspectral imaging combined with a novel ensemble chemometric model

文献类型： 外文期刊

第一作者： Wang, Youyou

作者： Wang, Youyou;Wang, Siman;Bai, Ruibin;Nan, Tiegui;Kang, Chuanzhi;Yang, Jian;Huang, Luqi;Li, Xiaoyong;Li, Xiaoyong;Yuan, Yuwei;Bai, Ruibin;Kang, Chuanzhi;Yang, Jian

作者机构：

关键词： Panax ginseng; Ginsenosides content; Hyperspectral imaging; Deep learning; Uncertainty evaluation; Effective wavelength

期刊名称：FOOD CHEMISTRY （ 影响因子：8.8； 五年影响因子：8.6 ）

ISSN： 0308-8146

年卷期： 2024 年 430 卷

页码：

收录情况： SCI

摘要： Panax ginseng C. A. Meyer (PG) is a health-promoting food, and its ginsenosides (Rb1, Rg1, Re) content, as the quality indicator, is affected by the planting modes (garden or forest ginsengs) and years. Effective prediction of this content remains to be investigated. In this study, hyperspectral (HSI) combined with ensemble model (CGRU-GPR) including the convolutional neural network (CNN), gate recurrent unit (GRU), and Gaussian process regression (GPR) realized a comprehensive evaluation of the prediction performance and predictive uncertainty. With effective wavelengths, the proposed CGRU-GPR model improved operation efficiency and obtained satisfactory prediction results with relative percent deviation (RPD) values all higher than 2.70 in three ginsenosides. Meanwhile, the interval prediction with a high prediction interval coverage probability (PICP) of 0.97 - 1.0 and a low mean width percentage (MWP) of 0.7 - 1.66 indicated a low prediction uncertainty. This study provides a rapid and reliable method for predicting ginsenosides contents in PG.

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