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

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

Food chemistry 2024 Jan 01

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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. Copyright © 2023 Elsevier Ltd. All rights reserved.

Citation

Youyou Wang, Siman Wang, Ruibin Bai, Xiaoyong Li, Yuwei Yuan, Tiegui Nan, Chuanzhi Kang, Jian Yang, Luqi Huang. Prediction performance and reliability evaluation of three ginsenosides in Panax ginseng using hyperspectral imaging combined with a novel ensemble chemometric model. Food chemistry. 2024 Jan 01;430:136917