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
作者： Wang, Youyou 1 ; Wang, Siman 1 ; Bai, Ruibin 1 ; Li, Xiaoyong 2 ; Yuan, Yuwei 4 ; Nan, Tiegui 1 ; Kang, Chuanzhi 1 ; Yang, Jian 1 ; Huang, Luqi 1 ;
作者机构： 1.China Acad Chinese Med Sci, Natl Resource Ctr Chinese Mat Med, State Key Lab Qual Ensurance & Sustainable Use Dao, Beijing 100700, Peoples R China
2.South China Normal Univ, Sch Environm, State SCNU Environm Res Inst, Guangdong Prov Key Lab Chem Pollut & Environm Safe, Guangzhou 510006, Peoples R China
3.South China Normal Univ, Sch Environm, MOE Key Lab Theoret Chem Environm, Guangzhou 510006, Peoples R China
4.Zhejiang Acad Agr Sci, Inst Agroprod Safety & Nutr, Key Lab Informat Traceabil Agr Prod, Minist Agr & Rural Affairs China, Hangzhou 310021, Peoples R China
5.Dexing Res & Training Ctr Chinese Med Sci, Dexing 334220, Peoples R China
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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