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Application of hyperspectral imaging assisted with integrated deep learning approaches in identifying geographical origins and predicting nutrient contents of Coix seeds

文献类型： 外文期刊

作者： Wang, Youyou 1 ; Xiong, Feng 1 ; Zhang, Yue 1 ; Wang, Siman 1 ; Yuan, Yuwei 2 ; Lu, Cuncun 3 ; Nie, Jing 2 ; Nan, Tiegui 1 ; Yang, Bin 4 ; Huang, Luqi 1 ; Yang, Jian 1 ;

作者机构： 1.China Acad Chinese Med Sci, Natl Resource Ctr Chinese Mat Med, State Key Lab Breeding Base Dao di Herbs, Beijing 100700, Peoples R China

2.Minist Agr & Rural Affairs China, Zhejiang Acad Agr Sci, Key Lab Informat Traceabil Agr Prod, Inst Agroprod Safety & Nutr, Hangzhou 310021, Peoples R China

3.Inst Basic Res Clin Med, China Acad Chinese Med Sci, Beijing 100700, Peoples R China

4.China Acad Chinese Med Sci, Inst Chinese Mat Med, Beijing 100700, Peoples R China

关键词： Coix seed; Hyperspectral imaging; Geographical origin; Nutrient content; Deep learning; 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： 2023 年 404 卷

页码：

收录情况： SCI

摘要： Coix seed (CS, Coix lachryma-jobi L. var. ma-yuen (Roman.) Stapf) has rich nutrients, including starch, protein and oil. The geographical origin with a protected geographical indication and high levels of nutrient contents ensures the quality of CS, but non-destructive and rapid methods for predicting these quality indicators remain to be explored. This paper proposed hyperspectral imaging (HSI) assisted with the integrated deep learning models of attention mechanism (AM), convolutional neural networks, and long short-term memory. The method achieved the effective wavelengths selection, the highest prediction accuracy for production region discrimination and the lowest mean absolute error and root mean squared error for nutrient contents prediction. Moreover, the wavelengths selected via the AM model were explicable and reliable for predicting the geographical origins and nutrient contents. The proposed combination of HSI with integrated deep learning models has great potential in the quality evaluation of CS.

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