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In Food chemistry

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.

Wang Youyou, Xiong Feng, Zhang Yue, Wang Siman, Yuan Yuwei, Lu Cuncun, Nie Jing, Nan Tiegui, Yang Bin, Huang Luqi, Yang Jian


Coix seed, Deep learning, Effective wavelength, Geographical origin, Hyperspectral imaging, Nutrient content