Fast prediction of diverse rare ginsenoside contents in Panax ginseng through hyperspectral imaging assisted with the temporal convolutional network-attention mechanism (TCNA) deep learning

Abstract

Combining hyperspectral imaging (HSI) with deep learning algorithms provides an effective and fast approach for evaluating the quality of food and agricultural by-products. This study comprehensively determined the quality of ginseng (Panax ginseng C. A. Meyer), an important medicinal and nutritional food, by evaluating the contents of diverse rare ginsenosides (RGs) using HSI technology. The results indicated that the combination of HSI with the deep learning temporal convolutional network-attention mechanism (TCNA) model achieved the best results in predicting the contents of six types of RGs (Rh1, Rh2, F1, Rg3, F4, and Rk1) simultaneously and effectively. Especially, the content detection of the six RGs based on the effective wavelengths showed that the TCNA model achieved coefficient of determination (R2) values above 0.890 and relative percentage deviation (RPD) values higher than 3.0, demonstrating excellent model performance. Meanwhile, the use of effective wavelengths makes the results of the TCNA model have better interpretability, and the simultaneous output of six RGs contents significantly improves prediction efficiency. The HSI assisted with the TCNA algorithm provides a rapid and effective detection approach for simultaneously predicting the content of diverse quality indicators. All these results will provide a new reference for developing convenient and rapid HSI equipment in the food and agricultural industry for direct and comprehensive quality inspection in markets in the future.