Accurate Prediction of Tea Catechin Content with Near-Infrared Spectroscopy by Deep Learning Based on Channel and Spatial Attention Mechanisms

作者：Mingzan Zhang, Tuo Zhang, Yuan Wang, Xueyi Duan, Lulu Pu, Yuan Zhang, Qin Li, Yabing Liu · 发表于：Chemosensors · 年份：2024 · DOI：10.3390/chemosensors12090184 · 被引用次数：13 · 研究领域：Spectroscopy and Chemometric Analyses、Advanced Chemical Sensor Technologies、Spectroscopy Techniques in Biomedical and Chemical Research

The assessment of catechin content stands as a pivotal determinant of tea quality. In tea production and quality grading, the development of accurate and non-destructive techniques for the accurate prediction of various catechin content is paramount. Near-infrared spectroscopy (NIRS) has emerged as a widely employed tool for analyzing the chemical composition of tea. Nevertheless, the spectral information obtained from NIRS faces challenges when discerning different types of catechins in black tea, owing to their similar physical and chemical properties. Moreover, the vast number of NIRS wavelengths exceeds the available tea samples, further complicating the accurate assessment of catechin content. This study introduces a novel deep learning approach that integrates specific wavelength selection and attention mechanisms to accurately predict the content of various catechins in black tea simultaneously. First, a wavelength selection algorithm is proposed based on feature interval combination sensitivity segmentation, which effectively extracts the NIRS feature information of tea. Subsequently, a one-dimensional convolutional neural network (CNN) incorporating channel and spatial–sequential attention mechanisms is devised to independently extract the key features from the selected wavelength variables. Finally, a multi-output predictor is employed to accurately predict the four main catechins in tea. The experimental results demonstrate the superiority of the proposed model ove...