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

文献类型： 外文期刊

作者： Zhang, Mingzan 1 ; Zhang, Tuo 2 ; Wang, Yuan 1 ; Duan, Xueyi 2 ; Pu, Lulu 2 ; Zhang, Yuan 2 ; Li, Qin 2 ; Liu, Yabing 2 ;

作者机构： 1.Guizhou Ind Polytech Coll, Dept Chem & Environm Engn, Guiyang 550025, Peoples R China

2.Guizhou Acad Agr Sci, Tea Res Inst, Guiyang 550025, Peoples R China

关键词： accurate prediction of catechin content; near-infrared spectroscopy; wavelength selection; convolutional neural network; attention mechanism

期刊名称：CHEMOSENSORS （ 影响因子：3.7； 五年影响因子：3.8 ）

ISSN：

年卷期： 2024 年 12 卷 9 期

页码：

收录情况： SCI

摘要： 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 over existing methods in terms of prediction accuracy and stability (R2 = 0.92, RMSE = 0.018 for epicatechin; R2 = 0.96, RMSE = 0.11 for epicatechin gallate; R2 = 0.97, RMSE = 0.14 for epigallocatechin; R2 = 0.97, RMSE = 0.32 for epigallocatechin gallate). This innovative deep learning approach amalgamates wavelength selection with attention mechanisms, provides a new perspective for the simultaneous assessment of the major components in tea, and contributes to the advancement of precision management in the tea industry's production and grading processes.

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