@article{XIA2025, 
author = {Gaofan XIA and Shengzhou MA and Huilin CHANG and Dengshan LI and Yu WANG and Qin OUYANG},
title = {Quality Evolution during Far-Infrared Radiation Withering of Black Tea and Its Monitoring Based on Data Fusion of Visible-Near Infrared Spectroscopy and Machine Vision},
year = {2025},
journal = {Food Science},
volume = {46},
number = {24},
pages = {9-17},
keywords = {black tea withering, far-infrared radiation, near infrared spectroscopy, machine vision, convolutional neural network},
url = {https://www.sciopen.com/article/10.7506/spkx1002-6630-20250721-165},
doi = {10.7506/spkx1002-6630-20250721-165},
abstract = {In this study, fresh tea leaves were subjected to three withering processes: natural withering, far-infrared radiation for 3 h, and far-infrared radiation for 6 h. The contents of major taste substances were determined according to the Chinese national standards, and visible-near infrared (Vis-NIR) spectroscopy and machine vision (MV) data of the withered samples were collected to build an improved one-dimensional convolutional neural network model integrated with a convolutional block attention module (CBAM-1DCNN). The results showed that the phenol/ammonia ratio after infrared radiation for 3 h followed by natural withering for 15 h decreased by 20.06% compared with fresh leaves, and this treatment group achieved the highest sensory score. The CBAM-1DCNN model based on the Vis-NIR-MV fused data exhibited stronger discrimination capacity than did the models based on the Vis-NIR and MV data with an accuracy of 99.11% for the training set and 96.00% for the prediction set. Far-infrared radiation significantly altered the contents of major taste substances, and Vis-NIR spectroscopy combined with MV enabled rapid discrimination of the withering degree of black tea.}
}