化学
红茶
质谱法
发酵
可转让性
傅里叶变换红外光谱
主成分分析
工艺工程
分析化学(期刊)
环境化学
色谱法
食品科学
计算机科学
化学工程
人工智能
机器学习
罗伊特
工程类
作者
Chongshan Yang,Leizi Jiao,Chunwang Dong,Xuelin Wen,Peng Lin,Dandan Duan,Guanglin Li,Chunjiang Zhao,Xinglan Fu,Daming Dong
出处
期刊:Food Chemistry
[Elsevier]
日期:2024-04-02
卷期号:449: 139211-139211
被引量:1
标识
DOI:10.1016/j.foodchem.2024.139211
摘要
Fermentation is the key process to determine the quality of black tea. Traditional physical and chemical analyses are time consuming, it cannot meet the needs of online monitoring. The existing rapid testing techniques cannot determine the specific volatile organic compounds (VOCs) produced at different stages of fermentation, resulting in poor model transferability; therefore, the current degree of black tea fermentation mainly relies on the sensory judgment of tea makers. This study used proton transfer reaction mass spectrometry (PTR-MS) and fourier transform infrared spectroscopy (FTIR) combined with different injection methods to collect VOCs of the samples, the rule of change of specific VOCs was clarified, and the extreme learning machine (ELM) model was established after principal component analysis (PCA), the prediction accuracy reached 95% and 100%, respectively. Finally, different application scenarios of the two technologies in the actual production of black tea are discussed based on their respective advantages.
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