化学计量学
代谢组学
气相色谱-质谱法
质谱法
可追溯性
指纹(计算)
化学
数据集
传感器融合
色谱法
模式识别(心理学)
数学
计算机科学
人工智能
统计
作者
Ge Jin,Yuanyuan Zhu,Chuanjian Cui,Yang Chen,Shaode Hu,Huimei Cai,Jingming Ning,Chaoling Wei,Aoxia Li,Ruyan Hou
出处
期刊:Food Chemistry
[Elsevier]
日期:2023-11-01
卷期号:425: 136538-136538
被引量:8
标识
DOI:10.1016/j.foodchem.2023.136538
摘要
The narrow geographical traceability of green tea is both important and challenging. This study aimed to establish multi-technology metabolomic and chemometric approaches to finely discriminate the geographic origins of green teas. Taiping Houkui green tea samples were analyzed by headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry and 1H NMR of polar (D2O) and non-polar (CDCl3). Common dimension, low-level and mid-level data fusion approaches were tested to verify if the combination of several analytical sources can improve the classification ability of samples from different origins. In assessments of tea from six origins, the single instrument data test set results in 40.00% to 80.00% accuracy. Data fusion improved single-instrument performance classification with mid-level data fusion to obtain 93.33% accuracy in the test set. These results provide comprehensive metabolomic insights into the origin of TPHK fingerprinting and open up new metabolomic approaches for quality control in the tea industry.
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