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- Title
Metabolomics integrated with machine learning to discriminate the geographic origin of Rougui Wuyi rock tea.
- Authors
Peng, Yifei; Zheng, Chao; Guo, Shuang; Gao, Fuquan; Wang, Xiaxia; Du, Zhenghua; Gao, Feng; Su, Feng; Zhang, Wenjing; Yu, Xueling; Liu, Guoying; Liu, Baoshun; Wu, Chengjian; Sun, Yun; Yang, Zhenbiao; Hao, Zhilong; Yu, Xiaomin
- Abstract
The geographic origin of agri-food products contributes greatly to their quality and market value. Here, we developed a robust method combining metabolomics and machine learning (ML) to authenticate the geographic origin of Wuyi rock tea, a premium oolong tea. The volatiles of 333 tea samples (174 from the core region and 159 from the non-core region) were profiled using gas chromatography time-of-flight mass spectrometry and a series of ML algorithms were tested. Wuyi rock tea from the two regions featured distinct aroma profiles. Multilayer Perceptron achieved the best performance with an average accuracy of 92.7% on the training data using 176 volatile features. The model was benchmarked with two independent test sets, showing over 90% accuracy. Gradient Boosting algorithm yielded the best accuracy (89.6%) when using only 30 volatile features. The proposed methodology holds great promise for its broader applications in identifying the geographic origins of other valuable agri-food products.
- Subjects
MACHINE learning; BOOSTING algorithms; METABOLOMICS; TEA; TIME-of-flight mass spectrometry; GAS chromatography
- Publication
NPJ Science of Food, 2023, Vol 7, Issue 1, p1
- ISSN
2396-8370
- Publication type
Article
- DOI
10.1038/s41538-023-00187-1