Food Science and Technology

Metabolomics integrated with machine learning to discriminate the geographic origin of Rougui Wuyi rock tea

Y. Peng, C. Zheng, et al.

This innovative study showcases a powerful method that merges metabolomics with machine learning to determine the geographic origin of Wuyi rock tea, boasting over 90% accuracy in results. Discover insights from this cutting-edge research conducted by Yifei Peng, Chao Zheng, Shuang Guo, Fuquan Gao, Xiaxia Wang, Zhenghua Du, Feng Gao, Feng Su, Wenjing Zhang, Xueling Yu, Guoying Liu, Baoshun Liu, Chengjian Wu, Yun Sun, Zhenbiao Yang, Zhilong Hao, and Xiaomin Yu.

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Abstract

This study developed a robust method combining metabolomics and machine learning (ML) to authenticate the geographic origin of Wuyi rock tea, a premium oolong tea. Volatiles of 333 tea samples were profiled using gas chromatography time-of-flight mass spectrometry. Multilayer Perceptron achieved the best performance with an average accuracy of 92.7% on training data. The model showed over 90% accuracy on independent test sets. Gradient Boosting yielded the best accuracy (89.6%) using only 30 volatile features. This methodology shows promise for broader applications in identifying geographic origins of other agri-food products.

Publisher

npj Science of Food

Published On

Mar 16, 2023

Authors

Yifei Peng, Chao Zheng, Shuang Guo, Fuquan Gao, Xiaxia Wang, Zhenghua Du, Feng Gao, Feng Su, Wenjing Zhang, Xueling Yu, Guoying Liu, Baoshun Liu, Chengjian Wu, Yun Sun, Zhenbiao Yang, Zhilong Hao, Xiaomin Yu

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