A comparative UHPLC-Q/TOF-MS-based metabolomics approach coupled with machine learning algorithms to differentiate Keemun black teas from narrow-geographic origins
Chuanyi Peng, Yin‐feng Ren, Zhi-hao Ye, Haiyan Zhu, Xiaoqian Liu, Xiaotong Chen, Ruyan Hou, Daniel Granato, Huimei Cai
Topics & Concepts
ChemometricsRandom forestSupport vector machineReceiver operating characteristicArtificial intelligenceLinear discriminant analysisPattern recognition (psychology)Artificial neural networkConfusion matrixMetabolomicsComputer scienceMachine learningMathematicsChemistryChromatographyMetabolomics and Mass Spectrometry StudiesTea Polyphenols and EffectsTraditional Chinese Medicine Analysis