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Improved geographical origin discrimination for tea using ICP‐MS and ICP‐OES techniques in combination with chemometric approach
Author(s) -
Liu Honglin,
Zeng Yitao,
Zhao Xin,
Tong Huarong
Publication year - 2020
Publication title -
journal of the science of food and agriculture
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.782
H-Index - 142
eISSN - 1097-0010
pISSN - 0022-5142
DOI - 10.1002/jsfa.10392
Subject(s) - linear discriminant analysis , principal component analysis , inductively coupled plasma , chemometrics , inductively coupled plasma mass spectrometry , partial least squares regression , mass spectrometry , hierarchical clustering , inductively coupled plasma atomic emission spectroscopy , pattern recognition (psychology) , artificial intelligence , artificial neural network , analytical chemistry (journal) , chemistry , cluster analysis , mathematics , chromatography , computer science , statistics , plasma , physics , quantum mechanics
BACKGROUND There is an urgent need to strengthen the testing and certification of geographically iconic foods, as well as to use discriminatory science and technology for their regulation and verification. Multi‐element and stable isotope analyses were combined to provide a new chemometric approach for improving the discrimination tea samples from different geographical origins. Different stoichiometric methods [principal component analysis (PCA), hierarchical cluster analysis (HCA), partial least squares‐discriminant analysis (PLS‐DA), back propagation based artificial neural network (BP‐ANN) and linear discriminant analysis (LDA)] were used to demonstrate this discrimination approach using Yongchuanxiuya tea samples in an experimental test. RESULTS Multi‐element and stable isotope analyses of tea samples using inductively coupled plasma mass spectrometry and inductively coupled plasma optical emission spectrometry easily distinguished the geographical origins. However, the clustering ability of the two unsupervised learning methods (PCA and HCA) were worse compared to that of the three supervised learning methods (PLS‐DA, BP‐ANN and LDA). BP‐ANN and LDA, with 100% recognition and prediction abilities, were found to be better than PLS‐DA. 86 Sr and 112 Cd were the markers enabling the successful classification of tea samples according to their geographical origins. Under the validation by ‘blind’ dataset, the prediction accuracies of the BP‐ANN and LDA methods were all greater than 90%. The LDA method showed the best performance, with an accuracy of 100%. CONCLUSION In summary, determination of mineral elements and stable isotopes using inductively coupled plasma mass spectrometry and inductively coupled plasma optical emission spectrometry techniques coupled with chemometric methods, especially the LDA method, is a good approach for improving the authentication of a diverse range of tea. The present study contributes toward generalizing the use of fingerprinting mineral elements and stable isotopes as a promising tool for testing the geographic roots of tea and food worldwide. © 2020 Society of Chemical Industry