Stable Isotope Ratio and Elemental Profile Combined with Support Vector Machine for Provenance Discrimination of Oolong Tea (Wuyi-Rock Tea)

This paper focused on an effective method to discriminate the geographical origin of Wuyi-Rock tea by the stable isotope ratio (SIR) and metallic element profiling (MEP) combined with support vector machine (SVM) analysis. Wuyi-Rock tea (n=99) collected from nine producing areas and non-Wuyi-Rock te...

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Main Authors: Yun-xiao Lou, Xian-shu Fu, Xiao-ping Yu, Zi-hong Ye, Hai-feng Cui, Ya-fen Zhang
Format: Article
Language:English
Published: Wiley 2017-01-01
Series:Journal of Analytical Methods in Chemistry
Online Access:http://dx.doi.org/10.1155/2017/5454231
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author Yun-xiao Lou
Xian-shu Fu
Xiao-ping Yu
Zi-hong Ye
Hai-feng Cui
Ya-fen Zhang
author_facet Yun-xiao Lou
Xian-shu Fu
Xiao-ping Yu
Zi-hong Ye
Hai-feng Cui
Ya-fen Zhang
author_sort Yun-xiao Lou
collection DOAJ
description This paper focused on an effective method to discriminate the geographical origin of Wuyi-Rock tea by the stable isotope ratio (SIR) and metallic element profiling (MEP) combined with support vector machine (SVM) analysis. Wuyi-Rock tea (n=99) collected from nine producing areas and non-Wuyi-Rock tea (n=33) from eleven nonproducing areas were analysed for SIR and MEP by established methods. The SVM model based on coupled data produced the best prediction accuracy (0.9773). This prediction shows that instrumental methods combined with a classification model can provide an effective and stable tool for provenance discrimination. Moreover, every feature variable in stable isotope and metallic element data was ranked by its contribution to the model. The results show that δ2H, δ18O, Cs, Cu, Ca, and Rb contents are significant indications for provenance discrimination and not all of the metallic elements improve the prediction accuracy of the SVM model.
format Article
id doaj-art-71c4a16689ba45a7b2d6ba0221d89927
institution Kabale University
issn 2090-8865
2090-8873
language English
publishDate 2017-01-01
publisher Wiley
record_format Article
series Journal of Analytical Methods in Chemistry
spelling doaj-art-71c4a16689ba45a7b2d6ba0221d899272025-08-20T03:33:42ZengWileyJournal of Analytical Methods in Chemistry2090-88652090-88732017-01-01201710.1155/2017/54542315454231Stable Isotope Ratio and Elemental Profile Combined with Support Vector Machine for Provenance Discrimination of Oolong Tea (Wuyi-Rock Tea)Yun-xiao Lou0Xian-shu Fu1Xiao-ping Yu2Zi-hong Ye3Hai-feng Cui4Ya-fen Zhang5Zhejiang Provincial Key Laboratory of Biometrology and Inspection & Quarantine, College of Life Sciences, China Jiliang University, Hangzhou 310018, ChinaZhejiang Provincial Key Laboratory of Biometrology and Inspection & Quarantine, College of Life Sciences, China Jiliang University, Hangzhou 310018, ChinaZhejiang Provincial Key Laboratory of Biometrology and Inspection & Quarantine, College of Life Sciences, China Jiliang University, Hangzhou 310018, ChinaZhejiang Provincial Key Laboratory of Biometrology and Inspection & Quarantine, College of Life Sciences, China Jiliang University, Hangzhou 310018, ChinaZhejiang Provincial Key Laboratory of Biometrology and Inspection & Quarantine, College of Life Sciences, China Jiliang University, Hangzhou 310018, ChinaZhejiang Provincial Key Laboratory of Biometrology and Inspection & Quarantine, College of Life Sciences, China Jiliang University, Hangzhou 310018, ChinaThis paper focused on an effective method to discriminate the geographical origin of Wuyi-Rock tea by the stable isotope ratio (SIR) and metallic element profiling (MEP) combined with support vector machine (SVM) analysis. Wuyi-Rock tea (n=99) collected from nine producing areas and non-Wuyi-Rock tea (n=33) from eleven nonproducing areas were analysed for SIR and MEP by established methods. The SVM model based on coupled data produced the best prediction accuracy (0.9773). This prediction shows that instrumental methods combined with a classification model can provide an effective and stable tool for provenance discrimination. Moreover, every feature variable in stable isotope and metallic element data was ranked by its contribution to the model. The results show that δ2H, δ18O, Cs, Cu, Ca, and Rb contents are significant indications for provenance discrimination and not all of the metallic elements improve the prediction accuracy of the SVM model.http://dx.doi.org/10.1155/2017/5454231
spellingShingle Yun-xiao Lou
Xian-shu Fu
Xiao-ping Yu
Zi-hong Ye
Hai-feng Cui
Ya-fen Zhang
Stable Isotope Ratio and Elemental Profile Combined with Support Vector Machine for Provenance Discrimination of Oolong Tea (Wuyi-Rock Tea)
Journal of Analytical Methods in Chemistry
title Stable Isotope Ratio and Elemental Profile Combined with Support Vector Machine for Provenance Discrimination of Oolong Tea (Wuyi-Rock Tea)
title_full Stable Isotope Ratio and Elemental Profile Combined with Support Vector Machine for Provenance Discrimination of Oolong Tea (Wuyi-Rock Tea)
title_fullStr Stable Isotope Ratio and Elemental Profile Combined with Support Vector Machine for Provenance Discrimination of Oolong Tea (Wuyi-Rock Tea)
title_full_unstemmed Stable Isotope Ratio and Elemental Profile Combined with Support Vector Machine for Provenance Discrimination of Oolong Tea (Wuyi-Rock Tea)
title_short Stable Isotope Ratio and Elemental Profile Combined with Support Vector Machine for Provenance Discrimination of Oolong Tea (Wuyi-Rock Tea)
title_sort stable isotope ratio and elemental profile combined with support vector machine for provenance discrimination of oolong tea wuyi rock tea
url http://dx.doi.org/10.1155/2017/5454231
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