Classification of Green and Black Teas by PCA and SVM Analysis of Cyclic Voltammetric Signals from Metallic Oxide-Modified Electrode

Food Analytical Methods

Volumn 7, Issue 2, 2014, Pages 472-480

  Liu, Nian ^a   Liang, Yizeng ^a   Bin, Jun ^a   Zhang, Zhimin ^a   Huang, Jianhua ^a   Shu, RuXin ^b   Yang, Kai ^b  

^a CENTRAL SOUTH UNIVERSITY   (China)

^b Shanghai Tobacco Group Co Ltd   (China)

Author keywords

Chemical sensors; Cyclic voltammetry; Principal component analysis; Support vector machine; Tea classification

Indexed keywords

CLASSIFICATION ACCURACY; CYCLIC VOLTAMMETRIC; GREEN AND BLACK TEAS; MULTIVARIATE DATA ANALYSIS; PATTERN RECOGNITION METHOD; POLYNOMIAL KERNELS; QUALITATIVE CLASSIFICATION; TEA CLASSIFICATIONS;

CHEMICAL SENSORS; CYCLIC VOLTAMMETRY; ELECTRODES; METALS; MULTIVARIANT ANALYSIS; NICKEL; OPTIMIZATION; OXIDES; PATTERN RECOGNITION; PRINCIPAL COMPONENT ANALYSIS;

SUPPORT VECTOR MACHINES;

EID: 84892489356     PISSN: 19369751     EISSN: 1936976X     Source Type: Journal    
DOI: 10.1007/s12161-013-9649-x     Document Type: Article

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