Quality grade identification of green tea using the eigenvalues of PCA based on the E-nose signals

Sensors and Actuators, B: Chemical

Volumn 140, Issue 2, 2009, Pages 378-382

  Yu, Huichun ^a,b   Wang, Jun ^a   Xiao, Hong ^a   Liu, Miao ^a  

^a ZHEJIANG UNIVERSITY   (China)

^b HENAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

BP neural network; Electronic nose; Feature extraction; Linear discrimination analysis; Principal component analysis; Tea

Indexed keywords

ANALYSIS RESULTS; BACK-PROPAGATION NEURAL NETWORKS; BP NEURAL NETWORK; DATA DIMENSIONS; EIGENVALUES; ELECTRONIC NOSE; FEATURE VECTORS; GREEN TEA; LINEAR DISCRIMINATION ANALYSIS; PRINCIPAL COMPONENTS; QUALITY GRADE; RESPONSE SIGNAL; TEA; TESTING RESULTS;

ARTIFICIAL ORGANS; BACKPROPAGATION; BEVERAGES; FACE RECOGNITION; FEATURE EXTRACTION; IMAGE RETRIEVAL; LAW ENFORCEMENT; NEURAL NETWORKS; VECTORS;

PRINCIPAL COMPONENT ANALYSIS;

EID: 67649354029     PISSN: 09254005     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.snb.2009.05.008     Document Type: Article

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