Rapid detecting total acid content and classifying different types of vinegar based on near infrared spectroscopy and least-squares support vector machine

Food Chemistry

Volumn 138, Issue 1, 2013, Pages 192-199

  Ji Yong, Shi ^a   Xiao Bo, Zou ^a,b   Xiao Wei, Huang ^a   Jie Wen, Zhao ^a   Yanxiao, Li ^a   Limin, Hao ^c   Jianchun, Zhang ^c  

^a JIANGSU UNIVERSITY   (China)

^b Key Laboratory of Modern Agricultural Equipment and Technology   (China)

^c The Research Center of China Hemp Materials   (China)

Author keywords

Back propagation artificial neural network; Least squares support vector machine; Near infrared spectroscopy; Partial least square; Principle component analysis; Total acid content; Vinegar

Indexed keywords

ACID CONTENT; BACK PROPAGATION ARTIFICIAL NEURAL NETWORK (BPANN); LEAST SQUARES; LEAST SQUARES SUPPORT VECTOR MACHINES; PRINCIPLE COMPONENT ANALYSIS; VINEGAR;

AROMATIC COMPOUNDS; BACKPROPAGATION; CLASSIFIERS; FORECASTING; FRUITS; INFRARED DEVICES; MEAN SQUARE ERROR; NEAR INFRARED SPECTROSCOPY; NEURAL NETWORKS; PATTERN RECOGNITION; PRINCIPAL COMPONENT ANALYSIS; RADIAL BASIS FUNCTION NETWORKS; SUPPORT VECTOR MACHINES;

ACETIC ACID;

VINEGAR;

ACCURACY; ACIDITY; ALGORITHM; ARTICLE; CHEMICAL ANALYSIS; CHINA; CONTROLLED STUDY; FERMENTATION; KERNEL METHOD; NEAR INFRARED SPECTROSCOPY; PREDICTION; PRINCIPAL COMPONENT ANALYSIS; QUALITY CONTROL; SUPPORT VECTOR MACHINE;

ACETIC ACID; ACIDS; CHINA; LEAST-SQUARES ANALYSIS; PRINCIPAL COMPONENT ANALYSIS; QUALITY CONTROL; SPECTROSCOPY, NEAR-INFRARED; SUPPORT VECTOR MACHINES;

EID: 84870152927     PISSN: 03088146     EISSN: 18737072     Source Type: Journal    
DOI: 10.1016/j.foodchem.2012.10.060     Document Type: Article

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