Application of principal component-radial basis function neural networks (PC-RBFNN) for the detection of water-adulterated bayberry juice by near-infrared spectroscopy

Journal of Zhejiang University: Science B

Volumn 9, Issue 12, 2008, Pages 982-989

  Xie, Li Juan ^a   Ye, Xing Qian ^a   Liu, Dong Hong ^a   Ying, Yi Bin ^a  

^a ZHEJIANG UNIVERSITY   (China)

Author keywords

Adulteration; Bayberry juice; Chemometrics technique; Near infrared (NIR) spectroscopy; Principal component radial basis function neural networks (PC RBFNN)

Indexed keywords

WATER;

ANALYTIC METHOD; ARTICLE; BERRY; CHEMOMETRIC ANALYSIS; FOOD ANALYSIS; FOOD PROCESSING; FOOD QUALITY; FOOD SPOILAGE; FRUIT JUICE; NEAR INFRARED SPECTROSCOPY; NONHUMAN; PRINCIPAL COMPONENT ANALYSIS; PRINCIPAL COMPONENT RADIAL BASIS FUNCTION NEURAL NETWORK; PRIORITY JOURNAL; PROCESS OPTIMIZATION; PROCESS TECHNOLOGY; RADIAL BASED FUNCTION; SENSITIVITY ANALYSIS; SEPARATION TECHNIQUE;

BEVERAGES; FOOD CONTAMINATION; MYRICA; NEURAL NETWORKS (COMPUTER); PRINCIPAL COMPONENT ANALYSIS; SPECTROSCOPY, NEAR-INFRARED;

MORELLA;

EID: 57549083391     PISSN: 16731581     EISSN: 18621783     Source Type: Journal    
DOI: 10.1631/jzus.B0820057     Document Type: Article

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