title: Study on classification of soy sauce by electronic tongue technique combined with artificial neural network.
abstract: Electronic tongue as an analytical tool coupled with pattern recognition was attempted to classify 4 different brands and 2 categories (produced by different processes) of Chinese soy sauce. An electronic tongue system was used for data acquisition of the samples. Some effective variables were extracted from electronic tongue data by principal component analysis (PCA). Backpropagation artificial neural network (BP-ANN) was applied to build identification models. PCA score plots show an obvious cluster trend of different brands and different categories of soy sauce in the 2-dimensional space. The optimal BP-ANN model for different brands was achieved when principal components (PCs) were 2, and the identification rate of the discrimination model was 100% in both the calibration set and the prediction set, and the optimal BP-ANN model for different categories had the same result. This work demonstrates that electronic tongue technology combined with a suitable pattern recognition method can be successfully used in the classification of different brands and categories of soy sauce.
authors.affiliation: School of Food & Biological Engineering, Jiangsu Univ., Xuefu Road 301# Zhenjiang City, Jiangsu Province, 212013, PR China.
authors.forename: Qin
authors.initials: Q
authors.lastname: Ou-Yang
authors.forename: Jie-Wen
authors.initials: JW
authors.lastname: Zhao
authors.forename: Quan-Sheng
authors.initials: QS
authors.lastname: Chen
authors.forename: Hao
authors.initials: H
authors.lastname: Lin
authors.forename: Xing-Yi
authors.initials: XY
authors.lastname: Huang
mesh_terms: D002138:Calibration; D055664:Electrochemical Techniques / Q000379:methods; D004581:Electronics; D016571:Neural Networks, Computer; D025341:Principal Component Analysis / Q000379:methods*; D045730:Soy Foods / Q000032:analysis* / Q000145:classification*; D019265:Spectroscopy, Near-Infrared / Q000379:methods