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.

Discrimination of five brands of instant vermicelli seasonings by HS-SPME/GC–MS and electronic nose

“…To a certain extent, brand represents the quality of products in the market. Instant vermicelli seasonings of different brands have different qualities (such as the chemical ingredients and flavor profile) (Liu and He 2008;Yang et al 2011). Flavor is one of the most important indicators of the quality of seasoning.…”

Evaluating taste contribution of brown sugar in chicken seasoning using taste compounds, sensory evaluation, and electronic tongue

“…[15,16] The electronic tongue, which can simulate the human sense of taste, is a good tool for distinguishing the differences between samples. [17] It has been used to discriminate the brands and categories of soy sauce [18] , compare the different recipes of flavor enhancers [19] , and optimize sweetener blends. [20] Takahashi et al [21] used the taste sensor method to detect the sweetness, umami, saltiness, astringency, and mineral-based bitterness of brown sugar sourced from different parts of the sugarcane.…”