Xingyi Huang

Study on Classification of Soy Sauce by Electronic Tongue Technique Combined with Artificial Neural Network

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.

Rapid determination of fish freshness using colorimetric sensor array

Freshness is the most important factor in fish quality evaluation. This research was intended to develop an artificial olfaction system based on colorimetric sensor array for rapid evaluation of fish freshness. Nine chemically responsive dyes including metalloporphyrins and pH indicator were selected according to their sensitivity to volatile compounds typical of spoilage processes in fish. The colorimetric sensor array was made by printing these selected dyes on a reverse phase silica gel plate. Fish of chub (‘Hypophthalmichthys molitrix’), purchased from a local market, were detected every 24 hours beginning from two hours after death of the chubs and lasting for 7 days using a system based on the sensor array. A color change profile for each sample was obtained by differentiating the images of the sensor array before and after exposure to the analyte. The digital data sets converted from the color change images were analyzed using principal component analysis. Principal component analysis showed the possibility of grouping chub samples into three freshness groups, corresponding to day 1, day 2-5, and day 6-7. A radial basis function neural network was employed to classify the degree of freshness, achieving an overall classification accuracy of 87.5%. This research suggests that the system can be useful for quality evaluation of fish and perhaps other food containing high protein.