Determination of rice sensory quality with similarity analysis-artificial neural network method in electronic tongue system

Abstract

Lack of effective data processing methods has limited the application of electronic tongues in rice sensory analysis. In this paper, a novel similarity analysis-artificial neural network (SA-ANN) method was developed for an electronic tongue system to determine rice sensory quality. Characteristic data were extracted from signals and arranged in data matrix. The obtained matrix data were analyzed using the similarity analysis method by comparing the data to those of a control sample, resulting in a similarity degree that was used as the input variable for the artificial neural network. The SA-ANN method was tested and compared to traditional sensory evaluation. The correlation coefficients (R) for odor, appearance, palatability, texture, and overall scores were 0.9669, 0.9711, 0.9760, 0.8654 and 0.9848, respectively. Results indicated that the developed SA-ANN technique is an efficient data processing method for use in an electronic tongue system to characterize and predict rice sensory quality.