A Fusion Parameter Method for Classifying Freshness of Fish Based on Electrochemical Impedance Spectroscopy

Abstract

Compared with using a single characteristic parameter of electrochemical impedance spectroscopy (EIS) to classify the freshness of fish samples from different origins, more characteristic parameters could bring higher accuracy as well as complexity, subjectivity, and uncertainty. In order to eliminate the disadvantages of the multiparameter model, a data fusion method based on model similarity (DFMS) was proposed in this study. The similarity relation between the freshness models based on EIS characteristic parameters and physicochemical indicator was analyzed and quantified accordingly, and then, the weighting factors of the fusion model were determined. The classification accuracy rate of fish freshness based on DFMS was 9.2∼15% greater than that of a single EIS characteristic parameter. The novel dimensionless fusion parameter method proposed in this article might provide a simple yet effective indicator for EIS-based food quality evaluation.