In order to establish a fast, convenient and low-cost method to detect the quality of beef, we compared and analyzed the changes of the impedance characteristics of the beef with different storage days and different storage methods (fresh/frozen-thawed) in the frequency range of 10<sup>1</sup>-10<sup>5</sup>Hz. And some rules of the change were explained in this paper. To verify the feasibility of detecting beef quality by using impedance characteristics, we established the discriminant model by combining back propagation algorithm artificial neural network (BPANN) with impedance characteristics of different quality beef. Results showed that combining BPANN with impedance characteristics could classify the samples (fresh/frozen-thawed) into two groups according to the mode of storage and classify the samples into three groups according to different storage days (0d, 15d, 30d). The accuracy of the model established by using BPANN to distinguish fresh beef from frozen-thawed beef could reach 99%, and the accuracy of the distinguish of different storage days could reach 98%. A PLS was applied to establish a quantitative prediction model of impedance spectroscopy and TVB-N, and the correlation coefficients of training set and test set were 0.9312 and 0.9277, respectively, showing good performances for predicting TVB-N. Since TVB-N was one of the most important parameters to determine meat freshness, the impedance characteristics could be used to predict the freshness of beef. In summary, the combination of impedance properties and chemometrics was a simple, effective and promising application for the detection of beef quality.

Detection of beef quality by using impedance characteristics

2019 Fall National Meeting

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