Adulteration discrimination and analysis of fresh and frozen-thawed minced adulterated mutton using hyperspectral images combined with recurrence plot and convolutional neural network

Abstract

This paper presented a method to detect adulterated mutton using recurrence plot transformed by spectrum combined with convolutional neural network (RP-CNN). For this, 100 adulterated samples of mutton mixed with different proportions (0.5-1-2-5-10% (w/w)) of pork and 20 pure mutton samples were prepared. The results of the classification model of adulterated mutton and the quantitative prediction model of pork content established by this method were comparable for fresh, frozen-thawed and mixed datasets. It shows that the classification accuracies of adulteration mutton on three datasets were 100.00%, 100.00% and 99.95% respectively. Moreover, for the pork content prediction of adulterated mutton, the R² on three datasets of fresh, frozen-thawed and mixed samples were 0.9762, 0.9807 and 0.9479, respectively. Therefore, the hyperspectral combined with RP-CNN proposed in this paper shows great potential in the classification of adulterated mutton and the pork content prediction of adulterated mutton.

Keywords: Adulterated mutton; Convolutional neural network; Hyperspectral; Qualitative discrimination; Quantitative prediction; Recurrence plot.