Detection of adulteration in mutton using digital images in time domain combined with deep learning algorithm

A novel method based on digital images in time domain combined with convolutional neural network (CNN) is proposed for discrimination and analysis of the adulterated mutton. For this, 195 sample images during the constant temperature heating process (about 10 min) were combined with CNN for qualitative discrimination and quantitative prediction of adulterated mutton. Furthermore, the hypothesis that temperature disturbance can improve the detection ability of adulterated mutton was confirmed by comparing the model performance of the initial heating stage and the entire heating process. The experimental results show that the performance of the latter was superior to that of the former. The accuracy of the qualitative discriminant model was increased by 7.33%, the R2 and RPD of the quantitative prediction model of the duck/pork in adulterated mutton were increased by 0.08/0.07 and 0.85/0.87 respectively, while the RMSE decreased by 0.01/0.01. Consequently, the proposed method can be used for detecting adulterated mutton effectively and accurately. •This study proposes a novel method to classify and quantify mutton adulteration with pork.•The digital image of the ROI during heating is used as the input to the CNN model.•The CNN model of qualitative classification and quantitative prediction are developed.•The heating effect was demonstrated by comparing model performances at different heating stages.