12/20/2023 0 Comments Duck duck g0This study indicated that the proposed 1DCNN-RFR framework could be used as an effective tool for the quantitative detection of meat adulteration. The proposed 1DCNN-RFR framework performed best in the quantitative detection of beef adulterated with pork. The effectiveness of the 1DCNN-RFR framework was verified by comparing it with four other models (support vector regression model (SVR), RFR, backpropagation neural network (BPNN), and 1DCNN). The RFR improved the regression performance due to its strong prediction ability. The 1DCNN backbone extracted a sufficient number of features from a multichannel input matrix converted from the raw E-nose data. In this study, a novel framework in which a one-dimensional convolutional neural network (1DCNN) serves as a backbone and a random forest regressor (RFR) serves as a regressor, named 1DCNN-RFR, is proposed for the quantitative detection of beef adulterated with pork using electronic nose (E-nose) data. Meat adulteration is a global problem which undermines market fairness and harms people with allergies or certain religious beliefs.
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