TY - JOUR
AU1 - Huang, Changquan
AU2 - Gu, Yu
AB - Meat adulteration is a global problem which undermines market fairness and harms people with allergies or certain religious beliefs. 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. The 1DCNN backbone extracted a sufficient number of features from a multichannel input matrix converted from the raw E-nose data. The RFR improved the regression performance due to its strong prediction ability. 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 proposed 1DCNN-RFR framework performed best in the quantitative detection of beef adulterated with pork. This study indicated that the proposed 1DCNN-RFR framework could be used as an effective tool for the quantitative detection of meat adulteration.
TI - A Machine Learning Method for the Quantitative Detection of Adulterated Meat Using a MOS-Based E-Nose
JF - Foods
DO - 10.3390/foods11040602
DA - 2022-02-20
UR - https://www.deepdyve.com/lp/multidisciplinary-digital-publishing-institute/a-machine-learning-method-for-the-quantitative-detection-of-qqos7pXfrv
SP - 602
VL - 11
IS - 4
DP - DeepDyve
ER -