TY  - JOUR
A1  - Huang, Changquan
A1  - Gu, Yu
T1  - A machine learning method for the quantitative detection of adulterated meat using a MOS-based e-nose
T2  - Foods
N2  - 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.
KW  - meat adulteration
KW  - electronic nose
KW  - one-dimensional convolutional neural network
KW  - random forest regressor
Y1  - 2022
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/69241
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-692419
SN  - 2304-8158
N1  - Data Availability Statement: The data presented in this study are available at Figshare (https://doi.org/10.6084/m9.figshare.19200284.v1).
N1  - Funding: This research was funded by the National Natural Science Foundation of China [Grant No. 61876059].
VL  - 11
IS  - 4, art. 602
SP  - 1
EP  - 17
PB  - MDPI
CY  - Basel
ER  -