Abstract
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.
Highlights
Meat is one of the best nutritional sources of protein for humans and is consumed worldwide due to its highly appreciated taste [1]
This study indicated that the proposed 1DCNN-random forest regressor (RFR) framework could be used as an effective tool for the quantitative detection of meat adulteration
A novel framework 1DCNN-RFR, consisting of a 1DCNN backbone and an RFR, was proposed for the quantitative detection of beef adulterated with pork using an metal oxide semiconductor (MOS)-based E-nose
Summary
Meat is one of the best nutritional sources of protein for humans and is consumed worldwide due to its highly appreciated taste [1]. A recent report issued by the Organization for Economic Cooperation and Development and Food and Agriculture Organization (OECD-FAO) revealed that the average annual global meat consumption surpassed 327 million tons (carcass weight equivalent) from 2018 to 2020 [2]. Due to differences in prices, unethical producers sometimes blend expensive meat with lower priced meat, such as by supplementing beef with pork to increase profits [3]. An economic loss of USD 45.6 million occurred in Europe due to beef products being adulterated with horse meat [4]. The illegal activity of fraudulent substitution raises serious concerns about food safety, public health, religion, and ethics [5]. It is important to develop a reliable method for the detection of adulterated meat
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