A machine learning method for the quantitative detection of adulterated meat using a MOS-based e-nose

  • 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.

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Author:Changquan HuangORCiD, Yu GuORCiD
URN:urn:nbn:de:hebis:30:3-692419
DOI:https://doi.org/10.3390/foods11040602
ISSN:2304-8158
Parent Title (English):Foods
Publisher:MDPI
Place of publication:Basel
Document Type:Article
Language:English
Date of Publication (online):2022/02/20
Date of first Publication:2022/02/20
Publishing Institution:Universit├Ątsbibliothek Johann Christian Senckenberg
Release Date:2022/11/08
Tag:electronic nose; meat adulteration; one-dimensional convolutional neural network; random forest regressor
Volume:11
Issue:4, art. 602
Article Number:602
Page Number:17
First Page:1
Last Page:17
Note:
Data Availability Statement: The data presented in this study are available at Figshare (https://doi.org/10.6084/m9.figshare.19200284.v1).
Note:
Funding: This research was funded by the National Natural Science Foundation of China [Grant No. 61876059].
Institutes:Biochemie, Chemie und Pharmazie
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
6 Technik, Medizin, angewandte Wissenschaften / 64 Hauswirtschaft und Familie / 640 Hauswirtschaft und Familie
Sammlungen:Universit├Ątspublikationen
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International