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A Machine Learning Method for the Quantitative Detection of Adulterated Meat Using a MOS-Based E-Nose.

Changquan Huang1, Yu Gu1,2,3,4

  • 1College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China.

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Summary

A new method using a one-dimensional convolutional neural network and random forest regressor (1DCNN-RFR) accurately detects pork adulteration in beef using electronic nose data. This framework offers an effective tool for quantitative meat adulteration detection.

Keywords:
electronic nosemeat adulterationone-dimensional convolutional neural networkrandom forest regressor

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Area of Science:

  • Food Science
  • Analytical Chemistry
  • Machine Learning

Background:

  • Meat adulteration, particularly beef with pork, is a significant global issue affecting consumers and market integrity.
  • Accurate quantitative detection methods are crucial for food safety, allergen management, and religious compliance.

Purpose of the Study:

  • To develop and validate a novel machine learning framework for the quantitative detection of pork adulteration in beef.
  • To assess the performance of the proposed framework against existing analytical models.

Main Methods:

  • A hybrid model, 1DCNN-RFR, was designed, utilizing a 1DCNN for feature extraction from electronic nose (E-nose) data and an RFR for regression analysis.
  • Raw E-nose data was preprocessed into a multichannel input matrix for the 1DCNN.
  • The 1DCNN-RFR model's performance was benchmarked against Support Vector Regression (SVR), Random Forest Regressor (RFR), Backpropagation Neural Network (BPNN), and 1DCNN models.

Main Results:

  • The 1DCNN-RFR framework demonstrated superior performance in the quantitative detection of beef adulterated with pork compared to all other evaluated models.
  • The 1DCNN effectively extracted relevant features, while the RFR enhanced predictive accuracy.

Conclusions:

  • The proposed 1DCNN-RFR framework is a highly effective tool for the quantitative analysis of meat adulteration.
  • This approach holds significant potential for ensuring food authenticity and safety in the meat industry.