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Updated: Oct 2, 2025

Species Determination and Quantitation in Mixtures Using MRM Mass Spectrometry of Peptides Applied to Meat Authentication

Species Determination and Quantitation in Mixtures Using MRM Mass Spectrometry of Peptides Applied to Meat Authentication

Published on: September 20, 2016

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

Changquan Huang¹, Yu Gu^1,2,3,4

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

Foods (Basel, Switzerland)

|February 25, 2022

Summary

This summary is machine-generated.

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 nose meat adulteration one-dimensional convolutional neural network random 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.