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Author Spotlight: Improving Beef Cattle Nutrition and Production with a Focus on Feed Efficiency and Meat Quality Traits Through Advanced Biochemical and Molecular Assays

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Beef Traceability Between China and Argentina Based on Various Machine Learning Models.

Xiaomeng Xiang^1,2, Chaomin Zhao^2,3, Runhe Zhang^2,3

¹Research Institute for Doping Control, Shanghai University of Sport, Shanghai 200438, China.

Molecules (Basel, Switzerland)

|February 26, 2025

Summary

This summary is machine-generated.

Accurate beef origin prediction is now possible using elemental analysis and stable isotopes. Machine learning models, particularly PLS-DA, achieved high accuracy in tracing beef provenance, ensuring food safety and quality.

Keywords:

beef traceability classification model elemental analysis machine learning stable isotope ratio

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Area of Science:

Food Science
Analytical Chemistry
Computational Biology

Background:

Consumer demand for high-quality beef necessitates reliable origin tracing.
Current beef origin identification methods require enhancement for accuracy and efficiency.
Production region significantly impacts beef's nutritional value and quality.

Purpose of the Study:

To develop a robust classification model for predicting beef origin.
To analyze elemental content and stable isotopes for origin determination.
To compare the performance of different machine learning algorithms for beef traceability.

Main Methods:

Elemental analysis of 52 elements using ICP-MS and ICP-OES.
Stable carbon isotope ratio determination via EA-IRMS.
Construction and evaluation of machine learning models including PLS-DA, CNN, and Random Forest.

Main Results:

PLS-DA model achieved 98.8% classification accuracy and 94.12% prediction accuracy.
Key elements (Fe, Cs, As, Co, V, Sc, Rb, Ru) and δ13C were identified as crucial for origin prediction.
PLS-DA model showed superior performance with R² of 0.924 and Q² of 0.787.

Conclusions:

Combining elemental and stable isotope analysis with machine learning effectively traces beef origin.
The developed models enhance food safety and meet consumer demand for verified beef provenance.
This approach provides a reliable method for differentiating beef from various production regions.