Abstract
Beef, as a nutrient-rich food, is widely favored by consumers. The production region significantly influences the nutritional value and quality of beef. However, current methods for tracing the origin of beef are still under development, necessitating effective approaches to ensure food safety and meet consumer demand for high-quality beef. This study aims to establish a classification model for beef origin prediction by analyzing elemental content and stable isotopes in beef samples from two countries. The concentrations of elements in beef were analyzed using ICP-MS and ICP-OES, while the stable carbon isotope ratio was determined using EA-IRMS. Machine learning algorithms were employed to construct classification prediction models. A total of 83 beef samples were analyzed for the concentrations of 52 elements and the stable carbon isotope ratio. The classification accuracy of the PLS-DA model built on these results was 98.8%, while the prediction accuracy was 94.12% for the convolutional neural network (CNN) and 82.35% for the Random Forest algorithm. The PLS-DA model demonstrated higher classification accuracy compared to CNN and Random Forest, with an explanatory power (R(2)) of 0.924 and predictive ability (Q(2)) of 0.787. Combining the analysis of 52 elements and the stable carbon isotope ratio with machine learning algorithms enables effective tracing and origin prediction of beef from different regions. Key factors influencing beef origin were identified as Fe, Cs, As, δ(13)C, Co, V, Sc, Rb, and Ru.