Rapid discrimination of beef, pork and rabbit meat using HS-SPME coupled with GC-MS and chemometric analysis

Species adulteration in meat products remains a persistent challenge for food safety, regulatory compliance, and consumer confidence, especially in products labeled as rabbit, pork, or beef. This study developed and validated a rapid, non-targeted volatilomic strategy based on headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME–GC–MS) for the authentication of minced samples from these three species. A total of 28 volatile compounds (VOCs), predominantly aldehydes and fatty acids associated with lipid oxidation pathways, exhibited statistically significant interspecies differences and distinct species-specific distribution patterns. Multivariate data exploration using t-Distributed Stochastic Neighbor Embedding (t-SNE) demonstrated complete cluster separation among rabbit, pork, and beef samples. Supervised machine-learning algorithms, including Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN), achieved high classification performance, with overall accuracies ranging from 90.3% to 94.4%; the ANN model yielded the best predictive performance. Rabbit meat was distinguished with near-perfect precision, primarily attributable to distinctive fatty acid-derived volatile markers. The proposed method requires less than 60 min per sample, involves minimal sample preparation, and relies on widely available analytical instrumentation, supporting its applicability as a practical, cost-effective tool for routine meat authenticity assessment in both raw and processed products.