This analysis demonstrates accurate breed classification and boar taint identification using metabolic fingerprinting, indicating potential for industry applications.
Abstract To help counteract food fraud and meet consumer expectations, the pork industry requires reliable quality-monitoring and traceability systems. In this context, Rapid Evaporative Ionization Mass Spectrometry (REIMS) could be rolled out as a real-time, accurate metabolic fingerprint-based classifier of pork meat characteristics and quality issues like e.g. genetic origin and taint. Here, fingerprinting of > 3000 pig neck fat samples enabled highly accurate pig breed classification (pairwise comparison of Commercials (Pietrain x Hampshires x Durocs, Large-Whites, Durocs), Hampshires and Large-Whites, where data modelling using Support Vector Machine (SVM, all pairwise comparisons > 89%) and Orthogonal Partial Least Squares - Discriminant Analysis (OPLS-DA, >90%) outperformed Random Forest (RF, 72.0–79.5%). Boar taint classification showed comparable results between OPLS-DA, RF, and SVM (93.5–96.0%), but strategies to avoid false negatives and positives, including the construction of balanced models (tainted vs. non-tainted), proved imperative.
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Gkarane et al. (2025) studied this question.
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