Towards real-time pork breed and boar taint classification using rapid evaporative ionisation mass spectrometry

V. Gkarane, M. De Graeve, C. Stephens, A. I. Decloedt, P. Vangeenderhuysen, J. Balog et al.

2025 npj Science of Food Cited 0 times

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 ionisation mass spectrometry (REIMS) could be rolled out as a real-time, accurate metabolic fingerprint-based classifier of pork meat characteristics and quality issues, such as genetic origin and boar taint. Here, fingerprinting of >3000 pig neck fat samples enabled highly accurate pig breed classification (pairwise comparison of Commercials (Pietrain × Hampshires × 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 it was important to apply strategies to avoid false negatives and positives, including the construction of balanced models (tainted vs. non-tainted).

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BibTeX

@article{Gkarane2025,
  title = {Towards Real-Time Industry-Proof Pork Breed and Boar Taint Classification Using Rapid Evaporative Ionisation Mass Spectrometry (Reims)},
  author = {Gkarane, Vasiliki and De Graeve, Marilyn and Stephens, Clive and Decloedt, Anneleen and Vangeenderhuysen, Pablo and Balog, Julia and Elliott, Christopher and Stead, Sara and Birse, Nick and Hemeryck, Lieselot and others},
  journal = {Available at SSRN 5087143},
  year = {2025},
  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 (RV, 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.},
}