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<p>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 &gt;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 &gt; 89%) and orthogonal partial least squares-discriminant analysis (OPLS-DA, &gt;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).</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月8日

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  • V. Gkarane,
  • M. De Graeve,
  • C. Stephens,
  • A. I. Decloedt,
  • P. Vangeenderhuysen,
  • J. Balog,
  • C. Elliott,
  • S. L. Stead,
  • N. Birse,
  • L. Y. Hemeryck,
  • L. Vanhaecke

摘要

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).