ArticleNPJ science of food2026
Towards real-time pork breed and boar taint classification using rapid evaporative ionisation mass spectrometry.
Article in NPJ science of food, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
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).
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.