Assessment of meat authenticity using bioinformatics, targeted peptide biomarkers and high-resolution mass spectrometry
Bibliographic record
Abstract
In recent years a significant increase of food fraud has been observed, ranging from false label claims to the use of additives and fillers to increase profitability. Recently in 2013 horse and pig DNAs were detected in beef products sold from several retailers. Mass spectrometry (MS) has become the workhorse in protein research, and the detection of marker proteins could serve for both animal species and tissue authentication. Meat species authenticity is performed in this paper using a well-defined proteogenomic annotation, carefully chosen surrogate tryptic peptides and analysis using a hybrid quadrupole-Orbitrap MS. Selected mammalian meat samples were homogenised and proteins were extracted and digested with trypsin. The samples were analysed using a high-resolution MS. Chromatography was achieved using a 30-min linear gradient along with a BioBasic C8 100 × 1 mm column at a flow rate of 75 µl min(-1). The MS was operated in full-scan high resolution and accurate mass. MS/MS spectra were collected for selected proteotypic peptides. Muscular proteins were methodically analysed in silico in order to generate tryptic peptide mass lists and theoretical MS/MS spectra. Following a comprehensive bottom-up proteomic analysis, we detected and identified a proteotypic myoglobin tryptic peptide (120-134) for each species with observed m/z below 1.3 ppm compared with theoretical values. Moreover, proteotypic peptides from myosin-1, myosin-2 and β-haemoglobin were also identified. This targeted method allowed comprehensive meat speciation down to 1% (w/w) of undesired product.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".