Detection of gelatin adulteration using bio-informatics, proteomics and high-resolution mass spectrometry
Bibliographic record
Abstract
Following the internationalisation of food production and manufacturing, a significant increase of food fraud was discovered, ranging from false label claims to the use of additives and fillers to increase profitability. The accidental or fraudulent mixing of animal products or by-products from different species is an important preoccupation for consumers with health or ethical concerns. Gelatin is widely used during food processing as well as in cosmetics, nutraceutics and medical formulations. It contains mainly type I, II and III collagen polypeptides. Gelatin speciation was performed using a well-defined proteogenomic annotation, carefully chosen surrogate tryptic peptides and analysis using a hybrid quadrupole-Orbitrap MS. Gelatin samples were dissolved in ammonium bicarbonate buffer and proteins were digested with trypsin. The samples were analysed using high-resolution MS. Chromatography was achieved using a 30-min linear gradient on a Thermo 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 and using a data-dependent top-10 method. Collagen proteins were methodically analysed in silico in order to generate tryptic peptide mass lists. Following comprehensive MS and MS/MS analyses, we detected and identified several type I collagen peptides and fully characterised the proteotypic peptides [831–846], [847–879], [949–974] and [975–996] (accession number F1SFA7). Additionally, the method was successfully tested with gelatin used in charcuterie meats obtained from grocery stores, fruit-snacks and gelatin capsules. This targeted method allowed comprehensive gelatin speciation and adulteration detection down to 0.1% (w/w) of undesired pork gelatin.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".