Mass Spectrometry-Based Untargeted Proteomics for the Assessment of Food Authenticity: The Case of Farmed Versus Wild-Type Salmon
Bibliographic record
Abstract
Abstract Background: Omics technologies have been widely applied in different fields, among which, proteomics is gaining increasing interest for its application to the authenticity of food products. MS, typically coupled with LC, represents a key technique for proteomics-related studies dedicated to fish and other seafood products by using a bottom-up approach. Objective and Methods: In this paper, the optimization of an untargeted proteomics-based method using LC separation and MS detection relying on a quadrupole time-of-flight mass spectrometer is described and applied to the analysis of Canadian farmed and wild-type salmon, followed by statistical analysis based on principal component (PC) analysis. Results and Conclusions: This untargeted approach, using a data-independent acquisition MS scheme, demonstrated the ability to effectively discriminate salmon belonging to the two classes. Furthermore, selected peptides showing high loadings on PC1 could represent potential candidate peptide markers able to discriminate farmed from wild-type salmon samples in the future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".