LC/MS/MS analysis of biogenic amines in foods and beverages
Bibliographic record
Abstract
Biogenic amines are a group of biologically active organic compounds produced by decarboxylation of free amino acids. They are found in bacterially contaminated food, particularly in fish, and are therefore potential quality indicators. Histamine is the main causative agent in scombroid fish poisoning. Other biogenic amines are also of interest as their presence enhances the toxicity of histamine. Biogenic amines can also react with nitrites to form potentially carcinogenic nitrosamines. Analysis by traditional RPLC is difficult because of poor retention. Derivatization methods are time consuming, ion-pairing agents can inhibit LC/MS analyses, and both can adversely affect method reproducibility. In this study we have investigated HILIC and fluorinated packing materials for the analysis of a wide range of biogenic amines, namely: histamine, cadaverine, 2-phenylethylamine, putrescine, serotonin, spermidine, spermine, tryptophan, tryptamine, tyramine and urocanic acid. The Pinnacle® DB PFPP works well with 0.05% trifluoroacetic acid for all of these amines with detection limits in the low ppb range. Over 200 different food and beverage samples have been tested in triplicate injections for reproducibility and robustness of this new method. This method was also applied to a study of how time and storage conditions affected concentrations of these biogenic amines in fish.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.004 | 0.002 |
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".