Determination of<i>N</i>-nitrosamines in processed meats by liquid extraction combined with gas chromatography-methanol chemical ionisation/mass spectrometry
Bibliographic record
Abstract
A simple, accessible and reproducible method was developed and validated as an alternative for the determination of nine volatile N-nitrosamines (NAs) in meat products, using a low volume of organic solvent and without requiring specific apparatus, offering the possibility of practical implementation in routine laboratories. The NAs were extracted with dichloromethane followed by a clean-up with phosphate buffer solution (pH 7.0). The extracts were analysed by gas chromatography-chemical ionisation/mass spectrometry (GC-CI/MS) in positive-ion mode using methanol as reagent. Limits of detection and quantification, recovery and reproducibility were determined for all NAs (N-nitrosodimethylamine, N-nitrosomethylethylamine, N-nitrosodiethylamine, N-nitrosopyrrolidine, N-nitrosodipropylamine, N-nitrosomorpholine, N-nitrosopiperidine, N-nitrosodibutylamine and N-nitrosodiphenylamine). Satisfactory sensitivity and selectivity were obtained even without concentrating the extract by solvent evaporation, avoiding the loss of the nine NAs studied. Limits of detection ranged from 0.15 to 0.37 µg kg(-1), whereas limits of quantification ranged from 0.50 to 1.24 µg kg(-1). Recoveries calculated in cooked ham that had been spiked at 10 and 100 µg kg(-1) were found to be between 70% and 114% with an average relative standard deviation of 13.2%. The method was successfully used to analyse five samples of processed meat products on the day of purchase and 7 days later (after storage at 4°C). The most abundant NAs found in the analysed products were N-nitrosodipropylamine and N-nitrosopiperidine, which ranged from 1.75 to 34.75 µg kg(-1) and from 1.50 to 4.26 µg kg(-1), respectively. In general, an increase in the level of NAs was observed after the storage period. The proposed method may therefore be a useful tool for food safety control once it allows assessing the profile and the dietary intake of NAs in food over time.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".