An optimised HS-SPME-GC-MS method for the detection of volatile nitrosamines in meat samples
Bibliographic record
Abstract
A method to detect volatile nitrosamines in meat samples was developed using headspace sampling by solid-phase microextraction (HS-SPME), with analysis by GC-MS. A 50/30 µm divinylbenzene/carboxen/polydimethylsiloxane fused silica fibre was selected to extract a total of nine volatile nitrosamines: N-nitrosodimethylamine, N-nitrosomethylethylamine, N-nitrosodiethylamine, N-nitrosodi-n-propylamine, N-nitrosomorpholine, N-nitrosopyrrolidine, N-nitrosopiperidine, N-nitrosodi-n-butylamine, and N-nitrosodiphenylamine. Extraction at 65°C for 45 min with 36% (w/v) NaCl were the optimal conditions determined for the extraction of nine nitrosamines. Excellent linearity was obtained for all analytes with determination coefficients greater than 0.997. Recovery rates were between 92 and 113%. The relative standard deviation ranged from 0.81 to 8.0% for six of the nine compounds, and from 16 to 32% for the other three. For seven out of nine nitrosamines, limits of detection were below 3.6 µg kg−1 and the limits of quantification were below 12 µg kg−1. The nitrosamine levels in four varieties of processed meat products were investigated to assess the applicability of the method. Based on the results, the developed HS-SPME-GC-MS method proved to be a simple and efficient technique to detect seven out of nine nitrosamines in meat products with adequate sensitivity, accuracy and precision.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".