A reference isotope dilution headspace GC/MS method for the determination of nitrite and nitrate in meat samples
Bibliographic record
Abstract
Summary A novel method for the determination of nitrite and nitrate in meat products is presented. The samples were ground and extracted in hot water with the presence of and internal standards. The solution was buffered with sodium bicarbonate and reacted with triethyloxonium tetrafluoroborate to convert nitrite and nitrate into EtNO2 and EtONO2. Such derivatives could be detected by headspace GC/MS in positive chemical ionisation mode with 0.05 µg g−1 and 1.0 µg g−1 LOD. The method was used for and quantitation in the 0.5‐300 and 2.5‐300 µg g−1 ranges. The method was applied for the analysis of fifteen meat products. Despite minimal sample preparation, the headspace sampling ensured a clean chromatography for over 135 analyses (throughput ten samples per hour). The proposed method offers selective GC/MS detection combined with high‐precision isotope dilution calibration, it is suitable for metrological applications and can support regulations on meat safety (European Commission, 2011).
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".