Rapid determination of heterocyclic amines in ruminant meats using accelerated solvent extraction and ultra-high performance liquid chromatograph–mass spectrometry
Bibliographic record
Abstract
Cooking techniques such as grilling confer several benefits to meat during food preparation including improved palatability, digestibility, preservation, and safety, as well as enhancing the sensory characteristics and net nutritional gain. However, grilling can lead to the formation of harmful compounds such heterocyclic amines (HCAs). HCAs are potent carcinogenic and mutagenic nitrogen containing compounds produced during certain cooking conditions of protein rich foods. Dietary intake of HCAs is associated with increased risk factors for cancers in humans. As such, there is overwhelming interest in identifying improved methods for rapid and accurate determination of heterocyclic amines in food matrices that is sensitive and avoids exhaustive sample preparation steps. Herein, we describe an approach that involves first extracting HCAs by pressurized accelerated solvent extractor using methanol as solvent, followed by addition of internal standard and quantification of HCAs by ultra-high performance liquid chromatography-high resolution accurate mass spectrometric detection (UHPLC-HRAMS). This method is fast, accurate, reproducible and does not require exhaustive sample pre-treatments prior to UHPLC-HRAMS analysis compared to existing/traditional methods for HCA analysis. •The method is automated, fast and uses tunable pressurized liquid extractor to selectively extract HCAs•Method does not require exhaustive cleanup and preconcentration steps prior to UHPLC/HRAMS analysis of HCAs•Validation showed method to be accurate, precise, and useful for routine multi-sample HCA analyses.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".