A rapid, sensitive and solvent‐less method for determination of malonaldehyde in meat by stir bar sorptive extraction coupled thermal desorption and gas chromatography/mass spectrometry with <i>in situ</i> derivatization
Bibliographic record
Abstract
RATIONALE: The traditional methods for analysis of malonaldehyde (MDA), such as the thiobarbituric acid (TBA) assay, require strong acidity at high temperature for derivatization and lack specificity in analysis. Stir bar sorptive extraction (SBSE) coupled with thermal desorption-gas chromatography/mass spectrometry (TD-GC/MS) with in situ derivatization using pentafluorophenylhydrazine (PFPH) under mild conditions is an emerging technique for MDA analysis. METHODS: MDA in meat was derivatized with PFPH at pH ~4 for 1 h at room temperature, forming a relative stable derivative of MDA-PFPH. The derivative of MDA-PFPH was simultaneously extracted using SBSE. Then, MDA-PFPH was thermally released and quantitatively analyzed by GC/MS in selected ion monitoring (SIM) mode. RESULTS: The method of SBSE-TD-GC/MS for MDA analysis with in situ derivatization was optimized and validated with good linearity, specificity and limit of detection/quantification (LOD/LOQ). The method was successfully applied for analysis of MDA in raw and cooked meat (pork). CONCLUSIONS: The SBSE-TD-GC/MS method was suitable to monitor and analyze MDA in meat samples at trace levels. The simple, sensitive and solvent-less method with moderated in situ derivatization can be applied for analysis of MDA in a wide variety of foods and biological samples.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".