Cyclohexanone/sulfonated polymer catalyst: a new simple derivatizing procedure for <scp>GC</scp>‐<scp>MS</scp> determination of 2‐ and 3‐monochloropropanediols
Bibliographic record
Abstract
Abstract Assessment of contamination of foods with monochloropropanediols (MCPD) and subsequent mitigation of their formation is an important current issue of a global food security. Methods for the determination of 2‐ or 3‐MCPD in foods at low μg/kg levels require analyte derivatization prior to gas chromatography‐mass spectrometry (GC‐MS) determination. All existing methods suffer from various drawbacks associated with current derivatization schemes. We have developed a new derivatization scheme, which uses cyclohexanone as a derivatization agent and a sulfonated polymer as a solid‐phase acidic catalyst. This derivatization uses a readily available derivatization reagent and does not require any postderivatization workup. The respective 2‐MCPD 1,3‐dioxane and 3‐MCPD 1,3‐dioxolane derivatives are stable with storage, produce characteristic molecular ions, and chromatograph well on nonpolar GC columns. This derivatization procedure was applied to the analysis of free 2‐ and 3‐MCPD, bound 2‐ or 3‐MCPD (in the form of fatty acid esters after acidic hydrolysis), and also to simultaneous analysis of free and bound forms. The method was tested on soy sauce, commercial palm oil, palm oil noodles from an instant soup, and olive oil, which was spiked with bound 2‐ and/or 3‐MCPD. The results obtained using derivatization with cyclohexanone agreed with the data obtained using traditional heptafluorobutyryl imidazole derivatization. Additionally, data for soy sauce and palm oil matrices obtained through interlaboratory testing programs had z‐scores <1. The method detection limit is 1–3 μg/kg for free 2‐ and 3‐MCPD (sample weight dependent) and 100 μg/kg per fat for bound 2‐ and 3‐MCPD.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".