Evaluation of the Performance of a Portable Mid‐Infrared Analyzer for the Rapid Determination of Total <i>Trans</i> Fat in Fast Food
Bibliographic record
Abstract
Abstract The performance of a novel, transmission‐mode, portable, Fourier transform infrared (FTIR) analyzer was evaluated and compared to that of a benchtop attenuated total reflection (ATR)‐FTIR spectrometer. The total concentration of trans fatty acids in the fat extracted from 19 representative fast foods was rapidly (<5 min) quantified in a single measurement after conversion to fatty acid methyl esters (FAME). While the FTIR determination is rapid, the time required for extraction and derivatization is not. For all extracts, the total trans FAME concentration varied from approximately 0.5 to 11 % (of total FAME) as determined using the portable FTIR analyzer. The trans fat contents (mean ± SD), expressed in grams per serving and calculated on the basis of total fat content and FTIR determination of trans fat content, were found to be 1.00 ± 0.42 for hamburgers, 0.67 ± 0.78 for chicken tenders, 1.00 ± 1.24 for French fries, and 0.27 ± 0.23 for apple pies. Determinations of total trans‐unsaturated FAME were consistent with those obtained by use of ATR‐FTIR and GC official methods (AOCS Cd 14e‐09 and AOCS Ce 1j‐07, respectively). These results indicate that the portable FTIR analyzer is suitable for the rapid and routine quantification of total trans fat measured as FAME prepared from fats extracted from fast foods.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".