Rapid FT‐NIR Analysis of Edible Oils for Total SFA, MUFA, PUFA, and <i>Trans</i> FA with Comparison to GC
Bibliographic record
Abstract
Abstract Declarations of the total content of trans fatty acids (FA) and saturated FA (SFA) are mandatory on food labels in the US and Canada. Gas chromatography (GC) has been the method of choice for the determination of FA composition. However, GC is time consuming and requires conversion of fats and oils to their FA methyl esters. In the present study, a recently published Fourier transform near‐infrared (FT‐NIR) spectroscopic procedure was applied to the rapid (<5 min) determination of total SFA, monounsaturated FA (MUFA), polyunsaturated FA (PUFA), and trans FA contents of 30 commercially available edible fats and oils. Good agreement was obtained between the GC and FT‐NIR methods for the determination of total SFA, MUFA, and PUFA contents. Differences between the two methods were apparent for the determination of trans fat at trans fat levels <2 % of total fat. The analytical determinations of total SFA, MUFA, and PUFA contents for many of the oils examined differed from the respective values declared on the product labels. Our findings demonstrate that the FT‐NIR procedure serves as a suitable alternative method for the rapid determination of total SFA, MUFA, PUFA and trans FA contents of neat vegetable oils.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".