Improvement of the Fourier Transform Near Infrared Method to Evaluate Extra Virgin Olive Oils by Analyzing 1,2‐Diacylglycerols and 1,3‐Diacylglycerols and Adding Unesterified Fatty Acids
Bibliographic record
Abstract
Extra virgin olive oils (EVOO) command higher prices because they contain health-promoting nutrients and desirable sensory characteristics. Many targeted methods have limited success in determining olive oil authenticity. Therefore, attention has been paid to rapid spectroscopic methods that provide the composition of multiple components. A Fourier transform near infrared (FT-NIR) method was reported that identified five major fatty acids and volatiles in EVOO, plus four models that identify common adulterants and their content. However, it did not include diacylglycerol (DAG) and unesterified fatty acids (FFA) known to be associated with freshness of the oil. The newly improved FT-NIR method now includes 1,2-DAG and 1,3-DAG models based on the DAG isomer content in freshly prepared EVOO, and a FFA model based on quantitative addition of oleic acid. The new FT-NIR method was used to reassess previously used EVOO products to evaluate their freshness. Based on these results and review of the published data, we propose several revisions to the EVOO regulation: limit FFA to ≤0.5%, include 1,2-DAG and 1,3-DAG in standard, place no limit on 1,2-DAG because it characterizes the oils, set the 1,3-DAG content to ≤1.0%, and lower the content of 18:2n-6 to 1.5%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".