First Application of Newly Developed FT‐NIR Spectroscopic Methodology to Predict Authenticity of Extra Virgin Olive Oil Retail Products in the USA
Bibliographic record
Abstract
Economically motivated adulteration (EMA) of extra virgin olive oils (EVOO) has been a worldwide problem and a concern for government regulators for a long time. The US Food and Drug Administration (FDA) is mandated to protect the US public against intentional adulteration of foods and has jurisdiction over deceptive label declarations. To detect EMA of olive oil and address food safety vulnerabilities, we used a previously developed rapid screening methodology to authenticate EVOO. For the first time, a recently developed FT-NIR spectroscopic methodology in conjunction with partial least squares analysis was applied to commercial products labeled EVOO purchased in College Park, MD, USA to rapidly predict whether they are authentic, potentially mixed with refined olive oil (RO) or other vegetable oil(s), or are of lower quality. Of the 88 commercial products labeled EVOO that were assessed according to published specified ranges, 33 (37.5%) satisfied the three published FT-NIR requirements identified for authentic EVOO products which included the purity test. This test was based on limits established for the contents of three potential adulterants, oils high in linoleic acid (OH-LNA), oils high in oleic acid (OH-OLA), palm olein (PO), and/or RO. The remaining 55 samples (62.5%) did not meet one or more of the criteria established for authentic EVOO. The breakdown of the 55 products was EVOO potentially mixed with OH-LNA (25.5%), OH-OLA (10.9%), PO (5.4%), RO (25.5%), or a combination of any of these four (32.7%). If assessments had been based strictly on whether the fatty acid composition was within the established ranges set by the International Olive Council (IOC), less than 10% would have been identified as non-EVOO. These findings are significant not only because they were consistent with previously published data based on the results of two sensory panels that were accredited by IOC but more importantly each measurement/analysis was accomplished in less than 5 min.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".