Discriminating Extra Virgin Olive Oils from Common Edible Oils: Comparable Performance of PLS-DA Models Trained on Low-Field and High-Field 1H NMR Data
Bibliographic record
Abstract
Olive oil, the oil derived from the olive tree (Olea europaea L.), is used in cooking, cosmetics, and soap production. Due to its high value, some producers adulterate olive oil with cheaper edible oils or mislabel cheaper oils to increase profitability. These other edible oils can have chemical profiles similar to extra virgin olive oil but can cause allergies in sensitive individuals. Given these consequences, there is a need for methods to rapidly authenticate olive oils. Nuclear magnetic resonance (NMR) has been used for this purpose, as it requires minimal sample preparation and is non-destructive. By utilizing NMR spectra of the samples and machine learning models trained on known olive oil and edible oils, oil samples can be classified and authenticated. While high-field NMRs are commonly used due to their superior resolution and sensitivity, they are generally prohibitively expensive to purchase and operate, for routine screening purposes. Low-field benchtop NMR presents an affordable alternative. Here, we compared the predictive performance of partial least squares discrimination analysis (PLS-DA) models trained on low-field 60 MHz benchtop 1H NMR and high-field 400 MHz 1H NMR spectra. We demonstrated that PLS-DA models trained on low-field spectra perform comparably to those trained on high-field spectra.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".