Identification of Adulteration of Olive Oil with Other Edible Oils by LED-induced Fluorescence and Multivariate Calibration
Bibliographic record
Abstract
The most common adulterants found in extra-virgin olive oil are refined olive oil and other vegetable oils, such as sunflower, soybean, corn, and canola. In addition to constituting economic fraud, adulteration can cause serious damage to the health of the consumer. This study focuses on the detection and quantification of the adulteration of extra-virgin olive oil with edible oils, using spectrofluorimetry and chemometrics. The data were analyzed by Principal Components Analysis (PCA) and Partial Least Squares (PLS) analysis. Through PCA, it was possible to separate the samples into two distinct areas, olive oil and other edible oils, based on their chemical composition. The PLS model, built with the spectra of mixtures of soybean oil in extra-virgin olive oil, exhibited an R2 of 0.99412 and low RMSEP (Root Mean Square Error of Prediction) (3.59), RMSEC (Root Mean Square Error of Calibration) (2.32) and bias (4.77. 10-7) values. Thus, the PLS model was considered exact for calibration and prediction.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".