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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 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".