Quantitative determination of free fatty acids in extra virgin olive oils by multivariate methods and Fourier transform infrared spectroscopy considering different absorption modes
Bibliographic record
Abstract
A methodology based on Fourier transform infrared (FTIR) spectroscopy with different absorption modes, combined with chemometrics techniques, was developed as a tool to quantify the free fatty acids (FFAs) in extra virgin olive oils (EVOOs). The range of FFA contents of calibration samples was extended by adding 0–1.00% oleic acid to refined and deodorized olive oil (RDOO) containing 0.00% FFA. Calibration models were implemented using the partial least-squares (PLS) regression technique. Two absorption modes, reflection and transmission, and two pretreatments, normal and first derivative spectra, were tested in several infrared spectral regions. In order to construct the calibration models, 15 calibration samples were scanned in different absorption modes, and 10 different brands of EVOOs were used for checking the predictive capacity of the best calibration model. The results showed that the best predictions were achieved using normal spectra in the transmission mode using 100 µm CaF2 flow cell with the highest correlation coefficient (R2) of 0.99979 and the lowest root mean square error of calibration (RMSEC) of 0.00441 and root mean square error of cross-validation (RMSECV) of 0.0107 in the infrared spectral region 1724–1646 + 3324–3023 cm−1. The method developed is fast, environment-friendly, and it could be easily used in olive oil industries for the rapid and reliable quantification of FFA content in EVOOs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".