Cultivar discrimination and prediction of mixtures of Tunisian extra virgin olive oils by FTIR
Bibliographic record
Abstract
Fourier‐transform infrared spectroscopy, followed by multivariate treatment of the spectral data, was used to classify Tunisian extra virgin olive oils (EVOOs) according to their cultivar. Moreover, these data were also employed to establish the composition of binary mixtures of EVOOs from different cultivars. For this purpose, the spectra were divided in 20 regions, being the normalized peak areas within these regions used as predictors. Using linear discriminant analysis, an excellent resolution between EVOOs from different cultivar was obtained. Moreover, multiple linear regression models were used to predict the composition of binary mixtures of EVOOs, being in all cases capable of predicting the percentage of one of the oils with average validation errors below 6%. Thus, the FTIR method proposed in this work, followed by chemometric analysis, could be used in an industrial setting since no complicated laboratory facilities are required, which could save great deal of time and money. Practical applications: EVOO quality can be affected by different parameters, such as cultivar and maturity index, among others, consequently, it is necessary to develop analytical methods able to verify EVOO quality, and also to control its origin since olive oil producers have to include in their manufactured products the cultivar (monovarietal oils) from which the oil was obtained. Currently, almost all the research related to Tunisian EVOOs has been mainly focused on the improvement and characterization of the two main cultivars (Chétoui and Chemlali). Thus, to diversify Tunisian olive oil resources and improve the quality of olive oil produced in Tunisia, research on additional cultivars needs to be conducted. Thus, the present methodology (including the application of chemometric techniques) could be used as an authentication tool for olive oil industry to assess cultivar of EVOO. ATR‐FTIR data of Tunisian EVOOs are used to predict EVOO cultivar using LDA.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".