Impact of Growing Area and Technological Aspects on Lebanese Olive Oil: Characterization by Unsupervised Methods
Bibliographic record
Abstract
The influence of growing-area and technological aspects on the Lebanese Soury variety is presented. The pedoclimatic conditions, quality parameters, and fatty acids of oil samples were examined using chemometric tools to demonstrate the typicality of Lebanese olive oil. Furthermore, the effect of several technological factors on olive oil quality was studied. Accordingly, olive oil samples were collected across Lebanon corresponding to two climatic zones (Zone A: Low-altitude regions; Zone B: High-altitude regions). Principal component analysis was capable of discriminating the two zones with distinct fatty acid profiles. Zone A exhibited a fatty acid profile mostly dominated by linoleic, linolenic, palmitic, and palmitoleic acid content. As for zone B, it showed a unique fatty acid profile distinguished by oleic acid. These results are mostly attributed to the climatic effect and the altitude of the growing area. Moreover, independent component analysis, coupled to one-way ANOVA, demonstrated that significant differences (p<0.05) were found in quality indices (acidity and peroxide value) as well as fatty acid contents (oleic and linoleic acid) when comparing uncontrolled to controlled extraction methods. This study provides a baseline for future intensive characterization of Lebanese olive oil and detects the non-compliances attributed to the disqualification of olive oil virginity.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".