Characterisation of Arbequina Extra Virgin Olive Oil from Uruguay
Bibliographic record
Abstract
Since the year 2002, the Uruguayan oil-producing sector has been growing at a steady peace, utilizing over 10,000 seeded hectares across the country. The aim of this study was to characterise both the chemical and sensory properties of the extra virgin olive oil variety known as Arbequina through two consecutive harvests. The work was carried out using olives with three different ripening indices; the oil was extracted using an Abencor system. The moisture of the olive, yield extraction, and yield on both a dry and wet basis were determined. The obtained oils were characterized by determining the free acidity, main antioxidant compounds (polyphenols and tocopherols), composition of fatty acids, and sensory profiles. Olive moisture was over 57%, which led to production of pomaces characterized as “difficult pastes”, explaining the low yield obtained. The harvest year and ripening index affected different parameters such as Abencor yield and, on a dry basis, the free acidity, polyphenolic content, oleic acid, and linoleic acid contents and fruity, bitter, and pungent intensities. To determine the best time of harvest, it is important to consider the balance between yield (which is significantly greater in olives with a ripening index above 3) and oil quality, as a higher ripening index decreases positive sensory attributes such as pungency and bitterness intensities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".