Chemical characterization of oil from four Avocado varieties cultivated in Morocco
Bibliographic record
Abstract
The notable growth in the use of avocado oil in the nutritional and cosmetic field was the main objective to valorize the oil production of important varieties of avocados existing in Morocco by analyzing its chemical composition in fatty acids, sterols, tocopherols and its physico-chemical properties. Oleic acid is the main fatty acid in the oil; they constitute between 50 and 65% of the total fatty acids. The study of the unsaponifiable fraction revealed that avocado oil contains 3259.9–5378.8 mg/kg sterols and 113.13–332.17 mg/kg tocopherols. Chemo-metric tools were employed in manner optimization, such as principal component analysis, agglomerative hierarchical clustering, analysis of variance, and classification trees using Chi-squared Automatic Interaction Detector. Chemo-metric tools revealed a difference in the composition of fatty acid, sterols, and tocopherol of avocado oil samples. This difference resulted from a variety of avocado fruits. Agglomerative Hierarchical Clustering (AHC) method was efficient distinguishing avocado oil samples based on fruit variety using fatty acids, tocopherols, sterol compositions and total sterol. Principal component analysis (PCA) method allowed the distinction the set avocado oil dataset based on fruit varieties, supplied a correct discrimination rate of 95.44% for avocado fruit varieties using the fatty acid. Chi-squared Automatic Interaction Detector (CHAID) carried out using the same variables, also provided an acceptable classification rate of 50% for avocado fruit varieties using the total tocopherol content. Besides, a comparative study of the physico-chemical properties in terms of acidity index, saponification index, iodine index, chlorophylls, carotenoids, and methyl and ethyl esters was performed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".