Fatty acids and fatty alcohol esters as novel markers of authenticity and extraction method of commercial avocado oil
Bibliographic record
Abstract
The lack of regulations on avocado oil authenticity enabled producers to blend it with cheaper oils without declaring it on the label. This study explored fatty acids (FA) and fatty alcohol esters (FAE) as innovative markers of avocado oil purity and extraction method. Only 40 % of the samples met the FA standards. Regarding FAE, extra virgin samples showed a maximum concentration of 2674.12 ± 570.98 mg total FAE·kg −1 oil, significantly higher than the 640.98 ± 220.06 mg total FAE·kg −1 oil observed in refined samples, making FAE novel markers of extraction method. Furthermore, a canonical discriminant analysis using Wilk's statistic (Λ = 0.008, F 126, 1028.2 = 8.87, p < 0.0001), based on fault concentrations (high, medium, low) of the allegedly pure samples, revealed that the high- and medium-fault clusters aligned closely with declared blends containing canola or safflower oils . FA and FAE are promising molecules to detect adulteration in avocado oil.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".