Evaluation of phenolics in the analysis of virgin olive oil using near infrared spectroscopy
Bibliographic record
Abstract
Olive oil is an indispensable part of the diet in Mediterranean regions, and is appreciated worldwide for its sensory characteristics, combining a fine aroma and pleasant flavor with the high nutritional value of specific chemical compounds. However, making rapid measurements of phenolic compounds is a major challenge for the olive oil sector. The development of a new method based on near infrared (NIR) spectroscopy may be considered an important advance for the sector, as it is rapid, low-cost, non-contaminant and non-destructive. In this study, three different NIR instruments − one FT-NIR benchtop instrument (671.82–2702.70 nm) and two low-cost portable devices (900–1700 nm and 1350–2150 nm) − were used to analyze a collection of virgin olive oil samples from various Mediterranean regions. To predict both the individual and total concentration of phenols in the olive oil, four signal pretreatment methods and modified partial least squares regression analyses were employed to develop the predictive models. The results showed that the benchtop FT-NIR instrument performed better than the other portable instruments when measuring the phenolic compounds in virgin olive oil. The best models of hydroxytyrosol derivatives, tyrosol derivatives, total phenols and “EFSA phenols” showed R 2 cv values of 0.84, 0.85, 0.88 and 0.89, while the RPD cv values were 2.51, 2.61, 2.93 and 2.95, respectively. Meanwhile, for the portable NIR instruments, the prediction models for hydroxytyrosol derivatives, total phenols and “EFSA phenols”(the sum of the hydroxityrosol and tyrosol derivatives) showed R 2 cv values ranging between 0.75 and 0.81 and an RPD cv between 2 and 2.5. These results indicate the great potential of NIR, both with benchtop and portable devices, to detect phenolic compounds in virgin olive oil, which can guarantee the quality of virgin olive oil and thus aid progress in the olive oil industry. Moreover, this is the first published work to determine phenolic compounds in virgin olive oil using portable NIR instruments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".