Fluoresence Spectra From Vegetable Oils Using Violet And Blue Ld/Led Exitation And An Optical Fiber Spectrometer
Bibliographic record
Abstract
In this article the possibility to detect adulteration of costly olive oils with cheaper vegetable oils using fluorescence spectroscopy is studied. Total luminescence spectra were recorded by measuring the emission spectra in the range 350 nm to 720 nm for excitation wavelengths from 375 nm to 450 nm. Fluorescence spectra of 12 types of olive oil samples were studied. Ten of the olive oil types were purchased locally, while two (samples 1 and 4) were obtained directly from Greek olive oil producers. Analysis of the fatty acid and the tocopherol contents has been performed. Two of the samples exhibit the content of sunflower oils, two are admixtures of sunflower and olive oils, while the remaining eight samples are natural olive oils. The samples show differences in their fluorescence spectra. The latter fact shows that fluorescence spectroscopy can be used for the quick identification of possible adulterations of olive oil, although a more detailed gas chromatographic analysis is needed for the exact quantitative determination of the content of the adulterant.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".