Bibliographic record
Abstract
Olive oil is an extensively used product and extra virgin olive oil is much costlier than other edible oils. Hence, purity of olive oil is a very significant issue. Fluorescence spectroscopy is a largely acceptable, simple, reliable and quick technique for adulteration detection and fingerprinting of olive oil. In this project, principal component analysis has been performed on fluorescence spectral data of 100 samples including pure extra virgin olive oil and adulterated ones with sunflower oil. The analysis has been able to successfully map the samples in a clear pattern for adulteration detection. The maximum tolerance limit for detection of adulteration is ±4.71% for the range of 0%-80% adulterated samples and ±5.67% for the range of 80%-100% adulterated samples. Also, by using two third of the samples as training set, this system can detect the rest one third samples (test set) quite accurately with an average tolerance of only ±3.42%. It has also been found that, short time exposure to laser, as a crude indication of possible long time exposure to sunlight, can definitely affect the fluorescence emission spectra. The two most significant wavelengths have been found (using variability) and validated (by principal component loading), that can replace the use of spectrometer with two color fiber optic probe. In this way, the computational complexity can be reduced to a great extent to make the adulteration detection system more affordable at retailer level.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".