Identification of adulteration in the market samples of saffron using morphology, HPLC, HPTLC, and DNA barcoding methods
Bibliographic record
Abstract
L., is the most expensive spice used for culinary, medicinal, dye, and cosmetics purposes. It is highly adulterated because of its limited production and high commercial value. In this study, 104 saffron market samples collected from 16 countries were tested using morphology, high-performance liquid chromatography (HPLC), high-performance thin-layer chromatography (HPTLC), and deoxyribonucleic acid (DNA) barcoding. Overall, 45 samples (43%) were adulterated. DNA barcoding identified the highest number of adulterated saffron (44 samples), followed by HPTLC (39 samples), HPLC (38 samples), and morphology (32 samples). Only DNA barcoding identified the adulterated samples containing saffron and other plants' parts as bulking agents. In addition, DNA barcoding identified 20 adulterant plant species, which will help develop quality control methods and market surveillance. Some of the adulterant plants are unsafe for human consumption. The HPLC method helped identify the saffron samples adulterated with synthetic safranal. HPLC and HPTLC methods will help identify the samples adulterated with other parts of the saffron plant (auto-adulteration).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".