Application of principal component and hierarchical cluster analysis to classify different spices based on in vitro antioxidant activity and individual polyphenolic antioxidant compounds
Bibliographic record
Abstract
This study investigated the variations in antioxidant profiles between spices using pattern recognition tools; classification was achieved based on the results of global antioxidant activity assays (2,2-diphenyl-1-picrylhydrazyl [DPPH], oxygen radical absorbance capacity [ORAC], ferric reducing antioxidant power [FRAP], microsomal lipid peroxidation [MLP] and 2,2′-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) [ABTS]), levels of different polyphenolic compounds (gallic acid [GA], carnosol [CAR], carnosic acid [CRA], caffeic acid [CA], rosmarinic acid [RA], luteolin-7-O-glucoside [LOG], apigenin-7-O-glucoside [APOG] and total phenols [TP]) of spices namely rosemary, oregano, marjoram, sage, basil, thyme, fennel, celery, cumin and parsley, commonly consumed in Ireland were analyzed. Rosemary showed the highest antioxidant activity measured by the DPPH (11.02 g Trolox/g DW) assay, whereas oregano had the highest activity in the ORAC (28.31 g Trolox/g DW) test. By contrast, parsley showed the lowest antioxidant activity in both of the assays. Interrelationships of these assays and the spices were investigated by principal component analysis (PCA) and hierarchical cluster analysis (HCA). PCA revealed that the first two components represented 73% of the total variability in antioxidant activity and different antioxidant groups. HCA classified samples into four main groups on the basis of the measured parameters.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".