Application of principal component and hierarchical cluster analysis to classify different spices based on in vitro antioxidant activity and individual polyphenolic antioxidant compounds
Why this work is in the frame
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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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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it