Comparative Study of Capsaicinoid Composition in <i>Capsicum</i> Peppers Grown in Brazil
Bibliographic record
Abstract
Twenty different varieties of Capsicum pepper cultivars belonging to four species (Capsicum chinense, Capsicum annuum, Capsicum frutescens, and Capsicum baccatum) were characterized in terms of their capsaicinoid and total phenolic content. The peppers were sown in a farm in the southeastern region of São Paulo State. The determination of capsaicinoids was performed by ultra-performance liquid chromatography. The total phenolic content was determined spectrophotometrically with the Folin-Ciocalteu reagent. Results were expressed as µg capsaicinoid/g fresh pepper and as Scoville heat unit. A wide variation was observed among the compositions of capsaicinoids. Capsaicin and dihydrocapsaicin were the most abundant peaks. Capsaicinoids were not identified in the pepper varieties Cheiro Verde, Cambuci Verde, Cambuci Vermelha, and Biquinho. The spiciest pepper was Naga Jolokia (119,016 Scoville heat unit). Regarding the phenolic contents, a large variability was observed. Total phenolic content ranged from 0.35 mg gallic acid equivalent/g in Cambuci Verde to 3.06 mg gallic acid equivalent/g in Naga Jolokia. The current study may benefit consumers, the food, and pharmaceutical industries due to the increasing interest in pharmacological compounds present in hot and sweet Capsicum peppers.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".