Phytochemical Characterization of By-Products of Habanero Pepper Grown in Two Different Types of Soils from Yucatán, Mexico
Bibliographic record
Abstract
By-products of edible plants may contain potentially useful phytochemicals. Herein, we valorized the by-products of Capsicum chinense by phytochemical characterization of its leaves, peduncles and stems. Plants of habanero pepper were grown in a greenhouse, in polyethylene bags with two soils that were named according to the Maya classification as: K’ankab lu’um (red soil) and Box lu’um (black soil). Habanero pepper by-products were dried using an oven, the extracts were obtained by Ultrasound Assisted Extraction, and phytochemical quantification in all the extracts was conducted by Ultra Performance Liquid Chromatography coupled to Diode Array Detector (UPLC-DAD). Differences in the phytochemical content were observed according to the by-product and soil used. Catechin and rutin showed the highest concentrations in the peduncles of plants grown in both soils. The leaves of plants grown in black soil were rich in myricetin, β-carotene, and vitamin E, and the stems showed the highest protocatechuic acid content. While the leaves of plants grown in red soil were rich in myricetin and vitamin C, the stems showed the highest chlorogenic acid content. This novel information regarding the phytochemical composition of the by-products of C. chinense may be relevant in supporting their potential application in food and pharmaceutical industries.
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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 | 0.000 |
| 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 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".