Effect of Thermal Process and Filtration on the Antioxidant Activity and Physicochemical Parameters of Agave atrovirens Extracts
Bibliographic record
Abstract
Recently agave plants are being used in the production of syrups that are consumed by diabetic people in order to control their blood glucose levels; unfortunately a deep characterization of this kind of products has not been made. In this study the juice obtained from Agave atrovirens leaves (CE) was filtered and cooked (FE) and evaporated until it has a solid content of 20 ºBx (TE), and the effect of the process in some parameters like pH, acidity, solid content, 5-Hydroxymethylfurfural (5-HMF) and compounds with biological properties like saponins and phenolic compounds as well as antioxidant activity were evaluated. Once FE was concentrated, a dark brown liquid with a pH of 5.32 was obtained; the content of phenolics and saponins undergoes slightly modifications trough the complete process. 5-HMF was only detected in CE but not in FE and TE. Filtering decrease acidity, total phenolics, saponins, 5-HMF and the antiradical activity (ARA); and evaporation increase the content of reducing sugars and tends to increase the ARA value. For this study, we can conclude that the negative impact associated to a thermal process on bioactive compounds like phenols and antioxidant activity is negligible hence Agave atrovirens leaves could be a source of a product with phytochemicals.
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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 | 0.000 | 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 | 0.001 |
| 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".