In-Vitro Antioxidant Capacity and Bioactive Compounds Preservation Post-Drying on Berrycacti (Myrtillocactus geometrizans)
Bibliographic record
Abstract
Berrycactus is a cactus which does not require special agronomic attention, the berries are consumed locally and its commercialization is rather scarce because of the extremely short shelf-life. The significance of the application of any drying methods used to extend the shelf-life on the berrycacti is currently unknown. The aim of this work was to preserve berrycacti (Myrtillocactus geometrizans) and test the bioactive compounds and antioxidant capacity using two distinctive drying methods, freeze-drying (FD) and Instant Controlled Pressure Drop (DIC). Ripe berrycacti was chosen for the drying procedures because the antioxidant capacity and levels of soluble phenols and betalains were at their peak. Colour, phenols, non-extractable polyphenols, tannins, betalains, and antioxidant capacity were considered as factors to determine drying efficacy. Only colour parameters could discriminate between FD and DIC, concluding that both methods are suitable and efficient for preservation of antioxidant properties and retention of bioactive compounds. Both drying methods demonstrated higher in-vitro antioxidant capacity compared to the fresh fruit; highlighting the increase of non-extractable polyphenols and condensed tannins, and good retention of betalains and ascorbic acid after the drying treatments. This research points to use this sustainable crop to provide added value to berrycacti while considering this fruit as functional food due to the antioxidant capacity present even after being processed.
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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 | 0.000 |
| 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".