Effect of processing on the preservation of bioactive compounds in traditional and exotic fruits: a review
Bibliographic record
Abstract
Bioactives are natural substances that may function as antioxidant, anti-inflammatory, antimicrobial, and anticarcinogenic agents. They include phenolic compounds, carotenoids, and vitamins that can exert health-promoting effects. Conventional fruit processing (e.g., heat treatment) can negatively affect the content and possibly the integrity of bioactives in the source material. Meanwhile, non-conventional techniques, such as high pressure processing and pulsed electric field, may increase the extractability of bioactives from the food matrix and enhance their availability for intestinal absorption. Although berries are usually perceived as outstanding sources of antioxidants, other conventional fruits also stand out, such as apple, banana, grape, mango, and orange. Nevertheless, exotic fruits, such as Buriti, mamey, açaí, pitanga, camapu, and tucumã are less frequently consumed, even though they can provide relevant bioactives. Additionally, fruit processing generates by-products containing high-value bioactives that can re-enter the industry cycle while minimizing the quantity of waste generated. Future studies should further examine the potential of exotic fruits using their discarded portions. Thus, identifying the best techniques for their use and maximum phytochemical extraction would be essential to reducing their environmental impact. Additionally, novel functional foods and nutraceuticals can be obtained by exploring the bioactive potential of these feedstocks and their processing discards.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".