Papaya seeds (Carica papaya L. var. Formosa) in different ripening stages: unexplored agro-industrial residues as potential sources of proteins, fibers and oil as well as high antioxidant capacity
Bibliographic record
Abstract
The use of whole fruits and vegetables, including the parts usually discarded during food processing, is an alternative to reduce the quantity of agro-industrial wastes. This study aimed to evaluate papaya seeds' nutritional and bioactive potential in two ripening stages. The seeds in the stages 0 and 5 of ripening were analyzed regarding their physicochemical composition, while the oil and the hydroethanolic extracts of the seeds were studied in respect of their fatty acid profile, total phenolic content, antioxidant and antimicrobial capacities. The seeds in both ripening stages show good nutritional quality, because they are sources of protein, fiber, and oil. The oil extracted from the seeds is majorly composed by oleic fatty acid (around 70%). The seeds extracts did not present antimicrobial activity against Salmonella enteritidies, Escherichia coli e Staphylococcus aureus. However, they presented high contents of total phenols (58.1 and 36.0 mg GAE⁄g dry extract for seeds in the ripening stages 0 and 5, respectively) and good antioxidant capacity, according to the FRAP and ABTS●+ assays. Papaya seeds provide nutrients and bioactive compounds and their use is a promising alternative to reduce the disposal of food wastes in the environment.
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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".