Interaction between orange juice and < 1 kDa leaf peptides: effect on the antioxidant and antidiabetic related enzyme inhibitory activities
Bibliographic record
Abstract
Abstract Designing a good vehicle for functional ingredients is the major focus of this study. Small molecular weight peptides (< 1 kDa) extracted from amaranth leaf protein (ALP), eggplant leaf protein (ELP) and fluted pumpkin leaf protein (FLP) were incorporated into freshly prepared orange juice at an effective and inhibitory concentrations of the peptides. The rate of degradation of ascorbic acid was more in the control juice (140.06 to 18.43 mg/mL) when compared with juice containing peptides at both storage conditions (140.08 to 32.32 mg/mL). However, the rate of ascorbic acid reduction during storage (refrigerated and ambient) was least in the juice containing peptide, isolated from ELP when compared with the juice samples that contained peptides isolated from ALP and FLP. After the eighth week of storage, juice that contained FLP peptide had greater amounts of residual total phenolic content (370.53 & 432.33 µg GAE/100 mL), juice that contained ALP peptide retained better ability to scavenge DPPH radicals (52.32 & 66.84%) while juice sample that contained ELP retained more metal chelating activities (44.82 and 51.03%). The results of antidiabetic property showed that juice containing peptide isolated from ALP contained greater amounts of α-amylase inhibitory activity (41.50 and 46.89%) while greater amounts of α-glucosidase inhibitory activities were retained in juice that contained peptide isolated from FLP. The results concluded that orange juice may be considered a veritable vehicle for functional ingredients for improved health. Graphical Abstract
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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".