Antioxidant and antimicrobial activities and UPLC-ESI-MS/MS polyphenolic profile of sweet orange peel extracts
Bibliographic record
Abstract
With growing consumer awareness, exploitation of renewable resources is cost-effective and environment friendly. This work examines the potential of citrus peels as natural antioxidants and antimicrobials for food preservation. Extraction yield, total soluble phenols and flavonoids of various citrus peels (sweet orange, lemon, tangerine and grapefruit) were optimized by varying the solvent type. While the highest extract yield (~16 g/100g) was obtained from the sweet orange peels in methanol, extraction with ethanol maximized the concentration of total phenols and flavonoids (~80 mg catechol equivalents/100 g dry weight). In addition, sweet orange peel extract showed the highest DPPH, ABTS and hydroxyl radical scavenging values. UPLC-ESI-MS/MS analysis of aqueous and ethanolic extracts of sweet orange peels revealed more than 40 polyphenolic compounds including phenolic acids and flavonoids, some of which have not been previously reported. The predominant polyphenols were narirutin, naringin, hesperetin-7-O-rutinoside naringenin, quinic acid, hesperetin, datiscetin-3-O-rutinoside and sakuranetin. The incorporation of sweet orange peel extract into two vegetable oils enhanced their oxidative stability. In addition, all citrus peel extracts possessed high antimicrobial activity against several food-borne pathogens, and the activity was highest for the sweet orange peel extract. Overall results suggested the great potential of sweet orange peels as natural antioxidant and antimicrobials, which can be efficiently extracted using a simple and low-cost method, for enhancing the storage stability and safety of vegetable oils.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".