Enhancing Oxidative Stability and Shelf Life of Cooking Oils Using Citrus Peel Extracts
Bibliographic record
Abstract
Citrus peels are nonedible by-products that are often discarded. This work explores the bioactive compounds extracted from the peels of 4 local citrus fruits, their antioxidant and antimicrobial activities and applicability as natural antioxidants for vegetable oils. Total soluble phenols and total flavonoids were extracted from orange, lemon, tangerine and grapefruit peels using various solvents. Orange peel methanol extract produced the highest yield (~16 g/100g), however using ethanol maximized the concentration of total phenols (~345 mg Gallic acid equivalents/100g dry weight) as well as total flavonoids (~80 mg catechol equivalents/100g). In general, extracts with high total phenolic contents exhibited high antioxidant capacities. The orange peel ethanol extract showed the highest DPPH and ABTS values while its methanol extract exhibited the highest hydroxyl radical scavenging value. In addition, all citrus peel extracts possessed high antimicrobial activity against several food-borne Gram-negative and Gram-positive pathogenic bacteria and fungi. The composition of polyphenolic compounds in orange peel extracts analyzed by ultra-performance liquid chromatography combined with mass spectrometry (UPLC-ESI-MS/MS) revealed the presence of 22 and 32 compounds in the aqueous and ethanolic extracts, respectively. The predominant compounds were narirutin, naringin, hesperetin-7-O-rutinoside naringenin, quinic acid, hesperetin, datiscetin-3-O-rutinoside and sakuranetin. Importantly, incorporation of orange peel extract into vegetable oils greatly enhanced their oxidative stability compared to a synthetic antioxidant (BHT). Overall results support the potential of citrus peels as natural antioxidants and antimicrobials for enhancing the shelf life, storage stability and safety of food oils.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".