Clove Polyphenolic Compounds Improve the Microbiological Status, Lipid Stability, and Sensory Attributes of Beef Burgers during Cold Storage
Bibliographic record
Abstract
This study investigated the phenolic composition of clove powder extract (CPE), determined by high-pressure liquid chromatography, as well as the effect of the clove powder (CP) concentration (0, 2, 4, and 6%) on the quality of beef burgers during 21 days of cold storage at 4 °C. The CPE contained a high amount of total phenolic content (455.8 mg Gallic acid equivalent/g) and total flavonoid content (100.4 mg catechin equivalent/g), and it exhibited high DPPH antioxidant scavenging activity (83.9%). Gallic acid, catechol, and protocatechuic acid were the highest phenolic acids (762.6, 635.8, and 544.9 mg/100 g, respectively), and quercetin and catechin were the highest flavonoid acids (1703.1 and 1065.1 mg/100 g, respectively). Additionally, the CPE inhibited the growth of both Gram-positive and Gram-negative bacteria effectively at 100 μg/disc. The addition of the CP had no discernible influence on the pH of the meat patties. The addition of CP at 4 and 6% increased the phenolic content and antioxidant activity of the beef patties, which consequently resulted in reduced lipid oxidation and microbial spoilage throughout the storage period. Furthermore, the CP significantly (p ≤ 0.05) improved the beef burger cooking characteristics (cooking yield, fat retention, moisture retention, and shrinkage). Additionally, the sensory acceptability was higher (p ≤ 0.05) for the burgers that contained 2% and 4% CP compared with the other treatments. In conclusion, the bioactive compounds in CP can extend the shelf life and improve the safety of beef burgers.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".