Exploring the Bioactive Compounds in Some Apple Vinegar Samples and Their Biological Activities
Bibliographic record
Abstract
Apple vinegar is highly recommended for nutrition due to its health benefits and bioactive components. However, the apple cultivar greatly influences the quality of the vinegar. In this research, our focus was on examining the impact of four different apple cultivars on the physicochemical attributes, chemical composition, as well as biological properties—including antidepressant and anti-inflammatory activities—of vinegar. Interestingly, the physicochemical properties of vinegar and the contents of acetic acid and polyphenols depend on the apple cultivars. HPLC chromatographic analysis showed that citric acid (820.62–193.63 mg/100 g) and gallic acid (285.70–54.40 µg/g) were mostly abundant in the vinegar samples. The in vivo results showed that administration of Golden Delicious apple vinegar (10 mL/kg) to adult Wistar rats reduced carrageenan-induced inflammation by 37.50%. The same vinegar sample exhibited a significant antidepressant effect by reducing the rats’ immobility time by 31.07% in the forced swimming test. Due to its high acidity, Golden Delicious vinegar was found to be more effective against bacteria, particularly Bacillus subtilis and Candida albicans, resulting in a MIC value of 31.81 mg/mL. Furthermore, the antioxidant activity of various vinegar samples was found to be powerful, displaying optimal values of IC50 = 65.20 mg/mL, 85.83%, and 26.45 AAE/g in the DPPH, β-carotene decolorization and TAC assays, respectively. In conclusion, the apple cultivars used in this study impact the chemical composition and biological activities of vinegar, which may help demonstrate the importance of raw material selection for the production of vinegar.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".