Technological and Nutritional Aspects of Incorporating Jamun (Syzygium cumini (L.) Skeels) Fruit Extract into Yoghurt
Bibliographic record
Abstract
The study aimed to evaluate the technological suitability of incorporating the jamun extracts into Yoghurt. The light focused on the effect of the extraction method and rate of addition on the flavonols profiles, antioxidant activity and sensorial characteristics of the final Yoghurt product. Jamun fruit was subjected to either mechanical cold extraction or steam extraction and introduced to milk at rates of 5 and 10%. The results indicated that the extraction technique had no effect on the values of protein, fat, ash and titratable acidity. The steam extraction led to increase the total solids, pH, total hydrolysable tannins, antioxidant activity, color, flavor and overall sensorial acceptability of Yoghurt. While the cold mechanical extraction led to increase the total flavonols, thickness and smell scoring. Increasing the percentage of jamun extract addition led to reduce the total solids, protein, fat, appearance and thickness in a concentration depending way, as well as to increase all the detected flavonols, tannins and antioxitant power indicators. The 5% juice containing Yoghurt was distinguished with the highest scores of color, flavor, taste, smell and overall acceptability. Jamun fruit may be a promising source for fortifying Yoghurt with flavonols and enhancing its antioxidant power.
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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".