Technological and Nutritional Aspects of Incorporating Jamun (Syzygium cumini (L.) Skeels) Fruit Extract into Yoghurt
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 it