Physicochemical and Sensory Properties of Ginger Spiced Yoghurt
Bibliographic record
Abstract
The physicochemical and sensory properties of ginger spiced yoghurt were investigated in the present study. Four yoghurt samples: A, B, C and D were prepared by addition of 0, 0.5, 1 and 1.5% (W/V) of ginger powder. Physicochemical properties of yoghurt samples determined at day 0 included pH, titratable acidity, dry matter, ash, fat and non-fat solid (NFS). The pH and titratable acidity were also evaluated during 30 days of storage at refrigerated conditions (4 – 6°C). The sensory attributes assessed were colour, odour, taste, texture and overall acceptability. From the results, ginger powder did not affect (P>0.05) the pH and titratable acidity of yoghurt but increased (P≤0.05) the dry matter, fat, NFS and ash content especially when spiced at 1% and 1.5% level. The spiced yoghurt did not show significant changes (P>0.05) in titratable acidity during storage as opposed to the unspiced yoghurt that increased (P<0.05) with storage time. The pH values of spiced yoghurt were not significantly affected (P>0.05) by storage contrary to the unspiced yoghurt. At the end of storage, the unspiced yoghurt presented the lowest (P≤0.05) pH and the highest (P≤0.05) titratable acidity. Results of sensory evaluation revealed the low appreciation of the spiced yoghurt with an increase in the proportion of ginger powder. However, yoghurt with 0.5% ginger powder was appreciated equally (P>0.05) with the unspiced sample. Spicing yoghurt with ginger powder therefore has positive effect on its physicochemical properties and shelf –life. The yoghurt spiced with 0.5% ginger powder could therefore be recommended.
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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) | 0.001 | 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 | 0.001 |
| 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".