Physico-Chemical and Microbiological Characteristics of Gundpak – A Traditional Milk Product of Nepal
Bibliographic record
Abstract
Gundpak, is a popular khoa based traditional milk product of Nepal and commonly used as a sweet. Twelve market samples of Gundpak were collected from the different areas of Kathmandu valley. The physico-chemical, sensory and microbiological analyses of the samples were investigated. The commercial samples were not consistent in their chemical compositions. The moisture, fat, protein, carbohydrates and ash were varied from 10.1 to 21.2, 10.6 to 16.5, 16.8 to 30.3, 29.0 to 54.8, 2.4 to 3.7 percentages, respectively. The microbiological analysis showed that Total Plate Count, Yeast and Mold, and Coliform were varied from 0 to 5 × 104, 0 to 8.0 × 103, 0-1.1 × 102, whereas there was no growth of Staphylococci. The hardness, cohesiveness, gumminess, springiness, chewiness and adhesiveness values were varied from 31.7 to 245.3 N, 90.03 to 296.3, 7.84 to 22.06 N, 2.36 to 7.62, 1.45 to 16.2 N.mm, and 0.3 to 6.8 mNm, respectively, among the samples. In the commercial samples, colour parameter, L*values was varied from 17.12 to 42.08, indicating wide variations in appearance from light to dark brown. The overall sensory quality did not show significant variations. The minerals, calcium, magnesium, sodium, potassium, iron, zinc and copper were in the ranges from 390.7-527.15, 25.56-40.43, 188.86-215.93, 282.0-378, 0.41-0.52, 1.31-2.58 and 0.11-0.16 mg/100 g sample, respectively. These results indicated that Nepalese Gundpak significantly vary in physico-chemical characteristics and hence require optimization of product ingredients and processing technology to get uniform high quality.
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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 | 0.001 | 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 | 0.000 |
| 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".