FORMULATION AND DEVELOPMENT OF JAMS PRODUCT OF LOCAL FRUITS HAVING POTENTIAL NUTRITIONAL VALUE
Bibliographic record
Abstract
Apple, guava and strawberry are important fruits extensively grown in Pakistan. Owing to their considerable nutritional significance, often marketed as "super-fruits". Due to inappropriate handling, transportation and processing 40-45% of the fruits are spoiled. These losses of the seasonal surplus of the fruits can be avoided by processing and preserving the fruit into different products like mixed jam, juice, nectar and jelly. Keeping in view the perishable nature of fruits, current study was designed with an objective to prepare different treatments of mixed jam having acceptable quality parameters as well as consumer acceptability. For this purpose, five treatments of jam were prepared at laboratory scale. After preliminary analysis of fruits, all the five treatments were analyzed for physico-chemical (pH, TSS, Titratable acidity, reducing sugars and non-reducing sugars), and sensory analysis for an interval of 7 days during 1 month storage period. The results of different treatments of jam showed a highly variable trend. pH, non-reducing sugars and all sensory parameters showed a decreasing trend during storage. Opposite is the case with acidity, total soluble solids, as well as reducing sugars. Sensory analysis indicated that the order of preference for jam treatments was T4>T3>T2>T0>T1>T5. Study suggests that losses in fresh fruits can be curtailed by processing it into mixed jam.
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.
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 | 0.000 |
| 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) | 0.002 | 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".