Effects of Different Solar Drying Methods on Quality Attributes of Dried Meat Product (Kilishi)
Bibliographic record
Abstract
This study was conducted to evaluate the efficiency of four methods of sundrying kilishi after preparation. They included Direct Sundrying Method (DSM) as control, Gujarat Energy Development Agency Method (GEDAM), National Institute of Oceanography Method (NIOM) and Kwatia Drying Method (KDM) each of the methods constituted a treatment viz, A, B, C and D. Meat (Beef) weighing 640 g was purchased and used for this study. The meat was divided into 4 equal parts of 160 g per treatment. They were sliced into length between 0.17 and 0.20 cm in thickness and dried between 4 and 5 hours to reduce the moisture to at least 40% before slurry infusion. The slurry ingredient components were ground and mixed to form a paste. Semi-dried meat were immersed in the slurry for one hour and later stabilized by roasting on charcoal fire for 5 minutes and later dried out in drying media tested in this study. The yield, chemical and sensory properties of kilishi were determined. The results showed that method B gave the highest (P < 0.05) yield of kilishi, chemical attributes as well as sensory properties of kilishi followed by method C. It is suggested that method B and C be developed and produced in commercial quantity for use in drying kilishi in the tropics due to their high efficiency.
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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".