Effects of the Type and Point of Inclusion of Soy-Melon-Protein Supplement on the Sensory Qualities of “Gari” Semolina
Bibliographic record
Abstract
The effects of the type of enrichment and the stage of application of supplement on the sensory qualities of Soy-melon protein-enriched gari semolina were studied. Three protein supplements (Full fat, Defatted and Milk residue) were added to the gari meal before fermentation, after fermentation and after toasting. After toasting and cooling, the samples were subjected to sensory evaluation at weekly interval over a period of 32 weeks by 20 member sensory panel of the Federal University of Technology, Akure, Nigeria in order to determine its shelf life. The panel members were students and staff members who were used to the consumption and sensory evaluation of gari. They were instructed to evaluate differences in overall sensory quality between the control and other samples packaged in HDPE film and Woven sack and subjected to storage at 20, 30 and 40°C. A nine-point hedonic scoring system was used for the evaluation, where 1 = extremely disliked and 9 = extremely liked. Results obtained from other data on the flavor difference scores for each product were subjected to regression analysis based on the critical minimum panel mean score of 5.0 for shelf stability of the samples. The shelf lives for the samples at different storage conditions were determined from the slopes of the regression equations. Results showed that enrichment with soy-melon flour reduced the shelf life at a high temperature above 40°C from 148 weeks to 17 weeks. The shelf lives of samples packaged in HDPE were significantly higher than those packaged in woven sack. The shelf life was reduced significantly by increase in temperature which exhibited a negative correlation with the flavor scores.
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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 | 0.001 |
| 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 | 0.001 |
| 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".