Utilization and Influence of Condiments Prepared From Fermented Legumes on Quality Profile of Meat
Bibliographic record
Abstract
This study was conducted to investigate the utilization and influence of condiments prepared from four fermented legumes; African locust-bean (Parkia biglobosa), melon seeds (Citrullus vulgaris), soybean (Glycine max) and cotton seeds (Malvaceae gossypium). They were processed, dried, milled and 25% solution of each condiment was made using purified water. 200 g fresh beef from the thigh cut of White Fulani bull (Bos indicus) was purchased, divided into 4 parts of 50 g and 20 ml of condiments solution was injected into each beef with a syringe and needle each condiment and 50 g beef constituted a treatment thus; TO = control (No condiment), TI = Beef steak + locust bean condiment, T2 = Beef + melon seeds condiment, T3 = Beef + soybean condiment, T4 = Beef + cotton seeds condiment. The injected beef steaks were wrapped in foil paper and broiled in oven at 170 ºC for 20 mins. Data were collected on physicochemical, microbiological and sensory properties of processed beef and were subjected to analysis of variance (ANOVA) at p = 0.05 in a completely randomized design experiment. The results showed that cooking loss and shear force were lower in TI, while water holding capacity (WHC) and yield were higher. Protein and ash were high in TI followed by T3 while fat and fibre were significantly lower. Aerobic and anaerobic bacteria were significantly the same across the treatments while coliform and fungal counts were lower except in T2 and T4. Treatment1 was adjudged higher in all the eating qualities except colour and was well accepted. It is therefore, recommended that locust bean condiment be used in processing meat followed by soybean condiment. However, further investigation should be carried out on varied levels of locust bean condiment to determine the level that will give better colour which can improve the meat product acceptability and consumption.
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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".