Evaluation of Suya (Tsire) – An Intermediate Moisture Meat Product in Ogun State, Nigeria
Bibliographic record
Abstract
A study was conducted to evaluate suya (tsire) an intermediate moisture meat product in Ogun State. Sixty suya sticks were used. Twelve suya sticks were prepared in the laboratory while 12 suya sticks were collected from each zone of the state namely: Yewa, Egba, Remo and Ijebu. They were analyzed for physical, chemical, microbiological and organoleptic characteristics. The results showed that there were significant (P < 0.05) differences in physical properties of suya samples analyzed with suya from Yewa zone having the highest (P < 0.05) water holding capacity and suya prepared in the laboratory and those from Egba zone had the highest (P < 0.05) shear force, while the pH was least (P < 0.05) in suya prepared from laboratory. Moisture content was least (P < 0.05) in suya samples prepared in the laboratory and from Egba zone, while ash content was higher (P < 0.05) in suya from Yewa, Remo and Ijebu Zones. Aerobic bacteria and coliform counts were least (P < 0.05) in suya prepared in the laboratory and from Egba Zone, while lactic acid bacteria were higher (P < 0.05) in suya prepared in the laboratory and from Egba Zone. The results revealed that suya samples prepared in the laboratory were accepted more (P < 0.05) followed by those from Egba and Remo Zones. However, microbial loads observed on Suya (tsire) samples in this study were not as high as those reported by previous workers. Nonetheless, efforts should be made to educate meat and meat products (Suya) processors in Ogun State on the importance of hygiene and proper packaging and preservation to avoid contamination and spoilage of meat products during processing and sale.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".