Quality Evaluation of Beef Preserved With Food Grade Organic Acids at Room Temperature
Bibliographic record
Abstract
The objective of this study was to investigate the effects of three common food grade organic acids – citric, acetic and ascorbic on quality properties of fresh beef preserved for 14 days. 1 kg of fresh beef (thigh muscle) of White Fulani cow was purchased at Ayetoro market in Yewa North local government Area of Ogun State and was divided into 4 equal parts of 250 g per treatment replicated three times. The acids were purchased at Federal Institute of Industrial Research Oshodi (FIIRO) Lagos. 5% each of the organic acid was prepared and constituted an experimental treatment, freezing was used as control. Thus: T1 = Freezing (control), T2 = Citric acid, T3 = Acetic acid, T4 = Ascorbic acid. 10ml of each organic acid solution was injected into 250 g fresh beef with a needle and syringe and immersed in the same solution in covered plastic containers, stored at room temperature (27 ºC). The results showed that most of the physicochemical properties of the preserved beef were better (P < 0.05) in treatment 3, also. Lipid oxidation and microbial values were lower (P < 0.05) in the same treatment. However, acceptability of beef in treatment3 was lower (P < 0.05) because colour and flavour scores beef were lower (P < 0.05). It was suggested therefore, that lower concentrations of acetic acid be tested in a separate study to ascertain concentration level that will confer higher colour flavour and acceptability scores on beef since acetic acid favoured almost all tested properties and of preserved beef in this study.
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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".