Does Spray Mango Kernel (Mangifera indica Linn.) Prolong the Shelf Life of Beef Sausages?
Bibliographic record
Abstract
The study was aimed to look at the effect of different forms of mango kernels (MK) on the shelf life of refrigerated beef sausages over 12 days of cold storage. The (MK) was chemically and microbiologically analyzed. Beef sausages were treated with MK in 3 states, as dry ground (1.5%), an extract (1.5%) and spray MK extract (1.5%) over minced beef of sausages. Two controls were used; BHT 0.02% and no additives. A series of analyses were performed after treatments; thiobarbituric acid reactive substances (TBARS), analysis of color, myoglobin and odor. The results indicated that different forms of MK added to the beef sausages had different effects on its shelf life. Furthermore, the sprayed MK extract has significantly (P ?0.05) lowered metmyoglobin (MMb) and TBARS and increased oxymyoglobin (MbO2), odor score and a* (redness) than other forms. The potential effects of the sprayed MK may be due to a cloud of droplets cover the large surfaces of minced beef sausages with efficient extracted antioxidants. MK is source of flavonoids 142mg/g F.W. GAE. The spraying of MK at 1.5% showed an improvement of E. coli from minced beef and beef sausages that were less than 10 cfu g-1. Also the concentrations of yeasts and moulds were not detected at day 12 of storage. Hierarchically, sprayed MK extract gave best results than ground MK or MK extract form which shows effective inhibitor of lipid oxidation and microbial growth of beef sausages.
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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".