Bibliographic record
Abstract
Problem statement: Extremely perishable meat provides favorable growth condition for various microorganisms. Meat is also very much susceptible to spoilage due to chemical and enzymatic activities. The breakdown of fat, protein and carbohydrates of meat results in the development of off-odors, off-flavor and slim formation which make the meat objectionable for human consumption. It is, therefore, necessary to control meat spoilage in order to increase its shelf life and maintain its nutritional value, texture and flavor. Approach: A comprehensive literature review was performed on the spoliage mechanisms of meat and meat products and preservation techniques. Results: Historical data reveals that salting, drying, smoking, fermentation and canning were the traditional methods used to prevent meat spoilage and extend its shelf life. However, in order to prevent wholesomeness, appearance, composition, tenderness, flavor, juiciness, and nutritive value, new methods were developed. These included: cooling, freezing and chemical preservation. Wide range of physical and chemical reactions and actions of microorganisms or enzymes are responsible for the meat spoilage. Microbial growth, oxidation and enzymatic autolysis are three basic mechanisms responsible for spoilage of meat. Microbial growth and metabolism depends on various factors including: pre-slaughter husbandry practices, age of the animal at the time of slaughtering, handling during slaughtering, evisceration and processing, temperature controls during slaughtering, processing and distribution, preservation methods, type of packaging and handling and storage by consumer. Microbial spoilage causes pH change, slime formation, structural components degradation, off odors and appearance change. Autoxidation of lipids and the production of free radicals are natural processes which affect fatty acids and lead to oxidative deterioration of meat and off-flavour development. Lipid hydrolysis can take place enzymatically or non-enzymatically in meat. In muscle cells of slaughtered animals, enzymatic actions are taken place naturally and they act as catalysts for chemical reactions that finally end up in meat self deterioration. Softening and greenish discoloration of the meat results due to tissues degradation of the complex compounds (carbohydrates, fats and protein) in the autolysis process. Conclusion: Microbial, chemical and enzymatic activities can be controlled by low temperature storage and chemical techniques in the industry. Proper handling, pretreatment and preservation techniques can improve the quality of meat and meat products and increase their shelf life. Combination of chemical additives (TBHQ and ascorbic acid) and low temperature storage (5C) in darkness are well recognized techniques for controlling the spoilage (microbial, enzymatic and oxidative) of meat and meat products. Understanding of the intrinsic factors and extrinsic factors at every meat processing stage (from preslaughtering to meat product development) is necessary before developing proper handling, pretreatment and preservation techniques for meat.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".