Effects of intramuscular fat on the flavor of fresh sheep and goat meat: Recent insights into pre-mortem and post-mortem factors
Bibliographic record
Abstract
Sheep and goat meat products are becoming increasingly popular among consumers due to their unique flavor derived from intramuscular fat (IMF), which contributes to formation of the distinctive odor. However, there is currently a dearth of reviews on the impact of IMF on the flavor of sheep and goat meat. The present review aims to discuss the relationships between IMF and flavor through lipid composition and fatty acid (FA) distribution, provide an overview of characteristic flavor compounds affecting the flavor of sheep and goat meat, and shed light on the impacts of pre-mortem and post-mortem factors on meat flavor attributed to changes in FAs and flavor compounds. Controlling pre-mortem practices and adjusting post-mortem harvesting methods are key factors in shaping and/or driving the flavor of sheep and goat meat products. This review enhances the comprehensive understanding of the impact of IMF on the flavor of sheep and goat meat. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) • The content and distribution of lipids are crucial factors of sheep and goat meat odor. • The odors of sheep and goat meat are influenced by pre-mortem factors. • Rumen microorganisms exert an impact on the odor of sheep and goat meat. • Post-mortem harvesting is a key factor of sheep and goat meat odor.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".