Electrical stimulation to improve meat quality: Factors at interplay, underlying biochemical mechanisms and a second look into the molecular pathways using proteomics
Bibliographic record
Abstract
Ensuring consistent beef eating quality is paramount for meeting consumer demands and sustaining the meat industry. Electrical stimulation (ES) is a post-slaughter intervention used to accelerate post-mortem glycolysis, to avoid cold shortening, to control the tenderization rate of meat through sophisticated physical, chemical and biochemical mechanisms including proteolysis, to improve beef tenderness and to achieve normal pHu that might lead to positive impact on color. This review comprehensively examines the multifaceted effects of ES on beef quality, encompassing factors and settings influencing its efficacy and the underlying biochemical mechanisms revealed using traditional biochemistry methods. It then delves into the molecular pathways modulated by ES, as unveiled by muscle proteomics, aiming to provide a second look and an unprecedented understanding of the underlying biochemical mechanisms through an integrative proteomics analysis of low-voltage ES (LVES) proteomics studies. The proteins changing as a result of ES were gathered in a compendium of 67 proteins, from which 14 were commonly identified across studies. In-depth bioinformatics of this compendium allowed a comprehensive overview of the molecular signatures and interacting biochemical pathways behind electrically stimulated beef muscles. The proteins belong to interconnected molecular pathways including the ATP metabolic process and glycolysis, muscle structure and contraction, heat shock proteins, oxidative stress, proteolysis and apoptosis. Understanding the intricate interplay of molecular pathways behind ES could improve the efficiency of beef production, ensuring consistent meat quality and meeting consumer expectations. The integrative analysis approach performed in this study holds promise for the meat industry's sustainability and competitiveness.
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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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".