Characterization of firmness in fermented sea bass (Lateolabrax japonicas) by multidimensional integration strategies: Insights from proteomic and microstructural analyses
Bibliographic record
Abstract
Fermented sea bass, recognized for its firmness and chewy texture, provides a distinct sensory experience.This study investigated the texture and microstructural properties of fermented sea bass during fermentation. Proteomics analysis identified the key proteins involved in firmness development, revealing the molecular mechanisms behind these changes. Water migration and myofibril thickening were significant contributors to the increased firmness, hardness, and chewiness. Label-free proteomics revealed 881 proteins, with 426 were differentially expressed, leading to the identification of 155 key protein biomarkers linked to texture. Structural proteins such as myosin light chain and actin correlated positively with hardness, chewiness, and adhesiveness. Fermentation also increased phosphorylation, enhancing protein degradation and texture attributes. Signaling pathways such as PI3k-AkT, HIF-1, and calcium ion signaling pathways were implicated in metabolism and myosin assembly, fortifying muscle tissue firmness. This study provided critical insights into the quality control and production optimization in the fermented sea bass industry.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".