Effects of interactions between microorganisms and lipids on inferior volatile compound production during cold storage of grouper (Epinephelus coioides)
Bibliographic record
Abstract
The interaction between microorganisms, proteins, and lipids plays a critical role in the odor production of fish. To explore the specific impact of the interaction between lipids and microorganisms on the overall odor of grouper, this study excluded the influence of proteins and assessed lipid (POV and TBARS) and microbial characteristics (biofilm mass and ATP content) in lipid solutions. The Results showed that microbial growth and lipid oxidation mutually promote each other. Lipidomics analysis identified 44 differential lipids, and microbial diversity analysis pinpointed five key microorganisms ( Carnobacterium , Pseudomonas , Gluconacetobacter , Vagococcus , and Shewanella ). Furthermore, 20 key volatile compounds (VOCs) related to odor changes in the grouper lipid solution were identified using HS-SPME-GC–MS. Correlation network analysis revealed potential microbial and lipid contributions to VOC categories, including alcohols, aldehydes, ketones, and nitrogen- and sulfur-containing compounds. This study provides new insights into the roles of microorganisms and lipids in flavor formation, offering valuable knowledge for improving seafood quality control. • Lipid oxidation and microbial growth are mutually reinforcing processes. • 44 differential lipids induced by microbial growth were screened. • The potential microbial and lipid sources of different types of VOCs were identified.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".