Uncovering the spoilage metabolism of spoilage bacteria in large yellow croaker (Larimichthys crocea) under cold chain logistics: A novel perspective of amino acids degradation
Bibliographic record
Abstract
To characterize the spoilage potential of amino acid degradation and related quality changes attributed to microorganisms, three large yellow croaker spoilage bacteria were inoculated to fillets ( in vivo ) and amino acid solutions ( in vitro ). The results showed that Aeromonas salmonicida exhibited stronger potential for the accumulation of trichloroacetic acid (TCA)-soluble peptides and deamidation activities and produced 174.23 μg/g ammonia on fillets on day 10.5, with the highest total volatile basic nitrogen (TVB-N) value of 22.39 mg/100 g. Pseudomonas fluorescens exhibited an active ability to utilizing glucose and lactate. Meanwhile, Shewanella putrefaciens had potent in ornithine and lysine decarboxylation activities (1106.59 and 1378.45 U/kg) and putrescine producing activities, releasing 21.83 mg/kg putrescine in fillets. The arginine decarboxylase pathway was evaluated as the most effective pathway to putrescine production for spoilage bacteria. This study provided insights into spoilage mechanisms and the development of quality control measures for seafood during cold chain logistics.
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 | 0.000 | 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 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".