Emerging Seafood Preservation Techniques to Extend Freshness and Minimize Vibrio Contamination
Bibliographic record
Abstract
Globally, the popularity of seafood consumption is increasing exponentially. To meet the demands of a growing market, the seafood industry has increasingly been innovating ways to keep their products fresh and safe while increasing production. Marine environments harbor several species of indigenous microorganisms, some of which, including Vibrio spp., may be harmful to humans, and all of which are part of the natural microbiota of the seafood. After harvest, seafood products are often shipped over large geographic distances, sometimes for prolonged periods, during which the food must stay fresh and pathogen proliferation must be minimized. Upon arrival there is often a strong desire, arising from both culinary and nutritional considerations, to consume seafood products raw, or minimally cooked. This supply chain along with popular preferences have increased challenges for the seafood industry. This has resulted in a desire to develop methodologies that reduce pathogenic and spoilage organisms in seafood items to comply with regulations and result in minimal changes to the taste, texture, and nutritional content of the final product. This mini-review discusses and compares several emerging technologies, such as treatment with plant derived natural compounds, phage lysis, high-pressure processing, and irradiation for their ability to control pathogenic vibrios, limit the growth of spoilage organisms, and keep the desired organoleptic properties of the seafood product intact.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".