Applications of Vibrational Spectroscopy to the Analysis of Fish and Other Aquatic Food Products
Bibliographic record
Abstract
Abstract Nowadays, people have come to realize the importance of fish and seafood in their diet. Various studies and researches have proved that the best sources of good fats, vitamins, and minerals to promote good health can actually be found in different seafoods. However, the quality of fish and fishery products has always been difficult to define, and is typically based on the general perception of the consumer evaluating the product. With increasing globalization of fishery product sales, processors, consumers, and regulatory officials have been seeking rapid and reliable methods for determining the authenticity, freshness as well as quality of these products. During the last decade spectroscopic techniques have become established as one of the more important and powerful tools of modern industrial analysis; This includes the use of these techniques in the food sector, especially for on‐line, in‐line or at‐line analysis. In the seafood industry, vibrational spectroscopy is the most widely used technique for quantitative and qualitative analysis of fish and related products. Here, we have summarized the latest practical vibrational spectroscopic techniques for assessing, measuring, and predicting the quality of fish and other seafood.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".