Monitoring on the 4‐Hexylresorcinol in various shrimp and crab meat products
Bibliographic record
Abstract
4‐Hexylresorcinol, one of the Generally Recognized Safe (GRAS) food additives, has been used as an antioxidant to prevent from melanosis (black spot) in some crustacean including shrimp and crab shell. It is authorized with maximum residue levels of 2 mg/kg in European Union, of 1.0 mg/kg in China and Canada but unauthorized in Korea. In our previous study, we developed a sensitive and simple analytical method to identify 4‐hexylresorcinol using high‐performance liquid chromatography with fluorescence detection (HPLC‐FLD) and with mass spectrometry (HPLC‐MS/MS). Therefore, this study was to demonstrate the effective application of the established analytical method on real various food samples such as uncooked frozen shrimps, cooked frozen shrimps, and crab meats. Our results show that all of the tested samples were not detected with the 4‐hexylresorcinol. The data from this study will be valuable source for data base construction of science‐based satefy and management for the 4‐hexylresorcinol in foods.
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 | 0.001 | 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) | 0.001 | 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".