Flow injection determination of copper and iron in seafoods by a continuous ultrasound-assisted extraction system coupled to FAAS
Bibliographic record
Abstract
Copper and iron were extracted on-line from solid seafood samples by a robust, fast and simple continuous ultrasound-assisted extraction system (CUES). CUES is connected to a flow-injection manifold, which allows on-line flame atomic absorption spectrometric determination of copper and iron. Experimental designs were used to optimize the continuous leaching procedures. These methods allowed a total sampling frequency of 46 and 18 samples per hour, with relative standard deviations of 1.6% and 0.3%, for copper and iron, respectively (for a sample containing 13.6 µg/g of copper and 217.3 µg/g of iron (dry mass)). The limits of detection for 30 mg of sample were 0.3 µg/g for copper and 0.6 µg/g for iron (dry mass). Analytical procedures were verified by the analysis of a standard reference material (lobster hepatopancreas marine, TORT-1) and were applied to several real seafood samples from the estuaries of Galicia (Spain) with satisfactory results.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".