Routine, High-Sensitivity, Cold Vapor Atomic Absorption Spectrometric Determination of Total Mercury in Foods after Low-Temperature Digestion
Bibliographic record
Abstract
A cold vapor atomic absorption spectrometric method was developed for the subnanogram-per-gram determination of total Hg in a wide variety of foods. Foods were weighed into 50 mL polypropylene centrifuge tubes and dried without charring at 55 degrees C in a circulating oven. Samples were then digested at 58 degrees C with HNO3, HCl, and H2O2. After matrix modification with solutions of 2% Mg(NO3)2, 0.01% Triton X-100, and Cu(II) at 10 microg/mL, samples were analyzed by using a CeTAC Technologies M-6000A dedicated Hg analyzer. Based on a 2 g sample weight, the detection limit of the method over 12 batches averaged 0.30 ng/g wet weight and ranged from 0.03 to 0.6 ng/g. Recoveries of Hg added to 17 different foods, analyzed in a routine manner, averaged 97%, and individual recoveries ranged from 77 to 107%. Accuracy was confirmed by analysis of 7 biological reference materials from the National Research Council of Canada and the National Institute of Standards and Technology. Stabilization of low concentrations of Hg in solutions containing no sample was required to prevent loss of Hg from blanks. In a comparison of NaCl, potassium dichromate, and Au(II), chloride was much more effective for stabilization than the other two, and HCl was used for subsequent stabilization.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".