Interlaboratory comparison of heavy metal testing in animal diagnostic specimens and feed using inductively coupled plasma–mass spectrometry
Bibliographic record
Abstract
We compared inductively coupled plasma-mass spectrometry (ICP-MS) test results for the analysis of heavy metals (As, Ba, Cd, Hg, Pb, and Se) in pet foods and routine veterinary diagnostic specimens using intralaboratory and interlaboratory comparisons. Four laboratories, 1 principal laboratory and 3 collaborating laboratories, conducted instrument comparison (limit of detection [LOD], limit of quantification [LOQ], and linear dynamic range [LDR] on 24 data sets), in-house method comparison (accuracy and precision on 120 data sets), and interlaboratory comparison (reproducibility on 528 data sets using Horwitz equation analysis). Matrices tested included 2 types of pet food jerky treats (chicken and sweet potato), bovine blood, and bovine liver and kidney. The instrument comparison study confirmed that ICP-MS provided the sensitivity necessary for the analysis of all heavy metals tested at concentrations below the level of concern for routine diagnostic testing. The "in-house" method comparison samples, spiked at low (0.04 µg/g), medium (0.4 µg/g), and high (8.0 µg/g; note: the high validation level spike for mercury was 2 µg/g) concentration levels, indicated that ICP-MS can meet U.S. FDA acceptance criteria for both accuracy (90-105% recovery) and precision (< 6% coefficient of variation). The interlaboratory comparison studies showed that ICP-MS is a reproducible method for the analysis of heavy metals (HorRat value of 0.5-2.0) except for mercury in one laboratory, which used a different sample preparation method (open block rather than microwave digestion). Overall, our study showed that ICP-MS is a reproducible method for the analysis of heavy metals in spite of minor differences in methodology.
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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 | 0.029 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".