Quantification of Arsenic Species in Wheat Flour Samples by Ion Chromatography Coupled to High Resolution Inductively Coupled Plasma-Mass Spectrometry (IC-HR-ICP-MS)
Bibliographic record
Abstract
A method was developed for arsenic speciation of South African wheat flour by microwave-assisted extraction with ion chromatography (IC) separation and detection with high resolution inductively coupled plasma–mass spectrometry (IC-HR-ICP-MS). Method optimization included development of the extraction and elution methods for baseline separation of the As-species in the samples. The As-species were successfully extracted using deionized water and baseline separation was attained using a gradient elution method with 0.5 mM HNO3 (pH 3.4) and 50 mM HNO3 (pH 1.4). Method validation parameters, including trueness (bias), precision, linearity, limit of detection (LOD), limit of quantification (LOQ) and selectivity were evaluated to assess the quality of the results. Three certified reference materials (CRMs), NIST SRM 1568b (rice flour), NMIJ CRM 7533-a (brown rice flour), and ERM BC211 (brown rice flour) were used to evaluate the trueness of the developed method. Low limits of detection and quantification were achieved (0.3 to 2.6 pg g−1 and 1.1 to 8.6 pg g−1, respectively) for the As-species. The dominant peaks in the wheat flour were arsenite (As(III)), dimethyl arsenic acid (DMA), and arsenate (As(V)). The concentrations of the inorganic (iAs), i.e., As(III) and As(V) in the wheat flour were very low from 6.8 to 17.8 ng g−1, with a relative expanded uncertainty (U) of <6.9% (k=2), which is below the permissible level of iAs in food and food products proposed by the World Health Organization (WHO).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".