Rapid and Accurate ED-XRF Quantification of Trace Arsenic in Rice-Based Foods Employing ANNs to Resolve Lead Spectral Interference.

Abstract read

In one paragraph

Article in Foods (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Murphy CarrollDepartment of Food Science, University of Massachusetts Amherst, 102 Holdsworth Way, Amherst, MA 01003, USA.ORCID 0009-0002-4106-6594

Zili GaoDepartment of Food Science, University of Massachusetts Amherst, 102 Holdsworth Way, Amherst, MA 01003, USA.

Lili HeDepartment of Food Science, University of Massachusetts Amherst, 102 Holdsworth Way, Amherst, MA 01003, USA.

Funding

USDA National Institute of Food and Agriculture Hatch Project MAS00559

6 · The paper itself

Abstract

Trace quantifications of arsenic (As) in foods by energy-dispersive X-ray fluorescence (ED-XRF) spectrometry are hindered by spectral overlap from lead (Pb) at characteristic emission lines. This study employed artificial neural networks (ANN) to statistically model and correct for As/Pb spectral overlap, enabling accurate As quantifications in rice-based foods. Calibration standards were prepared by pelletizing milled rice spiked with As and Pb, and validation was performed using a certified reference material, commercial rice-based foods, and Pb-spiked commercial foods. As calibration metrics were great (R

Indexed as

arsenicartificial neural networkfood safetyleadquantificationregressionX-ray fluorescence spectrometry

Identifiers

PMID41976424

DOI10.3390/foods15071130

PMCPMC13072912

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.