Assessing arsenic species in foods using regularized linear regression of the arsenic K-edge X-ray absorption near edge structure

Jahrman, Evan P.; Yu, Lee L.; Krekelberg, William P.; Sheen, David A.; Allison, Thomas C.; Molloy, John L.

doi:10.1039/d1ja00445j

Assessing arsenic species in foods using regularized linear regression of the arsenic K-edge X-ray absorption near edge structure

Journal Article · 01 January 2022 · Journal of Analytical Atomic Spectrometry

DOI:https://doi.org/10.1039/d1ja00445j· OSTI ID:1982207

^[1]; ^[1]; Krekelberg, William P. ^[1]; ^[1]; Allison, Thomas C. ^[1]; Molloy, John L. ^[1]

Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA

Solvent-free quantifications of arsenic species in foods are attained by applying lasso regression to the analysis of X-ray absorption spectra and assigning uncertainties by bootstrapping.

Research Organization:: Brookhaven National Laboratory (BNL), Upton, NY (United States). National Synchrotron Light Source II (NSLS-II)

Sponsoring Organization:: USDOE Office of Science (SC)

DOE Contract Number:: SC0012704

OSTI ID:: 1982207

Journal Information:: Journal of Analytical Atomic Spectrometry, Vol. 37, Issue 6; ISSN 0267-9477

Publisher:: Royal Society of Chemistry

Country of Publication:: United States

Language:: English

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