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Bridging the Gap Between Chemical Analysis and Applied Statistics to Expand the Applications of Atomic Spectrometric Techniques

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abstract
The combination of (trace) elemental analysis with data science techniques is an important contribution to atomic spectrometry and its application to a broad range of research fields. With temperatures as high as 10,000 K, the inductively coupled plasma (ICP) is a robust source of atomization, excitation and ionization, capable of providing trace level information for the vast majority of elements on the periodic table. Recently, data science has catalyzed research across science by helping to process and interpret large amounts of complex data acquired from instrumental techniques. The proposed body of work shows the merit of using data-driven algorithms to enhance the analytical capabilities of modern atomic spectrometry in two ways: (i) furthering the interpretation and use of data acquired using traditional calibration methods for environmental and health-related applications, and (ii) identifying and resolving matrix effects that can compromise analyses based on the traditional external standard calibration method (EC).
subject
Drinking water
Inductively coupled plasma
Lead
Machine learning
Matrix effects
Non-invasive diagnostics
contributor
Carter, Jake Alexander (author)
Jones, Bradley T (committee chair)
Calloway, Clifton P (committee member)
Colyer, Christa L (committee member)
Geyer, Scott M (committee member)
King, Stephen B (committee member)
date
2020-08-28T08:35:23Z (accessioned)
2020-08-28T08:35:23Z (available)
2020 (issued)
degree
Chemistry (discipline)
identifier
http://hdl.handle.net/10339/96944 (uri)
language
en (iso)
publisher
Wake Forest University
title
Bridging the Gap Between Chemical Analysis and Applied Statistics to Expand the Applications of Atomic Spectrometric Techniques
type
Dissertation

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