Role of the Power Function Value in Linearity and Universality for Charged Aerosol Detectors: Theoretical Elucidations from a Validated Model

High-throughput drug discovery on the microgram scale is now common, making analyte quantitation without molecule-specific calibration imperative. The charged aerosol detector (CAD) was invented to be a next-generation universal liquid chromatography (LC) detector with excellent response universality for nonvolatile analytes as well as sensitivity for nonchromophoric compounds. Although the CAD is a mass flow-sensitive detector, its response to mass is inherently nonlinear, which challenges traditional quantitation. In CAD software, there is a "power function value" (<i>p</i>) setting that can be used to linearize the signal through digital signal processing. The exact workings of this power function value algorithm remain unknown; however, its optimization is a crucial aspect of analytical method development for LC-CAD. Herein, we developed a theoretical relationship that can be used to predict the chromatogram (plus peak area, width, and height) at any <i>p</i> if the data are collected at <i>p</i> = 1. This model was validated using a diverse dataset comprising 1440 measurements including peak heights, areas, and widths. Predicted areas had an average error of less than 2% showing excellent agreement between calculated and experimental results. An open-access automated code is tested and provided, which predicts the power function value that produces the most linear response. It is vital to note that optimizing the power function value affects peaks of different heights disproportionately. Low-level impurities were shown to be minimized and eventually eliminated by increasing the power function value. This model provides an easy-to-implement tool (MATLAB or Excel) that assists in choosing the optimal <i>p</i> for each LC-CAD method, increasing the speed of method development and improving the accuracy of quantitative workflows.

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Publication Details

Journal
Analytical Chemistry
Published
2024-09-26
DOI
https://doi.org/10.1021/acs.analchem.4c03714
Citations
5
Primary Topic
Air Quality Monitoring and Forecasting
Type
article
Field-Weighted Citation Impact
1.03
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article

Role of the Power Function Value in Linearity and Universality for Charged Aerosol Detectors: Theoretical Elucidations from a Validated Model

Wesley W. Barnhart, Imad A. Haidar Ahmad, Daipayan Roy, Troy T. Handlovic
5 citations
Analytical Chemistry
Air Quality Monitoring and Forecasting
1.03
article

Role of the Power Function Value in Linearity and Universality for Charged Aerosol Detectors: Theoretical Elucidations from a Validated Model

Wesley W. Barnhart, Imad A. Haidar Ahmad, Daipayan Roy, Troy T. Handlovic
article en
5 citations

Abstract

High-throughput drug discovery on the microgram scale is now common, making analyte quantitation without molecule-specific calibration imperative. The charged aerosol detector (CAD) was invented to be a next-generation universal liquid chromatography (LC) detector with excellent response universality for nonvolatile analytes as well as sensitivity for nonchromophoric compounds. Although the CAD is a mass flow-sensitive detector, its response to mass is inherently nonlinear, which challenges traditional quantitation. In CAD software, there is a "power function value" (<i>p</i>) setting that can be used to linearize the signal through digital signal processing. The exact workings of this power function value algorithm remain unknown; however, its optimization is a crucial aspect of analytical method development for LC-CAD. Herein, we developed a theoretical relationship that can be used to predict the chromatogram (plus peak area, width, and height) at any <i>p</i> if the data are collected at <i>p</i> = 1. This model was validated using a diverse dataset comprising 1440 measurements including peak heights, areas, and widths. Predicted areas had an average error of less than 2% showing excellent agreement between calculated and experimental results. An open-access automated code is tested and provided, which predicts the power function value that produces the most linear response. It is vital to note that optimizing the power function value affects peaks of different heights disproportionately. Low-level impurities were shown to be minimized and eventually eliminated by increasing the power function value. This model provides an easy-to-implement tool (MATLAB or Excel) that assists in choosing the optimal <i>p</i> for each LC-CAD method, increasing the speed of method development and improving the accuracy of quantitative workflows.

Analytical ChemistryVol. 96(40)
Amgen (United States) (US), The University of Texas at Arlington (US), Université Bourgogne Franche-Comté (FR)
Affordable and clean energy
Openalex Percentile: Top 26%
Air Quality Monitoring and Forecasting
1.03
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