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

• Published in: Analytical chemistry (Washington) 2024-10, Vol.96 (40), p.16045-16052
• Main Authors: Handlovic, Troy T., Roy, Daipayan, Barnhart, Wesley W., Haidar Ahmad, Imad A.
• Format: Article
• Language: English
• Subjects: Aerosols; Algorithms; CAD; Computer aided design; Digital signal processing; Error analysis; Impurities; Linearity; Liquid chromatography; Mass flow; Nonlinear response; Optimization; Quantitation; Sensitivity analysis; Sensors; Signal processing
• ISSN: 0003-2700; 1520-6882; 1520-6882
• DOI: 10.1021/acs.analchem.4c03714

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” (p) 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 p if the data are collected at p = 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 p for each LC-CAD method, increasing the speed of method development and improving the accuracy of quantitative workflows.