Revealing the inner workings of the power function algorithm in Charged Aerosol Detection: A simple and effective approach to optimizing power function value for quantitative analysis

算法 功能(生物学) 信号(编程语言) 幂函数 功率(物理) 探测器 计算机辅助设计 再现性 线性化 噪音(视频) 生物系统 化学 计算机科学 人工智能 物理 数学 色谱法 数学分析 电信 生物化学 量子力学 非线性系统 进化生物学 图像(数学) 生物 程序设计语言
作者
Imad A. Haidar Ahmad,Andrei Blaskó,James P. Tam,Narayan Variankaval,Holst M. Halsey,Robert Hartman,Erik L. Regalado
出处
期刊:Journal of Chromatography A [Elsevier BV]
卷期号:1603: 1-7 被引量:32
标识
DOI:10.1016/j.chroma.2019.04.017
摘要

In recent years, charged aerosol detection (CAD) has become a valuable tool for fast and efficient quantitative chromatographic analysis of drug substances with weak UV absorption. In analytical method development using CAD, the power function settings available in the instrument software are key for linearization of the signal response with respect to analyte concentration. However, the relatively poor understanding of the power function algorithm has limited a more widespread use of CAD for quantitative assays, especially in the late stage of method validation and GMP laboratories. Herein, we present an approach to understand the inner workings of the power function value (PFV), the PFV optimization algorithm, as well as a method to determine the optimum PFV based on the signals acquired at PFV = 1 (default CAD settings). The exponent and the constant in the PFV equation used for modeling follow a trend as a function of PFV. The CAD signal at any PFV was modeled based on the signal acquired at PFV = 1, the modelling was successful for two analytes at different concentration levels on two different CAD detectors of the same model. This method reveals the functionality of the PFV which substantially simplifies the workflow needed to optimize the detector signal. The accuracy between the experimental and theoretical results showed high correlation and always resulted in the same optimum PFV determined by both ways. The approach described in this investigation simplifies the selection of the optimum PFV at which the signal is more linear, the signal-to-noise is higher, and the area reproducibility is better. The power function algorithm elucidated herein enables determination of optimum PFV from minimal experimental output and excellent overall accuracy. This paper provides an approach that includes no data transformation outside the vendor software, a very important requirement to easily validate and report results in a GMP environment.
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