4.5 Article

Correcting bias in log-linear instrument calibrations in the context of chemical ionization mass spectrometry

Journal

ATMOSPHERIC MEASUREMENT TECHNIQUES
Volume 14, Issue 10, Pages 6551-6560

Publisher

COPERNICUS GESELLSCHAFT MBH
DOI: 10.5194/amt-14-6551-2021

Keywords

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Funding

  1. Alfred P. Sloan Foundation [P-2018-11129]

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This study examines the bias in log-linear calibration relationships in the context of CIMS and proposes a parameter-explicit solution for completely removing the inherent bias. A simplified correction method is suggested for cases where comprehensive bias correction is not possible, showing an average elimination of bias but not for each individual compound.
Quantitative calibration of analytes using chemical ionization mass spectrometers (CIMSs) has been hindered by the lack of commercially available standards of atmospheric oxidation products. To accurately calibrate analytes without standards, techniques have been recently developed to log-linearly correlate analyte sensitivity with instrument operating conditions. However, there is an inherent bias when applying log-linear calibration relationships that is typically ignored. In this study, we examine the bias in a log-linear-based calibration curve based on prior mathematical work. We quantify the potential bias within the context of a CIMS-relevant relationship between analyte sensitivity and instrument voltage differentials. Uncertainty in three parameters has the potential to contribute to the bias, specifically the inherent extent to which the nominal relationship can capture true sensitivity, the slope of the relationship, and the voltage differential below which maximum sensitivity is achieved. Using a prior published case study, we estimate an average bias of 30 %, with 1 order of magnitude for less sensitive compounds in some circumstances. A parameter-explicit solution is proposed in this work for completely removing the inherent bias generated in the log-linear calibration relationships. A simplified correction method is also suggested for cases where a comprehensive bias correction is not possible due to unknown uncertainties of calibration parameters, which is shown to eliminate the bias on average but not for each individual compound.

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