Parts-Per-Billion Mass Measurement Accuracy Achieved through the Combination of Multiple Linear Regression and Automatic Gain Control in a Fourier Transform Ion Cyclotron Resonance Mass Spectrometer

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Abstract
Fourier transform ion cyclotron resonance mass spectrometry has the ability to achieve unprecedented mass measurement accuracy (MMA); MMA is one of the most significant attributes of mass spectrometric measurements as it affords extraordinary molecular specificity. However, due to space-charge effects, the achievable MMA significantly depends on the total number of ions trapped in the ion cyclotron resonance (ICR) cell for a particular measurement. Even through the use of automatic gain control (AGC), the total ion population is not constant between spectra. Multiple linear regression calibration in conjunction with AGC is utilized in these experiments to formally account for the differences in total ion population in the ICR cell between the external calibration spectra and experimental spectra. This ability allows for the extension of dynamic range of the instrument and for the mean MMA values to remain less than 1 part-per-million (ppm). In addition, multiple linear regression calibration is used to account for both differences in total ion population in the ICR cell as well as relative ion abundance of a given species, which also affords mean MMA values at the parts-per-billion (ppb) level.