Statistical Evaluation of Internal and External Mass Calibration Laws Utilized in Fourier Transform Ion Cyclotron Resonance Mass Spectrometry

Abstract

The statistical evaluation of two common and three new calibration laws utilized in Fourier transform ion cyclotron resonance mass spectrometry are presented. Electrospray ionization was used to prepare a series of mass spectra of ammonium-adducted polypropylene glycol (PPG) with an average molecular weight of 1000 Da. The singly charged PPG-1000 oligomers allowed for the description of a broad range of m/z and abundance values within each mass spectrum. The hexapole accumulation time was varied to afford a range of total ion abundance values of about an order of magnitude. To examine each of the calibration laws, we utilized cross-validation both "within-spectrum" and "between-spectra" for internally and externally calibrated data, respectively. In addition, we used t-statistics to ensure that each calibration coefficient was statistically significant and necessary to accurately describe the variation in the data. In comparison to commonly used calibration laws for internal calibration, our new calibration law based on multiple linear regression offered a 2-fold improvement in mass measurement accuracy (MMA). In comparison to external calibration laws without automatic gain control, our new calibration law using multiple regression improved the MMA by >10-fold; this improvement would increase further as the dynamic range of the measurement increases (e.g., a biological system). For both our internal and external calibration laws, the median MMA was less than 1 part-per-million. Furthermore, we investigate the number of calibrant ions as well as their required m/z range in order to successfully achieve high MMA.