Rapid deconvolution of low-resolution time-of-flight data using Bayesian inference
Date
2019
Journal Title
Journal ISSN
Volume Title
Publisher
AIP Publishing LLC
Abstract
The deconvolution of low-resolution time-of-flight data has numerous advantages, including the ability to extract additional information from the experimental data. We augment the well-known Lucy-Richardson deconvolution algorithm using various Bayesian prior distributions and show that a prior of second-differences of the signal outperforms the standard Lucy-Richardson algorithm, accelerating the rate of convergence by more than a factor of four, while preserving the peak amplitude ratios of a similar fraction of the total peaks. A novel stopping criterion and boosting mechanism are implemented to ensure that these methods converge to a similar final entropy and local minima are avoided. Improvement by a factor of two in mass resolution allows more accurate quantification of the spectra. The general method is demonstrated in this paper through the deconvolution of fragmentation peaks of the 2,5-dihydroxybenzoic acid matrix and the benzyltriphenylphosphonium thermometer ion, following femtosecond ultraviolet laser desorption.
Description
CITATION: Pieterse, C. L., et al. 2019. Rapid deconvolution of low-resolution time-of-flight data using Bayesian inference.
The Journal of Chemical Physics, 151:244307, doi:10.1063/1.5129343.
The original publication is available at https://aip.scitation.org
The original publication is available at https://aip.scitation.org
Keywords
Bayesian statistical decision theory, Time of flight mass spectrometry -- Deconvolution, Lucy-Richardson algorithm, Femtosecond lasers
Citation
Pieterse, C. L., et al. 2019. Rapid deconvolution of low-resolution time-of-flight data using Bayesian inference.
The Journal of Chemical Physics, 151:244307, doi:10.1063/1.5129343