scholarly journals Rapid deconvolution of low-resolution time-of-flight data using Bayesian inference

2019 ◽  
Vol 151 (24) ◽  
pp. 244307
Author(s):  
Cornelius L. Pieterse ◽  
Michiel B. de Kock ◽  
Wesley D. Robertson ◽  
Hans C. Eggers ◽  
R. J. Dwayne Miller
Keyword(s):  
Bayesian Inference ◽  
Time Of Flight ◽  
Resolution Time ◽  
Low Resolution ◽  
Flight Data

scholarly journals Rapid Deconvolution of Low-Resolution Time-of-Flight Data using Bayesian Inference

2019 ◽  
Author(s):  
Cornelius Pieterse ◽  
Michiel B. De Kock ◽  
Wesley D. Robertson ◽  
Hans C. Eggers ◽  
R. J. Dwayne Miller
Keyword(s):  
Time Of Flight ◽  
Stopping Criterion ◽  
Amplitude Ratios ◽  
Resolution Time ◽  
Low Resolution ◽  
Flight Data ◽  
Additional Information ◽  
Bayesian Prior ◽  
Accurate Quantification ◽  
General Method

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 by 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 is implemented to ensure these methods converge to a similar entropy, and that local minima are avoided, respectively. Improvement by a factor of two in mass resolution allows more accurate quantification of the spectra. The general method is demonstrated in this paper by the deconvolution of fragmentation peaks of the DHB matrix, as well as the BTP thermometer ion, following femtosecond ultraviolet laser desorption.

scholarly journals Rapid Deconvolution of Low-Resolution Time-of-Flight Data using Bayesian Inference

2019 ◽  
Author(s):  
Cornelius Pieterse ◽  
Michiel B. De Kock ◽  
Wesley D. Robertson ◽  
Hans C. Eggers ◽  
R. J. Dwayne Miller
Keyword(s):  
Time Of Flight ◽  
Fragmentation Pathway ◽  
Amplitude Ratios ◽  
Resolution Time ◽  
Low Resolution ◽  
Flight Data ◽  
Additional Information ◽  
Bayesian Prior ◽  
Accurate Quantification ◽  
General Method

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 by various Bayesian prior distributions and show that a prior of second-differences of the signal outperforms the standard Lucy-Richardson algorithm by accelerating the rate of convergence by a factor of two and preserving the peak amplitude ratios of a larger fraction of the total peaks. A stopping criterion and boosting mechanism is implemented to ensure that these methods converge to the same final entropy and that local minima are avoided. Improvement by a factor of two in mass resolution of the signal allowed more accurate quantification of the spectra. The general method is demonstrated in this paper by the deconvolution of fragmentation pathway peaks of the benzyltriphenylphosphonium thermometer ion following femtosecond ultraviolet laser desorption.<br>

scholarly journals Rapid Deconvolution of Low-Resolution Time-of-Flight Data using Bayesian Inference

2019 ◽  
Author(s):  
Cornelius Pieterse ◽  
Michiel B. De Kock ◽  
Wesley D. Robertson ◽  
Hans C. Eggers ◽  
R. J. Dwayne Miller
Keyword(s):  
Time Of Flight ◽  
Stopping Criterion ◽  
Amplitude Ratios ◽  
Resolution Time ◽  
Low Resolution ◽  
Flight Data ◽  
Additional Information ◽  
Bayesian Prior ◽  
Accurate Quantification ◽  
General Method

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 by 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 is implemented to ensure these methods converge to a similar entropy, and that local minima are avoided, respectively. Improvement by a factor of two in mass resolution allows more accurate quantification of the spectra. The general method is demonstrated in this paper by the deconvolution of fragmentation peaks of the DHB matrix, as well as the BTP thermometer ion, following femtosecond ultraviolet laser desorption.

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