Analysis of TCSPC data: a comparative evaluation of deterministic and probabilistic approaches

We review various methods for analysing time-resolved fluorescence data acquired using the time-correlated single photon counting (TCSPC) method in an attempt to evaluate their benefits and limitations. We have applied these methods to both experimental and simulated data. The relative merits of usi...

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Bibliographic Details
Published inMethods and applications in fluorescence
Main Authors Smith, Darren Andrew, McKenzie, Grant, Jones, Anita C, Smith, Trevor
Format Journal Article
LanguageEnglish
Published England 18.07.2017
Subjects
2-Aminopurine
Exponential series method
Iterative reconvolution
Deterministic and probabilistic analysis
TCSPC
Basis pusuit denoising
Maximum entropy method
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Summary:We review various methods for analysing time-resolved fluorescence data acquired using the time-correlated single photon counting (TCSPC) method in an attempt to evaluate their benefits and limitations. We have applied these methods to both experimental and simulated data. The relative merits of using deterministic approaches, such as the commonly used iterative reconvolution method, and probabilistic approaches, such as the smoothed exponential series method (SESM), the maximum entropy method (MEM) and recently proposed basis pursuit denoising (BPDN, compressed sensing) method are outlined. In particular, we show the value of using multiple methods to arrive at the most appropriate choice of model. We show that the use of probabilistic analysis methods can indicate whether a discrete component or distribution analysis provides the better representation of the data.
ISSN:2050-6120