What Is the Best Method to Fit Time-Resolved Data? A Comparison of the Residual Minimization and the Maximum Likelihood Techniques As Applied to Experimental Time-Correlated, Single-Photon Counting Data

dc.contributor.author Song, Xueyu
dc.contributor.author Smith, Emily
dc.contributor.author Song, Xueyu
dc.contributor.author Smith, Emily
dc.contributor.author Vaswani, Namrata
dc.contributor.author Petrich, Jacob
dc.contributor.department Ames National Laboratory
dc.contributor.department Chemistry
dc.date 2018-09-11T07:07:47.000
dc.date.accessioned 2020-06-30T01:16:17Z
dc.date.available 2020-06-30T01:16:17Z
dc.date.copyright Fri Jan 01 00:00:00 UTC 2016
dc.date.issued 2016-02-10
dc.description.abstract <p>The need for measuring fluorescence lifetimes of species in subdiffraction-limited volumes in, for example, stimulated emission depletion (STED) microscopy, entails the dual challenge of probing a small number of fluorophores and fitting the concomitant sparse data set to the appropriate excited-state decay function. This need has stimulated a further investigation into the relative merits of two fitting techniques commonly referred to as “residual minimization” (RM) and “maximum likelihood” (ML). Fluorescence decays of the well-characterized standard, rose bengal in methanol at room temperature (530 ± 10 ps), were acquired in a set of five experiments in which the total number of “photon counts” was approximately 20, 200, 1000, 3000, and 6000 and there were about 2–200 counts at the maxima of the respective decays. Each set of experiments was repeated 50 times to generate the appropriate statistics. Each of the 250 data sets was analyzed by ML and two different RM methods (differing in the weighting of residuals) using in-house routines and compared with a frequently used commercial RM routine. Convolution with a real instrument response function was always included in the fitting. While RM using Pearson’s weighting of residuals can recover the correct mean result with a total number of counts of 1000 or more, ML distinguishes itself by yielding, in all cases, the same mean lifetime within 2% of the accepted value. For 200 total counts and greater, ML always provides a standard deviation of <10% of the mean lifetime, and even at 20 total counts there is only 20% error in the mean lifetime. The robustness of ML advocates its use for sparse data sets such as those acquired in some subdiffraction-limited microscopies, such as STED, and, more importantly, provides greater motivation for exploiting the time-resolved capacities of this technique to acquire and analyze fluorescence lifetime data.</p>
dc.description.comments <p>This document is the Accepted Manuscript version of a Published Work that appeared in final form in Santra, Kalyan, Jinchun Zhan, Xueyu Song, Emily A. Smith, Namrata Vaswani, and Jacob W. Petrich. "What is the best method to fit time-resolved data? A comparison of the residual minimization and the maximum likelihood techniques as applied to experimental time-correlated, single-photon counting data." <em>The Journal of Physical Chemistry B</em> 120, no. 9 (2016): 2484-2490. copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see DOI: <a href="http://dx.doi.org/10.1021/acs.jpcb.6b00154" target="_blank">10.1021/acs.jpcb.6b00154</a>. Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/chem_pubs/1054/
dc.identifier.articleid 2059
dc.identifier.contextkey 12800365
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath chem_pubs/1054
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/14353
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/chem_pubs/1054/2016_SongXueyu_WhatIs.pdf|||Fri Jan 14 18:23:09 UTC 2022
dc.source.uri 10.1021/acs.jpcb.6b00154
dc.subject.disciplines Atomic, Molecular and Optical Physics
dc.subject.disciplines Chemistry
dc.subject.disciplines Physical Chemistry
dc.title What Is the Best Method to Fit Time-Resolved Data? A Comparison of the Residual Minimization and the Maximum Likelihood Techniques As Applied to Experimental Time-Correlated, Single-Photon Counting Data
dc.type article
dc.type.genre article
dspace.entity.type Publication
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