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拟合时间分辨数据的最佳方法是什么?应用于实验时间相关单光子计数数据的残差最小化和最大似然技术的比较

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.

作者信息

Santra Kalyan, Zhan Jinchun, Song Xueyu, Smith Emily A, Vaswani Namrata, Petrich Jacob W

机构信息

Department of Chemistry, Iowa State University , Ames, Iowa 50011, United States.

U.S. Department of Energy , Ames Laboratory, Ames, Iowa 50011, United States.

出版信息

J Phys Chem B. 2016 Mar 10;120(9):2484-90. doi: 10.1021/acs.jpcb.6b00154. Epub 2016 Feb 22.

Abstract

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.

摘要

例如,在受激发射损耗(STED)显微镜中,测量亚衍射极限体积内物质的荧光寿命,需要应对探测少量荧光团以及将随之产生的稀疏数据集拟合到合适的激发态衰减函数这一双重挑战。这种需求促使人们进一步研究两种通常称为“残差最小化”(RM)和“最大似然”(ML)的拟合技术的相对优点。在一组五个实验中,获取了特征明确的标准品孟加拉玫瑰红在室温下于甲醇中的荧光衰减(530±10皮秒),其中“光子计数”的总数分别约为20、200、1000、3000和6000,且各自衰减最大值处约有2 - 200个计数。每组实验重复50次以生成合适的统计数据。使用内部程序通过ML和两种不同的RM方法(残差加权不同)对这250个数据集进行分析,并与常用的商业RM程序进行比较。拟合过程中始终包含与实际仪器响应函数的卷积。虽然使用皮尔逊残差加权的RM在计数总数为1000或更多时可以恢复正确的平均结果,但ML的独特之处在于,在所有情况下,其产生的平均寿命都在公认值的2%以内。对于总数为200及以上的计数,ML始终提供的标准偏差小于平均寿命的10%,甚至在总数为20个计数时,平均寿命的误差也仅为20%。ML的稳健性表明它适用于稀疏数据集,如在某些亚衍射极限显微镜(如STED)中获取的数据集,更重要的是,这为利用该技术的时间分辨能力来获取和分析荧光寿命数据提供了更大的动力。

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