title: Single particle raster image analysis of diffusion.
abstract: As a complement to the standard RICS method of analysing Raster Image Correlation Spectroscopy images with estimation of the image correlation function, we introduce the method SPRIA, Single Particle Raster Image Analysis. Here, we start by identifying individual particles and estimate the diffusion coefficient for each particle by a maximum likelihood method. Averaging over the particles gives a diffusion coefficient estimate for the whole image. In examples both with simulated and experimental data, we show that the new method gives accurate estimates. It also gives directly standard error estimates. The method should be possible to extend to study heterogeneous materials and systems of particles with varying diffusion coefficient, as demonstrated in a simple simulation example. A requirement for applying the SPRIA method is that the particle concentration is low enough so that we can identify the individual particles. We also describe a bootstrap method for estimating the standard error of standard RICS.
authors.affiliation: Department of Mathematical Sciences, Chalmers University of Technology and University of Gothenburg, Gothenburg, Sweden.
authors.forename: M
authors.initials: M
authors.lastname: Longfils
authors.affiliation: SP Food and Bioscience, Structure and Material Design, Gothenburg, Sweden.
authors.forename: E
authors.initials: E
authors.lastname: Schuster
authors.affiliation: SP Food and Bioscience, Structure and Material Design, Gothenburg, Sweden.
authors.forename: N
authors.initials: N
authors.lastname: Lorén
authors.affiliation: Department of Mathematical Sciences, Chalmers University of Technology and University of Gothenburg, Gothenburg, Sweden.
authors.forename: A
authors.initials: A
authors.lastname: Särkkä
authors.affiliation: Department of Mathematical Sciences, Chalmers University of Technology and University of Gothenburg, Gothenburg, Sweden.
authors.forename: M
authors.initials: M
authors.lastname: Rudemo
keywords: Bias correction; bootstrap; confocal laser scanning microscopy; diffusion; fluorescent beads; maximum likelihood