Single particle raster image analysis of diffusion
Journal article, 2017
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.
Confocal laser scanning microscopy
Bias correction
Bootstrap
Diffusion
Fluorescent beads
Maximum likelihood
Author
Marco Longfils
Chalmers, Mathematical Sciences, Mathematical Statistics
University of Gothenburg
Erich Schuster
SIK – the Swedish Institute for Food and Biotechnology
Niklas Lorén
SIK – the Swedish Institute for Food and Biotechnology
SuMo Biomaterials
Aila Särkkä
University of Gothenburg
SuMo Biomaterials
Chalmers, Mathematical Sciences, Mathematical Statistics
Mats Rudemo
University of Gothenburg
Chalmers, Mathematical Sciences, Mathematical Statistics
SuMo Biomaterials
Journal of Microscopy
0022-2720 (ISSN) 1365-2818 (eISSN)
Vol. 266 1 3-14Subject Categories
Other Physics Topics
Probability Theory and Statistics
Areas of Advance
Materials Science
DOI
10.1111/jmi.12511