title: Principal-component analysis of particle motion.
abstract: We demonstrate the application of principal-component analysis (PCA) to the analysis of particle motion data in the form of a time series of images. PCA has the ability to resolve and isolate spatiotemporal patterns in the data. Using simulated data, we show that this translates into the ability to separate individual frequency components of the particle motion. We also show that PCA can be used to extract the fluid viscosity from images of particles undergoing Brownian motion. PCA thus provides an efficient alternative to more traditional particle-tracking methods for the analysis of microrheological data.
authors.affiliation: Department of Nuclear Science and Technology, Fudan University, Shanghai 200433, China.|Department of Physics and Astronomy, University of Western Ontario, London, Ontario, Canada N6A 3K7.
authors.forename: H Y
authors.initials: HY
authors.lastname: Chen
authors.affiliation: Montefiore Institute, Université de Liège, 4000 Liège, Belgium.
authors.forename: Raphaël
authors.initials: R
authors.lastname: Liégeois
authors.affiliation: Department of Physics and Astronomy, University of Western Ontario, London, Ontario, Canada N6A 3K7.
authors.forename: John R
authors.initials: JR
authors.lastname: de Bruyn
authors.affiliation: Department of Physics and Astronomy, University of Western Ontario, London, Ontario, Canada N6A 3K7.
authors.forename: Andrea
authors.initials: A
authors.lastname: Soddu