Principal-component analysis of particle motion

Authors

H. Y. Chen, Fudan University
Raphaël Liégeois, Université de Liège
John R. De Bruyn, The University of Western Ontario
Andrea Soddu, The University of Western OntarioFollow

Document Type

Article

Publication Date

4-15-2015

Journal

Physical Review E - Statistical, Nonlinear, and Soft Matter Physics

Volume

91

Issue

4

URL with Digital Object Identifier

10.1103/PhysRevE.91.042308

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.