title: Supervised discriminant analysis for droplet micro-magnetofluidics.
abstract: We apply the technique of supervised discriminant analysis (SDA) for in-flow detection in droplet-based magnetofluidics. Based on the SDA, we successfully discriminatebivariantdroplets of different volumes containing different encapsulated magnetic content produced by a GMR-based lab-on-chip platform. We demonstrate that the accuracy of discrimination is superior when the correlation of variables for data training is included to the case when the spatial distribution of variables is considered. Droplets produced with differences in ferrofluid concentration of 2.5 mg/ml and volume of 200 pl have been identified with high accuracy (98 %), indicating the significance of SDA for e.g. the discrimination in magnetic immuno-agglutination assays. Furthermore, the results open the way for the development of a unique magnetofluidic platform for future applications in multiplexed droplet-based barcoding assays and screening.
authors.affiliation: Institute for Integrative Nanosciences, IFW Dresden, Helmholtzstr. 20, 01069 Dresden, Germany ; Material Systems for Nanoelectronics, Chemnitz University of Technology, Reichenhainerstr. 70, 09107 Chemnitz, Germany.
authors.forename: Gungun
authors.initials: G
authors.lastname: Lin
authors.affiliation: Institute for Integrative Nanosciences, IFW Dresden, Helmholtzstr. 20, 01069 Dresden, Germany.
authors.forename: Vladimir M
authors.initials: VM
authors.lastname: Fomin
authors.affiliation: Institute for Integrative Nanosciences, IFW Dresden, Helmholtzstr. 20, 01069 Dresden, Germany.
authors.forename: Denys
authors.initials: D
authors.lastname: Makarov
authors.affiliation: Institute for Integrative Nanosciences, IFW Dresden, Helmholtzstr. 20, 01069 Dresden, Germany ; Material Systems for Nanoelectronics, Chemnitz University of Technology, Reichenhainerstr. 70, 09107 Chemnitz, Germany.
authors.forename: Oliver G
authors.initials: OG
authors.lastname: Schmidt
keywords: Droplet microfluidics; Ferrofluids; GMR sensor; Supervised discriminant analysis