Abstract
We apply the technique of supervised discriminant analysis (SDA) for in-flow detection in droplet-based magnetofluidics. Based on the SDA, we successfully discriminate bivariant droplets 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.Electronic supplementary materialThe online version of this article (doi:10.1007/s10404-015-1579-z) contains supplementary material, which is available to authorized users.
Highlights
Micro-magnetofluidics has emerged and received growing interest owning to the extensive contribution of magnetism to microfluidics (Pamme 2007; Frenz et al 2008; Wang 2008; Gijs et al 2010; Nguyen 2012; Misuk et al 2013)
Droplets are produced under definite flow parameters that are characterized with a certain size and encapsulated magnetic content
For droplets produced with a certain set of flow parameters, the data are featured with a distribution of detection events of droplets by means of Ci, Sj and respective dispersions (ΔC, ΔS) in a typical multiparametric diagram (Fig. 1d)
Summary
Micro-magnetofluidics has emerged and received growing interest owning to the extensive contribution of magnetism to microfluidics (Pamme 2007; Frenz et al 2008; Wang 2008; Gijs et al 2010; Nguyen 2012; Misuk et al 2013). The key step to demonstrate the multiparametric analytical capability of a magnetofluidic platform is its feasibility to discriminate individual detection events into discrete populations, which lays the foundation for its further applications in analytical assays, clinical diagnostics and cell research This step has still not yet been addressed for magnetic in-flow detection. By taking into account the correlation of the data with covariance matrix for the training, a first example of droplets produced with a concentration difference of 2.5 mg/ml and a volume difference of 200 pl can be successfully discriminated with high accuracy (~98 %), demonstrating its relevance for the discrimination of droplet changes for e.g. magnetic immunoagglutination assays It paves the way for future development of a droplet-based magnetofluidic platform based on a magnetic encoding scheme for combinatorial analysis, highly multiplexed droplet assays or diagnostics
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