Optofluidic microdevice for algae classification : a comparison of results from discriminant analysis and neural network pattern recognition

A.M. Schaap, Y.J. Bellouard, T. Rohrlack

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademic

3 Citations (Scopus)
1 Downloads (Pure)

Abstract

The early detection of changes in the level and composition of algae is essential for tracking water quality and environmental changes. Current approaches require the collection of a specimen which is later analyzed in a laboratory: this slow and expensive approach prevents the rapid identification of changes in algae species dynamics and hinders a quick response to potential outbreaks. In a recent work, we presented a microfluidic chip for classifying and quantifying algae species in water. Here, we study the device performance and specifically compare the difference in results obtained by using a discriminant analysis classification approach and a neural network pattern recognition approach. Using both of these methods, we demonstrate the classification of algae by species, of microspheres by size, and of a detritus/cyanobacteria mixture by type. In each of the demonstrations here, the neural network outperforms the discriminant analysis method.
Original languageEnglish
Title of host publicationProceedings of the Photonics West Conference : Microfluidics, BioMEMS, and Medical Microsystems X, 23 January 2012, San Francisco, California
EditorsH. Becker, B.L. Gray
PublisherSPIE
Pages825104-
DOIs
Publication statusPublished - 2012
Eventconference; Photonics West Conference: Microfluidics, BioMEMS, and Medical Microsystems X -
Duration: 1 Jan 2012 → …

Publication series

NameProceedings of SPIE
Volume8251
ISSN (Print)0277-786X

Conference

Conferenceconference; Photonics West Conference: Microfluidics, BioMEMS, and Medical Microsystems X
Period1/01/12 → …
OtherPhotonics West Conference: Microfluidics, BioMEMS, and Medical Microsystems X

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