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

Allison Schaap; Thomas Rohrlack; Yves Bellouard

doi:10.1117/12.907012

14 February 2012 Optofluidic microdevice for algae classification: a comparison of results from discriminant analysis and neural network pattern recognition

Allison Schaap, Thomas Rohrlack, Yves Bellouard

Author Affiliations +

Proceedings Volume 8251, Microfluidics, BioMEMS, and Medical Microsystems X; 825104 (2012) https://doi.org/10.1117/12.907012
Event: SPIE MOEMS-MEMS, 2012, San Francisco, California, United States

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.

Citation Download Citation

Allison Schaap, Thomas Rohrlack, and Yves Bellouard "Optofluidic microdevice for algae classification: a comparison of results from discriminant analysis and neural network pattern recognition", Proc. SPIE 8251, Microfluidics, BioMEMS, and Medical Microsystems X, 825104 (14 February 2012); https://doi.org/10.1117/12.907012

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