14 February 2012 Optofluidic microdevice for algae classification: a comparison of results from discriminant analysis and neural network pattern recognition
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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.
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Allison Schaap, Allison Schaap, Thomas Rohrlack, Thomas Rohrlack, Yves Bellouard, 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); doi: 10.1117/12.907012; https://doi.org/10.1117/12.907012
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