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- W2007169600 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." @default.
- W2007169600 created "2016-06-24" @default.
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- W2007169600 date "2012-02-09" @default.
- W2007169600 modified "2023-10-16" @default.
- W2007169600 title "Optofluidic microdevice for algae classification: a comparison of results from discriminant analysis and neural network pattern recognition" @default.
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- W2007169600 doi "https://doi.org/10.1117/12.907012" @default.
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