Abstract
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the Photonics West Conference : Microfluidics, BioMEMS, and Medical Microsystems X, 23 January 2012, San Francisco, California |
| Editors | H. Becker, B.L. Gray |
| Publisher | SPIE |
| Pages | 825104- |
| DOIs | |
| Publication status | Published - 2012 |
| Event | conference; Photonics West Conference: Microfluidics, BioMEMS, and Medical Microsystems X - Duration: 1 Jan 2012 → … |
Publication series
| Name | Proceedings of SPIE |
|---|---|
| Volume | 8251 |
| ISSN (Print) | 0277-786X |
Conference
| Conference | conference; Photonics West Conference: Microfluidics, BioMEMS, and Medical Microsystems X |
|---|---|
| Period | 1/01/12 → … |
| Other | Photonics West Conference: Microfluidics, BioMEMS, and Medical Microsystems X |
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Optofluidic microdevice for algae classification : a comparison of results from discriminant analysis and neural network pattern recognition. / Schaap, A.M.; Bellouard, Y.J.; Rohrlack, T.
Proceedings of the Photonics West Conference : Microfluidics, BioMEMS, and Medical Microsystems X, 23 January 2012, San Francisco, California. ed. / H. Becker; B.L. Gray. SPIE, 2012. p. 825104- (Proceedings of SPIE; Vol. 8251).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic
TY - GEN
T1 - Optofluidic microdevice for algae classification : a comparison of results from discriminant analysis and neural network pattern recognition
AU - Schaap, A.M.
AU - Bellouard, Y.J.
AU - Rohrlack, T.
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
U2 - 10.1117/12.907012
DO - 10.1117/12.907012
M3 - Conference contribution
T3 - Proceedings of SPIE
SP - 825104-
BT - Proceedings of the Photonics West Conference : Microfluidics, BioMEMS, and Medical Microsystems X, 23 January 2012, San Francisco, California
A2 - Becker, H.
A2 - Gray, B.L.
PB - SPIE
ER -