Date of Award
Campus Access Dissertation
Chemistry and Biochemistry
Michael L Myrick
Phytoplankton biomass is highly variable over space and time. The development of sensors for the in situ discrimination of phytoplankton size and community composition is necessary to better understand the oceanic carbon cycle. Our research focuses on developing instrumentation to classify single-cell phytoplankton cells in situ. Excitation spectra are collected using a single-cell optical trapping instrument built in house. Linear discriminant analysis (LDA) has been used to classify individual phytoplankton cells based on the fluorescence excitation spectra for individual cells in the wavelength range 350-650 nm. Interference filters called multivariate optical elements (MOEs), are fabricated based on the linear discriminant analysis results of the single-cell spectra. The transmission spectrum of each MOE mimics the performance of one linear discriminant function required for classification of a phytoplankton species. Target cells are excited with a light source filtered through a spinning series of MOEs that is incorporated into an imaging multivariate optical computing (IMOC) system. IMOC uses fluorescence excitation spectral information combined with optical discriminant analysis to identify different phytoplankton taxa. The fluorescence response of the target cells produces a classifying ''bar code' in a camera image. A set of MOEs has been fabricated that successfully classifies the three different phytoplankton species, six different phytoplankton species, and a set that indicates nitrogen status of phytoplankton species. The optical system used for experimental confirmation of the modeling will be illustrated and the classification of the phytoplankton cells will be shown.
Hill, L. S.(2011). System Design, Construction, Implementation, and Validation For Rapid Single-Cell Classification Using Imaging Multivariate Optical Computing. (Doctoral dissertation). Retrieved from https://scholarcommons.sc.edu/etd/687