Taxonomic Classification of Phytoplankton with Multivariate Optical Computing, Part I: Design and Theoretical Performance of Multivariate Optical Elements

Abstract

Phytoplankton are single-celled, photosynthetic algae and cyanobacteria found in all aquatic environments. Differential pigmentation between phytoplankton taxa allows use of fluorescence excitation spectroscopy for discrimination and classification. For this work, we applied multivariate optical computing (MOC) to emulate linear discriminant vectors of phytoplankton fluorescence excitation spectra by using a simple filter-fluorometer arrangement. We grew nutrient-replete cultures of three differently pigmented species: the coccolithophore Emiliania huxleyi, the diatom Thalassiosira pseudonana, and the cyanobacterium Synechococcus sp. Linear discriminant analysis (LDA) was used to determine a suitable set of linear discriminant functions for classification of these species over an optimal wavelength range. Multivariate optical elements (MOEs) were then designed to predict the linear discriminant scores for the same calibration spectra. The theoretical performance specifications of these MOEs are described.

Fluorescence Excitation Spectroscopy for Phytoplankton Species Classification Using an All-Pairs Method: Characterization of a System with Unexpectedly Low Rank

“…1). [27][28][29] This is a method of detecting the accessory pigments in phytoplankton, and is therefore a rough optical analog of pigment analysis via HPLC except that it can be performed rapidly on single cells. Our efforts can be divided into the tasks of: (1) recording sufficient spectra of different species; (2) data exploration to identify spectroscopic features or patterns that best differentiate species from one another; (3) selecting optical filter elements for these features; and (4) associating class information with responses on the sensors.…”

Host cell pigmentation in Scenedesmus dimorphus as a beacon for nascent parasite infection

“…Field deployable commercial spectrometers are readily available. We also envision that a non‐imaging, non‐spectral based, measurement could be developed using the spectrally separated Chl and carotenoid/autofluorescence emissions and standard filters or a specially designed multivariate optical element (Swanstrom et al, ). Further, as this is an optical approach, we hypothesize that remote sensing technologies (Reichardt et al, ) could monitor these spectral regions and this is an area of current investigation by our group.…”

Development ofIn SituSensors for Chlorophyll Concentration Measurement

“…Because of their size and power requirements, broadband lamps are seldom used for in situ measurement at present. However, some special applications such as single-cell fluorescence excitation spectroscopy systems [54] and multivariate optical computing instruments [55] require super high power Xe arc lamps (75 W).…”

“…Very recently, Swanstrom et al [96] developed a dynamicflow fluorescence imaging filter photometer, which uses an imaging CCD array to record fluorescence. Before this development, the theoretical characteristics of the designs were described in determining which optical elements were selected for fabrication [55]. The same researchers present a semiautomatic approach for extracting fluorescence intensities from the imaging photometer data along with a quantitative analysis of factors contributing to noise, including the detector read and dark noise.…”