Taxonomic Classification of Phytoplankton with Multivariate Optical Computing, Part II: Design and Experimental Protocol of a Shipboard Fluorescence Imaging Photometer

Abstract

Differential pigmentation between phytoplankton allows use of fluorescence excitation spectroscopy for the discrimination and classification of different taxa. Here, we describe the design and performance of a fluorescence imaging photometer that exploits taxonomic differences for discrimination and classification. The fluorescence imaging photometer works by illuminating individual phytoplankton cells through an asynchronous spinning filter wheel, which produces bar code-like streaks in a fluorescence image. A filter position is covered with an opaque filter to create a reference dark position in the filter wheel rotation that is used to match each fluorescence streak with the corresponding filter. Fluorescence intensities of the imaged streaks are then analyzed for the purpose of spectral analysis, which allows taxonomic classification of the organism that produced the streaks. The theoretical performance and signal-to-noise ratio (SNR) specifications of these MOEs are described in Part I of this series. This report describes optical layout, flow cell design, magnification, depth of field, constraints on filter wheel and flow velocities, procedures for blank subtraction and flat-field correction, the measurement scheme of the instrument, and measurement of SNR as a measurement of filter wheel frequency. This is followed by an analysis of the sources of variance in measurements made by the photometer on the coccolithophore Emiliania huxleyi. We conclude that the SNR of E. huxleyi measurements is not limited by the sensitivity or noise attributes of the measurement system, but by dynamics in the fluorescence efficiency of the E. huxleyi cells. Even so, the minimum SNR requirements given in Part I for the instrument are met.

Asymmetric Versus Symmetric Filter Wheels and Associated Processing Algorithms: Results from Asynchronous Fluorescence Imaging Photometer Measurements of Phytoplankton

“…Previous work from our laboratory indicated a source of dynamic variability in phytoplankton signals whose origin was unclear. 2 This dynamic variability leads to increased ''noise'' in measurement ratios as the individual signals are measured further apart in time. The final motivation for redesigning the filter wheel was to reduce the time between filter pair measurements without changing the filter wheel speed and signal intensities, with the aim of reducing the apparent noise.…”

“…The original work reported in Swanstrom et al used only the conventional six-position filter wheel design with a blocker and the older image processing algorithm described there. 2 The asymmetric filter wheel data cannot be processed with that original algorithm because of the need for streak deconvolution, thus the newer Table II. Standard deviation and sample sizes for distant and adjacent streak ratio distributions generated using the image processing algorithm, described by Pearl et al, 3 and a novel image processing algorithm on a set of 2000 fluorescence imaging photometer images of Thalassiosira pseudonana.…”

“…In past reports, our lab has described the use of a filter wheel on the excitation arm of an asynchronous fluorescence imaging photometer (FIP) for classification of phytoplankton cells. [1][2][3][4] The asynchronous character of the measurements resulted from the fact that phytoplankton entered the field of view of the instrument at random times, so there was no exact correlation between the appearance of a phytoplankton cell and the position of the filter wheel. Instead, the position of the filter wheel had to be inferred from measurements of the phytoplankton fluorescence recorded on a single charge-coupled device (CCD) camera frame as the cell passed through the field of view of the camera.…”

Development ofIn SituSensors for Chlorophyll Concentration Measurement

“…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].…”

Filter design for molecular factor computing using wavelet functions

“…Nelson et al designed a filter for multivariate optical computing (MOC) based on the loadings from principal component analysis (PCA), and applied it in the quantitative analysis of ethanol in water [6]. Then, the MOC filters based on the loading of linear discriminant analysis (LDA) were designed and used for classification of phytoplankton [7][8][9]. Unlike the molecular filters, MOC filters were made by depositing metallic oxides to a glass substrate [10][11][12][13].…”