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.

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.

A new Matlab-based software suite called Tilt-A-Whirl has been applied to XRD data from textured gold films electro-deposited onto nickel substrates. The software routines facilitate phase identification, texture analysis via pole figure visualization, and macrostrain determination. The use of principal component analysis with multivariate curve resolution (PCA/MCR) revealed the extraction of texture components. The unusual hardness properties of one Au film (deposited from a 30% gold depleted BDT-200 bath) were found to be dependent on the (210) out-of-plane preferred orientation of the polycrystalline gold film. The progressive nucleation of Au crystallites during electro-plating has been tied to improved hardness properties of this film.

We describe the automatic analysis of fluorescence tracks of phytoplankton recorded with a fluorescence imaging photometer. The optical components and construction of the photometer were described in Part I and Part II of this series in this issue. An algorithm first isolates tracks corresponding to a single phytoplankter transit in the nominal focal plane of a flow cell. Then, the fluorescence streaks in the track that correspond to individual optical elements on the filter wheel are identified. The fluorescence intensity of each streak is integrated and used to calculate ratios. This approach was tested using 853 fluorescence measurements of the coccolithophore Emiliania huxleyi and the diatom Thalassiosira pseudonana. Average intensity ratios for the two classes closely follow those predicted in Part I of this series, with a distribution of ratios in each class that is consistent with the signal-to-noise ratio calculations in Part II for single cells. No overlap of the two class ratios was observed, yielding perfect classification.

Multivariate optical computing (MOC) is a compressed sensing technique with the ability to provide accurate spectroscopic compositional analysis in a variety of different applications to multiple industries. Indeed, recent developments have demonstrated the successful deployment of MOC sensors in downhole/well-logging environments to interrogate the composition of hydrocarbon and other chemical constituents in oil and gas reservoirs. However, new challenges have necessitated sensors that operate at high temperatures and pressures (up to 230°C and 138 MPa) as well as even smaller areas that require the miniaturization of their physical footprint. To this end, this paper details the design, fabrication, and testing of a novel miniature-sized MOC sensor suited for harsh environments. A micrometer-sized optical element provides the active spectroscopic analysis. The resulting MOC sensor is no larger than two standard AAA batteries yet is capable of operating in high temperature and pressure conditions while providing accurate spectroscopic compositional analysis comparable to a laboratory Fourier transform infrared spectrometer.