Miniature Stereo Spectral Imaging System for Multivariate Optical Computing

Priore, Ryan J.; Haibach, Frederick G.; Schiza, Maria V.; Greer, Ashley; Perkins, David L.; Myrick, Michael L.

doi:10.1366/0003702041389418

Cited by 6 publications

(2 citation statements)

References 14 publications

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“…Since the measurements being made are spectrally convoluted, the question remains of what the optimal single or set of filtering elements would be to achieve a particular purpose. As we found in our previous work with multivariate optical computing, − any sensing system that makes use of filtering elements arrives at much the same question. In addition, any optical or spectroscopic system for which multiple choices of components can be made faces a similar issue when the performance of the system as a whole is not a linear summation of the performances of individual elements.…”

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confidence: 77%

Multimode Imaging in the Thermal Infrared for Chemical Contrast Enhancement. Part 2: Simulation Driven Design

et al. 2010

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We present a simulation-driven process to design an infrared camera system that is tuned to specific analytes of interest based on "molecular factor computing". There are many factors involved in optimizing discrimination using optical filtering aids, including, but not limited to, the detector response, optical throughput of the system, optical properties of the samples, and optical properties of the materials for sensitizing films/filters. There are nearly infinite possible setups for the system, which means it is neither cost nor time efficient to physically test each one. In lieu of this, we developed routines in MATLAB (The Mathworks, Natick, MA) that simulate the camera output, per pixel, given specific conditions. Beginning with measured spectra of calibration samples or standards and using an objective function or figure of merit (FOM) to measure simulated performance, these routines evaluate large numbers of combinations of chemical films as filters for discrimination based on linear discriminant analysis (LDA). In this study, the FOM was the Fisher ratio between a neat fabric and one stained with either a polymer film or blood.

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confidence: 77%

Multimode Imaging in the Thermal Infrared for Chemical Contrast Enhancement. Part 2: Simulation Driven Design

et al. 2010

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“…Our previous studies showed that optical measurements based on spectral pattern analysis can be performed with MOC. 8,9 At the center of MOC is the design of interference filters that we call multivariate optical elements (MOEs). These interference filters have complex spectral transmission functions that make possible a variety of multivariate measurements.…”

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confidence: 99%

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

et al. 2013

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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.

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