Michael Simcock

Tuning D* with modified thermal detectors

We report on the fabrication and characterization of a modified thermopile detector that has a spectral detectivity, D*, primarily determined by the absorbance of a polymer film. This was done by coating the detector with a metal mirror, followed by the polymer film, so that the film absorbances are responsible for most thermal conversion. The detector is designed to tailor the spectral response of optical systems more specifically to analytes in order to improve precision in methods such as multivariate optical computing and simple photometry. Interference effects in the thin-film response are eliminated by the textured surface of the silicon thermopile, which makes the spectral response relatively simple. The maximum detectivity due to a 1-micrometer-thick film is found to be 20% of the detectivity of the original wide-band detector at 10 Hz modulation frequency. We estimate the thermal diffusion length in the polymer at 10 Hz to be 40 micrometers. We also suggest that the detectivity of the modified detector can be approximated as the product of the D* of the underlying thermal detector and the absorbance of the modifying film, provided the modulation frequency is low and interference effects are defeated.

Precision in imaging multivariate optical computing

Multivariate optical computing (MOC) is a method of performing chemical analysis using a multilayer thin-film structure known as a multivariate optical element (MOE). Recently we have been advancing MOC for imaging problems by using an imaging MOE (IMOE) in a normal-incidence geometry and employing normalization by the 1-norm. There are several important differences between the previously described 45 degree and the normal-incidence imaging, one of which is the measurement precision due to photon counting. We compare this precision to 45 degree MOC. We also discuss how MOE models with similar values of standard errors of calibration and prediction and similar gain values may vary in precision because of the sign or offset of the regression vector encoded in the IMOE spectrum. Experimental verification of a key result is provided by near-infrared imaging of slides coated with a dye-doped polymer film.

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

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.