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Frederick G Haibach

Publications and source records attributed to Frederick G Haibach.

4 recordsLinked to original sources
PubMed2004

Precision in multivariate optical computing.

Multivariate optical computing (MOC) is an instrumentation design concept for optically demultiplexing the spectroscopic signals in radiometric measurements. The advantages of optically demultiplexing are improved precision, optical throughput, improved reliability, and reduced cost of instrumentation. Conceptually, the instrument implements a multivariate regression vector whose dot product with the spectrum yields a single value related to a spectroscopically active physical property of interest. Instrumentation designs for implementing MOC are diverse, and there has been no systematic comparison of the performance of these designs. This report develops a general expression for comparing the precision of the different instrumentation designs of MOC. Additionally, an expression is given for the transition from low- to high-signal-limited performance of MOC instrumentation. These two general expressions are applied to the traditional multivariate analysis and five examples of MOC.

Journal Article↗
PubMed2003

On-line reoptimization of filter designs for multivariate optical elements.

An automated method for producing multivariate optical element (MOE) interference filters that are robust to errors in the reactive magnetron sputtering process is described. Reactive magnetron sputtering produces films of excellent thickness and uniformity. However, small changes in the thickness of individual layers can have severe adverse effects on the predictive ability of the MOE. Adaptive reoptimization of the filter design during the deposition process can maintain the predictive ability of the final filter by changing the thickness of the undeposited layers to compensate for the errors in deposition. The merit function used, the standard error of calibration, is fundamentally different from the standard spectrum matching. This new merit function allows large changes in the transmission spectrum of the filter to maintain performance.

Journal Article↗
PubMed2002

Design of angle-tolerant multivariate optical elements for chemical imaging.

Multivariate optical elements (MOEs) are multilayer optical interference coatings with arbitrary spectral profiles that are used in multivariate pattern recognition to perform the task of projecting magnitudes of special basis functions (regression vectors) out of optical spectra. Because MOEs depend on optical interference effects, their performance is sensitive to the angle of incidence of incident light. This angle dependence complicates their use in imaging applications. We report a method for the design of angle-insensitive MOEs based on modification of a previously described nonlinear optimization algorithm. This algorithm operates when the effects of deviant angles of incidence are simulated prior to optimization, which treats the angular deviation as an interferent in the measurement. To demonstrate the algorithm, a 13-layer imaging MOE (IMOE, with alternating layers of high-index Nb2O5 and low-index SiO2) for the determination of Bismarck Brown dye in mixtures of Bismarck Brown and Crystal Violet, was designed and its performance simulated. For angles of incidence that range from 42 degrees to 48 degrees, the IMOE has an average standard error of prediction (SEP) of 0.55 microM for Bismarck Brown. This compares with a SEP of 2.8 microM for a MOE designed by a fixed-angle algorithm.

Journal Article↗