- Multivariate optical computing
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Multivariate Optical Computing is an approach to the development of spectroscopic instruments, particularly for industrial applications such as process analytical support. "Conventional" spectroscopic methods often employ multivariate methods to extract the concentration (or other analytical information) from data collected at many different wavelengths. Multivariate optical computing uses an optical computer to analyze the data as it is collected. The goal of this approach is to produce instruments which are simple and rugged, yet retain the benefits of multivariate techniques for the accuracy and precision of the result.
An instrument which implements this approach may be described as a multivariate optical computer. Since it describes an approach, rather than any specific wavelength range, multivariate optical computers may be built using a variety of different instruments (including FTIR[1] and Raman[2]).
The "software" in multivariate optical computing is a Multivariate optical element (MOE) which is specific to the particular application. The MOE is designed for the specific purpose of measuring the magnitude of a multi-wavelength pattern in the spectrum of a sample, without actually measuring a spectrum.
Multivariate Optical Computing allows instruments to be made with the mathematics of pattern recognition designed directly into an optical computer, which extracts information from light without recording a spectrum. This makes it possible to achieve the speed, dependability, and ruggedness necessary for realtime, in-line process control instruments.
References
- ^ Myrick, Michael L.; Haibach, Frederick G. (2004-04-01), "Precision in Multivariate Optical Computing", Applied Optics 43 (10): 2130–2140, doi:10.1364/AO.43.002130, PMID 15074423, http://ao.osa.org/abstract.cfm?id=79362, retrieved 2006-12-18
- ^ Nelson, MP; Aust, JF; Dobrowolski, JA; Verly, PG; Myrick, Michael L. (1998), "Multivariate optical computation for predictive spectroscopy", Analytical Chemistry 70 (1): 73–82, doi:10.1021/ac970791w
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