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Multivariate optical computing, also known as molecular factor computing, is an approach to the development of compressed sensing spectroscopic instruments, particularly for industrial applications such as process analytical support. "Conventional" spectroscopic methods often employ multivariate and chemometric methods, such as multivariate calibration, pattern recognition, and classification, to extract 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 and Raman].
The "software" in multivariate optical computing is encoded directly into an optical element spectral calculation engine such as an interference filter based multivariate optical element , holographic grating, liquid crystal tunable filter, spatial light modulator , or digital micromirror device and is specific to the particular application. The optical pattern for the spectral calculation engine is designed for the specific purpose of measuring the magnitude of that 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 real time, in-line process control instruments.
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