This article includes a list of references, related reading, or external links, but its sources remain unclear because it lacks inline citations. Please help improve this article by introducing more precise citations.(April 2009) (Learn how and when to remove this message)
A multivariate optical element (MOE), is the key part of a multivariate optical computer; an alternative to conventional spectrometry for the chemical analysis of materials.
It is helpful to understand how light is processed in a multivariate optical computer, as compared to how it is processed in a spectrometer. For example, if we are studying the composition of a powder mixture using diffuse reflectance, a suitable light source is directed at the powder mixture and light is collected, usually with a lens, after it has scattered from the powder surface. Light entering a spectrometer first strikes a device (either a grating or interferometer) that separates light of different wavelengths to be measured. A series of independent measurements is used to estimate the full spectrum of the mixture, and the spectrometer renders a measurement of the spectral intensity at many wavelengths. Multivariate statistics can then be applied to the spectrum produced.
In contrast, when using multivariate optical computing, the light entering the instrument strikes an application specific multivariate optical element, which is uniquely tuned to the pattern that needs to be measured using multivariate analysis.
This system can produce the same result that multivariate analysis of a spectrum would produce. Thus, it can generally produce the same accuracy as laboratory grade spectroscopic systems, but with the fast speed inherent with a pure, passive, optical computer. The multivariate optical computer makes use of optical computing to realize the performance of a full spectroscopic system using traditional multivariate analysis. A side benefit is that the throughput and efficiency of the system is higher than conventional spectrometers, which increases the speed of analysis by orders of magnitude.
While each chemical problem presents its own unique challenges and opportunities, the design of a system for a specific analysis is complex and requires the assembly of several pieces of a spectroscopic puzzle. The data necessary for a successful design are spectral characteristics of light sources, detectors and a variety of optics to be used in the final assemblage, dispersion characteristics of the materials used in the wavelength range of interest, and a set of calibrated sample spectra for pattern-recognition-based analysis. With these pieces assembled, suitable application specific multivariate optical computer designs can be generated and the performance accurately modeled and predicted.
and 25 Related for: Multivariate optical element information
(FTIR) and Raman). The "software" in multivariateoptical computing is encoded directly into an opticalelement spectral calculation engine such as an...
devices based on MultivariateOptical Computing have been demonstrated. These devices have been based on the MultivariateOpticalElement spectral calculation...
absorption of gamma rays. See also Mössbauer effect. Multivariateoptical computing is an all optical compressed sensing technique, generally used in harsh...
digital certificates for secure communications Correspondence analysis, a multivariate statistical technique Cultural algorithm, a type of evolutionary computation...
measurements, a sum signal from several analytes is measured which means that multivariate data analyses such as neural networks have to be used for quantification...
of the data matrix. PCA is the simplest of the true eigenvector-based multivariate analyses and is closely related to factor analysis. Factor analysis typically...
technique for real-time, in situ biochemical characterization of wounds. Multivariate analysis of Raman spectra has enabled development of a quantitative measure...
Survey Sampling theory Statistical modelling Biostatistics Epidemiology Multivariate analysis Structural equation model Time series Reliability theory Quality...
component analysis (ICA) is a computational method for separating a multivariate signal into additive subcomponents. This is done by assuming that at...
libraries, but more recently practically all data is stored on hard drives or optical discs. However, in contrast to paper, these storage devices may become...
1997). "Risks of the minimal access approach for laparoscopic surgery: multivariate analysis of morbidity related to umbilical trocar insertion". World Journal...
Aomoto and independently by Ribando. It expresses them as an infinite multivariate Taylor series: Ω = Ω d | det ( V ) | ( 4 π ) d / 2 ∑ a → ∈ N 0 ( d 2...
terms Knuth–Bendix completion algorithm: for rewriting rule systems Multivariate division algorithm: for polynomials in several indeterminates Pollard's...
series. Expressed as optical power, two thin lenses of optical powers P1 and P2 in series is equivalent to two thin lenses of optical power Pam, their arithmetic...
effect, Anderson localization, quantum dots, and superconductors In multivariate statistics, random matrices were introduced by John Wishart, who sought...
network with optical components. Dynamic neural networks address nonlinear multivariate behaviour and include (learning of) time-dependent behaviour, such as...
"Graphical exploration of gene expression data: a comparative study of three multivariate methods". Biometrics. 59 (4): 1131–1139. CiteSeerX 10.1.1.730.3670. doi:10...
many losses would, therefore, be charged twice as much. More complex multivariate analyses are sometimes used when multiple characteristics are involved...
similar contexts. LSI is also an application of correspondence analysis, a multivariate statistical technique developed by Jean-Paul Benzécri in the early 1970s...
spectroscopy (FTIR) secondary structure deconvolution is also used for multivariate analysis techniques including singular value decomposition, partial least...
performing shallow network. For instance, it was proved that sparse multivariate polynomials are exponentially easier to approximate with DNNs than with...
are usually excited by light. Non-Polar Optical: Energy is exchanged with an optical mode. Non-polar optical phonons must generally be considered in covalent...
more overlapped than for the fundamental bands seen in the MIR. Often, multivariate methods are used to separate spectral signatures of sample components...