Abstract: Spectra data collected from a mixture defines an n-dimensional data space (n is the number of data points), and application of PCA techniques yields a subset of m-eigenvectors that effectively describe all variance in that data space. Bach member of a library of known components is examined based by representing each library spectrum as a vector in the m-dimensional space. Target factor testing techniques yield an angle between this vector and the data space. Those library members that have the smallest angles are considered to be potential mixture members and are ranked accordingly. Every combination of the top y library members is considered as a potential solution and a multivariate least-squares solution is calculated using the mixture spectra for each of the potential solutions. A ranking algorithm is then applied and used to select the combination that is most likely the set of pure components in the mixture.

Abstract: A set of spectral data is collected from a mixture and corrected to remove instrumental artifacts. The collected mixture spectra define an n-dimensional data space, where n is the number of data points in the spectra. Principal component analysis (PCA) techniques are applied to the n-dimensional data space to generate and select a subset of m eigenvectors that effectively describe all variance in the original data space. The members of a spectral library of known, pure components are examined based on this reduced dimensionality data space using target factor testing techniques. Each library spectrum is represented as a vector in the m-dimensional reduced data space, and target factor testing results in an angle between the library vector and the data space for each spectral library member. Those spectral library members that have the smallest angles with the data space are considered to be potential members, or candidates, of the mixture and are submitted for further testing.

Abstract: The present invention is directed generally toward the field of spectral analysis and, more particularly, toward an improved method of identifying unknown components of a mixture from a set of spectra collected from the mixture using a spectral library including potential candidates. For example, the present method is directed to identifying components of a mixture by the steps which comprise obtaining a set of spectral data for the mixture that defines a mixture data space; ranking a plurality of library spectra of known elements according to their angle of projection into the mixture data space; calculating a corrected correlation coefficient for each combination of the top y ranked library spectra; and selecting the combination having the highest corrected correlation coefficient, wherein the known elements of the selected combination are identified as the components of the mixture.

Abstract: A method is disclosed to reduce the time needed to analyze data generated from very similar samples, emphasizing the differences obtained, from the combined technique of liquid chromatography/mass spectrometry, a technique used to analyze the composition of materials. The method quickly compares each mass chromatogram between similar samples and displays the limited number of mass chromatograms that are different to the analyst.

Abstract: A method of enhancing a series of images comprising the steps of: providing x digital image files, each image file including a matrix of n rows.times.m columns of pixels; producing a matrix of the x digital image files of x rows of m.times.n pixels per row, where each row is an unfolded image of an x digital image; splitting the produced matrix into two data sets A and B, each data set with an equal amount of images less than x; applying singular value decomposition (SVD) to data set A and limiting the SVD space to a number of expected components; calculating the projections A.sup.p and B.sup.p of data set A and data set B into the SVD space of A, with the eigenvalues of the SVD representing the projection of A; calculating the eigenvalues and eigenvectors of the product of A.sup.p and the inverse of B.sup.p ; and transforming the results of the latter calculation back to the original data set and calculating the resolved images and their contributions into the original images.

Abstract: A method of identifying and quantifying the chemical components of a mixture of organic material comprises subjecting the organic material to chromatography to separate the components of the mixture and subjecting the separated materials to spectrometry to detect and identify the components. A variable selection procedure is described that results in well resolved chromatography which facilitates the proper interpretation.

Abstract: An apparatus for analyzing spectral data of a mixture having a plurality of components by determining the pure variables, each of which has a contribution from only one of the mixture components, includes a programmed computer for performing steps of a) providing spectral data of the mixture, b) determining purity values of variables from said data to provide a first purity spectrum, c) finding the maximum of the purity values to determine the first pure variable, d) calculating a determinant-based weight function with respect to said first pure variable, e) multiplying the first purity spectrum by the determinant-based weight function to provide a second purity spectrum, f) finding the maximum in the second purity spectrum to yield the second pure variable, g) repeating steps d),e) and f) for successive determinant-based weight functions with respect to preceding pure variables to provide successive purity spectra from which successive pure variables are found.