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A method of determining the properties of a sample from measured spectral data collected from the sample by performing a multivariate spectral analysis. The method can include: generating a two-dimensional matrix A containing measured spectral data; providing a weighted spectral data matrix D by per

A method of determining the properties of a sample from measured spectral data collected from the sample by performing a multivariate spectral analysis. The method can include: generating a two-dimensional matrix A containing measured spectral data; providing a weighted spectral data matrix D by performing a weighting operation on matrix A; factoring D into the product of two matrices, C and ST,by performing a constrained alternating least-squares analysis of D=CST,where C is a concentration intensity matrix and S is a spectral shapes matrix; unweighting C and S by applying the inverse of the weighting used previously; and determining the properties of the sample by inspecting C and S. This method can be used to analyze X-ray spectral data generated by operating a Scanning Electron Microscope (SEM) with an attached Energy Dispersive Spectrometer (EDS).

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A method of determining the properties of a sample from measured spectral data collected from the sample by performing a multivariate spectral analysis. The method can include: generating a two-dimensional matrix A containing measured spectral data; providing a weighted spectral data matrix D by per

A method of determining the properties of a sample from measured spectral data collected from the sample by performing a multivariate spectral analysis. The method can include: generating a two-dimensional matrix A containing measured spectral data; providing a weighted spectral data matrix D by performing a weighting operation on matrix A; factoring D into the product of two matrices, C and ST,by performing a constrained alternating least-squares analysis of D=CST,where C is a concentration intensity matrix and S is a spectral shapes matrix; unweighting C and S by applying the inverse of the weighting used previously; and determining the properties of the sample by inspecting C and S. This method can be used to analyze X-ray spectral data generated by operating a Scanning Electron Microscope (SEM) with an attached Energy Dispersive Spectrometer (EDS). method using the neural network approach", Chemometrics and Intelligent Laboratory Systems, 1999, pp. 135-150, vol. 49. R. R. Hashemi, et al., "Identifying and Testing of Signatures for Non-Volatile Biomolecules Using Tandem Mass Spectra", Sigbio newsletter, ACM Press, Dec. 1995, pp. 11-19, vol. 15, No. 3. I. Belic, et al., "Neural network methodologies for mass spectra recognition", Vacuum, 1997, pp. 633-637, vol. 48, Nos. 7-9. W. Werther, et al., "Classification of mass spectra, A comparison of yes/no classification methods for the recognition of simple structural properties", Chemometrics and Intelligent Laboratory Systems, 1994, pp. 63-76, vol. 22. A. Y. Cairns, et al., "Towards the Automated Prescreening of Breast X-Rays", Digest of the IEE Colloquium, Applications of Image Processing in Mass Health Screening, University of Dundee, pp. 1/1-1/5. M. Astion, et al., The Application of Backpropagation Neural Networks to Problems in Pathology and Laboratory Medicine, Arch Pathol Lab Med, Oct. 1992, pp. 995-1001, vol. 116. R. Goodacre, "Rapid identification of urinary tract infection bacteria using hyperspectral whole-organism fingerprinting and artificial neutral networks", Microbiology, 1998, pp. 1157-1170, vol. 144. J. Taylor, "The deconvolution of pyrolysis mass spectra using genetic programming: application to the identification of some Eubacterium species", FEMS Microbiology Letters, 1998, pp. 237-246, vol. 160. R. Goodacre, et al., Discrimination between methicillin-resistant and methicillin-susceptible Staphylococcus aureus using pyrolysis mass spectrometry and artificial neutral networks, Journal of Antimicrobial Chemotherapy, 1998, pp. 27-34, vol. 41. J. Chun, et al., "Long-term Identification of Streptomycetes Using Pyrolysis Mass Spectrometry and Artificial Neural N