Abstract: An improved regression-based qualitative analysis algorithm useful when the mixture to be analyzed contains a compound not in the library spectra, a so-called unknown. A regression of a measured spectrum is computed against the library spectra. This regression is referred to as a “master” regression. Estimated mixing coefficients for an estimated spectrum are computed from the regression. Next, a residual error is computed between the estimated spectrum and the measured spectrum. Peaks in the residual error are identified that extend in a direction opposite to that of peaks in the measured spectrum. These peaks are referred to as “negative” peaks. A regression is performed on the peaks. This is referred to as a “retro-regression” to be distinguished from the master regression performed on the measured spectrum. Using information from the retro-regression, corrected mixing coefficients are computed and the process repeats.

Abstract: A method and apparatus for performing continuous variance analysis (CVA) to characterize a data set. Data set values may be associated with any source, including measurements of a received signal and/or measurements of natural and/or man-made phenomena. CVA generates an output matrix that contains a measure of variation for a plurality of ranges (or windows) of data elements within a data set positioned at known locations within the data set. CVA output can be interpreted visually by a technician and/or using automatic numerical analysis. CVA is compatible with any apparatus/approach that uses numerical analysis to generate a predicted model based upon stored library models and/or linear/nonlinear components. CVA is compatible with any programming language and can be readily added to new and/or existing apparatus to compliment existing capabilities. CVA is less complex than conventional techniques, and requires less computer processing capacity, yet results in more readily interpretable results.