Sandia Corporation

National Technology & Engineering Solutions of Sandia, LLC

Mark H Van Benthem

Michael R Keenan

Christopher Stork

James A Ohlhausen

Paul G Kotula

The present invention relates to methods for analyzing a chemical sample. For instance, the methods herein allow for global analysis of spectroscopy data in order to extract useful chemical properties from complicated multidimensional data. Such analysis can optionally employ data compression to further expedite computer-implemented computation. In particular, the methods herein provide global analysis of data matrices explained by both linear and non-linear terms.

Global analysis peak fitting for chemical spectroscopy data

The method of the present invention provides a fast, robust, and automated multivariate statistical analysis of gas chromatography/mass spectroscopy (GC/MS) data sets. The method can involve systematic elimination of undesired, saturated peak masses to yield data that follow a linear, additive model. The cleaned data can then be subjected to a combination of PCA and orthogonal factor rotation followed by refinement with MCR-ALS to yield highly interpretable results.

Method for factor analysis of GC/MS data

A fast and rigorous multivariate curve resolution (MCR) algorithm is applied to remotely sensed spectral data. The algorithm is applicable in the solar-reflective spectral region, comprising the visible to the shortwave infrared (ranging from approximately 0.4 to 2.5 &#x3bc;m), midwave infrared, and thermal emission spectral region, comprising the thermal infrared (ranging from approximately 8 to 15 &#x3bc;m). For example, employing minimal a priori knowledge, notably non-negativity constraints on the extracted endmember profiles and a constant abundance constraint for the atmospheric upwelling component, MCR can be used to successfully compensate thermal infrared hyperspectral images for atmospheric upwelling and, thereby, transmittance effects. Further, MCR can accurately estimate the relative spectral absorption coefficients and thermal contrast distribution of a gas plume component near the minimum detectable quantity.

Method to analyze remotely sensed spectral data

A fast combinatorial algorithm can significantly reduce the computational burden when solving general equality and inequality constrained least squares problems with large numbers of observation vectors. The combinatorial algorithm provides a mathematically rigorous solution and operates at great speed by reorganizing the calculations to take advantage of the combinatorial nature of the problems to be solved. The combinatorial algorithm exploits the structure that exists in large-scale problems in order to minimize the number of arithmetic operations required to obtain a solution.

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Middletown, DE, United States of America

Mark H Van Benthem