Estimation of water interference for spectral correction

Inventors

Judge, Kevin • Andersson, Greger • Zou, Peng

Assignees

Smiths Detection Inc

Abstract

A method includes decomposing a training set to obtain a principal component matrix having a plurality of principal component vectors. The method also includes variably rejecting portions of a sample spectrum vector that do not correspond to a selected one of the plurality of principal component vectors by incrementally providing a coefficient indicative of the weighting of the selected principal component vector for selected sub-regions. A corrected spectrum vector can be obtained by excluding certain sub-regions of the sample spectrum vector and corresponding principal component vector, multiplying the sample spectrum vector with the principal component matrix for non-excluded sub-regions, providing a predicted interference vector, and subtracting the predicted interference vector from the sample spectrum vector.

Core Innovation

The invention relates to estimating and correcting FTIR vapor-phase water interference in optical spectrometry by decomposing a training set of water vapor spectral data using principal component analysis (PCA) to obtain a principal component matrix with a plurality of principal component vectors. The training set corresponds to measured spectra of water vapor under at least one of different temperatures or different pressures.

For a sample spectrum vector generated for a sample mixture, the invention incrementally processes the spectrum using sub-regions and corresponding sub-regions of a selected principal component vector. It computes incrementally provided coefficients indicative of the weighting of the selected principal component vector for the selected sub-regions, variably rejecting sub-regions that do not correspond to the selected principal component vector based on the incrementally provided coefficients, and then recomputes a weighting vector using the sample spectrum vector with the principal component matrix for non-excluded sub-regions.

The invention multiplies the weighting vector by the principal component matrix to provide a predicted interference vector corresponding to water vapor interference masking a presence or an absence of one or more chemicals of interest in the sample mixture. It subtracts the predicted interference vector from the sample spectrum vector to provide a corrected spectrum vector, then compares the corrected spectrum vector to known spectrum data for one or more chemicals of interest to determine whether the corrected spectrum vector is representative of a chemical of interest, including triggering visual or audible indicia if a match is found.

Claims Coverage

The independent claim coverage includes three independent claims that share the same PCA-based framework for estimating a predicted interference vector from an optical spectrometry sample spectrum, subtracting it to form a corrected spectrum, and comparing to known spectrum data; the coverage contains a total of nine main inventive features across the independent claims, with one additional output/activation feature in one independent claim and a computer-readable storage medium form in another.

Across the independent claims, the method and medium use PCA on water vapor training spectra (under different temperatures or different pressures), incrementally compute sub-region coefficients to variably exclude non-corresponding sub-regions, compute a weighting vector and predicted interference vector, subtract the predicted interference vector to obtain a corrected spectrum vector, compare the corrected spectrum vector to known spectrum data for chemicals of interest, and (in one independent claim) activate visual or audible indicia when the corrected spectrum is representative.

Stated Advantages

Documented Applications