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Abstract

Raw data inputs are treated as independent signal sources to reduce computational lag without adversely affecting signal-to-noise ratio (SNR). Applications include spectroscopy, multiple linear regression, mass balance quantitation and the calculation of physical properties. The input-specific averaging has been applied to Raman spectroscopy, where the inputs are averaged spectra from which peak heights or areas are obtained from integration. Alternatively, peak areas or heights can be obtained from unaveraged spectra and are then averaged before use in further calculations as inputs to produce a desired output. The output(s) are linear or nonlinear combinations of the peak heights or areas, coupled with weighting factors which relate the raw inputs to a quantitative output such as concentration of a chemical species. Each specific input can use a different type of averaging. The overall goal may be optimization for best precision, and/or optimization for minimum lag time.

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First Claim