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Stochastic Regression Modeling of Chemical Spectra

Published

Author(s)

Anthony J. Kearsley, William E. Wallace, Yutheeka Gadhyan

Abstract

A stochastic regression method has been developed that decomposes chemical spectra into separate contributions from signal and from noise. The numerical results of regressing in this way on sample spectra are presented. The results suggest that this strategy offers an effective and efficient framework for in-depth noise estimation and analysis. From this analysis more effective means of feature extraction in chemical spectra can be created.
Citation
Chemometrics and Intelligent Laboratory Systems

Keywords

Signal to noise, stochastic differential equation, nonparametric, estimation, mass spectrometry, spectroscopy

Citation

Kearsley, A. , Wallace, W. and Gadhyan, Y. (2014), Stochastic Regression Modeling of Chemical Spectra, Chemometrics and Intelligent Laboratory Systems, [online], https://doi.org/10.1016/j.chemolab.2014.08.002 (Accessed April 18, 2024)
Created August 14, 2014, Updated November 10, 2018