Abstract

Identification and quantification of molecular species are central applications of molecular spectroscopy. In complex multicomponent systems like tissue samples, linear parametric models are often used to estimate the relative concentrations of the biochemical components of the sample. In situations where not all of the components of the sample are known or modeled, such parametric models can suffer from omitted variable bias and result in skewed estimates of component concentrations. We propose a semi-parametric approach that tries to avoid this omitted variable bias by effectively including unknown covariates as a non-parametric term in the regression equation. Constituent concentrations estimated with such partial linear models should outperform strict parametric linear models when the user has limited information on the composition of a multi-constituent system.

Constrained nonlinear method for estimating component spectra from multicomponent mixtures

Keiji Sasaki, Satoshi Kawata, and Shigeo Minami
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Multicomponent analysis using a confocal Raman microscope

Zhengyuan Tang, Sinead J. Barton, Tomas E. Ward, John P. Lowry, Michelle M. Doran, Hugh J. Byrne, and Bryan M. Hennelly
Appl. Opt. 57(22) E118-E130 (2018)

Simple parametric model for intensity calibration of Cassini composite infrared spectrometer data

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Spectral reflectance estimation from camera responses by support vector regression and a composite model

Wei-Feng Zhang and Dao-Qing Dai
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Raman active components of skin cancer

Xu Feng, Austin J Moy, Hieu T. M. Nguyen, Jason Zhang, Matthew C. Fox, Katherine R. Sebastian, Jason S. Reichenberg, Mia K. Markey, and James W. Tunnell
Biomed. Opt. Express 8(6) 2835-2850 (2017)