Title: Probabilistic partial least squares regression for quantitative analysis of Raman spectra

Authors: Shuo Li; James O. Nyagilo; Digant P. Dave; Wei Wang; Baoju Zhang; Jean Gao

Addresses: Computer Science and Engineering Department, The University of Texas at Arlington, Arlington, Texas 76013, USA ' Bioengineering Department, The University of Texas at Arlington, Arlington, Texas 76013, USA ' Bioengineering Department, The University of Texas at Arlington, Arlington, Texas 76013, USA ' College of Physics and Information Science, Tianjin Normal University, Tianjin 300387, China ' College of Physics and Information Science, Tianjin Normal University, Tianjin 300387, China ' Computer Science and Engineering Department, The University of Texas at Arlington, Arlington, Texas 76013, USA

Abstract: With the latest development of Surface-Enhanced Raman Scattering (SERS) technique, quantitative analysis of Raman spectra has shown the potential and promising trend of development in vivo molecular imaging. Partial Least Squares Regression (PLSR) is state-of-the-art method. But it only relies on training samples, which makes it difficult to incorporate complex domain knowledge. Based on probabilistic Principal Component Analysis (PCA) and probabilistic curve fitting idea, we propose a probabilistic PLSR (PPLSR) model and an Estimation Maximisation (EM) algorithm for estimating parameters. This model explains PLSR from a probabilistic viewpoint, describes its essential meaning and provides a foundation to develop future Bayesian nonparametrics models. Two real Raman spectra datasets were used to evaluate this model, and experimental results show its effectiveness.

Keywords: partial least squares; probabilistic PLS regression; SERS; surface-enhanced Raman scattering; Raman spectroscopy; quantitative analysis; bioinformatics; Raman spectra; in vivo molecular imaging; principal component analysis; probabilistic PCA; probabilistic curve fitting; parameter estimation; Bayesian nonparametric models.

DOI: 10.1504/IJDMB.2015.066768

International Journal of Data Mining and Bioinformatics, 2015 Vol.11 No.2, pp.223 - 243

Received: 09 Aug 2012
Accepted: 20 Aug 2012
Published online: 05 Jan 2015 *

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