Probabilistic partial least squares regression for quantitative analysis of Raman spectra. (6th January 2015)

• Record Type: Journal Article
• Authors: Li, Shuo; Nyagilo, James O.; Dave, Digant P.; Wang, Wei; Zhang, Baoju; Gao, Jean
• Is Part Of: International journal of data mining and bioinformatics. Volume 11:Number 2(2015)
• Page Start: 223
• Page End: 243
• Publication Date: 2015-01-06
• Subjects: 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; Data mining -- Periodicals; Bioinformatics -- Periodicals; 006.312
• Languages: English
• ISSNs: 1748-5673

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.