title: Probabilistic partial least squares regression for quantitative analysis of Raman spectra.
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.
authors.forename: Shuo
authors.initials: S
authors.lastname: Li
authors.forename: James O
authors.initials: JO
authors.lastname: Nyagilo
authors.forename: Digant P
authors.initials: DP
authors.lastname: Dave
authors.forename: Wei
authors.initials: W
authors.lastname: Wang
authors.forename: Baoju
authors.initials: B
authors.lastname: Zhang
authors.forename: Jean
authors.initials: J
authors.lastname: Gao
mesh_terms: D000465:Algorithms; D045424:Complex Mixtures / Q000032:analysis* / Q000737:chemistry*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D016018:Least-Squares Analysis; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013059:Spectrum Analysis, Raman / Q000379:methods*
chemical_list: D045424:Complex Mixtures