Using Multivariate Regression Methods to Resolve Overlapped Electrochemical Signals

This paper proposes the application of Gaussian process regression (GPR) as an alternative regression model to resolve the hard overlapped electrochemical signals belonging to the 2,4,6trichlorophenol/2,6-dichlorophenol (TCP/DCP) system. Gaussian process derives from the perspective of Bayesian non-parametric regression methods, in terms of the parameterization of the covariance function, results in its good performance for the development of a calibration model for both linear and non-linear data sets. The multivariate regression model developed by GPR was compared with some traditional regression methods such as partial least squares regression (PLSR), and support vector regression (SVR). The comparative results were satisfied. The satisfactory results obtained through GPR method suggest that it can be used as a more effective and promising tool for multivariate regression tasks than the others.