title: High and low frequency unfolded partial least squares regression based on empirical mode decomposition for quantitative analysis of fuel oil samples.
abstract: Accurate prediction of the model is fundamental to the successful analysis of complex samples. To utilize abundant information embedded over frequency and time domains, a novel regression model is presented for quantitative analysis of hydrocarbon contents in the fuel oil samples. The proposed method named as high and low frequency unfolded PLSR (HLUPLSR), which integrates empirical mode decomposition (EMD) and unfolded strategy with partial least squares regression (PLSR). In the proposed method, the original signals are firstly decomposed into a finite number of intrinsic mode functions (IMFs) and a residue by EMD. Secondly, the former high frequency IMFs are summed as a high frequency matrix and the latter IMFs and residue are summed as a low frequency matrix. Finally, the two matrices are unfolded to an extended matrix in variable dimension, and then the PLSR model is built between the extended matrix and the target values. Coupled with Ultraviolet (UV) spectroscopy, HLUPLSR has been applied to determine hydrocarbon contents of light gas oil and diesel fuels samples. Comparing with single PLSR and other signal processing techniques, the proposed method shows superiority in prediction ability and better model interpretation. Therefore, HLUPLSR method provides a promising tool for quantitative analysis of complex samples.
authors.affiliation: State Key Laboratory of Separation Membranes and Membrane Processes, Tianjin Polytechnic University, Tianjin, PR China; School of Environmental and Chemical Engineering, Tianjin Polytechnic University, Tianjin, PR China. Electronic address: bianxihui@163.com.
authors.forename: Xihui
authors.initials: X
authors.lastname: Bian
authors.affiliation: School of Environmental and Chemical Engineering, Tianjin Polytechnic University, Tianjin, PR China.
authors.forename: Shujuan
authors.initials: S
authors.lastname: Li
authors.affiliation: State Key Laboratory of Separation Membranes and Membrane Processes, Tianjin Polytechnic University, Tianjin, PR China.
authors.forename: Ligang
authors.initials: L
authors.lastname: Lin
authors.affiliation: State Key Laboratory of Separation Membranes and Membrane Processes, Tianjin Polytechnic University, Tianjin, PR China.
authors.forename: Xiaoyao
authors.initials: X
authors.lastname: Tan
authors.affiliation: Tianjin Green Security Technology Co. Ltd, Tianjin, PR China.
authors.forename: Qingjie
authors.initials: Q
authors.lastname: Fan
authors.affiliation: School of Environmental and Chemical Engineering, Tianjin Polytechnic University, Tianjin, PR China.
authors.forename: Ming
authors.initials: M
authors.lastname: Li
keywords: Complex sample analysis; Empirical mode decomposition; Ensemble modeling; Partial least squares regression; Unfolded strategy