High and low frequency unfolded partial least squares regression based on empirical mode decomposition for quantitative analysis of fuel oil samples
作者：Xihui Bian*, Shujuan Li, Ligang Lin, Xiaoyao Tan, QingjieFan, Ming Li
關鍵字：Empirical mode decomposition, Unfolded strategy, Partial least squares regression, Ensemble modeling, Complex sample analysis
論文來源：期刊
具體來源：Analytica Chimica Acta, 2016, 925, 16-22
發表時間：2016年
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.