Variational Mode Decomposition Unfolded Extreme Learning Machine for Spectral Quantitative Analysis of Complex Samples.
Spectrochimica acta Part A, Molecular and biomolecular spectroscopy（2025）
School of Chemical Engineering and Technology
Abstract
Considering the advantages of variational mode decomposition (VMD) in mathematical decomposition and extreme learning machine (ELM) in data modeling, a new regression model named variational mode decomposition unfolded extreme learning machine (VMD-UELM) is introduced for spectral quantitative analysis of complex samples. Firstly, mode components (uk) are obtained by decomposing spectra in VMD. Then the mode components are unfolded into an extended matrix. Ultimately, a quantitative model is built between the matrix and the target values by ELM. Efficiency of VMD-UELM is validated by quantitative analysis of hemoglobin, diaromatics and Panax notoginseng (PN) in blood, fuel oil and adulterated herb datasets. Results show that VMD-UELM model demonstrates better or similar performance compared with partial least squares (PLS) and ELM. Therefore, VMD-UELM is an efficient approach for spectral quantitative analysis.
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