Variational mode decomposition weighted multiscale support vector regression for spectral determination of rapeseed oil and rhizoma alpiniae offcinarum adulterants
writer：Xihui Bian*, Deyun Wu, Kui Zhang, Peng Liu, Huibing Shi, Xiaoyao Tan, Zhigang Wang
keywords：Variational mode decomposition, Support vector regression, Adulteration, Quality control, Chemometrics
source：期刊
specific source：Biosensors, 2022, 12, 586
Issue time：2022年
The accurate prediction of the model is essential for food and herb analysis. In order to exploit the abundance of information embedded in the frequency and time domains, a weighted multiscale support vector regression (SVR) method based on variational mode decomposition (VMD), namely VMD-WMSVR, was proposed for the ultraviolet-visible (UV-Vis) spectral determination of rapeseed oil adulterants and near-infrared (NIR) spectral quantification of rhizoma alpiniae offci narum adulterants. In this method, each spectrum is decomposed into K discrete mode components by VMD first. The mode matrix Uk is recombined from the decomposed components, and then, the SVR is used to build sub-models between each Uk and target value. The final prediction is obtained by integrating the predictions of the sub-models by weighted average. The performance of the proposed method was tested with two spectral datasets of adulterated vegetable oils and herbs. Compared with the results from partial least squares (PLS) and SVR, VMD-WMSVR shows potential in model accuracy.