Weighted multiscale support vector regression for fast quantification of vegetable oils in edible blend oil by ultraviolet-visible spectroscopy
writer：Xinyan Wu, Xihui Bian, En Lin, Haitao Wang, Yugao Guo, Xiaoyao Tan
keywords：Empirical mode decomposition, Ensemble modeling, Support vector regression, Edible blend oil analysis
source：期刊
specific source：Food Chemistry, 2021, 342, 128245
Issue time：2021年
Weighted multiscale support vector regression combined with ultraviolet-visible (UV-Vis) spectra for quantitative analysis of edible blend oil is proposed. In the approach, the UV-Vis spectra of the training set are decomposed into a certain number of intrinsic mode functions (IMFs) and a residual by empirical mode decomposition (EMD) at first, then support vector regression (SVR) sub-models are built on each IMF and residual. For prediction set, the spectral are decomposed as the training set and the final predictions are obtained by integrating SVR sub-model predictions by weighted average. The weight of the sub-model is the reciprocal of the fourth power of the root mean square error of cross-validation (RMSECV). For prediction peanut oil in binary blend oil and sesame oil in ternary blend oil, the proposed method has superiority in root mean square error of prediction (RMSEP) and correlation coefficient (R) compared with SVR and partial least squares regression (PLSR).