Weighted multiscale support vector regression for fast quantification of vegetable oils in edible blend oil by ultraviolet-visible spectroscopy

• Published in: Food chemistry 2021-04, Vol.342, p.128245-128245, Article 128245
• Main Authors: Wu, Xinyan, Bian, Xihui, Lin, En, Wang, Haitao, Guo, Yugao, Tan, Xiaoyao
• Format: Article
• Language: English
• Subjects: Data Analysis; Edible blend oil analysis; Empirical mode decomposition; Ensemble modeling; Informatics - methods; Least-Squares Analysis; Plant Oils - chemistry; Spectrophotometry, Ultraviolet; Support Vector Machine; Support vector regression; Time Factors
• ISSN: 0308-8146; 1873-7072
• DOI: 10.1016/j.foodchem.2020.128245

•Weighted multiscale support vector regression was proposed for fast quantification of binary and ternary edible blend oil.•UV–Vis spectra were decomposed into intrinsic mode functions (IMFs) by empirical mode decomposition (EMD).•The proposed method has superiority compared with support vector regression (SVR) and partial least squares (PLS). Weighted multiscale support vector regression combined with ultraviolet–visible (UV–Vis) spectra for quantitative analysis of edible blend oil is proposed. In the approach, UV–Vis spectra of the training set are decomposed into a certain number of intrinsic mode functions (IMFs) and a residue by empirical mode decomposition (EMD) at first. Then support vector regression (SVR) sub-models are built on each IMF and residue. For prediction set, the spectra are decomposed as done on 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 predicting 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 (PLS).