Identification of Edible-vegetable-oil Types Based on Multi-kernel Learning and Multi-spectral Fusion

Abstract

Aiming at the identification of edible oil quality, this study proposed a multi-spectral data fusion combined with multi-kernel learning support vector machine (SVM) method. This method used serial and wavelet fusion approaches to fuse Raman and near infrared spectral data, and established an identification model for edible-oil types, with the aid of the multi-kernel learning support vector machine (MKL-SVM). The performances of the single spectral model and spectral fusion model were compared, demonstrating that the spectral fusion could effectively improve the prediction accuracy and generalization ability of the model.