Identification of gasoline blending by infrared spectroscopy based on deep belief networks

In order to realize the fast nondestructive identification of blended gasoline, an identification method based on t-distributed stochastic neighborhood embedding(t-SNT) combined with deep belief networks was proposed to solve the nonlinear relationship between high-dimensional feature vectors in machine learning. Taking 92^#, 95^#, 98^# and fixed ratio blended gasoline as the research objects, the projection spectrum measurement data in original infrared band was preprocessed by multivariate scattering correction algorithm, and the dimension reduction of spectral data was carried out by using t-SNE nonlinear method. The spectral identification model of gasoline types was established by using deep belief networks and extreme learning machine respectively, and the identification accuracy of the two methods was compared and analyzed. The research shows that the gasoline identification model constructed by this method has better performance, and the prediction accuracy of gasoline types is as high as 92.5%, which verifies the effectiveness of this method in gasoline identification. The results of this research can provide technical support for the identification and traceability of blending refined oil products.