Intelligent component detection of quaternary blended oil based on near infrared spectroscopy technology

Abstract

In this study, near infrared spectroscopy (NIRS) technique was used for quantitative detection of quaternary blended oil. After a series of preprocessing, the prediction effects of the three models and their preprocessing combinations were compared. Taking soybean oil content prediction as an example, random forest (RF) model had better performance after second derivative (D2) optimization. In feature selection, a two‐step feature selection method was adopted to extract the feature wavelength. First, the elastic net (EN) was used for the initial screening of feature wavelengths, and most irrelevant features were eliminated. The number of feature wavelengths was reduced from 1048 to 134. After that, the competitive adaptive re‐weighted sampling (CARS) method was used to screen the remaining characteristic wavelengths more carefully, and 20 effective characteristic wavelengths were selected. Finally, a quantitative detection model was established based on 20 effective characteristic wavelengths selected by EN + CARS. Evaluated by the test set, The correlation coefficient of determination (R2), root‐mean‐square error of prediction (RMSEP) and Relative Percent Difference (RPD) values of 2D + EN + CARS + RF model were 0.97953, 1.34306 and 7.08875, respectively. The results showed that the two‐step feature selection method can effectively extract the feature wavelength, and the NIRS technology can realize the intelligent detection of blended oil components.