Quantitative determination of wheat moisture content based on microwave detection technique combined with multivariate data analysis

Abstract

In this study, a new method is proposed for the quantitative detection of wheat moisture content based on double heterodyne mixing microwave transmission technique. First of all, the microwave detection device received the command from the computer and detected the wheat in the frequency domain of 2.5 GHz–11.5 GHz, while acquiring the transmission index corresponding to each frequency point. Then, competitive adaptive reweighted sampling (CARS) was used for coarse feature selection of the transmission index after least partial squares filtering, and Genetic Algorithm (GA) was used for secondary feature refinement of the coarse selected feature variables, and they were sorted and combined according to their weights. Finally, Support vector regression (SVR) was used to establish quantitative models with different combinations of feature variables to achieve rapid detection of wheat moisture content. The results showed that the CARS-GA-SVR model had better detection performance for wheat moisture content determination compared to the CARS-SVR model. Among them, the CARS-GA-SVR model had the best detection effect when the combination of feature variables is 6-dimensional, with coefficient of determination (RP2) of 0.9756 and relative prediction deviation (RPD) of 6.3234. Compared to the SVR model established by the full transmission index, the RPD improved from 3.7089 to 6.3234. The results reveal that rapid and high-precision detection of wheat moisture content can be achieved by using a self-made miniaturized microwave device combined with a suitable chemometric method.