Quantitative determination of cadmium content in peanut oil using microwave detection method combined with multivariate analysis

Abstract

This research introduced an innovative application of microwave detection technology to quickly and precisely quantify cadmium concentration in peanut oil. Using the principle of free space measurement, a compact microwave detection device was fabricated, and obtain a transmission index with a frequency range of 2.5 GHz to 11.5 GHz and a step size of 10 MHz. Firstly, the preprocessed transmission index is selected by using three feature extraction algorithms: competitive adaptive reweighting sampling (CARS) algorithm, boot­strapping soft shrinkage (BOSS) algorithm, and iteratively variable subset optimization (IVSO) algorithm, and quantified them with the least square support vector regression (LSSVR) model. Then, two intelligent optimization algorithms, sine cosine algorithm (SCA) and firefly algorithm (FA) were used to optimize the parameters of the LSSVR model. Compared with the original LSSVR model, the coefficient of determination (RP2) is improved after three feature extraction algorithms and two parameter optimization algorithms, and the root average square error (RMSEP) is also decreased. Among these algorithms, feature extraction algorithm CARS and parameter optimization algorithm FA have the best prediction results. When the two are coupled, the CARS-FA-LSSVR model shows the best generalization performance, with RP2 of 0.9944 and RMSEP of 1.3075 mg∙kg-1. Through this research, it can be seen that high-precision detection of heavy metal cadmium content in peanut oil can be achieved by using microwave detection method combined with the stoichiometric model and can be extended to the detection of heavy metal content in other foods, providing a new way of idea for food safety detection.