Detection of Heat-Damaged Soybeans Using Hyperspectral Imaging Technology and MLSPTSVM Model
In the process of transportation,imported soybeans are easy to cause heat damage due to high storage temperature.Heat damage aggravates the quality of soybean protein and oil and affects the quality of soybean.In this paper,hyperspectral imaging technology and multiple least squares recursive projection twin support vector machine(MLSPTSVM)were used to detect heat-damaged soybeans.Hyperspectral imaging detection system was used to ob-tain the spectral images of normal,mild heat-damaged,and severe heat-damaged soybean in the range of 400~1 000 nm.A variety of preprocessing methods were used for spectral pretreatment,and the effectiveness of the pre-processing methods for improving the model detection performance was analyzed.The experimental results indicated that both the MLSPTSVM model of a linear kernel with multiplicative scatter correction preprocessing and the ML-SPTSVM model of a nonlinear kernel with the original spectral data can achieve 100%detection accuracy,much higher than other classical detection models.Moreover,the detection performance of the proposed model would not decrease when the number of experimental samples was significantly reduced.Therefore,the application of hyper-spectral imaging technology combined with MLSPTSVM model could realize accurate,rapid and non-destructive de-tection of heat-damaged soybeans with good robustness.
hyperspectral imagingheat damagesoybeanprojection twin support vector machinenonde-structive detection