ABSTRACT
The first step of developing aflatoxin intelligent sorter is to determine the key wavelengths for aflatoxin detection. In order to find more accurate wavelengths, in this chapter, three kinds of sensor system are built separately: the first sensor is hyperspectrometer by ASD spectrometer, the second is the multispectral camera system based on liquid crystal tunable filter (LCTF), and the third one is the hyperspectral camera based on grating spectrometer module (GSM). Under 365 nm UV LED illumination, using these three systems, three hyperspectral datasets of peanut samples, 45, 41, and 73, before and after aflatoxin contamination have been collected separately. In order to select the best key wavelengths, four feature selection methods (Fisher, SPA, BestFist, and Ranker) and four classifier models (KNN, SVM, BP-ANN, Random Forest) were analyzed and compared. Using all selected wavelengths based on different datasets, a weighted voting method was proposed and ten key wavelengths (440, 380, 410, 460, 420, 370, 450, 490, 700, and 600 nm) were selected. Based on the best model (Random Forest), the best integrated average recognition rate is 94.5%. And then, using these key wavelengths and the best classification model, a new design system for aflatoxin sorter base on a polygon mirror was proposed. Although the structure of this system is simple, its detection accuracy is high, which can be applied to online sorting of aflatoxin detection.
