title: Utilization of spectral-spatial characteristics in shortwave infrared hyperspectral images to classify and identify fungi-contaminated peanuts.
abstract: It's well-known fungi-contaminated peanuts contain potent carcinogen. Efficiently identifying and separating the contaminated can help prevent aflatoxin entering in food chain. In this study, shortwave infrared (SWIR) hyperspectral images for identifying the prepared contaminated kernels. Feature selection method of analysis of variance (ANOVA) and feature extraction method of nonparametric weighted feature extraction (NWFE) were used to concentrate spectral information into a subspace where contaminated and healthy peanuts can have favorable separability. Then, peanut pixels were classified using SVM. Moreover, image segmentation method of region growing was applied to segment the image as kernel-scale patches and meanwhile to number the kernels. The result shows that pixel-wise classification accuracies are 99.13% for breed A, 96.72% for B and 99.73% for C in learning images, and are 96.32%, 94.2% and 97.51% in validation images. Contaminated peanuts were correctly marked as aberrant kernels in both learning images and validation images.
authors.affiliation: College of Geosciences and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China.
authors.forename: Xiaojun
authors.initials: X
authors.lastname: Qiao
authors.affiliation: College of Geosciences and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China. Electronic address: jjb@cumtb.edu.cn.
authors.forename: Jinbao
authors.initials: J
authors.lastname: Jiang
authors.affiliation: College of Geosciences and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China.
authors.forename: Xiaotong
authors.initials: X
authors.lastname: Qi
authors.affiliation: College of Geosciences and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China.
authors.forename: Haiqiang
authors.initials: H
authors.lastname: Guo
authors.affiliation: College of Geosciences and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China.
authors.forename: Deshuai
authors.initials: D
authors.lastname: Yuan
mesh_terms: D000348:Aflatoxins / Q000032:analysis*; D010367:Arachis / Q000737:chemistry* / Q000382:microbiology; D005658:Fungi / Q000302:isolation & purification*; D013057:Spectrum Analysis / Q000379:methods*
chemical_list: D000348:Aflatoxins
keywords: Classification; Fungi-contaminated peanuts; Identification; SWIR hyperspectral image