中文摘要：众所周知，受到真菌污染的花生含有强致癌物。有效地鉴定并分离受污染的花生有助于阻止黄曲霉毒素进入食物链。本研究中，用短波红外(SWIR)高光谱图像鉴定受污染的花生仁。用方差分析(ANOVA)的特征选择方法和非参数加权特征提取(NWFE)的特征提取方法，将光谱信息集中到子空间，使受污染和健康的花生具有良好的分离性。然后用SVM对花生像素分类，用区域增长的图像分割法以花生仁大小的斑块进行图像分割并同时对花生仁计数。结果发现，不论在学习图像还是验证图像中，受污染的花生仁均被准确地标记为异常。
 
外文摘要：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.
外文关键词：SWIR hyperspectral image；Fungi-contaminated peanuts；Identification；Classification
作者：Qiao, XJ；Jiang, JB；Qi, XT；Guo, HQ；Yuan, DS
作者单位：China Univ Min & Technol
期刊名称：FOOD CHEMISTRY
期刊影响因子：4.052
出版年份：2017
出版刊次：1
点击下载：将光谱的空间特征用于短波红外高光谱成像来分类和鉴定受真菌污染的花生