Aflatoxin is highly toxic and is easily found in maize, a little aflatoxin can induce liver cancer. In this paper, we used hyperspectral data in the pixel-level to build the aflatoxin classifying model, each of the pixel have 600 hyperspectral bands and labeled ‘clean’ or ‘contaminated’. We use 3 method to extracted feature bands, one method is to select 4 hyperspectral bands from other articles: 390 nm, 440 nm, 540 nm and 710 nm, another method is to use feature extraction PCA to obtain first 5 pcs to shrink the hyperspectral volume, the third method is to use Fscnca, Fscmrmr, Relieff and Fishier algorithm to select top 10 feature bands. After feature band selection or extraction, we put the feature bands into Random Forest (RF) and K-nearest neighbor (KNN) to classify whether a pixel is polluted by aflatoxin. The highest accurate for feature selection is Relieff, it reached the accuracy of 99.38% with RF classifier and 98.77% in KNN classifier. PCA feature extraction with RF classifier also reached a high accuracy 93.83%. And the 600 bands without feature extraction reached the accuracy of 100%. Feature bands selected from other papers could reach an accuracy of. The result shows that the feature extraction performs well on its own data set. And if the computing time is not taken into account, we could use full band to classify the aflatoxin due to its high accuracy.

黄曲霉毒素具有很高的毒性，很容易在玉米中发现，一点黄曲霉毒素会诱发肝癌。在本文中，我们使用像素级的高光谱数据建立了黄曲霉毒素分类模型，每个像素具有600个高光谱带，并标记为“干净”或“受污染”。我们使用3种方法提取特征带，一种方法是从其他文章中选择4个高光谱带：390 nm，440 nm，540 nm和710 nm，另一种方法是使用特征提取PCA获得前5个收缩高光谱的PC第三种方法是使用Fscnca，Fscmrmr，Relieff和Fishier算法选择前10个特征带。在选择或提取特征带后，我们将特征带放入随机森林（RF）和K近邻（KNN）中，以对像素是否被黄曲霉毒素污染进行分类。特征选择的最高准确度是Relieff，在RF分类器中达到了99.38％，在KNN分类器中达到了98.77％。带有RF分类器的PCA特征提取也达到了93.83％的高精度。而没有特征提取的600个频段达到了100％的精度。从其他论文中选择的特征带可以达到的精度。结果表明，特征提取在其自己的数据集上表现良好。如果不考虑计算时间，由于黄曲霉毒素的准确性很高，我们可以使用全波段对其进行分类。从其他论文中选择的特征带可以达到的精度。结果表明，特征提取在其自己的数据集上表现良好。如果不考虑计算时间，由于黄曲霉毒素的准确性很高，我们可以使用全波段对其进行分类。从其他论文中选择的特征带可以达到的精度。结果表明，特征提取在其自己的数据集上表现良好。如果不考虑计算时间，由于黄曲霉毒素的准确性很高，我们可以使用全波段对其进行分类。