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| 高光谱图像技术在掺假大米检测中的应用 | |
| 引用本文： | 孙俊,金夏明,毛罕平,武小红,杨宁.高光谱图像技术在掺假大米检测中的应用[J].农业工程学报,2014,30(21):301-307. |
| 作者姓名： | 孙俊 金夏明 毛罕平 武小红 杨宁 |
| 作者单位： | 1. 江苏大学电气信息工程学院，镇江 212013; 江苏大学江苏省现代农业装备与技术重点实验室，镇江 212013 2. 江苏大学电气信息工程学院,镇江,212013 3. 江苏大学江苏省现代农业装备与技术重点实验室,镇江,212013 |
| 基金项目： | 国家自然科学基金资助项目（31101082）；江苏高校优势学科建设工程资助项目PAPD（苏政办发2011 6号）；农业部农业信息服务技术重点实验室课题（2014-AIST-03）；江苏省自然科学基金项目（BK20141165）。 |
| 摘 要： | 为了有效判别出优质大米中是否掺入劣质大米,该文研究了一种针对大米掺假问题的快速、无损检测方法。从市场上购买了东北长粒香大米和江苏溧水大米,按纯东北长粒香大米、3∶1、2∶2、1∶3和纯江苏溧水大米共5个掺合水平进行大米试验样本的制备。利用可见-近红外高光谱图像采集系统(390~1050 nm)获取了200个大米样本的高光谱图像。采用ENVI软件确定高光谱图像的感兴趣区域(region of interest,ROI),并提取出所有样本在ROI内的平均高光谱数据。采用支持向量机(support vector machine,SVM)建立全光谱波段下的大米掺假判别模型,径向基(radial basis function,RBF)核函数模型交叉验证准确率为93%、预测集正确率为98%。由于高光谱信息量大、冗余性强且受噪声的影响较大,该文采用主成分分析方法(principal component analysis,PCA)分别对大米高光谱图像和高光谱数据进行处理,从特征选择和特征提取2个角度对原始高光谱数据进行处理,通过主成分权重系数图选择了531.1、702.7、714.3、724.7、888.2和930.6 nm 6个特征波长,通过留一交叉验证法(leave-one-out cross-validation,LOOCV)确定并提取出PCA降维后的最优主成分数(number of principal component,PCs)为9。最后分别将优选出的特征波长和提取出的最优主成分数作为模型的输入,建立SVM模型。试验结果表明,基于特征波长SVM模型的交叉验证准确率为95%、预测集正确率为96%,基于最优主成分数SVM模型的交叉验证准确率为94%、预测集正确率为98%。该研究结果表明,该文建立的基于特征波长和基于最优主成分数的SVM模型均具有较优的预测性能,且利用高光谱图像技术对大米掺假问题进行检测是可行的。 |
| 关 键 词： | 无损检测 主成分分析 图像采集 大米掺假 支持向量机 高光谱图像 |
| 收稿时间： | 2014/7/23 0:00:00 |
| 修稿时间： | 2014/10/24 0:00:00 |
Application of hyperspectral imaging technology for detecting adulterate rice |
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| Sun Jun,Jin Xiaming,Mao Hanping,Wu Xiaohong and Yang Ning.Application of hyperspectral imaging technology for detecting adulterate rice[J].Transactions of the Chinese Society of Agricultural Engineering,2014,30(21):301-307. | |
| Authors: | Sun Jun Jin Xiaming Mao Hanping Wu Xiaohong and Yang Ning |
| Institution: | 1. School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China2. Jiangsu Provincial Key Laboratory of Modern Agricultural Equipment and Technology, Jiangsu University, Zhenjiang 212013, China;1. School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China;2. Jiangsu Provincial Key Laboratory of Modern Agricultural Equipment and Technology, Jiangsu University, Zhenjiang 212013, China;1. School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China;1. School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China |
| Abstract: | Abstract: Rice is an important food ration of Chinese people, which contains a great number of starch, protein, fat and some nutrient elements. However, rice adulteration is becoming one of the most urgent problems and it needs to be solved as soon as possible in Chinese rice market. Therefore, the purpose of this study was to develop a rapid, precise and nondestructive method to detect the rice adulteration. In this paper, some expensive rice with high quality (Chang-Li-Xiang) and some cheap rice with relatively low quality (Li-Shui) were purchased from the local Wal-Mart in Zhenjiang province, China. Then they were mixed together in five different proportions (0:4, 1:3, 2:2, 3:1 and 4:0) by using electronic scale and the sample of rice adulteration were obtained. The visible and near infrared (VIS-NIR) hyperpectral imaging system with the spectral range of 390-1050 nm was used to capture the hyperspectral images of 200 rice samples. ENVI software was adopted to determine the region of interest (ROI) in the hyperspectral image and extract the hyperspectral data by averaging the reflectance from all the pixels in the ROI images. Then the discriminative model for rice adulteration was established by using support vector machine (SVM) and the extracted hyperspectral data in the full spectral range. The performance of the SVM model was evaluated by using the indexes of cross validation accuracy and prediction accuracy. Finally, the cross validation accuracy was 93% and the prediction accuracy was 98% in the full-spectral-SVM. As there were a large number of noise and redundant information in the raw hyperspectral images and hyperspectral data, some data processing methods should be used to remove the noise, accelerate the processing efficiency and improve the performance of the models. In this paper, the traditional principal component analysis (PCA) method was respectively used to process the hyperspectral images and hyperspectral data from the two aspects of feature selection and feature extraction. For the aspect of feature selection, a total of six characteristic wavelengths (531.1, 702.7, 714.3, 724.7, 888.2 and 930.6 nm) were picked up according to the weight coefficient distribution curve of the first four principal component images under the full wavelengths. For the aspect of feature extraction, the optimal number of principal component (PCs) was determined as 9 by using the leave-one-out cross-validation (LOOCV). Finally, the two kinds of simplified SVM models were respectively developed by using the input data at the six characteristic wavelengths and at the optimal PCs. The experiment results showed that the cross validation and prediction accuracy in the model based on characteristic wavelengths were 95% and 96%, the cross validation and prediction accuracy in the model based on optimal PCs were 94% and 98%. It indicated that the two kinds of simplified models all achieved the promising results and they all had the comparable discriminant power for rice adulteration when compared with the full-spectral-SVM. The results demonstrated that it is feasible to use hyperspectral imaging technology for the detection of the problem of rice adulteration. |
| Keywords: | nondestructive examination principal component analysis image acquisition rice adulteration support vector machine hyperspectral imaging |
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