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当前位置: 首页 > 文章 > 高光谱图像对白萝卜糠心的无损检测 食品科学 2015,36 (12) 171-176
Position: Home > Articles > Detecting Hollowness of White Radish Based on Hyperspectral Imaging FOOD SCIENCE 2015,36 (12) 171-176

高光谱图像对白萝卜糠心的无损检测

作  者：
胡鹏程;孙晔;吴海伦;顾欣哲;屠康;郑剑;潘磊庆
单  位：
浙江农林大学农业与食品科学学院;南京农业大学食品科技学院
关键词：
高光谱图像;检测模式;白萝卜;糠心
摘  要：
为实现白萝卜异常品质糠心的无损检测,构建高光谱图像技术检测白萝卜糠心的检测系统。获取了光源透射、反射和半透射模式下白萝卜的高光谱图像信息,结合偏最小二乘分析(partial least squares discriminant analysis,PLS-DA)、支持向量机(support vector machine,SVM)、人工神经网络(artificial neural network,ANN)3种算法分别建立白萝卜糠心的识别模型。结果表明:3种检测模式中,基于透射模式的高光谱图像系统检测准确率最高;3种预测模型中,ANN模型优于PLS-DA和SVM模型。其中,基于透射模式的ANN模型,高光谱图像对萝卜糠心的检测总体准确率达94.3%,效果最好。因此,采用透射模式的高光谱图像技术对白萝卜糠心的检测是可行的。
译  名：
Detecting Hollowness of White Radish Based on Hyperspectral Imaging
作  者：
HU Pengcheng;SUN Ye;WU Hailun;GU Xinzhe;TU Kang;ZHENG Jian;PAN Leiqing;College of Food Science and Technology, Nanjing Agricultural University;School of Agricultural and Food Science, Zhejiang A&F University;
关键词：
hyperspectral imaging;;detecting model;;white radish;;hollowness
摘  要：
Hollowness is a common defect found in radish postharvest storage. In the present study, a prototype hyperspectral imaging system was designed for evaluating the internal quality of white radish. Three different detection models including semi-transmittance, reflectance and transmittance were evaluated and used to extract the hyperspectral imaging data of white radish, partial least squares discriminant analysis(PLS-DA), support vector machine(SVM), and artificial neural network(ANN) algorithms were then used to establish the hollowness model for radish identification and the recognition accuracy was calculated. The prediction accuracies based on PLS-DA, SVM, and ANN were 72.5%, 72.5% and 83.3% in semi-transmittance mode, 82.5%, 82.5% and 92.3% in reflectance mode, and 90.0%, 90.0% and 94.3% in transmittance mode, respectively. The results showed that hyperspectral transmittance imaging achieved the best prediction results among the three different detection models, ANN algorithm was the optimal algorithm to build hollowness discrimination model. Hyperspectral transmittance imaging in the combination with ANN gave the best results with a prediction accuracy of 94.3% for detecting the internal hollowness of white radish. Therefore, it was feasible to use hyperspectral transmittance imaging system for detecting the hollowness of white radish in postharvest storage.

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