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《Packaging and Food Machinery》 2010-06
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Detection of Water Content in Corn Based on Hyperspectral Imaging and Neural Network

LI Jiang-bo,SU Yi-nan,RAO Xiu-qin(College of Biosystems Engineering and Food Science,Zhejiang University,Hangzhou 310029,China)  
Water content is an important quality attributer.It was investigated that water content in corn was detected based on hyperspectral imaging and neural network.The detection wavelengths ragion between 450 and 900 nm.The spectrum features wavelengths for predicting the water content in corn were obtained by scatting spectral images.Subsequently,artificial neural network was used for developing a prediciton model to predict water content in corn.The prediction results showed that the maximal absolute value of error was 2.1182,the minimal absolute value of error was 0.0024,the average was 0.3090.Therefore,the hyperspectral imaging is an effective method for nondestructive assessing the water content in corn.
【Fund】: 国家科技支撑计划(2008BADA8B04)
【CateGory Index】: TS210.7
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