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《Transactions of the Chinese Society for Agricultural Machinery》 2009-S1
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Discrimination of Moldy Chinese Chestnut Based on Artificial Neural Network and Near Infrared Spectra

Zhou Zhu1 Liu Jie1 Li Xiaoyu1 Li Peiwu2 Wang Wei1 Zhan Hui1(1.College of Engineering & Technology,Huazhong Agricultural University,Wuhan 430070,China2.Oil Crops Research Institute,Chinese Academy of Agricultural Sciences,Wuhan 430062,China)  
The nondestructive discrimination of the shelled chestnuts was studied with near infrared spectra,which could provide a new method for quality detection of other shelled agricultural products.178 chestnut samples were prepared and the diffuse spectral reflectance of the samples were collected in the wave number range of 12 000~4 000 cm-1.First,six preprocessing methods including smooth、vector normalization、min-max normalization、standard normal variate transformation、multiplication scattering correction and first derivative were used to improve the original spectrum.Then,principal component analysis was applied to compress thousands of spectral data into several variables and to collect spectral information.The principal components extracted by PCA were employed as the inputs of the BP neural networks.Effects of the six preprocessing methods on the models based on BP neural network were compared.The results show that prediction precision varied to different preprocessing methods.The optimum network structure of 7-4-1 was obtained after vector normalization method.Discriminating rate of qualified chestnut,surface moldy chestnut and internal moldy chestnut in prediction reached 94.74%,94.44% and 92.31%,respectively.
【CateGory Index】: TS255.6
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