| 光谱学与光谱分析 |
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| Study on the Live State of Pieris Rapaes Using Near Infrared Hypserspectral Imaging Technology |
| SONG Ge-lian1, YU Jun-lin2, LIU Fei2*, HE Yong2*, CHEN Dan3, MO Wang-cheng3 |
1. Public Information Industry of Zhejiang Province Co., Ltd., Hangzhou 310006, China
2. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
3. Cixi City Vegetables Development Co., Ltd., Cixi 315326, China |
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Abstract Near-infrared hypserspectral imaging technology was applied for the discrimination of a variety of life states, the judgment of being alive or death. Discrimination models were built based on spectral data of pieris rapaes acquired during different life states. The wavelengths from 951.5 to 1 649.2 nm were used for analysis after the removal of spectral region with obvious noises at the beginning and the end. And the spectra data of 951.5～1 649.2 nm were preprocessed by different pretreatment methods. To discriminate the state of being alive or death of pieris rapaes, discrimination models were built based on the spectral data processed by different pretreatment methods. Results showed that the discriminant accuracy can approach or attain 100%. Thus the method was proved to be useful for the discrimination of the state of being alive or death of pieris rapaes. After the spectral data were preprocessed by moving average (MA) algorithm, 17 characteristic wavelengths were extracted based on weighted regression coefficient (Bw) and 20 were extracted based on successive projections algorithm (SPA) to identify the state of being alive or death of pieris rapaes. Four classification methods based on characteristic wavelengths, including partial least squares-discriminant analysis (PLS-DA), K-nearest neighbor algorithm (KNN), back propagation neural network (BPNN) and support vector machine (SVM) were used to build discriminant models for identifying the state of being alive or death of pieris rapaes. The discriminant accuracy all can approach or attain 100%.
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Received: 2014-03-09
Accepted: 2014-06-11
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Corresponding Authors:
LIU Fei, HE Yong
E-mail: fliu@zju.edu.cn;yhe@zju.edu.cn
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