Discrimination of variety and authenticity for rice based on visual/near infrared reflection spectra
Five different varieties of rice bought from the supermarket were identified with visual/near infrared spectroscopy (NIRS) technology. The spectra were collected by using ASD FieldSpec + 3 spectrometer, and 35 samples were obtained for each variety of rice. All the samples were divided randomly into...
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| Veröffentlicht in: | Hong wai yu hao mi bo xue bao 2009-10, Vol.28 (5), p.353-391 |
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| container_title | Hong wai yu hao mi bo xue bao |
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| creator | Liang, Liang Liu, Zhi-Xiao Yang, Min-Hua Zhang, You-Xiang Wang, Cheng-Hua |
| description | Five different varieties of rice bought from the supermarket were identified with visual/near infrared spectroscopy (NIRS) technology. The spectra were collected by using ASD FieldSpec + 3 spectrometer, and 35 samples were obtained for each variety of rice. All the samples were divided randomly into two groups, one group with 150 ones used as calibrated set, and the other with 25 ones as validated set. Then the samples were analyzed with the whole wave band and the characteristic wave band(400~500nm, 910~1400nm and 1940~2300nm) models, respectively. The samples data were pretreated by the methods of S.Golay smoothing and standard normal variable (SNV), and then the pretreated spectra data were analyzed with principal component analysis (PCA). The anterior 9 principal components computed by PCA were used as the input variables of back-propagation artificial neural network (BP-ANN) which included one hidden layer, while the values of rice varieties were used as the output variables of BP-ANN, and then the three |
| doi_str_mv | 10.3724/SP.J.1010.2009.00353 |
| format | Article |
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The spectra were collected by using ASD FieldSpec + 3 spectrometer, and 35 samples were obtained for each variety of rice. All the samples were divided randomly into two groups, one group with 150 ones used as calibrated set, and the other with 25 ones as validated set. Then the samples were analyzed with the whole wave band and the characteristic wave band(400~500nm, 910~1400nm and 1940~2300nm) models, respectively. The samples data were pretreated by the methods of S.Golay smoothing and standard normal variable (SNV), and then the pretreated spectra data were analyzed with principal component analysis (PCA). 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The spectra were collected by using ASD FieldSpec + 3 spectrometer, and 35 samples were obtained for each variety of rice. All the samples were divided randomly into two groups, one group with 150 ones used as calibrated set, and the other with 25 ones as validated set. Then the samples were analyzed with the whole wave band and the characteristic wave band(400~500nm, 910~1400nm and 1940~2300nm) models, respectively. The samples data were pretreated by the methods of S.Golay smoothing and standard normal variable (SNV), and then the pretreated spectra data were analyzed with principal component analysis (PCA). 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The spectra were collected by using ASD FieldSpec + 3 spectrometer, and 35 samples were obtained for each variety of rice. All the samples were divided randomly into two groups, one group with 150 ones used as calibrated set, and the other with 25 ones as validated set. Then the samples were analyzed with the whole wave band and the characteristic wave band(400~500nm, 910~1400nm and 1940~2300nm) models, respectively. The samples data were pretreated by the methods of S.Golay smoothing and standard normal variable (SNV), and then the pretreated spectra data were analyzed with principal component analysis (PCA). The anterior 9 principal components computed by PCA were used as the input variables of back-propagation artificial neural network (BP-ANN) which included one hidden layer, while the values of rice varieties were used as the output variables of BP-ANN, and then the three</abstract><doi>10.3724/SP.J.1010.2009.00353</doi><tpages>39</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Accuracy Artificial neural networks Back propagation Band spectra Calibration Computation Discrimination Infrared Infrared reflection Infrared spectroscopy Mathematical analysis Mathematical models Millimeter waves Principal component analysis Reflectance Reflectivity Rice Smoothing Spectra Spectrometers Supermarkets Visual |
| title | Discrimination of variety and authenticity for rice based on visual/near infrared reflection spectra |
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