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Tobacco Leaf Selection Method of the Near-infrared Spectroscopy Effective Feature Based on the Cluster
Pages: 659-664
Year: Issue:  10
Journal: Infrared Technology

Keyword:  near-infrared spectrumfeature selecttobacco groupingclusterSVM;
Abstract: The clustering method is applied to select the effective features from the original spectra. The effective features used for betterγ1 andγ2 is chose by analyzing the influence of intra-class parametersγ1 and inter-class parametersγ2. Part and color of tobacco leaves are classified by SVM method based on the near infrared reflecting spectra(1500 nm-2400 nm interval of 2 nm)of flue-cured tobacco leaves. The recognition rates of part and color are 100% for train sample, 96.22% and 92.79% respectively for test sample. After some irrelevant spectra are removed by clustering algorithm, the recognition rate can be improved to 97.23%and 95.52%respectively. Continue cutting spectra having low correlation with classification. The recognition rate will declined significantly when too many spectra are removed. The number of spectra can be reduced to about 200 from 451 with slightly low recognition rate. The experiment results show that the clustering method can not only improve the recognition rate but also greatly reduce the number of spectral data. This greatly lessens the time of collecting data and significantly improves the real-time and fast processing ability of the system.
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