Classification of Chinese Famous Tea Base on Visible and Near Infrared Hyperspectra Imaging

A total of 180 samples from six varieties of typical tea (30 for each variety) were collected for hyperspectral image classification. The first 2 principal components (PCs) explained over 97% of variances of all spectral information. Gray-level co-occurrence matrix (GLCM) analysis was implemented on the 2 principal component (PC) images to extract 24 textural feature variables in total. Least squares-support vector machine (LS-SVM) classification models were developed to classify Chinese famous tea based on (i) spectral variables, (ii) textural variables, (iii) spectral and textural combined variables, respectively. Satisfactory average correct classification rate (CCR) of 100% for the prediction samples based on (iii) was achieved, which was superior to the results based on (i) or (ii). The experimental results indicate that the proposed method is effective to recognize different types of Chinese famous tea by hyperspectral imaging technique. The overall results indicate that VIS/NIR hyperspectral imaging technique is promising for the reliable classification of Chinese famous tea.

[1]  Francesco Bianconi,et al.  Automatic classification of granite tiles through colour and texture features , 2012, Expert Syst. Appl..

[2]  J. Qin,et al.  Detection of citrus canker using hyperspectral reflectance imaging with spectral information divergence , 2009 .

[3]  Yong He,et al.  [Application PCA-ANN method to fast discrimination of tea varieties using visible/near infrared spectroscopy]. , 2007, Guang pu xue yu guang pu fen xi = Guang pu.

[4]  Pengcheng Nie,et al.  Using wavelet transform and multi-class least square support vector machine in multi-spectral imaging classification of Chinese famous tea , 2011, Expert Syst. Appl..

[5]  Wei Wang,et al.  [Rapid nondestructive detection of water content in fresh pork based on spectroscopy technique combined with support vector machine]. , 2012, Guang pu xue yu guang pu fen xi = Guang pu.

[6]  Xiangyang Wang,et al.  LS-SVM based image segmentation using color and texture information , 2012, J. Vis. Commun. Image Represent..

[7]  Xiuqin Rao,et al.  Development of algorithms for detecting citrus canker based on hyperspectral reflectance imaging. , 2012, Journal of the science of food and agriculture.

[8]  Wu Di,et al.  [Application of multispectral image texture to discriminating tea categories based on DCT and LS-SVM]. , 2009, Guang pu xue yu guang pu fen xi = Guang pu.

[9]  Yong He,et al.  Discrimination of varieties of tea using near infrared spectroscopy by principal component analysis and BP model , 2007 .

[10]  Roger T Hanlon,et al.  Hyperspectral imaging of cuttlefish camouflage indicates good color match in the eyes of fish predators , 2011, Proceedings of the National Academy of Sciences.

[11]  Yankun Peng,et al.  Application of Hyper-Spectral Imaging Technique for the Detection of Total Viable Bacteria Count in Pork , 2011 .