Texture discrimination of green tea categories based on least squares support vector machine (LSSVM) classifier

This research aimed for development multi-spectral imaging technique for green tea categories discrimination based on texture analysis. Three key wavelengths of 550, 650 and 800 nm were implemented in a common-aperture multi-spectral charged coupled device camera, and images were acquired for 190 unique images in a four different kinds of green tea data set. An image data set consisting of 15 texture features for each image was generated based on texture analysis techniques including grey level co-occurrence method (GLCM) and texture filtering. For optimization the texture features, 5 features that weren't correlated with the category of tea were eliminated. Unsupervised cluster analysis was conducted using the optimized texture features based on principal component analysis. The cluster analysis showed that the four kinds of green tea could be separated in the first two principal components space, however there was overlapping phenomenon among the different kinds of green tea. To enhance the performance of discrimination, least squares support vector machine (LSSVM) classifier was developed based on the optimized texture features. The excellent discrimination performance for sample in prediction set was obtained with 100%, 100%, 75% and 100% for four kinds of green tea respectively. It can be concluded that texture discrimination of green tea categories based on multi-spectral image technology is feasible.

[1]  Da-Wen Sun,et al.  Recent applications of image texture for evaluation of food qualities—a review , 2006 .

[2]  N. Togari,et al.  Pattern recognition applied to gas chromatographic profiles of volatile components in three tea categories , 1995 .

[3]  Nello Cristianini,et al.  An introduction to Support Vector Machines , 2000 .

[4]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[5]  Yiyu Cheng,et al.  Discriminating the Genuineness of Chinese Medicines Using Least Squares Support Vector Machines , 2006 .

[6]  Kurt C. Lawrence,et al.  Discriminant analysis of dual-wavelength spectral images for classifying poultry carcasses , 2002 .

[7]  Haruhiko Murase,et al.  Machine vision based quality evaluation of Iyokan orange fruit using neural networks , 2000 .

[8]  Fumiaki Tomita,et al.  Computer analysis of visual textures , 1990 .

[9]  Y. Zuo,et al.  Simultaneous determination of catechins, caffeine and gallic acids in green, Oolong, black and pu-erh teas using HPLC with a photodiode array detector. , 2002, Talanta.

[10]  Johan A. K. Suykens,et al.  Least squares support vector machines classifiers : a multi two-spiral benchmark problem , 2001 .

[11]  Noel D.G. White,et al.  Comparison of a Neural Network and a Non-parametric Classifier for Grain Kernel Identification , 2003 .

[12]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machines , 2002 .

[13]  Anette Kistrup Thybo,et al.  Prediction of sensory texture quality attributes of cooked potatoes by NMR-imaging (MRI) of raw potatoes in combination with different image analysis methods , 2004 .

[14]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[15]  A. G. González,et al.  Pattern recognition procedures for differentiation of Green, Black and Oolong teas according to their metal content from inductively coupled plasma atomic emission spectrometry. , 2001, Talanta.

[16]  Nurettin Acir,et al.  Automatic classification of auditory brainstem responses using SVM-based feature selection algorithm for threshold detection , 2006, Eng. Appl. Artif. Intell..

[17]  E. R. Davies,et al.  Texture analysis for foreign object detection using a single layer neural network , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).

[18]  Dwight D. Day,et al.  Fourier-Based Texture Measures with Application to the Analysis of the Cell Structure of Baked Products , 1996, Digit. Signal Process..

[19]  Yong He,et al.  Quantitative Analysis of the Varieties of Apple Using Near Infrared Spectroscopy by Principal Component Analysis and BP Model , 2005, Australian Conference on Artificial Intelligence.

[20]  Digvir S. Jayas,et al.  CLASSIFICATION OF CEREAL GRAINS USING MACHINE VISION: III. TEXTURE MODELS , 2000 .