A hyperspectral imaging technique was attempted to classify green tea. Five grades of green tea samples were attempted. A hyperspectral imaging system was developed for data acquisition of tea samples. Principal component analysis was performed on the hyperspectral data to determine three optimal band images. Texture analysis was conducted on each optimal band image to extract characteristic variables. A support vector machine (SVM) was used to construct the classification model. The classification rates were 98% and 95% in the training and prediction sets, respectively. The SVM algorithm shows excellent performance in classification results in contrast with other pattern recognitions classifiers. Overall results show that the hyperspectral imaging technique coupled with a SVM classifier can be efficiently utilized to classify green tea.
© 2009 Optical Society of America
Original Manuscript: March 9, 2009
Revised Manuscript: June 4, 2009
Manuscript Accepted: June 4, 2009
Published: June 22, 2009
Jiewen Zhao, Quansheng Chen, Jianrong Cai, and Qin Ouyang, "Automated tea quality classification by hyperspectral imaging," Appl. Opt. 48, 3557-3564 (2009)