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In order to detect and identify tea varieties more efficiently, an identification project based on Gray-Level Co-Occurrence Matrix (GLCM) was developed. First, different kinds of typical tea which are Anji white tea, Biluochun, Dahongpao, Tieguanyin and Yixing black tea need to be collected and used as first-hand information. Then, five different types of Chinese tea images were taken as raw inputs, each of which had 25 images. The 125 images were processed through MATLAB software. After a 7-fold cross-validation step, the project showed a good result. The results showed that the matching degree of tea varieties reached 86.4%. This scheme provides an effective approach for tea identification process [ABSTRACT FROM AUTHOR]

This study will provide a chemometric data analysis for fermented black tea that will help tea manufacturer to ensure the consistency and quality of black tea they produce. It will aim to create a device that will detect the aroma of black tea and generate the standard for the quality of black tea. The study utilized matlab and principal component analysis for the chemometric data gathered in black tea. Based on the analysis done, it can be concluded that the electronic nose was able to classify most of the black tea samples correctly. The overall recognition rate of the system is 75%. The system can be improved by increasing the number of training data. [ABSTRACT FROM AUTHOR]