Published in: Journal of Computer Science and Engineering in Innovations and Research (ISSN No: 3049-1762 online)
Publication Date: May 15, 2025
The traditional method for assessing tea quality relies on human sensory evaluation by "Tea Tasters." However, this approach is highly subjective and its reliability is questionable. Consequently, various instrumental setups have been explored in recent times. One such device is the Electronic Nose, which has been employed for tea quality assessment.
This electronic nose features an array of five AT-cut 10 MHz quartz crystal microbalance (QCM) sensors. These sensors have been exposed to the aroma of different types of black tea, such as cut-tear-curl (CTC) and orthodox, with responses monitored online through a data acquisition system. The collected data has been analyzed using principal component analysis (PCA), linear discriminant analysis (LDA), and independent component analysis (ICA). A comparative study of different clustering algorithms has been conducted based on cluster validity indices, and the BPMLP classifier has been utilized with a 10-fold cross-validation technique for data classification.
QCM, PCA, LDA, ICA, BPMLP, Dunn‘s Index