Qualitative identification of tea by near infrared spectroscopy based on soft independent modelling of class analogy pattern recognition

Near-infrared (NIR) spectroscopy has been successfully utilised for the rapid identification of tea varieties. The spectral features of each tea category are reasonably differentiated in the NIR region and the spectral differences provided enough qualitative spectral information for identification. Soft independent modelling of class analogy (SIMCA) as the pattern recognition was applied in this paper. In this study, both alpha-error (i.e. the rejection of correct samples from their class) and beta-error (i.e. the acceptance of objects that do not belong to that class) are focused on. Four tea classes from Longjing tea, Biluochun tea, Qihong tea and Tieguany in tea were modelled separately by principal component analysis (PCA). The results showed that at the 99% confidence level, the a-errors were equal to 0.1 only for the Longjing tea class when training and 0.2 only for the Biluochun tea class when testing, while the remaining alpha-errors and all beta-errors were equal to zero. The study demonstrated that NIR spectroscopy technology with a SIMCA pattern recognition method can be successfully applied as a rapid method to identify the class of tea.