Rapid and non-destructive discrimination of tea varieties by near infrared diffuse reflection spectroscopy coupled with classification and regression trees

Abstract

The current study attempted to rapidly and non-destructively discriminate the diverse varieties of tea (that is, Biluochun, Longjing, Maojian, Qihong, Tieguanyin, and Yinzhen) via utilizing near infrared (NIR) diffuse reflectance spectroscopy coupled with pattern recognition strategies. Before the recognition analysis, the original NIR spectra were pre-processed by second derivative treatment followed by informative wavenumber interval location. And then, non-linearity detection and outlier diagnosis were performed. When pattern recognition referred, principal component analysis (PCA) was firstly applied to ascertain the discrimination possibility with the NIR spectra. Classification and regression trees (CART), compared with linear discriminant analysis (LDA), and partial squares-discriminant analysis (PLS-DA), was then employed for establishing the discrimination rule. Experimental results showed that the tea quality could be accurately, rapidly, and non-invasively identified via NIR spectroscopy coupled with CART.

Key words: Near infrared diffuse reflection spectroscopy, classification and regression trees, and tea variety discrimination.