Predicting Rank of Japanese Green Teas by Derivative Profiles of Spectra Obtained from Fourier Transform Near-Infrared Reflectance Spectroscopy

Abstract

A rapid and easy method for extracting features from spectra obtained from Fourier transform near-infrared (FT-NIR) reflectance spectroscopy was examined by using the 1st and 2nd derivatives and Spearman's rank correlation. This method can select features from the overall wavelength. Therefore, this method can be considered suitable for the quality estimation of foods. Practically, a set of ranked green tea samples from a Japanese commercial tea contest were analyzed by FT-NIR in order to create a reliable quality-prediction model. The 2nd derivative was determined for reducing noise and amplifying the fundamental features. Feature selection from the amplified data was performed using relations between the tea ranks and the derivative coefficients. Finally, a reliable quality-prediction model of green tea was formulated by using single linear and PLS regressions. Furthermore, we discuss possibility of the derivative coefficients as feature representation in FT-NIR.