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 1 and 2 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 2 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.