Estimation of Catechins Concentration of Green Tea Using Hyperspectral Remote Sensing

Abstract

Models for the estimation of the concentration of catechins concentration in new green tea shoots were established using ground-based hyperspectral remote sensing. The coefficient of determination (R2) was determined to be more than 0.913, the root mean squared error of prediction (RMSEP) was determined to be less than 0.617 % and the relative error of prediction (REP) was determined to be less than 6.17%, except in the EGC model (R2=0.512, RMSEP=0.272%, and REP=15.7%). The regression coefficients of the green, red edge and near infrared regions were all changed, indicating that those regions were important for the estimation of catechin concentration. A similar trend was noted for the regression coefficients of ECg and EGCg. Therefore, the X -loadings of the first latent variables of ECg and EGCg (ester-type catechins) and EC and EGC (free-type catechins) were compared and the similarities between each type of catechin were determind. Therefore, each type of PLS regression model was designed based on date of the ester- and free-type catechins. The accuracy of the free-type model was as follows: R2=0.774, RMSEP=0.273% and REP=7.85%. The accuracy of ester-type model was as follows: R2=0.869, RMSEP=0.991% and REP=6.99%. The regression coefficients of the free-type catechins differed from those of the ester-type catechins. Large changes to the regression coefficients of the green to red, and red edge regions were also demonstrated.