Estimating Catechin Concentration of New Shoots in the Green Tea Field Using Groud-based Hyperspectral Image

摘要

Hyperspectral camera was applied to establish the models of catechin concentration for green tea. The possibility of improvement for the models was checked by the multi-year models and the mutual prediction. ECg, EGCg and the ester catechin (ECg & EGCg) decreased with the growth but EC, EGC and the free catechin (EC & EGC) were changed by the covering. In partial least square regression (PLSR) models for each catechin, R~2 (Relative Error for validation) was more than 0.785 (13.4%) for a single year data, 0.723 (13.3%) for two years data, and 0.756 (13.6%) for three years data except several catechins. It was possible to improve the precision and accuracy of models using the combination of catechin (free and ester type) or the combination of multi-year data. When each and each type of catechin model was predicted by the other year data, the accuracy of two years model improved comparing with it of a single year data. It means that the multi-year models might be more accurate than a single year models to predict the unknown data.