Estimating the catechin concentrations of new shoots in green tea fields using ground-based hyperspectral imagery

Abstract

Hyperspectral imagery was applied to estimating non-galloyl (EC, EGC) and galloyl (ECG, EGCG) types of catechins in new shoots of green tea. Partial least squares regression models were developed to consider the effects of commercial fertilizer (CF) and organic fertilizer (OF). The models could explain each type of catechin with a precision of more than 0.79, with a few exceptions. When the CF model was applied to the OF hyperspectral reflectance and the OF model was applied to the CF hyperspectral reflectance for mutual prediction, the prediction accuracy was better with the OF models than CF models. The prediction models using both CF and OF data (hyperspectral reflectances, and concentrations of catechins) had a precision of more than 0.76 except for the non-galloyl-type catechins as a group and EGC alone. These results provide useful data for maintaining and improving the quality of green tea.