NIR hyperspectral imaging for mapping of moisture content distribution in tea buds during dehydration

Abstract

This research employed hyperspectral imaging technique to determine the spatial distribution of moisture content (MC) in tea buds during dehydration. Hyperspectral images (874-1 734 nm) of tea buds were acquired in six dehydrated periods (0, 3, 6, 9, 14, 21 min) at 80°C. The spectral reflectance of tea buds were extracted from regions of interest (ROIs) in the hyperspectral images. Competitive adaptive reweighted sampling (CARS) algorithm was used to select effective wavelengths (EWs), which had the greatest influence on determining the MC of tea buds. As a result, the ten representing wavelengths at 1133, 1173, 1332, 1372, 1419, 1446, 1450, 1507, 1538, 1595 nm were selected. The quantitative relationships between spectral reflectance and the measured MC values of tea buds were built using partial least square regression (PLSR) based on full spectra and EWs. The quantitative model established using EWs, which provided a result of coefficient of correlation (RP) of 0.941 and root mean square error of prediction (RMSEP) of 5.31%, was considered as the optimal model for mapping MC spatial distribution. The optimal model was finally applied to predict the MC of each pixel within in tea bud hyperspectral images and built the MC distribution maps by using a developed image processing procedure. Results demonstrated that the hyperspectral imaging technique is promising for mapping the MC spatial distribution in tea buds during dehydrated process.