Abstract
The purpose of this research is to develop a laboratory-based near-infrared (NIR) hyperspectral imaging system to measure the two-dimensional distribution of nitrate concentrations in a vegetable leaf as a tool for precisely analysing nitrate metabolism. Komatsuna leaves were analyzed by hyperspectral reflectance in the range from 607 to 967 nm with a resolution of 9 nm. The reflectance was standardized by a reference plate and was converted to relative reflectance. An algorithm to select the effective wavelength to predict the nitrate concentration was developed in conjunction with partial least squares (PLS) regression and principal components regression (PCR). As for preprocessing methods for the spectra, mean-centre and standard normal variate transformations were examined. Estimation accuracy of the developed models was evaluated by the weighted average of standard error (WSE). The estimation accuracy of the wavelength-selected models was improved and the WSE was smaller than that of the full-spectrum model (41 wavelengths). The calibration model that used 21 wavelengths achieved the best WSE of 1446 ppm with a correlation coefficient of 0.870. The nitrate distribution in Komatsuna leaves were visualized in digital images with a spatial resolution of 2.5×10−4 mm/pixel. These images showed that the transporting route of nutrients contains higher nitrate ion concentration than other areas in the Komatsuna leaf.
