In this study, an approach to visualize the spatial distribution of sugar content in white strawberry fruit flesh using near-infrared hyperspectral imaging (NIR-HSI; 913–2166 nm) is developed. NIR-HSI data collected from 180 samples of “Tochigi iW1 go” white strawberries are investigated. In order to recognize the pixels corresponding to the flesh and achene on the surface of the strawberries, principal component analysis (PCA) and image processing are conducted after smoothing and standard normal variate (SNV) pretreatment of the data. Explanatory partial least squares regression (PLSR) analysis is performed to develop an appropriate model to predict Brix reference values. The PLSR model constructed from the raw spectra extracted from the flesh region of interest yields high prediction accuracy with an RMSEP and $R^2{}_p$ values of 0.576 and 0.841, respectively, and with a relatively low number of PLS factors. The Brix heatmap images and violin plots for each sample exhibit characteristics feature of sugar content distribution in the flesh of the strawberries. These findings offer insights into the feasibility of designing a noncontact system to monitor the quality of white strawberries.

Wood is relatively transparent to terahertz (THz) radiation with wavelengths in the submillimeter range. This radiation has a high potential for sensing and imaging wood with a good spatial resolution. THz is especially sensitive to moisture content, fiber alignment, and density – all of which are critical in the manufacturing of wood products. In this work, a systematic study was undertaken on 46 very different wood species by means of THz time-domain spectroscopy with density determination in focus. The dielectric response of wood was modeled based on the Maxwell-Garnett effective medium theory. The dielectric function of the cell wall material was found to be extremely consistent over this large number of species with very different properties. This renders possible to determine wood density by THz time-domain spectroscopy. A strong correlation between the measured and predicted densities has been observed for all the samples investigated.

From the viewpoint of combating illegal logging and examining wood properties, there is a contemporary demand for a wood species identification system.