Predict Compositions and Mechanical Properties of Sugar Beet Using Hyperspectral Scattering

Abstract

Sucrose, soluble solids, and moisture content and mechanical properties are important quality/property attributes of sugar beet. In this study, hyperspectral scattering images for the spectral region of 500–1000 nm were acquired, from which relative mean spectra were calculated. Prediction models were developed using partial least squares regression for both full spectra and selected wavelengths. The results showed that using relative mean spectra gave good predictions for the moisture, soluble solids, and sucrose content of beet slices with the correlations of 0.75–0.88 and the standard errors of prediction of 0.95–1.08 based on full-spectrum partial least squares regression (PLSR) models. PLSR models using wavelength selection with the uninformative variable elimination (UVE) method produced similar prediction accuracy. However, both modeling approaches gave poor predictions for the mechanical properties of beets with the correlation values of 0.46–0.63. The research demonstrated the potential of hyperspectral scattering imaging for measuring quality attributes of sugar beet.