Noncontact evaluation of soluble solids content in apples by near-infrared hyperspectral imaging

Abstract

Near-infrared (NIR) hyperspectral imaging was used to evaluate soluble solids content (SSC) in ‘Fuji’ apples [Malus sylvestris (L.) Mill. var. domestica (Borkh. Mansf.)]. Eighty ‘Fuji’ apples were analyzed by collecting four small block samples from each one (approximately 2.0 cm × 2.0 cm × 1.5 cm). Partial least squares (PLS) regression analysis was performed to determine the relation between SSC reference data and NIR spectral data measured from each sample. The cross-validation coefficient of determination (r2) between predicted and measured SSC values is 0.89 with a root mean squared error of cross-validation (RMSECV) of 0.55%. Then, we successfully mapped SSC at a high spatial resolution (375 μm per pixel). In addition, the absorption and reduced scattering coefficients of the measured samples were determined based on a diffusion theory model. The absorption coefficients are positively correlated to the SSC values (chemical information), whereas water cored tissue content (physical information) causes a characteristic change in light scattering coefficients. The fitting results were validated by Monte Carlo simulation, and the light penetration depth in ‘Fuji’ apples was estimated to be around 0.33 cm at 1198 nm and 0.17 cm at 1450 nm, respectively.