Can light penetrate through pomelos and carry information for the non-destructive prediction of soluble solid content using Vis-NIRS?

Abstract

The penetration depth of light and the distribution of components in fruit affect the accuracy of models in visible-near infrared spectroscopy (Vis-NIRS). These issues are particularly prominent for predicting the soluble solids content (SSC) of large fruit with thick rinds, such as pomelo. To describe the light penetration and distribution inside pomelos, three different modes of puncture measurement were performed by an original system with a puncture optical fiber. The asymmetrical changes and distribution of light intensity were observed in tissues. The semi-transmittance mode was adopted combined with the limited penetration depth, photon aggregation near the light source, and the distribution characteristics of SSC in pomelo. Subsequently, the multi-point spectra at six points in semi-transmittance mode were employed to evaluate the influence of the asymmetrical light and SSC distribution using cross-validation of partial least squares regression (PLSR). The models established by the mean spectra got good performances by eliminating the difference in light distribution. In addition, the probability distribution of SCC could affect the model performance more than the spatial distribution. Finally, the effective wavelengths were selected by competitive adaptive reweighted sampling (CARS) to establish the calibration model. The global model of CARS-PLSR using mean spectra and mean SSC of six points got the best performance in terms of root mean square error of prediction (RMSEP) and residual predictive deviation (RPD), with values of 0.25% and 2.62, respectively. Overall, multi-point detection in semi-transmittance mode could weaken the distribution difference caused by biological variability and allow the non-destructive prediction of SSC in pomelo. • An original system with a puncture optical fiber was designed. • The sensitive area and light distribution were explored by puncture measurement. • The influence of light and SSC distribution on multi-point detection were analyzed. • The mean spectra can effectively eliminate the influence of asymmetric light distribution on the model. • Pre-processing methods and wavelength selection by CARS were employed to improve the model.