Variable selection for the determination of the soluble solid content of potatoes with surface impurities in the visible/near-infrared range

Abstract

Non-destructive quality assessment of the inner content of potatoes is a key challenge in automatic grading of agricultural quality, especially when potatoes have surface impurities. This study compares different partial least-square regression (PLSR) models for the prediction of soluble solid content (SSC) of potatoes under conditions of surface cleanliness and surface impurities. Smoothing and spectral preprocessing with first-order derivatives and variable sorting for normalization (VSN) can effectively eliminate spectral noise. Variable selection algorithms were used to extract effective variables in order to further optimise the prediction models. The results showed that the method of the variable combination population analysis—iteratively retains informative variables (VCPA-IRIV) is the best method for selecting valid variables, and that the 35-variable VCPA-IRIV-PLSR prediction model could predict the potato SSC with a predictive correlation coefficient ( R p ), root-mean-square error of prediction ( RMSE P ), and residual predictive deviation ( RPD ) values of 0.831, 0.461 ○ Brix and 1.798, respectively. Therefore, the experimental results show the feasibility and applicability of the proposed SSC prediction method for potatoes with surface impurities using near-infrared spectroscopy. • Non-destructive detection of uncleaned potatoes (with surface impurities). • Good PLSR prediction model of potato SSC based on surface impurities. • Processing spectral data with multiple preprocessing algorithms to eliminate noise. • Proposed optimised variable selection algorithms for effective wavelength selection. • Application of VCPA-IRIV algorithm.