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.

马铃薯内在成分的无损质量评估是农业质量自动分级的关键挑战，尤其是当马铃薯表面有杂质时。本研究比较了不同的偏最小二乘回归 (PLSR) 模型在表面清洁度和表面杂质条件下预测马铃薯可溶性固形物含量 (SSC)。使用一阶导数和归一化变量排序 (VSN) 进行平滑和频谱预处理可以有效地消除频谱噪声。变量选择算法用于提取有效变量，以进一步优化预测模型。结果表明，变量组合总体分析方法——迭代保留信息变量（VCPA-IRIV）是选择有效变量的最佳方法，R _p )、预测均方根误差( RMSE _P ) 和残余预测偏差 ( RPD ) 值分别为 0.831、0.461 ^○ Brix 和 1.798。因此，实验结果表明了所提出的 SSC 预测方法的可行性和适用性，该方法使用近红外光谱法预测表面有杂质的马铃薯。