Sugar degree, one of the important indicators, is evaluating apples’ internal quality. When establishing a parsimonious model for analyzing apple sugar degree, the quality of calibrated samples and wavelengths affect the model’s accuracy, later update and maintenance.In this paper, 90 apples were taken as objects, a total of 1 044 wavelength points in the 350~1 150 nm spectra bands were collected. This paper studied the efficiency and feasibility of the Lasso implemented Least Angle Regression (LASSOLars) on sample and wavelength optimization.A combination of Norris derivative filtering, first-derivation and Variable Sorting for Normalization was used to preprocess. Considering the concentration ranking, split 75% of the sample dataset into the original train dataset (68 apples) and 25% into the test dataset (22 apples), and obtained the optimal train subset by LASSOLars. Compared LASSOLars with other two variables selection methods such as Monte Carlo Uninformative Variable Elimination and Competitive Adaptive Reweight Sampling respectively. Analyzing the model results, samples and wavelength sizes & distributions. The result shows that the optimal train subset compressed 16% of the original train dataset. At the same time, not changing the average level of the original train dataset, and the distribution was closer to the test dataset, the model quality was not weakened after reducing calibrated samples.The RMSECV of the optimal train subset and original train dataset were 0.460 and 0.491, the R2CV were 0.913 and 0.916, the RMSEP were 0.462 and 0.471, R2P were 0.909 and 0.906. LASSOLars selected out 40 wavelength points, the least size with the best results and highest signal-to-noise ratio, RMSECV, R2CV, RMSEP, R2P and RPD were 0.933, 0.400, 0.944, 0.373, 2.838. Based on the samples and wavelengths optimization by LASSOLars, which expanded the application of LASSOLars in subset selection, and provides ideas for optimizing, updating and maintaining the model.