This study utilized least angle regression (LARS) with the least absolute shrinkage and selection operator (LASSO) to select important wavelengths for rapid quantification of vitamin C (V-c) and reducing sugar (RS) in sweetpotato roots (SPR) using hyperspectral imaging (900-1700 nm). Nine wavelengths strongly correlated with V-c levels and twelve with RS were identified, achieving good predictions with partial least squares (PLS) regression (V-c: r(P) = 0.9704, RMSEP = 1.0098 mg/100 g; RS: r(P) = 0.9641, RMSEP = 0.2725 g/100 g). Validation with an independent sample set (n = 35) showed minimal deviation between predicted and actual values (V-c: 1.179-1.211 mg/100 g; RS: 0.316-0.324 g/100 g). Explainable AI and SHapley Additive exPlanations (SHAP) values were used to interpret the selected wavelengths. Chemical maps visually analyzed V-c and RS distribution in different SPR samples. This method effectively estimates V-c and RS levels in SPR, potentially aiding SPR quality assessment during post-harvest marketing and storage.