Improved prediction of vitamin C and reducing sugar content in sweetpotatoes using hyperspectral imaging and LARS-enhanced LASSO variable selection

Abstract

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