Improved prediction of vitamin C and reducing sugar content in sweetpotatoes using hyperspectral imaging and LARS-enhanced LASSO variable selection
作者：Hong-Ju He, Chen Zhang, Xihui Bian, Jinliang An, Yuling Wang, Xingqi Ou, Mohammed Kamruzzaman*
關鍵字：Variable selection, LARS-LASSO, Hyperspectral data, Sweet potato roots, Reducing sugar, Vitamin C
論文來源：期刊
具體來源：Journal of Food Composition and Analysis, 2024, 132, 106350
發(fā)表時間：2024年
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.