Evaluation of Sugar Content of Potatoes using Hyperspectral Imaging

Visible/near-infrared (VIS/NIR) hyperspectral reflectance imaging was evaluated as a technique toward rapid prediction of the glucose and sucrose percentages in two common fresh use and chipping potato cultivars. Tubers were sampled and held in multiple storage temperatures in an attempt to develop uniform and broad constituent distributions. Each tested sample was a 12.7-mm-thick slice cut uniformly from all tubers. Multiple features were extracted from samples including mean reflectance spectra and curve feature parameters yielded from an exponential model. Both glucose and sucrose ratios were measured using the Megazyme sucrose/D-glucose assay procedure as reference values. Partial least squares regression (PLSR), feed forward neural networks (FFNN), radial basis functions neural networks (RBFNN), and exact design radial basis functions (RBFNNE) neural networks were used for building calibration and prediction models. PLSR results demonstrated strongly correlated models built using mean reflectance spectra for glucose for Russet Norkotah (RN) with R (RPD) (or correlation coefficient (ratio of sample standard deviation to standard error of prediction)) values as high as 0.97 (3.58), whereas those values were 0.81 (1.70) for Frito Lay 1879 (FL). Sucrose models showed less correlation performance with R (RPD) values as high as 0.60 (1.14) for FL and 0.38 (1.00) for RN. Wavelength selection/prediction using interval partial least squares (IPLS) and genetic algorithm (GA) was conducted on the data, and PLSR and NN results were close to the full-wavelength models for the glucose and sucrose of both cultivars with a preference given to IPLS as it yields less selected variables than GA. Applying K-nearest neighbor (Knn) and partial least squares discriminant analysis (PLSDA) on mean reflectance spectra resulted in glucose misclassification errors of 14 % and 18 % for FL and RN, respectively. However, classification errors were higher for sucrose indicating lower accuracy for this sugar (34 and 30 % for FL and RN). The results in this study give promise to the possibility of measuring each of these sugars rapidly for quality control and monitoring in the potato industry.