The potential use of visible/near infrared spectroscopy and hyperspectral imaging to predict processing-related constituents of potatoes

Near-infrared (NIR) transmittance spectroscopy, visible/NIR interactance spectroscopy, and visible/NIR hyperspectral imaging modes were used to determine the potential for rapid electronic estimation of glucose, sucrose, specific gravity, primordial leaf count, and soluble solids of (FL) (chipping) and Russet Norkotah (RN) (table) potato cultivars. Whole tubers and 0.5 inch (12.54 mm) slices were evaluated. Partial least squares regression (PLSR) was used to obtain the prediction models. Models for leaf counts and glucose were promising for all modes with the optimum model obtained for leaf count from interactance with sliced samples resulting in R (RPD) values of 0.95(3.29) for FL, and 0.90(2.19) for RN. For glucose, interactance also yielded the best model with R (RPD) values of 0.90(2.14) for FL, and 0.95(3.12) for RN. Models of other constituents were inconsistent between systems. Interactance mode with sliced samples showed best performance for soluble solids for FL with R (RPD) values of 0.55(1.18). Also, interactance mode demonstrated optimal performance for sucrose estimation with R (RPD) values of 0.81(1.63) for FL obtained from sliced samples, and 0.81(1.64) from whole tubers. Transmittance mode with sliced samples showed the best prediction models for RN with R (RPD) values of 0.63(1.30) for sucrose. Finally, specific gravity under interactance and transmittance modes with sliced samples for FL and RN respectively yielded R (RPD) values of 0.61(1.27), and 0.59(1.22).