Evaluation of sugar content in potatoes using NIR reflectance and wavelength selection techniques

Near-infrared (NIR) diffuse reflectance has been extensively and successfully applied on quality assurance for fruits, vegetables, and food products. This study is principally aimed to extract the primary wavelengths related to the prediction of glucose and sucrose for potato tubers (of Frito Lay 1879 (FL), a chipping cultivar, and Russet Norkotah (RN), a table use cultivar, and investigating the potential of classification of potatoes based on sugar levels important to the frying industry. Whole tubers, as well as 12.7mm slices, were scanned using a NIR reflectance spectroscopic system (900–1685nm). To extract the most influential wavelength in the studied range, interval partial least squares (IPLS), and genetic algorithm (GA) were utilized. Partial least squares regression (PLSR) was applied for building prediction models. Prediction models for RN showed stronger correlation than FL with r(RPD) (correlation coefficient (ratio of reference standard deviation to root mean square error of the model)) values for whole tubers for glucose being as high as 0.81(1.70), and 0.97(3.91) for FL and RN; in the case of sliced samples the values were 0.74(1.49) and 0.94(2.73) for FL and RN. Lower correlation was obtained for sucrose with r(RPD) for whole tubers as high as 0.75(1.52), 0.92(2.57) for FL and RN; and the values for sliced samples were 0.67(1.31) and 0.75(1.41) for FL and RN respectively. Classification of potatoes based on sugar levels was conducted and training models were built using different classifiers (linear discriminant analysis (LDA), K-nearest neighbor (Knn), partial least squares discriminant analysis (PLSDA), and artificial neural network (ANN)), in addition to classifier fusion. To obtain more robust classification models for the training data, 4-fold cross validationwas used and results were tested using separate sets of data. Classification rates of the testing set for whole tubers, based on glucose, were as high as 81% and 100% for FL and RN. For sliced samples, the rates were 83% and 81% for FL and RN. Generally, lower classification rates were obtained based on sucrose with values of whole tubers of 71%, and 79% for FL and RN, and for sliced samples the rateswere 75%, and 82%which follows a similar trend as PLSR results. This study presents a potential of using selected wavelengths and NIR reflectance spectroscopy to effectively evaluate the sugar content of potatoes and classify potatoes based on thresholds that are crucial for the frying industry. ã 2015 Elsevier B.V. All rights reserved.