caa a22 4500 606243119 CHVBK 20210128101322.0 cr unu---uuuuu 210128e20150301xx s 000 0 eng 10.1007/s11694-014-9207-2 doi (NATIONALLICENCE)springer-10.1007/s11694-014-9207-2 Utilization of visible/near-infrared spectroscopic and wavelength selection methods in sugar prediction and potatoes classification [Elektronische Daten] [Ahmed Rady, Daniel Guyer] VIS/NIR spectroscopic systems have been extensively and successfully applied on quality assurance for fruits, vegetables, and food products. This study uniquely focused on the efficacy of selected wavelengths on predicting glucose and sucrose for potato tubers of Frito Lay 1879 (FL) and Russet Norkotah (RN) cultivars, and in turn 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 VIS/NIR interactance spectroscopic system (446-1,125nm). Interval partial least squares, and genetic algorithm were utilized for wavelength selection. Partial least squares regression (PLSR), and artificial neural networks were applied for building prediction models. Results showed that R(RPD) [correlation coefficient (ratio of reference standard deviation to root mean square error of the model)] for prediction models of glucose were as high as 0.78 (1.61), and 0.95 (3.02) for FL, and RN for slice samples, and 0.81 (1.72), and 0.97 (3.89) for FL and RN respectively in the case of whole tubers. For sucrose models, R(RPD) values were 0.71 (1.43), and 0.78 (1.57) for FL and RN for slice samples, and 0.80 (1.64), and 0.94 (2.82) for whole tubers. Classification of potatoes based on sugar levels was conducted and training models were built and validated using linear discriminant analysis, K-nearest neighbor, partial least squares discriminant analysis, and classifier fusion. Classification errors of the testing set for whole tubers, based on glucose, were as low as 18 and 0% for FL and RN. For sliced samples, the errors were 16 and 13% for FL and RN. Generally, higher classification errors were obtained based on sucrose with values of whole tubers as low as 26 and 14% for FL and RN, and for sliced samples the errors were 23 and 18% which follows a similar trend as PLSR results. This study presents a potential of using selected wavelengths in the VIS/NIR interactance range of 446-1,125nm to effectively predict sugars and classify potatoes based on thresholds that are crucial for the frying industry. Springer Science+Business Media New York, 2014 Near-infrared nationallicence Potatoes nationallicence Neural networks nationallicence Partial least squares regression nationallicence Classification nationallicence Knn nationallicence Glucose nationallicence Sucrose nationallicence Rady Ahmed Department of Biosystems and Agricultural Engineering, Michigan State University, 524 S. Shaw Lane, 48824, East Lansing, MI, USA aut Guyer Daniel Department of Biosystems and Agricultural Engineering, Michigan State University, 524 S. Shaw Lane, 48824, East Lansing, MI, USA aut Journal of Food Measurement and Characterization Springer US; http://www.springer-ny.com 9/1(2015-03-01), 20-34 2193-4126 9:1<20 2015 9 11694 https://doi.org/10.1007/s11694-014-9207-2 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s11694-014-9207-2 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Rady Ahmed Department of Biosystems and Agricultural Engineering, Michigan State University, 524 S. Shaw Lane, 48824, East Lansing, MI, USA aut NATIONALLICENCE 700 1- Guyer Daniel Department of Biosystems and Agricultural Engineering, Michigan State University, 524 S. Shaw Lane, 48824, East Lansing, MI, USA aut NATIONALLICENCE 773 0- Journal of Food Measurement and Characterization Springer US; http://www.springer-ny.com 9/1(2015-03-01), 20-34 2193-4126 9:1<20 2015 9 11694