Potato is a major crop around the world with special importance given in developed countries to the French frying, and chipping industries. Quality attributes of potatoes dramatically influence final product conditions and consequently affect product marketability. Many research studies have been conducted to investigate the feasibility of measuring quality attributes and external and internal defects of potato tubers using rapid and/or noninvasive methods (spectroscopic, vison, and sonic). An extensive review has been conducted of nondestructive techniques that have been studied for assessing quality attributes of raw potatoes as well as chips and French fries. Such factors included specific gravity, dry matter, water content, carbohydrates, protein, defects, and diseases. In addition, systems for sorting tubers based on various quality characteristics have been discussed in detail. Also, commercial systems are available in the market for sorting and grading tubers based on different quality factors. However, more deep studies are needed to enhance rapid measurement performance and investigate more attributes that are important to growers and industry. The main objectives of this study were to investigate the potential of using spectroscopic as well as hyperspectral systems to evaluate processing-related constituents and parameters of two common potato cultivars, Frito Lay 1879 (FL) and Russet Norkotah (RN), using partial least squares regression (PLSR), and several types of artificial neural network (ANN) along with wavelengths selection techniques being interval partial least squares (IPLS), and genetic algorithm (GA). In addition, classification of tubers based on sugar levels has been conducted using linear discriminant analysis (LDA) functions, k-nearest neighbor (Knn), partial least squares discriminant analysis (PLSDA), feed forward artificial neural network (FFNN), and classifier fusion. The first study in the 2008 season was conducted to evaluate five constituents for both FL and RN using NIR transmittance, and VIS/NIR interactance modes as well as VIS/NIR hyperspectral systems for 0.5'' (12.7 mm) sliced samples and whole tubers. Results showed that the interactance mode yielded most of the best PLSR results. For primordium leaf counts, glucose, sucrose, specific gravity, and soluble solids, the optimum prediction models obtained from the interactance mode resulted in R (RPD) values of 0.95 (3.29), 0.90 (2.14), 0.81(1.63), 0.61(1.27), and 0.55(1.18) respectively for FL. For RN, the R(RPD) values were 0.90 (2.19), 0.95 (3.12), 0.63(1.30), 0.59(1.22), and 0.37(1.08) respectively. Slightly lower performance was achieved for whole tubers with optimal R(RPD) values FL in the case of primordium leaf counts, glucose, sucrose, and specific gravity of 0.89(2.22), 0.88(1.78), 0.81(1.64), and 0.37(1.06) respectively. The R(RPD) values for RN were 0.77(1.50), 0.79(1.60), 0.43(1.10), and 0.51(1.08) for primordium leaf counts, glucose, sucrose, and specific gravity. Soluble solids for whole tubers showed weaker correlation than above constituents. Following preliminary results in the 2008 season, more concentration was given to glucose and sucrose as they significantly affect chip and French fry products quality. Also, based on preliminary results, transmittance mode was replaced by NIR reflectance mode. The second study was conducted in the 2009 and 2011 seasons using interactance, reflectance, and hyperspectral systems on the same cultivars and also using 0.5''(12.7 mm) sliced samples and whole tubers.Results of prediction models using PLSR and ANN along with models using IPLS and GA as wavelength selection techniques demonstrated strong correlation for VIS/NIR hyperspectral systems in which only sliced samples were used. For glucose prediction models, R(RPD) values were as high as 0.81(1.70) and 0.97(3.66) for FL and RN and those values for the best sucrose prediction models were 0.58(1.23) and 0.38(1.0) for FL and RN. For VIS/NIR interactance mode, promising results for glucose prediction were shown for FL and RN. FL and RN yielded R(RPD) values of 0.92(2.35) and 0.95(3.02) respectively for sliced samples, and 0.85(1.92) for FL and 0.97(4.16) for RN in the case of whole tubers. Sucrose prediction models resulted in strong correlation with R(RPD) values as high as 0.95(3.29) and 0.78(1.57) for FL and RN for sliced samples, and 0.94(3.01) for FL and 0.94(2.82) for RN in the case of whole tubers. NIR reflectance showed auspicious performance for both cultivars. The best glucose prediction models yielded R(RPD) values for FL and RN as high as 0.96(3.47) and 0.97(4.21) in the case of sliced samples, and 0.82(1.78) and 0.98(4.84) for FL and RN in the case of whole tubers. Sucrose also showed high correlation for sliced samples with R(RPD) values of 0.96(3.89) and 0.97(3.92) for FL and RN, and those values for the whole tubers were 0.96(3.80) and 0.97(3.78) for FL and RN. In general, prediction models based on selected wavelengths showed similar or better performance compared to full wavelengths models, and it is worth stating that GA yielded higher numbers of selected variables (wavelengths) than IPLS; thus, the latter method was preferred as it often produced similar results compared to GA models. Classification of potatoes based on sugar levels associated with the frying process showed encouraging results with the lowest classification error values of FL and RN obtained for glucose being 16% and 13%, for sliced samples, and 18% and 0% for whole tubers. In the case of sucrose models, error values in the case of sliced samples were 23% and 18%, and those values for whole tubers were 26% and 18% for FL and RN respectively. Such classification results for whole tubers demonstrated the feasibility of explaining more variation between samples when the data from interactance and reflectance modes was used, in the listed wavelengths ranges, and consequently applying both modes in an on-line system has the potential to enhance the sorting of tubers based on sugar levels.This research demonstrated the feasibility of hyperspectral imaging systems as well as spectroscopic systems, in reflectance and interactance modes, in rapidly and accurately measuring some important constituents for potato growers and processing industries. Such results, especially for whole tubers, proved that there is a possibility for conducting an on-line sorting system based on sugar levels, or a hand-held device for rapid evaluation of quality either in field or during storage, to maintain potato tubers quality and accurately estimate the suitable time for harvesting or processing.
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