## 3 Results and Discussion
### Cultivar Discrimination  
The need for developing a proper rapid technology able to properly classify the potato genotypes is further amplified by the demand to prevent a deceptive practice in the retail marketing of potatoes, which is the distribution of a mixed bunch of cultivars containing morphologically similar tubers, which are not, however, of the same quality standards. However, this practice is drifting the consumers' preference, purchase, and consumption to inferior quality potatoes inversely proportional to the increased purchase cost. As a result, popular areas, reputable in the production of optimal quality potatoes, such as of protected designations of origin (PDO) or protected geographical indications (PGI) are subjected to substantial profit loss. Consequently, the high value of traditional cultivars that are grown in the above areas is capitalized by retailers that distribute potatoes of similar external traits but of different cultivars with inferior quality grown in miscellaneous geographical sites. Color of the peel or the flesh resulted only in 66% accuracy (non-error rate) in both calibrated or cross-validated classification models, which implies that there were not significant differences of the peel or the inner tissue color among these three cultivars (Spunta, Banba, and Sylvana) and, therefore, color measurement cannot be considered as a reliable discrimination method. Neither did the normalized parameters of the digital images (R-G-B, R-G-NIR, or R-B-NIR) provide valuable information in separating the three potato cultivars with accuracy scores lower than 51%, 44%, and 34%, respectively, when captured above the peel of the tuber, and lower than 43%, 34%, and 47%, respectively, when captured in the flesh pith (data nor shown). Better discrimination models were generated when chlorophyll fluorescence parameters were tested with Fi, Fv, and Phi_Eo exhibiting the highest VIP scores (a measure of a variable's importance in the PLSDA model) (data not shown). However, when digital imaging indexes were combined with the above chlorophyll fluorescence parameters, several models with higher classification accuracy were generated. Therefore, a combination of (NIR - G)/(NIR + G), (NIR - B)/(NIR + B), Fi, and Fv measured on the external area of the tubers was able to separate the three cultivars by 76% in the cross-validated model and by only 61% in the internal flesh, confirming once more the assumption that the flesh components are more or less similar in Spunta, Banba, and Sylvana (Table 2). In our study, the best discrimination of the three potato cultivars, was achieved using the spectral reflectance measurements upon proper preprocessing of the raw data (Figure 6) of the skin of the tubers (99% and 93% of the calibration and the cross-validation sample sets), almost similar to the destructive spectral acquisition on the flesh after halving the tubers (96% and 95%, respectively) (Table 2). The implementation of genetic algorithm identified 458-516, 546-662, 721-809, and 956-984 nm as the most significant external reflectance regions and 458-516, 575-633, 663-692, and 721-779 nm (data not shown) as the most significant internal reflectance regions in classifying the potatoes into the respective cultivars (Figure 7). Similarly, chemometric treatment of the Fourier Transformed Infrared Spectroscopy (FTIT) spectra allowed the discrimination of eight different potato cultivars with a good accuracy [34]. Being a rapid, and/or noninvasive method, the spectroscopy technique is suitable in on-line applications combining less time consumption, greater robustness, higher reproducibility, and cost effective performance compared to human labor or other destructive laboratory methods used in quality assessment [24; 35]. Therefore, spectra reflectance data from the skin of the potato tubers may be reliably used in the assessment of postharvest storage life, as well as in the cultivar discrimination process.