## 3 Results and Discussion
### Postharvest Storage Assessment
Simultaneously, the fluorescence indexes have been successfully applied on potatoes, in order to assess postharvest changes during storage [1], and it has been shown that even though chlorophyll is not produced, the tubers still possess a photosynthetic apparatus whose physiological state can be measured by fluorometry. According to [PERSON] and [PERSON] [33], after light illumination of plant tissue, a small percentage of energy is emitted during photosynthesis in the form of fluorescence, upon the return of an exited electron back to its ground state. The partial least square regression analysis of spectra reflectance at the Vis/NIR region of the electromagnetic spectrum (400-1000 nm) that was captured at three distinct areas on the potato tubers, externally or internally, turned out to the best reliable indicator of the potato freshness (Table 1). In particular, the model that was generated based on all pooled data, irrespectively of the cultivar, had a cross-validated regression coefficient (Rcv) equal to 0.981 and 0.947, as determined by external and internal measurements, respectively, not significantly lower than when only each individual cultivar was tested (0.975-0.980 and 0.930-0.963, respectively) (Table 1). Data normalization followed by the Savitzky-Golay first derivatization algorithm combined with mean centering were identified as the most appropriate preprocessing techniques to be applied on the spectral data among several others tested (Figure 2) and partial least squares regression analysis was later performed using six latent variables based on the lowest root mean square error of cross-validation (RMSECV). However, the efficient use of a sorting system, despite its' accuracy, is dependent on the rate of productivity (t/h), which is a crucial factor in determining its functionality and marketability [32]. In line with the above, spectroscopy can be used as a tool to estimate optimal spectra based on specific criteria that can be commercially implemented in an online sorting system at appropriate speed for commercial use. Therefore, the genetic algorithm was selected as a feature extraction method in order to identify specific regions of interest that mostly affect the accuracy of the model and indeed it was concluded that the spectra captured externally at the regions of 517-575, 604-633, 664-721, 780-809, 839-896, and 926-1000 nm and internally at 458-487, 546-633, 664-692, 721-750, 780-838, and 868-935 nm (Table 1) apparently provide the most useful information regarding the reliable assessment of potato postharvest storage life at 5 \({}^{\circ}\)C (Figure 3), without significantly reducing the efficacy of the model generated either from the pooled data or from each cultivar's reflectance measurements, while minimizing the number of latent variables to as low as 4 (Figure 4). \begin{table}
\begin{tabular}{l l l l l l} \hline
**Technique** & **Taber Site** & **RALL CVs** & **RSPUNTA** & **RABAN** & **RSPUNTA** \\ \hline Spectra (400–1000 nm) & Outside & 0.981 & 0.975 & 0.976 & 0.980 \\  & Inside & 0.947 & 0.930 & 0.922 & 0.963 \\ Spectra (517–575, 604–633, 664–721, 780–809, 839–896, 926–1000 nm) & Outside & 0.979 & 0.981 & 0.978 & 0.973 \\
458–487, 546–633, 664–692, 721–750, 780–838, 868–955 nm) & Inside & 0.945 & 0.951 & 0.944 & 0.952 \\  & Outside & 0.458 & 0.728 & 0.406 & 0.456 \\ Color (1, C, P) & Inside & 0.879 & 0.901 & 0.803 & 0.883 \\ (NIR-R/NIR+R), (NIR-R/NIR-B), Fi, Fe & Outside & 0.885 & 0.914 & 0.829 & 0.922 \\  & Inside & 0.780 & 0.768 & 0.827 & 0.712 \\ \hline \end{tabular}
\end{table} 
Table 1: Regression coefficients (R) of cross-validated partial least square analysis between storage period (0, 2, and 5 months) and color, digital imaging, and chlorophyll fluorescence as well as Vis/NIR spectroscopy data of the whole 400–1000 nm and the genetic algorithm (GA) selected regions. Data were either pooled from all three varieties (Spunta, Barba, or Syvaran) or individually within each one of them.