## 3 Results and Discussion  
### Postharvest Storage Assessment  
Given that the postharvest storage of potatoes cannot be easily estimated by judging sensorial indexes, such as visual color or firmness, it is of high importance to assess the freshness of a potato tuber, especially at low temperature using an objective remote sensing method. A prolonged storage of potatoes at low temperature is associated with chlorophyll and total glycoalkaloids increase, ascorbic acid decrease, and starch conversion into reducing sugars [30] that are involved both in quality deterioration such as brown discoloration during food preparation at high temperatures, as well as in safety issues, such as the synthesis of asparagine, an amino acid that is considered as a precursor of acrylamide synthesis, which in turn has been linked with health risks [6]. In our study, external color measurements proved to be an unreliable index of potato freshness, as long as cross-validated regression coefficient (Rcv) was less than 0.458, using the pooled data of all cultivars for the whole 5-month storage period at 5 \({}^{\circ}\)C (Table 1). Interestingly though, when data were tested in each individual cultivar, it turned out that external color changes were more pronounced in Spunta (Rcv = 0.728), than in Banba or Sylvana (Rcv = 0.406-0.456), implying that color in Spunta tubers is changing during storage, even in total darkness conditions. On the other hand, internal color changes were induced in all three cultivars (Table 1). The regression coefficients of color changes during 5 months of storage in each cultivar was 0.901, 0.803, and 0.883 for Spunta, Banba, and Sylvana, respectively, and indeed the regression coefficient Rcv = 0.859 of the cross-validated data set irrespectively of the cultivar can be considered as a reliable index of postharvest storage in potato, which in turn depicts color changes in the internal flesh tissue of the tubers. A model combining digital imaging indices (NIR - R/NIR + R), (NIR - B/NIR + B) and chlorophyll fluorescence parameters (Fi and Fv) resulted in a high correlation with postharvest storage period (RALL CVs = 0.885), which is also confirmed even by the models generated for each of the three individual cultivars (RSPUNTA = 0.914, R\({}_{\text{BANBA}}\) = 0.829, and R\({}_{\text{SYLVANA}}\) = 0.922). Fi expresses the fluorescence at the I-step (30 ms) of OJIP transient and Fv (=Fm-Fo) is an index of maximal variable fluorescence [31]. Fm expresses the maximal fluorescence, when all photosystem (PS) II reaction centers (RCs) are closed and Fo is the minimal fluorescence, when all PS II RCs are open (at \(t\) = 0). Therefore, the multiple regression model can be of high importance in assessing postharvest freshness of potato, not only due to the strong regression with storage time, but also given that each of the components that participate in that have a significant effect (\(p<0.05\)) and at the same time exhibit a VIF lower than 4, which was set as a threshold, in order to identify the best algorithm. In addition, multiple regressions were statistically tested in order to select the combination of specific parameters that: (i) provide a high regression coefficient with storage time, (ii) exhibit a significant effect in the generated model (\(p<0.05\)), and (iii) present a collinearity (VIF) score below 4, in order to be considered as reliable. A non-contact system based on analyzing images captured above potatoes has already been found to detect external defects on whole tubers (skin cutting, shatter bruise, common scab, greening, cracks, etc.) using color images with classification rates higher than even 95% [26; 32]. The basis of this method is the image analysis of the sample and comparison with a standard and later a decision making related to the acceptance or rejection of the samples [25].