# E-Eye-Based Approach for Traceability and Annuality Compliance of Lentils
## 3 Results and Discussion  
### ASCA for Exploring the Significance of the Harvesting Year and the Origin  
Once all the images were collected, colorgrams were obtained using in-house MATLAB (R2015b; The Mathworks, Natick, MA, USA) functions; examples of the collected images and colorgrams are reported in Appendix A (Figures A1 and A2, respectively). The ASCA was used to inspect the significance of two factors: the origin and harvesting year. To perform the ASCA, we decided to work on the fraction of the dataset that could provide a crossed-factor design with all the cells populated; therefore, only the samples of the three varieties produced in the years 2017 and 2018 were considered. Moreover, in order to have a balanced design, 40 samples per each combination of year and origin were selected out of the available number reported in Table 2, using the Kennard-Stone algorithm [39] on the colorgram data. This decision stems from the fact that the ASCA does not allow the independent interpretation of effect matrices for unbalanced experimental designs, as in our case [40]. The significance has been evaluated by the permutation test (104 permutations), and both effects and their interaction appeared to be significant. The inspection of samples projected onto the space spanned by the first SCs associated with the model of the original effect (Figure 1) reveals a clear grouping trend of individuals according to their harvesting area (L'Aquila-AQ, Colforito-COL, Castelluccio di Norcia-CDN). In fact, CDN samples (blue diamonds) fall at negative values of SC1 whereas COL (green squares) and AQ objects (red dots) present positive values of this component. Samples harvested in AQ seem divided into two subgroups: one falling at negative SC2 scores and one at slightly negative values of this component, overlapping with CDN samples. This similarity can certainly be attributed to the proximity among the diverse areas and to similar pedoclimatic conditions. However, it must be stressed that the class AQ consists of different ecotypes cultivated in the vast province of L'Aquila. This condition results in a class with a higher variance than the others considered. Figure 1: ASCAanalysis: SCA model of the effect of the _geographical origin_ factor. Sample scores are plotted onto the two SCs of the model after the projection of the residual matrix to show within-level variability. Legend: Red dots—AQ; Green Squares—COL; Blue diamonds—CDN. Inspection of the loadings for the SC model, reported in Appendix B (Figure A3), indicated that the CDN samples are characterized, in general, by a higher amount of pixels with higher intensity of the red channel and a lower intensity of the green and blue channels, and lightness. At the same time, the CDN samples have a higher amount of pixels with higher hue, saturation, and intensity values than the lentils from the other two origins. On the other hand, by looking at the loadings on SC2, along which COL and AQ are differentiated, it is possible to affirm that the images from COL samples are mostly characterized by a lower intensity of the red channel and intermediate intensity of the blue one, the lightness also being lower. With respect to AQ, they are also characterized by slightly higher hue, intermediate saturation, and lower intensity. The inspection of the SC1 associated with the year effect factor unveils a clear trend related to annuality (Figure 2). Indeed, the 2018 production year is characterized by predominantly positive scores, whereas 2017 is mostly described by negative values. The corresponding loadings (Appendix B, Figure A4), which were found to be statistically different from zero only for a few variables, indicate that the 2018 samples are characterized by a higher amount of pixels with an intermediate intensity of the red, green, and, to a lower extent, blue channels.