## 3 Results and Discussion
### Discrimination of Potato Varieties Using the PLS-DA of ATR-FTIR Spectra
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Class** & **GA** & **TU** & **MO** & **PI** & **DE** & **AG** & **KE** & **SP** \\ \hline Computed classes & 100.0 & 96.0 & 100.0 & 90.9 & 96.2 & 100.0 & 100.0 & 94.7 \\ Predicted classes & 75.0 & 72.7 & 84.6 & 80.0 & 81.8 & 90.9 & 77.8 & 75.0 \\ \hline \end{tabular}
\end{table} 
Table 3: Proportion (%) of correctly classified potato samples using PLS-DA in the calibration (computed classes) and external prediction (predicted classes). Inspection of Figure 6 reveals that all the calibration samples belonging to the accessions GA, MO, AG, and KE were correctly classified, while one or two classification errors can be observed for the other potato samples with the associated proportions of correct classifications ranging between 90.9% and 96.0% (Table 3). Concerning the external potato samples, the number of classification errors ranged between one (AG) and three (GA and TU). Because of the lower number of prediction samples compared to the calibration data, the percentage of correctly predicted classes was slightly worse than that in calibration. The observed values, ranging between 72.7% (TU) and 90.9% (AG), do however indicate a good predictive performance of the PLS-DA model. To further confirm the reliability of the classification model, PLS-DA was used to discriminate between the potato samples after shuffling the classes. To this end, 30 different random assignments of the 194 calibration samples into eight categories were generated and PLS-DA classification was applied for every repetition. The model predictive performance was evaluated using cross-validation with five cancellation groups. The trend  
Figure 6: Calculated/predicted PLS-DA responses versus sample index. The open circles identify the external data samples and the red dotted line indicates the class threshold. Asterisks indicate the accessions from Majella National Park. of prediction errors over the 30 repetitions is displayed in Figure 16 (Appendix A). It can be observed that the proportion of correctly assigned samples to individual groups only rarely surpassed 40% and the total error was less than 15%, much lower than that observed when PLS-DA was applied to the true potato classes. It follows that the ATR-FTIR spectra of tubers really contained information on the potato accession and the good prediction results provided by PLS-DA applied to the true classes is unlikely to have occurred by chance. The influence of the various regions of the ATR-FTIR spectrum in the discrimination of the potato samples using PLS-DA was quantified using VIP (variable importance in the projection) scores [54]. The variables with VIP indices greater than one are usually assumed to be significant. The results of the VIP analysis are displayed in Figure 7.  
The most influential regions of the ATR-FTIR spectrum (VIP \(>\) 1) in the potato discrimination that could be unequivocally assigned included the spectral regions around 3370 cm\({}^{-1}\) and 2900 cm\({}^{-1}\) associated with the O-H and C-H stretching signals, respectively, and the bands centered at 2100 and 1640 cm\({}^{-1}\), which were attributed to the vibrational modes of free water molecules and those bound to starch, respectively. The intensity of the infrared spectrum in these regions has been related to the level of hydration of starch [31], which can be considered a character of potato flesh that is mainly influenced by the kind of accession. It is worth noting that, as described in Section 2.2, the eight potato accessions investigated in this study were grown in the same experimental field and the plants were not artificially irrigated. Therefore, the effect of possible differences in watering on the potato flesh composition can be neglected.