## 3 Results and Discussion
### Discrimination of Potato Varieties Using the PLS-DA of ATR-FTIR Spectra  
A total of 279 ATR-FTIR spectra were collected by analyzing different slices extracted from 7-9 tubers of each accession (Figure 5a). ATR distortion of the relative intensities of the bands and  
Figure 5: (**a**) Raw and (**b**) standard normal variate (SNV)-scaled attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectra of potato samples. (**c**) Mean SNV-scaled spectra in the 1800–940 cm\({}^{-1}\) range for each accession. A: Absorbance. shifts occur at lower frequencies, which can crucially affect quantitative analyses or accurate band assignments; however, this was expected to have a negligible impact on the fingerprinting ability of the infrared spectra in the classification of the potato accessions. Therefore, ATR correction on the spectra was not performed. The data matrix was partitioned into calibration and prediction data sets consisting of 194 and 85 samples, respectively, via application of the duplex Kennard-Stone algorithm [52] to ensure a good representativeness of both groups. Finally, each potato category was represented using a variable number of calibration samples ranging from 19 (SP) to 29 (MO), whereas the external samples belonging to a given potato accession ranged from 8 (SP) to 13 (MO). The raw ATR-FTIR spectra were subjected to various pre-processing methods [53], namely standard normal variate (SNV), first- and second-derivative transformation, and their combinations, with the aim of removing spurious variability and/or enhancing the systematic differences within the spectra profiles. In particular, SNV consists of autoscaling on the rows such that every spectrum will have a mean of 0 and a standard deviation of 1 after scaling. The Savitzky-Golay approach with a 15-point window was applied in the first- and second-derivative transformation using second- and third-order polynomial fittings, respectively. Regardless of the pre-treatment mode applied to the ATR-FTIR spectra, PLS-DA was conducted on the autoscaled variables (autoscaling on columns). The influence of the spectra pre-treatment on the PLS-DA predictive performance was evaluated using leave-one-out cross-validation. The comparison of the proportion (%) of correctly classified samples for various pre-processing methods, reported in Table 2, revealed that SNV scaling (Figure 5b) provided the best results, with over 97% of classifications being correct. It is worth noting that discrimination based on the raw spectra was noticeably worse (87.1% of correct classifications in cross-validation), despite a relatively wide variability in their intensities. Such differences are probably related with variations in the extent of the contact of the potato flesh with the ATR crystal, which can only be partially controlled through the pressure monitoring system integrated with the instrument. SNV scaling seemed to remove this kind of random variability and to enhance the spectral differences due to the potato accession, especially at lower wavenumbers (Figure 5c). The proportion of correctly assigned potato samples in the calibration and external prediction is reported in Table 3, whereas Figure 6 graphically displays the calculated and predicted PLS-DA responses for each class. In each insert of Figure 6, the data above or below the line represent the samples accepted or refused, respectively, by a given class. \begin{table}
\begin{tabular}{c c} \hline
**Pre-Processing of ATR-FTIR Spectra** & **NER\% in Cross-Validation** \\ \hline None & 87.1 \\ First derivative & 86.1 \\ Second derivative & 81.4 \\ SNV 1 & 97.4 \\ SNV + first derivative & 88.7 \\ SNV + second derivative & 79.9 \\ \hline \end{tabular} \({}^{1}\) Standard Normal Variate.  
\end{table} 
Table 2: Proportion of correctly classified potato samples (non-error-rate, NER%) in leave-one-out cross-validation for different pre-processing methods of the ATR-FTIR spectra.