# Enhancing Traceability of Italian Almonds through IR Spectroscopy and Chemometric Classifiers
## 3 Results
### Results of PLS-DA Modeling  
As before, two different data sets were available for PLS-DA; models were created on raw (mean-centered) data, and after different data pretreatments, namely, SNV, first derivative (D1), second derivative (D2), SNV + D1, and SNV + D2. Consequently, six different cross-validated calibration models were calculated on shell and kernel spectra.  
The optimal model parameters, i.e., the appropriate data preprocessing and the most suitable number of LVs to be used to build the calibration model, were chosen by inspecting the total cross-validated correct classification rate (% CCRcv).  
In Table 5, only the details associated with the optimal model, together with its prediction on the external test set (% CCRpred), are shown.  
From Table 5, it is clear that the model allows for the correct classification of all test samples. Attaining a 100% accurate classification rate is noteworthy and indicates that the spectral data contain highly discriminating information about the geographical origin. This outcome can also be appreciated in Figure 3, where samples are projected onto the space spanned by the two canonical variates (CVs). From the plot, it is apparent that the three classes present three clear groups in the CV space. In fact, samples belonging to class Calabria (red diamonds) are the only ones presenting negative CV1 scores, whereas objects belonging to Sicily (blue stars) and Apulia (green squares) discriminate along CV2. Indeed,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **Calabria** & **Apulia** & **Sicily** \\ \hline Calabria & 77.5 & 65.0 & 80.0 \\ \hline Apulia & 70.0 & 75.0 & 40.0 \\ \hline Sicily & 72.5 & 40.0 & 75.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Class-by-class sensitivities and specificities for the SIMCA models calculated on the spectra collected on the almonds’ kernels.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **Pretreatment** & **PCs** & **Efficiency (\% CV)** \\ \hline Calabria & D2 & 10 & 67.0 \\ \hline Apulia & SNV & 6 & 70.8 \\ \hline Sicily & raw & 6 & 68.1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: SIMCA on the spectra collected on the almonds’ kernels.  
the greatest part of samples from Sicily fall at the positive CV2, whereas almonds from Apulia present negative CV2-scores.  
Similarly, PLS-DA models were built on kernel spectra; the outcome of the analysis is displayed in Table 6.  
The application of PLS-DA on the kernel spectra provides less accurate predictions. In fact, the optimal calibration model (built on data preprocessed by the first derivative) misclassified \(11\) over \(80\) test samples; in particular, of the \(40\) almonds from Calabria, \(2\) were predicted as belonging to class Apulia and \(1\) associated with Sicily; \(18\) (over \(20\)) objects belonging to class Apulia were properly classified, whereas \(2\) samples were attributed to the other two categories and \(3\) almonds harvested in Sicily were predicted as belonging to class Apulia (all the others were properly classified). This outcome confirms what was already observed while discussing SIMCA, i.e., the IR profiles collected on kernels are less suitable than shell spectra for the geographical classification of the investigated samples.