## 3 Results and Discussion
### External Validation of the Instrumental Screening Tool  
The models developed with the seven sets were used to classify the samples of the corresponding seven validation sets, using as classification criteria their respective uncertainty ranges. The results of the external validation of the proposed instrumental screening tool are collected in Table 2.  
Among the classified samples, a mean of 78.9% of the samples graded only by the instrumental screening tool were correctly assigned, being the EVOO category the one with the best performance. Regarding the uncertainty rate, a mean of 23.3% of the virgin olive oil samples were identified as boundary and would need the panel test assessment, meaning that the workload of the sensory panel would have been reduced to almost one-third of the total sampling. Here, we found differences among the categories; the LOO presented the fewest uncertain samples. This fact could be explained by the lack of fruity sensory attributes and the high presence of several defects, features that allow a clearer classification. Finally, bearing in mind that currently the sole and only reference method to sensory grade virgin olive oils is the panel test, a full correct classification is assumed from this evaluation. Therefore, considering that the uncertain samples would be assessed by the sensory panel and would be correctly graded, we can state that a mean reliable assignment of 84% would be reached by combining \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**1 st PLS-DA (EVOO vs. Non-EVOO)**} & \multicolumn{3}{c}{**2 nd PLS-DA (VOO vs. LOO)**} \\  & 7 LVs, Q2 \textgreater{}0.4; RMSEcv \textless{}0.37; ANOVA \textgreater{}Value \textless{}0.05 & 5-7 LVs, Q2 \textgreater{}0.4; RMSEcv \textless{}0.33; ANOVA \textgreater{}Value \textless{}0.05 & 5-7 LVs \textless{}Q2 \textgreater{}0.4; RMSEcv \textless{}0.33; ANOVA \textgreater{}Value \textless{}0.05 & 5-7 LVs \textless{}Q2 \textgreater{}0.4; RMSEcv \textless{}0.33; ANOVA \textgreater{}Value \textless{}0.05 & 5-7 LVs \textless{}Q2 \textgreater{}0.4; RMSEcv \textless{}0.33; ANOVA \textgreater{}Value \textless{}0.05 \\ \hline
**Set** & **Lower** & **Upper** & **Uncertain Samples** & **Lower** & **Upper** & **Uncertain Samples** \\  & **Threshold 1** & **Threshold 2** & **3 (\%)** & **Threshold 1** & **Threshold 2** & 3 (\%) \\ \hline
**Full** & 0.354 & 0.752 & 15 (49/301) & 0.434 & - & \\
**model** & 0.397 & 0.503 & 4.2 (10/241) & 0.473 & - & \\ \hline
**Subsets** & & & & & & \\ \hline Set 1 & 0.397 & 0.503 & 4.2 (10/241) & 0.473 & - & \\  Set 2 & 0.427 & 0.836 & 17.4 (2/241) & 0.418 & 0.514 & 2.8 (4/143) \\  Set 3 & 0.383 & 0.771 & 15.4 (37/241) & 0.468 & - & \\  Set 4 & 0.310 & 0.497 & 6.6 (16/241) & 0.442 & - & \\  Set 5 & 0.370 & 0.748 & 14.5 (35/241) & 0.491 & - & \\  Set 6 & 0.426 & 0.812 & 16.6 (40/241) & 0.483 & - & \\  Set 7 & 0.411 & 0.760 & 22.2 (27/241) & 0.425 & 0.502 & 2.1 (3/143) \\ **Mean** & 0.389 & 0.704 & 13.8 & 0.457 & 0.482 & 2.5 \\  SD & 0.041 & 0.142 & 6.31 & 0.029 & 0.024 & 0.5 \\ \hline \hline \multicolumn{7}{l}{PLS-DA: partial least squares-discriminant analysis, LY: latent variable, Q2 \textgreater{}2; prediction power of the model, RMSEcv: root-mean-square error of cross-validation, SD: standard deviation. 1 Models’ sensitivity was equal to 1 for predicted values under this threshold; 2 Models’ specificity was equal to 1 for predicted values above this threshold; 3 The numbers between parentheses correspond to the number of uncertain samples/number of samples in the training set.} \\ \end{tabular}
\end{table} 
Table 1: Uncertainty ranges obtained from receiver operating characteristics (ROC) curves and uncertain samples for each classification model, obtained during internal validation of seven different subsets (leave-10%-out cross-validation).