## 3 Results
### Classification  
The PLS-DA modelling based on rule 1, divides input data samples in 2 classes: FIBERGLASS/ORGANIC and ASBESTOS/AGGREGATES/MORTAR/BRICK. It shows a captured variance of 94.73% with seven latent variables. The Sensitivity and the Specificity of the model in calibration and cross validation are shown in Table 2.  
Figure 6: PCA score plot utilized to perform data calibration according to the different hierarchical modelling rules defined and applied for classification (**a**): rule 1—Standard Normal Variate (SNV) and Mean Center (MC), (**b**): rule 2—MC, (**c**): rule 3—SNV Smoothing and (**d**): rule—MC. The components allowing the greater separation among classes are shown in the score plots. The PLS-DA model following the rule 2, allows to perform an optimal discrimination between FIBER GLASS and/ ORGANIC material. It shows a captured variance of 99.53% with four latent variables. The Sensitivity and the Specificity of the rule 2 based model in calibration and cross validation are shown in Table 3.  
The PLS-DA model for the rule 3, discriminates AGGREGATES/MORTAR/BRICK from ACM. It shows a variance capture of 92.41% with seven latent variables. The Sensitivity and the Specificity of model rule 3 in calibration and cross validation are shown in Table 4. The results reported in Table 4 clearly outlined as one of the major challenge of the proposed approach is to further increase the recognition between ACM and aggregates, mortar and bricks. Such a result is probably linked to the adopted resolution (i.e., acquired image field). On the other hand the goal of this study was to test the methodology at macro-scale. A resolution increase, that is an higher number of acquired spectra for investigated surface unit, should produce, for the rule 3, an increase of sensitivity. To perform the identification of asbestos materials and to reduce the number of false positive in ACM, rule 4 was defined. It was thus possible to discriminate ACM from AGGREGATES MORTAR AND BRICK + ORGANIC. The PLS-DA model for rule 4 shows a variance of 98.64% with six latent variables. The sensitivity and specificity of the model rule 4 in calibration, cross validation was shown in Table 5. \begin{table}
\begin{tabular}{c c c} \hline \hline
**rule 3** & **ACM** & **AGGREGATES AND MORTAR + BRICK** \\ \hline Sensitivity (Cal): & 77.3 & 94.4 \\ Specificity (Cal): & 94.4 & 73.3 \\ Sensitivity (CV): & 72.8 & 94.4 \\ Specificity (CV): & 94.4 & 72.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Sensitivity and Specificity in calibration (Cal) and cross validation (CV) of the Partial Least Squares Discriminant Analysis (PLS-DA) rule 3 based classifiers. \begin{table}
\begin{tabular}{c c c} \hline \hline
**rule 1** & **AGGREGATES** & **FIBER GLASS + ORGANIC** \\ \hline Sensitivity (Cal): & 95.1 & 95.6 \\ Specificity (Cal): & 95.6 & 95.1 \\ Sensitivity (CV): & 95.1 & 95.5 \\ Specificity (CV): & 95.5 & 95.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Sensitivity and Specificity in calibration (Cal) and cross validation (CV) of the Partial Least Squares Discriminant Analysis (PLS-DA) rule 1 based classifier. \begin{table}
\begin{tabular}{c c c} \hline \hline
**rule 2** & **FIBER GLASS** & **ORGANIC** \\ \hline Sensitivity (Cal): & 100 & 97.3 \\ Specificity (Cal): & 97.3 & 100 \\ Sensitivity (CV): & 100 & 97.3 \\ Specificity (CV): & 97.3 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Sensitivity and Specificity in calibration (Cal) and cross validation (CV) of the Partial Least Squares Discriminant Analysis (PLS-DA) rule 2 based classifiers. The results of hierarchical PLS-DA, applied to the validation dataset, are reported in Figure 7. The presence of asbestos in the composition was correctly recognized, while only a few pixels were identified as false positive.