## 3 Results  
The data from the last 30 s of readings from the facilities were involved in model development. Principal component analysis was performed on the full dataset. According to the scatter plot shown in Figure 3, the appropriate number of principal components was equal to 2 or 3, but the percentage of explained variance by the first two components was almost 85%. For this reason, only the first two components were chosen to visualize the data. This decision turned out to be the right one, as the groups delineated by observations derived from the samples pertaining to other objects formed distinct, homogeneous clus \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample Name** & **Mold Bloom** & **Odor Nuisance** & **Object Features** & **Type of Object** \\ \hline B1 & Visible & Plain & Roof construction failure & House, day room \\ \hline B2 & None & Perceptible & No thermal insulation & House, day room \\ \hline B3 & None & Plain & Incorrect thermal insulation & House, wainscot \\ \hline B4 & Significant & High & No thermal and water insulation & House, basement \\ \hline B5 & None (salt efflorescence) & None & Incorrect water isolation & House, basement \\ \hline B6 & None & None & Poor ventilation & Multifamily, bedroom \\ \hline B7 & Significant & High & Incorrect insulation & House, living room \\ \hline B8 & None & None & None & House, living room \\ \hline B9 & Fine Stains & None & Poor ventilation & Multifamily, wardrobe \\ \hline Air & - & - & - & Clean air \\ \hline DT & Totally stricken & High & Fully covered in mold & Decayed timber \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Features of the analyzed objects and reference samples. Figure 3: Scree plot for the principal component analysis. ters that did not overlap with the observations derived from other samples in the main component space in Figure 4. The visibly separable groups were the clean air and decayed wood samples, which are located in the upper left and upper right corners of the graph, respectively. This agrees with intuition, as the air was completely uncontaminated by mold, while the tested wood was entirely covered with it. The remaining observations from the other samples were grouped mainly in the right half-plane. Furthest from the decayed wood sample were the data from samples B5 and B9, which, according to Table 2, were characterized by the absence of odor nuisance, but in sample B9, the experts noted a minor mold bloom. The remaining data were grouped into two clusters B1, B2, B3, and B4 in the first quadrant and B6, B7, and B8 in the fourth quadrant of the system. The indicated clusters did not represent groups with the same degree of mold infestation, but the closest relative to the reference wood sample was sample B4, in which significant mold bloom and high odor nuisance were also observed. The result of the multidimensional scaling metric can be found in Figure 5; again, the greatest difference was between the measurements from the decayed wood sample and the clean air sample. The closest to air was the B5 sample, which was characterized only by salt efflorescence. In contrast, the closest to decayed wood were the measurements from sample B7, which was characterized by significant mold bloom and a high odor nuisance, similar to the previously mentioned sample B4. The remaining results were arranged in a similar manner, as shown in Figure 4.  
The Kohonen map was made in a hexagonal topology, on a 20 \(\times\) 20 grid of neurons. The observations from the analyzed dataset were mapped onto the grid and labeled according to the sample to which they belonged, as can be observed in Figure 6. The map shows that all groups of observations separated from each other and formed disjoint groups. The observations from the clean air sample were not located in close proximity to those from the decayed wood sample. However, other groups of observations were not particularly well placed on the map, as, for example, the B9 measurements were next to those from the clean air reference sample.