# A Lab-Made E-Nose-MOS Device for Assessing the Bacterial Growth in a Solid Culture Medium
## 3 Results and Discussion
### Bacterial Species Discrimination Using the E-Nose-MOS Lab-Made Device  
The dataset comprising the resistance signals based feature extraction data generated by the E-nose sensors (9 sensors \(\times\) 7 feature extraction variables, for each vial inoculated with different pre-established CFU of a single bacterium) was used to evaluate the potential application of the lab-made E-nose for discriminating the four bacteria under study, which were grown in solid agar medium (BHI agar). \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Volatile Compounds**} & \multicolumn{2}{c}{**Gram-Negative Bacteria**} & \multicolumn{2}{c}{**Gram-Positive Bacteria**} \\ \cline{2-5}  & _E. coli_ & _P. aeruginosa_ & _E. faecalis_ & _S. aureus_ \\ \hline Alcohols & & & & \\
1-Butanol & 33.82 & nd & 51.68 & nd \\
1-Nonanol & 5.42 & nd & nd & nd \\
1-Pentanol & 53.96 & 95.53 & 869.56 & 51.09 \\
3-Chloro-2-methyl-2-pentanol & nd & nd & nd & 11.11 \\ Ethanol & nd & nd & 695.93 & nd \\ Phenylethyl alcohol & 15.46 & nd & 23.30 & nd \\ \hline Aldehydes & & & & \\ Phenol & 6.19 & nd & 6.87 & nd \\ \hline Alkanes & & & & \\ Isoectane & 5.11 & 5.21 & 5.55 & 5.41 \\ \hline Alkenes & & & & \\ (E)-1,4-Undeca & nd & 70.09 & nd & nd \\
1-Undecene & nd & 32.00 & 23.71 & 43.71 \\ \hline Carboxylic acids & & & & \\
2-Methylbutanoic acid & nd & nd & 13.63 & nd \\ Acetic acid & nd & nd & 29.40 & nd \\ Isovaleric acid & nd & 43.40 & 37.17 & nd \\ Undecane & 9.89 & 11.45 & 16.63 & 7.22 \\ \hline Esters & & & & \\ Methyl valerate & nd & 70.17 & nd & 114.72 \\ \hline Ketones & & & & \\
2-Tridecanone & 7.92 & nd & nd & nd \\ \hline Phellandrenes & & & & \\ \(\alpha\)-Phellandrene & nd & nd & nd & 6.81 \\ \hline Pyrazines & & & & \\
2.5-Dimethylpyrazine & 38.62 & 46.01 & 48.88 & 46.87 \\
2-Ethyl-6-methyl-pyrazine & 0.52 & nd & 0.79 & nd \\
3-Ethyl-2,5-dimethylpyrazine & 1.11 & nd & nd & nd \\ Trimethylpyrazine & nd & nd & nd & 0.59 \\ \hline Terpenes & & & & \\ Carphene & nd & nd & nd & 1.50 \\ D-Limonene & 0.74 & nd & nd & nd \\ \(\beta\)-Pinene & 2.56 & 4.78 & 4.71 & 3.08 \\ \hline Others & & & & \\
2.4-Thujadiene & 0.52 & nd & 1.47 & 0.59 \\ E-7-Dodeceen-1-ol acetate & nd & nd & 11.67 & nd \\ Indole & nd & 2.97 & nd & 32.75 \\ Methyl undecyl ether & nd & nd & 19.82 & nd \\ \hline nd: not detected. & & & & \\ \end{tabular}
\end{table} 
Table 1: Identified volatile compounds and respective average amounts (ng of compound as internal standard equivalents), for the _E. coli_, _P. aeruginosa_, _E. faecalis_, or _S. aureus_, grown overnight in BHI solid medium after incubation with a \(10^{-4}\) dilution for _E. faecalis_ and \(10^{-5}\) dilution for the other bacterial strains. Figure 1 shows that an E-nose-MOS-LDA-SA model could be established, based on 25 feature extraction parameters selected by the SA algorithm (S6_LP, S1_INT, S5_INT, S8_INT, S9_INT, S1_MAX, S2_MAX, S4_MAX, S6_MAX, S2_MIN, S4_MIN, S5_MIN, S6_MIN, S2_SUM, S3_SUM, S5_SUM, S6_SUM, S8_SUM, S9_SUM, S2_MEAN, S5_MEAN, S1_SD, S2_SD, S3_SD, and S6_SD), which first two DFs explained 99.4% of the data variability. The model enabled the full discrimination of the four bacteria (sensitivity and specificity of 100%, for the training/original grouped data). Moreover, it should be highlighted that, according to the 1 st DF, it was possible to successfully discriminate the two Gram-positive bacteria (_E. faecalis_ and _S. aureus_) from the two Gram-negative bacteria (_E. coli_ and _P. aeruginosa_), located in the positive and negative regions, respectively. The satisfactory classification performance of each single bacterial species as well as between Gram-positive and Gram-negative bacteria, could be attributed to the observed differences, in the number and respective amount, of the VOCs emitted during the bacterial growth of each species (Table 1).