## 2 Exhaled Breath (EB) Analysis
### EB Analysis
#### 2.2.1 Off-line Analysis: Gas-Chromatography (GC)
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Target VOCs (Putative Biomarkers) (LODs)** & **Methodology** & **Sample** & **Sensitivity/** & **Statistical Approach** & **Reference** \\  & & **(Patients/** & **Specificity** & **(\%)** & **(\%)** \\  & & **Conrocial cancer (CRC)** & & **(\%)** & **(\%)** \\ \hline \multicolumn{5}{c}{**Colorectal cancer (CRC)**} \\ \hline decanal; 1,3-dimethylbenzene; 1,2-pentadiene & \multirow{2}{*}{GC-MS} & 37/41 & 86/83 & **PNN** validated by the **LOO method** & [27] \\ Cyclohexane; Methyl cyclohexane; 4-methyloctane & & & & \\ \hline
10 discriminant VOCs & SPME/GC-MS & 20/20 & **-** & **PCA, PLS-DA** & [45] \\ \hline
4 discriminant VOCs & GC-MS & 26/22 & **-** & **PCA and cluster analysis** & [101] \\ \hline \multicolumn{5}{c}{**Gastric cancer**} \\ \hline
6 discriminant VOCs & sensors, GC-MS & 37,32, -61 & 89/90 & **LDA**; Wilcoxon/ & [105] \\  & & & Kruskal-Wallis ANOVA & \\ \hline \multicolumn{5}{c}{**Head-and-neck cancer**} \\ \hline
8 discriminant VOCs & c-nose, GC-MS & 22,25/40 & 100/100 & **PCA** with ANOVA and Student & [106] \\ \hline VOCs pattern recognition & e-nose & 36/23 & 90/80 & Logistic regression, ROC & [107] \\ \hline \multicolumn{5}{c}{**Liver cancer**} \\ \hline
2,3-dihydro-benzofuran, & \multirow{2}{*}{sensor, GC-MS} & \multirow{2}{*}{95.8/100} & **LDA**; [PERSON], Wilcoxon/ & [108] \\  & & & Kruskal-Wallis ANOVA & \\ \hline hexanal; 1-octen-3-ol; octane & SPME/GC-MS & 18/19 & 100/100 & **RSD-\(\chi\)** & [109] \\ \hline
3-Hydroxy-2-butanone, styrene, and decane & \multirow{2}{*}{GC-MS} & \multirow{2}{*}{30/27 + 36} & A: 86.7/91.7 & \multirow{2}{*}{ROC and DA using the defined markers} & [110] \\ \cline{3-3} \cline{5-5} \multicolumn{5}{c}{B: cross-validation} & & & **Pulmonary Diseases** & \\ \hline \multicolumn{5}{c}{**Airways inflammation**} \\ \hline VOCs pattern recognition & e-nose & 110/108 & 72.2/75.1 & \multirow{2}{*}{**k-NN voting rule to classify features extracted by PCA**} & [111] \\ \cline{3-3} \cline{5-5} \multicolumn{5}{c}{} & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._ \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Target VOCs (Putative Biomarkers) (LODs)** & **Methodology** & \begin{tabular}{c} **Sample** \\ **(Patients/** \\ **Controls)** \\ \end{tabular} & \begin{tabular}{c} **Sensitivity/** \\ **Specificity** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Statistical Approach** \\ **(_Pre-Processing Method_; \\ **Classification Method**; \\ **Performance Measures**) \\ \end{tabular} & **Reference** \\ \hline \hline \multicolumn{5}{c}{**Asthma**} \\ \hline \hline \multicolumn{5}{c}{Several discriminant VOCs, including acetone and many alkanes} & e-nose, GC-MS & 2020 & - &
\begin{tabular}{c} PCA; **cross-validation** value, \\ **LDA** on principal component reduction, \\ **M-distance** \\ \end{tabular} & [112] \\ \hline \hline \multicolumn{5}{c}{} \\ \hline \hline \multicolumn{5}{c}{} \\ \hline \hline \multicolumn{5}{c}{} \\ \hline \hline \multicolumn{5}{c}{} \\ \hline \hline \multicolumn{5}{c}{} \\ \hline \hline \multicolumn{5}{c}{} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Target VOCs (Putative Biomarkers) (LODs)** & **Methodology** & \begin{tabular}{c} **Sample** \\ **(Patients/** \\ **Controls)** \\ \end{tabular}  & \ \begin{tabular}{c} **Sensitivity/** \\ **Specificity** \\ **(\%)** \\ \end{tabular}  & \begin{tabular}{c} **Statistical Approach** \\ **(_Pre-Processing Method_, \\ **Classification Method**; \\ **Performance Measures)** \\ \end{tabular}  & **Reference** \\ \hline \multicolumn{5}{c}{**Pulmonary Tuberculosis**} \\ \hline
6 discriminant VOCs & GC/MS & 42/59 & 95.7/78.9 & _Fuzzy logic, Pattern recognition analysis_; **PLS, HCA, PCA, k-NN**; **PLS, HCA, PCA, k-NN**; **PC regression, ROC, SLMCA** \\ \hline VOCs pattern recognition & \ \begin{tabular}{c} POC device \\ (TD-GC-SAW) \\ \end{tabular}