# Advancements and Prospects of Electronic Nose in Various Applications: A Comprehensive Review
## 3 Application Areas
### Disease Diagnosis
#### 3.3.1 Human Clinical Pathology
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Application** & **Type of Sensor** &
\begin{tabular}{c} **Data Processing** \\ **Algorithm** \\ \end{tabular} & **Description** & **References** \\ \hline \hline \multirow{8}{*}{A Asthma} & \multirow{8}{*}{Cyanrose 320} & \multirow{8}{*}{PCA} & Exhaled breath samples were collected and analyzed from controls (10 patients & \multirow{8}{*}{\(57.3\pm 7.1\) years and 10 patients \(26.8\pm 6.4\) years), as well as patients with mild asthma (10 patients, \(25.1\pm 5.9\) years) and severe asthma (10 patients, \(49.5\pm 12.0\) years). Results showed distinct separation between mild asthma and young controls, as well as between severe asthma and old controls. Discrimination between mild and severe asthma was less accurate. Duplicate samples were collected to replicate the results.  
\end{table} 
Table 5: Biomedical applications developed using electronic noses. Figure 8: Analysis of exhaled breath VOCs in COVID-19 onset and post-COVID syndrome using e-nose [167]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Application** & **Type of Sensor** & \begin{tabular}{c} **Data Processing** \\ **Algorithm** \\ \end{tabular}  & **Description** & **References** \\ \hline
190 sensors belonging to 4 different & & \begin{tabular}{c} The electronic nose system described in this \\ study is implemented as part of the European \\ project U-Biopred. This project focuses on \\ analyzing exhaled breath samples from \\ \end{tabular}  \\
[169] & \begin{tabular}{c} Hierarchical \\ clustering \\ \end{tabular}  & \ \begin{tabular}{c} The developed hierarchical model, utilizing \\ exhaled breath condensate VOC analysis \\ through an e-nose, effectively distinguishes \\ individuals with asthma and accurately \\ identifies those in need of corticosteroid \\ therapy. This innovative approach offers \\ valuable insights for pediatric asthma diagnosis \\ and has the potential to contribute significantly \\ to precision therapy decisions \\ \end{tabular}  &
[170] & \ \begin{tabular}{c} The study utilized e-nose technology to analyze \\ exhaled breath samples from COPD patients \\ with and without alpha 1-antitrypsin (AAT) \\ deficiency. The results revealed significant \\ variations in the “smell prints” between \\ AAT-deficient COPD patients, those without \\ AAT deficiency, and healthy controls \\ (\(p<0.0001\)). Moreover, distinct differences were \\ observed in the smell prints of AAT-deficient \\ patients before and after human recombinant \\ AAT therapy (\(p=0.001\)). These findings \\ emphasize the e-nose’s potential to distinguish \\ between these groups based on volatile organic \\ compound (VOC) patterns in exhaled breath, \\ indicating its potential utility in aiding the \\ diagnosis of AAT deficiency. \\ \end{tabular}  &
[171] & \ \begin{tabular}{c} The study aimed to assess the diagnostic \\ potential of an electronic nose prototype \\ equipped with six semiconductor sensors for \\ detecting chronic obstructive pulmonary \\ disease (COPD). The research involved \\ analyzing reference mixtures containing \\ volatile organic compounds (VOCs) and \\ potential COPD markers, evaluating the \\ device’s capacity to distinguish these markers. \\ To improve early-stage COPD detection, the \\ study recommended the exploration of more \\ sensitive sensor technologies such as \\ SAW/BAW type sensors, as well as sensors \\ incorporating carbon nanotubes. \\ \end{tabular}  &
[172] & \