# Advancements and Prospects of Electronic Nose in Various Applications: A Comprehensive Review
## 3 Application Areas
### Disease Diagnosis
#### 3.3.3 Invasive Insect-Pest Infestations
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study** & **Method** & **Target** & **Results** \\ \hline \multirow{4}{*}{[PERSON] et al. [199]} & \multirow{4}{*}{Customized sensor array (Cyanose 320)} & \multirow{4}{*}{Sour skin disease in onions} & \multicolumn{2}{c}{SVM achieved 92\% classification accuracy for diseased samples using LOO validation and 85\% using a separate test dataset. PCA, LDA, and SVM used for classification.} \\ \cline{3-4}  & & & EOS 507C model discriminated infected samples based on pathogens with LDA average accuracy of 55\%. Factors affecting discrimination were humidity control, sensors’ characteristics, and optimal temperatures. \\ \hline \multirow{4}{*}{[PERSON] et al. [201]} & \multirow{4}{*}{Commercial electronic (Alpha MOS, Toulouse, France)} & \multirow{4}{*}{Fungal infection in jasmine brown rice} & \multicolumn{2}{c}{EOS 507C model discriminated infected samples based on pathogens with LDA average accuracy of 55\%. Factors affecting discrimination were humidity control, sensors’ characteristics, and optimal temperatures.} \\ \cline{3-4}  & & & LDA and SVM models achieved 100\% and 98\% classification accuracies for storage days. Volatile profiles correlated with storage time. PLS regression predicted fungal growth on brown rice (R2 0.969). \\ \hline \multirow{4}{*}{[PERSON] et al. [202]} & \multirow{4}{*}{PEN3 electronic nose (PEN3 AIRSENSE, Inc., Schwerin, Germany)} & \multirow{4}{*}{Stored rice infested with red flour beetles} & Fuzzy C-means algorithm achieved 94\%, 100\%, and 100\% classification accuracies for light, middle, and heavy infestations, respectively. BPNN regression highly correlated (R2 > 0.8) with actual pest numbers. \\ \cline{3-4}  & & & ANFIS models highly predicted insect numbers, uric acid content, and protein content (R > 0.97). Models were robust and validated with unknown samples. \\ \hline \multirow{4}{*}{[PERSON] et al. [204]} & \multirow{4}{*}{Laboratory prototype (9 MOX sensors) (Melbourne, Australia)} & \multirow{4}{*}{Aphid infestation in wheat (Melbourne, Australia)} & ANN models achieved up to 98\% accuracy in classifying infestation levels and predicting insect numbers. Electronic nose data comparable to NIR measurements. \\ \hline \multirow{4}{*}{[PERSON] et al. [205]} & \multirow{4}{*}{Sensor array (13 CP sensors) (Bloodohound model ST214, Scensive Technologies Ltd., Normanton, UK)} & \multirow{4}{*}{Infestations in cucumber, tomato, and pepper plants} & SVM classifier achieved 86\% and 91\% accuracy in multi-class classification of different plants. Electronic nose discriminated plants based on VOC profiles. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Electronic nose applications in invasive insect-pest infestations. sensors and data analysis algorithms, researchers have been able to differentiate between disease states, monitor disease progression, and even predict disease presence. These advancements hold significant promise for revolutionizing disease detection and monitoring practices, offering non-invasive, rapid, and accurate solutions. As we move forward, addressing the challenges inherent in electronic nose technology's practical implementation becomes paramount. Standardizing sampling techniques, sensor selection, and data processing methods will be essential for ensuring reliable and reproducible results. Additionally, interdisciplinary collaboration between scientists, engineers, and medical professionals will be crucial for refining these techniques and translating them into real-world clinical and field applications. Overcoming these challenges will pave the way for a future in which electronic noses play a pivotal role in early disease detection, enhancing healthcare outcomes, preserving wildlife populations, and ensuring the sustainability of agricultural practices.