## 4 Classification of Measurement Methods
### Chemical Analysis
In recent years, there has been a clear trend towards the miniaturisation of sensors, enabling the wide application of electronic noses in environmental monitoring (identification and quantification of odours and volatile organic compounds), in the food industry (quality and safety assessment) and in healthcare (monitoring and diagnosis of diseases using volatile biomarkers) [65]. The pattern recognition algorithm, which is tuned to the sensor array, is a key component of the device that is able to extract valuable information from the captured sensor signals. Pattern recognition methods used in electronic noses include conventional statistical methods such as principal component analysis (PCA), linear discriminant analysis (LDA), support vector machine (SVM) and k-nearest Neighbor (k-NN), as well as more advanced approaches such as artificial neural networks (ANNs), convolutional neural networks (CNNs) and recurrent neural networks (RNNs) [65]. The selection of a suitable algorithm is crucial for ensuring the accurate and efficient performance of the electronic nose in recognising specific gas groups. For simple applications, conventional algorithms such as PCA or LDA are recommended due to their computational efficiency and simple hardware implementation. However, these conventional algorithms face challenges when used in real-world environments with significant noise and interference. For applications such as environmental monitoring, breath analysis in medicine or analysing mixtures of very similar gases, more advanced AI-based algorithms are preferable. Therefore, electronic noses require appropriate preparation--the so-called training. The training consists of introducing standards into the device and creating a corresponding database [68]. The use of the electronic nose is limited to gases with a predictable composition. In order to train \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sensor Type**} & **Operating** & **Response** & \multirow{2}{*}{**Device Lifespan**} & **Signal Stability with** & **Signal Stability** \\  & **Temperature** & & & **Temperature** & **with Humidity** \\  & **Temperature** & & & **Changes** & **Changes** \\ \hline \begin{tabular}{c} Amperometric \\ Semiconductor \\ Polymeric \\ Polymeric \\ Photoionisation \\ Microgravimetric \\ Surface Acoustic \\ Wave (SAW) \\ \end{tabular} & 20–50 \({}^{\circ}\)C & 30–180 s & Up to 2 years & Low & Low/Medium \\ \
\begin{tabular}{c} 300–400 \({}^{\circ}\)C \\ Polymeric \\ \end{tabular} & 20–50 \({}^{\circ}\)C & 20–50 s & Up to 2 years & Low & High \\
\begin{tabular}{c} 300–50 \({}^{\circ}\)C \\ Microgravimetric \\ Surface Acoustic \\ Wave (SAW) \\ \end{tabular} & 20–50 \({}^{\circ}\)C & 20–50 s & Up to 2 years & High & High \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameters of selected gas sensors used for the detection of odorous substances [46]. the nose for the odours it is supposed to identify, a series of similar patterns must first be introduced. The training is always geared towards the type of measurement planned [47]. The use of the electronic nose for analysing odorous gases is somewhat limited due to the very low concentrations of VOCs they contain (ppb, ppt level) [65]. The stringent requirements for the detection limits, sensitivity, selectivity and accuracy of the electronic nose may be difficult to fulfil in environmental studies, which is why the human nose is still popularly used as a detector. Table 5 shows the difficulties associated with the standardisation and widespread use of electronic noses as well as the proposed solutions.