This change in resistance is directly related to the concentration of the detected gas, as well as type of sensor [36]. Individual sensors are not precisely selective, which is why they react with varying intensity to a range of different gas compounds. In the commercial market, there is a lack of MOS sensors dedicated to detecting all VOCs, especially mVOCs. However, air pollutants do not occur as single chemical compounds, and deteriorated air quality also means increased concentrations of compounds to which one of the many sensors reacts. In order to visualize multivariate data, the following methods can be applied: principal component analysis (PCA), metric multidimensional scaling, and Kohonen self-organizing map, which are unsupervised machine-learning methods. Hierarchical cluster analysis, random forest, and MLP neural network were used to classify objects. The principal component analysis method was first formalized in 1901 by [PERSON] [37], and then in 1933 by [PERSON] [38]. The main purpose of PCA is to reduce the dimensionality of the data, that is, to extract the information contained in \(n\) variables and store it in the form of a new system of orthogonal variables--principal components [39]. The PCA algorithm seeks a linear combination of the variables contained in dataset \(X\) with the greatest variance. If the mean variables of \(X\) are stored in the vector \(\mu\), while \(\Sigma\) denotes its covariance matrix, then the following matrix is sought:  
\[Y=\Gamma^{T}(X-\mu), \tag{1}\]  
where \(\Gamma\) is an orthogonal matrix. In turn:  
\[\Lambda=\Gamma^{T}\Sigma\Gamma, \tag{2}\]  
is a diagonal matrix with eigenvalues on the main diagonal that have a relationship with each other:  
\[\lambda_{1}\geq\lambda_{2}\geq\ldots\geq\lambda_{n}\geq 0. \tag{3}\]  
Thus, the \(i\)-th principal component is the \(i\)-th column of the matrix \(Y\) given by the equation  
\[Y_{i}=\Gamma_{i}^{T}(X-\mu), \tag{4}\]  
where \(\Gamma_{i}\) is the \(i\)-th column of the matrix \(\Gamma\), which is also called the factor charge vector [40]. Dimensionality reduction, which is the main goal of PCA, is usually used to represent multidimensional data in a two- or three-dimensional space. It is a very common technique used in visualization, including environmental engineering works [41; 42], but also for imaging electronic sense readings [28; 31; 43]. Multidimensional scaling was created by [PERSON] and described in 1952 [44]; this method was designed to detect similarity between observations. In multidimensional scaling, a distinction is made between metric and non-metric methods. The non-metric method is used when the dataset under consideration contains variables measured in different units, which prevents the calculation of the Euclidean distance between them. Metric scaling, on the other hand, is employed when all measurements are made in the same unit. In the dataset considered in this work, all measurements were made in one unit, so it was acceptable to use metric scaling. This method aims to preserve the distance between points in the reduced space. The algorithm is based on minimizing the difference between the points in the original space, and the reduced one. Thus, the stress function relying on these distances is minimized:  
\[STRESS=\sqrt{\frac{\sum_{i=1}^{N-1}\sum_{j=i+1}^{N}\left(\left\|x_{i}-x_{j} \right\|_{2}-\left\|y_{i}-y_{j}\right\|_{2}\right)^{2}}{\sum_{i=1}^{N-1}\sum_ {j=i+1}^{N}\left\|x_{i}-x_{j}\right\|_{2}^{2}}}, \tag{5}\]  
where \(\left\|\,.\,\right\|_{2}\) denotes the Euclidean norm, \(x_{i}\) is the \(i\)-th observation in the original dataset, while \(y_{i}\) is the \(i\)-th observation in the reduced space [45; 46]. Although this algorithm is called a metric, its solution is not obtained in a linear fashion. The model's answer is obtained iteratively--in a \(k\)-dimensional space, where \(1\leq k\leq n-1\), and the points are moved to minimize the \(STRESS\) function [47]. Multidimensional metric scaling such as PCA can be used to visualize data in low-dimensional space.