# Breath Analysis: A Promising Tool for Disease Diagnosis--The Role of Sensors
###### Abstract
Statistical pattern-recognition algorithms and classification techniques are used for the establishment of analyte-specific response patterns, combining the responses of the sensor array elements [38]. It is worth noting that even analytical techniques, e.g., GC-MS, are progressively used for the analysis of total patterns of VOCs, instead of targeting a stand-alone biomarker [17]. Multivariate data analysis, a fundamental tool in breath analysis, improves the human perception of experimental data [16]. Response data obtained after sensor array exposure to a complex chemical mixture are processed by multivariate data analysis [40] in order to assess the discriminating ability of the sensor array [16, 41], as well as for the elimination of potential confounding variables (i.e., environmental temperature and humidity) [40]. Multivariate data analysis is also useful for breath analysis using analytical techniques, permitting the identification of the most discriminant VOCs between the different groups studied [32]. Numerous multivariate analysis methods are used in e-Nose systems, including canonical discriminate analysis (CDA), partial least squares regression (PLS regression), discriminant function analysis (DFA), and principal component analysis (PCA) (Figure 3) [40]. PCA comprises the most commonly used method in e-Nose systems [40], while DFA is also frequently used. PCA is an unsupervised learning technique in which the multidimensional data space is reduced to its main components [16]. Linear combinations of original data (i.e., sensor values) capturing the maximum variance between all data points are acquired [16; 42], leading to a reduced set of variables [41], called principal components (PCs). The differentiation of the PCs maintaining most of the original data information from the PCs with the minimum effects, which are excluded, is achieved by employing an appropriate algorithm [41]. PCs define new orthogonal axes for the representation of multidimensional data only in two or three dimensions [16; 42]. Thus, a visualized statistical analysis is obtained, which is a measure of the similarity between the two components. Figure 3: Statistical analysis of the response of a nanomaterial-based, cross-reactive chemiresistor for real-world samples of sick and healthy subjects. The use of PCA permits the differentiation of the groups. Notably, relative humidity compensation reduces the dispersion of different clusters thereby improving the discrimination between healthy and sick subjects. Representative 2D breath-analysis PCA plots for prostate cancer diagnosis: (**a**) without relative humidity compensation; (**b**) with relative humidity compensation. PCA plots for breast cancer diagnosis: (**c**) without relative humidity compensation; (**d**) with relative humidity compensation. Adapted with permission from Ref. [42] Copyright © 2012, American Chemical Society. Figure 2: Schematic representation of the working principle of selective sensors and artificially intelligent cross-reactive sensor arrays. Selective sensors contain highly selective elements in order to detect a specific gas-analyte in the presence of a composite gas-mixture. Cross-reactive arrays feature sensors that are sensitive to the majority of the gases present in the gas-mixture. In any case, detecting analyte concentration above a critical value leads to the differentiation between sick and healthy subjects. The response of gas-sensing arrays can be then processed by employing artificial intelligence, machine-learning, and pattern recognition techniques. Reprinted with permission from Ref. [6] Copyright © 2015 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim. permitting discrimination of otherwise entangled data [41]. PC1 is characterized by the greatest response variance, while the magnitudes of variance are diminished from PC2 to PC3 and so on [16; 42]. DFA is a linear supervised pattern recognition method, also used for multidimensional experimental data reduction [43]. DFA aims to separate known data groups to the best possible extent [44].