# Breath Analysis: A Promising Tool for Disease Diagnosis--The Role of Sensors
###### Abstract
The determination of the discrimination classes is conducted prior to the analysis [43]. Input variables are linearly combined to achieve the maximum variance between classes and the minimum variance for each class [43; 44]. DFA output is a set of canonical variables (CVs) that fulfill the above two requirements. The first CV is the most powerful discriminating dimension [43]. In order to evaluate sensor array suitability for disease diagnosis and to assess the ability to correlate VOCs with the appropriate disease, machine learning techniques [43] such as support vector machines (SVM), k-nearest neighbor (k-NN), and artificial neural networks (ANN) [40] are also needed. Machine learning techniques and artificial intelligence in general aim at enabling machines to mimic a specific behavior by designing and developing algorithms based on empirical data that are representative of the relation between the observed attributes [45]. The automated \"learning\" of complex patterns and intelligent decision-making based on current data comprise one of the principal research fields in machine learning [45] and are especially useful in the development of e-Noses for disease diagnosis. ANN is a commonly used machine learning technique, inspired by the human nervous system. The most discriminant sensing features are firstly determined and comprise the input data of the ANN. The input data are connected with the output (i.e., the classification of samples to specific disease) by the ANN, using a set of appropriate functions. The classification from the available inputs is improved by optimizing specific parameters, such as the number of neurons (calculation centers) that are responsible for the system calculations [43]. A number of requirements have to be fulfilled for the use of a sensor in breath analysis and therefore disease diagnosis. Reproducibility [6], high sensitivity, and good resolution [15], are three fundamental parameters. Low limit of detection (LOD) [13; 15] (ppb [38]), wide range of response [38], and increased selectivity are also of great importance, in order to detect the exhaled VOCs in the presence of water vapor found in the humidified clinical samples [15]. The stable baseline in the absence of gas-target biomarkers [6], short response, and recovery times are imperative [13; 15], while full recovery of the sensor after analyte removal is also essential. Alternatively, disposable sensors that are simple, cost-effective, and therefore suitable for mass-production can be used [15]. Last but not least, operation at RT is important [38]. In the context of this fast-growing field, novel nanomaterials and sensing devices are constantly being developed. The first part of this review aims to provide an up to date overview of the main classes of sensors investigated for the analysis of exhaled gas-analytes (i.e., VOCs, inorganic gases) for disease diagnosis. After highlighting the importance of nanomaterials, examples of applications of either selective or cross-reactive self-developed and commercial sensing devices are discussed. The second and final part of the paper discusses and highlights the capacity of breath analysis towards differential diagnosis, disease staging and phenotyping. There is currently a strong and urgent need for clinically applicable, portable and non-invasive diagnostic tools that can lead to the distinction of different diseases with similar symptoms; the current review paper evaluates the potential of sensing devices of varying technology, as well as analytical techniques, to serve as reliable breath analysis tools for differential diagnosis while it also discusses advantages and weaknesses that need to be addressed.