## 4 E-Nose: Principles and Main Agri-Food Applications  
In the past (up to the 1990s), sensory analysis was generally used to detect the different olfactory traits of many agri-food products, but it was often imprecise, not reproducible and too subjective. Hence, human senses, including the sense of smell, as discussed above, are influenced by physical and mental status and also by different exogenous factors. Therefore, sensory analysis was often coupled and compared with analytical instruments (e.g., gas chromatography). However, the latter requires time, expertise and expensive machinery [52]. Furthermore, this kind of analytical approach often requires sample preparation, which is complex and time consuming, and it is not always compatible with the modern food industry, which requires an easy and rapid detection tool for food quality evaluation. To overcome the above-mentioned drawbacks, in recent years, tools somewhat mimicking the biological sense of smell, namely the E-nose, have become some of the most used instruments in the food industry. Commonly, thanks to the technological capabilities of the sensing part (the sensor array constituting the device), the E-nose is somewhat able to transform volatile compounds contained within a biological matrix or in the environment into some detectable--often digital--electronic signal, which is, in turn, properly analyzed, mostly during post-processing, to extrapolate a possible pattern of some significance for the given analysis. Under such premises, it becomes clear that the main part constituting an E-nose tool, and that can be customized upon the desired application, is represented by the sensor array. Historically, the majority of E-nose systems relied on conducting polymers (CP), metal oxide semiconductors (MOS), metal oxide semiconductor field-effect transistors (MOSFET) and mass-sensitive (such as quartz microbalance), acoustic and optic sensors. Among them, MOS sensors are probably the most widely used since the early E-nose applications, due to their affordability, good performances, low tendency to drift and good sensitivity to various volatiles. Their principle of operation relies on the change in conductivity brought by the reactions between the detected volatile and adsorbed oxygen. However, they are poorly selective and quite prone to being poisoned by weak acids. On the other hand, MOSFET sensors are based on the variations in the electrostatic potential. They are considered to be particularly robust to environmental conditions, although a fine control of surrounding temperature is desirable for data interpretation. Commonly, mass-sensitive devices, such as piezoelectric sensors, take advantage of the piezoelectricity phenomenon to translate a mechanical variation due to the mass of the ligands into a change in resonance frequency. They are particularly selective, even if their stability to changes in temperature and humidity is quite poor. Finally, when it comes to CP sensors, they are considered particularly sensitive and resistant to the poisoning effects, different from MOS devices, but also feature a limited reproducibility. Therefore, in order to maximize the positive characteristics of all those principles of operation, reducing their associated drawbacks, thanks to the technological advances in the field, mainly in terms of device miniaturization, many hybrid solutions were adopted for a plethora of applications, even in the food industry, and mainly depending on the biological matrices to be investigated. This approach goes also far beyond the E-nose principles, as it is also applied to the synergy between E-nose and E-tongue or, in some instances, E-eye, or even to the combination between E-nose and analytical instruments to provide for a more accurate characterization demanded by a given task [53]. The other pivotal functionality of the E-nose tools is represented by the data processing and interpretation, which is normally performed under the Machine Learning principles.