## 6 Summary, Future Perspectives and Conclusions  
Over the past two decades, the potential of VOCs as biomarker in a wide variety of clinical conditions have raised considerable interest in the medical field. This is well illustrated by the increasing number of clinical studies addressing this matter. However, in contrast to other industries [79], widespread application of VOCs in daily clinical practice has still not been realized. Before a VOC-based medical tool can be developed and eventually implemented in clinical practice, several obstacles need to be addressed. First, for each clinical condition, the substrate of choice needs to be selected. In this particular review, we focus on studies using VOCs derived from feces. Fecal VOCs provide us with valuable information about resident gut microbiota composition, and both local and systemic (patho)physiological processes. Consequently, fecal VOCs harbor great potential as diagnostic and monitoring tool in both intra- and extra-intestinal clinical conditions. Major advantage of fecal VOCs is that stools can be collected in a noninvasive manner and they have logistic advances over for example exhaled breath, since transport and storage is relatively easy. However, analysis of fecal VOCs also harbors several important limitations. Despite an increasing acceptance of fecal sampling for medical purposes, (collection of) feces is still considered a taboo in large parts of the world [80; 81], potentially resulting in decreased sampling compliance. Another limitation is the fact that stools cannot be obtained on request at set times. However, in such situations, the use of rectal swabs could be an acceptable alternative [78]. Another item on the research agenda is the selection of optimal analytical techniques. In the majority of the currently reviewed studies, eNose devices are used in which discrimination between subpopulations is based on pattern recognition algorithms. Although such devices allow for fast and inexpensive measurements, resulting in relatively easy interpretable outcomes, they are limited by their inability to identify individual VOCs present in a gaseous mixture. Furthermore, since each eNose device has a different operating mechanism, comparison of studies using different devices is hampered, and eNose sensors are vulnerable for sensor drift. On the other end of the spectrum are chemical analytical techniques such as SIFT-MS and GC-MS. Although they allow for the identification of individual VOCs, application in daily clinical practice is not feasible due to their relatively expensive and time consuming measurements in combination with complex (statistical) analyses. We hypothesize that the development of a medical device, implementable in clinical practice and allowing for accurate disease detection based on VOC profile, can be achieved in two different ways. The first method is by collecting a large amount of healthy and diseased fecal VOC profiles, analyzed by means of an eNose. Subsequently, newly obtained fecal VOCs profiles are compared with the profiles already available (VOC cloud), determining a possible diagnosis. Connecting the eNose to a self-learning algorithm will optimize eNose accuracy with each new sample analyzed. A clear example of such a self-learning eNose is the SpiroNose. Being connected to an artificial intelligence application named BreathCloud, it is currently used to detect pulmonary diseases based on the VOCs present in exhaled breath [82]. The second method includes identification of disease-specific volatiles using chemical analytical techniques. With this information, a tailored eNose may be developed, specifically designed to accurately detect the presence or absence of key volatiles and consequently associated clinical condition. To date, several studies have attempted to identify discriminating volatiles using chemical analytical techniques. Recently, metal oxide-based gas sensors (MOS) are increasingly studied in its application for VOC analysis.