The increased occurrence of chronic diseases related to lifestyle or environmental conditions may have a detrimental effect on long-term health if not diagnosed and controlled in time. For this reason, it is important to develop new noninvasive early diagnosis equipment that allows improvement of the current diagnostic methods. This, in turn, has led to an exponential development of technology applied to the medical sector, such as the electronic nose. In addition, the appearance of this type of technology has allowed the possibility of studying diseases from another point of view, such as through breath analysis. This paper presents a bibliographic review of past and recent studies, selecting those investigations in which a patient population was studied with electronic nose technology, in order to identify potential applications of this technology in the detection of respiratory and digestive diseases through the analysis of volatile organic compounds present in the breath.

In the field of electronic noses (e-noses), it is not very usual to find many applications to wine detection. Most of them are related to the discrimination of wines in order to prevent their illegal adulteration and detection of off-odors, but their objective is not the identification of wine aromas. In this paper, an application of an e-nose for the identification of typical aromatic compounds present in white and red wines is shown. The descriptors of these compounds are fruity, floral, herbaceous, vegetative, spicy, smoky, and microbiological, and they are responsible for the usual aromas in wines; concentrations differ from 2-8/spl times/ the threshold concentration humans can smell. Some of the measured aromas are pear, apple, peach, coconut, rose, geranium, cut green grass, mint, vanilla, clove, almond, toast, wood, and butter. Principal component analysis and linear discriminant analysis show that datasets of these groups of compounds are clearly separated, and a comparison among several types of artificial neural networks has been also performed. The results confirm that the system has good performance in the classification of typical red and white wine aromas.