In most electronic noses, each sensor is sensitive to all volatile molecules but each in their specific way. However, in bio-electronic noses, receptor proteins which respond to specific odor molecules are used. Most electronic noses use sensor arrays that react to volatile compounds on contact: the adsorption of volatile compounds on the sensor surface causes a physical change of the sensor. A specific response is recorded by the electronic interface transforming the signal into a digital value. Recorded data are then computed based on statistical models. Bio-electronic noses use olfactory receptors - proteins cloned from biological organisms, e.g. humans, that bind to specific odor molecules. One group has developed a bio-electronic nose that mimics the signaling systems used by the human nose to perceive odors at a very high sensitivity: femtomolar concentrations. The more commonly used sensors for electronic noses include   Some devices combine multiple sensor types in a single device, for example polymer coated QCMs. The independent information leads to vastly more sensitive and efficient devices. In recent years, other types of electronic noses have been developed that utilize mass spectrometry or ultra-fast gas chromatography as a detection system. The computing system works to combine the responses of all of the sensors, which represents the input for the data treatment. This part of the instrument performs global fingerprint analysis and provides results and representations that can be easily interpreted. Moreover, the electronic nose results can be correlated to those obtained from other techniques (sensory panel, GC, GC/MS). Many of the data interpretation systems are used for the analysis of results. These systems include artificial neural network (ANN), fuzzy logic, pattern recognition modules, etc. Artificial intelligence, included artificial neural network (ANN), is a key technique for the environmental odour management. As a first step, an electronic nose needs to be trained with qualified samples so as to build a database of reference. Then the instrument can recognize new samples by comparing a volatile compound's fingerprint to those contained in its database. Thus they can perform qualitative or quantitative analysis. This however may also provide a problem as many odors are made up of multiple different molecules, which may be wrongly interpreted by the device as it will register them as different compounds, resulting in incorrect or inaccurate results depending on the primary function of a nose. The example of e-nose dataset is also available. This dataset can be used as a reference for e-nose signal processing, notably for meat quality studies. The two main objectives of this dataset are multiclass beef classification and microbial population prediction by regression.