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, chemometrics methods, pattern recognition modules, etc. Artificial intelligence, included artificial neural network (ANN), is a key technique for the environmental odour management. Performing an analysis 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. Applications Electronic nose instruments are used by research and development laboratories, quality control laboratories and process & production departments for various purposes: In quality control laboratories Conformity of raw materials, intermediate and final products Batch to batch consistency Detection of contamination, spoilage, adulteration Origin or vendor selection Monitoring of storage conditions Meat quality monitoring. In process and production departments Managing raw material variability Comparison with a reference product Measurement and comparison of the effects of manufacturing process on products Following-up cleaning in place process efficiency Scale-up monitoring Cleaning in place monitoring. In product development phases Sensory profiling and comparison of various formulations or recipes Benchmarking of competitive products Evaluation of the impact of a change of process or ingredient on sensory features. Possible and future applications in the fields of health and security The detection of dangerous and harmful bacteria, such as software that has been specifically developed to recognise the smell of the MRSA (Methicillin-resistant Staphylococcus aureus). It is also able to recognise methicillinsusceptible S. aureus (MSSA) among many other substances. It has been theorised that if carefully placed in