Computerised recognition methods of data of QCM sensor array sensing volatile gases

Chemical sensor arrays have come to have an important role in the analysis of volatile analytes. Such arrays gather data which have broad overlapping sensitivity profiles, which require substantial data analysis, often involving pattern recognition methods to solve the problems being addressed. Among the various frameworks in which pattern recognition has been traditionally formulated the statistical approach has been most intensively studied and used in practice. More recently, neural network techniques and methods imported from statistical learning theory have been receiving increasing attention. The design of a recognition system requires careful attention to the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples, and performance evaluation. In spite of almost 50 years of research and development in this field, the general problem of recognizing complex patterns with arbitrary orientation, location, and scale remains unsolved. In this study; our goal is to look over and compare of the well known methods used in recognition and perceive stages of volatile analytes. Keywords: Error Estimation, Chemical Sensor Array, Pattern Recognition, Feature Selection, Classification, Neural Network.