Artificial intelligence methods for selection of an optimized sensor array for identification of volatile organic compounds

We have investigated two artificial intelligence (AI)-based approaches for the optimum selection of a sensor array for the identification of volatile organic compounds (VOCs). The array consists of quartz crystal microbalances (QCMs), each coated with a different polymeric material. The first approach uses a decision tree classification algorithm to determine the minimum number of features that are required to classify the training data correctly. The second approach employs the hill-climb search algorithm to search the feature space for the optimal minimum feature set that maximizes the performance of a neural network classifier. We also examined the value of simple statistical procedures that could be integrated into the search algorithm in order to reduce computation time. The strengths and limitations of each approach are discussed.