Pattern analysis for machine olfaction: a review

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Metal oxide nanoscience and nanotechnology for chemical sensors (2013, background): “Nevertheless, being metal oxide gas sensors not selective, their typical configuration to gain selectivity is their use in an electronic nose, thus requiring a sequence of exposures to selected gases/atmospheres to accomplish the training and validation tests intrinsic in electronic nose calibration”
A review of unsupervised feature learning and deep learning for time-series modeling (2014, methodology): “The traditional approach of analyzing e-nose data involves extracting information in the static and dynamic phases of the signals [44] for the use of static pattern analysis techniques (PCA, discriminant function analysis, cluster analysis and neural networks).”
Domain Adaptation Extreme Learning Machines for Drift Compensation in E-Nose Systems (2015, methodology): “An excellent overview of the E-nose and techniques for processing the sensor responses can be referred to as [32], [33].”
Signal and Data Processing for Machine Olfaction and Chemical Sensing: A Review (2012, background): “Ten years ago, Gutierrez-Osuna [3] reviewed the building blocks of pattern recognition systems for machine olfaction.”