Advanced Molecularly Imprinting Materials 2016
DOI: 10.1002/9781119336181.ch14
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Molecular Imprinted Polymers for Sensing of Volatile Organic Compounds in Human Body Odor

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“…Various types of chemical sensor arrays have been developed for odor identification in the form of quartz crystal microbalance (QCM), field-effect transistors (FET), surface acoustic wave (SAW), metal oxide semiconductors (MOS), conducting polymers, and nanomechanical sensors. In contrast to the conventional chromatographic odor analysis, these chemical sensor arrays are capable of responding to a wider range of chemicals by measuring physicochemical interactions induced by the sorption of target analytes to the receptor layer and discriminate each odor by a pattern recognition approach using the multidimensional data set. This approach has been shown to discriminate various types of odors including human body odors and/or their components using QCM, MOS, SAW, and nanomechanical sensors. In addition to pattern-recognition-based chemical sensor approaches, recent advances in artificial intelligence and machine learning have led to further applications of artificial olfaction. For example, the specific target components can be quantified through machine learning, such as alcohols in liquors. , To improve the accuracy of these analyses in combination with machine learning, however, large data sets are required.…”
mentioning
confidence: 99%
“…Various types of chemical sensor arrays have been developed for odor identification in the form of quartz crystal microbalance (QCM), field-effect transistors (FET), surface acoustic wave (SAW), metal oxide semiconductors (MOS), conducting polymers, and nanomechanical sensors. In contrast to the conventional chromatographic odor analysis, these chemical sensor arrays are capable of responding to a wider range of chemicals by measuring physicochemical interactions induced by the sorption of target analytes to the receptor layer and discriminate each odor by a pattern recognition approach using the multidimensional data set. This approach has been shown to discriminate various types of odors including human body odors and/or their components using QCM, MOS, SAW, and nanomechanical sensors. In addition to pattern-recognition-based chemical sensor approaches, recent advances in artificial intelligence and machine learning have led to further applications of artificial olfaction. For example, the specific target components can be quantified through machine learning, such as alcohols in liquors. , To improve the accuracy of these analyses in combination with machine learning, however, large data sets are required.…”
mentioning
confidence: 99%