Bioinspired methodology for artificial olfaction.
Artificial olfaction is a potential tool for noninvasive chemical monitoring. Application of "electronic noses" typically involves recognition of "pretrained" chemicals, while long-term operation and generalization of training to allow chemical classification of "unknown" analytes remain challenges. The latter analytical capability is critically important, as it is unfeasible to pre-expose the sensor to every analyte it might encounter. Here, we demonstrate a biologically inspired approach where the recognition and generalization problems are decoupled and resolved in a hierarchical fashion. Analyte composition is refined in a progression from general (e.g., target is a hydrocarbon) to precise (e.g., target is ethane), using highly optimized response features for each step. We validate this approach using a MEMS-based chemiresistive microsensor array. We show that this approach, a unique departure from existing methodologies in artificial olfaction, allows the recognition module to better mitigate sensor-aging effects and to better classify unknowns, enhancing the utility of chemical sensors for real-world applications.
Raman
Baranidharan
B
Chemical Science and Technology Laboratory, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, USA.
Hertz
Joshua L
JL
Benkstein
Kurt D
KD
Semancik
Steve
S
D032701:Biomimetics / Q000379:methods*; D002623:Chemistry Techniques, Analytical / Q000295:instrumentation*; D008670:Metals / Q000737:chemistry; D055617:Micro-Electrical-Mechanical Systems; D010087:Oxides / Q000737:chemistry; D015203:Reproducibility of Results; D012903:Smell; D013696:Temperature; D013997:Time Factors
D008670:Metals; D010087:Oxides