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What’s that smell? The sense of smell is difficult to mimic. Researchers have tried to reproduce the biological response in the lab by creating artificial olfactory sensors for applications such as noninvasive chemical monitoring and the detection of toxic substances. But many solid-state sensors exhibit poor signal reproducibility because the responses drift significantly as the sensor ages. In addition, the conventional, all-at-once approach identifies an analyte by comparing it to the reference substances in a training data set and cannot generalize the training data to identify unknowns. In a recent AC paper (2008, DOI 10.1021/ac8007048), Joshua Hertz (now at the University of Delaware), Baranidharan Raman, Kurt Benkstein, and Steve Semancik of the U.S. National Institute of Standards and Technology tackled these problems by more closely mimicking nature and identifying general classes of odors first, before refining the identification to members within the class. “The analogy we like to use is when you first walk into a coffee shop, all you smell is coffee,” says Hertz. “Only after a little while do you smell that they freshly baked bread that dayOthe fine points of the odor.” For their method, the investigators produced extensive response databases and then subdivided the olfactory recognition task in a hierarchical fashion. Their new approach can be used for any sensor array, but the authors initially tested the method with a micro-hotplate array of 16 metal oxide sensors. To generate training data, the sensor was exposed to three simple oxides, two alcohols, two ketones, two alkanes, and two aromatics. After analytes were introduced at uniform concentrations in a background of dry air, the temperature of the array was ramped from 50 to 500 °C. The conductance of each of the 16 sensors was measured between 151 and 500 °C in 1 °C increments, yielding 5600 data points for each analyte. The researchers validated their approach with a test set containing many of the ana-
Optical micrograph of the 16-sensor micro-hotplate array. Individual thin-film sensors are composed of varying ratios of SnO2, TiO2, WO3, and Ru; the array contains two replicates of each composition.
lytes in the training set plus four new alcohols and ketones. As an additional challenge, the group aged the sensor before the test phase. A differential sensor response automatically corrected the readout drift that commonly occurs as sensors age. The investigators used the same training and test data sets to compare the all-at-once method with their new hierarchical method of analyte identification. For the all-at-once approach, the authors applied principal component analysis and hierarchical cluster analysis to find relationships among the data points. Neither analysis could detect similarities in the responses for analytes with common or similar features. The researchers then tested the new hierarchical model. The overall rate of correct characterization during the test phase (including analytes not in the training set) was 87%. In addition, the investigators used their method to successfully distinguish among three differ-
10.1021/AC802215X 2008 AMERICAN CHEMICAL SOCIETY
Published on Web 11/04/2008
ent concentrations of methanol. However, compounds that did not share common functionalities could not be correctly identified as members of an arbitrary group. The authors attribute the success of the drift mitigation in the hierarchical approach to two factors. The first was the use of subsets of data for the specific recognition tasks. “When we’re answering the problem of whether something is ethanol or methanol, we only use the training data collected in ethanol and methanol,” explains Hertz. The second factor was the independent selection of high-S/N responses for each recognition task. As with other chemical sensors, the physical basis for the sensor response is not well understood. Nevertheless, Hertz says, “it’s really a fairly unique way of being able to use chemical sensors to detect unknown chemicalsOeven if they are not members of the training setOand being able to understand at least some of the [chemical] qualities of the analytes.” Next, the group plans to continue working on the challenges of applying this approach to the detection of analyte mixtures and analytes in the presence of interfering backgrounds. —Christine Piggee
DECEMBER 1, 2008 / ANALYTICAL CHEMISTRY
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