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Bio-benchmarking of electronic nose sensors.

Berna AZ, Anderson AR, Trowell SC - PLoS ONE (2009)

Bottom Line: The comparison also highlights some important questions about the molecular nature of fly ORs.The comparative approach generates practical learnings that may be taken up by solid-state physicists or engineers in designing new solid-state electronic nose sensors.It also potentially deepens our understanding of the performance of the biological system.

View Article: PubMed Central - PubMed

Affiliation: CSIRO Entomology and CSIRO Food Futures Flagship, Canberra, Australian Capital Territory, Australia.

ABSTRACT

Background: Electronic noses, E-Noses, are instruments designed to reproduce the performance of animal noses or antennae but generally they cannot match the discriminating power of the biological original and have, therefore, been of limited utility. The manner in which odorant space is sampled is a critical factor in the performance of all noses but so far it has been described in detail only for the fly antenna.

Methodology: Here we describe how a set of metal oxide (MOx) E-Nose sensors, which is the most commonly used type, samples odorant space and compare it with what is known about fly odorant receptors (ORs).

Principal findings: Compared with a fly's odorant receptors, MOx sensors from an electronic nose are on average more narrowly tuned but much more highly correlated with each other. A set of insect ORs can therefore sample broader regions of odorant space independently and redundantly than an equivalent number of MOx sensors. The comparison also highlights some important questions about the molecular nature of fly ORs.

Conclusions: The comparative approach generates practical learnings that may be taken up by solid-state physicists or engineers in designing new solid-state electronic nose sensors. It also potentially deepens our understanding of the performance of the biological system.

Show MeSH
Cluster analysis for responses of MOx sensors (A) and dORs (B) to 25 compounds from five chemical classes.All responses are scaled to constant concentration: 4.20×10−8 M for MOx sensors, unknown for dORs. The chemical classes were: carboxylic acids (orange), alcohols (green), aldehydes (light blue), esters (maroon), terpenoids (dark blue).
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pone-0006406-g005: Cluster analysis for responses of MOx sensors (A) and dORs (B) to 25 compounds from five chemical classes.All responses are scaled to constant concentration: 4.20×10−8 M for MOx sensors, unknown for dORs. The chemical classes were: carboxylic acids (orange), alcohols (green), aldehydes (light blue), esters (maroon), terpenoids (dark blue).

Mentions: To investigate this difference further, we performed cluster analysis (Figure 5A) for a set of 25 compounds (42 nM) from five chemical classes using MOx sensor data. We observed three statistically significant clusters corresponding to carbonyls (esters, aldehydes and carboxylic acid); 3-methyl butanol and several non-carbonyls; all other alcohols and terpenes. The clustering was broadly congruent with chemical functionality. A representative set of 12 dORs, also generated three clusters with significantly different semi-partial R2 values (Figure 5B). Three terpenes formed one cluster, octanoic acid a second and the other 21 compounds a third cluster. We could detect no statistically significant correlation with any external molecular characteristics, notwithstanding a weak association with mean molecular weight. Therefore, discrimination by odorant receptors, although robust, was based on subtle, undefined and highly compound-specific features of individual odorant molecules. The dissociation of the dOR-generated clusters from conventional molecular descriptors is a necessary corollary of the sensors being independent of each other.


Bio-benchmarking of electronic nose sensors.

Berna AZ, Anderson AR, Trowell SC - PLoS ONE (2009)

Cluster analysis for responses of MOx sensors (A) and dORs (B) to 25 compounds from five chemical classes.All responses are scaled to constant concentration: 4.20×10−8 M for MOx sensors, unknown for dORs. The chemical classes were: carboxylic acids (orange), alcohols (green), aldehydes (light blue), esters (maroon), terpenoids (dark blue).
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2712691&req=5

pone-0006406-g005: Cluster analysis for responses of MOx sensors (A) and dORs (B) to 25 compounds from five chemical classes.All responses are scaled to constant concentration: 4.20×10−8 M for MOx sensors, unknown for dORs. The chemical classes were: carboxylic acids (orange), alcohols (green), aldehydes (light blue), esters (maroon), terpenoids (dark blue).
Mentions: To investigate this difference further, we performed cluster analysis (Figure 5A) for a set of 25 compounds (42 nM) from five chemical classes using MOx sensor data. We observed three statistically significant clusters corresponding to carbonyls (esters, aldehydes and carboxylic acid); 3-methyl butanol and several non-carbonyls; all other alcohols and terpenes. The clustering was broadly congruent with chemical functionality. A representative set of 12 dORs, also generated three clusters with significantly different semi-partial R2 values (Figure 5B). Three terpenes formed one cluster, octanoic acid a second and the other 21 compounds a third cluster. We could detect no statistically significant correlation with any external molecular characteristics, notwithstanding a weak association with mean molecular weight. Therefore, discrimination by odorant receptors, although robust, was based on subtle, undefined and highly compound-specific features of individual odorant molecules. The dissociation of the dOR-generated clusters from conventional molecular descriptors is a necessary corollary of the sensors being independent of each other.

Bottom Line: The comparison also highlights some important questions about the molecular nature of fly ORs.The comparative approach generates practical learnings that may be taken up by solid-state physicists or engineers in designing new solid-state electronic nose sensors.It also potentially deepens our understanding of the performance of the biological system.

View Article: PubMed Central - PubMed

Affiliation: CSIRO Entomology and CSIRO Food Futures Flagship, Canberra, Australian Capital Territory, Australia.

ABSTRACT

Background: Electronic noses, E-Noses, are instruments designed to reproduce the performance of animal noses or antennae but generally they cannot match the discriminating power of the biological original and have, therefore, been of limited utility. The manner in which odorant space is sampled is a critical factor in the performance of all noses but so far it has been described in detail only for the fly antenna.

Methodology: Here we describe how a set of metal oxide (MOx) E-Nose sensors, which is the most commonly used type, samples odorant space and compare it with what is known about fly odorant receptors (ORs).

Principal findings: Compared with a fly's odorant receptors, MOx sensors from an electronic nose are on average more narrowly tuned but much more highly correlated with each other. A set of insect ORs can therefore sample broader regions of odorant space independently and redundantly than an equivalent number of MOx sensors. The comparison also highlights some important questions about the molecular nature of fly ORs.

Conclusions: The comparative approach generates practical learnings that may be taken up by solid-state physicists or engineers in designing new solid-state electronic nose sensors. It also potentially deepens our understanding of the performance of the biological system.

Show MeSH