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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
PCA loading plots for the responses of MOx sensors (A) and dORs (B) to 21 esters.The responses to all esters (compounds 22-42, Table S3) were tested at or scaled to 1/100 dilution and corrected to equivalent concentrations using vapour pressure data. The 12 dORs are those asterisked in Table S4 and were chosen to represent a full range of tuning half-widths. Pearson pairwise correlation coefficients between MOx or dOR pairs are given in Table S5.
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pone-0006406-g003: PCA loading plots for the responses of MOx sensors (A) and dORs (B) to 21 esters.The responses to all esters (compounds 22-42, Table S3) were tested at or scaled to 1/100 dilution and corrected to equivalent concentrations using vapour pressure data. The 12 dORs are those asterisked in Table S4 and were chosen to represent a full range of tuning half-widths. Pearson pairwise correlation coefficients between MOx or dOR pairs are given in Table S5.

Mentions: To compare the sensor correlations we derived the pairwise Pearson correlation coefficients between all pairs within both classes of sensors (Table S2). For dORs, only five of the 276 possible sensor pairings were highly correlated (≥0.7 [15]) and the mean correlation was 0.24±0.28, whereas for the twelve MOx sensors all 66 of the possible sensor pairings were highly correlated with a mean correlation of 0.89±0.08. We next compared the independence of MOx sensors and dORs within a representative sub-region of odorant space defined by 21 esters. Esters form a chemical class of environmental significance for Drosophila and are also strong stimuli for MOx sensors. We selected only esters for which we had reliable vapour pressure information (Table S3) so that we could correct Hallem's data to minimise variation from this source [16], [17]. We also eliminated any bias due to different numbers of sensors by choosing a representative set of 12 dORs, spanning the full range of tuning curves (Table S4). Within the ester sub-region of odorant space, the 12 dORs (Figure 3B) were substantially more widely distributed than the MOx sensors (Figure 3A). Furthermore, we observed no increase in the correlations between pairs of dORs (Table S5; mean Pearson correlation = 0.12±0.4). The correlation among MOx sensor correlations was not significantly reduced (mean Pearson correlation = 0.77±0.25).


Bio-benchmarking of electronic nose sensors.

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

PCA loading plots for the responses of MOx sensors (A) and dORs (B) to 21 esters.The responses to all esters (compounds 22-42, Table S3) were tested at or scaled to 1/100 dilution and corrected to equivalent concentrations using vapour pressure data. The 12 dORs are those asterisked in Table S4 and were chosen to represent a full range of tuning half-widths. Pearson pairwise correlation coefficients between MOx or dOR pairs are given in Table S5.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0006406-g003: PCA loading plots for the responses of MOx sensors (A) and dORs (B) to 21 esters.The responses to all esters (compounds 22-42, Table S3) were tested at or scaled to 1/100 dilution and corrected to equivalent concentrations using vapour pressure data. The 12 dORs are those asterisked in Table S4 and were chosen to represent a full range of tuning half-widths. Pearson pairwise correlation coefficients between MOx or dOR pairs are given in Table S5.
Mentions: To compare the sensor correlations we derived the pairwise Pearson correlation coefficients between all pairs within both classes of sensors (Table S2). For dORs, only five of the 276 possible sensor pairings were highly correlated (≥0.7 [15]) and the mean correlation was 0.24±0.28, whereas for the twelve MOx sensors all 66 of the possible sensor pairings were highly correlated with a mean correlation of 0.89±0.08. We next compared the independence of MOx sensors and dORs within a representative sub-region of odorant space defined by 21 esters. Esters form a chemical class of environmental significance for Drosophila and are also strong stimuli for MOx sensors. We selected only esters for which we had reliable vapour pressure information (Table S3) so that we could correct Hallem's data to minimise variation from this source [16], [17]. We also eliminated any bias due to different numbers of sensors by choosing a representative set of 12 dORs, spanning the full range of tuning curves (Table S4). Within the ester sub-region of odorant space, the 12 dORs (Figure 3B) were substantially more widely distributed than the MOx sensors (Figure 3A). Furthermore, we observed no increase in the correlations between pairs of dORs (Table S5; mean Pearson correlation = 0.12±0.4). The correlation among MOx sensor correlations was not significantly reduced (mean Pearson correlation = 0.77±0.25).

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