Computing Odor Images - Journal of Agricultural and Food Chemistry

Mar 12, 2017 - This perspective examines psychophysical methods that may reveal the algorithms that encode odor images by integrating current data fro...
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Encoding odorant images Madeleine Marie Rochelle, Géraldine Julie Prévost, and Terry Edward Acree J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.6b05573 • Publication Date (Web): 12 Mar 2017 Downloaded from http://pubs.acs.org on March 14, 2017

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Journal of Agricultural and Food Chemistry

Computing Odor Images Madeleine M. Rochelle Cornell University Food Science Department 114 Tower Road Ithaca , NY 14853 [email protected] Géraldine Julie Prévost 1 Cornell University Food Science Department 114 Tower Road Ithaca , NY 14853 [email protected] Terry E Acree * Cornell University Food Science Department, 347 114 Tower Road Ithaca , NY 14853 [email protected] 1

present address Procter & Gamble Services Company Temselaan 100 1853 Strombeek-Bever Belgium

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Abstract

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This perspective examines psychophysical methods that may reveal the algorithms that

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encode odor images by integrating current data from sensory measurement into a

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computational model of odor perception. There is evidence that algorithms used by the

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nervous system to process odor sensations require input from only a few odorants,

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between 3 and 8.1-7 Furthermore, the number of recognizable odors in foods that

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contribute anything to the aroma of all foods is approximately 250.8 This may imply that

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it is the ratio of a small number of key odorants (KOs) that create a multitude of food

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odor. Studies with large mixtures of odorants (formulated to be of equal potency) show

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that a subject’s ability to detect individual odorants in these mixtures was vanishingly

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small. These large mixtures had weak and nondescript but similar odor character. If only

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a few stimulants are used to represent complex images, it is direct evidence of the

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simplicity and therefore the tractability of the computational process.9

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Keywords: sniff olfactometry, odor image, odorant mixtures, Laing limit, olfactory white

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Introduction

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Multitudes of odorants surround us every day. From fresh cut grass to a pot of brewing

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coffee, the odors we encounter in our daily routines capture our attention, modify our

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memories, and shape our experiences. It is a truth universally acknowledged that there

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are hundreds of odorants working behind the scenes, subliminal, while still activating

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our olfactory receptors. Nonetheless, a number of issues remain unresolved in this

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context, in particular the relation between the olfactory stimulus and percept.

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In a seminal study from 1989, David Laing tested the human ability to identify individual

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components in complex odor mixtures. For this, 127 human subjects were trained to

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associate labels with 7 odorants until they could do so accurately. The subjects were

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asked to identify all individual elements in the mixtures, containing up to 5 of the 7

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odorants. The frequency at which subjects could correctly identify both odorants in

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binary mixtures was less than 35 %, all three in tertiary mixture was less than 14%, and

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identifying all 4 in a quaternary mixtures was an insignificant 4%.1 However, Laing’s

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subsequent comparative studies with 10 more rigorously trained subjects yielded

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different results. Here, subjects were directed to perform a different task, namely to

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identify a single odorant in a complex mixture, and they could perform this task

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accurately for mixtures containing up to 8 odorants. 5, 10-12

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This represents an example of one of the three levels of analysis described for the study

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of visual perception, as described by the visual scientist David Marr. These three levels

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involve a “computational” element, referring to what the system does, an “algorithmic” or

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rule-based one that apply to the input during computation to yield an output, and the

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“implementation,” meaning the biology that does the work (the “wetware”).9 If we apply

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this model to olfaction, the few odor limits to the analysis of mixtures appears as a

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computational limit that is imposed by some algorithm and that allows us to ignore most

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odorants in a mixture, even though these odorants are above their threshold and

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activating receptors. In addition to such filtering effects, the perception of a mixture of

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odorants in small numbers is modulated by associative learning. For example, the

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enhanced ability to discriminate binary mixtures after association of one of the pairs with

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aversive shocks and the shift in odor quality when two of the odorants are paired

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together in a binary mixture indicates the profound and perhaps unavoidable effects of

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associative learning on odor mixture perception.7, 13-16 Somewhere, in the

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neuroanatomy are cellular implementations and neuronal connections housing the

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algorithms that produced these computational results.9, 17 Noticeably, the reproducibility

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of psychophysical experiments indicate that the olfactory system operates on a robust

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encoding-decoding process.18, 19 The psychophysical experiments described here are

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intended to provide insight into the computational behavior of a sensing organism and to

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define the algorithms that underlie them. Central to these experiments is the

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standardization of experimental techniques and parameters to assure a sufficient

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comparability of results obtained from different models.20

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Recognizable odorants in mixtures.

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According to Laing (Figure 1), after the limiting number of 3, there is a sharp drop in

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the ability to correctly identify all components of a mixture with significance. Moreover,

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the ability to recognize a single odorant in presence of 9 to 60 odorants is

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insignificant.1, 21 Training and expertise showed little to no effect on the number of

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recognizable odorants.4, 22 While the type of odorants used did not change the number

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of odorants detected, familiarity with the odorants increased identification accuracy,

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indicating that memory, as well as odorant concentration, contribute to the rapid

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identification of odorants in mixtures.1, 4 This remains true even when the odor is

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misidentified as something familiar. In multiple studies, reviewed in “Learning to Smell,”

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the relationship between familiarity and perceived intensity of an odor were directly

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correlated.23 This restricted capacity of the human olfactory system to recognize more

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than a few odorants in a mixture may seem like a weakness at first. However, it was

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suggested that, "Kthe apparent inability may in fact reflect a highly efficient neural

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encoding mechanism which facilitates the rapid discrimination and identification of

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multicomponent object odors in the environment." 4, 24 From this perspective, further

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analysis of such a limit to the number of odorants may account for particular analytical

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components in odor perception that could prove integral in the future of the study of

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olfaction and of enormous practical importance to the flavor industry.25

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A limit of recognizable odorants implies that any real complex odor mixture can be

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recreated with just a few odorants, something that perfumers and flavorists are

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convinced they do every day. There is considerable evidence that a few odorants in a

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complex mixture can be detected analytically and/or they can also be configured into a

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single perception from this pattern of individual responses to each of the component

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odorants. In philosophical terms, the whole may be more than the sum of its parts. For

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example, a study of tertiary mixtures of odorants that smelled like pineapple to

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humans, while none of the individual components had a pineapple smell on their own,

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is evidence that mixtures of odorants can be perceived as a single configuration of

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elements (pineapple) instead of a pattern of elemental odors (violet, caramel, and

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strawberry).26, 27 Similar results were obtained with newborn rabbits using the same

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three compounds. 28-30 The ongoing question is how processing such few odorants

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can explain the discriminatory skill of a sommelier to identify wine, the ability of many

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animals to use odorant mixtures to understand their environment, and the models of

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olfactory processing emerging from neurobiology9. In other words, how do organisms

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decode odor mixtures? How do they use salient and subliminal information extracted

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from a whiff to stimulate perception and behavior? An answer to this question must be

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able to explain the 3 KO phenomena. Also, it must account for the related issue of the

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simultaneous suppression of odorants in mixtures and their adaptation in sequential

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presentations.

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Suppression and Adaptation

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It is likely that the limit on the number of recognizable odorants in mixtures is due to

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the interaction between odorant signals in the network of neurons that process

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odorants into odors. Suppression and adaptation are among the chief modulating

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effects observed many times in the study of mixture perception, and these effects

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indicate that odorants interact with each other in different ways. When this

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phenomenon was examined in binary mixtures both components remained detectable,

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but some features of each component were suppressed with the addition of others.31

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Notably, the suppression and adaptation in odor mixtures can be somewhat predicted

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from the similarities and differences in odorant qualities. For example, the structurally

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similar “citrus” smelling odorants C8, C10, C11 n - aldehydes, cross-adapted each

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other but did not adapt to the green smelling and structurally similar n - C6 aldehyde,

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hexanal.32 Furthermore, the 3 similar smelling “citrusy” aldehydes do not suppress

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each other in mixtures while they do suppress hexanal, and vice versa.33 It is not

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surprising then that the rI7 receptor, first de-orphanized in 1998, has all the citrus

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smelling odorants in its receptive field while hexanal is not.34 Examining the interaction

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of C6, C8, and C10 aldehydes in binary mixtures, the similar smelling C8 and C10

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aldehydes cross-adapt but the dissimilar smelling aldehydes, C6 and C8, suppressed

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each other when mixed. It seems that odorants cross-adapt when they smell alike and

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suppress each other when they do not.33 At the very least, this implies that the

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processes that regulate suppression are somewhat different from those that govern

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adaptation. In other studies of binary odorant mixtures the overall intensity was less

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than the sum of the intensities of the odorants individually, indicating suppression, but

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always stronger than the mean intensities of the individual odorants and follow a vector

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model of addition.35, 36

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Non-human models also show non-additive effects of odorants in mixtures. For

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example, rats are instinctively attracted to, or repelled by, specific odors for safety and

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reproductive purposes. However, when these attractive and aversive odorants are

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combined in mixtures, the rats respond more to the attractive odorant, indicating that

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the attractive odorant is suppressing the aversive odorant.37 The effect of suppression

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and adaptation can also be seen in olfactory studies related to time. In both humans

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and non-human models, it has been shown that when faced with mixtures of similar

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smelling odorants, subjects tend to take longer to identify the individual odorants

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present in the mixture. Perhaps suppression is occurring in mixtures of similar

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odorants, making the analytical process of identifying odorants more difficult.38

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Mixture perception

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Contrary to the phenomena that limits the number of detectable odorants in a mixture,

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humans seem to be unable to detect a single component in mixture of 8 different

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odorants when they are at the same odor intensity.5 This phenomenon was more

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dramatically demonstrated when a mixture of 60 different odorants was prepared at

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concentrations of similar odor intensities. None of the component odorants could be

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recognized in the mixture, but there was a faint nondescript smell. Furthermore, when

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the mix of 60 was subdivided into 2 random mixtures of 30 components they both

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smelled the same.21 It may seem that the computational mechanisms of the human

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olfactory system process the signals from complex mixtures through a small subset of

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the individual components that are present at an odor potency somewhat larger than

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the remainder of the constituents. Gas chromatography-olfactometry (GCO) data

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published in the last 30 years shows the same pattern in natural products. A few Key

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Odorants (KO) dominate natural product GCO data, as supported by the publications

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(~ 900) that make up the Flavornet39. Furthermore, an analysis of 119 publications that

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fit rigorous criteria for odor activity involving 220 foods, only 230 unique odorants were

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found.8 There are over 113,000,000 possible distinguishable odorant patterns from a

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combination of any 4 of these 230 food odors – more than enough patterns to encode

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ecologically important features of any organism’s olfactory space. This does not even

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include the number of patterns that can be created with different ratios of the 4

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odorants. For this reason, a small number of stimulants may be all that is needed to

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encode complex images, and the limit on recognition may be evidence of the

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simplifying of algorithms involved in computational processing.

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To the extent that the encoding process is similar in rats and humans, maps of the

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neural projections from the glomerulus to the anterior piriform cortex indicate that

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connections between these two bodies are unique but not ordered in any simple way,

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e.g. the map in the main olfactory bulb is not a major feature of the “cortical response

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mosaic”. However, reproducible psychophysical behavior indicates that a robust

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encoding – decoding process must be in operation.18, 19 Although this perspective

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examines odor perception in terms of top-down processing, recent experiments with

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heterologous expression systems using recombinate odorant mixtures for “butter”

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containing the three KOs, diacetyl, butanoic acid, and racemic δ-decalactone showed

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that 1.) the receptor activity pattern of the recombinate did not just appear as the sum

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of patterns of the single compounds (at least in class-I ORs), and that 2.) at the level

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of a single receptor response, there was a synergism of the single compounds in the

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binary mixtures and in the recombinate.40 It is likely that evidence for the

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computational processes will also be visible at the receptor level, in the peripheral

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nervous system, or even in the circulatory system where these cultured butter KOs are

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expressed in leukocytes.40 The challenge will be to determine how humans process

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relatively simple mixtures of odorants first into a simple input code and then into more

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complex output: a multitude of unique odor objects representing our olfactory space.

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Sniff Olfactometry

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Most research on odor mixture perception in humans has relied on the correlation of

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psychophysical and sensory measurements of intensity with measures of odorant

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concentration. These methods tend to generate data on individuals very slowly. As

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pointed out by Wilson & Stevenson “[There is a] ... need to develop new approaches to

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testing olfactory discrimination that enhance both the sensitivity and speedK [Most

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sensory tests] ...are very time consuming and yield relatively little data per participant.

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Techniques such as those pioneered by Rabin and Cain41, which involve the

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identification of a target odor in a mixture are the sort of thing we have in mind.” 23

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Sniff Olfactometry (SO) was developed to address these questions. To improve the

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study of odor images in humans, stimulated by simple mixtures of key odorants, an

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olfactometer was used to deliver defined compositions with minimal stimulus exposure

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to 98%), and 2-ethyl-3,5-dimethylpyrazine CAS number 27043-

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05-6 (>95%) (2E3,5DP) were from Sigma Aldrich (St Louis, USA). Solutions were

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made in distilled water containing 10% v/v ethanol (food grade). Test solutions ranged

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from 1 ppm to 200 ppm for MAL, 10ppm to 1000ppm MOL and 50ppb to 300ppb

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2E3,5DP.

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Subjects – Two female subjects both members of the lab were tested

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Sniff Olfactometer - The design criteria for the SO was based on 250 ml PFA

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squeeze bottles.46 The PFA exhibits low odorant absorption, can contain a model

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bolus to represent retronasal smell, a headspace designed to deliver any odorant

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concentration in air released from model mixtures, or material representing many

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different ecological odorant sources: foods, beverages, ingredients, etc. The PFA

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bottles are easily managed when pre-installed in a 3-bottle assembly that maximizes

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sample exchanges (