Computing Odor Images - Journal of Agricultural and Food Chemistry

Subscriber access provided by HACETTEPE UNIVERSITESI KUTUPHANESI

Perspective

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

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Journal of Agricultural and Food Chemistry is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 30

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

1 ACS Paragon Plus Environment


Page 2 of 30

1

Abstract

2

This perspective examines psychophysical methods that may reveal the algorithms that

3

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

4

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

5

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

6

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

7

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

8

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

9

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

10

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

11

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

12

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

13

simplicity and therefore the tractability of the computational process.9

14

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


Page 3 of 30


15

Introduction

16

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

17

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

18

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

19

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

20

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

21

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

22 23

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

24

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

25

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

26

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

27

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

28

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

29

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

30

subsequent comparative studies with 10 more rigorously trained subjects yielded

31

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

32

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

33

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

34 35

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

36

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

37

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



Page 4 of 30

38

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

39

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

40

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

41

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

42

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

43

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

44

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

45

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

46

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

47

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

48

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

49

neuroanatomy are cellular implementations and neuronal connections housing the

50

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

51

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

52

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

53

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

54

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

55

standardization of experimental techniques and parameters to assure a sufficient

56

comparability of results obtained from different models.20

57 58

Recognizable odorants in mixtures.

59

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

60

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


Page 5 of 30


61

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

62

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

63

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

64

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

65

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

66

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

67

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

68

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

69

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

70

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

71

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

72

encoding mechanism which facilitates the rapid discrimination and identification of

73

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

74

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

75

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

76

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

77 78

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

79

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

80

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

81

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

82

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

83

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



84

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

85

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

86

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

87

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

88

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

89

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

90

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

91

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

92

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

93

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

94

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

95

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

96

simultaneous suppression of odorants in mixtures and their adaptation in sequential

97

presentations.

Page 6 of 30

98 99

Suppression and Adaptation

100

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

101

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

102

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

103

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

104

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

105

phenomenon was examined in binary mixtures both components remained detectable,

106

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


Page 7 of 30


107

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

108

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

109

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

110

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

111

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

112

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

113

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

114

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

115

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

116

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

117

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

118

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

119

processes that regulate suppression are somewhat different from those that govern

120

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

121

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

122

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

123

model of addition.35, 36

124 125

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

126

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

127

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

128

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

129

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



130

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

131

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

132

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

133

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

134

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

Page 8 of 30

135 136

Mixture perception

137

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

138

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

139

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

140

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

141

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

142

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

143

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

144

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

145

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

146

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

147

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

148

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

149

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

150

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

151

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

152

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


Page 9 of 30


153

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

154

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

155

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

156

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

157

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

158

simplifying of algorithms involved in computational processing.

159 160

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

161

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

162

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

163

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

164

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

165

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

166

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

167

heterologous expression systems using recombinate odorant mixtures for “butter”

168

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

169

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

170

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

171

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

172

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

173

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

174

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

175

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



176

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

177

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

Page 10 of 30

178 179

Sniff Olfactometry

180

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

181

psychophysical and sensory measurements of intensity with measures of odorant

182

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

183

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

184

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

185

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

186

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

187

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

188 189

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

190

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

191

olfactometer was used to deliver defined compositions with minimal stimulus exposure

192

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

204

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

205

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

206

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

207

2E3,5DP.

208 209

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

210 211

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

212

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

213

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

214

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

215

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

216

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

217

sample exchanges (

Computing Odor Images - Journal of Agricultural and Food Chemistry

Recommend Documents