Relatório final
Autor: A.C. Ciconelle | Publicado: 2011-02-03T15:02:00-02:00

Olfactory BrownCiconelle, Ana C.M.,  Brazil; Batscha, Jonathan, USA;Weiss, TaliDepartment of NeurobiologyAbstract&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;In  vision and audition exist, respectively, the phenomena of “brown color”  and “white noise.” However, such a phenomenon is much more elusive  in olfactory perception, as how physiochemical properties are translated  into olfactory precepts is not so well understood.&nbsp; In order to  create such an “olfactory brown,” two tests were made, one rating  the similarity of different mixtures and another rating the composition  of the same mixtures according to 26 verbal descriptors taken from Dravnieks’  atlas. Results have shown that mixtures of 15, 20, 25 and 30 components  have a similar percept; however, they are significantly different from  4 component mixtures. Those results suggest that mixtures containing  large numbers of different odors that span a greater perceptual olfactory  space may generate a similar percept: olfactory brown. 
Introduction &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;In  studies of vision and audition, the physical stimuli can be systematically  varied by wavelength and frequency, but there is no equivalent metric  for olfaction. Furthermore, the mapping of various physical stimuli  onto perception is well understood in vision and audition as being directly  related to color and luminance or decibel level and pitch, respectively,  but in olfaction rules governing the relationship between the physicochemical  features of molecules and their perceived odor  remain unknown. A significant obstacle to such understanding  is the high dimensionality of features describing both percepts  and molecules. The perceptual space  is made of odorants that can be described by a large number of  verbal descriptors. Dravnieks’ Atlas of Odor Character Profiles (1982)  characterized 144 monomolecular odorants with 146 perceptual descriptors.  Each odorant is therefore represented by a point in a highly multidimensional  sensory space. One of the methods to reduce dimensionality  in both odor percepts and physicochemical descriptors is PCA. It was  found that the primary axis of perception  is odor pleasantness, and that the primary axis  of physicochemical properties reflected the primary axis of  olfactory perception (Khan et al, 2007).&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Most  of the research in olfaction has relied on studies using pure neat molecules  as stimuli.&nbsp; The natural olfactory world, however, rarely contains  such neat compounds.&nbsp; The world of odor is made of mixtures containing  anywhere between several and several-thousand components.&nbsp; For  example: a rose may contain up to ~400 different components (Ohloff  and Demole, 1987).&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;In  this project we tried to test the assumption that mixtures containing  large numbers of equally intense odorants may generate a similar percept.  In vision, a mixture that combines all wavelengths in the visible spectrum  at equal energy is perceived as "white”, and a mixture of primary  paint colors is perceived as “brown”.&nbsp; In fact, a careful mixture  of just three primary paints – red, blue, and yellow - is sufficient  to generate a brown. In a previous study in this lab, 28 and 14 component  mixtures were carefully chosen from Dravnieks list based on algorithm  which divided the odorants so they will span the greatest possible perceptual  olfactory space. In a three-alternative forced-choice discrimination  test, discrimination accuracy between two different 28-component mixtures  was equal to discrimination accuracy between two 14-component mixtures;  both were significantly different from the discrimination accuracy between  two-component mixture and one component odorants (unpublished data).  These results suggest that the concept of “brown” is also valid  in olfaction.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;We  continued to explore this assumption by generating mixtures of various  components and comparing their similarity using Psychophysics tests.  In the next step we compare the similarity results to the perceptual  and chemical characters of their ingredients.&nbsp;
Experimental
Odorants:  80 odorants were diluted to equal intensity. Ten mixtures were made  of 4, 15, 20, 25 and 30 components. There were two versions of each  mixture; the odorants in each mixture were chosen randomly.Procedures:  All experiments were conducted in rooms specially designed for olfaction  experiments.&nbsp; These rooms are coated in stainless-steel to prevent  odor adhesion. All interaction with subjects was by computer-generated  digitized voice, and subjects provided their replies through a computer  keyboard.&nbsp; In order to minimize the effect of olfactory adaptation,  a 40 second inter-sample interval was maintained between mixture presentations. Psychophysics test:  Five subjects participated in this study. The task was to rate the similarity  between two mixtures in a range of 1 to 9.&nbsp; Each subject came twice,  on successive days. On the first day, the comparison was between mixtures  4A, 15A, 15B, 20A, 20B, 25A, 30A and on the second day, the comparison  was between the mixtures 4B, 15A, 15B, 20A, 20B, 25B, 30B, summing the  total of 42 comparisons including repeated ones.Statistical Analysis:  We used the Mann-Whitney U-test as well as a two-tailed T-test to discern  whether or not data gathered from both the descriptor quality survey  and comparison test was statistically significant. Although the Mann-Whitney  U-test and two-tailed T-test are designed to measure the same basic  idea, that is statistical significance, they have one fundamental difference;  while a two-tailed T-test is applicable to parametric data, the Mann-Whitney  U-test is applicable to non-parametric data as ours turned out to be. Physicochemical  olfactory space: Each of the 12 odorants we used was described  using 3224 chemical properties obtained using Dragon analytical chemistry  software. These properties range from simple values such as molecular  weight, number of atoms and bonds etc., and on to complex derived indices.  These individual physiochemical measures were then Z-scored to allow  for equal treatment.Perceptual space:  Ten different subjects rated each mixture using 24 verbal descriptors  that were chosen based on their STDEV of 144 odorants from 146 descriptors  in Dravnieks' Atlas (1982).&nbsp; 10 subjects rated the applicability  of each descriptor to the said mixtures on a scale from zero to five.  A score of zero denotes that the descriptor is not present and a score  of five denotes that the descriptor is highly appropriate to the odor  evaluate. The rating of each descriptor for each odor was calculated  by considering both the number of subjects that answered the rating  question and the average rating they provided.&nbsp; This allowed us  to generate a space with 146 descriptor-dimensions containing the 12  odorants we used.Two-dimensional  perceptual space: In addition to representing each odorant  with 24 descriptors, we also represented each odorant with only two  descriptors, intensity and pleasantness, which subjects scored from  zero to ten (zero for unpleasant and/or no odor and 10 for pleasant  and/or intense odor).PCA analysis:  To reduce the dimensionality of perceptual descriptors and physiochemical  descriptors, we used the principal component (PC) function in the Statistics  Toolbox of Matlab. Principal  component analysis (PCA) takes a data set consisting of N points  in an M-dimensional space (for example, 12 odorants in the  24-dimensional odor descriptor space) and finds a rotation matrix which  rotates the N points onto a new M-dimensional  space with certain special  properties. These are such that (1) the new dimensions are orthogonal  and (2) the new dimensions, called principal components  (e.g., PC1, PC2, ..., PCM), are ordered so that each successive  PC has the maximal possible variance. Thus, PC1 explains the  most variance of any linear transform of the original data,  PC2 explains the next biggest amount of variance, and so on.  Because the PCs are orthogonal, they explain sets of data that are not  necessarily mutually inclusive.Spanning analysis:  To measure the span of the mixture’s components, we analyzed the average  minimum distance between the Dravnieks’s odorants and the mixtures  components. For this procedure, we used the Statistics Toolbox of Matlab  to generate 10,000 random possibilities of mixtures and compute the  prevalence of the minimum distance between the odorants in all 146 dimensions  (each one corresponding to one verbal descriptor). In general, the lower  the average minimum distance, the greater the span across the perceptual  space.&nbsp;
ResultsMixtures whose components  spanning the space and are in equated intensity are expected to have  a similar smell. While there are many colorful shades of brown, there  may be an even greater variety of “olfactory browns”. The similarity  test intends to measure the similarity between those “olfactory browns”  which are not necessarily indistinguishable. Above 15 components  there is a similar perception  
   Mixtures   P-value   Mixtures   P-value
4 vs 15&nbsp;       0.001   15 vs 25    0.19
4 vs 20       &lt;0.001   15 vs 30    0.9
4 vs 25       &lt;0.001   20 vs 25    0.28
4 vs 30       0.0013   20 vs 30    0.95
15 vs 20       0.96   25 vs 30    0.25
Table1.    Comparison between the similarity of mixtures (T-test).
&nbsp;&nbsp; &nbsp;Similarity between mixtures  of four components and each of the other mixtures are significantly  different. Furthermore, the similarity between all of the other odorant  mixtures is not significantly different (Table 1).Two-dimensional  perceptual spaceThe results from the  psychophysics test in which participants were asked to rate the intensity  and pleasantness of various odorant mixtures is shown below. In this  graph, one can see the stark differences between mixture 4A and the  rest of the mixtures, as that particular mixture was significantly intense  and unpleasant. Other mixtures that showed similar levels of odor intensity  were mixtures 4B and 25A. All the other mixtures are not significantly  different in their pleasantness and intensity. (Figure is missing)&nbsp;
The perception  matrix of the mixture and evaluation of the spanning:As explained before,  we used PCA to reduce the number of dimensions. The effective dimensionality  of the odor profile space was much smaller than 24, with the first two  PCs accounting for 80% of the total variance. The mixtures 4C, 15A,  20A and 25A show substantial differences from the rest of the mixtures  (figure is missing). 
&nbsp;In order to measure the  quality of mixtures and be able to compare the results of the psychophysics  test, we compute the spanning algorithm. Intuitively, as the number  of odorants in a particular mixture increases, the average minimums  distance decreases, which is indicative of a greater spanning across  the perceptual space (figure 3). The 4-components and 20-components  mixtures have a significant difference around the average minimum distance;  while 20A and 4D have a good spread, 20C and 4C are clustered in space,  which is indicative of poor spanning of the perceptual space.

  Discussion
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; If  we wish to make a brown color, it is intuitive for us to mix between  paints which span the color spectrum; if we were to mix pink, red and  maroon, we would not get a brown color. The same principle applies to  olfaction; if we were to choose odorants of apple, pineapple and peach,  they would not create the perception of what we term a ‘brown smell’.  This is why the spanning of the olfactory perceptual space is so crucial.  Since in olfaction well-known physical or perceptual spaces are not  recognized, the creation of such a smell is more problematic. We took  advantage of the recent development of an olfactory perceptual space  for measuring smells (Khan et al, 2007, Haddad R 2008). This  space allowed us to evaluate if the odorants that were chosen for each  mixture actually spanned the perceptual spaces. We define the distance  between individual components, that is, odorants, using a Euclidean  distance metric. We chose the components of the different  mixtures randomly from a pool of odorants that were both available in  the laboratory and appear in Dravnieks' Atlas. After a random selection  we checked how the components of each mixture span the space using a  mathematical definition.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;In  the first experiment, all four-components mixtures have a low similarity  compared to other mixtures (P-value &lt; 0.001). However, all compared  mixtures with elements containing 15 odorants or more were highly similar.  Once it is known that four compounds aren’t enough and 15 is sufficient  to create the brown, we can assert that the minimum amount of compounds  required to create an olfactory brown lies between 4 and 15. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Since  the pleasantness and intensity of most of the mixtures were not significantly  different, we can assume that the similarity test was valid and that  participants were not affected by differences in intensity. However,  the intensity of the 4 component mixtures, as well as one of the 25  component mixtures, was significantly higher, and thus our data from  this test is not entirely conclusive and requires further study. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Our  analysis of the 4C mixture indicated a relatively high average minimum  distance, thus indicating a poor spanning of the perceptual space, making  it somewhat of an outlier. Nonetheless, the 20A mixture also shows up  as an outlier despite its good spanning, and for this reason we cannot  determine if the cluster represents the desired “olfactory brown.”  For this reason, we believe this topic is deserving of further study.

References
Dravnieks A (1985) Atlas of    odor character profiles. PA: ASTM Press.
Haddad R, Lapid H, Harel D,    Sobel N (2008) Measuring smells. Current opinion in neurobiology 18:438-444.
Khan R, Luk C, Flinker A,    Aggarwal A, Lapid H, Haddad R, Sobel N (2007) Predicting Odor &nbsp;    Pleasantness from Odorant Structure: Pleasantness as a Reflection of    the Physical World. &nbsp; Journal of Neuroscience.
Ohloff G, Demole E (1987)    Importance of the odoriferous principle of Bulgarian rose oil in flavour    and fragrance chemistry. Journal of chromatography 406:181-183.
&nbsp;