the magnetic properties of conductivity-promoting additives or iron oxide contamination. These were investigated using the highest contents of additives and contaminants that might be found in jet fuels (1 mg/L ASA-3-an additive containing chromium and calcium compounds-and 50 mg/L black iron oxide). No effect on the values determined for hydrogen content could be detected. Dissolved oxygen content was also shown to have no effect. In conclusion it appears that the use of low resolution proton NMR to determine hydrogen content of aviation turbine fuels is an extremely promising approach. It is simple to use, requires a very short time for determination (about 5 min) and
is capable of giving results to an accuracy better than those of techniques currently employed.
LITERATURE CITED ( I ) American Society for Testing and Materials, Standard specification for aviation turbine fuels, D.1655. (2) International Air Transport Association, Guidance material for aviation turbine fuels. Amended Dec. 1975. Corrected Jan. 1976. (3) C. R. Martel and L. C. Angello, USAF, Technical Report AFAPL-TR-72103.
RECEIVEDfor review October 7,1976. Accepted January 12, 1977.
Intrinsic Dimensionality of Smell James R. McGlll and Bruce R. Kowalskl* Laboratory for Chernornetrics, Department of Chemistry, University of Washington, Seattle, Wash., 98 195
The human sense of smell has long been a toplc of scholarly dlscusslon. Varlous emplrlcal odor classification schemes have been deflned In an attempt to describe the human sensory spectrum. The four most prominent theories are shape sensors, vlbratlonal coupllng, membrane puncture, and acld-base Interactlon. Thls paper attempts to determlne the lntrlnslc dlmenslonalltyof the human sensory space by flrst flttlng multlple emplrlcal odor slmllarltles using physlcal and chemical measurements of the molecules Involved. The axes of the space were then rotated, via the Karhunen-Loeve transform, whlch Indicated that there are only two meaningful axes In the space. By use of both the orlglnal physlcal and chemlcal measurements, and theoretlcal values calculated for the molecules by semlemplrlcalmethods, these axes were found to relate to the molecule’s electron donor ablllty and Its dlrected dlpole.
The human sense of smell has been a source of scholarly discussion since the time of the Greeks. Aristotle (I) (400B.C.) suggested the first empirical classification, dividing odors into sweet, bitter, pungent, sharp, and oily smells. Lucretius (2) (47 B.C.) was the first to try to define a theoretical basis for odors when he speculated that unpleasant smells were produced by hooked, jagged particles, and pleasant smells by smooth, round particles. After Lucretius, scholarly interest in the sense of smell became more empirically inclined, culminating with the work of Zwaardemaker (3) who, in 1895, proposed an empirical classification system which divided smells into nine classes (ethereal, aromatic, fragrant, ambrosial, alliaceous, empyreumatic, caprylic, repulsive, and nauseating), and 30 subdivisions. These 30 are listed in column 1 of Table I. Attempts to define a unifying theory behind sensations of smell were initiated by Hemming ( 4 ) in 1916 when he proposed an “olfactory prism”. He defined the prism with flowery, fruity, and foul forming the upper triangle and spicy, resinous, and burnt forming the lower. Each member of the former triangle was connected vertically to each member of the latter (i.e., spicy and flowery are vertically connected, etc.). In 1927, Crocker and Henderson ( 5 ) postulated that only four sensors, detecting fragrant, acid, burnt, and caprylic odors, were necessary to explain all smells. They defined the maximum intensity of any odor to be 8 and the minimum to be 0. This classification scheme formed the basis of the early quantitative experiments in odor classification. As research progressed, this four-sensor scheme was found to be too lim596
ANALYTICAL CHEMISTRY, VOL. 49, NO. 4, APRIL 1977
ited, and Amoore (6) postulated seven sensors: ethereal, camphoraceous, musky, floral, minty, pungent, and putrid. How these relate to Zwaardemaker’s system is shown in column 2 of Table I. Amoore noticed that in the first five of these categories, models made of compounds from the same category had similar shapes. This led him to revive Lucretius’ idea that the shape of a molecule defines its scent and to postulate shape sensors which fit the molecular forms in a lock-and-key manner. Amoore has since done extensive work using twodimensional projections of atomic models and has had some success fitting scent classifications with them. A separate school of odor theory has developed, based on the observation of Dyson (7),in 1937,that molecules of similar odor seem to have bands in the same region of their Raman spectra (1400-3500 cm-l) and he speculated that this frequency region was where the osmotic membranes of the nose would respond to vibrations. This theory has been extensively studied and modified by Wright (8) who calculated that only vibrations below 500 cm-l are excited at room temperature, and thus focused his attention on the Raman region in the 500 to 50 cm-1 range. He, like Amoore, has had some success fitting experimental data to his theory. A major empirical study of the human odor sensory space was conducted by R. H. Wright and K. M. Michaels (9)which used nine standards for comparative classification (shown in relation to Zwaardemaker’s and Amoore’s schemes in column 3 of Table I). A major attempt at unifying these two theories was conducted by Schiffman (IO). She used Guttman’s (11)method of nonlinear mapping to produce a two-dimensional representation of the nine-dimensional space produced by Wright’s study (9).She observed that molecules of similar smell clustered together, and that there seemed to be some correlation of the structure of the compounds and their smell. Using the structural formulas of the compounds, she counted a number of chemical features, such as the number of double bonds, number of alcoholic, aldehydic, and acidic groups, number of sulfurs and nitrogens, etc., along with other variables, such as molecular weight, and attempted to fit the axes produced by Guttman’s method. She also included Raman spectral information. Using a weighting method which she and co-workers had developed, she was able to generate measures of how important each feature was in explaining the data space. She had some success fitting the axes of the reduced dimensional space and found that a few measures were important (particularly molecular weight).
Table I. Comparison of Three Major Odor Classification Schemes Amoore Zwaardemaker
Hexyl Acetate (9)
Fruity Waxy Ethers Camphor Clove Cinnamon Aniseed Minty Thyme Rosy Citrous Almond Jasmine Orange Blossom Lily Violet Vanilla
Ethereal
Aromatic
Fragrant
Wright
Ethereal Camphor
Eugenol (1) Benzothiozol(6)
Minty Citral ( 7 ) Floral
5-8001 [Lily of the Valley] (8)
Turpentine (2)
Ambrosial
Musky Allyl Disulfide (4)
Alliaceous Empyreumatic Caprylic Repulsive Nauseating
Hydrogen Sulfide (5)
{~
~
~
(Caproic Cat Urine Narcotic Bed Bug Carrion {Fecal
~
~
l
i
c
{
Putrid Pugent
Two new olfactory models have been proposed recently. Davies (12) has correlated odor to the heat of absorption and molecular dimensions and has postulated that odorant molecules penetrate through neural membranes, disrupting the neuron wall, and thus initiating a response. The other new theory came from a study of the effect of electron withdrawing groups on the odor of molecules conducted by Brower and Schafer (13)from which they concluded that the acid-base character of a molecule is important, although they did not speculate as to the mechanism involved. While all of these theories are illuminating and stimulating, both of the major theories have distinct problems. Much dissection and biochemical work has been done on olfactory areas of humans and other animals, but no macroscopic shape sensors have been found. Neither have vibration sensitive structures been found. The only structures seem to be basal cells, ciliated support cells which probably form a very efficient dust trap, and ciliated nerves which are exposed directly to the passing air (14). There is also a yellow-brown pigment present in the interior of the olfactory layers which has long been thought important in odor detection, but recent work seems to refute this (14). The two more recent theories are less well developed, so it is difficult to comment on their consequences, but clearly, none of the available models is complete, as all of their authors themselves comment and there is much research to be done. We approached Wright’s study as analytical chemists wondering how much spectral and physical information could elucidate the human odor response space defined by Wright’s study. Initially, we used only instrumentally measured information, as we felt it was the least biased source available. We first found what part of these data correlated to each of the nine similarity continua defined by the nine standards, and how well each of the continua could be fit. Encouraged by these results, we then studied the structure of the total nine-
Propionic Acid (3)
dimensional response space by applying a linear transform to extract orthogonal axes and fitting the resulting axes with the physical and spectral data. These results led us to postulate that a minimum of two sensors was necessary to explain the response space. In order to define them more exactly, we calculated theoretical values, using the semiempirical CNDO (15)(Complete Neglect of Differential Overlap) method, and fit the transformed axes with these values. The results supported and considerably refined the postulated sensors.
EXPERIMENTAL The study using nine odor standards conducted by Wright ( 9 )was the starting point for the work discussed in the present paper. In Wright’s study, 84 subjects were presented 50 samples (45 distinct compounds, with five duplicates) and asked to rank them on a sixpoint scale as to their similarity to each of nine standards. The score reported for the 50 samples was the summation of the points given by the 84 subjects. Schiffman’spaper (10) brought Wright’s work to the attention of this laboratory and, as her results showed scentstructurecorrelation, it made us wonder if spectral information would not lead to a better understanding of Wright’s results. Therefore the infared, ultraviolet, and nuclear magnetic resonance spectra for each specific compound were obtained from the literature. A few of the compounds used by Wright were chemical mixtures or proprietary compounds, so spectral information was unobtainable. Considerable effort was made to obtain the Raman spectra. While there were general rules for correlation of spectral regions to structural features available, the specific spectra were available for only a minority of compounds, and so were not included. The molecular weight, melting point, boiling point, density, specific rotation, and solubility in water and alcohol for each chemical were also included. Most of this information was obtained from the “Chemical Rubber Company Spectral Data Handbook” (16). The few values which were not found in the handbook were obtained from the chemical literature. The coding of spectral data for use in this study was a difficult problem. As all of the spectral methods measure a continuum of information, but numerical methods need a discrete series of values, it was necessary to partition the spectra in some manner which loses ANALYTICAL CHEMISTRY, VOL. 49, NO. 4, APRIL 1977
597
Table 11. Comparison of Fit Values Category Step 1 2
1.3 2.3
3 4 5 6 7 8 9 Average
0.9 2.8
3.2 1.7 1.1 1.8
1.7 1.8
Features used Least (10) 21 18 27 20 20 24 23 26 22 22
3.3 2.8 2.8
4.2 6.3 3.4 2.6 4.8 3.1 3.7
Least (5)
Category Error
4.5 3.5 3.6 6.4. 8.0 4.8 4.2 6.6 4.4 5.1
6.6 5.5 8.2 10.7 10.1 9.5 4.4 3.2 5.1 7.0
a minimum of information. For the infrared and NMR data, this was not too difficult, as their spectra have regions in which specific types of molecular structures are represented. Partitioningthe ultraviolet spectra was not as straightforward because, while it is an excellent probe for aromaticity, the frequency of maximum absorption is not substructure specific. The simplest approach was to plot a histogram of all the ultraviolet spectral maxima of the data set and use the natural nodes to determine the partitioning of the ultraviolet spectra. As the study progressed, this partitioning was found to be too fine and several of the regions were combined. Thus the full data set used for fitting the nine sets of similarity scores consisted of 47 compounds and, initially, 43 measurements of spectral and physical properties. The values associated with each section of the UV, IR, and NMR spectra were the number of peaks reported in the published spectrum for that region. (Le., if the IR spectrum of a compound had four peaks reported between 6.0 and 6.5 nm, the compound was assigned a value of 4 for variable 13). This method of coding spectra is admittedly less than ideal, but experimental results indicate that it was adequate.
RESULTS AND DISCUSSION Before presenting the experimental results, a short review of the terminology used in this study will be helpful. All of the information which is known about a particular sample (in this study 43 measurements on each molecule) defines a sample vector. These measurements are referred to as features after they are modified by mathematical operations. As there are 43 measurements made on each of the 47 molecules, the features define a 43-dimensional space with 47 points plotted in it. This is called the data space. The numerical value which defines what is to be explained about the sample (the odor score in this case) is termed the property of that sample. When the 9-dimensional space defined by the nine properties measured on each of the molecules in the data set by Wright's subjects is discussed, it is referred to as the property space and each of the nine axes is referred to as a property continuum. It should be noted that while this laboratory is involved primarily with pattern recognition (17)all but one of the methods used in this study are standard statistical analysis tools. These are slightly more demanding of the data than pattern recognition methods, because they assume that the data are approximately normally distributed. Approximate normality was determined by plotting histograms of the values of each of the features over the entire data set. All of the features were found to be distributed normally. The initial experimental problem was to determine how well the measurements explain the variations in each of the nine property continua. Two distinct questions were asked: how well can the property trends be explained using the entire data space, and which of the features in the data space are the most important for this explanation. To answer the first question, step-wise regression was used. This technique fits the property values by sequentially incorporating the features which explain the maximum amount of the remaining, unaccounted for variance, stopping at a predetermined limit of significance. 598
ANALYTICAL CHEMISTRY, VOL. 49, NO. 4, APRIL 1977
Eugenol
N
Feeture
Paeninp
Correlation
Select YeIoht
1
Molecul8r welpht
.46
.23
2
N.M.R.
3-4
* 37
.11
3
1.9.
e36
e
4
N.M.R.
5
U.Y.
210-235
my
6
1.9.
3.5-3.0
nn
7
1.R.
14.0-15.0
8
I.R.
4.0-Y.4
ppn
nn
b.0-6.5 8-9
ppm
nn
-.I1
e05
-.I5
.04
.El nm
08
-.
-06
25
a07
-06
a04
.at
e03
.31
e03
Flgure 1. Ten most hlghly correlated features to eugenol
The resulting percent fit measures how much of the property variance can be explained using the entire data space. Using multiple regression presents a problem because there are 43 features and only 47 patterns, which is a pattern to feature ratio of 1.09. Our laboratory strives for a ratio of more than 10.0 and does not go below 4.0. Thus if one could pick out the five or 10 most important features and fit the property using them, the results would be much more trustworthy. One could use the first 10 features chosen by the step-wise regression, but we prefer to use variables chosen on the basis of correlation rather than variance. Thus we use a technique called SELECT (18)which picks out the feature most correlated to the property, makes the rest of the features orthogonal to it, picks out the most correlated feature from the remaining features, etc. until it runs out of features or the correlation to property of the features falls below a preset limit. This yields an ordering of the features which is then used to pick the top untransformed features for a least squares fit of the property. One can also determine the meaning of each feature and determine whether it is plausible considering the standard odor used for that property. Table I1 shows the average percent error in the property values calculated by stepwise regression and by multiple regression using the five and ten best features as determined by SELECT, for each of the nine properties. Also listed are the number of features used by the stepwise regression for its fit, the estimated experimental error of each property continuum, calculated in a manner which is discussed below, and the average of the above values over all nine properties. As can be seen, in most cases the errors for the calculations are less than the estimated errors in the properties, and are in a fairly constant ratio to the property errors. This is reassuring because it suggests that the predominant source of error in the study is the experimental property values. Thus even though the spectral coding is not ideal, it does not obscure the trends in the properties. Looking a t Table I1 it can be seen that for all nine properties the stepwise regression has an error of 3.2% or better using around half of the features. The least squares fit using 10 features does slightly worse, with an error of 6.3% or better in all cases and the least squares fit using five features is somewhat poorer, with an error of 8.096 or better for all nine. Paradoxically, this latter fit is the most acceptable considering the level of error in the data (the last column of Table 111,
A I
.%
O e a o 7-
8 e
n
8
Figure 2. Plot of K-L
axis 1 vs. K-L axis 2
because when the fit of a property is better than the error in the property, random information is being fit, and the results are questionable. What the 10 best features are and how they relate to the structure of the standard is of immediate interest. For one of the standards, this is an impossible question to answer as the standard for class 8, S-8001,is a proprietary compound of the Schimmel Corporation. For the eight categories with known standards, the first 10 features do quite a good job of indicating the molecular substructure of the standards. In eight of the nine standards one of the top three features was molecular weight (in fitting to turpentine, the molecular weight was not picked), which was the variable that Schiffman (10) also found to be the most important. Taking eugenol as an example, the standard for property 1,its structure and the meaning of each of the top 10 features chosen by SELECT are shown in Figure 1along with their correlation to the property. The presence in eugenol of a phenol, an aromatic ring, and an
isolated double bond are reflected in the most highly correlated features. As the correlation gets lower, the structural relationship becomes more vague, but there is some indication of the presence of a methyl group. The first 10 features selected for the other eight standards show similar trends. From this i t seems that the similarity of one smell to another is related to chemical substructures. But this does not indicate whether or not the nose senses the separate substructures. In order to address the more general question of what the nose actually perceives, a better understanding of the space defined by the nine properties is mandatory. Since the standards were chosen to span the odor spectrum as completely as possible (Table I), hopefully the property space defined by the responses of the subjects in Wright's study describes human odor discrimination reasonably well. To get some idea of the structure of the property space, a two-dimensional projection of the nine-dimensional space will be useful. A number of algorithms have been developed for plotting spaces ANALYTICAL CHEMISTRY, VOL. 49, NO. 4, APRIL 1977
599
Table 111. Correlation of Nine Standards 1 2 3 4 5 6 7 8 9
1
2
3
4
5
6
7
8
9
1.0
-0.16
-0.54 -0.06 1.0
-0.49 -0.27 0.53 1.0
-0.57 -0.27 0.66 0.88 1.0
-0.57 -0.11 0.50 0.73 0.88 1.0
0.51 -0.05 -0.63 -0.64 -0.73 -0.73 1.0
0.52 -0.17 -0.72 -0.65 -0.75 -0.74 0.74 1.0
0.43 0.03 -0.59 -0.65 -0.73 -0.70 0.68 0.63 1.0
1.0
in reduced dimensions. The object of them all is to map as good a representation of the n-dimensional data space as possible into fewer, usually two, dimensions. Two general types exist, referred to as linear and nonlinear mappings (19). Linear mappings are those algorithms that use linear combinations of the original axes to align the first axis with the direction of greatest variance in the data space, the second axis with the second greatest, etc. while maintaining orthogonality between the axes. The second category of algorithms actually reduces the number of axes to whatever number is specified by the user and in doing so tries to minimize the loss of information to the exclusion of all else, including orthogonality. Both of these methods have their advantages and disadvantages. The nonlinear mappings produce the best possible reduced dimensional representation of the relationship between the points in the n-dimensional space but both the relationship between the resulting axes and of those axes to the original space is unclear. The linear transforms do not produce such optimum maps but have the advantage that the new axes are orthogonal and linear combinations of the axes in the original space. Schiffman was interested in an optimal map and thus chose to use Guttman’s method, a nonlinear mapping algorithm. In our study we desired mathematically simple axes for subsequent analyses and, as we have also noticed that linear methods normally produce maps comparable to those of nonlinear methods at a fraction of the cost, we were led to use the first two axes of the Karhunen-Loeve transform (K-L), often referred to as an eigenvector projection. Figure 2 shows the K-L plot of the property space; 74.3% of the variance in the 9-dimensional property space has been retained. To determine how much of the variance in the property space is real variation, and how much is noise, the error in each of the nine property axes must be calculated. Five of the compounds in the data set were presented to the subjects twice (the pairs of connected points in Figure 2). Dividing the difference between each pair of scores for a single compound by the sum, and averaging the five numbers gives an estimate of the error in each property axis. This ranges from 3.2% for standard 8 to 10.7% for standard 4, with the average error for the whole property space of 7.0% error per axis (Table 11). If the nine axes were totally independent, there would be 48% error in the position of each of the points in the 9-space, but from Table I11 (the correlation matrix of the nine properties) one can see that eight of the properties are correlated, so the error must be less. The only property which is not strongly correlated is number 2 for which turpentine is the standard. This can be seen in the K-L projection (Figure 2 ) , for of the eight property standards that were included in the 45 substances tested (indicated by the number of the property for which they were the standard following their structure), seven lie near the horizontal axis while much of the vertical axis is due to standard 2. In order to measure the stability of the nine K-L axes and thus determine which are real and which are purely noise, we 600
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4, APRIL 1977
repeatedly took the raw property values, added 7% random error to each value, took the K-L transform of the modified data, and cross-correlated the new axes with the K-L axes generated from the original data (see reference 20 for a full discussion of this technique). This indicated that only the first three K-L axes are meaningful, and the third only marginally so. Upon applying SELECT we were unable to find any feature with a meaningful correlation to the third axis, possibly indicating that the actual error in the property axes is greater than 7%. Thus, rather suprisingly, there seem to be only two identifiable axes in the property space. Looking at these axes in the K-L plot (Figure 2), there seem to be chemical trends associated with them. Following the abscissa from left to right, one first encounters symetrically substituted sulfurs, followed by short chain acids, long chain acids, alcohols, sparsely substituted aromatics, aldehydes, and finally highly substituted aromatics. An increase in molecular weight from left to right is also seen. Thus the chemical trend seems related to electron distribution and symmetry. Along the ordinate from bottom to top, one first finds sulfurs and highly substituted aromatic systems, grading into simpler aromatics and organic acids, and alcohols. This chemical trend appears related to the degree of electron delocalization in the molecules. Fitting the K-L axes with the features from the data space should indicate whether this chemical intuition is valid. Figure 3 lists the five best features from SELECT for fitting the two K-L axes. These features fit 73%of the variance of the first vector and 70% of the second vector, respectively. Chemical intuition seems correct in regard to the ordinate being related to electron distribution and the abscissa being related to the occurence of polar groups in various bonding situations and molecular weight. We suspect that the importance of the molecular weight is an artifact resulting from most of the high molecular weight aromatics having similar smells, and thus clustering. While these results support the detected trends in the K-L plot they do not give a simple model of what the nose is sensing, though they do give some tantalizing hints and suggest that maybe only two sensors, rather the 7 to 30 ( 6 )that have been postulated, are necessary to explain the data. A method for obtaining more fundamental information about the molecules under study is necessary, and the methods developed by quantum chemistry offer an attractive possibility. These techniques can be used to calculate electronic orbitals, dipole moments, and partial charges on atoms from a minimum of information. While ab initio calculations are the most elegant, they are too expensive to be justified by this study. Semiempirical calculations, which start out with a bit more information about the real world and which, while not absolutely correct in energies, give good relative values, should be quite adequate for testing purposes. In particular, the CNDO (15) calculations were used. Even these became inordinately expensive or technically complex as the number of atoms con-
VECTOR 1 ( a b s c l s s i )
Feature
N
Reining
1
Molecular Velpht
2
I.R.
14.0-15.0
3
U.V.
gt.
300 m u
nm
Correlation .50
-.
-CH2-;WC
38
extended conjugation
~ 3 6 -a32
-.
23
VECTOR 2
(ordinate) -.42
-e39
.37 -e27
5
Melting Point
.?7
Figure 3. Five most highly correlated features to each K-L axis
sidered increased so only those compounds symbolized by the solid circles in Figure 2 were calculated. CNDO calculations were performed on 35 of the 45 compounds tested by Wright and as these are well distributed over the space they should give a reasonably good representation of the data. The values generated by the CNDO calculations were the total electron dipole moment, the electron dipole moment in the x (longest), y, and z (shortest) directions with the molecule along with all the ratios of these values, the energy of the top filled u, T , and total orbital, and the bottom unfilled u, T , and total orbital and the energy differences between the filled and unfilled u, T , and total orbitals were used. The molecular weight, the molecular dimensions, and the ratios of the dimensions were also included as they are fundamental parameters of a molecule and previous work had shown them to be important. This generated a 26-dimensional “theory” space which was used to fit the K-L axes. The results were better than expected. The SELECT weights indicate that in fitting the first K-L vector only the molecular weight and the ratio of the electronic dipole along the molecule’s x (longest) axis to the dipole along its z (shortest) axis, are of major importance in fitting the first K-L axis. The least squares fit results show that these two features explain 63.7% of the variance along the axis. Addition of all other features chosen, which all express information about electron dipole moment, explains 82.6% of the variance. From the SELECT weights on the second K-L axis, only a single feature, the separation between the top filled and the bottom unfilled u orbitals, is of major importance. From the least squares fit results, it contains 60.3% of the variance along the second K-L axis. Addition of the other features chosen explains 77.8% of the variance. The molecular dimensions and ratios thereof were not incorporated in the fit of either axis by step-wise regression. This explanation of over 60% of the variance along each of the two meaningful K-L axis with only three features from the theory space suggests that perhaps some fundamental understanding of the problem of odor sensing has been achieved.
CONCLUSIONS Some conclusions can be drawn from the foregoing. From the first part of the experimental work, it is clear that the
trends in the similarity scores of the molecules in relation to the standards are correlated to spectral and physical measurements. Since the features which are most useful for fitting the similarity trends are those that express substructure information about the standard, it seems evident that the nose is either sensitive to a wide range of substructural features, or to phenomena which are affected by subtle changes in substructures. The second part of the experimental work, the K-L transform (or, in essence, the factor analysis) of the property space, yields 2 identifiable axes. Furthermore, fitting these axes with the features from the data space, indicates that the first (x) axis is related to molecular weight and something like polarizability, while the second ( y )axis seems to be related to ease of electron promotion. In the last part of the experimental work, the calculation of theoretical values by the semiempirical CNDO method yields values that are correlated with the K-L axes from the second part. The features which are the most useful for fitting the two axes indicate that the first is a combination of the molecular weight and the ratio of the electron dipole moment in the x direction (the longest axis of the molecule) to that in the z direction (the shortest axis) and the second related to the difference between the top filled and the bottom unfilled u orbital in the molecule. What all this means and how it relates to the four current theories of smell, vibration, shape, penetration, and acid-base character is not unambiguous. With such a small data set one cannot come to final decisions, but we feel our results imply that the first two theories, while useful approximations, are focused on effects rather than causes. In the first part of our research it became clear that, despite the relatively poor quality of the information, spectral data would fit the response scores of each standard. In the third part, the fitting of the K-L axes from the second part made it clear that the electron dipole moment of a molecule was very important. Both these results lead one to expect that infrared and Raman spectral information will fit the response scores well, which is exactly what has been found. But correlation does not imply causation. As mentioned in the introduction, no vibration sensitive structures have been found, and furthermore the importance of the first electron excitation energy of the molecule seems to rule out vibration as sole cause of the smell of a substance. The shape theory also should have some success in fitting the response scores, which it does, for external shapes and internal electronic phenomena are correlated. But this does not indicate whether shape is sensed directly or is a secondary phenomena. Studying the areas where the shape theory has difficulties may allow resolution. One of the major problems with the shape theory is that all smells are not unified under a single mechanism. For pungent and putrid smells (the left half of Figure 2), there has been no unifying form found with which to deduce the shapes of sensors sensitive to them. Therefore, non-shape-specific sensors must be invoked to explain human sensitivity to them. In our results, these compounds mesh smoothly into the sensory spectrum, as chemicals with low excitation energy and a very small electronic dipole. Shape sensors also have the problem that they are exclusive by their very nature, sensing only the presence of a very small number of specific substructures on a molecule. Thus to explain the vast number of odors an expert can distinguish, one is forced to postulate multiple, partial fits or a vast number of different sensors and complex summation procedures in the brain to determine a smell. A two-dimensional continuum, on the other hand, allows for infinitely fine gradations, limited only by the patience and skill of the observer. The two more recent theories, penetration and acid-base character, are not contrary to our analysis. In fact, we feel that our results support both. The first axis of theory space corANALYTICAL CHEMISTRY, VOL. 49, NO. 4, APRIL 1977
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relates to the directed dipole of a molecule which one could argue would determine the size and shape of the puncture a molecule made in the dipolar membrane of a neuron. Possibly one could even eliminate the necessity of full penetration of the molecule into the interior of the neuron, and hypothesize that the presence of the molecule on the surface and the consequent disordering of the membrane would be sufficient. The second axis correlates to the first u electron excitation energy, which one would expect to correlate to Lewis acidity. Whichever one is chosen, it could possibly be sensed by the effect of the molecule on some biochemical pathway which involved electron transfer. ACKNOWLEDGMENT The authors thank Dave Kalman, who helped us decipher the CNDO data, Carl Appellof and Jim Koskinen for writing the 3-dimensional model building program, and M. da Koven for moral support. LITERATURE CITED (1) Aristotle, "De Anima", book II, Chap. 9, 400 B.C. (2) T. C. Lecretius, "On the Nature of the Universe", 47 B.C. (3) H. Zwaardemaker, "Die Psycoiogie des Geruchs", Engiemann, Leipzig, 1895. (4) H. Hemming, "Der Geruchs", Leipzig, 1912. (5) E. C. Crocker and L. F. Henderson, Am. Perfum., 227, 325, 356 (1927).
(6) J. E. Amoore, "Molecular Basis of Odor", C. C Thomas Co., Springfield, Ill., 1970. (7) G. M. Dyson, Chem. Ind. (London), 1938, 647 (1938). (8) R. H. Wright, "The Science of Smell", Basic Books New York, N.Y., 1964. (9) R. H. Wright and K. M. Michaels, Ann. N. Y. Acad. Sci., 116, 535 (1964). (IO) S.S.Schiffman, Science, 185, 112 (1974). (11) L. Guttman, Psychometrica, 33,469 (1968). (12) J. T. Davies, "Olfactory Theories" in "Handbook of Sensory Phenomina IV. Chemical Senses 1. Olfaction", L. M. Beidler, Ed., Springer-Verlag, New York, N.Y., 1971. (13) K. R. Brower and R. Schafer, J. Chem. Educ., 52, 538 (1975). (14)D.G. Moulton, "Detection and Recognition of Odor Molecules" in "Gustation and Olfaction", G. Ohloff and A. F. Thomas Ed., Academic Press, New York, N.Y., 1971. (15) J. A. Pople and D. L. Beverige, "Approximate Molecular Orbital Theory", McGraw, Hili, New York, N.Y., 1970. (16) "Atlas of Spectral Data and Physical Constants for Organic Compounds, J. G. Grasselli, Ed., C. R. C. Press, Cleveland, Ohio, 1974. (17) B. R. Kowalski, "Pattern Recognition in Chemical Research", in "Computers in Chemical and Biochemical Research", Vol. 2, C. E. Klopfenstein and C. L. Wilkins, Ed., Academic Press, New York, N.Y., 1974. (18) B. R. Kowaiski and C. F. Bender, Pattern Recognition, 8, 1 (1976). (19) B. R. Kowalski and C. F. Bender, J. Am. Chem. Soc.,95, 686 (1973). (20) J. L. Fasching, D. L. Duewer, and B. R. Kowalski, Anal. Chem., 48, 2002 (1976).
RECEIVEDfor review October 26,1976. Accepted December 20, 1976. This paper was presented at the 172nd National Meeting of the American Chemical Society, San Francisco, Calif., August 1976. This work was supported by the National Science Foundation under grant number MPS 74-00818 A01.
Mercury-Gold Minigrid Optically Transparent Thin-Layer Electrode Michael L. Meyer,' Thomas P. DeAngelis,* and William R. Heineman* Department of Chemistry, University of Cincinnati, Cincinnati, Ohio 4522 1
An optlcaliy transparent thin-layer electrode (OTTLE) with characterlstics of mercury has been prepared by electrodepositing a thin film of mercury on a 500-lpi gold minigrid. The negative potential range of this Hg-Au OTTLE was 500 mV greater than that obtained on a Au OTTLE and 200 mV greater than that reported for a Hg-NI OllLE. The large hydrogen overvoltage is attributed to the good overvoltage characteristics of the gold substrate, which is quite soluble in mercury, and to the formation of a continuous mercury film rather than droplets. The optical transparency of the gold minigrid (60%) was not measurably diminished by deposition of the mercury film. The extended negative potential range Is useful for observing electrode processes with large negative reduction potentialsas illustrated by vitamin BI2 and glyoxyllc acld.
Optically transparent electrodes (OTEs) enable spectral monitoring of electrode processes during an electrochemical experiment by virtue of an optical beam passing through the electrode itself (1-3, and references therein). Spectroelectrochemistry with OTEs has been used to study the kinetics of homogeneous chemical reactions coupled to electrode processes; obtain UV, visible, and infrared spectra of intermediates and products of electrode reactions; measure E '' and n values of biological redox components; and observe surface Present address, Institute of Forensic Medicine, Toxicology, ana Criminalistics, 3159 Eden Ave., Cincinnati, Ohio 45219. Present address, Corning Glass Works, Corning, N.Y. 14830. 602
ANALYTICAL CHEMISTRY, VOL. 49, NO. 4, APRIL 1977
phenomena. In these studies two types of OTEs are commonly employed. The first type of OTE consists of a thin film of a metal (platinum, gold) or a semiconductor (SnO2,InO2, carbon) which is coated on a transparent substrate such as glass, quartz, or germanium, depending on the spectral region of interest. The transparency of these electrodes depends on the thinness and the optical properties of the conducting film. A second category of OTE is the minigrid electrode which consists of a metal (gold, nickel, silver, copper) grid with from 100 to 2000 wires per inch ( 4 ) . In this case the transparency is due to the physical holes in the minigrid structure. Mercury has been used extensively as an electrode material, in part because of its large hydrogen overvoltage which offers a substantially greater negative potential range compared to many other electrode materials such as platinum. Mercury has also proven to be very suitable for the reduction of metal ions to metals which form amalgams. These important properties of mercury have stimulated the development of mercury OTEs. Of particular importance is the extended negative potential range which would make accessible the study of electrode processes obscured by hydrogen evolution on the existing OTEs. "Mercury" OTEs have been reported for both of the two categories of OTEs described above by electrodepositing a thin mercury coating on a platinum-film OTE (5) and on a nickel minigrid OTE (6). While both of these electrodes exhibited substantial mercury character including a hydrogen overvoltage increase of 300-600 mV, the influence of dissolved substrate (Pt, Ni), and the formation of mercury droplets rather than a continuous film prevented the attainment of an
