826
Anal. Chem. 1981, 53, 826-831
Computer-Enhanced High-Resolution Gas Chromatography for the Discriminative Analysis of Tobacco Smoke Mllton E. Parrlsh,' Bennle W. Good, Francis S. Hsu, Frank W. Hatch,' Danlel M. Ennls, David R. Douglas, Janet H. Shelton, and Duane C. Watson Philip Morris, Inc., Research and Development, P.O. Box 26583, Richmond, Virginia 2326 1
Charles N. Rellley Department of Chemistry, University of North Carolina, Chapel Hill, North Carolina 275 14
The isolation, separation, and identification of the volatile components of cigarette smoke have become increasingly important in tobacco research because of the heightened emphasis placed on the development of lower delivery products. This paper describes a semiautomated microprocessor-controiied glass capillary gas chromatographk procedure which provides for automatic processing of cigarette smoke with improved reproducibility compared to nonautomated systems. More importantly, the technique provides for the transmission of the digitized chromatographic data from the microprocessor to a host computer facility for postrun processing: profile averaging, proflie subtractlon, and graphic plotting. Examples are presented that both demonstrate the procedures used and lilustrate the value of this approach to a wide range of applications, especially in the area of sensory evaluation. Results which show important parallels between pattern similarity and dlscrimination by an expert panel are presented.
Intense activities in the isolation, separation, and identification of the organic volatile components present in cigarette smoke began in the mid-l950s, mostly due to the development of two new analytical instruments, the gas chromatograph and the gas chromatograph-mass spectrometer (GC/MS). In 1957, Seligman and co-workers (1) published one of the first papers which described the use of GC/MS in the determination of 17 components in cigarette smoke. Subsequent work (2-11) by researchers within the tobacco industry demonstrated that there was considerable interest directed toward characterizing different cigarette types by monitoring the volatile components of cigarette smoke. The capability of high-resolution chromatographic techniques for the routine analysis of the volatile smoke components is challenged by today's low delivery cigarettes. The application of glass capillary gas chromatography, (GC)2,in the analysis of the volatile components in cigarette smoke was pioneered by Grob beginning in 1962 (12-19). His work not only laid the foundation for cigarette smoke research but also served to inform analytical chemists of the advantages of using high-performance glass capillary columns for the separation of complex mixtures. It was due to the advent of microcomputers in 1971 by Intel Corp. that the (GC)2technique became more precise, easy to control, and moderately priced (20). Due to the use of high-resolution glass capillary columns, various selective detectors, sampling schemes, and microprocessors which reproducibly control parameters such as temperature, flow, and injection (21,22),the chromatographer is presented today with a data set apparently richer in in-
* Present address: Boehrin er In elheim, Research and Development, P.O. Box 368, Ridgefieyd, C'f 06877.
formation and more sensitive toward subtle differences than ever before. However, the task of zeroing in on subtle differences and determining their significance is a formidable one. The cigarette system offers a large data base to study, because it involves analysis of a complex and dynamic mixture. Cigarette mainstream smoke is usually broken down into three areas: the gas phase smoke, the semivolatile fraction, and the particulate phase. The work presented here deals only with the organic gas phase portion of cigarette smoke and is arbitrarily defined as that portion of the whole mainstream smoke which passes through a Cambridge filter (allows only particles less than 0.3 pM to pass) during standard smoking conditions (35-mL puff of 2 s duration/min). The organic constituents of gas phase smoke, comprising not more than 1.5% of the whole smoke, are highly diluted, and include an estimated 500-1000 compounds (I7), incorporating the full spectrum of organic functional groups. These compounds are products of pyrolytic and volatilization processes and exist in an unstable equilibrium between the gaseous and particulate phases. The high reactivity of many smoke components makes artifact formation and sampling a difficult problem but is curtailed a t liquid nitrogen temperature when the smoke is condensed in a cryogenic trap. In addition, quantitative efforts are limited by the tobacco and cigarette to cigarette variability. These problems are compounded without the data manipulation and enhancement necessary for transforming the data into meaningful information. The above considerations have frustrated most efforts to make wide-ranging fully descriptive comparisons of closely related cigarette types. The need for applying chemometric techniques for the characterization and differentiation of various cigarette types is clear. These techniques have been utilized in other areas (23-25). In the food and beverage industries, the desire to correlate sensory data with gas chromatographic data has been the subject of much research (26-37). Generally the approach taken has been to reduce the number of analytical measurements (such as GC peak areas) to a minimum set which most highly correlates with a specific sensory property. Conclusions from this type of analysis, however, cannot be easily generalized and, since many of the compounds isolated are often highly correlated to each other, the multiple regression equations are often no more than models. These models certainly cannot be used as psychophysical relationships of fundamental importance. Because of the complexity of cigarette smoke, it is difficult to define and standardize specific sensory properties. For this reason, we have focused our attention on estimating the difference between two samples without regard to the properties leading to discrimination. In relating analytical measurements to a sensory difference, we prefer to use all of the available information to improve reliability. Reduction of the analytical information to a limited set of peaks would only be of importance if a well-defined dimension was uncovered and we wished to design samples exhibiting different intensities.
0003-2700/8 1 /0353-0826$01,2510 0 1981 American Chemical Society
ANALYTICAL CHEMISTRY, VOL. 53, NO. 6, MAY 1981
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Table 11. GC/MS Parameters
Table I
GC
GiC Conditions carrier gas gas velocity column head pressure initial oven temperature initial hold time rate of temperature program final oven temperature final hold time detector temperatwe slope sensitivity
helium 30 cm/s for CH, 18 psi -25 "C 1 5 min 2 OC/min 130 OC 25 rnin 200 OC 0.25
Chronology of Timed Events time, min
function
0.1 5.9 6.0 11.0 11.5 95.0
switch sampling valve to inject position liquid nitrogen flow to trap off heater for trap on heater for trap off begin integration switch sannding: valve to sample position
The results presented here are obtained by using a semiautomated microprocessor-controlled (GC)2procedure which has been previously described (38). It provides for automatic analysis of cigarette smoke compared to nonautomated systems and provides for the transmission of the digitized chromatographic data from the microprocessor to a host computer facility for postrun processing. The application of chemometric techniques t o the transmitted chromatographic data is discussed, with emphasis placed on determining the significant quantitative differences of two sample types. Examples are presented that both demonstrate the procedures used and illustrate the values of this approach to a wide range of applications, including some preliminary results correlating pattern and sensory ditscrimination.
EXPERLMENTAL SECTION Gas Chromatography. A Hewlett-Packard 5830 microprocessor-controlledgas chromatograph (Hewlett-Packard, Avondale, PA) equipped with a flame ionization detector (FID)and cyrogenic temperature programming capability was employed to acquire all profiling data. The column used was a Pyrex glass capillary column (0.5 mm i.d., 98 m long) prepared in this laboratory according to the procedure of Schomburg (39) and Pretorius (40). The liquid phase was a combination of UCON 50-HB-280Xand UCON 50-HB-5100. A four-port smoking machine was adjusted to standard smoking conditions: cigarettes were lit sequentially, one every 2 min such that a composite of 2nd, 4th, 6th, and 8th puffs was obtained. Halwever, in cases where cigarettes had different puff counts, the following sequences were employed for the 10-puff cigarettes, the 2nd, 5th, 7th, and 10th puffs were obtained, for 11-or 12-puff cigarettes,the 2nd, 5th, 8th, and 11th puffs were obtained, and for 13- or 14-puff cigarettes, the 2nd, 6th, 9th, and 13th puffs were obtained. The composite puff was then injected into the capillary column via the previously described procedure (38). The chromatographic conditions are listed in Table I. All the cigarettles were conditioned at 74 O F and 60% relative humidity for at least 48 h prior to smoking. Normally four replicate runs were made for each sample type. GC/MS Identification. A Varian 2800 gas chromatograph, equipped with a cryogenic multilinear temperature programmer interfaced with a Finnigan 3000 mass spectrometer (Finnigan Corp, Sunnyvale, CA) was employed (41). Total ion current chromatograms were obtained by integrating the ion intensities over the mass range of m / e 35-170. A sample of gas phase smoke was obtained manually under standard conditions using a 50-mL glass syringe as a vacuum source and subsequent injection of the smoke into a 4-mL sample loop attached to a manually operated six-port Carle gas sampling valve (Carle Special Products, Anaheim, CA). The 4-mL gas phase sample was concentrated in a liquid nitrogen trap for 10 min before
temperature program: -25 OC to 95 "C at 2 OC/min 95 OC to 150 "C at 4 OC/min carrier gas: helium, 30 cm/s injection port temperature: 100 OC GC/MS interface temperature: 200 "C MS electron energy: 70 eV emission current: 0.5 mA electron multiplier: 2.0 kV preamplifier: lo-' A/V scan range: 35-170 amuls UNADJUSTED
RETENTION TIME, minutes
Flgure 1. Rubberband adjustment of retention times.
being flashed onto the inlet of the capillary column using hot top water and a heat gun. Identification of gas phase components was accomplished by using GC retention time data and by comparing experimentalmass spectra with reference mass spectra from an in-house mass spectral compilation and ref 42. One second scan times were necessary for the narrow peaks from capillary columns. However, manual scanning prohibited the acquisition of more than one or two scans over the elution of a GC peaks. In most cases, coeluting compounds were observed in the mass spectrum and emphasis was placed on identificationof the major components. The complete set of GC/MS conditions is given in Table 11. Data Preparation. The Hewlett-Packard 5830 GC final report was transmitted to our laboratory host computer via a RS-232C serial ASCII interface. A Lear Siegler (Lear Siegler, Inc., Anaheim, CA) ADM-3A terminal provided a RS-232C extension interface for connection to the GC and a RS-232C modem interface for connection to the host computer. The host was a Xerox Sigma 9 with a CP-V operating system. All programs were written in FORTRAN IV. A Hewlett-Packard 7221A graphics plotter (Hewlett-Packard, San Diego, CA) was used for comparison plots and Gaussian facsimiles. Software provided for chromatograms from repetitive injections of the "same'' sample to be averaged, resulting in a composite chromatogram with adjusted retention times and averaged areas. The averaging was performed in three steps. The first was the adjustment of the retention times. The user selected a set of peaks, referred to as the reference chromatogram. Those peaks needed to be present in each of the chromatograms and recognizable by the user. Once entered, the program set each of the reference peaks to the time assigned by the reference chromatogram. All peaks that fell between two reference peaks were assigned a time based on a rubber band function between the two reference peaks. Peaks before the fiist and after the last reference peak were merely shifted by the offset necessary to align the first and last sample chromatogram peaks with their corresponding reference peaks. This is illustrated in Figure 1. The second stage of the averaging process was to locate similar peaks in all sample runs. This was done on the bases of two window factors selected by the user: a time and an area tolerance. The best match for a sample peak within the two tolerance windows that was found in all the chromatograms was averaged.
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ANALYTICAL CHEMISTRY, VOL. 53, NO. 6, MAY 1981
RUN 3
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The new time and new area in the composite or averaged chromatogram were an average of all times and areas that were found. An example of this step is seen in Figure 2 (case I). Because we were not separating our mixture completely, there were cases where a peak may be resolved in one chromatogram but not in another, as shown in Figure 2 (cases 11-IV). This was handled by the final averaging step. When two or more of the remaining peaks fell within twice the window-average tolerance, their areas were added together and their retention times averaged. Then using only the time criterion, times and meas of peaks found in at least half of the chromatograms were averaged. At that point all unmatched peaks, which were generally small, case I inconsistent, were zeroed. The composite chromatogram was then stored on disk in the host for subsequent evaluation, along with the standard deviation of each peak area. Another program provided a means to plot these composite chromatograms on a Hewlett-Packard 7221A graphics plotter. Since we only knew peak times and areas, we plotted only Gaussian line shapes with a user specified width at half height on a flat base line. Comparing Composite Chromatographic Peaks. Once composite chromatograms had been created with the same reference chromatogram, they were retention time consistent and may be compared pairwise. To compare two chromatograms, the user selected a tolerance window for time and area within which a peak in both chromatograms must be to be paired for direct comparison. There was no other guarantee that the same compound in each chromatogram will be compared, without further instrumental investigation. But in cases where the two compared samples were fundamentally similar, the qualitative differences were few and therefore alignment as to retention time was adequate. Once two peaks were paired for comparison, BEPET, a subroutine from the IMSL Library (International Mathematical and Statistical Libraries, Inc., Houston, TX), was utilized for calculating the significance of the difference between the two means. The calculation was a two-sided t-test where the result indicated the significance that the first mean is greater than or less than the second mean. With a calculated significance for each peak pair, a plot program can easily select peaks with a significance value greater than a user-specified level to be plotted. Sensory Discrimination. The two greatest problems facing the panelist in assessing cigarettes are cigarette variability and fatigue. Traditional discriminative techniques, such as paired (43,44),triangle (&-a), and duo-trio tests (49), run into serious problems when the stimulus is a cigarette. Real differences between populations, detectable if 20 or more samples are evaluated, become washed out by the large differences between identical samples. In order to overcome this problem, we utilized a degree of difference test which involves the assessment of “identical” and “different” samples by scoring the pairs on the degree of difference between them on a nine-point scale, where one is not different and nine is extremely different. The pairs were presented to 11 panelists in an unknown sequence 10 min apart. Each panelist received both pairs. Mineral water was used as an oral rinse to relieve fatigue and optimize discrimination. A paired t-test using BMDPBD (Department of Biomathematics, University of California-Los Angeles, Los Angeles, CA) was used to compare the “identical” and “different” means. Since we were
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RETENTION TIME, minutes
Figure 3. Chromatogram of gas phase smoke reproduced using Gaussian line shapes.
only interested in cases where the “different” mean was higher than the “identical” mean, the test was one-tailed.
RESULTS AND DISCUSSION Identification. The results of the GC/MS investigation produced 113 identifications, which included 14 molecular weight and 37 empirical formula identifications. GC retention time data were obtained for 129 compounds. Fifty-two compounds were confirmed by both GC retention and GC/MS information. A total of 190 full or partial identifications were made. A typical chromatogram of gas phase smoke is shown in Figure 3. Application. The foundation for developing the software necessary for the computer analysis of the organic gas phase chromatographic data is based on the availability of a microprocessor-controlled gas chromatograph. This instrumentation has allowed the development of a semiautomated cryogenic injection system compatible for (GCY analysis. The advantages of this system are twofold. First, the precision of the work is increased relative to the manually operated procedure previously employed. Second, less time is required by the analyst to accomplish the chromatography. The saved time can now be more efficiently used to analyze the data obtained. In addition, the microprocessor GC offers the capability of automatidy recording the retention time and area count of each resolved peak and then transmitting this to a host computer for postprocessing analysis. Because of these capabilities, the reproducibility of the data collection is improved. Reduction of the cigarette to cigarette variability is necessary in order to determine real differences between two similar cigarette brands. This is improved by sampling from 140 mL of gas phase smoke, which is made up of puffs from four cigarettes. Averaging of replicate runs further serves to reduce the effects of sample variability. An experiment was conducted with two low-delivery cigarette brands which were determined to be similar in values for puff count, dilution, tar,nicotine, and additional analytical tests. The composite chromatographic data obtained were subjected to Gaussian reproductions (Figure 4) in order to visually illustrate the problem of trying to quantitatively determine the significant differences of 150 peaks present in the gas phase chromatogram. The similarities are remarkable and one could conclude that the gas phase analysis may not have the capability of detecting the differences. This is precisely the problem earlier researchers faced, made even more severe because many were using only packed columns. The chromatographic data which have been averaged were
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ANALYTICAL CHEMISTRY, VOL. 53, NO. 6, MAY 1981
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Flgure 6. Comparison at 95% signlficance of two lowdelivery cigarette brands. Control Is brand A. Sample is brand B.
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Flgure 5. Comparison at 0% significance of two lowdelivery cigarette brands. Control is brand A. Sample is brand B.
then subjected to the comparison programs so that peaks having similar retention times were matched and their areas compared and their differences detected. Because it is a difficult test to determine the existence of an overall trend from digital data above, it was determined desirable to visually display the comparison results using the HP7221A graphics plotter. Figure 5 shows an example of this comprehensive presentation. This arrangement yields a great deal of significant information to the analyst. The chromatograms are presented as bar graphs simply because the only information available to the computer for each peak is the retention time and area count. The y axis is a logarithmic scale over a user-selected range which allows the presentation of peak areas to be made over a wider dynamic range than possible using the conventional linear scale. A double hash system shows the range of the areas of each peak within one standard deviation, i.e., one hash mark at log (area + standard deviation) and one at log (area - standard deviation). The actual area is not denoted. The top bar graph represents the control composite chromatogram, in this case brand A. Below it in reverse polarity is the sample composite chromatogram, brand B. The bottom set of bar graphs represents the subtractive differences of each peak in the control minus its counterpart in the sample. Here the hash marks are placed at log (area f standard deviation of the difference). If the analyst wants all peaks presented, a significance level of zero percent is entered, as is the case in Figure 5. Figure 6 has been obtained by selecting the percent significance of 95%. This means that only those differences having significance greater than 95%
11 10
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40 50 68 70 RETENTION TIME, MINUTES
80
90
100
Flgure 7. Comparison at 9 5 % significance of Kentucy Reference
cigarettes, modifled wlth CA fllters (control) and activated charcoal filters (sample).
based on the number of runs made to form the composite chromatograms are presented. It is shown that the organic gas phase delivery of the more volatile components of brand A is greater than that for brand B. With the peaks now prescreened by this t-test, efforts can be made to identify these peaks which may be responsible for the sensory properties of the particular brand under investigation. Another application of interest is to determine those peaks which are affected by different filter materials or designs. Kentucky Reference (50) cigarettes were modified such that one sample type contained a standard cellulose acetate (CA) filter and a second sample was fitted with an activated charcoal filter. It would be expected to see results indicating minimum differences of the more volatile components and maximum differences in the remaining gas phase Components. These results were confirmed and the comparison plot is shown in Figure 7. The majority of peaks present in the negative portion of the difference bar graph occur because the integration performed by the HP5830 did not detect their counterparts in the CA fiiter sample. Only three peaks eluting near the end are considered not to be artifacts, two of which have been tentatively identified as 1,2,3-trimethylbenzeneand l,4-diethylbenzene. Their increased concentration due to the presence of the charcoal filter indicates possibly a saturation effect. These components are present in an unstable equi-
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ANALYTICAL CHEMISTRY, VOL. 53, NO. 6, MAY 1981
Table 111. Degree of Difference Test for Two Cigarettes of Different Tobacco Blends panelist no. 1 2 3 4 5 6
I
“identical” “different” difference 2 1 3 1.5 1 4 2
8
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librium between the gas phase and particulate phase. The CA filter removes or decreases many of the components having higher boiling points and molecular weights than compounds eluting during the first two-thirds of the chromatogram. The final example involves two full-flavored cigarette types which are similar in every respect except the composition of the blends differed. Discriminative panelists have determined that the two cigarette types were substantially different sensorially and have measured the extent of difference. By use of the degree of difference test, results of which are shown in Table 111, the difference is significant with >95% confidence. Earlier analytical studies demonstrated that it was difficult to determine the differences of organic gas phase smoke generated by different tobacco blends. Also, sensory differences are difficult to discriminate analytically using organic gas phase smoke. This is primarily because the gas phase fraction is composed of compounds formed mostly from the pyrolysis of cellulose, while compounds responsible for distinctive flavor differences in different tobacco types are usually less volatile and are found mostly in the semivolatile fraction of cigarette smoke. As stated earlier, the organic gas phase is only a small portion of the whole smoke sample. This difficult discrimination pushes these computer enhancement techniques to their limit. The comparison results obtained from analytical measurements are given in Figure 8. As can be seen, there are many components present in the gas phase of the sample in greater concentration than found in the control which is interesting since only the tobacco blends are different in the two cigarettes. This example demonstrates that these computer enhancement techniques can distinguish between different tobacco blends which also were found to be different by a sensory panel. This method provides for the detection of differences between two samples that previously were not easily observable. It is the access to this information that allows instrumental/sensory correlation. The potential of these techniques, however, is limited by our reliance on just retention time data. To extend the chemometric techniques into the realm of pattern mapping and discrimination, we need to be assured that the peaks that are matched and compared to one another originate from the same component or components. Mass spectral information yields the credibility required for consistent component classification. This means that our criteria for peak matching will now include both retention time and mass spectral data. By utilizing various pattern dissimilarity techniques, we hope to develop an analytical rationale for declaring two products to be the same or different. It is important to establish the limits for dissimilarity coefficients within which sensory differences cannot be perceived. It may be presumptive to expect to achieve product discrimination on the basis of gas phase data alone. Ultimately, sensory and analytical correlations must encompass every
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RETENTION TIME, MINUTES Figure 8. Comparison at 9 5 % signiflcance of two full-flavored clgarete models of different tobacco blends and found to be different by sensory panels.
aspect of the cigarette which can be measured analytically. However, organic gas phase analysis is a useful starting point from which pattern dissimilarity techniques can be developed. It is possible that the intensity of gas phase components may be correlated with the intensity of non-gas-phase components, so that the organic gas phase analysis may prove to be a useful screening tool for obtaining an index of dissimilarity.
ACKNOWLEDGMENT The authors thank Robert Wiley for his helpful advice concerning cigarette conditioning, screening, and testing and Anne Donathan and Jim Day for their help in the preparation of the manuscript.
LITERATURE CITED (1) Seligman, R. 6.; Resnlk, F. E.: O’Keeffe, A. E.; Holmes, J. C.; Morreil, F. A.; Murrlll, D. P.; Gager, F. L. rob. Scl. 1957, I , 124. (2) Johnstone, R. A. W.; Plimmer, J. R. Chem. Rev. 1959, 59, 885. (3) Johnstone, R. A. W.; Quan, P. M.; Carruthers, W. Nature (London) 1952. 795. 1267. (4) Phllippe, R.’ J.; Moore, H.; Honeycutt, R. G.: Ruth, J. M. Anal. Chem. 1984. 36, 859. (5) Jarrell, J. E.; de la Burde, R. Tob. Sci. 1965, I X , 5. (6) Williamson, J. T.; Graham, J. F.; Allman, D. R. Beitr. Tabakforsch. 1985, 3, 233. (7) Wlillamson, J. T.; Allman, D. R. Beitr. Tabakforsch. 1986, 9 , 590. (8) Caroff, J.; VBron, J.; BurdB, R.; Gulllerm, R. J. Gas Chromafogr. 1985, 3. 196. .. (9) Newsome, J. R.; Norman, V.; Keith, C. H. Tob. Scl. 1965, I X , 102. (10) Watson, D. C.; Ikeda, R. M.; Resnlk, F. E. Tob. Sci. 1966, X , 88. (11) Norman, V.; Newsome, J. R.; Keith, C. H. Tob. Sci. 1966, X I I , 216. (12) Grob, K. Beitr. Tabakforsch. 1962, 7 , 285. (13) Grob, K. Be&. Tabakforsch. 1982, 1 , 315. (14) Grob, K. J. Gas Chromatogr. 1965, 3 , 52. (15) Grob, K. Beitr. Tabakforsch. 1065, 3 , 243. (16) Grob, K. Beltr. Tabakforsch. 1968, 3, 403. (17) Grob, K. NCI Monogr. 1986, 28, 215. (18) Grob, K. Chem. Ind. (London) 1973, 248. (19) Grob, K.; Grob, 0. Chromatographla 1972, 5, 3. (20) Cram, S. P.; Yang, F. Y. Ind. ReslDev. 1976, 20, 89. (21) Leung, A. T.; Hubbard, J. R.; Miller, L. A. J. Chromafogr. Sci. 1976, 14, 166. (22) Crockett, I. L.; Mikkelsen, L. J . Chromatogr. Sci. 1976. 14, 169. (23) McConnell, M. L.; Rhodes, G.; Watson, U.; Novotny, M. J . Chromafogr. W70, 162, 495. (24) Clark, H. A.; Jurs, P. C. Anal. Chem. 1979, 57, 616. (25) Saxberg, B. E. H.; Duewer, D. L.; Booker, J. L.; Kowalski, B. R. Anal. Chlm. Acfa 1976, 103, 201. (26) Hoff, J. T.; Herwig, W. C. J . Am. SOC.Brew. Chem. 1975, 34, 1. (27) Glanturco, M. A.; Blggers, R. E.; Ridllng, B. H. J . Agrlc. Food Chem. 1974, 22, 758. (28) Biggers, R. E.; Hilton, J. J.; Gianturco, M. A. J. Chromatogr. Sci. 1969, 7 , 453. (29) Teranishi, R. Am. Lab. (Fairfield, Conn.) 1979, 7 7 , 6, 51. (30) McCarthy, A. I.; Palmer, J. K.; Shaw, C. P.; Anderson, E. E. J . Food Scl. 1983, 28, 379. (31) Akesson, C.; Persson, T.; von Sydow, E . Roc., Inf. Congr. FoodScl. Technol., 4th 1974, 11, 183. (32) Powers, J. J.; Qulnlan, M. C. J. Agric. Food Chem. 1974, 22, 745.
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Anal. Chem. 1981,53, 831-836 (33) Merrltt, C.; Robertson, C). H.; Cavagnaro, J. F,; Graham, R. A,; Nichols, T. L. J. Agric. Food Clbm. 1974, 22, 750. (34) Alshima, T.; Nobuhara, A. Agric. Biol. Chem. 1977, 47, 1841. (35) Tressl, R.; Friese, L.; Fendenck, F.; Koppler, H. J. Agric. Food Chem. 1978. 26. 1422. (36) von Sydow, E.; Anderson, J.; Anjou, K.; Karlsson, G. Lebensem.-Wiss. Techno/. 1970, 38, 11. (37) Galetto, W. 0.; Bednarozyk, A. A. J. Food Sci. 1975, 40, 1165. (38) Parrlsh, M. E.; Higgins, C. T.; Douglas, D. R.; Watson, D. C. HRC CC, J. Hlgh Resolut. Chromatogr. Chromatogr. Commun. 1979, 2 , 551. (39) Schomburg, G.; Husmann, H.; Weeke, F. J. Chromatogr. 1974, 99, 63. (40) Schieke, J. D.; Comlne, N. R.; Pretorius, V, J. Chromatogr. 1975, 772, 97. (41) Hatch, F. W.; Parrlsh, hh. E. Anal. Chem. 1978, 50, 1165. (42) "Eight Peak Index of Meiss Spectra", Mass Spectrometry Data Centre, UK, 1974. (43) Guilford, J. P. "Psychometric Methods", 2nd ed.; McGraw-HIII: New York, 1954.
(44) (45) (46) (47) (48) (49) (50)
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Thurstone, L. L. Am. J. Psycho/. 1927, 38, 368. Helm, E.; Trolle, B. Wallefstein Lab. Commun. 1946, 9, 181. Frljters, J. E. R. Br. J. &th. Stat. Psycho/. 1979, 32, 229. Gridgeman, N. T. J. Food Sci. 1970, 35, 87. Byer, A. J.; Abrams, D. Food Techno/. (Chicago) 1959, 7, 185. Peryam, D. R.; Food,Techno/.(Chicago) 1958, 72, 231. Atkinson, W. 0. Proceedings of the Tobacco and Health conference"; University of Kentucky: Lexlngton, KY, Feb 1 9 7 0 Conference Report No. 2, p 28.
RECEIVED for review August 25,1980. Accepted February 9, 1981. This paper has been presented in part at a Symposium on Advances and Applications of High Resolution Chromatography a t the 31st Pittsburgh Conference on Analytical Chemistry and Applied Spectrscopy in Atlantic City, NJ, March 11, 1980.
Separation of Nitrogen Compound Types from Hydrotreated Shale Oil Products by Adsorption Chromatography on Basic and Neutral Alumina C. D. Ford,' S. A. Holmes," L. F. Thompson, and D. R. Latham Laramie Energy Technology Center, Department of Energy, P.O. Box 3395, Laramle, Wyoming 82071
The nltrogen compounds In hydrotreated shale oll products derived from Paraho shale oll are separated Into speclflc nitrogen-type fractions by using baslc/neutral alumina adsorption chromatographly. The separatlon scheme, whlch gives fast and reproducilble results, Is successfully applled to shale oil products produced under dlfferent hydrotreating condltlons. These products vary In the level of nltrogen and/or In the dlstillation range. Infrared spectrometry of the fractlons identified three major nltrogen compound types: pyrldlne-type nitrogen, pyrrole-type nltrogen, and amide-type nitrogen. The distribution of these nitrogen types In varlous hydrotreated shale oil products Is shown,,
The detrimental effect of nitrogen-containing compounds in transportation fuels and in fuel oils is demonstrated by relatively high NO, emisriions and by fuel product instability. Separation and characteriizationprocedures used in identifying nitrogen compound types can be used to help design refining processes that eliminate these detrimental compounds. A study of the nitrogen types in fuel products derived from shale oil requires a suitable separation scheme to selectively concentrate nitrogen compound types and to facilitate their characterization. The separation scheme should give rapid results and clean fractiondl of specific nitrogen compound types and should apply to samples independent of nitrogen level or distillation range. Numerous separation methods have been used in the analysis of nitrogen compounds in shale oil (1-4). Earlier work using ion-e rchange and coordination-complex chromatography by Holmes et al. (5) indicated difficulty in adequately separating anjd concentrating nitrogen compound types present in hydrotreated shale oil products. This work suggested that sample alteration occurring during any par'Present address: University of Utah, Salt Lake City, UT.
ticular separation is a primary concern when analyzing hydrotreated shale oil products. Snyder et al. (6) and Schiller et al. (7) successfully employed alumina adsorption chromatography to separate various compound types in petroleum and coal liquids, respectively. More recently Guerin (8) developed a separation scheme to effectively isolate nitrogen compound types from petroleum substitutes utilizing acidbase extraction, alumina and silica adsorption chromatography, and Sephadex LH-20 column chromatography. The latter separation schemes involve a considerable amount of time and effort and generate numerous fractions that in this study are not necessary for characterization. This work presenk a useful scheme incorporating basic and neutral alumina adsorption chromatography for separating the nitrogen compound types present in hydrotreated shale oil products. The separation scheme is applied to several hydrotreated shale oil products produced under different refining conditions. These products vary in boiling range and in total nitrogen. The composition of the fractions collected by the separation and the stability of the hydrotreated shale oil products are discussed. The separation procedure is also applied to a crude shale oil sample with poor results.
EXPERIMENTAL SECTION Hydrotreated Shale Oil Products. Five hydrotreated shale oil products produced by the Standard Oil Co. of Ohio (Sohio), Toledo, OH, and two hydrotreated shale oil products produced by Chevron Research Co., Salt Lake City, UT, were studied in this work. All products were derived from Paraho crude shale oil containing about 2.2 w t % nitrogen. Details regarding the hydrotreating conditions can be found elsewhere (9,lO). In the Sohio operation, the crude shale oil was allowed to settle, run through an alumina guard bed to remove particulates and metals, and then catalytically hydrotreated over a nickel-molybdenum catalyst. The hydrotreabd whole product was distilled into four fractions: gasoline (11vol %),jet fuel (26 vol %), diesel fuel marine (31 vol %), and residuum (32 vol %). The Sohio products studied in this work include the following: the hydrotreated whole product which included recycled bottoms; two jet
This article not subject to U.S. Copyrlght. Published 1981 by the Amerlcan Chemical Society
