2858
Anal. Chem. 1985, 57,2858-2864
24696-54-6; ( ~ - B U ) ~ S ~(RO =2-quinolinyl), R 98218-18-9; (nBu),SnSR (R = benzyl), 23728-85-0; ( ~ - B U ) ~ S(R ~ S=Rbutyl), 17390-72-6; (n-Bu),SnOR (R = 8-quinolinyl), 5488-45-9; (nBu),SnOR (R = 1-methylcyclohexyl),98218-19-0;l19Sn, 1431435-3; 29Si,14304-87-1;1-phenyl-2-[(trimethylsilyl)oxy]benzene, 1022-21-5; l-phenyl-4-[(trimethylsilyl)oxy]benzene,1023-13-8; 3,4-dimethylphenol, 95-65-8; 2,3-dimethylphenol, 526-75-0; 4methylphenol, 106-44-5;2-methylphenol, 95-48-7;2,6-dimethylphenol, 576-26-1; ethylphenol, 25429-37-2; phenol, 108-95-2; rn-cresol, 108-39-4.
LITERATURE CITED Whitehurst, D. D.; Mltchell, T. 0.; Farcasiu, M.; Dickert, J. J. EPRI Project 410-1; Mobil Research and Development Corp., 1979. McClennen, W. H.; Meuzelaar, H. L. C.; Metcalf, G. S.; Hill, G. R. Fuel 1983, 62, 1422-1429. Hara, T.; Jones, L.; LI, C.; Tewarl. K. C. Fuel 1981, 60, 1143-1148. White, C. M.; LI, N. C. Anal. Chem. 1982, 5 4 , 1564-1570. White, C. M.; Li, N. C. Anal. Chem. 1982, 5 4 , 1570-1572. Ogan, K.; Katz, E. Anal. Chem. 1981, 53, 160-163. Chao, G. K.-J.; Suatoni, J. C. J. Chromatogr. Sc;. 1982, 2 0 , 436-440. Scharbon, J. F.; Hurtublse, R. J.; Sllver, H. G. Anal. Chem. 1979, 51, 1426-1433. Martin, R . W. J. Am. Chem. SOC. 1952, 7 4 , 3024-3025. Pierce, A. E. "Silylation of Organic Compounds"; Pierce Chemical Co.: Rockford, IL, 1979. Snape, C. E.; Smith, C, A.; Bartle, K. D.; Matthews, R. S. Anal. Chem. 1082. 20-25. ..._. 5.4 ,. _. _. Llndeman, L. P.; Nickslc, S. W. Anal. Chem. 1984, 3 6 , 2414-2417. Schweighardt, F. K.; Retcofsky, H. L.; Friedman, S.; Hough, M. Anal. Chem. 1978, 50, 368-371. Leader, G. R. Anal. Chem. 1973, 45, 1700-1706.
(15) Ho. F. F.-L. Anal. Chem. 1974. 46. 496-499. i16) Bartle, K. D.; Matthews, R. S.; Stadelhofer, J. W. Appl. Specb'OSC 1980, 34 (6), 615-617. (17) Manatt, S. L. J. Am. Chem. SOC. 1966, 88, 1323-1324. (18) Konishl, K.; Morl, Y.; Tanlguchl, N. Analyst (London) 1969, 94 1002-1005 . - - - - - -. (19) Dorn, H. C.; Sleevi, P. S.; Koller, K; Glass, T. Prepr. Pap.---Am. Chem. SOC.,Div. FuelChem. 1979, 2 4 , 301-309. (20) Sleevi, P.; Glass, T. E.; Dorn, H. C. Anal. Chem. 1979, 51, 1931-1934. (21) Coleman, W. M.; Boyd, A. R. Anal. Chem. 1982, 5 4 , 133-134. (22) Larsen, J. W.; Nadar, P. A.; Mohammadl, M.; Montano, P. A. Fuel 1962. 6 1 . 889-893. (23) Poller, R. C. "The Chemlstry of Organotln Compounds"; Logos Press: London, 1970; pp 70-136. (24) Harrls, R. K.; Kennedy, J. D.; McFarlane, W. "NMR and the Periodic Table"; Harris, R. K., Mann, B. E., Eds.; Academic Press: London, 1976; pp 342-366. (25) Davies, A. G.; Smlth, P. J. "Comprehensive Organometallic Chemlstry"; Wllklnson, G., Ed.; Pergamon Press: New York, 1982; pp
. 1
__
519-627 - .
(26) Smith, P. J.; Smlth, L. Inofg. Chlm. Acta Rev. 1973, 7 , 11-33. (27) Blunden, S. J.; Frangou, A.; Gillles, D. G. Org.Magn. Reson. 1982, 20, 170-174. (28) Morris, G. A.; Freeman, R. J. Am. Chem. SOC. 1979, 101, 760-762. (29) Doddrell, D. M.; Pegg, D. T.; Brooks, W.; Bendall, R. J. Am. Chem. SOC. 1981, 103, 727-728. (30) Llotta, R.; Brons, G. J. J . Am. Chem. SOC. 1981, 103, 1735-1742. (31) Smith, P. J.; White, R . F. M.; Smlth, L. J. Organomet. Chem. 1972, 40, 341-353. (32) Pertersen, J. C.; Plancher, H. Anal. Chem. 1981, 5 3 , 786-789.
RECEIVED for review December 28, 1984. Accepted July 10, 1985.
Crude Oil Characterization and Correlation by Principal Component Analysis of I3C Nuclear Magnetic Resonance Spectra Olav M. Kvalheim,* Dagfinn W. Aksnes, Trond Brekke, Magnus 0. Eide, and Einar Sletten Department of Chemistry, University of Bergen, N-5000 Bergen, Norway Nils Telnaes Norsk Hydro AIS, Lars Hilles g.30,N-5000 Bergen, Norway
Prlnclpal component analysls (PCA) Is applied to I3C nuclear magnetlc resonance spectra of the naphtha fractlon of crude oil from wells located on the Norweglan shelf. Unsupervlsed PCA correlates oil samples from the same geographlcal area. The correlatlon follows from properties related to the composltlon of the oils, e.g., long-chalned vs. short-chained alkanes, branchlng, cycllzatlon, and aromatlzatlon. Several of the major constituents present In the 011s are Identifled. The link between chemical composition of the crude crlls and the geochemlcal processes of blodegradatlon, water washlng, and maturatlon suggests a slmpllfled characterlzatlon of the oil samples. Also, by use of component scores the posslblllty of communlcatlon between the wells Is uncovered. Separate modellng of replicated spectra of each sample by the use of supervlsed PCA (SIMCA) conflrmed the results of the unsupervlsed classlflcatlon.
Extensive research during the last years reflects the growing need of rapid and quantitative methods for structural characterization of crude oils. 13Cand lH NMR spectroscopy have proved especially useful for this purpose, either separately
(1-10) or in combination with other spectroscopic techniques (10-12), element analysis (13, 14), or gas chromatography/ mass spectrometry (10, 15). The advantage of NMR spec-
troscopy for the analysis of complex hydrocarbon mixtures is easily explained. Gas chromatography (GC) or mass spectroscopy (MS) gives poor resolution due to severe overlap among the numerous peaks. The NMR spectra contain comparatively fewer peaks because carbon atoms in identical structural environments within the nearest four neighbors possess the same chemical shift (16). Since the major constituents in crude oil are made up of rather few molecular fragments, 13C NMR spectroscopy produces spectra with well-resolved parts even from fractions containing a large number of constituents. The limitation imposed on the interpretation of the NMR spectra, using molecular substructures rather than individual constituents, turns out to be an advantage in the present context. Spectra of complex hydrocarbon mixtures can be interpreted in terms of average properties, i.e., average chain length, aromatic vs. aliphatic content, and so on (15). However, as pointed out recently by Ward and Burnham in their investigation on shale oil, even identification of the major individual constituents is possible (17). The functional group
0003-2700/85/0357-2858$01.50/00 1985 Amerlcan Chemical Society
ANALYTICAL CHEMISTRY, VOL. 57, NO. 14, DECEMBER 1985
analysis introduced by Petrakis et al. is another route to a condensed description of oil samples (13, 14). Although not always explicitly formulated, one of the main reasons for the considerable activity in the field of structural characterization of crude oils is the need of quantitative parameters for the correlation of oils from the same geographical area (18). Since similar oil samples may differ only in relative amounts of the same constituents, the chemical shifts alone are of limited value. However, the intensity distribution in a spectrum reflects the chemical composition and is unique for each oil. The exploration of this spectral pattern points out another route to structural characterization offering a quantitative measure of similarity between oil samples. Pattern recognition methods have proved useful in many branches of chemistry, including the interpretation of NMR chemical shift data of pure constituents (19,20). In order to extend this approach to the interpretation of the intensity patterns of mixtures, a set of quantitative intensities are needed. Wilkins et al. (21) integrated segments of 1ppm over the entire 13Cppm range to obtain intensity parameters for pure compounds. These intensities were used as input to a pattern-recognition method known as linear discriminant function analysis. When analyzing mixtures of hydrocarbons, a simpler and easier interpretable approach is possible, namely identifying and digitizing recognized peaks in the spectra. Since each constituent gives rise to several peaks in the spectra, many of the peaks contain information about the same constituent. Also, because many constituents are made up of the same molecular fragments, they contribute intensity at the same chemical shifts. These dependencies among the peaks make it impossible to correlate structural similarity to intensities of single chemical shifts. Principal component analysis (PCA) is especially well-suited to the interpretation of samples characterized by correlated variables (22-24). Hence, PCA may prove useful in a statistical interpretation of the NMR intensity patterns. The aim of the present4nvestigation can be divided into two related parts First, since the patterns shown by the NMR spectra reflect differences and similarities in the composition of the crude oils, similarity between these patterns can be correlated with the geological and/or geographical information. This correlation can be utilized to indentifyingoils originating from the same source rock (oil-sourcecorrelation) and to reveal communication between wells. This goal is accomplished by grouping the oil samples into classes, with each class comprising oils with similar compositions. One important aspect of this grouping is the extraction of the features (peaks, or ultimately constituents or groups of related constituents) contributing most to the classification. This leads to the second goal of this investigation: the extraction of the chemical information contained in the NMR spectra of the crude oils. Following earlier investigations (12, 15), this chemical information is expressed as the relative amount of straight-chained alkanes to branched alkanes, aliphatic content vs. aromatic, degree of cyclization, average chain length, and so on. This information may be translated into geochemical terms as degree of biodegradation (partial or total removal of n-alkanes and low-branched alkanes), water washing, and maturation shown by the crude oils (25).
THEORY Principal component analysis (PCA) is now a well-established tool for the interpretation of chemical data. All basic features of the method are thoroughly documented together with numerous chemical examples (22-24). Hence, this section provides only a brief description of the features necessary for the interpretations in the present work. PC Model. The major step in PCA is the extraction of the eigenvectors from the variance-covariance matrix to get un-
2859
correlated new variables called principal components (PC’s) or factors. Since the PC’s are linear combinations of all the original variables, they can be viewed as composite average variables, located in the directions explaining most of the variation in the data set. The explanatory importance of a PC is measured through the ratio of its variance to the total variance contained in the original variables. If the original variables are highly correlated the first few PC‘s explain most of the total variance so that the data can be approximated with a PC model with F product terms (24)
x = 18 + TB + E
(1)
where X is the 1 X m matrix of the mean values of each variable, 1 is a n X 1 matrix of l’s, B is the F X m matrix of the coefficients (loadings) of the F first PC’s in B, T is the n X F matrix of projections (scores) of the samples along the F PC’s, and E is the n X m matrix of the residuals. Score Plot. One of the nice features of PCA is that nearly every result can be represented graphically. The scores along the PC’s reveal the relationships among the samples. This information is displayed in a score plot, also called the eigenvector projection (26). Similar samples group together in clusters (classes) in the score plot. Also, since the PC’s are orthogonal, the Euclidean distance between samples can be used to measure similarity quantitatively. Loading Plot. Interpretation of a single PC and the features of a PC model is possible through the connection to the original variables. A loading plot displays both the importance of each variable to the interpretation of a PC and the relationship among variables in that PC (27). The coordinates of a variable in a loading plot are its loadings along the orthogonal and normalized PC’s. First, a variable’s contribution to a PC is directly proportional to the squared loading. Thus, the distance of a variable to the origin along a PC is a quantitative measure of the importance of that variable in the PC. A variable near the origin carries little or no information in the PC, while a large distance from the origin (high loading) means that the variable is important in the interpretation of the PC. Second, the mutual location of the variables reflects the coherences among them. Variables grouped together hold the same information in the PC. Also, variables located on the same side of the origin are positively correlated, while variables located on opposite sides are negatively correlated. The larger the separation of two negatively correlated variables, the stronger is the negative correlation. These statements can be generalized to the interpretation of the PC model. Supervised and Unsupervised PCA. PCA can be performed at two levels, either in an unsupervised learning approach to uncover groups of similar samples or as a supervised learning method to separate modeling of samples of known groups. Usually, the supervised approach is also preceded by PCA of the whole data set to display the samples in a score plot. In the following the term unsupervised PCA is used to indicate that no previous knowledge of grouping is assumed, while supervised PCA is introduced to implicate separate modeling of known groups. In these terms, the SIMCA method (28) is a highly developed variant of supervised PCA. Scaling of NMR Intensities. Usually, the initial step in PCA is some preprocessing of raw data. With NMR intensities normalization is necessary to outweigh the effect of different peak heights due to different signal-to-noise ratios in the spectra. Since the largest peaks also have the largest absolute variance, systematic variation in small peaks can be masked in PCA. With gas chromatographic data this problem is overcome by using logarithmic or autoscaled data (24),reducing the weight of the dominant peaks in the analysis. A nonlinear transformation destroys the ratio between peaks originating from the same constituent, which excludes the use of the logarithmic transformation on NMR intensity data.
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ANALYTICAL CHEMISTRY, VOL. 57, NO. 14, DECEMBER 1985
i
No.1
I
0
10
20
30 KM
12
Figure 1. Relative geographlcal locatlon of each well.
Also, since autoscaling is questionable in unsupervised PCA (29), the use of normalized intensities without further transformation seems preferable. 40
EXPERIMENTAL SECTION The fraction between 25 and 200 "C was obtained from crude oil samples from 14 wells covering a large geographical area in the Norwegian part of the North Sea (Figure 1). From well 3 two distillates were obtained and from well 12 both distillate and condensate were produced, giving finally 16 oil samples to be analyzed on a Bruker 90-MHz Fourier transform nuclear magnetic resonance (FT-NMR) spectrometer. NMR Measurement. The samples were prepared in 10 mm 0.d. NMR tubes using 11% deuterated benzene as lock signal and 3% tetramethylsilane (Me,Si) as internal reference to compute the chemical shifts. The 13Cspectra of all samples were obtained by using a normal one-pulse sequence with broad-band proton decoupling. Typical spectra consisted of 2000 transients using 32K points over a 4300-Hz bandwidth with a 3 - ~(20O) s rf pulse. A repetition time of 3.8 s was chosen to ensure quantitative intensities for most of the quaternary carbons. The signal-to-noise ratio was improved by exponential apodization, which introduced 0.15-Hz line broadening. To ease the interpretation of the spectra, a few samples were further examined by using a spin echo sequence (heteronuclear J-modulation) to separate primary and tertiary carbons from secondary and quarternary carbons ( I , 3, IO). To test the instrumental precision, some of the samples were run several times during a period of 3 months. Finally, in each spectrum a total of 59 peaks at the same chemical shift positions were selected and the peak heights recorded for use in the data analysis of the intensity patterns. Fifty-five peaks were picked from the alkane region, while only four assignments were possible in the aromatic region due to the overlapping peaks of the lock medium in this chemical-shiftdomain. The recorded intensities for each spectrum were collected in a matrix with each row containing all the 59 peaks of one spectrum. Due to differences in signal-to-noiseratio the absolute intensities were normalized to the mean total intensity. No further preprocessing of the data was performed. Data Analysis. The data were analyzed in two steps. First, 13 of the oil samples, 9 of them replicated, were analyzed by unsupervised PCA. (Sample 12 was excluded because of severe biodegradation. A preliminary PCA showed that the other samples were compressed into a small domain if sample 12 was included.) Next, supervised PCA was performed to separate PC modeling of replicated spectra of crude oils from four wells located in two different geographical areas. The classification ability of these separate models was calculated and compared with the result found in the unsupervised calculation. All calculations were performed by using the SIMCA program (24,27,28,30-32),implemented to run on a UNIVAC 1100/82 computer (33). TO optimize the predictive power of the models, cross-validationwas used in all calculations (34). RESULTS AND DISCUSSION Similarities and Differences by Inspection of Spectra. Figure 2 shows the NMR spectra of crude oils from two wells
35
30
25
20
15
,
.
'
'
16 Po*
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1 PPM
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Figure 2. 13C NMR spectra of sample 1 and sample 2.
i 40
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40
35
30
25
20
15
'
30
25
M
I5
'
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Figure 3. I3C NMR spectra of the nelghboring wells 3 and 4.
located in the same geographical area. No obvious difference is observed by inspection of the spectra. Figure 3 shows the spectra of two oil samples from a different area. These two also seem mutually very similar. However, they are both clearly different from those in Figure 2. The kind of patterns shown in Figures 2 and 3 are found to be typical for the whole set of spectra. Although revealing some information, this kind of comparison is extremely time-consuming and tedious when used on a large data set. Also, the limitation imposed by not being able to compare all the spectra simultaneously makes it very difficult to uncover any pattern explaining similarities and differences among the spectra. The need for a statistical approach is obvious. Oil-Oil Correlations. Figure 4 shows the location of 22 samples in the space spanned by the two significant PC's determined by cross-validation. These PC's explain more than 82% of the total variance of the 22 X 59 matrix, proving that strong correlations exist among the spectral intensities used to characterize the samples. The score plot displays the relationships among the oil samples. Samples close together are considered similar, while dissimilar samples will tend toward separation. By comparing the well numbers in the score plot with the well numbers of the spectra shown in Figures 2 and 3, we find that our first visual impressions of similarities and differences between spectra are only partly confirmed. Crude oil from well 3 and well 4 (Figure 3) are mutually similar compared to all other oils. However, the
ANALYTICAL CHEMISTRY, VOL. 57,NO. 14, DECEMBER 1985
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01
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23
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Flgure 4. Score plot of samples from all wells (sample from well 12 excluded). distance between replicates shows beyond doubt that they have different composition. The same conclusion follows from the locations of crude oil from well 1 and well 2 (Figure 2). The score plot also indicates that crude oil from well 2 is almost as similar (or different) to oil from well 4 as to oil from well 1. The Euclidean distances between replicated oil samples can be used to reveal wells with similar oil types (35). The distances were all in the range 0.12-0.64 compared to 0.17 between wells 7 and 14. Crude oils from well 7 and well 14 are so similar that we anticipate communication to exist between these wells. The geographical location of the wells supports this conclusion (Figure 1). Values in the range 0.17-0.88 between wells 6 and 9 suggest that these wells also are connected. This presents a cautious approach to oil-oil correlation, since only samples that have suffered the same degree of biodegradation/ water washing and have reached the same maturity level are considered similar. The limitation imposed by the rather small number of samples prevents any attempt to correct for oil alteration due to the mentioned geochemical processes. Peak Assignments. Our next task is to explain the proposed classification scheme in terms of the main features characterizing each PC. These features are determined through the interpretation of a PC in terms of the original variables. Just as the score plot displays the relationships among samples, a loading plot shows the connection between peak intensities. Inspection of the loading plot (Figure 5) shows that relatively few peaks are responsible for most of the variance explained by the two PC’s. To ease the interpretation of the loading plot, the 13Cspectrum (Figure 6) of one oil sample was acquired by using the spin echo technique (1,3,10). This pulse sequence separates secondary carbons from primary and tertiary carbons. By comparison of the chemical shifts in this spectrum with the shifts of pure constituents (16,36-%), the tentative assignmentsin Table I were made. The most dominating peaks in the spectra are due to nalkanes, and these constituents also dominate the loading plot. Peak 54 is mainly due to C1 in n-alkanes but also gains some intensity from long, singly methylated alkanes. Cz’s of nalkanes with at least six carbons and long singly methylated alkanes are responsible for peak 38, while peak 39 corresponds to the chemical shift a t the same position in pentane. Peak 17 refers to the chemical shift on C3 in n-alkanes with chain lengths in the range 8-11, while peaks 18 and 20 are the corresponding shifts of heptane and hexane, respectively. The five peaks originating from secondarycarbons located around 30 ppm are all due to n-alkanes and singly methylated alkanes. Peaks 25 and 26 gain intensity from shorter alkanes, while peaks 22, 23, and 24 correspond to chemical shifts of more
18
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.
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Figure 5. Loading plot when treating ail wells jointly. Coordinates are the loadings (coefficients)of the varlables In PC 1 and PC 2.
8
135
130
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125
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,
40
35
30
25
20
15
,
, PP*
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Flgure 6. 13C NMR spectrum of oll from well 1 using the spin echo technique (I). long-chained alkanes. This last group of peaks is located in the same domain in the loading plot, confirming the dependency between them. As expected, the aromatic peaks are also concentrated in one area of the loading plot. The loading plot can be used to simplify the assignments of the peaks in a spectrum. The shift of peak 10 is well-resolved and very specific of the C3 position in pentane. If no other constituent interferes, we expect to observe the peaks corresponding to C1 and Cz in pentane in the same domain of the loading plot. This is fulfilled for Cz,which is identified as peak 39. We also expect to fiid other short-chainedalkanes in this domain. By following the same line of reasoning as for pentane, peaks 19 and 43 belong to 2-methylbutane, while peaks 1,28, and 53 are due to 2-methylpentane. The other shifts of these constituents interfere with other alkanes and are displaced to other regions in the loading plot. The same procedure as carried out above can be repeated in the assignment of a cyclic region in the loading plot. Peak 31 belongs to cyclohexane, while peaks 5,33, and 44 are mainly from methylcyclohexane. Table I shows that all the peaks in this area belong to five- and six-membered cyclic alkanes. Due to the small amount of doubly methylated alkanes, these are located near the origin of the loading plot together with peaks corresponding to more specific shifts of singly meth-
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ANALYTICAL CHEMISTRY, VOL. 57, NO. 14, DECEMBER 1985
Table I. Shift Assignment of the Most Abundant Constituents of the Atialyzed Oilsa pentane (4%) 3-methylheptane (1%) 5
hexane (4.5%)
5
50
4-methylheptane (0.8%)
w 48
54&6-
heptane (4.5%)
2-methyloctane (0.8%) 18
octane (4.5%)
3-methyloctane (0.8%) 17
nonane (4.5%) decane (4%)
v 38
gr(
38
24
4-methyloctane (0.5%) 52
37
24
cyclopentane (0.5%) 54w
undecane (4%)
methylcyclopentane (2%) I
2-methylbutane (2%)
cyclohexane (3.5%)
43&*
2,3-dimethylbutane (0.8%) 2-methylpentane (2%)
methylcyclohexane (6%)
Ap3J
cis-l,3-dimethylcyclohexane ( 1.5%)
5
truns-1,2-dimethylcyclohexane(0.8%)
42
3-methylpentane (1%) 5
y
&
2,4-dimethylpentane (0.3%)
41
w
2-methylhexane (0.5%)
truns-l,4-dimethylcyclohexane(0.5%)
41
3-methylhexane (1.5%) 5
5
w
benzene (1.5%) toluene (3%)
2,5-dimethylhexane (0.3%)
5 956a 4 5 41&
2,4-dimethylhexane (0.3%)
6
&
43
1,a-xylene (1.5%) 1,4-xylene (1%)
2-methylheptane (1.5%) 41
Concentration determined by gas chromatography. Chemical shift labels refer to composite spectra. The stars indicate chemical shift not labeled in composite spectra. ylated alkanes. Some of these are identified in Table I. Geochemical Interpretation. Figure 7 displays the main correlations in the two PC’s. In the first PC, the n-alkanes and long-chained singly methylated alkanes with branching in the 2 or 3 position are negatively correlated to cycloalkanes and methylated cycloalkanes. The second PC shows negative correlation of short and branched alkanes to longchained alkanes and aromatic constituents. Since the loading plot is oriented in accordance with the directions in the score plot, the explanation of the position of the samples in Figure 4 is now straightforward. Along the first PC, from sample 8 to sample 3, there is a gradual decrease in n-alkanes and singly methylated alkanes and a corresponding increase in cycloalkanes and methylated cycloalkanes. This variation can be attributed to different degrees of biodegradation,with sample 3 being the most degraded. A C1, n-alkane-to-pristane ratio of 1.93 for sample 8 compared to 0.06 for sample 3 strongly supports this interpretation. Moving from sample 8 to sample 1along the second PC corresponds to a decrease of aromatic content and long-chained alkanes toward shorter and more branched alkanes. Since the microorganisms responsible for
the biodegradation are brought into the reservoir through water contact, biodegradation is often accompanied by water washing, reducing also the aromatic content during the process (39). The other dominant factor observed in the second PC, from longer toward shorter alkanes, is probably due to different degrees of maturation attained by the oils (25),with sample 1 being the most mature. This interpretation is supported by the ratios of monoaromatic to triaromatic steranes found in the oils. Validation of the Analytical Procedure. In the last part of this investigation, SIMCA was used to separate modeling of six to seven replicated spectra of each oil from wells 1-4. This was necessary to examine the precision in acquisition and treatment of the spectra. Two replicated distillates from one well were acquired and incorporated in the same model to also test the analytical procedure. Cross-validation gave no significant PC in either model, indicating that the variation in peak intensities between spectra of oils from one well is randomly distributed. For each group of spectra the average distance from all points in the group to the center of gravity was calculated. The ratio between inter- and intragroup
ANALYTICAL CHEMISTRY, VOL. 57, NO. 14, DECEMBER 1985
2863
n-ilkanes and slnlle-methylated alkanes i
_
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Long straight alkanes
Aromaficc
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115
-iL_i-diiiii_iuL-i
40
30
spectra of synthetic fuel (lower) and corresponding crude oil sample (upper). The upper spectrum is shifted 1.25 ppm.
Flgure 8. 13C NMR
crude oil sample. The differences between the spectra can be attributed to slightly different composition due to neglect of minor constituents present in the oil and inaccurate gas chromatographic determination of the concentrations. Solvent effects of the synthetic fuel are shown to be within 0.2 ppm, posing no serious objections on the proposed assignments.
Flgure 7.
Main correlations in PC 1 and PC 2.
distances gives a quantitative measure of the separation between groups and corresponds to class distance between models as defined by Albano et al. (27). Ratios greater than 3-4 correspond to good separation. The ratios were found in the range 4.8-7.1 for all six pairs of oils except for 3 and 4, the ratio for which was calculated to be 2.5. Oils 3 and 4 are from neighboring wells (Figure 1). However, none of the replicates from well 3 and well 4 showed ratios less than 2.0, the ratio being calculated between the distances from each replicate to the centers of gravity. Further validation was gained by treating spectra of wells 3 and 4 jointly with one PC explaining 63% of the total variation. The scores of the samples were utilized to coqpare the inter- and intragroup variation of the samples from the two wells. A F ratio of 1488 with 1 and 11 degrees of freedom confirms the previous conclusion at the 95% significance level. The good classification properties of the I3C NMR spectra in spite of small class distance must be attributed to tight grouping of spectra obtained from one well, indicating high precision in the analytical procedure. Before this work is concluded, the possibilities of misinterpretations must be examined. The reliability of the proposed classifications rests on three assumptions: First, the labeling of a peak must be the same in all spectra. Since the peak identifications are based on the chemical shifts, this condition is fulfilled if solvent effects are similar in the oils. This assumption is justified since the crude oils analyzed are composed of the same constituents, differing only in relative amounts. Second, the intensities must be quantitative. The one-pulse technique used meets this requirement except for quarternary carbons with extra long relaxation times. The third assumption is connected to the normalization step in the data analysis. Two oils with the same relative amounts of major constituents, but differing in total amount of these constituents, show the same intensity pattern. However, the absolute intensities are different in the two spectra. Normalization cancels these differences indicating two identical oils. This situation is not likely to occur. Also, this ambiguity can be solved by using an internal standard to calculate intensities. Tetramethylsilane is too volatile for this purpose. The assignments of the observed spectra are based on pure compounds. By use of the concentrations of the 33 constituents in Table I a synthetic fuel was mixed and the 13C spectrum acquired. Figure 8 shows the spectrum of the synthetic fuel together with the spectrum of the corresponding
CONCLUSION The results of the combined spectroscopic and statistical approaches used in the present work are encouraging. Separate modeling proves, beyond doubt, that I3C NMR is able to reveal unambiguous classification of oils. The 13C NMR spectrum provides effectively a fingerprint of each oil. Further improvements in multiple-pulse NMR techniques toward quantitative intensities can be expected. This implies that more information will be accessible and that statistical interpretation may be of crucial importance in future applications. Finally, we would like to point out that the approach used here is not limited to oil analysis, but may be useful also in other areas of chemistry, for example, in vivo NMR studies of biological systems. Registry No. Pentane, 109-66-0;hexane, 110-54-3;heptane, 142-82-5;octane, 111-65-9;nonane, 111-84-2;decane, 124-18-5; undecane, 1120-21-4;2-methylbutane,78-78-4; 2,3-dimethylbutane, 79-29-8; 2-methylpentane,107-83-5;3-methylpentane,96-14-0; 2,4-dimethylpentane, 108-08-7; 2-methylhexane, 591-76-4; 3methylhexane, 589-34-4; 2,5-dimethylhexane, 592-13-2; 2,4-dimethylhexane, 589-43-5;2-methylheptane,592-27-8;3-methylheptane, 589-81-1;4-methylheptane,589-53-7;2-methyloctane, 3221-61-2; 3-methyloctane,2216-33-3;4-methyloctane, 2216-34-4; cyclopentane, 287-92-3;methylcyclopentane, 96-37-7;cyclohexane, 110-82-7; methylcyclohexane, 108-87-2; cis-1,3-dimethylcyclohexane, 638-04-0; trans-1,2-dimethylcyclohexane,6876-23-9; trans-1,4-dimethylcyclohexane,2207-04-7; benzene, 71-43-2; toluene, 108-88-3; 1,3-xylene, 108-38-3;1,4-xylene,106-42-3. LITERATURE CITED Cookson, D. J.; Smith, B. E. Org. Magn. Reson. 1981, 16, 111-116. Cookson, D. J.; Smith, B. E.; Fuel 1982, 61,1007-1.013. Snape, C. E. fuel1982, 61, 1164-1167. Cookson, D. J.; Smith, B. E. Fuel 1983, 62, 986-987. Cookson, D. J.; Smith, 8. E. Fuel 1983, 62,34-43. Holak, T. A.; Aksnes, D. W.; Stclcker, M. Anal. Chem. 1984, 56, 725-728. (7) Hagaman, E. W.; Scheli, F. M.; Cronauer, D. C. Fuel 1984, 63, 9 15-919. (6) Barron, P. F.; Bendall, M. R.; Armstrong, L. G.; Atkins, A. R. fuel 1984, 63, 1276-1280. (9) Cookson, D. J.; Smith, B. E. J. Magn. Reson. 1984, 5 7 , 355-368. IO) "Magnetlc Resonance-Introduction, Advanced Topics and Applications to Fossil Energy"; Petrakis, L., Fralssard, J. P., Eds.; Reidel: Dordrecht. 1984. 11) Seshadri, K. S.; Cronauer, D. C. fuel 1983, 62, 1436-1444. 12) Qian, S. H.; Li, C.-F.; Zhang, P . 2 . Fuel 1984, 63, 268-273. 13) Petrakis, L.; Alien, D. T.; Gavalas, G. R.; Gates, B. C. Anal. Chem. 1983, 55, 1557-1564. (14) Alien, D. T.; Petrakis, L.;Grandy, D. W.; Gavaias, G. R.; Gates, B. C. fuel. 1984, 63, 803-809. (15) Netzel, D. A.; McKay, D. R.; Heppner, R. A.; Guffey, F. D.; Cooke. S. D.; Varie, D. L.; Llnn, D. E. fuel 1981, 60,307-320. (16) Lindeman, L. P.; Adams, J. Q. Anal. Chem. 1971, 4 3 , 1245-1252. (17) Ward, R. L.; Burnham, A. K. Fuel 1984, 63,909-914. (18) Dygard, K.; Ulvclen, S.;Grahl-Nieisen, 0. Roc. Nordic Symp. Appl. Stat. Christie, 0. H. J., Ed.; Stokkand Forlag Publisher: Stavanger, 1983; pp 100-112A. (1) (2) (3) (4) (5) (6)
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(19) Varmuza, K. “Pattern Recognition in Chemistry”; Springer-Verlag: New York. 1980. (20) Gray, N. A. B. frog. Nod. Magn. Reson. Spectrosc. 1982, 15, 201-248. (21) Wilkins, C. L.; Wililams, R. C.; Brunner, T. R.; McComble, P. J. J Am. Chem. SOC. 1974, 9 6 , 4182-4185. (22) Malinowski, E. R.; Howery, D. G. ”Factor Analysis in Chemistry”: Wlley: New York, 1980. (23) Massart, D. L.; Kaufman, L. “The Interpretation of Analytical Chemical Data by the Use of Cluster Analysis”; Wiley: New York, 1983. ; Dunn, W. J., 111; Edlund, U.; Esbensen, K.; GeiaJohansson, E.; Lindberg, W.; Sjostrom, M. I n “Chemometrlcs-Mathematics and Statistics In Chemistry”: Kowalski, B. R., Ed.; Reldel: Dordrecht, 1984; pp 17-95. (25) Tlssot, B. P. Welte, D. H. “Petroleum Formation and Occurrence-A new approach to 011 and gas exploration”; Springer-Verlag: BerllnHeidelberg-New York, 1978. (26) Kowalski, B. R.; Bender, C. F. J . Am. Chem. SOC. 1972, 9 4 , 5632-5639. (27) Albano, C.; Btomqvist, G.; Coomans, 0.; Dunn, W. J., 111; Edlund, U.; Eiiasson, 8.; Hellberg, S.;Johansson, E.; NordBn, 6.; Johnels, D.; Sjostrom, M.; SMerstrom. B.; Wold, H.; Wold, S. Proc. Nordic Symp. Appl. Stat. Hijgskuldsson, A., Conradsen, K., Jensen, B. Sloth, Esbensen, K., Eds.; Copenhagen, 1981; pp 183-218.
(28) Wold, S. Pattern Recognit. 1078, 8 , 127-139. (29) Kvalhelm, 0. M. Anal. Chlm. Acta, In press. (30) Wold, S.; Sjostrom, M. I n “Chemometrlcs, Theory and Application”; Kowalski, B., Ed.; American Chemical Society: washington, DC, 1977; Am. Chem. SOC.Symp. Ser. 52. pp 243-282. (31) Albano, C.; Dunn, W., 111; Edlund, U.; Johansson, E.; Norden, 6.; Sjostrom, M.; Wold, 5. Anal. Chim. Acta 1978, 103, 429-443. (32) Esbensen, K. H.; Wold, S. R o c . Nordic Symp. Appl. Stat. Christie, 0. H. J., Ed.; Stokkand Forlag Publisher: Stavanger, 1983: pp 11-36. (33) Kvalhelm, 0. M. “SIMCA User’s Guide”; University of Bergen, Bergen, Norway, 1985. (34) Wold, S. Technometrics 1978, 20, 397-405. (35) Kvalhelm, 0. M. submitted for publication In Geochlm. Cosmochlm. Acta. (36) Brekke, T. Cand. Sc. Thesis, University of Bergen, Norway, 1985. (37) “The Sadtler Guide to Carbon-13 NMR Spectra”; Slmons, W. W., Ed.; Sadtler: Phlladelphla, PA, 1983. (38) Brelmaier, E.; Haas, 0.; Voelter, W. “Atlas of Carbon-13 NMR Data”; Heyden: London, 1979; Vol. I and 11. (39) Deroo, G.; Tlssot, 6.; McCrossan, R. G.; Der, F. Can. SOC.Pet. Geol. Mem. 1974, 148-167, 184-189.
RECEIVED for review May 14,1985. Accepted August 1,1985.
Determination of Alcohols in Gasoline/Alcohol Blends by Nuclear Magnetic Resonance Spectrometry George E. Renzoni,* Eric G. Shankland, Julie A. Gaines, and James B. Callis Analytical and Spectral Services Division and Center for Process Analytical Chemistry, Department of Chemistry, BG-10, University of Washington, Seattle, Washington 98195
A simple, rapid procedure for the determlnatlon of alcohols in gasoline/aicohol mixtures is described. The method takes advantage of a window In the proton nuclear magnetic resonance (‘H NMR) spectrum of gasollne that extends from a chemical shift of 6 2.8 to 6.8 ppm. Forelgn substances that have resonances in this region may be readlly determlned. We have quantitated methanol In gasollne by lntegratlon of the methyl slnglet at 6 3.4 ppm. The method gives ilnear callbratlon curves in the range of 0-25% methanol with a detection llmlt of less than 0.1 %. The application Qf thls technique to other alcohols is also presented.
Oxygenated organic compounds such as alcohols and ethers can be added to gasoline to act as fuel extenders and octane improvers ( I ) . An example is gasohol, a motor fuel containing about 10% ethanol in unleaded gasoline. Methanol is a particularly attractive blending agent because it provides the greatest octane improvement. However, problems arise from phase separation of gasoline/methanol/water mixtures, which may result in potentially dangerous engine failure-a special concern of the aviation industry (2). In this case, other alcohols, (ethanol, propanols, and butanols) caq be used as cosolvents to reduce or eliminate the phase separation. Another aspect of alcohol/gasoline blends is their fraudulent representation to the public as pure gasoline. Until now there has been no simple way to determine the composition of gasoline/alcohol blends or to analyze for adulterants of this type. It is this latter concern that has prompted us to develop an analysis for methanol in gasoline. Previous analyses of gasoiine have included both separation and spectrometric techniques. Gas-liquid chromatographic (GLC) analysis has often been employed in the determination of simple oxygenates in gasoline blends (3-8). GLC analyses 0003-2700/85/0357-2864$01.50/0
of the aqueous extract of gasoline blends have been used for the determination of methanol, ethanol, and 2-methyl-2propanol as well as methyl tert-butyl and diethyl ether ( 3 , 4 ) . The direct determination of C1-C4 alcohols in gasoline/alcoho1 blends has also been reported (5). Recently, more sophisticated GLC analyses of oxygenates in gasolines involving multiple column (6, 7) and capillary column (8) chromatography have appeared. A direct liquid chromatographic method for the determination of Cl-C3 alcohols and water in gasoline/alcohol blends has also been described (9). In addition, the amount of ethanol in gasoline has been quantified by near-infrared (lo),infrared (11,12),microwave (13),and mass spectrometric techniques (14). Gasoline is a complex mixture of normal and branched aliphatic hydrocarbons, ranging from C5 to C12 alkanes, and aromatic hydrocarbons including benzene, toluene, and the xylenes among others. Our decision to use ‘HNMR spectrometry for this determination was based on the observation that the NMR spectrum of gasoline consists of three distinct regions, as shown in Figure 1: an aliphatic region (6 0.8-1.8 ppm), a benzylic region (6 2.1-2.8 ppm), and an aromatic region (6 6.8-7.5 ppm). In contrast, the carbinolic protons of alcohols resonate in the vicinity of 6 3.5 ppm, a region free of gasoline resonances. Thus, NMR spectrometry seemed to be particularly well-suited for the determination of alcohols.
EXPERIMENTAL SECTION The alcohols used in preparing the standards were of analytical grade and were not further purified. The NMR solvent, carbon tetrachloride,and reference, tetramethylsilane, were spectroscopic grade and also used without purification. The gasolines were obtained from commercial sources. The 60-MHz proton spectra were obtained with a Varian EM 360L NMR spectrometer with the following spectrometer settings: rf power 0.05 mG, time constant 0.05 s, spectrum amplitude 500, sweep width 10 ppm, and sweep time 2 min. Integrals were measured by using a 5 ppm 0 1985 American Chemical Society
