Letter pubs.acs.org/ac
Hexagonal Class Representation for Fingerprinting and Facile Comparison of Petroleomic Samples Konstantin O. Zhurov, Anton N. Kozhinov, and Yury O. Tsybin* Biomolecular Mass Spectrometry Laboratory, Ecole Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland ABSTRACT: Because of the high complexity of petroleomictype samples, there is a need for efficient ways of visualizing and interpreting the resulting data in mass spectrometry-based petroleomics. Over the years, several graphing approaches have become widespread, yet they mostly deal with a particular subset of compounds detected within a given sample. Here, we present an alternative and complementary sample visualization method, the hexagonal class representation, based on relative abundance vs compound classes plot. The representation can be used to “fingerprint” a petroleomic-type sample, provide a simple means of sample comparison, as well as allow for a fast overview and detection of any compound of interest based on its elemental composition and chemical properties.
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Recently, with the improvement of key characteristics of available mass spectrometers, samples of ever higher complexity can be analyzed. To deal with increased sample complexity, various additional data visualization approaches have been proposed, including 2D/3D van Krevelen plots,28 3D Kendrick counterplots,29 abundance vs carbon content plots,30 and statistical methods, such as principal component analysis and hierarchical clustering31 as well as various other methods.7,9,32,33 Most of the commonly used graphs and proposed improvements present data for a single class. Here, we propose a complementary method of visualizing the data that characterizes the entire sample, the relative abundance vs compound classes plot in a 3D vector space. By taking advantage of the ordered nature of the molecules found in petroleomic samples, the presented method facilitates overall sample analysis and intersample comparison as well as permits fast sample fingerprinting by highlighting the relationship between the compound classes identified in a sample.
or decades, there has been a significant interest in elucidating the composition and physical properties of fossil fuels and related samples,1−10 including coal,11 oil sands,12 and recently, biofuels.5,13−15 One of the key analytical methods for characterizing these samples is mass spectrometry (MS), often coupled to various other spectroscopic and fractionation techniques.16−19 Since petroleomic-type samples usually contain hundreds, if not thousands of molecules, analyses of mass spectra of these samples result in assignment of numerous elemental compositions to the peaks present therein.20 Hence, data visualization tools which would allow for fast and convenient grouping of all identified molecules along pertinent criteria for efficient sample analysis are needed. Generally, most molecules in such samples tend to be hydrocarbons containing one or more atoms of S, N, or O.21 The molecules then can be grouped by their elemental formulas: those molecules which have the same number and type of heteroatom (e.g., one N atom) but vary in their C and H count are grouped into a class. Within a class, the molecules may vary by both their degree of unsaturation as well as by the number of CH2 units attached to a molecular core; the latter then forms a homologous series. As such, the distribution of elemental compositions in petroleomic samples presents ordered data sets which then may be conveniently represented by several types of graphs. Typical graphs included in the analysis and characterization of a given petroleomic sample are: plot of relative abundance vs compound classes identified in the entire sample,22 Kendrick plot of mass defect vs nominal mass using the Kendrick mass scale,23,24 double bond equivalents (DBE) vs carbon number graphs,25,26 and van Krevelen plots which map C/H vs O/H ratios.27 The three latter graphs deal with data within a given class, while the first plot provides a general overview of the sample. © XXXX American Chemical Society
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EXPERIMENTAL SECTION Data Treatment. Noting that most classes within the petroleum and petroleum related samples contain only two of the three heteroatoms,7,11,12,14,26,34−37 the following graphical representation is proposed. Using a 3D vector space, each axis represents one of the three commonly found heteroatoms: N, O, and S. Thus, each class location can be represented by (N, O, S) notation giving its coordinate in space. Provided that most samples contain a mixture of classes represented by only two heteroatoms, we propose a “corner-on” view along the vector passing through (0, 0, 0) and (n, n, n). Hence, any given Received: February 7, 2013 Accepted: May 22, 2013
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Figure 1. Template of hexagonal class representation: includes a hexagon based on the CH and NOS classes as well as provides framework for including metal containing species.
Figure 2. Hexagonal class representation of identified classes of an asphaltene feed from an APPI FT-ICR mass spectrum published by Purcell et al.26
hexagon will contain the (n, n, n) class, and classes along the x = n, y = n, and z = n planes. As such, the CH class is represented by the (0, 0, 0) coordinate, and the (1, 1, 1)
coordinate is the NOS class. Any classes of type X or XY can then be found in the three planes, which can be superimposed to form a series of expanding hexagons, Figure 1. B
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fingerprint of the chemical composition and directly compare samples of different origin. Figure 3 demonstrates the data published by Hughey et al.22 (Figure 7 within) which compares three crude oils of different
The hexagonal representation can be extended to include classes which contain all three heteroatoms: they are all then conveniently placed on the next hexagon, with the point of origin being the NnOnSn class. Figure 1 includes the hexagons for the (0, 0, 0) and (1, 1, 1) cases: for the latter, any classes with at least one atom from two elements and one atom of the third element may be placed. Hence, a new hexagon can be created for any combination of classes of the form Xn+αYn+βZn (α ≥ 0, β ≥ 0) with XnYnZn class at the center. Relative abundance values presented here were calculated from the data published in Hughey et al.22 and Purcell et al.26 Additionally, the procedure was applied in resin and maltene sample analysis in Zhurov et al.38 The abundance scale representation is user-defined. Here, the abundances were converted to a decimal log abundance scale using the HSL (hue, saturation, and lightness) representation of RGB color,39 with S = 255, L = 150, and H = 155 − 50 log(I), where I is relative abundance, in percent, of a given class. This scale covers 4 orders of magnitude, such that low-abundance classes may also be clearly represented in the graph. The converted abundance points can then be mapped on a template file, Figure 1. Note that metal porphyrins can also be included; see the Results and Discussion for details.
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RESULTS AND DISCUSSION
Sample Fingerprinting. The hexagonal class representation gives a clear relationship between different classes: one can simply add up the N, O, S vectors from the origin to arrive at any class on these planes, with a given class always occupying a predetermined point in space. As such, there are clearly defined regions of space, where one or other heteroatom dominates, e.g., any classes found in the lower-right region of the hexagon will be sulfur-containing, and the further out from the center one goes, the more heteroatoms there shall be. Hence, if a certain compound class presents a particular interest, such as sulfur-containing species,40 it will be possible to determine whether they have been detected in the sample by considering the appropriate region of the graph. For example, Figure 2 shows the schematic representation of identified classes from an atmospheric pressure photoionization (APPI) mass spectrum of an asphaltene feed (Purcell et al.,26 Figure 2 therein). As can be seen from the figure, there are 40 classes of compounds present, spanning over 3 orders of magnitude, with clear dominance of sulfur containing classes and only trace amounts of oxygen containing molecules present, along with the sample having significant heteroatom class diversity, including classes with up to 5 heteroatoms. Furthermore, making a simultaneous hexagonal representation of basic, acidic, and nonpolar compounds found in a sample can also be possible as, for example, the data from a (+)ESI, (−)ESI, and an APPI mass spectra can be represented on a single graph, by means of splitting the color-blot into equal parts (e.g., thirds, halves, or more, depending on how many data sets are being compared). Thus, one may have, on a single graph, relevant information with regards to both the presence of typical species which may affect fuel processing41−44 as well as their chemical responses based on which ionization technique was used.22,36 Sample Comparison. Given that geological origin affects the chemical composition of petroleomic-type samples,22,45,46 and biofuel type gives rise to distinct chemical distributions,14,34 for a given sample, it should be possible to generate a
Figure 3. (Top) Hexagonal class representation of identified classes of a Chinese crude oil from a (−)ESI FT-ICR mass spectrum published by Hughey et al.22 (Bottom) Hexagonal class representation of identified classes of a North American and Middle Eastern crude oils from (−)ESI FT-ICR mass spectra published by Hughey et al.22
geochemical origin. As can be seen from Figure 3 top, the Chinese crude has a smaller variation on class types, whereas the Middle Eastern and North American crude are much more similar, Figure 3 bottom. However, both the Middle Eastern and North American crudes have one unique class absent in the other, and the color-blots indicate that the North American crude has significantly more NO and N2 species; subtle variations in relative abundances of the OnS classes can also be readily observed. Metal Porphyrins. Finally, the presence of metal porphyrins in petroleomic samples needs to be addressed, as they contain additional elements to consider.1,42 It is thus proposed that a separate hexagon is used to indicate the presence of metal porphyrins, with N4 at the center, and the metal elements placed around the corners of the hexagon. As, by far, the most commonly encountered metal porphyrins C
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(14) Jarvis, J. M.; McKenna, A. M.; Hilten, R. N.; Das, K. C.; Rodgers, R. P.; Marshall, A. G. Energy Fuels 2012, 26 (6), 3810−3815. (15) Smith, E. A.; Brown, R. C.; Lee, Y. J. Applications of High Resolution Mass Spectrometry in the Analysis of Biofuels. In Abstracts of the 44th Midwest Regional Meeting of the American Chemical Society, Iowa City, IA, October 21−24, 2009; MWRM-277. (16) Rodgers, R. P.; McKenna, A. M.; Robbins, W. R.; Hsu, C. S.; Lu, J.; Hendrickson, C. L.; Reddy, C. M.; Nelson, R. K.; Marshall, A. G. Petroleomics: Past, present, and future. In Abstracts of Papers, 242nd ACS National Meeting & Exposition, Denver, CO, August 28− September 1, 2011; FUEL-367. (17) Rodgers, R. P.; McKenna, A. M. Anal. Chem. 2011, 83 (12), 4665−4687. (18) Mullins, O. C. Annu. Rev. Anal. Chem. 2011, 4, 393−418. (19) Merdrignac, I.; Espinat, D. Oil Gas Sci. Technol.-Rev. IFP 2007, 62 (1), 7−32. (20) Xian, F.; Hendrickson, C. L.; Marshall, A. G. Anal. Chem. 2012, 84 (2), 708−719. (21) Marshall, A. G.; Rodgers, R. P. Acc. Chem. Res. 2004, 37 (1), 53−59. (22) Hughey, C. A.; Rodgers, R. P.; Marshall, A. G.; Qian, K.; Robbins, W. K. Org. Geochem. 2002, 33 (7), 743−759. (23) Kendrick, E. Anal. Chem. 1963, 35 (13), 2146−54. (24) Hughey, C. A.; Hendrickson, C. L.; Rodgers, R. P.; Marshall, A. G.; Qian, K. Anal. Chem. 2001, 73 (19), 4676−4681. (25) Marshall, A. G.; Rodgers, R. P. Proc. Natl. Acad. Sci. U.S.A. 2008, 105 (47), 18090−18095. (26) Purcell, J. M.; Merdrignac, I.; Rodgers, R. P.; Marshall, A. G.; Gauthier, T.; Guibard, I. Energy Fuels 2010, 24 (4), 2257−2265. (27) van Krevelen, D. W. Fuel 1950, 29, 269−284. (28) Wu, Z.; Rodgers, R. P.; Marshall, A. G. Anal. Chem. 2004, 76 (9), 2511−2516. (29) Corilo, Y. E.; Vaz, B. G.; Simas, R. C.; Lopes Nascimento, H. D.; Klitzke, C. c. F.; Pereira, R. C. L.; Bastos, W. L.; Santos Neto, E. n. V.; Rodgers, R. P.; Eberlin, M. N. Anal. Chem. 2010, 82 (10), 3990−3996. (30) Wu, C.; Qian, K.; Nefliu, M.; Cooks, R. G. J. Am. Soc. Mass Spectrom. 2010, 21 (2), 261−267. (31) Chiaberge, S.; Fiorani, T.; Savoini, A.; Bionda, A.; Ramello, S.; Pastori, M.; Cesti, P. Fuel Process. Technol. 2013, 106 (0), 181−185. (32) Barrow, M. P.; Headley, J. V.; Peru, K. M.; Derrick, P. J. Energy Fuels 2009, 23 (5), 2592−2599. (33) Shi, Q.; Zhao, S.; Xu, Z.; Chung, K. H.; Zhang, Y.; Xu, C. Energy Fuels 2010, 24 (7), 4005−4011. (34) Smith, E. A.; Park, S.; Klein, A. T.; Lee, Y. J. Energy Fuels 2012, 26 (6), 3796−3802. (35) Klein, G. C.; Kim, S.; Rodgers, R. P.; Marshall, A. G.; Yen, A.; Asomaning, S. Energy Fuels 2006, 20 (5), 1965−1972. (36) Purcell, J. M.; Hendrickson, C. L.; Rodgers, R. P.; Marshall, A. G. Anal. Chem. 2006, 78 (16), 5906−5912. (37) Shi, Q.; Hou, D.; Chung, K. H.; Xu, C.; Zhao, S.; Zhang, Y. Energy Fuels 2010, 24 (4), 2545−2553. (38) Zhurov, K. O.; Kozhinov, A. N.; Tsybin, Y. O. Energy Fuels 2013, DOI: 10.1021/ef400203g. (39) Joblove, G. H.; Greenberg, D. Comput. Graph. 1978, 12, 20−27. (40) Purcell, J. M.; Juyal, P.; Kim, D.-G.; Rodgers, R. P.; Hendrickson, C. L.; Marshall, A. G. Energy Fuels 2007, 21 (5), 2869−2874. (41) Hsu, C. S.; Dechert, G. J.; Robbins, W. K.; Fukuda, E. K. Energy Fuels 2000, 14 (1), 217−223. (42) McKenna, A. M.; Purcell, J. M.; Rodgers, R. P.; Marshall, A. G. Energy Fuels 2009, 23 (4), 2122−2128. (43) Rodgers, R. P.; Hendrickson, C. L.; Emmett, M. R.; Marshall, A. G.; Greaney, M.; Qian, K. Can. J. Chem. 2001, 79 (5−6), 546−551. (44) Liu, P.; Shi, Q.; Chung, K. H.; Zhang, Y.; Pan, N.; Zhao, S.; Xu, C. Energy Fuels 2010, 24 (9), 5089−5096. (45) Belitskaya, E. A.; Serebrennikova, O. V. Pet. Chem. 2009, 49 (6), 458−465. (46) Fang, Y.; Liao, Y.; Wu, L.; Geng, A. J. Asian Earth Sci. 2011, 41 (2), 147−158.
contain vanadyl or nickel ions, those have been placed on opposite apexes, Figure 1. Placement of other metal containing species is then left to the discretion of the user, with recommendation of said placement then being reserved for that particular element in ensuing publications. Given an elemental identification of other metal-containing species, such as sulfur-containing vanadyl porphyrins47 or other organometallics, a hexagon with the appropriate heteroatom class at the center may be created, with identified metals associated with said class placed at the hexagon apexes.
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CONCLUSIONS A data visualization scheme of the relative abundance vs class type graph in a 3D vector space has been proposed. Applications to sample fingerprinting and sample comparison have been demonstrated. The universality of the scheme allows petroleomic and related data types to be easily converted into this format. The authors believe that the hexagonal class representation allows for straightforward sample reference and overview and may become a simple, yet useful tool for petroleum-type sample analysis.
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AUTHOR INFORMATION
Corresponding Author
*Address: Prof. Yury O. Tsybin, EPFL ISIC LSMB, BCH 4307, 1015 Lausanne, Switzerland. E-mail: yury.tsybin@epfl.ch. Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS We thank Luca Fornelli, Kristina Srzentić, and Ü nige A. Laskay for helpful discussions with regards to the log color scale. The work was supported by the Swiss National Science Foundation (Projects 200021-125147, 200021-147006, and 128357) and the European Research Council (ERC Starting Grant 280271).
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REFERENCES
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