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Standardizing the Novel Method for Chemical Fingerprinting of Oil and Petroleum Products Based on Positive Electrospray Mass Spectrometry and Chemometrics Kolbjørn Zahlsen and Ingvar Eide* Statoil Research Centre, N-7005 Trondheim, Norway ReceiVed July 14, 2005. ReVised Manuscript ReceiVed NoVember 1, 2005
A novel method for chemical fingerprinting of oil and petroleum products has recently been developed. The method is based on full-scan positive electrospray mass spectrometry (ESI-MS) using a single quadrupole LC-MS instrument, however, with direct injection (no chromatographic separation) and without fragmentation of the molecules. The aim of the present study was to establish recommended or standardized conditions able to produce mass spectrometric data of high quality with respect to pattern diversity, recognition, precision, and repeatability utilizing a standard single quadrupole ESI-LC-MS instrument. Performance was evaluated by means of Principal Component Analysis (PCA) to track changes in spectra. A mobile phase consisting of acetonitrile and 50 mM ammonium acetate (90:10) was found to give the optimum result, and instrument parameters to produce unfragmented spectra were established. The method can be considered as a first attempt to establish standardized conditions and parameters applicable to modern single quadrupole LC-MS instruments for the purpose of performing chemical fingerprinting using the combination of ESI-MS and chemometrics.
Introduction In a recent paper we described a novel method for chemical fingerprinting of oil and petroleum products.1 The method is based on full-scan mass spectrometry using a single quadrupole LC-MS instrument, however, with direct injection (no chromatographic separation) and without fragmentation of the molecules. In the previous work, positive electrospray ionization was used (ESI-MS); however, the method is applicable to any other ionization technique. Pattern recognition of the spectra is performed using multivariate data analysis (chemometrics). Oils, even the most complex heavy crude oils, can be analyzed directly without pretreatment except dissolution in dichloromethane (DCM). One analysis takes one minute to perform, and the combination of ESI-MS and chemometrics (multivariate data analysis) makes it possible to classify and discriminate the highly complex spectral data in a manner that has not been achieved previously. The spectra can also be used for calibration purposes.1 Previous studies have shown that crude oils and petroleum products contain a large number of compounds that are ionized positively by electrospray ionization.1-7 These compounds are primarily heterocyclics containing N and possibly also O and S, as demonstrated with high-resolution mass spectrometry (FT* Corresponding author. Phone: +47 90997296. Fax: +47 73967286. E-mail:
[email protected]. (1) Eide, I.; Zahlsen, K. Energy Fuels 2005, 19, 964-967. (2) Zahn, D.; Fenn, J. B. Int. J. Mass Spectrom. 2000, 194, 197-208. (3) Qian, K.; Rodgers, R. P.; Hendrickson, C. L.; Emmett, M. R.; Marshall, A. G. Energy Fuels 2001, 15, 492-498. (4) Hughey, C. A.; Rodgers, R. P.; Marshall, A. G. Anal. Chem. 2002, 74, 4145-4149. (5) . Marshall, A. G.; Rodgers, R. P. Acc. Chem. Res. 2004, 37, 53-59. (6) Qian, K.; Edwards, K. E.; Diehl, J. H.; Green, L. A. Energy Fuels 2004, 18, 1784-1789. (7) Rostad, C. E. Energy Fuels 2005, 19, 992-997.
ICR-MS)3,5 and confirmed with single quadrupole ESI-MS using model substances.6 Spectra obtained with positive single quadrupole ESI-MS may have more than 1000 lines, one distinct line per integer mass number (m/z). Each line represents a limited number of compounds with the same mass number. As shown in our previous work,1 characteristic distributions for odd- and evennumbered masses and repetitive spacings of 14 Da (CH2) and 2 Da (saturated versus double-bond analogues) give a detailed, fine structure that is an ideal basis for multivariate pattern recognition techniques. Different solvents and conditions during infusion and electrospray ionization have been shown to create different patterns of the mass spectra.2 Therefore, it seems extremely favorable to establish standardized conditions that will increase reproducibility and robustness when our combined “mass spectrometricchemometric” methodology is applied to an increasing number of different samples and matrixes. The aim of the present study was to establish recommended or standardized conditions that are able to produce mass spectrometric data of high quality with respect to pattern diversity, recognition, precision, and repeatability using a single quadrupole ESI-LC-MS instrument. Emphasis was placed on mobile-phase composition and fragmentor voltage. Changes in spectra as well as similarities and differences between spectra were evaluated by means of Principal Component Analysis (PCA)8 as demonstrated in our previous work.1 PCA was also used to evaluate repeatability. Materials and Methods Chemicals and Crude Oils. The following chemicals were used: methanol (HPLC), acetonitrile (HPLC), and dichloromethane (8) Jackson, J. E. A User’s Guide to Principal Components; John Wiley: New York, 1991.
10.1021/ef050213f CCC: $33.50 © 2006 American Chemical Society Published on Web 12/06/2005
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Figure 1. Positive ESI-MS spectra of ASC crude analyzed with the four different mobile phases: ACN-AmAc, MeOH-AmAc, ACN-Fac, and MeOH-Fac.
(HPLC) from LabScan, Dublin, Ireland. Formic acid (p.a.) and ammonium acetate (p.a.) from Acros Organics, Geel, Belgium. Water (>18 MΩ) was produced on a MilliQ Gradient system from Millipore, Billerica, MA. Three different crude oils were used in the present study: GFA (North Sea), GLI (North Sea), and ASC (Asian crude). Sample Dilution Solvent. Dichloromethane (DCM) was chosen as the preferred sample dilution solvent. Initially, three different sample dilution solvents were evaluated: dichloromethane, acetonitrile, and methanol. Solubility was tested with a variety of crude oils, including heavy crude oils, crude oil fractions, vacuum residues, etc., within the concentrations range of 0.5 to 10 mg/mL. The conclusion was that dichloromethane was found to dissolve all samples completely. Instrumentation and Injection. The following instrumentation was used: an Agilent MSD 1100 liquid chromatography-mass spectrometry system (Agilent Technologies, Palo Alto, CA) consisting of a G1379A degasser, a G1367A autosampler, a G1312A binary pump, and a G1956B LC MSD SL single quadrupole mass spectrometer. Samples were injected by the autosampler and led into the mass spectrometer by a 70-cm PEEK tubing (i.d. 0.18 mm), without separation on a chromatographic column (details in previous work 1). Full-scan spectra were collected in the range m/z 651000. Mobile Phases, Fragmentor Voltage, Repeatability. The following binary mixtures were evaluated as mobile phases: acetonitrile and 50 mM ammonium acetate (90:10); methanol and 50 mM ammonium acetate (90:10); acetonitrile and 25 mM formic acid (90:10); methanol and 25 mM formic acid (90:10). These four mobile phases will be abbreviated ACN-AmAc, MeOH-AmAc, ACN-Fac, MeOH-Fac, respectively. The following fragmentor voltages were evaluated: 50, 75, 100, 125, 150, and 200V. Repeatability (drift) was tested by multiple (140) consecutive injections. Instrumental Parameters. Some instrumental parameters that normally are under strict control according to instrument specifications were also evaluated initially. The parameters were tested at three different levelssone below and one above the aimed standardized value. The parameters and the values evaluated were
the following: nebulizer pressure: 20, 25, and 30 psi; mobile phase flow: 0.100, 0.150, and 0.200 mL/min; injection volume: 1, 2, and 3 µL; and drying gas flow: 7, 8, and 10 L/min. The influence on the spectra was evaluated by PCA and score plots.1 Altogether, varying these parameters had no or only minor effects on the resulting spectra (results from this part of the study are not shown). In any case, these parameters were standardized with the following intermediate values: nebulizer pressure: 25 psi; mobile-phase flow: 0.150 mL/min; injection volume: 2 µL; and drying gas flow: 8 L/min. Data Processing and Multivariate Analysis. One average spectrum was obtained from each individual analysis, calculated from approximately 10 individual spectra obtained at half peak height after background subtraction (using a post-run macro from Agilent). Mass numbers are rounded off to integer mass numbers. Prior to multivariate analysis, the abundance data (spectral line values) were normalized to a constant sum within each row, and the normalized data were centered. Principal Component Analysis (PCA) and score plots 8 were used to evaluate similarities and differences between spectra and were performed with Simca-P 10.5 (Umetrics, Umeå, Sweden). Further details are described in our previous work.1
Results Figure 1 shows full-scan spectra of one of the crude oils (ASC) from the four mobile phase experiments: acetonitrile and ammonium acetate (ACN-AmAc); methanol and ammonium acetate (MeOH-AmAc); acetonitrile and formic acid (ACN-Fac); and methanol and formic acid (MeOH-Fac). As can be seen, all four spectra show similarities with respect to overall patterns. The main common characteristic of all four spectra is high complexity and a distribution of masses with increasing abundance from m/z ) 100 up to m/z ) 300-400. Then a gradual decrease is observed from m/z ) 400 up to m/z ) 1000. All spectra are occupied with one line per mass number, characteristic distributions can be seen for odd- and
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Figure 2. (a) PCA score plot of GLI, GFA, and ASC crude oils analyzed with the four different mobile phases (9: ACN-AmAc; 2: MeOHAmAc; +: ACN-Fac; *: MeOH-Fac). (b) PCA score plot of GLI, GFA, and ASC crude oils analyzed with the two different mobile phases containing AmAc (9: ACN-AmAc; 2: MeOH-AmAc).
even-numbered masses, and repetitive spacings of 14 and 2 Da appear in all spectra (more visible in an expanded figure in our previous work1). Characteristic differences can also be observed between the four spectra from the four mobile phases. The most characteristic and visible difference in pattern and mass distribution is seen between the mobile phases containing ammonium acetate and mobile phases containing formic acid. Generally, the ammonium acetate-containing mobile phases display more complex spectra containing several distinct distributions and sub-distributions in contrast to the ones with formic acid. It is likely that these subdistributions represent different classes of heterocyclics that differ typically with spacings of 14 Da. As judged by visual inspection, the ammonium acetate spectra thus theoretically would be capable of reflecting the variety of compound classes in a complex petroleum product better than formic acid spectra. Figure 2, parts a and b, shows the score plots resulting from the PCA of the spectral data from the three crude oils GFA, ASC, and GLI analyzed using different mobile phases. Each crude oil was injected 10 times (per mobile phase), and all 10 data points are displayed to show the variability and repeatability (precision) between injections. Figure 2a shows all four mobile phases together and separately labeled in the same score plot. The first Principal Component (PC) explains 42% of the variation, and the second PC explains another 25%. The lower part of the figure shows the ammonium acetate containing mobile phases, where the GLI, GFA, and ASC data sets with the same mobile phase are linked
together by lines. MeOH-AmAc is the data set with dotted lines and ACN-AmAc is the data set with unbroken lines. The upper part of the figure shows the mobile phases with formic acid (inclined labels). The score plot shows clearly that the AmAc data are more precise than the Fac data. In contrast to AmAc, the Fac data points show a systematic linear distribution and a poorer precision. From this plot it was concluded that the mobile phases with AmAc (both with ACN and MeOH) were superior to the mobile phases with Fac. Figure 2b shows the score plot obtained after PCA on the AmAc data alone. The first PC now explains 56% of the variation, and the second PC explains another 22%. The upper three groups of points linked with unbroken lines are ACNAmAc and the three lower groups of points linked with dotted lines are MeOH-AmAc. From this plot, the effect of ACN versus MeOH can be seen when combined with AmAc as buffer in the mobile phase. The results are comparable with respect to both precision and distance between groups of different crudes. Due to the fact that a slightly better precision generally was achieved with ACN-AmAc as the mobile phase, this combination was chosen as the preferred one rather than MeOH-AmAc in further experiments. Besides, ACN-AmAc was used in our previous work.1 As a consequence, all experiments below are performed with ACN-AmAc as the mobile phase. The purpose of the fragmentor experiment was to ensure that no (or minimal) fragmentation occurs when using the recommended conditions. It is important to avoid fragmentation to
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Figure 3. (a) PCA score plot of GLI, GFA, and ASC crude oils analyzed with ACN-AmAc with different fragmentor voltages: 50, 75, 100, 125, 150, and 200 V (O: 50, 75, and 125 V; 9: 100 V; +: 150 V, *: 200 V). Values in plot are fragmentor voltage (V). (b) PCA score plot showing the results from GLI, GFA, and ASC crude oils anlyzed with ACN-AmAc with different fragmentor voltages (O: 50, 75, and 125 V; 9: 100 V). Data for 150 and 200 V not used in PCA. Values in the plot are fragmentor voltage (V).
ensure high repeatability and reproducibility. Figure 3a shows the score plot for the three crude oils (with ACN-AmAc as mobile phase) with 50, 75, 100, 125, 150, and 200 V. The first PC explains 67% of the variation, and the second PC explains another 11%. The score plot shows that the spectra for each of the three oils do not change (“move in score plot”) with fragmentor voltage in the range 50-125 V for all three reference oils. A fragmentor voltage of 200 V clearly changes the spectra for all three oils. Figure 3b shows the same data without 150 and 200 V. The first PC explains 74% of the variation, and the second PC explains another 13%. The results indicate that no fragmentation occurs below 150 V, and that no systematic trend occurs when fragmentor voltage is increased within the range 50-125 V. Consequently, the standard procedure with 100 V is well within the range that does not cause fragmentation. Repeatability (drift) was tested in an experiment where the three crude oils were injected several times. First, the two crude oils GFA and ASC were injected 10 times each. Then GLI was injected one hundred times, and finally GFA and ASC were injected another 10 times each, giving a total of 140 consecutive injections. The score plot obtained after PCA on the spectral data from this experiment are shown in Figure 4. For weighting and visibility purposes, only the first 10 and the last 10 of the 100 GLI injections are shown (the 80 injections between will only enlarge the cluster slightly). The first PC explains 78% of the variation, and the second PC explains another 13%. The score plot illustrates the excellent repeatability within a large analytical series. It is emphasized that most of the variation is
along the first PC, discriminating the oils, and only 13% along the second PC. Discussion The combination of ESI-MS and PCA has been shown to distinguish not only between different samples, but also between small variations due to changes in methodological parameters. PCA detects, with high sensitivity, minute changes in very complex spectra and differences that cannot be seen with the naked eye. This is extremely useful and implies in addition that the conditions may be optimized to serve different purposes. Furthermore, it also shows the need for standardized conditions. The present study has shown that it is possible to define standardized conditions that are satisfactory for controlled production of mass spectra. The issue of major importance in the present study was to establish controlled chemical conditions for mass spectrometric analysis that would produce mass spectrometric patterns that was optimal for fingerprinting and chemometrics. In this context, optimal means (1) sufficient complexity to create enough data (m/z values and abundance values) within each sample, (2) sufficient diversity that makes it possible to distinguish between similar samples, and (3) satisfactory repeatability for pattern recognition. Generally, the percentage of explained variance was very high for all data sets, demonstrating that most of the variation in the spectra is systematic and can be explained with a low number of principal components (PC).
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Figure 4. PCA score plot showing the repeatability results from GLI, GFA, and ASC crude oils analyzed with ACN-AmAc with 140 consecutive injections (O: first 10 injections; b: last 10 injections). Values in the plot refer to injection number.
The different mobile phases tested were chosen because they are “compatible” with modern LC-MS technology. They are based upon the solvents with most widespread use in reverse phase applications, namely, acetonitrile (ACN) and methanol (MeOH). In addition the volatile buffers ammonium acetate (AmAc, 50 mM) and formic acid (Fac, 25 mM) were chosen due to their volatility, as volatile buffers are mandatory in modern LC-MS interfacing systems. Initial tests showed that the pure solvents, ACN and MeOH, cannot be used as mobile phases alone without buffer, due to poor and inconsistent results (results not shown). This seems reasonable because the loss of pH control may indeed introduce a risk for lower repeatability and poorer precision. Our results show that ESI-MS analyses of crude oils give different spectra using different binary combinations of acetonitrile, methanol, ammonium acetate, and formic acid. However, the patterns are reproducible with the ability to distinguish between the three test oils GFA, ASC, and GLI. In fact, all of the four mobile-phase compositions tested can be used to distinguish between the three crude oils, as shown in Figure 2a. However, we found the combination of ACN and 50 mM AmAc (90:10) to perform better than the other mobile phases. This conclusion was based upon better precision and better discrimination (increased distance) between the crudes in the score plot. From the extensive work on high-resolution mass spectrometry,3,5 it is clear that ESI-MS is very suitable for the detection of N-containing heterocyclics, possibly also combined with S and O. From the high-resolution studies, it is also clear that crude oils and related products and fractions contain a vast number of groups of compounds with a complexity that has not been documented earlier. This fact is a perfect basis for producing “fine” fingerprints, but is also a challenge for repeatability and reproducibility as ionization of different groups of compounds may relay on extremely complex equilibriums for the compounds to be charged/ uncharged in the gas phase. It is therefore likely that the different spectral patterns obtained with different mobile phases reflect specific differences in ionization efficiency. This may be advantageous, and it may be used to discriminate between groups of heterocyclics. Therefore, it cannot be ruled out that one of the three other mobile phases, in certain situations, may perform better than the presently recommended ACN-AmAc (90:10). The purpose of the fragmentor experiment was to ensure no (or minimal) fragmentation. The fragmentor voltage is the
voltage difference between the end of the inlet capillary and the skimmer at the entry of the vacuum chamber of the mass analyzer. An increase in this voltage will propagate ions from the capillary inlet via the skimmer and in the direction of the quadrupole unit. The ions collide with gas molecules, and with increased kinetic energy (increased voltage difference), collisioninduced dissociation (CID) fragmentation starts to occur. Figure 3 shows that a fragmentation voltage of 100 V is suitable for the ESI-MS analysis. A too-low value will result in reduced sensitivity. Changes in spectra, indicating fragmentation, are only seen at 150 V and higher. Unfragmented patterns are desirable for at least two reasons: first, it is known that it can be difficult to reproduce fragmentation (i.e., constant ion ratios between parent ion and fragment ions) on LC-MS instruments, and second, mass spectrometric patterns of unfragmented molecules make pattern recognition easier as the molecular weight distribution is displayed directly (as M+1). The assumption that ESI-MS produces singly charged ions was evaluated initially with model compounds such as seven different carbazoles, three acridines, and one porphyrine which demonstrated that these compounds form singly charged M+1 adducts with all the four different mobile phases. Spectra with only one line in each are not shown; besides the findings are in accordance with previous studies.5,6,9 Excellent repeatability with multiple injections is shown clearly in Figure 4. Satisfactory repeatability is required for large analytical series. We are currently also evaluating reproducibility between series and between instruments of the same brand, using the three crude oils as reference samples in all other analytical series. The results from these long-term studies will be published later. If a sufficient reproducibility can be achieved between instruments, the idea of library-searchable plots can be realistic for recognition of complex products without reference material. Conclusions The present methodological study has shown that it is possible to define standardized conditions that are satisfactory for controlled production of mass spectrometric patterns of sufficient complexity and repeatability to distinguish between crude oils and related samples by multivariate pattern recognition techniques. The controlled parameters comprise solubilization, (9) Wu, Z.; Jernstro¨m, S.; Hughey, C. A.; Rodgers, R. P.; Marshall, A. G. Energy Fuels 2003, 17, 946-953.
270 Energy & Fuels, Vol. 20, No. 1, 2006 Table 1. Preferred Conditions for Chemical Fingerprinting with Positive ESI-MSa
a
parameters
recommended values
mobile phase mobile phase flow injection volume fragmentor voltage nebulizer pressure capillary voltage drying gas flow drying gas temperature scan range spectrum data storage
ACN and 50 mM AmAc (90:10) 0.15 mL/min 2 µL 100 V 25 psi 3000 V 8 L/min 350 °C m/z 65-1000 condensed
Some of the parameter values refer to Agilent equipment only.
introduction, gas-phase volatilization and ionization, and finally mass spectrometric analysis. The study also shows that the combination of ESI-MS and chemometrics (PCA) can be based
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on simple all-purpose single quadrupole LC-MS equipment with standard configuration without additional equipment. This type of standard LC-MS instrumentation now shows a rapidly increasing widespread use worldwide, and theoretically has a potential to be a new standard for complementary handling of highly complex hydrocarbon mixtures that contain analytes that are ionized positively by electrospray ionization. The preferred conditions for chemical fingerprinting with positive ESI-MS are summarized in Table 1 (some of the parameter values refer to Agilent equipment only). Acknowledgment. The authors are grateful to Hege Kummernes, Toril Berg, and Hans Konrad Johnsen, Statoil Research Centre, Trondheim, Norway, for valuable support. EF050213F
