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Identification and Quantification of Surfactants in Oil Using the Novel Method for Chemical Fingerprinting Based on Electrospray Mass Spectrometry and Chemometrics Ingvar Eide,* Kolbjørn Zahlsen, Hege Kummernes, and Gunhild Neverdal Statoil Research Centre, N-7005 Trondheim, Norway ReceiVed December 19, 2005. ReVised Manuscript ReceiVed March 15, 2006
A novel method for chemical fingerprinting of oil and petroleum products based on electrospray mass spectrometry (ESI-MS) and chemometrics has been used to identify and quantify surfactants in crude oil. The method is based on full-scan positive ESI-MS using a single quadrupole LC-MS instrument, however, with direct injection (no chromatographic separation) and without fragmentation of the molecules. The method was evaluated using two complex surfactant mixtures, Tween 80 and 85, which were added to a crude oil. Two dilution series were prepared with either Tween 80 or Tween 85 in the oil in concentrations ranging from 0.01 to 5.0%. In addition, samples of pure crude oil and pure surfactants were prepared. The samples were analyzed by positive ESI-MS. Similarities and differences between spectra were evaluated by means of principal component analysis (PCA). Projections to latent structures (PLS) was used for the multivariate calibration. With this method it was possible to identify and differentiate between the two surfactants at concentrations as low as 0.01%, although the method was optimized for a concentration range relevant for oil dispersants, which may be 100 times higher.
Introduction Surfactants, surface-active compounds, are used as emulsifiers, dispersants, and detergents. The applications are many: oil spill combat, oil well treatment, lubricants in gasoline, emulsifiers in food and pharmaceuticals, biotechnology, biochemical research, etc. Surfactants are usually complex mixtures of polymers and are often contained in other complex mixtures. The identification and quantification of one complex mixture contained in another is a real challenge. Furthermore, most surfactants are difficult to analyze by GC-MS due to their low volatility. In general, surfactants may give rise to considerable analytical challenges due to their amphipatic nature, especially in chromatographic systems. Strong bonding of analytes to the stationary phase may cause a risk of selective discrimination and loss of specific analytes, with analytical errors and artifacts as a result. In addition, they may adhere to the surfaces of bottles, tubings, needles, and analytical equipment. Direct infusion and positive electrospray ionization has successfully been used to obtain spectra of detergents in gasoline.1 Similarly, MALDI-TOF MS has been used to obtain spectra of polysorbate emulsifiers.2 Electrospray ionization has also been used to obtain spectra of crude oils and petroleum products.3-11 In two recent papers we described a novel method for chemical fingerprinting of oil and petroleum products.10,11 * Corresponding author. Phone: +47 90997296. Fax: +47 73967286. E-mail:
[email protected]. (1) Carraze, B.; Delafoy, J.; Bertin, J.; Beziau, J.-F.; Lange, C. M. Rapid Commun. Mass. Spectrom. 2004, 18, 451-457. (2) Frison-Norrie, S.; Sporns, P. J. Agric. Food Chem. 2001, 49, 33353340. (3) Zahn, D.; Fenn, J. B. Int. J. Mass. Spectrom. 2000, 194, 197-208. (4) Qian, K.; Rodgers, R. P.; Hendrickson, C. L.; Emmett, M. R.; Marshall, A. G. Energy Fuels 2001, 15, 492-498. (5) Hughey, C. A.; Rodgers, R. P.; Marshall, A. G. Anal. Chem. 2002, 74, 4145-4149. (6) Wu, Z.; Jernstro¨m, S.; Hughey, C. A.; Rodgers, R. P.; Marshall, A. G. Energy Fuels 2003, 17, 946-953. (7) Marshall, A. G.; Rodgers, R. P. Acc. Chem. Res. 2004, 37, 53-59.
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 other ionization techniques (we have used negative ionization for organic acids and photoionization for less polar compounds; these results will be published later). Pattern recognition of the spectra is performed using multivariate data analysis (chemometrics). The previous papers demonstrated that 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 makes it possible to classify and discriminate the highly complex spectral data. The spectra were also used for calibration purposes in order to correlate chemical fingerprints (X matrix) to the percentage of one crude oil blended with another in binary mixtures (Y matrix).10 The aim of the present study was to use the novel “mass spectrometric-chemometric” method for chemical fingerprinting of petroleum10,11 to identify and quantify surfactants in crude oil. Two complex surfactant mixtures, Tween 80 and Tween 85, were added to one crude oil, one surfactant at a time, in concentrations up to 5%, a concentration range relevant for oil dispersants. The samples were analyzed by positive ESI-MS. Similarities and differences between spectra were evaluated by means of principal component analysis (PCA).12 Projections to latent structures (PLS)13 was used for the multivariate calibration. (8) Qian, K.; Edwards, K. E.; Diehl, J. H.; Green, L. A. Energy Fuels 2004, 18, 1784-1789. (9) Rostad, C. E. Energy Fuels 2005, 19, 992-997. (10) Eide, I.; Zahlsen, K. Energy Fuels 2005, 19, 964-967. (11) Zahlsen, K.; Eide, I. Energy Fuels 2006, 20, 265-270.
10.1021/ef050423s CCC: $33.50 © 2006 American Chemical Society Published on Web 04/08/2006
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Figure 2. Positive ESI-MS spectra of crude oil with (a) 0.01% Tween 85 and (b) 0.05% Tween 85. Abundance versus m/z.
Figure 1. Positive ESI-MS spectra of (a) the crude oil, (b) Tween 85, and (c) Tween 80. Abundance versus m/z.
Materials and Methods The following chemicals were used: acetonitrile (HPLC grade) and dichloromethane (HPLC grade) from LabScan, Dublin, Ireland, and ammonium acetate (p.a.) from Acros Organics, Geel, Belgium. Water (>18 MΩ) was produced on a MilliQ Gradient system from Millipore, Billerica, MA. Tween 80 and Tween 85 were from Sigma-Aldrich (P1754 and P4634, respectively), St. Louis, MO. The oil was a light, low-sulfur crude oil with API ) 36.8° from the Norwegian sector in the North Sea. Two dilution series were prepared with 0.01, 0.05, 0.1, 0.25, 0.5, 1.0, and 5.0% of either Tween 80 or Tween 85 in the crude oil. In addition, the dilution series with Tween 85 was expanded with 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, and 4.5% Tween 85 in crude oil in order to perform multivariate calibration. Also, samples of pure crude oil and pure surfactants were prepared. The samples were dissolved in dichloromethane, the oil samples (with or without surfactants) in a concentration of 2 mg/mL and the pure surfactants in a concentration of 0.04 mg/mL. The samples were analyzed by full-scan mass spectrometry on an Agilent 1100 Series LC/MSD system (Agilent Technologies Inc., Palo Alto, CA). The system consisted of a G1322A mobile phase degassing unit, a G1311A quaternary pump with gradient mixer for up to four mobile phase constituents, a G1367A autosampler, and a G1946D single quadrupole mass spectrometer. Samples of 2 µL were injected by the autosampler and fed into the mass spectrometer by 70-cm PEEK tubing (i.d. 0.18 mm) without separation on a chromatographic column. Each sample was analyzed five times. The mass spectrometer was operated in the scan mode using atmospheric pressure (12) Jackson, J. E. A User’s Guide to Principal Components; John Wiley: New York, 1991. (13) Wold, S.; Ruhe, A.; Wold, H.; Dunn, W. J., III. SIAM J. Sci. Stat. Comput. 1984, 5, 735-743.
Figure 3. Positive ESI-MS spectra of crude oil with (a) 0.05% Tween 80 and (b) 0.25% Tween 80. Abundance versus m/z.
electrospray positive ionization (AP-ESI). Introductory, the crude oil and the surfactants were analyzed in the m/z range 65-3000; however, there were very few and only small spectral lines above 1400. As a consequence, the instrument was operated in the m/z range from 65 to 1400 for the present work. The mobile phase consisted of acetonitrile and ammonium acetate (50 mM) at a ratio of 90:10, and the mobile phase flow was 0.15 mL/min. Fragmentor voltage was 100 V. Further details are described in previous work.11 With direct injection, each analysis takes 1 min and gives only one peak in the chromatographic direction. One average spectrum was obtained from each individual analysis, calculated from approximately 10 individual spectra obtained at half peak height, after background subtraction. The process of background subtraction and acquisition of spectra was performed by a postrun macro to ensure identical data collection between different injections. Each average spectrum was tabulated to a row with numbers where each
Fingerprinting Surfactants in Oil
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Figure 4. (a) PCA score plot showing the two dilution series of Tween 85 and Tween 80 in crude oil (each sample was analyzed five times). (b) Expanded view of PCA score plot showing the lower concentration range of the two dilution series of Tween 85 and Tween 80 in crude oil. (+, crude oil; ∆, 0.01% Tween 85; O, 0.01% Tween 80; *, 0.05% Tween 80; b, all other samples).
number represents the height of each spectral line. The unit resolution ESI-MS gives m/z values with one decimal. To construct a compressed matrix from all the individual analysis, the mass numbers were 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 (performed in Microsoft Excel). The matrix was exported to SimcaP+ 10.5 (Umetrics, Umeå, Sweden), and the normalized data were mean centered. PCA12 was used to evaluate similarities and differences between spectra.. Multivariate calibration (regression) was performed with PLS13 to correlate chemical fingerprints (X matrix) to the percentages of surfactant in crude oil (Y matrix). The PCA and PLS models were validated with respect to explained variance and goodness of prediction (shown as Q2), the latter obtained after cross validation.14 The PLS model was in addition evaluated with respect to goodness of fit (R2). A final validation was performed by distinguishing between a training set and a test set. Each sample was analyzed five times, and four of the five analyses were used for the training set and the fifth for the test set. Further details are described in our previous works.10,11
Results and Discussion Figure 1, parts a-c, show full-scan spectra of the crude oil and the two surfactants. The crude oil spectrum has the characteristic overall shape shown in our previous papers.10,11 (14) Wold, S. Technometrics 1978, 20, 397-405.
The spectra of the two surfactants are visually different from the oil and from each other. The surfactants have higher molecular weight distribution, compared to the crude oil. It is obviously beneficial for the identification and quantification of the surfactants in crude oil that the oil and the surfactants have different but overlapping molecular weight distribution; however, it is not a prerequisite. More important is the high response of the surfactants, especially Tween 85, implying high sensitivity in the positive ESI-MS analyses with the chosen conditions. Figures 2 and 3 show the spectra of crude oil with 0.01 and 0.05% Tween 85 and 0.05 and 0.25% Tween 80. The lowest concentrations of surfactants in crude oil that can be observed visually are 0.05% Tween 85 and 0.25% Tween 80. However, lower concentrations and smaller differences between spectra are detected using multivariate data analysis. Although the spectra may look unresolved, there is one distinct line per integer mass number. Repetitive spacings of 14 Da (CH2) and 2 Da (saturated versus double-bond analogues) are typical for the crude oil (confer expanded spectrum in previous paper10). The surfactant spectra show repetitive spacings of 44 Da, which corresponds to the oxaethylene (ethoxylate) units (-OCH2CH2-). Polysorbate surfactants share the common characteristics that they contain a polar sorbitol derivative core (sorbitan and/or isosorbide), chains of polymerized oxaethylene (POE) units, and
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alkyl units of unpolar fatty acids esterified to one or more of the oxaethylene chains. The chemistry of the complex polymeric surfactants with repetitive units is described in detail elsewhere.2 The functions of the polysorbates as surfactants is dependent on the structure of the polar sorbitol derivative core, the length and number of the unpolar alkyl chains, and the number of polymerized oxaethylene units.2 According to the product information,15 Tween 80 (sorbitan monooleate POE 20) is a complex mixture of molecules “ideally” built from one sorbitan unit, four polyoxaethylene chains with an average total number of 20 oxaethylene units, and one fatty acid (predominantly oleic acid). Tween 85 (sorbitan trioleate POE 20) corresponds to Tween 80, but with the difference that the oxaethylene chains are esterified to three fatty acids (predominantly oleic acid). Furthermore, one ideal molecule of sorbitan monooleate POE 20 would have a theoretical molecular weight of 1308.15 In a study of Tween 80K by MALDI-TOF-MS,2 spectral lines were seen up to m/z 2500. In our study the majority of spectral lines were found below m/z 1200, most likely due to the existence of double charges. This theory is strongly supported by the observation of repetitive spacings of 22 Da in our ESI-MS spectra. Nevertheless, as long as the spectra are unique and repeatable they will serve the purpose of classification and multivariate calibration. Excellent repeatability is demonstrated in Figure 4a and illustrates the importance of method standardization.11 Satisfactory reproducibility was evaluated using reference samples at the beginning and at the end of each analytical series (not shown). Figure 4a shows the score plot obtained from the PCA of the spectral data from the analysis of the two dilution series. Each sample was injected five times, and all five data points are displayed to show the repeatability (precision) between injections. The small changes in the content of surfactant in crude oil can easily be detected, and the two “rays” shows each dilution series, “intercepting” in the crude oil cluster. The first and the second principal components (PCs) explain 87 and 10% of the variation in the data, respectively. Figure 4b is an expanded view of the score plot. It demonstrates that PCA discriminates crude oil with 0.01% Tween 85 and 0.05% Tween 80 from each other and from the crude oil. The ESI-MS methodology itself, the data preprocessing, and the multivariate data analysis may be further optimized to improve sensitivity in a lower concentration range, for example by using multivariate classification to establish class models. The concentration range used in the present work is relevant for oil dispersants. The PLS analysis of the fingerprint data of the binary mixtures of crude oil with Tween 85 versus the blend matrix resulted in very good PLS models with both goodness of fit (R2) and goodness of prediction (Q2) of 0.97. Figure 5 shows the observed versus predicted percentages of Tween 85 in crude oil and illustrates the very good calibration curve and the predictions of the test set. (15) www.Sigma-Aldrich.com.
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Figure 5. Observed versus predicted percentage of Tween 85 in crude oil (each sample was analyzed five times). (+, crude oil; 4, 0.01% Tween 85; b, all other samples training set; and O, test set).
Conclusion Fingerprinting using the combined “mass spectrometricchemometric” methodology has proved useful for the identification and quantification of surfactants in crude oil. The methodology has resolved the challenge of identifying and quantifying one complex mixture contained in another and may be useful for many applications.10 Furthermore, direct injection and positive ESI-MS have been demonstrated to overcome the difficulties in analyzing complex surfactant mixtures per se. In fact, the response of the surfactants is very high when analyzed with positive ESI-MS with the instrumental conditions used in the present work. PCA detects, with high sensitivity, minute differences between complex spectra with more than 1000 lines and differences that cannot be observed visually. Excellent repeatability with multiple injections was obtained. 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). This makes it possible to detect, identify, and differentiate between small amounts of Tween 80 and Tween 85 in oil. This is demonstrated with Tween 80 and Tween 85 down to a concentration of 0.05 and 0.01%, respectively, although the method was optimized for a concentration range relevant for oil dispersants, which may be 100 times higher. Multivariate calibration was used to correlate spectra to the amount of surfactant added to the oil. The PLS model was validated both by cross validation and by predicting the amount of surfactant in a test set. Acknowledgment. The authors are grateful to Toril Berg and Hans Konrad Johnsen, Statoil Research Centre, Trondheim, Norway, and Jorun Hokstad and Janne Resby, SINTEF, Trondheim, Norway, for valuable support. EF050423S
