3702
Energy & Fuels 2007, 21, 3702–3708
Chemical Fingerprinting of Biodiesel Using Electrospray Mass Spectrometry and Chemometrics: Characterization, Discrimination, Identification, and Quantification in Petrodiesel Ingvar Eide* and Kolbjørn Zahlsen Statoil Research Centre, N-7005 Trondheim, Norway ReceiVed June 19, 2007. ReVised Manuscript ReceiVed July 30, 2007
A novel method for “chemical fingerprinting”, originally developed for oil and petroleum products, has been applied to biodiesel. The method is based on electrospray mass spectrometry (ESI-MS), efficient data processing, and multivariate data analysis (chemometrics). The method was applied on biodiesel from rapeseed (three different), palm, soybean, used frying, recycled vegetable, and salmon oil (two different), sheep tallow, and animal fat. In addition, dilution series were prepared with rapeseed or salmon biodiesel in petrodiesel in concentrations ranging from 0.5% to 10%. It is demonstrated that ESI-MS and chemometrics can be used to discriminate between biodiesel from different feedstocks and manufacturers, to identify fatty acid methyl esters (FAME) and free fatty acids, and to identify and quantify blend composition. Positive and negative ionization have been used complementarily to specifically identify FAME or free fatty acids, respectively.
Introduction It has been known for more than 100 years that it is possible to run diesel engines on vegetable oils. For example, a small diesel engine was operated on peanut oil by the French Otto Co. at the Paris Exposition in 1900.1 However, biodiesel today refers primarily to fatty acid methyl esters (FAME) obtained after transesterification of triglycerides with methanol. The methyl esters can be produced by different techniques from many different triglyceride sources, of which rapeseed is the most important, followed by sunflower seed oil, soybean, and palm oil. Other sources are linseed oil, tallow, and used frying oil.1,2 Biodiesel has been used commercially in several countries for up to 20 years, either as pure FAME or blended with petrodiesel.3 The European and the American standards for biodiesel (EN 14214 and ASTM D 6751) specify allowable limits for a large number of physical and chemical parameters. Rapeseed methyl ester (RME) meets these quality specifications whereas FAME from other sources may have to be blended to meet the requirements.3 There are considerable analytical challenges associated with the control of product quality during and after production, and a variety of different methods have to be used. For some of these methods problems with interferences have been identified.2 Mass spectrometry has been used to characterize crude oil and to some extent fossil fuels4–7 and vegetable oils.8 In three * Corresponding author: phone +47 90997296; Fax +47 73967286; e-mail
[email protected]. (1) Knothe, G.; Krahl, J.; Van Gerpen, J., Eds. The Biodiesel Handbook; AOCS Press: Champaign, IL, 2005. (2) Mittelbach, M.; Remschmidt, C. Biodiesel—The ComprehensiVe Handbook; Karl-Franzens University: Graz, Austria, 2004. (3) Lebedevas, S.; Vaicekauskas, A.; Lebedeva, G.; Makareviciene, V.; Janulis, P.; Kazancev, K. Energy Fuels 2006, 20, 2274–2280. (4) Zahn, D.; Fenn, J. B. Int. J. Mass Spectrom. 2000, 194, 197–208. (5) Roussis, S. R.; Fedora, J. W. Rapid Commun. Mass Spectrom. 2002, 16, 1295–1303. (6) Porter, D. J.; Mayer, P. M.; Fingas, M. Energy Fuels 2004, 18, 987– 994.
recent papers we described a novel method for chemical fingerprinting of oil and petroleum products based on mass spectrometry, efficient data processing, and multivariate data analysis (chemometrics).9–11 The method has so far been used on crude oils and polymeric surfactants. The aim of the present study was to use the method to discriminate between biodiesel of different origin, to identify FAME and free fatty acids, and to identify and quantify the content of biodiesel in petrodiesel. Materials and Methods Eleven different samples of biodiesel of different origin were used in the present work: rapeseed oil (three manufacturers), palm oil, soybean oil, used frying oil, recycled vegetable oil, salmon oil (two manufacturers), sheep tallow, and animal fat (unspecified from slaughterhouse). In addition, dilution series were prepared with rapeseed or salmon oil methyl ester in regular petrodiesel (grade #2) in concentrations ranging from 0.5% to 10%. Also, a sample of the pure petrodiesel was prepared. The samples were dissolved in dichloromethane (DCM) in a concentration of 2 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 led into the mass spectrometer by a 70 cm PEEK tubing (i.d. 0.18 mm), without separation on a chromatographic column. Each sample was injected (analyzed) 10 times. The mobile phase consisted of acetonitrile and ammonium acetate (50 mM) at a ratio of 90:10, and the mobile phase flow was 0.2 mL/min. Fragmentor voltage was 100 V. Each sample was analyzed 5 or 10 times. (7) Marshall, A. G.; Rodgers, R. P. Acc. Chem. Res. 2004, 37, 53–59. (8) Wu, Z.; Rodgers, R. P.; Marshall, A. G. J. Agric. Food Chem. 2004, 52, 5322–5328. (9) Eide, I.; Zahlsen, K. Energy Fuels 2005, 19, 964–967. (10) Zahlsen, K.; Eide, I. Energy Fuels 2006, 20, 265–270. (11) Eide, I.; Zahlsen, K.; Kummernes, H.; Neverdal, G. Energy Fuels 2006, 20, 1161–1164.
10.1021/ef700342f CCC: $37.00 2007 American Chemical Society Published on Web 09/22/2007
Chemical Fingerprinting of Biodiesel
Energy & Fuels, Vol. 21, No. 6, 2007 3703
3704 Energy & Fuels, Vol. 21, No. 6, 2007
Eide and Zahlsen
Figure 1. Positive ESI-MS spectra of the 11 different samples of biodiesel. Intensity versus m/z.
Figure 3. Positive mass spectrum of the petrodiesel. Intensity versus m/z.
with Projections to Latent Structures (PLS)13 to correlate spectra (X matrix) to the percentages of biodiesel in petrodiesel (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). Further details are described in our previous works.9–11 Figure 2. 3D PCA score plot differentiating between the 11 samples of biodiesel including two batches of salmon and three batches of rapeseed methyl ester. Each sample was analyzed 10 times with positive ESI-MS.
Results and Discussion
Positive electrospray ionization was used to detect the fatty acid methyl esters. Negative electrospray ionization was used for the detection of free fatty acids. The instrument was operated in full scan mode in the m/z range from 65 to 1300, at scan rate of approximately 1 scan/s. With direct injection, each analysis takes 1 min and gives only one peak in the “chromatogram”. 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 reprocessing of spectra was performed by a postrun macro to ensure identical data collection between different injections. Each average spectrum was tabulated as mass and intensity, with rounding of the decimal mass to an integer. Matrix construction was performed by a specially designed macro in Microsoft Access. Multivariate data analysis (chemometrics) was performed with Simca P+ 11.5 (Umetrics, Umeå, Sweden). Prior to multivariate data analysis, the data were normalized (in Microsoft Excel) and mean centered. Principal component analysis (PCA)12 was used to evaluate similarities and differences between spectra (illustrated in score plots). Multivariate calibration (regression) was performed
Identification of FAME in Positive ESI-MS Spectra. Figure 1 shows the spectra of the 11 samples of biodiesel obtained using positive ESI-MS. The spectra reflect the distribution of different fatty acid methyl esters in each sample. There is one distinct ion per integer, dominated by fatty acid methyl esters. Some characteristic ions seem to be common in all samples of biodiesel used in this study, although their relative abundance may differ. Two such ions are m/z 314 and 312, which corresponds to the ammonium adducts [M + NH4]+ of the methyl esters of oleic (18:1) and linoleic acids (18:2), respectively. These two unsaturated fatty acids are the most abundant fatty acids in rapeseed, soybean, sunflower, and palm oil.2 In our test material, 314 and 312 were prominent in biodiesel from rapeseed, soybean, sheep tallow, and animals fat. (Pure standards of methyl esters of oleic and linoleic acids have been analyzed in separate experiments confirming ammonium adducts with ions at m/z 314 and 312, respectively. The ammonium adducts are the major adduct for the methyl esters of oleic and linoleic acids with less than 5% [M + H]+ and [M + Na]+ insignificant.) Two other ions that seem to appear in several biodiesels in this study are m/z 374 and 379. These ions may correspond to
(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. (14) Wold, S. Technometrics 1978, 20, 397–405.
Chemical Fingerprinting of Biodiesel
Energy & Fuels, Vol. 21, No. 6, 2007 3705
Figure 4. PCA score plot showing the two dilution series of 0.5–10% rapeseed or salmon methyl ester in petrodiesel. Each sample was analyzed 10 times with positive ESI-MS.
Figure 5. Observed versus predicted percentage of rapeseed methyl ester in petrodiesel. Each point represents the mean of 10 analyses of each sample with positive ESI-MS.
the ammonium and sodium adducts, respectively, of monoolein. Monoolein is the monoacylglycerol (glycerol esterified to one oleic acid molecule, in either the 1- or 2-position). Monoolein possess a molecular weight (M) of 356, which generates ions (m/z) of 374 and 379 with the ammonium (+18) and sodium (+23) adducts, respectively. Both ammonium and sodium adducts are frequently encountered with ESI-MS using ammonium buffers and often display a characteristic pattern with lines with a distance of 5 mass units in the mass spectra, which is easily recognized visually. (Pure standards of monoolein have not been available, and the formation of these adducts has not yet been verified in separate experiments.) The spectra of recycled vegetable and used frying oils show bimodal patterns with a second group of compounds with m/z values of 600–640. The patterns are definitely different from most of the other biodiesels where the majority of ions are distributed from about m/z 250 to about 400. The larger ions may be due to contamination from meat frying or to the heating itself. However, the origin of these oils is not specified. The palm oil spectrum has a third mode of ions at mass range m/z 900–1000. The tallow spectrum has a third mode of ions at mass range m/z 800–900, with a maximum at m/z 876.
Salmon oil has a characteristic pattern with a single ion of m/z 360 that dominates over the other ions. An m/z of 360 is consistent with an ammonium adduct of the methyl ester of DHA (docosahexaenoic acid) which has a molecular weight of 342. DHA is known as a major fatty acid in fish oil.2 Multivariate Data Analysis of Positive ESI-MS Data. Figure 2 shows a 3D score plot obtained by multivariate analysis (PCA) of the spectral data from the positive ESI-MS analysis of the 11 different samples of biodiesel, each sample analyzed 10 times. The score plot illustrates similarities and differences between the altogether 110 spectra. The score plot shows the excellent repeatability of the fingerprint analyses and illustrates the importance of method standardization.10 The score plot illustrates that the biodiesels are grouped according to their origin. The two different batches from salmon are located in one part of the score plot. The methyl esters of the vegetable oils (palm, soybean, and three batches of rapeseed) are located in another part of the plot. The two animal samples are located in one area, and the used frying oil and the recycled vegetable oil are located in a fourth part of the 3D score plot. In addition, the score plot demonstrates that samples of different suppliers and batches can be distinguished from each other. The first,
3706 Energy & Fuels, Vol. 21, No. 6, 2007
Eide and Zahlsen
Chemical Fingerprinting of Biodiesel
Energy & Fuels, Vol. 21, No. 6, 2007 3707
Figure 6. Negative ESI-MS spectra of the 11 different samples of biodiesel. Intensity versus m/z.
second, and third principal component (PC) explain 48, 18, and 11% of the variation in the data, respectively. Figure 3 shows the spectrum of the petrodiesel. This spectrum is visually different from the biodiesel spectra with a lower molecular weight, unimodal, highly alkylated distribution of ions superimposed on ions throughout the mass range. It is obviously beneficial for the identification and quantification of biodiesel in petrodiesel that the biodiesel and the petrodiesel have different but overlapping molecular weight distribution; however, it is not a prerequisite. Small differences between spectra can be detected using multivariate data analysis. Figure 4 shows the score plot obtained from the PCA of the spectral data from the analysis of the two dilution series with either rapeseed or salmon methyl ester in petrodiesel. Each sample was injected 10 times, and all 10 data points are displayed to show the repeatability (precision) between injections. The individual points from repeated analyses are apparently not clustered as tightly as in the score plot in Figure 2; however, this is primarily due to mutually more similar samples expanding the distance between points from repeated analyses in the score plot in Figure 4. However, the small changes in the content of biodiesel in petrodiesel can easily be detected, and the two “rays” show each dilution series, “intercepting” at the petrodiesel cluster. The first and the second principal component (PC) explain 68 and 29% of the variation in the data, respectively. Since there are similarities between the spectra from the two biodiesels, the two “rays” are not orthogonal. The PLS analysis of the fingerprint data of the binary mixtures of crude oil with rapeseed methyl ester versus the blend matrix resulted in very good PLS models. Figure 5 shows the observed versus predicted percentages of rapeseed methyl ester in petrodiesel and illustrates the very good calibration curve (goodness of fit R2 ) 0.98 and goodness of prediction Q2 ) 0.98). Identification of Free Fatty Acids in Negative ESI-MS Spectra. Figure 6 shows mass spectra from the 11 samples of biodiesel obtained after negative ionization. On the basis of the specificity and selectivity of negative ionization, the ions in the spectra likely represent free fatty acids. It is emphasized that free fatty acids are undesired in biodiesel. All sources display characteristic patterns either with characteristic ions or with different relative responses. However, some ions seem to be common for many feedstocks. It is also characteristic that double or triple lines appear that differ with 2 mass units. This is typical for all compounds that contain fatty acids with different degree of saturation and represents the presence of different double bond analogues of the fatty acids. Almost all biodiesel sources show spectra with m/z 281, which is the [M – H]- ion of oleic acid (18:1). Linoleic acid (18:2) can be seen at m/z 279. Both these unsaturated fatty acids are
Figure 7. 3D PCA score plot differentiating between the 11 samples of biodiesel including two batches of salmon and three batches of rapeseed methyl ester. Each sample was analyzed five times with negative ESI-MS.
abundant in most biodiesels. In biodiesel from palm oil, sheep tallow, and animal fat, the saturated palmitic acid (16:0) is abundant at m/z 255. This is consistent with the fact that these sources contain more saturated fatty acids. It can be seen from Figure 6 that the soybean sample contains very little free fatty acids with ions at m/z 255 and 281. The spectrum of biodiesels made from salmon are significantly more complex than the others. The reason for this is the presence of several polyunsaturated fatty acids as DHA (docosahexaenoic acid, 22:6) with m/z 327 and EPA (eicosapentaenoic acid, 20:5) with m/z 301. From a technical point of view, polyunsaturated fatty acids are unfavorable because of lower oxidation stability, a tendency to polymerize (especially fatty acids with more than four double bonds), and a potential to form deposits on injector nozzles in the engines during combustion. This problem can be solved by hydrogenation of the polyunsaturated fatty acids, and the (successful) result of the hydrogenation process would be easy to detect with the present method. Multivariate Data Analysis of Negative ESI-MS Data. Figure 7 shows a 3D score plot obtained by multivariate analysis (PCA) of the spectral data from the negative ESI-MS analysis of the 11 different samples of biodiesel, each sample analyzed five times. The score plot is analogous to the plot in Figure 2; however, the relative distributions of the different biodiesels are somewhat different. Especially the rapeseed and palm oils have very distinct areas of the plot. A major conclusion is that
3708 Energy & Fuels, Vol. 21, No. 6, 2007
negative ESI-MS can be used to specifically detect free fatty acids and that spectra from the different biodiesels can be distinguished from each other. Furthermore, the repeatability is excellent. The first, second, and third principal component (PC) explains 51, 24, and 16% of the variation in the data, respectively. Concluding Remarks This is the first time the combined methodology of electrospray mass spectrometry and chemometrics has been used on biodiesel. The methodology has proved useful to discriminate between biodiesels from different feedstocks and manufacturers, to identify fatty acid methyl esters (FAME) and free fatty acids, and to identify and quantify the content of biodiesel in petrodiesel. Positive and negative ionization have been used complementarily to specifically identify FAME or free fatty acids, respectively. The multivariate data analysis 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 injec-
Eide and Zahlsen
tions was obtained. The positive and negative spectra provide two diverse types of distinction for biodiesel. Other possible applications are quality control, tracking changes due to aging and refining, and predicting blend composition of mixtures of FAME of different origin. The latter is particularly interesting since blending may be required to meet specifications. The method has also a potential to identify other lipids like triglycerides and phospholipids. Furthermore, we believe it is possible to correlate spectral data with chemical and physical properties by multivariate calibration, thus obtaining a large number of parameters from one ESI-MS spectrum. In addition, spectra that describe “ideal” blends may be identified and used as “targets” in blend optimization and refining. Acknowledgment. The authors are grateful to Gunhild Neverdal, Hege Kummernes, Toril Berg, and Hans Konrad Johnsen, Statoil Research Centre, Trondheim, Norway, for valuable support. We acknowledge the collaboration with Statoil’s Product Technology and Customer Service Centre, Mongstad, Norway, and their supply of biodiesel. EF700342F
