Energy Fuels 2010, 24, 3661–3664 Published on Web 05/28/2010
: DOI:10.1021/ef100274c
Detection of 5 ppm Fatty Acid Methyl Ester (FAME) in Jet Fuel Using Electrospray Ionization Mass Spectrometry and Chemometrics Ingvar Eide,*,† Gunhild Neverdal,† and Frank Westad‡ †
Statoil Research Centre, N-7005 Trondheim, Norway, and ‡CAMO Software AS, N-0158 Oslo, Norway Received March 9, 2010. Revised Manuscript Received May 12, 2010
Positive electrospray ionization mass spectrometry (ESI-MS) and multivariate regression (chemometrics) have been used for the identification and quantification of fatty acid methyl ester (FAME) in jet fuel in concentrations from 3 to 35 ppm. The jet fuel samples were injected directly and undiluted into the ion source. Each analysis takes less than 1 min to perform. Calibration series with rapeseed methyl ester (RME) and soybean methyl ester (SME) alone or in combination were used to create regression models with excellent prediction properties. An independent test set with known amounts of RME and SME was made several weeks later, and the regression model was used to predict the concentration of RME and SME with a root-mean-square error of prediction (RMSEP) of 2.6 and 1.2 ppm, respectively.
Previous work on FAME covered the concentration range from 0.5 to 10% FAME in petrodiesel, corresponding to 5000 to 100 000 ppm. The aim of the present study was to use the method to quantify FAME in jet fuel at the 5 ppm level based on recognition of the FAME spectrum after direct injection of undiluted jet fuel.
Introduction Jet fuel is a highly specified transportation fuel, but the airline industry has long recognized that a small amount of cross-contamination with fatty acid methyl ester (FAME) is unavoidable in a shared fuel system. It is agreed that jet fuel must contain less than 5 ppm FAME. At high enough concentrations, FAME can impact the thermal stability of jet fuel, leading to coke deposits in the fuel system. FAME contamination can also impact the freezing point of jet fuel, resulting in fuel gelling. Such conditions can result in engine operability problems and possible engine flameout.1 It has been a challenge to identify and quantify trace amounts of FAME. A recently proposed standard method uses gas chromatography-mass spectrometry (GC-MS) with a long polar column to separate the polar FAME species relative to the nonpolar hydrocarbon matrix of the jet fuel.2 Simultaneous selected ion monitoring (SIM) and full-scan mode is recommended, and alternatively, the samples are run twice, once in SIM mode and once in full-scan mode. The retention time is approximately 40 min. In five recent papers, we have described a novel method for chemical fingerprinting based on electrospray ionization mass spectrometry (ESI-MS) with direct injection, efficient data processing, and multivariate data analysis (chemometrics).3-7 Each analysis takes less than 1 min to perform. The method has thus far been used on crude oils, polymeric surfactants, bio-oils from wood, in addition to petrodiesel and FAME.
Materials and Methods A calibration series was prepared using a jet fuel sample guaranteed free from FAME. Rapeseed methyl ester (RME) and soybean methyl ester (SME) were added to the jet fuel alone or combined in concentrations ranging from 0 to 35 ppm. The calibration set consisted of a total of 30 samples. An independent test set consisting of 13 new samples with RME and/or SME in jet fuel in concentrations ranging from 0 to 10 ppm was prepared a few weeks later. Meanwhile, service and repair had been carried out on the MS instrument. The jet fuel samples, with or without RME and/or SME, were injected directly and undiluted into the ion source. In addition, pure RME and SME samples were diluted in dichloromethane (DCM) in a concentration of 2 mg/mL because of the very high response of the FAME in the mass spectrometer. The samples were analyzed by positive ESI-MS on an Agilent 1100 series liquid chromatography/mass selective detector (LC/MSD) system (Agilent Technologies, Inc., Palo Alto, CA). The system consisted of a G1322A mobile-phase degassing unit, a G1311A quaternary pump with a gradient mixer, 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, without separation on a chromatographic column. Each sample was injected 5 times. The mobile phase consisted of acetonitrile and 50 mM ammonium acetate at a ratio of 90:10, and the mobile-phase flow was 0.2 mL/min. The fragmentor voltage was 100 V. The instrument was operated in full-scan mode in the m/z range from 65 to 1300, at a scan rate of approximately 1 scan/s. With direct injection, each analysis takes less than 1 min and gives only one peak in the chromatographic direction. One average spectrum was obtained from each analysis, calculated from approximately 10 individual spectra obtained at half peak height, after background subtraction. The process of background subtraction and preprocessing of spectra was performed by a postrun macro to ensure identical data collection between different
*To whom correspondence should be addressed. Telephone: þ4790997296. E-mail:
[email protected]. (1) Special Aviation Airworthiness Information Bulletin, SAIB Nol. NE-09-25, Jan 5, 2009. (2) IP PM-DY/09. Determination of fatty acid methyl esters (FAME) derived from biodiesel fuel, in aviation turbine fuel;GC-MS with selective ion monitoring/scan detection method. The Energy Institute, London, U.K. (3) Eide, I.; Zahlsen, K. Energy Fuels 2005, 19, 964–967. (4) Zahlsen, K.; Eide, I. Energy Fuels 2006, 20, 265–270. (5) Eide, I.; Zahlsen, K.; Kummernes, H.; Neverdal, G. Energy Fuels 2006, 20, 1161–1164. (6) Eide, I.; Zahlsen, K. Energy Fuels 2007, 21, 3702–3708. (7) Gellerstedt, G.; Li, J.; Eide, I.; Kleinert, M.; Barth, T. Energy Fuels 2008, 22, 4240–4244. r 2010 American Chemical Society
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Energy Fuels 2010, 24, 3661–3664
: DOI:10.1021/ef100274c
Eide et al.
heterocycles, amines, ethoxylates, glycerides, esters, phenols, etc.3,5-7,10 Nonpolar hydrocarbons, such as paraffins and aromatics, are largely not ionized by ESI. The spectra of RME and SME reflect the distribution of different FAMEs, and as demonstrated in a previous paper, the spectra from the two feedstocks can be distinguished from each other,6 although they are quite similar. The spectrum of jet fuel reflects polar compounds containing N, S, or O. The spectrum of undiluted jet fuel resembles the spectrum from a petrodiesel after dilution to 2 mg/mL, as shown in our previous paper; however, the content of polar compounds and, hence, the response in the mass spectrometric analysis are much lower in jet fuel. There is one distinct line per integer mass number in the spectra. Some characteristic ions seem to be common in all samples of biodiesel as described previously.6 Preprocessing. Several preprocessing methods were investigated, among them detrending, area normalization, SNV, and EMSC. EMSC allows us to include spectra of known constituents and subtract these spectral features from the spectra. Below, the detailed results from SNV are reported. Models with EMSC gave similar cross-validated error as SNV. However, because the EMSC is based on a set of calibration samples and is not an individual transform as SNV, EMSC may be more susceptible to unwanted variation in new samples. In this case, it resulted in a larger error for the independent test set because of a bias between the predicted and the reference. From Figure 1, it can be seen that the main m/z (hereafter named variables) regions for the pure jet fuel and RME and SME were non-overlapping. One consideration was if the regression models should include variables from the region of pure jet fuel or just the RME/SME range. Including some of the variables for the jet fuel could help in setting the baseline level and normalizing the data in the preprocessing step. On the other hand, different jet fuel batches can have rather different main peaks, which makes it difficult to find variables that are consistent over batches. Regression models with alternative sets of variables were computed, and the conclusion was that the jet fuel region did not improve RMSECV. After this initial analysis, 103 variables ranging from m/z 283 to 385 were selected for the subsequent models. Another aspect is normalization of the spectra as the total intensity in the spectra varies between samples. If this is performed on the full spectrum, it would be highly influenced by the jet fuel region, which can introduce unwanted effects in the normalized spectrum. Also, normalization works best when the spectral patterns are similar; i.e., the same variables are found in all samples, and any baseline effect will be detrimental to the normalized spectra. In the period of time between the measurements, it became evident by projecting the test samples onto the calibration samples that there had been a change in the instrument response after service and repair, including the fact that a vacuum pump change had been carried out. By grouping the spectra into calibration and test set (Figure 2), a change in both baseline and amplification (gain) was present. This unwanted effect of change in baseline and amplification was handled with preprocessing methods. The first step was to remove the baseline by detrending the spectra with a seconddegree polynomial for the range m/z 283-395 only because this was the range selected for building the calibration models. Because there still was a difference as a result of amplification, this had to be handled by a second preprocessing step. One method that is tailored for removing path-length phenomena in traditional absorbance spectroscopy is SNV, which
Figure 1. ESI-MS spectra of undiluted jet fuel and RME and SME diluted to 2 mg/mL in DCM. Intensity versus m/z.
injections. After each average spectrum was tabulated as mass and intensity, the m/z values were rounded off to integer mass numbers. The matrix construction was performed by a specially designed macro in Microsoft Access. Further details are described in previous papers.3,6 Multivariate data analysis (chemometrics) was performed with Unscrambler 9.8 (CAMO Software, Oslo, Norway). Prior to the multivariate data analysis, several preprocessing methods were investigated, such as detrending, area normalization, standard normal variate (SNV), and extended multiplicative signal correction (EMSC). Multivariate calibration (regression) was performed with partial least squares regression (PLSR)8 to correlate spectra (x matrix) to the concentration (in ppm) of FAME in jet fuel (y matrix). The PLSR models were validated with respect to explained calibration and validation variance, with the latter obtained after crossvalidation,9 performed with mean centering inside the validation segments. The PLS models were in addition evaluated with respect to root-mean-square error (RMSE), a measure in the same unit as the concentration (ppm). The terms RMSEC, RMSECV, and RMSEP were used to indicate whether the error is estimated from calibration, cross-validation, or prediction, respectively. Although cross-validation may give a reasonable estimate of future prediction error depending upon the cross-validation scheme, the final validation should always be performed with the prediction of an independent test set.
Results and Discussion Figure 1 shows the spectrum of the jet fuel injected undiluted and the spectra of RME and SME injected after dilution to 2 mg/mL in DCM. FAME gives a significantly higher response compared to jet fuel in the mass spectrometric analysis. Furthermore, jet fuel and FAME cover different ranges of mass numbers (m/z) with almost no overlap. These two features are obviously advantageous for the detection of trace amounts of FAME in jet fuel. ESI typically occurs by the addition or loss of a proton, but sometimes positive ionization occurs by adducts. Positive ESI-MS detects polar compounds, such as nitrogen-, oxygen-, or sulfur-containing (8) Martens, H.; Næs, T. Multivariate Calibration; John Wiley: New York, 1993. (9) Wold, S. Technometrics 1978, 20, 397–405. (10) Qian, K.; Edwards, K. E.; Diehl, J. H.; Green, L. A. Energy Fuels 2004, 18, 1784–1791.
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Figure 5. Cross-validated predicted versus measured plot for RME.
Figure 2. ESI-MS spectra for the range m/z 283-385 for calibration (blue) and test set (red).
Figure 6. Cross-validated predicted versus measured plot for SME.
is normalization of individual samples to have a variance of 1. The samples should also preferably have for this method the same distinct variables. Figure 3 shows the data after SNV, and the spectra are now of similar intensity. The change in baseline and amplification is not desired but may sometimes occur after extensive service, repair, source cleaning, etc. Therefore, it is important to know that such changes can be identified and handled by the chemometric software. The performed preprocessing steps for correcting the shift in baseline (polynomial detrending) and change in amplification will in general also handle possible future changes and contribute to a robust methodology. Regression Modeling. In latent variable regression methods, such as PLSR, the number of components should reflect the complexity of the system that is observed. In this case, because the RME/SME region was selected only and observing that the jet fuel signals could be ignored for this region, the complexity of the system is only two. Thus, the score plot should represent the concentrations of RME and SME in the standards if the preprocessing has removed baseline and intensity effects. Figure 4 shows the score plot from a model with RME as the response variable for the first two components, with the parts per million level for the standards depicted in the plot. Five variables were selected in the final model: m/z 310, 314,
Figure 3. Calibration and test data after detrending and SNV preprocessing.
Figure 4. Score plot of the model with detrending and SNV for RME.
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Energy Fuels 2010, 24, 3661–3664
: DOI:10.1021/ef100274c
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Figure 9. Prediction of test samples with SME.
Figure 7. Score plot of the model with detrending and SNV for SME.
Figures 8 and 9 show the predicted versus reference for the prediction of 13 test samples for RME and SME, respectively. The RMSEP is 2.6 with a bias of -0.82 for RME and 1.6 with a bias of 1.2 for the SME. Concluding Remarks The combined methodology of ESI-MS and chemometrics has proven useful for the quantification of trace amounts of FAME in jet fuel. Furthermore, undiluted jet fuel was injected directly, and each analysis takes less than 1 min to perform. This implies that there is a potential for online monitoring. The multivariate data analysis detects minute differences between complex spectra and differences that cannot be observed visually. Excellent repeatability and reproducibility were obtained. The regression model was confirmed by the prediction of an independent test set made and analyzed weeks later. Although an error of approximately 1.5-2.5 ppm may be considered as a high relative error in the low parts per million range, the use of ESI-MS for quantitative analysis of FAME in the parts per million range is a novelty. There is potential for improving the methodology, for example, by using outlier detection in terms of the residual distance to the model for new samples to flag that prediction results are uncertain and that a recalibration in terms of model updating is required. Although the PLS model was made with major lines in the FAME spectra to minimize the influence of other compounds, the regression model should be further verified and possibly be made even more robust by adding data from other jet fuels and FAME samples. On the other hand, the models use parts of the MS signal where jet fuels do not give significant intensity, implying that new fuels should not affect the prediction precision. It is also demonstrated that baseline and amplification effects can be identified and handled adequately.
Figure 8. Prediction of test samples with RME.
317, 319, and 331. The direction of increased concentration of RME is indicated. The second direction follows the increasing SME concentration. This model explained 94% of the variance in calibration and 91% for full cross-validation. Figure 5 shows the cross-validated predicted versus measured plot for this model, with the target line included. The RMSEC was 1.8 ppm, while the RMSECV was 2.2 ppm. The corresponding model for SME with all 103 variables had a RMSEC of 2.1 ppm and a RMSECV of 2.9 ppm. The difference between RMSEC and RMSEP was mainly due to the highest concentration of 35 ppm not being wellinterpolated when that sample was not in the internal cross-validation model, as shown in Figure 6. Figure 7 shows that the mixtures of RME and SME are reflected well in the two components found to be optimal. A model with only seven variables (m/z 310, 312, 314, 315, 317, 318, and 319) was also investigated, but for the RMSECV, it did not improve significantly.
Acknowledgment. We acknowledge the collaboration with Statoil’s Product Technology and Customer Service Centre and their supply of fuel samples.
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