Resolution and Quantification of Complex Mixtures of Polycyclic

ARTICLE pubs.acs.org/ac

Resolution and Quantification of Complex Mixtures of Polycyclic Aromatic Hydrocarbons in Heavy Fuel Oil Sample by Means of GC GC-TOFMS Combined to Multivariate Curve Resolution Hadi Parastar,†,‡ Jagos R. Radovic,‡ Mehdi Jalali-Heravi,† Sergi Diez,‡ Josep Maria Bayona,‡ and Roma Tauler*,‡ † ‡

Department of Chemistry, Sharif University of Technology, Tehran, Iran Department of Environmental Chemistry, IDAEA-CSIC, Jordi Girona, 18, Barcelona 08034, Spain ABSTRACT: Comprehensive two-dimensional gas chromatography timeof-flight mass spectrometry (GC GC-TOFMS) combined to multivariate curve resolution-alternating least-squares (MCR-ALS) is proposed for the resolution and quantification of very complex mixtures of compounds such as polycyclic aromatic hydrocarbons (PAHs) in heavy fuel oil (HFO). Different GC GC-TOFMS data slices acquired during the analysis of HFO samples and PAH standards were simultaneously analyzed using the MCR-ALS method to resolve the pure component elution profiles in the two chromatographic dimensions as well as their pure mass spectra. Outstandingly, retention time shifts within and between GC GC runs were not affecting the results obtained using the proposed strategy and proper resolution of strongly coeluted compounds, baseline and background contributions was achieved. Calibration curves built up with standard samples of PAHs allowed the quantification of ten of them in HFO aromatic fractions. Relative errors in their estimated concentrations were in all cases below 6%. The obtained results were compared to those obtained by commercial software provided with GC GC-TOFMS instruments and to Parallel Factor Analysis (PARAFAC). Inspection of these results showed improvement in terms of data fitting, elution process description, concentration relative errors and relative standard deviations.

D

epleting resources and increasing demand motivate the petroleum industry to consider more unconventional sources, such as heavier oils and shales, and to intensify offshore deeper drilling activities.1 In addition to occasional large, high profile incidents, operational oil spills are occurring on daily basis, especially along coasts in areas of intense offshore oil production and maritime transportation routes.2 Large-scale oil spills (more than 10.000 ton) in the marine environment do not occur very often, but incidents with below 1000 tons of spilled oil are rather common. Currently, available remote sensing techniques allow for their detection. In this context, continuous development and improvement of strategies for characterization of oils and spills is essential. Compositional elucidation is particularly cumbersome in the case of heavy fuel oils (HFOs), also called residual oils, which are produced by blending residues from different refinery distillation cuts or cracking processes to achieve a target viscosity. Diverse nature of refinery components used in their generation creates complex and variable mixtures of relatively high molecular weight compounds difficult to separate and characterize. Moreover, in the case of HFO spills, compositional information is crucial to assess their fate and effects in the marine environment.3 In the past decade, comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometer (GC GC-TOFMS) emerged as a powerful technique suitable for the separation of complex mixtures because of its high resolution and r 2011 American Chemical Society

high peak capacity.48 However, complete separation of all detectable components cannot be achieved because of the extremely high complexity of many real samples, and because of limitations in experimental and instrumental conditions.4,5,9 Chemometric resolution techniques can be used to address incomplete separation issues during GC GC-TOFMS analysis.1012 However, the performance of most of these techniques depends on the reproducibility of retention times in both dimensions, which cannot be guaranteed during GC GC-TOFMS analysis. Uncontrollable fluctuations in instrumental parameters such as temperature and pressure, as well as matrix effects and stationary phase degradation, can cause shifts in retention times especially in the second dimension between chromatographic runs (between run shifts).13 Several chemometric methods have been proposed to correct retention time shifts between GC GC runs, such as rank alignment,14 correlation optimized shifting,15 piecewise alignment,13 two-dimensional correlation optimized warping (2D-COW),16 and dynamic time warping (DTW).17 In addition, shifts in retention time can occur during a single GC GC-TOFMS analysis (within run shifts).18 Very often, to have a faster chromatographic run and a better chromatographic Received: July 13, 2011 Accepted: November 10, 2011 Published: November 11, 2011 9289

dx.doi.org/10.1021/ac201799r | Anal. Chem. 2011, 83, 9289–9297

Analytical Chemistry resolution, the temperature of the second column is increased throughout a run along with the temperature of the first column, which causes analyte retention times on the second column decrease from “slice-to-slice” taken from the first column. Unfortunately, both types of retention time shifts can affect significantly the chemometric results, because they can cause deviation from trilinearity model assumptions. Parallel Factor Analysis (PARAFAC)19 has been proposed for multivariate resolution of GC GC-TOFMS data.10,20,21 However, this method is based on the fulfillment of the trilinear model. Although GC GC-TOFMS data can be considered as being three-way (data can be organized in a data cube according to its three modes, the two chromatographic ones and the spectral one), they do not strictly follow the trilinear model due to the retention time shifts observed especially in second dimension, as previously mentioned. In practice, PARAFAC can still yield reasonable and useful results even when some deviation from strict trilinearity is present. However, large retention time shifts must be corrected before PARAFAC. PARAFAC222 is another version of PARAFAC analysis that has been applied to GC GCTOFMS data.10,18 In this case, strict trilinearity fulfillment needed in PARAFAC is not required, and data analysis is possible when retention time shifts are in one of the two separation dimensions.18 However, PARAFAC2 is computationally more complex and expensive, and it does not allow for the application of constraints like non-negativity or unimodality and therefore unreasonable negative values and multimodal peaks can appear in the results. Also, selecting the application of constraints only to some selected components is not possible. Thus, there is still the need for the development of simpler and more reliable methods of multivariate resolution of coeluted peaks in GC GCTOFMS analysis which should consider their specific chromatographic nature. Multivariate Curve Resolution Alternating Least Squares (MCR-ALS)2325 and Tucker3ALS methods26 can be used for this purpose. However, Tucker3 is a complex model which is usually difficult to interpret in chemical terms, because of the presence and interpretation of the interaction components and also because its flexibility implies nonunique solutions and the presence of rotation ambiguities.27 The potential of the MCR-ALS method has been already shown12,2833 for analysis of different types of chromatographic data. Since measured mass spectra do have the same mass-tocharge (m/z) ratios in all second column slices during the different GC GCTOFMS runs, column-wise data augmentation keeping in common these m/z values (in the columns of augmented data matrix) is an optimal strategy for MCR bilinear modeling of GC GCTOFMS data. In this strategy (see below), both retention time shifts, within and between runs (in the rows of the augmented data matrix), can be properly handled during the resolution process without any need to correct them before chemometric analysis. Baseline in the three data modes can also be modeled using this method. In this work, the strategy based on column-wise superaugmentation of different slices taken from the first column in different chromatographic runs and then applying MCR-ALS is presented to resolve coeluted peaks in GC GCTOFMS data. The potential of the proposed method is shown for qualitative and quantitative analysis of ten polycyclic aromatic hydrocarbons (PAHs) in aromatic fraction of HFO sample in the presence of interferences using standard mixtures of PAHs.

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’ EXPERIMENTAL SECTION Chemicals and Reagents. All analytical-grade chemicals (purity >95%) included in this study were purchased from Dr Ehrenstorfer (Augsburg, Germany). All the solvents used were SupraSolv-grade from Merck (Darmstadt, Germany). Silica gel and aluminum oxide for column chromatography was also obtained from Merck. Sample and Sample Preparation. Properties of HFO vary widely, usually falling into the ranges that are reported in literature.34 HFO sample used in this study is characterized by increased content of aromatic compounds (55% from total by TLC-FID, and sulfur (2.6 wt %)). HFO sample was first fractionated into aliphatic, aromatic and polar fractions by open column liquid solid chromatography according with previously described procedures.35 Briefly, a column was packed with slurry of silica gel (60100 mesh, 5% H2O-SiO2; 40 g) under alumina (grade 1-neutral; 1.5% H2OAl2O3; 20 g). The oil sample was applied in hexane (2 mL) to the top of the column and the column eluted with hexane (150 mL), dichloromethane (200 mL) and methanol (200 mL) to provide aliphatic, aromatic and polar fractions, respectively. Solvent was then removed by rotary evaporation and the fractions transferred to a vial, evaporated under the stream of N2 and weighed. The mass of the aromatic (DCM) fraction was 239.1 mg. This aromatic fraction contained a wide range of compounds from naphthalene (MW = 128 g mol1) to dibenzoanthracene (MW = 278 g mol1). GC GC-TOFMS. GC GC-TOFMS system was a Pegasus 4D (LECO, St. Joseph, MI), which is an Agilent Technologies 6890 GC (Palo Alto, CA) equipped with a split/splitless injector, a secondary oven to fit the secondary column, and a ZX1 (Zoex, Houston, TX) two stage thermal modulator. Liquid nitrogen was used to cool down the nitrogen gas for cold pulses. The GC column set consisted of a TRB-5MS coated with 5% diphenyl, 95% dimethylpolysiloxane from Teknokroma (Sant Cugat del Valles, Spain) (20 m 0.18 mm i.d. 0.18 μm film thickness) as the first dimension and a TRB50-HT coated with 50% diphenyl 50% dimethylpolysiloxane from Teknokroma (2.0 m 0.10 mm i.d. 0.10 μm film thickness) as the second dimension. The primary oven temperature was 65 °C for 1 min, ramped at 10 °C min1 to 315 °C, and then held for 3 min. The secondary oven temperature program had a temperature 10 °C higher than that of the primary one and with similar ramp. Carrier gas flow rate was 0.60 mL min1 using helium. The modulation period (PM) was 6 s with 0.5 s hot pulse duration and a 15 °C modulator temperature offset versus the primary oven temperature. The MS transfer line was held at 250 °C, and the TOFMS was operated in the electron ionization (EI) mode with a scan mass range of 50300 m/z. The ion source temperature was 250 °C, the detector was operated at 1650 V, the applied electron energy was 70 eV and the acquisition rate was 80 spectra s1. In addition, 1.00 μL of each sample was injected via autosampler (Agilent 7890) in split-less injection mode. GC GC-TOFMS Experimental Data Arrangement. Figure 1 shows the general procedure for the global strategy proposed in this work for GC GC-TOFMS data arrangement and their MCR-ALS analysis. In Figure 1a, the GC GC-TOFMS instrumentation design and signal acquisition is described where the sample is injected to the first column, then the eluted compounds are preconcentrated in the modulator and they are reinjected into the second column after a modulation period, PM.36 In this way, the entire first column chromatogram is 9290

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Figure 1. (a) GC GC-TOFMS instrumentation design, (b) signal acquisition of two overlapped components X and Y eluting from the first column and injected to the second column and corresponding 2D contour plot of the second column collected chromatograms, (c) three-way data arrangement of the whole GC GC-TOFMS signal acquired during the analysis of a single sample, (d) Xaug, is the column-wise superaugmented matrix built using all augmented data matrices corresponds to different samples. Xstd are the individual augmented data matrices corresponding to the GC GC-TOFMS analysis of every standard mixture samples and Xsample is the individual augmented data matrix corresponding to the unknown HFO sample. In all cases, data matrices were arranged with their mass spectral mode in common (for more details see explanation in the text and eq 2). Caug is the matrix of elution profiles of every component in the standard mixture samples, Cstd, and in the unknown sample, Csample, and ST, is the pure mass spectra of every component resolved by MCR-ALS. Eaug is the residual matrix, and (e) external calibration strategy to obtain quantitative information from the peak areas of the MCR-ALS resolved elution profiles.

sliced into a series of high-speed short secondary chromatograms of a length equal to PM, which are continuously recorded by TOFMS detector (Figure 1b). Every slice produces a data matrix, X(I,J) where I is the number of collected data points (retention times) in second column and J is the number of m/z. The slices obtained at the different retention times in the first dimension can be then combined for every m/z to describe the elution pattern by means of 2D-contour plots (I,K) as shown in Figure 1b, in which K is the number of slices taken from the first column of the total run. This contour plot will be obtained for every m/z in the GC GC-TOFMS analysis. When all the m/z values (J) are simultaneously considered, the whole set of acquired data in one GC GC run (in the analysis of a single sample) gives a threeway data cube with dimensions equal to (I,J,K) where I, J, and K are like before (Figure 1c). When several standard and unknown samples are simultaneously analyzed, a four-way hypercube data set will be obtained with dimensions equal to (I,J,K,L), where I, J, and K are the same as before, and L is the number of analyzed samples. This complex data arrangement can be simplified by joining the augmented data matrices obtained in the analysis of every individual sample with dimensions (I K, J) to give the column-wise superaugmented data matrix with dimensions (I K L, J) for the simultaneous analysis of the different L samples (Figure 1d). Since the number of m/z is equal for all slices and for all chromatographic runs, the column-wise data arrangement shown in the Figure 1d results to be very flexible and adequate to bilinear modeling requirements. For instance, the number of chromatographic ranges analyzed can be different in each slice and the presence of time shifts among different slices and among different samples do not destroy the bilinear model assumption associated to the column-wise data augmentation strategy. It is important to note that each slice in the superaugmented data

matrix can have different number of rows (retention times in the first and second dimensions). This is a very flexible property of matrix augmentation, which adapts very well to GC GCTOFMS data because of ubiquitous retention time peak shifts of the eluted components from slice-to-slice (in second column) and from sample-to-sample (in first and second columns). Furthermore, with this superaugmented data arrangement, there is no limitation in the number of included submatrices (number of slices and samples). For quantitative analysis for instance, it is possible to include the second dimension slices taken from the first column for the different standard mixture and unknown samples, and their replicates in the same column-wise superaugmented data matrix and perform their simultaneous analysis in one shot (see below). Chemometrics Methodology. MCR25 techniques are based on the bilinear decomposition of a measured mixed signal into their pure contributions (pure elution and spectra profiles in chromatography hyphenated to spectroscopic detection). This general MCR bilinear model is as follows: X ¼ CST þ E

ð1Þ

where for GC GC-TOFMS data, X(I J) is one of the second column data slices taken from the first column, C(I N) is the matrix containing second dimension elution profiles for this slice, ST(N J) is the matrix of pure mass spectra, and E(I J) is the residual matrix with the data variance unexplained by the bilinear model CST. In addition, N is the number of chemical components considered in the factor matrices. MCR-ALS2325 solves eq 1 for C and ST, using an iterative algorithm based on two constrained linear least-squares steps. It requires an initial estimation of the elution, C, or of the spectra, ST, profiles. Although finding 9291



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appropriate solutions of eq 1 is ambiguous when only information about the data matrix X is provided, the use of constraints like non-negativity, unimodality, selectivity, component correspondence, and trilinearity can decrease significantly the presence of rotation ambiguities. A more detailed discussion about the MCR-ALS method can be found in previous works.2325 One of the most important advantages of MCR-ALS is that it can be easily extended to the simultaneous analysis of several chromatographic runs (e.g., third- and fourth-order data) and to obtain relative quantitative information. The same MCR bilinear model like eq 1 can be used in this case as follows: 3 3 3 2 2 2 C11 X11 E11 7 7 7 6 6 6 6 X12 7 6 C12 7 6 E12 7 7 7 7 6 6 6 6 l 7 6 l 7 6 l 7 7 7 7 6 6 6 6 X1K 7 6 C1K 7 6 E1K 7 7 7 7 6 6 6 7 7 7 6 6 6 Xaug ¼ 6 l 7 ¼ 6 l 7ST þ 6 l 7 7 7 7 6 6 6 6 XL1 7 6 CL1 7 6 EL1 7 7 7 7 6 6 6 6 XL2 7 6 CL2 7 6 EL2 7 7 7 7 6 6 6 6 l 7 6 l 7 6 l 7 5 5 5 4 4 4 X LK CLK ELK ¼ Caug ST þ Eaug

ð2Þ

where Xaug is the column-wise superaugmented data matrix with the same number of columns (m/z values) for all slices. Since, in the analysis of each sample, K slices are taken from the first column to be analyzed in the second column, the number of submatrices (slices) in Xaug for L samples results to be L K. Therefore, the number of rows for Xaug is equal to I L K. After MCR-ALS analysis a superaugmented concentration matrix containing the pure second dimension elution profiles for different slices of different samples, Caug, and pure mass spectra profiles, ST, for N components as well as residual matrix, Eaug, containing noise and unresolved background will be obtained (Figure 1d). The Caug contains the second dimension elution profiles in all L K slices for the N resolved components. To get first dimension elution profiles for every component in each sample analyzed, every column in Caug was appropriately refolded to give a matrix of dimensions (I,K) for each sample analyzed (L samples). The column mean of this refolded data matrix gives the corresponding first dimension elution profile of dimensions (1,K). Therefore, for each sample, a matrix of first dimension elution profiles of dimensions (N,K) will be obtained. A single pure mass spectra matrix (of dimensions N,J) will be obtained from the MCR-ALS analysis of the superaugmented data matrix, which can be used for the identification of the resolved components. In addition, resolved second dimension elution profiles can be used for quantitative purposes. Quantitative Analysis. Since GC GC-TOFMS produces a series of modulated peaks for each resolved component, quantification is based on the summation of the peak areas generated by the modulation process.9 Thus, relative quantitative information for one target compound can be then directly derived from the comparison of MCR-ALS resolved second dimension elution profiles for different samples under the assumption of linear relation between relative peak areas (summation of the modulated peaks for each component in different slices) of the resolved elution profiles and their relative concentrations (Figure 1e). In this study, a standard mixture of PAHs solutions was used.

After dilution, the external calibration samples were in the concentration ranges 0.025.00 (ng 3 μL1) (seven concentrations were spanned in this range to build calibration curves) for naphthalene, 1-methylnaphthalene, fluorene, dibenzothiophene, phenanthrene, anthracene, 3,6-dimethylphenantherene and pyrene and in the rages 0.023.33 (ng 3 μL1) (six concentrations were spanned in this range to build calibration curves) for 2,3,5-trimethylnaphthalene and 1-methylpyrene. Finally, the obtained concentration for each PAH in HFO sample were corrected to the initial mass of HFO aromatic fraction (ng 3 mg1). The aromatic fraction was dissolved in 1.5 mL of hexane and then 2 μL of the obtained solution was diluted with hexane to final volume of 200 μL. Finally, 1 μL of this solution was injected to GC GC-TOFMS. Therefore, the obtained concentrations from the calibration curves could be corrected to the initial mass of HFO aromatic fraction by considering the initial mass of HFO aromatic fractions and the dilution ratios. Data Analysis. Data acquisition was carried out using ChromaTOF software version 3.32 (LECO Corp., St. Joseph, MI). Data was then exported in comma separated values (CSV) format and imported into MATLAB, version 7.7 (Mathworks Inc., Natick, MA). MCR-ALS toolbox programs23 were downloaded from the MCR Web site.37 In addition, the MATLAB code for PARAFAC belongs to N-way toolbox.38 NIST MS Search Program v. 2.0 for windows was used to compare the resolved mass spectra with those of the standards.

’ RESULTS AND DISCUSSION Resolution and Quantification of PAHs in Standard Mixture Samples. Data obtained in the GC GC-TOFMS analysis

of standards mixture samples of ten PAHs were analyzed by using the proposed strategy given in Figure 1. Initial pure estimations of the mass spectra were obtained using the simple-to-use interactive self-modeling mixture analysis (SIMPLISMA) approach.39 In addition, non-negativity, unimodality, spectra normalization, and component correspondence constraints were applied during the ALS optimization procedure. In the analysis of standard mixtures, some of the compounds were coeluted and the background contribution was also high for all samples, especially for those samples with lower analyte concentrations. For example, phenanthrene and anthracene had a strong coelution in both dimensions in standard mixture of PAHs and because of their very similar mass spectra and to the presence of strong baseline, background, and interference signals, the identification and construction of quantitative calibration curves for these two compounds is difficult by conventional means. Figures 2ac show the elution profiles and mass spectra of the pure species resolved by MCR-ALS for standard mixture of 1.00 (ng 3 μL1) of phenantherene and anthracene. Lack of fit value (LOF) was only of 2.85% for this case, which was at the level of experimental noise in the data. In Figure 2a, resolved second column elution profiles for the five simultaneously analyzed second column slices are shown. The corresponding first column elution profiles are shown in Figure 2b. As it can be seen, the two components, 2 and 3, were strongly coeluted in the second dimension and they were also heavily overlapped in the first dimension. In addition, baseline contributions in both dimensions could be resolved also by MCR-ALS. Comparison of the MCR-ALS resolved mass spectra with those stored in NIST MS database confirms the identification of dibenzothiophene, phenanthrene, anthracene, as well as of a 9292



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Figure 2. MCR-ALS resolved elution and mass spectra profiles for chromatographic region containing phenanthrene and anthracene; (a) MCR-ALS resolved 2nd column elution profiles in the five data slices taken from the first column, (b) MCR-ALS resolved 1st column elution profiles, and (c) pure mass spectra profiles for dibenzothiophene (1), phenanthrene (2), antheracene (3) and baseline. The identification of the resolved components gave RMF values of 876, 965, and 954 for components 1, 2, and 3, respectively.

Table 1. Analytical Results Obtained by MCR-ALS and ChromaTOF for the Resolution and Quantification of Ten PAHs in Standard Mixtures and HFO Samples conc. in HFO (ng 3 mg1)c

RE (%)b r,slope, intercepta

MCR

naphthalene

0.9956, 27.3, 1.08

5.12

8.77

1-methylnaphthalene

0.9921, 23.7, 1.01

5.74

3.88

2,3,5-trimethylnaphthalene fluorene

0.9935, 8.52, 0.45 0.9980, 14.5, 1.56

5.70 3.20

dibenzothiophene

0.9987, 10.3, 1.20

phenanthrene

0.9996, 13.2, 2.09

anthracene

0.9956, 10.7, 2.71

5.63

3,6-dimethylphenanthrene

0.9979, 6.78, 0.82

3.25

pyrene

0.9976, 12.1, 2.91

3.19

1-methylpyrene

0.9961, 7.91, 0.64

4.80

PAHs

ChromaTOF

ChromaTOF

MCR

38.71

54.34

2.53

4.44

94.10

110.23

1.91

1.17

7.40 3.75

184.19 120.51

229.48 124.56

1.47 5.18

4.44 4.72

2.39

3.45

197.05

162.95

5.62

7.61

2.24

3.47

260.60

196.49

5.35

208.34

ND

9.92

3.60

369.39

391.20

5.12

5.40

4.64

167.94

164.55

5.04

4.13

6.37

205.46

188.98

5.89

7.15

NDe

MCR

RSD (%)d ChromaTOF

5.79 ND

a

Correlation coefficients, slopes, and intercepts in the calibration curves obtained between resolved MCR-ALS elution profiles and standards with known concentrations of the target compounds. b Relative error (RE) in quantification using equation: RE(%) = (∑i(ci ^ci))1/2/(∑i(c2i ))1/2, where ci is the known concentration of the target compound in the standard i and ^ci is its calculated concentration using MCR-ALS (MCR) and ChromaTOF software. c Concentration of selected PAHs in aromatic fraction of HFO using MCR-ALS (MCR) and ChromaTOF software. d Relative standard deviation (RSD, %) of the peak areas obtained by MCR-ALS (MCR) and ChromaTOF software for three replicates (n = 3). e Not identified and quantified by ChromaTOF software.

significant baseline/background signal in the resolved mass spectra (Figure 2c). Reverse match factor (RMF) values (which is the square-root of the normalized dot product between the MCR-ALS resolved spectra and the possible standard mass spectrum candidates from NIST MS library) resulted to be 876, 965, and 954 for dibenzothiophene, phenanthrene and anthracene, respectively. It is clear from Figure 2c that the mass spectra for phenanthrene and anthracene are extremely similar but there is still a very small difference in their relative ionic abundances which allowed their MCR-ALS resolution. As it is shown in Table 1, relative errors (REs) for the concentrations estimated for phenantherene and anthracene were respectively 2.24% and 5.63%. A similar procedure was used for

the MCR-ALS resolution of all other ten PAHs in the standard mixture samples and their calibration curves were built up. Correlation coefficients (r), slopes, intercepts, and relative errors (REs) obtained for each PAH are given in Table 1. Good linear relationships between peak areas and concentrations were found in all cases (see Table 1). REs for the concentrations of the 10 PAHs in standard mixture samples were always below 6% and this confirms the validity of the proposed strategy. To compare these results obtained by MCR-ALS with those obtained by ChromaTOF software, additional calibration curves and quantification of the 10 PAHs were also obtained. REs for concentrations of the 10 PAHs obtained using ChromaTOF are also presented in Table 1. It can be seen that in all cases (except for 9293



Figure 3. 2D-contour plot for the TIC signal of the aromatic fraction of a HFO sample. Insert shows the selected chromatographic region used to test the resolution and quantification of the proposed strategy.

1-methylnaphthalene) relative errors obtained by the proposed method were better (lower RE) than those obtained by ChromaTOF. Moreover, the simultaneous determination of phenanthrene and anthracene was not possible by ChromaTOF, whereas MCR-ALS gave their correct resolution and quantification. As an additional advantage, using MCR-ALS strategy, contributions of interferences and especially of background were properly resolved, and this allowed better estimations of analyte concentrations and better description of the whole separation process in both columns. In addition, slopes of calibration curves obtained with MCR-ALS were higher than those obtained by ChromaTOF (for brevity these results are not shown) confirming the increase in calibration sensitivity when MCR-ALS was used. Resolution and Quantification of PAHs in a Heavy Fuel Oil. To evaluate the performance of the method on real samples, the identification and quantification of the previous ten PAHs were performed in the aromatic fraction extracted from a HFO sample. Here, according to the strategy shown in Figure 1d, the individual data matrices from the six different standards (each one with same number of slices for each standard) and the data matrices from the different slices of the unknown sample were arranged one on top of the other in a column-wise superaugmented data matrix, and then analyzed using MCR-ALS. Figure 3 shows the GC GC-TIC contour plot for the analysis of the aromatic fraction of the HFO extract. Although the overall separation seems to be reasonably good, most of the components were not well resolved in none of the two columns. This is due to the presence of an extremely large number of isomers (different C1-, C2-, and C3-alkylated derivatives) with similar chemical properties making their total chromatographic separation extremely difficult. In the insert of Figure 3, the TIC of one of these selected chromatographic segments is shown. The target compound in this region was 3,6-dimethylphenanthrene. There are other different compounds in the same chromatographic region including some of the isomers of dimethylphenanthrene with similar mass spectra. In addition, due to the complexity of this HFO sample, the number and type of interferences were changing in second column from slice-to-slice. This means that in the different slices taken from the first column, the number of components could be different. Since, there was very little chromatographic resolution of the components in this chromatographic

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segment, the resolution and quantification of target compounds was very challenging. MCR-ALS was applied to the corresponding superaugmented data matrix, which included the HFO extract sample. A number of components as high as 10 was needed to take into account for baseline/background signal contributions and also for the possible interferences present in the different simultaneously analyzed slices. In the MCR-ALS analysis of such a complex data set, the correspondence between resolved components in the different slices was an important constraint27 to consider, because it fixed the presence or absence of the known components in the different data submatrices of the standards, and it helped to overcome the problem of changing interferences from slice to slice. Figure 4a shows the MCR-ALS resolved elution profiles in the five different second column slices taken from first column containing the peak cluster of interest (dotted line). MCR-ALS provided a very successful resolution of target compound as well as of unknown interferences. Finally MCR-ALS resolved and standard mass spectra of target compound are shown in Figures 4b and 4c, respectively. It is clear that a good match (RMF = 944) between resolved and standard mass spectra of the target compound was obtained. Resolved mass spectra could be used to identify interferences, for example the green peak in Figure 4a was identified as being from 2,8-dimethyldibenzothiophene and the peaks in cyan and brown were identified to be the two isomers of dimethylphenanthrene, 4,5- and 2,5-dimethylphenanthrene, respectively. Using the known concentrations of ten PAHs in the standard samples, the concentrations (ng mg1) of these PAHs in the aromatic fraction of HFO were calculated by the proposed MCRALS strategy and by the ChromaTOF software, and the mean values of them are reported in Table 1. For those cases where peak resolution was easier, the results obtained by both methods (MCR-ALS and ChromaTOF) were comparable. However in cases like 3,6-dimethylphenenthrene or 2,3,5-trimethylnaphthalene, the concentrations estimated by ChromaTOF were systematically higher than those obtained by MCR-ALS. This is related to the presence of unresolved interference in peak area calculation by ChromaTOF software, whereas in MCR, due to the possibility of resolution of second dimension elution profiles in different slices, more reliable results could be obtained. Again, for the case of anthracene, ChromaTOF software was completely unable to perform its quantification, whereas MCR-ALS could do the proper quantification of both anthracene and phenantrene in the HFO sample. Values of relative standard deviation (RSD) for three replicates are also given in Table 1. In this case, quantification errors in terms of RE% cannot be given because the actual concentrations of the analytes in HFO samples were unknown and only RSD values of the predicted concentration from replicates could be used for comparison. As it can be seen, the RSD values for the MCR-ALS resolved profiles were lower than 6% except for anthracene (9.92%). The possible reason explaining this larger value for this compound can be related to its highly overlapped elution profile with phenanthrene in both dimensions and also because of their similar mass spectra. Furthermore, other possible experimental problems associated with sample preparation and with GC GC-TOFMS analysis can also contribute to the loss of reproducibility for the signal of this compound. RSD values for peak areas obtained by ChromaTOF are also included in Table 1. It can be also seen that the RSD values obtained by MCR-ALS are in general better than those of ChromaTOF 9294



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Figure 4. MCR-ALS resolved elution profiles and pure mass spectra corresponding to the selected chromatographic region given in Figure 3. (a) MCRALS resolved 2nd dimension elution profiles in five slices taken from the first dimension containing 3,6-dimethylphenanthrene target compound which are marked with a blue dotted line and a black arrow, (b) MCR-ALS resolved MS for the target compound and (c) NIST MS standard spectrum for the 3,6-dimethylphenanthrene. The RMF value for the target compound is 944.

confirming the potential of the proposed strategy for target analysis in complex matrices. Comparison of MCR-ALS and PARAFAC Results. In this section, some of the results obtained by MCR-ALS are compared to those obtained by PARAFAC. A more throughout comparison between these two approaches is out of scope of the present work, and it would require a deeper investigation, including also the use of PARAFCA2. For comparison purposes, first the chromatographic segment related to the phenantherene and anthracene coelution (Figure 2) was also analyzed by PARAFAC. Trilinear decomposition (TLD)40 was used as a method for initialization of ALS algorithm in PARAFAC and non-negativity and unimodality constraints were applied during analysis considering the presence of four components. In this case, LOF increased up to 7.52%, higher than the one obtained for MCRALS (2.85%), meaning a worse fit when a trilinear model is used. In addition, relative errors for the estimated concentrations, increased up to 5.56% and 7.19% for phenantherene and anthracene, respectively. As second column retention times for each component were changing from slice-to-slice (shifting), the trilinear model may not be strictly fulfilled, and this could attribute to the PARAFAC results being worse in this case. In contrast, these shifts do not affect MCR-ALS resolution since the bilinear model assumptions still hold for the column-wise data arrangement considered in this work, with mass spectra in the common mode. To further test this deviation from the trilinear model, the same data were arranged in two related superaugmented data matrices, one column-wisely way (m/z values in the common mode) and another row-wisely way (second dimension retention times in the common mode). In case of fulfilling the trilinear model, they

both should have the same chemical rank (mathematical rank in absence of noise) and therefore, they should give the same number of large significant components24,25 when their Singular Value Decomposition (SVD)41 is performed. The results obtained in this case for the analysis of the chromatographic segment where phenantherene and anthracene coeluted (Figure 2), showed that the number of significant singular values was four for the column-wise matrix (two for these two components, another for the baseline/background contribution and another one for an interference contribution, see also results from Figure 2), whereas the number of large singular value components for the row-wise superaugmented data matrix increased from four up to six, because of peak shifting effects in the augmented row mode. These results confirmed that in this case the experimental data deviated from the trilinear model. See refs 24 and 25 for a more detailed discussion about the effects on deviations from trilinearity. PARAFAC analysis was also performed for the analysis of chromatographic segment shown in Figure 3 (HFO sample). As example, for the standard samples of 3,6-dimethylphenanthrene, the relative error in the concentrations estimated by PARAFAC was 4.70% (compared to 3.25% for MCR-ALS, in Table 1). PARAFAC resolution of the chromatographic segment containing this target compound in HFO sample gave a LOF value of 9.34%, higher than that of MCR-ALS (3.52%). Furthermore, the RSD value for PARAFAC results was 7.12% which was again higher (worse) than MCR-ALS one (5.12%, in Table 1). All these results confirmed again that retention time shifts affected the performance of PARAFAC results because of deviation from trilinear model. On the contrary, this problem did not affect bilinear 9295


Analytical Chemistry MCR-ALS modeling using the proposed column-wise super augmented data matrix arrangement with m/z values in the common matrix mode. Finally, from the calculation time point of view, the proposed strategy based on MCR-ALS resulted to be faster than the one used by PARAFAC. To prevent the effects of retention time shifts between runs on PARAFAC, the analysis of the four-way GC GC-TOFMS data set, implies that each sample (a data cube) has to be analyzed individually. This makes the analysis much more time-consuming. Moreover, this is also more inaccurate, because quantitative results relied on having resolved exactly the same MS spectrum for the component of interest in the different PARAFAC analysis of the individual samples, requiring therefore their further normalization and matching manipulation. In contrast, in the proposed MCR-ALS strategy, all data matrices have been simultaneously considered in the same analysis, including the standard samples and the HFO sample, in one single shot.

’ CONCLUSIONS A new strategy for the analysis of GC GC-TOFMS data based on the application of the MCR-ALS method is proposed in this work. The application of this method resulted to be a powerful and fast method for the multicomponent resolution, identification and quantification of target PAHs in the GC GCTOFMS simultaneous analysis of mixtures of these compounds in standard samples and in a complex HFO sample. Problems associated to retention time shifts for within and between runs in GC GC-TOF-MS analysis were efficiently solved using MCRALS bilinear modeling of the column-wise superaugmented data matrix containing selected second column slices from different chromatographic runs. Pure elution profiles in both dimensions (i.e., in both columns) and pure mass spectra for target PAHs in complex mixtures of HFO aromatic fractions were correctly resolved in the presence of unknown overlapped and/or embedded peak interferences, as well as in the presence of baseline/ background strong contributions. Good quantitative results were also obtained for the target compounds using the proposed strategy with REs below 6%. These results were comparable or even better than those obtained by commercial firmware GC GC-TOFMS instruments software, especially for those chromatographic segments with complex coeluted peaks and with strong peak shifting within and between runs. The results of this work confirmed the great potential of proposed strategy and its proposal for its general and extended use for resolution and quantification of target compounds in GC GC-TOFMS analysis of very complex samples such as the oils and petrochemical samples analyzed in this work. Although many of previous published works for chemometric resolution of GC GC-TOFMS data have been based on PARAFAC, the results obtained using MCRALS confirmed that this approach can be used advantageously in many cases. PARAFAC is based on the satisfactory fulfillment of the trilinear model, which may not always be the case for GC GC-TOFMS because of retention time shifts especially in second dimension within and between GC GC runs. ’ AUTHOR INFORMATION Corresponding Author

*E-mail: [email protected]. Tel.: +34 93 400 61 40. Fax: +34 93 204 59 04.

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’ ACKNOWLEDGMENT Financial support was obtained from MCINN (Projects ref. . CTQ2009-11572, CTM2008-02718-E, RAMOCS, and CTM200802721-E/MAR, TOXPROF) and the project “European concerted action to foster prevention and best response to accidental marine pollution-AMPERA” (ERAC-CT2005-016165) within the framework of the EU ERA-Net Initiative (sixth Framework Program). J.R.R. kindly acknowledges a predoctoral fellowship (JAE Predoc) from the Spanish National Council of Research (CSIC). ’ REFERENCES (1) Jernelv, A. Ambio 2010, 39, 353–366. (2) Kirby, M. F.; Law, R. J. Mar. Pollut. Bull. 2010, 60, 797–803. (3) Díez, S.; Jover, E.; Bayona, J. M.; Albaiges, J. Environ. Sci. Technol. 2007, 41, 3075–3082. (4) Adahchour, M.; Beens, J.; Brinkman, U. A. T. J. Chromatogr. A 2008, 1186, 67–108. (5) Mondello, L.; Tranchida, P. Q.; Dugo, P.; Dugo, G. Mass Spectrom. Rev. 2008, 27, 101–124. (6) Wang, Y.; Chen, Q.; Norwood, D. L.; McCaffrey, J. J. Liq. Chromatogr. Relat. Technol. 2010, 33, 1082–1115. (7) Matamoros, V.; Jover, E.; Bayona, J. M. Anal. Chem. 2010, 82, 699–706. (8) Blumberg, L. M.; David, F.; Klee, M. S.; Sandra, P. J. Chromatogr. A 2008, 1188, 2–16. (9) Amador-Mu~ noz, O.; Marriott, P. J. J. Chromatogr. A 2008, 1184, 323–340. (10) Pierce, K. M.; Hoggard, J. C.; Mohler, R. E.; Synovec, R. E. J. Chromatogr. A 2008, 1184, 341–352. (11) Augusto, F.; Poppi, R. J.; Pedroso, M. P.; De Godoy, L. A. F.; Hantao, L. W. LC-GC Eur. 2010, 23. (12) de Godoy, L. A. F.; Hantao, L. W.; Pedroso, M. P.; Poppi, R. J.; Augusto, F. Anal. Chim. Acta 2011, 699, 120–125. (13) Pierce, K. M.; Wood, L. F.; Wright, B. W.; Synovec, R. E. Anal. Chem. 2005, 77, 7735–7743. (14) Fraga, C. G.; Prazen, B. J.; Synovec, R. E. Anal. Chem. 2001, 73, 5833–5840. (15) Van Mispelaar, V. G.; Tas, A. C.; Smilde, A. K.; Schoenmakers, P. J.; Van Asten, A. C. J. Chromatogr. A 2003, 1019, 15–29. (16) Zhang, D.; Huang, X.; Regnier, F. E.; Zhang, M. Anal. Chem. 2008, 80, 2664–2671. (17) Vial, J.; Noc-airi, H.; Sassiat, P.; Mallipatu, S.; Cognon, G.; Thiebaut, D.; Teillet, B.; Rutledge, D. N. J. Chromatogr. A 2009, 1216, 2866–2872. (18) Skov, T.; Hoggard, J. C.; Bro, R.; Synovec, R. E. J. Chromatogr. A 2009, 1216, 4020–4029. (19) Bro, R. Chemom. Intell. Lab. Syst. 1997, 38, 149–171. (20) Hoggard, J. C.; Synovec, R. E. Anal. Chem. 2007, 79, 1611–1619. (21) Hoggard, J. C.; Synovec, R. E. Anal. Chem. 2008, 80, 6677–6688. (22) Bro, R.; Andersson, C. A.; Kiers, H. A. L. J. Chemom. 1999, 13, 295–309. (23) Jaumot, J.; Gargallo, R.; De Juan, A.; Tauler, R. Chemom. Intell. Lab. Syst. 2005, 76, 101–110. (24) Tauler, R.; Smilde, A.; Kowalski, B. J. Chemom. 1995, 9, 31–58. (25) Tauler, R. Chemom. Intell. Lab. Syst. 1995, 30, 133–146. (26) Bro, R. Crit. Rev. Anal. Chem. 2006, 36, 279–293. (27) Tauler, R.; Maeder, M.; De Juan, A. In Comprehensive Chemometrics: Chemical and Biochemical Data Analysis; Brown, S., T. R., Walczak R., Ed.; Elsevier: Oxford, U.K., 2009; Vol. 2, p 473503. (28) Tauler, R.; Barcelo, D. Tr. Anal. Chem. 1993, 12, 319–327. (29) Pere-Trepat, E.; Lacorte, S.; Tauler, R. J. Chromatogr. A 2005, 1096, 111–122. (30) Pere-Trepat, E.; Tauler, R. J. Chromatogr. A 2006, 1131, 85–96. (31) Pere-Trepat, E.; Lacorte, S.; Tauler, R. Anal. Chim. Acta 2007, 595, 228–237. 9296



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Resolution and Quantification of Complex Mixtures of Polycyclic

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