Data Reduction in Comprehensive Two-Dimensional Gas

Jul 9, 2012 - Data Reduction in Comprehensive Two-Dimensional Gas Chromatography for Rapid and Repeatable Automated Data Analysis. Paul McA. Harvey an...
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Data Reduction in Comprehensive Two-Dimensional Gas Chromatography for Rapid and Repeatable Automated Data Analysis Paul McA. Harvey and Robert A. Shellie* Australian Centre for Research on Separation Science (ACROSS), University of Tasmania, Private Bag 75, Hobart, 7001 Australia S Supporting Information *

ABSTRACT: A rapid approach for comprehensive two-dimensional gas chromatographic data analysis is introduced, providing important environmental metrics including total petroleum hydrocarbon concentration as well as chemical-class distribution. The approach, comprising transformation of exported data files to two-dimensional retention time arrays, blank subtraction, alignment and projection onto new axes, subdivision of the aligned two-dimensional data matrix, and compilation of summary data is able to be performed without user intervention. The approach satisfies critical goals of rapid batch analysis with repeatability and traceability. Application to assessment of petroleum hydrocarbon contaminated soil samples from Macquarie Island, a remote Southern Ocean island, is shown to illustrate the utility of this new data analysis strategy.

pressure programmed GC × GC, classes of compounds typically appear as curved bands in the two-dimensional separation space. Thus, Cordero et al. described use of direct image comparison and template-based fingerprinting18 in which nonoverlapping polygonal panels were used to delineate chromatographic features such as peaks or peak sets, followed by quantification of the response in each region. The peak response in each polygon can be used for further data analysis, statistical comparisons, etc. Likewise, Arey et al. interpreted oil weathering by projecting solute vapor pressure and aqueous solubility contours onto GC × GC chromatograms and determining the peak response within each of the discrete regions bound by these contours.6 Although distinct curved bands are easily identified by visual inspection, nonlinear classes complicate automatic delineation of chromatographic features such as those based upon analyte class and/or environmentally relevant categories. Retention time variability also impacts the performance of such approaches; thus, correction of retention time variability is also vitally important. A number of approaches for whole-ofchromatogram correction are described elsewhere.19,20 The present approach is similar in part to those described elsewhere;6,7,18 namely, the two-dimensional separation space is divided into regions that distinguish chromatographic features, and then the response in each region is quantified. In this communication we incorporate a method of transforming the data so that class patterns can be allocated to rectangular bins. Harynuk and co-workers illustrated how projecting the data into alternate dimensions can facilitate interpretation of the

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etroleum spills are one of the most extensive and environmentally damaging on-land and near-shore marine pollution problems in Antarctica1 and the sub-Antarctic. To this end we have developed an approach to (i) measure the concentration of fuel in the ground and (ii) assess the extent of contaminant attenuation by evaporation, solubilization, and biodegradation. As petroleum attenuation progresses, the gas chromatographic profile of soil extracts appears as a baseline hump composed of an unresolved complex mixture (UCM)2 with few discernible features. Previous studies have shown that comprehensive two-dimensional gas chromatography (GC × GC) provides sufficient analytical resolution to partially unravel the UCM.3−11 GC × GC analysis offers a widely heralded advantage of increased peak capacity over conventional gas chromatography. A critical consideration for petroleum characterization also arises from the ordered chromatograms obtained when analysis conditions are properly optimized.12,13 The aim of the present investigation was to harness the ordered two-dimensional class separation information provided by GC × GC analysis in a way to permit rapid, detailed characterization of petroleum hydrocarbons in soil. Numerous approaches have been described before, such as use of point-bypoint subtraction, division, and addition, of the chromatographic response,4 and extension of this to calculation of mean GC × GC chromatograms from replicate analyses followed by statistical comparison.14 Point-by-point comparisons rely on highly reproducible retention times beyond realistic expectation.15 Approaches for retention time alignment/correction were introduced to account for run-to-run variability,16,17 whereby regions of the two-dimensional separation space could be realigned. The latter of these approaches lead to stepwise entire-chromatogram alignment. An alternative approach compares regions rather than points. In temperature and © 2012 American Chemical Society

Received: March 7, 2012 Accepted: July 9, 2012 Published: July 9, 2012 6501

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complex patterns found in GC × GC chromatograms,21 and we propose that this will be beneficial for automated interpretation of ordered two-dimensional petroleum hydrocarbon chromatograms. The transformation described here has a second benefit, in that y-axis chromatogram variability is minimized, since all points in the y-axis are projected as relative rather than absolute retention. Development and implementation of a methodology, comprising transformation of the exported ASCII vector data file to two-dimensional retention time array, blank subtraction, alignment and projection onto new axes, subdivision of the aligned two-dimensional data matrix, and compilation of summary data, without user intervention is described here.

and compilation of summary data was performed using Matlab 2007a. GC × GC chromatograms are presented in this paper using Transform (Fortner Software) for clarity.

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DISCUSSION Method Development. A typical GC × GC chromatogram of a reference diesel sample fortified with additional marker compounds using the acquisition approach described herein is shown in Figure 1. In order to align the data into rectangular class patterns, we implemented a transformation akin to Harynuk and co-workers’ transformation for fatty acid methyl esters, in which the second-dimension retention was presented as second-dimension retention factor ( 2k).21 Calculation of 2k relies on precise determination of the hold up time in the second-dimension column (2tM). Klee and Blumberg have shown that 2tM can be reliably predicted for flow-based modulation approaches,24 but it cannot be conveniently determined from the GC × GC chromatogram without making additional analyses for specific 2tM measurement. To provide for general applicability, i.e., for thermal and flow-based modulation, we elected to calculate relative seconddimension retention (2r). Although r is strictly defined as the ratio of the retention time of each component relative to that of a standard obtained under identical conditions, in the present investigation every data point in the two-dimensional data array was transformed to 2r. To this end, the acyclic aliphatic hydrocarbon elution line was selected as the standard(s) to which 2r was calculated modulation-by-modulation. The acyclic aliphatic hydrocarbons appear in a discrete elution bands in the first-separation dimension; therefore, many modulation slices do not contain a reference compound, so a surrogate had to be employed. The retention time of these surrogates was determined by interpolation between peak apexes of adjacent acyclic aliphatic hydrocarbons by fitting a curve through the maxima of the acyclic aliphatic hydrocarbons. The second dimension retention times (2tR) of the peak apexes were extracted using a maxima-searching algorithm developed in Matlab 2007a. To this end, a rectangular portion of data was extracted about the particular GC × GC data point of interest. A local maximum was identified if the point was the maximum in the extracted region and greater than a preset absolute value (peak detection threshold). A line is drawn in Figure 1 to indicate 2tR of the acyclic aliphatic hydrocarbon elution line. A curve through the maxima of these peaks was described by a cubic function from 10.92 to 23.28 min and a linear function after 23.28 min. All peak maxima were within 50 ms of the fitted 2tR versus modulation (number) curve. Peak identification of several key compounds was performed by injection of authentic reference standards; an abbreviated list of identified compounds is presented in Table 2. Transformation of the y-axis scale from 2tR into 2r was performed modulation-by-modulation using Matlab 2007a by dividing each data point in a given modulation slice by the time of the imaginary elution band fitted through the acyclic aliphatic hydrocarbon elution line. The resultant two-dimensional plot is presented in Figure 2. A plot corresponding to a soil extract following identical data transformation is presented in Figure 3A. By definition, those peaks falling on the acyclic aliphatic hydrocarbon elution line have 2r = 1.0 and now appear as a straight band in the two-dimensional separation space. It is also notable that the identifiable aromatic classes are horizontally aligned after the transformation procedure. Naphthalene as well as the methylnaphthalenes, C2 and C3

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EXPERIMENTAL SECTION The 76 samples analyzed in this investigation were excavated from bore holes near the Macquarie Island Fuel Farm in the 2006−07 and 2007−08 Austral summers according to an established method.22 Hydrocarbons were extracted from a 10 g subsample of homogenized wet soil by tumbling overnight with a mixture of 10 mL of deionized water, 10 mL of hexane, and 1 mL of internal standard mixture. The internal standard mixture comprised cyclooctane (0.2498 mg/mL) and 1bromoeicosane (0.2501 mg/mL) in hexane. Samples were centrifuged for 10 min at relative centrifugal force 208 g using a Haraeus Multifuge 3SR Plus centrifuge (Thermo Scientific), and the hexane fraction was collected. The soil residue was dried at 105 °C for 24 h and the dry mass recorded. Special Antarctic blend diesel (SAB) calibration standards (with total petroleum hydrocarbon (TPH) values of 0, 3000, 6000, 12 000 mg/kg) were prepared in 10 mL of hexane, and 1 mL of internal standard mixture. GC × GC analysis was performed using an Agilent 6890 GC that was retrofitted with a custom-built differential flow modulator based on ref 23. The dimensions of the modulator ensemble and separation columns are provided in Table 1. All Table 1. Details of the Differential Flow Modulator Column Ensemblea component

dimensions

stationary phase

precolumn first-dimension separation column second-dimension separation column sample loops (L1, L2) restrictors (R1, R2)

1.00 m × 0.075 mm i.d. 12 m × 0.22 mm i.d. 4.8 m × 0.25 mm i.d.

n/a 0.25 μm BP-1 0.25 μm HT-8

0.500 m × 0.25 mm i.d 40 mm × 0.05 mm i.d.

n/a n/a

a

A diagram of the modulator assembly is provided as Supporting Information.

tubing and separation columns were from SGE Analytical Science. The flow rates of the first and second dimension columns were 0.25 mL/min and 15 mL/min, respectively (constant flow). The carrier gas in both separation dimensions was hydrogen, and the modulation period was 3.6 s. Samples were introduced by split injection (4 μL injection; 16:1 split ratio) at 340 °C. The oven temperature program was 40 °C (hold 4 min) 40−360 °C (7 °C/min), 360 °C (hold 3.5 min). FID detector temperature was 350 °C. Data were acquired at 100 Hz. Transformation of the exported ASCII vector data file to two-dimensional array, blank subtraction, alignment, subdivision of the aligned two-dimensional data matrix, integration, 6502

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Figure 1. Two-dimensional separation space for the GC × GC analysis of a reference special Antarctic blend diesel sample, fortified with additional reference compounds listed in Table 2. The internal standards, cyclooctane (*) and 1-bromoeicosane (#), are marked accordingly.

MS methods used for the analysis of a crude oil spill and have been used extensively to monitor the long-term environmental fate of aromatics from the Exxon Valdez spill in Alaska.25,26 While the 2r class assignments are not definitive, they allow the data to be binned into regions that will have similar environmental interpretation, such as evaporation and water solubility.6,7 These relative retention bands are likely to be appropriate for assessment of contaminant attenuation by biodegradation and weathering. The general order of aliphatic biodegradation is thought to be n-alkanes > branched > cyclic > polycyclic alkanes while alkylated aromatic hydrocarbons have increasing resistance to biodegradation with increasing branching and cyclization of the alkyl group(s).27 Determination of total peak response within these relative retention bands provides a snapshot of the total abundance of each of these chemical classes. The vertical lines drawn in Figure 3A permit further refinement of the bins used to characterize the soil extracts. Subdivision of the x-axis was driven principally by location of the n-paraffins. Bin divisions were placed at an approximate linear retention index 850, 1050, 1250 ..., 2850 using an automated procedure. This procedure (which is described later) was designed to account for retention time drift during a long analysis sequence. A spacing of two carbon units was chosen so that a sufficient number of bins were available to preserve the overall shape of the fuel envelope while keeping carbon number spacing often found in regulatory methods.28−30 Forty-four bins cover the SAB elution region in the twodimensional separation space; 16 of these were omitted because they did not contain a response for any of the samples analyzed, and were not considered further. In the work of Arey et al.6 the spacing of data bins was determined by calculating contours of

Table 2. Identities of Numbered Peaks in Figure 1 number

name

number

name

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

C9H20 C10D22 C10H22 C12H26 C14H30 C16H34 C17H36 pristane C18H38 phytane C20H42 C24D50 C24H50 naphthalene 2-methylnaphthalene 1-methylnaphthalene acenaphthylene

18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34

acenaphthene fluorene d10-anthracene phenanthrene anthracene fluoranthene pyrene 1,1′:4′,1″-terphenyl 2,2′-dimethyl-1,1′-binaphthalene benz(a)anthracene chrysene benzo(b)fluoranthenes benzo(k)fluoranthenes benzo(a)pyrene indeno(1,2,3-cd)pyrene dibenzo(a,h)anthracene benzo(g,h,i)perylene

naphthalenes, all appear within a narrow band (1.21 < 2r < 1.29). Following detailed examination of the transformed twodimensional separation space, four relative retention bands, shown by the horizontal delineations in Figure 3A are apparent within the defined region that encapsulates petroleum hydrocarbons. Row 1 (0.94 < 2r < 1.09) is composed of acyclic aliphatic hydrocarbons and alkyl cyclohexanes, row 2 (1.09 < 2r < 1.21) is populated by alkyl benzenes and polycyclic aliphatics, row 3 (1.21 < 2r < 1.35) contains all of the alkyl naphthalenes and other alkylated two-aromatic ring structures, and row 4 (1.35 < 2r < 1.49) is home to alkyl phenanthrenes, anthracenes, and other alkylated three-aromatic ring structures. These compounds are typically targeted with isotope dilution GC/ 6503

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Figure 2. Transformed two-dimensional separation space shown in Figure 1.

Figure 3. (A) Transformed two-dimensional separation space for a petroleum hydrocarbon contaminated soil extract from Macquarie Island. The internal standards are marked with an asterisk. (B) Heat map representation of the binned data indicating relative total peak response.

shifting of a single component near a bin boundary from one bin to another bin in a different analysis is reduced so the additional step of partially assigning data near cell boundaries to multiple cells6,7 was not required. The use of midpoints between the n-paraffin retention times for the bin divisions also means that the abundant n-paraffins (in fresh SAB and diesel) are always binned correctly and not artificially split into different bins from alignment problems and/or column overloading.31,32 Automated subdivision was performed as follows: For each sample, the peak apex of the internal standard 1-bromoeicosane was aligned to the observed maximum of 1-bromoeicosane in the reference standard by carrying out a simple, discrete integer

hydrocarbon vapor pressure and aqueous solubility across the raw GC × GC chromatograms. That approach used ∼150 bins in the n-C11 to n-C24 range and implemented a method of partially assigning data near cell boundaries to multiple cells in order to overcome spurious data analysis problems arising from slight retention shifts of large GC × GC peaks near cell boundaries. The spacing used in the present investigation is somewhat less frequent than the environmental partitioning used in refs 6 and 7 but more frequent than analysis methods developed by regulators for contaminated site assessment.28−30 By having a lower number of bins the relative total peak-area between bins becomes less sensitive to retention time alignment problems. In this way the relative effect from the 6504

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shift in the x and y directions, without stretching or skewing. The positions of the vertical lines delineating bins were assigned as a defined modulation slice relative to the modulation slice containing the 1-bromoeicosane peak maximum with the bounds for the binning defined using the retention marker mixture. This approach was validated by comparison of the maxima for the d10-anthracene and cyclooctane internal standards to confirm that any peak alignment gives stable retention locations across the analysis sequence. The greatest discrepancy was in the first dimension retention time of cyclooctane, which varies by a maximum of two modulations from the expected position. This is due to some migration of cyclooctane in the first dimension separation column during the initial isothermal oven portion. Cyclooctane varies significantly more than n-paraffins larger than n-C11. Using this alignment approach, samples with high concentrations of analytes that are eluted in the ∼n-C9−n-C10 region will suffer from the greatest problems with bin assignments. This is essentially restricted to the fresh SAB calibration samples and any sample containing SAB that has undergone minimal evaporation. The analysis sequence contained numerous repeats of the SAB calibration standards from 3000 mg/kg to 12 000 mg/kg, and bin assignments were not problematic. TPH Determination. In addition to the 16 bins without diesel-range-organics response being discarded, four additional bins containing internal standards were also removed. Assessment of total petroleum hydrocarbon (TPH) concentration was carried out by summation the response in all remaining 24 bins. TPH spans a wide dynamic range from 2 mg/kg to 12 800 mg/kg among the 76 samples analyzed in the present investigation. TPH determination of the same samples had previously been performed using a validated GC approach, and excellent congruence between GC × GC and GC data was observed, with the caveat being that GC × GC offered a much lower minimum detectable quantity (2 mg/kg vs 150 mg/kg TPH). Assessment of the Extent of Contaminant Attenuation. Owing to the wide dynamic range of TPH concentration, absolute peak responses of individual bins are not informative. Our reasoning was that a sample with lowmoderate TPH with a high proportion of toxic aromatic compounds is more environmentally concerning than a higher TPH sample containing only innocuous compounds. Thus, all bins were scaled using a total internal normalization so that the sum of the responses was 1. In this way each bin value represents a fraction of the total diesel-range-organics. The result from applying the automated binning procedure is shown in Figure 3B. A summary of the analysis of 83 samples (including randomly selected replicates and 7 SAB reference standards) is presented in Figure 4 as a heat map image. This representation provides a readily interpretable overview of the extent of contaminant attenuation. The 7 SAB standards are plotted at the bottom of the figure. It is immediately apparent that essentially all of the samples have undergone some degradation by comparing to SAB standards. To investigate further, the summary data (28 bins) from samples determined to contain more than 20 mg/kg TPH were analyzed by classical multidimensional scaling (Torgerson−Gower scaling). The result of this classical multidimensional scaling is presented in Figure 5. The color of the markers is indicative of measured TPH concentration, and the position in the plot reflects chemical composition. A series of ellipses are drawn to

Figure 4. Heat map representation of petroleum hydrocarbon distribution in soil extracts from Macquarie Island.

highlight the different classes of samples present in the environment. Those samples on the left-hand side are closest in composition to the SAB reference standards and are fresh spilled SAB. Those in the middle ellipse are representative of samples containing biodegraded and evaporated diesel. The samples in the ellipse on the right-hand side of the figure are highly degraded, containing only old petroleum hydrocarbon remnants. Following the multidimensional scaling the complete data were remapped in the heat map format from mostdegraded samples to least-degraded samples according to the MDS result. This format again provides a readily interpretable overview of sample composition and to the extent of contaminant attenuation. We see a fall in relative abundance of bins in row 1 as biodegradation becomes more advanced, from top-to-bottom in Figure 6. Interestingly, some samples appear to contain a high abundance of water-transported aromatic compounds as well as varying amounts of old, degraded fuel remnants in the raw GC × GC data. These samples are shown in the upper-most ellipse in Figure 5 with the sample containing the greatest proportion of water transported aromatics (2 replicates) highlighted by the circle placed in Figure 6. Complete interpretation of these Macquarie Island fuel spill analysis results requires detailed investigation and will be the subject of future correspondence.

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CONCLUSION GC × GC is highly suitable for assessment of sites contaminated with petroleum hydrocarbons. Although a wide range of homemade and commercial instrument solutions for GC × GC analysis exists, data analysis often still presents a bottleneck. In the present investigation we developed a userfriendly methodology for dealing with GC × GC data that alleviates this bottleneck by reducing time and technical training requirements. A novel data transformation permits straightforward binning of data into environmentally appropriate compartments. This is highly advantageous for subsequent analysis of large sample sets, since the methodology is rapid, repeatable, and traceable. 6505

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Figure 5. Classical multidimensional scaling (Torgerson−Gower scaling) plot revealing petroleum hydrocarbon distribution in soil extracts ≥20 mg/ kg TPH from Macquarie Island.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Phone: +61-3-6226-7656. Fax: +61-3-6226-2858. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This research was supported under Australian Research Council’s Discovery Projects funding scheme (Project DP110104923). R.A.S. is the recipient of an Australian Research Council Australian Research Fellowship (project number DP110104923). The authors gratefully acknowledge the helpful discussions with Dr. Ben Raymond, Prof. Ian Snape, and Mr. Greg Hince (Australian Antarctic Division).

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Figure 6. Reordered heat map showing petroleum hydrocarbon distribution in soil extracts from Macquarie Island after multidimensional scaling. Samples are plotted from top-to-bottom with increasing degradation as indicated from multidimensional scaling. A circle is drawn to bring attention to two separate GC × GC analysis results of the sample containing elevated concentration of water-transportable aromatic compounds without a background signature from a low concentration of highly degraded fuel.

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REFERENCES

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ASSOCIATED CONTENT

S Supporting Information *

Additional information as noted in text. This material is available free of charge via the Internet at http://pubs.acs.org. 6506

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