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Data analysis programs for comprehensive two-dimensional chromatography
Journal of Chromatography A, 1216 (2009) 2923–2927
Contents lists available at ScienceDirect
Journal of Chromatography A journal homepage: www.elsevier.com/locate/chroma
Data analysis programs for comprehensive two-dimensional chromatography Minna Kallio, Maarit Kivilompolo, Sami Varjo, Matti Jussila, Tuulia Hyötyläinen ∗ Laboratory of Analytical Chemistry, Department of Chemistry, University of Helsinki, P.O. Box 55, FI-00014 Helsinki, Finland
a r t i c l e
i n f o
Article history: Available online 19 November 2008 Keywords: Comprehensive two-dimensional liquid chromatography Comprehensive two-dimensional gas chromatography Data analysis
a b s t r a c t User-friendly and easy-to-use laboratory-written programs for visualisation and interpretation of comprehensive two-dimensional chromatographic data were developed. The programs that are not tied to any particular commercial instrument, and data obtained either by comprehensive two-dimensional liquid (LC × LC) or gas (GC × GC) chromatography can be analysed. Operations of the programs allow visualisation of 2D and 3D plots, comparison of two 2D plots at a time, as well as determination of retention times and peak heights and volumes. © 2008 Elsevier B.V. All rights reserved.
1. Introduction Obtaining full advantage of data obtained by comprehensive two-dimensional liquid (LC × LC) and gas (GC × GC) chromatography depends upon the existence of data analysis software for visualisation and interpretation. Comprehensive two-dimensional (2D) separation is visualised as a contour or colour plot obtained by slicing a raw chromatogram according to a specified modulation period and then aligning the slices in parallel. Sometimes a three-dimensional (3D) plot is used, in which the peak intensity is reported along the z-axis. For creations of the plots, no commercial visualisation programs are available for LC × LC at present; for GC × GC some commercial software has been developed although many researchers still rely on in-house written software. Since comprehensive 2D separations provide information on the whole sample and its components, the technique is often used to characterise differences between samples. A direct, qualitative and well-known approach is to compare 2D chromatograms by eye but to describe the differences quantitatively demands sophisticated approaches. In the LC × LC traditional side-by-side comparison has so far been prevailing technique. In GC × GC side-by-side comparison has been used as well, but also sophisticated approaches using difference [1–4], ratio and addition [4] chromatograms have been reported. Chemometrical tools become necessary when the differences are searched from large amount of data. Increasing demand to perform quantitative analyses set demands for software development. The number of quantitative GC × GC analyses is increasing, partly due to commercial tools
∗ Corresponding author. Tel.: +358 919150252; fax: +358 9 19150253. E-mail address: tuulia.hyotylainen@helsinki.fi (T. Hyötyläinen). 0021-9673/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.chroma.2008.11.037
available, but quantification in LC × LC is still rare [5,6], mainly due to lack of suitable software. The traditional procedure to calibrate demands the determination of summed peak areas or peak volumes; peak heights are rarely used. When peak areas are determined from a raw chromatogram with traditional LC or GC software, and when a spreadsheet program is used for summing and further calculations, no special software is necessarily required. Peak volume determinations, on the other hand, always require special software. In addition, chemometric approaches can be used [7–10]. The aim of this paper is shortly to present laboratory-written programs for visualisation of 2D and 3D plots, comparison of 2D plots and quantification of data obtained by comprehensive twodimensional chromatography. The software was originally written for GC × GC, but the same scripts can be applied to LC × LC separations as well. Because the programs are not tied to any particular commercial instrument, LC × LC or GC × GC data produced by any instrument can be analysed. The data analysis approach do not try to compete with commercial software, but to introduce userfriendly, easy-to-use and easy-to-adopt solution for average analyst working with LC × LC or GC × GC. Applicability of the programs is demonstrated with selected LC × LC and GC × GC analyses. 2. Experimental 2.1. Chemicals and samples Phenolic acids (caffeic, chlorogenic and syringic acids) were from Sigma–Aldrich (Steinheim, Germany). Stock solution prepared in methanol was diluted with deionised water for preparation of standard solutions (c = 5–30 g/ml). The beverage and herb samples were purchased from a local supermarket or a liquor store.
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Beverage samples were filtered and diluted with deionised water (1:1 or 1:2). Herbs were milled into fine powder in a laboratoryscale mill. For sample preparation ultrasonic assisted extraction with 60% (v/v) ethanol was used [6]. For GC × GC, aerosol samples were collected onto quartz fibre filters with a high volume sampler at the Station for Measuring Forest Ecosystem-Atmosphere Relations (SMEAR II) in Hyytiälä, Finland [11]. For sample preparation ultrasonic assisted extraction with dichloromethane or n-hexane-acetone (1:1, v/v) was used. Nocaryophyllene aldehyde standard was synthesised, purified and characterised in our laboratory [12]. 2.2. Comprehensive two-dimensional liquid chromatography The LC × LC apparatus consisted of a Hewlett-Packard 1100 HPLC with diode array detector and an extra pump (Jasco PU-980, Tokyo, Japan). A 10-port high-pressure interfacing valve (C2-1000EP, VICI Valco, Houston, USA) was controlled by laboratory-written C++ program. For both applications the first column was Atlantis C18 column (150 mm × 2.1 mm I.D., 3 m, Waters, MA, USA) with a flow rate of 0.1 ml/min. The amount injected was 10 l. The eluent in the 1st dimension was a 0.5% acetic acid–acetonitrile gradient for both applications. In the analysis of beverages the second column was XBridge C18 (50 mm × 3 mm I.D., 2.5 m, Waters) and the eluent was 15 mM tetrapentylammonium bromide in acetonitrile with 0.05% acetic acid (21:79%, v/v) at a flow rate of 1.35 ml/min. For the herb analyses the second column was an Ultrasphere cyano column (75 mm × 4.6 mm I.D., 3 m, Beckman, CA, USA). The eluent was acetonitrile–0.05% acetic acid (35:65%, v/v) at a flow rate of 1.9 ml/min. The loop size was 200 l (beverages) or 130 l (herbs) and the modulation period was 90 s (beverages) or 35 s (herbs). For
the herb analyses a MicroTOF mass spectrometer (Bruker Daltonics, Germany) with an electrospray ion source was used. The MS operated in negative mode (100–800 m/z) with acquisition frequency of 2 Hz. The LC flow was split in ratio 1:7. 2.3. Comprehensive two-dimensional gas chromatography A GC × GC system was an HP 6890 GC equipped with an HP 7683 automatic injector and a flame ionisation detection (Agilent Technologies, CA, USA). Splitless injection with helium as carrier gas in constant flow mode was applied. The FID was held at 300 ◦ C. Modulation was performed with a laboratory-made semirotating cryogenic modulator [13,14] with modulation period of 3 or 4 s. In the identification of -nocaryophyllene aldehyde, a 10 m × 0.25 mm I.D. HP-1701 (Agilent Technologies) column (film thickness 0.25 m) coupled to a 0.7 m × 0.10 mm I.D. HP-1 column (film thickness 0.12 m) was used. Temperature program was as follows: 60 ◦ C (6 min); 5 ◦ C/min to 280 ◦ C (10 min). Otherwise the aerosol samples were analysed with a 20 m × 0.25 mm I.D. HP-5MS column (film thickness 0.25 m) connected to a 0.7 m × 0.1 mm I.D. BGB-1701 (BGB Analytik, Zürich, Switzerland),) column (film thickness 0.1 m). Temperature program was as follows: 60 ◦ C (isothermal 4 min); 5 ◦ C/min 300 ◦ C (15 min). 2.4. Data analysis Chemstation was used for data acquisition with data acquisition rates of 20–50 Hz for LC × LC and 100 Hz for GC × GC. Data files were exported as comma separated value files for further analysis. Inhouse Matlab (The MathWorks, Natick, MA, USA) written scripts were used in the data analysis.
Fig. 1. Visualisation of black currant wine analysed with LC × LC–UV system. (A) Raw chromatogram with suggested baseline, (B) raw chromatogram with adopted baseline, (C) 2D plot, and (D) 3D plot.
M. Kallio et al. / J. Chromatogr. A 1216 (2009) 2923–2927
Fig. 2. Overlaid LC × LC–UV plots of black currant wine (green) and juice (red).
3. Results and discussion The operations of the two in-house written are summarized in Table S1. The developed programs allowing basic and comparison operations are suitable for analysis of LC × LC and GC × GC data. In this study, we concentrate on cases with limited number of samples and on cases where expertise of the analyst is needed. 3.1. Program operations Basic operations include baseline adoption, generation of the 2D and 3D plots, and peak defining (determination of 1st and 2nd dimension retention times, peak volume and height). Setting of the baseline into the raw chromatogram can be done either automatically with a polynomial function or manually. The benefit of
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the manual break-line adoption is that any offsets or drifts in the baseline level can then be accurately followed and removed or straightened. This feature is particularly useful for LC × LC data in which the baseline is less stable and often includes large void peaks in the beginning of the run (Fig. 1A and B). Manual baseline adoption is not a practical option for large number of samples, and in such cases automated algorithms are a better option. Manual baseline adoption takes less than 10 s and its primary validation of the correctness is done visually. Further, it has been shown that calibration curves obtained by summed areas and peak volumes are equal [15]. In 2D plots (Fig. 1C), contours indicate heights of the peaks. Since no smoothing is used, peak shape slightly angular in the first dimension due to low number of data points along the first-dimension axis. This is more severe in LC × LC due to longer modulation periods (tens of seconds) than in GC × GC in which modulation period is usually less than 10 s. In 3D-plots (Fig. 1D), colour scale indicates the peak intensities. In manual peak defining, the user defines a square around a peak, for which the program determines the retention times in the first and second dimensions, the peak height and the volume. Retention times and peak heights are related to peak apex, i.e. to the highest fraction of the modulated peak envelope while peak volume is calculated by multiplying the data point height by the corresponding area and summing these sub-columns of the peak together. The peak volume determination can also be used to determine the peak volume for a compound group. Further, there is an option for automated peak detection, which searches peaks on the basis of a predetermined minimum peak height. For the comparison of 2D plots, the program handles two files, one of which is considered the reference and the other the data file. Comparison operations include baseline adoption (similar to that in the basic operations), signal normalisation with internal standard, adjustment of the position of the data file relative to the reference,
Fig. 3. (A) LC × LC–MS overlaid plot of oregano (red) and thyme (green), total ion chromatogram. (B) EIC with masses 353 for chlorogenic and 197 for syringic acid. (C) Subtracted EIC.
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intensity of the positive or negative values but all positive points are depicted with the same red and likewise all negative points with a fixed blue colour in the 2D plot. As an example of this operation, standards containing -nocaryophyllene aldehyde at two different concentrations can be seen in Fig. S1. Semi-quantitative estimation of the amount of differences can be obtained by looking at the subtracted 3D plot, in which the colour scale indicates the numeric value (Fig. S2). An option to integrate the subtracted peaks and to get a numeric value for a negative or a positive peak will be considered to in the future. 3.2. Applications
Fig. 4. Identification of -nocaryophyllene aldehyde (indicated with arrow) in forest aerosol sample analysed by GC × GC-FID. Green signal is the original sample and the red the spiked one.
and the subtraction of peak intensities from another. Retention times can be scaled to match by moving the data plot separately in the direction of the x- and y-axes or by using a scaling factor. The presented technique that aims for visual comparison can be applied to any 2D separation. Best alignment for GC × GC runs is achieved when relatively small regions are selected one at a time instead of the whole run. For LC × LC the whole run can be handled at once due to lower number of data points. Correction made on the 2D plot ensures that the retention time correction is performed reliably in both dimensions without any problems in 2D plot generation. Particularly for LC × LC data the reproducibility of the retention times is not as good as for GC × GC, and thus the retention time scaling is a very useful option. Finally, possibility to subtract overlaid runs from each other allows comparison of concentration differences. For clarity of visualisation no colour scale indicate the
The developed programs were utilised in the LC × LC analysis of antioxidant phenolic compounds in herbs and beverages. Phenolic compounds are of particular interest as they are associated with reduced risk of oxidative stress mediated diseases such as cancer and cardiovascular disease. LC × LC–UV profiles of black currant juice and wine were compared using comparison operations. As can be seen from Fig. 2, black currant wine contains more peaks (green signal) than black currant juice (red signal). Caffeic acid was present in both samples and was identified by comparing the retention times with those of standard runs. Concentration of the caffeic acid in black currant wine was 10 g/ml; in black currant juice the amount was below 5 g/ml. Other phenolic compounds identified in the samples included catechin, gallic acid, para-coumaric acid, ferulic acid, isoquercitrin, myricetin and quercetin with concentrations ranging from 5 to 30 g/ml. Previously, comparison of LC × LC plots has only been done side-by-side instead of computer-assisted overlaying program which also makes quick semi-quantitative evaluation possible. Fig. 3 shows a comparison of LC × LC–MS analyses of two herbal samples, namely oregano and thyme. In Fig. 3A the total ion chromatograms (TIC) of herbs are overlayed with each other and in Fig. 3B and C the extracted ion chromatograms (EIC) of target ana-
Fig. 5. Subtracted GC × GC-FID plots of pinonaldehyde (circled) and verbenone (squared). Signal of a day when particles formation occurred (March 17, 20, 24, 25, April 7 and 9) were subtracted from the day when formation did not occur (March 22, 27 and April 10). Red indicates positive value and blue negative value. Relative peak volumes are shown above the peak.
M. Kallio et al. / J. Chromatogr. A 1216 (2009) 2923–2927
lytes are overlayed and subtracted. In TIC plot, only the major peaks are visible, more peaks and the improved 2D separation could be seen when EIC mode and suitable zooming was applied in the comparison operation. The comparison operation proved to work well both for comparison of the overall separation and in the comparison of selected areas, which were zoomed for visualisation of minor compounds. Further, the programs were used in the analysis of aerosols samples by GC × GC. Biogenic compounds, and especially their oxidation products, are of special interest since they are believed to take part in the growth of newly formed aerosol particles. Special emphasis was put on the oxygenated biogenic compounds, namely -nocaryophyllene aldehyde (an oxidation product of -caryophyllene) and pinonaldehyde (an oxidation product of ␣pinene), which both are emitted by coniferous trees. By using the comparison program -nocaryophyllene aldehyde could be positively identified in an ambient aerosol sample (Fig. 4). Using the quantification features of the basic program and the calibration curve constructed using peak volumes, the concentration of -nocaryophyllene aldehyde was 6 ng/m3 . At such low concentration, GC × GC with modulation was required to increase the sensitivity and to spot the oxidation product properly. The separation efficiency obtained with GC × GC was also essential because in one-dimensional GC -nocaryophyllene aldehyde co-eluted with a non-polar compound that was present in high concentration and it could not be located from GC-FID chromatogram. The identification and quantification was clearly more reliable than with the GC–MS in which the coelution with aliphatic compounds interfered the analysis [12]. Further analysis utilising the comparison program showed differences in the profiles of oxygenated compounds during particle formation. For example, pinonaldehyde which is present in relatively high concentrations (from 10 to 100 ng/m3 ) in all of the samples showed higher concentration (blue peaks are dominant) on days when particles were not formed (Fig. 5). In the comparison, two signals at the time were normalised, overlaid and subtracted from each other. The visual estimation of concentration differences were confirmed by determining the relative peak volumes reported in the figure. Verbenone, another oxygenated biogenic compound, showed rather similar trend. Note that the peaks are not fully overlaid due to slight differences in peak shape. Holligsworthy et al. [2] reported overcoming of the problem through the use of fuzzy difference chromatograms, where individual pixels were not compared with one-by-one but rather with values in the near neighbourhood.
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4. Conclusions The programs developed for two-dimensional data handling proved useful in the visualisation and interpretation of GC × GC and LC × LC data. Basic operations allowed the visualisation and determination of parameters needed in qualitative and quantitative analysis. Overlaying of 2D plots allowed easy comparison and subtraction of the plots provided an estimate of the differences in concentration. In future, the programs will be utilised in a variety of LC × LC and GC × GC analyses. Acknowledgements Funding was provided by the Academy of Finland, the Maj and Tor Nessling Foundation, the Alfred Kordelin Foundation and the University of Helsinki. Päivi Raimi and Sari Järvimäki are thanked for assistance in the laboratory. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.chroma.2008.11.037. References [1] R.A. Shellie, W. Welthagen, J. Zrostlikova, J. Spranger, M. Ristow, O. Fiehn, R. Zimmermann, J. Chromatogr. A 1087 (2005) 83. [2] B.V. Hollingsworthy, S.E. Reichenbach, Q. Tao, A. Visvanathan, J. Chromatogr. A 1105 (2006) 51. [3] T.C. Tran, P.J. Marriott, Atmos. Environ. 41 (2007) 5756. [4] R.K. Nelson, B.M. Kile, D.L. Plata, S.P. Sylva, L. Xu, C.M. Reddy, R.B. Gaines, G.S. Frysinger, S.E. Reichenbach, Environ. Forensics 7 (2006) 33. [5] J. Pól, B. Hohnová, M. Jussila, T. Hyötyläinen, J. Chromatogr. A 1130 (2006) 64. [6] M. Kivilompolo, T. Hyötyläinen, J. Chromatogr. A 1145 (2007) 155. [7] L. Mondello, M. Herrero, T. Kumm, P. Dugo, H. Cortes, G. Dugo, Anal. Chem. 80 (2008) 5418. [8] C.G. Fraga, B.J. Prazen, R.E. Synovec, Anal. Chem. 72 (2000) 4154. [9] V.G. van Mispelaar, A.C. Tas, A.K. Smilde, P.J. Schoenmakers, A.C. van Asten, J. Chromatogr. A 1019 (2003) 15. [10] K.J. Johnson, B.J. Prazen, D.C. Young, R.E. Synovec, J. Sep. Sci. 27 (2004) 410. [11] T. Rissanen, T. Hyötyläinen, M. Kallio, J. Kronholm, M. Kulmala, M.-L. Riekkola, Chemosphere 64 (2006) 1185. [12] J. Parshintsev, J. Nurmi, I. Kilpeläinen, K. Hartonen, M. Kulmala, M.-L. Riekkola, Anal. Bioanal. Chem. 390 (2008) 913. [13] M. Kallio, T. Hyötyläinen, M. Jussila, K. Hartonen, S. Palonen, M.-L. Riekkola, Anal. Bioanal. Chem. 375 (2003) 725. [14] M. Kallio, M. Jussila, P. Raimi, T. Hyötyläinen, Anal. Bioanal. Chem. 391 (2008) 2357. [15] M. Kallio, T. Hyötyläinen, J. Chromatogr. A 1125 (2006) 234.
Contents lists available at ScienceDirect
Journal of Chromatography A journal homepage: www.elsevier.com/locate/chroma
Data analysis programs for comprehensive two-dimensional chromatography Minna Kallio, Maarit Kivilompolo, Sami Varjo, Matti Jussila, Tuulia Hyötyläinen ∗ Laboratory of Analytical Chemistry, Department of Chemistry, University of Helsinki, P.O. Box 55, FI-00014 Helsinki, Finland
a r t i c l e
i n f o
Article history: Available online 19 November 2008 Keywords: Comprehensive two-dimensional liquid chromatography Comprehensive two-dimensional gas chromatography Data analysis
a b s t r a c t User-friendly and easy-to-use laboratory-written programs for visualisation and interpretation of comprehensive two-dimensional chromatographic data were developed. The programs that are not tied to any particular commercial instrument, and data obtained either by comprehensive two-dimensional liquid (LC × LC) or gas (GC × GC) chromatography can be analysed. Operations of the programs allow visualisation of 2D and 3D plots, comparison of two 2D plots at a time, as well as determination of retention times and peak heights and volumes. © 2008 Elsevier B.V. All rights reserved.
1. Introduction Obtaining full advantage of data obtained by comprehensive two-dimensional liquid (LC × LC) and gas (GC × GC) chromatography depends upon the existence of data analysis software for visualisation and interpretation. Comprehensive two-dimensional (2D) separation is visualised as a contour or colour plot obtained by slicing a raw chromatogram according to a specified modulation period and then aligning the slices in parallel. Sometimes a three-dimensional (3D) plot is used, in which the peak intensity is reported along the z-axis. For creations of the plots, no commercial visualisation programs are available for LC × LC at present; for GC × GC some commercial software has been developed although many researchers still rely on in-house written software. Since comprehensive 2D separations provide information on the whole sample and its components, the technique is often used to characterise differences between samples. A direct, qualitative and well-known approach is to compare 2D chromatograms by eye but to describe the differences quantitatively demands sophisticated approaches. In the LC × LC traditional side-by-side comparison has so far been prevailing technique. In GC × GC side-by-side comparison has been used as well, but also sophisticated approaches using difference [1–4], ratio and addition [4] chromatograms have been reported. Chemometrical tools become necessary when the differences are searched from large amount of data. Increasing demand to perform quantitative analyses set demands for software development. The number of quantitative GC × GC analyses is increasing, partly due to commercial tools
∗ Corresponding author. Tel.: +358 919150252; fax: +358 9 19150253. E-mail address: tuulia.hyotylainen@helsinki.fi (T. Hyötyläinen). 0021-9673/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.chroma.2008.11.037
available, but quantification in LC × LC is still rare [5,6], mainly due to lack of suitable software. The traditional procedure to calibrate demands the determination of summed peak areas or peak volumes; peak heights are rarely used. When peak areas are determined from a raw chromatogram with traditional LC or GC software, and when a spreadsheet program is used for summing and further calculations, no special software is necessarily required. Peak volume determinations, on the other hand, always require special software. In addition, chemometric approaches can be used [7–10]. The aim of this paper is shortly to present laboratory-written programs for visualisation of 2D and 3D plots, comparison of 2D plots and quantification of data obtained by comprehensive twodimensional chromatography. The software was originally written for GC × GC, but the same scripts can be applied to LC × LC separations as well. Because the programs are not tied to any particular commercial instrument, LC × LC or GC × GC data produced by any instrument can be analysed. The data analysis approach do not try to compete with commercial software, but to introduce userfriendly, easy-to-use and easy-to-adopt solution for average analyst working with LC × LC or GC × GC. Applicability of the programs is demonstrated with selected LC × LC and GC × GC analyses. 2. Experimental 2.1. Chemicals and samples Phenolic acids (caffeic, chlorogenic and syringic acids) were from Sigma–Aldrich (Steinheim, Germany). Stock solution prepared in methanol was diluted with deionised water for preparation of standard solutions (c = 5–30 g/ml). The beverage and herb samples were purchased from a local supermarket or a liquor store.
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Beverage samples were filtered and diluted with deionised water (1:1 or 1:2). Herbs were milled into fine powder in a laboratoryscale mill. For sample preparation ultrasonic assisted extraction with 60% (v/v) ethanol was used [6]. For GC × GC, aerosol samples were collected onto quartz fibre filters with a high volume sampler at the Station for Measuring Forest Ecosystem-Atmosphere Relations (SMEAR II) in Hyytiälä, Finland [11]. For sample preparation ultrasonic assisted extraction with dichloromethane or n-hexane-acetone (1:1, v/v) was used. Nocaryophyllene aldehyde standard was synthesised, purified and characterised in our laboratory [12]. 2.2. Comprehensive two-dimensional liquid chromatography The LC × LC apparatus consisted of a Hewlett-Packard 1100 HPLC with diode array detector and an extra pump (Jasco PU-980, Tokyo, Japan). A 10-port high-pressure interfacing valve (C2-1000EP, VICI Valco, Houston, USA) was controlled by laboratory-written C++ program. For both applications the first column was Atlantis C18 column (150 mm × 2.1 mm I.D., 3 m, Waters, MA, USA) with a flow rate of 0.1 ml/min. The amount injected was 10 l. The eluent in the 1st dimension was a 0.5% acetic acid–acetonitrile gradient for both applications. In the analysis of beverages the second column was XBridge C18 (50 mm × 3 mm I.D., 2.5 m, Waters) and the eluent was 15 mM tetrapentylammonium bromide in acetonitrile with 0.05% acetic acid (21:79%, v/v) at a flow rate of 1.35 ml/min. For the herb analyses the second column was an Ultrasphere cyano column (75 mm × 4.6 mm I.D., 3 m, Beckman, CA, USA). The eluent was acetonitrile–0.05% acetic acid (35:65%, v/v) at a flow rate of 1.9 ml/min. The loop size was 200 l (beverages) or 130 l (herbs) and the modulation period was 90 s (beverages) or 35 s (herbs). For
the herb analyses a MicroTOF mass spectrometer (Bruker Daltonics, Germany) with an electrospray ion source was used. The MS operated in negative mode (100–800 m/z) with acquisition frequency of 2 Hz. The LC flow was split in ratio 1:7. 2.3. Comprehensive two-dimensional gas chromatography A GC × GC system was an HP 6890 GC equipped with an HP 7683 automatic injector and a flame ionisation detection (Agilent Technologies, CA, USA). Splitless injection with helium as carrier gas in constant flow mode was applied. The FID was held at 300 ◦ C. Modulation was performed with a laboratory-made semirotating cryogenic modulator [13,14] with modulation period of 3 or 4 s. In the identification of -nocaryophyllene aldehyde, a 10 m × 0.25 mm I.D. HP-1701 (Agilent Technologies) column (film thickness 0.25 m) coupled to a 0.7 m × 0.10 mm I.D. HP-1 column (film thickness 0.12 m) was used. Temperature program was as follows: 60 ◦ C (6 min); 5 ◦ C/min to 280 ◦ C (10 min). Otherwise the aerosol samples were analysed with a 20 m × 0.25 mm I.D. HP-5MS column (film thickness 0.25 m) connected to a 0.7 m × 0.1 mm I.D. BGB-1701 (BGB Analytik, Zürich, Switzerland),) column (film thickness 0.1 m). Temperature program was as follows: 60 ◦ C (isothermal 4 min); 5 ◦ C/min 300 ◦ C (15 min). 2.4. Data analysis Chemstation was used for data acquisition with data acquisition rates of 20–50 Hz for LC × LC and 100 Hz for GC × GC. Data files were exported as comma separated value files for further analysis. Inhouse Matlab (The MathWorks, Natick, MA, USA) written scripts were used in the data analysis.
Fig. 1. Visualisation of black currant wine analysed with LC × LC–UV system. (A) Raw chromatogram with suggested baseline, (B) raw chromatogram with adopted baseline, (C) 2D plot, and (D) 3D plot.
M. Kallio et al. / J. Chromatogr. A 1216 (2009) 2923–2927
Fig. 2. Overlaid LC × LC–UV plots of black currant wine (green) and juice (red).
3. Results and discussion The operations of the two in-house written are summarized in Table S1. The developed programs allowing basic and comparison operations are suitable for analysis of LC × LC and GC × GC data. In this study, we concentrate on cases with limited number of samples and on cases where expertise of the analyst is needed. 3.1. Program operations Basic operations include baseline adoption, generation of the 2D and 3D plots, and peak defining (determination of 1st and 2nd dimension retention times, peak volume and height). Setting of the baseline into the raw chromatogram can be done either automatically with a polynomial function or manually. The benefit of
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the manual break-line adoption is that any offsets or drifts in the baseline level can then be accurately followed and removed or straightened. This feature is particularly useful for LC × LC data in which the baseline is less stable and often includes large void peaks in the beginning of the run (Fig. 1A and B). Manual baseline adoption is not a practical option for large number of samples, and in such cases automated algorithms are a better option. Manual baseline adoption takes less than 10 s and its primary validation of the correctness is done visually. Further, it has been shown that calibration curves obtained by summed areas and peak volumes are equal [15]. In 2D plots (Fig. 1C), contours indicate heights of the peaks. Since no smoothing is used, peak shape slightly angular in the first dimension due to low number of data points along the first-dimension axis. This is more severe in LC × LC due to longer modulation periods (tens of seconds) than in GC × GC in which modulation period is usually less than 10 s. In 3D-plots (Fig. 1D), colour scale indicates the peak intensities. In manual peak defining, the user defines a square around a peak, for which the program determines the retention times in the first and second dimensions, the peak height and the volume. Retention times and peak heights are related to peak apex, i.e. to the highest fraction of the modulated peak envelope while peak volume is calculated by multiplying the data point height by the corresponding area and summing these sub-columns of the peak together. The peak volume determination can also be used to determine the peak volume for a compound group. Further, there is an option for automated peak detection, which searches peaks on the basis of a predetermined minimum peak height. For the comparison of 2D plots, the program handles two files, one of which is considered the reference and the other the data file. Comparison operations include baseline adoption (similar to that in the basic operations), signal normalisation with internal standard, adjustment of the position of the data file relative to the reference,
Fig. 3. (A) LC × LC–MS overlaid plot of oregano (red) and thyme (green), total ion chromatogram. (B) EIC with masses 353 for chlorogenic and 197 for syringic acid. (C) Subtracted EIC.
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intensity of the positive or negative values but all positive points are depicted with the same red and likewise all negative points with a fixed blue colour in the 2D plot. As an example of this operation, standards containing -nocaryophyllene aldehyde at two different concentrations can be seen in Fig. S1. Semi-quantitative estimation of the amount of differences can be obtained by looking at the subtracted 3D plot, in which the colour scale indicates the numeric value (Fig. S2). An option to integrate the subtracted peaks and to get a numeric value for a negative or a positive peak will be considered to in the future. 3.2. Applications
Fig. 4. Identification of -nocaryophyllene aldehyde (indicated with arrow) in forest aerosol sample analysed by GC × GC-FID. Green signal is the original sample and the red the spiked one.
and the subtraction of peak intensities from another. Retention times can be scaled to match by moving the data plot separately in the direction of the x- and y-axes or by using a scaling factor. The presented technique that aims for visual comparison can be applied to any 2D separation. Best alignment for GC × GC runs is achieved when relatively small regions are selected one at a time instead of the whole run. For LC × LC the whole run can be handled at once due to lower number of data points. Correction made on the 2D plot ensures that the retention time correction is performed reliably in both dimensions without any problems in 2D plot generation. Particularly for LC × LC data the reproducibility of the retention times is not as good as for GC × GC, and thus the retention time scaling is a very useful option. Finally, possibility to subtract overlaid runs from each other allows comparison of concentration differences. For clarity of visualisation no colour scale indicate the
The developed programs were utilised in the LC × LC analysis of antioxidant phenolic compounds in herbs and beverages. Phenolic compounds are of particular interest as they are associated with reduced risk of oxidative stress mediated diseases such as cancer and cardiovascular disease. LC × LC–UV profiles of black currant juice and wine were compared using comparison operations. As can be seen from Fig. 2, black currant wine contains more peaks (green signal) than black currant juice (red signal). Caffeic acid was present in both samples and was identified by comparing the retention times with those of standard runs. Concentration of the caffeic acid in black currant wine was 10 g/ml; in black currant juice the amount was below 5 g/ml. Other phenolic compounds identified in the samples included catechin, gallic acid, para-coumaric acid, ferulic acid, isoquercitrin, myricetin and quercetin with concentrations ranging from 5 to 30 g/ml. Previously, comparison of LC × LC plots has only been done side-by-side instead of computer-assisted overlaying program which also makes quick semi-quantitative evaluation possible. Fig. 3 shows a comparison of LC × LC–MS analyses of two herbal samples, namely oregano and thyme. In Fig. 3A the total ion chromatograms (TIC) of herbs are overlayed with each other and in Fig. 3B and C the extracted ion chromatograms (EIC) of target ana-
Fig. 5. Subtracted GC × GC-FID plots of pinonaldehyde (circled) and verbenone (squared). Signal of a day when particles formation occurred (March 17, 20, 24, 25, April 7 and 9) were subtracted from the day when formation did not occur (March 22, 27 and April 10). Red indicates positive value and blue negative value. Relative peak volumes are shown above the peak.
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lytes are overlayed and subtracted. In TIC plot, only the major peaks are visible, more peaks and the improved 2D separation could be seen when EIC mode and suitable zooming was applied in the comparison operation. The comparison operation proved to work well both for comparison of the overall separation and in the comparison of selected areas, which were zoomed for visualisation of minor compounds. Further, the programs were used in the analysis of aerosols samples by GC × GC. Biogenic compounds, and especially their oxidation products, are of special interest since they are believed to take part in the growth of newly formed aerosol particles. Special emphasis was put on the oxygenated biogenic compounds, namely -nocaryophyllene aldehyde (an oxidation product of -caryophyllene) and pinonaldehyde (an oxidation product of ␣pinene), which both are emitted by coniferous trees. By using the comparison program -nocaryophyllene aldehyde could be positively identified in an ambient aerosol sample (Fig. 4). Using the quantification features of the basic program and the calibration curve constructed using peak volumes, the concentration of -nocaryophyllene aldehyde was 6 ng/m3 . At such low concentration, GC × GC with modulation was required to increase the sensitivity and to spot the oxidation product properly. The separation efficiency obtained with GC × GC was also essential because in one-dimensional GC -nocaryophyllene aldehyde co-eluted with a non-polar compound that was present in high concentration and it could not be located from GC-FID chromatogram. The identification and quantification was clearly more reliable than with the GC–MS in which the coelution with aliphatic compounds interfered the analysis [12]. Further analysis utilising the comparison program showed differences in the profiles of oxygenated compounds during particle formation. For example, pinonaldehyde which is present in relatively high concentrations (from 10 to 100 ng/m3 ) in all of the samples showed higher concentration (blue peaks are dominant) on days when particles were not formed (Fig. 5). In the comparison, two signals at the time were normalised, overlaid and subtracted from each other. The visual estimation of concentration differences were confirmed by determining the relative peak volumes reported in the figure. Verbenone, another oxygenated biogenic compound, showed rather similar trend. Note that the peaks are not fully overlaid due to slight differences in peak shape. Holligsworthy et al. [2] reported overcoming of the problem through the use of fuzzy difference chromatograms, where individual pixels were not compared with one-by-one but rather with values in the near neighbourhood.
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4. Conclusions The programs developed for two-dimensional data handling proved useful in the visualisation and interpretation of GC × GC and LC × LC data. Basic operations allowed the visualisation and determination of parameters needed in qualitative and quantitative analysis. Overlaying of 2D plots allowed easy comparison and subtraction of the plots provided an estimate of the differences in concentration. In future, the programs will be utilised in a variety of LC × LC and GC × GC analyses. Acknowledgements Funding was provided by the Academy of Finland, the Maj and Tor Nessling Foundation, the Alfred Kordelin Foundation and the University of Helsinki. Päivi Raimi and Sari Järvimäki are thanked for assistance in the laboratory. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.chroma.2008.11.037. References [1] R.A. Shellie, W. Welthagen, J. Zrostlikova, J. Spranger, M. Ristow, O. Fiehn, R. Zimmermann, J. Chromatogr. A 1087 (2005) 83. [2] B.V. Hollingsworthy, S.E. Reichenbach, Q. Tao, A. Visvanathan, J. Chromatogr. A 1105 (2006) 51. [3] T.C. Tran, P.J. Marriott, Atmos. Environ. 41 (2007) 5756. [4] R.K. Nelson, B.M. Kile, D.L. Plata, S.P. Sylva, L. Xu, C.M. Reddy, R.B. Gaines, G.S. Frysinger, S.E. Reichenbach, Environ. Forensics 7 (2006) 33. [5] J. Pól, B. Hohnová, M. Jussila, T. Hyötyläinen, J. Chromatogr. A 1130 (2006) 64. [6] M. Kivilompolo, T. Hyötyläinen, J. Chromatogr. A 1145 (2007) 155. [7] L. Mondello, M. Herrero, T. Kumm, P. Dugo, H. Cortes, G. Dugo, Anal. Chem. 80 (2008) 5418. [8] C.G. Fraga, B.J. Prazen, R.E. Synovec, Anal. Chem. 72 (2000) 4154. [9] V.G. van Mispelaar, A.C. Tas, A.K. Smilde, P.J. Schoenmakers, A.C. van Asten, J. Chromatogr. A 1019 (2003) 15. [10] K.J. Johnson, B.J. Prazen, D.C. Young, R.E. Synovec, J. Sep. Sci. 27 (2004) 410. [11] T. Rissanen, T. Hyötyläinen, M. Kallio, J. Kronholm, M. Kulmala, M.-L. Riekkola, Chemosphere 64 (2006) 1185. [12] J. Parshintsev, J. Nurmi, I. Kilpeläinen, K. Hartonen, M. Kulmala, M.-L. Riekkola, Anal. Bioanal. Chem. 390 (2008) 913. [13] M. Kallio, T. Hyötyläinen, M. Jussila, K. Hartonen, S. Palonen, M.-L. Riekkola, Anal. Bioanal. Chem. 375 (2003) 725. [14] M. Kallio, M. Jussila, P. Raimi, T. Hyötyläinen, Anal. Bioanal. Chem. 391 (2008) 2357. [15] M. Kallio, T. Hyötyläinen, J. Chromatogr. A 1125 (2006) 234.