The LCGC Blog: Time Interval Deconvolution as an Alternate Strategy to Peak Integration Using Gas Chromatography–Vacuum Ultraviolet Spectroscopy


Precise and accurate quantitative analysis based on chromatographic measurements has historically relied very heavily on careful peak integration. Seasoned analysts know that while automated algorithms exist in modern chromatography software, it is a best practice to manually check that the integration points-the points at the beginning and end of a peak, between which the peak will be integrated to obtain a peak area-are appropriately specified.

Precise and accurate quantitative analysis based on chromatographic measurements has historically relied very heavily on careful peak integration. Seasoned analysts know that while automated algorithms exist in modern chromatography software, it is a best practice to manually check that the integration points-the points at the beginning and end of a peak, between which the peak will be integrated to obtain a peak area-are appropriately specified. Column and instrument manufacturers know that reliable and reproducible peak integration is best achieved when target analytes are well resolved from any coeluted interferences, and when peaks are highly symmetrical. Instruments are designed to minimize dead volumes, especially at points of connection between components, and active sites, where target analytes may stick or react. Stationary phases in columns are designed to limit or remove interactions between analytes and residual or underlying silanol units that cause tailing, as well as to provide novel selectivities for challenging separations. In many cases, these practices will remain, but in some cases, where gas chromatography coupled to vacuum ultraviolet spectroscopy (GC–VUV) (1,2) can be used for classification or speciation of a complex mixture, peak integration can possibly be supplanted by a new approach called time interval deconvolution (TID) (3).

GC–VUV involves the spectroscopic detection of species based on their absorption between 120 and 240 nm. Virtually all chemical species absorb and have unique gas-phase absorption signatures in this region of the electromagnetic spectrum. When two or more species are coeluted, the recorded spectrum is a simple sum of the spectra of the overlapping species, the contributions of which are scaled based on the relative abundance of each species. Libraries of pure spectra for compounds can be recorded, and with such libraries, it is straightforward to project the contribution of individual components to an overlapping peak-this process has been termed deconvolution (4).

Now, imagine that we take a chromatogram and break it up into regular time intervals. For example, every 0.03 min we will say is a new time interval. For any time interval in the chromatogram, regardless of whether it is from the front of a peak, the middle of a peak, or multiple peaks, one can see a spectrum associated with all of the species present that absorb light. If multiple components are present, and their pure reference spectra are in the library, then their individual contributions to the total absorption can be determined. We can start at the beginning of a chromatogram, and then proceed through to the end, interval by interval, determining the contribution of different compounds to each interval. This is the key part of TID.

When this process is complete (a matter of a few seconds for the instrument software), the data can be processed for reporting. Library entries not only contain a reference spectrum; one can also assign a particular classification for that analyte (for example, an aromatic or a saturate) and a relative response factor (RRF). The abundance of any individual species of interest detected in the chromatographic separation can be specified for reporting (for example, ethanol in gasoline). The total absorption for that compound can be converted to a mass percent, or other unit of interest, using its RRF. Incidentally, determining and recording RRFs for any compound only requires that a known amount of that compound be analyzed together with a known amount of reference having a previously determined RRF. Similar individual compounds of interest can also be binned into the previously specified classes and reported as an aggregate (for example, total aromatics in gasoline). Class-specific RRFs can also be recorded; within a class of compounds, perhaps varying by a homologous series, RRFs are generally very predictable (3). Users have the freedom to define what species or classes should be reported for any given analysis. And again, it is important to note that peak shape or the defining of a beginning or ending of a peak has no bearing on the recorded total absorbance for that compound. I have also written about this situation in the context of column overload in a prior LCGC Blog post (5).

The example of species and classes defined for a gasoline sample above is deliberate. The characterization of finished gasoline often involves the determination of its chemical content in terms of linear, branched, cyclic, aromatic, unsaturated, and oxygenated hydrocarbons, as well as several specific compounds, like benzene and ethanol. A number of fairly laborious ASTM methods exist to carry out this analysis. The use of GC–VUV and TID for this application was demonstrated in the end of 2016 (3). In March 2017, a new ASTM method (D8071) was approved using this new technology. Compared to previous standard methods, the new approach is faster, simpler, and more rugged, and it returns data as accurate and precise as those provided by older methods.


We also recently demonstrated that mixtures of polychlorinated biphenyl (PCB) compounds, so-called Aroclor mixtures, could be accurately speciated using GC–VUV and TID (6). There are 209 ways that one to 10 chlorines can be arranged around a biphenyl molecule. Mixtures of PCBs have been used in a variety of industrial settings, but now they are banned by most countries and represent the most highly analyzed environmental contaminant. In a GC–VUV analysis, each one of the 209 congeners has a unique and differentiable absorption signature. We chose to classify each Aroclor mixture (each one can contain well over 100 PCBs) according to its content of different classes of isomers (for example, monochlorinated species, dichlorinated species, and so forth). The combined output of the TID analysis matched very well the total degree of chlorination specified for each mixture.

We are currently working on other applications of TID for GC–VUV analysis. Where routine or repeated analyses of reasonably well-defined complex mixtures is desired, this approach can be more powerful and rugged than traditional peak integration approaches. Deciding whether a sample is appropriate for TID analysis, assuming it is amenable to gas chromatographic analysis, requires one to answer a few questions:

  • Are the pure spectra for the compounds or classes or interest already in the GC–VUV library, and are they accompanied by RRFs?

  • If not, can pure standards be obtained to record pure library spectra and RRFs?

  • Can you assign retention indices for your compounds of interest based on the separation conditions chosen? This process is generally straightforward-library searches for compounds to deconvolute can be restricted to those that have a retention index loosely matching (for example, ± 30–50 index units) a specified time interval.

  • Can the majority of the sample be characterized by compounds that are in the library or can be added to the library? Although both gasoline and Aroclor samples are complex mixtures, their content is generally well known. In some types of samples from sources like the environment or food, the background can vary considerably and can be hard to characterize. That said, could some type of sample preparation be applied to better isolate the compound class(es) of interest and reduce the number of background unknowns introduced into the instrument? Alternatively, could a specific spectral range be considered through the application of a spectral filter to accentuate the compound class(es) of interest and greatly reduce the number of unknown background compounds that are projected in the final chromatogram? Although some unknowns that are not represented in the library can be accommodated in the methodology, if there exists a large amount of variable and unknown background, then the presence of individual species of interest cannot be deconvolved from the background.

If answers to the above questions reveal the potential to apply TID over traditional peak integration for your analysis, then I would suggest that TID is a better option. It will significantly reduce the burden on chromatographic separations to achieve optimal resolution and peak symmetry. While it may not be a license to perform poor chromatography, a TID approach will be more rugged and forgiving. I would be more than happy to discuss with any reader regarding whether their application could benefit from this approach. Simply send me an email to discuss.

Disclaimer: I am a member of the Scientific Advisory Board for VUV Analytics, Inc.



            1.     I.C. Santos and K.A. Schug, J. Sep. Sci.40, 138–151 (2017).

2.     K.A. Schug, I. Sawicki, D.D. Carlton Jr., H. Fan, H.M. McNair, J.P. Nimmo, P. Kroll, J. Smuts, P. Walsh, and D. Harrison, Anal. Chem.86, 8329–8335 (2014).

3.     P. Walsh, M. Garbalena, and K.A. Schug, Anal. Chem.88, 11130–11138 (2016).

4.     J. Schenk, X. Mao, J. Smuts, P. Walsh, P. Kroll, and K.A. Schug, Anal. Chim. Acta945, 1–8 (2016).

5.     K.A. Schug, “Column Overload in Gas Chromatography with Vacuum Ultraviolet Detection,” The LCGC Blog, November 10, 2015.

6.     C. Qiu, J. Cochran, J. Smuts, P. Walsh, and K.A. Schug, J. Chromatogr. A1490, 191–200 (2017).   


Kevin A. Schug is a Full Professor and Shimadzu Distinguished Professor of Analytical Chemistry in the Department of Chemistry & Biochemistry at The University of Texas (UT) at Arlington. He joined the faculty at UT Arlington in 2005 after completing a Ph.D. in Chemistry at Virginia Tech under the direction of Prof. Harold M. McNair and a post-doctoral fellowship at the University of Vienna under Prof. Wolfgang Lindner. Research in the Schug group spans fundamental and applied areas of separation science and mass spectrometry. Schug was named the LCGCEmerging Leader in Chromatography in 2009 and the 2012 American Chemical Society Division of Analytical Chemistry Young Investigator in Separation Science. He is a fellow of both the U.T. Arlington and U.T. System-Wide Academies of Distinguished Teachers.      

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