The LCGC Blog: Gas Chromatography–Vacuum Ultraviolet Spectroscopy for Fatty Acid Analysis

In 2009, the founders of VUV Analytics, Inc. approached my group about the potential of coupling a vacuum ultraviolet absorption spectroscopy detector to a gas chromatograph (GC–VUV).

In 2009, the founders of VUV Analytics, Inc. approached my group about the potential of coupling a vacuum ultraviolet absorption spectroscopy detector to a gas chromatograph (GC–VUV). For five years, we worked together, back-and-forth secretly, on different alpha and beta instruments, until a commercial instrument was launched in 2014. I was given the honor of presenting the first research talk on GC–VUV at the 2014 International Symposium on Capillary Chromatography and GCxGC conference ( We published the first article later that year (1), and we have been working closely with the company since then to expand the application base for the system- to see what it can and cannot do.

Myriad GC–VUV applications have been reported in the intervening years (2). These have included analysis of petroleum hydrocarbons (3,4), fixed gases (5), terpenes (6), pesticides (7), polychlorinated biphenyls (PCBs) (8) and designer drugs (9), to name just a few. The take-home message for these applications has largely been that the GC–VUV is highly complementary to gas chromatography–mass spectrometry (GC–MS), in cases of analysis where significant numbers of isomers need to be differentiated, especially if they are difficult to resolve chromatographically. Gas phase molecular absorption spectroscopy can be superior to fragment ion spectra, both for differentiating isomers and for deconvolving overlapping signals. In the 120–240 nm range where the GC–VUV records signal, virtually all molecules absorb and have unique absorption signatures. Further, overlapping signals are additive, so with the aid of an absorption spectral reference library, individual contributions to the combined signal can be rapidly determined.

An application area where these detector attributes are especially attractive is for fatty acid analysis (10,11). For example, cis- and trans-isomers for mono-unsaturated fatty acids can be quite distinctively differentiated based on the shape of their absorption spectra. Additionally, the degree of saturation or unsaturation is very evident in acquired spectra of fatty acids. It is worth noting that esterification to create fatty acid methyl esters (FAMEs) is necessary to make the fatty acids more volatile, and GC is needed to separate homologous series of FAMEs that are less directly differentiable based on spectral profiles (for example, C10:0 and C12:0 FAMEs will have similar absorption spectra but are easily separable chromatographically). Even so, GC–VUV has some definite advantages over GC–MS for qualitative speciation of fatty acids.

Initially, we reported determinations of fatty acids in vegetable oils (10). We then used them as a model analyte set to explore the coupling of online comprehensive two-dimensional GC (GCxGC) to VUV detection in collaboration with the Mondello group in Italy (12). Most recently, we have reported applications of GC–VUV for the determination of clinically relevant fatty acids in blood plasma (13). We also showed that fatty acid profiles can be used to classify and discriminate bacteria (14), and we monitored changes in bacterial fatty acid profiles as the microorganisms were exposed to stressful conditions commensurate with potential environmental contamination events (15).

Levels and relative levels of fatty acids in human blood plasma can provide indications of metabolic disorders or nutritional deficiencies, which makes their determination highly desirable in the clinical setting. This application is also extremely challenging given the wide range of relative abundances of a large number of fatty acids desired to be monitored. The current gold standard is GC–MS analysis, but the method of choice requires two different injections (one in split and one in splitless mode) to cover all of the analytes (16). Although GC–MS is generally more sensitive than current GC–VUV instruments, we incorporated large-volume injection to reach the necessary detection limits.

Programmed temperature vaporizer (PTV) inlets are very versatile and can be used to accomplish many different types of injections for GC analyses. For large-volume injections, the injector is programmed to inject a large volume (50 µL in our case) of sample into the injection port. A solvent venting mode is then used to reduce the volume and remove the large excess of solvent before introducing the sample on column. Although the solvent venting mode eventually limited our ability to see FAMEs below C10:0, we were able to achieve detection limits in the low nanomolar concentration with uncertainties and errors in accuracy largely below 10% for 32 different FAMEs from C10:0 to C26:1. The total run time was 45 minutes, which is not ideal for a clinical assay, but the ability to deconvolve poorly resolved analytes, even those overlapping in highly disparate quantities, allowed the determinations to be made from a single injection.


In a completely different realm, our group has been interested in environmental microbiology associated with potential environmental impacts of unconventional oil and gas extraction activities. We had previously used matrix-assisted laser desorption-ionization – mass spectrometry (MALDI-MS) to identify microbes present in both groundwater (17) and oilfield wastewater (18). In the former, some potentially hazardous microbes were detected in supplies of groundwater being used for drinking water, and their presence was often accompanied by other irregularities in water quality. In other words, compromised water quality in terms of elevated total organic carbon or total dissolved solids can lead to increased microbial levels. Although this is in and of itself a concern-you do not want opportunistic pathogens in your drinking water- we began to wonder whether we could also detect indicators of stress in bacteria, when they grow in the presence of different chemical compounds, which may be toxic.

MALDI-MS and GC–VUV were used to measure protein and fatty acid profiles of bacteria, respectively, after the bacteria were exposed to potential environmental contaminants, such as high salt levels, benzene, or alcohol. While protein profiles were also slightly altered, clear changes in the fatty acid profiles were seen using GC–VUV. Interestingly, bacteria will alter their fatty acid profile to decrease membrane permeability against exogenous constituents. Cells exposed to chemical contaminants exhibited increased saturated: unsaturated ratios, as well as displayed the presence of more branched and cyclopropane fatty acids. Thus, it is conceivable to use bacteria (and some are more responsive than others) to monitor ecological health; the presence of potentially unknown or unmeasured toxicants could manifest in measurable changes of bacterial protein and fatty acid compositions.

Although hydrocarbon analysis has been the most prominent application of GC–VUV technology to date, fatty acid analysis is a close second in terms of reports in the literature. Fatty acids are present in so many places of interest, including in our food, in our body, and in the microbiome. Degrees of saturation and particular conformations (cis- vs. trans-) are important for biological effects in both prokaryotic and eukaryotic systems. Thus, speciation of fatty acids is important. Although GC–MS may have greater sensitivity, GC–VUV offers some distinct advantages for speciation of fatty acid mixtures. I would also expect fatty acid analysis to be more amenable to a time interval deconvolutions procedure, as has been demonstrated for gasoline and PCBs (4,8), but that remains still to be developed. Further application of GC–VUV in the food industry, for determining trans-fat and omega-fatty acid content, also seems quite promising, and is being explored.


Disclaimer: Kevin A. Schug is a member of the scientific advisory board for VUV Analytics, Inc.



  1. K.A. Schug, I. Sawicki, D.D. Carlton Jr., H. Fan, H.M. McNair, J.P. Nimmo, P. Kroll, J. Smuts, P. Walsh, D. Harrison, Anal. Chem.86, 8329-8335 (2014).
  2. I.C. Santos, K.A. Schug, J. Sep. Sci.40, 138-151 (2017).
  3. J. Schenk, X. Mao, J. Smuts, P. Walsh, P. Kroll, K.A. Schug, Anal. Chim. Acta945, 1-8 (2016).
  4. P. Walsh, M. Garbalena, K.A. Schug, Anal. Chem.88, 11130-11138 (2016).
  5. L. Bai, J. Smuts, P. Walsh, H. Fan, Z.L. Hildenbrand, D. Wong, D. Wetz, K.A. Schug, J. Chromatogr. A 1388, 244-250 (2015).
  6. C. Qiu, J. Smuts, K.A. Schug, J. Sep. Sci.40, 869-877 (2017).
  7. H. Fan, J. Smuts, P. Walsh, K.A. Schug, J. Chromatogr. A 1389, 120-127 (2015).
  8. C. Qiu, J. Cochran, J. Smuts, P. Walsh, K.A. Schug, J. Chromatogr. A 1490, 191-200 (2017).
  9. L. Skultety, P. Frycak, C. Qiu, J. Smuts, L. Shear-Laude, K. Lemr, J.X. Mao, P. Kroll, K.A. Schug, A. Szewczak, C. Vaught, I. Lurie, V. Havlicek, Anal. Chim. Acta971, 55-67 (2017).
  10. H. Fan, J. Smuts, L. Bai, P. Walsh, D.W. Armstrong, K.A. Schug, Food Chem. 194, 265-271 (2016).
  11. C.A. Weatherly, Y. Zhang, J.P. Smuts, H. Fan, C. Xu, K.A. Schug, J.C. Lang, D.W. Armstrong, J. Agric. Food Chem.64,1422-1432 (2016).
  12. M. Zoccali, K.A. Schug, P. Walsh, J. Smuts, L. Mondello, J. Chromatogr. A 1497, 135-143 (2017).
  13. I.C. Santos, J. Smuts, M.L. Crawford, R.P. Grant, K.A. Schug, Anal. Chim. Acta (In Press, 2019). DOI: 10.1016/j.aca.2018.12.007
  14. I.C. Santos, J. Smuts, W.-S. Choi, Y. Kim, S.B. Kim, K.A. Schug, Talanta182, 536-543 (2018).
  15. I.C. Santos, A. Chaumette, J. Smuts, Z.L. Hildenbrand, K.A. Schug, Environ. Sci. Process. Impacts (In Press, 2019). DOI: 10.1039/C8EM00338F
  16. S.A. Lagerstedt, D.R. Hinrichs, S.M. Batt, M.J. Majera, P. Rinaldo, J.P. McConnell, Mol. Genet. Metabol.73, 38-45 (2001).
  17. I.C. Santos, M.S. Martin, M.L. Reyes, D.D. Carlton Jr., P. Stigler-Granados, M.A. Valerio, K.W. Whitworth, Z.L. Hildenbrand, K.A. Schug, Sci. Tot. Environ.618 ,165-173 (2018).
  18. Z.L. Hildenbrand, I.C. Santos, T. Liden, D.D. Carlton Jr., E. Varona-Torres, M.S. Martin, M.L. Reyes, S.R. Mulla, K.A. Schug, Sci. Tot. Environ.634, 1519-1529 (2018).

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 LCGC Emerging 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.