Food and beverage companies rely on accurate and detailed aroma profiling to deliver high-quality products with consistent key flavour compound profiles and to develop new products. Additionally, off-flavour compounds, which are occasionally present at ultra-trace levels, need to be carefully monitored. Quality and reliability of aroma analysis by capillary gas chromatography (GC) is greatly affected by the applied sample preparation technique and by the type of mass spectrometric detector used. Conventional solvent-based extraction techniques are increasingly being replaced by solventless techniques, such as dynamic headspace sampling (DHS), which is suitable for the characterization of aromas in food and beverages. The technique can also be fully automated and delivers full aroma profiling covering a wide volatility range. Hyphenation of DHS with capillary GC equipped with time-of-flight mass spectrometry (TOF-MS) further extends the range of aroma compounds detected. TOF-MS offers high sensitivity in full scan mode which is ideal for untargeted screening. Additionally, spectral continuity and increased acquisition speed allow deconvolution of chromatographically coeluting aroma compounds. Applications of DHS–GC–TOF-MS for the analysis of strawberry yoghurt, chocolate, and red wine are described.

Aroma is one of the most important characteristics of food and beverage products and has a major impact on the overall perception of quality by consumers. Aroma profiling analysis is therefore of utmost importance in the food and beverage industries. Accurate aroma profiling helps manufacturers to ensure quality through proper selection of the raw materials and to provide product consistency. Additionally, it supports the development of new products and the understanding of the influences of ingredients on the aroma of the final products. Aroma monitoring also allows us to investigate the influence of processing, such as roasting, development of the desirable odours during wine and cheese aging, and the shelf‐life.

Aromas are characterized by volatile and semi‐volatile compounds that reach our sense of smell through the air we inhale. Food products contain aroma compounds with a broad range of odour thresholds, values ranging from high parts per million (ppm, or mg/L, or mg/kg) for solutes such as 2,5‐dimethylpyrazine (roasted nut smell) to low or sub parts per trillion (ppt, or ng/L, or ng/kg) for solutes such as 2,4,6‐trichloroanisol (cork off‐flavour). This means that quantitatively minor compounds can have a major odour contribution, while highly abundant constituents might have little to no olfactory significance (1). A specific product odour can consist of a few up to hundreds of different aroma molecules of which key aroma compounds are often present at very low levels. In general, only a fraction of the volatile compounds are aroma‐active compounds, also called key aroma compounds, like furaneol in strawberry, citral for lemon aroma, 1‐octen‐3‐one for mushrooms, and so on. Approximately 10,000 different volatile and semi‐volatile compounds have been identified in foods and beverages covering nearly all chemical classes and functionalities (2). During food processing and storage, many chemical reactions involving sugars, amino acids, and lipids occur, for example, Maillard reactions, oxidations, and so forth. These chemical reactions lead to the formation of additional volatile aroma compounds and, potentially, to the formation of important off‐flavours. Complex food matrices—such as coffee, chocolate, and cooked/roasted meat—are known to contain as much as 1,000 aroma compounds (3). Many of these compounds are common to several different foods, such as pyrazines in both coffee and chocolate. On the other hand, aroma loss as well as off-flavour development is a serious problem in the food industry. Off‐flavours are caused by the presence of one or several molecules that are formed during processing but not expected above their threshold value in the final food product. A typical example is diacetyl (2,3‐butanedione) giving a buttery note to beer. Other examples are the formation of 3‐methyl‐2‐butene‐1‐thiol under light by reaction of the bitter acids and S‐containing amino acids in beer stored under non‐optimal conditions or the contamination of wine by 2,4,6‐trichloroanisol from non‐product‐related sources such as corks. Additionally, in recent years aroma profiling is more frequently used in food adulteration control and wine fraud. Several studies have already shown that aroma analysis of food products can be highly specific and can allow the discrimination of an original product from its adulterated counterpart (4) and even allows classification of botanical origin, such as is the case of wine (5).

The analysis of aroma compounds in food and beverages is challenging due to their presence at a broad concentration range (ppt to ppm), broad polarity range, volatility (high vapour pressures), and the instability of some important classes of aroma compounds. Aroma profiling can be performed either by sensory or instrumental analysis. There are numerous analytical approaches that can be used for aroma profiling, all involving isolation and concentration via extraction, separation, identification, and quantification of individual compounds. The choice of the instrumental method greatly depends on the goal of the analysis, namely research or quality control, on the expected concentration range of the aroma compounds and on the complexity of the aroma profile. The instrumental methods allow us to identify the individual compounds that are responsible for characteristic sensorial observations (either key aroma compounds or off‐flavours). Furthermore, instrumental methods implemented to run in routine laboratories to provide continuous information about the quality of the products (QA/ QC) should be easily validated. Proper isolation and correct identification of minor components with aroma significance are major challenges. Often key aroma compounds or specific off‐flavours are present at concentrations below the detectability of state‐of‐the‐art instrumentation. A solution can be found in combining the most exhaustive extraction techniques with the most sensitive mass detectors able to operate in full scan mode. Additionally, major aroma contributors often coelute with compounds that are less relevant or have no sensory significance, making their identification even more challenging. Instrumental methods can also be combined with olfactometric methods (such as sniffing port) providing correct insight into the aroma profile of the analyzed food and beverage products. If needed, organoleptic evaluation can be combined with heart‐cutting techniques to trap and enrich important aroma compounds for further fractionation and characterization. For isolation of aroma compounds from foods and beverages, classical liquid–liquid extractions (LLE) with organic solvents have been used for decades. However, these methods are costly, time consuming, labour intensive, and above all harmful to the environment. Furthermore, these methods are prone to analyte degradation and artefact formation, and the presence of significant amounts of co‐extracted non‐volatile material contaminating the instrumentation that is used. In recent years, headspace analysis has become a much more sustainable method for extraction of aroma compounds because the headspace contains most of the volatiles responsible for the overall odour of the food and beverage samples. Headspace sampling techniques include static headspace (SHS), solid phase micro‐extraction (SPME), headspace sorptive extraction (HSSE), dynamic headspace (DHS), and purge and trap (P&T). All these techniques are extensively used because of their automation, solventless character, and absence of interferences from the non‐volatile matrix.

From the range of headspace sampling techniques applied in our laboratory, DHS has proven to be one of the most exhaustive extraction techniques for aroma analysis. In DHS, a flow of an inert gas (helium or nitrogen) is continuously passed through the headspace of the sample (liquid or solid) so the equilibrium between the gas and liquid/solid phases is continuously shifted. The volatile and semi‐volatile compounds are then enriched on a trap that is subsequently heated to desorb the solutes to a cold inlet for focussing. Finally, the inlet is rapidly heated to inject the compounds into the capillary column. DHS sampling is one of the most versatile sampling techniques for trace compounds because of its flexibility in terms of volume of purge gas, sample incubation conditions, and the range of available trapping materials. A range of different traps filled with adsorbent (Tenax, carbon, for example) or absorbent (polydimethylsiloxane or PDMS) is available for different chemical classes of compounds. Tenax traps are probably the most frequently used for aroma analysis in food and beverage analysis because of the high retention of aroma compounds and low affinity towards water and ethanol, making them especially suitable for alcoholic and non‐alcoholic beverages. Additionally, recent developments in DHS techniques, such as full evaporation DHS (FEDHS), which is suitable for analysis of hydrophilic and low vapour pressure aroma compounds (6) and multi‐volatile DHS methods (MVM‐DHS), applying multiple traps in combination with a single gas chromatography (GC) run (7) have extended the applicability range.

In this article, the benefits of combining DHS with gas chromatography time‐of‐flight mass spectrometry GC–TOF‐MS are illustrated with three representative food samples: strawberry yoghurt, chocolate, and red wine.

 Red wine, strawberry yoghurt, and milk chocolate were purchased in a local store. Samples were stored according to the instructions on the labels, before analysis. For DHS and SPME analysis, 5 mL of red wine, 2 g of yoghurt, or 1 g of chocolate were placed in 20 mL headspace vials. 

 All analyses were conducted on an Agilent 7890A GC (Agilent Technologies), coupled to a BT‐TOF mass spectrometer (Leco Corporation, St. Joseph, Michigan, USA). The gas chromatograph was equipped with a split/splitless inlet (for comparative analyses by SPME), a Gerstel CIS4 PTV inlet, and a Gerstel thermal desorption unit (TDU) (Gerstel). Enrichment via DHS, desorption, and injection were fully automated using the MPS‐Robotic Pro autosampler (Gerstel) and operated using Maestro software (Gerstel).

For wine analysis, DHS was performed at 70 °C. The sample headspace was purged with 300 mL helium and the volatile compounds were trapped on a Tenax trap. A dry purge step, flushing the Tenax trap with 2500 mL helium was applied to remove excess alcohol and water. For yoghurt, DHS was performed at 60 °C using a DHS sampling volume of 300 mL and a dry purge volume of 1500 mL helium. For chocolate, DHS was performed at 80 °C using a 1000 mL sampling volume. No dry purge was required for this sample type. Thermal desorption from the Tenax trap and injection of samples was carried out using the TDU.The TD trap was desorbed at 280 °C for 6 min onto the cold trap at −40 °C. The injection was carried out by ramping the cold trap from −40 °C (0.1 min) to 250 °C (7 min) with a ramp of 12 °C/s. A solvent vent injection with a 1:10 split ratio was used to introduce the aroma compounds on the column. The column was a DB‐WAX Ultra Inert (UI) (30 m × 0.25 mm i.d. × 0.25‐μm df, Agilent Technologies, Folsom, California, USA) and the oven temperature was programmed from 40 °C (5 min) to 250 °C (9 min) with a rate of 10 °C/min. The carrier gas was helium, at a flow of 1 mL/min. The column effluent was subsequently ionized by electron impact at 70 eV. Scan range was m/z 35–450 and acquisition was done at a rate of 10 spectra/s. The interface temperature was 260 °C and the ion source was maintained at 250 °C. 

‐alkanes (C7–C30) were analyzed for calculating retention indices (RI) using the integrated automated functions in the ChromaTOF software. Data acquisition and data processing were performed using ChromaTOF (Leco, v. 4.72). Data processing included peak finding, mass spectral deconvolution, peak area integration, and library searching with retention index match. For peak detection, an S/N cutoff was set at 25, and detected peaks were tentatively identified using the NIST 2017 library (700 minimum similarity was applied) and using retention index information (± 30 RI tolerance was considered). Comparative analyses were performed on an equivalent system equipped with an Agilent 5977B MSD, with an extractor lens source.

The exhaustive extraction performance of DHS is illustrated by the analysis of a strawberry yoghurt sample. Such a matrix is representative for dairy products. The profiles obtained by SPME and DHS are compared in Figure 1. Although it is true the selectivity of the SPME fibre was optimized for this experiment (divinylbenzene/ Carboxen/polydimethylsiloxane, DVB/ CAR/PDMS fibre), some very volatile compounds as well as semi‐volatile compounds were not detected by SPME sampling. For the majority of detected compounds, peak abundances obtained by DHS are 2–10 times higher compared to SPME. A detailed view of the elution region between 14.0 and 15.5 min shows that, using DHS, more “aroma‐relevant” compounds could be detected and identified. The odour compounds detected in this elution window are listed in Table 1, with their retention time (

), identification based on the NIST mass spectral library search, library RI from the NIST library, experimental retention index, and detectability by SPME and DHS.

The success of aroma profiling of foods and beverages also greatly depends on the efficiency of the separation and the deconvolution performance and sensitivity of the detector used.

Although it has been demonstrated that comprehensive GC (GC×GC) has the potential to substantially increase peak capacity, one‐dimensional GC is still the industry standard, both in research and for quality control (QC). Therefore we preferred to focus on a standard GC approach using a 30 m capillary column operated at 1 mL/min constant flow rate and using a 10 °C/min. Estimated peak capacity under these conditions is in the order of 300–350.

Hyphenation of capillary GC with mass spectrometry (MS) provides the most powerful and effective method for aroma characterization. The characterization of complex aroma samples is done by comparing recorded mass spectra with mass spectral libraries after spectral deconvolution. The spectral match of an acquired mass spectrum with a library mass spectrum, expressed in % or promille, is a first indication of correct identification of the analyte. Additional confirmation can be further clarified using RI and comparison of experimental RI with RI databases. Comparison of retention indices is of especially high value for aroma profiles containing a large number of terpene isomers with similar mass spectra.

While single quadrupole mass spectrometers can be considered as the workhorse in gas chromatography‐mass spectrometry, time‐of‐flight (TOF) analyzers are gaining interest in the past decade. This is mainly due to their high sensitivity, while acquiring “full scan” spectra. Additionally, the acquisition speed is independent from the mass range for analysis, unlike the case with a quadrupole analyzer. Higher scan speeds of TOF analyzers offer the possibility to reduce GC run time and increase sample throughput, while maintaining correct peak detection (sufficient datapoints per peak), correct deconvolution (without mass skewing) and sensitivity. This is illustrated by the analysis of an artificial food aroma mixture, analyzed by DHS–GC– TOF‐MS under different GC conditions. As can be seen from Figure 2(a) and 2(b), analysis with a slow temperature program of 2.5 °C/min (run time per sample, including DHS method: (120 min) ensures complete separation of caryophyllene, terpinene‐4‐ol and hexyl hexanoate. However, when working with such a method, no more than 12 samples can be analyzed in 24 h. Increasing the oven temperature rate to 5 °C/min (run time, including DHS method: 80 min) will still allow separation of these compounds but with a sample throughput of 18 samples/24 h. By increasing the oven temperature rate to 10 °C/min, the throughput increases to 25 samples/24 h, but now caryophyllene and terpinene‐4‐ol are no longer chromatographically separated (Figure 2c and 2d). When operating with an acquisition speed below 5 scans/s, as usually done with the quadrupole analyzer, deconvolution and correct identification of terpinene‐4‐ol was not possible due to the lack of sufficient data points to extract a high quality mass spectrum from the caliper (= nondeconvoluted) mixed spectrum. Operating at higher acquisition speeds allows the deconvolution algorithms to extract a high‐quality mass spectra for both compounds, resulting in good matches with library spectra and correct identification.

It should be noted that high acquisition speed also reduces the apparent peak width (full width at half height [FWHH]). This is illustrated in Figure 3, showing the separation of two closely eluting solutes in the artificial food aroma mixture. When analyzed at 4 scans/s, hexanoic acid methyl ester coelutes with limonene (valley > 80% of peak heights). When MS acquisition is done at 10 scans/s and 20 scans/s, separation increases, and at 20 scan/s almost baseline separation (valley < 20% peak height) is obtained. In addition, better spectral match factors of identified compounds are observed. This is illustrated in Table 2. While 5‐(hydroxymethyl) dihydrofuran‐2(3H)‐one is not identified when acquisition speed was 4 scans/s, the similarity increases to almost 800 when analyzing at 20 scans/s. The influence of scan speed on FWHH is also confirmed for these compounds. Mass spectra are
not “deformed” or altered by scan speed, but the deconvolution algorithm works much better at high acquisition speed, resulting in mass spectra that correspond better with library spectra. This explains the data presented in Table 2.

Analysis of a red wine sample further illustrates the high analytical performance of a TOF mass analyzer combined with DHS sampling (Figure 4). Deconvolution and NIST library search (match factor threshold of ≥ 700) provides identification of about 240 aroma compounds in the sample analyzed by TOF‐MS. When the analysis was performed on a parallel single quadrupole MS system, about 120 aroma compounds were identified in the same sample using the same DHS and GC conditions (Table 3). The difference in the number of identified compounds is, however, not only caused by the difference in sensitivity of the mass analyzer but mainly by the acquisition speed difference. When operating at 10 scans/s some of the coeluted compounds were efficiently deconvoluted which could not be done when operating at lower acquisition speeds.

As an illustration of the deconvolution power of a fast scanning TOF‐MS, several compounds—including some known aroma compounds such as 1‐octen‐3‐ol and methional, co‐eluting with the tailing acetic acid peak—were successfully identified (Figure 4b and Table 4). These compounds are present at trace levels compared to acetic acid. Nonetheless, thanks to the extended dynamic range of the TOF analyzer, the low abundant ions in the total ion chromatogram (TIC) are correctly detected which allows correct deconvolution and identification as illustrated for methional (Figure 5). The relative abundance of the ions originating from methional are present below 1% relative ratio compared to the abundances of the ions originating from acetic acid (

 43, 60). Still this compound could be identified with a relatively high similarity factor and was additionally confirmed by its retention index (1454 library retention index vs. 1455 experimental retention index).

Finally, the analysis of a milk chocolate sample further illustrates how a complex aroma profile can be unravelled by DHS–GC–TOF‐MS (Figure 6). The complex chocolate aroma profile originates from the processing steps in the chocolate production, including cocoa beans fermentation, drying, and roasting. In this analysis, almost 280 different aroma compounds were identified in the chocolate headspace, which is about two times more than a similar analysis with a single quadrupole mass analyzer in full scan mode (Table 3). The identified aroma compounds belong to different chemical classes including acids and esters, pyrazines and furans, lactones, monoterpenes, aldehydes, ketones, and sulfur compounds. Thanks to the high sensitivity of the detector, trace sulfur compounds such as dimethyl disulfide and dimethyl trisulfide could easily be detected by the TOF analyzer with signal‐to‐noise ratios of 340 and 450, respectively. Again, deconvolution was of utmost importance in the elucidation of dimethyl trisulfide. In Figure 6 (b), the TIC profile at 12.832 min shows one distinctive symmetrical peak. After deconvolution, two compounds could be clearly identified and quantified, namely dimethyl trisulfide (extracted ion chromatogram [EIC] at 126) and 1‐hydroxy‐2‐butanone (EIC at 57) even though the apex difference was less than 500 ms. Identification of such closely coeluting compounds is possible due to the high acquisition speed which allowed us to obtain sufficient data points for deconvolution.

The combination of DHS with capillary GC–TOF‐MS is a great tool for untargeted analysis of aroma compounds in food and beverages. Compared to SPME sampling, DHS sampling provides a broader coverage of very volatile, volatile, and semi‐volatile aroma compounds due to dynamic purging and concentration of the headspace above the sample, resulting in a more exhaustive extraction and increased sensitivity. Dynamic headspace sampling is a universal technique for aroma analysis in various matrices, but DHS parameters—such as sample equilibration temperature, sampling volume, and dry purge conditions—need to be carefully optimized depending on the matrix type.

TOF‐MS offers unique features in terms of sensitivity and acquisition speed that can be exploited in fast untargeted screening. Even in one‐dimensional GC, the advantages of high acquisition speed, spectral integrity (lack of mass skewing), and low picogram sensitivity result in more detected, and positively identified compounds in a given matrix using a generic chromatographic separation. The TOF mass analyzer is without any doubt a valuable addition to the portfolio of mass spectrometers that can be successfully hyphenated to capillary GC.

Odour thresholds: Compilations of odour threshold values
in air, water, and other media

 (Oliemans Punter & Partners, Utrecht, 2011).

(2) B. d’Acampora Zellner, P. Dugo, G. Dugo, and L. Mondello, 

Encyclopedia of Food Sciences and Nutrition

, L. Trugo and P.M. Finglas, Eds. (Cambridge, Massachusetts, USA, Academic Press, 2nd ed., 2003.)

(4) M.A. Farag, N. Hegazi, E. Dokhalahy, and A.R. Khattab, 

(5) A. Springer, J. Riedl, S. Esslinger, T. Roth, M. Glomb, and C. Fauhl‐Hassek, 

(6) N. Ochiai , K. Sasamoto, A. Hoffmann, and K. Okanoya, 

(7) N. Ochiai, J. Tsunokawa, K. Sasamoto, and A. Hoffmann, 

 are Senior Scientists at RIC group, Kortrijk, Belgium. 

 is Founder and advisor of RIC group and Emeritus Professor, Ghent University, Belgium. Direct correspondence to the Editor-in-Chief, Alasdair Matheson: 

Articles

Cover Story

This article looks at the benefits of combining dynamic headspace sampling (DHS) with capillary GC–TOF-MS as a tool for untargeted analysis of aroma compounds in food and beverages. Applications for the analysis of strawberry yoghurt, chocolate, and red wine are described.

High Analytical Performance for Untargeted Aroma Profiling by Combining Dynamic Headspace Sampling, Capillary Gas Chromatography and Time‐of‐Flight Mass Spectrometry

“GC Connections” presents the column’s annual review of new developments in the field of gas chromatography (GC) seen at Pittcon and other venues in the past 12 months.

I am pleased to present our annual review of new products in gas chromatography, introduced between spring 2020 and spring 2021. Many of these products were showcased at the virtual 72nd meeting of the Pittsburgh Conference on Analytical Chemistry and Applied Spectroscopy (Pittcon), USA, from March 8–12, 2021. While the interface and events at the virtual conference were presented well, with easy navigation and tonnes of content at the fingertips, the interaction was not nearly as rich as an in‐person meeting.

Pittcon 2022 was held from March 5–9, 2022 in Atlanta, Georgia, USA. I strongly encourage all readers to consider attending a gas chromatography (GC)‐related conference or participate in your local or regional chromatography discussion group in the next year. I have used the rise of the virtual conferences to attend meetings that I would not have attended otherwise due to the travel costs, but I will still attend my usual set of in‐person meetings when they come back. The past year of 2020–2021 has been a rough period for conference organizers and for the many vendors, contractors, and economies that conferences support. They need your help. Conference and meeting attendance is still the most effective and efficient way to train and educate yourself about GC and improve your skills and performance.

The exposition included virtual vendor booths virtually displaying new instrumentation and products. Most booths included video content and links to instrument brochures, application notes, and other technical literature. Vendor personnel were on hand to chat or video conference with attendees. The interface was presented to look a lot like the actual conference booths, which was a nice touch. One plus was that the basic navigation for all the booths was the same, whether a large or small vendor was displaying. This seemed to somewhat level the playing field for user attention.

The virtual technical programme at Pittcon 2021, which ran over five days from Monday through Friday included a very strong GC presence. Pittcon continues to be a leading showcase for the latest GC, gas chromatography–mass spectrometry (GC–MS), and two‐dimensional gas chromatography (GC×GC)‐related research. While none of the major awards were presented to gas chromatographers this year, the technical programme included numerous oral presentations and posters presenting new GC‐related techniques and applications.

There were several symposia, oral sessions, poster sessions, and networking events with significant GC presence, enough to keep attendees very busy on all days. Overall, sessions related to cannabis analysis, biofuels, food and beverages, forensics, pharmaceuticals, and general applications of both GC and GC×GC were offered. GC itself is trending toward being less the driver of session topics than its applications to scientific problems. Searching the Pittcon technical programme website using “GC” as the keyword shows relatively few sessions with GC in the title, but many GC talks and posters. The Pittcon 2021 virtual conference website content is available on demand until June 2021, effectively extending the conference. Readers are encouraged to take advantage.

The information presented in this article is based on vendors’ responses to questionnaires and additional information from press releases, websites, and product literature, not on actual use or experience of the author. Every effort has been made to collect accurate information but because of the preliminary nature of some of the material, LCGC Europe cannot be responsible for errors or omissions. This column cannot be considered a complete record of all new GC products introduced in the past year because not all vendors chose to respond to the questionnaire, not all had a strong presence at Pittcon, nor is all the submitted information necessarily included here, because of the limited available space and the editors’ judgment as to its suitability. Table 1 provides a listing of the vendors highlighted in this review. For a more complete picture of the GC‐space today, I encourage you to review the two previous years’ reviews (1,2).

In new instruments, trends seen over the past few years toward smaller, more automated systems that are more specialized, and spectrometric detectors continue with the product introductions at Pittcon 2021. Table 2 shows a list of new instruments introduced over the past year, including a miniature GC system, several all‐in‐one systems, and several new GC‐MS systems featuring new mass spectrometers.

Capillary column stationary phases continue to be updated by most of the vendors, with nearly all making incremental improvements or showing new applications in their online application note libraries. The two new stationary phases highlighted in Table 3 provide examples of an important and ongoing trend in capillary column development: new stationary phase chemistries for specific problems. Both stationary phases are examples of classical stationary phase chemistries that have been modified to suit specific problems—in these cases, separations of fatty acid methyl esters and dioxins.

Developments in new accessories and consumables continue to trend toward simplifying analysis, and making chromatographers’ work easier. Both accessories and consumables are also developing to improve inertness in the sample flow path. Sample preparation is not covered broadly here. For more information on sample preparation, see the specific sample preparation article by Douglas Raynie in the upcoming May issue of 

. As with the GC reviews, it is worthwhile to review the past two years as well for a complete picture of sample preparation products (3,4). Table 4 provides a listing of products displayed at Pittcon 2020.

I encourage readers to continue your education and training. Besides several short courses associated with Pittcon, additional training and educational resources are available for 2021. The pandemic has resulted in much growth in online training and seminar offerings by vendors and training organizations. Opportunities for in‐person short courses, seminars, and training are beginning to start up again and are offered by most major instrument vendors and through many conferences and organizations, including the American Chemical Society, Eastern Analytical Symposium (Princeton, New Jersey, USA, November 2021) and local and regional chromatography discussion groups. LCGC Europe’s online training system, CHROMacademy, launched a completely revamped online platform in 2020, and is hosting a virtual symposium in May, 2021. I will be offering online short courses on Advanced GC for the Chromatography Forum of the Delaware Valley (www.cfdv.org) on June 15, 2021, and for the Eastern Analytical Symposium (www.eas.org) in early July.

As GC approaches 70 years since its conception, development and invention are still going strong. As seen at Pittcon 2021, GC‐related developments and innovations are ongoing and include all areas: sampling and supplies, new instruments, columns, detectors, data systems, and educational opportunities. I look forward to more innovation and advancements throughout the 2020s, and I hope to see many of you at EAS 2021 in my home state of New Jersey or at Pittcon 2022 in Atlanta, USA.

Nicholas H. Snow is the Founding Endowed Professor in the Department of Chemistry and Biochemistry at Seton Hall University, USA, and an Adjunct Professor of Medical Science. During his 30 years as a chromatographer, he has published more than 70 refereed articles and book chapters and has given more than 200 presentations and short courses. He is interested in the fundamentals and applications of separation science, especially gas chromatography, sampling, and sample preparation for chemical analysis. His research group is very active, with ongoing projects using GC, GC–MS, two-dimensional GC, and extraction methods including headspace, liquid–liquid extraction, and solid-phase microextraction. Direct correspondence to: 

GC Connections

“GC Connections” presents the column’s annual review of new developments in the field of gas chromatography seen at Pittcon and other venues in the past 12 months.


New Gas Chromatography (GC) Products for 2020–2021

Hydrophobic interaction chromatography (HIC) is one of the historical analytical methods applied for the separation and purification of proteins. In the first part of this series, the elution and separation mechanisms were discussed. In this second part, some practical considerations such as method development, selection of the phase system, combined salt systems, and possibilities to combine HIC with other chromatographic modes are explained. Applications are critically discussed and the hyphenation to mass spectrometry (MS) briefly tackled.

Historically, a kosmotropic salt— typically 1.5–2 M ammonium‐sulfate—has been used as a mobile phase additive in hydrophobic interaction chromatography (HIC) (as well as 50–100 mM phosphate buffer). However, various salts can be applied, for example, ammonium acetate, sodium sulfate, ammonium chloride, sodium chloride, or sodium citrate. Salts follow the lyotropic (Hoffmeister series for their protein precipitation affinity in aqueous solutions. Kosmotropic salts are known to promote hydrophobic interactions and protein precipitation.

Salt nature may affect retention to a different extent on different stationary phases and for different types of proteins (1,2). This is probably due to several processes occurring at the same time as a mix of different mechanisms, including binding, unfolding, repulsion, steric effects, and so on, as discussed in Part 1 (3). Therefore, the effect of a salt is hardly predictable, but should be experimentally determined as an early step of the method development (1). Depending on the lyotropic strength of the salts, different concentrations are required to maintain the same salting‐out effect. Stronger salts result in appropriate retention already at relatively low concentration (such as 1–1.5 M), while weaker salts require higher concentration (up to 3–5 M) to maintain the same retention.

Peak widths might also vary with salt concentration because it impacts the gradient steepness and therefore the gradient band‐focusing effect. It has been demonstrated that for antibody drug conjugates (ADCs) and drug‐to‐antibody ratio (DAR) separations, quite similar selectivity could be achieved with any type of salts, once the lyotropic strength is corrected on a given stationary phase (1,2). Equivalent molarities of the salt systems can be determined for various columns on the basis of hydrophobicity index, that is, can be derived from the parameters of the linear solvent strength model (2).

An important practical aspect—in case of a mono salt system—is the required salt concentration, which has to be maintained as low as possible. In addition, it is suggested to dedicate high and ultra‐high performance liquid chromatography (HPLC/UHPLC) systems for hydrophobic interaction chromatography (HIC) measurements because there is a huge risk of salt precipitation and therefore clogging. Systems and columns need to be regularly rinsed and purged with water to avoid precipitation through the entire system. Dedicated inert HPLC/UHPLC systems more resistant to high salt concentration have, in recent years, been commercially introduced as well.

Salt Mixtures (Binary, Ternary) and Mobile Phase pH

Using salt mixtures instead of single salts can be interesting for HIC separations. Not only retention and selectivity, but also loading and binding capacity can be improved with salt mixtures (4–6). Besides the salt nature, the mobile phase pH also impacts the retention of proteins as a part of the electrolyte system. Depending on pH, both the proteins’ net charge and their conformation can change. In addition, hydrophobic interactions are stronger when the mobile phase pH is close to the isoelectric point of the protein. Therefore, the electrostatic repulsion between the protein molecules becomes smaller, favouring a closer arrangement on the surface of the stationary phase. A recent study reported on the effect of the pH on protein adsorption from mobile phase containing mixed salts (7). Binary and ternary salt mixtures of sodium chloride, ammonium chloride, sodium sulfate, and ammonium sulfate, as well as pure salts were used. The mobile phase pH was adjusted so that protein carried either positive, negative, or zero net charge. For each studied pH, the salt composition was varied keeping the overall ionic strength constant at a given value. Mixed salts could have either positive, negative, or no cooperative effects. The magnitude and sign of the effect depended on the net charge of the protein.

Besides mobile phase pH optimization and the use of binary salt systems, ternary salt systems are not really considered today for HIC method development, but it can be an interesting topic for future work.

In HIC, mildly hydrophobic stationary phases are commonly used (less hydrophobic than in reversed‐phase liquid chromatography [RPLC]). Most common stationary phase modifications include short n‐alkyls (butyl, ethyl), phenyl, ether, or alkylamide, linked to silica or polymeric based materials (1).

The strength of hydrophobic interactions between the protein and stationary phase ligand can be controlled mostly by the ligand density (8). Ligand density also impacts the access to the hydrophilic parts of the stationary phase. In addition, ligand hydrophobicity may impact the rate of protein unfolding in adsorbed state, while ligand size can influence steric effects. Aromatic ligands can provide some additional π–π interactions which can increase retention (9). An updated version of the most popular HIC columns (10) applied for the analytical separations of biopharmaceuticals is provided in Table 1. Most HIC columns are packed with 2.5–5 μm particles, but columns packed with sub‐2‐μm particles have also been commercialized. Some of the providers offer columns dedicated for mAb and ADC analysis.

Historically, most HIC applications use butyl phases operated in ammonium sulfate and phosphate buffer as mobile phase. However, any salts having salting‐out properties (and appropriate solubility) can be considered as potential buffer components for HIC separations. Mobile phase pH, salt concentration and type, as well as organic modifiers and temperature can also change selectivity and retention. Therefore, it is valuable to perform a systematic method development rather than just applying the historical conditions or executing random or trial‐and‐error runs. Here, we provide a brief overview on the steps of a systematic approach (more details can be found elsewhere [1,11]).

As a first step, it is recommended to find the most appropriate phase system. For this purpose, a few stationary phases (such as butyl, propyl, and phenyl phases) can be tested with different salt systems (for example, ammonium‐sulfate, sodium‐acetate, and sodium‐chloride) by running a generic linear gradient, and then the retention window, selectivity, peak shape, and recovery should be checked. Based on these scouting gradient experiments (corresponding to nine experiments when screening three columns and three salts), the best combination of the column and salt can be selected.

After the screening procedure, the gradient program and other variables, such as organic modifier, pH, or temperature, can be optimized by performing experimental design or retention modelling (11). Both require only a limited number of experiments (typically 4–9 experiments). The important thing is the selection of the most appropriate variables. The gradient steepness (program) can always be considered as an influential variable. Besides that, the organic modifier often impacts both the selectivity and recovery (especially for very hydrophobic samples) and sometimes mobile phase temperature has a significant impact as well. Figure 1 shows the schematic workflow of HIC method development.

Applications of HIC for Therapeutic Proteins

HIC is currently considered a reference technique for the analysis of cysteine‐linked ADCs (to determine DAR distribution and average DAR), for monoclonal antibody (mAb) analysis at intact and sub‐unit levels, for monitoring post‐translational modifications (such as oxidation, deamidation, and isomerization), and for determining the heterodimerization efficiency when the parental mAbs of a bispecific antibody (bsAb) possess different hydrophobicity (1,11–16).

The drug‐loading, distribution, and conjugation sites of cysteine‐linked ADCs are known to influence pharmacokinetics, toxicity, and therapeutic index, therefore average DAR and distribution are considered as some of the most important quality attributes. A fully conjugated IgG1 type ADC has a maximum DAR of 8, while an IgG2 type has a maximum DAR of 12. IgG1 ADCs are composed of a heterogeneous mixture of 0, 2, 4, 6, and 8 DARs while IgG2 ADCs of 0, 2, 4, 6, 8, 10, and 12 DARs (17). An odd DAR number (for example, DAR1 or DAR3) of the conjugated drug is typically indicative of incomplete conjugation or degradation, and mostly observed in small amounts (12). A given DAR value can correspond to different positional isomers (for example, DAR4 of IgG1 ADC is possible to be formed in four different positional isomers). Because the cytotoxic drug linked to the IgG has a lipophilic character, the conjugation increases the hydrophobicity of the higher DAR species, and thus retention increases with the conjugation number. In most cases, a simple linear inverse salt gradient (such as 1.5–0 M ammonium‐sulfate) enables the separation of all different DAR species. Figure 2 shows a typical DAR separation performed by running linear inverse salt gradient.

The average DAR can then be calculated from the HIC profile. Chromatograms have to be acquired at 214 or 280 nm (using UV detection) or at λ

: 350 nm (using fluorescence detection). Peak area percentages of the different species have to be determined. Then, the weighted peak areas have to be calculated by multiplying the peak area percentage by the corresponding drug load. Finally, weighted average DAR can be obtained by summing the weighted peak area percentages and dividing their sum by 100 (18,19). Please note that the recovery of the most hydrophobic DARs (DAR6 and DAR8) is incomplete in some cases. Therefore, the addition of a small portion (5–15%) of organic modifier can be helpful. In such cases, the apparent average DAR may depend on the amount of organic modifier (20). Non‐linear gradients can also be applied to improve peak resolution for the late‐eluting species (21).

Very recently, HIC was applied for site‐specific PBD‐ADCs (PBD: pyrrolobenzodiazepine dimer) to separate DAR0, DAR1, and two DAR2 species (22). The HIC method had the advantage to determine not only DAR values and drug‐load distribution, but also to separate unique DAR2 conformations existing as a resultof the N‐glycan heterogeneity.

HIC is also a useful tool to separate hydrophilic and hydrophobic intact mAb variants (23). For example, several variants can be separated for infliximab, rituximab, and ofatumumab (23). However, HIC analysis at sub‐unit level is more informative and often used to separate oxidized Fab and Fc variants, which are less retained than the native forms (24). Figure 3 shows the HIC analysis of trastuzumab highlighting a deamidation event in the Fab fragment.

BsAbs are composed of fragments of two different mAbs (mAbA and mAbB) and therefore bind to two different types of antigens. If the parental antibodies show significant differences in charge and/or hydrophobicity, the heterodimerization efficiency of the bsAb can be evaluated by chromatographic techniques (15,16). In HIC, the bsAb peak elutes between that of parental antibodies. Residual parental mAb content and the distribution of mAbA, mAbB, and bsAb species can be determined on the basis of peak area percentages as illustrated in Figure 4. Recently, an interesting combination of peptide mapping and HIC has been developed to quantify succinimide in bsAbs (25). Deamidation product, unmodified bsAb, and succinimide were efficiently separated and the method was applied in a quality controlled environment for release and stability testing studies.

Two‐dimensional liquid chromatography (2D‐LC) is a powerful technique for mAb and ADC characterization, since it enables the hyphenation of a non‐mass spectrometry (MS)‐compatible chromatographic mode (for example, HIC as the first dimension) to MS instrumentation, via a suitable interface (such as by using RPLC or size‐exclusion chromatography [SEC] as a desalting step prior to MS) (Figure 5) (1). In addition, such setup improves peak capacity too and therefore increases peak resolution. One of the very first 2D‐LC applications of HIC was developed by Birdsall et al. who showed the potential of an on‐line HIC‐RPLC–MS setup for the characterization of isoforms of cysteine‐conjugated ADCs (26). The methodology was tested using an IgG1 mAb modified by cysteine conjugation with a non‐toxic drug mimic. Structural elucidation of peaks observed in the HIC analysis (first dimension) were successfully identified based on their unique sub‐unit masses via MS techniques once dissociation occurred under denaturing reversed‐phase conditions (second dimension).

A comprehensive on‐line HIC×RPLC–MS method was also successfully developed and applied to obtain important information on drug load profile and structural information on positional isomers of DARs of a commercial cysteine‐linked ADC (27). The method was applied for stability studies and confirmed that odd DAR species can be produced by heat stress.

Another comprehensive 2D‐LC approach was based on the combination of HIC and SEC (HIC×SEC), as coupled to ion mobility spectrometry and mass spectrometry (IM–MS) for the analytical characterization of ADCs under non‐denaturing conditions (28). The DAR species were separated in the HIC phase while the SEC dimension was used as desalting process. The role of the SEC dimension was simply to separate HIC salts from ADC species and to direct the HIC salts towards the waste. With this setup, all fractions eluted from the SEC column could be continuously infused into the electrospray ionization (ESI) source, followed by ion‐mobility spectrometry (IMS) and MS dimensions.

The loops in a heart‐cutting system can be replaced by a HIC phase. In such a 2D‐LC system, HIC can work as a capture phase between Protein A and SEC separation for mAb analysis (29).

On‐line coupling of HIC with RPLC and charged aerosol detector (CAD) and MS was performed to characterize polysorbates in therapeutic protein formulations (30). Polysorbates are complex mixtures of over a thousand components possessing a wide range of hydrophobicity. This online 2D setup was used to evaluate the heterogeneity and stability of polysorbates. Adding a low concentration of formic acid and organic solvent in the mobile phase, enabled the use of the HIC column to separate small molecule excipients (including major components of polysorbates) and the large protein molecules by a mixed mechanism. The protein and the charged excipients, which eluted early from the HIC column, were directed to the waste, while the polysorbates and other neutral excipients, which eluted later from the HIC column, were directed to the RPLC column for further separation and analysis.

HIC is inherently incompatible with MS because of the significant amount of salts used in the mobile phase. However, fraction collection and off‐line connection is feasible or on‐line 2D‐LC–MS making use of desalting in the second dimension (see above). Debaene et al. applied HIC for off‐line native MS characterization of a commercial ADC (31). HIC fractions have been collected, desalted, and then analyzed by IMS and native MS. Conventional HIC provides good separation of proteins under non‐denaturing conditions, but requires high concentrations of nonvolatile salts. Therefore, a new strategy has been proposed, to increase the retentivity of the HIC column (pentyl‐, hexyl‐, and heptyl-phases were developed), while decreasing salt concentration—and preferentially use ammonium acetate instead of ammonium sulfate—and also to add about 50% organic modifier into the mobile phase (32).

This way the highly retentive column can be directly coupled to MS. This methodology is probably a hybrid form of conventional HIC and RPLC. The salt seemed to preserve protein structure rather than promoting only retention (according to the authors). This approach has been applied for top‐down proteomics and intact mAb characterization (33).

HIC is a reference method to separate the hydrophobic variants of protein species. In most HIC separations, the same (historical) conditions are usually applied (such as a 1.5–2 M ammonium‐sulfate buffer at pH 6–7 for the mobile phase with a butyl stationary phase), although many other options and conditions can be used that could improve the quality of the separation. For example, using salt mixtures instead of a single (mono) salt, working at a pH less than 6 or using phenyl‐, amide‐, or pentyl stationary phases could all have an impact on the overall quality of the separation in terms of selectivity, peak width, analysis time, and so on. This article has reviewed these possibilities hoping that chromatographers will benefit from them. In addition, a systematic method development scheme was also provided, which can significantly decrease the time spent on method optimization. Finally, some common and recent applications have been presented including uni‐ and multi‐dimensional (2D) separations. Possibilities of coupling HIC to MS detection were also reviewed.

1) S. Fekete, J.L. Veuthey, A. Beck, and D. Guillarme, 

2) A. Cusumano, D. Guillarme, A. Beck, and S. Fekete, 

3) S. Fekete, A. Murisier, K. Sandra, and D. Guillarme, 

5) E. Müller, J. Vajda, D. Josic, T. Schröder, R. Dabre, and T. Frey, 

6) Z.E. Rassi, L.F. de Ocampo, and M.D. Bacolod, 

10) S. Fekete, J.-L. Veuthey, and D. Guillarme, 

11) S. Fekete, I. Molnár, and D. Guillarme, 

12) B. Wiggins, L. Liu-Shin, H. Yamaguchi, and G. Ratnaswamy, 

14) J.V. Douglass, A. Wallace, and A. Balland,

15) S. Paul, J. Connor, T. Nesspor, P. Haytko, K. Boakye, M.L. Chiu, and H. Jiang, 

16) A.F. Labrijn, J.I. Meesters, P. Priem, R.N. de Jong, E.T.J. van den Bremer, M.D. van Kampen, A.F. Gerritsen, J. Schuurman, and P.W.H.I. Parren, 

17) L.N. Le, J.M. Moore, J. Ouyang, X. Chen, M.D. Nguyen, and W.J. Galush, 

Methods in Molecular Biology: Antibody-Drug Conjugates

, L. Ducry Ed. (Humana Press, New Jersey, USA, 2013).

19) K.J. Hamblett, P.D. Senter, D.F. Chace, M.M.C. Sun, J. Lenox, C.G. Cerveny, K.M. Kissler, S.X. Bernhardt, A.K. Kopcha, R.F. Zabinski, D.L. Meyer, and J.A. Francisco, 

20) B. Bobaly, A. Beck, J.L. Veuthey, D. Guillarme, and S. Fekete, 

21) B. Bobaly, G.M. Randazzo, S. Rudaz, D. Guillarme, and S. Fekete, 

22) T. Janaratne, X.C. Wang, C. Becker, Y. Zhao, R. Leanna, and W. Pritts, 

23) A. Goyon, V. D’Atri, B. Bobaly, E. Wagner-Rousset, A. Beck, S. Fekete, and D. Guillarme, 

24) https://theanalyticalscientist.com/app- notes/analysis-of-oxidised-monoclonal- antibodies-using-biopro-hic-bf

25) M. Cao, S.H.R. Mulagapati, B. Vemulapalli, J. Wang, S.V. Saveliev, M. Urh, A. Hunter, and D. Liu, 

26) R. Birdsall, H. Shion, F.W. Kotch, A. Xu, T.J. Porter, and W. Chen, 

27) M. Sarrut, A. Corgier, S. Fekete, D. Guillarme, D. Lascoux, M.C. Janin-Bussat, A. Beck, and S. Heinisch, 

28) A. Ehkirch, V. D’Atri, F. Rouviere, O. Hernandez- Alba, A. Goyon, O. Colas, M.Sarrut, A. Beck, D. Guillarme, S. Heinisch, and S. Cianferani, 

29) L. Wang, H.K. Trang, J. Desai, Z.D. Dunn, D.D. Richardson, and R.K. Marcus, 

30) Y. He, P. Brown, M.R.B. Piatchek, J.A. Carroll, and M.T. Jones, 

31) F. Debaene, A. Boeuf, E.W. Rousset, O. Colas, D. Ayoub, N. Corvaia, A.V. Dorsselaer, A. Beck, and S. Cianférani, 

32) B. Chen, Y. Peng, S.G. Valeja, L. Xiu, A.J. Alpert, and Y. Ge, 

33) B. Chen, Z. Lin, A.J. Alpert, C. Fu, Q. Zhang, W.A. Pritts, and Y. Ge, 

 is a Scientific Collaborator at the University of Geneva, Switzerland, in the Analytical Pharmaceutical Chemistry group. He was the awardee of the

 Emerging Leader Award in Chromatography in 2020. 

 is a PhD student at the University of Geneva, in Switzerland. Her PhD thesis focuses on novel chromatographic and electrophoretic techniques for the analysis of therapeutic proteins in the group of Jean-Luc Veuthey and Davy Guillarme. 

 is an Associate Scientist at RIC group (Kortrijk, Belgium) and industrial PhD student at Ghent University (Ghent, Belgium). 

 is a Senior Lecturer and Research Associate at the University of Geneva, in Switzerland. He is also an editorial board member of LCGC Europe. 

 is the editor of “Biopharmaceutical Perspectives”. He is CEO at RIC group (Kortrijk, Belgium) and Visiting Professor at Ghent University (Ghent, Belgium). He is also a member of 

’s editorial advisory board. Direct correspondence to: 

Biopharmaceuticals Perspectives

In the second part of this review of the current state of HIC, some practical considerations are explained, including method development, selection of the phase system, combined salt systems, and possibilities to combine HIC with other chromatographic modes.

Hydrophobic Interaction Chromatography (HIC) for the Characterization of Therapeutic Monoclonal Antibodies and Related Products, Part 2: Practical Considerations

 spoke to Stefan Van Leeuwen and Bjorn Berendsen from Wageningen Food Safety Research (WFSR), in Wageningen, The Netherlands, about their novel research into non-targeted screening of per- and polyfluoroalkyl substances (PFAS) in surface water using liquid chromatography high-resolution mass spectrometry (LC–HRMS) with fragment ion flagging (FIF).

Q. Why are per- and polyfluoroalkyl substances (PFAS) gaining so much attention at the moment and why is the analysis of these analytes important? 

 PFAS are a class of chemicals that are extremely stable and resistant to degradation. Their surfactant properties give these chemicals unique characteristics for application in a wide array of products and processes. PFAS are used as surfactants for water and dirt repellency of clothing and furniture, as surfactants in aquous fire fighting foams, for grease- and water-proofing food contact materials, and in many other applications. Unfortunately, their persistency and toxicity has become a major concern for society. The European Food Safety Authority (EFSA) recently evaluated that some specific PFAS show adverse effects on the immune system at low levels (1). 

 Several pollution incidents in the USA, Europe, and Asia involving PFAS manufacturing plants polluting river water and drinking water, among other incidents, has brought the PFAS problem to the surface. Policy makers are now putting regulatory measures in place to regulate PFAS in products, the environment, food, and drinking water, and to stimulate the search for alternative chemicals.

Q. What techniques have been used to analyze these compounds historically, and do they have any limitations?

 The attention for these compounds started off in the early 2000s, with the targeted analysis of perfluorooctane sulfonic acid (PFOS) and perfluorooctanoic acid (PFOA). Labs, including Wageningen Food Safety Research (WFSR), mainly used liquid chromatography (LC) with triple quadrupole (QqQ) mass spectrometry (MS/MS) to achieve the desired selectivity and sensitivity (2,3). Gas chromatography tandem mass spectrometry (GC)–MS/MS) methods were used for the highly fluorinated volatile PFAS. Over the years other PFAS were added and today some 20–40 compounds are analyzed routinely. SVL: In the meantime, it became clear that PFAS is a very diverse group of chemicals, covering very different chemical families (4). Today, more than 4700 PFAS have been registered in an Organisation for Economic Co-operation and Development (OECD) database (5), and several of these lead to environmental and human exposure through food and drinking water. Moreover, several PFAS can actually degrade abiotically and biotically through pathways yielding unknown intermediates. With these developments, the need for identification of unknowns became more apparent.

 Your team recently developed a method for the non-targeted analysis of per- and polyfluoroalkyl substances in surface water using liquid chromatography high-resolution mass spectrometry (LC–HRMS) with fragment ion flagging (FIF)?

 That’s right. Together with a student, Thijs Hensema, we have developed this approach, as we felt that current approaches for non-target identification of unknown PFAS had some drawbacks that we wanted to get around. We called the approach fragment ion flagging (FIF) (6).

 What is fragment ion flagging and what benefits does it offer the analyst for this particular application?

 FIF is a method for the identification of unknown PFAS, by looking at the fragmentation patterns of these substances. Specific characteristic fragments of PFAS will then be used as a lead towards structural elucidation of the parent PFAS. We have developed and tested this FIF workflow, and the analyst can easily follow this stepwise procedure for identification of unknown PFAS. FIF can be used in any HRMS system (coupled to GC or LC) that allows the use of all ion fragmentation (AIF) and data independent acquisition (DIA).

 What sample preparation technique did you use?

 Most studies so far were conducted on samples with relatively high levels of PFAS, for example, downstream from a manufacturing plant. We wanted to investigate the samples with background contamination that were likely to have lower contamination levels. To do so, we used solid-phase extraction (SPE), in our case a weak anion exchange SPE column, to isolate and concentrate anionic PFAS. This provided us with a 1000-fold concentration factor. In the future, we will expand this further to other isolation modes, such as cationic and mixed-mode isolation.

 What is novel about your approach and what benefits does it offer the analyst for this application compared to other techniques?

 The methods applied so far for identification of unknown PFAS were based on full scan feature selection, such as homologue series detection, mass defect filtering, and intensity threshold filtering. Although these approaches have proven benefits, they come with some drawbacks. Homologue series detection only works in cases where the unknown PFAS are part of a homologue series. PFAS that do not fit in that definition may be missed out from the identification. Mass defect detection works well for highly fluorinated PFAS, but the mass defect will be counteracted if multiple hydrogens are present in a molecule. Threshold filtering is a way to reduce the number of signals, however, one may overlook small but relevant signals.

 After some brainstorming, we realized that we should make use of the typical fragments that originate from the ionization and fragmentation of several PFAS. This overcomes some of the drawbacks of the previously mentioned approaches. We have developed the FIF workflow and this includes five steps. First, we performed all ion fragmentation (AIF) and a full scan with the system we used. Secondly, we assessed the data screening for the typical fragments C

O or neutral loss. The third step was to select the corresponding precursor ion in the full-scan data, followed by step four, recording of the product ion mass spectrum of the precursor ion, and finally perform the structure elucidation.

 What were the main obstacles you had to overcome and how did you overcome them?

 We challenged ourselves by searching for PFAS in surface waters that were suspected to contain relatively low levels of PFAS. As a result we observed many interfering background signals, and selection of relevant signals for PFAS was hampered. By applying selective sample preparation to clean and concentrate the sample we were able to increase the signal intensity. By comparing sample extracts with different dilution factors we were able to eliminate background signals that originated from the instrument and solvents used. Altogether, we must also acknowledge that FIF does not guarantee that all PFAS present in a sample will be detected, but we were able to detect and identify several PFAS in the tested samples, of which some have never been reported before.

 With the FIF methodology we detected 40 structures in Dutch surface water, and we tentatively identified four new structures that were not reported before. Next to that, we identified fragments of what we believe are the larger molecular analogues of GenX (hexafluoropropylene oxide-dimer acid [HFPO-DA]). These are heavier analogues (hexafluoropropylene oxide-timer acid [HFPO-TrA], hexafluoropropylene oxide-tetramer acid [HFPO-TeA], and so on) that we have not previously detected in the regular targeted analysis. To me, this shows the strength of the FIF identification approach.

 For some of the PFAS structures, the final step of unambiguous identification by comparing with a reference standard could not be achieved, due to the lack of such a reference standard. This problem is faced more generally by non-target identification methods for pollutants, metabolites, and so on. Ideally, this will be solved by the synthesis of many new reference standards, but that is a very resource-demanding task.

 Are you planning to develop this research further?

 Yes, we want to develop this process further, as there are still some challenges ahead. The process of data handling is quite labour intensive and could benefit from automation, which will stimulate a broader application. Furthermore, for many of the 4700 PFAS compounds, no reference standards are available, which hampers quantification. This is not a unique situation for PFAS, but broadly for non-target analysis, and we will investigate how to perform approximate quantification of these compounds where we don’t have access to reference standards. 

 Also, we are aiming to apply FIF to food samples, which introduces an additional challenge as such samples are much more diverse and require more intensive sample clean-up.

 Could FIF be useful for other environmental (or food) applications?

 We demonstrated FIF on surface water, and I’m confident that FIF will also work on other environmental matrices. Our next step is to apply it to foods, particularly those where foods were derived from animals exposed to PFAS, such as fish from Dutch rivers. Moreover, one can think of packaged foods where they are wrapped in food contact materials (FCMs) that have been treated with a wide range of PFAS in the past to make them water- and greaseproof.

 Will FIF become an important technique in PFAS analysis?

 PFAS contaminants will stay with us for a long time, and the need for identification tools will therefore remain. With FIF, a valuable identification approach is added to the non-target identification toolbox, which will also be applicable to other food and environmental contaminant fields.

1) EFSA Panel on Contaminants in the Food Chain (EFSA CONTAM Panel), “Scientific Opinion: Risk to human health related to the presence of perfluoroalkyl substances in food”, 

Portal on Per and Poly Fluorinated Chemicals

, http://www.oecd.org/chemicalsafety/portal-perfluorinated-chemicals/

Featured Interview

LCGC Europe spoke to Stefan Van Leeuwen and Bjorn Berendsen from Wageningen Food Safety Research, The Netherlands, about a novel non-targeted approach to analyze PFASs using LC–HRMS with fragment ion flagging (FIF).

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