From Raw Material Identification to Fine and Specialty Chemicals Analysis

The Global Chemical Industries Summit 

ChromSoc Advances in Gas Chromatography 

Their Application to Achiral and Chiral Separations

Evaluating Two Dioscin-Based Silica Stationary Phases

The Future of Separation Science: Goodbye Old Friends

Analyzing Per- and Polyfluoroalkyl Substances in Drinking Water

Rising Stars of Separation Science: Emanuela Gionfriddo

But My Peaks Are Not Gaussian! Part II: Physical Causes of Peak Asymmetry

Nominate a colleague or yourself for the 2023 LCGC Lifetime Achievement in Chromatography Award

 is seeking nominations for the 15th Annual Lifetime Achievement in Chromatography award, to be awarded in 2023. 

 Lifetime Achievement in Chromatography Award, as its name implies, recognizes a separation scientist for his or her overall contributions to the field of chromatography. The winner, chosen by an independent committee, will be featured on the cover of a 2023 issue of 

 and in an interview that will be featured in our electronic newsletter, e-Separation Solutions, and on the 

	A list of past winners can be found on the 


	To nominate a candidate, please use this 

to provide the information listed below about the person you are nominating.

	Please email the completed form to Laura Bush, the editorial director of 

		Candidate’s full name:           

		Current job title (if retired, please indicate that and include last known job title before retirement):      

		Area(s) of focus of work (maximum 200 words):      

		Short summary, in 100 words, of the candidate’s achievements or contributions (more details can follow in the sections below):      

		Please list the candidate’s most important contributions to the field of chromatography and explain briefly the significance of each (maximum 800 words):

		Number of oral and poster presentations at scientific conferences: 

		Service provided to the scientific community, such as volunteer work in scientific societies, organizing conferences, work as a journal reviewer, teaching short courses, mentoring young scientists, and so on. Please quantify where possible (e.g., number of students in short courses): 

	Nominations should be sent to Laura Bush, the editorial director of 

	Questions about the submission process should be directed to Laura Bush at 

	Please note that this call for nominations is for the 2023 award.

Articles

2023 Lifetime Achievement in Chromatography Award Call for Nominations

Nominate a colleague or yourself for the 2023 LCGC Emerging Leader in Chromatography Award 

 is seeking nominations for the 15th Annual Emerging Leader in Chromatography award, to be awarded in 2023.  

 Emerging Leader in Chromatography award recognizes the achievements and aspirations of a talented young separation scientist who has made strides early in his or her career toward the advancement of chromatographic techniques or applications. The winner will be featured on the cover of a 2023 issue of 

	A list of past winners can be found on the 

 Emerging Leader in Chromatography award must work in the field of separation science. In 2023, candidates must be within 10 years of receiving their highest degree, and must have made significant practical contributions to the field of separation science that have helped advance the state of the art in the fundamentals or application of chromatography. Weight will be given to candidates who have taken a leadership role at their university, institute, or place of business. 


	You may nominate a colleague or yourself. To nominate a candidate, please email the following documents to Laura Bush, the editorial director of 

1. INFORMATION ABOUT THE PERSON YOU ARE NOMINATING 

, please provide the following information about the person you are nominating:

		Year highest academic degree was earned and from where (list degree, year, institution, and location). 

Important: Candidates must be within 10 years of receiving their highest degree in the year the award is presented.

		Short summary, in 100 words, of the candidate’s achievements or contributions (more details can follow in the sections below)

		Please list the candidate’s five most important pieces of work (such as papers in peer-reviewed journals, technical reports written in an industrial community, patents, or other) and explain briefly the significance of each. 

		Other key contributions to the field of separation science not captured above

		Number of oral and poster presentations at scientific conferences


	Please include a maximum of two letters of support from members of the separation science community. (Additional letters will not be considered during the voting process.) Information in the supporting letters should complement, not repeat, the information contained in the nomination form.


	Please provide a current resume or CV of the candidate in a Word or PDF file.


	Include a high-resolution headshot of the nominee in a .JPG format.

	Submissions should be sent to Laura Bush, the editorial director of 

	Questions should be directed to Laura Bush at 

	Please note that this call for nominations is for the 2023 award. 

2023 Emerging Leader in Chromatography Award Call for Nominations

Good chromatography begins with determining the correct amount of analyte to inject onto the column. There are two types of overload: mass and volume. Mass overload displaces the peak, which shifts the retention time, with the front half sloping and the second half perpendicular to the 

-axis. Volume overload results in symmetrical peaks with widths increasing by 10%. Both conditions can compromise the resolution of closely eluting compound pairs, interfering with identification and quantification. Capacity is the maximum amount of sample before peak distortion occurs, and is directly proportional to film thickness, column length, column internal diameter, and phase solute compatibility. Mathematical approaches to calculate peak capacity have difficulty determining the solubility of specific analytes in stationary phases. Here we will take an empirical approach and examine concentrations for a wide range of compound polarities on several stationary phases, and determine overload as measured by symmetry.

A common question gas chromatographers ask is: “How much sample can I put on my column?” Many of us refer to Dean Rood’s book, 

A Practical Guide to the Care, Maintenance, and Troubleshooting of Capillary Gas Chromatographic Systems 

as a good starting point, since his work is the result of over 15,000 customer inquiries. On page 44 is a table of typical column capacities, and that information has found its way into many seminars over the years (1). There are times when we recommend a range, and the feedback is that our estimate was not even close. Jaap de Zeeuw references “loadability” with different film thicknesses on a 0.32 mm internal diameter (i.d.) column presenting a broader estimate of 2 ng on‑column to 7500 ng on-column (2), whereas Dean Rood’s estimate is 30 ng on-column to 1500 ng on-column. Both estimates consider that the amount that can be injected onto a column is directly related to the amount of stationary phase. One way to get more sample on-column and maintain Gaussian peak shapes is the use of a larger column internal diameter and a thicker film (3). Saturation of the mobile and stationary phase results in overloading effects, which are commonly seen as distorted peak shapes and are categorized as 

. Volume overload leads to broader peak shapes, which may remain symmetrical, whereas mass overload is the saturation of the stationary phase and will be our main focus.

Three columns with different stationary phases were chosen: 100% polydimethylsiloxane (PDMS), “1-type phase”; polyethylene glycol (PEG), “wax phase”; and a 14% cyanopropylphenyl–86% polydimethylsiloxane phase, “1701-type phase”. Column dimensions were the same for the phases evaluated (30 m × 0.53 mm, 0.5-μm 

f columns [Restek]). Our choice of compounds is designed to cover highly polar (acetic acid and propionic acid), polar (1-butanol), mid-polarity (butyraldehyde, 2-butanone, acetonitrile, chloroform) and nonpolar (benzene, cycloheptane, octane, octene, octyne). Using data from our previous article (4), where we describe the impacts of solvents on retention and peak shape, we were able to determine that using 2-ethoxyethanol (EGEE) as a solvent with split rates of 1:25 or higher had no measurable effects on peak shape or retention for the compounds used in that study. This became a logical starting point and at first glance 10 of our 12 compounds were soluble in 2-ethoxyethanol. Oven conditions started at 40 °C with a hold time of 10 min followed by a 30 °C/min to 330 °C, with a final hold of 2 min. Compounds eluted during the hold time allowed a more controlled comparison between columns. Column flow was 5 mL/min, 37 cm/s. Two split ratios were used—25:1 and 50:1—since higher split ratios were unable to maintain correct pressures. Sample concentrations were adjusted from 500 parts per million (ppm) to 200,000 ppm, and on‑column concentrations were at points 100, 250, 500, 1000, 2000, and 4000 ppm. Initial peak identification was performed using a gas chromatography–mass spectrometry (GC–MS) system and capacity experiments were run on a flame ionization detector (FID). Samples were analyzed at the same concentration on-column using different splits and starting concentrations to verify performance (Figure 1).

 Analyzing acetic acid and propionic acid required water as the solvent, different concentrations, and a change in the wax column starting temperature as elution of propionic acid at 40 °C would have taken 193 min (5). Oven conditions for the wax columns were adjusted to 120 °C isothermal with a final bakeout to 230 °C, while the 1701 and 1-type columns remained at 40 °C. Final on-column concentrations ranged from 2 to 2000 ppm.

 Determination of fronting was performed using Agilent’s Chemstation calculation of peak symmetry where a Gaussian peak is 1.0 and values greater than 1.0 are considered fronting. A peak where there is no inflection point is calculated as S = a1+a2/a3+a4, where a1 is the start of the peak, a2 is the 2nd slice, a3 is the 2nd half slice, and a4 is the end of the peak. Chemstation values are more sensitive as they are not measured at 5% or 10% peak height and therefore they do not inversely correlate to 

 or FDA asymmetry calculations. We defined an overloaded analyte as one with a symmetry value of 1.05 or greater, with verification of peak shapes using bracketing with different concentrations as a confirmation.

Determination of peak fronting for chloroform, butanol, and the acids proved to be difficult since these compounds did not exhibit classical fronting peaks for all stationary phases, such as we observed with the other compounds, such as cycloheptane (Figure 2). High concentration samples produced a double focusing effect with chloroform. In addition, chloroform was not linear beyond the 2000 ppm on‑column concentration, which suggests a potential solubility issue in 2-ethoxyethanol. Peak tailing was observed on both the 1701 and 1-type phases for butanol, and while our work with 2-ethoxyethanol in a previous article showed no observable effects, butanol under our given conditions and thinner film ratio eluted within one minute of the solvent. Propionic acid and acetic acid at 2 ppm on-column had a symmetry value of 1.61 and 1.81, suggesting that even lower concentrations would result in fronting (Figure 3). Surprisingly, fronting was observed on the wax column at 500 ppm and very distorted at 1000 and 2000 ppm. Differences in degree of compound saturation did not appear to change solubility enough to make measurable differences. Octane, octene, and octyne offered similar performances on the different stationary phases; for instance, on the 1-type phase, peak tailing at 500 pmm concentration was 1.16, 1.17, and 1.16, respectively (Figure 4). Figure 4 represents the point at which the peak symmetry exceeded 1.05.

This brief examination of column capacity using a 0.53 mm internal diameter and a 0.5 µm film thickness column with three different stationary phases determined that the capacity range for columns is much larger than what has been published. For example, a 1-type phase is saturated at the 2 ng on‑column level or below for acids. Compounds that are not soluble in a particular stationary phase will saturate at very low levels. Single bond, double bond, and triple bonded compounds did not measurably change solubility profiles. Further work with smaller column internal diameters will yield more accurate results as lower concentration standards and higher split ratios can be used, which will minimize solvent and allow a wider range of split ratios.

A Practical Guide to the Care, Maintenance and Troubleshooting of Capillary Gas Chromatographic Systems

 (Huthig, Heidelberg, Germany, 1991), p. 44.

Impact of GC Parameters on The Separation Part 4: Choice of Film Thickness

, https://www.restek.com/globalassets/pdfs/literature/Impact-of-GC-Parameters_Part4.pdf

Impact of GC Parameters on The Separation Part 2: Choice of Column Internal Diameter

, https://www.restek.com/globalassets/pdfs/literature/impact-of-gc-parameters_part2.pdf

 has managed a team of chemists in Restek’s innovations laboratory since 2004. Before taking the reins of the laboratory, he spent seven years as an environmental chemist and was critical to the development of Restek’s current line of volatile GC columns. Prior to joining Restek, he operated a variety of gas chromatographic detectors conducting method development and sample analysis. Chris holds a B.S. in environmental science from Saint Michael’s College, USA.

In the latest instalment of “Practical GC”, Chris English takes an empirical approach and examines concentrations for a wide range of compound polarities on several stationary phases, and determines overload as measured by symmetry. 

How Much Sample Can I Put on My GC Column?

This month we interview Emanuela Gionfriddo, Assistant Professor of Chemistry at the Department of Chemistry and Biochemistry of the University of Toledo in Ohio, USA, about her work quantifying per- and polyfluoroalkyl (PFAS) in water using solid-phase microextraction liquid chromatography tandem mass spectrometry (SPME–LC–MS/MS) and the evolving field of sample preparation.

Q. When did you first encounter chromatography and what attracted you to the subject?

 I encountered chromatography for the first time while working on my bachelor of science thesis at the University of Calabria in Italy. I was investigating the changes in the aroma profile of tomatoes based on their origin. The method involved the use of solid‑phase microextraction (SPME) coupled to gas chromatography–ion trap/mass spectrometry (GC–IT/MS). I was fascinated by the fact that these techniques allow to extract and separate so many chemicals and unveil the molecular composition of food aroma.

Q. Can you tell us more about your Ph.D. thesis?

My Ph.D. thesis focused on developing new derivatization methods for the analysis of challenging environmental contaminants such as hydrazine (1), food quality markers such as seleno amino acids (2), and neurotransmitters (3). Part of my thesis was also focused on the fundamental understanding of sorption mechanisms and displacement phenomena on solid SPME sorbents (4).

Q. What chromatographic techniques have you worked with?

 I have worked with one-dimensional and two-dimensional gas chromatography coupled to mass spectrometry. In my laboratory, we are also working with high and ultrahigh-performance
liquid chromatography (HPLC and UHPLC).

Q. You recently published a paper detailing how to minimize the effects of transient microenvironments (TME) by hyphenating for the first time SPME Arrow to MS via thermal desorption (TD) and direct analysis in real time (DART). What is novel about this approach and what does it offer over other methods?

This methodology involves heated enclosed desorption of Arrow SPME for DART-MS analysis. This approach allows to confine the SPME desorption and analytes’ ionization region, providing a more efficient introduction of the analytes into the MS system (5). This, in turn, helps to minimize interferences and background noise generated by the surrounding environment, with a significant potential to expand the use of this technology for on-site analysis. Compared to other approaches, this method allows for more precise interday measurements by DART-MS and for an easy and consistent introduction of Arrow SPME devices into the desorption chamber.

Q. What challenges did you encounter when combining these two technologies and how did you overcome them?

The main challenge for this work was to create a system that would allow for reproducible sample introduction and would maximize the desorption efficiency while improving ion transmission to the MS system. We overcame these challenges by modifying the design of a thermal desorption unit used for DART-MS and by studying which sorbent thickness/length would provide the sharpest desorption profiles with optimum signal‑to‑noise ratio (

Q. You have also used SPME–liquid chromatography (LC)–MS/MS to quantify per- and polyfluoroalkyl (PFAS) in water. Could you talk a little about this work?

This work used a hydrophilic-lipophilic balance weak anion exchange (HLB-wax) sorbent immobilized on SPME devices for the selective extraction of PFAS from various environmental samples. The SPME extracts were injected in a ultrahigh-performance liquid chromatography-laminar flow tandem mass spectrometry system. Analysis of PFAS is extremely challenging due to potential contamination at all stages of analysis, which can result in false-positive results, especially at the low concentrations (part per trillion) at which these analytes are usually detected in environmental samples. The main issue is that contamination sources in the laboratory are not well characterized and can arise from the use of many laboratory products, including sample preparation supplies. For this reason, it is important to minimize sample handling and the various stages needed for extraction: SPME can achieve this. In fact, the protocol developed by my group not only establishes SPME as a reliable preconcentration method for the ultratrace analysis of PFAS in aqueous matrices but also lays the groundwork for future studies involving the analysis of PFAS in more complex samples (6).

In this work we assessed that SPME can be considered an alternative sample preparation strategy to existing methods for the analysis of PFAS, such as dilute-and-shoot performed in accredited methods by the U.S. EPA. We also investigated how the selectivity of the extraction process is dependent not only on hydrophobic interactions but also on anion exchange mechanisms. Moreover, we assessed matrix effects for these analytes in various environmental samples.

Q. What projects are you working on next?

I certainly want to continue working on PFAS research for analysis of more complex samples by expanding the range of PFAS simultaneously analyzed and pushing down the limits of detection achievable with the preconcentration ability of SPME devices. We are also working on the development of new extraction phases obtained from low-cost and renewable materials for extraction of environmental toxins.

Q. What are the emerging trends in sample preparation?

The field of sample preparation has been developing at an exponential rate in the past 10 to 15 years. And I am glad to see that this field is gaining more attention and respect in the separation science community. Certainly, emerging trends include “greening” sample preparation as much as possible, providing streamlined protocols compatible with automation. Several research groups are expanding the capability of extraction technologies, such as microextraction, to in vivo methods, and also to the analysis of metals and nucleic acids, which is paving the way to a very broad range of innovative applications.

Recent trends also involve developing sample preparation solutions that include a completely green cycle: starting from a green sorbent synthesis to minimization of waste produced during the extraction procedure.

Q. How do you see the role of the next generation of academic separation scientists in modernizing the strategies for teaching separation science? What particular challenges are facing you?

Separation science research is evolving at a very fast speed; however, separation science education can’t always catch up with modern trends in the field. The separation science community is currently making efforts to bring innovative strategies into the classroom and raise awareness on the importance that separation processes have in our everyday life. In this way we can motivate the next generation of scientists to explore degrees and careers in this field. One of the main challenges I am facing is to make my students understand that chromatographic systems are not “black boxes”, and that to control what happens in a chromatographic column we need to understand the chemistry of the interactions between molecules and the stationary phase. Only in this way can we fully control the chromatographic process and tune it to fulfill our analytical needs. I am also trying to integrate into my class curriculum alternative topics in separation science, such as extraction technologies, and provide the students with hands-on experience that I am sure will be extremely useful for their future careers.

Q. What advice would you give to someone who is thinking of going into separation science teaching?

Be creative and strive to link your educational content with topics of broad interest for your students. Explore new ways to let your students acquire hands-on experience in separation processes in the laboratory, even when chromatographic equipment is not readily available. Many interesting strategies have been proposed in the literature that can be easily implemented in the teaching laboratories nowadays.

Last but not least, strive to link all the separation processes to the fundamental interactions of molecules with stationary or extraction phases, to teach students the chemistry behind separation science.

E. Gionfriddo, A. Naccarato, G. Sindona, and A. Tagarelli, 

, 37–45 (2014). https://doi.org/10.1016/j.aca.2014.05.038.

E. Gionfriddo, A. Naccarato, G. Sindona, and A Tagarelli, 

, 58–66 (2012). https://doi.org/10.1016/j.aca.2012.08.016.

A. Naccarato, E. Gionfriddo, G. Sindona, and A. Tagarelli, 

E. Gionfriddo, É.A. Souza-Silva, and J. Pawliszyn, 

(16), 8448–8456 (2015). https://doi.org/10.1021/acs.analchem.5b01850.

, 145789 (2021). https://doi.org/10.1016/j.scitotenv.2021.145789.

A.A. Olomukoro, R.V. Emmons, N.H. Godage, E. Cudjoe, and E. Gionfriddo, 

, 462335 (2021). https://doi.org/10.1016/j.chroma.2021.462335.

 is an Assistant Professor of Chemistry at the Department of Chemistry and Biochemistry of The University of Toledo in Ohio, USA. Research work in Dr. Gionfriddo’s laboratory focuses on the development of advanced analytical separation tools for the analysis of complex biological and environmental matrices, with an emphasis on alternative green sample preparation methodologies. She received her B.Sc. (2008) and M.Sc. (2010) in chemistry and her Ph.D. in analytical chemistry (2013) at the University of Calabria (Italy). She joined Professor Pawliszyn’s group at the University of Waterloo (Ontario, Canada) in 2014 as postdoctoral fellow and manager of the gas chromatography section of the Industrially Focused Analytical Research Laboratory (InFAReL), and within three years became a research associate. She has currently authored over 50 peer-reviewed contributions including a patent on PTFE‑based SPME coatings. Dr. Gionfriddo is one of the founding members of the Dr. Nina McClelland Laboratory for Water Chemistry and Environmental Analysis at The University of Toledo and she is appointed to the Ohio Attorney General Yost’s Environmental Council of Advisors. She also serves on the executive committee of the American Chemical Society Analytical Division Subdivision on Chromatography and Separations Chemistry. Her research programme is currently funded by the National Oceanic and Atmospheric Administration and several industrial partnerships.

, will be running a series of interviews in 2021, featuring the next generation of separation scientists. If you would like to nominate a “rising star” for consideration, please send the name of the candidate and why they deserve recognition to Alasdair Matheson, Editor-in-Chief, LCGC Europe at amatheson@mjhlifesciences.com

This month we interview Emanuela Gionfriddo, Assistant Professor of Chemistry at the Department of Chemistry and Biochemistry of the University of Toledo in Ohio, USA, about her work quantifying per- and polyfluoroalkyl (PFAS) in water using SPME–LC–MS/MS and the evolving field of sample preparation.

Despite the complex matrices present in toxicology testing, fast, accurate results should be possible with easy-to-implement sample preparation and liquid chromatography with tandem mass spectrometry (LC–MS/MS) analysis. Often, to prepare a diverse sample set, the quickest method may have unintended negative impacts. Sample preparation methods need to be selected based on the pain management drugs being analyzed, cleaning up the matrix without compromising recovery, column lifetime, or to reduce additional system maintenance in the future. This article will explore the same 39 analytes in urine, whole blood, and plasma as well as the differences between the workflows to get consistent, fast MS results.

Traditional pain management drug testing poses many analytical challenges for laboratories, including the use of sample preparation techniques that are often time‑consuming and unreliable. In addition, a wide variety of sample matrices exist and can potentially provide inaccurate results. Each of these matrices present different challenges that could complicate switching an assay or updating validated methods due to the specific clean‑up requirements. Whole blood, serum, and urine are common matrices that all require sample preparation before liquid chromatography tandem mass spectrometry (LC–MS/MS) analysis, yet laboratories struggle to find reproducible, accurate, and reliable workflows. While the preparation of biological samples has not changed significantly in the past 10 years, different techniques have evolved to process more samples in less time while also yielding more sensitive results, all due to advancements in instruments. The implementation of simple sample preparation methods before selecting an LC column is an important step towards a consistent, efficient workflow for analyzing pain management drugs.

The first step for developing a new method is to evaluate the matrix, as well as what level of sensitivity is needed for the assay. This can differ between toxicology laboratories based on their needs and the type of analytes they are working to separate. Most laboratories have great resources for optimizing or developing methods from chromatography vendors. However, understanding method requirements will help in implementing a cohesive, dependable method. To get a robust sample preparation method, analysts should select a sample preparation technique based on the starting matrix and then proceed to evaluate an LC column. Sample preparation is usually an afterthought, even though it could directly impact the LC separation, resolution, matrix effects, and more. While most laboratories are not establishing completely new methods or assays, the need for laboratories to be able to evolve to meet specific analyte levels must be considered; this requires an understanding of selecting sample preparation methods based on diverse matrices, as well as a variety of analytes with different chemical structures.

As a sample matrix, urine seems relatively straightforward. However, the clean‑up techniques may not be. A universal drug test is often performed using urine, which is thought of as an easy matrix to work with. While there are many clean‑up techniques, filtration, dilutions, and solid‑phase extraction (SPE) are some of the most frequently used. To determine the most appropriate method, analysts and method developers need to weigh the cleanliness, costs, and results. One of the downsides of testing urine is that drugs have already been metabolized—meaning they have undergone a glucuronidase reaction in the liver to increase polarity so the drug moiety can be absorbed into the kidneys (1). The result is a drug structure that must be hydrolyzed to be analyzed by LC–MS/MS. After hydrolysis, the drug as well as a large protein are present in the sample. For some laboratories, a dilution is a simple solution. However, the trade-off of this method is that LC columns will fail prematurely, affecting the reliability of results and causing more expenses for the laboratory down the line. By using filtration, some of the large proteins and particulates can be removed prior to analysis while maintaining the sensitivity in the sample that may be required downtream. SPE is a selective sample preparation technique that results in the cleanest extraction, but it can take a significant amount of method development and time. An SPE method that does not require a conditioning or equilibration step—about 40% of the protocol—allows analysts to achieve the benefits of SPE in less time, with high recoveries and low variation between samples (2). Table 1 shows the effectiveness of a method based around drug extractions to save method development time but still yield accurate, high recoveries.

For drug analysis, plasma and serum allow for better real-time detection data then urine. Plasma and serum act as good indicators, providing accurate LC results. However, the sample is more complex than urine; a simple dilution will not effectively clean up the sample for LC–MS/MS. These types of matrix require either a protein precipitation, phospholipid removal, or SPE prior to analysis. Spending time developing an LC method with the correct selectivity and separation will be ineffective if the proteins and phospholipids in the samples are not removed prior. The phospholipids present in the sample can cause ion suppression or enhancements in the MS system at specific transitions, introducing further inaccuracies and a change in the baseline. Figure 1 shows an analysis that was performed to determine 39 pain management analytes in urine and the method is detailed below.

 LC–MS/MS: Column: 50 × 3.0 mm, 2.6-µm Kinetex Biphenyl; (Phenomenex) part no.: 00B-4622-Y0; mobile phase: A: 0.1% formic acid in water, B: 0.1% formic acid in methanol; gradient: 0 min 15% B, 3.5 min 95% B, 5 min 95% B, 5.01 min 15% B, 7 min 15% B; flow rate: 0.5 mL/min; injection volume: 5 µL; temperature: ambient; detection: Sciex 4500 MS/MS (ESI+)

200 µL of serum was aliquoted into a tube and 600 µL of chilled (0 to -20 ºC) 95:5 acetonitrile–methanol was added and vortexed for 5–10 s. The tube was centrifuged at 300 rpm for 10 min, the supernatant was collected, and 25 µL of 1% formic acid was added. Load: pre-treated sample onto the Phree 96‑well plate; part no.: 8E-S133-TGB; vacuum: 4–5 psi to collect supernatant; dry down: sample under a gentle stream of nitrogen at 40–45 ºC; reconstitute: in 200 µL initial mobile phase. The difference in the chromatography between samples that had undergone a simple protein precipitation and those which used phospholipid removal is shown in Figure 2. Phospholipid removal is one of the easiest sample preparation methods to implement and balances time, recovery, and matrix cleanliness. It does not require method development—the time spent is equivalent to protein precipitation—and it can be easily automated for high‑throughput samples. Cleanup can also be done using a solid‑phase extraction method if additional solvent switching or concentration are concerns, but the time spent on method development and the protocol itself will be increased in this case. By using phospholipid removal, the phospholipid profile will be significantly cleaned up, leading to better MS results, longer column lifetimes, lower backpressure, and overall improvement in the chromatography (3). The recovery results of the extraction still remain high, with a low percentage of variation as seen in Table 1.

Whole blood as a sample matrix is commonly used within toxicology testing laboratories. Like plasma or serum and unlike urine, the drugs present in whole blood have not yet been metabolized, so the hydrolysis step can be skipped. However, whole blood as a matrix has disadvantages, including the complexity and the required pretreatment steps needed even before preparing the appropriate sample. Whole blood must be broken down; phospholipid removal or solid-phase extraction is required for clean up. SPE is the best solution for laboratories that need to add sensitivity at low levels. When working with a diverse panel of drug analytes, it is imperative to have a solution unique to the matrix and drug panel. While many SPE sorbents and tube formats are available, the basic chemical reaction remains the same: the tube is primed by the conditioning and equilibration steps, the sample is loaded, solvents are used to wash the tube, analytes are bonded to the sorbent, and a strong organic solvent is used to “release” the analytes. Then they are ready for LC separation for identification. Phospholipid removal also works similarly after the whole blood pretreatment to serum or plasma; if a laboratory needs a relatively fast, clean sample, they typically implement phospholipid removal techniques and subsequently improve the chromatography (4). Results for the two-step phospholipid removal are seen in Table 1. The results are comparable to what you would get with an SPE method for whole blood.

Liquid Chromatography (LC) Column Selection

Selecting an LC column for the separation and analysis of drug analytes is a more straightforward approach than sample preparation. Additionally, troubleshooting issues with the separation method is typically simpler than troubleshooting the sample preparation method. Method development requires optimizing the stationary phase as well as overall conditions to achieve the separation of target analytes. For the majority of pain panel or pain management testing, a selectivity with 100% aqueous stability is recommended for reversed phase separations. To understand why a biphenyl selectivity is often adopted for different drug assays, a comparison of the chemical attributes on a core–shell backbone is highlighted in Figure 3. Both the C18 and biphenyl LC columns are bonded on a core–shell solid support, which offers an efficient way to potentially increase relative peak intensity while operating under typical high performance liquid chromatography (HPLC) pressure (<400 bar). An example of the separation for 39 different pain management drugs using a biphenyl bonded on a core–shell LC particle, with the sample preparation method from serum and with good chromatography results, is shown in Figure 1. In the future, there will be MS instruments that guarantee analyte detection at lower levels and that incorporate the advantages of micro- and nano-LC technology. With these advances, sample preparation will become an even more important part of the workflow. Nano- and micro‑LC columns cannot be used without appropriate sample preparation, making technique selection even more critical. As previously mentioned, dilution is a traditional method for more sensitive MS instruments, but will not be a viable option with low flow chromatography.

With new specifications, drug moieties, and detection limits, sample preparation methods need to be adaptable for laboratories that work in a fast-paced separation environment. When working with a diverse array of matrices for different pain management extraction, each type of matrix provides a slew of options for sample preparation. Since not all samples are uniform, neither are the sample preparation options. However, laboratories should consider how to balance clean-up techniques with their scheduling and budgetary requirements.

Quantification of glucuronide metabolites in biological matrices by LC-MS/MS, tandem mass spectrometry—applications and principles

 (2012). www.intechopen.com/books/tandem-mass-spectrometry-applications-andprinciples/quantification-of-glucuronide-metabolites-in-biological-matrices-by-lc-ms-ms

Fast Clean Up of Whole Blood Using Phree Phospholipid Removal and Analysis of 39 Pain Management Drugs by Kinetex 2.6 µm Biphenyl

graduated from Vanguard University of Southern California (California, USA) with a B.S. in biology and a minor in chemistry. She now works as the Senior Manager of the Global Product Marketing Team at Phenomenex, Inc. located in Torrance, California, USA.

graduated from Baylor College of Medicine (Texas, USA) with a Ph.D. in translational biology and molecular medicine. He is the Technical Marketing Writer at Phenomenex, Inc. located in Torrance, California, USA.

is a senior scientist within the applications lab at Phenomenex. She holds a MS in biochemistry from California State University, Los Angeles, USA. Her work involves analytical method development, optimization, and troubleshooting.

The importance of sample preparation for analyzing pain management drugs in different matrices

Novel Sample Preparation Methods for Analyzing Pain Management Drugs in Complex Matrices

Tony Taylor is Group Technical Director of Crawford Scientific Group and CHROMacademy. His background is in pharmaceutical R&D and polymer chemistry, but he has spent the past 20 years in training and consulting, working with Crawford Scientific Group clients to ensure they attain the very best analytical science possible. He has trained and consulted with thousands of analytical chemists globally and is passionate about professional development in separation science, developing CHROMacademy as a means to provide high-quality online education to analytical chemists. His current research interests include HPLC column selectivity codification, advanced automated sample preparation, and LCâMS and GCâMS for materials characterization, especially in the field of extractables and leachables analysis.

I’m very much a “big picture” type of thinker. By that, I mean unless I can understand all of the working parts of a problem and understand how they interact, I find it difficult to decide the best approach to figuring out how to solve the issue. While this is certainly a personality trait, I often wonder how chromatographers with some thirty-plus years less experience than myself begin to fathom the complexities of separation science and so begin to develop their own approach to practical problem solving.

The Purnell Equation shown in Figure 1 does an excellent job of describing the change in chromatographic Resolution (

) obtained when altering each of the principle chromatographic variables, namely efficiency (

). When I’m teaching chromatography, it’s at the point we begin to roll out the governing equations that I see that glazed look come over many students, as the old guy “tries to tie everything back to theory,” “when was the last time I ever used an equation in the laboratory,” and “when was the last time he ever did any real chromatography.” Most times I can retrieve this situation by answering, “yesterday,” and by relating the mathematical relationships to the last time they stood in front of their HPLC equipment scratching their head for inspiration! Let’s do the same thing here and see how this particular equation can help with practical problems.

Figure 1 should immediately tell us that when trying to optimize resolution, the selectivity gives us by far the biggest return on our investment. It also seems to be the gift that keeps on giving with no real “plateau” in resolution as the selectivity increases. This will be empirically obvious to most chromatographers. Next most powerful perhaps is efficiency, with a pronounced improvement in resolution as we ramp up the plate numbers. This is borne out by the industries drive over the past decade and a half to switch to narrower internal diameter columns and smaller particles to improve inherent column efficiency and the improved pump hardware which is required to cope with the increased back pressures created as we reduce the particle diameter of the stationary phase. Least effective of all in improving resolution is retention factor, which seems to rapidly improve from 

 values of 0 to 5 and then hit a definite plateau that coincides with the point at which band broadening due to diffusion counteracts any gains in separation factor. This is true for isocratic separations, but I’ll talk more later on how useful k values can be in gradient analysis. OK, so that describes what is shown in Figure 1, but how can we translate this into action plans for getting out of chromatographic dire straits?

Let’s suppose we have an isocratic separation as shown in Figure 2. What’s the most efficient way to tackle this 

Clearly, we have a separation issue between the first two peaks within the chromatogram and need to think about how to improve the separation. We have seen that changing the selectivity of the separation makes a big impact on resolution, so how can that be done in this case? Well, my own personal golden rules of selectivity optimization follow a “lazy chromatographers” pathway:

Optimize eluent pH (for ionizable analytes)

Why do I put such a big-hitting selectivity variable, such as the stationary-phase chemistry, last? Well, I’m lazy, and I’d like to see if I can get the separation done with the variables at hand, without having the change the column. And it’s not just about choosing a different phase, equilibrating the new column, and performing an injection. What if the new phase doesn’t work? Remember there are around 200 C18 stationary phases on the market to choose from, and they will all (or close to all), give a different selectivity. You might get lucky. You might not!

As can be seen from Figure 3, by following my rules of thumb, I managed to affect a good resolution for the separation in a very reasonable analysis time. I increased the % methanol as a first intent, because lowering it just gives me longer analysis time, and if I can get away with a faster analysis, then so much the better. The same is true with temperature. Higher temperatures give faster separations, although I should note that I got a little lucky with this separation, and temperature doesn’t always have such a profound effect on the separation of non-ionogenic analytes. All of this was achieved simply by changing settings in the data system. I didn’t touch the HPLC at any time during this optimization, only the data system.

Of course, if I was dealing with ionizable analytes, I could have gone on to optimize the eluent pH. But I wasn’t, primarily because I wanted to keep this article a little shorter! One could argue that optimizing eluent pH and changing the stationary phase are both complex and can be iterative processes if optimization software isn’t used. And that would be right, but I bet these very processes are going around in laboratories all over the world, right now. For pH optimization I would urge the reader to select one of the analytes in the mixture, find out its pKa value, and then adjust eluent pH so that the analyte is fully non-ionized (eluent pH two units above the pka for bases and two units below the pKa for acids). This will be a good starting point for the optimization. For stationary-phase selection, use a website such as 

 to better understand what type of stationary phase is orthogonal to your current phase. You can choose the same chemistry (such as another C18) or try a different chemistry. I would recommend the use of polar-embedded phases for lower Log P analytes, Phenyl Hexyl for aromatic analytes, and Penta Fluoro Phenyl (PFP) for analytes that are closely structurally related.

Of course, one could try to change the efficiency using a different column diameter or smaller particle size or even optimizing the eluent flow rate, but as we saw in Figure 1, it’s better to start with the big-hitting variables in order to quickly get to the result you want. On that latter point of changing flow rate, remember that capacity factor in isocratic separations does not appreciably change with flow rate as can be seen from Equation 1 for calculating 

 is the elution time of an unretained component.

In isocratic separations, as the retention time increases or decreases with flow, so does the hold-up time (t

) and therefore we can’t expect to influence resolution or selectivity (Equation 2, the ratio of the capacity factor of two adjacent peaks) by changing flow rate.

So, what about gradient separations? Well, most of what is said above holds true with gradient-mode analysis, with the exception of capacity factor. Because the eluent composition is constantly changing in gradient separations, the capacity factor will also be changing, and this variable becomes much more useful to us, in the form of this nice equation for the “average” retention factor (

 is the eluent flow rate, D%B is the change in eluent composition expressed as decimal (such as 10–90% change in component B would be 0.8), V

 is the column eluent volume (approximately 1 mL for the column in question) and S is a shape factor that can be assumed to be 5 for small molecule applications).

This equation can be very useful in helping us to adjust the gradient parameters to see if we can affect a separation. An interesting aspect of 

 in gradient HPLC, as opposed to k in isocratic HPLC, is that flow rate changes will have an effect on 

—if the flow rate is doubled, then the k* value (and hence analyte retention relative to the hold-up time) will double. Selectivity in gradient elution is defined in the same manner as in Equation 3, except that 

 values for adjacent peaks are used instead of isocratic 

 values. It is important to note that the relative peak spacing (a) changes if only the flow-rate is changed. The same is true when altering the gradient time tg, and changes in either of these parameters can be counterbalanced by lengthening or shortening the gradient range 

I’m particularly focussing on these parameters as they appeal to my inner laziness—that is, one needs to do nothing other than to make changes to the method via the computer data system and no changes need to be made to the HPLC system, column, or eluent composition. Figure 4 shows some illustrative examples:

Figure 4 (a) shows a gradient separation of 4 neutral test compounds of various LogP values, which isn’t quite working out as we had hoped. Using Equation 3, we first doubled the flow rate and, as can be seen, the average capacity factor (

) of the peaks increased by a factor of two. The same effect can be seen when halving the gradient change in %B (gradient range) or doubling the gradient time.

What should be obvious is that in each case the resolution of the critical pair of peaks has improved! Figure 4 (c) is somewhat of a compromise separation in that most of the peaks are eluting in the “isocratic” phase at the end of the gradient. In practical terms, the conditions set out Figure 4(d) may be the most promising in terms of a basis for further optimization, given that most of the peaks elute within the gradient range. Of course, one might suspect that isocratic mode would have been more successful for this separation, but that will need to be reserved for a future technical tip.

I should state that this is quite a simple separation, and I did get a little lucky on this particular example, however, the fact remains that by doing not very much "practical" work, it has been possible to make some informed decisions, based on basic chromatographic relationships, which presented possible solutions for the optimization of the separation.

I realize that many of us working in HPLC don’t plan or even optimize separations based on equations. Many of us develop a “feel” for what is required through many years of experience. However, I do strongly believe that this “feel” can be very usefully backed up and even better informed when one understands and can use some of these simple relationships to understand the cause-and-effect nature of our work.

is the Chief Scientific Officer of Arch Sciences Group and the Technical Director of CHROMacademy. His background is in pharmaceutical R&D and polymer chemistry, but he has spent the past 20 years in training and consulting, working with Crawford Scientific Group clients to ensure they attain the very best analytical science possible. He has trained and consulted with thousands of analytical chemists globally and is passionate about professional development in separation science, developing CHROMacademy as a means to provide high-quality online education to analytical chemists. His current research interests include HPLC column selectivity codification, advanced automated sample preparation, and liquid chromatography–mass spectrometry (LC–MS) and gas chromatography (GC)–MS for materials characterization, especially in the field of extractables and leachables analysis.

I’m very much a “big picture” type of thinker. By that, I mean unless I can understand all of the working parts of a problem and understand how they interact, I find it difficult to decide the best approach to figuring out how to solve the issue.

Back to School—Solving Practical HPLC Problems with Basic Theory

Rachael Szafnauer received an M.Sci in forensic science from the University of South Wales, UK, where her final‑year project focused on fingerprinting emerging psychoactive substances using advanced techniques such as GC×GC–TOF MS, in collaboration with Markes International. She later took up the role of thermal desorption product specialist at Markes, providing technical and application support to the commercial team, before taking on her current role at the start of this year, specialising in the development of applications using extraction and enrichment techniques.


Elinor Hughes obtained her B.Sc. in chemistry and Ph.D. in organic chemistry at Bangor University, UK. After working for a chemical manufacturing company for three years, she moved to the Royal Society of Chemistry where she worked in journals publishing for six years and on Chemistry World magazine for four years. This was followed by five years as a freelance copyeditor and science writer. Her current role is technical copywriter at Markes International.

Multi-step enrichment (MSE) enhances the performance of headspace–solid-phase microextraction–trap (HS–SPME–trap). Using this method combined with gas chromatography–mass spectrometry (GC–MS), the aroma profile of olive oil was analyzed to identify volatile and semivolatile organic compounds (VOCs/SVOCs). The technique was used to distinguish between olive oil samples by identifying unique VOCs as authenticity markers.

Food crime is serious fraud and criminality in food supply chains can be seriously harmful to consumers, food businesses, and the wider food industry (1). Fraudulent acts include adulteration (including a foreign substance that is not on the product’s label to lower costs or fake a higher quality), substitution (replacing a food or ingredient with another substance that is similar but inferior), and misrepresentation (marketing or labelling a product to wrongly portray its quality, safety, origin, or freshness).

To aid the prevention of food fraud, volatile organic compounds (VOCs) from olive oil samples are being analyzed to build profiles that can be used to discriminate between different commercial classes of olive oil (2,3). The profiles will make it easier to identify inferior products, or cheap products being misrepresented as more expensive varieties.

It is hoped that the data will help the International Olive Council (IOC) (4) identify analytical criteria for detecting fraud and determining the quality of olive oils. Currently, the IOC uses a sensory (or panel) test to classify oils in which human evaluators record their sensory responses to products being tested. Although a panel can be repeatable, reproducibility among panels is more challenging. Support from a sensitive analytical method would be highly beneficial to avoid misclassification of products.

Solid-phase microextraction (SPME) is a solvent-free sample preparation technique in which samples are extracted by immersive or headspace (HS) sampling. SPME is used for a wide range of sample classes and applications, including foods and beverages, environmental, clinical, industrial, and defence. This is made possible by the availability of a variety of SPME fibre phases (including polydimethylsiloxane [PDMS], polyacrylate and multi-phase divinylbenzene/carboxen/polydimethylsiloxane [DVB/CAR/PDMS]), which allow analyte selectivity to be optimized. However, workflows for conventional (“direct”) SPME sometimes suffer from its limited sensitivity. This stems from the small volume of sorptive phase on the fibre (typically ~0.5 μL of PDMS), as well as from the relatively slow heating rate of commonly used gas chromatography (GC) injection ports, resulting in broad peaks.

The primary focus of this work was to assess the incremental gain in peak area, and therefore sensitivity, for a range of representative analytes in olive oils when using an automated sample extraction and enrichment platform. Linear retention index values were calculated to confirm peak identification with mass spectrometry (MS) results from the NIST17 database. A commonly used multi-phase fibre for edible oils (DVB/CAR/PDMS) was selected for extraction. A balance between extraction time and sensitivity (across the broad volatility range of compounds in the aroma profile) needed to be achieved. A single sample extraction was compared to cumulative threefold and sixfold extractions from the same vial using various incubation times. Furthermore, the advantage of using shorter extraction times for MSE compared to an equivalent single extraction time is shown, for example, 3 × 10 min (total extraction time 30 min) is compared to a single 30-min extraction time. The successful application of MSE to the distinction of extra virgin olive oil, virgin olive oil, and lampante olive oil has been extensively shown by research conducted by Purcaro 

An extra virgin olive oil (EVO) sample purchased from a supermarket was used for a comparative analysis of single and multi-step SPME–trap extraction and enrichment (Figure 1).

Five EVO, two virgin (VO), and four lampante (LO) olive oils were analyzed as part of a case study based on the MSE method developed in Section A.

Samples (1.5 g ± 0.05 g) were weighed into 20-mL screw‑top headspace vials, which were then sealed.

Instrument: Centri (Markes International).

Fibre: Multi‑phase DVB/CAR/PDMS, 10 mm, 50/30 μm 

 (MilliporeSigma), Sampling depth: 30 mm, Pre-sampling: 43 °C (5 min), Extraction: 43 °C (10 min or 30 min), Agitation: 300 rpm, Fibre desorption: 250 °C (2 min), Enrichment: 3 and 6 extractions (same vial), Enrichment delay: 5 min, Replicates: 3 for each cumulative analysis, Flow path: 180 °C, Focusing trap: “Material emissions” (part no. U-T12ME-2S), Purge flow: 50 mL/min (1 min), Trap low: 0 °C, Trap high: 300 °C (10 min), Split ratio: 5:1.

 Column: SLB-5ms (silphenylene polymer), 30 m × 0.25 mm, 0.5-μm (MilliporeSigma), Oven program: 30 °C (5.5 min), 10 °C/min to 310 °C (0 min), Constant velocity: 35.9 cm/s (He),
Transfer line: 280 °C, Ion source: 200 °C, Mass range: 

 Section A: ChromSpace (SepSolve Analytical). Section B: ChromCompare+ (SepSolve Analytical). MS library: NIST17 database. Experimental linear retention index within ±15 units.

Section A: Comparing the Sensitivities of Single Extraction and Multi-Step Enrichment: 

The EVO sample was extracted with a multi-phase porous fibre (DVB/CAR/PDMS). When using this fibre, short extraction times are recommended to reduce competitive adsorption between the VOCs (5). The EVO sample was extracted once, then three and six times from the same vial (allowing 10 min for each extraction) and injected each time onto the same focusing trap of the extraction and enrichment platform, such that the multiple extractions were then analyzed in a single GC–MS run. The focusing trap was maintained at a sub‑ambient temperature for all injections and then rapidly heated to release the compounds as a narrow band into the capillary column. Figure 2 shows a comparison between the three extraction modes (single extraction and the three and six extractions), which demonstrates an increase in abundance that is proportional to the number of extractions. The gain in sensitivity using MSE was evaluated by dividing the peak area of each compound by the results obtained after a single extraction. The overall extraction ratio showed an average 3.01- and 5.96-fold increase (for the three and six extractions, respectively) in abundance, indicating that the headspace is likely saturated for extraction and after subsequent equilibrations, it is quickly replenished, providing a linear cumulative increment in analyte response compared to the single extraction. A representative selection of 10 compounds is shown in Figure 3 and Table 1 to further highlight this.

Comparison Between MSE and a Single Extraction Using the Same Total Sampling Time:

 When designing an analytical system, the sampling method is an important consideration because the characteristics of some compounds, such as polarity or volatility, can affect the profile of a sample. For example, when sampling with a SPME fibre, profiles are altered both by the selectivity of the coating itself and by competitive adsorption processes, which may be related to the time taken to extract samples. A longer extraction time facilitates this displacement effect, which can occur with some heavier analytes. To better understand the effect of sampling time, a comparison between one extraction for 30 min and three extractions for 10 min (to give the same total sampling time) was carried out. Figure 4 shows that the single extraction (1 × 30 min) did not necessarily provide better results than the shorter repeat extractions. Differences are more evident with the semivolatile organic compounds (SVOCs), for which, in some cases, a 10-min extraction provided a significantly higher yield (inset in Figure 4[a]). For 2-pentadecanone, dibutyl adipate, n-butylbenzenesulfonamide, and 1-hexadecanol, the abundances for the 3 × 10-min extractions are between four and six times higher than those of the 1 × 30-min extractions (Figure 4[b]). This suggests that short repeat samplings enable the detection of more compounds in the volatile profile of extra virgin olive oil than the single extraction. Dibutyl adipate and n-butylbenzenesulfonamide are plasticizers, likely to have leached from the oil’s packaging.

Section B: Identification of Specific VOC Markers from Different Olive Oils by Multi-Step Enrichment:

 There is an increasing interest in the use of VOCs as markers of authenticity in food matrices. Finding a reduced set of volatile markers offers a rapid and potentially inexpensive screening tool. Here, VOC profiles of several olive oils were automatically compared using the software platform. It identified a reduced set of VOCs with the potential to act as markers of quality to distinguish between olive oil samples.

Eleven samples of olive oils (five EVOs, two VOs, and four LOs) were analyzed. Six SPME–trap extractions were performed, each for 10 min, which avoided the competitive adsorption discussed earlier. See Purcaro 

 (2) for a statistical analysis of the results. A reduced set of VOCs can be used to distinguish between EVOs (red) and LOs (pink), while the VOs (blue) are at the median position between the two, as shown in the principal components analysis (PCA) score plot (Figure 5). The results correlate with the compositions of the oils: VOs present some compositional defects, but not as strongly as the LOs, and maintain important positive attributes contained in the EVOs.

Interestingly, two of the four compounds in the reduced set of VOCs were not detected when a single extraction was performed (the late-eluting compounds cyclododecanol and octacosane), an observation that highlights another benefit of using MSE with SPME–trap.

This work was carried out to investigate the potential of SPME when combined with multi-step enrichment to provide an enhanced characterization of the aroma profiles of a range of olive oil samples. The results demonstrate that MSE SPME can improve the overall sensitivity for the VOCs and SVOCs in these products and enhance the understanding of information from cross‑sample studies. Using MSE SPME with shorter extraction times is preferable to an equivalent sampling time for a single extraction, both in terms of overall signal response and for limiting the competitive mechanisms that occur when adsorption fibres are used. This study suggests that a reduced number of VOCs could be used to distinguish expensive extra virgin olive oil from other types, so the technique could help confirm authenticity and prevent fraud.

https://www.food.gov.uk/safety-hygiene/food-crime

has a Ph.D. in food science from the University of Udine, Italy. She spent part of her postdoc at the University of Messina in Italy, and at Dartmouth College in the USA, before being appointed as Analytical Chemistry Professor at the University of Liège, Belgium. Her main areas of research applications are related to contaminants analysis in food, mainly hydrocarbons (polycyclic aromatic hydrocarbons [PAHs] and mineral oils), and food quality, primarily the volatile profile, by using hyphenated chromatographic techniques such as liquid chromatography (LC)–GC(

received an M.Sci. in forensic science from the University of South Wales, UK, where her final‑year project focused on fingerprinting emerging psychoactive substances using advanced techniques such as GC×GC–time-of-flight (TOF)‑MS, in collaboration with Markes International. She later took up the role of thermal desorption product specialist at Markes, providing technical and application support to the commercial team, before taking on her current role at the start of this year, specializing in the development of applications using extraction and enrichment techniques.

obtained her B.Sc. in chemistry and Ph.D. in organic chemistry at Bangor University, UK. After working for a chemical manufacturing company for three years, she moved to the Royal Society of Chemistry where she worked in journals publishing for six years and on 

 magazine for four years. This was followed by five years as a freelance copyeditor and science writer. Her current role is technical copywriter at Markes International.

The potential of SPME when combined with MSE for the characterization of olive oil aroma profiles is presented.

Tackling Food Fraud: Solid-Phase Microextraction Using Multi-Step Enrichment to Enhance Aroma Profiling in Olive Oil

Agilent Technologies Inc. (California, USA) has announced Jason H. Yang as the recipient of the 2021 Agilent Early Career Professor Award. Yang is an Assistant Professor of Microbiology, Biochemistry, and Molecular Genetics in the Center for Emerging and Re-Emerging Pathogens at Rutgers New Jersey Medical School, USA.

“It is an immense honour to be named for this year’s prestigious Agilent Early Career Professor Award. This competitive award will enable us to expand our research capabilities and allow us to make new quantitative, real-time measurements into understanding how immune cells make cellular decisions,” said Yang.

Yang’s multidisciplinary research programme integrates high‑throughput, quantitative experimentation with network modelling and machine learning to study mechanisms underlying the progression and cure of human diseases. The award will support research to reverse-engineer the cell circuitry of human macrophages to enable the development of synthetically engineered macrophages as new cell‑based therapies for treating infections, autoimmunity, and cancer.

“We are delighted to contribute to and accelerate Dr. Yang’s research,” said Jack Wenstrand, Director of University Relations and External Research at Agilent.

The Agilent Early Career Professor Award is an annual programme that recognizes and supports promising research from professors who, early in their careers, show outstanding potential for future research in areas of importance to the communities Agilent serves.

Agilent Technologies Inc. have announced Jason H. Yang as the recipient of the 2021 Agilent Early Career Professor Award.