Volatile organic compounds (VOCs) are ubiquitous chemicals of great interest in the study of aromas and flavours of foods. Many recent studies present optimized headspace (HS) and dynamic headspace (DHS) methods for specific sample types; however, the literature does not present ­(to the best of our knowledge) a generalized procedure for the thorough optimization of a DHS extraction. This article presents an approach using design of experiments (DoE) for the optimization of DHS extraction parameters. The approach is demonstrated for two different food sample types with diverse populations of VOCs: active sourdough colony as an example with a high moisture content, and sourdough bread as an example with a lower moisture content. Optimized methods are assessed for VOC extraction reproducibility and exhaustiveness; guidelines for DHS optimization are presented.

Mixtures of volatile organic compounds (VOCs) emanate from foods and beverages during human consumption, and these compounds blend together to create flavours and aromas. While many VOCs are present endogenously in food, others can be added intentionally as flavour enhancers, or unintentionally through contact with other materials or packaging during handling, preparation, and storage. Additionally, VOC profiles can evolve and change due to storage and handling. These VOCs then contribute to both pleasant and unpleasant or “off” flavours in foods and beverages (1). Headspace sampling techniques have been shown to be the most effective approach for the extraction of VOCs from a variety of food and beverage matrices, including wine, bread, and milk (2–4). Aroma profiles can be obtained with little disturbance to the bulk sample during headspace sampling. The most commonly employed technique for these applications is headspace solid-phase microextraction (HS-SPME) (5–7). Though widely applied for its simplicity and relatively low cost, the fibre coatings introduce selectivity into the extraction process, as the coating will preferentially sorb certain compounds over others, depending on the chemistry of the fibre and analyte. Generally, SPME also favours analytes with moderate volatilities and is highly susceptible to matrix effects.These two features of SPME can make quantification and comparison of relative abundances of different compounds between sample types challenging. Dynamic headspace (DHS) has been suggested as a powerful alternative to HS-SPME because it provides better sensitivity and has fewer concerns related to selectivity of the extraction to matrix effects (8–10).

DHS was developed to overcome some limitations of static headspace sampling (11). It involves flowing a constant stream of inert gas through the headspace of a sample vial, purging volatiles that partition into the headspace onto a sorbent trap. DHS offers enhanced recoveries of analytes over other headspace techniques; however, it has multiple additional parameters that need to be optimized. DHS experimental conditions include sorbent type, sample incubation temperature, purge flow rate and volume, and sorbent trap temperature (12). To achieve optimal DHS conditions, the one‑factor-at-a-time method of optimization has been popular. This classical approach is a single-factor experiment wherein each variable to be optimized is studied individually until an optimal value has been reached. Although this univariate approach has been commonly used in DHS studies, there are many disadvantages to this method. It is costly in terms of sample volume, as well as instrument and analyst time, to investigate all the variables involved in DHS individually (13). More importantly, this method is not capable of evaluating interactions among the variables and their combined effects on the process. Consequently, the optimal conditions found through a series of single-factor experiments may not be globally optimal conditions and this univariate approach is not recommended for the optimization of multiple DHS parameters (14). To the best of the authors’ knowledge, there has yet to be a report in the literature that sets out general guidelines for the optimization of DHS extractions.

Prior studies have used a multivariate design for optimization of DHS parameters, which allows several variables and their interactions to be evaluated simultaneously (15−17). A statistical approach, referred to as 

 (DoE) (18), was employed to investigate multiple DHS parameters that may collectively impact the extraction of analytes. DoE is suitable for an experimental design with the goal of obtaining sufficient information through a reduced number of experiments, thus allowing several experimental parameters to be varied systematically and simultaneously (19). In the context of DoE, independent variables or experimental parameters are called

, and different values for each factor in designed experiments are called 

. Although DoE is a suitable approach to explore the multiple DHS experimental parameters and their interactions, the appropriate choice of factors and levels is absolutely crucial in building a DoE model, as there are sample-dependent considerations to be made (20).

The aim of this study was to develop a generalized analytical procedure to guide DHS system optimization using DoE. While previous studies have reported optimized methods for one sample type (12,14–17,19,20), the literature does not, to the best of our searching, provide an easily accessible strategy that can be applied to any sample type. Given the increasing popularity of DHS, such a strategy would benefit the community. The proposed optimization procedure involves the use of experimental designs for both liquid and solid samples. For both studies, the Box-Behnken design was selected to generate modelling of the response surface using a smaller number of required runs than other designs, such as full factorial or central composite (21). To this end, active sourdough colony and sourdough bread (made from the same colony) were chosen as representative liquid and solid samples, respectively, for developing a standardized optimization strategy for DHS extraction methods.

Sourdough is commonly added to a variety of breads to enhance its aroma and flavour (22). Due to the variety of VOCs present, sourdoughs produce an intense aroma: the influential chemical classes include alcohols, aldehydes, ketones, and esters (23). There are several factors that influence the volatile profile of sourdough colony and bread. Sourdough volatiles are affected by the type and amount of flour used, hydration, type of fermentation, how much sourdough is used, and any extra ingredients added (24). VOCs can be determined between strains of sourdough and bread through headspace extraction techniques. Here, automated DHS extraction followed by comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC×GC–TOF‑MS) was used to explore VOCs from sourdough colony and sourdough bread.

Sourdough Colony Preparation for DHS Extraction: 

Sourdough colony samples (100.0 mg ±1.5 mg) were weighed into 10 mL headspace vials (Chromatographic Specialties Inc.). Vials were capped tightly with magnetic screw caps (Canadian Life Sciences) with PTFE-faced silicone septa (see supplementary information; Figure S1). All sealed sample vials were placed on a heating mantle set to 70 °C for 5 min to deactivate the colonies and prevent further VOC production. After colony deactivation, samples were placed in a refrigerator at 4 °C until analysis. Details of sourdough colony preparation and maintenance are presented in the supplementary information.

Sourdough Bread Preparation for DHS Extraction:

 Sourdough bread samples were prepared by weighing 100.0 mg (±1.5 mg) of crumb or crust into 10 mL headspace vials and capping tightly (see supplementary information; Figure S1). Samples were placed in a refrigerator at 4 °C until analysis. Details of bread preparation are presented in the supplementary information.

A DoE model was produced in Minitab 17.0 statistical software (Minitab LLC) for both sample types. Both models were constructed using the Box-Behnken approach with a three-factor design. In total, 15 DHS experiments were performed with three replicates on the centre point. A summary of DoE factors and levels is provided in Table 1. Values for each level were determined based on initial range finding experiments (data not shown).

The three designated factors for DoE models were chosen based on their expected impact on the amount of volatiles extracted. Though classified as a pre-extraction parameter, incubation time is a crucial step in augmenting VOCs extraction. A minimum amount of time must pass while heating the sample to facilitate pre-equilibration of analytes with the headspace prior to purging and trapping. Though there are multiple extraction parameters, purge flow rate and purge flow volume were selected for the same rationale. An optimal volume of purge gas must be flowed at an optimal rate to maximize the amount of VOCs extracted. A more thorough discussion of the rationale for excluding other parameters from optimization is included in the supplementary information.

Modelled methods were optimized based on the total number of detected compounds and the total summed peak area of analyte signals for both sample types using response surface methodology (25,26). Briefly, this statistical method fits a predictive model to optimize a response (for example, total summed peak area) affected by different quantitative factors (purge flow rate and volume) and their interactions. The result is a response surface plot, with a local maximum calculated as the optimal level for each factor.

Bread samples were incubated at 80 °C; sourdough colony samples were incubated at 40 °C. A trap temperature of 25 °C was selected to trap purged volatiles on Tenax TA adsorbent tubes (Gerstel). The purging gas was high-purity (4.8) nitrogen (Praxair).

DHS extraction was achieved using an Automated Dynamic Headspace Module (Gerstel). Trapped analytes were desorbed from sorbent tubes by a thermal desorption unit (TDU2, Gerstel) and cryogenically focused in a programmable‑temperature vaporization inlet (PTV; model CIS4, Gerstel) prior to GC×GC separation. Desorption of sourdough colony analytes was accomplished in solvent vent mode (at the TDU2) with the following parameters: solvent vent time 5 min, initial temperature 50 °C (hold 5 min) ramped to 250 °C at 720 °C/min and held for 10 min with a desorption flow of 75 mL/min (helium, high-purity 5.0, Praxair). Desorption of bread analytes was achieved in splitless mode (at the TDU2), and all other desorption parameters remained the same as the sourdough colony parameters. The water content of the sourdough colony samples yielded a wet headspace even when heated at 40 °C, so DHS water management steps (solvent venting) must be taken to avoid creating ice blockages between the TDU and the PTV inlet. Cryogenic focusing of analytes was maintained in the PTV, with an initial temperature of -100 °C ramped to 250 °C at 12 °C/s.

Details of other DHS parameters, GC×GC–TOF-MS analyses, and data processing are provided in the supplementary information.

The goal of this study was to develop a cost-effective, general procedure for the optimization of DHS extraction. Although previous investigations have used the DoE approach to optimize HS and DHS parameters, the factors that were selected for optimization varied significantly across the studies and were sample-specific (27−33). On average, each study used 3–5 factors with 2–3 levels per factor in order to keep the number of experiments reasonable. Out of several variables, an effective choice of factors is critical to maintain a practical number of experiments while covering a broad range of important variables. In some studies that discuss the optimization of HS/DHS parameters, the choice of factors included in DoE were not significant enough to affect the overall procedure. For example, one study included the sorbent amount as a factor to be investigated for macroalgae volatiles (27); another assessed the effect of sample weight on the extraction efficiency of melon volatiles (28). Overall, the larger adsorbent mass increased the number of compounds extracted and the overall chromatographic peak area for macroalgae. Similarly, the largest sample size of melons yielded the highest extraction efficiency. In another study, the purge flow was kept constant while other parameters of interest were varied (29). In general, the standardization of an optimization protocol would be beneficial to offer basic guidelines for new or regular users, to help fine-tune DHS parameters through DoE for any sample type.

Prior to DoE, preliminary experiments must be performed for range finding, that is, appropriate ranges for factor levels must be determined before modelling. Some questions to consider prior to experimental design are provided in Figure 1.

These questions (and their necessity) should be assessed on a per-study basis, since a priori knowledge of the sample and its behaviour during extraction will dictate how DHS experiments are designed. The number of experiments required will escalate rapidly as the number of factors or levels increases.

An entire DHS sampling procedure can be subdivided into three stages: equilibration, extraction, and injection. Table 2 summarizes the relevant parameters for each category.

Out of the three stages, the purge‑and-trap step (that is, extraction of volatiles) is the most crucial step for DHS optimization. The Box-Behnken design used herein focuses on optimizing the extraction parameters. After modelling, the optimal method was assessed for extraction reproducibility and the obtained VOC profile (that is, visual assessment of chromatographic patterns).

The entire DHS process was divided into three steps (Table 2), though a model was only constructed for the extraction step. A response surface plot was built; the sample regression equations output from the statistical software for sourdough bread (B) and colony (SD) are shown in equations 1−4. “TPA” refers to the total peak area or the total summed peak areas of analyte signals. Model coefficients for both sample types per optimizing metric are provided in the supplementary information.

The DoE model was calculated using a system of linear equations to derive the model coefficients in equations 1–4, though these outputs offered no insight as to which factor was the most important in the modelled process. Similarly, the model coefficients and regression equations contain no specific information about optimal levels for each factor (for example, 344.3 mL DHS purge volume) (16). To visualize these relationships, Pareto charts and optimization surface plots were created in the statistical software. A Pareto chart considering the TPA of sourdough colony is provided in Figure 2. It displays not only the single factor with the most impact on the model but also the interaction(s) of two combined factors.

For this model, the two most important factors affecting the DHS extraction were purge flow rate and purge flow volume. The interaction between these two parameters was similarly important—the modulation of both may enhance the abundance of analytes extracted. In this case, the incubation time did not significantly influence the outcome of the extraction. More generally, the purge flow rate and purge flow volume are the two most critical factors to optimize for any DHS extraction. Pareto charts are informative, but they provide no optimized parameters: optimization surface plots are needed. A sample of these plots obtained for the sourdough bread model considering TPA is provided in Figure 3.

The optimized conditions for both sample types were chosen based on models considering the TPA and total number of peaks. These values from the response surface plot were rounded for convenience and to account for the autosampler control software only allowing whole numbers. The optimal regions on the plots considering both total peaks and TPA for all factors were broad enough to allow the slight adjustment. For sourdough bread samples, optimal parameters were 2100 mL purge volume, 50 mL/min purge flow rate, and 10 min incubation time. Optimal parameters for sourdough colony samples were 1100 mL purge volume, 80 mL/min purge flow rate, and 25 min incubation time. Both optimized methods were reproducible; %RSD was less than 20% for living sourdough colony and less than 9% for sourdough bread for replicate samples.

VOC Profiles of Sourdough Colony and Bread: 

VOC profiles of sourdough colony and bread were sampled by the optimized DHS methods. A typical chromatogram obtained from each sample type is shown in Figure 4. Automatic mass spectral filtering scripts for GC×GC–TOF‑MS omics data (34) were applied to rapidly characterize certain chemical classes present in all samples.

Optimized DHS methods recovered a variety of compound classes in both sample types. Chemical families such as alcohols, aldehydes, fatty acid ethyl esters, and terpenoids were the predominant classes identified by filtering scripts for GC×GC–TOF‑MS data. In colony samples, acetoin, isoamyl alcohol, and isoamyl acetate were identified; bacterial and fermentation by-products responsible for active sourdough odour were also found (35). The presence of isooctane esters (plasticizers) (36) solely in bread samples was most likely caused by the plastic bag used for sample storage. Interestingly, fatty acid ethyl esters were only detected in sourdough colony, while only their linear aldehyde equivalents were identified in sourdough bread. As expected, Maillard reaction products such as furfural and 5-methylfurfural were only detected in bread samples (37).

Overall, the general procedure laid out in this work can be summarized by a series of four steps for any sample type: plan, design, interpret, and apply. The first step is to plan the entirety of the DHS extraction process (factor selection), sample preparation, sample considerations, and range finding (Figure 1). Then, a DoE model must be constructed of factors and levels based on the range finding results, and the subsequent designed DHS experiments performed. The Box‑Behnken approach was selected by the authors to save analysis time, though other designs are equally valid. Next, results obtained from designed DHS experiments must be optimized using at least two metrics (for example, TPA and total number of peaks) to reliably interpret the optimization plots. Once an optimized method is obtained, it must be applied to replicate samples to assess extraction reproducibility and robustness.

In the present study, a protocol for the optimization of dynamic headspace sampling relying on multivariate experimental design was presented. Prior understanding of the entire DHS process was required for proper selection of the most significant factors in DoE models. Troubleshooting a DHS extraction was complicated; the exhaustive extraction process required many considerations based on sample type and qualities prior to analyses. Initial DHS experiments must be performed to determine suitable conditions such as sample mass, that is, concentration, addition of salt or saline solution, purge flows and volumes (breakthrough prevention), and water or humidity mitigation. Sample water content was a significant consideration that should be handled through one or a combination of the following parameters: lower extraction temperatures, lower trap temperatures, solvent venting, or DHS drying (extra gas purge). Optimized methods should be assessed for reproducibility after design. Future GC(×GC) studies that require DHS sampling should adopt a similar strategy to the one proposed here.

The link to the supplementary information can be found here: http://bit.ly/3OeXrY4

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is a Ph.D. candidate at the University of Alberta, currently conducting research in James J. Harynuk’s laboratory and The Metabolomics Innovation Centre (TMIC). His work involves plant metabolomics and terpene profiling by two-dimensional gas chromatography for a variety of applications. Ryan completed his B.Sc. in chemistry and forensic chemistry at Towson University in Towson, Maryland, USA.

studied analytical chemistry at the University of Alberta under the supervision of James J. Harynuk and received her Ph.D. degree in January 2021. She is currently a postdoctoral fellow in the Harynuk laboratory. Her research focuses on metabolomics using GC×GC.

 is a Masters student in the Harynuk group at the University of Alberta. Her research focuses on environmental air and human breath analysis using thermal desorption and two-dimensional gas chromatography. Sheri completed her B.Sc. degree in chemistry at the University of Alberta.

 is an analytical chemist and the scientific coordinator for James Harynuk’s node of TMIC. Her research interests lie in chemometrics, machine learning, and developing methods for metabolomics research by GC and GC×GC.

 is a professor of chemistry at the University of Alberta, and one of the principal investigators for TMIC. He completed his Ph.D. at the University of Waterloo in 2004, studying and developing tools in the then-emerging field of GC×GC. After completing postdoctoral research in Melbourne, Australia, he returned to Canada and joined the University of Alberta in 2007. Current research interests include the development of new software tools for handling GC×GC data.

Articles

Peer Reviewed

A generalized approach using DoE is presented for the optimization of DHS extraction parameters.

Multivariate Optimization Procedure for Dynamic Headspace Extractions Coupled to GC(×GC)

Matrix effects continue to be an issue in small molecule quantitation in food analysis, with researchers exploring different approaches to circumvent the issue. LCGC Europe talked to Rudolf Krska and Michael Sulyok from the University of Natural Resources and Life Sciences (BOKU), Vienna, Austria, about their work to develop a multi-analyte approach using liquid chromatography tandem mass spectrometry (LC–MS/MS), and the issues surrounding agrocontaminants in animal feeds.

Matrix effects continue to be an issue in small molecule quantitation in food analysis, with researchers exploring different approaches to circumvent the issue. 

 talked to Rudolf Krska and Michael Sulyok from the University of Natural Resources and Life Sciences (BOKU), Vienna, Austria, about their work to develop a multi-analyte approach using liquid chromatography tandem mass spectrometry (LC–MS/MS), and the issues surrounding agrocontaminants in animal feeds.

Q. Liquid chromatography–tandem mass spectrometry (LC–MS/MS) has become ubiquitous for the quantitative determination of small molecules in food and feed samples. However, according to your 2020 paper (1), matrix effects continue to be an issue for those using an LC–MS/MS method—in particular for multi-analyte approaches. What are the issues surrounding matrix effects in this area?

Matrix effects are an issue in various analytical techniques. For LC–MS using both high-resolution (HR)MS and tandem MS, matrix components coeluting with analytes of interest influence the ionization efficiency of the latter. This decreases—and on rare occasions increases—their analytical response in the sample extract compared with the same analyte concentration in a neat solvent standard, and thus compromises the accuracy of the method. The classical approach of dedicated sample clean-up (such as by an immune-affinity clean-up) to remove the matrix components is limited to single target analytes or a group of analytes, although there are approaches involving combining different antibodies (2,3). An example of this would be for the mycotoxins addressed by regulatory limits. The same is true for stable isotope labelled internal standards that compensate losses during ionization and are therefore the preferable option for official control analysis, but they are available for only approximately 20 compounds (4). This leaves the option of matrix matching, which can be defined as preparing the calibration curve in sample extracts instead of solvent-based standards for methods that target a broader range of compounds. However, there are some difficulties in executing this approach, including the lack of samples that are true blanks for all the analytes, or accommodating samples from different matrices in one analytical sequence.

Q. The method you published in your 2020 paper was a “dilute-and‑shoot” method and covered more than 500 secondary metabolites, including all mycotoxins addressed by regulatory limits as well as emerging and masked mycotoxins in seven different food matrices. What did the obtained data indicate about the applicability and practicability of the current guidelines for method validation on such a broad method?

Following non-LC–MS-specific guidelines results in a considerable consumption of time for data analysis, particularly for levels close to the estimated limit of detection/limit of quantification (LOD/LOQ), which requires manual inspection of each peak. As it took close to two years to evaluate and compile all data for our 2020 publication, it was our aim to come up with a proposal to reduce this burden without compromising the overall assessment of the method performance. We believe that spiking experiments at low concentration levels are not essential as we found recoveries to be independent of the concentration level, and also it is feasible to come up with a conservative estimation for LOD/LOQ that is independent of the matrix. Instead, the focus should rather be on absolute and relative matrix effects, which can be studied at high concentrations that result in peaks that are reliably processed by automatic peak integration. In addition, our data did not support the concept of matrix groups mentioned in some guidelines. Differences in apparent recovery between the individual nut, grain, and dried fruits matrices indicated that matrix effects should be characterized for each new matrix, even if it is similar to another commodity that has already been evaluated.

Q. What conclusions were reached on the current guidelines and what were the recommendations for improvement?

There is a lack of recommendation to what extent matrix effects are acceptable, which is true both for the absolute extent, namely the difference in signal intensity to the identical concentration of a given analyte in neat solvent, as well as for the difference in matrix effects between different individual samples, such as different varieties, brands, and so forth of a given matrix, which is known as 

. Even extreme absolute matrix effects may be compensated for in principle by matrix matching, but it may be questioned whether a method can be considered valid in such a case. In contrast, the issue of relative matrix effects cannot be overcome by matrix matching and is neglected in many publications focusing on method validation. This may be partially explained by the fact that many guidelines foresee determination of repeatability on technical replicates, such as “identical test items” or “18 aliquots of a blank material”, which implies to some extent that any individual sample is representative for all samples of the same matrix. In our work, we emphasize that different individual samples should be included in the validation set.

Q. A further paper published in 2020 presents a comprehensive quantitative LC–MS/MS approach for the analysis of agrocontaminants in animal feeds. What challenges exist in the analysis of animal feeds and why are they important?

Animal feed is particularly challenging because the fractions of the main ingredients vary to a large extent, thus emphasizing the issue of relative matrix effects. In addition, finding samples that are true blanks for all analytes of interest is close to impossible, which makes it difficult to find a suitable sample set for method validation. We have proposed to prepare model samples from the related single feed ingredients mirroring the common ranges of the exact composition.

Q. The paper mentions the construction of a prevalence database. What is the function of this database?

The database intends to link occurrences of mycotoxins and fungal metabolites with complementary data, such as origin, weather, or agricultural practice. This serves to identify particular hot spots of mycotoxin contamination in the global supply chain of feed ingredients and finished feed, and also to monitor changes in the pattern of mycotoxins/fungal metabolites on a larger time scale due to global warming.

Q. Did you encounter any challenges in carrying out this research with regards to the instruments and methodologies available?

 In our multi-class paper, we explored the limits of data acquisition in tandem mass spectrometry, which is strictly consecutive, meaning that for a given time period (dwell time) on the scale of tens of milliseconds, only one MS/MS signal of one given analyte is scanned. This runs into the problem of not having sufficient acquisition time in the case of hundreds of analytes, even if the “scheduled multiple reaction monitoring mode” is applied, namely scanning a particular analyte only within a pre-defined period at the expected LC retention time. We have shown that reducing the number of data points per peak while keeping the dwell time as high as possible results in an acceptable data quality. However, we think our method is close to the limit of what is feasible in terms of number of analytes—if an even faster scanning technology is not feasible in tandem mass spectrometry, methods comprising even more analytes need to be transferred to HRMS in full-scan mode.

Q. Does the method have applications in other areas of research?

Our multi-class paper has proven that our “dilute‑and‑shoot” approach is not limited to mycotoxins, although other contaminants might pose problems, such as the solubility and stability issues of certain sub‑classes of veterinary drugs. We believe that our considerations on method validation for such broad methods can be extrapolated to LC–MS‑based methods dealing with other contaminant classes and matrices.

Our current work in the area of LC–MS/MS method development focuses on various aspects. We are investing a lot of time and effort in the validation of a further extended multi‑class method, including some toxic metabolites such as byssochlamic acid, in processed grain-based products. Considering the plethora of secondary metabolites of plants and fungi present in food crops, even the 1250+ method does not suffice. We are also studying the metabolization of mycotoxins by insects and plants by utilizing a stable isotope-assisted LC–HRMS metabolomics-based approach in a functional genomics context.

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, 139–143 https://doi.org/10.1016/j.chroma.2014.05.069

www.romerlabs.com/shop/inter_en/reference-materials/biopure-standards/mycotoxins

(Bio-)Analytics and Organic Trace Analysis

 at the University of Natural Resources and Life Sciences, Vienna (BOKU). He is head of the Institute of Bioanalytics and Agro-Metabolomics at BOKU´s Department IFA-Tulln. Currently, the former A/Chief of Health Canada´s Food Research Division in Ottawa is also Head of Strategic Research at the Austrian Competence Centre for Feed and Food Quality, Safety and Innovation (FFoQSI). Rudolf Krska has received 12 scientific awards and is (co‑)author of more than 440 SCI publications (h-index: 69, Scopus). In 2018, he has become jointly appointed Professor within the Institute for Global Food Security at Queen´s University, Belfast, UK. Since 2020 Prof. Rudolf Krska serves as President of the Austrian Society of Analytical Chemistry.


 studied Chemical Engineering with specialization in Analytical Chemistry at the Vienna University of Technology and received his PhD in 2004 on the Synthesis and Characterization of Monolithic Stationary Phases for Liquid Chromatography. After that, he changed to the Department IFA-Tulln of the University of Natural Resources and Life Sciences Vienna, where he specialized on methods based on liquid chromatography coupled to tandem mass spectrometry for the simultaneous determination of hundreds of mycotoxins and fungal metabolites. In his work there is a strong focus on method validation in order to characterize the performance of modern LC-MS based multi-analyte methods with minimal or even no sample clean-up. The other focus is on the application of the method to study the overall pattern of mycotoxins and fungal metabolites in food- and feedstuffs and in the indoor environment. This has resulted in numerous international cooperations, more than 290 papers and the award of the Clarivate Highly Cited Researcher in 2018 and 2019.

Food Analysis

Rudolf Krska and Michael Sulyok from the University of Natural Resources and Life Science in Vienna, Austria, discuss their recent work developing a multi‑analyte approach using liquid chromatography tandem mass spectrometry (LC–MS/MS), and the issues surrounding agrocontaminants in animal feeds.

Taking the Red Pill: Alleviating Matrix Effects for Small Molecule Quantitation in Food and Feed

Several liquid chromatography (LC) troubleshooting topics never get old because there are some problems that persist in the practice of LC, even as instrument technology improves over time. There are many ways for things to go wrong in an LC system that ultimately manifest as deviations from expected retention times. Developing a short list of the likely causes of these deviations can help streamline our troubleshooting experience when retention-related problems occur.

Writing this “LC Troubleshooting” column and thinking about topics each month is interesting in the sense that there are some topics that never get old. Although certain topics or ideas become obsolete once they are displaced by newer and better ideas in the chromatography research world, there are certain topics in the troubleshooting world that have remained relevant since the first troubleshooting article appeared in this magazine(

 Magazine at that time) in 1983 (1). Over the last few years, I’ve focused several “LC Troubleshooting” instalments on contemporary trends (the relatively recent advances in our understanding of the effects of pressure on retention [2]) in liquid chromatography (LC) that are affecting the way we approach our interpretation of LC results and approach troubleshooting with modern LC instruments. In this instalment, I am continuing a series I started in January of 2022 (3) that focuses on some of the “bread and butter” topics of LC troubleshooting—those elements that are essential for any troubleshooter, no matter the vintage of the system we are working with. The topics at the heart of this series are highly related to the well-known “LC Troubleshooting” wall chart (4) that hangs in many laboratories. For the second instalment in this series, I focus on problems related to retention (too low, too high, and changing unexpectedly) in reversed-phase LC separations. I hope LC users young and old will find some useful tips and reminders related to this important topic.

More often than not, I find myself responding to troubleshooting questions with “everything is possible”, which might seem like an easy out when considering observations that are hard to explain. However, I find that this response is appropriate more often than not. 

’s “LC Troubleshooting” wallchart lists no less than 18 possible causes for retention to be too high, too low, or changing. One could surely add to this list if the size of the wallchart were not a constraint and if we wanted to be really thorough. The main point is that it is important to keep an open mind when thinking of reasons that the observed retention is not right.

A critical step in any troubleshooting exercise—but one that I think is unappreciated—is recognizing that there is a problem to be solved. Recognizing that there is a problem usually amounts to recognizing that what is happening with the instrument is different from what is expected to happen, and our expectations are formed from theories, empirical knowledge, and experience (5).

Before getting into details about what we can expect about retention, a few words to clarify what retention is and how it is measured are warranted. First, 

) is directly observable from a chromatogram, and is most commonly taken as the time corresponding to the apex of the chromatographic peak. Second, 

) is a commonly used measure of retention that is calculated from measurements of the retention time of a retained analyte (

), which is a measure of the elution time of an “unretained” marker compound.

 is challenging because of the difficulty of obtaining accurate dead time values (6), there are several advantages of 

, some of which lead to more repeatable and reproducible measures of retention over time and between laboratories and instruments:

 is nominally independent of parameters including flow rate, column length, column diameter, and particle size, which makes comparing retention under conditions where these parameters are varied more straightforward than directly comparing retention times.

Small changes in flow rate because of changes in pump performance— which do directly affect 

 because the effect of a small change in the flow rate on retention will be similar to the effect on the dead time, and thus, the differences cancel out.

Finally, a third measure of retention that is common in practice is relative retention time (RRT):

r, without the difficulty associated with accurate determination of column dead time. Figure 1 shows retention data from some recent isocratic measurements under reversed-phase conditions in my laboratory. Panels A and B show retention times obtained for anisole (A) and butylphenyl ether (B) from 75 replicate injections over a period of approximately 48 h. The relative standard deviations (RSD) of the retention times are 0.11% and 0.13%, respectively. Although these RSDs are reflective of the excellent retention stability we can expect from ultrahigh-performance LC (UHPLC), panel C shows that the 

can be even more stable. In this case, we see that the RSD drops by nearly one-half to 0.06%. Panel D shows that the 

r for the two compounds are highly correlated (the two compounds were injected as a mixture for each replicate injection), which is why the 

Bearing in mind these details about the different ways of measuring retention, it is important to understand that there is no single standard we can use to judge whether the retention we see in a routine assay today is reasonable relative to prior work. Different applications with different conditions run on different instruments and in different laboratories will be characterized by different levels of variability in retention. Although the repeatability of retention shown in Figure 1 is excellent, it will not always be this good, even when using the finest equipment. The user must be aware of what constitutes “typical” performance. In the best-case scenario, a control chart or some other indicator of retention over time (days, weeks, or months) is used. It is only against this background that one can assess whether a retention observation today seems too low or high, or is varying too much.

Situations Involving Retention That Is Decreasing or Lower Than Expected

There are a few common potential causes of retention that either appears to be too low or decreasing over time.

Harsh mobile phase conditions can lead to loss of the component of the stationary phase that imparts retentive properties to the packing material. In reversed-phase LC, we frequently refer to this as the “bonded phase”, and for silica-based materials, this bonded phase is a ligand covalently bonded to the silica surface through siloxane (Si–O–Si) bonds. These bonds are susceptible to hydrolysis under acidic conditions (pH <2), and once the bond is hydrolyzed, the ligand can be washed out of the column, leading to a decrease in retention (7). This process manifests as a slow loss of retention over days or weeks, rather than a sudden, abrupt decrease in retention. Confirming that this is, in fact, happening is difficult. However, if such phase loss is suspected, it is best to adjust the mobile phase conditions to a less acidic pH, use a different, more chemically stable stationary phase, or both.

If gradually decreasing retention is observed as the mass of analyte injected is increased (but the injection volume is constant), this could be a sign of mass overload (8). Mass overload can be a complex phenomenon, but one mechanism that seems to occur for charged analytes involves mutual repulsion of injected analytes by charged compounds already adsorbed to the stationary phase surface. One potential solution to this problem is to increase the ionic strength of the mobile phase; ionized mobile phase components (for example, sodium or ammonium ions) can interrupt the charge-repulsion mechanism and minimize the effect of the injected mass of analyte on retention time.

The composition of the matrix of the injected sample can also have a dramatic effect on apparent

. For example, in reversed-phase LC, if the sample contains more organic solvent than the mobile phase, this can lead to a decrease in retention (9). In hydrophilic interaction liquid chromatography (HILIC) separations, the same effect can be observed, but in this case, samples that contain more water than the mobile phase lead to a decrease in retention (10). Whereas the effect of mass overload on retention tends to be gradual, with volume overload the change in retention can be sudden and dramatic because of a “breakthrough” of the analyte band as some of it travels with the band of sample solvent through the column (11). This effect is relatively easy to study by varying the volume and composition of the injected sample over ranges of interest to the application at hand and looking for a correlation with the observed 

. If volume overload appears to be a problem, a variety of approaches can be used to mitigate this effect, including decreasing the injection volume, preparing the sample with less “strong solvent” (10), or installing a mixer between the injector and the column (12).

 Most stationary phases designed for reversed-phase LC are, by design, quite hydrophobic. When highly aqueous mobile phases (<2% organic solvent) are used with the phases, the mobile phase can actually be physically expelled from the narrow pores of the particles because of the low thermodynamic favorability of the highly aqueous phase interacting with the highly hydrophobic stationary phase (13,14). This process leads to an apparent loss of retention because analytes simply cannot diffuse into pores that have no solvent in them. This type of retention loss is usually sudden. The good news is that it is reversible by simply flushing the column with a few column volumes of mobile phase rich in organic solvent, and that is usually sufficient in restoring retention (13). This problem can be avoided by not using purely aqueous mobile phases with highly hydrophobic reversed-phase phases (for example, C8, C18, or phenyl). If highly aqueous mobile phases must be used, then more hydrophilic reversed-phase phases should be considered (for example, C1 or CN).

Situations Involving Retention That Is Increasing or Higher Than Expected

There are a few common potential causes of retention that either appears to be too high or increases over time.

 If the flow rate through the LC column has decreased, or is slowly decreasing over time, the 

 of an analyte will be higher than expected or slowly increase. Such a decrease in flow rate could be because of leaks outside of the pump (for example, connection to the column, sampler needle seat, sampler valve rotor seal), in which case the pump is working properly but not all of the mobile phase actually goes through the column. Also, the pump itself may not be producing the flow rate indicated by the software because of a leaky check valve or piston seal (3). These decreases in flow rate could be sudden or gradual depending on the root cause of the change. If decreasing flow rate is the suspected cause of increasing retention, it is helpful to compare both 

 and retention factor (or RRT) to historical values. Whereas 

r is dependent on flow rate, retention factor and RRT should be independent of flow rate. If a difference in the dependence of 

 on flow rate is observed here, this difference is an important clue. Resolving the problem may or may not be tricky depending on the exact cause.
Leaky check valves do not produce leaks that we can see with our eyes in a way that a leaky needle seat or column connection would (see [3] for suggestions about troubleshooting leaky check valves). If the nominal flow rate is 2 mL/min, then a leak of 5% (100 µL/min.) because of a poor connection should be easy to spot. However, if the flow rate is 50 µL/min, then a leak rate of 5% only corresponds to 2.5 µL/min, which is harder to spot.

 Given the strong dependence of retention on the mobile phase composition in reversed-phase LC, small changes in the mobile phase delivered by the pump can significantly increase or decrease retention. Changes in the composition delivered by the pump could be because of a leaky check valve in just one of the two channels of a binary pump. For example, if the acetonitrile channel is leaky,
then the contribution of organic solvent to the mobile phase will be lower than expected, and retention will increase. In pumps (usually quaternary) that use electric solenoids to open and close the flow path between a solvent bottle and the pump, a valve that fails to open correctly will lead to changes in the composition of the mobile phase delivered to the column (see [15] for differences between these pump designs). In this case, both 

 and retention factor would be affected, but RRT would not be as affected. Comparing all three values to historical data may provide important clues. The control software used with modern LC instruments has built-in diagnostic tests that can be used to determine whether or not the pump is delivering the expected mobile phase composition both accurately and precisely (16).

Situations Involving Retention That Appears to Be Fluctuating

The “LC Troubleshooting” wallchart lists 11 different potential causes for fluctuating retention. Here, I discuss the two that I see causing problems most frequently.

Inadequate Buffering of the Mobile Phase pH: 

Retention of ionogenic compounds that have a p

 in the range of pH values normally used in reversed-phase LC (that is, from 2–10) can be sensitive to small changes in mobile phase pH. When working with these compounds, it is important to use a buffer with a good buffer capacity (typically more than 10 mM of the buffering agent, such as phosphate or formate), and under conditions where the target pH is within 1 pH unit of the p

 of the buffering agent. For example, the p

 of formic acid is approximately 3.8 in water, so formate buffers should only be used in the range between 2.8–4.8. Working outside of this range, or with a too low concentration of the buffering agent, would hinder the ability of the buffer to do its job. In other words, it would not be a buffer at all, and it would not be able to stabilize the mobile phase pH.

The retention of most compounds analyzed by reversed-phase LC decreases with increasing column temperature, thus the stability of retention over time is dependent on the stability of the column temperature over time. Most modern LC instruments have the capacity both to preheat the mobile phase before it enters the column and to control the temperature of the column environment (17). However, some instruments are used without these features, and this can be a source of variability in retention. For some highly sensitive compounds, retention can vary because of temperature fluctuations even with state-of-the-art LC equipment. In these cases, controlling the room temperature as tightly as practically possible is helpful (18).

In this second instalment on essential topics in LC troubleshooting, I discussed situations where the observed analyte retention is somehow different from what is expected or normal. Effective troubleshooting for this type of problem begins with a sense for what the expected retention behaviour is, so that a deviation from those expectations is noticeable. There are many different potential causes of retention related problems (too low, too high, or varying). In this instalment, I discussed some of the causes I see most frequently in detail. Understanding these details provides a good place to start troubleshooting but does not capture all possibilities. Readers interested in learning about a deeper list of causes and solutions are referred to the “LC Troubleshooting” wallchart.

I would like to thank Tina Dahlseid and Carter Henning for their effort in collecting the data shown in Figure 1.

LCGC Troubleshooting Wallchart. https://www.chromatographyonline.com/view/troubleshooting-wallchart.

D. Cabooter, H. Song, D. Makey, D. Sadriaj, M. Dittmann, D. Stoll, and G. Desmet, 

, 461852 (2021). https://doi.org/10.1016/j.chroma.2020.461852.

S. Chapel, F. Rouvière, V. Peppermans, G. Desmet, and S. Heinisch, 

, 462399 (2021). https://doi.org/10.1016/j.chroma.2021.462399.

Z. Breitbach, C. Randstrom, J. Chang, M. Lesslie, G. Webster, and D.R. Stoll, 

K. Broeckhoven, K. Shoykhet, and M. Dong, 

 is the editor of “LC Troubleshooting”. Stoll is a professor and the co-chair of chemistry at Gustavus Adolphus College in St. Peter, Minnesota, USA. His primary research focus is on the development of 2D-LC for both targeted and untargeted analyses. He is also a member of 

’s editorial advisory board. Direct correspondence to: amatheson@mjhlifesciences.com

LC Troubleshooting

There are some problems with LC separations that will probably never go away, such as shifting retention times.

Essentials of LC Troubleshooting, Part 2: Misbehaving Retention Times

Used by chemists and nonchemists alike, gas chromatography (GC) is considered mature and among the most widely used instrumental techniques for chemical analysis. Because instruments have become both more sensitive and easier to use, columns have achieved higher resolution and stationary phases have greater selectivity. As a result, gas chromatographs have taken on a “black box” view. With greater sensitivity, resolution, and advanced data handling capabilities, a new set of experimental uncertainties emerge that may not be apparent to most users, especially those who were not formally trained as analytical chemists. In this instalment, we examine these uncertainties in typical GC methods, especially as they relate to quantitative analysis. We look at hidden experimental uncertainty, especially in the glassware used for sample preparation. We also comment on injection and detection with an eye towards understanding the sources of the errors. It is important to understand that experimental error and uncertainty are inherent in all analytical techniques; they can be reduced but cannot be eliminated.

An increasing portion of the chromatographic literature of today describes applications and quantitative analysis rather than fundamental advances in chromatographic techniques and principles. I am not offering an opinion about whether this is good or bad, but it is apparent. Articles describing chromatographic methods show quantitation at parts per billion (ppb) and lower levels are now commonplace. However, much of this literature shows common mistakes and problems with experimental uncertainty in quantitative analysis. Most commonly, uncertainties, usually in the form of standard deviations, are presented with too many significant digits. Uncertainty should be expressed in the least significant digit. The uncertainty then determines the number of significant digits in the result regardless of the number of digits provided by the data system. Often, I see both uncertainties and quantitative results presented with too many significant digits.

In addition to this first challenge, the basic reporting of results often confuses the presentation of both the experimental result and the uncertainty. Classically, results larger then 10 and smaller than 0.1 should be reported using scientific notation. This guideline is often stretched to 0.01–100, but it should not be stretched further. Strict adherence to this rule by itself reduces confusion in result reporting and significant figures.

An additional challenge relates to the common use of the standard deviation and relative standard deviation (RSD) as a figure of merit for the precision of chromatographic results. As we know from basic population statistics, one standard deviation from the mean indicates that approximately 68% of results should be expected to be in that range. The true experimental uncertainty is larger, typically around two standard deviations, which would include approximately 95% of the results, or three standard deviations, which would include about 99.7% of expected results. However, most quantitative analytical chemistry experiments do not generate enough results for population statistics, so these generalizations do not hold. Classically, as taught in quantitative and instrumental analysis texts for decades, a 95% confidence interval about the data is best used as the experimental uncertainty, which for small populations of data would be significantly wider than one standard deviation. In much of the chromatography literature, this additional step is not performed. In short, much of the literature underestimates experimental uncertainty and overestimates significant figures.

This observation is not to denigrate the work of the many scientists developing and optimizing gas chromatography (GC) methods. Because GC has become increasingly popular over the years, it has gone from a technique largely performed by formally trained analytical chemistry specialists to a much broader range of scientists, who may not have had formal training in quantitative analysis and analyzing experimental error and uncertainty. In the rest of this instalment, we examine some common cases where experimental uncertainty arises in GC methods.

Even simple “dilute-and-shoot” methods have hidden experimental uncertainties. We start with this case because nearly all other sample preparation techniques and methods involve dilution in the preparation of samples and standards. Many analysts make the incorrect assumption that volumetric glassware is perfect. Figure 1 shows a close-up look at a typical “Class A” 100 mL volumetric flask, with the class indication circled. For any volumetric glassware, it is best to use Class A glassware and avoid any glassware that does not indicate class or has the markings worn off. If you look closely, you can note the uncertainty value of ±0.08 mL provided in the printing on the flask. Although the uncertainty seems small, the rules for propagation of errors tell us that each additional dilution step will add to the experimental uncertainty of the result.

Table 1 shows the uncertainties involved with some Class A volumetric flasks and transfer pipettes. It illustrates one of the interesting problems in analytical method development. Because we are pressured to reduce the use of solvents, smaller volumes introduce higher relative experimental errors in each step. Table 1 shows that there is flask-to-flask and pipette-to-pipette experimental uncertainty involved with volumetric glassware that must be considered when developing methods. Drawing a sample to the mark in a pipette or filling to the mark in a flask may not deliver the exact volume stated on the device. To this end, experimental procedures for gravimetrically verifying the volume delivered by a pipette or contained in a flask have been published by the National Institute of Standards and Technology (NIST) (1).

Consider the experimental uncertainty involved in a 1:100 dilution of a pre-prepared stock standard solution by two different procedures. In the first procedure, a 1-mL class A transfer pipette is used to deliver 1 mL of the stock solution into a 100-mL class A volumetric flask and the flask is filled to the mark with the dilution solvent. In the second procedure, a 1-mL class A transfer pipette is used to deliver 1 mL of the stock solution into a 10-mL class A volumetric flask that is filled to the mark with the dilution solvent. Using a second 1-mL class A transfer pipette, 1 mL of the diluted solution is then transferred to a second 10-mL Class A volumetric flask, which is then filled to the mark with the dilution solvent. In procedure 1, a total of about 100 mL of dilution solvent is used; in procedure 2, the amount of dilution solvent is reduced by 80% to about 20 mL. Table 2 shows the propagation of errors comparison of procedures 1 and 2. Detailed equations and discussion of propagation of errors are not provided here but can be referenced in nearly any textbook on analytical chemistry or instrumental analysis (2). In the chapter in reference 2, I share a specific example of a propagation of error analysis applied to a pharmaceutical analysis method. Table 2 shows that the serial dilution procedure increases the uncertainty from 0.6% to 0.9% over the single dilution, which is an increase of 50% in the uncertainty.

As seen in Table 2, the serial dilution procedure, which most analysts would prefer today because it uses far less solvent, has significantly higher experimental uncertainty, and this experimental uncertainty would then be added to any uncertainty in the stock standard. Note that when making extremely low concentration standards, which is a common need with
the extremely sensitive methods and instruments of today, the experimental uncertainty involved in using even the most precise available glassware to prepare standards and samples in multiple dilution steps can lead to relatively large uncertainties that may reduce the number of significant figures in the experimental results. As a result, it is likely that any determinations at sample concentrations lower than parts per million (ppm) level should be reported with no more than one or two significant figures.

The inlet and injection process in GC is well-known to provide hidden uncertainty, as discussed in two recent “GC Connections” instalments and a classic book (3–5). As discussed above, as pipettes and sample delivery system volumes get smaller, relative experimental uncertainty in the delivered volume increases. Most microlitre-volume syringes are accurate to approximately 1% of their nominal maximum volume. A typical 10 μL syringe has graduations representing 0.1 μL. Interestingly, it is common to inject 1 μL of liquid using a 10 μL syringe, providing a nominal error of up to 10% in the accuracy of the delivery. Furthermore, the graduations on the syringe barrel do not account for the internal volume of the syringe needle, which is approximately 0.6 μL. Therefore, our 1 μL injection using a 10 μL syringe may be an injection of 1.5–1.7 μL, depending on the accuracy of the syringe.

With the use of an auto-injector and proper maintenance, a syringe can remain highly reproducible for hundreds or possibly thousands of injections. Syringe manufacturers provide quick guides on how to take care of and perform maintenance on syringes (6) that include suggestions for extending syringe lifetime. With the high precision of auto-injectors, users can typically expect injections with less than 1% RSD in the resulting peak areas from injection to injection. Difficulty can arise when the syringe is changed; you can expect peak areas to vary by several percentage points up or down from the original syringe. With effective calibration, and either internal or external standard, this change may not be noticed unless working at or near the limit of detection (LOD) or limit of quantitation (LOQ), where a few percent of reduced injected sample volume might move results lower than the LOD or LOQ.

Uncertainty in Detection and Quantitation

In a recent instalment, we discussed several hidden challenges in measuring the LOD for an instrument or as part of the method validation process (7). We saw that the classical International Union of Pure and Applied Chemistry (IUPAC) calculation for LOD includes terms only related to uncertainty in the measured signal, not in the calibration curve. We saw an alternate calculation, based on propagation of errors, for determining the LOD that includes terms for uncertainty in both the slope and 

-intercept of the calibration curve. Earlier in this column, we discussed uncertainty in simple “dilute-and-shoot” procedures, which are often similar to the procedures used to generate calibration curves.

In general, greater increases in experimental uncertainty from calculations occur when subtraction and division are used in equations and formulas. In both cases, the calculation result becomes smaller while the uncertainty becomes larger. Table 3 illustrates this principle with some simple calculations, in which the values 1.100 × 10

 ± 0.2 are combined by addition, subtraction, multiplication, and division. Note that both initial values would be seen as highly precise. As seen in Table 3, the original data have a relative uncertainty of 0.2%. When the data points are multiplied or divided, the uncertainty is determined from the relative uncertainties, so the final uncertainty is the seam on a relative basis for both. Since experimental uncertainties are additive for addition and subtraction, adding the two data points results in a decreased relative uncertainty as the result increases faster than the uncertainty. However, subtracting them results in a much greater relative uncertainty, since the result is now smaller and the uncertainty larger. In this case, the four significant figure original data are reduced to three in the result. The relative experimental uncertainty increases from a fraction of a percent to nearly 3% in the subtraction case. When I examine GC methods with unsatisfactory reproducibility, the first places to look for the source of the problem are calculations and glassware choices, followed by injection and detection.

Error analysis and the consideration of experimental uncertainty may seem like a chore. However, it is one of the most important aspects of method development, optimization, and validation. GC instruments and data systems are highly precise, and they generate raw data that may have three, four, or more significant figures. In the past, we would look first at potential instrumental challenges when troubleshooting and optimizing problems with precision and accuracy. Today, I look first at the glassware and calculations, followed by the instrument. Chromatographers should be cautious and recognize that there are hidden uncertainties in almost all quantitative chromatographic methods that may increase experimental uncertainty, reduce the number of significant figures, and make the data coming from the data system look more precise and accurate than it really is.

NISTIR 7383: Selected Procedures for Volumetric Calibrations (2019 Edition

) (National Institute of Standards and Technology, Gaithersburg, Maryland, USA, 2019).

G.D. Christian, P.K. Dasgupta, and K.A. Schug, 

 (John Wiley and Sons, Hoboken, New Jersey, USA, 2013), pp. 81.

Split and Splitless Injection for Quantitative Gas Chromatography: Concepts, Processes, Practical Guidelines, Sources of Error, 4th, Completely Revised Edition 

(John Wiley and Sons, Hoboken, New Jersey, USA, 2008).

 https://www.hamiltoncompany.com/laboratory-products/support/documents/care-and-use-guide. (Accessed April 2022).

 is Founding Endowed Professor in the Department of Chemistry and Biochemistry at Seton Hall University, USA, and Adjunct Professor of Medical Science. Direct correspondence to: amatheson@mjhlifesciences.com

GC Connections

With the greater sensitivity, resolution, and advanced data handling capabilities now available comes a new set of experimental uncertainties.

Is the Solution Dilution? Hidden Uncertainty in Gas Chromatography Methods

This article covers liquid chromatography (LC) columns and accessories commercially released after Pittcon 2021 through to this year’s virtual conference. Like in the past, 

 sent out a survey in late 2021 and early 2022 asking vendors to supply information on products launched over the past year. Note that new products for gas chromatography (GC), LC instrumentation and software, and sample preparation are covered elsewhere. The information for this article was obtained over several months, therefore, it is possible that some information was missed or misinterpreted. The reader is encouraged to check with specific vendor sites for additional products as well as more detailed information on product usage and attributes. Links to vendor sites are provided where applicable.

The vendors that responded to the survey and their new liquid chromatography (LC) products are listed in Table 1. The products vary in targeted analyte type as well as in mode of chromatographic operation. The columns were initially categorized as addressing small molecule or large molecule separations. Within these categories, the products were further separated based on the mode of separation, including reversed-phase LC, hydrophilic-interaction LC (HILIC), chiral, size-exclusion chromatography (SEC), and affinity chromatography. Two additional subcategories extracted this year are treated as separate entities—new nano- and micro-column format offerings and columns targeting oligonucleotides are also discussed.

The products launched over the past year revealed that there was an emphasis in the industry to reduce or eliminate analyte–metal interactions. Most of the categories this year include some type of advance in metal passivation or new uses of established “non-metal contact” hardware, such as PEEK (polyetheretherketone)-lined stainless steel. Unlike in previous
years where there were enough new products in given categories to generate illustrative sub-tables, the new products introduced this year are spread evenly across the categories, leaving only a few in any given section.

Our annual review of new liquid chromatography columns and accessories introduced recently. 

May 2022 | LCGC Europe

Multivariate Optimization Procedure for Dynamic Headspace Extractions Coupled to GC(×GC)

Taking the Red Pill: Alleviating Matrix Effects for Small Molecule Quantitation in Food and Feed

Essentials of LC Troubleshooting, Part 2: Misbehaving Retention Times

Is the Solution Dilution? Hidden Uncertainty in Gas Chromatography Methods

New Liquid Chromatography Columns and Accessories for 2022

New Sample Preparation Products and Accessories 2021–2022