Untargeted analysis using liquid chromatography with tandem mass spectrometry (LC–MS/MS) chemical profiling has become a valuable tool for providing insight into the variations found in a sample set. However, the large data sets often generated, frequently yielding upwards of 20,000 features or more, can be overwhelming. Key elements in creating an experimental design are one of the most pivotal aspects of verifying statistical variance in a sample set. Therefore, care should be taken when an experimental design is developed and when statistical tools are used to perform data reduction and processing.

People have always wanted to expand their limits of knowledge. Regarding chemical analysis, the use of an untargeted approach with high-resolution accurate mass instrumentation can aid in the process of gaining a more comprehensive understanding of sample composition, compared to a targeted approach. Untargeted analysis is a technique that focuses on global chemical profiling. It is considered a hypothesis-generating or a discovery approach. However, this pursuit of expanded knowledge has some advantages and disadvantages relative to targeted analysis.

For a typical targeted approach to chemical analysis using liquid chromatography-mass spectrometry (LC–MS), the user would perform absolute quantitation using a triple quadrupole (TQ) or single quadrupole (SQ) on a subset of analytes known to potentially be in the sample. The target list of compounds that are being evaluated to determine they are present does not comprise everything that could be in the sample, but includes key features that are of interest to a specific industry. For example, the determination of pesticides in water resources is of high interest. For this process, a nominal mass or unit resolution mass instrument is acceptable, because the analyte of interest is preselected and standards are available to confirm retention times, the precursor ion, and, when using TQ, the product ions. The workflow is centered around verifying the presence of an analyte and calculating the concentration.

Alternatively, untargeted analysis is considered a more comprehensive approach. Less selective sample preparation techniques allow for a range of chemical compounds to be analyzed together. A goal is to broaden our understanding of a biological process, discover variations in a sample set, or just gain a deeper understanding of a sample. For example, researchers have used untargeted analysis to explore the environmental impacts on bacterial growth through the analysis of the extracellular metabolite profiles (1). This technique has also been applied to beer styles to determine the key chemical components that define different styles of beer (2). While an untargeted approach can obtain all the data to paint a more detailed picture of the sample set, it comes with some challenges. Untargeted analysis requires high-resolution accurate mass instrumentation such as a quadrupole time of flight (QTOF) mass spectrometer. Such instrumentation is more costly than instrumentation options with a lower resolving power. Additionally, the large data sets require extensive data processing that can take weeks to months, versus targeted analysis data, which typically only requires a few hours to process. Due to the increased challenges associated with untargeted analysis, a highly skilled scientist is required to operate the instrument in addition to collecting and analyzing the data. Moreover, programming skills are frequently required to write specialized in-house applications to analyze the data. Therefore, untargeted analysis is primarily limited to the research and development stage at this point. The workflow for untargeted analysis is centered around the experimental design with only one independent variable being modified between experimental groups to easily associate statistically significant features with the independent variable.

When implementing untargeted analysis, statistical tools are typically used to measure variability within a sample set. Therefore, it is important to consider where variability can be introduced and proactively take steps to prevent introducing unwanted variability while planning the experimental design and batch sequence. For example, when evaluating the environmental impacts on the extracellular metabolite profile of bacteria, the same strain of bacteria should be used throughout the study. Additionally, the conditions such as the time point in the growth curve that the bacteria are collected, and the growth environment temperature should be consistent. Finally, the setup used to replicate the environmental conditions should be consistent. In other words, enough media should be prepared in one setting for all replicates of each type of bacteria. Furthermore, enough mobile phase should be prepared for the entire batch sequence to eliminate variability between different preparations. These extra precautions aid in minimizing the variability detected with statistical tools.

When the experiment is designed, replicates should be included for redundancy to ensure measured variations are reproducible. Biological replicates increase the robustness of the finding and show biological variation. Technical replicates (also known as 

) encompass the entire experimental procedure and involve repeat injections of the same extract. The combination of replicates provides confirmation that the measured variation is real. The use of quality controls is critical to monitor the performance and stability of the instrument. A quality control sample can further validate measured variation across experiments. Ideally, pooled samples (prepared from equal portions of each sample) are evaluated throughout the batch, as well as various reference standards that cover a wide 

range to ensure mass accuracy for all observed ions (3). Finally, randomization in the batch sequence provides additional confirmation that the measured variation is credible (Table I). While minor variation will still potentially be measured in statistical tools such as principal component analysis (PCA), variation between sample sets should be significant and easily identifiable (Figure 1).

After the experimental design has been implemented and samples analyzed, the next focus becomes data analysis, which includes bucketing, data reduction, data alignment, statical analysis, feature picking, and feature annotation. Open-source software, such as MS-DIAL (4), can be used to aid in the data processing stage. This type of software allows several data analysis steps to be completed at the same time.

, consists of slicing or grouping data from each sample into retention time (RT):

pairs and can be completed by setting a peak and mass width. Evaluating the 

differences of the calibrant and reference solution to the exact mass can provide a foundation for the 

slice width. Once data from each sample has been bucketed, the next steps are 

based on MS1 results. Data reduction is continuous throughout the data analysis process, but it can begin by defining an acquisition time and 

range to eliminate features that elute in the void volume or during column re-equilibration. Next, the data are aligned to evaluate the variation or statistical differences within the sample set. When aligning data, the window or range of the RT and 

tolerance will directly impact the alignment of the samples (Figure 1). The variation measured in 

and RT of the standard solution should be evaluated to verify that set tolerances are acceptable. Finally, further data reduction can be completed during the data alignment step by electing to remove features detected that are also found in the blanks and have a fold change under a prescribed limit.

Once samples have been aligned, the goal is to 

determine differences between samples and highlight key features

. A comparison between data from each sample is completed typically using statistical tools to visualize which characteristics are most influential in the differentiation of samples. Open-source software, such as MetaboAnalyst and MS-DIAL, can aid in this process (4). This process can further aid in data reduction by determining which features are significant enough to devote time to annotating. Focusing primarily on statistically significant features (

value < 0.05) is beneficial in reducing extraneous data. It is critical that the statistical significance of features is determined using more than one statistical approach, such as univariate analysis, multivariate analysis, and machine learning (Figure 2). Each technique can potentially introduce a unique bias; therefore, the inclusion of multiple tools prevents overfitting the data (5,6). Once data reduction is complete and the key features that are the most statistically significant are determined, confirmation of a Gaussian chromatographic peak should then be verified. Each feature can be defined as a compound once a quality signal for a feature has been verified.

The last (and possibly the most challenging) step of the process is 

. Authors should clearly differentiate and report the level of identification rigor for all compounds reported. The Metabolomics Standards Initiative (MSI) has defined four levels of identification (7,8). Level 1 is the highest level consisting of identified compounds using two or more orthogonal properties and a chemical standard that is compared to experimental data from the same laboratory and analytical methods. Level 2 refers to putatively annotated compounds. For these, matching chemical standards is not required. Annotation is based upon physicochemical properties or spectral similarity with public or commercial spectral libraries. Putatively characterized compound classes are referred to as Level 3. In this group, compounds are classified based on characteristic physicochemical properties of a chemical class of compounds, or by spectral similarity to known compounds of a chemical class. Frequently, only the elemental formulas are known. Finally, the most common is Level 4: unknown compounds. These are “unique unknowns” differentiated based upon unique experimental spectral or chromatographic features (RT:

While untargeted analysis is a time-consuming process that requires a highly skilled scientist, it can provide a wealth of new information and open the doors to new horizons. Untargeted analysis can be used to define features for the discrimination between samples or identify a particular sample difference. For example, having a broader range of metabolites for food authentication confirmation. Having these features, one can then devise a targeted chemical analysis through the development of MRMs for important features, thus making the desired determination routine. Despite the many hurdles and challenges that a scientist must overcome through this inductive approach, many nuggets of gold can be retrieved from the mountain of information.

(1) Zanella, D.; Liden, T.; York, J.; Franchina, F. A.; Focant, J. F.; Schug, K. A. Exploiting Targeted and Untargeted Approaches for the Analysis of Bacterial Metabolites under Altered Growth Conditions. 

(2) Anderson, H. E.; Liden, T.; Berger, B. K.; Zanella, D.; Manh, L. H.; Wang, S.; Schug, K. A. Profiling of Contemporary Beer Styles Using Liquid Chromatography Quadrupole Time-of-Flight Mass Spectrometry, Multivariate Analysis, and Machine Learning Techniques. 

(3) Broadhurst, D.; Goodacre, R.; Reinke, S. N.; Kuligowski, J.; Wilson, I. D.; Lewis, M. R.; Dunn, W. B. Guidelines and Considerations for the Use of System Suitability and Quality Control Samples in Mass Spectrometry Assays Applied in Untargeted Clinical Metabolomic Studies. 

(4) Tsugawa, H.; Rai, A.; Saito, K.; Nakabayashi, R. Metabolomics and Complementary Techniques to Investigate the Plant Phytochemical Cosmos. 

(5) Broadhurst, D. I.; Kell, D. B. Statistical Strategies for Avoiding False Discoveries in Metabolomics and Related Experiments. 

(6) Saccenti, E.; Hoefsloot, H. C. J.; Smilde, A. K.; Westerhuis, J. A.; Hendriks, M. M. W. B. Reflections on Univariate and Multivariate Analysis of Metabolomics Data. 

(7) Viant, M. R.; Kurland, I. J.; Jones, M. R.; Dunn, W. B. How Close Are We to Complete Annotation of Metabolomes? 

(8) Sumner, L. W.; Amberg, A.; Barrett, D.; et al. Proposed Minimum Reporting Standards for Chemical Analysis: Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). 

 are with the Department of Chemistry and Biochemistry at The University of Texas at Arlington, in Arlington, Texas. 

 is with Shimadzu Scientific Instruments in Columbia, Maryland. Direct correspondence to: 

Events

Untargeted analysis using liquid chromatography with tandem mass spectrometry (LC–MS/MS) chemical profiling is a valuable tool, but care needs to be taken with experimental design.

An Overview of the Untargeted Analysis Using LC–MS (QTOF): Experimental Process and Design Considerations

poke to Hans-Gerd Janssen from the Unilever Foods Innovation Centre in The Netherlands about the advantages of a novel, rapid method to analyze antioxidants that inhibit oxidation, and he explains that the maturity of gas chromatography (GC) technology is actually an advantage to the analyst.

Q: You recently developed a fast gas chromatography–mass spectrometry (GC–MS) method for efficacy assessment of natural antioxidants (1). Why is this type of analysis important?

As part of Unilever’s commitment to help people transition towards healthier diets, we are reducing the levels of unhealthy saturated fats in our products by replacing them with more healthy unsaturated oils. These unsaturated lipids are more prone to lipid oxidation, resulting in a poor smell and a rapid loss of product quality. To avoid this, antioxidants are needed. Up until now synthetic chemicals were used to this end, but we now try to use “nature’s ingenuity”. Nature has not only provided us with unsaturated fatty acids, but also with natural antioxidants! Many spices and herbs, for example, have antioxidant properties. It is just that you need to find which spices and herbs work, and for that, you need to test them. But because there are many potential natural antioxidant sources, you need to test a lot of them. Hence, the method needs to be fast.

Q: What is novel about the method you developed, and what benefits does it offer the analyst?

 In literature you will find many methods for testing antioxidant efficacy. Unfortunately, most of these methods are very far from reality. They test radical scavenging activity, metal chelation capacity, or oxygen scavenging. We wanted a test where we are much closer to reality. We wanted to monitor a lipid over its shelf life and monitor the products that are causing the rancid smell of oxidized lipids. Hexanal is one of these species. We have therefore developed an assay where we oxidize lipids under realistic, yet accelerated, conditions and can monitor the effects of antioxidants by following a key product of the oxidation reaction. The main novelty for me is the much more realistic nature of our assay. Moreover, the method is very fast. One measurement takes less than 2 min. The method is also safe for the analyst. There are no toxic chemicals involved. Just sunflower oil, salt, and air.

Q: Were there any particular difficulties you had to overcome when developing the method?

There were numerous difficulties. First, there are many potential food ingredients in nature that have an antioxidant capacity. So, we had to narrow the area in which we would search. Spices and herbs were a logical focus area because we wanted to stabilize dry soups and sauces. But even with this focus there were still many possibilities to test and we also knew there could be rather strong synergy effects. We clearly needed a fast method. We also wanted to be realistic, meaning that we wanted to study the actual oxidation reaction, not some type of partial aspect of the complex process of oxidation. So, we needed to accelerate the aging in a realistic way. We studied many routes for acceleration and asked our sensory experts to make sure that the off-smell in the accelerated aging was the same as in real aging. We had already fixed hexanal as the read-out for the assay, but we needed to find a fast method to measure it rapidly at very low levels. GC–MS with selected ion monitoring provided the solution here. A final problem was caused by the heterogeneity of many spices and herbs. We needed to take at least 50 to 200 mg to be representative. But this meant that a rather large volume of oil was needed. In the end our assay was done in 250-mL jars. There were many of them, some up to 200 mL, so we needed a large climate cabinet for aging.

The main findings we have patented. We found mixtures of three to five common spices or herbs in specific ratios that exert very strong antioxidant effects. From an analytical perspective, I would say the main finding was the high speed and reliability of headspace GC–MS measurements of hexanal. By now we are probably close to 10,000 injections without any major instrumental problems. The rapid hexanal method is now used not only to find new antioxidants, but also to study shelf life stability of our new range of plant-based meat alternatives. We are also thinking of using fast GC–MS on short columns in other applications, such as the dynamic release of flavors from ice cream or the formation of Maillard reaction flavors in cooking.

Q: Are there any specific method development strategies that can turn a “standard” GC method into a “fast GC–MS” method in food analysis?

We are very much used to GC runs that take half an hour and basically take that for granted. Admittedly, with fully automated systems this means you can still do some 50 analyses each day. Such a speed you can probably not meet with the sample preparation, data interpretation, and all the paperwork. I think very often the actual GC run is not the rate-determining step. But if you are interested in a shorter run time, the use of a column with a slightly reduced inner diameter is a very simple option. We used a 150 micrometer column. Such a column is roughly twice as fast as the standard 320‑μm column. Reducing the column length is also a simple way to reduce time, at least if you have over-resolution. Finally, GC–MS allows you to separate compounds that are not chromatographically resolved simply by exploiting mass spectral differences. This is also a good method for reducing the analysis time.

Q: GC is often regarded as a mature technology. Are there any technology developments in GC that offer the analyst “new horizons” in food analysis or any areas of GC that you find particularly innovative?

 GC is absolutely a mature technology. But that should not be interpreted as the technique is dull, no longer important, and not relevant anymore. GC continues to be a very important workhorse in the analytical laboratory. GC being mature also means it is reliable, it has proven its value, and, just as for people, we should think of “lifelong learning and improvement” rather than allow people or techniques to dwindle into oblivion. Field-portable instruments that can be used by less‑experienced users, stable retention times that can be repeated in other laboratories, and elimination of the need for calibration are just a few fields where there is still a lot of work to be done. Recent developments that I think are important include the better software we now have for comparisons of chromatograms, hyphenated systems for complex analyses such as mineral oil saturated hydrocarbons/mineral oil aromatic hydrocarbons (MOSH/MOAH) analysis, and high-resolution mass spectrometry. We should also not forget improvements in the sample preparation area, such as SPME Arrow and thin-film microextraction.

(1) Janssen, H.-G.; Gah, C.; Steenbergen, H.; Rosing, E.; Spraul, M. A Fast GC–MS-Based Method for Efficacy Assessment of Natural Anti-Oxidants for Inhibiting Lipid Oxidation. 

 is Science Leader Chromatography and Mass Spectrometry at the Unilever Foods Innovation Centre, in Wageningen, The Netherlands. He is also a part‑time professor at Wageningen University on the special chair of recognition‑based analytical chemistry. His research interests include the development of chromatographic systems, methods, and theories for food analysis and other applications. He has a PhD in analytical chemistry from Eindhoven University (The Netherlands) and has over 35 years of experience in chromatography research. Direct correspondence to: 

Articles

LCGC spoke to Hans-Gerd Janssen from the Unilever Foods Innovation Centre in The Netherlands about the advantages of a novel, rapid method to analyze antioxidants that inhibit oxidation.

Fast GC–MS for Analyzing Antioxidants & Industrial Food Analysis Applications

Chlorinated paraffins (CPs) are an emerging and ubiquitous group of environmental pollutants associated with adverse effects on human health, including endocrine disruption and possible carcinogenicity. In this study, supercritical fluid chromatography (SFC) and ultrahigh-performance liquid chromatography (UHPLC)—both coupled with high-resolution mass spectrometry (HRMS)—methods for the analysis of short-, medium-, and long-chain CPs in fish oil-based dietary supplements were developed and validated at concentration levels of 0.6 and 3.0 µg/g lipid weight (lw). The recoveries were in the range of 80–96% and repeatabilities, expressed as relative standard deviations, were <19%. The limits of detection for the UHPLC–HRMS method (from 0.03 to 0.05 µg/g lw) were 5 to 10 times lower than those obtained by SFC–HRMS (from 0.13 to 0.50 µg/lw).

Chlorinated paraffins (CPs) are widespread environmental pollutants used predominantly as additives (such as plasticizers and flame retardants) in plastic materials (1,2), high temperature and pressure resistant lubricants, metal‑working fluids, and flame retardants in adhesives and textiles (3). CPs are divided into three main groups by their carbon chain lengths: (i) short- (SCCPs; C

); and (iii) long-chain chlorinated paraffins (LCCPs: C

SCCPs are endocrine disruptors (5) and possible carcinogens to humans (6); therefore, they were added to the Stockholm Conven‑ tion (Annex A) in 2017 as persistent organic pollutants (POPs) (7). The data on both MCCPs and LCCPs adverse effects on human health are very limited, and so they are not currently regulated (8). On the other hand, MCCPs are under evaluation for listing in the Stockholm Convention, and the risk assessment and risk profile are being performed (7).

The instrumental analysis of CPs in food and biological samples (usually at trace levels) (9,10) is a demanding task. In recent years, several gas chromatography (GC) or liquid chromatography (LC)-based methods have been published (9–12). Nevertheless, CPs cannot be completely separated to individual compounds, which has led (together with the lack of well‑characterized standard mixtures) to the need for unconventional quantification approaches (13–15). The LCCP standards have the least available options on the market.

The main goal of this study was to develop and validate super‑ critical fluid chromatography (SFC) and ultrahigh- performance liquid chromatography (UHPLC)—both coupled with high-resolution mass spectrometry (HRMS) with electrospray ionization operated in negative mode (ESI-)—methods for the determination of CPs (particularly LCCPs as those cannot be analyzed by GC–HRMS because of low vapor pressure). Fish oil dietary supplements were selected as the sample matrix, as an example of an adequately complex type of sample. The validated methods are necessary for future studies to assess the CPs levels in food and the environment, particularly for LCCPs, which are the least studied among the major CP groups.

LGC Standards provided 31 standard mixtures of SCCPs, MCCPs, and LCCPs of 10 µg/mL or 100 µg/ mL cyclohexane with various chlo‑ rine contents (from 36.0% to 65.3%) for this study. The detailed list is documented in our previous paper (16). Isotopically labeled internal standard of β-hexabromocyclododecane (

-β-HBCD; 50 µg/mL toluene) was purchased from Wellington Laboratories.

Acetonitrile, methanol, n-hexane, ethyl acetate, and isopropanol were obtained from Honeywell. Dichloromethane, isooctane, sul‑ furic acid, ammonium formate, and silica gel 60 (particle size 0.063– 0.200 mm) were purchased from Merck, whereas sodium sulfate (anhydrous) was bought from LachNer. Deionized water was made by a Milli-Q water purification system (Merck). Technical gases (carbon dioxide 4.8 and nitrogen 4.0) were supplied by SIAD.

In this study, a sample of fish oil‑based dietary supplements with no measurable CPs contamination found in a previous study (16) were chosen for method development and validation. Four other samples (with higher levels of CPs contamination previously determined by GC–HRMS) were analyzed to compare the new methods. A multilayer solid-phase extraction (SPE) was used in this study for the sample preparation. Briefly, the CPs were isolated from fish oil on an SPE column (78 × 12 mm with Luer tip) (LCTech GmbH) filled with (from bottom to top) silica gel (0.5 g; deactivated by 2% deionized water, w/w), sodium sulfate (1 g; anhydrous baked for 4 h at 600 °C), and sulfuric acid-modified silica gel (1 g; 40% of H2SO4, w/w). Each column was washed with 3:1 (v/v) n-hexane–dichloromethane and then conditioned with n-hexane. A 100-mg measure of the sample diluted in n-hexane was loaded onto the SPE column and the analytes were eluted with 3:1 v/v n-hexane–dichloromethane. The sample was subsequently concentrated by a rotary vacuum evaporator followed by drying in a gentle stream of nitrogen. It was then dissolved in 500 µL of n-hexane, and the residual lipids were subsequently mineralized by a few drops of concentrated sulfuric acid. After one hour, an aliquot of 250 µL was evaporated and redissolved in 250 µL of the syringe standard (50 ng/mL 

The LC–ESI(-)–HRMS analysis of CPs was performed by Dionex UltiMate 3000 UHPLC system (Thermo Fisher Scientific) coupled with a TripleTOF 6600 HRMS system (Sciex) with ESI operated in a negative mode. The method was developed from initial conditions published elsewhere (17). In this study, the target analytes (injection volume 5 µL acetonitrile) were separated on a 100 × 2.1 mm, 1.7-µm Waters Acquity UPLC BEH C18 column maintained at 40 °C. Methanol (A) and a mixture of 65:30:5 (v/v/v) isopropanol–methanol–water (B) were used as the mobile phases. The initial conditions were 10% B for 1 min followed by a gradient with the following steps: to 30% B at 1.5 min; to 60% B at 2 min; to 80% at 3 min; to 90% B at 3.5 min; and to 100% B at 4 min (3.5 min isocratic hold). The total run time was 11 min, including the return to the initial state and equilibration. The mobile phase flow rate was 0.2 mL/min.

Regarding MS source conditions, the desolvation temperature was set to 450 °C, and the capillary voltage was -4.5 kV. The acquisition speed was 2 spectra/s and the mass range 100 to 1500 

A previously published SFC-based method (16) was used in this study with several modifications (gradient of mobile phases, composition of mobile phase B, and make‑up solvent were changed). The supercritical fluid chromatograph Acquity UPC

 coupled with a Synapt G2 Si high-resolution mass spectrometer (both Waters) with electrospray ionization operated in a negative mode was employed. The target analytes (injection volume 3 µL acetonitrile) were separated on a 100 × 3.0 mm, 1.8-µm Viridis HSS C18 SB (Waters) column maintained at 70 °C. Super‑ critical CO2 was used as mobile phase A and 5 mM ammonium formate in 99:1 (v/v) methanol–water was employed as mobile phase B. The initial conditions were 100% A for 0.5 min followed by a gradient to 35% B at 5 min (1 min isocratic hold; the total run time was 8 min including the return to the initial state and equilibration). The mobile phase flow rate was 1.8 mL/min. After the separation under supercritical conditions, the CO

 evaporated and had to be substituted by another mobile phase—a make-up solvent—which leads the analytes into the ion source. In this study, the make-up solvent was a mixture of 45:45:10 (v/v/v) methanol– ethyl acetate–dichloromethane (flow rate 0.5 mL/min).

In the MS system, the desolvation gas temperature was 250 °C and the capillary voltage was set to -3 kV. The acquisition speed was 2 spectra/s and the mass range 250 to 1500 

The analytical method for the determination of SCCPs, MCCPs, and LCCPs was validated by the analysis of artificially contaminated samples (at two different concentration levels, each level prepared in six parallels). The selected spike levels were 0.6 and 3 µg/g lw for each of the CP groups. The standard mixtures used were C

 49.0% Cl. The limits of detection (LODs) were determined as the lowest standard level at which any CP congener group was integrable (with S/N ≥ 10).

The matrix effects were evaluated by comparing matrix standards (prepared in duplicate) with solvent standards at a level of 0.6 µg/g lw. The equation used for the evaluation was described elsewhere (12). The sample preparation was performed as described in the “Methods and Materials” section. For a comparison, matrix effects of the extract with residual lipids (without sulfuric acid treatment) were evaluated as well.

Comparison of Method Performance Characteristics of SFC and UHPLC-Based Methods for the Analysis of CPs in Fish Oil-Based Dietary Supplements

The method performance characteristics of SFC–HRMS and UHPLC–HRMS methods for analysis of SCCPs, MCCPs, and LCCPs are shown in Table I. There are no officially recommended reference values for recovery and repeatability in the analysis of CPs; therefore, the acceptable values (recovery from 60 to 120% and repeatability <20%) were adopted from 

 (18) concerning the sampling and analysis of polychlorinated diben‑ zodioxins/furans and polychlorinated biphenyls (other chlorinated POPs) in foodstuffs. The recoveries varied from 84 to 95% (SFC‑based instrumental method) and from 80 to 96% (UHPLC-based instrumental method), respectively. The repeatabilities (expressed as relative stan‑ dard deviations, RSDs) were <19%. Therefore, both methods were successfully validated on levels of 0.6 and 3 µg/g lw, with no signifi‑ cant differences regarding recov‑ ery and repeatability. The UHPLC instrumentation proved to yield lower LODs (5 to 10 times lower, see Table I) than the SFC method (probably because of the presence of splitter on the interface of SFC and MS). On the other hand, the SFC proved to be a robust method regarding injection solvent; that is, the use of acetonitrile in this study and isooctane used elsewhere (16), where the samples prepared for GC–HRMS analysis (in isooctane) were also measured by SFC–HRMS (as a complementary method for screening of LCCPs).

The matrix effects are illustrated in Figure 1. The positive effect of a sulfuric acid treatment is documented there. In samples without the sulfuric acid treatment, there can be seen a strong signal suppression, which was more significant in SFC (with a response decrease of up to 69% for the MCCPs). The signal suppression was presumably caused by methyl or ethyl esters of fatty acids, as these esters are sometimes a dominant form of fatty acids in concentrated fish oil-based dietary supplements (19). The free fatty acids released from ester bonds by sulfuric acid were then separated from CPs on the chromatographic column, decreasing effectively the matrix effects, which was verified by successful validation.

The Determination of CPs in Fish Oil-Based Dietary Supplements

For the developed and validated methods final comparison, the methods were used for determination of CPs in four fish oil-based dietary supplements (Figure 2). Chromatograms illustrating the separation differences of several chromatographic systems are shown in Figure 3. The concentrations of SCCPs and MCCPs obtained employing SFC and UHPLC systems were comparable (considering 30% uncertainty). The concentrations were in the range of 1.01–37.22 µg/g lw (SCCPs) and 0.83–37.76 µg/g lw (MCCPs), respectively. These results were lower than concentrations obtained by GC–HRMS in our previously published study (16). The differences may have been caused by still remaining matrix effects, even after sulfuric acid treatment of the samples. Similarly, concentrations of LCCPs obtained by UHPLC–HRMS (0.10–0.36 µg/g lw) were in samples 3 and 4, both of which were lower than those obtained by SFC–HRMS (0.20–1.59 µg/g lw). The differences might have been caused by slightly higher matrix effects (Figure 1).

This article describes a validation of two analytical approaches (employing SFC- and UHPLC‑based methods) with a single sample preparation procedure for the analysis of SCCPs, MCCPs, and LCCPs in fish oil-based dietary supplements. The recoveries ranged from 80 to 96% (with RSDs <19%). The UHPLC instrumentation showed lower LODs than SFC, but SFC demonstrated a good robustness because acetonitrile or isooctane extracts could be analyzed under the same conditions.

The methods were then used to determine CPs contamination in four samples of dietary supplements. The SCCP and MCCP concentrations obtained by both systems were comparable, whereas the LCCP concentrations differed (with UHPLC yielding lower results). The SCCP and MCCP levels were also compared with results obtained employing GC–HRMS (which were previously published). The results obtained by SFC and UHPLC were slightly lower than those obtained by the GC-based method. This might have been caused by matrix effects in some samples, and in the following studies further research is needed (that is, selection of a 

C-labeled CP internal standard and/or use of a more complex clean-up procedure). Finally, the methods are planned to be verified in interlaboratory studies.

This work was financially supported by the Czech Science Foundation (21-19437S). The support from the grants of specific university research —grants No. A1_FPBT_2022_005 and A2_FPBT_2021_018—are also gratefully acknowledged.

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(2) Wang, C.; Gao, W.; Liang, Y.; et al. Migration of Chlorinated Paraffins from Plastic Food Packaging into Food Sim‑ ulants: Concentrations and Differences in Congener Profiles. 

Risk Management Evaluation: Short-Chain Chlorinated Paraffins

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(5) Li, H.; Gao, S.; Yang, M.; et al. Dietary Exposure and Risk Assessment of Short-Chain Chlorinated Paraffins in Supermarket Fresh Products in Jinan, China. 

http://monographs.iarc.fr/ENG/Classification/index.php

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(9) Perkons, I.; Abdulajeva, E.; Bartkiene, E. et al., Short- and Medium-Chain Chlorinated Paraffins in Commercial Complementary Baby Food Produced in Different European Countries: Occurrence, Congener Group Profiles, Portion-Based Dietary Intake, and Risk Assessment. 

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(11) Kratschmer, K.; Schachtele, A.; Vetter, W. et al. Short- and Medium-Chain Chlorinated Paraffin Exposure in South Germany: A Total Diet, Meal, and Market Basket Study. 

(12) Tomasko, J.; Stupak, M.; Hajslova, J.; et al. Application of the GC-HRMS Based Method for Monitoring of Short- and Medium-Chain Chlorinated Paraffins in Vegetable Oils and Fish. 

(13) Meziere, M.; Kratschmer, K.; Perkons, I. et al. Addressing Main Challenges Regarding Short- and Medium-Chain Chlorinated Paraffin Analysis Using GC/ECNI-MS and LC/ESI-MS Methods. 

(14) Bogdal, C.; Alsberg, T.; Diefen‑ bacher, P. S.; et al. Fast Quantification of Chlorinated Paraffins in Environmental Samples by Direct Injection High-Resolution Mass Spectrometry with Pattern Deconvolution. 

(15) Reth, M.; Zencak, Z.; Oehme, M. J. New Quantification Procedure for the Analysis of Chlorinated Paraffins Using Electron Capture Negative Ionization Mass Spectrometry. 

(16) Tomasko, J.; Hrbek, V.; Kourimsky, T.; et al. Are Fish Oil-Based Dietary Supplements a Significant Source of Exposure to Chlorinated Paraffins? 

(17) Li, T.; Wan, Y.; Gao, S.; et al. High-Throughput Determination and Characterization of Short-, Medium-, and Long-Chain Chlorinated Paraffins in Human Blood. 

Commission Regulation (EU) 2017/644 (2017).

(19) Pieck, C. A.; Crampon, C.; Charton, F. A. New Model for the Fractionation of Fish Oil FAEEs. 

Jakub Tomasko, David Maxa, Klara Navratilova, Tomas Kourimsky, Vojtech Hrbek, Jana Hajšlová,

are with the Department of Food Analysis and Nutrition at the University of Chemistry and Technology Prague, in Prague, Czech Republic. Direct correspondence to: 

Scientists used supercritical fluid chromatography (SFC) and ultrahigh-pressure liquid chromatography (UHPLC)—both coupled with high-resolution mass spectrometry (HRMS)—to analyze various types of chlorinated paraffins (CPs) in fish oil-based dietary supplements.

Application of Liquid- and Supercritical Fluid Chromatography Coupled with High-Resolution Mass Spectrometry for the Analysis of Short-, Medium-, and Long-Chain Chlorinated Paraffins in Dietary Supplements

How can low-resolution mass spectrometry (LRMS) and high-resolution (HR) MS work together to provide unambiguous results in clinical and forensic toxicology?

The search for xenobiotics in analytical toxicology is based on different screening approaches depending on the context. Targeted analysis is preferred when some toxicants are suspected, but in some cases an untargeted analysis can be performed to detect compounds without preconceptions. Whatever the case, there is a strong need for certainty in the identification of compounds. In this context, the “gold standard” in analytical toxicology is liquid chromatography combined with mass spectrometry (LC–MS). Most of the time, toxicological laboratories are equipped with low-resolution mass spectrometers (LRMS). However, the principle of LRMS is that it can only discriminate compounds by their nominal mass, which can lead to false positives in the case of isobaric components. To solve this relative lack of specificity, high-resolution mass spectrometry (HRMS) is necessary to confirm (or deny) LRMS results.

In mass spectrometry, resolution (R) can be defined by the full width at half maximum (FWHM, Figure 1). Mass spectrometers that achieve a resolution of at least 20,000 are called 

high-resolution mass spectrometers (HRMS)

. The main difference between HRMS and LRMS is the precision in the determination of the mass; the so-called “mass accuracy”. To confirm a compound identification in HRMS, error in the mass determination (Δm) has to be < 5 ppm (equation 1).

According to this, HRMS systems are known to be able to discriminate compounds by their accurate masses, contrary to LRMS systems, which can only reach a so-called “nominal mass” (± 1 Da) (1).

Roughly, the high selectivity of HRMS ensures certainty in the identification of precursors in MS

, while LRMS cannot differentiate two different compounds when they are isobaric (same nominal masses) and/or share similar fragments and retention times (RT). However, a major drawback of HRMS is that it usually exhibits lower sensitivity and dynamic ranges (2). Consequently, LRMS and HRMS approaches can be seen as complementary tools for laboratories that have to perform screen analysis in multiple contexts. This article presents two real cases that were faced by our forensic unit and for which both LRMS and HRMS approaches were needed to obtain unambiguous results.

The first analysis concerned a whole blood in a case of driving under the influence of drugs (DUID).

The second case concerned a whole blood analyzed in the context of drug‑facilitated sexual assault (DFSA). In the two cases, the objective of the HRMS analysis was to confirm the presence of compounds detected by LRMS.

Sample Preparation and Chromatographic Separation

In both cases, the following extraction procedure was applied: to 100 μL of whole blood, 200 μL of glacial acetonitrile (-20 °C) was added for sample deproteinization. Then, 40 mg of QuEChERS salts (4 g MgSO

/1 g NaCl/1 g sodium citrate dihydrate/0.5 sodium citrate sesquihydrate) supplied by UCT were added. The mixture was centrifugated and 30 μL of the upper phase was added to 90 μL of aqueous mobile phase and 5 μL was then injected.

For LRMS and HRMS approaches, the chromatographic system consisted of two Shimadzu LC-30 AD pumps (Nexera X2), a CTO 20 AC oven, and a SIL-30 AC-MP autosampler (Shimadzu Corporation). Chromatographic separation was performed on a 100 × 2.1 mm, 2.7‑μm Shim‑Pack Velox biphenyl column (Shimadzu Corporation), using a gradient of (A) ammonium formate 2 mM/0.002% formic acid buffer, and (B) methanol ammonium formate 2 mM/0.002% formic acid buffer as mobile phase at a flow-rate of 0.3 mL/min (0.6 mL/min 11–16.2 min to accelerate column conditioning and equilibration), as follows: 0.00–1.00 min, 5% (B); 1.00–2.00 min, 5 to 40% (B); 2.00–10.50 min, 40 to 100% (B); 10.50–11.00 min, 100% (B); 11.01–13.00 min, 100% (B); 13.00–13.01, 100 to 5% (B); 13.51–18 min, 5% (B). Oven temperature was set at 40 °C.

Mass Spectral Conditions for LRMS and HRMS

A fully validated and routinely employed method allowing the analysis of 48 illicit drugs was used. It was based on a multiple reaction monitoring (MRM) mode with two transitions per molecule. The LRMS system was a triple quadrupole LCMS-8050 (Shimadzu Corporation) (3).

The HRMS system was an LCMS‑9030 (quadrupole time-of-flight [QTOF]) (Shimadzu Corporation). A targeted acquisition mode was used (“MSMS mode”). Briefly, using this acquisition mode, the candidate precursor was searched using full-scan MS

 analysis, while its fragments were secondarily searched using MS

 acquisition after obtaining the exact mass of the precursor.

The LRMS targeted acquisition highlighted a signal corresponding to 2C-B, an amphetamine (Figure 2). The RT was consistent with that of our library, and the two transitions (260.10 > 243.05 and 260.10 > 228.10) were detected with similar ratios to those of the standard sample (± 20%). However, 2C-B consumption is not very common in France, and the expert in forensic toxicology asked for a confirmation of its presence in the whole blood of the DUID case by using HRMS (Figure 3). In a whole blood sample spiked at 100 ng/mL of 2C-B, a signal was obtained with a measured mass of 260.02759 

; Δm < 2 ppm). When analyzing the whole blood of the driver, a precursor mass of 260.16391 

was obtained. Similarly, the fragments obtained in the patients were not those expected (243.13686 

). The observed signals corresponded to a mass error > 500 ppm for the precursor and the fragments. As mentioned above, Δm < 5 ppm is mandatory to unambiguously identify a molecule. Consequently, the molecule detected by LRMS was not 2C-B.

The LRMS screening procedure identified alfuzosin, an alpha-blocker used for functional symptoms of benign prostatic hyperplasia. As the presence of alfuzosin was hardly compatible in the case of a woman and was also not known as a compound employed in DFSA cases, the expert asked for a confirmation using HRMS. The analysis of the whole blood revealed a compound with a mass of 390.21291 

which was favorably compared with the expected mass of alfuzosin (390.2136 

Δm < 2 ppm). In addition, fragments were also very close to those expected (235.11833 

). For the detected precursor ion and the fragment, Δm was < 5 ppm, which confirmed the presence of alfuzosin (Figure 4).

Is There a Place for HRMS in Clinical or Forensic Toxicology?

These two real cases, observed in the routine activity of a forensic toxicology unit, illustrate the obvious added-value of using both LRMS and HRMS approaches in a laboratory. LRMS targeted or untargeted screening procedures ensure sensitivity, but these two cases highlighted situations where their specificity could be insufficient. In the first case, isobaric compounds with very similar precursor ions and fragments—which should have led to a false positive result without a secondary analysis by HRMS—were analyzed. In the second case, the detected compound was not consistent with the context and the expert refused to consider the molecule before confirmation by HRMS.

Depending on the laboratory, we believe that HRMS can be used either in combination with LRMS, or as a kind of all-in-one solution, taking into account the recent improvement in the sensitivity and dynamic ranges (4,5).

(1) Ojanperä, I.; Kolmonen, M.; Pelander, A. Current use of high-resolution mass spectrometry in drug screening relevant to clinical and forensic toxicology and doping control. 

(2) Kaufmann, A. The current role of high-resolution mass spectrometry in food analysis. 

(3) Dulaurent, S.; El Balkhi, S.; Poncelet, L. et al. QuEChERS sample preparation prior to LC-MS/MS determination of opiates, amphetamines, and cocaine metabolites in whole blood. 

(4) Grund, B.; Marvin, L.; Rochat, B. Quantitative performance of a quadrupole-Orbitrap-MS in targeted LC–MS determinations of small molecules. 

(5) Fedorova, G.; Randak, T.; Lindberg, R.H.; Grabic, R. Comparison of the quantitative performance of a Q-Exactive high-resolution mass spectrometer with that of a triple quadrupole tandem mass spectrometer for the analysis of illicit drugs in wastewater. 

are with the Unit of Biological and Forensic Toxicology, Department of Pharmacology and Toxicology, at Limoges University Hospital, in Limoges, France. Direct correspondence to: 

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