International Symposium on Capillary Chromatography

 (ISCC) has established its reputation as a forum for microcolumn separation techniques. Since the first meeting in Hindelang in 1975, the most important developments in capillary gas chromatography, microcolumn liquid chromatography and electromigration techniques have been presented in this symposium series.  

This year the Palazzo dei Congressi in Riva del Garda, Italy, will accommodate the 34th meeting from 

. The four-day event will feature recent findings from leading academic and industrial experts in the form of lectures and posters. The conference offers sessions on capillary gas chromatography, microcolumn liquid chromatography, electromigration methods and microfabricated analytical systems, which are expected to cover lab-on-a-chip, column technology, coupled and multidimensional techniques, comprehensive techniques, hyphenated techniques, sampling and sample preparation, trace analysis and automation. 

Application sessions include environmental applications, energy/petrochemical/industrial applications, biomedical/pharmaceutical applications and the analysis of natural products, food, flavours and fragrances. Workshop-seminars of instrument manufacturers and an extensive exhibition of instrumentation, accessories and supplies will run in parallel to the scientific programme. 

At the meeting, the 2010 Marcel Golay Award will be presented in recognition of outstanding contributions in the field of separation science. Outstanding research work presented as oral or poster contributions by youngsters will be awarded with the Leslie Ettre Award for research on capillary gas chromatography applied to environmental or food analyses and with the Pfizer Awards for advances made in pharmaceutical and bio-analysis. 

To encourage scientific exchange and friendship building, the scientific programme will be spiced with the well-known "Riva Social Programme", which consists of a welcome reception, cocktail party, classical concert, wine and cheese evening, disco night and farewell cocktail. 

Considering the interest in comprehensive techniques, the 

 will be organized during the same period to allow scientists to attend both meetings. The 

 with a course presented by experts in the field covering the fundamental aspects of comprehensive techniques and a plenary session on 

For both meetings, abstracts for consideration as lecture or poster presentations can be submitted on-line at 

. All abstracts will be reviewed on the basis of scientific merit, novelty and practical application. Presenters at the meeting may also have their work published in a special volume of 

5th IMMS MMX — MS, Ion Mobility and other Techniques in Life Sciences

Hynes Convention Center & Sheraton Boston Hotel, Boston, USA

The 28th International Symposium on Chromatography

The 13th International Symposium on Preparative Industrial Chromatography

Articles

Events

Over the years, the International Symposium on Capillary Chromatography (ISCC) has established its reputation as a forum for microcolumn separation techniques. Since the first meeting in Hindelang in 1975, the most important developments in capillary gas chromatography, microcolumn liquid chromatography and electromigration techniques have been presented in this symposium series.

Event News

Broad spectrum chemical analysis of trace level components is a continuing challenge for any analytical chemist. This challenge is further confounded when chemical impurities may be present in common organic solvents or when chemical artifacts may be formed, produced and introduced during an analytical procedure. Minimizing and understanding these chemical artifacts is critical for trace level detection and is crucial for accurate analytical conclusions. Comprehensive gas chromatography is an excellent analytical tool to help address these complex mixture challenges. This work examines the impurities present in a variety of acetone sources using comprehensive gas chromatography. This work highlights the extreme variability possible in solvent sources and hence the importance of understanding the impurities that may confound an analytical method or result.

General research or specific experimental protocols involving trace and ultra-trace chemical analysis and detection is an extremely challenging and daunting task for scientists. As well as the inherent chemical background that is present when a sample is collected, numerous chemical analyses and methods rely on a pure solvent for experimental procedures. For example, many chemical extraction approaches use a solvent to extract target chemicals of interest from a substrate (e.g., soil) or collection sorbent. After the extraction step, an evaporation/condensation/concentrating step is performed to reduce the extraction volume to improve detection of trace components. Unfortunately, this volume reduction step will also concentrate many of the impurities present within a solvent and can further confound research efforts. Many of the solvent artifacts introduced into a sample are from chemical impurities that are not completely removed by standard manufacturing processes or techniques for the solvent, as well as the glassware and storage vials used. Regardless of the source, minimizing and understanding these chemical artifacts is critical for trace level detection and is crucial for unambiguous analytical results. 

Acetone is one of the most widely used solvents in the laboratory environment. A common chemical production method for acetone is through the cumene process.

  In the cumene process benzene is alkylated with propene resulting in cumene (isopropylbenzene), which is subsequently oxidized to give phenol and acetone. A wide variety of impurities may exist in the final crude acetone fraction (e.g., aliphatic aldehydes, olefins and carbonyl impurities, and more specifically diacetone alcohol, mesityl oxide and hydroxyacetone), which is subsequently purified to obtain the quoted purity. Once the acetone has been obtained in the laboratory, further use or storage may form additional chemical artifacts. Hunchak and Suffet

  examined acetone–hexane artifacts produced during a soxhlet extraction process. The chemical artifacts produced through the various procedures and reactions include diacetone alcohol and mesityl oxide as well as phorone, mesitylene and isophorone.

The work presented here examines the purity of various acetone sources using comprehensive gas chromatography and mass spectrometry (GC×GC–MS) for broad spectrum chemical analysis. The purpose of this work is to characterize the impurities present in a solvent as a possible approach to track or match acetone sources for chemical forensic investigations. In particular, in the preparation of the toxin ricin, acetone is a common solvent used for the extraction of the castor oil. Previous work developed a simple solid-phase microextraction (SPME) approach to detect the use of acetone as an extraction solvent in the final crude ricin preparation.

  The motivation for this study is to provide additional sample processing information regarding the type/grade of acetone used in the production of ricin or other extracted materials. As broad spectrum chemical analysis becomes increasingly important GC×GC–MS has emerged as a powerful analytical tool. Comprehensive GC×GC is a multidimensional separation technique that separates a sample sequentially on two different columns, which are chosen to separate mixtures based on distinct — ideally orthogonal — chemical properties. Multidimensional separations have the advantage of greatly improved separations compared to conventional single-dimension approaches.

  The work presented here is the first step, examining the impurities present in various acetone sources. However, the overall importance of understanding the possible impurities present in a solvent is vital and can be applied to any trace analytical method. 

Table 1 lists the 21 acetone sources obtained and examined for this study. The mention of trade names or commercial products in this work does not constitute an endorsement, criticism or recommendation for their use. 

Chemical analyses were performed on a Leco Pegasus 4D GC×GC–MS system (LECO, St Joseph, Michigan, USA) equipped with a Gerstel cooled injection system (CIS4) and multipurpose sampler (MPS2) (Gerstel, Baltimore, Maryland, USA). The separation used ultra-high purity helium as the carrier gas, set at 1 mL/min, constant flow. Because the type and number of impurities were largely unknown for each acetone source, two different comprehensive GC×GC column sets were used (see Table 2, A×C and B×C) for all 21 acetone sources. The first dimensional column was either a polar or non-polar type and the secondary column was of moderate polarity and remained constant throughout the study. Unless stated otherwise the experimental conditions are listed in Table 2. LECO ChromaTOF software (version 3.32) was used for data collection and analysis. Higher level data analysis was performed using an in-house software tool (Data Analysis Tool Extension, DAnTE, 

Table 2: Experimental conditions for chemical analysis.			

In this study 21 different acetone sources (brands) were analysed to examine the trace impurities inherently present within the neat acetone source. Table 1 lists the 21 acetone sources, which range from high purity laboratory grade to hardware store grade to commercial finger nail polish remover. Within this acetone sample set the supply containers included dark amber glass, clear glass, plastic and metal. All acetone sources were quoted above 99% purity. In comparison to the extraction solvent artifacts referenced by Hunchak and Lafleur,

  this study attempts to examine the impurities inherently present within an acetone source. To minimize acetone-related artifacts during the analysis, a cooled injection approach is used where 5 µL of acetone is slowly injected into a cooled injection system (CIS) with the majority of injected acetone vented during this process (see Table 2 for experimental details). Following the acetone injection process into the CIS, the solvent vent is closed and the CIS is rapidly heated to inject the remaining collected residual impurities. 

To offer a baseline perspective of the purity of a low grade acetone source, Figure 1 shows a comparison between a 1 µL split injection of moderate split ratio (50:1) and 5 µL CIS injection of acetone A11 (hardware store brand) obtained using column set A×C. In these and the following 2D chromatograms, the blue color represents the baseline signal and the red represents higher signal values of compounds. As can be seen in Figure 1, at this 1 µL split injection level (top chromatogram) a number of trace compounds (peaks) are visually apparent, but with acetone the dominant compound in this separation. The major impurity compounds are listed in Figure 1 and include 2-pentanone, 4-hydroxy-4-methyl- (diacetone alcohol) and toluene. Pentane is observed as carryover from the syringe wash solvent used between injections. The horizontal solute bands at the end of the chromatogram are due to column bleed components. In general even this low grade acetone source appears to be relatively pure at this injection level. However, when a 5 µL CIS injection of the same A11 is examined the number of impurities observed dramatically increases as shown in the lower chromatogram in Figure 1 where the vertical dashed line denotes the start of the data acquisition for the larger injection volume. These impurity components encompass a wide range of polarity, volatility and analytical stability. Depending upon the source and type of acetone, numerous trace impurities are found in all 21 acetone sources and all the sources can easily be differentiated by visual inspection of the chromatograms.  

Figure 2 summarizes the separations obtained for five of the acetone sources (A3, A9, A20, A23 and A27) for both column sets (i.e., A×C and B×C) acquired using the 5 µL CIS injection approach. A number of observations are illustrated in Figure 2. In general, among all the acetone sources the number and amount of impurities can be extremely large and variable. The general chemical classes observed include saturated and unsaturated hydrocarbons, organic acids, ketones and branched aromatic hydrocarbons according to their chromatographic elution order and mass spectral information. Interestingly, regardless of the column set used (i.e., A×C or B×C) various solute zones appear as large streaks typically early in the 2D chromatograms, which was also observed in Figure 1. This solute zone streaking is probably due to the chemical mismatch between the solute and stationary phase (e.g., non-polar solute and a polar stationary phase and vice versa) of the 1st dimensional column so that there is a lack of adequate solute focusing during the injection process. For example, in acetones A3 and A9, both acetic acid and propanoic acid are found by using column set A×C with good solute zone shape; however, acetic acid is absent within the data collection window used and propanoic acid appears as a large streak when using column set B×C (Figure 2). Obviously the presence of these trace acids in the solvent could have deleterious effects for some solutes (e.g., reactions producing artifactual solutes) and could confound trace level applications. Importantly, these organic acids could easily go undetected if only a non-polar type column were used. Conversely, many of the other acetone sources have a large number of what appear to be hydrocarbon type impurities according to the mass spectral information. These hydrocarbon type compounds appear as streaks early in the 2D chromatograms when using column set A×C (e.g., acetone sources A9 and A27 in Figure 2, as well as A11 in Figure 1 as highlighted by black outlined regions), but appear as narrow solute zones with column set B×C. This hydrocarbon source region is outlined for acetone source A9 in Figure 2 and contains numerous straight chain, branched and cyclo hydrocarbon compounds as indicated by their electron impact mass spectra. Varying amounts of these hydrocarbons can be found in all the acetone sources examined and the solute zone shape is more clearly defined when using column set B×C. Again, these solutes may go undetected if using column set A×C due to the lack of solute focusing/streaking. This result highlights the importance of using a number of different primary columns of different chemical nature when examining for trace level components in a broad spectrum chemical analysis. (Note: As an aside, when using a common 2nd dimensional column and changing only the 1st dimension column the down time of the GC×GC–MS instrument is minimal, as the mass spectrometer may not need to be vented during this column switch.)  

The hydrocarbon region outlined in A9 in Figure 2 is examined more closely in Figure 3 with a comparison between acetone sources A9 and A27. In comprehensive 2D separations compared to conventional 1D GC separations, chemically-similar compounds will create patterns or groupings of solute zones (e.g., a homologous series of saturated hydrocarbons) within the 2D separation plane. These chemical groupings or families in a 2D chromatogram can be of help in identifying unknowns, when no standard components are available, in the absence of commercial MS library spectra or when the resulting experimental MS spectra are very similar to others. In Figure 3, three 

 values are selected to highlight groupings of saturated hydrocarbons (

 = 69) and alkyl substituted benzene rings (

 = 91), which also captures possible mesitylene, cumene and their isomeric impurities. In general, as expected, the A9 acetone source appears to be of higher chemical purity than A27 as depicted by the smaller number of solute zones. Interestingly, acetone source A9 also appears to have a greater fraction of saturated and unsaturated hydrocarbon compounds that elute later in time. This implies that A9 has a larger fraction of higher boiling point hydrocarbons than does A27, which may indicate a certain type or degree of purification process. Finally, a series/family of alkyl substituted benzyl compounds (

 = 91) are observed in both acetone sources, but the amounts present in acetone A27 are much larger. Again the presence of these benzyl compounds may indicate the type or extent of the purification process used. 

All 21 acetone sources were found to contain four of the common acetone impurities expected; diacetone alcohol (2-pentanone,4-hydroxy-4-methyl-), mesityl oxide (3-penten-2-one,4-methyl-), phorone (2,5-heptadien-4-one,2,6-dimethyl-) and phenol, but at varying amounts. Figure 4 illustrates a heatmap plot summarizing the results obtained from these four compounds for the 21 acetone sources using the A×C column set. In Figure 4, the solute zone volumes (absolute detector counts) are based upon the average of three replicate injections of the acetone source, which were analysed in a random order. The average solute volumes illustrated in Figure 4 are calculated based on a log

 scale, where the blue color represents the lowest signal intensity and the red colour represents the largest signal intensity of the compound. As illustrated in Figure 4 there appears to be a large variation in the amount of these four compounds for each acetone source. This indicates that acetone CIS injection process is not contributing appreciably to the presence of these compounds and that the components are likely residual from the manufacturing or distribution process. From Table 1, acetone sources A23, A24 and A25 are from the same distributor and the same lot (i.e., different bottles); acetone sources A9 and A10 are from the same distributor, but different lots (this is also true for acetone sources A5 and A6; however, A5 displayed a large isopropyl alcohol impurity of approximately 1:1 relative to acetone, chromatogram not shown); acetone sources A11, A27, A28 are commonly available hardware store brands. These 'source' trends are also observed in Figure 4, where these acetone sources tend to cluster closer to each other. The exception maybe the closer clustering of A9 and A20 to each other compared to the A9 and A10. Again this clustering is only using these four expected impurities, where the actual number of compound impurities is extremely large. As shown in Figure 2 the number, type and amount of impurities present between these two sources (A9 and A20) is very different. 

For broad spectrum screening or trace component detection against complex background mixtures, impurities present within a solvent can significantly confound research efforts. In this work comprehensive gas chromatography–mass spectrometry is used to assess the impurities present in various commercial acetone brands. A large number and broad range of different chemical impurities were found, dependent upon the acetone source. These chemical impurities ranged in complexity from simple organic acids, alkanes and olefins, to a variety of substituted benzyl compounds (to highlight just a few). In general, dependent upon the analytical questions trying to be answered, a chemist should not become complacent and should be cautious of the impurities that may be present (and how they may change with different brands) and interfere with a trace component analysis. This is highlighted by the presence of the organic acids present in various acetone sources and the subsequent need for two different column sets to better capture and understand the range of impurities present. Moreover this work addresses only those compounds that are stable and volatile for the gas chromatographic system and does not address those components more labile or nonvolatile that may also be present in a solvent to alter or affect results. 

Funding for this work was provided through contract AGRHSHQDC07X00451 to Pacific Northwest National Laboratory by the United States Department of Homeland Security, Science and Technology Directorate. 

 is a senior research scientist in the Chemical and Biological Signature Sciences Group at Pacific Northwest National Laboratory. He has a PhD in Analytical Chemistry from Michigan State University and a BS in Chemistry from Michigan Technological University. Dr Wahl's principal research interests focus on interdisciplinary chemical and bioanalytical problems that require advanced high-resolution separation techniques. Multidimensional separations for ultratrace analysis methods for chemical warfare agents and related compounds, biological, explosives and other signature compounds of interest are a main focus. 

 received a BS in Biochemistry from Portland State University and a MS in Forensic Science from National University. She has experience in a variety of analytical techniques including separations, mass spectrometry and spectrophotometry in addition to writing standard operating procedures and quality control manuals, and applying statistical data analysis.  

 is a staff scientist within the Chemical and Biological Signature Sciences Group at Pacific Northwest National Laboratory in Richland, Washington, USA. Dr Wahl has a PhD in Analytical Chemistry from Michigan State University and a BA in Chemistry from The College of Wooster. Dr Wahl's research interests include the development of MALDI mass spectrometry for microorganism analysis and the development of analytical methods for chemical and biological forensics.  

Regular Article

This work examines the impurities present in a variety of acetone sources using comprehensive gas chromatography.

Investigating Solvent Purity Using Comprehensive Gas Chromatography: A Study of Acetones

In this Practical Data Handling column, method development for drug impurity profiling is discussed. This first part consists of the first two steps when developing a drug impurity profile: the selection of a suitable chromatographic column and the optimization of the mobile phase pH. Prior to the impurity profiling, the selection of a set of dissimilar chromatographic columns is made (i.e., columns with different selectivities) to be screened with the impurity mixture for selectivity differences.

According to the International Conference on Harmonization of Technical Requirements for the Registration of Pharmaceuticals for Human Use (ICH) guideline on impurities in new drug substances,

  an impurity is defined as 'any component of the new drug substance that is not the chemical entity defined as the new drug substance'. Impurities can be formed during drug synthesis, manufacturing, and/or storage.

Depending whether or not certain threshold (concentration) limits are exceeded, these drug impurities should be identified, qualified and/or quantified.

For this purpose, many pharmaceutical industries develop a drug impurity profile, which is defined as 'a description of the identified and unidentified impurities present in a new drug substance'.

  Chromatographic impurity profiles are most often developed using reversed-phase high-performance liquid chromatography (RP-HPLC). The chromatographic impurity profile should allow detecting and separating all (un)identified impurities in each new active compound. However, the analysis of impurities is not evident because initially neither their number, nor their structures are known. Moreover, impurities are often only present in small amounts, thus sensitive analysis methods are mandatory. Furthermore, impurities often have a similar structure as the main compound and/or other impurities, thus also selective analysis methods are recommended. In general, different steps can be distinguished when developing chromatographic drug impurity profiles. The different steps are represented in Figure 1.  

  we have discussed the experimental design approach in method development and optimization. In such an approach, a screening design was applied to identify the most important factors, followed by a response surface design to determine the best conditions for these factors. In the development of an impurity profile, one will change the selectivity properties between experiments to find suitable conditions. Because the factors most affecting the selectivity are known, a screening design set-up is not required. The factors most influencing the selectivity are the type of stationary phase, the pH for compounds with acidic/basic properties (thus for most pharmaceuticals), the organic modifier and to a lesser extent the slope of the gradient and the temperature. Most often, a sequential approach is followed, where the factors are optimized in the sequence of their influence on selectivity. The subsequent steps are 

 selection of a set of dissimilar chromatographic columns, such as those with different selectivities; 

 selection of a suitable chromatographic column and optimization of the mobile phase pH; 

 optimization of the organic modifier composition; and 

 optimization of the gradient slope and the temperature. The organic modifier composition can be optimized using a mixture design-based approach,

  while the gradient slope and the temperature can be studied in a response surface design.

  In this first data-handling column on drug impurity profiling, we will mainly focus on steps 

, the selection of the column and the pH.  

Selection of a Set of Dissimilar Chromatographic Columns

To reveal and/or separate all impurities in a new drug substance, several chromatographic profiles with different selectivities will be created. In impurity profiling, as mentioned previously, the stationary phase largely influences the selectivity of the chromatographic system.

  Therefore, one possibility to obtain a set of profiles with different selectivities is to use a set of dissimilar (or orthogonal) RP-HPLC columns to screen the drug impurities mixture. This selection can be based on several approaches,

  which will not be discussed in detail in this paper. Basically, one injects a series of diverse compounds on each column and then selects those columns showing the most diverse selectivities. For this selection, several chemometric approaches have been applied.

  A comparison among them was made and it was seen that the most dissimilar systems are selected using the Kennard and Stone algorithm.

  Another possibility is to characterize the columns by chromatographic parameters representing given column properties. Comparison of the parameters between columns also allows indicating similar or dissimilar columns.

  It often is preferred to use only silica-based columns, which are widely available. 

Optimization of the Mobile Phase pH and Selection of a Suitable Column

After selecting a set of dissimilar chromatographic columns, the new drug impurities mixture can be screened. To further introduce different selectivities, the selected RP-HPLC columns are screened at different mobile phase pH values and/or organic modifier compositions. 

Because pH influences the selectivity more than the organic modifier composition, a given optimization strategy can, for example, optimize the pH and the organic modifier composition sequentially. Then the second step to develop drug impurity profiles consists of screening the drug substance and its impurities on all possible combinations of the selected dissimilar columns (column

), while keeping the other factors, such as organic modifier type (acetonitrile or methanol), the applied gradient and the column temperature, constant. For instance, four or five dissimilar columns in combination with three or four different pH values in the range 2–9 are then screened. For example, five columns in combination with four pH values results in 20 systems. The initial selection of the columns was already described shortly above. To select the pH range, one should take into account the allowed pH range, in which the column remains stable, as is certified. Then this range can be divided into two or three equal parts in order to define the intermediate screening pH values. 

After screening on the chromatographic systems, several approaches to select the best column and pH are possible. However, first one will try to determine the number of compounds occurring in the drug impurities mixture and to identify the corresponding peaks in the different chromatograms. Preferably LC–MS measurements are performed for this purpose (resulting in molecular mass information), and peak matching algorithms exist to indicate the corresponding peaks. 

One possibility to determine the best column and pH is to select the column and pH combination with the best separation from the systems screened for further method optimization. 

Another possibility consists of predicting the optimal pH on each column. This is done by modelling, for each impurity, the retention times tR as a function of the pH on a given column.

  The goal when developing a drug impurity profile is to select the experimental conditions, here the pH, so that the worst-separated peak pair is best separated (i.e., so that the smallest resolution at a given pH is maximal). 

Modelling the resolution directly as a function of the pH should not be done. It can only be done properly in situations with only two peaks that elute in the same sequence in the entire pH domain. In situations with more than two peaks, this approach is not feasible because modelling the resolution can not be done appropriately due to different reasons. Many resolutions, such as those between all peak pairs, can be considered, while only those between consecutive peaks are relevant. However, which peak pairs form the consecutive peaks may change in the examined pH domain. Furthermore, different situations with a similar separation degree between two peaks but a reversed elution order can not be distinguished from the calculated resolution (i.e., they are represented by the same resolution). However, somewhere between these two cases, the resolution goes through zero, meaning the peaks co-elute. Considering both positive and negative resolutions might solve the latter problem, but not the first.  

As a result, the most appropriate approach to predict resolutions at given pH values is represented in Figure 2. It consists of first modelling the retention of the peaks as a function of the pH and also the peak width in case of isocratic elution. For gradient elution, the peak width can be considered constant and does not need to be modelled. Different types of curves can be evaluated for this modelling.

  For a compound with acidic or basic properties, retention changes with pH, following a sigmoidal curve. However, depending on the pK

 of the impurities and the examined pH range, the whole curve is not considered. This makes modelling t

 as a function of pH challenging, as can be seen in Dumarey et al.

  For instance, depending on the situation, a second- or third-degree polynomial model can be tested. When a proper model is selected, the t

 is predicted for every step, Δ pH, starting from pH

, where Δ pH is considered a feasible step size, for instance, Δ pH = 0.1 or 0.2. This is done for each impurity individually. 

On a given column and at a given pH value, the predicted retention times and their corresponding peak widths are then sorted. Resolutions between consecutive peaks are then calculated and the minimal resolution, Rs

 determined at each pH value on the given column. These Rs

 are plotted as a function of the pH values for each column (Figure 3). The pH value with maximal Rs

 the one with the best separation for the worst-separated peak pair, is considered the optimum on the considered column. For example, on column

 is indicated on Figure 3. The same is done for all columns (Figure 3), and on each column the pH value with maximal Rs

 is determined. Finally, the overall maximal Rs

 then specifies the best pH and column. For example, on Figure 3, the pH with the overall maximal Rs

Then, the real separation at these best conditions is performed and evaluated. When the separation needs to be further improved, one might optimize the organic modifier composition. However, one also might reconsider the gradient slope. For instance, the begin and end conditions of the organic modifier fraction can be changed depending on the behavior of the analytes. The gradient time also may be adapted when the peaks are eluting in a too narrow time frame, such as when the overall maximal Rs

 still is largely below baseline separation. After gradient adaptation, the pH optimization might be reconsidered. The reason is that, in such a situation, during the initial pH optimization, because of the complexity of the mixture, the best separation was largely below baseline separation in whatever condition. Thus in the end the predictions may even become unreliable (when the error on the prediction and the prediction itself become of similar magnitude). After adapting the gradient, peaks should be eluting further away from each other and pH predictions should be better. 

The following optimization steps, the optimization of the organic modifier composition and a rather fine tuning of the method considering the gradient slope but also the column temperature, will be discussed in the next data-handling column. 

The first two steps in a method development approach for drug impurity profiling were presented and discussed. In the first step, a set of dissimilar chromatographic columns is selected. In a second step, a suitable chromatographic column is selected and the mobile phase pH is optimized after screening on, for instance, 12–20 systems. The possible remaining steps in the impurities profile development will be discussed in the next data-handling column. 

 is a professor at the Vrije Universiteit Brussel, Belgium, department of Analytical Chemistry and Pharmaceutical Technology, and heads a research group on chemometrics and separation science.  

 is a former PhD student from the same department [funded by a specialization grant awarded by the Institute for the Promotion of Innovation by Science and Technology in Flanders (IWT)] and is currently working as a postdoctoral fellow at the Umea University, Umea, Sweden. 

 is a postdoctoral fellow of the Fund for Scientific Research (FWO, Vlaanderen, Belgium) working at the same department on experimental designs and their applications in method development and validation, and on the development and data analysis of herbal fingerprints. 

1.  International Conference on Harmonisation of Technical Requirements for the Registration of Pharmaceuticals for Human Use (ICH) guideline Q3A(R2), 

Practical Data Handling

In this Practical Data Handling column, method development for drug impurity profiling is discussed.

Method Development for Drug Impurity Profiling: Part 1

Normally we use the calibration standards in a liquid chromatography (LC) method as a tool for quantitative analysis so that we can measure the concentration of an analyte in our samples. In this month's "LC Troubleshooting", we'll look at another use of the standards — to monitor the quality of the data, with a goal toward helping us in troubleshooting instrument problems. 

As an example, we'll use a high-precision method, such as might be encountered for a drug content method in the pharmaceutical industry or an active ingredient determination in a pesticide formulation. This kind of method is characterized by signal-to-noise ratios of >100 and peak area imprecision of ≤1% for several consecutive injections of the same sample. By examining a trend through a batch of samples or looking at individual outliers, we often can get enough information to help isolate the problem source to a particular module or submodule of the LC system. 

As with most problems, it is difficult to determine if something is wrong unless we know the normal behaviour of the system. For example, let's consider the behaviour of the calibration standards for a batch of samples. 

In this case, three different calibrators (STD A, B and C) of nominally the same concentration were injected in duplicate, followed by 6–10 samples in duplicate, then another set of standards, and so forth, finishing with a final set of standards. An example of the results for standards in a normal run is shown in Table 1. 

Table 1: Calibration data for a well-behaved method			

In Table 1, we can see that the variability of peak area is very small, whether we examine the percent relative standard deviation (%RSD) for each calibrator over the entire run or compare the percent difference between consecutive injections. In all cases, the values are well below 1%. These data are typical for this method and will serve as our reference values for the problems discussed later. 

When the variability in peak area increases, the autosampler often is the first module to suspect. A good first step in troubleshooting is to make two or more injections from the same vial of the suspect sample or samples. If you normally inject duplicate calibrators from the same vial (a good idea), you will have this information as a normal part of the data set without having to go back and make special injections for troubleshooting purposes. If the variability in peak area is much larger than normal, this means that the same sample volume is not being injected each time, pointing to an autosampler problem. Check for obvious problems, such as a blocked or partially blocked autosampler needle, or insufficient sample. Once these sources are eliminated, look for mechanical problems with the autosampler. 

Many people erroneously assume that fluctuating area counts or response factors are associated with only the injection valve; however, there is another part of the autosampler in addition to the injection valve that contributes to the precision of the data and is subject to wear and tear, as well: the sampling unit, the part of the system that withdraws sample from the vial and transfers it to the sample loop. 

An example of problem data is shown in Table 2. Here, a coworker's data shows mostly good agreement with area counts, with a few exceptions shown in bold italics. Notice that the occasional erratic peak areas throw off the %RSD values for the calibrator sets as well as the %-difference for the individual injection pairs. Understanding the functionality of the parts of the modules is crucial for troubleshooting. The injection valve directs flow to the column once the sampling unit has drawn the sample into the sample loop; therefore, if a cross-port leak was indeed the culprit, the data should show more variability than it does. The response would be less than normal as well, because a cross-port leak usually causes sample to leak into the waste stream. Hence, the injection valve was summarily dismissed as the problem, so we are left with the sampling unit as another potential problem source. Upon inspection of the instrument, it was discovered the metering drive, the part of the sampling unit that operates the sampling syringe, was loose. After tightening the bolts that hold the metering head in place, excellent precision was restored. 

Table 2: Data set showing occasional abnormal injections			

In this example, the method normally gave very small %RSD values for standards, usually ≤0.5%. However, we noticed a trend in the data shown in Table 3, with the overall %RSD about twice that normally observed (compare with Table 1). Notice that, although each pair of consecutive standards agrees closely with each other, the trend is to larger variation through the run. Because each pair of injected standards was consistent (only STD A is shown here for brevity), we did not suspect initially that the autosampler was the problem. Again, in our experience, autosampler problems tend to show random variations in area, whereas in this case, there is a trend over the run. We suspected that this was due to increased noise from a detector lamp that was failing.  

Table 3: Data showing higher-than-normal %RSD			

The condition of the lamp was confirmed by three different observations. First, according to the lamp life meter, the lamp had logged more than 1000 h. Lamps typically last between 1000 and 2000 h. If the lamp has logged >1000 hours and other symptoms exist, it is prudent to change the lamp. Second, this method uses a detector wavelength of 205 nm; lower wavelengths are the first to fail the intensity test performed on the lamp. This particular lamp barely passed the intensity test in the 200–220 nm range, so it was replaced. The final convincing argument for a bad lamp was the drop in %RSD from 0.80 to 0.21 when a new lamp was installed (Table 4). In addition, increased baseline noise is a common symptom of a failing lamp, but often this occurs only after the earlier symptoms are noted; increased baseline noise was not observed in this case. 

Table 4: Data from same system and method as Table 3 after detector lamp change			

To avoid premature lamp replacement after being misled by a drift in precision, as in Table 3, it is important to allow the lamp to warm up for 30–45 min before analysis. A simple way to convince yourself of this requirement is to equilibrate the column for a method and load standards into the autosampler tray. Turn on the detector and immediately start injecting. Then compare the variability of early injections to those after the lamp is fully warmed up.  

An example of this is shown in Table 5 for a 15-min run. The column was equilibrated, and then the detector was turned on and injections were begun immediately. Although the %-difference in area between injections is abnormally high only for the first two injections, it can be seen that the overall %RSD improves if the first few injections are dropped. This is summarized at the bottom of Table 5, where 

 = 12 is for all samples except the first one, and so forth. There is a noticeable reduction in the %RSD when the first one or two samples are dropped; after that, the value settles down, which would suggest that a 30 min detector warm-up would be sufficient. However, note that for maximum performance in this example, if 1.5 h elapsed before data were used (starting with injection 7), the %RSD drops to ≤20% of what it was with all the injections included. And yes, this is one data set on one detector, so specific warm-up time recommendations are not possible for all detectors and methods, but the pattern is consistent: detector performance improves if adequate warm-up time is allowed. 

Table 5: Effect of lamp warm-up on peak area			

The last example is for the same high precision method, but an unusually high variation of areas was observed when comparing the early and late runs for the standards (see Table 6). Note that the areas for runs 45, 65, 66 and 67 (bold) are approximately 8% larger than the other runs. Because this instrument had been serviced recently and the precision for subsequent runs was acceptable, the autosampler was ruled out as the most likely cause of the problem. When the data were examined more closely, it was noted that the retention times also changed in conjunction with the area counts. A close examination of the system showed that the tubing between the aqueous solvent reservoir and the pump was filled with bluish particulate matter. This was microbial growth resulting from a phosphate buffer that was allowed to sit idle on the LC for an extended period of time without flushing with fresh water. The tubing was disconnected from the pump and hot water was forcefully flushed through the tubing, including the tubing inside the in-line degasser. This water was collected and filtered through a 0.45 μm PTFE filter to collect the residue. The result is shown in Figure 1, where the rinse water residue is compared with the residue from filtering an equal volume of HPLC-grade water. 

There are at least three possible consequences of this contamination. First, the frit in the mobile phase reservoir could be blocked. Second, the proportioning valve on the low-pressure mixing manifold could be malfunctioning. Third, the check valves in the pump could stick as a result of this fouling. Each of these three possible results was considered in light of the symptoms. A blocked reservoir frit would restrict flow of the aqueous solvent to the mixer. Because a constant flow of mobile phase is pumped from the mixer, when the organic solvent valve opens, excess organic solvent is delivered to make up the shortfall of the aqueous solvent. This means that the concentration of organic solvent in the mobile phase would be higher than normal. This is an ion chromatography method, where the buffer is the strong solvent, so higher concentrations of buffer would reduce retention times. Without sufficient buffer reaching the column, the analyte would be retained more, consistent with the observations. The inlet filter was changed, given the amount of microbial growth found in the low pressure lines. Finally, a partially blocked or intermittently sticking check valve would result in a lower flow rate than normal and this would increase the retention time as with the Table 6 data. A good practice is to note the stability of the pump pressure while the system is equilibrating and during system suitability. The pressure should be steady, varying no more than 1–2 bar (15–30 psi). If the pressure varies more than this, either there is a bubble in the pump that needs to be removed, additional mobile phase degassing is needed, a check valve is sticking or leaking, or some other flow-related problem is present. It is not clear in retrospect which possible problem source was the root cause of the problem, or perhaps if a combination of causes existed. It is also not immediately obvious why the peak areas changed as much as was observed. In any event, flushing the system to remove residual particulate matter, replacement of the inlet frit in the reservoir and sonication of the check valves resulted in a restoration of the LC system to satisfactory operation. 

Table 6: Data Obtained from contaminated LC system			

This example drives home a point that has been mentioned in this "LC Troubleshooting" column regularly over the last 25 years: Do not turn off the LC system with buffer in it! Sure, a few minutes won't hurt, but extended standing with buffers encourages evaporation of the liquid phase and formation of buffer crystals as well as microbial growth inside the system. A good habit is to replace the buffer once a week, although many laboratories will use buffer for two weeks without problems. And before you shut down the system, flush all the buffer lines with water. It is best never to reuse a buffer reservoir — use all the buffer, then replace the reservoir with a clean one. A used reservoir can inoculate the next batch of buffer with microorganisms contained in the last batch. If you find yourself in possession of a contaminated system, as was the case here, replacement of the tubing would be a good idea, and flushing with a dilute solution of chlorine bleach also would ensure that any microbial contaminants had been deactivated (obviously, you shouldn't pump bleach through the column!). 

The examples cited this month illustrate several problems that were identified initially by studying the behaviour of calibration standards after a sample batch was completed. In addition to calibration properties, these standards can serve as a powerful indicator of the health of the LC system. In each case, the symptoms pointed to a general part of the LC system. Further observation and interpretation of the data helped to isolate the problems so that they could be corrected. 

 is an analytical chemist with Syngenta Crop Protection Inc. She is responsible for the development and validation of methods for herbicide products as well as the on-demand troubleshooting and repair of 30 departmental LC systems. 

 is vice president of LC Resources, Walnut Creek, California, USA; and a member of 

 editorial advisory board. Direct correspondence about this column to "LC Troubleshooting", 

, Poplar House, Park West, Sealand Road, Chester CH1 4RN, UK, or e-mail Alasdair Matheson, the editor, at 

LC Troubleshooting

There's more to the data than you might expect.

Getting the Most Out of Calibration Standards

Generic methods based on chromatography with full scan MS detection are maturing. Progress has been made in the development of software for automated detection or identification of the analytes, but this still is the bottleneck inhibiting implementation for routine analysis. Validation of qualitative wide-scope screening is another hurdle to be taken before application. An overview of current chromatography-based food toxicant screening is presented.

During production, processing, storage and transport of food and feed, a variety of potentially hazardous compounds may enter the food chain. These include residues left after treatment of crops and animals with pesticides and veterinary drugs, natural toxins produced by fungi (mycotoxins) and plants, environmental contaminants (persistent pollutants) and processing contaminants. The potential presence of these compounds is an important issue in the field of food and feed safety. Consequently, extensive legislation has been established to protect consumers from unnecessary or excessive exposure to these substances. Analytical chemists are facing a huge challenge when it comes to efficient verification of compliance of a wide variety of products with this legislation and, preferably at the same time, to provide occurrence data for 'new' contaminants which are not yet regulated. In short, hundreds of different products need to be analysed for thousands of known contaminants and more. 

Within each class of contaminants, the current 'golden standard' to deal with this challenge is the use of multi-analyte methods based on LC or GC with tandem MS detection. Such methods typically cover tens to several hundreds of analytes and are well established in the field of pesticides,

  other fields following the same concept (e.g., mycotoxins

 ). The instrumental methods involve targeted acquisition where detection of each compound is individually optimized during instrumental method development. The methods are typically extensively validated with respect to quantitative performance and data handling usually involves a manual review of extracted ion chromatograms to verify peak assignment and integration. 

A logical next step is to combine multi-methods beyond their contaminant class. The feasibility of this approach has recently been demonstrated

  by simultaneous determination of pesticides, veterinary drugs, mycotoxins and plant toxins in a variety of food and feed commodities. Sample preparation for such integrated methods is very generic and straightforward (as simple as a single extraction/dilution step). Because the anticipated number of analytes to be covered in this approach is beyond what can easily be accommodated by tandem MS, full scan MS is the detection method of choice. Here the measurement is non-targeted, which makes the instrumental analysis much more straightforward (i.e., no application-specific instrument adjustments or optimization needed, no acquisition-time windows). In principle, the scope of the method is unlimited. As long as analytes elute from the column and are ionized in the source, they can be detected. The moment of setting the scope of the method shifts from before, to after the instrumental analysis.  

The conclusion from the trend described above is that both sample preparation and instrumental analysis are becoming more and more generic and, to a certain extent, independent of the analyte of current or future interest. This also means that the main effort in terms of development, optimization and validation is clearly moving from sample preparation and instrumental analysis towards data processing to ensure reliable detection of the compounds of interest. 

This paper will provide a brief overview of the full scan MS options currently available for chromatography-based wide-scope screening of food toxicants. Aspects related to automated detection and identification will be discussed based on literature and experiences within the authors' laboratory, with emphasis on data handling. An in-house developed software package (MetAlign), which features instrument independent and uniform data preprocessing and automated identification, will be presented.  

General Considerations on Automated Detection/Identification After Full Scan MS Measurement

The raw data files obtained after GC or LC with full scan MS detection, are typically 20–500 Mb and contain information on retention time (1 or 2 dimensional), 

 = 50–1000 (nominal or accurate mass) and abundance (from noise to saturation). Getting meaningful results out of this huge amount of information in an efficient manner is not a trivial task. In principle two approaches can be pursued: non-targeted and targeted data evaluation. With non-targeted data analysis, the raw data file is processed to obtain a list of all individual peaks present in the entire chromatographic run. The number of peaks can be in the 10000s, which rules out a manual identification. Without any 

 information, one could restrict the evaluation to the major peaks, but these are usually originating from non-toxic endogenous compounds, rather than contaminants, which are mostly present at low levels. Another option is to filter out contaminants by comparing overall LC–MS or GC–MS profiles of samples, with profiles obtained after analysis of the corresponding non-contaminated product. For this, extensive databases for the different food commodities would be required, which still need to be generated. Consequently, a more feasible option for the time being is to perform a targeted data evaluation based on libraries containing information on the target compounds. All individual peaks assigned after data (pre)processing can then be matched against the information in the library. Alternatively, the software could use the target library as starting point and restrict the search to compound-specific retention time windows and ion traces from the raw data file. Either way, some sort of match between experimental data and library data is obtained. Depending on the MS device used, this match can be based on a combination of retention time(s), ions ratios, mass spectra, isotope patterns and/or mass accuracy. Criteria will have to be set for each of the match parameters in such a way that the number of so-called 'false negatives' and 'false positives' are within acceptable limits. False negatives being compounds known to be present in the sample but missed by the automated screening method, false positives being compounds known to be absent but appearing on the list of (provisionally) detected compounds. 

Various options for full scan MS detection are available for GC. Single quadrupole and ion trap instruments have been commonly applied for food analysis since the 1990s, especially for the determination of pesticide residues in vegetables and fruits. Sensitivity limitations encountered in the past when using full scan acquisition have been overcome by large volume injection using PTV injectors

  and improvements in MS devices. A big advantage of GC–MS is that the MS spectra obtained after electron ionization are highly characteristic and, to a large extent, are instrument independent. This means that spectra generated elsewhere can be used and that libraries can be purchased. Despite the screening potential, the instruments were and still are mainly used for quantitative analysis rather than for screening. One reason for this is that the spectra of the analytes in the sample often contain interfering ions from other compounds, which complicates automated matching against library spectra. To a certain extent, the quality of sample extraction can be improved by software alogorithms, such as deconvolution, that resolve spectra from overlapping peaks and background. Deconvolution software tools are available both commercially and as free downloads (for example, AMDIS from NIST

 ). A second reason for the limited exploitation of the screening potential is the lack of dedicated sofware for automatic detection. The standard sofware that comes with the GC–MS instrument is usually able to perform searches of sample spectra against libraries, but is often not really suited and not user-friendly with respect to automated identification and report generation in routine practice. This has caused users to develop in-house solutions without

  For the user, deconvolution tools that are integrated into the instrument's data analysis software are most convenient. Some instrument manufacturers offer this as default

  or as an optional package combined with dedicated libraries that not only include the mass spectra but also retention times for prescribed GC conditions.

 For extracts of increasing complexity and/or in case of lower analyte levels, GC with full scan quadrapole or ion trap MS lacks selectivity, even when applying preprocessing algorithms such as deconvolution. Currently, there are two options to improve selectivity in GC with full scan MS. The first is the use of high resolution/accurate mass MS detectors (GC–hrTOF-MS). The higher selectivity arises from the ability to separate ions that have the same nominal mass but differ in their exact mass. A main disadvantage is that, to take full advantage of this, exact mass library spectra are needed. Because these are not (yet) available, they would need to be generated by the user. In addition, the current mass resolving power (~6000) and dynamic range are not adequate for all applications. The second option to improve selectivity is through enhanced chromatographic resolution. The current-state-of-the-art here is comprehensive GC (GC×GC). Compounds are typically first separated by volatility on a regular GC column and then, by polarity, on a second short narrow-bore column. A visualization of the resulting separation is shown in Figure 1. For full scan MS detection a high scan speed is required (~100–250 Hz) in order to have sufficient data points across the narrow (100 ms) chromatographic peaks. TOF-MS detectors allowing scan speeds up to 500 Hz are available (nominal resolution only). Cleaner spectra are obtained in the first place due to the enhanced GC separation and also here data preprocessing for peak purification can be performed. Given the comprehensiveness of this type of analysis, its main application lies in profiling and classification of samples in various areas including metabolomics, petrochemicals, food and environmental,

  but several applications for qualitative and quantitative determination of residues and contaminant have been reported.

  Due to the complexity and the amount of data obtained after comprehensive GC analysis, data handling is a major challenge. Both proprietary software and in-house developed software tools for data handling have been described. They have been excellently reviewed by Piece et al.

  At the moment, no general 'ready-to-use' solution is available for automated detection. Consequently, in our laboratory data handling after GC×GC–TOF-MS analysis is performed using a combination of the instrument software for initial processing and deconvolution and an Excel macro for matching retention times, data reduction and generation of a list of detected compounds. Currently, an in-house developed alternative for preprocessing/resolving overlapping peaks called MetAlgin is being evaluated. 

For full scan measurement of low levels of toxicants in food and feed after LC separation, high resolution/accurate mass MS detectors are required. During ionization, little or no fragmentation occurs and compounds are detected through the accurate mass of their (de)protonated molecule or adduct. TOF-MS has been used in most investigations. Especially over the past few years, resolution, mass accuracy, dynamic range, scan speed and sensitivity have greatly improved. In addition, a single stage Orbitrap-MS has been introduced, making a resolving power up to 100000 an affordable option for food toxicant analysis. 

For selective and efficient automated detection, a reliable high mass accuracy is essential. The instrument specifications are typically within 5 ppm or even better. However, in practice this mass accuracy can only be achieved when co-eluting compounds with the same nominal mass can be mass spectrometrically resolved. Consequently, for real-life samples the resolving power of the MS is an important parameter as well. This has been shown recently by Kellmann et al.

				 The resolving power required depends on the application, that is, on the complexity of the extract (sample complexity, sample preparation), the analyte (sensitivity) and its concentration. For generic extracts of honey, a resolving power of 25000 was sufficient for obtaining a mass accuracy of <2 ppm for pesticides, natural toxins and veterinary drugs, down to the 0.01 mg/kg level. For a much more complex animal feed matrix 100000 was needed. The effect of resolving power on assigned mass accuracy is illustrated in Figure 2.  

While the hardware to enable screening is becoming more and more fit-for-purpose, development of software and databases for automated detection is still in full progress, with major improvements being made in the past two years. In its simplest form, analytes can automatically be detected in the raw data through matching the accurate mass of peaks found against a list of target compounds with their exact mass and retention time. However, accurate mass and retention time alone are often not sufficiently unique for selective automatic identification. Depending on the tolerances set for matching retention time and accurate mass, the response threshold, the complexity of the matrix and the number of compounds in the target database, the number of potential detections triggering further confirmatory (data) analysis may be too high for screening purposes. This is illustrated in Figure 3. To increase specificity, isotope patterns can be used. Like the exact mass, they can be calculated and no prior experimental determination is required. However, for small molecules the isotope ions (except chlorine and bromine isotopes) have substantially lower abundance, thereby increasing the limit of identification. Another option to improve specificity in automated detection is through analyte fragments. Such fragments can be induced during ionization (so-called in-source collision induced ionization, IS-CID) or in a collision cell (e.g., a quadrupole of a QTOF) with the remark that no precursor ion selection is performed and fragmentation is done using fixed generic fragmentation conditions. The formation of fragment ions, to some extent, but certainly their relative abundance, depends on the instrument and conditions applied. Therefore, in contrast to GC–MS spectral databases, fragmentation patterns can not easily be extrapolated and used in other laboratories and will need to be generated by the user (or vendor) for each instrument.  

Toward Generic Data Processing and Data Mining

While methods for generic extraction and subsequent full scan instrumental analysis are maturing, universal approaches for data (pre)processing, detection of toxicants and formats for archiving preprocessed result files hardly exist. This means that the data files containing comprehensive information on a wide variety of food and feed samples and the (un)known toxicants they may contain, can only be (further) explored by the laboratory that generated the data. That laboratory might only be interested in pesticides (and just perform a targeted data analysis limited to that), while others might be interested in natural toxins in the same commodity. Furthermore, libraries used for automated detection may differ in scope, which affects the output. The issue, as such, is not unique for food toxicant analysis but, unlike other areas such as metabolomics, has hardly been addressed so far. In our institute, as a spin-off from development of data handling software for application in the field of metabolomics, preprocessing tools are being applied and dedicated search tools have been developed for food toxicant screening. The preprocessing tool, called MetAlign, is freely available and described in detail elsewhere.

  One of the main features of MetAlign is that it can handle, either direct or after automated conversion, data from different vendors covering a broad range of GC–MS and LC–MS instruments, both with nominal and accurate mass. Data preprocessing involves baseline correction, noise elimination and peak picking. The software also deals with detector saturation and brand and instrument specific artifacts. The output is a 50–500× reduced data file in a uniform format which can be exported to Excel, or formats allowing visual review through proprietary instrument software already available in the laboratory (see Figure 4). Data alignment can be performed as well, which can be of interest in searching for truly unknowns through comparison of profiles of the same products.   

The resulting files can be searched against target databases using dedicated modules for GC–MS, GC×GC–MS (application to be published elsewhere) and LC–MS. Due to the strongly reduced size, files can be easily stored and searching is fast. A comparison of the automated detection performance after GC×GC–TOF-MS between the instruments software and MetAlign/GCGCMS_ search showed that in feed samples spiked with 106 analytes, more compounds (32% vs 62% at the 0.0025 mg/kg level) were found using the MetAlign software, without increase in the number of false positives. A generic approach for data preprocessing can facilitate data sharing and data mining, thereby making much more efficient use of (existing) information available at different laboratories (Figure 5).  

Validation of Chromatography-based (Qualitative) Screening Methods

Once automated detection and reporting have been optimized, the overall method (i.e., sample preparation + instrumental analysis + automated data processing and reporting) has to be validated before it can be applied in routine practice. This essentially means that for a certain matrix (or group of matrices), at the anticipated reporting level, the level of confidence of detection of the target analytes needs to be established. False negatives need to be minimized, for obvious reasons. False positives are undesirable because they will trigger further manual data evaluation and confirmatory analyses which takes time and effort.  

Up to now, only explorative evaluation of screening performance has been done using limited numbers of spiked samples or by comparison of the detection rate of the automated screening method with (earlier) results from established quantitative. Lee et al.

				tested automated identification using a test set of 106 pesticides and contaminants spiked to a complex compound feed sample at various levels, using GC×GC–TOF-MS.

  Detection rates were clearly concentration dependent and varied from 100% to 73% to 17% for 0.10, 0.01 and 0.001 mg/kg, respectively. Mezcua et al.

  investigated the potential of LC–TOF-MS based screening for pesticides in vegetables and fruits. Automated detection was done using an in-house compiled database and instrument software. Most pesticides found by the conventional LC–MS–MS method were also automatically found by the LC–TOF-MS screening method. 

Very recently, two groups did a more extensive verification of automated detection of pesticides using GC–MS (single quadrupole) analysis combined with Agilent's Deconvolution Reporting Software tool. Mezcua et al.

  spiked 95 pesticides to eight vegetable and fruit samples, at levels between 0.02 and 0.10 mg/kg. The evaluation of Norli et al.

  involved a test set of 177 pesticides spiked in triplicate to 3 matrices at 0.02 and 0.1 mg/kg. The studies revealed that the detectability is, as expected, compound, matrix and concentration dependent and that, even in cases of repeated analysis of the same extract, inconsistencies occurred with respect to automated detection. In all studies mentioned, the data generated were too limited to derive a confidence level for detectability of the target analytes in a certain matrix (group). On the other hand, through analysis of real samples, the studies did show that, compared to the conventional methods, more pesticides could be found in less time, clearly demonstrating the potential of the approach. This has been encouraging enough to apply the methods as an extension to the established quantitative multi-methods because any additional toxicant automatically found can be considered worthwhile.  

To summarize the above, systematic validation studies of the qualitative screening methods are still lacking. The limited availability of appropriate software and/or target compound libraries up to now might be one reason for this. Another reason might be that, in contrast to quantitative methods, validation procedures and criteria for qualitative chromatography-based methods were not very well addressed in guidance documents for residues/contaminant analysis. For veterinary drugs, EU directive 657/2002 states that screening methods should be validated and that the 'false compliant rate (false negatives) should be <5% at the level of interest',

  without providing much detail on how to establish this. Fortunately, beginning this year both the veterinary drug

  community in the EU came up with supplemental and updated guidance documents addressing this issue. Essentially, both documents prescribe that in an initial validation, at least 20 samples spiked at the anticipated screening reporting level (< MRL) need to be analysed and the target analyte(s) need to be detectable in at least 19 out of 20 samples (corresponding to the <5% false negative rate). The initial validation needs to be continuously supplemented by analysis of spiked samples during routine analysis and periodical re-assessment of the data needs to be performed to demonstrate validity based on the extended data set. 

With the recent guidelines in mind, a first retrospective evaluation of automatic detection of pesticides and contaminants in feed commodities after GC×GC–TOF-MS analysis was done in the authors' laboratory. The evaluation was based on spiked samples concurrently analysed with routine samples over a period of 9 months. The results for 100 target compounds at the 0.05 mg/kg level are summarized in Figure 6. It shows that, at the software detection settings applied (which were not yet exhaustively optimized) >90% of the target compounds are detected with a confidence level >70%. In a retrospective validation exercise, for wheat the validation criterion was met for 70% of the analytes from the test set. Across different feed commodities, this percentage was substantially lower, although it should be mentioned that this type of application is one of the most challenging in food/feed toxicant analysis.  

Chromatography combined with advanced full scan mass spectrometry is developing into a universal tool for screening (and quantitative determination) of a wide variety of food toxicants. Time spent on sample preparation and the set-up of instrumental acquisition methods is declining. The opposite is true for data processing, optimization of software parameters for automated detection and finding the fit-for-purpose balance between false positives and false negatives; but progress is being made. The screening methods have already proven their added value. In the foreseeable future, such methods are likely to become more dominating, thereby enabling more efficient and effective control of food safety and providing more comprehensive, and more rapidly available, data for risk assessment. 

 is a senior scientist and heads the group of National Toxins and Pesticides at RIKILT. He has over 15 year's experience in residue and contaminant analysis in the food chain using chromatography with a range of mass spectrometric techniques. 

 (BSc) is a research chemist in chromatography with various mass spectrometric detection techniques, applied to pesticide residues and mycotoxins in feed and food matrices. 

 is a senior scientist focusing on the development of data preprocessing and alignment software and adaption of this software for various applications ranging from metabolomics, profiling to targeted screening. Other areas of expertise include NMR. 

 (BSc) is a research chemist using gas chromatography, mainly focusing on GC×GC–TOF-MS analysis of food and feed with an emphasis on data evaluation and reporting. 

 is a junior scientist with extensive experience in GC×GC–TOF-MS analysis of pesticides and environmental contaminants. His current work also focuses on elemental speciation analysis using chromatography with ICP-MS. 

 is finishing his PhD on the analysis of marine biotoxins. As well as his continued involvement in this area, his current research topics also include nano-LC–MS and the development and the application of software tools for data evaluation in chromatographic screening methods and data mining. 

4. M. Spanjer, P.M. Rensen and J.M. Scholten, 

An overview of current chromatography-based food toxicant screening is presented.

LCGC Europe-04-01-2010

Event News

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Getting the Most Out of Calibration Standards

Data Handling and Validation in Automated Detection of Food Toxicants Using Full Scan GC–MS and LC–MS