The goal of quantitative chromatography is to separate two or more substances sufficiently so that they can be measured with a desired degree of accuracy and precision. The resolution of a peak pair provides a standardized measurement of the extent of a separation but strictly speaking, resolution is defined only for two Gaussian peaks of equal size. This month's "GC Connections" discusses resolution and the effects that relative peak size have on area measurements. 

The resolution of a pair of adjacent peaks is defined in the chromatographic literature and in standard nomenclatures such as IUPAC

For a Gaussian shaped peak the width at half-height is related to the width at base by a factor of 1.699: 

It is usually more convenient to calculate resolution from the width at half-height of the second peak using this related formula: 

The width at half-height is more easily measured than the width at base, and it is measured and used by many chromatography data-handling systems in, for example, system suitability calculations. The width of the second peak is used because it will generally be either equal to or slightly greater than the first peak's width and thus, produces a more conservative result while not requiring measurement of the widths of both peaks.  

Most chromatographers are familiar with the appearance of partially resolved, baseline resolved and fully resolved peak pairs. Figure 1 shows an ideal pair of generated Gaussian peaks of equal sizes and widths with partial resolution at 

				= 1.0 [Figure 1(a)], baseline resolution at R

= 1.5 [Figure 1(b)], and full resolution at 

= 2.0 [Figure 1(c)]. In this illustration, resolution increases as the separation between the peaks increases, while the same peak shape is maintained. Keeping the same separation while decreasing the peak widths would also increase their resolution proportionately. Figure 1(c) illustrates the relationship between the peak width at half-height and base, and it also shows how the width at base is determined from the baseline intersection points of lines drawn tangent to the peak's upward and downward slopes. A common misconception is that the width of a peak at base is the time between when the peak starts to rise above the baseline until it subsides back into the baseline — the peak integration start and stop times — but in fact, the base width of a Gaussian peak is measured at 13.4% of the peak height above the baseline, at the times corresponding to the tangent–baseline intersection points. 

 Figure 1: Resolution and measurement of a pair of identical Gaussian peaks. Peak 1 (green dotted line); Peak 2 (red dotted line). Chromatogram:(blue solid line). Overlapping area of Peak 2 inside Peak 1 (red shaded area). Overlapping area of Peak 1 inside Peak 2 (green shaded area). (a) Resolution = 1.0; (b) Resolution = 1.5; inset shows extent of peak overlap; (c) Resolution = 2.0, dotted grey lines show tangents to the peaks and measurements of width at base.			

Table 1 lists the retention times, peak width measurements and resolution for the three cases shown in Figure 1. At a resolution of 1.0 the valley point between the peaks corresponds with the end of the first peak's base width and the start of the second peak's base width. At baseline resolution, where 

= 1.5, the ending and starting base widths of the first and second peaks, respectively, are separated by 0.5 times the base width. At resolution 2.0, the ending and starting base widths of the first and second peak are each spaced at intervals equal to the base width. 

Table 1: Resolution measurements from Figure 1. (a), (b) and (c) refer to the parts of Figure 1.			

The identification of specific peaks by their retention times becomes more certain as resolution increases. It is possible to identify peaks on the basis of a partial separation, but the certainty of identification improves with increasing resolution. The detector signal comes closer to baseline between the peaks and the presence of additional components becomes more apparent. A mass-selective detector helps to identify merged peaks on the basis of their mass spectra, but closely eluted peaks often are isomeric or otherwise chemically similar. They may have similar spectra that preclude unambiguous identification by spectral library matching.  

Each component in a group of two or more merged peaks should be identified with separate analyses of the pure components and the influence of matrix components should be determined through blank injections. Bear in mind, too, that the elution order of close peaks will be influenced by changes in the column temperature conditions: a change in temperature programme rate of as little as 2 °C/min can reverse elution order. When faced with the partial resolution of a critical peak pair, or with the presence of a new matrix compound that interferes, make sure that the current separation conditions are optimal and then consider upgrading the column resolving power by installing a longer or narrower-bore column. If all else fails, then try changing to a different stationary phase that better separates the peaks of interest. 

Measurement of a partially resolved peak pair can be difficult to achieve with acceptable levels of accuracy. The simple but unrealistic case of two equal-sized peaks is a good starting point for understanding the effects that colliding peaks have on quantitative analysis. Both the influence of the portion of one peak that extends into another and the effects that merged peak shapes have on peak integration play strongly into the accuracy of quantitative measurement of merged peaks. 

Returning to Figure 1, the degree of area overlap of the coinciding portions of the peaks decreases as resolution increases. At 

 = 1.0 in Figure 1, the contribution of either peak to the other's total area is about 2.3%, as measured from where a vertical dividing line for peak area integration is dropped from the valley point between the two peaks to the baseline. I measured the overlap areas with a measurement tool in my data system for the first generated peak alone by starting at the time of the valley point, which corresponds with the end of the peak's segment, and extending to the end of the peak. In this case, the peaks are symmetrical and identical so the overlap and total areas are the same for each. 

The overlapping areas are highlighted in colour in Figure 1(a) and in the inset in Figure 1(b). As the resolution increases to 1.5 — baseline resolution — the overlap between the two peaks drops toward 0.1% and at a resolution of 2.0 no measurable overlap remains. Table 2 gives the area measurements from Figure 1. The values for each peak are very close to the known areas as set when the peaks were generated. 

Table 2: Peak area measurements from Figure 1. Overlap is the amount of area or percentage of one peak into another. (a), (b) and (c) refer to the parts of Figure 1.			

In this idealized situation, there is no significant error between the measured and known peak areas, even down at a resolution of 1.0. However, real peaks are not equal in area and width because their amounts, detector response factors and shapes vary. Also, real-world peaks incorporate uncertainty in their measurement due to injection variability, detector noise and slight shifts in retention time from run to run. It is instructive to consider what happens when one peak is considerably larger than the other and what the effects imply about the influence of peak resolution on quantitative analysis. 

Increasing the size of the first peak in our example affects both the peak separation and area measurements. The disparity in peak sizes changes the degree of peak overlap, but more significantly, it has a profound effect on quantitative measurements. Figure 2 shows the same three cases as in Figure 1, but the first peak is now 10 times larger. A question arises of whether the classical resolution measurement, which is based upon two equal-sized peaks, is applicable when the peak sizes are significantly different. 

Figure 2: Resolution of peaks of unequal size. The peak widths and separations are the same as in Figure 1. The area of the Peak 1 is ten times the area of Peak 2.			

We can use the degree of area overlap between the peak pair as an indicator of the degree of peak resolution and compare the case of equal-sized peaks to that of unequal peaks. Looking first at Figure 2(a) (the case of partial resolution of 1.0 of peaks with a size ratio of 10:1), the first peak's percentage of overlap into the second, as listed in Table 3, is similar to that of a pair of equal-sized peaks when there is a simple vertical division between the two peaks at the valley point. In the case of baseline resolution, in Figure 2(b), Table 3 lists the overlap of the larger first peak into the smaller second peak at 0.31% of the second peak's area, while the second peak's overlap into the first is only 0.06%. The overlap for the unequal peaks is small and similar to the degree of overlap of 0.14% for a pair of equal-sized peaks, however, so the definition of the resolution of equal-sized peaks seems to extend to at least at a peak area ratio of 10:1. In the case of resolution equal to 2.0, in Figure 2(c), there is no measurable influence of either peak on the other: they are indeed fully resolved and independent. 

Table 3: Peak areas and overlap of unequal peaks. Overlap is the amount of area or percentage of one peak into the other. (a), (b) and (c) refer to the parts of Figure 2.			

Quantitative area measurements in the case of baseline or better resolution of unequal peaks are fairly accurate. The measured and known areas in Table 3 for cases of baseline resolution [Figure 2(b)] and full resolution [Figure 2(c)] compare very well. Going from a 1:1 area ratio to a 10:1 area ratio with a pair of peaks at baseline resolution or better should yield a nearly linear trend between peak size and measured area. At some point, as the area ratio increases above 10:1, however, the degree of influence of the larger peak on the smaller one's measured area will become significant and the linear relationship between peak size and measured area will break down. Greater resolution between the peaks will raise this ceiling on linearity.  

= 1.0 the story is quite different. Now, there is a much larger overall error in measured area counts with a peak size ratio of 10:1, which was not hinted at when only the similar degree of area overlap for equal and nonequal peak sizes was considered. Not surprisingly the largest errors occur for the smaller peak. 

The error level depends strongly upon the type of baseline allocation selected for the data system. Figure 3 illustrates three common choices. First is a simple vertical separator dropped from the valley point to the baseline, which we've used throughout this discussion so far, shown [Figure 3(a)]. This choice produces an error of about +1% for the larger peak, while the smaller peak comes back with an area that's about 10% smaller than the known amount, as listed in the first line of Table 4. This effect is seen graphically in the inset of Figure 3(a), where the relative amount of the second peak that's overlapped into the first is a significant fraction of the second peak's size. 

Figure 3: Influence of integration type on the measured areas of unequal peaks. Peak pair with RS = 1.0 from Figure 2(a). (a) Vertical drop from valley to baseline; (b) Tangential skim underneath the second peak; (c) Exponential skim off the first, larger peak. The insets show the baselines in more detail.			

A tangent skim baseline choice puts a straight line at the valley point and tangential to its intersection with the declining side of the smaller peak. When a tangent skim is selected, the error in the second peak's area becomes unacceptably huge at -63%. This effect is visualized easily by examination of the inset in Figure 3(b), where a very large portion of the second peak is lost into the area of the first. This shift of area also biases the first peak's area upward by 4.5%. 

Table 4: Influence of integration type on areas of partially resolved unequal peaks RS = 1.0. (a), (b) and (c) refer to parts of Figure 3.			

Finally, using an exponential skim or curve fit off of the first peak reduces the error relative to the tangent skim, but for the second peak the loss of area is still very large at -31%. Looking at the inset in Figure 3(c), the curve-fit of the exponential skim to the first peak is fairly good and overestimates the peak size by only 1%. The loss of a significant portion of the second peak is obvious.  

The errors associated with these three different baseline allocation choices depend on the relative sizes of the peaks and their resolution. What I've shown here is just a snapshot of one artificial situation. Chromatography data-handling systems include a conditional code that allows users to select boundaries for cases in which different baseline allocations are to be applied, based upon relative peak heights, valley points and other parameters. This means that the choices for baseline allocation must be made carefully and validated across the entire targeted range of analyte concentrations.  

The resolution of a pair of Gaussian chromatographic peaks provides a useful metric for gaugeing the ability to achieve reliable qualitative peak identification as well as predicting the accuracy of area measurement. As long as peak resolution is at baseline or better levels, quantitative errors can be very low, even for adjacent peaks at size ratios up to ten to one. When the resolution falls below the baseline level, the measurement of peak areas becomes difficult. Careful choices for area integration in data-handling systems can minimize the errors but must be checked across the entire operating range of a method. Selecting conditions and columns that better resolve critical peak pairs can be a better choice than relying on the data system's rules of area integration to slice away partially resolved peaks from each other. 

 is a senior research scientist at Serveron Corp., Hillsboro, Oregon, and a member of 

Direct correspondence about this column should be sent to Alasdair Matheson, editor, 

, Advanstar Communications, Poplar House, Park West, Sealand Road, Chester CH1 4RN, UK or e-mail: 

 IUPAC Compendium of Chemical Terminology

 (International Union of Pure and Applied Chemistry, Research Triangle Park, North Carolina, 27709–3757, USA, 1997). 

Standard Practice for Gas Chromatography Terms and Relationships", Annual Book of Standards

(6) (ASTM International, Conshohocken, Pennsylvania). 

Articles

GC Connections

A look at the effect of two overlapping peaks on peak quantificiation and resolution

When Peaks Collide!

 Waters Corporation, Milford, Massachusetts, USA, 

 Pfizer, Neuroscience Research Unit, Groton, Connecticut, USA. 

Fast, flexible platforms for peptide quantification are needed, particularly for a discovery setting. This type of methodology would be especially advantageous in the case of amyloid beta (aβ ) peptides. The deposition/formation of insoluble aggregates, or plaques, of aβ peptides in the brain is considered to be a critical event in the progression of Alzheimer's Disease (AD) and thus has the attention of many researchers. Historically, quantification of aβ peptides in biological fluids has relied mainly on the use of immunoassays, such as ELISA. These assays are time consuming, expensive to develop and labour intensive. They are subject to cross reactivity and an individual assay is required for each peptide. To meet the throughput requirements and constant flow of demands for new peptide methods in a discovery setting, there is a need for a highly specific yet flexible methodology based on an LC–MS–MS platform. In this work, this platform is coupled with selective sample preparation for the simultaneous quantification of multiple a  peptides. This work focuses on methods for the 1-38, 1-40 and 1-42 aβ peptides, in support of preclinical studies.  

 Figure 1: Sequence, MW and pI information for amyloid Î²  peptides. 

, 300 Å, 2.1 × 150 mm, 1.7 µm, Peptide Separation Technology 

SPE device: Oasis MCX µElution 96-well plate 

MS was performed in positive ion mode because CID of the 4+ precursor ion yielded several distinct product ions corresponding to inherently specific b sequence ions (representative spectrum shown in Figure 2). 

Figure 2: Representative ESI+ MSâMS spectrum for amyloid  Î² 1-42 with fragment sequence ions labelled. 

Separation of the three amyloid β  peptides is shown in Figure 3. 

Figure 3: UPLCâMSâMS analysis of amyloid Î²  1-38, 1-40 and 1-42 peptides extracted from artificial CSF + 5% rat plasma. 

SPE was performed using Oasis MCX, a mixed-mode sorbent, to enhance selectivity of the extraction. The sorbent relies on both reversed-phase and ion exchange retention mechanisms to selectively separate the aβ fraction from other high abundance polypeptides in complex CSF samples. The Oasis µElution plate (96-well format) provided significant concentration while eliminating evaporation and reconstitution, minimizing peptide losses. 

Figure 4: Representative chromatogram showing basal levels of amyloid Î²  1-42 extracted from 3 sources of human and 1 source of monkey CSF.

N15-labelled internal standards were used for each peptide. Standard curves for all 3 aβ peptides were linear (1/x weighting) from 0.1 to 10 ng/mL in artificial CSF + 5% rat plasma. Basal levels of amyloid β  1-42 extracted from 3 sources of human and 1 source of monkey CSF are shown in Figure 4. Overspike QC samples were prepared in 3 sources of pooled human CSF and 1 source of pooled monkey CSF at 0.2, 0.8, 2 and 6 ng/mL. Accuracy and precision values for all 3 peptides met the regulatory criteria for LC–MS–MS assays. Representative results from QC sample analysis are shown in Table 1. 

Table 1: Representative results from analysis of QC samples prepared in pooled human CSF, source 3. 

• An SPE-UPLC–MS–MS bioanalytical method was developed and validated for the simultaneous quantification of multiple amyloid β  peptides in human and monkey CSF.  

• 96 samples can be extracted and ready for injection in < 30 minutes, providing the sample prep throughput required for pre-clinical and clinical studies. 

• The method described herein eliminates time-consuming immunoassays or immunoprecipitation steps for pre-clinical work. 

• This approach also allows one assay for the simultaneous measurement of several different amyloid β peptides from a single sample. This single assay provides a high degree of selectivity and specificity in a high-thoughput format while still achieving the high sensitivity required for low level endogeneous amyloid β peptides.  

• The use of a single UPLC–MS–MS assay represents a significant advantage over an ELISA assay, which would require multiple assays with multiple antibodies to quantify each of the relevant peptides. 

 34 Maple Street, Milford, Massachusetts 01757 

tel. +1 508 478 2000  fax +1 508 478 1990 

Departments

A Flexible SPE-LC–MS–MS Platfrom for the Simultaneous Quantification of Multiple Amyloid β Peptides in Cerebrospinal Fluid

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. 

 Figure 1: Three-dimensional GCÃGCâTOFâMS image obtained after a 10 ÂµL injection of a standard solution containing >200 pesticides (for more details see reference 19).			

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 <2ppm 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. 

Figure 2: Effect of resolving power on mass assignment of residues and contaminants (0.025 mg/kg) in a complex compound feed matrix (for details see reference 22).			

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. 

Figure 3: Effect of retention time tolerance, mass accuracy tolerance and number of entries in the target library on the number of provisionally detected compounds in a non-contaminated samples (low response threshold). Sample preparation details (see reference 8). LCâMS analysis (see reference 22).			

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.  

Figure 4: LCâTOF-MS data file (a) before and (b) after preprocessing using MetAlign (see reference 23).			

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/GC×GC–MS 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). 

Figure 5: Data (pre)processing: MetAlign as interface between instrument raw data and a uniform database for data mining.23			

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.  

Figure 6: Reliability of automated detection of residues and contaminants in feed commodities analysed with GCÃGCâTOF-MS. Data processing and automatic detection: ChromaToF 4.1 + Excel macro.			

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, 

10. National Institute of Standards and Technology, Automated Mass Spectral Deconvolution and Identification System (AMDIS). 

Regular Article

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

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

The method developed runs on a fully automated platform integrating solid-phase extraction (SPE) and LC (Symbiosis Pico) without any manual pretreatment step(s). Automatic pretreatment results in high throughput, low CVs and high precision. Sample preparation has been simplified to just one operator step before the system takes over the automated analysis of the samples.  

A Spark Holland Symbiosis PICO system with a Mistral column oven and an Applied Biosystems/MDS Analytical Technologies API 4000 LC–MS–MS system are used for this application. The API 4000 is equipped with a Heated Nebulizer (APCI) probe. 

 is Spark Holland's unique (u)HPLC solution with integrated on-line SPE-LC–MS automation. The system offers large flexibility in processing different types of samples selecting one of the three fully automated operational modes LC; XLC (extraction liquid chromatography) and mXLC (multidimensional on-line SPE) 

Sample preparation consists of diluting a human sample with a special protein disrupting buffer, followed by mixing. Samples are then placed in the Symbiosis autosampler. 100 µL of sample is injected on a Spark HySphere C8EC-SE sorbent SPE cartridge for on-line sample clean-up and concentration.  

The mobile phase consists of formic acid, water and methanol with separation on a Merck KGaA Chromolith SpeedROD RP-18e (endcapped), 50 × 4.6 mm HPLC column. An example chromatogram of the separation achieved is shown in Figure 1.  

 Figure 1: Chromatogram representing 100 ng/mL 25-mono-hydroxy-vitamin D3 (red) and 25-mono-hydroxy-vitamin D2 (Blue) spiked in human plasma.

A calibration curve was created by injecting a full set of calibration standards ranging from 5 to 500 ng/mL spiked human plasma. The calibration curve is calculated with a R

  of 0.999 with a 1/X weighting for both compounds.  

Table 1: Accuracy and precision of the QC samples. 

This method was evaluated using spiked human sodium heparin plasma samples. The developed on-line SPE-MS–MS method has an absolute recovery of more then 90%, and calibration curve with a linear range from 5–500 ng/mL (R = 0.999 for both compounds). The developed method has an accuracy ranging from 89–117% and precision of 0.1–7.9 %CV. The estimated quantification limits for both analytes are more than sufficient to allow the analytical method to be used for therapeutic drug monitoring. When injecting 100 µL, containing 50 µL of plasma, a detection limit of 5 ng/mL is achieved. 

2. Applied Biosystem Vitamin D i-method (User Contributed i-method by Spark). 

For more information please send an e-mail to: 

P. de Keyserstraat 8, PO box 3887800 AJ Emmen, The Netherlands

Determination of 25-mono-hydroxy-vitamin D2 and D3 in Human Plasma Using a Symbiosis Pico System: ACN Free On-line SPE-LC–MS–MS

This paper describes the use of the AB SCIEX TripleTOF 5600 LC–MS–MS system for targeted and non-targeted screening for pesticide residue in food samples. High resolution and accurate mass MS information was used to quantify compounds and MS–MS spectra were used to identify compounds based on mass spectral library searching. 

Fruit and vegetable samples were extracted using a QuEChERS (Quick, Easy, Cheap, Effective, Rugged and Safe) procedure.

  Extracts were diluted 10× to minimize possible matrix effects. UHPLC separation was achieved using a Shimadzu UFLCXR system with a Restek Ultra II Aqueous C18 2.2 µm (100 × 2.1 mm) column and a gradient of water and methanol with ammonium formate buffer. The AB SCIEX TripleTOF 5600 LC–MS–MS equipped with Turbo V source and electrospray ionization (ESI) probe was used. Full scan MS spectra were acquired over a mass range of 100–1000 Da with an accumulation time of 100 ms. Information dependent acquisition (IDA) was used to collect up to 30 MS–MS spectra with an accumulation time of 25 ms for compound identification based on mass spectral library search. Collision energy settings were standardized to acquire library spectra and data of unknown samples: collision energy (CE) = 35 V with collision energy spread (CES) = ±15 V. 

A catalogue of accurate mass molecular ions was generated containing the information of ~1700 pesticides from an on-line database to screen for pesticides in food samples.

Targeted pesticides were quantified with high selectivity in fruit extracts from extracted ion chromatograms using an extraction window of ±10 mDa. The sensitivity of the developed method was sufficient to allow 10× extract dilution to minimize possible matrix effects, such as ion suppression and still reach the target limit of quantification for all analytes of less than 10 ppb (µg/kg). Example chromatograms, reproducibility and a calibration curve for cyprodinil is shown in Figure 1. 

 Figure 1: Extracted ion chromatograms, reproducibility and calibration curve (0.05 to 50 ppb of Cyprodinil). 

An example of positive findings of pesticide in a red bell pepper sample is shown in Figure 2. The accurate mass MS and MS–MS information was used to identify acephate, carbaryl, clothianidin, dimethoate, imidacloprid, methamidophos, metalaxyl, myclobutanil, omethoate, propamocarb, spinosyn A, spinosyn D and thiamethoxam with high confidence. MS–MS library searching resulted in a FIT values greater than 80%  

Figure 2: Identification of Acephate in a red bell pepper sample using accurate mass and high resolution MS and MSâMS data. 

The new AB SCIEX TripleTOF 5600 LC–MS–MS system was used to screen for, quantify and identify pesticide residues in food extracts. High sensitivity of detection allowed dilution of QuEChERS extracts to minimize possible matrix effects. Accurate mass MS–MS spectra were searched against an existing LC–MS–MS library of pesticides to confirm the identity of quantified analytes. 

 353 Hatch Dr., Foster City, California 94404, USA 

Pesticide Residues Screening in Food Samples Using Hih resolution and Accurate Mass LC–MS–MS

Besides vehicle emissions and industrial sources, biomass burning is one of the largest sources of air pollution. Approximately 3 billion metric tons of biomass are annually burned. Main contributions derive from residential wood burning, the burning of leaves, grass and trash as well as from wildfires. The evolving atmospheric aerosol particles — suspensions of fine solid particles or liquid droplets in a gas — can persist for days to weeks in the troposphere and thus significantly contribute to long-range particle transport. Most of the released smoke aerosol particles are smaller than 10 microns in size (PM

) and can easily enter the human lung and cause adverse health effects. Moreover, smoke aerosol particles affect cloud formation and precipitation and thus the climate in general. For this reason, properties and chemical composition of aerosol particulate matter have to be investigated.  

Aerosols consist of a complex mixture of inorganic and organic compounds. Especially, the water-soluble organic compounds (WSOC) make up a major fraction of the atmospheric aerosol. Within the WSOC, the monosaccharide anhydrides levoglucosan (1,6-anhydro-beta-D-glucopyranose) and, to a lesser extent, its stereoisomers mannosan (1,6-anhydro-beta-D-mannopyranose) and galactosan (1,6-anhydro-beta-D-galactose) can be determined as thermal degradation products of structural polysaccharides present in biomass. While levoglucosan arises from the pyrolysis of cellulose, the main building material of wood, its stereoisomers mannosan and galactosan stem from the pyrolysis of hemicellulose. During the combustion of fossil fuels no anhydrosugars are released. Levoglucosan fulfils all important requirements of an ideal molecular marker necessary for source apportionment: it is abundant in wood smoke, highly source specific and relatively stable in the atmosphere. In contrast, monosaccharides are released by microrganisms, plants and animals; sugar alcohols are characteristic for fungi, lichens and bacteria. In particular, arabitol and mannitol, which are storage substances in fungal spores, can be used as valuable tracers for quantifying airborne fungal spores.

Anhydrosugars are typically extracted into an organic solvent, derivatized with a silylation reagent and most commonly determined via gas chromatographic (GC) separation with mass spectrometric detection (MS). Due to their polar nature, these compounds are challenging to analyse by GC–MS. A faster and simpler method, which does not require relatively sophisticated and expensive analytical instrumentation, organic solvents, sample workup and derivatization, is ion chromatography (IC) followed by pulsed amperometric detection (PAD). PAD, the oxidation of electrochemical active molecule groups at a gold electrode, works well for carbohydrates and combines the advantages of an excellent sensitivity and good selectivity over other classes of compounds. Moreover, IC-PAD allows a straightforward water extraction of the large WSOC fraction of aerosols and thus excels by the possibility to handle wet samples, such as rain, fog or cloud water.

  Characterization of these samples is important for understanding the role of aerosols in cloud formation and precipitation.  

This study presents a straightforward ion chromatographic method with amperometric detection for the simultaneous determination of the biomass burning tracer levoglucosan, its stereoisomeric anhydrosugars mannosan and galactosan as well as the sugar alcohols arabitol and mannitol (Figure 1).   

 Figure 1: Chemical structures of the anhydrosugars  levoglucosan, mannosan and galactosan as well as of the sugar alcohols arabitol and mannitol. 

Levoglucosan, the sugar alcohols and glucose were reagent grade and purchased from Fluka (Sigma Aldrich, Buchs, Switzerland). Mannosan and galactosan were obtained from Carbosynth Ltd (Berkshire, UK). All standard solutions and eluents were prepared with deionized water having a specific resistance higher than 18 M Ω.cm. A carbohydrate standard mix covering the concentration range 80...800 µg/L served to determine the system characteristics. An air monitoring agency provided the aerosol winter and summer filter sub-samples for method development. 

Figure 2: PAD chromatograms of an 8-component carbohydrate standard using a Metrosep A Supp 15 â 150/4.0 column in series with a Metrosep Carb 1 â 150/4.0 (a) and a Metrosep Carb 1 â 250/4.6 (b). The following determinations were performed with the shorter Metrosep Carb 1 â 150/4.0 column; Table 1 summarizes the chromatographic parameters and the PAD settings of the method. 

A problem frequently encountered in the analysis of atmospherically relevant saccharidic compounds in aerosols is the poor chromatographic separation of the two compound pairs arabitol/levoglucosan and mannitol/mannosan.

  Because both pairs represent different compound classes and are important tracers for different aerosol sources, chromatographic separation is crucial. To overcome this drawback, the optimized IC-PAD method uses two columns in series, a Metrosep Carb 1 – 150/4.0 and a Metrosep A Supp 15 – 150/4.0, and thus achieves an excellent separation of anhydrosugars and sugar alcohols in a single isocratic run in less than 17 minutes [Figure 2(a)]. Separation, especially between levoglucosan and mannosan, can be further improved by using a weaker eluent or a longer carbohydrate separation column [250 mm instead of 150 mm, Figure 2(b)]. 

Table 1: Chromatographic parameters and PAD settings of carbohydrate determinations. 

The saccharidic tracers are determined by applying a triple-step potential waveform: a positive potential E1 to determine the carbohydrates followed by a stronger pulse E2 for oxidative desorption of adsorbed species and finally a third, negative potential E3 to reactivate the electrode's surface (Table 1). The entire sequence lasts one second and is continuously repeated. PAD detection is highly sensitive and as shown in Figure 3, calibration curves are linear over a wide concentration range. Calibration curves for the atmospherically important saccharidic compounds have correlation coefficients (R) better than 0.9998 (Table 2). The reproducibility of the IC-PAD method was evaluated for the peak height of each carbohydrate with RSDs equal to or less than 2%. The detection limit for levoglucosan was estimated to be below 10 µg/m

  using a 100 µL loop. With an extraction volume of 4 mL, 5 ng/m

  levoglucosan aerosol concentration can be determined. These data show the suitability of IC-PAD to accurately quantify water-extractable saccharidic compounds in atmospheric aerosols. 

Figure 3: Calibration curves of (a) the sugar alcohols inositol, erythritol, arabitol and mannitol as well as of (b) the anhydrosugars levoglucosan, mannosan and galactosan using linear regression.  

Contribution of biomass smoke and biogenic sources to ambient aerosol levels is determined by collecting the aerosol fraction of a defined air volume on a filter. Before extraction, the 15 cm diameter glass fibre filter, through which 80 m

  air was drawn, was subdivided into 4.7 cm diameter filter samples. After addition of 14 mL ultrapure water, the filters were treated by ultrasonic extraction for about 30 minutes. Before injection, samples were filtrated through a 0.45 µm syringe filter. Alternatively, Metrohm's Inline Ultrafiltration was used. In case concentrations on the filters are very low, the extraction volume may be reduced to 4 mL.   

Table 2: Linearity and precision of the developed method. 

Seasonal variations in summer and winter samples

Table 3 shows the results of the determined carbohydrate concentrations of three analysed winter and four summer filter extracts. Figure 4 contains the chromatograms of an exemplary winter (ID 13) and summer sample (ID 408); corresponding aerosol concentrations in ng/m

  are plotted in the bar chart of Figure 5. The anhydrosugars levoglucosan and mannosan as well as the sugar alcohols arabitol and mannitol and the monosaccharide glucose were detected in each filter. Galactosan was only quantified in one summer filter in the low µg/L range. In both summer and winter samples, levoglucosan was the most dominant identified carbohydrate. The highest levoglucosan concentration, approximately ten times higher than that detected in summer, was observed in winter, when residential wood burning for heating purposes was most intense.    

Table 3: Overview of analysed glass fibre winter and summer samples and corresponding carbohydrate concentrations. 

In summer, concentrations of combustion-derived anhydrosugars strongly decreased whereas the contributions of biogenic sugars and sugar alcohols increased. Arabitol and mannitol summer aerosol concentrations even achieved mannosan levels. These results are consistent with the fact that in summer the biological activity and thus the emission of biogenic volatile organic compounds such as fungal spores and plant pollen is at their maximum.  

Figure 4: PAD chromatogram obtained from the analysis of (a) the winter sample 13 and (b) the summer sample 408.  

Ion chromatographic determination coupled with pulsed amperometric detection is highly sensitive and selective to saccharidic aerosol tracers. After straightforward water extraction of the filters, atmospheric tracers for biomass combustion and for biogenic sources can be determined at the ng/m

  scale. The method requires no complex sample pretreatment or derivatization and is well suited to handle a large number of samples. The IC-PAD method is an invaluable tool for the distinction between anthropogenic and natural aerosol emissions and offers novel insights for elucidating the impact of atmospheric aerosols on the climate.    

Figure 5: Bar chart comparing the detected carbohydrate concentrations in (a) the winter sample 13 and (b) the summer sample 408. 

Oberdorfstrasse 68, CH-9101 Herisau, Switzerland

Determining Saccharidic Tracers in Atmospheric Aerosols

Catecholamines are important markers for the diagnosis and management of tumour diseases of the sympathoadrenal system. The major catecholamines are dopamine, norepinephrine and epinephrine. Urine tests have shown to be applicable to measure the level of catecholamines in the human body. Various separation methods have been used for the clean-up of catecholamines in biological fluids: solvent extraction, adsorption on alumina, ion-exchange and solid-phase extraction of a diphenylboronic acid-catecholamine complex. From the point of simplicity, reproducibility and automation the last method is the most suitable and can be realized by on-line SPE–LC. Diphenylborate forms a negatively charged complex with the diol groups of catecholamines. These complexes which are strongly retained on a polystyrene-divinylbenzene cartridge in alkaline medium (pH 8.5) can be eluted in a second step from the SPE unit directly onto the HPLC column. The separation of catecholamines is performed in isocratic mode by adjusting the pH value in the acidic range. 

All parts in the fluid path, except the SPE and analytical columns, require passivation with a nitric acid solution. Because of the high buffer content in SPE eluent and mobile phase it is highly recommended that the filter and sieve combinations, the eluent filter and the precolumn be replaced after 250 injections. The first column inlet filter unit should be installed between the autosampler and switching valve of the on-line SPE unit. A second in-line filter unit should be placed on the outlet of the HPLC pump. For quality control of the analytical determinations we recommend using the ClinChek urine controls. These controls are available for the normal range as well as for the pathological range. The injection volume of the calibration and sample solution is 50 µL. 

 Figure 1:  Urine calibrator + 20 ÂµL IS. 

 Eurospher 100-5 C18 100 × 4 mm, with precolumn 

 SPE cartridge for catecholamines (Recipe) 

 Mobile phase for catecholamines (Recipe) 

The on-line SPE sample preparation unit 6300 with its HPLC pump in combination with fluorescence detection dramatically streamlines the urine analysis for the determination of catecholamines. Automated sample handling, proprietary column chemistry and sensitive fluorescence detection have been combined. The system requires only a fraction of a typical sample volume. The limit of detection can be calculated with 1 µg/L and the linearity range goes up to 1500 µg/L. Its increased speed, improved accuracy and precision clearly demonstrate the advantage of this hyphenation technique over standard procedures.  

 T.G. Rosanjo, T.A. Swift and L.W. Hayes, 

Wissenschaftliche Gerätebau Dr. Ing. Herbert Knauer GmbH

 tel. +49 30 809727 0  fax +49 30 801501 0 

On-line SPE-LC of Catecholamines in Urine with Fluorescence Detection

Occasionally the chromatogram generated by a liquid chromatography (LC) method looks strange. One of those oddly appearing cases is when the peak doesn't seem to belong with the neighbouring peaks in the chromatogram. This month's "LC Troubleshooting" looks at one such case. An example is shown in Figure 1, where the peaks at 1 min and 2.75 min look normal, but there is a broad peak that shows up at approximately 1.5 min. I do not know the chromatographic conditions for this run, but for the present discussion, let's assume that it is a reversed-phase method run on a C18 column. As a general rule, all the peaks in a particular part of the chromatogram, whether it is an isocratic or gradient run, should be about the same width. So when a broad peak appears, it is a sign that something is wrong.

One of the most likely causes of broad peaks in the presence of normal, narrower ones is late elution — a peak that belongs to a previous run. This is simple to determine by extending the run time until the peak emerges. I've shown this in the pair of simulated chromatograms shown in Figure 2. In Figure 2(a), a broad peak appears at 2.2 min, obviously out of place when compared with its neighbours. In Figure 2(b), the run time was extended and another peak appears at 7.2 min. This peak has the expected width for its position in the chromatogram. This tells us that the 2.2 min peak comes from the previous injection.

Let's go back to the broad peak of Figure 1 to consider another way to figure out where the extra peak really belongs. To do this, we need to do a few simple calculations. First, we need to determine the column plate number 

 for the peaks that behave as expected. Recall that the plate number (also called the column efficiency) is calculated as

 is the width of the peak at the baseline, measured between tangents drawn to the sides of the peak. We can use equation 1 to calculate the plate number for a normal peak in the chromatogram. In this case I chose the third peak of Figure 1. I expanded the chromatogram and did my best to measure the retention time and peak width, based on the supplied time scale: 

 = 0.1 min. Using equation 1 with these values gives 

 = 12100 for peak 3. I have assumed that all peaks in the chromatogram have the same plate number, which is reasonable for this estimation technique.  

A broad peak where it does not belong is a classic sign of late elution.

LCGC Asia Pacific-09-01-2010

When Peaks Collide!

A Flexible SPE-LC–MS–MS Platfrom for the Simultaneous Quantification of Multiple Amyloid β Peptides in Cerebrospinal Fluid

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

Determination of 25-mono-hydroxy-vitamin D2 and D3 in Human Plasma Using a Symbiosis Pico System: ACN Free On-line SPE-LC–MS–MS

Pesticide Residues Screening in Food Samples Using Hih resolution and Accurate Mass LC–MS–MS

Determining Saccharidic Tracers in Atmospheric Aerosols

On-line SPE-LC of Catecholamines in Urine with Fluorescence Detection

Where Did That Peak Come From??

Accurate, High Sensitivity Analysis of Impurities

New Developments in the Field of Chromatography Monoliths

Rapid UHPLC Determination of Common Preservatives in Cosmetic Products