New sorbent materials for solid-phase extraction (SPE), dispersive solid-phase extraction (dSPE), solid-phase microextraction (SPME), and other solid–liquid extraction methods continue to be developed both in academia and the commercial sector. Here, we cover some areas of significant development, including molecularly imprinted polymers (MIPs), dSPE for QuEChERS, nanomaterials, and mixed-mode and silicone monolith sorbents.

Solid–liquid extraction is probably the most popular method for sample preparation in the chromatography laboratory. Solid–liquid extraction techniques such as solid-phase extraction (SPE), solid-phase microextraction (SPME), stir-bar sorbent extraction (SBSE), and dispersive solid-phase extraction (dSPE) continue to see increased application in diverse markets such as environmental, food and food safety, forensics, and the life sciences. As sample sizes are continually decreased, more selective sorbents are required to eliminate matrix compounds that may cause interference in the analytical measurement. With the increased use of liquid chromatography–tandem mass spectrometry (LC–MS–MS) and gas chromatography–tandem mass spectrometry (GC–MS–MS), the potential for ion suppression and ion enhancement, which affects signal intensity, continues to draw attention to sample preparation technologists. In this instalment, I cover some newer sorbent materials that have the potential to help in solving specific laboratory problems. In next month's instalment, I will look at entirely new sample preparation technologies that may break away and become the next technique to help in eliminating the laboratory sample preparation bottleneck. 

Molecularly imprinted polymers (MIPs) are among the most selective phases used in SPE. The technique is sometimes referred to as molecularly imprinted solid-phase extraction (MIP-SPE). Molecular imprinting is a technique that has been used in areas where selective recognition is required for complex separations or sample cleanup. An introductory article (1) outlines the basics of MIP technology, and review articles (2–5) and several books (6–8) provide detailed information on the use and potential of MIPs in SPE.  

A MIP is a highly stable polymer that possesses recognition sites that are adapted to the three-dimensional shape and functionalities of an analyte of interest. The most common approach through the use of noncovalent imprinting involves a print molecule (template) that is chemically coupled with one of the building blocks of a polymer. After polymerization, the resulting bond must be cleaved to obtain a free selective binding site (receptor). The synthesis process is shown schematically in Figure 1. The selective interactions between the template and the monomers are based on hydrogen bonding and ionic or hydrophobic interactions. The most often used monomers are based on methacrylic acid or methacrylates. The basic idea of a MIP is the "lock and key" concept in which a selective receptor or cavity on the surface of a polymer perfectly fits the template analyte that was used to prepare the MIP. The receptor site is complementary to the template in terms of its size, shape, and functionality. The concept is similar to immunoaffinity SPE phases, but obtaining and linking a suitable antibody for these immunoaffinity sorbents can be very time-consuming and expensive.  

Figure 1: Synthesis of a molecularly imprinted polymer stationary phase. 			

The removal of the template from the polymeric MIP is important not only to make the interaction sites available for increased sample capacity, but also to ensure that the analyte to be isolated can be measured quantitatively. The lack of removal of the template molecules, even with exhaustive extraction, has been one of the main problems with the acceptance of MIPs. The template molecules frequently bleed, sometimes give baseline drifts, and interfere with the quantitation of the desired analyte, especially at low levels. One approach to overcome this limitation is to use a template that is similar to the analyte of interest. An example would be to use a brominated analogue template rather than a chlorinated molecule of interest. If the analogue can be separated from the analyte of interest, then the MIP will function as desired. 

With aqueous mobile phases, MIPs can display reversed-phase and ion-exchange interaction because selective polar interactions are impaired. The MIP phases show the greatest selectivity when the experimental conditions are chosen that generate the selective interactions that are usually obtained in organic solvents used for the MIP synthesis. This approach allows the MIP to be used for trapping analytes from aqueous solution by hydrophobic or ionic interactions, then washed with a solvent that breaks selective binding of matrix components, and finally with an organic solvent that disrupts the strong bonds between the analyte and the MIP polymer matrix. 

As the SPE packing material is a polymer, depending on the degree of crosslinking, there may be some swelling or shrinkage with a change in solvent. Such a physical change can modify the size of the receptor and change the selectivity of the MIP for the target analyte. In this regard, perhaps the synthesis of molecularly imprinted organic–inorganic hybrid polymers (9) may generate a more rigid substructure that does not swell and shrink. 

A disadvantage of the MIP approach to SPE is the fact that each sorbent must be custom made. One determines the specificity of the MIP by choosing the appropriate template molecule. The MIP can be synthesized in the laboratory using published procedures, or the template molecule can be sent to a specialty laboratory that will make a custom MIP. Because of the relatively long process involved in making a MIP for SPE, one can justify it only if the application will frequently be required or if there is no other way to perform sample cleanup. 

Off-the-shelf MIPs have been introduced. These standard MIP phases have been designed for specific analytes that are popularly encountered in complex matrices. Among those currently available from Supelco and Biotage are sorbents optimized for: 					

Beta agonists: Multiresidue extractions in urine and tissue samples. 

NNAL (4-methylnitrosamino-1-[3-pyridyl]-1-butanol): tobacco-specific nitrosamine in biological matrices.

Riboflavin (vitamin B2) in aqueous samples.

Triazines: Multiresidue extraction in water, soil, and food products.

Chloramphenicol: Antibiotic in biological matrices.

Beta blockers: Multiresidue extractions in water and biological samples.

MIPs are generally prepared by traditional bulk polymerization and must be ground and sieved to produce desired particles in an appropriate size for use. This tedious and time-consuming process often produces particles that are irregularly shaped that do not pack as well as spherical particles might. Monolithic polymers consist of a continuous bed with no interstitial volume but only internal porosity. Monolithic beds result in fairly homogenous flow characteristics with a low pressure drop. By combining MIP technology with monolithic technology, SPE and high performance liquid chromatography (HPLC) columns can be prepared that provide the advantages of both technologies (10–12). Yan, Qiao, and Ho Row (12) synthesized a MIP–monolithic column and used it for the selective on-line SPE extraction of enrofloxacin and ciprofloxacin from urine. Enrofloxacin is a fluoroquinolone with a broad antibacterial spectrum and high bacterial activity against major pathogenic bacteria. Its primary metabolite is found in diseased animals.

Because this synthesis of the MIP-monolith was carried out in an aqueous environment (5:1 methanol–water as a porogenic solvent), a high specificity for enrofloxacin and ciprofloxacin in aqueous systems was obtained. Common to many MIP synthesis, to prevent template leakage, a structural analogue of each target molecule was used to prepare the imprinted polymers. The downstream HPLC separation was sufficient to separate the small amount of template from the enrofloxacin and ciprofloxacin peaks when the sample was eluted onto the HPLC column. One beauty of monolithic columns is that direct injection of liquid samples is frequently possible. In this case, the authors were able to inject 0.5 mL of a spiked urine sample directly onto the on-line SPE column via a column switching setup. There were no apparent deleterious effects on the MIP, but a washing step was used to ensure that there was no interference from any matrix compounds in the urine. The method showed good linearity, acceptable accuracy and precision, and recoveries in excess of 88% at three spike levels. 

To make a spherical MIP specific for triazine herbicides, Matsui and colleagues (13) used suspension polymerization. The synthesis was performed in a two-phase system consisting of chloroform–atrazine (the template)–ethylene glycol dimethacrylate and azobis(dimethylvarelonitrile), which was poured into water containing poly(vinyl alcohol). After a considerable time spent heating and stirring, the resultant particles were filtered and thoroughly washed with methanol–acetic acid to remove the template. A nonimprinted polymer (NIP) was also prepared to compare the MIP to a similar polymer prepared without a template present. Although several triazine herbicides were used to show retention characteristics, simazine was chosen as the analyte for actual samples with various spiked impurities. However, when the simazine and impurity mix was applied to the MIP and washed with dichloromethane only the simazine remained behind indicative of the strong binding based on molecular recognition. Methanol was then used to elute the simazine. Recovery was determined to be 91%. When a NIP was used as the sorbent, the capacity factor for simazine was only 10% versus the MIP indicating the greatly increased MIP specificity for the herbicide.  

Commercial stir bars for SBSE are coated with polydimethylsiloxane, which is apolar in nature. A novel twist of combining a MIP with a stir bar was developed by Chen and colleagues (14). A stir bar with a selective coated phase represents a fairly simple approach to selectively enrich trace analytes in a liquid sample. The stir bar just has to be dropped into a solution and the stirrer turned on for a finite period of time, usually in the range of 30–45 min. In this case, the workers prepared a bensulphuron-methyl (BSM) imprinted polymer monolith on a glass stir bar as well as a nonimprinted polymer monolith. Besides BSM, both stir bars were used to extract several sulphonylurea herbicides from water. After extraction, the adsorbed analytes were released by immersing the stir bars in an acetonitrile–trifluoroacetic acid solution. After the stir bar was removed, the solvent was evaporated to dryness and reconstituted in acetonitrile and injected into an HPLC system equipped with a UV detector. For BSM, the selectivity factor for the MIP-coated bar was 260% of the nonimprinted bar. For several other herbicides similar in structure to BSM, the MIP stir bar also showed a selectivity factor of around 30% higher than the nonimprinted stir bar, indicating that there is also some enhancement for similar compounds. The MIP stir bars were used at least 150 times without any ill effects. 

Some similar studies were performed with a molecularly imprinted nylon 6 (polycaprolactam) stir bar for the enantioseparation of amino acids. Zhu and Zhu (15) synthesized a stir bar with a high affinity for L-glutamine compared to its isomer and analogues. A reference NIP stir bar showed no selectivity toward the enantiomers. Scientists in Spain (16) have developed a procedure using a MIP stir bar that will extract the fungicide thiabendazole from citrus fruits. They used chemical coating by first derivatizing the stir bar with a silylating reagent and immersing it in a mixture of methacrylic acid and crosslinkers containing thiabendazole as the template molecule. The template was removed by washing with a solution of acetic acid-methanol. After the MIP stir bar was immersed in homogenized extracts of fruit peels or pulps treated with thiabendazole for 45 min, the stir bars were removed, rinsed, and the analyte was released with methanolic acetic acid, the extracts were analyzed for the fungicide by HPLC with fluorescence detection.  

As mentioned earlier, a common problem with MIPs is the unextractable template remaining behind in the polymer matrix. Despite exhaustive extraction of the MIP by Soxhlet, ultrasonic, or other normal methods, traces of template still remain. These traces often leach (or bleed) from the template, thereby fouling up quantitation. Working around this involves coming up with a template similar to, but not the same as the target compounds. This bleeding template is intended to be separated by the HPLC or GC separation step after the analyte enrichment. Batlokwa and colleagues from the Chemistry Department at Rhodes University in South Africa (17) came up with a novel solution to the template removal problem that uses pressurized hot water extraction. Surprisingly, as water is heated above its boiling point there is a steady decrease in its permittivity, viscosity, and surface tension as well as an increase in its diffusivity characteristics. With enough pressure to maintain water in the liquid phase at elevated temperatures, its dielectric constant decreases so that it has the "solvent power" of methanol or ethanol, depending on the temperature and pressure. Moreover, water is a safe, nontoxic, easily obtained, and easily disposed of solvent and when used for extraction it decreases the use of organic solvent. Using three different distinctly coloured MIPs, template removal was investigated using hot water extraction and UV absorbance. Washing conditions were optimized for the three MIPs and it was observed that extraction efficiency of template removal was more than 99.6% (compared to <94.5% for Soxhlet and ultrasonics) with no subsequent or minimal template bleed (<0.01%). The extraction of template was not only more complete, but also faster than Soxhlet and ultrasonics (70 min compared to 18 h). So, this safe and low-cost technique may go a long way to reduce the bleeding template problem for MIPs. 

Removal of Pigments in Dispersive SPE for QuEChERS 

By now, many chemists are familiar with the principles of QuEChERS (quick, easy, cheap, effective, rugged, and safe) sample preparation. The use of QuEChERS has become a standard sample preparation for pesticide analysis in fruits and vegetables and beyond (18). We have previously discussed the basics of the technique (19) as well as interviewed the inventors on the latest developments (20). QuEChERS consists of two steps: Salting out extraction and dispersive solid-phase extraction. Both extractions are based on the use of acetonitrile as an extracting solvent for analytes of interest, with the intention of leaving the matrix compounds behind. In the dSPE step, a sorbent is chosen that will retain matrix compounds and leave the solutes of interest in solution. For example, for samples with high fat content, primary–secondary amine mixed with C18 sorbent is recommended; for samples with moderate, and high levels of chlorophyll and carotenoids (for example, spinach, carrots), primary–secondary amine is mixed with graphitized carbon black (GCB) to reduce these coloured compounds. Although the addition of GCB helps with the partial removal of chlorophyll and other pigments, there is an accompanying partial loss of certain structurally planar pesticides such as carbendazim and cyprodinil. It is believed that the increased adsorption of planar pesticides in GCB is caused by the platelet structure of the carbon where the pesticides can be tenaciously held between the layers. The planar pesticide recovery can be increased by the addition of toluene in the extraction solution (21); however, toluene may have an impact on the successful extraction of other compounds in the solution. Newer sorbents that will remove chlorophyll without adsorbing the planar pesticides have been under investigation and some of the newer approaches are reported here briefly. 

Although GCB is an excellent sorbent for removing pigments and other hydrophobic compounds during the dSPE step, it is quite difficult to work with. It is light and fluffy, difficult to pack, sticks to the walls of the extraction vessel, may carry over to the injection into the HPLC or GC column, and makes everything it touches turn black. United Science has developed a material called CarbonX for QuEChERS that consists of carbon deposited on high-surface-area porous substrates (for example, silica and alumina). The material is designed to provide higher recoveries of the planar pesticides without the handling problems of GCB and without the use of toluene. The material is denser and structurally stronger than GCB and provides high recovery for pesticides in pigmented fruits and vegetables, such as broccoli, spinach, red pepper, and strawberries. Figure 2 shows that CarbonX yields much higher recoveries of planar pesticides than GCB without the use of toluene to desorb them from the carbon phase (22). Besides use as a bulk material in dSPE, the media can also be packed into a column (cartridge) and used for regular SPE experiments. 

Figure 2: Comparison of planar pesticide recoveries after cleanup with CarbonX (United Science) for QuEChERS and graphitized carbon black (GCB). Reproduced with permission from reference 22. 			

As was discussed earlier, MIPs are generally used for removing selected analytes from complex matrices. MIPs also might be used for removing matrix compounds instead. A novel MIP was prepared by Batlokwa and colleagues (23) that was selective for chlorophyll, a common pigment in green plant extracts. Chlorophyll must be removed or substantially reduced before injection into a chromatographic column. To prepare the chlorophyll MIP, chlorophyll a, methacrylic acid, ethylene glycol dimethacrylate, and azobisisobutyronitrile in the mole ratio 1:5:25:0.4 were dissolved in dichloromethane and refluxed in an oil bath at 75 °C for 9 h. The resultant rigid polymer was ground to a powder and the template was removed by pressurized hot water extraction as described earlier. A NIP was also prepared. The materials were compared to GCB in QuEChERS experiments. For an equivalent reduction in chlorophyll in a standard buffered solution (2% w/v), 850 mg of the chlorophyll MIP had to be used compared to 300 mg of GCB. For planar pesticides, the MIP did not show any selective binding and the percentage of chlorophyll removed was more than 99.75%. The percentages bound by the NIP were very low, less than 3.7% in all cases. A spinach extract showed a reduction in chlorophyll absorbance to as low as 0.092, which is considered acceptable for quantitative analysis. To the naked eye, the green characteristic colour of several plant extracts virtually disappeared after exposure to the chlorophyll MIP. 

Supelco, a division of Sigma Aldrich, has introduced three sorbents for the sample cleanup of complex matrices by removing more fat and colour from sample extracts than traditional dSPE phases for QuEChERS methods. Supleco's SupelQuE Z-Sep sorbent is recommended for samples containing hydrophobic analytes in fatty matrices. SupelQuE Z-Sep+ is for samples containing greater than 15% fat, and SupelQuE Z-Sep/C18 is for samples containing less than 15% fat. Figure 3 presents a bar graph for the relative retention of the zirconia on silica products compared to other phases typically used in SPE. For the experiment, a 25-mg portion of each sorbent was used for a 1-mL solution of mono-, di-, and triglycerides in 90:10 (v/v) acetonitrile–water. By eliminating problematic matrix interferences, these new sorbents provide more robust LC–MS methods. This proprietary technology can replace both C18 and primary–secondary amine in current methods without additional method development. Some published applications of these new sorbents showed a decreased presence of fat in the studies of organic contaminants in catfish (24) and in the analysis of 100 veterinary drugs in bovine muscle (25). 

Figure 3: Relative retention of mono-, di-, and tri-glycerides on various sorbents. Courtesy of Sigma-Aldrich, Supelco. 			

Nanomaterials for Use in Sample Preparation

Nanotechnology is one of the most important trends in material science. Because of their ultra-small size, nanomaterials possess unique physical and chemical properties. Thus, it is no surprise that nanomaterials have found their way into sample preparation applications. A nanomaterial is used to denote structures that have one of their dimensions in the nanometer size range, 1–100 nm (26). A recent review article discusses the use of nanomaterials in sample preparation (27), and so only brief coverage of the technologies used in sample preparation will be covered here. The so-called nanoscale effect comes into play with particles in the nano-range because they display optical, electrical, thermal, magnetic, catalytic, and other effects that non-nanomaterials do not display. Their larger surface areas allow smaller amounts to be used for extraction and make them ideal for miniaturization of sample preparation protocols. Many nanomaterials can be functionalized with various chemical groups to increase their affinity towards target compounds. A wide variety of nanomaterials are being studied for sample preparation. Metallic and metallic-oxide nanoparticles are on the forefront of research in chromatography and sample preparation applications. Gold nanoparticles have been modified with organic molecules with thiol or amino groups to form self-assembled monolayers. Derivatized nanoparticles can be used as coatings for SPME by assembling them on a stainless steel wire, which can then be used to extract polynuclear aromatic hydrocarbons from water. Silver nanoparticles have also been used for SPME. Metal oxide nanoparticles such as TiO

 have been widely used for extraction and separation of pollutants both as SPE sorbents and as coatings for SPME. When coated onto SPME fused-silica fibres or rods, compared to commercial phases, new selectivities can be introduced and the SPME devices are often more rugged. The metal oxide nanoparticles can also be further functionalized with small organic or inorganic compounds or polymers.  

Among the more interesting nanoparticles are those that are based on magnetic adsorption materials. The materials can be used in a dSPE mode. These materials are easily manipulated via magnets and can save time compared to using SPE manifolds or centrifuges for separating the solid phase from the supernatant liquid. SPE columns do not need to be packed and since no liquid sample loading of a cartridge is required, the experiment is much faster. Typically, the magnetic material has an iron oxide core and a coating of nanoparticles to extract different analytes. In a paper by Tian and colleagues (27), a table of various applications of magnetic nanoparticles is shown. 

Other types of nanoparticles that have been used include metal–organic framework materials. These are organic–inorganic hybrids and can be fashioned with different metallic cations and different organic electron donor linkers to provide derivatizable surfaces, which can be used for many different applications including sample preparation such as SPE and SPME. They can also be magnetized for easy handling. 

Many people are familiar with carbonaceous nanomaterials such as carbon nanotubes, fullerenes (buckyballs), and carbon nanocones. Graphene is the basic building block of carbon sorbents such as graphitic carbon black, which was mentioned earlier. Graphene powder has been used as an SPE sorbent for the extraction of chlorophenols from water but, as mentioned earlier, it has a tendency to aggregate so immobilizing it on a substrate such as silica gel or converting it into graphene oxide can make it more useful for sample preparation. It has also been used in SPME. Carbon nanotubes are the hottest carbon nanostructure material. Because of their unique geometry, carbon nanotubes exhibit excellent mechanical and thermal properties. Carbon nanotubes can be functionalized and used in SPE and SPME. There are many publications on the use of carbon nanotubes in sample preparation (27). 

Silica nanoparticles are most similar to those used in regular SPE experiments including the incorporation of molecular imprinting to provide high affinity and selectivity to the nanoparticles compared to normal C18 and other common groups. 

Nanomaterials should continue to see rapid development, especially as further miniaturization takes place in chromatography and sample preparation. 

New sorbent materials are continually coming out of the research laboratories, some of which may prove to be the next universal sorbent or be the most selective ever. A couple of examples are shown below. 

In the past, mixed-mode behaviour of sorbents was considered to be a nuisance because it was harder to use one's chromatographic knowledge to develop a method. Workers in the field preferred pure reversed-phase mechanisms so one could predict how the change in organic modifier or pH would affect retention. Nevertheless, mixed mechanisms still occurred that often complicated things. Nowadays, mixed mechanisms are now looked upon as good, especially in sample preparation, because one can now manipulate the conditions to vary the relative interactions between the analyte, matrix, and sorbent to come up with the best set of conditions to isolate the analyte of interest. Manufacturers are now introducing SPE phases with dual- or even trifunctionality. For example, Biotage has introduced several SPE phases that provide both nonpolar and ion-exchange functionalities that can be used to remove interferences that would potentially cause ion suppression or enhancement in LC–MS–MS analysis. The products are resin-based, but silica-based mixed-mode sorbents are also available to perform the same type of extractions. 

Moral and colleagues from Spain (28) synthesized their own multifunctional sorbents based on sodium dodecyl sulphate (SDS) and mixed tetrabutylammonium (TBA)-SDS hemimicelles and micelles adsorbed onto gamma-alumina. These sorbents provided different retention mechanisms (such as hydrophobic, ionic, and π-cation interactions). They used these mixed-mode sorbents for the extraction of acidic, basic, and neutral pesticide multiresidues from natural waters. Recoveries ranged between 92% and 107% for most of the pesticides tested. Using UV detection, the workers had detection limits lower than 100 ng/L for these pesticides. 

Fontanals and colleagues (29) synthesized a copolymer based on 

-vinylimidazole–divinylbenzene that showed mixed-mode and both weak and strong anion-exchange properties. The SPE copolymer could be protonated at a certain pH and used as an anion exchanger, and at other pH values its hydrophilic and reversed-phase properties could be exploited. The novel copolymer was compared to commercially available polymeric SPE sorbents and showed unique selectivity.  

At HPLC 2013, Kazuki Nakanishi and coworkers from Kyoto University and GL Science in Japan (30) reported on recent developments in a macroporous poly(methylsilsesquioxane) (PMSQ) monolith family that produced a novel type of siloxane-based macroporous polydimethylsiloxane (PDMS) analogues suitable for many kinds of sample preparation purposes. These so-called "marshmallow gels" are very soft, low density, highly hydrophobic, and highly permeable monoliths with micrometre-range continuous pores and are synthesized by controlled copolymerization of methyltrimethoxysilane and dimethyldimethoxysilane (MTMS-DMDMS) precursors. Because of the presence of dense surface methyl groups, all the surfaces of micrometre-range skeletons are highly hydrophobic and exhibit a similar or faster equilibration rate than those of other PDMS-based SPE devices. Physical properties are comparable to conventional PDMS materials; marshmallow gels exhibit no degradation under temperatures as high as 600 K, and no obvious glass transition at temperatures as low as 150 K. Furthermore, even under liquid nitrogen (77 K) the material still exhibits rubbery elastic deformation and recovery. In the above broad temperature range, the extraction of hydrophobic substances from a polar solvent system can be dramatically accelerated by absorbing the mixture and simply squeezing it out. These gels may find use in both in GC and LC sample preparation purposes. Not limited to the PDMS analogue, the chemical modification of the pore surface is easy by choosing appropriate precursors. Typical processing requires only a few hours in any shape and size below 100 °C. The use of this unique polymer for SPE is depicted in Figure 3. Because the sorbent is selective for hydrophobic compounds, pyrene in water was selected as a test solute. In Figure 4, only 10 µL of the test sample (9.57 mg/20 mL acetonitrile) was added to 100 mL of water and the pyrene recovery was 98.2%; using HPLC with UV detection, the pyrene showed good linearity over the 0.05–5 mg/L concentration range. 

Figure 4: Application of marshmallow silicone monolith in solid-phase extraction. (Courtesy of Gen Hayase, Kyoto University [28]). 			

In this instalment, I tried to select some significant new approaches to sorbent development that might result in better, faster, more efficient, and cleaner sample preparation than previous sorbent materials used in the general category of solid–liquid extraction. Further development in MIPs seems especially strong in Europe with new developments in phases, format (that is, stir bars), and preparation or template removal techniques. The dSPE portion of the QuEChERS extraction has been getting attention as workers try to come up with better sorbents to selectively remove matrix compounds that may interfere with downstream LC–MS–MS and GC–MS–MS analysis. Nanoparticles have attracted lots of attention but few commercial products are currently available. Nevertheless, the future looks particularly bright as smaller sample sizes and miniaturization of sample preparation advances further. New sorbent materials continue to be introduced for solid–liquid sample preparation, and this trend should continue in both the academic and commercial sectors.  

 "Sample Preparation Perspectives" Editor 

 is an analytical consultant and is a member of

editorial advisory board. Direct correspondence about this column should be addressed to "Sample Preparation Perspectives", 

4A Bridgegate Pavilion, Chester Business Park, Wrexham Road, Chester, CH4 9QH, UK, or e-mail the editor-in-chief, Alasdair Matheson, at 

Articles

Sample Preparation Perspectives

In this instalment Ron Majors discusses new sample preparation media that could result in improved or even "breakaway" performance for solid–liquid extractions. Some of these sorbents are based on new novel bonded phases to existing media while others are based on new extraction phases, such as carbon on silica, other metal oxides, or polymeric media. With resulting new selectivities, sample preparation could be performed more quickly in fewer steps than typical of usual adsorbents

Novel Sorbents for Solid–Liquid Extraction

Advantages of comprehensive two-dimensional gas chromatography (GC×GC) include increased peak capacity, improved resolution, and unique selectivity compared to conventional one-dimensional gas chromatography (1D GC). Despite the maturing status of the technique, there are still some outstanding issues that spark discussion and controversy. One of them is the sensitivity enhancement in GC×GC separations compared to conventional 1D separation. In this article, the sensitivity of two-dimensional gas chromatography coupled to two different detectors 

 a time-of-flight mass spectrometer (GC×GC–TOF-MS) and a flame ionization detector (GC×GC–FID) 

was compared to the sensitivity of conventional one-dimensional gas chromatography (GC–TOF-MS and GC–FID) by determining method detection limits (MDLs) for a series of different compounds with different polarities. 

GC×GC was first introduced by Phillips in the 1990s (1), and it soon proved to be a very powerful separation technique. GC×GC provides highly structured separations with high resolving power. In spite of the many applications described in the literature, little attention has been devoted to quantitative evaluation of the technique and to quantitative comparison of these systems with the one-dimensional (1D) counterparts. The sensitivity enhancement in GC×GC separations compared to conventional 1D separations is still considered a somewhat controversial issue. In GC×GC, two columns of different properties are connected in series through a special interface (modulator). The modulator collects portions of the first dimension effluent, and injects them at regular intervals to the second dimension. Cryogenic modulators collect the effluent fractions at sub-oven temperatures and re-inject them in the form of a very narrow pulse when the temperature of the modulator is brought back up (2). In theory, this should increase the signal-to-noise ratio (

), because the mass flow rate of the solute into the detector is increased. This band recompression is generally considered to result in increased sensitivity. Increasing the frequency of the chemical signal entering a detector is an excellent way to enhance the 

 ratio (3). Philips and Liu used thermal desorption modulation between the outlet of the column and the inlet of the detector to enhance chromatographic sensitivity and 

 ratio (4). An increase by a factor of 10 was observed (4). Kinghorn and Marriott used a longitudinally modulated cryogenic system (LMCS) for the same purpose, that is, 

 enhancement in capillary gas chromatography. An increase of 

 ratio by a factor of 10 was also reported (5, 6). The increase in peak amplitude using GC×GC compared to a single column has been qualitatively discussed by DeGeus (7). Habram and Welsch reported a 10–27× increase in the 

 ratio through modulation (8). Lee et al. proposed a theoretical model for simple calculation of sensitivity enhancement in GC×GC over 1D separation (9). Contrary to that, a paper published in the 

 in 2003 (10) claimed that there is no increase in the sensitivity of GC×GC over 1D GC and stated that "(…) addition of the second dimension does not change the system MDC (minimum detectable concentration) for any solute that is sufficiently separated in one-dimensional GC and in GC×GC and has the same retention in both cases." The authors mentioned that the detector electronic noise is the main contributor in the determination of MDC, and this noise cannot be reduced below a certain level limited by the white noise (10). In a GC system, under controlled conditions, the noise consists primarily of the sum of two slowly varying components: a steady-state standing-current offset (GC detector noise) (11) and "chemical" or "chromatographic" noise which includes temperature-induced column-bleed and solvent tail. The main goals of this study were to compare the sensitivity in GC×GC and 1D GC using the US Environmental Protection Agency (EPA)-recommended method (12) to determine the method detection limits (MDLs) for both techniques, to study the effect of noise on the sensitivity of the method, and to determine major noise contributors (electronic noise and chromatographic noise, for example). 

 The GC×GC system consisted of an Agilent 6890 GC (Agilent Technologies) equipped with a single jet, liquid nitrogen cryogenic modulator, coupled to a Pegasus III time-of-flight mass spectrometer (TOF-MS) and a flame ionization detector (FID) (LECO Corp.) (13). The column set consisted of a 30 m × 0.25 mm, 1.00 µm 

 VF-1MS (Varian) as a primary column coupled to a 1.5 m × 0.25 mm, 0.25 µm 

 SolGel-Wax phase second dimension column (SGE). Modulation periods of 2 s, 4 s, 6 s, and 8 s were used with the cryogenic trap cooled to –196 °C using liquid nitrogen. The separation was performed using the following temperature programme: Initial temperature 50 °C, kept for 0.2 min, and ramped at 4 °C/min to 150 °C (mixture 1); initial temperature 40 °C, kept for 0.2 min, ramped at 30 °C/min to 240 °C, and then ramped at 4 °C/min to 280 °C and held for 3 min (mixture 2). The injector was operated at 280 °C and 1 µL injections were performed in pulsed splitless mode, with a splitless time of 1 min. Helium was used as the carrier gas at a constant flow of 1.4 mL/min for TOF-MS and 1.6 mL/min for the FID. The MS transfer line was maintained at 250 °C. Ions in the mass range 35–400 amu were acquired at a rate of 100 spectra/s. The ion source temperature was 225 °C and the detector voltage was set to -1800 V. Flame ionization detection was performed at 350 °C, with data collected at 100 Hz. Three different types of inlet ferrules were used: 100% graphite ferrules (with mixture 1); vespel–graphite and SilTite ferrules (with mixture 2). 			

 Two mixtures were used for this study. Mixture 1 consisted of 

-hexane (freshly distilled before use). Mixture 2 was composed of 

. Hexane and all the standards were obtained from Sigma–Aldrich. CS

 was obtained from Fisher Scientific. Helium (99.999% purity) was delivered by Praxair. 

 at the same concentration. The concentrations used with TOF-MS and FID (Table 1) were prepared by dilution of the appropriate volumes into 

Table 1: Sample concentrations used in method detection limit (MDL) determination. 

 The EPA approach as defined in the U.S. EPA Electronic Code of Federal Regulations (12) was used for the calculation of MDLs. The EPA method detection limit approach utilizes a single-concentration design estimator. The first step is to determine an estimate of the detection limit (EDL). An EDL is defined as a concentration value which maintains an instrument 

 ratio in the range of 2.5–5. The EDL is then used to choose the concentration at which standards should be prepared. The EPA recommends using a concentration that is between 1 and 5× the EDL. Eight aliquots of the sample concentration (Table 1) were prepared and the standard deviations for the peak heights of replicate measurements were calculated. The MDL was calculated as follows: 

Where MDL is the method detection limit, t

 is the student's t-value appropriate for a 99% confidence level and a standard deviation estimate with 

 is the standard deviation of the replicate analyses. In this study, the MDLs were estimated using the tallest second-dimension peak for a given analyte, because at or close to the MDL only this peak would be visible in most cases. 

Figure 1: (a) Total ion current (TIC) of n-C9, n-C10, and n-C12 (5 pg/ÂµL, red, and 80 pg/ÂµL, white) using 1D GCâTOF-MS separation. (b) m/z = 71 extracted ion chromatogram of n-C9, n-C10, and n-C12 (5 pg/ÂµL, red, and 80 pg/ÂµL, white) using 1D GCâTOF-MS.			

 To make an "absolute" comparison of the MDLs in 1D GC and GC×GC, both separations should be performed under carefully optimized conditions. This would potentially require the use of different column geometries, temperature programming rates, and data acquisition rates, for example. However, from the authors' experience, most potential users would like to use GC×GC as a simple "plug-in" that they add to a system without changing the method they have developed and validated. On top of that, conditions that are optimal with respect to sensitivity (for example, the optimal temperature programming rate) quite often cannot be used in the analysis of real samples, for instance because of insufficient selectivity. Consequently, we decided to perform the comparison under similar conditions for both methods, the only difference being the absence of modulation and the second dimension column in 1D separations. We believe that such a comparison is valuable, because it is applicable to most practical scenarios. 

Figure 2: GCÃGCâTOF-MS separation of 5 pg/ÂµL n-C9 peak from the solvent tail (4 s modulation, m/z = 71).			

The primary reason the EPA procedure was used rather than the simplistic (but popular) conventional approach based on the magnitude of the 

 ratio is that the latter largely ignores the variability of the response close to the MDL, in effect leading to overly optimistic results. This is particularly important in GC×GC, where the MDL estimation is based on the individual second dimension chromatogram ("slice") with the highest intensity of the response (peak height). This peak height in GC×GC is affected not only by the random factors that also affect 1D separations, but also by random variations in the modulation phase (a case in point: Different slices sometimes had to be used for the same analyte if the modulation was close to perfectly out-of-phase). Thus, even though the

 ratio in any single analysis might suggest lower MDL, the increased variability of the height of the most intense peak in GC×GC (compared to the height variability of a single, well defined peak in 1D separations) makes the actual MDL comparably higher. 

Table 2: MDL values of 1D GCâTOF-MS, 1D GCâFID, GCÃGCâTOF-MS, and GCÃGCâFID using 100% graphite inlet ferrules.			

Another problem with the conventional approach might be over-reliance on software-reported 

 values. From the authors' experience, some popular software packages used in GC×GC report 

 values that can be as much as an order of magnitude higher than the actual values determined from the chromatogram, leading to artificially low MDL values. The EPA procedure, on the other hand, is based on multiple measurements, and therefore it accounts for the increased variability of the response close to the MDL. If an analyte is detected according to the EPA procedure, there is a 99% probability that it is actually present in the sample (on the flip side, if it is not detected, there is still a 50% chance that it is present there). Consequently, we believe that the EPA procedure should be used whenever a method is characterized in terms of MDL. 

Figure 3: GCÃGCâFID separation of 5 pg/ÂµL n-C9 peak from the solvent tail (6 s modulation).			

The "chemical" or "chromatographic" noise in GC may result from a number of sources, such as the solvent tail and the column bleed. In this study, the solvent tail was created by using 100% graphite ferrules for the inlet. These ferrules are porous, and therefore they sorb the solvent during injection and release it during the GC run, causing solvent tailing (graphite acts as a "chemical sponge" causing a near-continuous release of the solvent during the run). Mixture 2 was composed of high boiling-point analytes that eluted with the column bleed at the end of the run. Vespel–graphite and SilTite inlet ferrules were used with mixture 2. To compare the sensitivity between 1D GC and GC×GC, the two mixtures were analysed using both approaches and applying the EPA-recommended method to calculate the MDLs. The limits of detection for TOF-MS were calculated using unique masses for each analyte to enhance the sensitivity. For 

 was 71; 83 was selected for 3-octanol, 57 for 

Figure 4: (a) Total ion current (TIC) of 100 pg/ÂµL n-C20, n-C22, and n-C24; (b) m/z = 57 extracted ion chromatogram of 100 pg/ÂµL n-C20, n-C22, and n-C24 using 1D GC.			

Figure 1(a) shows the 1D GC total ion current (TIC) traces of 5 pg/µL and 80 pg/µL of 

. Even using the extracted ion chromatogram at 

 = 71, the analytes were not detected at 5 pg/µL concentration levels within the high background noise levels of the solvent tail (Figure 1[b]). However, in GC×GC the analytes were detected at the 5 pg/µL concentration levels. The main reason was that the analytes were separated from the high background noise. This is illustrated in Figure 2, where the small 

 peak was separated from the solvent tail. The MDL values obtained in the experiments are shown in Table 2. The same scenario was then applied to the FID, which is a non-selective detector. Consequently, the selectivity and sensitivity enhancement through the use of extracted ions with MS was lost, making good chromatographic separation particularly important. This is illustrated in Figure 3, where the small peak of 

 was well separated from the solvent tail and could be detected at 5 pg/µL. 

Table 3: MDL values of 1D GCâTOF-MS and GCÃGCâTOF-MS using vespelâgraphite and silTite inlet ferrules.			

The second type of chromatographic noise is the temperature-induced column bleed. Figure 4(a) demonstrates the TIC trace of 100 pg/µL of 

 using 1D GC. No distinct peaks of the analytes could be observed in this high background noise. One of the main advantages of TOF-MS is its selectivity. Therefore, by selecting the unique masses of the analytes, their peaks could be detected (Figure 4[b]). Despite the selectivity of the MS, MDLs of 1D GC were still higher than those of GC×GC (Table 3) because of a lack of band compression in the former technique. 

Figure 5: GCÃGCâFID separation of n-C20, n-C22, and  n -C24 from column bleed (6 s modulation).			

A selective detector is an advantage in chromatography; however, not all laboratories can afford TOF-MS. In such cases, sensitivity is highly affected by the chromatographic separation efficiency. The analyte peaks of 

 could be efficiently separated from the column bleed using GC×GC and detected using FID with much lower MDLs than in 1D GC (Figure 5 and Table 4). The effect of chromatographic separation can be clearly shown and explained in the separation of pyrene in mixture 2 from the column bleed. In Figure 6, the pyrene peak was wrapped around when a 2 s modulation period was used in GC×GC and coeluted with the column bleed. The selectivity of the TOF-MS could solve this problem by quantitation at 

 = 202, the unique mass of pyrene. The MDL was calculated and it was still much better than in 1D GC (Table 3). However, with the FID, the pyrene peak could not be separated from the noise (Figure 7). Therefore, MDLs of pyrene at 2 s and 4 s modulation could not be calculated. The only solution to this problem is the chromatographic separation of the pyrene peak from the column bleed, which can be achieved with GC×GC, but not with 1D GC. This is illustrated in Figure 8, where the pyrene peak was completely separated from the column bleed by increasing the modulation period to 6 s and could be detected with low MDLs (Table 4). 

Figure 6: GCÃGCâTOF-MS separation of 15 pg/ÂµL pyrene (PYN), 2 s modulation (m/z = 202).			

The results shown in Tables 2, 3, and 4 show that MDLs increased somewhat with the increase in the number of modulations per peak (that is, the highest MDLs were obtained with 2 s modulation periods and the lowest MDLs with 8 s modulation periods). The results show that a 6- to 18-fold (for GC×GC–FID) and 6- to 13-fold (for GC×GC–TOF-MS) increase in sensitivity could be achieved when the first dimension peak was modulated into one or two second dimension peaks (8 s modulation). When the peaks were modulated three to four times (as in 2 s modulation), sensitivity improvements of 5- to 14-fold (for FID) and 5- to 9-fold (for TOF-MS) were achieved. These findings could be considered unexpected by some, as the differences in MDLs between 2 s and 8 s modulation periods were in fact small and not at all proportional to the differences in analyte mass delivered to the detector in a single second dimension peak. Common sense seems to dictate that a second dimension peak obtained at 8 s modulation period should be ~4 times taller than a peak obtained at 2 s modulation with virtually unchanged noise, hence the 

 ratio should be ~4 times higher. Consequently, one expects a 4-fold decrease in the MDL when looking at the 

 ratio only. However, this view is somewhat simplistic. As explained above, the MDL of any analytical method is determined not only by the value of the 

 ratio in a single experiment, but also by how reproducible it is in repeat experiments. This often ignored factor actually explains the findings of this study. With long modulation periods, a first dimension peak is sampled into only a few second dimension peaks. Small changes in modulation phase lead to significant variations in the heights of those peaks (and, consequently, large standard deviations of peak heights). On the other hand, with more frequent modulation, these variations become much less pronounced, and the standard deviations of peak heights decrease. Thus, while in general the 

 ratios were proportional to the length of the modulation period, the increased standard deviations of peak heights at longer modulation periods erased most of the gains in MDLs. Nevertheless, MDLs in GC×GC were still very good and significantly better than those in 1D GC. 

Figure 7: (a) Contour plot of GCÃGCâFID separation of 300 pg/ÂµL pyrene (PYN), 4 s modulation; (b) Surface plot of GCÃGCâFID separation of 300 pg/ÂµL pyrene (PYN), 4 s modulation.			

The results for 3-octanol and pyrene indicated that sensitivity enhancement strongly depended on the second dimension retention time of the analyte. The increased retention times of both 3-octanol and pyrene, which eluted close to the end of the run, resulted in broader first dimension peaks that were sampled into more second dimension peaks, and thus in lower sensitivity gains compared to earlier eluting analytes. 

Figure 8: GCÃGCâFID separation of 15 pg/ÂµL pyrene (PYN), 6 s modulation.			

As has been mentioned by Blumberg (10), the noise in a GC system is composed of several types, such as detector and chemical noise. Detector noise is limited by the white noise, (14) therefore, it cannot be reduced below a certain limit. However, in reality it is the chemical noise that is the major contributor to the overall noise in any GC system. When the magnitude of the analyte peak becomes comparable to that of the background noise, the analyte peaks of interest begin to merge with the noise and can no longer be distinguished unless they can be chromatographically separated from that noise. Therefore, the practical limit in sensitivity is usually imposed by the chemical or chromatographic noise rather than the inherent sensitivity of the detector. The ability of GC×GC to separate the analytes from themselves and from the chromatographic noise is of utmost importance with complex mixtures. 1D GC MDL is directly influenced by any closely eluting interfering compounds or noise, and, in this case, GC×GC MDL enhancement becomes greater. 

Table 4: MDL values of 1D GCâFID and GCÃGCâFID using vespelâgraphite and SilTite inlet ferrules.			

The sensitivity of GC×GC was compared to that of 1D GC. Using an 8 s modulation period, GC×GC offered approximately an order of magnitude (on average) improvement in MDL. Electronic noise cannot be reduced below a certain limit, and is therefore a limiting factor for theoretical sensitivity improvements. However, this is not the case when chromatographic or chemical noise is the main contributor to overall noise. In such cases chromatographic separation strongly affects the MDL. The ability of GC×GC to produce peak isolation with true baseline is of significant importance with complex mixtures. MDL in 1D GC is directly affected by any closely eluting interferences (for example, other analytes, column bleed, and solvent tail). Since GC×GC offers a much better chance of separating the interferences, it can significantly enhance the MDLs. The gains, however, are not as great as would be expected from the theoretical 

 increase because of band compression by the modulator as a result of the increased variability of the individual peak heights in GC×GC caused by random shifts in the modulation phase. As a result, the MDL values for significantly different modulation periods change only slightly.  

The authors thank the Natural Sciences and Engineering Research Council of Canada (NSERC) for the funding of this research.  

 is a professor at the Department of Chemistry, University of Waterloo (Ontario, Canada). He obtained his M.Sc. Engineer (1981) and PhD (1986) degrees from the Gdansk University of Technology, Poland, and his Professor of Chemical Sciences degree (2009) from the President of the Republic of Poland. Professor Górecki's scientific interests include comprehensive two-dimensional gas chromatography (GC×GC), passive sampling, enhanced extraction techniques, environmental analysis, field analysis, pyrolysis GC–MS, and HPLC. He is the author and co-author of 22 book chapters, over 140 peer-reviewed papers, nearly 240 conference contributions (including 41 invited lectures), and six patents.  

 received his PhD in Analytical Chemistry in 2012 from The University of Waterloo, Ontario, Canada. He was awarded his M.Sc. degree in Pharmaceutical Sciences in 2005 and his B.Sc. degree in Analytical Chemistry in 2000 from the Faculty of Pharmacy, Suez Canal University, Egypt. He is currently a visiting researcher in the Department of Chemistry at the University of Waterloo. His research interests include chromatographic techniques including HPLC, GC–MS, and GC×GC and their applications in the study of complex matrices, in addition to chemometrics and its application in pharmaceutical analysis. He is an author and co-author of over 40 papers, book chapters, and conference presentations.  

(2) A. Mostafa, M. Edwards, and T. Górecki, 

(3) W.J. Taraszewski, D.T. Haworth, and B.D. Pollard, 

(7) H. De Geus, J. De Boer, J.B. Phillips, E.B. Ledford Jr., and U.A.T. Brinkman, 

(9) A.L. Lee, K.D. Bartle, and A.C. Lewis, 

Features

In this article, the sensitivity of two-dimensional gas chromatography coupled to two different detectors - a time-offl ight mass spectrometer (GCXGC–TOF-MS) and a fl ame ionization detector (GC?GC–FID) - was compared to the sensitivity of conventional one-dimensional gas chromatography (GC–TOF-MS and GC–FID) by determining method detection limits (MDLs) for a series of different compounds with different polarities.

Sensitivity of Comprehensive Two-dimensional Gas Chromatography (GCXGC) Versus One-dimensional Gas Chromatography (1D GC)

Why do some peaks front and others don't in the same method?

I often get asked how I come up with the topics that are covered in my "LC Troubleshooting" column every month. Most of the time, the source is either questions I receive via e-mail (see instructions at the end of this column if you have a question to ask me) or in live classes that I teach around the world. This month, the topic originates in the latter category. One course attendee had observed a problem with her liquid chromatography (LC) method in which a peak fronted. Most of us are familiar with tailing peaks, in which the peak rises from the baseline rapidly, but returns to the baseline more slowly, resulting in a distorted peak, where the second half of the peak is broader than the first half, forming a "tail". In the present case, injections of the reference standard gave normal, nearly symmetrical peaks, but when the samples were injected, the peaks rose more slowly from the baseline than normal, resulting in a fronting peak, where the first half of the peak is broader than the second half. The question, of course, was what was going on and how to correct the problem. 

One of the troubleshooting principles that I frequently mention is what I call the "divide-and-conquer" technique. This is simply the process of sequentially dividing the problem into (usually) two major parts by changing some variable and observing the result. The goal is to find changes that either do or do not make a difference in the observed problem. This helps to eliminate possible causes of the problem. As we go through a series of such steps, we progressively eliminate potential problem sources until we are left with the root cause of the problem. For example, in the first step, we may eliminate half of the possible causes, in the second, half of the remaining causes, and so on. It doesn't take long to be left with just one or two things that may have to be sorted out to correct the problem. 

A second principle of troubleshooting is to use the scientific method and change just one variable at a time. In this manner, we will know which variables influence the problem and which do not. Often it is tempting to change several items together, just to get the problem solved and get back to work, but in the long run, this approach doesn't help us identify the real problem source so that we can avoid or minimize future problems. For example, for a problem observed in the chromatogram, we might change the column, guard column, in-line filter, and make a fresh batch of mobile phase. This may fix the problem, but it does not get to the root cause of the problem — for example, use of the wrong pH for the mobile phase — and therefore may waste money (we may have discarded a perfectly good column) and invite future problems (perhaps we should add a step to double-check the mobilephase pH). 

Usually, we apply the divideandconquer technique along with changing just one thing at a time during troubleshooting. This may be a conscious or subconscious process. Let's try this approach with the present problem. 

The first round of troubleshooting has already been done for us; the remaining ones we'll have to speculate on, but they will help to define experiments that will help us identify the root cause of the problem. We know that injection of the reference standards results in acceptable peak shapes, but sample injections give fronting peaks. We also know that the two injection types consistently give distinctly different results (our first divide-and-conquer experiment). What do these two items have in common, and what is different between them?  

Both the standards and samples use the same mobile phase, instrument, and column, so it is unlikely that any of these items is at fault. As a side observation, peak tailing is a much more common problem with LC separations. Tailing is most commonly attributed to unwanted secondary interactions between the analyte molecule and the column. Usually this results from strong interactions between acidic silanol groups on the column packing and basic functional groups on the analyte. When this happens, usually the analyte in both the reference standards and samples gives the same tailing peak shape. The different peak shape between the two cases in the present example is further evidence that the column is not at fault. The most common cause of peak fronting that is observed today is the collapse of the column bed, but when column collapse occurs, both standards and sample will generate the same fronting peak shape, so we know this is not the source of the current problem. However, because column collapse is the most common cause of fronting peaks, it is covered briefly at the end of the present discussion. 

The present method was used to determine the potency, or concentration of the analyte, in a formulated drug product. We will assume that during method development and validation it was found that acceptable precision and accuracy were obtained with the reference standard prepared in a water–organic solution, as is often the case for this type of method. The most likely source of the problem is something that is different between the standard and the sample. Let's make a list of some possible differences, then we can devise experiments to isolate the root cause. Here are some possibilities: 					

aqueous–organic ratio of the injection solvent

I'm sure I've missed something obvious in this list — you readers are always helpful to point this out! Let's look at each item in turn. 

: Injection of too large a volume can distort peaks, producing fronting, tailing, or split peaks, often worse for peaks that are eluted near the front of the chromatogram. Injection volume problems are often made worse if too much of too strong a solvent is injected (see next item). A simple rule of thumb to determine the maximum injection volume is that up to ~15% of the volume of the first peak of interest can be injected if the sample is dissolved in the mobile phase. I'm not sure what column size or packing particle size was used for the present case, but I have shown some estimates for various column configurations in Table 1 that can be used for comparison. For the various columns shown, these assume a realistic column plate number and a retention factor (

) of 1 (as is usual for such presentations, I have rounded values for clarity). For example, a 150 mm × 4.6 mm column packed with 5-μm diameter particles will produce a 

 = 1 peak with a volume of ~126 μL, corresponding to a maximum injection volume of ~19 μL. Contrast this with a 50 mm × 2.1 mm, 2-μm particle column, where the corresponding injection volume is only ~1.4 μL. Usually, but not always, the injection-volume effects will be the same for standards and samples, so I do not suspect that injection volume alone is the cause of the problem. However, the experiment is simple — just reduce the injection volume by a factor of two or more and see if the peak shape improves. 

 Table 1: Estimated peak and maximum injection volumes for various columns.			

Aqueous–Organic Ratio of the Injection Solvent

: The estimates of maximum injection volume just mentioned assume that the injection solvent (sample diluent) is the mobile phase or a weaker solvent. For reversed-phase separations, this means the injection solvent can have no larger organic content than the mobile phase for the referenced injection volumes. Even if the injection volume is smaller than that referenced in Table 1, it may be too large if there is more organic solvent in the sample diluent than in the mobile phase. Furthermore, different aqueous–organic ratios used for the standard and sample injections can be the source of the problem. My first guess is that the sample and standard diluents are different, and this, perhaps in combination with the injection volume, is the problem source. It is common to prepare reference standards in a highly aqueous solution. For example, the initial dilution of the standard may be in a small volume of methanol or acetonitrile to ensure that it is fully dissolved. Then subsequent dilutions are made with water or buffer. As a result, the injected standard may be in <1% organic solvent. In contrast, the sample may contain significantly more organic solvent because of the sample preparation scheme. The test in the present case is simple. It may be inconvenient to reduce the organic content of the sample diluent, but by rediluting the reference standard with solutions containing the same percentage of organic solvent as the sample, you will produce standards and samples in equivalent diluents. If the newly diluted standards front, you will have identified the problem source. Reducing the injection volume or modifying the sample preparation scheme to result in a more aqueous final sample should fix the problem. For example, if 50% methanol–water is used to prepare the sample to fully dissolve it, it may be possible to initially dissolve the sample in one fourth of the volume of 50% methanol, then adjust the sample to the final volume with water for a 12.5% methanol–water injection. 

: Ideally, the injection solvent pH should be adjusted to match the pH of the mobile phase. Also, the pH of the diluent for the reference standard and sample should be the same. Often an unbuffered or weakly buffered diluent will not be a problem if the injection size is sufficiently small so that the pH of the injection solvent is immediately shifted upon injection by a properly buffered mobile phase. But if the pH of the standards or samples do not match the mobile phase, try the experiment and match the mobilephase pH to see if the problem is corrected. As with the organic solvent in the injection, pH effects may be exacerbated by injections that are too large. 

: A final product usually contains "inert" ingredients that facilitate shelf life, sample stability, dissolution, or other desirable characteristics of the sample. Often these components of the matrix contain buffers or acidic or basic components. Such components, when dissolved or extracted, can affect the final pH of the sample solution, making it different from the reference standard solution. This should be easy to check by determining the pH of the respective solutions. If they are different, adjustment of the sample solution to match the reference standard may be necessary. Similarly, other matrix components can influence the chromatography. It may be possible to obtain each of the excipients or matrix components individually and formulate experimental sample solutions deficient in one or more components to determine if this is the problem source. 

: Just as matrix components can affect the chromatography, it is possible that other analytes in the sample may cause problems. I do not suspect that this is the case, because the reference standard should contain all the analytes. But if it does not, it would be easy to check this by combining all the analytes in a single reference standard solution. 

You can see from the discussion above that the different potential error sources often influence each other. For example, if you inject a small enough volume, you can inject just about any sample without problems, no matter how incompatible the solution appears to be. The mobile phase will often dissolve, adjust, or disperse problem conditions or components if their concentration is small enough. All of these potential problem sources point to the importance of robustness testing during method development and validation. It is a good idea to check the influence of the variables you can adjust — injection volume, organic solvent concentration, analyte concentration, pH, and so forth — both alone and in combination to be sure the method will produce acceptable results when one or more variables are changed slightly. The analogy is if you are standing on a cliff to watch the ocean. There is a much greater chance of falling off because of a wind gust or an unstable surface if you are right at the edge of the cliff. You can see almost as well if you stand back from the edge, and you are much safer. In a similar manner, you need to find the "edge of the cliff" for your methods and adjust conditions so they are not too close to this zone of potential disaster. 

Figure 1: Peak fronting: (a) Normal peak shape; (b) fronting after ~500 injections. See text for details.

As I mentioned earlier, the most common cause of peak fronting is a physical degradation of the packing bed inside the column. This is fairly rare today, because most columns are packed much better and are more stable than those from the past. With some of the older, low-purity, type-A columns that were predominant before the mid-1990s, a physical shock to the column, such as dropping it or even pressure pulses during injection, could eventually cause the column to collapse. The chromatograms of Figure 1 show what might happen. These are from a method in our laboratory that used a pH and column temperature that were significantly in excess of those recommended for the column. After approximately 500 injections, the peak shape changed from the normal peak of Figure 1(a) to the fronting peak of Figure 1(b). This affected all chromatograms — sample and reference — and could not be reversed. The cause is illustrated in the cartoon of Figure 2. In Figure 2(a), a normal column is shown, packed with particles. These particles can be thought of much like popcorn balls, where the final particle is made up of many smaller particles, and the spaces between the smaller particles are the pores in the packing. Such particles and the packed bed are very stable, often for thousands of injections, if the mobile-phase conditions are not too aggressive. For most C18 columns, this means temperatures of ≤70 °C and pH ≤ 8. There are columns that will withstand more rigorous conditions and some are more fragile. In this case, the column limits were 40 °C and pH 7, with a warning that the combination of conditions near the limits could severely limit the column lifetime. The column was operated at 70  °C and pH 9, and these conditions gradually dissolved the column. In Figure 2(b), you can see that some of the tiny particles have dissolved, but the overall matrix is still stable. But at some point, enough of the silica has dissolved that both the individual particles and the packing bed become very fragile. A pressure pulse from the injector cycling may be enough to cause the collapse of a particle; then a chain reaction starts and the column bed can settle, as in Figure 2(c). If the top of the bed is distorted, as is shown in Figure 2(c), the injected sample stream (arrows) reaches the top of the column at different points, distorting the flow profile. Because some of the molecules flow farther down the column before they start moving chromatographically, they get a "head start" over the others and result in a fronting peak. There is no fix for this problem other than replacing the column and changing conditions to prevent it from happening in the future. 

Figure 2: Illustration of column collapse: (a) New column; (b) column after partial dissolution; (c) column after collapse; arrows showing injection flow stream. See text for details.			

We have seen that, although peak fronting most commonly results from column collapse, there are other potential sources. The best approach is to do a mental analysis of the potential causes, as we did here, then judiciously devise experiments to help prove or disprove your hypothesis of what caused the problem. After the problem source is identified and confirmed, you can figure out how to adjust the experimental conditions to eliminate or minimize the problem. 			

is vice president of LC Resources, Walnut Creek, California, USA. He is also a member of 

editorial advisory board. Direct correspondance about this column should go to "LC Troubleshooting" 

4A Bridgegate Pavillion, Chester Business Park, Wrexham Road, Chester, CH49QH, UK, or email the editor-in-chief, Alasdair Matheson, at 

LC Troubleshooting

Peak Fronting... Some of the Time

An excerpt from LCGC's e-learning tutorial on optimizing GC–MS methods at 

There are many "minor" variables in gas chromatography–mass spectrometry (GC–MS) that can be optimized to improve selectivity or sensitivity, and knowledge of these is important in getting the most from your analytical methods. What follows is a brief summary of some of the simpler ways to improve your GC–MS analyses — go to 

Column bleed should be avoided in GC–MS because it significantly lowers detection limits and adds to the spectral noise when qualitative analysis is required. Ensure you have high-capacity oxygen and hydrocarbon traps fitted to the carrier-gas line, use "MS" designated (low-bleed) columns where possible, and condition columns properly before installation into the detector — this will mean passing carrier gas through the column at room temperature for at least 10 min before thermally cycling the column, which will usually not require more than 30 min at the isothermal temperature limit. To check for column bleed one would investigate a spectrum of the baseline of a total ion chromatogram (TIC) and note ions at 207, 267, and 281 — these ions indicate the presence of siloxane column bleed products. 

Ensure that Vespel (Dupont)/graphite ferrules are used to prevent oxygen permeation, and avoid wider bore or short columns that require very low carrier gas head pressures because the carrier flow may not be reproducible with vacuum outlet conditions. Always work within the specified tolerances of the mass spectrometer pumping equipment to maintain a good level of vacuum within the instrument and improve sensitivity (this will typically be a 2–4 mL/min flow of carrier gas depending upon instrument design). The quality of the vacuum (and hence instrument sensitivity) will be directly influenced by the quality of the roughing pump oil, which should be replaced and "ballasted" to expel impurities on a weekly basis to optimize instrument sensitivity. 

Most systems use perfluorotributylamine (PFTBA) as a tuning compound to optimize the ion source settings and mass analyzer calibration. This compound is suitable because it is volatile at room temperature under vacuum, contains no mass deficit because of the lack of hydrogen atoms, and fragments reliably to give ions over a wide mass range. Most manufacturers will use ions at 

 69, 219, and 502 to perform the sensitivity optimization and mass axis calibration of the instrument; although these ions represent a wide range of masses and relative abundance, they may not be suitable for "target tuning" the equipment for high sensitivity at masses relevant to your particular analysis. The relative "normative" benefits of autotuning are outside the scope of this piece; however, to obtain the maximum performance from GC–MS equipment, one needs to appreciate the "manual" tuning benefits. 

Many electrostatic components of a modern ion source will be "tunable" by monitoring ion abundance against applied voltage for a range of ions, which are typically user selectable. Selecting the voltage corresponding to optimum ion abundance for an ion whose mass is close to your target ions for the repeller or pusher electrode, for example, can often lead to an increase in instrument sensitivity in selected ion monitoring (SIM) mode. Other ion source components may be optimized using such voltage "ramping" techniques, and one may need to iterate ion source voltages for beam formation, focusing, and acceleration to find the optimum combination of settings. 

The energy of the ionizing electrons is of great importance and when using spectral libraries for qualitative work, the electron energy should be set at 70 eV. For all other purposes, the use of 70 eV is purely arbitrary and it may be that altering the electron energy will aid optimization of a method. Reducing electron energy can lead to the promotion of a more prominent molecular ion, although one should note that the absolute abundance of all ions will decrease exponentially. Increasing the electron energy may improve the abundance of target ions in SIM analysis and hence improve the sensitivity of a method. These parameters should be explored as part of the method optimization where better qualitative or quantitative performance is required. Note that altering electron energy may also affect relative abundances of target and qualifier ions. 

The mass analyzing device will almost certainly be capable of tuning to a higher specification than achieved by the autotune routine. For example, most quadrupole mass analyzing devices can be tuned for resolution and sensitivity by altering the magnitude of the applied voltages (DC and AC). An increase in the applied DC voltage will increase spectral resolution, but it will reduce sensitivity and affect all masses to the same extent. An increase in the gain (slope) of applied DC versus applied AC voltages will also reduce sensitivity and improve resolution, but it will affect higher masses to a greater extent. Of course, an increase in sensitivity and a reduction in resolution can be achieved by lowering the applied DC and the gain. These techniques are important approaches to optimizing a GC–MS analysis and more information should be sought from your manufacturer. 

For scan data acquisition, keep the mass range as narrow as possible and the number of sampling iterations at each mass as high as possible to obtain good quality spectra; consider only spectra that have been properly background subtracted and are an average of all spectra across the peak apex from half height. For SIM analysis, optimize the ion dwell or recording time and keep the numbers of ions per group at a minimum to ensure optimum sensitivity. 

Ensure that the transfer device, ion source, and mass analyzer are at the correct temperature. The optimum temperatures will vary between manufacturer and between electron ionization and chemical ionization modes. 

The Essentials

There are many "minor" variables in GC–MS analysis that can be optimized to improve selectivity or sensitivity. Knowing how to manually optimize the detector settings leads to better quality data.

Optimizing GC–MS Methods

Field-flow fractionation (FFF) is a family of techniques that is increasingly used for separating and characterizing macromolecules. This review discusses recent advances in the characterization of biological, natural, and synthetic polymers. Applications of FFF are contrasted with size-exclusion chromatography to illustrate practical considerations when characterizing macromolecules. The use of different FFF fields allows separations based on size, mass, composition, and architecture. The open channel design and subsequent low shear rate is well suited for analyzing weakly bound complexes, highly branched polymers, high molar mass analytes, and aggregates. Other benefits of FFF that are highlighted in this paper include simplified sample preparation, flexibility in carrier fluid choice, and on-line removal of low-molecular-weight contaminants. 

Macromolecules are ubiquitous in many areas of science and technology. Depending on the macromolecule, it is important to analyze properties like size, molar mass (MM), chemical composition, degree of branching, and their respective distributions to understand their behaviour. However, because of the complex nature of polymers, current separation techniques are not always capable of comprehensive analyses. Size-exclusion chromatography (SEC) is widely regarded as the workhorse for polymer characterization, but is limited by high molar mass (HMM) macromolecules, weakly bound complexes and aggregate species, and highly branched polymers. Field-flow fractionation (FFF) is a versatile family of techniques that complements SEC with additional separation capabilities based on analyte size, mass, composition, or architecture depending on the field used (Figure 1). 

The open channel FFF design results in a soft separation mechanism that is well suited for analysis of high and ultrahigh MM polymers and samples containing microgel. Some key advantages of FFF over SEC arises from its ability to separate analytes over a broad size range (0.001 to 100 µm) using a single channel, and the absence of column packing, which greatly reduces shear degradation. SEC of protein aggregates often requires the addition of cosolvents or preconditioning of columns to reduce adsorption (1). However, addition of cosolvents may induce aggregation, dissociate aggregates, or cause sample specific adsorption (Figure 2) (2). Preconditioning columns is often practised but not reported in the literature, and even when preconditioning is used poor recoveries and sample specific adsorption have been observed (3). In FFF the ability to use formulation buffer allows separations and measurements under solution conditions that are more representative of actual use. For polymer analysis, the shear degradation and co-elution of small and large analytes observed in SEC for highly branched polymers are attributed to effects caused by the column packing material (4). 

 Figure 1: Types of FFF separation. (a) In AF4 a crossflow passes through a semi-permeable membrane and porous frit. (b) In HF5 a cylindrical semipermeable membrane is used and a radial outward flow creates the perpendicular field. (c) In ThFFF a temperature gradient (ÎT) is formed between a hot wall and a cold wall, and sample migrates towards the cold wall because of thermal diffusion (DT).			

In practice, FFF offers users additional benefits. Prior to SEC, filtering is often implemented as a sample preparation step to remove large components and help prolong the life of the column. Sample filtering has been shown to remove soluble and insoluble microgels leading to erroneous MM and polydispersity results (Figure 3) (5). Filtering is not required in FFF and soluble polymers and microgels can be simultaneously characterized. Many syntheses require the addition of excess reagents, which may interfere with subsequent product analyses. Such reagents or interfering low MM sample components either elute in the void peak or can be removed on-line through a semi-permeable membrane used in some FFF techniques. 

Figure 2: An IgG1 recombinant fully humanized monoclonal antibody was analyzed by FFF in two different carrier fluids: (a) 0.1% acetic acid containing 50 mM magnesium chloride and (b) 10 mM phosphate buffer pH 7.1. High molar mass aggregates (peak at ~18.5 min) present in (a) are absent in (b) as a result of weak aggregate interactions stabilized by the magnesium chloride. Adapted and reproduced with permission from B. Demeule, M.J. Lawrence, A.F. Drake, R. Gurny, and T. Arvinte, (2007), Biochim. Biophys. Acta-Proteins. Proteomics 1774, 146-153. Â© Elsevier.			

Separations in FFF are dependent on the strength of an externally applied field which can be easily adjusted. Therefore, resolution and separation speed are readily controlled without the need to change channels. In addition, the open channel design greatly reduces the chance of contamination and inexpensive membranes can be replaced when contaminated. Finally, FFF is easily coupled on-line with detectors frequently used for SEC analysis, including multi-angle light scattering (MALS), differential refractive index (dRI), and mass spectrometry (MS) detectors. For those interested in FFF, building a simple homemade system requires a FFF channel and standard high performance liquid chromatography (HPLC) components common to many laboratories. The recent advances in FFF over the last three years are highlighted in this review. 

Figure 3: ThFFFâMALSâdRI analysis of unfiltered (solid line, black symbols) and 0.5-Âµm filtered (dashed line, grey symbols) microgel-containing poly(vinyl acetate). The lines and symbols represent the dRI fractograms and rg, respectively.  Significant polymer loss in the filtered sample is evident in the lower MM distribution. Adapted and reproduced with permission from D. Lee and S.K.R. Williams, (2010), J. Chromatogr. A. 1217, 1667-1673. Â© Elsevier.

Separation takes place in a thin, open, ribbon-like channel where carrier fluid transports components down the separation axis of the channel. Frictional drag at the channel walls creates a parabolic flow profile across the channel thickness, 

, with the fastest flows in the middle of the channel and the slowest flows near the walls (Figure 1). An external field (flow, thermal, or sedimentation) is applied perpendicular to the separation axis of the channel to drive components towards the accumulation wall. This field-induced transport is counteracted by diffusion of components away from the high concentration region near the accumulation wall. Equilibrium is reached when the two transport processes are balanced and there is no net flux of sample in either direction. The equilibrium position is different for each sample component depending on the magnitude of their interaction with the applied field and their diffusion coefficient. Components of smaller sizes diffuse further into the channel than larger components based on the inverse relationship between diffusion coefficient (

) given by the Stokes-Einstein equation for spherical analytes. The smaller components experience the faster flows further from the accumulation wall and therefore elute before larger components in normal mode FFF. This normal mode elution order is the reverse of that observed for SEC. 

The main FFF techniques relevant to this review are asymmetrical flow field-flow fractionation (AF4), hollow fibre flow field-flow fractionation (HF5), and thermal field-flow fractionation (ThFFF). AF4 utilizes a single permeable wall that allows a crossflow to act as the perpendicular field (Figure 1[a]). The permeable wall is composed of a porous frit covered with a semipermeable membrane, the latter acting as the sample accumulation wall. The retention time (

where η is the carrier fluid viscosity, 

In HF5 a semipermeable hollow fibre membrane is used and an outward radial flow acts as the perpendicular force (Figure 1[b]). The benefits of HF5 over AF4 are lower sample volumes and a potentially disposable channel. Thermal FFF (ThFFF) employs a hot and cold wall to create a temperature difference (

) that subsequently induces thermal diffusion of components towards the cold wall (in most cases) (Figure 1[c]). The retention time is given in equation 2 				

Biopolymers are a diverse class of macromolecules that includes polypeptides, polynucleotides, and polysaccharides. The versatility of AF4 separation and the characterization of biopolymers is well established. Several review papers and a book focusing on the analysis of biological polymers using FFF have recently been published (6–8). 

Characterizing protein—protein and protein—macromolecule complexes is important for understanding the efficacy and functions of proteins. AF4's gentle separation mechanism is well suited to analyze complexes with weak interactions (Figure 4) (8). Current techniques for characterizing protein dissociation constants (K

), such as surface plasmon resonance (SPR), analytical ultracentrifugation (AUC), and SEC, are limited in their ability to analyze more than two components or to detect weak binding affinities (K

 > µM). Protein—protein binding between a neonatal Fc receptor (FcRn), immunoglobulin (IgG), and human serum albumin (HSA) was recently studied by AF4 (9). FcRn is involved in removing IgG proteins from lysosomal degradation pathways and IgG transportation in the body. AF4 separation of the IgG-FcRn complex allowed for the determination of a relatively low binding affinity (K

 of 3.74 µM). In addition, FcRn, HSA, IgG, and their associated complexes were separated using AF4. By using an internal standard curve, the formation of multi-protein complexes were determined, including a previously unreported protein complex (HSA/FcRn/IgG/FcRn, 303 kDa). The separation of intact, weakly bound protein complexes shows great promise for AF4 studies of protein pharmacokinetics and aggregation kinetics. Analysis of protein aggregates, especially those in the submicron size range, is of particular interest in the development of therapeutic proteins. Development of an AF4 method for separating IgG monomer and submicron IgG aggregates was recently shown by Hawe et al. (3). Better size resolution and recoveries of submicron IgG aggregates were achieved by AF4 compared to SEC. 

Figure 4: A comparison of FFF to other currently used techniques (analytical ultracentrifugation [AUC];  surface plasmon resonance [SPR]) for proteinâprotein characterization. The open channel FFF design and flow-based separation extends the current ability to detect weak proteinâprotein interactions into the Î¼M binding affinity range. Adapted and reproduced with permission from J. Pollastrini, T.M. Dillon, P. Bondarenko, and R.Y.T. Chou, (2011), Anal. Biochem. 414, 88â98. Â© Elsevier.			

Lipoproteins are assemblies of proteins and lipids that function as carriers for lipids and cholesterols in blood. AF4 has been used to analyze low-density lipoproteins (LDL) and high-density lipoproteins (HDL) (10). LDLs have been associated with an increased risk of coronary artery disease (CAD). In addition to the conventional AF4 channel, a hollow fibre guard channel placed before the AF4 channel was evaluated using serum from healthy patients and CAD patients. The guard channel removed contaminants and improved reproducibility in retention, and fluorescence detection reduced adsorption of serum proteins to the membrane and reduced the amount of serum required for each injection (0.13 µL). 

On-line coupling with a variety of detection methods has expanded the breadth of AF4's characterization ability in recent years. The use of MALS, dRI, and quasi-elastic light scattering (QELS) detectors has become more common for characterizing macromolecules. Characterization of 

), and chemical composition was shown for a PEGylated protein conjugate and its aggregates using on-line AF4–UV-MALS–QELS–dRI, SEC–UV-MALS–QELS–dRI, and matrix-assisted laser-desorption/ionization time-of-flight mass spectrometry (MALDI–TOF­­­-MS) as complementary techniques (11). PEGylated protein, unreacted protein traces, and aggregated species were detected by both AF4 and SEC, with AF4 providing superior size resolution, while MALDI–TOF-MS was unable to detect aggregates. Detection by UV–MALS-QELS–RI enabled chemical composition characterization of PEGylated proteins (1/1 PEG to protein ratio) and allowed identification of aggregates present using different storage buffers. 

Interest in characterizing protein complexes by 2D off-line coupling of AF4 with other separation techniques and a variety of detection methods has grown in recent years. Lectin-treated N-linked glycopeptides in serum from lung cancer and healthy patients were separated by AF4 and subsequently analyzed by nanoflow liquid chromatography-electrospray ionization–tandem mass spectrometry (nLC–ESI-MS–MS) (12). Binding of various lectin types to glycoproteins enabled a size-based separation by AF4. Removal of non-lectin bound glycopeptides and size sorting of lectin-glycopeptide complexes during AF4 allowed semi-quantitative analysis and improved identification of biomarkers by nLC–ESI–MS–MS. Similarly, characterizing cholesterols and triglycerides in lipoprotein complexes is also important for understanding their function in the body. Off-line coupling of AF4 and gas chromatography (GC)–MS allowed cholesterols and triglycerides to be profiled from human serum samples, and results showed agreement with the current enzymatic determination methods (13).  

The use of FFF has shown promise as a pre-MS separation technique in proteomics analyses. To improve MS detection of poorly soluble proteins, the effect of protein–SDS complexation on protein solubility was examined by HF5 and nLC–MS (14). SDS-denatured serum samples, unfractionated or fractionated, showed improved solubility of the SDS–protein complexes, and allowed for a greater number of proteins to be identified by nLC–MS. Furthermore, the HF5 process was shown to remove low MM (< 30 kDa) components, which subsequently led to lowered background noise in the MS spectrum.  

Protein phosphorylation is a post-translational modification which plays an important role in protein regulation and can be used as a biomarker for diseases like cancer. The 2D on-line coupling of an isoelectric focusing (IEF) step and AF4 step prior to nLC–ESI–MS–MS enabled separation of phosphorylated proteins from a proteome sample based on isoelectric point (pI) and 

 (15). IEF-AF4 separation was evaluated for unphosphorylated and phosphorylated α-casein. Peptides with higher degrees of phosphorylation eluted in the lower pH channels and at longer AF4 retention times as expected. Relative abundances of phosphorylated protein biomarkers were determined by IEF–AF4 and  nLC–ESI–MS-MS for a prostatic cancer line and a normal cell line. In another study, improvements in direct on-line coupling of AF4 with ESI–MS were shown in a small chip-type channel for top-down proteomics that operates in the micro-flow rate regime (Figure 5[a]) (16). The chip-type channel effectively separated carbonic anhydrase (29 kDa) and transferrin (78 kDa) while using much lower, and more ESI–MS compatible, channel flow rates (<12 µL/min) than previous on-line studies (Figure 5[b]) (17,18). Resolution of monomer and aggregate species as well as desalting during AF4 led to higher signal-to-noise for ESI–MS detection (Figure 5[c] and 5[d]). Lipodomic analysis of HDL and LDL from human serum was also shown by chip-type AF4 (19). On-line desalting improved ionization of the HDL and LDL lipids, and in a CAD plasma sample 28 phospholipids, 18 triacylglycerides, and six cholesteryl esters were identified. 

Figure 5: (a) A schematic of the chip-type miniaturized AF4 channel interfaced with electrospray ionization mass spectrometry (AF4âESIâMS); (b) Base peak fractogram (BPF) of AF4âESIâMS for the separation of CA and transferrin (V.out/V.c = 0.012/0.49Â mL/min; (c) Full scan ESIâMS for peak #2 (transferrin) after AF4 shown in (b); (d) Full scan ESIâMS spectrum of transferrin (0.01Â Î¼g/Î¼L) without AF4. Considerably better S/N is observed in the fractionated transferrin as a result of monomer/dimer resolution and contaminant removal during AF4. Adapted and reproduced with permission from K.H. Kim and M.H. Moon, (2011), Anal. Chem. 83, 8652â8658. Â© American Chemical Society.			

Starches are macromolecules essential to human beings and are used in a variety of industrial and food applications. The properties of starches (for example, digestion and thickening abilities) are dependent on starch structure, which in turn is dependent on the degree of branching. However, wide size distributions and variation in branching makes characterizing starches difficult (20). AF4 coupled with MALS and dRI detectors has been successfully used to determine starch 

 (21,22). An in-depth review of FFF characterizing food macromolecules has been recently published (23).  

A review of the latest trends in field-flow fractionation (FFF) for various types of polymer analysis.

LCGC Europe-12-01-2013

Novel Sorbents for Solid–Liquid Extraction

Sensitivity of Comprehensive Two-dimensional Gas Chromatography (GCXGC) Versus One-dimensional Gas Chromatography (1D GC)

Peak Fronting... Some of the Time

Optimizing GC–MS Methods

Field-Flow Fractionation for Biological, Natural, and Synthetic Polymers: Recent Advances and Trends

Vol 26 No 12 LCGC Europe December 2013 Regular Issue PDF