In a recent liquid chromatography (LC) method development class in Ireland, a problem with an LC–tandem mass spectrometry (MS-MS) method was presented as a example of the type of problem that can be encountered. In the present example, the primary complaint was that peak tailing was excessive. Rather than jump in with both feet with a discussion of peak tailing problems and their sources, we decided to examine a chromatogram. The original chromatogram is shown in Figure 1a, with obvious tailing of the first peak and less tailing of the second one. Peak tailing certainly is a problem here, but as we examined the chromatogram more thoroughly some surprises appeared. This month's "LC Troubleshooting" will cover some of the things we can learn from examining a chromatogram that doesn't look great, but that doesn't look too bad on first glance.  

The method is a based on the modification of an existing method. The first peak is the target analyte for the new method, and the second peak is another drug that was analyzed under the same chromatographic conditions in another validated method. The second peak will be used as the internal standard in the new method. 

The equipment is from a major manufacturer of LC and LC–MS equipment and is only a year old. The manufacturer's service specialist installed it and confirmed its performance. The column is a 150 mm × 2.1 mm, 3-µm 

 C18 column, also from a major column supplier, and operated at 0.2 mL/min with a mobile phase of 54% acetonitrile and 46% 0.1% formic acid in water. The conditions are similar to hundreds of other LC–MS-MS methods with the exception that a 50 mm × 2.1 mm column size is much more common than 150 mm × 2.1 mm size. 

At this early stage of development, reference standards were being used to generate the initial conditions. Later, the drug would be spiked into plasma and extracted before injection. For the present work, the standards were dissolved in acetonitrile and 20-µL injections were made. 

The primary complaint was that the peak tailing was excessive, so peak shape is a logical place to start. The chromatogram of Figure 1a has been marked with dashed lines in Figure 1b to show the various measurements. We chose to use the asymmetry factor, 

, rather than the tailing factor more commonly used in the pharmaceutical industry, because it is a bit easier to measure from the chromatograms. The conclusions with either technique of measurement should be the same. The asymmetry factor is calculated with the formula 

 are the front and back half-widths of the chromatogram, measured at 10% of the peak height. The data of Figure 1b yielded 

 ≤ 2.0 can be tolerated in many cases. So the first peak has excessive tailing, but the second one isn't too bad. 

When there is more tailing for early-eluted peaks than later ones, one of the common causes is excessive extracolumn effects. Factors such as injection volume, injection solvent, and plumbing effects can be sources of extracolumn band-broadening. We'll come back to these later.  

Figure 1: Chromatograms for an LCâMS-MS method: (a) original; (b) with added notations to aid calculation of chromatographic parameters. Down-arrow, t0; vertical lines, peak centers; horizontal lines, baseline and 10% of peak height; double-arrow, width at half-height. 

Another symptom of extracolumn band-broadening is low values of the column plate number, 

, for early peaks, as contrasted with later-eluted ones. So the next step was for us to measure the plate number for the peaks. You'll recall that the plate number can be calculated from the peak width at baseline or half-height. The two formulas are 

 is the baseline peak width measured between tangents drawn to the sides of the peak, and 

 is the width at half the peak height. Because the half-height technique is easier (we don't have to draw tangents), we chose to use it. We started with the second peak and measured the width at half-height (double-ended arrow in Figure 1b). Using equation 3, we calculated 

 (Note, if you are repeating our calculations, we've rounded the numbers for display convenience, and these are estimates, so a high degree of precision and accuracy isn't necessary.) Whoa! 322 plates? Something is wrong. We double-checked our measurements and calculations and indeed 

 be for this column? We can use a shortcut calculation to estimate the plate number of a column under realistic conditions. This will be less than that the manufacturer obtains with ideal solutes under ideal conditions, so it is a better number for comparison than the data from the column test chromatogram. The shortcut is 

 is the column length in millimeters and 

 is the column packing particle diameter in microns. For the present column, we get 

 should be within 20% or so of this for well-retained compounds. We're only off by 50-fold! Immediately we suspected that the column is very dead. At this stage the next move would be to replace the column with a new one. However, there a few more things to evaluate about the chromatogram. 

 should be close to the estimate for well-retained compounds. A retention time of 5 min is well-retained for an LC–MS separation — or is it? A better way to evaluate the retention is to use the retention factor, 

 is the column dead-time. So we need to figure out what 

 is. With UV methods, it isn't hard, because there usually is a huge peak at the beginning of the chromatogram for unretained material in the sample. We can use the time at which the peak starts up from the baseline as an estimate of 

. However, we don't have that possibility with most LC–MS methods, because the mass analyzer isn't tuned to see these compounds. The following estimate can be used for such cases, or to confirm the visual observation of 

 is the internal diameter, both in millimeters. Convert 

 by dividing by the flow rate (remember to keep the units sorted out). 

 ≈ 0.33 mL/0.2 mL/min ≈ 1.65   min.  

Now the retention factor for peak 1 can be calculated as 

From a standpoint of "good" chromatography, 

 ≥ 1 is desired for the first peak. This provides conditions that tend to be less susceptible to loss of resolution with small changes in retention and helps to avoid interferences that are eluted at the front of the chromatogram. Because of the potential for ion suppression in LC–MS, 

 ≥ 2 for the first peak is a much better choice. In either case, 

 = 0.5 is well below the limit. Such short retention times are also much more susceptible to extracolumn effects exhibited as increased tailing and decreased plate numbers. The bottom line is that the retention of the first peak is much too small. 

Although the observations discussed above suggest that column failure is the most likely cause of the low plate numbers, it is useful to determine the risk of problems due to extracolumn effects because replacing the column with a new one will only exaggerate such problems. With a retrospective analysis of the problem, we don't have any way to evaluate the plumbing in the system as a source of extracolumn effects; it is unlikely that they are significant because the LC system was designed for use with MS and installed according to the manufacturer's guidelines. In such cases, plumbing problems should be minimal. Injection problems, however, may be a different story. 

A good rule of thumb for injection volume and injection solvent is that you can inject ≈15% of the peak volume of the first peak of interest without causing significant peak broadening if the injection solvent is mobile phase. What does this mean for the present example? It doesn't make much sense to use the observed peak widths for this estimate, because they are extremely broad, likely due to column failure. But we can estimate the peak width based on the expected performance of a new column. To estimate the peak width, we need to solve equation 2 for the peak width at baseline, 

 = 0.082 min × 0.2 mL/min =   0.016 mL = 16 µL 

Similarly, we can use the retention time of 5.26 min to calculate 

 = 34 µL. Multiply each of these values by 15% to get the permissible injection volume of ≈2 µL and ≈5 µL for peaks 1 and 2, respectively. And this is in mobile phase, not 100% acetonitrile. 

How do these recommendations compare with the current method? They are certainly much smaller than the 20 µL injection of standards in acetonitrile. If we use an injection solvent stronger than the mobile phase (>54% acetonitrile), we need to reduce the injection volume even more. So even if we replace the column with a new one, we expect that the peak widths are going to be greater than they should be because of the injection of too large a volume of too strong a solvent.  

So where do we go with all this information? At the very least, the column should be replaced with a new one. This should make a major improvement in the chromatogram. The peaks will be narrower, taller, and should tail less. The next step would be to reduce the injection volume and use a different injection solvent. For example, start by decreasing the injection volume to 5 µL and diluting the injection solvent to the mobile phase strength or lower. It is possible to inject larger volumes of sample if the injection solvent is significantly weaker than the mobile phase. For example, with the present conditions, an injection solvent 20% weaker than the mobile phase (that is ≤54 – 20 = 34% acetonitrile) should allow injection of ≥20 µL without problems. For this reason, you could dilute the current injection solvent to 35% acetonitrile, but stay with an injection volume of 5 µL unless larger volumes are required. The concentration of the reference standard and internal standard could be adjusted to allow for injection of the same mass of sample as in the current method, but it is unlikely that this will be necessary because the peak heights will be so much larger with the new column. 

-values that are too small. First, check the retention with the new column before making mobile phase adjustments, but we suspect that they will be necessary, even with the new column. We can use the 

 to make an estimate of the effect of a change in the solvent strength on retention. The Rule of Three states that a 10% change in organic solvent (acetonitrile in the present case) will change 

 by approximately threefold. So a change from 54% acetonitrile to 44% acetonitrile should increase 

-values from ≈0.5 and ≈2 to ≈1.5 and ≈6. It is a good idea to have a little more retention of the first peak for an LC–MS method, so 40% acetonitrile would be a good choice for the next mobile phase; it could be adjusted further, as necessary. 

We have seen that the application of a few simple calculations and estimates to a problem chromatogram can give us a great deal of insight into possible sources of chromatographic problems. In the present example, peak shape problems were confirmed with a calculation of the asymmetry factor. These suggested possible extracolumn effects, so we calculated the column plate number, only to find that the column needed to be replaced. We also took a look at retention in terms of the retention factor and discovered that the first peak was being eluted much too early and was likely to have problems with ion suppression when real samples were analyzed. We also could estimate that problems were likely to occur because too much of too strong a solvent was used for injection. A solution to this problem was to reduce the injection volume and use a weaker solvent. Finally, we were able to estimate mobile phase conditions that could be tried to help increase the retention factors for fewer problems when real samples are analyzed.  

It is likely that we would have drawn different conclusions only on the initial complaint of peak tailing. We might have assumed that everything else was OK in the separation and it was just a problem of mobile phase pH or some other factor related to peak shape. Instead, inspection of the chromatogram made it all possible. A picture really is worth 1000 words — or 2000, in the case of the present discussion! 

Articles

LC Troubleshooting

A few simple calculations and estimates can provide insight into a troublesome chromatogram.

A Picture Is Worth a Thousand Words

For most of us, change isn't easy. Anyone who has tried to kick an ingrained habit knows this. I've been saying for years that I should give up diet cola, but it hasn't happened yet. (Actually, it did happen once, and then I started up again. 

For more complex changes, there are a variety of other factors involved, beyond the psychological ones that affect personal behaviors. The challenges of adopting more environmentally friendly practices, at home or in the laboratory, present a good example of this. 

Cost, for example, is an important consideration. "Green" options often are, or at least appear to be, more expensive than the alternatives. Yet comparing the true costs of standard and green approaches often requires looking at long-term effects that reach beyond oneself or one's company. For example, a t-shirt made from organically grown cotton may have a higher pricetag than one produced from cotton grown using pesticides. But if the pesticides pollute drinking water, or give people cancer (through environmental or workplace exposure), environmental cleanup or medical treatment will be required. We might bear the financial burden of those remedies indirectly, if the government or company involved passes them on to us through higher prices or taxes, but their poor visibility can make it hard for us to choose a green option that has higher direct costs.  

Then, once one commits to the investment of time and money to go green, the next step is to sort out what steps to take. Often, people who don't really want to make the effort will question whether alternative methods truly are better for the environment, simply to delay or obfuscate. But making the right choices can indeed be complicated. 

Fortunately, when it comes to applying green concepts to the rigors of analytical chemistry, help is available. As Majors and Raynie explain in this month's "Sample Prep Perspectives" column (p. 118), several organizations have developed helpful rubrics for assessing the environmental impact of analytical methods. For example, the American Chemical Society's Green Chemistry Institute has developed a set of four "greenness criteria" that have been used to evaluate many of the methods listed on the website of the United States National Environmental Methods Index (

). The alternative "Green Assessment Summary" incorporates similar criteria, but includes energy consumption. Majors and Raynie provide clear examples of how to apply these assessments.  

But even without using a scorecard, the authors explain, one can begin to implement green chemistry simply by seeking analytical methods that reduce or eliminate hazardous substances. In chromatography, particularly in reversed-phase LC and HILIC, the simplest way to do that is to use smaller columns that require less solvent as the mobile phase. But because capillary and nano-scale columns are not the norm, other approaches may be needed, such as identifying alternative solvents, or reducing solvent consumption in the mobile phase by altering experimental conditions. (See the column to learn about the status of these efforts). 

The use of hazardous solvents also can be reduced in sample preparation. Furusawa (p. 162), for example, has developed a rapid and environmentally friendly method of sample preparation, followed by reversed-phase HPLC with diode-array detection, to quantify melamine in milk. No organic solvents are used at any stage of the analysis, and the total analytical time is <20 min for each sample. In addition, the method is inexpensive. Sometimes, it seems, green methods can be easy, fast, and cheap. 

As with any important change, implementing green analytical chemistry certainly requires effort and commitment. But with help like this, it is getting easier.

From the Editor

For most of us, change isn't easy. Anyone who has tried to kick an ingrained habit knows this. I've been saying for years that I should give up diet cola, but it hasn't happened yet. (Actually, it did happen once, and then I started up again. Sigh.)

It's Getting Easier to Be Green

The aggregated singletons for automated purification (ASAP) workflow was introduced at Pfizer (Groton, Connecticut) in April 2009. Singletons are unique, individual final compounds synthesized for in-vitro testing. The main driver and objective behind this initiative was to save synthesis costs and time. Typically a majority of the singletons are made in 100–300 mg amounts. Datasets across various projects show that only ~20% of the singletons survive primary and secondary screenings and hence ~80% of the compounds are made unnecessarily on the large scale. Pharmaceutical chemists can save considerable time and cost by making smaller (30–50 mg) batches. The chemists can submit synthesized compounds (30–50 mg) to ASAP to be purified, registered, and delivered to the materials management department as 30 mM dimethyl sulfoxide (DMSO) solutions for biological screening. The workflow for compound synthesis to biological screening is shown in Figure 1. The turnaround time from samples received for purification to delivery to the materials management department is two to three business days. Centralizing this activity in the purification group allows greater time for higher value tasks to be completed by practicing chemists; the purification scientists can provide expert-level service and technology, and also more opportunity for harmonization with screening (consistent and high quality samples delivered for biological assays).  

Figure 1: Flow chart showing the steps in the process from compound synthesis to screening.			

Following are the steps for sample submission to ASAP: The samples submitted are final products (compounds going for biological screening — no intermediates). The crude weight of the compound is 10–50 mg. Some type of an initial sample workup (for example, liquid–liquid extraction, solid-phase extraction, and so forth) is highly recommended to remove metals or reagents used during the synthesis. The crude sample is fully dissolved in 900 μL of DMSO, filtered, and placed in a 2D bar-coded, matrix capped tube. The crude sample is then registered as an "in-production singleton" sample via Chemistry e-Notebook electronic laboratory notebook software (CambridgeSoft, Cambridge, Massachusetts) using the software's Global Registration tool, which has been customized for Pfizer. The biology assay–screen panel is assigned and the sample is submitted for purification using Pfizer's Auto-Purification submission website. The sample is then dropped off in the assigned submission dry boxes in a Singleton Matrix-Box container. 

The ASAP process is outlined in Figure 2. A total of 10–12 singletons are aggregated in a plate format and associated with a bar code using a 2D Matrix tube reader (Thermo Fisher Scientific, Waltham, Massachusetts). An analytical plate of fixed concentration (1 mg/mL) is created using a Tecan Freedom Evo liquid handling system (Männedorf, Switzerland). The samples in the analytical plate are then analyzed using reversed-phase liquid chromatography–mass spectrometry (LC–MS) and evaporative light scattering detection (ELSD) techniques. The analytical system comprised a Waters 2795 Alliance high performance liquid chromatography (HPLC) system and ZQ mass spectrometer (Milford, Massachusetts) and a Polymer Labs 2100 ELSD system (Agilent Technologies, Santa Clara, California). The initial purity of the sample can range from 5% to 80%. The pre-quality control (QC) gradient methods are 5 min in length with 2-mL/min flow rates. Different column chemistries, modifiers, and gradient conditions are used to develop methods best suited for purification. Even though the samples are aggregated in a miniplate and analyzed in a high-throughput mode, a method is developed for each individual sample. The best method and conditions are then transferred to the preparative purification step. A Waters Auto-Purification Fractionlynx system is used for purification. Fraction collection is mass triggered. The preparative methods are gradients run over 10 min. The collected fractions are evaporated using Genevac Mega evaporators (Gardiner, New York). The dry fraction tubes are then weighed on Tecan weighing stations.  

Figure 2: Outline of the ASAP process.			

The dried, purified material is dissolved in DMSO to make 30 mM solutions. A maximum of 900 μL of the 30 mM DMSO solution is transferred to a plate. A daughter analytical plate is also created by transferring a 5-μL aliquot and adding enough DMSO to result in a 0.5-mg/mL concentration. Excess 30 mM DMSO solution is transferred to bar-coded 2-mL vials. Tecan liquid handling systems are used for dispensing the DMSO and transfer of samples to the plates and vials. The samples in the vials are evaporated using a Genevac HT-12 evaporator in a two-step evaporation process. 

The final QC is performed using the HPLC–MS–ELSD system or Waters Acquity UPLC/SQD/PL 2100 ELSD units, and the samples are registered if they meet the purity criteria (>80% purity by UV at 215 nm, >85 % purity by ELSD, and >50% mass spectral purity). Orthogonal QC methods are selected to ensure the final purity of the samples. The solubilized plate (maximum up to 900 μL of 30 mM DMSO stock) is sent to the materials management department for assay preparations. The compound is then released for the requested screening. The dry compounds are registered and shipped to the Pfizer Neat Store. The pre-QC and final QC data are uploaded in Pfizer's Global Analytical database as a PDF and the file also gets linked to the chemist's e-Notebook submission page. The recovery amount, purity, and registration data are available through Pfizer's Research database. An e-mail notification is sent to the chemists to inform them that the samples have been delivered to the materials management department. A results spreadsheet is attached that provides detailed information regarding the samples, including the QC gradient conditions, mass observed, retention time, purity data, and total recovery after purification. The entire process and data flow are handled through customized software. The software has been specifically designed for automated batch-singleton and library purification workflow. The software enables a user to handle multiple plates with accurate data flow. It also provides structures, chemical properties, CLogD data, and acid–base labile prediction information for the compounds associated in the plate. This information helps minimize the time required for method development.  

The ASAP process was introduced in Pfizer R&D (Groton) in April 2009. A major contributor to the success of ASAP is the strong partnership between the company's Analytical Chemistry and Medicinal Chemistry lines. A point of contact (POC) was established for each project using ASAP to facilitate communication between the two lines. Training sessions were conducted for each project joining ASAP. There was a gradual ramp-up of projects added to ASAP to ensure continuous progress of implementation.  

Figure 3: Singleton compound analyzed using ASAP: (a) pre-QC analysis, (b) preparative run, and (c) final QC analysis. (a) 50 mm Ã 4.6 mm, 5-Î¼m Waters XBridge C18; mobile phase A: 0.03% ammonium hydroxide in water (v/v); mobile phase B: 0.03% ammonium hydroxide in acetonitrile (v/v); gradient: 5â95% B (linear) in 4.0 min, hold at 95% B to 5.0 min; flow rate: 2.0 mL/min. (b) Column: 100 mm Ã 19 mm, 5-Î¼m Waters XBridge C18; mobile phase A: 0.03% ammonium hydroxide in water (v/v); mobile phase B: 0.03% ammonium hydroxide in acetonitrile (v/v); gradient: 5â50% B (linear) in 8.0 min, 50â100% B (linear) to 8.5 min, hold at 100% B to 10.0 min; flow rate: 25 mL/min. (c) Column: 50 mm Ã 4.6 mm, 5-Î¼m Waters Atlantis dC18; mobile phase A: 0.05% trifluoroacetic acid in water (v/v); mobile phase B: 0.05% trifluoroacetic acid in acetonitrile (v/v); gradient: 5â95% B (linear) in 4.0 min, hold at 95% B to 5.0 min; flow rate: 2.0mL/min.			

Figures 3 and 4 show pre-QC, prep, and final QC data for samples analyzed by ASAP. Figure 3 is an example of a sample that was submitted to ASAP with an initial purity of ~15%. The target mass was isolated and enough material was delivered to the materials management department for screening. Figure 4 is an example of a complex sample. Under acidic conditions peak 4 in Figure 4a was coeluted with the target peak. However, after a method was developed in basic conditions, separation was obtained and the purified compound easily passed the final QC (Figure 4c).  

Figure 4: Singleton compound analyzed using ASAP: (a) pre-QC analysis, (b) preparative run, and (c) final QC analysis. (a) Column: 50 mm Ã 4.6 mm, 5-Î¼m Waters XBridge C18; mobile phase A: 0.03% ammonium hydroxide in water (v/v); mobile phase B: 0.03% ammonium hydroxide in acetonitrile (v/v); gradient: 10â95% B (linear) in 4.0 min, hold at 95% B to 5.0 min; flow rate: 2.0 mL/min. (b) Column: 100 mm Ã 19 mm, 5-Î¼m Waters XBridge C18; mobile phase A: 0.03% ammonium hydroxide in water (v/v); mobile phase B: 0.03% ammonium hydroxide in acetonitrile (v/v); gradient: 10â100% B (linear) in 8.5 min, hold at 100% B to 10.0 min; flow rate: 25 mL/min. (c) 50 mm Ã 4.6 mm, 5-Î¼m Waters Atlantis dC18; mobile phase A: 0.05% trifluoroacetic acid in water (v/v); mobile phase B: 0.05% trifluoroacetic acid in acetonitrile (v/v); gradient: 5â95% B in 4.0 min, hold at 95% B to 5.0 min; flow rate: 2.0 mL/min.			

ASAP Workflow Deliverables and Advantages

A high quality of purified sample is delivered to the materials management department with a 2–3 day turnaround time. 

The process provides a high level of purification expertise and technology. ASAP can purify compounds that for chemists find difficult to purify using conventional techniques.

The process provides a high success rate (>90% samples are registered).

Postpurification analysis: The final purity is assessed by MS–UV–ELSD and QC data are uploaded into the Global Analytical database.

Final purity information (purity and weight recovery) is available in the research database. 

An e-mail notification is sent to the chemist. Data can be accessed through electronic laboratory notebook software. 

The process increases the likelihood of running mini-arrays to generate 5–10 analogs in a final step rather than one or two, with the same 2–3 day turnaround time.

The ASAP process allows projects to choose synthetic routes that diverge at a late stage, which is the most effective way to prepare large sets of analogs.

The ASAP process saves time and lets chemists focus on high-value activities such as making compounds for preliminary toxicity studies.

To date, more than 5700 singletons have been submitted and >90% of the samples have been successfully purified. ASAP is currently supporting approximately 25 projects across different therapeutic areas. Although the samples are purified in an automated purification mode, the versatility of the technology allows purification of diverse molecules with respect to compound solubility, polarity, and molecular weight. The next steps involve enhancing purification technologies such as adding normal-phase chromatography, supercritical fluid chromatography, and on-column solvent exchange techniques to increase the scope of available chemical space. 

The authors would like to thank David Price, Rose Gonzales, and Mark Noe for sponsoring ASAP and providing resources and staffing. 

is an Associate Research Fellow in the Analytical Chemistry department at Pfizer Global R&D (Groton, Connecticut). Direct correspondence to 

A multistep workflow process provides purified samples for in-vitro testing in just two or three business days.

Aggregated Singletons for Automated Purification Workflow

What first intrigued you about chromatography? Please tell us about some of your early research.

Well, my earliest research experience with chromatography went back to my sophomore year in undergrad when I was doing some gas chromatography work. I was asked to separate two isomers of a photoreactive compound so I was using packed column GC and collecting the fractions of the isomers in Pasteur pipettes, and then using those fractions for UV and NMR experiments. I then went on, when I was an undergrad in another laboratory, to do some analytical extractions of humic compounds found in soil samples and to do some GC–MS. That was when I really developed a pretty strong passion for chromatography. I went on to graduate school and ended up in the lab of Dan Armstrong, of course a world-renowned chromatographer. From then on I just took more of a liking to chromatography and got into doing more GC, chiral GC, HPLC, and so on.  

 Emerging Leader Award last year, you were also honored with the 2009–2010 Faculty Excellence Award from the College of Arts & Sciences. Has winning these awards had an impact on your research or funding?

Well first of all, it was a great honor to be chosen for 

 Emerging Leader Award. When I was an undergraduate student there was an analytical course I took where the professor used to give us articles from 

. So, at a very early stage of my undergraduate career I was aware of 

 and it was a great educational magazine. So to be chosen as an emerging leader award winner from such a reputable magazine, I was so honored. Also, to be chosen for the Faculty Excellence Award at my home institution was also great. I think what helps me in the research aspect is to know that I can continue doing the things that I really enjoy — that is, going off in new directions and research, to start to examine new projects or new ideas that I once thought were outside of my expertise. I can read up more about those, arm myself with a great lab of grad students, and start to explore different areas. I think that's really what being awarded these honors allows you to do: It gives you the confidence to look at areas of research that you think are very important and that maybe when you first started out building your lab you thought, "Maybe I shouldn't go into those areas because I'll be met with many obstacles." But now, you know that the community respects you and that you can start to go into these different areas. 

In your group's research on solid-phase microextraction (SPME), you developed polymeric ionic liquid (PIL)-based materials as SPME sorbent coatings. Can you discuss the process you went through developing those coatings? 

Well, I was always really interested in solid-phase microextraction. I never used SPME when I worked in Dan Armstrong's group. So when I started my independent career I decided to do so, since I had worked with ionic liquids and materials before in Dan's group and I was always interested in combining ionic liquids as solvent systems with sample prep. When I started at the University of Toledo, I had three very good grad students that joined my group and we started working on trying to develop the ionic liquids that were compatible with SPME. Of course SPME is a very popular technique, developed by Janusz Pawliszyn at the University of Waterloo (Ontario, Canada).  

We went into this area not knowing a lot about how to make these fibers stable, and we were discouraged at first when we started designing the ionic liquids. We found that when we made them as coding materials for SPME and started to do our desorption and GC, the ionic liquid materials would flow off the fiber and we would get very poor reproducibility in our extractions. That's when we started to design new polymeric ionic liquids in which we could polymerize the monomers of the ionic liquid to give the material more strength and make it more resilient to flowing at higher temperatures during the desorption step. It took us some time to get the polymerization chemistry down and choose the right monomers, but eventually we made some coatings that work very well when compared to other commercial coatings available. 

How were these materials an improvement over previous SPME sorbents? 

Some of the improvements that these have over commercial materials is that we can design the properties of the monomer of the ionic liquid to interact specifically with the analytes that we want to extract out of the matrix. For example, we've designed some newer generation polymers in which we add specific functional groups to the polymer and those groups will interact, or react in some cases, with an analyte like carbon dioxide, and we can selectively extract the carbon dioxide out of a gas sample. These are some examples that we've been working on in a variety of different matrices, from extracting pollutants from water or extracting carbon dioxide, methane, or nitrogen from gas. Now we're starting to work on extracting borates from water and genotoxic impurities from pharmaceutical formulations. 

This interview was edited for length and clarity.

An interview with Jared Anderson of the University of Toledo.

An Emerging Leader: One Year Later

Melamine is an organic base compound rich in nitrogen and is widely used in plastics (for example, dishware) and adhesives. Recently, there have been reports from several countries of melamine contamination of a variety of food products, including products containing milk and milk-derived ingredients from China (1). According to the World Health Organization (WHO), in China, where adulteration has occurred, water has been added to raw milk to increase its volume. As a result of this dilution the milk has a lower protein concentration. Companies using the milk for further production (for example, of powdered infant formula) normally check the protein level through a test measuring nitrogen content. Adding melamine increases the nitrogen content of the milk and therefore its apparent protein content (2). Significant exposure to melamine may result in bladder and kidney stones, which, in turn, may result in acute or chronic renal failure and, in rare cases, death. Health Canada has decided to adopt the WHO's new recommended tolerable daily intake (TDI) level of 0.2 mg/kg b.w./day and has reassessed its maximum tolerable limit for melamine in food products containing milk to a maximum of 2.5 ppm (3). Milk is a very important and basic food because it is highly nutritious, inexpensive, and readily available. Monitoring the presence of melamine in milk is, therefore, an important means of guaranteeing food safety. 

In response to recent expansion in the internal food trade, the development of international standardized methods to determine chemical residues in foods is essential to guarantee equitable international trade in these foods and to ensure food safety for consumers. The optimal harmonized method for routine monitoring of chemicals in foods should be simple, quick, economical, and cause negligible harm to the environment and analyst.  

Previous high performance liquid chromatography (HPLC) techniques combined with a selective detection technique for determining melamine in foods including milk have a crucial drawback: All of the methods consume organic solvents in the HPLC and LC–mass spectrometry (MS)-MS mobile phases as well as for extraction and deproteinization in sample preparation (4–6). Thus, the risk associated with these solvents extends beyond direct implications to human health by affecting the ecosystem in which we all reside. Additionally, the disposal of waste organic solvents through incineration has steadily increased over the past decade and costs large amounts of money. Thus, eliminating organic solvents is an important goal in terms of protecting the environment, human health, and the economy (7,8).  

This article describes a rapid and inexpensive technique to strictly monitor melamine residue in milk without organic solvent consumption.  

 A melamine standard and octanesulfonic acid (sodium 1-octanesulfonate) as a reagent for ion-pairing were purchased from Wako Pure Chem. Ltd. (Osaka, Japan). Distilled water was of HPLC grade (Wako). A stock standard solution of melamine was prepared by dissolving the melamine standard in water to a concentration of 100 µg/mL. Working mixed standard solutions of the compound were prepared by diluting the stock solution with water. A handheld ultrasonic homogenizer (model HOM-100, 2-mm i.d. probe, Iwaki Glass Co., Ltd., Funabashi, Japan), a microcentrifuge (Biofuge fresco, Kendo Lab. Products, Hanau, Germany), and a Microcon Ultracel YM-3 (3000 normal molecular weight limit, 0.5-mL maximum volume; Millipore, Bedford, Massachusetts) centrifugal ultrafiltration device were used in the sample preparation. The following five types of nonpolar sorbent columns (250 mm × 4.6 mm, 5-µm 

) with their guard columns (5 mm × 4.6 mm) for HPLC were used: Column A: Wakosil 5TMS (C1) (Wako); Column B: Mightysil RP-4GP (C4) (Kanto Chemical Co., Inc., Tokyo, Japan); Column C: Lichrospher 60 RP-selectB (C8) (Merck, Darmstadt, Germany); Column D: Mightysil RP-18 (C18) (Kanto); Column E: Inertsil ODS-4 (C18) (GL Science, Tokyo, Japan) (Table I). 

 Table I: Physical and chemical specifications of the reversed-phase columns* used and chromatographic melamine separation obtained under the HPLC condition ranges examinedâ 			

 The HPLC system included a model PU-980 pump and DG-980-50 degasser (both from Jasco Corp., Tokyo, Japan), as well as a model SPD-M10A VP photodiode array (PDA) detector (Shimadzu Scientific Instruments, Kyoto, Japan). The analytical column was a 250 mm × 4.6 mm, 5-µm 

 Mightysil RP-4GP column (Kanto Chemical Co., Inc., Tokyo). The isocratic mobile phase was 0.05 mol/L octanesulfonic acid and the flow rate was 1.0 mL/min. The PDA detector scanned over the range of 190–300 nm, detecting melamine at 202 nm (a maximum for melamine). The column temperature was maintained at 40 °C. The injection volume was 20 µL. 

 An accurately weighed 0.2-mL sample (pasteurized cow's milk) was transferred to a microcentrifuge tube and homogenized with the ultrasonic homogenizer for 30 s with 0.6 mL of water. After homogenization, the capped tube was centrifuged at 12,000

 for 5 min. A 100-µL portion of the supernatant liquid was placed into an Ultracel YM-3 centrifugal ultrafiltration device and centrifuged at 5000

 for 5 min. The ultrafiltrate was injected into an HPLC system. 

The main aim of this work was to develop a technique for determining melamine levels in milk without using any organic solvents.  

 The present sample preparation procedure realized a space-saving extraction and easy purification of melamine in a short time while completely eliminating the consumption of organic solvent. The procedure resulted in a high recovery rate and high reproducibility with considerable cost savings compared to established methods.  

 To achieve the separation with a 100% aqueous mobile phase and optimize a faster separation, the author tested five types of reversed-phase columns. Table I lists the physical-chemical specifications of the columns. This study used water or 0.05 mol/L octanesulfonic acid as the isocratic aqueous mobile phase and examined column temperatures from 25 to 45 °C, HPLC flow rates ≥0.8 mL/min, and HPLC retention times ≤15 min. Because the HPLC separations were performed serially, the time per run was critical for routine residue monitoring. The short run time also affected the method-development time. The five columns were compared with regard to the separation between melamine and its interfering peaks. The chromatographic separation obtained under the condition ranges examined also are presented in Table I 

Columns A, B, and D had difficulty separating melamine and the interfering milk extract throughout the examined condition ranges. No melamine was eluted from Columns C or E. The complete separation of melamine and interference peaks, a symmetrical peak, and a short retention time were achieved with Column B using an isocratic mobile phase of 0.05 mol/L octanesulfonic acid with a flow rate of 1.0 mL/min and a column temperature of 40 °C. This HPLC analysis achieved optimal separation in <5.5 min without the need for a gradient system to improve the separation or pre-column washing after the analysis. The elevated column temperature, 4% carbon content in the column, and ion-pair action of the octanesulfonic acid mobile phase used here were necessary to obtain the findings described. From the data shown in Table I, it is difficult to prove the critical parameter in the column with regard to the retention of melamine and its peak form. 

Figure 1 displays typical chromatograms for milk samples obtained following the procedure developed here. The data shown in the figure demonstrate that the present method can provide quantitation and identification of melamine by HPLC, with the PDA detector set at 202 nm (the maximum absorbance for melamine). The present HPLC system easily confirmed the peak identity of the target compound. Melamine was identified in a milk sample by its retention time and absorption spectrum. The melamine spectrum obtained from the sample is practically identical to that of the standard. The present HPLC system did not require the use of mass spectrometry, which is very expensive and is not available in a lot of laboratories for routine analysis, particularly in developing countries.

Figure 1: Chromatograms obtained from the HPLC system for a blank milk sample (upper profile) and a milk sample spiked with melamine (1 Âµg/mL) (lower profile). The PDA detector was set at 202 nm. Peak 1 = melamine (retention time = 5.1 min).			

 The present method was qualified in terms of analytical performance parameters calculated according to guidance from the United States Food and Drug Administration (9). Table II summarizes the main method validation parameters.

Table II: Method validation data for melamine-fortified milk samples			

The accuracy and precision (average recoveries and their relative standard deviations [RSDs]) are well within the method acceptable criteria for residue analysis established by the United Nations Food and Agriculture Organization's and the World Health Organization's Codex Alimentarius Commission: average recoveries of 70–110% with RSDs < 20% when the spiked level for the analyte is 0.01–0.1 µg/mL, and average recoveries of 80–110% with RSDs < 15% when the spiked level is ≥0.1 µg/mL (10). In terms of selectivity, the target spectrum from the milk sample was practically identical to that of the standard. Because of the satisfactory purification and the high absorbance of melamine, the PDA detector was able to detect melamine at trace levels. The time and budget required for the analysis of a single sample were <20 min and approximately US $6.10 as of January 20, 2011, respectively. For sequential analyses, a batch of 24 samples could be analyzed in 3 h. 

Milk was purchased from a number of convenience stores in Osaka, Japan, and used as real milk samples and analyzed using the proposed method. No samples contained detectable concentrations of melamine. The chromatograms were free from interference. 

In conclusion, the present study describes an easy, space-saving, and organic solvent–free method for determining melamine in milk. The entire procedure, which has a negligible impact on the environment and humans (that is, because there is no solvent consumption), has a short analysis time (<20 min/sample), is inexpensive (approximately US $6.10 per sample), and reproducible (with recoveries ≥88.3% with RSDs ≤3.2%). The method validation data demonstrate the reliability of the method. Given these results, this procedure is proposed as an international standardized method for the routine monitoring of melamine in milk.3 

 is with the Grad-uate School of Human Life Science, Osaka City University, Osaka, Japan. The author can be contacted via e-mail at 

(1) Health Canada. Melamine (web page, accessed January 17, 2011). 

http://www.hc-sc.gc.ca/fn-an/securit/chem-chim/melamine/index-eng.php#who

(2) World Health Organization, Melamine and Cyanuric acid: Toxicity, Preliminary Risk Assessment and Guidance on Levels in Food, 25 September 2008; updated 30 October 2008. 

http://www.who.int/foodsafety/fs_management/Melamine.pdf

(3) Health Canada. Questions and Answers – Melamine. (web page, accessed January 17, 2011). 

http://www.hc-sc.gc.ca/fn-an/securit/chem-chim/melamine/qa-melamine-qr-eng.php#8

(4) S. Tittlemier, Health Canada, Background Paper on Methods for the Analysis of Melamine and Related Compounds in Foods and Animal Feeds, Ottawa, Ontario, 1–4 December 2008. 

http://www.who.int/foodsafety/fs_management/Melamine_1.pdf

(5) Health Canada, Bureau of Chemical Safety, Food Directorate, Health Products and Food Branch, LPS-005. Determination of Melamine in Various Milk-Containing Products by Liquid/Liquid Extraction and Cation Exchange Solid Phase Extraction to Prepare Samples, and Electrospray Positive Ionization Liquid Chromatography Tandem Mass Spectrometry to Quantitate Melamine. Ottawa, Ontario, November 2008. 

http://www.hc-sc.gc.ca/fn-an/alt_formats/hpfb-dgpsa/pdf/pubs/melamine_milk-lait-eng.pdf

(6) M. Rambla-Alegre, J. Peris-Vicente, S. Marco-Peiró, B. Beltrán-Martinavarro, and J. Esteve-Romero, 

 (Oxford University Press, Oxford, United Kingdom, 1998). 

(8) T. Yoshimura, T. Nishinomiya, Y. Homda, and M. Murabayashi, 

Green Chemistry – Aim for the Zero Emission of Chemicals 

(Sankyo Publishing Co. Ltd. Press, Tokyo, Japan, 2001). 

(9) United States Food and Drug Administration, 

Guidelines for Submitting Samples and Analytical Data for Methods Validation,

http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/ucm123124.htm

(10) United Nations Food and Agriculture Organization's and World Health Organization, Codex Alimentarius. Standard CAC/GL 67, Model Export Certificate for Milk and Milk Products. 2008. 

http://www.codexalimentarius.net/web/standard_list.do?lang=en

High recovery, reproducibility, and cost savings are among the advantages of this environmentally friendly method.

A Rapid and Space-Saving Method for Determining Melamine in Milk Under Organic Solvent–Free Conditions

Mass spectrometry (MS) has been a cornerstone technology of the chemical industries including petroleum research and related commercial product manufacturing in polymers. The American Society for Mass Spectrometry (ASMS) will celebrate its 60th anniversary next year. Although christened under its current name in 1969, it began as "ASTM Committee E-14" in 1952. The work of John Fenn and others that brought us electrospray ionization (ESI) and heralded the modern era where biological use of MS predominates was not seen until the 1980s. Before the 1980s gas-phase interests predominated and techniques were commonly performed in vacuum as opposed to atmosphere today and often as a set experiment using a solids probe rather than a flowing or chromatographic serial sample introduction. 

Where is the interest among practitioners today? What generates the most problem solving interest today in the chemical industry? What tools do they employ with good success and which ones do they look forward to — or wish they had? I had an opportunity to speak with Colin Moore, Fellow and Technology Leader in Mass Spectrometry at Chemtura Corporation (Middlebury, Connecticut) about the types of analytical problems his group has been asked to solve recently and what they have learned in the process of solving them; this discussion evolved into a short tutorial on chemical industry practice.

A UK native, Moore worked for Shell Research for seven years doing analytical work on agrochemicals and simultaneously became a graduate of the Royal Society of Chemistry. Postgraduate studies at the University of Southampton were followed by three years at Warwick University and a Ph.D. with Professor Keith Jennings. He joined Uniroyal (now Chemtura Corporation) in 1994 as a member of the MS group, becoming a manager in 1997 and a Research Fellow in 2002. He has 20 publications in peer-reviewed journals and 24 posters and presentations at conferences to his credit. 

Sample analysis in Moore's laboratory generally falls into one of the following areas:  

Confirmation of the chemical structure of the main components and identification of impurities to help synthetic chemists improve the synthesis.

Identification of minor components in a product that shouldn't be there (for example, color bodies).

Identification of additives in a polymer or oil (for example, antioxidants in engine oil).

Figure 1: GCâMS response for an oil sample (upper is a methanolic extract; lower is the oil sample dissolved in methylene chloride). (Courtesy of Colin Moore, Chemtura Corporation.)  

For highly pure samples, analysis by nuclear magnetic resonance (NMR) spectroscopy is probably the best way to obtain detailed structural information and therefore most of the samples that mass spectrometrists are asked to analyze are mixtures. The analyst's first decision therefore is to determine which separation technique will be used: gas chromatography (GC), liquid chromatography (LC), gel permeation chromatography (GPC), solid-phase microextraction (SPME) or some type of liquid–liquid extraction. For example, if an engine oil sample in methylene chloride is shaken with methanol, then the phenolic and aminic antioxidants will be concentrated in the methanol. GC–MS analysis of the methanol extract makes it easy to identify the major antioxidants in the oils (Figures 1 and 2). 

Figure 2: EI spectrum of peak at 13.82 min in the methanol extract shown in Figure 1. (Courtesy of Colin Moore, Chemtura Corporation.) 

An interesting aspect of identifying unknowns, which Moore says he has not seen in the literature, stems from making use of both electron ionization (EI) and ESI spectra of an unknown since the fragmentation patterns can be "complementary" (see the EI and ESI spectra in Figure 3). 

Figure 3: The complementary diagnostic nature of EI and ESI spectra of the same compound. (Courtesy of Colin Moore, Chemtura Corporation.) 

In the EI spectrum the first major loss is a C

 alkyl radical whereas in the ESI spectrum it is loss of the C

 alcohol. For those interested in a quick overview on mass spectra a recent column was devoted to that topic (1) that  also highlights James Little's insights from his experiences solving problems at Eastman Chemical in Tennessee. 

A significant difference in analytical practice between the pharmaceutical and specialty chemical industries is the level of dependence of the latter on GC–MS. Today the pharmaceutical world favors LC–MS. For those interested in the aspects of how LC–MS became what we think of as open access in the pharmaceutical world, I chronicled the insights of a few practitioners on how LC–MS transitioned from a relatively obscure novelty to become the beneficial tool we know today (2). A recent article also examined the ongoing need for development in MS ionization and related technologies to support work in areas where "electrospray just isn't enough" to do the job (3). According to a survey among practitioners in various disciplines, those identified with the chemical materials industry arguably rely on GC three times more than LC. A quick look around Moore's laboratory supports the observation. 

One issue that all laboratories need to address is productivity. Two of Moore's GC–MS systems, an Agilent 5970 (Agilent Technologies, Palo Alto, California) and a Waters GCT (Waters Corporation, Milford, Massachusetts), are fitted with two GC columns in the injection port using a two-hole ferrule. One column is connected to the mass spectrometer and the other to an ancillary detection system such as flame ionization detection (FID). As Moore explained, "As soon as you tell somebody what an unknown peak is in a GC–MS trace, the next question is usually, How much [of it] is there? Collecting the FID data in parallel with the GC–MS data allows us to give our customers semiquantitative data as well as MS identification in a single experiment." Such a practice is analogous to LC–MS data acquisition with an in-line UV detector. 

Moore's future interests include installing a nitrogen–phosphorus detection (NPD) system as well as FID on one GC system because many of the antioxidants that Chemtura makes are amines. Amines as a class are often easily ionized by ESI. Yet in some cases the analytes are just not amenable to the technique. Insufficient polarity, the inability to capture a proton, or perhaps excessive volatility that precludes transport and separation by condensed phase means in an LC could all contribute to ESI failure. Certainly due to its popularity great strides have occurred in recent years to increase the sensitivity, resolution, and overall utility of LC–ESI-MS instruments. Today techniques such as atmospheric pressure gas chromatography (APGC) and atmospheric solids analysis probes (ASAP), analogous to the vacuum solids probes used for years in GC–MS, are viable without sacrificing performance, which was not the case years ago (3,4). 

The ability of software-driven applications to amass increasingly refined data streams has unleashed a data handling problem that crosses into all practices and disciplines. So much so that handling complex data has become a recurring workshop topic at the Conference on Small Molecule Science (CoSMoS) in recent years (

Moore's laboratory uses MassLynx mass spectrometry software (Waters Corporation) to process its MS data. For GC–MS data, workers export the files using the NetCDF converter option in ChemStation software in the three Agilent systems (Agilent Technologies) and then convert the files to the MassLynx format using the Waters DBridge program. NetCDF does not produce a file for the FID system that can be read by MassLynx, and therefore processing the FID trace has to be done using ChemStation. On the GCT system the FID trace is recorded as analog data by MassLynx that can be processed with the MS data. The retention time differences between the two traces can be time-aligned in the chromatogram plots (the peaks come out earlier in the MS data because the vacuum system of the MS increases the helium flow rate in the column connected to the mass spectrometer). 

Understanding the Problem and Designing the Experiment

As an analytical problem, identifying color bodies in polymers is neither trivial nor obvious. In a 2005 paper (5), Moore shows there is often more than one way to solve a problem using mass spectrometry. Analysis by LC–MS with an inline photodiode-array (PDA) detector is probably the best way to identify a new color body. Techniques like time-of-flight secondary ionization mass spectrometry (TOF SIMS) can be used to quickly confirm that the color body on fresh samples is the same as on previously determined samples. 

Moore's 2005 work detailed the analysis of a yellow discoloration on the surface of a compounded ethylene–propylene–diene monomer (EPDM) rubber sample. Surface discoloration of a polymer can result from various phenomena, including contamination, component migration, oxidation, and other chemical reactions. Components rising to the surface can give rise to "bloom," a process in which one component of a polymer mixture (usually not a polymer) undergoes phase separation and migration to an external surface of the mixture, according to the IUPAC definition. Protective waxes can be beneficial, but thiazoles (mercaptobenzothiazole) leading to discoloration of the product are undesirable. For example, oxidation of antioxidants can form color bodies (that is, phenolic antioxidants can form quinone methides) (6). The first step in identifying a color body is to separate it from the polymer often by washing the surface with a suitable solvent. 

The washings result in a complex mixture of the color bodies, additives, and other surface contaminants. Identification of the colored components requires further separation of the mixture, analysis of the separated components, and the ability to ascertain which of the components are colored. The combination of LC–MS-MS with an inline PDA detector is able to do the complete analysis in a single experiment. Moore recalls that when he joined Uniroyal Chemical in 1994, identifying a color body often involved pooling fractions from multiple LC runs to acquire enough material for the particle beam LC–MS system (an early 1990s rather short-lived technique that, although not very sensitive, produced EI spectra from typical LC-amenable analytes not volatile enough for GC–MS). The much greater sensitivity of ESI sources and TOF mass spectrometers has made the process much quicker and easier, illustrating a central point in mixture analysis: the importance of matching the separation technique with the sensitivity of the final analysis technique. 

Matching aspects of the analytical technique is not as simple as it sounds. When light reflects off a colored substance, the reflected light has the complementary color to the wavelength or wavelengths absorbed. Yellow light covers the wavelength range 570–585 nm, but the complementary color to yellow is indigo over the range 420–430 nm. So when processing the LC–MS data, Moore looked for a component with strong absorption over that range of wavelengths. He found one component that yielded the UV and LC–MS spectra shown in Figure 4. Note that the color body is the neutral Cu(II) dibutyl dithiocarbamate, but for it to be detected by the LC–MS system it must be oxidized in the electrospray process to the positively charged Cu(III) compound. A quick review of spectral color properties can be found at 

www2.chemistry.msu.edu/faculty/reusch/VirtTxtJml/Spectrpy/UV-Vis/spectrum.htm

Figure 4: Mass spectrum of the color body (inset) and its UV spectrum. (Courtesy of Colin Moore, Chemtura Corporation.) 

Color bodies are often polar molecules, which means that they are easy to ionize in an ESI source. For some time now, following improvements in the instrument capabilities, accurate mass MS has been recognized as an efficient means of enhancing separation of closely related chemical species. As Moore points out, when "the electrospray spectrum contains few (if any) fragment ions identifying unknowns, [that] requires that either an LC–MS-MS spectrum is acquired and/or exact mass measurements [7] are performed to get the elemental formula of the pseudo-molecular ion." An inline UV detector is sometimes an overlooked adjunct as well in LC–MS. Here the color of the offending samples of course indicates distinct chromophoric benefits. Components separated by the high performance liquid chromatography (HPLC) column absorb at the appropriate wavelength to give the observed discoloration with the added advantage of well-characterized algorithms employed by PDA detectors to distinguish differences in homologous components on the samples that the unaided eye cannot. 

Moore noted another frequently overlooked element in the analytical chemist's trade that bears mentioning: Structure elucidation is made much easier if the analyst has a thorough knowledge of the sample chemistry and its history. 

He also points out that "imaging mass spectrometry is a powerful technique for mapping the concentration of a compound on the surface of a matrix." The technique applies in many fields, including the analysis of inorganic materials, polymers, and biological materials. An early publication discusses TOF-SIMS analysis in which a TOF system measures secondary ions produced by bombarding a surface with high-energy particles (8). TOF-SIMS has been used to detect light stabilizers (9) and antioxidants (10) on the surface of a polymer as well as to characterize the bulk polymer (11). 

In the few years since Moore published his work, a number of techniques operating by various mechanisms on or near the surface of a material (as opposed to techniques requiring analytes of interest be in solution — desorption electrospray ionization [DESI], direct analysis in real time [DART], ASAP, and a few others) have been examined in some detail in this column (4,12,13).

DESI can be used in combination with chemical reactions to improve the selectivity and sensitivity of the analysis. Moore's studies with Keith Jennings and attending presentations by Graham Cooks and John Beynon that emphasized the utility of chemical reactions in MS encouraged him to attempt using novel chemical ionization (CI) reagent gases to help solve problems. "Many antioxidants are alkylated aromatic amines and therefore a paper by Buchanan [14] was of great interest," he says. As shown by the spectra in Figure 5, the technique not only gives the number of aminic protons in the molecular ion, but it also helps identify fragment ions. The 

Figure 5: Deuterated (ND3) CI and EI spectrum of 4,4'-methylenedianiline. (Courtesy of Colin Moore, Chemtura Corporation.) 

Moore has also updated a method first reported by Morgan and colleagues (15) for analyzing zinc dialkyldithiophosphate (ZDDP) in engine oils. Engine oils are complex mixtures of base oils and performance enhancing multifunctional additives, like ZDDP. They are excellent antiwear agents and effective oxidation and corrosion inhibitors (Figure 6). 

Figure 6: Potential alkyl group positions. (Courtesy of Colin Moore, Chemtura Corporation.) 

The original work used negative ion CI to produce chloride ion adducts of the oil without any prior separation. Moore has used an atmospheric pressure chemical ionization (APCI) source and a mobile phase containing methylene chloride to give similar results.  

Note that the mass spectrum in Figure 7 yields two complementary pieces of information about the ZDDP sample and gives the molecular weight of any phenolic antioxidants in the oil. The phenolic antioxidant present in the oil is evident by the response at 

Figure 7: Chloride ion APCI spectrum of an engine oil sample dissolved in methylene chloride. (Courtesy of Colin Moore, Chemtura Corporation.) 

The chloride adduct pseudomolecular ion [ZnL2Cl]

  permits calculating the total number of carbon atoms in the four alkyl groups (R

 , tell us whether the ZDDP was prepared by blending individual ZDDPs or was produced using a mixture of alcohols. Table I illustrates how we can deduce that a mixture of C

Table I: Understanding the comparative diagnostic value of the ZDDP spectrum* 

Though effective for determining the carbon chain length of the R groups, the chloride adduction technique does not show whether the chains are linear or branched. However, collision-induced dissociation (CID)–ion mobility spectrometry (IMS)–CID data may hold the key to solve that puzzle (16). 

If the chromatographic conditions are not ideal for interfacing with MS or if other analytical techniques are going to be used to assist in the identification then LC fraction collection may be the best methodology. Polyalkylmethacrylates (PAMAs) are used as viscosity modifiers in oils (Figure 8)

Figure 8: Generic structure for PAMAs. (Courtesy of Colin Moore, Chemtura Corporation.). Moore has used fraction collection from a GPC system (Figure 9),

Figure 9: GPC trace for oil sample. 99.5% of the sample yields an Mp of 564 and 0.5% having an Mp of 8630 (Mp, molecular weight). (Courtesy of Colin Moore and John Mannello, Chemtura Corporation.)  then pyrolysis GC–MS (Figure 10) and IR analysis to identify PAMAs. If R

 are likely to be either H or methyl and R

 is one of a mixture of alkanes, pyrolysis of this type of polymer gives two series of fragment ions: alkenes and alkyl methacrylates. Thus if R

 then one gets dodec-1-ene and dodecyl methacrylate (Figure 11). 

Figure 10: Pyrolysis GCâMS at 550 Â°C of a PAMA standard (top) and the high mass component from the oil. (Courtesy of Colin Moore, Chemtura Corporation.) 

Moore visited Graham Cooks at Purdue to try using a DESI source to detect the color body (17). Simply spraying the yellow polymer with acetonitrile indeed gave a small signal for the Cu dibutyl dithiocarbamate. Nevertheless, the signal was enhanced when the oxidizing agent I

Figure 11: Acquired EI spectrum(top) and best library match for the peak at 11.79 min found in the oil extract (Figure 10). (Courtesy of Colin Moore, Chemtura Corporation.)

An extension of the thermal investigations coming back into favor may in the not-too-distant future provide yet another chapter for these studies by combining thermal MS capabilities with the surface information derived by atomic force microscopy (AFM) (Figure 12). 

Identifying unknown compounds continues to challenge analysts.

LCGC North America-02-01-2011

A Picture Is Worth a Thousand Words

It's Getting Easier to Be Green

Aggregated Singletons for Automated Purification Workflow

An Emerging Leader: One Year Later

A Rapid and Space-Saving Method for Determining Melamine in Milk Under Organic Solvent–Free Conditions

Problem Solving in the Chemical Industry

The Greening of the Chromatography Laboratory

Still a Young Technology, Chiral Chromatography Makes Big Strides in Pharma

Vol 29 No 2 LCGC North America February 2011 Regular Issue PDF