The first instalment of this series on Troubleshooting Basics (1) was an overview of classifying a troubleshooting problem, and considered some rules of thumb that may be useful to isolate the source of various problems.  

This month we'll be more specific, with a look at pressure. Often the first sign that something isn't right with your liquid chromatograph is an abnormal pressure reading — the pressure is too high, too low, cycling or erratic. In this month's "LC Troubleshooting" we'll look at ways to estimate what normal system pressure should be, as well as some likely causes of various pressure abnormalities. 

Before we can determine if there is a pressure problem, we need to know what the normal system pressure is for a given configuration of hardware, column and mobile phase. Pressure is a result of the resistance to flow of the mobile phase through the system, and the column is the major cause of resistance. Thus, the length, diameter and particle size of the column are important. The mobile phase viscosity and flow-rate are the other main factors. For conventional LC systems (< 6000 psi; < 400 bar), the hardware (pump, autosampler, tubing and detector) contributes little to the pressure, and can usually be ignored. With ultrahigh-pressure LC (UHPLC, > 6000 psi), however, narrow-bore tubing and in-line frits can result in 1000 psi or more of pressure in addition to the column, so the hardware cannot be ignored. 

The easiest way to identify a pressure problem is to compare the current pressure to the normal value. I like to use two kinds of normal reference values. The first is a method-independent pressure measurement, which I'll call the "system reference pressure." To check this, install a new column that is typical of what you normally use, such as a 150 × 4.6 mm, 5 µm particle size (

) C18 column, and an easy-to-replicate mobile phase, such as 50/50 v/v methanol/water. Set the flow-rate and column temperature, for example, 2 mL/min and 30 °C, respectively, and allow the system to equilibrate. Record the pressure under these conditions and you can use it in the future as a reference point. To be thorough, I would also progressively disconnect the fittings at the column outlet, column inlet, in-line filter inlet (if used) and pump outlet; record the pressure after each step. Now you should have a list of pressures at various places in the flow path under these standard conditions. These reference pressures may be handy to help track down pressure abnormalities in the future. 

The second reference value, which I'll call the "method reference pressure," is obtained in a similar manner, but using the normal method settings. If a gradient method is used, record the pressures under the starting conditions. You may want to shortcut the process and just record the pressure with all components installed, with the column inlet disconnected, and with the in-line filter (if used) disconnected — these will isolate the most common sources of system blockage for future reference. Because method pressure rises normally over time as frits and filters collect debris, I like to track the pressure. A convenient way to do this is to add a "starting pressure" item to the data recorded at the beginning of each batch of samples (column serial number, sample batch number, notebook reference and so forth). This can be used for future reference or plotted over time as a control chart to help anticipate pressure problems before they occur. 

You may want to have an estimate of what the normal system pressure should be, just as a cross-check. The technique I like for this uses Equation 2.13a from reference 2: 

 (psi), is a function of the column length, 

 (µm), as well as the mobile phase viscosity, η (cP) and flow-rate, 

 (mL/min). For pressure in bar, divide by 14.5. The mobile phase viscosity will depend on the components in the mobile phase and the temperature. Methanol (MeOH) and acetonitrile (ACN) are the most common organics for reversed-phase LC, mixed with water or buffer. Both MeOH and ACN are more viscous when mixed with water, with a maximum viscosity for MeOH of 50% MeOH in water and for ACN of 10% ACN in water. I have summarized the viscosities of mixtures of MeOH and ACN with water at several compositions and temperatures in Table 1. For a more complete listing, see Table 1.5 in reference 2.

 Table 1: Viscosity of mixtures of methanol or acetonitrile with water. Data from Table 1.5 of reference 2. 

Now we can use Equation 1 to estimate the pressure for a selected method. Several examples are given in Table 2. I have chosen maximum-viscosity mobile phases (50/50 MeOH/water and 10/90 ACN/water) and 30 °C. For example, a 150 × 4.6 mm, 5 µm column run at 2 mL/min with the methanol mobile phase will generate approximately 2000 psi (140 bar) under these conditions. Pressure estimates, such as these, are just that — estimates — and in my experience they may be off by ±20% in many cases, and ±50% in some cases. This is because the resistance to flow of some columns may differ because of packing techniques, and the quoted nominal particle size may not be the true value. For example, a 0.1 µm difference in a nominally 2 µm particle will make a 10% difference in the calculated pressure.

Table 2: Estimated pressures for several column conditions. 

I have included the pressure calculated for the above example and several other common column configurations in Table 2; in each case the columns are selected to give approximately the same plate number, 

, so a similar separation should be obtained in each case (assuming identical column chemistry). A few general observations are in order. ACN generates approximately 60% of the pressure of MeOH, which is one reason it is favoured for UHPLC mobile phases. This is highlighted by comparing the last two rows of each section of Table 2: 1 mL/min with a 75 × 2.1 mm, 1.8 µm column generates too much pressure (18800 psi) with MeOH to operate even under UHPLC conditions, whereas the 11800 psi with ACN makes this flowrate feasible. For comparison, I have also included a shell-type particle. The particle size (2.7 µm) dictates the pressure, but the efficiency of the shell configuration makes them behave like a 2 µm particle in terms of plate number. The right-hand column of Table 2 lists the column dead-time, 

, which can be used to compare run times for the various columns. For example, it may come as a surprise, but a separation on the 100 × 4.6 mm, 3 µm column takes approximately one third longer than the 150 × 4.6 mm, 5 µm column at the same pressure. Also, a 1.8 µm UHPLC column will cut the run time by about four-fold compared with a 3 µm or 5 µm column on a conventional LC system when all columns are run at reasonable system pressures. And finally, the shell-type 2.7 µm particle column shortens the run time by two-fold when compared with a 1.8 µm UHPLC column when both are operated at the same pressure. 

Now that we have a technique to approximate the column pressure, we can see how calculated values compare with the observed values under the system reference or method reference conditions. If you are using a UHPLC, you'll need to add to the calculated value the system pressure observed when the column is removed, which may be 500–1000 psi. 

A gradual increase in pressure over time is a normal symptom of column aging, and excessive pressure is often the first indicator that something is wrong with the system. In some cases, the pressure increase may be large enough to trigger the upper-pressure limit, and system shut-down may occur. High pressure is a symptom that something in the flow path is partly or completely blocked. The most common location for this will be the first frit after the autosampler because it accumulates debris from the sample or other sources. This is one of the reasons I strongly recommend using an in-line frit just downstream from the autosampler. This is a 0.5 µm porosity frit when columns with particles > 2 µm are used; a 0.2 µm porosity frit is used for ≤2 µm columns. This frit is smaller porosity than the frit at the head of the guard column or column, so it will become blocked first. The frit in the in-line filter is easy and inexpensive to change, making it a quick fix of the most common high-pressure problem and a simple way to protect the expensive column from damage. 

Isolate the location of the blockage by progressively loosening fittings, as described earlier, until you find the source of the pressure increase. Remember that when the column is removed, conventional systems (<6000 psi; ≤400 bar) should have negligible pressure, but UHPLC systems may normally have measureable back pressure. 

If the frit at the head of the column becomes blocked, you may be able to correct the problem by back-flushing the column; this is effective approximately one-third of the time. Just reverse the column direction and pump 20–30 mL of mobile phase through the column to waste (not to the detector). If the pressure drops, you can leave 5 µm columns reversed. Check with the column care and use sheet for ≤3 µm columns to see if they can be safely reversed for extended use. If back-flushing does not restore the column, replace the column with a new one. It may be wise to add an in-line filter and/or guard column if column blockage is common. 

If some other component apart from the column, guard column or in-line filter is the source of the blockage, sequentially remove connections until you isolate the location of the blockage. If the tubing is blocked, replace it. Other parts, such as injection valves, may be require disassembly and reconditioning. 

Low pressure usually results from air in the pump, a faulty check valve, or a leak. First, check for the obvious: make sure the flow-rate is set properly and that there is sufficient mobile phase in the reservoir(s). Purge the pump of any bubbles by opening the purge valve and increasing the flowrate to flush 5–10 mL of mobile phase through the pump. If this does not correct the problem, verify that the pump is working properly. Perform a simple check of pump delivery by doing a timed collection of 10 mL of mobile phase in a volumetric flask; the flowrate should be within ±1% of the set point. If the pump still doesn't deliver properly, check to be sure there is sufficient solvent at the inlet to the pump. Remove the supply tubing at the pump inlet (or with a low-pressure mixer, the tubing at the proportioning manifold) and measure the flow in a graduated cylinder. Siphon flow should deliver at least ten times as much solvent to the pump inlet as you need. For example, if you normally run 1–2 mL/min, expect to see at least 20 mL/min of siphon flow to the pump. If there is insufficient solvent at the pump, check for blocked frits in the reservoir or blocked tubing. Still another possible pump problem is a leaky pump seal; replacement of pump seals every 6–12 months should prevent this from happening for most applications. 

Once you are happy with the pump operation, check for leaks elsewhere in the system. You may have been alerted by a leak detector. If this is the case, the leak location should be easy to identify. Otherwise, check each fitting, especially upstream from the column, where the connections are under the most pressure. Look for visible signs of leaks, such as drops of mobile phase or white buffer residues left when leaked mobile phase evaporates. Sometimes a scrap of paper can be useful to help probe for leaks; thermal printer paper works best for this, but it is hard to come by today, so copier paper can be used instead. Cut a triangle of copier paper ≈1 cm across the base and ≈5 cm along the sides. Touch the narrow end to any suspect fittings and it will act as a wick and soak up any small leak, which should be easily visible (thermal paper will turn black). If fittings need to be tightened, it is good practice to do this with the flow off. This is especially true with finger-tightened polyether ether ketone (PEEK) fittings, because the tubing can slip in the fitting if there is pressure in the system when the fitting is adjusted. Any fittings that still leak after being tightened a quarter turn or so past their normal setting should be replaced with new parts. 

Pressure readings that bounce around are usually the result of a faulty check valve or air in the pump. Cycling pressure usually coincides with the piston stroke of one or more pumps. The fixes to this problem are to purge the pump, clean or replace the check valves, and/or replace the pump seals. Persistent problems may be associated with inadequate mobile phase degassing. 

Although most of us now use automatic in-line degassers, these can fail, too. I have had several cases reported to me in the last few months where in-line degasser failure caused system shut-down, pressure fluctuations or erratic retention times.  

If you are running gradients, don't forget that the pressure will increase during a gradient. For example, with a methanol/water gradient of 0–100% MeOH, the setup for the first line of Table 2 would give a starting pressure of ≈1100 psi, would rise to ≈2000 psi mid-gradient, and would end up at ≈700 psi for 100% MeOH. 

We have looked at several pressure-related aspects of the LC system. First, we saw the importance of having a record of normal system pressures that can be used for comparison when a change in pressure is encountered. Equation 1 can be used to estimate the pressure produced by columns of different dimensions and particles sizes. We looked at some of the causes of, and corrections for, high, low and cycling or erratic pressures. These should help you to isolate and eliminate the most common sources of pressure problems. But as one of my colleagues used to say, "Don't forget to check stupid!" It is amazing how often problems are related to something silly that we've done, such as let the reservoir run dry or mis-programmed the controller. 

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

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

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

2. L.R. Snyder, J.J. Kirkland and J.W. Dolan, 

Introduction to Modern Liquid Chromatography

Articles

LC Troubleshooting

What do you do when the system pressure is not what it is supposed to be?

Troubleshooting Basics (Part 2): Pressure Problems

The materials examined by mass spectrometry may have hundreds to thousands of unique components in a wide concentration range. The spectra are very complex — even to a trained eye. Two mass spectra of related but different samples are virtually indistinguishable. So how does one decipher and tabulate thousands of chemical formulas and relative abundance without losing the important details?

As mass spectrometrists, we regularly encounter complex samples. Frequently, we're asked to isolate, identify and quantify analytes from complex matrices such as body fluids or tissue, soil, water, agricultural commodities or process streams.  

When performing examinations in the presence of impurities and chromatographic or spectral interferents, we apply a variety of separation tools and protocols. Eventually, we produce an aliquot of material that is, we expect, clean enough to provide unambiguous results. Yet even then, the extreme lengths we took to produce that aliquot might have failed to deliver us from our dilemma. At such times, we resort to our most sensitive means of analysis — highly selective mass spectrometry (MS) techniques such as ion mobility or multiple reaction monitoring (MRM) — to eliminate isobaric interferences.

For another group of researchers, though, the whole sample 

 the analyte. For them, extracting and analysing a part of the sample is of no use, and only a compositional characterization of the entire sample provides the information required to address the experiment's goals. The materials examined by these scientists may have hundreds to thousands of unique components, in a wide concentration range. The spectra are very complex. Even to a trained eye, two mass spectra of related but different samples are virtually indistinguishable, and it is the identification and relative quantification of their components that tells the story. Nevertheless, looking at tabulations of thousands of chemical formulas and relative abundances can quickly glaze the eyes of any observer, while important details get lost in the blur of numbers.  

The MS analysis of petroleum is a case in point. The March 2008 instalment of this column (1) described in detail the techniques developed by Alan Marshall, Ryan Rodgers and their research team from the National High Magnetic Field Laboratory (NHMFL) at Florida State University. The petroleum samples this group handles are probably some of the most complex materials ever analysed, often yielding mass spectra that include nearly 30000 peaks. Using high performance instruments and specialized software tools to analyse these perplexing spectra, they can assign a chemical formula and relative abundance to each of those peaks.  

Yet even the ability to perform such a remarkable feat is not answer enough. The development of visualization tools that condense tables of chemical formulas into easily interpretable plots and images useful to pipeline operators and refinery process engineers plays a major role in the analysts' success. With such pictures, they can readily convey the composition of a single sample and compare samples from different sources or processing conditions. 

Over the course of more than 20 years of research in petroleum analysis, the NHMFL scientists have become widely recognized as world leaders in this field, with an extensive publication record covering their analysis and visualization techniques. Now, the introduction of a new generation of affordable, high-resolution, high-accuracy mass spectrometers makes the routine generation of high-quality mass spectral data outside the confines of a national laboratory possible. Thus, scientists working in domains other than petroleum have investigated ways to apply these petroleum-analysis techniques to their samples, some of which can be as complex as petroleum. In this column, we will meet three such scientists, from industrial, academic and government research groups, who work with such diverse samples as formulated products, swamp water and atmospheric aerosols. 

A key concept in the analysis and characterization of complex samples is the mass defect. Based in nuclear physics, the mass defect is defined as the difference between the mass of an atom and the sum of the masses of the individual subatomic particles that comprise it. Because of the binding energy that holds the atom together, its mass is always less than the total unbound masses of the particles. In molecular terms, the mass defect is the difference between the sum of the monoisotopic mass of the individual atoms and their mass number, which is the sum of the number of nucleons in the nuclei of those atoms. As described in the earlier column (1), the "exact" monoisotopic mass of the water molecule, consisting of two atoms of hydrogen (

 O) is 2 × 1.0078 + 15.9994 = 18.0106 u. The sum of nucleons is 18, so the mass defect of the H

O molecule is 0.0106 u. The IUPAC standard assigns the unified atomic mass unit to be 1/12 the mass of a C atom; thus, 

 C has an atomic mass of 12.0000 u and a mass defect of zero. 

Given that every isotope of every atom has a slightly different mass defect, each chemical composition also has a unique defect. If two compounds are related by some chemical difference, then their mass defects differ by the mass defect of the difference formula. For example, methane (CH

; 14.0156 u). Their mass defects differ by the mass defect of methylene, 0.0156 u. In recent years, this property has been applied in drug metabolite analysis to perform mass defect filtering of spectra. This technique, developed by Zhang, Zhu and coworkers at Bristol Myers Squibb (2,3), simplifies liquid chromatography (LC)–MS spectra by removing all peaks that do not fall within a narrow mass defect window relative to the substrate. The defect window used is characteristic of a particular metabolic transformation, such as oxidation. The peaks remaining after filtering are likely to be caused by such oxidative metabolites. 

The same property applies for any series of homologous compounds: The mass defect difference between any two successive members of the series is the mass defect of the repeating unit of the series. In 1963, Kendrick (4) recognized that this property could prove useful when analysing petroleum by MS. By scaling the mass axis of a hydrocarbon spectrum by a factor equal to the ratio of the nominal mass of CH

 to its exact mass (14/14.01565  0.9989), the mass of CH

 in the resultant spectrum is exactly 14.000, and all peaks of a given homologous series are exactly 14 u apart. 

As an additional consequence of this rescaling, the mass defects of successive members of the same homologous series are precisely the same because, on this scale, the mass defect of the repeating methylene unit is zero. Two hydrocarbon series that differ by one degree of unsaturation (that is, a chemical difference of H

) are offset by the scaled mass defect of H

. Likewise, when the difference results from the presence of one or more non-CH atoms in the core formula of the hydrocarbon, all pairwise members of the two homologous series (those of the same CH

 count) differ by the same mass defect. Rodgers and coworkers exploit this effect. They create Kendrick dot plots, or images of Kendrick mass defect vs Kendrick mass. Thus, in a two-dimensional dot plot, the peaks of each homologous series form a horizontal row of dots, each row offset from the others by a difference in unsaturation or core composition. 

Such dot plots serve as the basis of the algorithm for assigning chemical composition to these complex spectra. By assigning a 

 chemical formula to one dot (peak) in a row, all other dots in the same row can also be assigned a chemical formula simply by adding multiples of CH

. After one row is assigned, the algorithm jumps to another row using the Kendrick H

 mass defect, because all the dots in the latter row are related to all the dots in the first row by a chemical difference of H

. Thus, the assignment procedure works its way through the entire array of dots, one row at a time, first by assigning a 

 composition to one dot, then tracking through all related dots by chemical formula differences. 

 assignment of an initial composition depends on high mass accuracy. The inherent mass accuracy of the instrument must generally be improved by internal recalibration of the spectrum using a series of target peaks of known composition spaced throughout the spectrum. The NHMFL group has developed several recalibration methods, the most recent of which allows a low parts-per-billion mass accuracy throughout the spectrum (5). At this level of accuracy, a peak of relatively low molecular weight can be assigned a composition unambiguously. 

After a chemical composition is assigned, a petroleum sample can be grouped into subsets by heteroatom content (chemical class) and by degree of unsaturation (the double bond equivalent, or DBE). The DBE is easily computed from the chemical formula: 

where the letters represent the counts of the respective atoms, and the lowest valence of N and P is assumed. The DBE is a measure of the number of rings plus double bonds, and for petroleum samples, higher DBE values are generally associated with highly condensed and aromatic ring systems. In addition to DBE, the chemical formula provides other measures: atom counts and ratios of atom counts. In hydrocarbons, the carbon count is roughly equivalent to molecular weight, so it is common to create smoothed images of DBE versus carbon number for members of the same hydrocarbon class. By colour coding such images, using a gradient based on relative abundance, the image readily imparts a visual indication of the ranges of both DBE and molecular weight, as well as the mean and standard deviation (SD) of each. Comparing two images (such as before and after catalytic cracking to reduce molecular weight and increase the concentration of low-boiling aromatics) should show a shift of the image along both axes to a lower carbon number and a lower DBE. 

Ratios of atomic compositions are used to produce so-called van Krevelen plots. A typical 2D van Krevelen plot uses the H:C ratio as the ordinate and a heteroatom:C ratio (O:C, N:C) as the abscissa. On these plots, compounds of related composition form rows of dots along the horizontal, vertical or diagonals. For example, in a H:C versus O:C van Krevelen plot, compounds related by hydrogenation or dehydrogenation form vertical lines; compounds differing by oxygen count (oxidation or reduction) form horizontal lines. Compounds related by decarboxylation, hydration or dehydration, or methylation or demethylation, form various diagonal lines. 

Solving Formulation Problems Using Graphics

Robert Strife of Procter & Gamble (P&G) is responsible for the characterization of "known unknowns", which are formulated materials whose constituents are all known commercial compounds, but whose identities and relative amounts are unknown to the investigator. In an oral presentation at the September 2010 Conference on Small Molecule Science (CoSMoS) (6), Strife presented two examples of how "mass mapping" (his term for two-dimensional plotting of chemical compositions) allows him to diagnose and solve problems with complex raw materials destined for use in P&G's food or cosmetics products. 

In one application, polyglycerol esters (PGEs), which consist of a polyglycerol backbone esterified with coconut-oil fatty-acid esters (compositions C

), are used as a "green" raw material to form emulsions. To perform adequately in this application, the number of glycerol units and the degree of esterification must fall within an acceptable range and be present in appropriate concentrations. The number of combinations of glycerol units, esterification positions, fatty acid types and multiple adducts makes for spectra of astounding complexity: For a polyglycerol with five glycerol units in the backbone (a "G

"), between 1 and 7 esterification sites, and the five fatty acids listed above, there are 774 combinations. Add to this the possibility of G

, or higher, oligomers, a potential contaminant resulting from cyclization of a glycerol subunit and loss of H

O, and molecular ions from electrospray ionization with H

  adducts, and the spectra become forests of peaks (Figure 1). In such spectra, it is impossible to discern by eye anything more than gross differences between good or bad samples. Often, the difference between a material that performs successfully and one that does not is a subtle variation in the relative amounts of each of the polyglycerol esters.

 Figure 1: Infusion ESI+ Fourier transform orbital trapping MS spectrum of a polyglycerol polyester. 			

To make sense of all this, Strife adapted the Kendrick mass defect plot. Using C

) results in a normalization of the mass axis to one in which molecules with successive glycerol backbone units are exactly 74 u apart. Along the 

axis, the mass defect scale is also normalized to glycerol. Thus, every polyglycerol of the same ester composition, differing only by glycerol count, manifests the same mass defect and falls on a horizontal line. Cyclic glycerol impurities fall on horizontal lines as well, offset from the noncyclic PGEs by the scaled mass and mass defect of water. For a given glycerol count, differences in ester chain length fall on diagonal lines. Finally, the mono-, di-, triesters and higher form neatly separated groups (Figure 2). By transforming and mapping these complex spectra in this way, Strife converts an indescribably complex spectrum into easily interpretable maps by which one can readily discern differences in composition or the presence of unacceptable levels of impurities.

 Figure 2: A Kendrick mass defect map C3H6O2. 			

In a second example, Strife borrows a visualization method from the proteomics field. In a 2009 paper (7), Roman Zubarev and coworkers proposed a two-dimensional plot with the ordinate representing a normalized isotopic shift (NIS) and the abscissa a normalized mass defect (NMD). These two quantities can be calculated from careful measurements of isotope clusters in experimental spectra or from calculations based on chemical formula as follows: 

 is the nominal molecular mass, respectively. The NIS is effectively the normalized difference between the monoisotopic mass and the centroid of the molecular ion cluster's isotopic envelope. Thus, it generally increases with increasing molecular weight (as the abundance of 

 C-containing isotopic peaks increase relative to the monoisotopic 

 C peak) and, more importantly, increases dramatically with the addition of atoms with significantly abundant higher isotopes, such as S, Cl or Br. The NMD axis varies with degree of unsaturation as well as heteroatom content. In his paper, Zubarev used this 2D plot to illustrate how subtle differences in the composition of a peptide could result in large shifts along one or both axes of the plot. 

Strife illustrated how to adapt this "Zubarev plot" to map compositional differences in complex mixtures. As an example, he created a plot of an artificial mixture of polyglycerol esters, alkyl ethoxylates (EO) and alkyl ethoxylates phosphate mono- and diesters. As shown in Figure 3, the various classes are clearly separated, with related materials forming linear clusters of dots. The presence of an impurity would be glaringly obvious as an out-of-place dot on such a map.

Figure 3: A "Zubarev plot" demonstrating clustering and class separation of polyglycerol polyesters (PGE) and various alkyl polyethoxylate (EO) and EO phosphate ester species.			

Mapping the Complexity of Natural Organic Material

Rachel Sleighter, research manager for the group led by Patrick Hatcher at Old Dominion University in Norfolk, Virginia, USA, manages a large research team studying the composition of natural organic matter in dissolved water, soil and sediment, aerosol, fossilized and so forth. The most difficult aspect of this work, Sleighter says, is relating the organic characterization to other environmental observations in a meaningful way (for example, water quality and pollution transport, air mass trajectories or carbon cycling between sources and sinks). The group's primary tool for molecular-level characterization is electrospray ionization, Fourier-transform ion cyclotron resonance mass spectrometry (ESI-FT-ICR–MS). Other tools, such as gas chromatography–mass spectrometry (GC–MS), high performance liquid chromatography (HPLC), nuclear magnetic resonance (NMR) spectroscopy and Fourier-transform infrared (FT-IR) spectroscopy are used to characterize specific components or for bulk-level characterization. 

The FT-ICR–MS spectra include thousands of individual peaks that are carefully calibrated to ensure high mass accuracy. Using a molecular formula calculator developed by the NHMFL team and guided by knowledge of the expected composition of the natural organic matter, Sleighter's group can assign a chemical formula to the majority of the peaks in the spectrum. Given the formulas, Sleighter can apply the visualization methods developed for petroleum samples. Compared to petroleum, natural organic matter is generally highly oxygenated, so van Krevelen plots of H:C versus O:C composition tend to be the most frequently used. On such a plot, various chemical classes within natural organic matter, such as humic or fulvic acids, cellulosic materials and lignins form distinct clusters. Comparison of plots of samples that have undergone chemical or biological transformation reveal characteristic shifts associated with the degree of degradation. 

To better understand these changes, the Hatcher group has recently begun using multivariate statistics to compare large numbers of samples (8). "Because our FT-ICR–MS analyses translate into datasets containing thousands of molecular formulas, it has been a challenge to effectively compare a large number of samples and to determine subtle differences," Sleighter says. 

Multivariate statistical methods like principal component analysis (PCA) identify groups of formulas that seem to indicate important differences between samples. Sleighter commented n those methods when used in combination with van Krevelen plots that map the formula differences into chemical class differences. "We can design new experiments in order to understand these components better or to correlate them with other measurements and environmental observations," she says (Figure 4).

Figure 4: Principal component analysis on a subset of peaks detected in at least one of 38 DOM samples from various terrestrial, estuarine and marine locations and analysed by ESI-FTICR-MS: (a) the biplot of the formula loadings (variables) in the PCA indicate that high positive PC1 and PC2 values correlate to estuarine and marine samples, while high negative PC1 and PC2 values correlate to terrestrial samples; (b) the corresponding van Krevelen diagram of the assigned formulas, coloured according to location on the PCA biplot. Adapted from reference 8.			

In a final example of complexity reduction, the work of Julia Laskin, chief scientist in the Chemical and Materials Sciences Division of the Pacific Northwest National Laboratory, illustrates how the application of multiple orders of mass defect "folding" can reduce an extremely complex spectrum to a small set of points. One area of Laskin's research relates to the chemical characterization of the large organic polymers that constitute a significant fraction of secondary organic aerosols (SOA) and the study of their effects on physical properties of aerosols relevant to climate change. In a recent paper (9), Laskin and coworkers describe how successive applications of mass defect scaling transformation can reduce a complex spectrum to a set of a few dots on a two-dimensional mass defect plot. 

In an ordinary mass defect plot, a single "basis formula" is used to rescale the mass and mass defect axes. As we have seen, such rescaling results in a plot where each homologous series forms a horizontal row of dots, incremented by the nominal mass of the basis formula. Series that differ by degree of unsaturation or core composition form additional rows spaced along the mass defect axis. In this first-order plot, each dot represents a single monoisotopic peak in the mass spectrum. In Laskin's work, application of a second-order mass defect scaling reduces each row of dots to a single dot. In this second-order plot, the abscissa is replaced by the original mass defect axis (the ordinate in the first-order plot), and a new scaling of the ordinate is performed by scaling the first-order defect by a new defect for a second-basis formula. By factoring out the first-order defects in this way, each homologous series collapses into a single dot. This can be extended to a third order using yet another repeat formula as basis, and so on. Each of these folding steps reduces the complexity dramatically, at the loss of the detailed information factored out by the folding. 

Using a petroleum spectrum with nearly 13000 monoisotopic peaks as an example, the first-order mass defect plot (using CH

 as a basis) is a dark blob of unresolved dots. The secondorder of folding uses both CH

 as bases, reducing the 13000 dots into 25 horizontal lines containing a total of 480 dots. By folding both the chain length (CH

) into the ordinate, each of the 25 lines now represents a specific chemical class (containing a core composition of N, N

, S, and so forth). Every dot along a given horizontal class line represents a homologous series of the same degree of unsaturation. Folding a third time, using oxygen (O) as a basis, reduces the 480 dots into only 25 dots. Now each horizontal line represents a class of a specific, non-oxygen, heteroatom composition, with successive horizontal dots shifted by the the effect of zero, one, two, three or more oxygen atoms. From a spectrum of 13000 points, these successive foldings result in a plot of only 25 points, yet the complete heteroatomic composition of the sample is immediately comprehensible (Figure 5). For the analysis of petroleum, natural organic matter, and other such materials whose composition is highly complex but also highly regular, this reduction of complexity should be of great use in both the assignment of chemical composition and comparison between samples.

Figure 5: (a) A Kendrick mass defect plot of a crude oil sample. The mass defect on a CH2 basis is plotted versus the Kendrick m/z, also based on CH2. Each of the nearly 13000 dots represents a single monoisotopic peak in the mass spectrum. (b) The same spectrum, after three successive mass defect folding steps. The abscissa is the third order mass defect, based on CH2, H2 and O, plotted vs the second order defect based on CH2 and H2. In this plot, each horizontal row represents a heteroatom composition class as labelled. Successive dots in each row represent 0, 1, 2 ... additions of an oxygen atom to the core composition. Adapted from reference 9.			

 is the president of Sierra Analytics, Inc in Modesto, California, USA. He obtained a PhD in physical chemistry from the University of Maryland, College Park. He worked as an analytical chemist for the Shell Chemical Company and at E.I. du Pont de Nemours and Co., Experimental Station, and developed mass spectrometry software at HewlettPackard, Fissons Instruments, and Micromass. 

, is principal scientist, MS technology development, at Waters Corp. (Milford, Massachusetts, USA); a former adjunct professor and visiting scientist at Roger Williams University (Bristol, Rhode Island, USA); cofounder and current president of the Society for Small Molecule Science (CoSMoS) and a member of 

 editorial advisory board. Direct correspondence about this column should go to "MS: The Practical Art", 

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

How does one decipher and tabulate thousands of chemical formulaes and relative abundance without losing the important details.

Visualizing the Chemical Composition of Complex Samples

The transfer of capillary electrophoresis (CE) methods from the research and development (R&D) laboratories to routine quality control (QC) laboratories requires that the methods can be repeated in laboratories outside the method development/validation laboratories and possibly on a variety of CE instruments. Method details are also included in marketing applications to regulatory authorities who may select to run the methods. 

 CE historically had a reputation for lack of reproducibility and precision. CE methods were initially developed and reported but attempts to repeat them from literature were often unsuccessful. This perceived lack of repeatability hampered the implementation of routine CE methods because they were considered a high risk. 

The early CE methods developed and validated by industrial laboratories in a variety of sectors — including forensic, clinical and pharmaceutical — were found to be more reproducible because these methods were generated to produce important data that would stand considerable scrutiny. 

The lessons learnt from the early work in industrial laboratories have been incorporated into current methods and CE repeatability has improved considerably. The performance of CE instruments has also improved. CE methods have now been incorporated into numerous regulatory filings for pharmaceuticals and successfully implemented into routine QC testing and batch release. CE is also used routinely in forensics and clinical laboratories. This article will discuss the optimization of repeatability using CE for small molecule analysis. The next installment of "CE Currents" will discuss repeatability for large molecule analysis by CE. 

Are there Reported Examples of Successful Inter-laboratory Repeatability Exercises in Small Molecule CE Analysis?

Many CE methods have been successfully transferred into QC laboratories. However, the confidential nature of work within industrial laboratories means this repeatability data is not published. However, some inter-laboratory repeatability exercises have been reported (1–7).  

A group of eight UK-based pharmaceutical companies conducted a three-part interlaboratory repeatability exercise. This was coordinated through the UK Pharmaceutical Analysis Science Group

The initial method (1) involved the chiral separation of clenbuterol using a low pH phosphate buffer containing cyclodextrin. All companies were able to obtain the separation on the first injection using their own equipment and reagents. One of the participants had never even used the equipment before! Precision and linearity was also successfully assessed and reported. The second method (2) involved use of a micellar method (a simple borate buffer containing 50 mM SDS) to test the dose uniformity of paracetamol content in capsules. All companies obtained the separation and obtained good precision and agreement of results across the group (304 mg/capsule) and with the label claim (300 mg/capsule) and HPLC results (304 mg/capsule) for the batch — an internal standard (IS) was used in the CE analysis. 

The final exercise (3) involved quantification of the sodium ion content in the sodium salt of a cephalosporin drug substance. Quantification used potassium as the IS and a simple low pH buffer containing imidazole to provide the background signal to perform indirect UV detection at 214 nm. All the companies again reported good accuracy (CE assay result was 5.46%w/w versus theoretical content of 5.47%w/w) and precision results. High sample concentrations were used to give large peaks which helped with quantification. These three methods were also successfully used for the practical training of FDA scientists. 

Workers at the University of Jena and Astra Zeneca used statistical experimental designs to optimize and validate a chiral CE method (4) for the impurity profiling of calcium levofolinate including the enantiomeric impurity. The method allowed detection of all the required impurities at 0.1%. The method was very highly described; in particular the preparation of the electrolyte. Various electrolyte and method combinations were assessed to understand the method robustness. Robustness testing was across instrument type and laboratories. The method was fully validated to International Conference on Harmonization (ICH) standards. 

Good repeatability of a micellar CE method (5) used to determine seven antibiotics in pharmaceuticals and feedstuffs. The researchers conducted a very thorough evaluation of factors affecting quantification, for example, noticing that peak height variation was higher in summer when evaporation losses from the samples in the non-temperature controlled autosampler increased. 

Eleven participating academic laboratories attempted to repeat a cyclodextrin-based non-aqueous CE method (6) for quantifying R-timolol content in S-timolol maleate. The trail was designed following ISO guideline 5725-2. Not all participants were able to reproduce the method performance. This was because of differences between the separations obtained on different types of CE instruments — this is discussed in more detail in a later section. Relative migration time precision was measured (n = 6 injections) and results ranged from 0.1–0.7% relative standard deviation (RSD). 

A precision study on CE method for metacycline (7) was performed in an inter-laboratory study across ten academic laboratories. The method conditions were complicated and involved on-capillary chelation with ethylene diamine tetra acetic acid (EDTA), an exact pH of 10.35 and a buffer containing 13%v/v of methanol. Only seven of the laboratories produced sufficient data to be included in the data analysis. A low level of isochlortetracycline was used as an IS that generated precision issues. Currents varied across the various instruments (80–150 µA), which had profound effects on the migration times and selectivity achieved — this was a result of the increased temperatures within the capillaries at higher operating currents. The temperature affects both the flow-rate and the level of complexation/chelation. The method was then redeveloped to improve performance. Modifications included use of a higher IS concentration and optimization of the electrolyte — the method showed improvements but could still only be operated on two of the three instruments.  

What can be Done to Improve Method Repeatability?

The flowchart in Figure 1 shows a method lifecycle to ensure good method repeatability. This is common to both CE and HPLC methods. 

 Figure 1: Method lifecycle to ensure good method repeatability. 

 The design method is the initial step where the purpose of the method is agreed and method development begins. Method development should ideally be performed on an actual sample because sample constituents may affect the separation. For example, the ionic strength of a sample solution may be high which will alter peak shape; this would not be observed for a standard simply dissolved in water. Often generic conditions (such as 50 mM phosphate pH 2.5, 15 mM borate or a standard micellar or microemulsion buffer) provide the required resolution. The method should be kept as simple as possible to maximize repeatability. For example, a low pH separation of two basic compounds relies solely on differences in their charge/size ratios which are physiochemical properties and are very robust. Similarly a simple borate (pH 9.3) buffer containing sodium dodecyl sulphate (SDS) micelles separates neutral compounds based on their solubility which, again, is very robust. The addition of solvents, for example, complicates methods as evaporation losses can cause variability. If a solvent must be added, then a less volatile solvent such as DMSO will give less variance. Variance in the purity of additives. such as cyclodextrins (8), can also affect method repeatability — therefore assess unmodified native cyclodextrins initially before moving to derivatized cyclodextrins that have variable degrees of substitution and each lot will give a slightly different separation.  

Use of kits or dynamic column coating approaches also greatly improves method repeatability and should be considered if appropriate to the separation. 

 The use of an IS greatly improves method performance and enhances repeatability (9). The selection of an IS is generally not complicated, especially if generic method conditions are used. Precision is also related to peak size and greater precision will be obtained when the analyte (and IS) peaks are large. Sample preparation robustness is again improved using an IS and avoiding small transfer/sample volumes. 

 This is a key activity to ensuring method repeatability and should ideally be performed during method development using an actual sample. Use of experimental designs during method development allows mapping of the space around the method conditions (4). For example if a method is set to run at pH 3.5 with a 50 mM buffer, then it is useful to know what performance will be achieved when the buffer is, for example, 55 mM/pH 3.3, 45 mM/pH 3.7 or 50 mM/pH 3.7. Other factors to include in the design are voltage, temperature and sample concentration.  

Data is generated by running attended sequences of methods on the instrument that equate to the various conditions being assessed. The results are then usually presented graphically as contour maps (4). A detailed robustness evaluation allows method limits to be included in the method, for example, the method may state pH 3.5 (± 0.2), 50 mM (± 5 mM) phosphate buffer. The results from the robustness study also show how the method fails and helps guide the selection of system suitability parameters. 

If a method will be run on more than one CE instrument type then this should also be included as a factor in the robustness study because this can have a considerable impact on method performance — see later section. 

 This is another key activity to ensuring method repeatability. It is vital to document all aspects of the method (4) so that it can be successfully repeated. It is insufficient to state use of a pH 2.5, 50 mM phosphate buffer because it does not specify how the buffer is prepared — it is necessary to state (10) what the initial buffer solution is (For example, 50 mM sodium dihydrogen orthophosphate), and what solution is used to adjust the pH. Failure to fully document buffer preparation leads to buffers of variable ionic strength that produce variable levels of current and flow inside the capillary that will spoil the repeatability. The filtration procedure for the electrolyte should also fully defined (10) as this can impact the resulting buffer (For example, some of the SDS in micellar solutions can be adsorbed onto filters, resulting in lower concentrations of micelles and altered selectivity). Factors affecting sensitivity, such as the capillary slit width, use of bubble cells and even detector sampling rates, should all be included in the method. The exact dissolving solvent for sample and standards should also be specified as this can have pronounced effects on the separation performance — once the solvent is determined it should not be altered. 

 This is another important step and is generally controlled via a transfer protocol which states pre-agreed testing and acceptance criteria for the results that will be generated. Method training is sometimes performed for complicated methods where an analyst from the transferring laboratory visits the receiving laboratory and assists the laboratory in using the method. The results for a series of testing by both the transferring laboratory and the receiving laboratory are statistically compared against pre-defined criteria. 

 This activity should happen periodically where an assessment of the method in routine use occurs. If there have been issues with the method then this should be assessed by suitable redevelopment, redefinition of system suitability parameters or tighter controls on the method.  

Why is There a Need to Perform Inter-instrument Type Repeatability in CE?

There are technical differences between CE instruments which can have a significant effect on method performance. The technical differences between HPLC instruments are very small and as such have a very limited impact on the performance of methods. Therefore HPLC methods give similar performance when run on different HPLC instrument types. Table 1 details some of the issues that can arise when methods are transferred between different instrument types. 

It is therefore important that method conditions are determined and fully documented for any instruments that the method will be used on. Regulatory submissions and methods often include two (or more) sets of operating conditions to allow use of multiple instrument types. As previously mentioned instrument type should be included in robustness studies if it is known that the method will be routinely run on more than one instrument type. 

 Table 1: Issues arising from transferring between different instrument types. 

Method repeatability was identified as an issue in the early days of CE implementation and this somewhat hampered the introduction of CE because it was a perceived risk with the technique. Improved knowledge and experience, and improved instrumentation have improved the performance of CE methods and has lead to increased useage. 

CE methods are sensitive to a variety of factors and these should ideally be assessed fully during method development by using experimental designs. If the method will be run on more than one CE instrument type then this should also be assessed during robustness studies.  

Documentation of the final method parameters is important to ensure that the method can be fully replicated and should include important factors such as the pH adjustment procedure which may previously have been overlooked but have a pronounced impact on method performance. Use of validated kits and dynamic coating systems can also markedly improved method repeatability.  

Many CE methods are now in routine use across a number of regulated industries following successful method transfer exercises. These transfers are confidential and therefore not published. It may seem that there is no evidence of CE method repeatability, but good method repeatability is possible and is being routinely achieved when good practices are followed.  

 is an associate director in the pharmaceutical development department at GlaxoSmithKline. He is editor of "CE Currents " and a member of 

editorial advisory board. Direct correspondence about this column should go to "CE Currents",

 , Advanstar Communications, Bridgegate Pavilion 4A, Chester Business Park, Wrexham Road, Chester CH4 9QH, UK, or e-mail the the editor of 

CE Currents

Why is method repeatability so important in capillary electrophoresis?

How to Optimize Method Repeatability in the Analysis of Small Molecules by Capillary Electrophoresis

Elucidation of the ageing chemistry of whiskey aged in oak barrels is important for an understanding of the final aroma and taste. Both volatile and non-volatile species comprise the additional compounds produced during maturation and their analysis requires the complimentary techniques of gas chromatography mass spectrometry (GC–MS) and liquid chromatography mass spectrometry (LC–MS). The recently introduced technology of GC large volume injection (LVI) with solvent venting is used for direct injection of samples without extensive sample pre-treatment. Data deconvolution of the resulting total ion chromatogram (TIC) allows rapid isolation of targets from other co-eluting — mainly fermentation — compounds. LC–MS then reveals additional subtle differences in compound profiles in whiskey matured in oak barrels from different wood species. Ion trap (IT) and time-of-flight (TOF) mass spectrometry (MS) techniques can than be used to give structural information and empirical formula determination on these relatively unknown species. A final important tool is use of an LC active splitter preparative system, where the specific MS signal of a compound is the trigger for isolation and collection. In this way unknown compounds can be collected in high purity for both further spectroscopic analysis and sensory evaluation.

For a premium commercial spirit, such as whiskey, wood ageing or maturation in oak barrels for extended periods is essential to give it the unique taste that consumers like.The various processing stages of barley brewing, fermentation and distillation all contribute a unique sub-set of flavours to the spirit, but it is clear that the final long ageing step in oak barrels has a significant influence on the final distinctive aroma and taste. During maturation the flavour compound changes that contribute to taste (1,2) can be subdivided into four areas:  

The reaction of wood and wood compounds with components of the unaged distillate, including the dominant component: ethanol 

Reactions involving only wood extractives

The processes involved and the final outcome are made more complex by additional variables, including the pre-treatment of the barrel when new, the type and origin of the wood used, and whether the barrel has been previously used in a maturation cycle (3) 

Pre-treatment of the barrel involves heating the inside of the cask and may vary from an intense charring over a fire to more gentle toasting with hot air. The intensity of heating will affect the type and levels of compounds produced from the degradation of wood lignin and macromolecules. In addition to heat pre-treatment an additional mechanism that contributes to flavour involves the production of a complex from wood lignin and ethanol in which ethanol acts as a solvent and a reactant.

The mild acidic hydrolysis of this complex produces simple aromatic phenolic compounds. The majority of published work in this area to date has concentrated on elucidating the mechanisms involved in this degradation of wood to give simpler flavour molecules. An examination of the literature indicates many studies on the development, determination, structural elucidation and sensory properties of oak wood ellagitannins, a large group of polyphenolic compounds widely distributed in plants (4,5). The usual practice is to chemically extract these ellagitannins directly from the corresponding heartwood of the oak species that has initially been converted into oak chip or sawdust form. Interesting results are presented but practically no linkage is established between the species identified and their possible presence in spirits that have undergone a "natural" maturation cycle. Methanol–water extracts of oak wood chips, and wine and spirit samples actually aged with oak chips have also been used to analyse and determine low-molecular-weight phenolic compounds (6,7). Again, the problem is that this much shorter, forced ageing with a hugely increased wood-to-spirit surface exposure cannot replicate the intricate complexity and result of a natural ageing cycle. 

In this article we concentrate entirely on the comparative analysis of the actual pot distillate at various intervals of barrel maturation extending to 20 years in both new 

 (European) wood. Six of each of these barrels were made to identical dimensions of 500 litres volume, were lightly toasted on the inside and had no additional treatments or seasoning to uniquely allow the wood effect to be expressed in the subsequent analyses.  

Figure 1 visually compares the unaged spirit (parent) before cask filling with the equivalent at 20 years from the two wood species and clearly illustrates the remarkable influence of the wood. Needless to say the sensory properties of the two aged samples in terms of both aroma and taste are also dramatically different. Gas chromatography–mass spectrometry (GC–MS) and liquid chromatography–mass spectrometry (LC–MS) analysis were performed. GC–MS was specifically used to investigate compounds associated with wood toasting and lignin breakdown. In our opinion comparative analysis of those compounds, mainly of fermentation origin, originally present in the unaged distillate, provides only limited information in the context of changes induced by maturation. 

 Figure 1: Visual changes in unaged parent whiskey (centre) after 20 years in new Quercus robur (left) and Quercus alba oak barrels. 

Direct large volume injection (LVI) of actual spirit samples with solvent venting of the ethanol–water matrix was used for GC–MS analysis. This injection technology removes the need for extensive sample preparation and concentration, and offers a boiling point recovery of target analytes when correctly optimized. Less volatile compounds were analysed by different LC–MS techniques using atmospheric pressure chemical ionization (APCI) in the negative mode. Ion trap mass spectrometry (IT-MS–MS) was used for structural information and the accurate mass time-of-flight (TOF) technique was used to determine the compound empirical formula. 

The most widely applied LVI technique uses a PTV (programmable temperature vaporization) inlet in solvent vent mode and important parameters are the PTV temperature, the carrier gas venting flow and the sample introduction rate. If the solvent is not removed at the same time as a vapour as it enters the injector liner, then analytes will simply be removed with liquid solvent through the split line. The goal is to recover and retain the maximum amount of analytes from the solvent for subsequent splitless transfer to the column.  

Research groups in Germany and Holland have pioneered research into the fundamentals of this technique and have explained the interactions between the variables involved (8–10). In the case of distilled spirits the "solvent" will be the natural ethanol-water matrix of the sample and while the maximum rate of injection for a particular solvent can be theoretically calculated from the solvent vapour pressure, split gas venting flow and the PTV temperature, an additional reduction of this maximum is necessary when water is present. This is because the high latent heat of vaporization of water induces an additional cooling effect in the liner, and the venting gas flow will have a lower than theoretical saturation with water vapour.  

For the results described in this article 100 µL of spirit samples were successfully injected with 60% water at a PTV temperature of 40 °C, a solvent vent gas flow of 500 mL/min and an injection rate of 12 µL/min. This latter parameter is critical because doubling the injection speed results in more than 80% of the analyte being lost compared with the optimum injection rate of 12 µL/min. Under optimum conditions recovery data for 100 µL injections of a compound test mix compared with standard 1 µL injections of prorata concentrated solutions show more than 90% recovery of analytes and good area reproducibility vis-à-vis added internal standards (ISs). Proper column choice is also important and we use an apolar CP-Sil 5 CB (Agilent Technologies, Santa Clara, California, USA) with dimensions 25 m × 0.15 mm i.d. × 2.0 µm d

Injection of 100 µL will also transfer substantial amounts of non-target ester and acid analytes to the column and the thick film with phase ratio β = 19 provides the high capacity needed for retention of good analyte peak shape. The large injections were performed on disposable 2 mm i.d. plain glass liners packed with glass wool. This is useful as more frequent liner changes are obviously necessary. Unattended sequences can be accommodated by using an automated liner exchange module (Gerstel, Mülheim an der Ruhr, Germany). 

A recent publication (11) describes a 5–10 µL solvent injection on an acidic wax column of distilled spirits for major fermentation esters and acids and a limited number of minor compounds associated with cereal raw material preparation and wood ageing. The present work is an extension of this successful approach, but introducing much larger volumes requiring injection speed programming and using a high temperature apolar phase for elution of less volatile analytes. Finally, because the mass spectra of target analytes will be known, expected fragment ion ratios can be used by deconvolution software (Ion Signature Technology, North Smithfield, Rhode Island, USA) to return a reconstructed ion chromatogram (RIC) of target analytes only, from the generic TIC. In this way a useful synergy is achieved with LVI simplifying the first rate limiting step of sample preparation and deconvolution the second rate limiting step of data reduction. 

Table 1 describes the target compound data base for deconvolution of these compounds from the standard total ion chromatogram (TIC). This database is first established, either from the spectral data of expected compounds (vanillin, etc), literature data on monomeric lignin-derived products after thermal degradation of wood (12,13), or similar compounds after pyrolysis GC–MS of plant and lignin materials (14,15). Lignins may be pyrolyzed reproducibly to produce a mixture of relatively simple phenols resulting from cleavage of ether and certain C-C linkages. Other sources for identification of candidate target compounds are LC–GC–MS and GC–GC–MS (16). Deconvolution processes the scan data file looking for the expected relative abundances (Table 1) associated with each target and provides a list of compounds found and their relative concentrations. This process is more comprehensive than selected ion monitoring (SIM) because the software identifies and discards contributions to the spectra that have not originated from the target compounds.

Table 1: Database for target compound GCâMS deconvolution after large volume injection with solvent venting. 

Figure 2 shows an overlay of the TIC and associated target RIC for a 100 µL injection of a composite 20-year-old sample from the 

 casks, and demonstrates the ability of this procedure to extract a more meaningful pattern from the standard TIC. 

Figure 2: Overlay of a GC total ion chromatogram (TIC) and corresponding target compound reconstructed ion chromatogram (RIC) after 100 ÂµL injection of aged whiskey. 

Figure 3 shows a comparison of the target RIC's for the 20 year old 

-matured samples. Additional complexity is apparent for the 

 sample, but this mainly reflects an increased response for many of the same target compounds. This type of rapid profiling can be very useful for cask quality assessment and will also in authenticity verification. We will now look at how LC–MS technology applied to the less volatile compounds reveals unique and interesting differences between these samples.

Figure 3: Comparison of GC RIC traces for 100 ÂµL injections of aged whiskey from Quercus robur (left) and Quercus alba oak barrels. 

In contrast to GC–based studies, relatively few publications feature HPLC–MS or the characterization of important oak wood compounds in aged distilled spirits (17). The principal application has been high performance liquid chromatography with diode array detection (HPLC–DAD) to quantify vanillin-type phenolic aldehyde structures, as well as their associated acids and coumaric acid side chain derivatives (18). Where LC–MS has been used the main ionization mode has been electrospray ionization (ESI), which requires care to avoid problems of ionization suppression and adduct formation. In this study atmospheric pressure chemical ionization (APCI) in the negative mode was used because this provides highly selective detection for compounds with an acidic ionizable phenolic hydroxyl group. Mass spectra show almost no fragmentation and predominantly give the [M—H]

  ion after proton abstraction with much reduced chromatographic background compared with electrospray mode. APCI and electrospray are relatively weak ionization techniques and therefore the molecular ion [M–H] is usually dominant in the mass spectra of compounds. This facilitates the use of extracted ion chromatograms (EIC) of the molecular ion for quantification. Good separation was achieved on a 250 × 4.6 mm Zorbax SP-Phenyl column (Agilent Technologies) in reversed phase (RP) mode using 5 mM formic acid and methanol as eluents. 

Figure 4 shows the comparison of the TIC–MS chromatograms obtained during the analysis of aged 

 samples and also the UV trace (λ = 290 nm) for an aged 

 sample. Compounds in the region up to 35 min are the welldocumented vanillin-type structures and are detected in both MS and UV modes. More apolar later eluting species, with typical molecular weights from 400–700 Da, are only amenable to MS detection. The TIC–MS chromatograms of whiskey from both woods at the same age shows that, for the earlier eluting phenolic aldehydes and acids, the concentrations are generally higher from 

. Later eluting compounds with higher molecular masses show substantial interwood concentration differences with significantly higher concentrations from 

Figure 4: Comparison of TICâMS APCI negative traces for aged whiskey from Quercus robur and Quercus alba oak barrels with the corresponding LCâUV trace for aged whiskey from Quercus robur barrels. 

The previous section has shown how a single mass analysis in APCI negative mode is well suited for method development, basic characterization of the sample composition and quantification of interesting compounds. However, more advanced MS techniques such as Ion trap (IT) and time-of-flight (TOF) offer the possibility of obtaining structural information of interesting compounds. Mass spectral databases for LC–MS techniques are generally not available and therefore structure determination of unknown compounds needs to be performed by complex experiments which involve all available LC–MS techniques. 

Table 2: Fragmentation and exact mass data on interesting low volatility compounds after LCâIon Trap and LCâTOF. 

Table 2 summarizes all LC–MS data on most of the compounds detected in whiskey from both wood species. With ion trap, up to MS

  fragmentations were performed to reveal interesting substructures of compounds. The masses of these fragments are listed in Table 2. Without changing the chromatography exact mass information was obtained from TOF measurements on the molecular ion [M—H]. Partial overlap of the signals as well as varying concentration can influence the accuracy of the mass determination. Therefore, the exact masses and calculated formulas were recorded only for those non-overlapping main compounds present at fairly high concentration. These are listed in Table 2. 

The use of these techniques can be illustrated by considering the fragmentation of compound 12 (Table 2), 

 = 419, identified as lyoniresinol, and the earlier eluting compound 11, 

 = 581, identified as glucolyoniresinol. Lyoniresinol (19) with its typical phenolic aldehyde structure has four methoxy groups resulting in the expected sequential losses of 

 = 404, 389, 374 and 359. Figure 5 shows the parent lyoniresinol structure and its MS, MS

  spectra together with the corresponding fragmentation patterns for glucolyoniresinol. In general, compounds which are formed by the addition or condensation of smaller molecules to lyoniresinol should show similar fragmentation and in this respect it can be seen that the MS

  spectra respectively of glucolyoniresinol are identical with the MS

  spectra of lyoniresinol. Compound 11 is a derivative of lyoniresinol and is also significantly more polar. The most probable formula obtained from the TOF measurement and proposed chemical structure of glucolyoniresinol is presented in Table 2. The reaction of phenolic compounds with glucose in plants has been extensively described in the literature (20), an additional new example is compound 5 in Table 2, identified as glucogallic acid. An analogous relationship can be established between compound 25 (

  pattern of compound 31. Compound 25 is again a glucoside of compound 31. Additionally, compound 31 has isomers with significantly lower concentrations and among these compound 28 is the isomer with the highest relative concentration and identical fragmentation to compound 31. 

Figure 5: Comparison of Ion Trap LCâMSâMS fragmentation of Lyoniresinol and Glucolyoniresinol. 

Fragmentation pattern and exact mass experiments can give information on structural details and the possible formula mass of a compound, but for the final determination of the compound chemical structure it may be necessary to isolate the compound for further spectroscopic examination.  

Compound isolation also provides pure material for sensory examination and spiking at various levels into spirits at various stages of wood maturation. Previous data has shown that the ultra-violet (UV) response of new unknown high molecular compounds is very low and therefore we use preparative LC–MS for the isolation of compounds. This system is outlined in Figure 6 and is based on the "active splitter" principle where the MS signal for the target compound is used to divert the compound to the fraction collector. The fractions were cut with the MS in selected ion mode and using the typical APCI negative 

 of the target. Progress so far has provided the following three main compounds at high purity: Compound 12 (

 = 501) at the milligramme level. Purified samples will be further investigated for structure determination by 

Figure 6: LCâMS configuration for preparative isolation of compounds. 

Various novel GC–MS and LC–MS techniques have been used to investigate quite obvious visual and sensory differences in whiskey matured in oak barrels made from different wood species. Substantial information can be obtained by using approaches that allow direct analysis of samples without extensive sample preparation. In GC–MS this is achieved by on-line concentration in the inlet by LVI with solvent venting of the dominant ethanol-water fraction followed by target compound deconvolution, and in LC–MS by using atmospheric pressure chemical ionization (APCI) as a selective detection mechanism for the compounds of interest. LC–IT-MS provides molecular fragmentation information to assist in structure determination and LC–TOFMS provides exact mass information for empirical formula determination.  

, PhD, is Head of Technical Development at Irish Distillers, an important subsidiary of Groupe Pernod Ricard. He has published extensively on modern GC sample introduction techniques applied to distilled spirits, where much larger volumes than standard can be introduced with matrix removal incorporated into the injection procedure. He represents Ireland on the Technical Committee of CEPS, the European Spirits Producers Association. 

, PhD, is a Senior Scientist at Irish Distillers, Pernod Ricard, and has experience in state-of-the-art separation science and chromatographic techniques. She previously worked in the environmental and pharmaceutical sectors with a specialty in LC–MS and LC–TOF-MS techniques. Her main focus is on the methods development, validation and compound identification. 

, PhD, is the owner and general manager of TeLA GmbH, a contract laboratory in the field of Food Safety Analysis. Norbert Helle has more than 15 years experience working in food safety and environmental analysis for various German Federal and State agencies, mainly in the field of HPLC–MS. He chairs two working groups under the German § 64 Foodstuffs and Commodities Act charged with developing food safety analysis methods for phycotoxins and biogenic amines. 

 is the Head of Research and Development at TeLA GmbH, including method development on HPLC–MS, HPLC–MS–MS and ICP–MS instruments for the determination of residues of pesticides, biotoxins and drugs. She oversees various national research projects on preparative and analytical HPLC–MS and HPLC–MS–MS work in the area of food safety. Meike Baden is a member of a working group under the German § 64 Foodstuffs and Commodities Act charged with developing food safety analysis methods for phytotoxins. 

Describing developments in LC–MS and GC–MS that reveal the ageing chemistry of whiskey aged in oak barrels.

LCGC Europe-09-01-2011

Troubleshooting Basics (Part 2): Pressure Problems

Visualizing the Chemical Composition of Complex Samples

How to Optimize Method Repeatability in the Analysis of Small Molecules by Capillary Electrophoresis

Advances in the Ageing Chemistry of Distilled Spirits Matured in Oak Barrels

Event News

Highlights of HPLC 2011