Have you wondered how your gas chromatography (GC) system sets and controls gas pressures, flows, and carrier gas velocities electronically? Here, we describe the requirements for and the operation of electronic gas control systems for GC columns and detectors.

Computerized or electronic pneumatic control (EPC) systems for carrier gas, split flow control, and detector gases abound in modern gas chromatographs (GC), as well as in headspace samplers and column switching systems. The accuracy and repeatability of EPC are superior to that of manual adjustment, and the improved control of instrument parameters greatly reduces the possibility for making gas-related mistakes. Computerized pneumatics excel at controlling column pressure drop or detector gas flow rates. An EPC system generally relieves operators from having to make repetitive adjustments and measurements with a flow meter and stopwatch. However, running an EPC system blindfolded, so to speak, by never cross-checking actual gas behaviour with selected set-points, only invites trouble. Like any computer system, the results can only be as good as the column and gas parameters that a user enters. Thus, a good working understanding of how an EPC system works and what goals are to be accomplished is essential for obtaining the best possible results. 

In an EPC system, gas flows from the gas supply input, through a metering valve, into a pressure or flow transducer, and then out to the device — inlet, detector, or other GC component — that consumes the gas. The GC system sends a set-point value to the EPC controller, which returns the measured flow or pressure value from its transducer. The set-point and actual values are compared in the EPC system, which adjusts the metering valve as required to maintain the desired set point. The EPC controller incorporates column and carrier gas characteristics to determine the necessary pressure drop at any given moment. Atmospheric and gas supply pressures plus controller temperatures are included as required to compensate for drift and instabilities. 

In the simplest configurations an EPC channel acts as a carrier gas flow controller for a packed column or controls a detector gas, such as air or hydrogen, for flame ionization detection (FID). Two controllers — one pressure and one flow — can provide the split flow and inlet pressure for a capillary column inlet splitter. Other more complex applications include pressure or flow controllers for auxiliary devices such as purge-and-trap or headspace samplers, or pressure-switching controllers for multidimensional column systems. 

Computer-controlled pneumatics cannot prevent operators from selecting inappropriate column pressures or split flow rates. An operator may easily establish incorrect conditions and become misled as to the reasons for a problem. Because of their complexity and flexibility, computerized pneumatic systems offer analysts more opportunities for errors. 

One of the most common applications for EPC is the control of capillary (open-tubular) column carrier gas. The column flow rate and the carrier gas linear velocity are complex functions of the column dimensions, oven temperature, and type of carrier gas. The mathematical relationships between GC column dimensions, temperature, and flow, pressure, and linear velocity are well understood. Fortunately for today's GC users, all of this is incorporated into the EPC system, which acts as a kind of gas calculator. 

A wide-bore 30 m × 530 μm column is a good example to help understand the ins and outs of EPC control. At 50 °C with helium carrier gas this column will require about 4.1 psig of inlet pressure to maintain a flow of 5.6 mL/min or an average carrier gas linear velocity of 40.0 cm/s. I know this because I used the EPC "calculator" in my GC system to compute the pressure, flow, and velocity by first entering the column dimensions, carrier gas type, oven temperature, and desired velocity of 40 cm/s. Because I am using a split inlet, I entered the split ratio as well. With this column, a desired split ratio of 20:1 results in a split-flow set point of 112 mL/min. 

An alert reader will realize at this point that they could have entered an incorrect column length or internal diameter, or perhaps failed to correct an existing set of parameters from the previously installed column. This type of error would, for the most part, go unnoticed by the GC system itself unless a required pressure or flow could not be attained by the controller. Suppose that the column internal diameter was mistakenly set at 320 μm. Entering a velocity of 40 cm/s would result in a pressure drop of 20.7 psig. This is achievable by the EPC controller, which upon increasing the pressure to that level would cause the actual 530-μm column flow to increase to 41.9 mL/min with an average linear velocity of 190 cm/s! The split ratio of 20:1 would still call for a split flow rate of 112 mL/min, and so the actual split ratio would be more like 2.67:1.  

These erroneous operating parameters are within the capabilities of the split inlet pressure and flow controllers, so the GC system would not report an error even though the column was operating very far from its desired set point. A subsequent injection might alert the operator to a problem; the peaks would be eluted too rapidly and they would be much larger than expected. If not identified immediately, this type of problem could cause serious difficulties later on. Of course, if the retention times were known under the correct conditions then this situation would be evident after one injection.  

Rather than wait until after a run is recorded to discover such errors, the operator can double-check flow rates or unretained peak times after a column has been installed and the preliminary setup completed. Time an unretained peak or measure the flow at the column outlet and compare that to what the EPC system says they should be. Bear in mind that direct flow measurement is more difficult with smaller internal diameter columns, and if necessary use an electronic flow meter that is rated for low flow rates. 

Small deviations in the actual column internal diameter or length will give rise to relatively small errors in actual velocities or flows, as discussed in more detail in another "GC Connections" instalment (1). For the best accuracy, it's a good idea to include the stationary phase film thickness, if greater than about 1 μm, with the column dimensions entered into the GC system. 

The pressure drop that's required for a particular column flow depends on the oven temperature as well as the column dimensions and carrier gas, so the operator must specify the temperature at which the desired flow is to be achieved — this is nearly always the same as the initial oven temperature. So far so good, but what happens when the column temperature increases during oven temperature programming? The column flow rate depends on the oven temperature, as well as many other factors, so the flow and velocity will change during temperature programming. The EPC split inlet controller maintains a set pressure level and does not directly control flow or velocity — it must instead calculate the pressure required to establish a desired flow or velocity. What are the effects of choosing these different pneumatic control modes? 

 As temperatures increase so does the carrier gas viscosity, which causes the flow and velocity to decrease at higher temperatures if the pressure is kept constant. Figure 1 shows the relationships of temperature and gas viscosity for three common GC carrier gases. EPC systems use these relationships to calculate carrier-gas viscosity dynamically as the oven temperature changes. Figure 1 gives a good idea of how large this viscosity effect can be. Going from 50 °C to 250 °C, for example, causes helium viscosity to increase by about 40%. The other carrier gases undergo viscosity changes of a similar magnitude.  

Figure 1: Carrier gas viscosity as a function of temperature (adapted from reference 2). The dots correspond to published measurements; the solid lines represent interpolated data and the dashed lines extrapolated data.			

With a constant inlet pressure drop, the column flow decreases during temperature programming. Figure 2 illustrates the effect on carrier gas flow and average linear velocity of changing the oven temperature while holding the pressure drop constant, using our example wide-bore column with a constant 4.1 psig of helium carrier gas. The column flow rate of 5.6 mL/min at 50 °C decreases to 2.5 mL/min at 250 °C, a 55 % loss. Across the same temperature range, the average linear velocity decreases from 40 cm/s at 50 °C to 28 cm/s at 250 °C, a 30% loss.  

Figure 2: Theoretical effect of column temperature on (a) corrected carrier gas flow and (b) average linear velocity with constant pressure drop. Column dimensions: 30 m Ã 0.530 mm; pressure drop: 4.1 psig; carrier gas: helium; column outlet pressure: 1 atm.			

When using a constant inlet pressure with temperature programming, the reduction in flow rate at elevated temperatures can cause unwanted peak broadening as the carrier gas velocity departs from optimum, and it also can extend elution times unnecessarily. Usually in this situation, choosing a different pneumatic operating mode will produce better results. 

An EPC system can be programmed to increase the column pressure drop sufficiently to maintain a constant carrier-gas flow rate as the temperature increases. Figure 3 shows how this choice affects the pressure drop and the average linear velocity. The EPC controller maintains a constant column flow of 6.0 mL/min by increasing the pressure drop from 4.5 psig at 50 °C to 9.1 psig at 250 °C. Note that the average linear velocity increases as the oven temperature goes up, which perhaps runs counter to intuition. But with a constant flow rate the linear velocity can remain in a more efficient region somewhat higher than optimum, which will largely avoid column efficiency losses. Peaks will be eluted sooner and at lower temperatures, reducing the total run time compared to constant pressure operation.  

Figure 3: Theoretical effect of column temperature on (a) carrier gas pressure drop and (b) average linear velocity while maintaining constant corrected column flow. Column dimensions: 30 m Ã 0.530 mm; column flow: 6.0 mL/min; carrier gas: helium; column outlet pressure: 1 atm.			

 Large shifts in column flow rate can affect detector operation. Constant column flow is desirable for consistent detector function during temperature programming. Mass spectrometry (MS) detection solute ionization fragmentation patterns depend somewhat on the source pressure, which in turn depends on the incoming flow rate, especially with higher column flows. Granted, the 530-μm column example discussed here is not the best choice for direct interfacing to a bench-top mass spectrometer, but the benefits can be significant in terms of consistent spectra and library searches for those narrower i.d. columns that can be connected directly.  

When FID is used with hydrogen carrier, it's a good idea to keep the total hydrogen flow through the detector constant. This can be accomplished in two different ways, either by maintaining a constant column flow rate or by programming the detector hydrogen flow to compensate for changes in the column flow, that is, by keeping the sum of the column and detector hydrogen flows constant. 

Ultimately, the most important consideration may be peak resolution and not necessarily detector performance, especially in situations where the separation is marginal. Deciding whether constant pressure or constant flow will yield a better temperature-programmed separation, let alone the effects of changing the temperature programme itself, is a more complex consideration that doesn't give itself over very well to purely theoretical modelling. Relative peak positions in a chromatogram depend heavily on the thermodynamic relationships between the stationary phase and a solute's chemical characteristics. As a result, individual peaks move in different ways relative to each other as the temperature changes. 

In general, the more the chemical natures of a pair of peaks differ, the greater the effect of changing the column temperature or temperature programme will be. At higher flows peaks will be eluted earlier in a temperature programme, and therefore at somewhat lower column temperatures. But this change of elution temperature may or may not improve the separation of a pair of peaks if both are not affected in the same way. This effect is illustrated in the series of test chromatograms shown in Figure 4. Here, a test mixture was injected with either constant pressure or constant flow programmed pneumatics, with the initial pressure drop the same in either case. The programme rate was doubled in each of four consecutive runs, from 3 °C/min to 6 °C/min, 12 °C/min, and finally 24 °C/min.  

Figure 4: Series of chromatograms at increasing oven temperature programme rates with constant pressure or constant flow controlled pneumatics. Temperature programming from 50 Â°C, hold 2 min, then to 250 Â°C at: (a) 3 Â°C/min; (b) 6 Â°C/min; (c) 12 Â°C/min; (d) 24 Â°C/min. Constant pressure at 4.1 psig (P) or constant flow at 6.0 mL/min (F). Column: 30 m Ã 0.530 mm, 1.3-Î¼m df EC-Wax. 1.0 Î¼L manual injections split 20:1 at 200 Â°C. Peaks: 1 = n-C10, 2 = n-C11, 3 = n-C12, 4 = n-C13, 5 = 2-octanone, 6 = 1-octanol, 7 = 2,6-dimethylaniline, 8 = 2,4-dimethylphenol. 			

The overall effects of constant flow compared to constant pressure operation are clear in these chromatograms: The peaks are all eluted sooner with constant flow. The last peak in the run, 2,4-dimethylphenol (DMP), is eluted at 24.85 min with constant pressure and at 22.79 min with constant flow at 6 °C/min. Similar reductions in retention times hold for the other peaks when the flow rate is kept constant, across all of the selected programming rates. 

The effects on retention times of increasing the temperature programme rate far outweigh the differences observed between constant pressure and constant flow pneumatic modes. The DMP peak moves in from 40.66 min at 3 °C/min (elution temperature = 166 °C) to 11.1 min at 24 °C/min (elution temperature = 250 °C). Again, there are no surprises here — the temperature effects are quite strong. 

The overall effect on peak resolution, however, is somewhat unexpected. The two closest-eluted peak pairs, 

-decane–2-octanone and 2,6-dimethlyaniline (DMA)–DMP, show opposite trends. Resolution increases with temperature programme rate for the first pair but decreases for the second pair, regardless of the pneumatic programming mode. Interestingly, the constant pressure mode gives slightly better resolution than the constant flow mode for the first pair, but makes little difference for the later peak pair. Figure 5 plots the resolution of these two peak pairs as a function of the temperature programming rate and the pneumatic mode.  

Figure 5: Peak resolution (RS) as a function of temperature programming rate and pneumatic mode for (a) n-C13 and 2-octanone, and (b) 2,6-dimethylaniline and 2,4-dimethylphenol. P = constant pressure and F = constant flow. Conditions and chromatograms from Figure 4.			

Although the resolution of DMA–DMP is high in all cases — there is no reason to be concerned with this peak pair itself — if there were other peaks between these two in a "real" sample, then it would be interesting to find an optimum set of conditions for the overall chromatogram instead of choosing the best case for the first peak pair. A temperature programming rate of 12 °C/min might be a good choice because it keeps the resolution of the first pair while still utilizing a good portion of the available resolving power between the second pair.  

A better choice might be to use a two-ramp temperature programme instead of a compromise single-ramp programme. At the time that the first peak pair is being eluted, the later-eluted DMA–DMP pair has not started to move along the column much, so cutting the temperature programming rate back to 6 °C/min or even 3 °C/min halfway through the run would make some sense. That way, the later-eluted pair would behave in much the same way as found by running the entire chromatogram at the lower rate, while the earlier pair would get the full advantage of the maximum available resolution. Of course, other solutions might include changing to a narrow-bore column or investigating the effects of a different stationary phase composition.  

Overall, these examples demonstrate that there can be significant differences in how peaks behave relative to each other as the column pneumatic and thermal conditions are varied. Clearly, such changes can affect resolution in ways that may not be anticipated. Chromatographers should always validate and confirm that required peak resolution and quantitation performance levels are met after making any changes to the chromatography.  

Computerized pneumatic control adds a very capable multipurpose tool to the chromatographer's tool belt. Such systems make setting up a GC system easier by automating some tasks. They can give more repeatable results from run to run and laboratory to laboratory. Some detector performance improvements can be obtained with constant carrier-gas flow control. And computerized pneumatics excel in facilitating advanced separations techniques. But when it comes to chromatographic performance gains in solute resolution, the choice of constant flow or constant pressure control can produce significant differences for multiple peak pairs in the same analysis. 

is a senior scientist at Serveron Corporation in Beaverton, Oregon, USA, and is a member of the 

 editorial advisory board. Direct correspondence about this column should be addressed to "GC Connections", 

, Honeycomb West, Chester Business Park, Chester, CH4 9QH, UK, or e-mail the editor-in-chief, Alasdair Matheson, at 

Introduction to Open-Tubular Column Gas Chromatography

Articles

GC Connections

Have you wondered how your GC system sets and controls gas pressures, flows, and carrier gas velocities electronically? Here, we describe the requirements for and the operation of electronic gas control systems for GC columns and detectors.

Electronic Control of Carrier Gas Pressure, Flow, and Velocity

A simple and rapid method for the simultaneous separation and determination of the inorganic ionic forms of As(III), As(V), Sb(III), Sb(V), Tl(I), and Tl(III) in river water samples is described. Analysis was performed using high performance liquid chromatography with inductively coupled plasma mass spectrometry (HPLC–ICP–MS). Gradient elution was applied with the use of two eluents of different composition. The total separation time was 16 min and limits of detection (LOD) were at the level of sub-μg/L. The method was successfully applied for the simultaneous determination of arsenic, antimony, and thallium inorganic ions in river water.

Speciation analysis was first described in the literature in 1993 (1), and since then it has been demonstrated that it is not the total content of an element that has an impact on the environment, but its various chemical forms. The International Union of Pure and Applied Chemistry (IUPAC) describes speciation as the occurrence of an element in different chemical forms defined by the isotopic composition, electron configuration, oxidation state, oxidation complex, or molecular structure (2).  

Although relatively expensive to undertake, speciation analysis is becoming more important. Speciation analysis is performed to study biochemical cycles of selected chemical compounds; determine toxicity and ecotoxicity of elements; ensure food and pharmaceutical quality control; and in technological process control and clinical analytics (3).  

Toxicological data analyses requires the constant reduction in analyte detection limits to lower and lower concentrations, but many current methods do not provide analysts with the appropriate tools. For this reason, hyphenated methods can be used coupling chromatographic methods to spectroscopic detection (4,5). Hyphenated methods should be selective towards analytes of interest, sensitive over a broad concentration range, and should identify detected compounds. Hyphenated method selection should be directed by the analyte nature, possible method combinations, required sensitivity, and apparatus availability.  

Photo Credit: David Henderson / Getty Images			

Speciation analyses are most often used for the determination of metal and metalloids (6); anthropogenic metalorganic pollution and its degradation products; inorganic disinfectant byproducts (7); and speciation forms of platinum metal group, radionuclides, and rare earth metals (8). Among the many toxic metals and metalloid ions, special attention is given to elements such as arsenic, antimony, and thallium. Arsenic and antimony are elements characterized by complex physical and chemical properties and are of interest to both toxicologists and analytical chemists because of their toxicity, bioavailability, and reactivity. On the other hand, thallium is a poorly recognized element, even though it's toxicity is higher than that of lead, mercury, or cadmium. The United States Environmental Protection Agency (US EPA) classify thallium as a priority pollutant. Priority pollutants rarely reach toxic concentrations in the environment, but their small toxic dose range and prevalence requires regulation. Monitoring of the elements antimony, arsenic, and thallium in the environment indicate contamination spread and changes in anthropogenic processes. 

The combination of high performance liquid chromatography (HPLC) or ion chromatography with mass spectrometry (MS) or inductively coupled plasma mass spectrometry (ICP–MS) detector (9) provides a good range of sensitivity, and also qualitative and quantitative determination of various ionic forms of many elements, including the simultaneous determination of arsenic, selenium, and antimony species (10). To the best of our knowledge, there is only a very limited amount of data about thallium speciation using hyphenated techniques (11); no work about simultaneous separation and determination of inorganic species of arsenic, antimony, and thallium species has been published. 

 The following reagents were used to form standards: sodium arsenate(V) dibasic heptahydrate; sodium (meta)arsenite(III); potassium hexahydroxoantimonate(V); thallium(III) nitrate(V)trihydrate and anhydrous thallium(I) nitrate(V); ethylenediaminetetraacetic acid disodium salt; and diethylenetriaminepentaacetic (all from Sigma-Aldrich). The antimony(III) standard solution (1000 mg/L), 65% nitric acid (V), and phthalic acid used in this study were from Merck. All the reagents had spectral purity. Calibration solutions were prepared by diluting an appropriate standard solution prepared from the suitable salt. All solutions and standards were prepared using high purity deionized water Milli-Q Gradient (Millipore) (<0.05 μS/cm).  

A 200 LC series speciation kit consisting of a Peltier oven Series 200 LC, autosampler Series 200 LC gradient pump Series 200 LC, and ICP–MS detector 6100 Elan DRC-e was used (Perkin Elmer). The analytical column was a 4 × 250 mm, 10-μm IonPac AS-7 (Dionex).  

The gradient elution programme used two eluents: eluent A: 1.5 mM/L phtalic acid + 10mM/L EDTANa

 + 2 mM/L DTPA. The elution programme was as follows: 0–4 min 100% eluent A, next 4–12 min. 100% eluent B, and washing 4 min. 100% by eluent A. The column temperature was 30 °C, and the injection volume was 150 μL. The ICP–MS detector parameters are shown in Table 1.  

 The validation process of the method was based on the parameters of method precision, detection and quantification limits, analyte recovery, and method selectivity. To determine the repeatability of the method, spiked drinking water and river water samples were measured. Each of the four test series (n = 5) was preceded by a calibration of the instrument. The same solutions were also used for the determination of analyte recovery. Detection limits were calculated on the basis of determining the standard deviation of the calibration curves intercept obtained in the presented study. The quantification limit was determined as three times the detection limit.  

Figure 1: Synthetic water sample containing 10 Î¼g/L of As(III), As(V), Sb(III), Sb(V), Tl(I), and Tl(III). 			

The developed method was characterized by high selectivity and low detection and quantification limits. Figure 1 presents the analysis of a synthetic water sample containing 10 μg/L of As(III), As(V), Sb(III), Sb(V), Tl(I), and Tl(III). Table 2 summarizes the method validation parameters. The repeatability and accuracy of the method (standard recovery) were therefore suitable for the analysis of trace amounts of inorganic species of arsenic, antimony, and thallium in water samples. Validation parameters were comparable with literature data (12–14), but it should be stated that these other studies have used other analytical methods and sample matrices (15–16). It is important to note that the main advantage of the developed method is the simultaneous separation and determination of inorganic species of arsenic, antimony, and thallium species in environmental water samples.  

One issue encountered when performing the developed method is the various types of interferences, specifically As/ArCl interference. The mono-isotopic character of arsenic makes it impossible to remove by means of mathematical correction. Polyatomic individual 

 ions are eluted from the column after arsenic forms, as shown in Figure 2. This avoids such interferences because potentially interfering ions Ar

  are eluted between As(III)/As(V) and Tl(I)/Tl(III) ions at a retention time of around 5.5 min (Figures 1 and 2).  

Figure 2: Sample containing 10 Î¼g/L As(III) and As(V).			

 Samples of river water were collected from Upper Silesia District rivers (Poland) and analyzed by HPLC–ICP–MS. Analytes ions were found at the following concentrations ranges: As(III) 0.6–5.56 μg/L, As(V) 3.00–35.87 μg/L, Sb(III) 0.63–4.56 μg/L, Sb(V) 1.93–23.09 μg/L, Tl(I) 0.01–4.97 μg/L, and Tl(III) <0.01–0.10 μg/L. The rich sample matrices and high concentrations of chloride occurring in these rivers did not interfere with the analysis of samples as illustrated in Figure 3.  

Figure 3: Water sample from the Klodnica river in Silesia, Poland. 			

Even though speciation analysis has made enormous progress in the last 30 years, it is still a relatively new area of analytical chemistry. Its further development depends upon many factors such as the development of new separation, detection, and sample preparation methods as well as the availability of new certified reference materials. Metals or metalloids ion determination and speciation have received significant attention in recent years, because of their strong environmental impact. The proposed method could be a useful tool for fast and reliable determination of inorganic species of arsenic, antimony, and thallium.  

HPLC–ICP–MS creates new and immense possibilities in speciation analysis. The main advantages of this technique are extremely low detection and quantification limits, insignificant interference influence, and high precision and repeatability of the determinations. Hyphenated methods also have their limitations, such as high price and apparatus complexity, and so are not in general laboratory use The application of hyphenated methods requires perfect understanding of analytical methods and apparatus. Those systems are expensive and often only used in scientific research, rather than for routine analyses. Nevertheless, progress in the discussed method is becoming more and more important and visible as the number of applications and works can testify (17). 

 is a doctor of analytical chemistry at the Department of Chemistry of Silesian Technology University in Gliwice, Poland. He also works as an assistant at the Institute of Environmental Engineering, Polish Academy of Sciences. The scope of his responsibilities includes performing analysis of soil, soil-like materials, and ash using energy dispersive X-ray fluorescence (EDXRF); performing analysis of water and sewage using ICP–MS; and determining species of selected elements by using HPLC–ICP–MS.  

 is an Associate Professor and the Deputy Director for Research at the Institute of Environmental Engineering, Polish Academy of Science in Zabrze, Poland. His current research interests include the application of IC, HPLC, and GC for the determination of inorganic and organic compounds in environmental samples, and the application of hyphenated techniques (IC–ICP–MS, IC–MS) in species analysis (water inorganic disinfection by-products, metal/metalloids ions). He has produced over 170 publications on the application of IC in environmental sample analysis. Please direct correspondence to 

(2) D.M. Templeton, F. Ariese, R. Cornelis, L.G. Danielsson, H. Muntau, H. Leeuwen, and R. Łobiński, 

(4) R. Michalski, M. Jabłońska, and S. Szopa, in 

Speciation Studies in Soil, Sediment and Environmental Samples

, Sezgin Bakirdere Eds., (Science Publishers/CRC Press/Taylor&Francis Group, 2013), pp. 242–262. 

(5) M. Popp, S. Hann, and G. Koellensperger, 

(6) R. Michalski, M. Jablonska, S. Szopa, and A. Łyko, 

(8) C.C. May, P.J. Worsfold, and M.J. Keith-Roach, 

(10) T. Lindemann, A. Prange, W. Dannecker, and B. Neidhart, 

(11) D. Nolan, E. Schaumlöffel, E. Lombi, L.Ouerdane, R. Łobiński, and M. McLaughlin,

(12) B. Krasnodebska-Ostrega, M. Asztemborska, J. Golimowski, and K. Strusińska, 

(13) T. Lindemann, A. Prange, W. Dannecker, and B. Neidhart, 

(14) L.O. Iserte, A.F. Roig-Navarro, and F. Hernandez, 

(15) T.I. Todorov, J.W. Ejnik, F.G. Mullick, and J.A. Centeno, 

(16) Y. Morita, T. Kobayashi, T. Kuroiwa, and T. Narukawa, 

(17) R. Michalski, M. Jablonska-Czapla, S. Szopa, and A. Łyko, in 

, R.A. Meyers Ed., (J. Wiley, Chichester, UK, 2013) DOI: 10.1002/9780470027318.a9291. 

Features

A simple and rapid method for the simultaneous separation and determination of the inorganic ionic forms of As(III), As(V), Sb(III), Sb(V), Tl(I), and Tl(III) in river water samples is described.

Simultaneous Determination of Inorganic Forms of Arsenic, Antimony, and Thallium by HPLC–ICP–MS

Both the volume of the sample injection and the solvent in which the sample is dissolved can affect the appearance of the chromatogram. 

At first glance, injection of sample onto a liquid chromatography (LC) column seems pretty simple — just put the sample in a vial, select the desired injection volume in the autosampler settings, and push the start button. However, before starting an LC method, we should check a few variables to be sure that we are not creating a potential problem. How does the injection volume affect the chromatogram? What about the injection solvent? Does the column size or packing particle diameter make any difference? What symptoms should we watch for that might indicate we have an injection problem? In this month's "LC Troubleshooting" instalment, we'll look at a few of these factors so that we can understand what is going on and how to avoid problems. 

Let's start with a conceptual look at the effect of injection volume on the appearance of a peak in the chromatogram. Throughout this discussion I'll assume ideal, Gaussian-shaped peaks, and apply distortion to them. I will also assume that the sample concentration is sufficiently small that neither the column or the detector experiences overload. The results will vary a bit with real peaks, which tend to tail somewhat, and are influenced by some LC system characteristics external to the column. Finally, in the present discussion, I will consider only isocratic separations, where the mobile-phase composition is constant; next month we'll look at what happens in a gradient.  

Figure 1: Simulated chromatograms showing the effect of increased injection volume on peak shape. Purple, green, and blue peaks for injection volume ratios of 1, 2, and 4, respectively, when no injection-related peak broadening is present. Red and black peaks represent mild and severe peak broadening because of excessive injection volume. Arbitrary time scale. See the text for details.			

If we start with a small injection volume of sample dissolved in the mobile phase, we might expect a peak such as the purple one in Figure 1. As we double the injection volume, then double it again, we would expect the peak to grow in height and area, as with the green and blue peaks, respectively. As the peak grows under nonoverload conditions, the column plate number, 

 is the baseline width of the peak between tangents drawn to the sides of the peak. If the peak is Gaussian in shape, the present assumption, the width will be 4 standard deviations, 4σ. If the plate number stays constant as the peak grows, and the retention time does not change, this means that the peak width will also stay constant. This can be seen as the peaks of Figure 1 grow from the purple to green to blue tracings. 

With a sufficiently small injection volume, we can assume that all the sample molecules arrive at the head of the column at the same time, and as they migrate down the column, the peak spreads into the familiar Gaussian shape. If the peak is too large, on the other hand, the sample molecules on the leading edge of the peak arrive at the column before those at the trailing edge. Because we're assuming that the injection solvent is identical to the mobile-phase composition, we can imagine that those first-on molecules get a head start in the chromatographic process and move down the column a bit before the last-on molecules get started. The sample molecules on the leading edge of the large injection behave exactly as they would in a smaller injection, so the front edge of the peak appears unchanged.  

You can see this in Figure 1, where the leading (left-hand) edge of the blue, red, and black peaks overlap perfectly. With a large injection, once the peak height reaches its maximum, the sample concentration is constant, and will remain so until the tail of the injection band is reached. Thus, when too large a sample volume is injected, the peak rises normally, then is flat-topped, then drops back to the baseline with the normal Gaussian tail. I have shown two different large-volume injections in Figure 1, red and black, where the tail (right edge) looks identical to the blue one, but offset to a later time. You can think of this as inserting a constant-concentration band in the middle of the peak, pushing the back edge to a later retention time. For the red peak, the injection volume is just a bit too large and the peak broadens and appears rounded on the top, but the black peak is quite a bit too large and the flat top on the peak is obvious. You can imagine that if the injection volume is big enough, the molecules on the leading edge of the peak could reach the detector before those on the trailing edge reach the inlet of the column. 

If we have two components in our sample, each one behaves independently. In Figure 2, I have shown what happens under the conditions of Figure 1 when two peaks are present. At small injection volumes, the peak sizes grow with injection volume for the purple, green, and blue peaks of Figure 2. Because there is no overload, the separation stays the same, so the valley between the peaks does not move. (The slight offset in the valley for the purple, green, and blue traces is an aberration in the way I smoothed the data.) In the present example, I have set the resolution (

) to 1.5, where the valley just touches the baseline (read more about resolution in last month's instalment [1]). When the injection volume is too large, as we saw above, the front edge of the peak behaves normally, but the back edge is shifted to a later time; this happens for each component of the sample. So in Figure 2, for the red and black cases of too large an injection, the front edges of each peak look just as they did for the blue peak. The molecules at the back edge of the first peak of the red and black examples are still at a sufficiently large concentration when the second peak begins to be eluted so that the valley between the peaks is raised and shifted to a later time, thus reducing resolution. This loss in resolution is small, but noticeable for the red case, but for the black tracing, the injection volume is much too large, and the loss of resolution is severe. Note also that the apparent peak centre (retention time) and valley shift to longer times as the magnitude of the excess injection volume increases.  

Figure 2: Simulated chromatograms showing the effect of increased injection volume on resolution. Same peak coding and conditions as in Figure 1. See the text for details.			

So to recapitulate, as we increase the injection volume, the peak height and area grow in proportion to the injection volume up to the point when the injection volume is too large. At that point, the peak broadens, and this peak broadening can reduce resolution between two adjacent peaks. The questions now are: How much can we inject before we inject too much? How much loss in resolution can we tolerate? 

Above we saw conceptually what happens when the injection volume becomes too large. Now we need to find out what happens quantitatively. (If you don't care about the calculations, skip to the next section.) You'll recall that resolution (

 are the retention times of the two peaks, respectively, and 

 are the corresponding baseline peak widths. If the peaks are closely eluted, they will have approximately the same peak width, so we can simplify the denominator of equation 2 to the average peak width, 

. For the present discussion, where we are interested in injection volume, it is more convenient to consider resolution in terms of volumes:  

 are the retention volumes of peak 1 and peak 2, respectively, and 

The peak volume observed in a chromatogram has two primary contributions, the volume (width) resulting from normal Gaussian band-broadening processes within the column, and the volume resulting from injecting too large a sample (as in the red and black samples of Figures 1 and 2):  

 is the volume of the peak for a very small volume sample (for example, the purple or green peaks of Figures 1 and 2). You can see that the resulting peak volume is simply the square root of the sum of squares of each contribution, with a little extra broadening for the injection volume because of factors (2) that we don't need to go into here. 

 from the retention volume (time) of a peak, 

The retention volume is calculated from the retention factor, 

And the dead volume (in millilitres) can be estimated from the column length, 

We then can combine equations 5, 6, and 7 to allow us to estimate 

 from the column dimensions, plate number, and sample 

As has been discussed in "LC Troubleshooting" many times in the past, it is best to have 

 ≥ 1 to take maximum advantage of the column and to avoid interferences with the solvent front. For the present discussion, we'll assume 

 = 1; the effects generally will be more severe with 

Now, we have all the tools we need to determine the effect of the injection volume on resolution using equations 3, 4, and 8. We can measure retention volumes, 

 with equation 8. The observed peak volume, 

, can then be estimated for different sample volumes, 

Next, let's look at the effect of injection volume on five popular column sizes, three commonly used for conventional LC (<6000 psi [400 bar]) with lengths of 150 or 100 mm, diameters of 4.6 or 2.1 mm, and packed with 5- or 3-μm diameter, 

, particles; one for LC with mass spectrometric detection (LC–MS), 50 mm × 2.1 mm with 3-μm particles; and one for ultrahigh-pressure LC (UHPLC, >6000 psi [400 bar]), a 50 mm × 2.1 mm column containing 1.7-μm particles. These are summarized in Table 1. I'll refer to these by their identification numbers 1–5 in the left-hand column of Table 1. I have included estimates of realistic plate numbers, column dead volumes, retention volumes (for 

 = 1), and peak volumes for small peaks, all determined from the equations referenced in the footnote to the table. (I'll issue my standard disclaimer here: The numbers in the various tables have been rounded or truncated for display purposes, so if you repeat these calculations, your results may vary slightly from mine.)  

Table 1: Column characteristics of selected columns.			

In Table 2, I've shown the calculated percent loss in resolution for injection volumes of 1–1000 μL for the columns of Table 1. We can see some general trends in the data. If we pick some arbitrary value for the acceptable loss in resolution, such as 5%, we can see that the most popular column for conventional LC, column 1, can tolerate between 20 and 50 μL of sample injected in mobile phase. Anything that is done to reduce the peak volume generated by the column will also reduce the acceptable injection volume. For example, column 2 has approximately the same plate number as column 1, so it should give the same separation, but its smaller volume will require a reduction in injection volume to something closer to 20 μL. If we try to conserve solvent and move to column 3, which is just a smaller diameter version of column 2, the column will tolerate only 5 μL of sample. For UHPLC, column 5 will require that we reduce the volume even further.  

Table 2: Percent resolution loss for various columns and injection volumes.			

Table 2 is useful if we want to know how much of an impact a given injection volume will have for a particular column configuration. An alternative way to look at the same data is shown in Table 3, where I've shown the maximum volume of sample (in microlitres) that can be injected in the mobile phase for a given loss in resolution (1%, 5%, or 10%) with each column configuration. For the example above, where we considered a 5% loss in resolution, column 1 can tolerate 37 μL of sample, whereas column 5 needs to be restricted to 2.6 μL.  

Table 3: Maximum injection volume for a specified loss in resolution.			

Although it is nice to have tables to use for data lookup, I find that it is even nicer to have a general rule of thumb that I can apply. If such rules are simple, they are easy to remember and apply even when the table isn't handy. We can create such rules by comparing the allowable sample volumes, 

, in Table 3 with the small-injection peak volumes, 

, for the corresponding conditions in Table 1, as summarized at the bottom of Table 3. From this, we can see that for a 1% loss in resolution, 

. That is, if we inject using mobile phase as the injection solvent, we can inject up to ~15% of the volume of the first peak of interest, which I call the 15% Rule. Similarly, if we can tolerate a 5% loss in resolution, the factor is ~30% of 

. I like to work on the conservative side of things, and I've often used the 15% Rule in "LC Troubleshooting" when recommending injection volumes. By using these rules, you don't have to worry about the 

-value or retention times — just look at the volume of the first peak of interest by converting a small injection peak width from time to volume (

In this discussion, we've looked in detail at what happens to the sample peak characteristics as we inject different volumes of sample dissolved in mobile phase onto an LC column under isocratic conditions. We've seen that the appearance of the resulting peaks is dependent on the peak volume (width) of a small-injection sample, which in turn is dependent on the length and diameter of the column, as well as the packing particle size and peak retention (

-value). As a general rule, any system change that reduces the volume of the peak generated by the column (shorter column, narrower column, smaller packing particles, or shorter retention) will also reduce the volume of sample we can inject before resolution is degraded. Based on the data presented here, we were able to generate some practical rules of thumb, such as the 15% Rule, which states that for a 1% loss in resolution, we can inject up to 15% of the volume of the first peak of interest if we inject in mobile phase. 

The discussion assumed that we were using the mobile phase as the injection solvent. If a stronger (higher percent organic in reversed-phase LC) or weaker (higher percent aqueous) injection solvent is used, or the method uses gradient conditions, the results will vary. We'll consider these in next month's instalment. We've also assumed that neither the column nor the detector was overloaded, either of which can change the appearance of the peaks. Finally, we've assumed that the injection volume is the only added factor contributing to peak broadening, which is rarely the case. From a practical standpoint, the injection band can broaden by as much as 50% (3) as it is washed from the sample injection valve. Additionally, extracolumn band broadening because of the system plumbing, detector cell design, and other factors can play a role in the observed peak widths.  

When all the above factors are taken into play, the recommended injection volumes in Tables 2 and 3, or the rules of thumb should be interpreted as guidelines for a good place to start. I recommend doing a simple empirical test to see if the selected conditions are acceptable. Pick a target injection volume for the maximum desired loss in resolution, such as from Table 3, then inject this volume plus an injection twice as large and half as large. If the results are acceptable for all three injections, the target volume should be safe. If you see significant changes in resolution or peak shape between the injections, you may need to further evaluate if the injection volume is sufficiently robust for your purposes. 

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

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

, Honeycomb West, Chester Business  Park, Wrexham Road, Chester, CH4  9QH, UK, or e-mail the editor-in-chief,  Alasdair Matheson, at 

(3) L.R. Snyder, J.J. Kirkland, and J.W. Dolan,

 Introduction to Modern Liquid Chromatography, 3rd ed. 

(Wiley, Hoboken, New Jersey, USA, 2010), p. 70. 

LC Troubleshooting

Both the volume of the sample injection and the solvent in which the sample is dissolved can affect the appearance of the chromatogram.

How Much Can I Inject? Part 1: Injecting in Mobile Phase

Professor Wolfgang Lindner spoke to Kevin A. Schug about his inspiring and influential career in chromatography.

Kevin A. Schug: When did you first discover chromatography and why did you remain in this field?

 Between 1969 and 1972, I carried out my PhD research in chemistry at the Institute of Organic Chemistry, University of Graz, Austria. It had an interdisciplinary focus bridging organic chemistry with analytics, with gas chromatography (GC) the method of choice.  

The PhD work dealt with the synthesis of highly reactive small molecules such as ketene and carbonsuboxide, and followed the reaction kinetics with other molecules. These reactive species and the products of pyrolysis reactions could be produced in the injection block of the GC system. Finally, the analytical part dealt with their identification in cigarette smoke using GC and GC coupled to mass spectrometry (MS). 

I became fascinated by the power of chromatography and the possibility of monitoring the reaction kinetics of highly reactive compounds and identifying them in very complex samples like cigarette smoke. The project actually also dealt with the performance of cigarette filters.   

KAS: Which separations scientists have inspired you most and why?

In the late 1960s, although at that time still working mainly with packed GC columns and the beginning of capillary columns (glass and steel), GC had matured to a high level. I soon realized during my PhD work the diverse limitations of GC and the great potential of liquid chromatography (LC). For GC, it was my supervisor Prof. Heinz Binder (Graz) and Prof. Conny Grob from ETH Zurich who inspired me most. In LC, it was the late Prof. J.F.K Huber who inspired me during a GDCh course on high performance liquid chromatography (HPLC) which I took part in 1973 in Essen (Germany).  

After I completed my PhD in chemistry in 1972 I moved to the Institute of Pharmaceutical Chemistry in Graz where I ordered one of the first HPLC instruments from Hewlett Packard (who bought Hupe and Bush) in 1973.  

For many years I worked in parallel with GC (and even preparative GC) and LC, so I grew up in both worlds. It was later that HPLC became my main passion when I realized the enormous potential of liquid phase separation science because of the "selectivity" options driven by the great variability of the mobile phase and stationary phase characteristics. In essence LC pinpoints the more chemistry-oriented part of separation sciences, aside from the more physico-chemistry related fundamentals describing the separation process. 

In 1973 I made my first stagier at Sandoz (now Novartis) in Basel in the late Prof. Roland Frei's department. This personal contact and friendship was highly influential to me and my further career in academia.  

During that time I also met Fritz Erni. We have been good friends ever since then, sharing for over 40 years the rapid and continuous success of HPLC as a technology with enormous impact in natural sciences. Again, it was the almost unlimited potential of HPLC in the field of pharmaceutical sciences that fascinated me. We were able to solve analytical problems that had not been feasible to approach before. 

In 1977 I participated for the first time in an HPLC meeting chaired by Joseph Huber that took place in Salzburg, Austria. There I met a lot of the gurus of HPLC — including Barry Karger, Jack Kirkland, John Knox, Georges Guiochon, Csaba Horvath, Cal Giddings, Istvan Halasz — as they presented their latest research results and enthusiastically discussed scientific arguments.  

One late night while at the bar of the conference hotel I decided to ask Prof. Karger if he would accept me as a post-doc in his laboratory in Boston. This idea was seconded by my two mentors Roland Frei (Basel, Switzerland) and Göran Schill (Uppsala, Sweden). Barry Karger agreed and in 1978–79 I stayed in Boston with my family. It was a fantastic year for all of us and the start of my career as an HPLC specialist and the beginning of a life-long friendship with Barry.  

In Boston and still at the beginning of my academic career, I started to work in the challenging field of chromatographic enantiomer separations, both in theory and in practice. My background in organic chemistry helped me significantly to dig deeper and to develop novel enantioselective chiral stationary phases. At the same time I worked with other stereoselective separation methods and approached the field as broadly as possible. Trying to understand the underlying stereoselective molecular recognition principles in all its various facets has marked my academic life up until the present day. Along the way I was able to file several patents, and some of the developments were licensed and led to commercialized products. It is an interesting way of being rewarded and having your work recognized — a good feeling. 

KAS: What do you regard as your biggest breakthrough in your research career? And your biggest mistake?

On the question "which was my biggest mistake" I have no answer as in chemistry we all have to make many "mistakes" along the way of making a "break through". Actually I am satisfied with my way of life, both in business and personally; ups and downs have to be faced everywhere and need to be overcome. Those who learn to deal with them and who learn quickly from the downs will be on the winning side at the end of the road. 

KAS: You have worked in both industry and academic laboratories. What are the pros and cons of each?

 In academia, the challenges are certainly different than in industry, where the achievements of the pre-set goals, the deadlines, and the productivity (results per time unit) count the most. In academia my goal was always to maintain a high level of research and education and to stimulate research as part of the education programme.  

Students need to find their individual limits and to learn to deal with them; once they have learnt to handle them in the course of a PhD project they become "fit for the business life". On this basis I have had fantastic teams of students and co-workers over the years, and I am in contact with most of them — even my first PhD students who finished in 1977! The research and the personal touch does not make it an easy-going lifestyle but at the end it is highly rewarding. 

The academia career was the right choice for me because of the enormous amount of freedom to follow research-driven ideas. This is risky because one can easily get lost, with the result that projects don't always get finished! But on the other hand, moments of inspiration can be followed, which would not be possible in industry.  

To be successful in academia, which is nowadays judged by the number of publications, the number of patents, the amount of money raised via research grants, and the number of PhD students you assisted, requires the personality of an "entrepreneur". One needs to be self-driven and constantly dedicated. It is not so very different to the founder of a start-up company, the only fundamental difference is that if you fail in academia you may not face unemployment so quickly and you can more easily argue that the university did not provide you with the adequate financial support and research environment. In all honesty, this reflects the old days of academia as an academic career is now at least as tough as working in industry, as the fight for research money has become very challenging. Not to mention the demands on flexibility in all aspects has become even more pressing.  

KAS: You were part of the Christian Doppler Laboratory for Molecular Recognition Materials in the Department of Analytical Chemistry and Food Chemistry at the University of Vienna. Can you tell us the origination of this centre?

In order to bridge the academically funded research, I also had to involve myself in industry-related research and development and therefore follow the concept of innovation  

It was actually not too difficult for me to find industry partners during my university career. In this context I was able to found 

 with my co-worker Michael Lämmerhofer (now at the University of Tübingen, Germany) the Christian Doppler Laboratory for Molecular Recognition Materials, based within the University of Vienna.  

Over 7 years financial support came from European industry partners and from the Ministry of Economics of Austria; and it was a very successful model of applied research funds established in Austria and led by the Christian-Doppler-Society (CDG). 

KAS: What areas of separation science are you focusing on at the moment?

Over the years I have been active mainly in the areas of GC, LC, capillary electrophoresis (CE), and capillary electrochromatography (CEC), but we also used separation technologies with membranes and crystallization concepts. Hyphenation of GC and LC with mass spectrometry (MS) has become a standard tool over the past 20 years.  

We worked on the separation and analysis of small molecules, but also on large ones such as proteins and antibodies. For all these fields we developed dedicated separation materials (stationary phases) and methods. We even tackled the analysis of very large molecules with molecular masses over 1 MDa, such as DNA-type analytes.  

These results contributed to one of the highlights of our research and was recognized even by biochemists; it documents the potential of working on the interface of different fields and the impact of separation sciences. The development of various novel chiral stationary phases to separate stereoisomers belongs to our core activities. In this context, the chiral ion exchange type materials are widely known as so-called "Lindner phases". Of course, dedicated sample preparation concepts, including the use of gold nanoparticles, needed to be integrated into our strategies as part of the total analysis.  

Central to all our work has been the involvement of organic chemistry — whether it was for the dedicated synthesis of chiral selectors, or for the development of derivatization (tagging) concepts of analytes to improve their detectability by MS, including their fragmentation profile in MS, or improving the chromatographic selectivity profile of the analytes in question. This can be summarized with the credo of molecular recognition concepts in separation sciences and with the development of separation materials where such principles are in force. 

In a way this focus has been with me all my life and it is based on my passion for organic chemistry. Every compound has a unique physico-chemical property (size, polarity, shape, functional groups, etc.). These unique characteristics and the fact that when a compound comes into contact with a molecular partner, as for example the solvents of the mobile phases and/or the ligands of stationary phases, is the basis for the almost unlimited selectivity space and its tuning in liquid phase separation techniques. 

KAS: Is there an area of separation science you would like to have been involved but didn't have the chance (or funding!) to do? What areas of separation science do you think are exciting outside your area of expertise?

I would have loved to work more with MS techniques, both in theory and in practice; this includes their use as fast separation tools. Modern ion mobility MS in particular has interested me. The underlying fragmentation pattern in MS relates directly to selectivity issues based on the compound's property. It is particularly visible in the various "omics" type research fields where hyphenated LC–MS technologies are the gold standard. Good analytical data are the basis for good science. Understanding of the analytical question, of the analytes, of the matrices, of the separation systems, and of the detection technology are in summary the demands on the qualified analytical chemist. 

KAS: How do you see chromatography evolving and what are its limits?

Chromatography has developed to a core technology in analytical sciences, in "omics" related biosciences, in food chemistry, environmental chemistry, and forensic chemistry. It is also important in purification sciences, whether it deals with biomolecules or with natural products or synthetic compounds. Preparative chromatography from the mg to the multi-ton range became indispensable in an industrial environment. The trend towards miniaturization and capillary technology will remain; it is not the chromatographic technology that is challenging 

, it is more the engineering aspect. Very small volumes and highly sensitive detection systems with (ultra)high pressure are challenges to be mastered. 

To a certain degree, supercritical fluid chromatography (SFC) will re-evolve, at least for the platforms of compound purification scenarios. 

I don't see clear limits for LC. Its fundamental advantage relates to the chemical selectivity tuning based on the inherently occurring intermolecular interaction principles between selectands (analytes) and selectors (ligands) of stationary phase and of an adsorption material, respectively. Partition type chromatography will remain, with the potential for further advancements. 

KAS: What major challenges should the younger generation of scientists work on?

This question cannot be answered very easily. Just to pick out one aspect though, there are clear challenges in bioscience analytics and attempting to emulate the biological world with the underlying chemical world, which will be tightly connected to technological and methodological advances of the analytical sciences. To de-convolute the chemical complexity of biological systems is a striking challenge. This includes medicinal chemistry, where in essence diagnosis is an analytical tool. To generate unbiased analysis data of biomarkers and disease markers is strongly connected to the establishment of laboratories with a high level of analytical competence. 

In industry a high demand for well educated chromatographers will remain. They should be able to play the keyboard of methodologies to adequately tackle the analytical questions being raised. 

KAS: What advice do you have for aspiring chromatographers?

 Chromatographers and analytical chemists should be more confident of the key role they fulfil not just in industry but also in (bio)chemistry- and medicinal chemistry-oriented sciences. If the analytical data are not valid, the projects will suffer substantially. Analytical scientists should be considered as equal partners in a team and should not be seen only as service units. 

History of Chromatography

Wolfgang Lindner

The quantification of proteins in a complex biological sample is an important and challenging task. Mass spectrometry (MS) is increasingly used for this purpose, not only to give a global survey of the components and their amounts, but also to precisely and accurately quantify specific target proteins. Here, we review the essential elements of MS approaches to protein quantification and critically compare the available options.

Proteins are the most abundant macromolecules in biological systems. Together with their smaller relatives, peptides, they are polymers comprising amino-acid building blocks joined through amide bonds. In contrast to the repeating units of other biopolymers (for example, polysaccharides and polynucleotides), the constituent amino acids are diverse in their chemical and physical properties. Consequently, the polymers derived from them are also a complex, chemically and physically diverse ensemble.  

This structural diversity lends itself to extensive functional diversity. Proteins serve as antibodies, enzymes, messengers, structural components, and transport or storage molecules. For that reason, the majority of drug targets are proteins. Significantly, the genetic machinery of the cell is tasked with synthesizing proteins. Accordingly, one might argue that much of each cell, and therefore any organism 

 — its structure, function, reproduction, repair, and regulation — relies on proteins. Understanding biology — function or dysfunction, health or disease — is therefore about defining and understanding proteins. 

Protein identification and quantification are therefore the two central objectives of many biological and biomedical studies. Historically, these tasks were performed on purified proteins that were exhaustively sequenced (such as Edman) or quantified by the immuno-based western blot or enzyme-linked immunosorbent assays (ELISAs). Today, however, because of advances in mass spectrometry (MS) and the development of a set of global, protein-analysis tools that some call the "proteomics toolbox," improved analytical strategies have evolved, and the objectives of researchers have changed. Typically, investigators now aim to study biological entities at the "systems" level; that is, they seek to resolve and identify a multitude of proteins simultaneously in a single sample and to quantify each in relative or absolute terms (See definitions in Table 1).  

Quantification is an important component of most studies. Defining differences or changes in protein abundances (or, more appropriately, the abundance of specific protein species including isoforms and post-translational variants) between two or more groups or states (such as control and test) is often at the heart of understanding function and regulation. 

As the proteomics toolbox evolves, new approaches to protein quantification by MS are continually reported. These methods can be categorized into several major classes, all of which share features and performance characteristics. Furthermore, though some additional considerations are specific to protein quantification, it is also important to state that the process of quantification remains essentially the same, regardless of the nature of the analyte (that is, small molecules versus biopolymers). Consequently, the principles and practices that have guided the development and evaluation of quantitative methods (for example, replicate measurements to characterize the variance of a method) are no less applicable in this setting. 

Non-MS Approaches to Protein Quantification

This column instalment focuses on MS methods for protein quantification because of their growing importance. Yet it is important to acknowledge the existence of other strategies and that, moreover, in certain settings those alternatives may be the methods of choice. For example, the mainstay for targeted protein quantification for almost 50 years has been the western blot immunoassay, in which antibodies are used to detect proteins transferred from polyacrylamide gels to nitrocellulose or polyvinylidene fluoride membranes. Refinements of the basic protocols yield detection limits in the attomolar range (1). Evolution of the principles underlying the western blot led to the development of the radioimmunoassay (RIA) and ELISA (2,3). In a clinical setting, specific proteins are commonly quantified by ELISA. Similarly, for several decades, quantification of multiple protein components in complex biological samples has relied on two-dimensional (2D) gel electrophoresis (discussed in some detail later). In addition, a growing array of multiplexed, selective capture methods, including aptamer and antibody arrays, are increasingly used for protein quantification. Numerous reviews discuss these and related strategies (4–9).  

 We categorize protein quantification by MS into several groups. These are outlined below, represented in Figure 1, and discussed in more detail in the sections that follow.  

Figure 1: Diagrammatic representation of the common strategies for protein quantification and the relationships between them.			

Global, System-Wide (Multicomponent) Strategies: 

Here, hundreds or thousands of proteins are compared in two or more samples and quantification is typically relative. Nevertheless, with modification to the basic method, absolute quantification is sometimes possible. 

These approaches involve resolution of a complex mixture of proteins (for example, by 2D gel electrophoresis) and quantification in their intact form. The approach often involves differential radio- or chemical-labelling of proteins in distinct samples (such as, difference gel electrophoresis [DIGE]). After tagging, the samples are combined, the proteins are resolved in two dimensions, and the relative amounts of the tagged proteins are measured. Protein spots are excised from the gel, digested, and identified by MS by means of peptide mass maps (that is, peptide masses), peptide sequences (that is, tandem mass spectrometry [MS–MS]), or a combination of both techniques. 

The quantification of proteins in a complex biological sample is an important and challenging task. Mass spectrometry (MS) is increasingly used for this purpose, not only to give a global survey of the components and their amounts, but also to precisely and accurately quantify specific target proteins.

LCGC Europe-10-01-2014

Electronic Control of Carrier Gas Pressure, Flow, and Velocity

Simultaneous Determination of Inorganic Forms of Arsenic, Antimony, and Thallium by HPLC–ICP–MS

How Much Can I Inject? Part 1: Injecting in Mobile Phase

Wolfgang Lindner

Quantifying Proteins by Mass Spectrometry

Advantages of Saliva Sampling in Bioanalysis Using Microextraction by Packed Sorbent and Dried Saliva Spot with LC–MS–MS

Vol 27 No 10 LCGC Europe October 2014 Regular Issue PDF