This month's instalment comes from a question submitted by a regular reader (Justin Chow), which developed into a joint troubleshooting project. Before we get into the question, let me remind and encourage you to send me questions about problems you are having (see contact information at the end of this article). I'm happy to give you my input, and who knows your problem may form the basis of one of the "

" discussions, as is the case this month.  

The problem concerns the differences in retention time that were observed when the same method was run on three different brands of liquid chromatographs, which we'll refer to as system A, system B and system C. As is often the case, the method was one that was transferred from another laboratory, so complete details about the logic behind the method are not available. The method comprises an isocratic segment for 62 min at 95% A and 5% B followed by a 23-min gradient segment of 5–95% B. Finally, the mobile phase is stepped back to the initial conditions and allowed to equilibrate for the next injection. Both mobile-phase A and B contain buffer and organic solvent, with more organic in B. Note that both buffers A and B are prepared using volumetric glassware to eliminate variations from graduated cylinders. The column is a 150 mm × 3.0 mm, 2.6-µm particle diameter (

), C18 column operated at 0.6 mL/min. As part of the method transfer process, the same method was used to compare the chromatography on the three LC systems, all of which are four-solvent, low-pressure mixing systems. The dwell volumes for all systems are approximately the same. The main peak gave different retention times on each system: 49 min (system A), 55 min (system B) and 61 min (system C). Although the variability between systems A and B (49 vs. 55 min) is within the range observed by workers in other laboratories, the retention time for system C (61 min) is excessively long. Furthermore, latereluted peaks that normally appeared in the isocratic segment now came off the column during the gradient segment and were not adequately separated. The problem is, of course, why are the retention differences observed, how can the problem source be verified and how can it be fixed? This issue also serves to illustrate how we can use some simple calculations to evaluate the probable cause of the problem.  

It is slightly unusual to have such a long isocratic portion in the method, but it works for the present application and is part of a validated method that is best not modified, if possible. However, it should be noted that the peak properties will suffer from such large retention times. The column plate number, 

 and w are the retention time and baseline peak width, respectively. We can estimate the plate number for a column when real samples are run as 

 is the column length (in millimetres) and 

 is the particle diameter (in micrometres). So for our 150 mm × 3.0 mm, 2.6-µm 

 ≈ 17000. Solving equation 1 for w and using 

 = 50, we can estimate w = 1.5 min or 0.9 mL at 0.6 mL/min flow rate. A peak eluted at 10 min would have one-fifth this width and consequently five times the height — not a small change in response. Therefore, the long retention time means that method sensitivity (peak height) may be poor. 

Another check we can make on the quality of the chromatography is to consider the retention factor, 

. For the best isocratic methods, we like 2 ≤ 

 ≤ 10, but in some cases all the peaks won't fit within this range so we accept 1 ≤ 

 ≤ 20. When the retention range exceeds this, a gradient method is usually preferred. Recall that 

 is the column dead time. The column volume can be estimated as  

 is the column volume (in millilitres) and

 is the column internal diameter (in millimetres). Our 150 mm ×  3.0 mm column will have a volume of ≈ 0.675 mL, and a flow rate of 0.6 mL/min, 

 = (50  – 1.125)/1.125 = 43; this is much larger than the maximum desired 

 ≈ 20, so a gradient method probably would be a better choice. But we have to work with the current method to separate some very closely related compounds, so let's try how to figure out what's wrong. 

Because the same method is used on all three instruments, one possible cause of the problem is that the systems are not producing the same blend of mobile phase. We can estimate how much the mobile phase will change retention by taking advantage of the relationship between mobile phase strength and retention:  

 is the retention time in 100% water and Φ is the %B as a decimal (Φ = 0.01 = 1% B). 

 is a constant for a given compound and can be estimated as 

 , where MW is the molecular weight. If we assume a 400 Da compound, we can use 

 = 5 for our present calculations. Let's consider two cases, first the 49 min (

 ≈ 47.9, system B) "normal" variation and the unexpected 52 min (

 ≈ 45.2, average for systems A and B) vs. 61 min (

For the first case, log 42.6 = 1.629 and log 47.9 = 1.680, so Δ log 

 = (1.680 – 1.629) = 0.05 (note that I've rounded the numbers for convenience, so if you try to reproduce these calculations, don't round during the process). Divide by 

 and we get ΔΦ = 0.010. This suggests that systems A and B differ by 1% B, which is marginally within the normal calibration range of ±1% B (see discussion below). For the second case, log 45.2 = 1.655 and log 53.2 = 1.726, so Δ log 

 = (1.726 – 1.655) = 0.070, so ΔΦ = 0.070/5 or 1.4% B. In other words, we can account for the difference between the longest retention time (61 min, system C) and the average retention from systems A and B (52 min) by a difference of 0.4% B in the mobile phase composition. This small variation is probably within normal error, but it is possible that system C may be out of calibration. Next, we need to check the calibration of the mixing system for system C. 

A check of the calibration of the mobilephase mixing system is part of system performance qualification (1), which we recommend performing every 6–12 months on each LC system in your laboratory. The mixing can be tested quite easily with the gradient step-test. This test is performed as follows:  

Remove the column and replace it with a piece of capillary tubing; ≈ 1 m of 0.125-mm (0.005-in.) i.d. tubing, such as the red PEEK (polyetheretherketone) tubing, which works well. This has an insignificant volume (≈ 12 µL) and generates enough back pressure that the pump works well. 

Replace the A-solvent with water and the B-solvent with water containing 0.1% acetone. If you want to check the C- and D-solvents, put the C-inlet tubing in the A reservoir and D in B. 

Set the flow rate to 2 mL/min and the UV detector wavelength to 265 nm, where acetone absorbs strongly. 

Programme and run a series of isocratic steps, 4 min each. Steps of 0, 10, 20, 30, 40, 50, 60, 70, 80, 90 and 100% B work well.

The test should be run for each LC system and each combination of solvents that might be used for a method. Currently we're only concerned about the A- and B-solvents, but C and D should be tested as well. 

The results for a test of system C are shown in Figure 1. Measure the difference between the 0% and 100% steps and use this to determine the height of each intermediate step. In Figure 1, you can see that most of the steps vary a little from the target values (even 10% increments), but the worst deviation is the 20% step, which comes out 0.6% low at 19.4%. Pump specifications typically are ± 0.5–1%, so ± 1% is a reasonable acceptance limit for this test. Everything looks OK for system C from the step test. 

 Figure 1: Gradient step-test results for system C using 10% steps from 0% B to 100% B; actual composition shown for each step. 

For cases in which subtle solvent proportioning errors are suspected, such as in the present example, it may make sense to make finer step sizes around the suspect region — 0–10% in 1% steps in this case. The results are shown in Figure 2. The absolute %B obtained is shown above each step and the actual step size shown to the right of each step. Here, as in the 10% steps of Figure 1, the absolute value of each of the steps is within 1% of the set-point, so the instrument passes specifications in the 0–10% region (note that from Figure 1, we know that this is 0–9.8%; Figure 2 reflects this). Furthermore, the anticipated 0.4% deviation at 5% B, determined from the earlier calculations, is not observed. The 5% step in Figure 2 measures 4.8%, only 0.2% from the ideal. However, the step sizes in Figure 2 are a bit odd. Note in Figure 1 that all the steps except 0 and 100% are low by 0.1–0.6%, with a general trend of larger deviations at lower %B-values. But in Figure 2, the step 1–2 and 2–3 intervals are 0.8% and the step 0–1 and 3–4 intervals are 1.2%. This inconsistency in step sizes was the only difference that could be linked to system C, so the proportioning valve assembly was replaced as a precautionary measure. System C with the new proportioning valve gave a retention time of 57.7 min for the main peak, which was still slightly longer than the other laboratories were finding. However, the poorly separated peaks that were eluted during the early part of the gradient segment are now eluted in the isocratic separation and have adequate separation. At this point, the problem was considered solved and the method was put into routine use on system C. 

Figure 2: Gradient step-test results as in Figure 1, but 1% steps from 0â10% B (actually 0â9.8% B based on the results of Figure 1). Actual composition shown above each step; step size to the right. 

The above problem highlights the challenge of obtaining sufficient precision and accuracy with on-line mixing to obtain consistent retention times for strongly retained peaks. A much simpler solution to the problem of instrument-to-instrument differences would be to use hand-mixed mobile phase for the isocratic portion. That is, hand mix a mobile phase of 95% A and 5% B and put this in the A-reservoir. Separately, hand mix a mobile phase of 5% A and 95% B and put this in the B-reservoir. Now, run the program for 62 min at 0% reservoir-B and then programme a gradient to 100% reservoir-B in 23 min. This suggestion was tested and the retention time of the main peak came out much later. A further minor adjustment of the initial mobile-phase composition (by adding a little more of the B-solvent for the initial isocratic hold) should bring the retention time back to the target value.  

We've looked at a problem of instrument-to-instrument differences in retention time and speculated that the most likely cause was a difference in mobilephase proportioning between the systems. We used simple backoftheenvelope calculations based on normal chromatographic behaviour to estimate the magnitude of mobile-phase composition errors. After we knew what to look for in terms of the scale of the problem, we used the gradient steptest as a means of checking instrument calibration. Although the test results showed compositional differences that would most likely be unimportant for methods with shorter retention times, a replacement of the mobilephase proportioning valve corrected the problem. If you would like to see more examples of problems related to proportioning errors, consult reference 3. 

is an associate scientist at Gilead Sciences, Inc., in Foster City, California, USA. He specializes in HPLC method development, separation of closely related compounds, impurity elucidation using LC–MS and instrument troubleshooting. 

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

 (Wiley-Interscience, Hoboken, New Jersey, 2007), section 5.5.3. 

Articles

What happens when the same method gives different retention times on different instruments?

Retention Time Changes

A look at the use dynamic coatings to improve performance in capillary electrophoresis.

What are Dynamic Coating Approaches Used? 

The migration time of a solute in capillary electrophoresis (CE) is dependent upon both the charge/size of solute ion and the level of electro-osmotic flow (EOF) generated in the capillary. The EOF is generated as a result of the charge on the capillary wall. In uncoated capillaries the negative charge is generated through dissociation of silanols on the capillary wall. Differences in the number of residual silanol contents between portions of capillaries from the same supplier and/or between capillaries from different suppliers leads to variable migration times and reduced method robustness. Use of a dynamically-coated system allows the amount of charge on the capillary wall to be closely controlled and consistent EOF levels and migration times across capillaries.  

The level of EOF is also highly pH dependent as the silanols are acidic with a pKa value in the pH 4–5 region. The capillary wall therefore has a low charge at low pH (and a small/zero EOF). The EOF and charge are high at high pH values. Small day-to-day variations in pH in the mid-pH range cause large differences in the migration times obtained. 

Solute adsorption is a significant problem for basic compounds as they are positively charged and are electrostatically attracted to the negatively charged capillary walls. This irreproducible adsorption can lead to peak broadening due to tailing, increased peak area variability and lower precision. Internal capillary surfaces can be chemically modified to give a permanent neutral, positive or negative charge which may assist in the elimination of peak tailing and/or alter the speed/direction of EOF. However, these internally coated capillaries have increased costs compared to uncoated capillaries and attention must be paid to the repeatability, stability and durability of the coatings. Dynamicallycoated capillaries can reduce adsorption and tailing issues and do not suffer from stability or durability issues. 

Manipulating EOF direction/speed and selectivity: 

Surface active additives such as cationic surfactants can alter the speed/direction of the EOF. This can beneficially affect selectivity and/or decrease analysis time. This is discussed later specifically for the analysis of inorganic anions. 

What Dynamic Coating Approaches Are Used?

 Coating systems have been developed which reproducibly dynamically coat the internal wall of the capillary. These are available commercially (1,2) or the components can be purchased independently (3) and used. Typically, a three-stage process is employed which initially involves flushing the capillary with NaOH solution to fully dissociate the surface silanols and generate a highly negatively-charged surface. An initiator solution, which contains a polyamine substance, is then flushed through the capillary. The multiply charged polyamine adsorbs onto the capillary wall which results in a complete and uniform coating and positive charge. The capillary is finally flushed (initiated) with a buffer solution at the required pH which contains a polyanion that gives (Figure 1) a uniform coating and a consistent negative charge to the capillary. The negative charge of the polyanion layer is unaffected by pH variation as the polyanion contains sulphonic acid groups that remain ionized over the entire pH range. 

The buffering agents generally used in these systems (1,2) are zwitterionic amino acids such as Tris, arginine and taurine. The buffers have a low ionic strength and a good buffering capacity. The low ionic strength is of benefit as this reduces the operating current compared to standard inorganic buffers used in CE such as phosphate. The lower currents generated permit use of either wider bore capillaries for improved sensitivity or higher applied voltages.  

Migration times are also reduced for the basic compounds at low pH values as the electrophoretic migration of the solutes is supplemented by an appreciable EOF. This combination of EOF and mobility can reduce migration times by up to 50%. 

A very systematic evaluation of the factors affecting the dynamic coating stability has been conducted (4) on non-commercial coating approaches.  

These include polybrene (hexadimethrine bromide) (5) and TTAB (tetradecyl trimethyl ammonium bromide) (6). Figure 2 shows the process and the effect obtained. The cationic surfactants ion-pair onto the negatively-charged capillary wall forming a mono-layer. The long-chain tails of the surfactants then interact to form a bi-layer. The bi-layer results in the capillary wall having an effective positive charge which causes the EOF flow to change direction compared to an uncoated capillary. The positively charged capillary wall repels cationic solutes which (5) eliminates peak tailing. Relatively low concentrations of cationic surfactants are employed (6) (for example, 0.5 mM TTAB) as micelles are formed at higher concentrations which dramatically alter the separation selectivity. 

What Levels Of Improvement Can Be Obtained With These Coating Approaches?

An experimental piece of glass tubing was obtained (1) which contained nineteen 25-µm channels. The benefits of employing this capillary format would be that improved injection loadings and sensitivity could be obtained compared to a standard wider bore capillary. The current (and heat) generated in each 25 µm channel would be low as heat should be effectively dissipated.  

Figure 3(a) shows the capillary used to analyse a solution of the bronchodilator salbutamol with a standard phosphate pH 2.5 buffer. This produced a series of split peaks due to different levels of EOF being generated in each channel. Figure 3(b) shows (1) the analysis of the same sample solution on the multi-bore capillary using a pH 2.5 polyanionic buffer after dynamically coating the surface. A single, higher intensity peak was generated which shows that the EOF is uniform across each of the channels. The peak time is reduced in Figure 3(b) due to the EOF present. This example particularly exemplifies the dramatic improvement in repeatability using a dynamic coating system. 

Control of EOF between capillaries is problematic as it relies on the silanol density which changes between capillaries. Extensive rinsing of the capillaries with NaOH to activate the silanols is recommended to improve reproducibility but variability still occurs which detracts from use in a routine quality control (QC) environment. Figure 4 shows separation (1) of 5 basic compounds on 3 freshly prepared capillaries. All capillaries were rinsed for 10 min with 0.1 M NaOH followed by 1 min with the polyamine solution and then 2 min with the pH 2.5 polyanionic buffer. All capillaries gave an operating current of 16 or 17 µA. The separations shown in Figure 4 were the first injection on each new capillary. The reproducibility of migration times for the 3 separate capillaries is considerably better than the repeatability for 3 normal, untreated CE capillaries using a conventional buffer – this is especially important in routine QC operations. 

 Table 1 shows data (1) from studies measuring precision. Injection sequences were performed using a test-mixture containing 3 basic drugs and aniline, which is used as an internal standard. These measurements were conducted on different days and on 3 separate capillaries. The improved peak shape, reduced tailing and improved peak time repeatability made accurate and more reproducible integration possible. The inherent injection volume variability in CE is shown in the 2–3% relative standard deviation (RSD) values obtained for peak areas (Table 1). This injection volume variability is successfully counteracted by the use of the internal standard approach, that is, the peak area ratio (PAR) values were calculated. 

Table 1: Injection precision measurements. 

What Applications Have Dynamically Coated Capillaries Been Used For?

Figures 3 and 4 show that the dynamic coating systems can be used (1) for routinely analysing basic drugs. Commercial coating systems covering a range of pH values are available and allow for example separation of acidic species.  

The low pH dynamic coating systems have been used (2) for fast screening of tobacco-specific N-nitrosamines (TSNA's), which are carcinogens. The reported method was faster, simpler and lower cost than the previously used methodology. The method was used to measure TSNA levels in serum samples. 

Dynamic coating buffers have been successfully applied to protein analysis (4) with intra-day RSD's of less than 1% being obtained with very good selectivity. Similar performance levels have been reported (3) for a range of peptides analysed using dynamicallycoated capillaries. 

Polybrenecoated capillaries have been used (5) to monitor nucleotide pyrophosphates/phospodiesterases reactions with various inhibitors. Sample throughput was high. For example, ATP, UMP and AMP were monitored with an analysis time of 5 min.  

The most common and frequent use of dynamic coated capillaries is in the determination of inorganic anions, such as chloride and sulphate, and simple organic acid ions such as citrate and maleate. TTAB for example (6) is added to the buffer to generate a negative EOF direction which matches the migration of the small negativelycharged solutes. A negative voltage is then used in separation mode. These methods are very robust and have found (6) routine use in a number of industries, including pharmaceuticals, agrochemicals and application in food analysis and forensics. 

The use of dynamic surface coating approaches can significantly improve the routine performance of capillary electrophoresis. Commercial and homemade dynamic coating systems are easy to use and significantly improve the repeatability of migration times between capillaries. The coating systems are available to cover a range of applications and are widely used in routine QC operations in many industries. 

Dynamic coatings are also useful for altering the selectivity and optimizing robust operating performance. This is particularly illustrated in the analysis of inorganic anions and simple acids where the methods developed are routinely applied across a wide number of industries and applications. 

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

 editorial advisory board. Direct correspondence about this column should go to "CE Currents", LCGC Europe, Advanstar Communications, 4A Bridgegate Pavilion, Chester Business Park, Wrexham Road, Chester, CH4 9QH, UK, or email the 

(2) J. Liao, Y. Pan, C. Li, D. Wen and H. Liu, 

(3) R. Nehme, C. Perrin, H. Cottet, M.D. Blanchin, H. Fabre, 

(4) R. Nehme, C. Perrin, H. Cottet, M.D. Blanchin, H. Fabre, 

(5) J. Iqbal, S.A. Levesque, J. Sevigny, C. Muller, 

CE Currents

The use of dynamic coating systems/approaches in CE can result in improved routine performance and/or selectivity.

Enhanced Capillary Electrophoresis Performance using Dynamic Surface Coatings

When does human error or a fat finger moment slide down the slippery slope to falsification and fraud? 

A central component of ensuring the integrity of data and results generated in any chromatography laboratory is the human element, that is, the chromatographer or the analytical chemist who will be involved with developing and validating methods or performing sample analysis. Mistakes or fat finger moments are part of human nature but where is the dividing line between this and falsification and fraud? We will discuss this further in this article. 

In the title of this column I have suggested that there are three types of data integrity deviation: fat finger, falsification and fraud. Here are my definitions of the terms:					

: an inadvertent mistake made by an analyst during the course of their work that can be made either on paper or electronically. 

: an individual who deliberately writes or enters data or results with the intention to deceive. 

collusion between two or more individuals who deliberately write or enter data or results with the intention to deceive.

I have drawn a clear distinction in the definitions of falsification and fraud: falsification is perpetrated by an individual and fraud by two or more people, however, the impact of both is the same — the intent to deceive.  

In writing this column, I have made the following assumptions:					

Each chromatographer working in a laboratory has a minimum level of scientific and professional training to undertake their work. For example,  in method development, method validation, analysis of samples, or review of work and the preparation of a report. 

All chromatographers will follow documented analytical methods and laboratory standard operating procedures (SOPs) or work instructions (WIs). 

The organization the individual works for has stated the ethical and professional standards expected of its staff at their induction and via regular training sessions thereafter.

Mistakes and fat finger moments? If we are honest, we all make them. That is why any quality system for laboratories (e.g. ISO 17025, Good Laboratory Practice and Good Manufacturing Practice) has the four eyes principle: one individual to perform the work and a second one to review the data produced to see that the procedure was performed correctly and that there are no typographical errors or mistakes with calculations. Errors are easy to make, you should see the number I'm making as I type this column using a new PC that has a different keyboard than I am used to.  

Many of the errors and mistakes that we make are self-corrected. For example, as you enter a number into a spreadsheet cell, database field or report, often you will find that while the brain says enter 12.3 your fingers magically enter 13.2 instead. This is a fat finger moment, but before committing the number to the cell or database you can correct this as you can see and have realised your own error. The equivalent moment on paper is when you actually write the wrong numbers down in your laboratory notebook and then correct it by striking through the original entry so as not to obscure it and entering the correct value along with your initials and date and possibly the reason for change. This is the paper version of an audit trail. 

Some other mistakes may not be noticed by a chromatographer but could be detected by the software application that they are using such as a spell checker, or by verification that the data entered fails to meets certain criteria, such as within a predefined range or specific format by a spreadsheet, Laboratory Information Management System (LIMS) or Chromatography Data System (CDS). So, using the example above, if the data verification range was 11.0 to 13.0, the software would have picked up the problem and warned you even if you had not noticed the error.  

However, that still leaves the mistakes you don't realise you have made.  For example, if the entry in the case above was 11.3, data verification would be useless and the error would have been entered without you or the software realizing that there was a problem.  

Don't assume that you will spot all of your own mistakes because we are all human and error prone, which is why we need the second pair of eyes to check our analytical data and calculated results. From my experience as a laboratory manager and as an auditor, supervisors know which members of their staff are diligent about their work and how well they check it and those individuals who are slapdash. A supervisor will adjust their second person reviews accordingly. So if you don't want a dubious reputation to precede you, be diligent and try your best to find and correct your own errors before passing your work over to be checked.  

What is the Fat Finger Rate in a Laboratory?

To a certain extent, this column is about airing dirty laundry which may not be a particular interesting problem to all but it is at the heart of any good laboratory quality management system: self audits coupled with effective corrective and preventative action planning. Quality is everybody's problem: it is not the sole responsibility of the quality assurance group to pick up the errors that the analytical laboratory has made. However, finding papers on how often we make mistakes in an analytical laboratory is hard to find — probably as we don't really want to go there. But this is the wrong approach to take and we should encourage studies to investigate this. 

Help is at hand however from clinical chemists working in hospitals who have published many studies on error rates in laboratories. For those that do not know, clinical chemistry is involved in the analysis of blood, urine and other bodily outputs to help in the diagnosis and management of diseases. Mistakes in this area can have a critical impact on the health of a patient and therefore the reduction in errors is essential.   									

One paper, entitled "The Blunder Rate in Clinical Chemistry", measured the rate of detected analytical errors before and after the introduction of a LIMS: it was reduced from about 5% to less than 0.3% following the introduction of computer system (1). 

Manual transcription errors in patient records were assessed for blood results recorded in a critical care setting by comparing the handwritten and printed laboratory results in 100 consecutive patients in the intensive care unit of a UK hospital. Out of 4664 individual values, there were 67.6% complete and accurate, 23.6% were not transcribed at all and 8.8% were inaccurate transcriptions of the results. Interestingly, this study found that the most accurate work was performed in the morning (2).  

An Australian study of transcribing hand-written pathology request forms to a computer system and chemical analysis of the samples found that error rates were both in the range 1–3% in the best laboratories. However, the worst laboratories had error rates of up to 39% in transcription and 26% in analytical results (3) which, in my view, would indicate a sloppy approach to quality by management in these organizations.

So, let us extrapolate from the clinical chemistry laboratory that the range of error rates in an average analytical laboratory is in the range of 0.3% to 3% depending on the degree of automation that exists there. The more manual input and transcription checking required, the greater the number of errors that need to be detected and captured. Therefore, laboratory errors are expected by external quality audits and regulatory inspections. Not finding these detectable errors raises suspicion of problems with the result being a further delving into laboratory records.  

Therefore, depending on the level of automation involved in an individual laboratory, the quality system supervisors and chromatographers need to be aware of the existence of errors and the ways that can be used to reduce them, such as training, awareness and responsibility for error prevention and detection, mechanisms for manual review of data, design of pro-forma documents to capture laboratory data to ensure data integrity and objectives for performance review. 

The Laboratory Notebook – Integrity or Falsification?

This brings us to a common issue that we all have experience with: the humble laboratory notebook. Typically this is a bound book with pre-numbered pages which are just there to prevent you tearing out a page to write down a shopping list or making a paper aeroplane from it, and, more importantly, as the first stage of ensuring data integrity in the laboratory. At the bottom of each page is space for you to sign and afterwards a reviewer/supervisor/witness/peer to sign after they have checked your work and accepted it as accurate. 

Here's the situation: You are a supervisor and you are checking a laboratory notebook for some current work and in turning the page you notice that your signature is missing from when you reviewed some earlier work. Three out of four pages of the old work are signed and dated but you have omitted to sign one of the pages: so what do you do? 

Temptation Time! You have the following options:

1. Ignore the problem and wait for somebody else to discover it?

2. Sign the page and date it the same as the other pages?

3. Sign the page but date it with the current date and add a note that you have just noticed the problem? 

So what are you going to do? It is a pity that the paper and electronic versions of this magazine do not come with the prospect of having a large hammer hit you over the head if you pick the wrong option. Most chromatographers should reject option one, especially if you are working in a research environment where product development and especially patent protection can be crucially dependent on the date of discovery.  

So we're down to options two and three. For option two is a little voice whispering in your ear "nobody will know if you put the same date that the other pages were signed on?" You are now on the brink of the abyss – on the sunny upland plateau is ethics and integrity and down the dark slippery slope is falsification and fraud. May I suggest to you that option three is the only option worth considering that will establish credibility for you and the laboratory? Reiterating the point in the section above and putting my auditor's hat on – I expect to see mistakes: if I don't find any I become suspicious. 

Let us move into the murkier world of falsification and fraud with the intent to deceive. Falsification and fraud can be classified into the following two main areas:					

— deliberately not revealing all data available and basing any decisions made on a selection of results that fit the desired outcome, for example, passing a test and ignoring the rest 

— deliberately altering data or fabricating data as shown with the substitution of the IR spectra or the Able Laboratories fraud case that we will discuss in a minute.

A good source for finding examples dealing with both these issues is the FDA warning letters section found on 

 The agency posts warning letters on their website under the US Freedom of Information Act, with the intent to name and shame. I quote these examples from the pharmaceutical industry as the FDA openly publishes this information, as opposed to the European regulators or ISO 17025 accreditation agencies who usually keep such findings confidential. However, it is important to note that falsification and fraud could occur in any laboratory in any industry. 

The following information is taken from five of the warning letters and regulatory issues that have emerged in this arena recently. 

1. The classic fraud case involving laboratory data is that of Able Laboratories from 2005 (4). The company was engaged in a systematic laboratory fraud to pass batches of drug product that failed to meet specifications by changing weights, conversion factors and even cutting and pasting chromatograms. Results that failed were manipulated and faked until they passed: an original result for dissolution testing was ~ 30% versus a specification of > 85% but after the magic fingers were applied the final result was ~ 89%! The company had passed several regulatory inspections until a whistleblower alerted the FDA to these practices. After a detailed inspection, the company withdrew several drug applications, recalled over 3100 batches of product and eventually went bankrupt. There was a subsequent criminal prosecution of four members of the company for fraud. 

2. During an inspection of Ohm Laboratories in 2009, suspicion was aroused in the stability testing laboratory about material that had been taken out from the stability chambers for analysis. The material had been signed out by the stability coordinator but 

the attendance record shows that your stability coordinator was absent from your firm during those dates in which the coordinator recorded the withdrawal of samples from the stability chambers 

(5). This is very similar to the laboratory notebook example we discussed above.  

3. A Chinese company, Xian Libang Pharmaceutical Co, (6) was found to have used the IR spectra from one batch of material to support the release of two subsequent batches. The warning letter noted that 

this practice is unacceptable and raises serious concerns regarding the integrity and reliability of the laboratory analyses conducted by your firm. It is essential that at least one test be conducted to verify the identity of each lot of incoming material. In addition, the laboratory control records should include complete documentation of all raw data generated during each test, including graphs, charts and spectra from laboratory instrumentation. These records should be properly identified to demonstrate that each raw material batch was tested and met the release specification before its use in production. .... A cursory review of records is not sufficient to ensure that other personnel did not manipulate or inaccurately report test data.

It is interesting to note that finding falsification in one analysis, the agency, quite rightly, casts doubt on all data generated by the whole laboratory. 

There was a further citation about the lack of controls to prevent manipulation of raw data during routine analytical testing and how measures would be put in place to stop unauthorized changes being made to data in the future. The agency wanted to see a process 

to prevent omissions in data, but also for recording any changes made to existing data, which should include the date of change, identity of person who made the change, and an explanation or reason for the change. All changes to existing data should be made in accordance with an established procedure.

4. A warning letter sent to Ranbaxy, an Indian generic drug manufacturer, in 2007 highlighted 

 made in drug submissions to the United States in the stability testing programmes run by the company (7). In item 5 of the warning letter it was noted that the results of an audit of existing drug submissions had found 

"2257 errors in entries for the dates of analysis; and errors in 1385 entries for stability test results, and tests for which corrections were made in specification limits"

. This is not a mass fat finger moment and I leave it to your interpretation if this is falsification or fraud.  

5. My last example cost a European generic drug manufacturer a loss of $3.3 million in 2010 (8). Acino, a Swiss generic drug manufacturer, contracted Glochem, an Indian company, to supply clopidogrel which is the active ingredient of Plavix. Following a visit by European inspectors to the Indian company, they found more than 70 original batch records in a rubbish skip at the site — all the records had been rewritten to be perfect with no errors, in total contradiction of Good Manufacturing Practice (GMP). The inspectors, again quite rightly, classified this as fraud and this triggered a recall of the material.  

The company thought that the inspectors response was too excessive and commissioned extensive third party analysis to demonstrate that the material met specifications. However, as the batch records had been copied and the originals were in the process of being destroyed the inspectors held to their original view.   

You can see from these few examples, by being diligent, honest and professional you can avoid the problems faced by these companies. The fifth example also illustrates that if a company outsources to a third party the first company is still accountable for the quality of the material going into their own supply chain and pro-active auditing will help prevent these issues. 

Questions of Quality

Mistakes or fat finger moments are part of human nature but where is the dividing line between this and falsification and fraud?

Fat Finger, Falsification or Fraud?

29th International Symposium on Chromatography (ISC)

The 29th International Symposium on Chromatography (ISC)

 in the picturesque gothic city of Torun, Poland.  

This is the first time that the Steering Committee of the International Symposium on Chromatography has awarded the organisation of the International Symposium on Chromatography (ISC) to the Polish Chemical Society and Commission for Chromatographic Analysis (PAS).

Poland is historically significant for separation science because the origins of chromatography began in Warsaw with M.S. Tswett and the foundations of the chromatographic process were laid by W. Nernst, who was born in Wabrzezno near Torun. The theme of this year's event is 

Chromatography & Separation Science: Past, Present and Future

, and will be devoted entirely to the role and applications of modern separation techniques in all application areas. Symposium topics will cover both theoretical and practical lectures including; 									

The organizers, together with the scientific committee, have planned a programme to inform and inspire, and to provide opportunities for increased collaboration between separation scientists. The programme includes plenary lectures, section lectures, oral and poster presentations, vendor seminars, workshops (concerning sample preparation, food quality control, miniaturization) and exhibitions. Invited to participate in the symposium are renowned, worldclass specialists in chromatography. Young scientists are encouraged to attend the symposium and present the results of their research. As well as the scientific programme there are many interesting social events that allow delegates to explore the city and region.   

Torun is well positioned and accessible to Central Europe. It is a city with a long-lasting tradition of political and scientific dialogue and cooperation, and is notable for being the home of the famous astronomer, Nicolaus Copernicus. Torun is also important for being the venue of two historical peace treaties in 1411 and 1466. 

The symposium is supported by the European Society for Separation Science (EuSSS), the Central European Group for Separation Science and the Nordic Separation Science Society. 

CMA4CH, Use of Multivariate Analysis and Chemometrics in Cultural Heritage and Environment

 Cma4ch research group at Rome University 

38th International Symposium on Capillary Chromatography (Riva 2012) and 9th GCXGC Symposium  

Luigi Mondello, Chromaleont, University of Messina, Italy 

13th Fera/JIFSAN Symposium 'Omics' Technologies in Food Science

ExTech 2012, 14th Symposium on Advances in Extraction Technologies

 Luigi Mondello, Chromaleont, University of Messina, Italy 

Departments

Event News

A dedicated GC–MS analyser was developed to address the increasing need for more sensitive catalyst poisoner analysis. The system combines the separation power and robustness of a classic backflush configuration with the selectivity and sensitivity of mass spectrometry.

The use of high-yield metallocene catalysts has dramatically increased both efficiency and selectivity of polymerization processes (1). Unfortunately, these catalysts are extremely prone to poisoning by feedstock impurities, such as arsine (AsH

), oxygenates (for example, dimethylether) and sulphur-containing compounds (mercaptanes, sulphides, etc) (2,3). Minute amounts of these compounds are sufficient to impose undesirable effects and induce immediate loss of catalytic activity and reaction yield. At the same time, trace contaminants at the part-per-billion (ppb) concentration levels can end up in the polymers and alter subsequent polymer properties and characteristics. 

For decades, process chemical and petrochemical analysts used to address their analytical challenges mainly by relying on superior chromatography and smart tools such as valve switching, backflush and Dean's heart-cut. In combination with relatively cheap, robust and selective detectors, they were capable of providing all information necessary to control and tweak petrochemical processes. 

Organic catalyst poisoners are usually determined using dedicated chromatographic analysers. These systems are, typically, equipped with a dual capillary column configuration with backflush and fitted with a flame ionization detector (FID). Under these conditions, limits-of-detection are usually situated around 100 ppb, depending on the compound investigated and the complexity of the matrix that is introduced (4). Unfortunately, this is far from sufficient to protect the latest catalysts, which start to deteriorate as soon as fed with low ppb amounts (5,6). An overview of some typical specifications for catalyst poisoners in polymer-grade hydrocarbons is given in Table 1. 

 Table 1: Typical specifications for catalyst poisoners in polymer grade hydrocarbons (6).			

Mass spectrometry (MS) is hardly used in petrochemical QC laboratories, which is primarily because of its apparent complexity and higher cost-of-ownership. Nonetheless, MS detection has several distinct advantages over classic analogue detectors. In full scan acquisition mode, for example, it allows tracking and identification of unknown components using spectral deconvolution and subsequent library matching. In selective ion monitoring (SIM) mode, on the contrary, MS permits trace and ultratrace quantification of target analytes which is often superior to classic selective detectors. Furthermore, MS permits the use of mass labelled internal standards (ISs) that behave identically to their native analogues, which has a positive effect on overall method precision and accuracy. It is no surprise that instrument manufacturers have invested substantially in solutions aimed at reducing overall MS complexity and total cost-of-ownership in the last couple of years. Easy tune and calibration functionalities, increased sensitivity and speed, new acquisition modes and elegant solutions that eliminate downtime, such as vacuum lock technology, have contributed largely in this respect. 

This article gives an overview of the main characteristics and performance of a new gas chromatography mass spectrometry (GC–MS) analyser that has been recently developed. The system combines the chromatographic separation power and backflush/Dean's heart-cut capabilities of a classic oxygenate analyser with the orthogonal separation power, sensitivity, selectivity and overall robustness of the latest generation single quadrupole mass spectrometers. 

Standard oxygenate reference mixture from Spectrum (Sugarland, Texas, US) at 10 ppm in hexane. For more details with respect to the composition of the test mixture, please consult Table 3. Calibration standards were prepared by gradual dilution in hexane at 0.01 ppm, 0.05 ppm, 1 ppm and 5 ppm. 

 The GC analyser consisted of a Thermo Trace GC (Austin, Texas, US) refurbished by Global Analyser Solutions (GAS, Breda, The Netherlands). The system was fitted with a gas sampling valve (GSV), a liquid sampling valve (LSV), a vapourizer, a standard split/splitless injector and a FID. Inside the GC oven, a universal pressure balanced Deans assembly was installed to carry out heartcut and/or backflush. Auxiliary pressure for balancing was provided and controlled by a separate Trace GC DCC unit. 

The first dimension column was a Restek Rtx-1 (Bellefonte, Pennsylvania, US) with the following dimensions: 15 mL × 530 µm i.d., 5 µm d

. The second dimension column was an Agilent CP-Lowox (Middelburg, The Netherlands) with the following dimensions: 10 mL × 530 µm i.d., 10 µm d

. Restrictions were 250 µm i.d. uncoated Siltek-deactived fused-silica capillary tubing (Restek) cut to the appropriate length. All connections were made using micro Siltite unions (SGE Analytical Science Victoria, Australia). Other relevant parameters are summarized in Table 2. 

: The GC analyser was hyphenated to a Thermo ISQ single quadrupole mass spectrometer. The system was applied in both full scan (range: 15–250 amu) and SIM (dwell time: 0.2 s) as full scan/SIM mode. All relevant MS settings are summarized in Table 3. 

Table 3: Peak identification and typical SIM ions.			

All data were acquired using Thermo QuanLab Forms. The MS was applied after running a full EI tune. System performance was verified using a daily tune check.  			

 The capillary column set comprises the true core of any classic catalyst poisoner analyser. The second dimension column is particularly important. Ultimately, it is here that separation of the analytes, from each other as well as from the aliphatic matrix in which they reside, occurs. A CP-Lowox column (Agilent) was used for this purpose. This column, which is based on a multilayer PLOT concept, is very polar and characterized by a high MAOT with virtually no bleed at temperatures as high as 350 °C (7). In combination with a backflushed apolar-coated capillary column in the first dimension, matrix separations up until C12 hydrocarbons are well within range. 

Unfortunately, the CP-Lowox column is not available in an MS-friendly  narrow bore dimensions. To avoid the MS vacuum from protruding the system, it is necessary to incorporate an adequate restriction at the back of the column. A schematic representation of system set-up for Lowox/MS applications is depicted in Figure 1. 

Figure 1: Schematic representation of the GCâMS analyser. BF = backflush; HCs = hydrocarbons.			

True backflush, as well as Dean's heart-cut, is achieved by increasing the auxiliary pressure at the column joint just above the first dimension residual head pressure at a certain moment in time. Debalancing induces full flow reversal, whilst maintaining a small flow over the Lowox column for chromatography. It is crucial to know the exact pressure at the column joint for this to be successful. When set too low, standard flow direction will be maintained and the first column is not backflushed. Conversely, when set too high, none of the target analytes will be able to reach the second dimension column and the detector. The easiest way to determine the pressure at the column joint involves setting the head pressure at regular and then reading the residual pressure at the auxiliary DCC, which is kept off at this stage. Five kPa differences are sufficient to induce flow reversal. Although less straightforward because of the vacuum conditions, a similar approach is applied in combination with MS. It also permits the user to determine the minimal length of the restriction capillary (Figure 1).  

: System suitability was evaluated by direct injection of the 10 ppm oxygenate standard. In order to compare with a classic analyser set-up, experiments were performed using both FID and MS as a detector. The MS was applied in both full scan and SIM modes. A typical chromatogram with the MS in full scan mode is depicted in Figure 2. Peak identification is referred to in Table 3.  

Figure 2: Chromatogram of the oxygenates standard at 10 ppm. The MS was applied in full scan mode. Peak identification is referred to in Table 2.			

A comparative overview of the results is given in Table 4. For each peak, the signal-to-noise ratio (

) was calculated (RMS) in full scan, extracted ion and SIM mode. These results were subsequently expressed relative to the 

The results in Table 4 clearly illustrate the significant gains in sensitivity that can be reached when using MS compared to FID. Minimal gain is 3.7 for ethanol. Unsurprisingly, sensitivity gains are particularly significant when the MS was used in SIM mode. Straight full scan mode proved to be less appropriate for target analysis, which is predominately due to the low molecular weight of the target compounds, and means having to include highly interfering masses such as 

 =18 (water), 28 (nitrogen) and 32 (oxygen) in the scan range. More convenient in this respect is the combined full scan/SIM mode, which is available on all major instrument brands nowadays. 

Table 4: Average sensitivity gains in different MS detection modes. FS = full scan; EIC = extracted ion chromatogram; SIM = selected ion monitoring. %RSD at the 10 ppm level (six analyses).			

Afterwards, calibration curves were constructed for each of the oxygenates in the standard mixture from 0.05 ppm to 5 ppm. Correlation coefficients were ≥ 0.995. Some typical SIM traces at the 0.10 ppm level are depicted in Figure 3.  

Figure 3: SIM traces at 0.01 ppm. (a) acetaldehyde; (b) ethanol; and (c) propyl ether.			

Method repeatability was determined as well. Results at the 10 ppm level are included in Table 4 (six consecutive analyses). 

: Naphtha is a complex mixture of hydrocarbons (C5–C12) in petroleum boiling between 30 °C and 200 °C. Oxygenates are routinely determined in these samples according to reference procedures such as ASTM D7423 (4) as their cracking product cause problems in the downstream separation processes (8). Naphthas are very complex and fully require the chromatographic separation power of the Lowox column. A typical chromatogram of a naphtha sample in SIM mode is depicted in Figure 4. Individual samples were introduced using the LSV of the GC–MS analyser. The insert shows the methanol trace (ion 29, 0.18 ppm); concentrations of acetaldehyde and TAME were 8.3 and 4.9 ppm, respectively. 

Figure 4: Typical SIM trace of a naphtha feed.			

When idle, the GC oven was kept at 200 °C with the backflush activated. This was necessary to prevent the accumulation of siloxane bleed from the precolumn. 

A GC–MS analyser is described that substantially expands the workable application range of a classic catalyst contaminants analyser. The use of mass spectrometry in FS/SIM mode permits identification of unknown contaminants in combination with reliable quantification at trace and ultratrace amounts.  

The authors acknowledge the financial support from the Long Term Structural Methusalem Funding by the Flemish Government – grant number BOF09/01M00409. KVG holds a Postdoctoral Fellowship of the Fund for Scientific Research Flanders and a BOF tenure track position at Ghent University.  

 is a Fulbright alumnus of the Massachusetts Institute of Technology, Massachusetts, USA, and assistant professor in thermochemical reaction engineering in the department of chemical engineering and technical chemistry at Ghent University, Belgium.    

 is a masters student in civil engineering in the department of chemical engineering and technical chemistry at Ghent University, Belgium. He is currently finishing his thesis on trace oxygenate analysis using mass spectrometric techniques. 

 is an analytical support engineer at IS-X. He specializes in instrument design and method development for petrochemical applications. 

A GC–MS analyser is described that is reported to substantially expand the workable application range of a classic catalyst contaminants analyser. The use of FS/SIM mode permits identification of unknown contaminants in combination with reliable quantification at trace and ultratrace amounts.

LCGC Europe-04-01-2012

Retention Time Changes

Enhanced Capillary Electrophoresis Performance using Dynamic Surface Coatings

Fat Finger, Falsification or Fraud?

Event News

Ultratrace Quantitative Analysis of Catalyst Poisoners using a Dedicated GC–MS Analyser

Introduction to SPME for In Vivo Analysis