Are You a Problem Solver?


The Column

ColumnThe Column-09-21-2016
Volume 12
Issue 17
Pages: 8–13

What are the problem-solving skills required to succeed in analytical chemistry?

Photo Credit: Mari/Getty Images

What are the problem-solving skills required to succeed in analytical chemistry?

I was recently asked to deliver a presentation on the important problem‑solving skills in analytical chemistry and which of these skills were particularly relevant to life in a chromatography laboratory.

It’s hugely difficult to rationalize a set of skills that embody everything required to solve any and every problem that may occur - so instead I collected my thoughts on how to solve chromatographic problems in the hope that they will reveal something of the skill sets needed to become an accomplished problem solver. I’ve shared these thoughts here, in the hope that you might use them as a yardstick to measure your own capabilities and areas for development. For the more experienced problem solvers, you may want to let me know what I have omitted!

Identify the Problem and State it

This is often the most difficult part of the whole process. Often we know that the chromatogram doesn’t “look right” or that the accuracy or precision of the data doesn’t meet specification, but we don’t know why. If that’s the case, then this is where you need to begin - with a simple statement of what is wrong. Statements I’ve written include:

  • It’s leaking from somewhere inside the autosampler.

  • Resolution between peaks 3 and 4 is lost somewhere around injection 10.

  • Although within specification, I’m not happy with the RSDs of the peak area for ‘X’, which appears to be much broader than usual.

  • The chromatogram doesn’t look like it did the last time I ran this method; peak tailing appears worse but critically the selectivity between several of the peaks (including matrix component peaks) has changed.

  • The baseline is cycling with a pattern that I don’t recognize.

  • The accuracy of QC checks is outside the specification limit.

Often, the ability to state a problem comes with experience of analytical science in general, but is heightened when one has a good grasp of that particular application. This is great when you have run the analysis before - but not so good when you are developing a new method for the first time!

At this point it’s absolutely critical to confirm that the problem actually exists, which usually involves repeating the experiment to obtain the same (unsatisfactory) result. Sometimes the problem will disappear - in which case you usually have more time to reflect on why the anomalous result was obtained. In other cases, the result may be a different - but still unsatisfactory - outcome, which may tell you something about the random nature of the problem. In any case, you will have confirmed that a problem actually exists rather than those mischievous lab gremlins, which can strike even the most experienced of us.

Writing down the problem often forces us to define and evaluate it more thoroughly. Interestingly, in my experience, it will also deepen our commitment to resolving the problem and form a “contractual” relationship between yourself and the issue - allowing us to “own” the problem and deepening our sense of commitment to solving it.

Laboratory neophytes used to be told to write down the problem and what investigations have so far been performed to inform the engineer when they come to repair the instrument. I suspect this may have come from shrewd managers to reduce on-site time, and therefore cost, but I don’t hear this very often nowadays. I’ve always found this practice very helpful, especially when you need to refer to others to help find a solution.


Critically Evaluate the Problem

We need to evaluate the problem more thoroughly and define it in more detail, or at the very least note what we do not know, which we will need to find out to overcome the problem. Again the extent to which we can do this will depend on our overall experience in analytical science and also of the particular technique or specific application that we are running. For less experienced readers, the crucial part here is to document in as detailed a way as possible the actual nature of the problem and contrast with what the expected behaviour should have been. For those who are more experienced, then a descriptive or quantitative assessment of the issue should be possible based on your wider experience. Extending the problems cited above, this phase of problem solving might include:

  • Removal of the autosampler covers to trace the source of the leak using the universal condensed phase leak detector (blue lab roll or tissues!!).

  • Stating if resolution is lost as a result of changes in selectivity, efficiency, or peak shape.

  • Comparison of RSDs using a statistical test to check significance of the larger recent values.

  • The extent (quantitative if possible or necessary) of the change in peak tailing and selectivity and which analytes this applies to.

  • Is the baseline pattern cycling in resonance with any known processes in the system? (Air conditioning, every 5th injection, each pump stroke, specific time after each injection, with each proportioning value activation, column switch, etc.).

  • Is it the accuracy of the data or the precision of the data that is the issue? By how much is accuracy of the QC sample data outside the limit? Is there discernible drift? Is there evidence of random variation in the data and

  • has this been checked with a statistical test?

So here you will see that good practical skills as well as knowledge of chromatography theory and statistics
can help a great deal when solving problems in analytical chemistry. This sentence is so important and all encompassing, it could have been my whole article this time - but I suspect
the editors would not have been so pleased.

Check the “Obvious” and the Ancillary

Here it’s recommended to take a step back and evaluate the factors that may have been overlooked. These factors usually lead to questions such as:

  • What did the chromatogram look like the last time it was performed?

  • Is the instrument calibrated and has this been done properly?

  • Is the eluent at the correct pH? (Use a different pH meter to that used to make up the original eluent.)

  • Have the instrument specification checks been performed and what were the results in comparison to past checks?

  • Has the autosampler vial been pierced and what is the liquid level compared to the other vials in the tray? (Has the correct sample been injected and has any of it gone from the vial?)

  • Are all of the frequently made connections leak-free (sample loops, autosampler syringe, column, detector, etc.)?

  • Is the column the correct geometry and chemistry?

  • Are all of the system variables correctly defined in the data system method? (Injection volume, detector wavelength, column temperature, etc.)

  • Have peaks been satisfactorily integrated in a random sample of chromatograms or for the problematic samples?

Whilst it’s sometimes difficult to draw ourselves away from the specific details of the problem to consider these more esoteric factors, it can save a lot of unnecessary work in the long term. I often make a list of all of the ancillary contributing factors that I need to check and work through these in systematic order, which can sometimes be quite time-consuming and frustrating. However, I would also comment that in my experience, the root cause or a significant clue will be found around half of the time during this step of the process!

Use Knowledge to Make Predictions and Design Experiments to Investigate the Predictions

So here we need to apply our knowledge - whatever level that may be - to make predictions of the cause of the problem and to design experiments to test our predictions. Here we assume that we have defined and confirmed the problem by repeating the analysis and undertaken the other steps above.

Of course, this is the difficult part, and our level of knowledge and experience will vary vastly, but you need to bring all of your understanding to design the correct investigative paths. Less experienced folks will probably take more experiments to find the root cause of the problems but do not lose heart - you will get there! Always remember that help forums, more experienced colleagues, instrument manufacturers, and the internet can all be hugely helpful in reducing the time and effort in solving problems. The key thing here is that you learn from the experience and file it in your “experience” folder to be brought to bear that next time you have a similar problem.

Again, from our original examples, this stage may involve experiments or actions such as:

  • Check torque on all autosampler hydraulic connections and observe the injection sequence to identify leaks at each stage of the sampling process.

  • Investigate the effects of accumulation of matrix components on the column by swapping out the column, using a guard column, or using appropriate sample preparation techniques.

  • Investigate the cause of the RSD problems by measuring precision of the instrument from the same vial or sample and selectively evaluating the effect of each major experimental variable or instrument component. This may include checking vial weights after each injection to assess sample volume reproducibility, checking peak area reproducibility at different detector wavelengths, or using a fresh column to check for sample adsorption issues.

  • Check the correct column is being used and swap out the column for a fresh one to investigate the column chemistry and degradation of the stationary phase.

  • Leak check the system and observe an entire analysis to map out the cycle against the frequency of processes within and around the instrumentation, including all valve switching and pumping processes.

  • Design an experiment to rigorously assess the reproducibility and intermediate precision of the experiment and remember that the test needs to assess both the short- and long-term performance of the instrument. Design the test in such a way as to identify the key variables and their performance - which will probably involve the use of MultiVariate analysis, design of experiments, and ANOVA type statistical analysis.


Critically Evaluate the New Data and Apply the Fix to the Original System

Results of investigative experiments will range from very simple (a loose nut was tightened) to the highly complex (a set of statistical weightings for experimental factors, which are important in determining accuracy or precision). Whatever the complexity of the data you are dealing with, it’s always good practice to leave the laboratory environment and write out your conclusions and the suggested corrective actions. If possible, share your findings with a colleague to check your thinking.

Hopefully the investigative experiments will have led to a clear conclusion or suggested corrective action for the problem. The next stage is to test your fix on the original system and sample (if possible) to test its effectiveness.

It is of great importance that, just as we needed to repeat the analysis to confirm the problem, we need to rigorously test the effectiveness of the solution. This may require multiple injections, multiple columns, and longer-term evaluation of the new methodology, or, in extreme cases, a revalidation of the method; we must be able to show that over the long term, our fix is effective.

Go to 1?

The most disheartening outcome of an investigation is to think that we have a
fix, which, under further scrutiny, does
not effectively address the original problem or reveals other underlying problems within the method. However, we must never be afraid to go back to the start of the process. We will almost certainly have learned something useful about the problem, which will help to inform our decision-making during the next cycle.

Present and Share the Findings

Sometimes this may involve us presenting to ourselves, by simply writing up our work. However, this will complete the contract we made with ourselves when we wrote out the problem initially and helps us to rationalize the problem and deepen our understanding of the theory and practice of chromatography.

For more complex and interesting problems, we may need to employ our team-working, report-writing, and presentation skills to effectively communicate the issues and their resolutions with our colleagues so that they may learn from the experience and improve their own knowledge, awareness, and problem-solving skills.

To conclude, I’d like to share a thought from Amit Kalantri, a young Indian author, who has encouraged me beyond all of the usual Einstein and Maslow quotes you see written in problem-solving and troubleshooting guides:

“All my problems bow before my stubbornness.”

Simple yet effective. How stubborn are you when solving problems?

Contact author: Incognito

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