A new competence centre in Southern Estonia

Last week I had a change to visit a brand new Polli laboratory of the Estonian University of Life Sciences together with our student Elmo. The Polli lab is located in Southern-Estonia near Karksi-Nuia and surrounded by breathtaking landscape with hills and lakes. The Polli laboratory is unique in Estonia as it combines equipment suitable for small scale industrial testing as well as high competence analytical laboratory.

The analytical laboratory is equipped, in addition to other high-tech instruments (including LC/MS etc), with a SFC/MS (super critical fluid chromatography – mass spectrometry) instrument which can be online coupled with SFE (super critical fluid extraction) instrument. The aim of this high-tech lab is to support the development of functional foods produced in Estonia but also to provide research on the functional components of natural products.

The industrial part contains several extraction instrumentation – microwave extraction, supercritical fluid extraction, pressurised liquid extraction - as well as different mixers, lyophilizators, sieves, etc. The extraction equipment is specially chosen so that it would allow production of extracts of functional components for small batches of foodstuff. Also Polli hosts a small lab space for companies interested in applications of functional components in non-foods – such as cosmetics etc.

According to the impression I got from the visit Polli lab is open-minded for different collaborations! Hope that our lab can do some collaborative project soon as we don’t have SFC instrumentation ourself yet and I would also encourage others to investigate the possibilities for collaboration with such nicely equipped centre.

Website of Polli lab: http://www.plantvalor.ee/index.php/home/

Motivation day!

Students I supervised last school year gave me a rafting trip with them from my birthday in May and on this Friday we vent to Võhandu rive for this trip. Altogether we passed 20 km containing both quite slow flow parts but also some cascades. Slow parts, though not that adventures, taught us some practical team work.

Mari, Me, Asko, Hanno and Piia (Jaanus is taking the picture).

In the end of the day we had a very nice grill and sauna at Mari’s place. For some strange reason we had agreed with Jaanus for a challenge in plank which ended up with everyone planking and finding out their best. Now we agreed to do it again on autumn seminar with the aim of improving personal best.

Hanno, Asko, Jaanus, Piia, Me and Mari.

It was a great day, thank you! Already looking for a next motivation day!

Optimizing or troubleshooting LC?

Hi!

Today I would like to introduce you to two books that I find very valuable and that can be freely downloaded from the internet. The “Five keys to successful LC methods” and “Controlling selectivity in reversed-phase LC” are collections of articles published in LC/GC in the section “LC Troubleshooting” by John W. Dolan. It’s useful whether you are a beginner learning on you own or a specialist working with LC every day.  I can assure that even the latter will find something refreshing in these writings. And of course I have to admit I am a big fan of John W. Dolan. His columns in LC/GC are both a good study material but often also a fascinating detective story for an analytical chemists (eg this one here: http://www.chromatographyonline.com/calibration-problems-case-study-1). 
Both books are freely available from the LC/GC homepage (http://www.chromatographyonline.com/lcgc-ebooks).

Here I would also like to come back to one of J.W. Doles thoughts that has really influenced me. I think everyone working with more than one compound or with matrix containing various stuff, have found themselves stuck with figuring out the best gradient to fit the resolution and speed desired. And of course this perfect gradient needs to be figured out fast to not waste any precious analyses time. Well, one really simple but hard to do thing to keep in mind is: First things first. Don’t mess with the middle part of your LC gradient before you have a needed separation for the early eluting peaks. Firstly, this is really pointless – changes in the beginning will very likely influence resolution in the middle part as well. Secondly, it just waists extra time – you will most probably have to do this once again after you have made the necessary changes in the beginning of the gradient. I know from my own experiences that it is hard to stick to this rule but it sure pays off! I would even extend this not only to LC gradient but also to other fields, eg sample prep – your solution composition most probably is influenced by the dissolving, pH regulation, etc steps you carry out in the beginning of the sample prep. Therefore before you start working out your SPE you should know in what form (including solvent etc your sample is by this stage. Of course, here this is even harder, as if SPE is not working at all (eg analyte flushes through the cartridge during sample introduction) it is also hard to optimize earlier steps (unless you can make injections without SPE).

For practitioners: do you need weighted linear regression?

In my statistics course, similarly to I guess all other teachers concerned in calibration, I teach that for instrumental analyses weighted linear regression should be used. Why? Non-weighted linear regression (the one we can use with LINEST, SLOPE and INTERCEPT in excel) assumes same signal precision (repeatability standard deviation in other words) for all concentrations in the calibration range. For instrumental analyses however the relative standard deviation of the signal is usually (there are some specific instruments where this does not apply) nearly constant over concentrations used.

Veronica Meyer¹ published in LC/GC a good simulation aiming to show how much results are influenced by either using or not using weighting in linear regression.

Their simulations effectively demonstrate that advantages of weighting are observed only if all following four things happen simultaneously:

1. Absolute repeatability standard deviation is not constant over given concentration range.

2. The calibration range is very wide.

3. Calibration points are distributed equally over the calibration range.

4. Sample result is at the lower end of the calibration range.

For example if calibration points 2, 1000, 2000, 3000 and 5000 units were used for calibration and the sample with actual concentration of 2.0 units was measured unweighted regression yielded answer of 8.3 but weighted resulted in 1.95 units. This simulation only included random errors.

On the other hand if a narrower calibration range – around one order of magnitude – would be used there is no significant difference in using or not using weighting.

So what to do if you are in the lab doing your actual analyses? I’d suggest you to prepare at least 5 point approximately equally spaced standard solutions for each order of magnitude your method needs to work in. For example if you samples concentrations may range from 10 – 1000 ppb I’d suggest following solutions: 10, 25, 50, 75, 100, 250, 500, 750 and 1000 ppb.

After analysing these solutions I would break up this calibration into two parts 1) 10-100 ppb and 2) 100-1000 ppb. This way you can assure that the highest concentrations on the calibration graph do not influence the accuracy of the samples in the lower end.

Good calibration!

¹ V. Meyer Weighted Linear Least-Squares Fit — A Need? Monte Carlo Simulation Gives the Answer, LC/GC 28 (2015) 204-210.