Choosing the Correct Column for Chromatographic Selectivity
When developing a method for the routine multiresidue analysis of pesticides in food commodities there are several factors that need to be considered when selecting which liquid chromatography (LC) column to use. The requirements include:
- Good retention for a range of physiochemical properties (polar, non-polar, base, acid).
- Moderate to high resolution (peak capacity).
- Sample extracts can be crude, so the column needs mechanical stability.
- Good peak shape for a wide range of analytes.
- Ability to transfer the separation to multiple instruments within the lab and to other labs.
In a previous blog, we described the importance of retention, how to calculate it and ensure your methods have enough to meet guidelines and give reliable results. In this blog I am going to consider some column stationary phase and mobile phase parameters that can impact the selectivity of chromatography methods.
When considering various factors of a liquid chromatography column that can impact method selectivity these can be described as the chemical factors as shown below:
What is selectivity?
Selectivity (α), is the capability of a method to distinguish between two or more analytes, it can be impacted by the choice of stationary phase and mobile phase solvents and additives.
An example of where selectivity can be important is where we have compounds which are structural isomers such as sebuthylazine and terbuthylazine. If separation of these two compounds is not achieved by our liquid chromatography method the mass spectrometry acquisition method will not be able to distinguish between the two compounds if they co-elute.
To calculate selectivity, we need to know a couple of things, we initially need the t0 which we explained how to calculate in the previous blog. We also need the retention times (tR1 and tR2) of our two compounds of interest. Once we have collated this information, we can use the following equation to calculate the retention factor (k) for each of our peaks of interest:
k = (tR – t0)/ t0
Calculation for retention factor (k)
Once we have our K1 and K2 values we can then calculate selectivity (α) between our two peaks using the following equation which gives a ratio of the two retention factors:
α = k2/k1
Calculation of the selectivity (α), ratio of retention factor (K) between two peaks
If we take sebuthylazine and terbuthylazine and calculate the α based on the separation shown below then we get a result of 1.02, the value will increase if the separation between the two peaks is improved or decrease if the peaks elute closer to each other.
Factors Effecting Selectivity?
By making changes either to the stationary phase or the mobile phase we change how our analytes will interact between these two phases. If our compounds of interest interact with these two phases differently then we will be able to separate them, the greater the difference the more the separation. This is measured as the retention factor (k) and we can measure the effects of changes in selectivity between analytes using the selectivity factor (α). In this blog we will focus on the impact of changing stationary phase can have on selectivity as well as the role of organic solvents. In a future blog we will cover how the use of mobile phase additives and buffers affect selectivity.
The use of C18 columns is common practice for the retention and separation of different pesticide classes in multiresidue methods. There are many different types of C18 columns and there will be retention and selectivity differences between them, depending on the ligand and base particles used. In the example below four different <2 µm Waters C18 stationary phases with the same dimensions (2.1 x 100 mm), have been tested using the same system and mobile phase to monitor the separation of sebuthylazine and terbuthylazine. These four C18 columns have a range of different characteristics which give them unique selectivity’s. The CORTECS C18 is a solid core 1.6 µm particle, providing high efficiency separations. The other three columns are fully porous particles, they include an ethylene bridged hybrid (BEH) C18, a charged surface hybrid (CSH) C18 and finally a high strength silica (HSS) T3 column. The HSS T3 column is a C18 stationary phase technology with proprietary end-capping, optimised pore size and ligand density to achieve optimum characteristics suited for polar compound retention under reversed phase conditions.
The choice of organic solvent in reversed phase liquid chromatography can produce different results when analysing compounds on the same column. Common solvents such as methanol and acetonitrile will yield different outcomes and the properties of these solvents should be considered when developing a method. Methanol is a protic solvent, due to the hydroxyl group (-OH), whereas acetonitrile is the stronger of the two solvents in terms of reversed phase elution, it is also an aprotic solvent as it does not have a hydrogen atom bonded to either an oxygen or a nitrogen.
In the case of our two isomers using acetonitrile in place of methanol has impacted our separation in two ways. Initially as expected the peaks have eluted faster, this is not surprising and expected with acetonitrile being the stronger elution solvent. In this instance however acetonitrile has also given a better selectivity between the two compounds than seen with methanol.
Different column stationary phase and organic solvents are chemical factors which can affect selectivity and chromatographic resolution. Even different columns which are classified as C18 can have different retention and selectivity characteristics dependent on the base particle and ligands utilised. In multiresidue pesticide analysis some compromise on chromatographic performance can be expected to analyse a wide range of compounds with different physiochemical properties. By initial testing of column stationary phase and the organic solvent, factors such as retention, key separations and peak shapes can be assessed to ensure that maximum chromatographic performance is achieved which has a positive impact on data quality.