LCGC North America
Get the most from your method by considering what you want it to do and setting appropriate chromatographic parameters.
Ultimately we need sensitive, reproducible, and robust chromatographic results that are fit for purpose, according to our analytical requirements. Having chromatographic performance targets to work toward will not only result in more robust chromatography, but they will be a great indicator of when you are heading down the wrong development path, or when there are underlying problems with the method or equipment.
There are many regulatory organizations that have set analytical method performance requirements. For the purposes of this discussion we will refer to the current United States Food and Drug Administration (FDA) values for the validation of chromatographic methods (1,2).
Retention factors (k) of 2–10 are commonly used (with the FDA stipulating k ≥ 2) but this may not work for all analyses. If k < 1 separations will be less stable and reproducible, exhibit greater susceptibility to chromatographic interferences at the beginning of the chromatogram (that is, peaks poorly resolved from unretained material at the column dead time t0), and retention of analytes with low k values will be more sensitive to small changes in mobile-phase composition. A k value of 1–2 may work well when faster chromatography is desired, where samples do not contain a lot of endogenous or matrix components, or where pH or buffer strength are not vital in controlling retention or selectivity. For complex mixtures, k values greater than 10 may be needed to resolve all peaks, however be aware of peak broadening of later-eluted peaks that may reduce resolution.
Efficiency can be increased by increasing column length, reducing internal diameter, or decreasing the particle size. It is better to use a smaller diameter packing than to increase the column length, which will increase analysis time. However, a decrease in particle size will result in an increase in system back pressure. The use of smaller particles and narrower column internal diameters both require minimized extracolumn dead volume to avoid efficiency losses. The FDA stipulates that the value for N should be >2000, which is typically easily achieved with modern high performance liquid chromatography (HPLC) columns.
Tailing peaks create issues with resolution, quantitation, and reproducibility. Peak shape is often the controlling factor when optimizing complex separations, especially when components are present in very different concentrations. Peak tailing can be minimized by using type II or III silica, minimizing system volumes, using the correct connections and fittings, employing the correct buffer type and strength, and by optimizing the sample diluent. A value within the recommended range (tailing factor TF ≤ 2) may not always be ideal and may need further optimization. For example, the analysis of a stability indicating sample at different buffer concentrations demonstrates the importance of the tailing factor. At a lower buffer concentration (13.4 mM) the degradant peak in the sample has a relatively high tailing factor of 1.72, which although it is in the FDA recommended range, results in poor resolution (Rs = 0.98) between the degradant and the preceding peak. Increasing the buffer concentration to 23.6 mM not only improves the peak tailing of the degradant peak (TF = 1.43), but also results in resolution of the two peaks (Rs = 1.69).
Injection precision is important for reproducible chromatographic results and should be estimated in the same way for each analysis. It is indicative of performance of the plumbing, column, and environmental conditions at the time of analysis. Assessment of injection reproducibility can be used to aid in the diagnosis of potential system problems such as leaks. It is expressed as relative standard deviation (RSD) and is measured by multiple injections of a homogeneous sample (RSD <1% for n ≥ 5). The injection precision of modern autosamplers is 0.15–0.9%, depending on the sample volume. Several factors that affect precision should be considered to minimize errors, such as pH, buffer type, sample degradation, mobile-phase stability, sample adsorption, accurate dilutions, homogeneous sampling, and the use of an internal standard to estimate loss during sample preparation.
Before setting a value for resolution it is important to ask a couple of questions that relate to the specific separation first. For example, what value is acceptable? What value is required for reliable quantitation? Resolution is a function of retention (k), selectivity (α), and efficiency (N), which can all be modified to improve resolution. Rs > 1.5 can often be easily obtained for samples containing five or less components; however, for complex mixtures Rs > 1.8 is required for rugged performance. “Real world” setting of resolution specifications requires experience in HPLC and the method under consideration. Although values of Rs > 2 are recommended, sometimes it is not practical with very complex samples or depending on the type or stage of analysis. The early-stage analysis of pharmaceutical impurities may be carried out successfully with a minimum Rs value of 1.2 to give reliable, reproducible quantitation of all impurities. Conversely, late-stage pharmaceutical impurity analyses require much more stringent Rs values (>4) to give reliable quantitation of impurities.
AI-Powered Precision for Functional Component Testing in Tea Analysis
October 11th 2024Analyzing functional foods reveals numerous health benefits. These foods are rich in bioactive compounds that go beyond basic nutrition, boosting the immune system and improving overall wellness. However, analyzing these compounds can be challenging. This article discusses AI algorithms to support automated method development for liquid chromatography, simplifying the process, enhancing labor efficiency, and ensuring precise results, making it accessible to non-experts for tea analysis.
Characterizing Cooked Cheese Flavor with Gas Chromatography
October 11th 2024A joint study by the Department of Food and Nutritional Sciences at the University of Reading and Synergy Flavours aimed to identify volatiles that contribute to the aroma of cooked cheese, including the role of fat content in development during cooking.