This month in "GC Connections" John Hinshaw examines the anatomy of chromatographic peaks with attention to features that
help determine the suitability of individual chromatographs for a specific analysis task.
The human sense of shape and pattern recognition can discern subtle nuances among groups of visual cues that no computer system
can reproduce faithfully. Yet when it comes to measuring and evaluating chromatograms, analysts put a great deal of faith
into their computerized data handling systems. Over the course of one year a practicing chromatographer might look at 10,000
or more peaks. After some time an observer develops a finely tuned sense of what constitutes a good or bad peak shape, which
peaks will be detected and measured correctly by the data system, and an overall idea of how the observed peaks indicate the
instrumentation operational health. This sense is formalized in system suitability software that determines an array of chromatogram
metrics from test analyses and compares them to goals or minimum performance levels. These programs confirm the suitability
of individual chromatography systems to perform specific analytical tasks, often on a daily basis. Such software relies upon
accurate designation of target peaks and parameters when set up: Otherwise it is bound to perform poorly and can fail to find
Chromatographers who use data-handling or suitability software or who make such measurements themselves should have a thorough
understanding of the various metrics that are extracted from a chromatogram. Without this awareness they relinquish control
over the quality of their results — they are using a tool without a working knowledge of its functions and limitations.
This month's "GC Connections" examines the basic measurements of a peak's size and shape as used for purposes of assessing
and monitoring chromatographic separations over a period of time. The calculations presented here represent some of the most
commonly used metrics of this type. Other related calculations are found throughout various commercially available performance
monitoring systems as well as in individual laboratories' quality control (QC) and quality assurance (QA) procedures. This
column installment is intended to aid in the general understanding of these calculations and it does not purport to present
computations that are any more or less appropriate than others.
Getting into Shape
As solutes enter and pass through a chromatography column and out to the detector they are subject to various processes that
modify the peaks' profiles as finally registered by a data system or chart recorder. Peaks must be constrained within certain
limits of retention time, width, and shape if minimum levels of peak resolution are to be maintained. By measuring their peak
shapes, chromatographers obtain information about the ability of their systems to perform a specific analysis as well as about
possible sources of peak shape distortion.
In an ideal chromatography system the separation process results in the normal Gaussian-shaped peak that is the basis for
many metrics. The Gaussian peak shape is a product of a statistical theoretical treatment of the solutes' transit through
a chromatography system. It represents a simple basis on which analysts can make a number of measurements by assuming that
it closely approximates their peak shapes. This assumption is justified by the random-walk and other theories of chromatography
that approximate the separation process as if it were the product of the bulk behavior of large populations of solute molecules.
The randomness of the process results in a Gaussian peak elution profile. Additional effects, primarily extracolumn in nature,
act to modify the peak profile. Slow detector response speeds and broad injection profiles, relative to band spreading in
the column, are the two most common extracolumn effects.