Why “Peak Area” is Used for Quantification in Chromatography
Author: Chromatography Mound (expert chromatographer, Welch's contract writer)
Table of contents
Introduction
Chromatographers often face a range of challenges, from poor resolution between adjacent peaks, interference from impurities, to insufficient response from certain analytes that leads to inaccurate quantification, etc. Many solutions can be applied to address these issues, including extending retention times, improving sample preparation, or applying standard addition techniques.
However, there is one surprisingly simple and highly effective solution that requires no changes to the analysis method at all. It only involves the choice of quantification data after the analysis is conducted.
How A Chromatogram is Formed?
Before begin the topic, let’s discuss the basics: how is a chromatogram generated? Take the commonly used diode array detector (DAD) as an example. These detectors typically cover the UV range (200–400 nm) and the visible range (400–800 nm). Most compounds with conjugated systems or strongly polar covalent bonds absorb within this wavelength span.
As shown below, a compound’s absorbance varies across wavelengths, and the DAD can scan and collect absorbance data across 200–800 nm. Combining that with retention time, we get a three-dimensional data set resembling a mountain range. The front view gives the familiar chromatogram; the side view provides a spectral profile used for compound identification.
Why Quantify Using Peak Area?
Knowing how chromatograms are formed, we can continue to our main topic: why is peak area, instead of height or volume, used for quantification?
Let’s first discuss the “meaning” of these values. Each point on a chromatographic peak represents the compound’s signal response at a particular wavelength. Peak height reflects the maximum signal intensity, or the top of the peak, peak area reflects the integrated signal over time, or the total response, and peak volume reflects the integration of the peak area across all wavelengths.
- Limitation of using peak volume
Peak volume considers all collected data points across wavelengths. This approach works well for pure standards, but if the target analyte is co-eluted with impurities, their peaks overlap in the 3D plot, leading to overestimated or skewed quantification. - Limitation of using peak height
In some cases, peak height can be a valid choice: when the central part of the peak is sharp and symmetrical, and interference affects only the flanks. Since only the maximum point is used, the influence of nearby impurities is minimal.
However, peak deformation often occurs in real-world samples due to pH differences, salt concentrations, or ionization states. These changes can result in broader and flatter peaks with the same area but reduced height, introducing quantification errors. - Pros of using peak area
Peak area offers a balance of robustness and precision. Regardless of changes in peak shape or height, as long as the retention time remains stable, the peak area generally remains consistent. This makes area-based quantification less susceptible to distortion and more reliable overall.
In theory, all of the three values can be used for quantification, but in practice, we most commonly use peak area because it yields the most consistent and reliable results. And in some cases when peak shape is satisfactory but adjacent peaks are unresolved, peak height can be used instead.
Other Quantification Approach Worth Trying
Similarly, analysts should be cautious when choosing detection wavelengths. Many compounds exhibit multiple absorbance maxima. For synthetic dyes like Allura Red or Sunset Yellow, if the chromatogram is extracted at a UV wavelength, interference is more likely, as many impurities absorb in that range. In such case, switching to a visible wavelength may significantly reduce interference, since most sample matrix impurities don't absorb visible light.
Conclusion
Accurate quantification depends not only on chromatographic separation but also on how the data is extracted and interpreted. With the right strategy, you can simplify your workflow, avoid unnecessary troubleshooting, and achieve reliable results more efficiently.
