Integration Errors in Chromatographic Analysis, Part I: Peaks of Approximately Equal Size

Apr 01, 2006
Volume 24, Issue 4, pg 402–414

Chromatographic situations are created for varying peak resolution and relative peak size. In this case, the peak size of the smaller peak is always at least 5% of the larger peak, and resolution is varied from 4.0 to 1.0. All separations are then integrated, using both area and height, by four different baseline methods: drop, valley, exponential skim, and Gaussian skim. Integration errors are calculated using reference calibration injections. The results demonstrate that the drop and Gaussian skim methods produce the least error in all situations. The valley method consistently produces negative errors for both peaks, and the skim method generates a significant negative error for the shoulder peak. Peak height is also shown to be more accurate than peak area. As the relative peak size increases, resolution below 1.0 will generate unacceptable errors, and resolution greater than 1.5 is necessary to minimize integration errors.

Integration of chromatographic peaks (determination of height, area, and retention time) is the first and most important step in the data analysis part of all chromatography-based analytical methods. This information is used for all subsequent calculations, including construction of calibration curves and calculation of unknown concentrations. Clearly, any error in measurement of peak size will produce a subsequent error in the reported result.


Figure 1
Analysts have several choices for integrating peaks. Figure 1 illustrates the four most common options for drawing the baseline between two peaks: drop, valley, exponential skim, and Gaussian skim. The drop method (Figure 1a) involves addition of a vertical line from the valley between the peaks to the horizontal baseline, which is drawn between the start and stop points of the peak group. The valley method (Figure 1b) sets start and stop points at the valley between the peaks, thus, integrating each peak separately. Skim procedures separate the small peak from the larger parent with separate baselines. The parent peak is integrated from its starting point to the apparent end of the peak group. The small peak's baseline starts at the valley between the peaks, and ends when the signal nears the baseline. The area "under" the skimmed peak is added to the parent peak, not the skimmed peak. This approach has been described also as a tangent integration method, and the small peak variously labeled a skim, shoulder, or rider peak.

Several variations of the skim procedure are possible. Early integration algorithms drew a straight line from the valley to the end of the peak. Figure 1c shows an exponential skim baseline. An exponential function is used to create curvature in the skim line, in an attempt to approximate the underlying baseline of the parent peak. While the exact procedures used to construct this line are a proprietary part of modern software algorithms, they all employ the same basic approach — use an exponential function to draw a curved baseline under the skimmed peak. More recently, another skim procedure has been developed, as shown in Figure 1d. Although referred to as a new exponential skim method, it will be described here as a "Gaussian" skim, because the intent is to more accurately reproduce the Gaussian shape of the parent peak.

For any of these methods, analysts must also choose between measuring peak size by either area or height. It is clear that these integration options are likely to generate significantly different analytical results, and analysts must decide which approach provides better accuracy.


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