Empower Tip: Baseline Noise

In this tip for Empower Chromatography Data System (CDS), we will resume our series on Baseline Noise and discuss Root Mean Squared Noise (RMS).

In general, RMS calculations will yield significantly smaller noise calculations, at least two thirds smaller than an equivalent Peak-to-Peak calculation. When working with drifting baselines, or baselines with infrequent noisy regions, averaging segments of time over the measured noise region can give smaller calculated noise numbers.

With an RMS noise calculation, we fit the noise to a best-fit line using the linear least squares method.

The difference between each noise point is subtracted from its corresponding calculated point on the best-fit line. These values are summed, squared, and then averaged. The square root of this average value is reported as the noise in the RMS calculation.

The noise is measured as distance away from the best-fit line. The noise region consists of parallel lines with the same slope as the original best-fit line. Because this noise is measured with a best-fit line following the slope of the baseline, this calculation is not dramatically impacted by a sloping baseline (figures 3).

Even though the calculation is not dramatically impacted by baseline slope the calculation may be broken into segments and averaged like the Peak-to-Peak calculation discussed in Tip 295. By doing this, you can average out the impact of outliers in the noise calculation.

If you break the noise region into thirty-second segments as before, you can do the linear least squares fit over each thirty-second segment.

For each segment, the noise will be calculated. By some mechanism the software will arrive at a final single number derived from these segments.

It’s that easy!