Describe › Signal & numerical

Peak analysis (find peaks)

Peak detection locates local maxima and reports each peak's position, height, prominence, and approximate width.

What is Peak analysis (find peaks)?

Peak finding is the core of chromatography, spectroscopy, and any 'count and characterize the bumps' workflow. A local maximum alone is noisy, so each candidate is characterized by its prominence — how far it stands above the higher of the two surrounding valleys — which separates a real peak from a ripple on a shoulder.

Width is measured at the peak base between the bounding minima. Height and prominence thresholds filter out small or non-salient peaks; the peak pattern tolerates flat tops and plateaus so rounded or quantized data isn't missed.

When should I use Peak analysis (find peaks)?

  • Counting and quantifying peaks in a spectrum, chromatogram, or any 1-D signal.
  • Extracting feature positions/areas before further analysis.

What data does it need?

One numeric signal column (+ optional X column for real units) + optional minimum height and minimum prominence.

What does it report?

A line plot with the detected peaks marked, plus a table of position, height, prominence, and width per peak.

What does it assume?

  • Peaks are local maxima; smooth first if the signal is noisy (see smoothing).
  • Prominence, not raw height, is the reliable saliency measure on a sloping baseline.

How do I interpret the result?

Rank peaks by prominence, not height, when the baseline drifts. Raise the prominence threshold to drop noise ripples.

See also

References

  • Borchers (2022). Practical Numerical Math Functions.