Reading presentations from existing tool providers I noticed that in order to detect peaks they first use bunching (average N points) and then use slope and curvature to detect peaks. I'm guessing they use bunching to get rid of high-frequency noise.

Aren't there better alternatives like removing high frequencies with FT? I'm concerned that bunching requires peak width to be known to define Bunching Factor. Which means we first need to detect peak width in some other way before using curvature. But I've seen more than 1 presentation talking about bunching, so there have to be reasons to use this approach instead of others.

  • $\begingroup$ Several chromatogram signals are made of three components: peaks (potentially merged), drift or baseline, and random stuff. Do you wish to qualify or quantify peaks? Any hints about peak width and noise? $\endgroup$ – Laurent Duval Dec 2 '18 at 18:02
  • $\begingroup$ I'd like to determine peak beginning/end and its area. I'm building a generic tool to process LCMS, thus there could be baseline drifts and different levels of noise. Need to automatically tag peaks within EIC and (maybe) TIC. $\endgroup$ – Stanislav Bashkyrtsev Dec 2 '18 at 18:27
  • $\begingroup$ Only 1D in a separable way? Any bound on peak width? $\endgroup$ – Laurent Duval Dec 2 '18 at 19:23
  • $\begingroup$ Only 1D, yes. No particular bounds on peak widths. Though I haven't seen peaks as wide as 30 seconds yet. But I assume they may exist if LC method is not selected properly. $\endgroup$ – Stanislav Bashkyrtsev Dec 3 '18 at 6:54

peak(s) determination tends to be application specific. There are many heuristics and one usually needs to try and test a lot.

It helps a lot if there is a reliable statistical model for your data.

One trick is to note that peak is larger than its adjacent neighbors. Peaks on boundaries can be determined by reflection.

Another issue is the set that you are finding peaks on. Some data consists of multiple scans that can be averaged to reduce noise. One can also do what you refer to as bunching and bunching has numerous variations such as summing, weighted summing, taking a median and combinations.

Another concern may be strong peaks bleeding into weak peaks or masking them entirely. Many applications have this bin bias problem and there are ways to approach that which are beyond what can be answered in a simple answer given here.

One can also use thresholds to isolate peaks and again that has many variations.

Usually, a community has conventions on how to approach problems like finding peaks and there is probably some documents that constitute a standard. You might want to look for those.


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