Peak picking problem

The role of peak-picking is to return only one position per peak, and in the presence of noise, this again poses problems. It is likely that most algorithms will start from a position of intensity maximum, and then search either side for the first point significantly lower, i.e., a multiplier of the noise. This defines a new peak region and the process could then be repeated. Some software additionally offers the facility of a horizontal threshold, above which values are returned and below which they are not. 

The noise-free Stern-Volmer lifetime plots are clearly curved, which indicates a failure of a two discrete site model. However, this is a difficult nonlinear least-squares fitting problem, and the unquenched apparent lifetimes are within a factor of two of each other. Thus, for real data, it is much more difficult to pick up on the nonlinearities and exclude a discrete two-site model. For distributions with smaller R s, of course, fitting becomes too difficult for reliable model testing at least at 104 counts in the peak channel. 

In this case the survey scan was set as a full scan and the dependent scan as a product ion scan. The problem with data dependent acquisition is to determine the selection criteria. In most cases the system picks up the most abundant ion in the full scan spectrum. An inclusion list with masses of potential metabolites or exclusion list of known interferences significantly improves the procedure. In the example shown in Fig. 1.39, a procedure called dynamic background subtraction (DBS) was applied. This procedure considers chromatographic peak shapes and monitors not the most abundant signal in the spectrum but the largest increase of an ion in a spectrum. The advantage is that once a signal of a peak has... 

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