Processing peak picking

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 basic processing of ID and 2D data requires obligatory processing steps for transforming the raw data (FID) into a "readable spectrum, i.e. Fourier transformation and phase correction to produce a spectrum with absorptive lineshapes. Finally, a few additional step.s (calibration, peak picking, integration) as discussed in chapter 4 are required before the spectrum is eventually plotted. 

After processing in the time domain, Fourier transformation, phasing and basic processing (calibration, peak picking, integration) ahs been performed, additional processing steps to improve spectral quality are at your disposal. This includes operations common to both ID and 2D spectra e.g. baseline correction in the frequency domain, as well as operations specific to these different type.s of data sets. 

Correction, Window Function (Exponential LB = 1.0 Hz) and FT. In the frequency domain select Phase Correction (6th Order), Peak Picking (positive Peaks only X Range whole Spectrum) of the whole region. Save Spectrum (set Processing Number Increment = 1) and Plot Spectrum (set the plot parameters according to your preferences). Execute the automatic processing and if you are satisfied with the result, store this job for processing 1D C raw data as C.JOB. 

The spectra can be processed in different ways in order to produce signals from individual samples. In the first method, results from a peak-picking routine in each acquired spectrum are correlated with a calculation of the shifts in resonant frequency expected for each sample from the values of the applied gradients and the positions of the coils with respect to the centre of the z-gradient. A second, more robust method involving spectral masking and subtraction, is shown in Figure 8.2.5. 

Standard baseline subtraction and smoothing methods are applied to each individual MALDI spectrum after measurement or on-the-fly during acquisition in order to reduce the influence of chemical noise (Figure 2D) (35). While this is performed independently on each mass spectrum, other processing steps such as normalization, recalibration, and peak picking have to account for the large number of individual spectra in MSI data. 

Both an automatic peak-picking routine in the processing software and visual inspection are used to identify peaks. Chemical shifts are then compared with literature reports. Table 2.1 shows some of the biological phosphorus compounds identihed in NMR studies of environmental samples, while Table 2.2 shows peak shifts for phosphorus minerals determined by solid-state spectroscopy. Although this chapter focuses on organic phosphorus, the overlap of peak shifts for biological and mineral phosphorus compounds must be noted. This may complicate the identification of phosphorus species in solid-state spectroscopy, because both biological and mineral phosphorus forms may be present. To further confirm peak shifts, standards such as methylene diphosphonic acid may be added directly to the sample or included as capillary tube inserts in solution NMR (e.g. Koopmans et aL, 2003). 

In addition to providing automated peak picking and computation of the corresponding chemical shift lists, APSY supports automated sequential resonance assignment. Thus, APSY is a valid alternative to related NMR techniques. APSY can be the first step, after sample preparation, in a fully automated process of protein stmcture determination by NMR with successive automated algorithms for the NOESY spectrum analysis and structure calculation. 

Many other data processing techniques are used to extract useful information from experimental NMR spectra. Signal intensities compiled by peak picking may be fitted to an exponential or Gaussian function, as in the determination of relaxation times and... 

Mass Spectrometry Aside from a peak for the molecular ion which is normally easy to pick out aliphatic carboxylic acids undergo a variety of fragmentation processes The dominant fragmentation m aromatic acids corresponds to loss of OH then loss of CO... 

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