The mass spectrometer outputs the graphical output where each spectrum of data points is shown using Gaussian curves for peptide identification.
If spectra with a different number of data points are to be compared, then the mean intensity or the RMS has to be used instead of the TIC or vector norm, respectively.
After de-noising and baseline correction, the algorithm used for peak picking and peak parameter estimation in mass spectra can be summarized as follows: 1. Locate valleys (local minima) in the smoothed and baseline-corrected spectrum and split the spectrum into groups of data points at the valleys.
Normalization is performed according to the calculation: <img src="http://journals.plos.org/plosone/article/asset?id=info?doi/10.1371/journal.pone.0018359.e001.PNG" class= inline-graphic"/> y = inline-graphicpoint of the spectrum and n = number of data points of the spectrum.
A high scan speed can allow for greater MS/MS spectral coverage, but a scan speed that is too high can affect the quality of the spectra due to the lack of data points for each extracted ion chromatogram (EIC).
Suppose the spectrum is split into k groups of data points.
2. For each group (j = 1,2,..., k) of data points in the spectrum, 2.1 Fit a peak to the data points in component j, using the modified Asymmetric Pseudo-Voigt (mAPV) model.
The frequency resolution of the spectra is determined by the number of data points used in the S-WPIA calculation, and this number is automatically adjusted to match the frequency resolution of the 65 kHz sampling mode.
We can determine its approximate mean μN and standard deviation σN by considering the first and last 10 % of data points in each spectrum.
Here, N, M, exp, and cal represent the number of data points in the spectra, the number of variable parameters in the model, the experimental data, and the calculated data, respectively.
where N is the number of data points in the spectra, M is the number of variable parameters in the model, and "exp" and "cal" represent the experimental and the calculated data, respectively [27].
