A spectrum can consist of several hundred to thousands of cross-peaks manifested as localized multidimensional spectral features that in the case of 2D NMR belong to individual pairs of atoms that possess a nuclear spin. Identification and quantitative characterization of cross-peaks critically affect all downstream analyses and can have a major impact on data interpretation. Each cross-peak is characterized by the position of its center (i.e. frequency coordinates corresponding to chemical shifts), its peak shape along each dimension (usually Voigt shape with variable amounts of Lorentzian or Gaussian components), and its peak amplitude (or volume). The parameters that define the cross-peaks represent the chemical and biological information of interest about the molecule(s) present in the sample. The analysis of an NMR spectrum invariably involves some or all of the following steps: (i) identification of the complete set of cross-peaks, known as peak picking (ii) assignment of each cross-peak to the atoms it belongs to and (iii) quantification of each cross-peak by the determination of the peak amplitude or volume. Despite many years of progress, the above steps can only be partially automated. This applies in particular to spectra of large molecular systems or complex mixtures containing many cross-peaks that tend to overlap, which makes their spectral deconvolution challenging without expert human assistance. However, due to the large number of cross-peaks, such work can be tedious, time-consuming, and subjective with results differing between experts and labs, thereby limiting the transferability of the analysis within the research community. This makes the availability of an approach necessary that accomplishes the above tasks both with high accuracy and high reproducibility.ĭifferent methods have been proposed for peak picking and spectral deconvolution. The simplest approach is to select local maxima as peak positions. However, because of spectral noise, not all local maxima belong to true peaks.
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