A unified framework for peak detection and alignment: application to HR-MAS 2D NMR spectroscopy

In this paper, we propose a new scheme to detect and align simultaneously peaks that correspond to different metabolites within a biopsy. The proposed peak detection and alignment scheme is based on the use of evidence theory, which is well suited to model uncertainty and imprecision characterizing the 2D NMR HR-MAS spectra. Consequently, we propose the coupling use of Bayesian and fuzzy set theories to model and quantify the imprecision degree, which is then exploited to define the mass function. We particularly show that our new mass function definition and the use of evidence theory for peak detection and alignment achieve consistently high performance compared to a Bayesian scheme on both synthetic and real spectra. The high quality of peak alignment precision reached by the use of evidence theory allows us to efficiently detect reliable biomarkers, which is an essential step for a better therapeutic and human complement system management in case of multiple sclerosis disease, cancer, etc.