An automatic L 1-based regularization method for the analysis of FFC dispersion profiles with quadrupolar peaks

Fast Field-Cycling Nuclear Magnetic Resonance relaxometry is a non-destructive technique to investigate molecular dynamics and structure of systems having a wide range of ap-plications such as environment, biology, and food. Besides a considerable amount of liter-ature about modeling and application of such technique in specific areas, an algorithmic approach to the related parameter identification problem is still lacking. We believe that a robust algorithmic approach will allow a unified treatment of different samples in several application areas. In this paper, we model the parameters identification problem as a con-strained L 1-regularized non-linear least squares problem. Following the approach proposed in [ Analytical Chemistry 2021 93 (24)], the non-linear least squares term imposes data con-sistency by decomposing the acquired relaxation profiles into relaxation contributions as-sociated with 1 H -1 H and 1 H -14N dipole-dipole interactions. The data fitting and the L 1 -based regularization terms are balanced by the so-called regularization parameter. For the parameters identification, we propose an algorithm that computes, at each iteration, both the regularization parameter and the model parameters. In particular, the regularization parameter value is updated according to a Balancing Principle and the model parameters values are obtained by solving the corresponding L 1-regularized non-linear least squares problem by means of the non-linear Gauss-Seidel method. We analyse the convergence properties of the proposed algorithm and run extensive testing on synthetic and real data. A Matlab software, implementing the presented algorithm, is available upon request to the authors.(c) 2022 Elsevier Inc. All rights reserved.

Automated summary

Fast Field-Cycling Nuclear Magnetic Resonance relaxometry is a non-destructive technique used to investigate molecular dynamics and structure in various applications. This paper models the parameter identification problem as a constrained L1-regularized non-linear least squares problem and proposes an algorithm to compute the regularization parameter and model parameters. The convergence properties of the algorithm are analyzed, and extensive testing on synthetic and real data is conducted.