Statistical Parameterization of Analytical Resonance in Chemical Spectroscopy.

SPARCS provides a robust and automated alternative by employing a sophisticated Bayesian nonparametric mathematical model of the NMR signal. The posterior distribution of this model is approximated using Markov Chain Monte Carlo (MCMC) sampling.

This allows the application to generate an MCMC chain that effectively maps the probability distribution of key spectral parameters. The resulting chain serves as a rich dataset for further detailed analysis, enabling deeper insights into the chemical properties of a biological sample. This tool is designed for chemists, biochemists, and graduate students working with 1D NMR spectroscopy.

Key Features

Import Raw 1D NMR Data: Directly import and process data from Bruker format file systems.
Bayesian Analysis: Generate robust MCMC chains from your data using a Bayesian nonparametric model.
Live Visualization: Optionally view a real-time display of the MCMC chain's progress, including the current spectrum with frequency locations and a trace of the log-posterior.
Background Processing: For long runs, an option to expand the chain in the background with a progress bar is available.
Data Export: The final MCMC chain is exported as a standard .mat file for easy downstream analysis in MATLAB.