Reduction in the complexity of 1D 1H-NMR spectra by the use of Frequency to Information Transformation

TL;DR

This paper introduces the frequency-information transformation (FIT), a novel method that simplifies 1H-NMR spectra by extracting relevant information, reducing variability, and improving automated molecule identification.

Contribution

The paper presents FIT, a new technique for transforming NMR spectra into an information spectrum that enhances pattern matching and reduces spectral variability compared to previous methods.

Findings

FIT successfully extracts relevant spectral information.

It decreases inter-class correlation coefficients.

It increases intra-class correlation coefficients.

Abstract

Analysis of 1H-NMR spectra is often hindered by large variations that occur during the collection of these spectra. Large solvent and standard peaks, base line drift and negative peaks (due to improper phasing) are among some of these variations. Furthermore, some instrument dependent alterations, such as incorrect shimming, are also embedded in the recorded spectrum. The unpredictable nature of these alterations of the signal has rendered the automated and instrument independent computer analysis of these spectra unreliable. In this paper, a novel method of extracting the information content of a signal (in this paper, frequency domain 1H-NMR spectrum), called the frequency-information transformation (FIT), is presented and compared to a previously used method (SPUTNIK). FIT can successfully extract the relevant information to a pattern matching task present in a signal, while discarding the remainder of a signal by transforming a Fourier transformed signal into an information spectrum (IS). This technique exhibits the ability of decreasing the inter-class correlation coefficients while increasing the intra-class correlation coefficients. Different spectra of the same molecule, in other words, will resemble more to each other while the spectra of different molecules will look more different from each other. This feature allows easier automated identification and analysis of molecules based on their spectral signatures using computer algorithms.