A Tutorial on Chemometrics for Low Field NMR Relaxation Data Part 1: Preparing the Dataset & Optimizing Experiments

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Abstract
Low resolution at low field NMR (LF-NMR) also known as time-domain nuclear magnetic resonance is a powerful analytical technique that probes molecular mobility mostly through relaxation dynamics (T₁, T₂) and diffusion offering valuable insights into physical-chemical properties across diverse matrices. However, the effective interpretation of LF-NMR data, whether univariate (e.g., relaxation times) or multivariate (e.g., full relaxation curves), requires rigorous experimental planning and robust chemometric workflows. This tutorial, the first of a two-part series, provides a comprehensive guide to plan LF-NMR experiments as well as to prepare datasets for chemometric analysis. It begins by distinguishing univariate and multivariate data structures and outlining appropriate statistical approaches for exploratory, regression, and classification objectives. Key preprocessing strategies for both rows (samples) and columns (variables) are critically examined, emphasizing the importance of physically meaningful transformations over empirically driven, uncritical data manipulation. The tutorial further introduces design of experiments (DOE) as an essential tool for optimizing acquisition parameters and systematically evaluating their influence on data quality. There are presented two practical examples, including a 2² factorial design for oleogel solidification and a 3³ design for CPMG parameter optimization. The results demonstrate how a structured experimental planning enhances reproducibility, minimizes artifacts, and lays the foundation for reliable chemometric models. Ultimately, this tutorial underscores that robust chemometric interpretations depend not on post-hoc data manipulation, but on the integration of sound experimental design, careful preprocessing, and a deep understanding of the underlying physical phenomena

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