Luís Eduardo Vitolano

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

LF-NMR signals, typically acquired as multi-exponential decays, contain rich information about molecular mobility, compartmentalization and sample composition (i.e., T₂ or T₁). However, their full potential is often underutilized due to a lack of chemometric integration. This tutorial begins with Exploratory data analysis using principal component analysis and hierarchical cluster analysis, emphasizing preprocessing strategies such as mean-centering and normalization to reveal latent structures. A case study on temperature-dependent egg measurements illustrates how preprocessing choices affect physicochemical interpretation. Next, regression methods, univariate, multiple linear regression, principal component regression, and partial least squares (PLS), are detailed with practical examples on pharmaceutical and food adulteration datasets, highlighting the importance of cross-validation, figures of merit (RMSE, R², LOD, LOQ, bias), and model robustness. Finally, classification techniques including K-nearest neighbors, PLS Discriminant Analysis (PLS-DA), Soft Independent Modeling of Class Analogy (SIMCA), and one-class data driven SIMCA are demonstrated on the same egg dataset. The tutorial concludes with emerging trends (i.e., non-linear models, SHapley Additive explanations interpretability, data fusion, class balancing) and best practices for building causal, interpretable, and generalizable LF-NMR chemometric models

Polysaccharide-based edible coatings are increasingly explored as sustainable strategies for maintaining quality of fresh produce, acting as barriers to gas exchange while improving mechanical and optical properties. However, their effectiveness depends not only on the intrinsic features but also on the structural and physiological diversity of fruits and vegetables, which vary in peel composition, hydrophobicity, and texture. This study investigated plant-derived polysaccharide films (cassava starch, potato starch, corn starch, carboxymethylcellulose, hydroxypropylmethylcellulose, and pectin) characterized for moisture resistance, solubility, permeability, thermal stability, hydrophilicity, opacity, gloss, and mechanical strength. Concurrently, different fruits and vegetables (fruit, root, and tubers) were analyzed for their surface hydrophilicity to establish correlations between film properties and peel characteristics. The findings emphasize that no single polymer can be universally applied. In addition, the choice of matrix must be guided by both film functionality and produce surface traits. Starch-based films presented high hydrophilicity, suggesting better wettability, while pectin and cellulose derivatives presented distinct advantages for less hydrophilic peels. This work highlights the importance of tailoring edible coatings according to the physicochemical compatibility between films and fresh produce surfaces, providing insights for improving post-harvest preservation strategies and guiding the development of effective, sustainable coatings for diverse horticultural commodities.