Prediction of Physicochemical Properties of Date Fruits from LF-NMR Relaxation: Regressions, Multivariate Analysis, and Machine Learning

Simple prediction of food composition and related properties could reduce reliance on complicated measurement methods that use costly instruments and chemicals. In this study, low-field nuclear magnetic resonance (LF-NMR) relaxation measurements ( T 1 and T 2 ) were used. Nine batches of date fruits (DFs) differing in quality were analyzed using relaxation descriptors extracted to characterize proton environments associated with different mobility states, with transverse ( T ₂ ) relaxation reflecting contributions from rigid, semirigid, and mobile proton populations. Descriptors associated with rigid, semirigid, and mobile proton populations were obtained using transverse ( T 2 ) relaxation. The relationships between these parameters and the physicochemical characteristics of the samples of date fruit were analyzed. LF-NMR T 1 and T 2 parameters showed linear correlations with studied physicochemical properties, including moisture, crude fiber, and pectin. This indicates the potential of LF-NMR for predicting physicochemical properties of date fruits. Multivariate analysis was used to examine clustering trends among date fruits based on LF-NMR relaxation behaviors. The decision tree model achieved high predictive performance for moisture content ( R 2 = 0.990) using T ₂ relaxation parameters. Artificial neural networks considering T 2 relaxation also showed strong internal predictive performance for sucrose ( R 2 = 0.998) and fiber (R 2 = 1.000). These results demonstrate that integrating LF-NMR relaxation with multivariate and machine learning approaches provides an effective framework for predicting key physicochemical attributes of date fruits.