Abstract text: Diferulic acids (DFAs) play a key role in cell wall cross-linking and strongly influence the mechanical properties of plant tissues. However, their routine quantification is limited by low-throughput and labor-intensive analytical methods. In this study, we developed and validated near-infrared spectroscopy (NIRS) calibration models to predict individual diferulate dimers and total diferulates (DFAT) in maize stem pith tissue. Calibration models (n = 384) showed strong predictive performance, with cross-validation coefficients of determination (R²cv) ranging from 0.86 to 0.96 across diferulate fractions, and ratio of performance to deviation (RPDcv) values above 2 for all traits, exceeding 3 for DFAT and major dimers. External validation with an independent dataset (n = 130) confirmed model robustness, yielding R²ev values between 0.75 and 0.92, slopes close to unity, and low standard errors of prediction. The most informative spectral regions were located around 1490 and 2130 nm, associated with phenolic hydroxyl groups and structural polysaccharides of the cell wall. Predictive accuracy was highest for total diferulates and the 8–O–4 and 8–5 dimers, enabling reliable high-throughput phenotyping. Attending to previous reports with other tissues, these results highlight strong tissue dependency in NIRS performance and identify maize stem pith as a robust substrate for diferulate prediction.