Starch-based nanocomposites (SNCs) are at the forefront of innovations in food science, offering unparalleled opportunities for enhancing the stability, bioactivity, and overall functionality of food systems. This review delves into the potential of SNCs to address contemporary challenges in food formulation, focusing on the synergistic effects of their components. By integrating cutting-edge technologies, such as artificial intelligence (AI) and nuclear magnetic resonance (NMR), we explore new avenues for enhancing the precision, predictability, and functionality of SNCs. AI is applied to optimize the design of SNCs, leveraging predictive modeling to fine-tune material properties and streamline production processes. The role of NMR is also critically examined, with particular emphasis on its capacity to provide high-resolution structural insights, monitor stability over time, and elucidate molecular interactions within food matrices. Through detailed examples, the review highlights the impact of NMR in unraveling the complex behaviors of bioactive compounds encapsulated in SNCs. Additionally, we discuss the integration of functional assays and AI-driven analytics in assessing the bioactivity and sensory properties of these systems, providing a robust framework for the rational design of advanced food products. The synergy between AI, NMR, and SNCs opens new pathways for developing tailored, high-performance food formulations that address both health and consumer preferences.

This study aimed to achieve a precise and non-destructive quantification of the amounts of total starch, protein, β-glucan, and fat in oats using near-infrared technology in conjunction with chemometrics methods. Eight preprocessing methods (SNV, MSC, Nor, DE, FD, SD, BC, SS) were employed to process the original spectra. Subsequently, the optimal PLS model was obtained by integrating feature wavelength selection algorithms (CARS, SPA, UVE, LAR). After SD-SPA, total starch reached its optimal state (R_p² = 0.768, RMSEP = 2.057). Protein achieved the best result after MSC-CARS (R_p² = 0.853, RMSEP = 1.142). β-glucan reached the optimal value after BC-SPA (R_p² = 0.759, RMSEP = 0.315). Fat achieved the optimal state after SS-SPA (R_p² = 0.903, RMSEP = 0.692). The research has shown the performance of the portable FT-NIR for a rapid and non-destructive quantification of nutritional components in oats, holding significant importance for quality control and quality assessment within the oat industry.