The rising global burden of chronic diseases highlights the limitations of traditional dietary guidelines. Precision Nutrition (PN) aims to deliver personalized dietary advice to optimize individual health, and the effective implementation of PN fundamentally relies on comprehensive and accurate dietary data. However, conventional dietary assessment methods often suffer from quantification errors and poor adaptability to dynamic changes, leading to inaccurate data and ineffective guidance. Machine learning (ML) offers a powerful suite of tools to address these limitations, enabling a paradigm shift across the nutritional management pipeline. Using dietary data as a thematic thread, this article outlines this transformation and synthesizes recent advances across dietary assessment, in-depth mining, and nutritional intervention. Additionally, current challenges and future trends in this domain are also further discussed. ML is driving a critical shift from a subjective, static mode to an objective, dynamic, and personalized paradigm, enabling a loop nutrition management framework. Precise food recognition and nutrient estimation can be implemented automatically with ML techniques like computer vision (CV) and natural language processing (NLP). Integrating with multiple data sources, ML is conducive to uncovering dietary patterns, assessing nutritional status, and deciphering intricate nutritional mechanisms. It also facilitates the development of personalized dietary intervention strategies tailored to individual needs, while enabling adaptive optimization based on users’ feedback and intervention effectiveness. Although challenges regarding data privacy and model interpretability persist, ML undeniably constitutes the vital technical support for advancing PN into practical reality.

The fibers from four wheat varieties (FT, XW 26, XW 45, and KW 1701) were selected and chemically modified with NaOH, epichlorohydrin, and dimethylamine to improve the adsorption capacity for anionic dye. The structure of the fibers with or without modification was characterized by scanning electron microscope (SEM), X-ray diffraction (XRD), and Fourier-transform infrared (FTIR) spectrometry. The modified products were studied from the aspects of adsorption capacities, adsorption kinetics, and thermodynamics to provide a reference for the utilization of wheat bran. By SEM, more porous and irregular structures were found on the modified fibers. The XRD results showed that the crystals from the original fibers were destroyed in the modification process. The changes in fibers’ infrared spectra before and after modification suggested that quaternary ammonium salts were probably formed in the modification process. The maximum adsorption capacity of wheat bran fibers for Congo red within 120 min was 20 mg/g for the unmodified fiber (XW 26) and 93.46 mg/g for the modified one (XW 45). The adsorption kinetics of Congo red by modified wheat bran fiber was in accord with the pseudo-second-order kinetic model at 40 °C, 50 °C, and 60 °C, indicating that the adsorption process might be mainly dominated by chemisorption. The adsorption was more consistent with the Langmuir isothermal adsorption model, implying that this process was monolayer adsorption. The thermodynamic parameters suggested that the adsorption occurred spontaneously, and the temperature increase was favorable to the adsorption. As mentioned above, this study proved that the wheat bran fiber could possess good adsorption capacities for anion dye after chemical modification.