The integration of artificial intelligence (AI) in the food industry has driven significant advancements in efficiency, safety, and sustainability. This review assesses the current state and future prospects of AI applications in key areas such as food traceability, safety, quality control, supply chain optimization, and intelligent packaging solutions. AI technologies, including machine learning (ML) algorithms and computer vision systems, are widely used to optimize supply chains, predict demand, reduce waste, and enhance food safety and quality monitoring. Advanced ML models are employed to analyze production data, monitor quality parameters, and predict shelf life, ensuring compliance with stringent regulatory standards. Despite these advancements, challenges related to data quality, system integration, computational demands, and ethical considerations remain, necessitating further research and collaboration among stakeholders. This review aims to elucidate these challenges while highlighting the transformative potential of AI in the food industry. By synthesizing recent developments and trends, this paper provides valuable insights for researchers, industry professionals, and policymakers, underscoring the pivotal role of AI in driving innovation and sustainability in the food sector. Graphical Abstract

In this paper, phosphoric acid (H₃PO₄), hydrochloric acid (HCl), and hydrogen peroxide (H₂O₂) were employed for the modification of oil-based drill cutting ash (OBDCA) for the first time. The adsorption of rhodamine B (RhB) on modified oil-based drill cutting ash (MOBDCA) in an aqueous medium was investigated. H₂O₂-modified OBDCA had the optimal adsorption efficiency for RhB. The physical and chemical properties of MOBDCA were analyzed using X-ray diffraction (XRD), scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), ζ-potential, N₂ adsorption–desorption isotherm, and pore size distribution. The effect of the pH value (3–11), reaction time (10–720 min), and initial RhB concentration (10–200 mg/L) on RhB adsorption was discussed. The adsorption kinetics highly fitted with the pseudo-second-order model (R² > 0.99), which indicated that the adsorption process was dominated by chemisorption. The adsorption isotherm fitted well with the Langmuir and Freundlich models (R² > 0.97), which indicated the monolayer adsorption process and the heterogeneous adsorption process, respectively. The theoretic adsorption capacity (50 mg/g) for RhB was achieved by H₂O₂-modified OBDCA. This paper provides a promising method of resource utilization of OBDCA to treat organic pollutants.

Carbon quantum dots (CQDs) were prepared by a chemical oxidation method using luffa sponge based activated carbon fiber as the raw material. The obtained CQDs were well characterized. The fluorescence quenching effect of Cr(VI) ion on CQDs was investigated. The results show that the addition of Cr(VI) changes the intensity of the ultraviolet characteristic absorption peak of CQDs, and causes static quenching of the fluorescence of CQDs. With the increase in the Cr(VI) concentration, the fluorescence of CQDs was gradually extinguished linearly.