Machine learning-driven optical nanomaterials typical applications in food quality assessment: Present obstacles and future horizons

Complex and variable food systems contain numerous interfering substances that produce intricate analytical signals within the relevant matrices. Analytical detection utilizing optical nanosensing often generates an extensive array of data points, characterized by high dimensionality or complex imaging maps. Consequently, the extraction of meaningful data from these vast datasets has emerged as a significant challenge. The integration of machine learning with optical nanomaterials has exhibited remarkable predictive capabilities and accuracy in the processing, analysis, and extraction of valuable information from large and complex food-related datasets. This review systematically elucidates the synergistic relationship between optical nanosensing and machine learning, encompassing: (1) the signal transduction mechanisms and data acquisition utilizing optical nanomaterials; (2) the design and optimization of nanomaterials driven by machine learning; (3) the applications of machine learning algorithms for food data processing and predictive modeling; and (4) the advancements in collaborative applications of machine learning and optical nanomaterials in food quality analysis. Building on this foundation, the paper addresses the challenges currently confronting research on the application of machine learning-driven optical nanomaterials in food quality analysis, constructs a research framework, and outlines potential directions for future exploration. This review offers novel insights into the integrated application of optical nanomaterials and machine learning, presents innovative solutions for the rapid and real-time detection of food quality, and significantly enhances the depth of research into food quality assessment.