Ensuring the quality and safety of nuts is essential due to their high economic value, vulnerability to contamination, and increasing global demand. Traditional quality assessment methods are often invasive, labor- intensive, and limited in scope. Hyperspectral imaging (HSI), a non-destructive technique that integrates spatial and spectral information, has emerged as a powerful tool for comprehensive nut quality evaluation. This review examines recent advancements in the application of HSI to major nut types, including walnuts, almonds, pistachios, hazelnuts, pecans, peanuts, and chestnuts. The surveyed studies demonstrate the successful use of HSI for assessing chemical composition, fungal contamination, aflatoxins, physical impurities, and varietal classification. Unlike earlier reviews that either broadly address plant-based products or focus narrowly on specific contaminants such as mycotoxins, this work synthesizes diverse postharvest HSI applications specific to nuts. By consolidating current knowledge, it underscores the potential of HSI as a comprehensive tool for nut quality monitoring and classification. This review identifies key gaps such as the need for standardized imaging pro tocols, enriched spectral libraries, and real-time processing capabilities, offering direction for future research and industrial adoption in nut quality monitoring.
