Shelf-life assessment techniques for fruit and vegetables: Recent trends and future prospects

Abstract

Fruit and vegetables (F&Vs) are rich in vitamins and bioactive compounds, contributing significantly to health, but their high perishability presents a major challenge by shortening their postharvest shelf-life. Accurate shelf-life assessment (SLA) of F&Vs is crucial for optimizing supply chains, reducing food waste, and ensuring high-quality produce for consumers. Various techniques have been used to qualify and quantify F&Vs' shelf-life throughout the supply chain, from harvest to retail. This comprehensive review highlights recent advances in machine learning (ML) and non-destructive techniques for evaluating F&Vs' shelf-life. Traditional methods, such as subjective quality assessments and invasive techniques, are compared with modern approaches, including kinetic models, ML algorithms, Internet of Things (IoT) technologies, and non-destructive methods like spectroscopy, hyperspectral imaging, and electronic sensing. Key factors influencing shelf-life, including intrinsic characteristics, pre-harvest practices, postharvest handling, and storage conditions, are thoroughly discussed. Additionally, this review examines the strengths and limitations of various shelf-life predictive technologies, emphasizing their role in real-time, accurate SLA throughout the supply chain. The prospects of SLA focus on integrating multi-sensor fusion, studying genome mutations, and utilizing advanced ML models to enhance real-time, accurate SLA in practical applications.