Artificial intelligence integrated in non-destructive technologies for monitoring food freshness: A review of next generation approach.

Food freshness is a significant quality and safety attribute, which contributes in consumers' health and their food choices. The adoption of rapid, accurate, and reliable food freshness detection novel approaches is of great significance. Non-destructive testing (NDT) offers precise, fast and efficient detection of food freshness, quality and safety but, still confronts multiple issues in data preprocessing, accuracy, reliability and adaptability. However, artificial intelligence (AI) integration in NDT upholds significant promise to overcome these issues. This review, spotlights the significance of integrating the AI in NDT (spectroscopy, imaging and other technologies) for boosting the performance of NDT. It further probes the merits, and demerits, and the principles of AI assisted NDT technologies. Moving forward, integrating AI in NDT technologies have significantly boosted the reliable and accurate freshness detection in complex food matrices, plant and animal derived foods. In spite of recent advancements in this field of research, there are still several existing challenges of compatibility, precision, and adaptability associated with the NDT approaches coupled with AI. Therefore, this review is comprehensively emphasized on the promising transformative prospects of AI integrated in NDT approaches for the non-destructive, rapid, reliable and accurate detection of food freshness based on its quality indicators accompanied with highlighting the future challenges, innovations and directions.