Advanced non-destructive detection methods for monitoring biological factors in post-harvest maize losses during transportation and storage

Post-harvest maize losses of 20–30% globally threaten food security and farmer livelihoods, especially in low- and middle-income regions. These losses stem from fungal growth, mycotoxin accumulation, insect infestations, and rodent damage, all of which are undetectable by conventional inspection and laboratory methods in their early stages. This comprehensive review examines recent advances in non-destructive detection technologies, including spectroscopic (near-, mid-, and Raman infrared), imaging (hyperspectral, X-ray, and computer vision), and volatile-sensing systems (colorimetric sensor arrays and electronic noses), highlighting their integration with artificial intelligence and the Internet of Things. Unlike previous reviews, it provides a comparative analysis of their performance and applicability in addressing multiple biological threats. Integrated sensing systems have shown potential to detect contamination well before visible symptoms, with spectroscopic and imaging methods achieving detection accuracies of 90–98% and volatile-sensing platforms reaching 92–100% in controlled laboratory studies. However, adoption remains limited by high equipment costs, lack of field validation in tropical settings, and absence of standardized calibration protocols. The transition from laboratory validation to field deployment requires advances in miniaturization, cost reduction, and environmental robustness, alongside system-level integration and standardized data protocols to enable the shift from reactive inspection to predictive prevention, strengthening food system resilience and global food security.