Microbial volatile organic compounds (MVOCs) serve as early, noninvasive indicators of food spoilage and microbial contamination. This review critically assesses current methods for MVOC detection, including gas chromatography-mass spectrometry (GC-MS), ion mobility spectrometry, chemiresistive sensors, biosensors, and colorimetric indicators. These technologies are compared in terms of sensitivity, selectivity, matrix compatibility, and deployment potential. Particular attention is given to challenges in signal drift, sensor specificity, and reproducibility in real-world food systems. Emerging materials, such as MXene composites and molecularly imprinted polymers, and biosensor platforms using enzymes, odorant-binding proteins, or live cells offer improved selectivity and operational stability. Integration with Internet of Things platforms and machine learning enhances the predictive capability, enabling real-time quality monitoring and dynamic shelf life estimation. Quantitative structure-activity relationship models and structure-informed tools such as OWSum further refine analyte prioritization and sensor design. Cross-commodity comparisons reveal both universal and food-specific spoilage volatiles, supporting the development of hybrid sensor arrays. Regulatory considerations and recent international initiatives are also discussed, highlighting the need for standardized performance criteria. Overall, this review outlines a shift toward adaptive, intelligent MVOC sensing systems that combine analytical rigor with data-driven design to support food safety, quality assurance, and waste reduction.
Ramadan et al. (2025) studied this question.