Smart Detection of Food Spoilage Using Microbial Volatile Compounds: Technologies, Challenges, and Future Outlook

Area of Science:

• Food Science
• Analytical Chemistry
• Sensor Technology

Background:

• Microbial volatile organic compounds (MVOCs) are crucial early indicators of food spoilage and contamination.
• Current detection methods face challenges in sensitivity, selectivity, and real-world application.

Purpose of the Study:

• To critically review and compare existing MVOC detection technologies.
• To explore emerging materials and platforms for enhanced sensor performance.
• To discuss the integration of MVOC sensing with data analytics for intelligent food quality monitoring.

Main Methods:

• Comprehensive literature review of MVOC detection techniques (GC-MS, IMS, sensors, biosensors, colorimetric indicators).
• Comparative analysis based on sensitivity, selectivity, matrix compatibility, and deployment potential.
• Evaluation of novel materials (MXene, MIPs) and biosensor platforms (enzymes, OBPs, live cells).

Main Results:

• Emerging technologies show promise for improved selectivity and stability in food matrices.
• Integration with IoT and machine learning enables real-time monitoring and dynamic shelf-life estimation.
• Quantitative structure-activity relationship models and tools aid sensor design and analyte prioritization.

Conclusions:

• A paradigm shift towards intelligent, adaptive MVOC sensing systems is evident.
• Data-driven design and analytical rigor are essential for advancing food safety and quality assurance.
• Standardized performance criteria are needed to facilitate regulatory acceptance and widespread adoption.