A cost-effective dual sensor system utilizing photoionization and metal-oxide sensors, combined with machine learning, for real-time detection of hexanal, a key marker of lipid oxidation in food spoilage.
This innovative solution offers a real-time, cost-effective method for detecting hexanal, a critical marker of lipid oxidation linked to food spoilage. By integrating a photoionization detector (PID) sensor with a metal-oxide (MOX) sensor, this technology leverages the strengths of both sensors to enhance sensitivity and selectivity. The PID sensor provides high sensitivity to volatile organic compounds (VOCs), while the MOX sensor enables differentiation of gas profiles through pattern recognition. Machine learning algorithms further refine the detection process, offering a viable alternative to traditional GC-MS analysis for monitoring food quality.
This technology is currently at Technology Readiness Level 4, indicating that it has been validated in a laboratory environment. Future validation will focus on further calibration and testing under various conditions to ensure accuracy and reliability in detecting hexanal concentrations.
East Carolina University is a comprehensive public research university in eastern North Carolina, combining broad academic breadth with a strong public-service mission. Integration with a major regional health system and teaching hospital enables clinically grounded collaboration and faster translation. Coastal field sites provide Atlantic test beds, and shared core labs and prototyping spaces support scale-up and validation. The university engages industry through flexible contracting, sponsored research, and tailored workforce partnerships. Research is backed by competitive federal funding, including NIH, NSF, and DoD, with state and industry support, and a dedicated technology transfer office manages IP, licensing, and startup formation.