Defect-Engineered WO Architectures Coupled with Random Forest Algorithm Enables Real-Time Seafood Quality Assessment.

Reliable and real-time monitoring of seafood decay is attracting growing interest for food safety and human health, while it is still a great challenge to accurately identify the released triethylamine (TEA) from the complex volatilome. Herein, defect-engineered WO architectures are presented to design advanced TEA sensors for seafood quality assessment. Benefiting from abundant oxygen vacancies, the obtained WO sensor exhibits remarkable TEA-sensing performance in terms of higher response (1.9 times), faster response time (2.1 times), lower detection limit (3.2 times), and higher TEA/NH selectivity (2.8 times) compared with the air-annealed WO sensor. Furthermore, the definite WO sensor demonstrates long-term stability and anti-interference in complex gases, enabling the accurate recognition of TEA during halibut decay (0-48 h). Coupled with the random forest algorithm with 70 estimators, the WO sensor enables accurate prediction of halibut storage with an accuracy of 95%. This work not only provides deep insights into improving gas-sensing performance by defect engineering but also offers a rational solution for reliably assessing seafood quality.