Defect-Engineered WO_3- Architectures Coupled with Random Forest Algorithm Enables Real-Time Seafood Quality...

Area of Science:

• Materials Science
• Chemical Sensing
• Food Science

Background:

• Accurate, real-time monitoring of seafood decay is crucial for food safety.
• Identifying specific spoilage markers like triethylamine (TEA) in complex gas mixtures remains challenging.

Purpose of the Study:

• To develop advanced triethylamine (TEA) sensors for seafood quality assessment.
• To investigate the impact of defect engineering on tungsten oxide (WO3-x) gas sensing properties.

Main Methods:

• Fabrication of defect-engineered WO3-x architectures, specifically WO2.91.
• Characterization of sensor performance, including response, detection limit, selectivity, and stability.
• Testing the sensor's ability to detect TEA during halibut decay.
• Application of a random forest algorithm for predicting storage time.

Main Results:

• The WO2.91 sensor demonstrated significantly enhanced TEA-sensing performance compared to WO2.96.
• Key improvements included a 1.9-fold higher response, 2.1-fold faster response time, 3.2-fold lower detection limit, and 2.8-fold higher TEA/NH3 selectivity.
• The sensor showed long-term stability and anti-interference capabilities.
• Accurate prediction of halibut storage time (0-48 h) with 95% accuracy was achieved using the WO2.91 sensor and a random forest algorithm.

Conclusions:

• Defect engineering in WO3-x is an effective strategy to enhance gas-sensing performance.
• The developed WO2.91 sensor provides a reliable solution for real-time seafood quality assessment.
• This approach offers valuable insights for designing improved gas sensors for various applications.