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Related Experiment Video

Updated: Jul 7, 2026

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus

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Published on: April 8, 2019

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

Ziqi Zhang¹, Junxuan Liang¹, Kai Liu¹

¹State Key Laboratory of Bio-Fibers and Eco-Textiles, College of Materials Science and Engineering, Qingdao University, Qingdao 266071, P. R. China.

|August 3, 2024

Summary

Engineered tungsten oxide (WO2.91) sensors detect triethylamine (TEA) for seafood quality. Defect engineering enhances sensor performance, enabling accurate prediction of fish spoilage.

Keywords:

defect-engineering gas sensors seafood quality triethylamine tungsten oxide

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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.