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Published on: October 1, 2016
Metal-modulated MOFzyme-based colorimetric sensor array for rapid detection and classification of perfluorinated
Lixin Kang1, Nuo Duan2, Zhouping Wang2
1School of Food Science and Technology, Jiangnan University, Wuxi, 214122, China.
Talanta
|June 8, 2026
Summary
A new sensor array using metal-organic framework artificial enzymes rapidly classifies per- and polyfluoroalkyl substances (PFAS) in food and water. This technology offers a high-throughput method for detecting these harmful contaminants, ensuring better food safety.
Area of Science:
- Analytical Chemistry
- Materials Science
- Environmental Science
Background:
- Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants found in aquatic products and water.
- Simultaneous, high-throughput screening of structurally similar PFAS remains a significant analytical challenge.
- Accurate detection is crucial for food safety and environmental monitoring.
Purpose of the Study:
- To develop a novel colorimetric sensor array for rapid classification of PFAS.
- To utilize metal-organic framework artificial enzymes (MOFzymes) for selective PFAS detection.
- To establish a high-throughput pattern recognition platform for PFAS screening.
Main Methods:
- A four-channel colorimetric sensor array was designed using PCN-Fe and PCN-Mn MOFzymes.
- MOFzymes with modulated metal nodes exhibited distinct peroxidase-like activities and crystal dimensions.
- Differential adsorption of PFAS within MOFzyme channels generated unique colorimetric patterns.
- Linear discriminant analysis (LDA) was employed for data interpretation and classification.
Main Results:
- The sensor array achieved 100% accurate identification of six representative PFAS.
- Quantitative tracking of PFAS showed high correlation (R² = 0.987).
- In real-world samples (seawater, shrimp, fish), the array demonstrated high classification accuracy (up to 97.14%) for individual and mixed PFAS contaminants.
Conclusions:
- The developed MOFzyme-based sensor array provides a reliable and high-throughput platform for rapid PFAS screening.
- This technology shows significant potential for enhancing food quality and safety control measures.
- The pattern recognition approach enables precise discrimination of complex PFAS mixtures in environmental and food matrices.
