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Sensitive and visual detection of sulfonamides using γ-CD-MOF@Eu-MOF fluorescence sensor assisted by smartphone and
Mingsha Jie1, Amei Zhu2, Zhendong Liu2
1College of Food and Bioengineering, Zhengzhou University of Light Industry, Zhengzhou, Henan Province 450002, PR China; Key Laboratory of Cold Chain Food Processing and Safety Control (Zhengzhou University of Light Industry), Ministry of Education, Zhengzhou 450002, PR China.
A novel fluorescence sensor detects sulfonamides (SAs) in food with high sensitivity and selectivity. This paper-based system, combined with machine learning, offers rapid, intelligent, and on-site visual detection for improved food safety.
Area of Science:
- Analytical Chemistry
- Materials Science
- Food Science
Background:
- Ensuring human health and food safety requires accurate detection of sulfonamide (SAs) residues.
- Existing methods for SA detection can be time-consuming and lack on-site applicability.
Purpose of the Study:
- To develop a rapid, selective, and visual fluorescence sensor for the quantitative detection of sulfonamides (SAs) in food.
- To integrate the sensor with machine learning for intelligent, high-throughput analysis.
Main Methods:
- Fabrication of a γ-CD-MOF@Eu-MOF based fluorescence sensor.
- Immobilization of fluorescent probes onto paper substrates for a paper-based sensor.
- Utilizing a smartphone-assisted machine learning algorithm for data analysis and prediction.
Main Results:
- The γ-CD-MOF@Eu-MOF sensor demonstrated high selectivity for SAs.
- Achieved low detection limits for specific sulfonamides: SMR (25.5 nmol/L), SM2 (36.7 nmol/L), and SMD (39.8 nmol/L).
- The machine learning model achieved a coefficient of determination of 0.99, indicating high accuracy.
Conclusions:
- The developed paper-based fluorescence sensor provides a stable, sensitive, and specific method for SA detection.
- The system enables intelligent, rapid, and high-throughput on-site visual detection of SAs.
- This approach offers a practical solution for food safety monitoring.
