Related Experiment Video
Updated: Mar 8, 2026
08:46
Electronic Tongue Generating Continuous Recognition Patterns for Protein Analysis
Published on: September 16, 2014
8.2K
From fluorescence imaging to intelligent quantification: An automated and intelligent platform for Al3+ detection in
Xu Liu1, Qinghui Bu1, Zheng Cheng1
1College of Chemistry and Chemical Engineering, Henan Key Laboratory of Function-Oriented Porous Materials, Luoyang Normal University, Luoyang 471934, China; College of Food and Bioengineering, Henan University of Science and Technology, Luoyang 471022, China.
Food Chemistry
|March 6, 2026
Summary
This study introduces an intelligent platform for aluminum ion (Al3+) detection using fluorescence and a neural network. The system offers rapid, selective, and accurate Al3+ sensing, suitable for high-throughput analysis.
Area of Science:
- Analytical Chemistry
- Biomedical Engineering
- Materials Science
Background:
- Accurate detection of aluminum ions (Al3+) is crucial for environmental and food safety monitoring.
- Existing methods for Al3+ detection can be time-consuming and require specialized equipment.
- Development of rapid, selective, and user-friendly sensing platforms is needed.
Purpose of the Study:
- To develop an intelligent detection platform for Al3+ sensing.
- To integrate a ratiometric fluorescence probe, smartphone imaging, and a feedforward neural network (FFNN) algorithm.
- To enable rapid, selective, and automated Al3+ quantification.
Main Methods:
- A ratiometric fluorescence probe exhibiting color transition upon Al3+ binding was synthesized.
- Smartphone imaging captured fluorescence images, processed to extract RGB values.
- A three-layer FFNN model was trained and validated for Al3+ concentration prediction.
Main Results:
- The probe showed a wide linear range (0.1-100 μM), high selectivity, low detection limit (11.6 μg/kg), and rapid response (30 s).
- The FFNN model achieved high predictive performance (R2 > 0.997) for Al3+ concentrations.
- The platform demonstrated accurate Al3+ detection in starch noodles with high recovery rates (98.60%-102.2%).
Conclusions:
- The integrated platform provides a user-friendly, rapid, and effective solution for automated Al3+ sensing.
- This approach enables high-throughput analysis of large sample sets under unmanned conditions.
- The results align with standard ICP-MS methods, validating the platform's accuracy.
