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Smartphone image analysis-based fluorescence detection of tetracycline using machine learning.

Maryam Mousavizadegan1, Morteza Hosseini1, Mahsa N Sheikholeslami2

  • 1Nanobiosensors Lab, Department of Life Science Engineering, Faculty of New Sciences & Technologies, University of Tehran, Tehran, Iran.

Food Chemistry
|November 11, 2022
PubMed
Summary

A new method uses BSA-protected bimetallic nanoclusters and machine learning to rapidly detect tetracycline (TC) in water and milk. This approach offers a quick and accurate way to identify this common veterinary drug in food products.

Keywords:
Bimetallic nanoclustersFluorescenceMachine LearningSupervised LearningTetracycline

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Area of Science:

  • Nanotechnology
  • Analytical Chemistry
  • Machine Learning

Background:

  • Tetracycline (TC) is a widely used veterinary antibiotic, necessitating reliable detection methods.
  • Current detection methods can be time-consuming and require specialized equipment.
  • Developing rapid, accessible sensors for TC is crucial for food safety.

Purpose of the Study:

  • To develop a rapid and sensitive detection method for tetracycline (TC).
  • To utilize BSA-protected Au/Ag bimetallic nanoclusters (BSA-BMNCs) as a sensing platform.
  • To integrate smartphone imaging and machine learning for quantitative analysis.

Main Methods:

  • Synthesis of BSA-protected Au/Ag bimetallic nanoclusters (BSA-BMNCs).
  • Observation of TC interaction-induced colorimetric shifts (red to yellow) in BSA-BMNCs.
  • Smartphone image acquisition and feature extraction (color, texture) for dataset generation.
  • Training machine learning algorithms (bagging, ANNs, decision trees) on generated datasets.

Main Results:

  • BSA-BMNCs exhibited a concentration-dependent color change upon interaction with TC.
  • High-performance ML models were developed: bagging and ANNs for water (R²=0.994), bagging and decision trees for milk (R²=0.999).
  • The developed sensor demonstrated high accuracy and sensitivity for TC detection in both matrices.

Conclusions:

  • BSA-BMNCs combined with smartphone imaging and ML provide a rapid and effective platform for TC detection.
  • This approach highlights the potential of ML in developing portable sensors for food safety applications.
  • The study demonstrates a feasible method for on-site monitoring of veterinary drug residues.