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Machine learning-based sensor array: full and reduced fluorescence data for versatile analyte detection based on gold

Hamada A A Noreldeen1,2, Shao-Bin He1,3, Kai-Yuan Huang1

  • 1Fujian Key Laboratory of Drug Target Discovery and Structural and Functional Research, School of Pharmacy, Fujian Medical University, Fuzhou, 350004, China.

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|October 24, 2022
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Summary
This summary is machine-generated.

This study introduces a novel sensor array using 6-Aza-2-thiothymine-gold nanoclusters (ATT-AuNCs) and machine learning for heavy metal ion detection. First-order fluorescence data with random forest and linear discriminant analysis proved highly accurate for distinguishing analytes.

Keywords:
Dimensionality reductionGold nanoclustersHigh-dimensional spectraMachine learningPublic healthSensor array

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Area of Science:

  • Analytical Chemistry
  • Nanotechnology
  • Machine Learning

Background:

  • Sensor arrays are crucial for detecting heavy metal ions and other analytes.
  • Comparing different fluorescence data acquisition methods and assessing sensor array extendability is limited.
  • Developing robust methods for distinguishing multiple analytes in complex matrices is essential.

Purpose of the Study:

  • To evaluate the performance of first- and second-order fluorescence data from ATT-AuNCs combined with machine learning for heavy metal ion discrimination.
  • To assess the extendability of this approach for anion detection.
  • To identify the most effective machine learning algorithms and data dimensionality reduction techniques for sensor array applications.

Main Methods:

  • Utilized 6-Aza-2-thiothymine-gold nanoclusters (ATT-AuNCs) as a single fluorescent probe.
  • Acquired and analyzed first- and second-order fluorescence spectral data.
  • Applied machine learning algorithms including random forest (RF) and linear discriminant analysis (LDA).
  • Performed dimensionality reduction on the acquired data.
  • Tested the sensor array's capability for quantitative detection of Cadmium (Cd2+).

Main Results:

  • First-order fluorescence data consistently outperformed second-order data across various machine learning models, both before and after dimensionality reduction.
  • Random Forest (RF) demonstrated superior stability and accuracy compared to other models.
  • Linear Discriminant Analysis (LDA) showed excellent separation ability for discriminating between heavy metal ions and anions, even in high-dimensionality data and real samples.
  • The method achieved a limit of detection (LOD) of 60.40 nM for Cadmium (Cd2+).

Conclusions:

  • The combination of ATT-AuNCs, first-order fluorescence data, and machine learning, particularly LDA, offers a powerful and extendable platform for sensitive heavy metal ion and anion detection.
  • This approach effectively integrates analytical chemistry principles with machine learning capabilities, paving the way for advanced sensor array technologies.
  • The study highlights the potential of LDA for analyzing high-dimensional spectral data in sensor array applications, enabling precise analyte discrimination.