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High-Performance Liquid Chromatography: Types of Detectors01:15

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The role of the detectors in High-Performance Liquid Chromatography (HPLC) is to analyze the solutes as they exit from the chromatographic column. The detector recognizes the solute's property and generates corresponding electrical signals, which are converted into a readable graph of the detector's response versus elution time called a chromatogram at the computer. There are several types of HPLC detectors, each with its own advantages and limitations, depending on the analyte...
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Updated: Aug 14, 2025

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Machine learning toward high-performance electrochemical sensors.

Gabriela F Giordano1, Larissa F Ferreira1,2, Ítalo R S Bezerra1,3

  • 1Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo, 13083-100, Brazil.

Analytical and Bioanalytical Chemistry
|January 13, 2023
PubMed
Summary
This summary is machine-generated.

Machine learning (ML) enhances bio/sensor data analysis, improving accuracy and enabling multi-target detection. ML models address common issues like fouling and interference, paving the way for practical, on-site applications.

Keywords:
AccuracyArtificial intelligenceClassificationData treatmentRegression

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Area of Science:

  • The integration of the fourth paradigm, or data-driven science, into the field of sensing technologies.
  • Focus on the application of machine learning (ML) in bio/sensor development and data interpretation.

Background:

  • Traditional sensing methods face challenges including poor signal-to-noise ratio, electrode fouling, chemical interferences, and matrix effects.
  • Supervised machine learning models trained on large datasets from electrical and electrochemical bio/sensors are a growing trend.
  • These ML models offer a pathway to overcome common analytical limitations.

Purpose of the Study:

  • To highlight the impact of machine learning on improving sensor performance metrics such as sensitivity, reproducibility, and accuracy.
  • To showcase the capability of ML, particularly multi-output regression models, in determining multiple targets from single measurements.
  • To provide an outlook on future trends and demonstrate how ML can facilitate the adoption of advanced testing technologies for real-world applications.

Main Methods:

  • Utilizing supervised machine learning models trained on extensive datasets generated by electrical and electrochemical bio/sensors.
  • Employing multi-output regression models for simultaneous determination of multiple analytes.
  • Reviewing literature examples demonstrating ML's effectiveness in mitigating common sensor interferences.

Main Results:

  • Machine learning significantly improves the accuracy, sensitivity, and reproducibility of bio/sensor measurements.
  • ML algorithms effectively handle complex analytical challenges like electrode fouling and chemical interferences.
  • Demonstrated ability to identify multiple targets from a single measurement using ML.

Conclusions:

  • Machine learning is a transformative tool in sensing, enabling robust and accurate analyses.
  • ML facilitates the transition of sensor technologies from laboratory settings to practical, on-site applications.
  • The trend article provides insights into the future potential of ML in advancing sensing capabilities.