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High-Performance Liquid Chromatography: Types of Detectors01:15

High-Performance Liquid Chromatography: Types of Detectors

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 properties and...

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Sampling and Identification of Microplastics in Groundwater
08:27

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Published on: November 7, 2025

Detection of Microplastics and Heavy Metals Using Electronic Tongues and Machine Learning.

Luis Angel Peña1, Juan P Hoyos-Sanchez1, Juan Daniel Sarmiento2

  • 1Dirección Académica, Universidad Nacional de Colombia, Sede de La Paz, La Paz 202017, Colombia.

Sensors (Basel, Switzerland)
|May 27, 2026
PubMed
Summary

This study introduces a novel method for detecting microplastics and heavy metals in seawater. An electronic tongue coupled with machine learning models achieved over 90% accuracy, offering a reliable solution for environmental monitoring.

Keywords:
AASEPSelectronic tonguesheavy metalsmachine learningmicroplastics

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Area of Science:

  • Environmental Science
  • Analytical Chemistry
  • Data Science

Background:

  • Microplastics (MP) and heavy metals (HM) are significant water pollutants, posing risks to ecosystems and public health.
  • MP originate from plastic degradation and product additives, while HM are natural but amplified by human activities.
  • Accumulation and biomagnification of HM in the food chain present toxicological risks.

Purpose of the Study:

  • To develop and validate a methodology for detecting microplastics and heavy metals in seawater.
  • To assess the efficacy of machine learning models combined with an electronic tongue for contaminant detection.
  • To investigate the adsorption of microplastics influenced by water properties.

Main Methods:

  • Utilized an electronic tongue sensor network coupled with various machine learning models.
  • Detected specific heavy metals (zinc, cadmium) and microplastics (expanded polystyrene) under simulated conditions.
  • Employed Atomic Absorption Spectroscopy (AAS) for heavy metal concentration validation.

Main Results:

  • Machine learning models accurately classified contaminated water samples.
  • Achieved Area Under the Curve (AUC) values exceeding 90% for seven different models.
  • Demonstrated strong adsorption of heavy metals onto microplastics, dependent on water characteristics.

Conclusions:

  • The electronic tongue and machine learning approach is reliable for detecting microplastics and heavy metals in seawater.
  • This integrated system offers a promising solution for environmental monitoring of water pollutants.
  • Further research can explore the adsorption dynamics and expand detection capabilities.