Application of laser-induced, deep uv raman spectroscopy and artificial intelligence in real-time environmental monitoring—solutions and first results

Claudia Post*, Simon Brülisauer, Kryss Waldschläger, William Hug, Luis Grüneis, Niklas Heyden, Sebastian Schmor, Aaron Förderer, Ray Reid, Michael Reid, Rohit Bhartia, Quoc Nguyen, Holger Schüttrumpf, Florian Amann

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

13 Citations (Scopus)

Abstract

Environmental monitoring of aquatic systems is the key requirement for sustainable environmental protection and future drinking water supply. The quality of water resources depends on the effectiveness of water treatment plants to reduce chemical pollutants, such as nitrates, pharmaceuticals, or microplastics. Changes in water quality can vary rapidly and must be monitored in real-time, enabling immediate action. In this study, we test the feasibility of a deep UV Raman spectrometer for the detection of nitrate/nitrite, selected pharmaceuticals and the most widespread microplastic polymers. Software utilizing artificial intelligence, such as a convolutional neural network, is trained for recognizing typical spectral patterns of individual pollutants, once processed by mathematical filters and machine learning algorithms. The results of an initial experimental study show that nitrates and nitrites can be detected and quantified. The detection of nitrates poses some challenges due to the noise-to-signal ratio and background and related noise due to water or other materials. Selected pharmaceutical substances could be detected via Raman spectroscopy, but not at concentrations in the µg/l or ng/l range. Microplastic particles are non-soluble substances and can be detected and identified, but the measurements suffer from the heterogeneous distribution of the microparticles in flow experiments.
Original languageEnglish
Article number3911
JournalSensors
Volume21
Issue number11
DOIs
Publication statusPublished - Jun 2021
Externally publishedYes

Keywords

  • Data processing
  • DUV Raman/fluorescence spectroscopy
  • Environmental monitoring
  • Machine learning
  • Real-time water analysis

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