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Related Experiment Video

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Benchmark classification dataset for laser-induced breakdown spectroscopy.

Erik Képeš1, Jakub Vrábel2, Sára Střítežská2

  • 1Central European Institute of Technology, Brno University of Technology, Purkyňova 123, 612 00, Brno, Czech Republic. erik.kepes@ceitec.vutbr.cz.

Scientific Data
|February 15, 2020
PubMed
Summary

This study introduces a comprehensive dataset of laser-induced breakdown spectroscopy (LIBS) spectra, ideal for training and evaluating LIBS classification models. This resource aids in developing new soil classification methodologies and pre-training models for data-scarce applications.

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Area of Science:

  • Analytical Chemistry
  • Spectroscopy
  • Machine Learning

Background:

  • Laser-induced breakdown spectroscopy (LIBS) is a key technique for in-situ and industrial elemental analysis.
  • LIBS is widely used for sample classification and clustering tasks in various fields.
  • Developing robust classification models requires extensive and well-characterized datasets.

Purpose of the Study:

  • To present a novel, extensive dataset of LIBS spectra for pre-training and evaluating LIBS classification models.
  • To support the development and testing of advanced classification and clustering methodologies for LIBS data.
  • To facilitate model development in scenarios with limited available training data.

Main Methods:

  • Acquisition of LIBS spectra from 138 soil samples across 12 distinct classes using a state-of-the-art LIBS system.
  • Detailed characterization of each soil sample's elemental composition, including uncertainties.
  • Dataset curated for direct application in machine learning model training and validation.

Main Results:

  • An extensive dataset comprising LIBS spectra for 138 soil samples is now available.
  • The dataset covers 12 distinct soil classes, providing diverse spectral information.
  • Accurate compositional data with uncertainties is provided for each sample, enhancing dataset utility.

Conclusions:

  • The presented LIBS spectral dataset is a valuable resource for advancing LIBS-based classification and clustering.
  • This dataset can significantly accelerate the development of pre-trained models, especially for data-limited applications.
  • The availability of this comprehensive dataset will foster innovation in soil analysis and related spectroscopic fields.