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Detection of minor compounds in complex mineral samples from millions of spectra: A new data analysis strategy in LIBS imaging
- Source :
- Analytica Chimica Acta, Analytica Chimica Acta, 2020, 1114, pp.66-73. ⟨10.1016/j.aca.2020.04.005⟩, Analytica Chimica Acta, Elsevier Masson, 2020, 1114, pp.66-73. ⟨10.1016/j.aca.2020.04.005⟩
- Publication Year :
- 2020
- Publisher :
- Elsevier BV, 2020.
-
Abstract
- International audience; Today, Laser-Induced Breakdown Spectroscopy (LIBS) imaging is in full change. Indeed, always more stable instrumentations are developed, which significantly increases the signal quality and naturally the analytical potential of the technique for the characterization of complex and heterogeneous samples at the micro-scale level. Obviously, other intrinsic features such as a limit of detection in the order of ppm, a high field of view and high acquisition rate make it one of the most complete chemical imaging techniques to date. It is thus possible in these conditions to acquire several million spectra from one single sample in just hours. Managing big data in LIBS imaging is the challenge ahead. In this paper, we put forward a new spectral analysis strategy, called embedded k-means clustering, for simultaneous detection of major and minor compounds and the generation of associated localization maps. A complex rock section with different phases and traces will be explored to demonstrate the value of this approach.
- Subjects :
- Chemical imaging
Hyperspectral imaging
Field of view
02 engineering and technology
01 natural sciences
Biochemistry
Clustering
Spectral line
Analytical Chemistry
Big data
[SPI]Engineering Sciences [physics]
[CHIM]Chemical Sciences
Environmental Chemistry
Spectroscopy
Cluster analysis
[PHYS]Physics [physics]
Detection limit
business.industry
Chemistry
010401 analytical chemistry
Pattern recognition
021001 nanoscience & nanotechnology
0104 chemical sciences
Characterization (materials science)
Laser-induced breakdown spectroscopy (LIBS)
Artificial intelligence
0210 nano-technology
business
Subjects
Details
- ISSN :
- 00032670
- Volume :
- 1114
- Database :
- OpenAIRE
- Journal :
- Analytica Chimica Acta
- Accession number :
- edsair.doi.dedup.....71a4e80786292b2048e57c2403240c5b
- Full Text :
- https://doi.org/10.1016/j.aca.2020.04.005