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Laser-induced breakdown spectroscopy: a tool for real-time, in vitro and in vivo identification of carious teeth

  • Samek, Ota1
  • Telle, Helmut H2
  • Beddows, David CS3
  • 1 Technical University Brno, Institute of Physical Engineering, Technická 2, Brno, 616 69, Czech Republic , Brno (Czechia)
  • 2 University of Wales Swansea, Department of Physics, Singleton Park, Swansea, SA2 8PP, United Kingdom , Swansea (United Kingdom)
  • 3 The University of Edinburgh, Department of Chemistry, West Main Roads, Edinburgh, EH9 3JJ, United Kingdom , Edinburgh (United Kingdom)
Published Article
BMC Oral Health
Springer (Biomed Central Ltd.)
Publication Date
Dec 19, 2001
DOI: 10.1186/1472-6831-1-1
Springer Nature


BackgroundLaser Induced Breakdown Spectroscopy (LIBS) can be used to measure trace element concentrations in solids, liquids and gases, with spatial resolution and absolute quantifaction being feasible, down to parts-per-million concentration levels. Some applications of LIBS do not necessarily require exact, quantitative measurements. These include applications in dentistry, which are of a more "identify-and-sort" nature – e.g. identification of teeth affected by caries.MethodsA one-fibre light delivery / collection assembly for LIBS analysis was used, which in principle lends itself for routine in vitro / in vivo applications in a dental practice. A number of evaluation algorithms for LIBS data can be used to assess the similarity of a spectrum, measured at specific sample locations, with a training set of reference spectra. Here, the description has been restricted to one pattern recognition algorithm, namely the so-called Mahalanobis Distance method.ResultsThe plasma created when the laser pulse ablates the sample (in vitro / in vivo), was spectrally analysed. We demonstrated that, using the Mahalanobis Distance pattern recognition algorithm, we could unambiguously determine the identity of an "unknown" tooth sample in real time. Based on single spectra obtained from the sample, the transition from caries-affected to healthy tooth material could be distinguished, with high spatial resolution.ConclusionsThe combination of LIBS and pattern recognition algorithms provides a potentially useful tool for dentists for fast material identification problems, such as for example the precise control of the laser drilling / cleaning process.

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