Progress towards an unassisted element identification from Laser Induced Breakdown Spectra with automatic ranking techniques inspired by text retrieval

Year: 2010

Authors: Amato G., Cristoforetti G., Legnaioli S., Lorenzetti G., Palleschi V., Sorrentino F., Tognoni E.

Autors Affiliation: ISTI-CNR, Area della Ricerca, Via Moruzzi 1, 56124, Pisa, Italy; IPCF-CNR, Area della Ricerca, Via Moruzzi 1, 56124, Pisa, Italy; Dipartimento di Fisica e astronomia, Università di Firenze, Polo Scientifico, via Sansone 1, 50019 Sesto Fiorentino (FI), Italy; Istituto di Cibernetica CNR, via Campi Flegrei 34, 80078 Pozzuoli (NA), Italy; Marwan Technology, c/o Dipartimento di Fisica “E. Fermi”, Largo Pontecorvo 3, 56127 Pisa, Italy; INO-CNR, Area della Ricerca, Via Moruzzi 1, 56124 Pisa, Italy

Abstract: In this communication, we will illustrate an algorithm for automatic element identification in LIBS spectra
which takes inspiration from the vector space model applied to text retrieval techniques. The vector space
model prescribes that text documents and text queries are represented as vectors of weighted terms
(words). Document ranking, with respect to relevance to a query, is obtained by comparing the vectors
representing the documents with the vector representing the query.
In our case, we represent elements and samples as vectors of weighted peaks, obtained from their spectra.
The likelihood of the presence of an element in a sample is computed by comparing the corresponding
vectors of weighted peaks. The weight of a peak is proportional to its intensity and to the inverse of the
number of peaks, in the database, in its wavelength neighboring.
We suppose to have a database containing the peaks of all elements we want to recognize, where each peak
is represented by a wavelength and it is associated with its expected relative intensity and the corresponding
Detection of elements in a sample is obtained by ranking the elements according to the distance of the
associated vectors from the vector representing the sample.
The application of this approach to elements identification using LIBS spectra obtained from several kinds of
metallic alloys will be also illustrated. The possible extension of this technique towards an algorithm for fully
automated LIBS analysis will be discussed.


Volume: 65 (8)      Pages from: 664  to: 670

KeyWords: Automatic processing; Element identification; Ranking techniques; LIBS; Spectral analysis
DOI: 10.1016/j.sab.2010.04.019

Citations: 22
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