http://rdf.ncbi.nlm.nih.gov/pubchem/patent/EP-3605062-A1

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filingDate 2018-08-03-04:00^^<http://www.w3.org/2001/XMLSchema#date>
inventor http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_00819f47be48c1d496a142ff03c2f457
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publicationDate 2020-02-05-04:00^^<http://www.w3.org/2001/XMLSchema#date>
publicationNumber EP-3605062-A1
titleOfInvention A method and apparatus for characterisation of constituents in a physical sample from electromagnetic spectral information
abstract The present invention is enclosed in the area of machine learning, in particular machine learning for the analysis of High or Super-resolution spectroscopic data, which typically comprises analysis of highly complex samples / mixtures of substances and/or data with low resolution, for instance Laser-Induced Breakdown Spectroscopy (LIBS). It is an object of the present invention a method of computational self-learning for characterization of one or more constituents in a sample, from electromagnetic spectral information of such sample, which changes the paradigm associated with prior art methods, by using only sub-optical spectral information, i.e., obtaining the resolution of the spectral information and thereby be able to extract spectral lines - thus determining a spectral line position - from such spectral information, hence avoiding all the uncertainty associated with pixel based methods. It is also an object of the present invention a computational apparatus configured to implement such method.
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