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.

Abstract: Biophotonic device for the point-of-care, real-time, non-invasive determination of parameters with diagnostic relevance, in particular an optical system for parameter characterization of an element of body fluid or tissue comprising an optical device which comprises: a light source for emitting light onto the element; and a spectrometer for recording the spectrum of light from the element, said light from the element being of transmittance, reflectance or Raman scattering of the emitted light by said element; the optical system further comprising a data processing module configured to: convert the recorded spectrum by a conversion matrix into a standardized spectrum, wherein said conversion matrix has been obtained by calibrating the optical system spectrum response against a spectrum reference; pre-process the converted spectrum; correlate, for parameter quantification, the converted pre-processed spectrum with pre-obtained spectral bands for each parameter; said spectrum being contained within uv-vis-nir wa

Abstract: The present invention is enclosed in the area of calibration of spectral information/spectroscopy devices, such as the calibration of spectral information which consist of high-resolution electromagnetic spectra, as Laser-induced Breakdown Spectroscopy (LIBS). It is an object of the present invention a calibration method of a spectroscopy device comprising a plurality of sensors and a method for transferring spectral information obtained from a first and a second spectroscopy devices. The method of the present invention provides the access to accurately defined spectral lines from the electromagnetic spectrum, as well as to obtain electromagnetic spectra in two different sites, with two different spectroscopy devices and physical samples still provide for the reliable comparison between the electromagnetic spectra obtained in each of such spectroscopy devices.

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.

Abstract: Spectrophotometry device and method for predicting a quantification of a constituent from a sample to be quantified, comprising the steps of: obtaining an electromagnetic spectrum from said biological sample; projecting said obtained spectrum into a sample point of a multiple dimension vector space associated with feature vectors, herewith a feature space, defined by a predetermined vector basis, wherein each of said dimensions is a prediction feature; selecting, if existing, a minimum of neighbouring sample points from sample points within said feature space, said sample points having been projected from previously obtained spectra each with a known constituent quantity, such that said minimum maximises the covariance of the projected spectrum of said sample to be quantified together with the projected spectra of the selected neighbouring sample points; predicting the quantification of the constituent from the sample to be quantified by correlating the known constituent quantity from the selected neighbourin

Abstract: Biophotonic device for the point-of-care, real-time, non-invasive determination of parameters with diagnostic relevance, in particular an optical system for parameter characterization of an element of body fluid or tissue comprising an optical device which comprises: a light source for emitting light onto the element; and a spectrometer for recording the spectrum of light from the element, said light from the element being of transmittance, reflectance or Raman scattering of the emitted light by said element; the optical system further comprising a data processing module configured to: convert the recorded spectrum by a conversion matrix into a standardized spectrum, wherein said conversion matrix has been obtained by calibrating the optical system spectrum response against a spectrum reference; pre-process the converted spectrum; correlate, for parameter quantification, the converted pre-processed spectrum with pre-obtained spectral bands for each parameter; said spectrum being contained within uv-vis-nir wa

Abstract: Biophotonic device for the point-of-care, real-time, non-invasive determination of parameters with diagnostic relevance, in particular an optical system for parameter characterization of an element of body fluid or tissue comprising an optical device which comprises: a light source for emitting light onto the element; and a spectrometer for recording the spectrum of light from the element, said light from the element being of transmittance, reflectance or Raman scattering of the emitted light by said element; the optical system further comprising a data processing module configured to: convert the recorded spectrum by a conversion matrix into a standardized spectrum, wherein said conversion matrix has been obtained by calibrating the optical system spectrum response against a spectrum reference; pre-process the converted spectrum; correlate, for parameter quantification, the converted pre-processed spectrum with pre-obtained spectral bands for each parameter; said spectrum being contained within uv-vis-nir wa

Abstract: Biophotonic device for the point-of-care, real-time, non-invasive determination of parameters with diagnostic relevance, in particular an optical system for parameter characterization of an element of body fluid or tissue comprising an optical device which comprises: a light source for emitting light onto the element; and a spectrometer for recording the spectrum of light from the element, said light from the element being of transmittance, reflectance or Raman scattering of the emitted light by said element; the optical system further comprising a data processing module configured to: convert the recorded spectrum by a conversion matrix into a standardized spectrum, wherein said conversion matrix has been obtained by calibrating the optical system spectrum response against a spectrum reference; pre-process the converted spectrum; correlate, for parameter quantification, the converted pre-processed spectrum with pre-obtained spectral bands for each parameter; said spectrum being contained within uv-vis-nir wa