Abstract: A method of determining a constituent related sample property of a multi-constituent sample comprising: subjecting the sample to a perturbation selected to induce a time dependent change in measurement data associated with a constituent related to the sample property to be determined; recording a time-series of measurement data following subjecting the sample to the perturbation; and determining the sample property from the application to the recorded time-series of measurement data of a calibration correlating the sample property with time-series of measurement data, said calibration being empirically derived from chemometric time-series modelling of time-series measurement data recorded for each of a plurality of reference samples following subjecting each reference sample to the perturbation, each reference sample having a different known values of the sample property.

Abstract: A method of determining a constituent related sample property of a multi-constituent sample comprising: subjecting the sample to a perturbation selected to induce a time dependent change in measurement data associated with a constituent related to the sample property to be determined; recording a time-series of measurement data following subjecting the sample to the perturbation; and determining the sample property from the application to the recorded time-series of measurement data of a calibration correlating the sample property with time-series of measurement data, said calibration being empirically derived from chemometric time-series modelling of time-series measurement data recorded for each of a plurality of reference samples following subjecting each reference sample to the perturbation, each reference sample having a different known values of the sample property.

Abstract: The present invention relates to a method of training a classification system for characterising a biological sample, a diagnostic classification system, as well as a method of characterising a condition in an animal or a human being by using parameters obtained from the sample. The invention relates to classification based on physical parameters obtained from luminescence spectroscopy on light emitted from the sample. The data obtained from a spectrofluorimetric analysis can be considered a finger-print of the sample. Each sample gives rise to a unique spectrofluorometric set of physical parameters. By analysing the fluorescence data, it is possible to classify samples into two or more classes based on the fluorescence spectra, such as classifying with respect to presence/abs of a specific disease, group of diseases or risk of later attaining a specific disease or a body condition, or concentration of a specific compound or medicine.

Abstract: The present invention relates to a method of training a classification system for characterising a biological sample, a diagnostic classification system, as well as a method of characterising a condition in an animal or a human being by using parameters obtained from the sample. The invention relates to classification based on physical parameters obtained from luminescence spectroscopy on light emitted from the sample. The data obtained from a spectrofluorimetric analysis can be considered a finger-print of the sample. Each sample gives rise to a unique spectrofluorometric set of physical parameters. By analysing the fluorescence data, it is possible to classify samples into two or more classes based on the fluorescence spectra, such as classifying with respect to presence/absence of a specific disease, group of diseases or risk of later attaining a specific disease or a body condition, or concentration of a specific compound or medicine.