Abstract: A kernel-based method determines the similarity of a first spectrum and a second spectrum. Each spectrum represents a result of spectral analysis of a material or chemical and comprises a set of spectral attributes distributed across a spectral range. The method calculates a kernel function which makes use of the shape of the spectral response surrounding a spectral point. This is achieved by comparing the value of an spectral attribute in a spectrum and each of a set of neighboring spectral attributes within a window around the spectral attribute. Weighting values can be applied to calculations when deriving the kernel function. The weighting values can assign different degrees of importance to different regions of the spectrum. The method can be used to: classify unknown spectra; predict the concentration of an analyte within a mixture; database searching for the closest match using a kernel-derived distance metric; visualization of high-dimensional spectral data in two or three dimensions.

Abstract: A kernel-based method determines the similarity of a first spectrum and a second spectrum. Each spectrum represents a result of spectral analysis of a material or chemical and comprises a set of spectral attributes distributed across a spectral range. The method calculates a kernel function which makes use of the shape of the spectral response surrounding a spectral point. This is achieved by comparing the value of an spectral attribute in a spectrum and each of a set of neighbouring spectral attributes within a window around the spectral attribute. Weighting values can be applied to calculations when deriving the kernel function. The weighting values can assign different degrees of importance to different regions of the spectrum. The method can be used to: classify unknown spectra; predict the concentration of an analyte within a mixture; database searching for the closest match using a kernel-derived distance metric; visualisation of high-dimensional spectral data in two or three dimensions.

Abstract: A method of and system for generating models with which to classify or quantify spectra of unknown mixtures of compounds to permit the specific identification or quantification of a target analyte in complex mixtures based on spectral data, the method comprising the steps of: providing a training set of training spectra, each spectrum representing a mixture of known compounds and each having a plurality of spectral attributes, each at a different wavelength, choosing a plurality of wavelengths, determining at least the value of the spectral attribute at each chosen wavelength in each training spectrum in the training set, and building a model for each chosen wavelength by correlating the determined attribute values at said chosen wavelength, a method and system for classifying the spectrum of a mixture of unknown compounds, and a method and system for quantifying the spectrum of a mixture of unknown compounds to determine concentrations therein, using said models.