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.

Abstract: The use of a fabric sheath (2), or other article, on a conduit (1), for example a pipe or flexible hose, to provide abrasion resistance or other forms of protection, for example impact protection or cut-through protection, thereto; and fabric sheaths suitable for such use.

Abstract: A fabric cover (9, FIG. 2a), which is shrinkable at least in part, for encapsulating at least part of an expandable object such as an automotive airbag (1), the fabric cover being provided with a predetermined region of weakness (13), preferably a catch thread holding the edges of a woven fabric together to form a tubular or flat cover, which region of weakness will rupture when the said object expands by greater than a predetermined amount, e.g. in an automobile accident. The region of weakness (13) and preferably fabric regions (11) immediately adjacent thereto may be of a contrasting color to facilitate alignment of the region of weakness in the direction of expansion of the expandable object.