Spectrometric analysis
地域：
Wilmslow
摘要
A method of spectrometric analysis comprises obtaining one or more sample spectra for a sample. The one or more sample spectra are subjected to pre-processing and then multivariate and/or library based analysis so as to classify the sample. The pre-processing involves deisotoping the sample spectra.
說明書
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This application is a national phase filing claiming the benefit of and priority to International Patent Application No. PCT/GB2017/050592, filed on Mar. 6, 2017, which claims priority from and the benefit of United Kingdom patent application No. 1603906.7 filed on Mar. 7, 2016 and United Kingdom patent application No. 1603907.5 filed on Mar. 7, 2016. The entire contents of these applications are incorporated herein by reference.
The present invention relates generally to spectrometry and in particular to methods of spectrometric analysis in order to classify samples.
In known arrangements, a sample obtained from a target substance is ionised so as to produce analyte ions. The analyte ions are then subjected to mass and/or ion mobility analysis so as to produce sample spectra. The sample spectra are then subjected to spectrometric analysis in order to classify the sample. For example, it is known to utilise statistical analysis of spectrometric data in order to help distinguish and identify different classes of sample.
It is desired to provide improved methods of spectrometric analysis in order to classify samples. For example, it is generally desired to provide methods of spectrometric analysis that result in more accurate classifications and/or that consume less processing power.
According to an aspect there is provided a method of spectrometric analysis comprising:
obtaining one or more sample spectra for a sample;
權(quán)利要求
The invention claimed is:1. A method of spectrometric analysis comprising:obtaining one or more sample spectra for a sample; pre-processing the one or more sample spectra, wherein pre-processing the one or more sample spectra comprises using isotopic deconvolution as a deisotoping process to generate a deisotoped version of the one or more sample spectra in which one or more isotopic peaks are reduced or removed; analysing the one or more pre-processed sample spectra, wherein analysing the one or more pre-processed sample spectra comprises performing at least one of a multivariate and library-based analysis on the deisotoped version of the one or more sample spectra; and classifying the sample using the at least one of a multivariate and library-based analysis on the deisotoped version of the one or more sample spectra, wherein classifying the sample comprises projecting at least one of a sample point and vector for the deisotoped version of the one or more sample spectra into a classification model space. 2. A method as claimed in claim 1 , wherein the deisotoped version of the one or more sample spectra is a lower dimensional representation of the one or more sample spectra; and the at least one of a multivariate and library-based analysis is performed on the lower dimensional representation of the one or more sample spectra. 3. A method as claimed in claim 1 , wherein the deisotoping process comprises using one or more of: nested sampling; massive inference; and maximum entropy to generate the deisotoped version of the one or more sample spectra. 4. A method as claimed in claim 1 , wherein the deisotoping process comprises generating a set of trial hypothetical monoisotopic sample spectra. 5. A method as claimed in claim 4 , wherein the deisotoping process comprises deriving a likelihood of the one or more sample spectra given each trial hypothetical monoisotopic sample spectrum. 6. A method as claimed in claim 4 , wherein the deisotoping process comprises generating a set of modelled sample spectra having isotopic peaks from the set of trial hypothetical monoisotopic sample spectra. 7. A method as claimed in claim 6 , wherein each modelled sample spectra is generated using known average isotopic distributions for one or more classes of sample. 8. A method as claimed in claim 6 , wherein the deisotoping process comprises deriving a likelihood of the one or more sample spectra given each trial hypothetical monoisotopic sample spectrum by comparing a modelled sample spectrum to the one or more sample spectra. 9. A method as claimed in claim 1 , wherein the deisotoping process comprises one or more of: a least squares process, a non-negative least squares process; and a Fourier transform process. 10. A method as claimed in claim 1 , wherein performing at least one of a multivariate and library-based analysis on the deisotoped version of the one or more sample spectra comprises developing at least one of a classification model and library using one or more reference sample spectra. 11. A method as claimed in claim 1 , wherein performing at least one of a multivariate and library-based analysis on the deisotoped version of the one or more sample spectra comprises performing one or more of: principal component analysis (PCA), linear discriminant analysis (LDA), and a maximum margin criteria (MMC) process on the deisotoped version of the one or more sample spectra. 12. A method as claimed in claim 1 , wherein performing at least one of a multivariate and library-based analysis on the deisotoped version of the one or more sample spectra comprises deriving one or more sets of metadata for the deisotoped version of the one or more sample spectra, wherein each set of metadata is representative of a class of one or more classes of sample, and each set of metadata is stored in an electronic library. 13. A method as claimed in claim 1 , wherein performing at least one of a multivariate and library-based analysis on the deisotoped version of the one or more sample spectra comprises using at least one of a classification model and library to classify the deisotoped version of the one or more sample spectra as belonging to one or more classes of sample. 14. A method as claimed in claim 1 , wherein performing at least one of a multivariate and library-based analysis on the deisotoped version of the one or more sample spectra comprises calculating one or more probabilities or classification scores based on the degree to which the deisotoped version of the one or more sample spectra correspond to one or more classes of sample represented in an electronic library. 15. A method of mass or ion mobility spectrometry comprising a method as claimed in claim 1 . 16. A spectrometric analysis system comprising:control circuitry arranged and adapted to: obtain one or more sample spectra for a sample; pre-process the one or more sample spectra, wherein pre-processing the one or more sample spectra comprises using isotopic deconvolution as a deisotoping process to generate a deisotoped version of the one or more sample spectra in which one or more isotopic peaks are reduced or removed; analyse the one or more pre-processed sample spectra, wherein analysing the one or more pre-processed sample spectra comprises performing at least one of a multivariate and library-based analysis on the deisotoped version of the one or more sample spectra; and classify the sample using the at least one of a multivariate and library-based analysis on the deisotoped version of the one or more sample spectra, wherein classifying the sample comprises projecting at least one of a sample point and vector for the deisotoped version of the one or more sample spectra into a classification model space. 17. A mass or ion mobility spectrometric analysis system or a mass or ion mobility spectrometer comprising a spectrometric analysis system as claimed in claim 16 . 18. A tangible computer readable medium comprising computer software code which, when run on control circuitry of a spectrometric analysis system, performs a method of spectrometric analysis comprising:obtaining one or more sample spectra for a sample; pre-processing the one or more sample spectra, wherein pre-processing the one or more sample spectra comprises using isotopic deconvolution as a deisotoping process to generate a deisotoped version of the one or more sample spectra in which one or more isotopic peaks are reduced or removed; analysing the one or more pre-processed sample spectra, wherein analysing the one or more pre-processed sample spectra comprises performing at least one of multivariate and library-based analysis on the deisotoped version of the one or more sample spectra; and classifying the sample using the at least one of a multivariate and library-based analysis on the deisotoped version of the one or more sample spectra, wherein classifying the sample comprises projecting at least one of a sample point and vector for the deisotoped version of the one or more sample spectra into a classification model space. 19. A method as claimed in claim 1 , wherein the deisotoping process comprises including one or more species with a known elemental composition in the deconvolution process with a correct mass and an exact isotope distribution.
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