Method And System Of Identifying A Sample By Analysing A Mass Spectrum By The Use Of A Bayesian Inference Technique

Abstract

A method and system for the identification and/or characterisation of properties of a sample using mass spectrometry. The method involves producing a measured data set from a sample using a mass spectrometer, deconvoluting the measured data set by Bayesian inference to produce a family of plausible deconvoluted data sets, inferring an underlying deconvoluted data set from the family of plausible deconvoluted data sets and using the underlying deconvoluted data set to identify and/or characterise the sample.

1 . A method of identifying or characterising at least one property of a sample, the method comprising the steps of:
 a. producing at least one measured data set from a sample using a mass spectrometer;   b. deconvoluting the at least one measured data set by Bayesian inference to produce a family of plausible deconvoluted data sets;   c. inferring an underlying deconvoluted data set from the family of plausible deconvoluted data sets; and   d. using the underlying deconvoluted data set to identify or characterise at least one property of the sample.   
     
     
         2 . The method of  claim 1  further comprising the step of identifying the uncertainties associated with underlying deconvoluted data set from the family of plausible deconvoluted data sets. 
     
     
         3 . The method of  claim 1 , wherein the deconvolution step further comprises assigning a prior using a procedure comprising at least two steps. 
     
     
         4 . The method of  claim 3 , wherein the procedure comprises first assigning a prior to the total intensity and then modifying the prior relative to the proportions for specific charge states. 
     
     
         5 . The method of  claim 1 , wherein the deconvolution step further comprises the use of a nested sampling technique. 
     
     
         6 . The method of  claim 1 , wherein the procedure comprises varying predicted ratios of isotopic compositions to identify or characterise the at least one property of the sample. 
     
     
         7 . The method of  claim 1  further comprising comparing at least one characteristic of the underlying deconvoluted data set with a library of known data sets to identify or characterise the at least one property of the sample. 
     
     
         8 . The method of  claim 1  further comprising comparing at least one characteristic of the underlying deconvoluted data set with candidate constituents to identify or characterise the at least one property of the sample. 
     
     
         9 . The method of  claim 1 , wherein the deconvolution step comprises the use of importance sampling. 
     
     
         10 . The method of  claim 1 , wherein the at least one measured data set comprises electrospray data. 
     
     
         11 . The method of  claim 1 , further comprising recording a temporal separation characteristic associated with the at least one measured data set and storing the underlying deconvoluted data set with the recorded temporal separation characteristic on a non-transitory memory means. 
     
     
         12 . The method of  claim 1 , further comprising recording a temporal separation characteristic associated with the at least one measured data set and using the recorded temporal separation characteristic to identify or characterise the or a further at least one property of the sample. 
     
     
         13 . The method of  claim 12  wherein the temporal separation comprises an ion mobility separation. 
     
     
         14 . A system for identifying or characterising a sample, the system comprising:
 a. a mass spectrometer for producing at least one measured data set from a sample;   b. a processor configured or programmed or adapted to deconvolute the at least one measured data set by Bayesian inference to produce a family of plausible deconvoluted data sets and infer an underlying deconvoluted data set from the family of plausible deconvoluted data sets; and   
       wherein the processor is further configured or programmed or adapted to use the underlying deconvoluted data set to identify or characterise at least one property of the sample. 
     
     
         15 . The system of  claim 14  further comprising a first non-transitory memory means for storing the underlying deconvoluted data set. 
     
     
         16 . The system of  claim 15  further comprising a second non-transitory memory means on which is stored a library of known data sets. 
     
     
         17 . The system of  claim 14  wherein a temporal separation characteristic is associated with the at least one measured data set and wherein the processor is further configured or programmed or adapted to use the temporal separation characteristic to identify or characterise the or a further at least one property of the sample. 
     
     
         18 . The method of  claim 17  wherein the temporal separation comprises an ion mobility separation. 
     
     
         19 . A mass spectrometer suitable for carrying out, or specifically adapted to carry out, a method according to  claim 1 . 
     
     
         20 . A retrofit kit for adapting a mass spectrometer to provide a mass spectrometer suitable for carrying out, or specifically adapted to carry out, a method according to  claim 1 , the kit comprising a computer program element including computer readable program code means for causing a processor to execute a procedure to implement that method.