Abstract:
Systems and methods for reducing background noise in a mass spectrum. The method includes the following steps of: (a) obtaining an original mass spectrum; (b) determining a noise mass spectrum corresponding to background noise in the original mass spectrum; and (c) determining a corrected mass spectrum by subtracting the noise mass spectrum from the original mass spectrum. Step (b) of the method may include the steps of: A) effecting a transformation of the original mass spectrum into the frequency domain to obtain an original frequency spectrum; B) identifying at least one dominant frequency in the original frequency spectrum; C) generating a noise frequency spectrum by selectively filtering for said dominant frequencies; and D) determining the noise mass spectrum by effecting a transformation of the noise frequency spectrum into the mass domain. Preferably for each correlated pair of original and noise intensity data points, the minimum value is determined and the noise mass spectrum is modified by making the noise intensity data point equal to the minimum value.

Description:
RELATED APPLICATIONS 
     This application is a continuation of application Ser. No. 12/023,873, filed on Jan. 31, 2008. This application also claims priority from U.S. provisional patent application No. 60/887,915 filed on Feb. 2, 2007. All of the above-noted applications are incorporated herein by reference. 
    
    
     FIELD OF THE INVENTION 
     The present invention relates generally to the field of mass spectrometry. 
     BACKGROUND OF THE INVENTION 
     Mass spectrometers are used for producing a mass spectrum of a sample to find its composition. This is normally achieved by ionizing the sample and separating ions of differing masses and recording their relative abundance by measuring intensities of ion flux. 
     Typically, the mass spectra are subject to background noise, obscuring the real signal. 
     The applicants have accordingly recognized a need for new systems and methods for reducing or removing noise from mass spectra. 
     SUMMARY OF THE INVENTION 
     In one aspect, the present invention is directed towards a method for reducing background noise in a mass spectrum. The method includes the following steps:
         (a) obtaining an original mass spectrum;   (b) determining a noise mass spectrum corresponding to background noise in the original mass spectrum; and   (c) determining a corrected mass spectrum by subtracting the noise mass spectrum from the original mass spectrum.       

     Step (b) of the method may include the steps of: 
     A) effecting a transformation of the original mass spectrum into the frequency domain to obtain an original frequency spectrum; 
     B) identifying at least one dominant frequency in the original frequency spectrum; 
     C) generating a noise frequency spectrum by selectively filtering for said at least one dominant frequency; and 
     D) determining the noise mass spectrum by effecting a transformation of the noise frequency spectrum into the mass domain. 
     With the method as claimed, the original mass spectrum may be provided with a plurality of original intensity data points and the noise mass spectrum may also be provided with a plurality of noise intensity data points such that each noise intensity data point correlates to an original intensity data point. The method may further include the following step: 
     E) for each correlated pair of original and noise intensity data points:
         (i) determining the minimum value; and   (ii) modifying the noise mass spectrum by making the noise intensity data point equal to the minimum value.       

    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The present invention will now be described, by way of example only, with reference to the following drawings, in which like reference numerals refer to like parts and in which: 
         FIG. 1  is a schematic diagram of a noise reducing system made in accordance with the present invention; 
         FIG. 2  is a graph illustrating an original mass spectrum as may be input into and manipulated by the system of  FIG. 1 ; 
         FIG. 3A  is a graph illustrating an original frequency spectrum determined by transforming the original mass spectrum of  FIG. 2  into the frequency domain; 
         FIG. 3B  is a magnified segment of the graph of  FIG. 3A ; 
         FIG. 3C  is a schematic diagram of a segment of a filter made and used in accordance with the present invention to filter the original frequency spectrum of  FIG. 3A , the segment corresponding to the original frequency segment illustrated in  FIG. 3B ; 
         FIG. 4  is a graph illustrating a noise frequency spectrum made in accordance with the present invention and determined by selectively filtering for dominant frequencies in the original frequency spectrum of  FIG. 3A ; 
         FIG. 5  is a graph illustrating a noise mass spectrum made in accordance with the present invention and determined by transforming the noise frequency spectrum of  FIG. 4  into the mass domain; 
         FIG. 6  is a graph illustrating a magnified portion of the noise mass spectrum of  FIG. 5  overlaid together with a corresponding magnified portion of the original mass spectrum of  FIG. 2 ; 
         FIG. 7A  is a graph illustrating the noise mass spectrum made in accordance with the present invention by determining the minimum value of each corresponding pair of intensity data points from the complete noise mass spectrum and original mass spectrum portions of which were illustrated in  FIG. 6 ; 
         FIG. 7B  is a graph illustrating a magnified portion of the noise mass spectrum of  FIG. 7A  corresponding to the magnified portions in  FIG. 6 ; 
         FIG. 8  is a graph illustrating a noise frequency spectrum determined by transforming the noise mass spectrum of  FIG. 7A  into the frequency domain; 
         FIG. 9  is a graph illustrating a noise frequency spectrum made in accordance with the present invention and determined by selectively filtering for dominant frequencies in the noise frequency spectrum of  FIG. 8 ; 
         FIG. 10  is a graph illustrating a noise mass spectrum made in accordance with the present invention and determined by transforming the noise frequency spectrum of  FIG. 9  into the mass domain; 
         FIG. 11  is a graph illustrating the noise mass spectrum made in accordance with the present invention by determining the minimum value of each corresponding pair of intensity data points from the complete noise mass spectrum of  FIG. 10  and the original mass spectrum of  FIG. 2 ; 
         FIG. 12  is a graph illustrating a corrected mass spectrum made in accordance with the present invention and determined by subtracting the noise frequency spectrum of  FIG. 11  from the original mass spectrum of  FIG. 2 ; and 
         FIG. 13  is a flow diagram illustrating the steps of a method of reducing noise in a mass spectrum, in accordance with the present invention. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     Referring to  FIG. 1 , illustrated therein is a noise reducing system, referred to generally as  10 , made in accordance with the present invention. The system  10  comprises a processor or central processing unit (CPU)  12  having a suitably programmed noise reduction engine  14 . The programming for the engine  14  may also be saved on storage media for example such as a computer disc or CD-ROM. An input/output (I/O) device  16  (typically including a data input component  16   A , and an output component such as a display  16   B ) is also operatively coupled to the CPU  12 . As will be understood, preferably the data input component  16   A  will be configured to receive mass spectrum and/or frequency domain data, and the display  16   B  will similarly be configured to graphs corresponding to mass spectra and frequency domains. 
     Data storage  17  is also preferably provided in which may be stored mass spectrum and frequency domain data. 
     As will be understood, the system  10  may be a stand-alone analysis system for reducing noise in a mass spectrum (or frequency domain data). In the alternative, the system  10  may (but does not necessarily have to) comprise part of a spectrometer system having an ion source  20 , configured to emit a beam of ions, generated from a sample to be analyzed. 
     A detector  22  (having one or more anodes or channels) may also be provided as part of the spectrometer system, which can be positioned downstream of the ion source  20 , in the path of the emitted ions. Optics  24  or other focusing elements, such as an electrostatic lens can also be disposed in the path of the emitted ions, between the ion source  20  and the detector  22 , for focusing the ions onto the detector  22 . 
     Referring now to  FIG. 2 , illustrated therein is a graph  30  illustrating an original mass spectrum  40  as may be input into and analyzed by the system  10 . The vertical axis  42  corresponds to signal intensity, while the horizontal axis  44  corresponds to m/z (mass/charge). The graph displays the original mass spectrum  40 , which will typically comprise a real signal combined together with and obscured by a background noise or signal. As will be understood, the data corresponding to the original mass spectrum  40  is preferably input into and stored in the data storage  17 , and typically the graph  30  is displayed on the display  16   B . 
       FIG. 13  sets out the steps of the method, referred to generally as  200 , carried out by the noise reducing system  10 . Data corresponding to an original mass spectrum  40  (illustrated in  FIG. 2 ) is received (typically via the I/O device or determined by the system  10  if the system  10  comprises a spectrometer) and typically stored in data storage  17 , and the noise reduction engine  14  is programmed to initiate the noise reduction analysis (Block  202 ). A noise mass spectrum corresponding to the background signal component in the original mass spectrum  40  is then determined (Block  204 ). As set out in the discussion relating to Blocks  206  to  232  below, this step may itself comprise a number of steps. 
     The engine  14  can be programmed to effect a transformation of the original mass spectrum  40  into the frequency domain (typically by subjecting the original mass spectrum  40  data to a Fourier Transformation, sine/cosine transform or any mathematical or experimental method known in the art) to obtain an original frequency spectrum  50 , as illustrated in the graph  52  of  FIG. 3A  (a magnified segment of which is illustrated in the graph  52 ′ of  FIG. 3B ) (Block  206 ). In the graph  52 , the vertical axis  54  corresponds to intensity while the horizontal axis  56  corresponds to frequency. 
     The original frequency spectrum  50  comprises distinct peaks  58  corresponding to dominant frequencies. As will be understood, background noise is often periodic in nature, typically having a period of one atomic mass unit. Accordingly, a significant portion of the intensity of the dominant frequencies  58  may often be attributed to the noise component of the original mass spectrum  40 . These dominant frequencies  58  will often correspond to the background noise&#39;s base frequency and corresponding harmonics thereof. 
     The engine  14  preferably identifies at least one and preferably all of the dominant frequencies  58  in the original frequency spectrum  50  (although as will be understood, this step could be performed manually by a system  10  user) (Block  208 ). Next, the original frequency spectrum  50  is filtered for the identified dominant frequencies  58 , in order to generate a noise frequency spectrum  60 , as illustrated in the graph  61  of  FIG. 4  (Block  210 ). 
     To accomplish this, a filter  62 , such as that depicted for illustrative purposes in the schematic graph  64  of  FIG. 3C , may be created to selectively filter for the identified dominant frequencies  58 . Typically the data filter  62  will be implemented through software in the reduction engine  14 , and will often not be displayed to the end user. As can be seen, the vertical axis  66  represents the ratio (from 0 to 1) of the original frequency spectrum  50  to be retained or filtered for. The horizontal axis  68  corresponds to frequency. The filter  62  preferably comprises a plurality of tabs  70  corresponding to the number of dominant frequencies  58  identified in Block  208 . As can be seen from the juxtaposition of  FIGS. 3A and 3B , via the tabs  70 , the filter  62  is configured to preserve or filter for 100% of the identified dominant frequencies  58  data. Conversely, the filter  62  discards the frequency data in the original frequency spectrum  50  not forming part of the identified dominant frequencies data  58 , resulting in the noise frequency spectrum  60  data. 
     Subsequently, the engine  14  is preferably configured to determine a noise mass spectrum  72  illustrated in the graph  74  of  FIG. 5 , typically by effecting an inverse Fourier transformation of the noise frequency spectrum  60  data into the mass domain (Block  212 ). 
     As will be understood, the noise mass spectrum  72  data represents an estimate of the background noise signal component of the original mass spectrum  40 . 
     Referring to  FIG. 6 , illustrated therein is a graph  76  overlay of a close-up segment of the original mass spectrum  40  with a corresponding magnified segment of the noise mass spectrum  72 . As will be understood, the noise  72  and original  40  mass spectrums are formed of many thousands of data points. Data points in both mass spectrums  72  and  40  may be correlated as one data point should exist in each spectrum  40 ,  72  corresponding to each m/z value. 
     Referring to exemplary data points  74 A and  74 B (and  75 A and  75 B) of the original mass spectrum  40  and the noise mass spectrum  72 , respectively, each pair is correlated to the same m/z value (as indicated by the dotted lines). It can be seen that the noise mass spectrum  72  may have a higher intensity value at certain m/z values than the original mass spectrum  40 . However, as will be understood, this indicates an artifact in estimation of the background noise signal component, as the noise component should not exceed the combined background and real signals of the original mass spectrum  40  (at corresponding m/z values). This artifact is a result of the real peak(s) in the original mass spectrum  40 , for example at points  74 A,  75 A where the original mass spectrum  40  has a higher intensity value than the corresponding points  74 B,  75 B on the noise mass spectrum  72 . 
     Accordingly, to further refine the background signal estimate, the noise mass spectrum  72  data is revised such that for each correlated data point in the noise mass spectrum  72  and original mass spectrum  40  (having the same m/z value), the minimum intensity value of the two data points is determined (Block  214 ). In turn, the noise mass spectrum is preferably modified by making the noise intensity data point equal to the minimum value (Block  216 ). 
     For the sake of clarity, the steps of Blocks  214  and  216  may be implemented using the function set out in Equation 1, below:
 
 f ′( x )=min( f ( x ), g ( x ))  EQ. 1:
 
where x represents m/z and f(x) represents the intensity value of the noise mass spectrum  72  and g(x) represents the intensity value of the original mass spectrum  40 , and f′(x) represents the modified noise mass spectrum.
 
     Completion of Block  216  for all of the correlated data points in the original and noise mass spectrums  40 ,  72 , results in a modified noise mass spectrum  80 , as illustrated in the graph  82  of  FIGS. 7A  (and  7 B) (Block  218 ). 
     Next, a transformation of the modified noise mass spectrum  80  into the frequency domain is effected (again, typically by subjecting the noise mass spectrum  80  data to a Fourier Transformation) to obtain a noise frequency spectrum  90 , as illustrated in the graph  92  of  FIG. 8  (Block  220 ). 
     Next, at least one and preferably all of the dominant frequencies  94  in the noise frequency spectrum  90  are identified (Block  222 ). The noise frequency spectrum  90  is then filtered for the identified dominant frequencies  94 , in order to generate a filtered noise frequency spectrum  98 , a portion of which is illustrated in the graph  99  of  FIG. 9  (Block  224 ). 
     Typically, the filter  62  of  FIG. 3B  created in reference to Block  210 , may be reused to selectively filter for the identified dominant frequencies  94 , in creating the noise frequency spectrum  98 . 
     Subsequently, a noise mass spectrum  100  as illustrated in the graph  102  of  FIG. 10  is generated, typically by effecting an inverse Fourier Transformation of the noise frequency spectrum  98  data into the mass domain (Block  226 ). 
     To further refine the background signal estimate, in a manner similar to that discussed in relation to Block  216 , the noise mass spectrum  100  data is revised such that for each correlated data point in the noise mass spectrum  100  and original mass spectrum  40  (correlated by sharing the same m/z value), the minimum intensity value of the two data points is determined (Block  228 ). In turn, the noise mass spectrum  100  is preferably modified by making the noise intensity data point equal to the minimum value (Block  230 ). As will be understood, the steps of Blocks  228  and  230  may be implemented using Equation 1, above. 
     Completion of Block  230  for all of the correlated data points in the original and noise mass spectrums  40 ,  100 , results in a modified noise mass spectrum  102 , as illustrated in the graph  104  of  FIG. 11  (Block  232 ). 
     The steps of Blocks  220  to  232  will preferably (but not necessarily) be repeated multiple times (as indicated by the line  233  in  FIG. 13 ), each repetition further refining the background signal estimate (noise mass spectrum  102 ) and making it more closely approximate the actual background signal. The steps of Blocks  220  to  232  may be repeated a predetermined number of times (for example from 1 to 20 times, typically, but more repetitions may be necessary in some instances), or the engine  14  may be programmed to discontinue the repetitions automatically once the difference between the respective versions of the modified noise mass spectrum  102  data and the noise mass spectrum  100  data falls within a predetermined range. 
     Once the final version of the modified noise mass spectrum  102  has been determined, the noise mass spectrum  102  is subtracted from the original mass spectrum  40 , resulting in a corrected mass spectrum  110  as illustrated in graph  112  in  FIG. 12  (Block  250 ). As will be understood, the corrected mass spectrum  110  corresponds to the intended real signal of the sample to be analyzed, with a substantial portion of the background noise (present in the original mass spectrum  40 ) removed. 
     In an alternate embodiment  200 ′, it has been found that improved results may sometimes be obtained by segmenting the original mass spectrum  40  into a plurality of initial windows  120  (as illustrated in  FIG. 2  and separated by dotted lines) prior to Block  206  (Block  234 ). Typically, the windows  120  are of equal dimensions, although this is not required. Preferably, Blocks  206  through  212  inclusive are each completed separately for one initial window  120 , before Blocks  206  through  212  are commenced and completed for another (typically successive) initial window  120 , as indicated by dotted line  236 . 
     Of course, as will be understood, the description above of each of Blocks  206  through  212  refer to mass spectrums and corresponding frequency domains as a whole. However, if the original mass spectrum  40  is to be processed by initial windows  120  separately pursuant to Block  234 , as appropriate, references to whole mass spectrums and frequency domains in the descriptions for the Blocks  206  through  212  should be understood to refer to the mass spectrum and frequency domain segments corresponding to the initial window  120  being processed during the specific iteration of those Blocks. 
     Once the segmentation of the original mass spectrum  40  into initial windows  120  pursuant to Block  222  and the subsequent completion of Blocks  206  through  212  for each initial window  12  and the modified noise mass spectrum  80  has been generated pursuant to Blocks  214  through  218 , the noise mass spectrum  80  is segmented into a series of a plurality of subsequent windows  130  (as illustrated in  FIG. 7A ) prior to Block  220  (Block  238 ). Preferably, the subsequent windows  130  in the series are configured such that no subsequent window  130  is coextensive with any initial window  12  in the mass domain. It is also preferable if (other than at the beginning and end of the mass spectrums), the windows  130  do not share a leading or termination edge (indicated by the dotted lines in  FIG. 7A ) with any initial windows  12 . 
     Accordingly, if the subsequent windows  130  are configured to be generally of the same size as the initial windows  12 , the subsequent window segments  130  will be shifted in the mass domain such that the first  130 ′ and last  130 ″ subsequent window segments will typically be smaller than the remainder of the subsequent windows  130 . 
     Each of Blocks  220  through  226  inclusive is completed separately for one subsequent window  130  (including  130 ′,  130 ″), before Blocks  220  through  226  are completed for another (typically successive) subsequent window  130 , as indicated by dotted line  240 . As with the initial embodiment discussed above, Blocks  220  through  232 , may be repeated—for each subsequent repetition (as indicated by dotted line  233 ′ instead of line  233 ) preferably a series of new subsequent windows is created in Block  238  such that no new subsequent window  130  is coextensive with any subsequent window  130  in any previous series. It is also preferable if (other than at the beginning and end of the mass spectrums), any new subsequent windows  130  do not share a leading or termination edge (indicated by the dotted lines in  FIG. 7A ) with any subsequent windows  120  in a previous series. 
     To avoid or minimize the overlap of leading or terminating edges, for each subsequent repetition, a series of new subsequent windows  130  may be configured to generally have the same size as previous series of windows  130 , but be shifted in location relative to m/z value. Alternatively, the size of the windows  130  may be changed for different series of windows  130  to minimize the overlapping of leading or terminating edges. 
     Thus, while what is shown and described herein constitute preferred embodiments of the subject invention, it should be understood that various changes can be made without departing from the subject invention, the scope of which is defined in the appended claims.