CN112415078A - Mass spectrum data spectrogram signal calibration method and device - Google Patents
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Abstract
The invention discloses a mass spectrum data spectrogram signal calibration method, which comprises the following steps: obtaining an original spectrogram; establishing a one-to-one mapping relation between standard sampling time points and mass spectrum data; constructing a two-dimensional wavelet transform matrix X (gamma, mu) to complete spectrum peak identification and peak position marking; establishing an overlapping peak splitting mathematical model, identifying overlapping peaks in spectral peaks and completing splitting; optimizing the center coordinate and half peak width of a spectrum peak by using an NM algorithm, and performing Gaussian fitting on each signal peak after the optimization is completed; searching a mark peak, and establishing a mapping relation between the accurate mass number of the mark peak and the actual corresponding sampling time; recording corresponding delta Pi when the data are not overlapped; and correcting mass spectrum data of the corresponding segment Si (x) by using the displacement quantity delta Pi of each segment to obtain an alignment spectrum S (x) under a target scale. The method has the remarkable advantages of simple and clear operation, high analysis efficiency, accurate calibration and the like, and effectively improves the accuracy and reliability of spectrogram data processing.
Description
[ technical field ] A method for producing a semiconductor device
The invention relates to the technical field of mass spectrometry, in particular to a mass spectrum data spectrogram signal calibration method and device.
[ background of the invention ]
In the experimental process of a mass spectrometry system, a spectrogram acquired by the system is interfered by various factors such as environmental temperature, sample introduction rate, electromagnetic interference, system noise and the like, the interference can cause distortion phenomena such as distortion, drift and the like of the acquired spectrogram, and even if the spectrogram is the same sample, the spectrograms measured under different instruments and conditions are different. Meanwhile, a plurality of random noises can be added into the spectrogram, and the noises can reduce the signal-to-noise ratio of effective signals and have adverse effects on identification. In the application process of the system, due to different sampling time, environment and other factors, the spectrogram acquired in each experiment is different, and if the spectrogram is not processed, the difference caused by the external factors covers the difference between different sample spectrograms, so that the recognition error is caused. However, the existing mass spectrum data processing model has the problems of complex operation, low efficiency and inaccurate calibration, and cannot meet the requirements for accuracy and reliability in spectrogram data processing.
In view of the above, it is desirable to provide a method and an apparatus for calibrating a spectrum signal of mass spectrum data to overcome the above-mentioned drawbacks.
[ summary of the invention ]
The invention aims to provide a mass spectrum data spectrogram signal calibration method and device, aims to solve the problems of complex operation, low efficiency and inaccurate calibration in the existing mass spectrum data processing model, realizes the alignment and unified analysis of multi-center and multi-node original data drift, effectively improves the accuracy and reliability of spectrogram data processing, and has the remarkable advantages of simple and clear operation, high analysis efficiency, accurate calibration and the like.
In order to achieve the above object, an aspect of the present invention provides a method for calibrating a spectrogram signal of mass spectrum data, comprising the following steps:
step S11: acquiring an original spectrogram, and setting S (x) as a spectrum to be aligned and R (x) as a reference spectrum; the original spectrogram is mass spectrum data of 2 columns of 31666 rows, the 1 st column records accurate mass number, and the 2 nd column records signal intensity;
step S12: setting a sampling time interval as t, and establishing a one-to-one mapping relation between standard sampling time points and the mass spectrum data; dividing the sampling time interval t equally by 10 to enlarge the sampling point from 31666 to 316660;
step S13: constructing a two-dimensional wavelet transform matrix X (gamma, mu); wherein, gamma and mu are respectively set as transformation scale and change displacement, the original spectrogram is subjected to continuous wavelet transformation within the set range of gamma and mu, and spectral peak identification and peak position marking are completed by judging the slopes of the rising edge and the falling edge of a spectral peak;
step S14: establishing a mathematical model for splitting the overlapped peaks, and identifying the overlapped peaks in the spectral peaks and completing splitting by combining the peak positions identified and marked by wavelet transform and adopting a smooth optimization processing algorithm and a Fourier deconvolution and space deconvolution processing method to obtain a plurality of signal peaks;
step S15: optimizing the center coordinate and the half-peak width of the spectrum peak by using an NM algorithm, and performing Gaussian fitting on each signal peak in the original spectrogram after the optimization is completed;
step S16: searching a mark peak, and establishing a mapping relation between the accurate mass number of the mark peak and the actual corresponding sampling time; wherein a spectrogram segment S of the spectrum to be aligned S (x) is seti(x) (i ═ 1, 2..) and calculates each segment Si(x) Amount of drift Δ P relative to the corresponding segment of the reference spectrum R (x)i(ii) a By Δ PiMoving the corresponding segment Si(x) If and adjacent to the segment Si-1(x) Or Si+1(x) Spectral peak overlap occurs such that Δ Pi＝ΔPi-1(ΔPiNot less than 0) until no overlapping of the spectrum peaks occurs, and recording the corresponding drift amount delta P when no overlapping occursi；
Step S17: by the corresponding drift amount Δ P when the segments are not overlappediCorrecting the corresponding segment Si(x) Obtaining the target scale from the mass spectrum dataAlignment spectrum s (x) of; and setting a standard spectrogram to comprise 35000 sampling points, and establishing a standard mass axis interval by using the increment step length of the uniform mass number.
In a preferred embodiment, the step S15 is followed by:
step S18: and establishing a mathematical model of noise distribution, and performing nonlinear suppression, smooth noise reduction and optimization processing on the noise in the original spectrogram.
In a preferred embodiment, the step S18 further includes:
and carrying out signal intensity zero setting processing on sampling points which do not participate in Gaussian fitting in the original spectrogram.
In a preferred embodiment, the step S17 is followed by:
step S19: setting an initial value of a Gaussian window function sigma, and a minimum step length delta sigma, judging whether the Gaussian window function sigma reaches a set minimum scale, and if so, ending the algorithm; if the result is negative, σ' ═ σ - Δ σ is set, and the process returns to step S13.
In a preferred embodiment, the step S17 is followed by:
step S20: and generating new spectrogram data from the alignment spectrum S (x) after the alignment treatment, and performing drawing treatment on the alignment spectrum S (x) and outputting the drawing treatment.
The second aspect of the present invention provides a mass spectrum data spectrogram signal calibrating apparatus, comprising:
the data initialization module is used for acquiring an original spectrogram, and setting S (x) as a spectrum to be aligned and R (x) as a reference spectrum; the original spectrogram is mass spectrum data of 2 columns of 31666 rows, the 1 st column records accurate mass number, and the 2 nd column records signal intensity;
the time mapping establishing module is used for setting a sampling time interval as t and establishing a one-to-one mapping relation between standard sampling time points and the mass spectrum data; dividing the sampling time interval t equally by 10 to enlarge the sampling point from 31666 to 316660;
the spectral peak identification module is used for constructing a two-dimensional wavelet transformation matrix X (gamma, mu); wherein, gamma and mu are respectively set as transformation scale and change displacement, the original spectrogram is subjected to continuous wavelet transformation within the set range of gamma and mu, and spectral peak identification and peak position marking are completed by judging the slopes of the rising edge and the falling edge of a spectral peak;
the overlapped peak splitting module is used for establishing an overlapped peak splitting mathematical model, identifying overlapped peaks in the spectrum peaks and completing splitting by combining the peak positions identified and marked by wavelet transformation and adopting a smooth optimization processing algorithm and a Fourier deconvolution and space deconvolution processing method to obtain a plurality of signal peaks;
the Gaussian fitting module is used for optimizing the center coordinate and the half-peak width of the spectrum peak by using an NM algorithm, and performing Gaussian fitting on each signal peak in the original spectrogram after the optimization is finished;
the calibration spectrogram drift module is used for searching for a mark peak and establishing a mapping relation between the accurate mass number of the mark peak and the actual corresponding sampling time; wherein a spectrogram segment S of the spectrum to be aligned S (x) is seti(x) (i ═ 1, 2..) and calculates each segment Si(x) Amount of drift Δ P relative to the corresponding segment of the reference spectrum R (x)i(ii) a By Δ PiMoving the corresponding segment Si(x) If and adjacent to the segment Si-1(x) Or Si+1(x) Spectral peak overlap occurs such that Δ Pi＝ΔPi-1(ΔPiNot less than 0) until no overlapping of the spectrum peaks occurs, and recording the corresponding drift amount delta P when no overlapping occursi；
A standard axis establishing module for using the corresponding drift amount delta P when each segment is not overlappediCorrecting the corresponding segment Si(x) Obtaining an alignment spectrum S (x) under a target scale according to the mass spectrum data; and setting a standard spectrogram to comprise 35000 sampling points, and establishing a standard mass axis interval by using the increment step length of the uniform mass number.
In a preferred embodiment, the method further comprises:
and the noise elimination module is used for establishing a mathematical model of noise distribution and carrying out nonlinear suppression, smooth noise reduction and optimization processing on the noise in the original spectrogram.
In a preferred embodiment, the noise cancellation module further includes:
and the sampling point zero setting unit is used for carrying out signal intensity zero setting processing on sampling points which do not participate in Gaussian fitting in the original spectrogram.
In a preferred embodiment, the method further comprises:
the correction judgment module is used for setting an initial value of a Gaussian window function sigma and a minimum step length delta sigma, judging whether the Gaussian window function sigma reaches a set minimum scale or not, and if so, finishing the algorithm; and if the result is negative, the sigma' is enabled to be sigma-delta sigma, and the spectrum peak identification module returns to process.
In a preferred embodiment, the method further comprises:
and the alignment spectrum output module is used for generating new spectrogram data from the alignment spectrum S (x) after alignment processing, and performing drawing processing on the alignment spectrum S (x) and outputting the alignment spectrum S (x).
The method for calibrating the spectrogram signal of the mass spectrum data can quickly read original spectrogram data, and cut and equally divide a single sampling interval by establishing the mapping relation between standard sampling time and an original spectrogram; by means of function processing such as spectral peak identification, overlapping peak splitting, Gaussian fitting and the like, mark peak searching and drift spectrogram calibration are achieved, a standard quality axis interval is further established, and a standard spectrogram is obtained from an original spectrogram. The method comprehensively utilizes a plurality of data processing functions, has the remarkable advantages of simple and clear operation, high analysis efficiency, accurate calibration and the like, effectively improves the accuracy and reliability of spectrogram data processing, and has wide practical application prospect.
Meanwhile, in the preferred embodiment, a probability-based statistical method is adopted, mass data are used for counting distribution functions of the frequency and the intensity of noise near each accurate mass number, and a mathematical model of noise distribution is established; meanwhile, by combining with wavelet transformation, nonlinear filtering and other digital signal processing methods, noise reduction and optimization processing such as nonlinear suppression, smoothing and the like are carried out on noise in the spectrogram through time domain and frequency domain processing means, noise reduction is realized, and the signal-to-noise ratio of the spectrogram is effectively improved, so that the characteristics of the spectrogram can be retained to the maximum extent in the identification process, and the identification accuracy is improved.
[ description of the drawings ]
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
FIG. 1 is a flow chart of a method for calibrating a spectrogram signal of mass spectrometry data according to the present invention;
FIG. 2 is a comparison graph of the peak spectra of the oxygen mass spectrum data signals before and after calibration by the mass spectrum data spectrum signal calibration method shown in FIG. 1;
FIG. 3 is a signal peak of oxygen mass spectrum data after calibration processing by the mass spectrum data spectrogram signal calibration method shown in FIG. 1;
fig. 4 is a block diagram of a mass spectrometry data spectrogram signal calibration apparatus provided by the present invention.
[ detailed description ] embodiments
In order to make the objects, technical solutions and advantageous effects of the present invention more clearly apparent, the present invention is further described in detail below with reference to the accompanying drawings and the detailed description. It should be understood that the detailed description and specific examples, while indicating the preferred embodiment of the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
In an embodiment of the present invention, on the one hand, a method for calibrating a mass spectrum data spectrogram signal is provided, which analyzes acquired spectrogram data, wherein the spectrogram data includes multiple spectrogram data, such as time-of-flight mass spectrometry data, ion trap mass spectrometry data, quadrupole mass spectrometry data, and ion mobility spectrometry data. It should be noted that, the plurality of data processing functions and models provided by the present invention may refer to the existing functions and models, for example, refer to Pathon plurality of data processing functions, and the present invention is not limited herein.
As shown in FIG. 1, the calibration method of the spectrum signal of the mass spectrum data comprises the following steps S11-S17.
Step S11: acquiring an original spectrogram, and setting S (x) as a spectrum to be aligned and R (x) as a reference spectrum; the original spectrogram is mass spectrum data of 2 columns 31666 rows, the 1 st column records the accurate mass number, and the 2 nd column records the signal intensity.
In this step, data initialization is performed for the calibration method. The spectrum to be aligned S (x) and the reference spectrum R (x) are original spectrogram data obtained by mass spectrum online detection.
Step S12: setting a sampling time interval as t, and establishing a one-to-one mapping relation between standard sampling time points and mass spectrum data; the sampling interval t is divided equally by 10 to expand the sampling point from 31666 to 316660.
It should be noted that, the method for establishing the time mapping between the sampling point and the mass spectrum data may refer to the prior art, and the invention is not limited herein. Meanwhile, the sampling time interval t is divided by 10 equally, so that the number of sampling points is increased, the sampling points caused by noise can be eliminated in the subsequent steps, the accuracy of spectrogram calibration is improved, and the alignment and the unified analysis of multi-center and multi-node original data drift are realized.
Step S13: constructing a two-dimensional wavelet transform matrix X (gamma, mu); wherein, gamma and mu are respectively set as transformation scale and change displacement, the original spectrogram is subjected to continuous wavelet transformation within the set range of gamma and mu, and spectral peak identification and peak position marking are completed by judging the slopes of the rising edge and the falling edge of the spectral peak. The specific two-dimensional wavelet transform matrix may refer to the prior art, and the present invention is not limited herein.
Step S14: establishing a mathematical model for splitting the overlapped peaks, combining the peak positions identified and marked by wavelet transformation, adopting optimization processing algorithms such as smoothing and the like and Fourier deconvolution and space deconvolution processing methods, identifying the overlapped peaks in the spectral peaks and completing splitting to obtain a plurality of signal peaks, thereby completing splitting of the overlapped peaks.
Step S15: and optimizing the center coordinates and the half-peak width (FWHM) of the spectrum peaks by using an NM algorithm, and performing Gaussian fitting on each signal peak in the original spectrum after the optimization is completed.
In this step, The spectral peaks are optimized for maximum accuracy of gaussian fit by NM algorithm (The Nelder-Mead simple search method).
Further, the method also includes step S18: and establishing a mathematical model of noise distribution, and performing nonlinear suppression, smooth noise reduction and optimization processing on the noise in the original spectrogram.
In the step, firstly, a probability-based statistical method is adopted, mass data are used for counting distribution functions of frequency and intensity of noise near each mass number, and a mathematical model of noise distribution is established. And then, noise reduction and optimization processing such as nonlinear suppression, smoothing and the like are carried out on the noise in the spectrogram by adopting methods such as nonlinear filtering and the like, so that the noise reduction is realized, the signal-to-noise ratio of the spectrogram is effectively improved, the characteristics of the spectrogram can be retained to the maximum extent in the identification process, and the identification accuracy is improved. Furthermore, the sampling points which do not participate in the Gaussian fitting in the original spectrogram are subjected to signal intensity zero setting processing.
Step S16: searching a mark peak, and establishing a mapping relation between the accurate mass number of the mark peak and actual corresponding sampling time (namely sampling points after the sampling time interval is divided by 10 equal parts); wherein a spectrogram segment S of the spectrum S (x) to be aligned is seti(x) (i ═ 1, 2..) and calculates each segment Si(x) The amount of drift Δ P of the corresponding segment relative to the reference spectrum R (x)i(ii) a By Δ PiMoving the corresponding segment Si(x) If and adjacent to the segmentSi-1(x) Or Si+1(x) Spectral peak overlap occurs such that Δ Pi＝ΔPi-1(ΔPiNot less than 0) until no overlapping of the spectrum peaks occurs, and recording the corresponding drift amount delta P when no overlapping occursi。
In this step, the extraction of the peak signal is completed by identifying, locating and integrating each peak. Meanwhile, spectrogram drift amounts are calculated in a segmented mode, so that the drift amount corresponding to each spectrogram segment is obtained, and the accuracy and the reliability of calibration are improved.
Step S17: by the corresponding drift amount Δ P when the segments are not overlappediCorrecting the corresponding segment Si(x) Obtaining an alignment spectrum S (x) under a target scale according to the mass spectrum data; and setting a standard spectrogram to comprise 35000 sampling points, and establishing a standard mass axis interval by using the increment step length of the uniform mass number. The method comprises the steps of establishing a standard axis, obtaining an alignment spectrum S (x) based on a standard mass axis interval, and processing an original spectrogram into a standard spectrogram.
Further, in an embodiment, step S17 is followed by:
step S19: setting an initial value of a Gaussian window function sigma, and judging whether the Gaussian window function sigma reaches a set minimum scale or not, if so, ending the algorithm; if the result is negative, σ' ═ σ - Δ σ is set, and the process returns to step S13.
In this step, in order to reduce the leakage error after signal interception, a gaussian window function (truncation function) is used to truncate the signal in the standard spectrogram. Firstly, setting an initial value and a minimum step length, if the Gaussian window function does not reach the preset minimum scale, re-performing steps of spectral peak identification, overlapped peak splitting and the like on the spectrogram, and finally enabling an iteration value sigma' of the Gaussian window function to reach the preset minimum scale through continuous iteration, namely enabling an error caused by leakage caused by signal interception in the standard spectrogram to be within a preset range. Specifically, the result can be returned to the detection terminal through the network, and the judgment is completed. The minimum scale of the gaussian window function σ is the minimum value of the preset gaussian window function σ.
Further, in an embodiment, step S17 is followed by:
step S20: and generating new spectrogram data from the alignment spectrum S (x) after the alignment treatment, and performing drawing treatment on the alignment spectrum S (x) and outputting the drawing treatment.
Specifically, new spectrogram data is generated from the alignment spectrum s (x) after the alignment processing, and is output and stored to the formulated path. And simultaneously, the alignment spectrum S (x) is subjected to drawing processing and output so as to examine and verify the reliability and accuracy of the result.
For example, as shown in fig. 2 and fig. 3, fig. 2 is a comparison graph of spectra before and after calibration, and fig. 3 is a signal peak of oxygen mass spectrum data after completion of calibration.
The first embodiment is as follows:
taking the peak of the oxygen mass spectrum data (m/z 32) as an example, it can be seen from FIG. 2 that: the right signal peak is oxygen mass spectrum data subjected to drift, and the detection mass number after the drift is 32.0102; the signal peak in fig. 3 is the oxygen mass spectral data after calibration, and the mass number detected after calibration is 31.98924, which is very close to the exact mass number 31.98984 of the oxygen signal peak. And as can be seen from fig. 3: after the calibration algorithm, the spectrogram noise reduction and smoothness are improved to a large extent, and the spectrogram is finally processed into a standardized histogram. Therefore, the mass deviation of mass spectrum data is fully corrected, and the accuracy and the effectiveness of the method are shown.
In summary, the method for calibrating the spectrogram signal of the mass spectrometry data provided by the invention can quickly read the original spectrogram data, and cut and equally divide the single sampling interval by establishing the mapping relation between the standard sampling time and the original spectrogram; by means of function processing such as spectral peak identification, overlapping peak splitting, Gaussian fitting and the like, mark peak searching and drift spectrogram calibration are achieved, a standard quality axis interval is further established, and a standard spectrogram is obtained from an original spectrogram. The method comprehensively utilizes a plurality of data processing functions, has the remarkable advantages of simple and clear operation, high analysis efficiency, accurate calibration and the like, effectively improves the accuracy and reliability of spectrogram data processing, and has wide practical application prospect.
Meanwhile, in the preferred embodiment, a probability-based statistical method is adopted, mass data are used for counting distribution functions of the frequency and the intensity of noise near each accurate mass number, and a mathematical model of noise distribution is established; meanwhile, by combining with wavelet transformation, nonlinear filtering and other digital signal processing methods, noise reduction and optimization processing such as nonlinear suppression, smoothing and the like are carried out on noise in the spectrogram through time domain and frequency domain processing means, noise reduction is realized, and the signal-to-noise ratio of the spectrogram is effectively improved, so that the characteristics of the spectrogram can be retained to the maximum extent in the identification process, and the identification accuracy is improved.
The second aspect of the present invention provides a mass spectrum data spectrogram signal calibrating apparatus 100, which is used for quickly reading original spectrogram data, establishing a standard mass axis interval, and obtaining a standard spectrogram from the original spectrogram. It should be noted that the implementation principle and the implementation mode of the mass spectrum data spectrogram signal calibration apparatus 100 are consistent with the mass spectrum data spectrogram signal calibration method described above, and therefore, the details are not repeated herein.
As shown in fig. 4, the mass spectrometry data spectrogram signal calibration apparatus 100 comprises:
a data initialization module 10, configured to obtain an original spectrogram, and set s (x) as a spectrum to be aligned, and r (x) as a reference spectrum; the original spectrogram is mass spectrum data of 2 columns of 31666 rows, the 1 st column records accurate mass number, and the 2 nd column records signal intensity;
the time mapping establishing module 20 is configured to set a sampling time interval to t, and establish a one-to-one mapping relationship between standard sampling time points and the mass spectrum data; dividing the sampling time interval t equally by 10 to expand the sampling point from 31666 to 316660;
a spectral peak identification module 30, configured to construct a two-dimensional wavelet transform matrix X (γ, μ); wherein, gamma and mu are respectively set as transformation scale and change displacement, the original spectrogram is subjected to continuous wavelet transformation within the set range of gamma and mu, and spectral peak identification and peak position marking are completed by judging the slopes of the rising edge and the falling edge of the spectral peak;
an overlapped peak splitting module 40, configured to establish an overlapped peak splitting mathematical model, identify overlapped peaks in spectral peaks and complete splitting by using a smooth optimization processing algorithm and fourier deconvolution and spatial deconvolution processing methods in combination with the peak positions identified and marked by wavelet transform, so as to obtain a plurality of signal peaks;
a gaussian fitting module 50, configured to optimize the center coordinate and half-peak width of a spectrum peak by using an NM algorithm, and perform gaussian fitting on each signal peak in the original spectrogram after the optimization is completed;
a calibration spectrogram drift module 60, configured to search for a marker peak, and establish a mapping relationship between an accurate mass number of the marker peak and an actual corresponding sampling time; wherein a spectrogram segment S of the spectrum S (x) to be aligned is seti(x) (i ═ 1, 2..) and calculates each segment Si(x) The amount of drift Δ P of the corresponding segment relative to the reference spectrum R (x)i(ii) a By Δ PiMoving the corresponding segment Si(x) If and adjacent to the segment Si-1(x) Or Si+1(x) Spectral peak overlap occurs such that Δ Pi＝ΔPi-1(ΔPi≧ 0) until no overlap of spectral peaks occurs, recording the corresponding Δ P when no overlap occursi；
A standard axis establishing module 70 for establishing a standard axis by using the displacement amount Δ P of each segmentiCorrecting the corresponding segment Si(x) Obtaining an alignment spectrum S (x) under a target scale according to the mass spectrum data; and setting a standard spectrogram to comprise 35000 sampling points, and establishing a standard mass axis interval by using the increment step length of the uniform mass number.
Further, in an embodiment, as shown in fig. 4, the mass spectrum data spectrogram signal calibration apparatus 100 further includes:
and the noise elimination module 80 is used for establishing a mathematical model of noise distribution and performing nonlinear suppression, smooth noise reduction and optimization processing on the noise in the original spectrogram.
Further, the noise cancellation module 80 further includes: and the sampling point zero setting unit (not shown in the figure) is used for carrying out signal intensity zero setting processing on sampling points which do not participate in Gaussian fitting in the original spectrogram.
Further, in an embodiment, as shown in fig. 4, the mass spectrum data spectrogram signal calibration apparatus 100 further includes:
a correction judging module 90, configured to set an initial value of the gaussian window function σ, and a minimum step size Δ σ, judge whether the gaussian window function σ reaches a set minimum scale, and if so, end the algorithm; if the result is negative, let σ' ═ σ - Δ σ, and go back to spectral peak identification module 30 for reprocessing.
Further, in an embodiment, as shown in fig. 4, the mass spectrum data spectrogram signal calibration apparatus 100 further includes:
the alignment spectrum output module 91 is configured to generate new spectrogram data from the alignment spectrum s (x) after the alignment processing, and perform mapping processing on the alignment spectrum s (x) and output the mapping processing.
In another aspect, the present invention provides a terminal (not shown in the drawings), where the terminal includes a memory, a processor, and a mass spectrum data spectrogram signal calibration program stored in the memory and executable on the processor, and when executed by the processor, the mass spectrum data spectrogram signal calibration program implements the steps of the mass spectrum data spectrogram signal calibration method according to any one of the above embodiments.
The present invention further provides a computer-readable storage medium (not shown in the drawings), in which a mass spectrum data spectrogram signal calibration program is stored, and when being executed by a processor, the mass spectrum data spectrogram signal calibration program implements the steps of the mass spectrum data spectrogram signal calibration method according to any one of the above embodiments.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and method steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed system or apparatus/terminal device and method can be implemented in other ways. For example, the above-described system or apparatus/terminal device embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The invention is not limited solely to that described in the specification and embodiments, and additional advantages and modifications will readily occur to those skilled in the art, so that the invention is not limited to the specific details, representative apparatus, and illustrative examples shown and described herein, without departing from the spirit and scope of the general concept as defined by the appended claims and their equivalents.
Claims (10)
1. A mass spectrum data spectrogram signal calibration method is characterized by comprising the following steps:
step S11: acquiring an original spectrogram, and setting S (x) as a spectrum to be aligned and R (x) as a reference spectrum; the original spectrogram is mass spectrum data of 2 columns of 31666 rows, the 1 st column records accurate mass number, and the 2 nd column records signal intensity;
step S12: setting a sampling time interval as t, and establishing a one-to-one mapping relation between standard sampling time points and the mass spectrum data; dividing the sampling time interval t equally by 10 to enlarge the sampling point from 31666 to 316660;
step S13: constructing a two-dimensional wavelet transform matrix X (gamma, mu); wherein, gamma and mu are respectively set as transformation scale and change displacement, the original spectrogram is subjected to continuous wavelet transformation within the set range of gamma and mu, and spectral peak identification and peak position marking are completed by judging the slopes of the rising edge and the falling edge of a spectral peak;
step S14: establishing a mathematical model for splitting the overlapped peaks, and identifying the overlapped peaks in the spectral peaks and completing splitting by combining the peak positions identified and marked by wavelet transform and adopting a smooth optimization processing algorithm and a Fourier deconvolution and space deconvolution processing method to obtain a plurality of signal peaks;
step S15: optimizing the center coordinate and the half-peak width of the spectrum peak by using an NM algorithm, and performing Gaussian fitting on each signal peak in the original spectrogram after the optimization is completed;
step S16: searching a mark peak, and establishing a mapping relation between the accurate mass number of the mark peak and the actual corresponding sampling time; wherein a spectrogram segment S of the spectrum to be aligned S (x) is seti(x) (i ═ 1, 2..) and calculates each segment Si(x) Amount of drift Δ P relative to the corresponding segment of the reference spectrum R (x)i(ii) a By Δ PiMoving the corresponding segment Si(x) If and adjacent to the segment Si-1(x) Or Si+1(x) Spectral peak overlap occurs such that Δ Pi＝ΔPi-1(ΔPiNot less than 0) until no overlapping of the spectrum peaks occurs, and recording the corresponding drift amount delta P when no overlapping occursi；
Step S17: by the corresponding drift amount Δ P when the segments are not overlappediCorrecting the corresponding segment Si(x) Obtaining an alignment spectrum S (x) under a target scale according to the mass spectrum data; and setting a standard spectrogram to comprise 35000 sampling points, and establishing a standard mass axis interval by using the increment step length of the uniform mass number.
2. The method for calibrating signals of a spectrum of mass spectrometry data as claimed in claim 1, wherein said step S15 is followed by the steps of:
step S18: and establishing a mathematical model of noise distribution, and performing nonlinear suppression, smooth noise reduction and optimization processing on the noise in the original spectrogram.
3. The method for calibrating a spectrum signal of mass spectrometry data as claimed in claim 2, wherein said step S18 further comprises:
and carrying out signal intensity zero setting processing on sampling points which do not participate in Gaussian fitting in the original spectrogram.
4. The method for calibrating a spectrum signal of mass spectrometry data of claim 3, wherein said step S17 is further followed by:
step S19: setting an initial value of a Gaussian window function sigma, and a minimum step length delta sigma, judging whether the Gaussian window function sigma reaches a set minimum scale, and if so, ending the algorithm; if the result is negative, σ' ═ σ - Δ σ is set, and the process returns to step S13.
5. The method for calibrating signals of a spectrum of mass spectrometry data of claim 4, wherein step S17 is followed by further comprising:
step S20: and generating new spectrogram data from the alignment spectrum S (x) after the alignment treatment, and performing drawing treatment on the alignment spectrum S (x) and outputting the drawing treatment.
6. A mass spectrometry data spectrogram signal calibration apparatus, comprising:
the data initialization module is used for acquiring an original spectrogram, and setting S (x) as a spectrum to be aligned and R (x) as a reference spectrum; the original spectrogram is mass spectrum data of 2 columns of 31666 rows, the 1 st column records accurate mass number, and the 2 nd column records signal intensity;
the time mapping establishing module is used for setting a sampling time interval as t and establishing a one-to-one mapping relation between standard sampling time points and the mass spectrum data; dividing the sampling time interval t equally by 10 to enlarge the sampling point from 31666 to 316660;
the spectral peak identification module is used for constructing a two-dimensional wavelet transformation matrix X (gamma, mu); wherein, gamma and mu are respectively set as transformation scale and change displacement, the original spectrogram is subjected to continuous wavelet transformation within the set range of gamma and mu, and spectral peak identification and peak position marking are completed by judging the slopes of the rising edge and the falling edge of a spectral peak;
the overlapped peak splitting module is used for establishing an overlapped peak splitting mathematical model, identifying overlapped peaks in the spectrum peaks and completing splitting by combining the peak positions identified and marked by wavelet transformation and adopting a smooth optimization processing algorithm and a Fourier deconvolution and space deconvolution processing method to obtain a plurality of signal peaks;
the Gaussian fitting module is used for optimizing the center coordinate and the half-peak width of the spectrum peak by using an NM algorithm, and performing Gaussian fitting on each signal peak in the original spectrogram after the optimization is finished;
the calibration spectrogram drift module is used for searching for a mark peak and establishing a mapping relation between the accurate mass number of the mark peak and the actual corresponding sampling time; wherein a spectrogram segment S of the spectrum to be aligned S (x) is seti(x) (i ═ 1, 2..) and calculates each segment Si(x) Amount of drift Δ P relative to the corresponding segment of the reference spectrum R (x)i(ii) a By Δ PiMoving the corresponding segment Si(x) If and adjacent to the segment Si-1(x) Or Si+1(x) Spectral peak overlap occurs such that Δ Pi＝ΔPi-1(ΔPiNot less than 0) until no overlapping of the spectrum peaks occurs, and recording the corresponding drift amount delta P when no overlapping occursi；
A standard axis establishing module for using the corresponding drift amount delta P when each segment is not overlappediCorrecting the corresponding segment Si(x) Obtaining an alignment spectrum S (x) under a target scale according to the mass spectrum data; and setting a standard spectrogram to comprise 35000 sampling points, and establishing a standard mass axis interval by using the increment step length of the uniform mass number.
7. The apparatus for calibrating mass spectrometry data spectrum signals of claim 6, further comprising:
and the noise elimination module is used for establishing a mathematical model of noise distribution and carrying out nonlinear suppression, smooth noise reduction and optimization processing on the noise in the original spectrogram.
8. The apparatus for calibrating a signal from a spectrum of mass spectrometry data of claim 7, wherein the noise cancellation module further comprises:
and the sampling point zero setting unit is used for carrying out signal intensity zero setting processing on sampling points which do not participate in Gaussian fitting in the original spectrogram.
9. The apparatus for calibrating mass spectrometry data spectrum signals of claim 8, further comprising:
the correction judgment module is used for setting an initial value of a Gaussian window function sigma and a minimum step length delta sigma, judging whether the Gaussian window function sigma reaches a set minimum scale or not, and if so, finishing the algorithm; and if the result is negative, the sigma' is enabled to be sigma-delta sigma, and the spectrum peak identification module returns to process.
10. The apparatus for calibrating mass spectrometry data spectrum signals of claim 9, further comprising:
and the alignment spectrum output module is used for generating new spectrogram data from the alignment spectrum S (x) after alignment processing, and performing drawing processing on the alignment spectrum S (x) and outputting the alignment spectrum S (x).
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