CN115879382B - Triple quadrupole mass spectrum automatic tuning method, equipment and medium based on improved PSO - Google Patents
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Abstract
The invention relates to a triple quadrupole mass spectrometry automatic tuning method, equipment and medium based on improved PSO, wherein the method comprises the following steps: initializing tuning parameters and model parameters; acquiring the spectrum peak intensity, half peak width and mass number deviation corresponding to the tuning parameters; judging whether the resolution parameter and the quality axis parameter meet the termination condition, otherwise, updating the quality axis parameter, updating the resolution parameter through an improved PSO algorithm model, and jumping to the step of acquiring the spectrum peak intensity, half-peak width and quality number deviation; if yes, outputting the resolution and the quality axis parameters, and judging whether the resolution parameters and the quality axis parameters meet the termination conditions; if yes, outputting parameters; otherwise, updating lens parameters, ion source parameters and resolution parameters through an improved PSO algorithm model, and jumping to a step of acquiring spectrum peak intensity, half peak width and mass number deviation; according to the invention, a simulated annealing algorithm is introduced, and the triple quadrupole mass spectrometer is automatically tuned by improving multi-inertia weight selection, so that the optimization of spectrogram quality is realized.
Description
Technical Field
The invention relates to the technical field of mass spectrometry, in particular to a triple quadrupole mass spectrometry automatic tuning method, equipment and medium based on improved PSO.
Background
With the continuous development of biological analysis and chemical research, mass spectrometry technology is increasingly gaining attention in scientific research and industrial production. Triple quadrupole mass spectrometry (QqQ-MS) instrument is a high-efficiency mass spectrometry instrument with good sensitivity and selectivity, and can be used for various analysis applications, such as identification and quantification of biomolecules, drug metabolism research and environmental monitoring. Mass spectrometer tuning refers to the process of optimizing mass spectrometer parameters to achieve optimal experimental results prior to sample analysis. Triple quadrupole mass spectrometers require a multitude of parameters to tune, including lens parameters, ion source parameters, resolution, mass axis, etc. Triple quadrupole mass spectrometer tuning should be divided into three parts, namely mass axis calibration, resolution calibration and mass spectrum parameter and ion source parameter optimization, which are not sequenced but alternate. Therefore, the QqQ-MS instrument needs to be efficiently tuned in practical application to ensure the accuracy and reliability of analysis results.
Traditional mass spectrometry tuning methods often rely on manual intervention, which is time consuming and error prone. To overcome these problems, automatic tuning techniques have been developed. However, in some mass spectrometers, most of the methods used for automatic tuning are single variable adjustment methods, which, although fast, have the problem of finding solutions far from the optimal solution. Therefore, how to solve the problem of optimal solution in automatic tuning is a focus of attention. The automatic tuning method based on the heuristic optimization algorithm is more suitable for multi-parameter optimization, such as genetic algorithm, particle Swarm Optimization (PSO) and the like.
The PSO algorithm is a heuristic optimization algorithm based on group intelligence, and has the advantages of simplicity, easiness in implementation, fast convergence, easiness in parallel calculation and the like. However, the conventional PSO algorithm has a certain limitation in solving the optimization problem of the multimodal function, such as poor population convergence of particles, poor global searching capability of the algorithm, and the like.
Disclosure of Invention
To achieve the above and other advantages and in accordance with the purpose of the present invention, a first object of the present invention is to provide a triple quadrupole mass spectrometry auto-tuning method based on an improved PSO, comprising the steps of:
initializing tuning parameters and model parameters; wherein the tuning parameters include lens parameters, ion source parameters, resolution parameters, and mass axis parameters; the model parameters comprise a speed boundary, a maximum iteration number, a weight boundary and an initial speed;
acquiring the spectrum peak intensity, half peak width and mass number deviation corresponding to the tuning parameters;
if the resolution parameter and the mass axis parameter meet the termination condition, outputting the resolution parameter and the mass axis parameter, and judging whether the resolution parameter and the spectral peak intensity meet the termination condition or not;
outputting the resolution parameter, the mass axis parameter, the lens parameter and the ion source parameter if yes;
otherwise, updating the lens parameters and the ion source parameters through an improved PSO algorithm model, updating the resolution parameters through the improved PSO algorithm model, and jumping to the step of acquiring the spectrum peak intensity, half-peak width and mass number deviation corresponding to the tuning parameters;
if the resolution parameter and the mass axis parameter do not meet the termination condition, updating the mass axis parameter, updating the resolution parameter through an improved PSO algorithm model, and jumping to the step of acquiring the spectrum peak intensity, half-peak width and mass number deviation corresponding to the tuning parameter.
Further, the termination conditions of the resolution parameter and the mass axis parameter are as follows
FW is the half-peak width of the current MASS spectrum peak, TFW is the target half-peak width of the MASS spectrum peak, MASS is the detected MASS number of the current MASS spectrum peak, and TMASS is the target MASS number of the MASS spectrum peak;
and
Is a non-negative constant, +.>
Is the set target value.
Further, the resolution parameter and the peak intensity are terminated by
Wherein I is the peak intensity of mass spectrum,
is a non-negative constant, +.>
Is the set target value.
Further, the formula for updating the mass axis parameter is as follows
wherein ,
for the DAC value obtained for the nth iteration, < >>
For the last DAC value, +.>
For slope, +>
For the target mass number>
The mass number corresponding to the nth detected spectrum peak.
Further, the model parameters also include a location boundary;
updating parameters through the improved PSO algorithm model comprises the following steps:
s1, randomly generating all particle positions
Obtaining the global optimal position at the current time
;
S2, for each particle
Speed under various inertial weights +.>
Respectively calculating according to the formula of
wherein ,
representing various inertial weights,/->
and
Is a random number +.>
For the historically optimal position of particle j +.>
Global optimal positions for all particles;
if it is
Then->
，
Is the minimum in the speed boundary;
if it is
Then->
，
Is the maximum value in the speed boundary;
s3, according to the obtained speeds under various inertia weights
Update every particle->
To obtain a plurality of new positions, and the position updating formula is as follows
wherein ,
representing the position coordinate g at time n+1;
calculating optimal solutions of a plurality of new positions, wherein the formula is
If it is
Then->
，
Is the minimum value in the position boundary>
If it is
Then->
，
Is the maximum value in the location boundary;
s4, calculating a global optimal position, wherein the formula is
Then observing whether an iteration stop condition is reached, if yes, ending, otherwise returning to the step S2; wherein the iteration stop condition is that
wherein ,
、
、
the resolution parameter vector, the lens and ion source parameter vector and the mass axis parameter vector are respectively; i is the intensity of a MASS spectrum peak, FW and TFW are the half-peak width of the current MASS spectrum peak and the target half-peak width of the MASS spectrum peak respectively, and MASS and TMASS are the detected MASS number of the current MASS spectrum peak and the detected target MASS number of the MASS spectrum peak respectively;
、
、
Is a non-negative constant.
Further, the plurality of inertial weights includes linear weights, exponential weights, power weights, random weights; wherein,
the formula of the linear weight is
The formula of the index weight is
The formula of the power weight is
The formula of the random weight is
wherein ,
for the maximum value in the weight boundary, +.>
N is the maximum iteration number and N is the algorithm iteration number for the minimum value in the weight boundary.
Further, the model parameters further include a location boundary update parameter
；
The method further comprises the following steps between the step S2 and the step S3:
dynamically updating location boundaries
The calculated formula is
Further, the model parameters also comprise simulated annealing parameters
；
The step S4 also comprises the step of updating the global optimal position and the particle history optimal position by using a simulated annealing algorithm, wherein the formula is
wherein ,
is [0,1]Random numbers generated in the uniform distribution.
A second object of the present invention is to provide an electronic device including: a memory having program code stored thereon; a processor coupled with the memory and which when the program code is executed by the processor implements a triple quadrupole mass spectrometry auto-tuning method based on an improved PSO.
A third object of the present invention is to provide a computer readable storage medium having stored thereon program instructions that when executed implement a triple quadrupole mass spectrometry auto-tuning method based on an improved PSO.
Compared with the prior art, the invention has the beneficial effects that:
the traditional optimization method generally adopts a mode of fixing other parameters by single-parameter scanning and rotating the scanning parameters for optimization, and the optimization mode can lead to sinking into a local optimal solution, so that the instrument is difficult to work in a better state. Aiming at the defect that the optimization process caused by the single parameter optimization iterative algorithm falls into a local optimal solution and cannot jump out, the particle swarm algorithm is used, and the concepts of a simulated annealing algorithm and an inertia weight are added to the particle swarm algorithm according to actual conditions in use, so that the method is more suitable for triple quadrupole mass spectrometry automatic tuning.
The invention provides an automatic tuning method of triple quadrupole mass spectrometry based on improved PSO, which uses the thought of particle swarm to optimize parameters of a triple quadrupole mass spectrometer, and in the tuning process of the triple quadrupole mass spectrometer, more parameters need to be optimized, and the possible combination is as many as tens of millions, if each parameter is optimized, the triple quadrupole mass spectrometer falls into unacceptable waiting. By applying the improved particle swarm optimization, the situation that the global optimal combination is reached in a short time is achieved, the global optimal solution is approximated or even reached, namely, the relatively optimal solution (parameter combination) can be found in a short time, so that the triple quadrupole mass spectrometer works in a good state, and a satisfactory experimental result can be obtained.
The foregoing description is only an overview of the present invention, and is intended to provide a better understanding of the present invention, as it is embodied in the following description, with reference to the preferred embodiments of the present invention and the accompanying drawings. Specific embodiments of the present invention are given in detail by the following examples and the accompanying drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiments of the invention and together with the description serve to explain the invention and do not constitute a limitation on the invention. In the drawings:
FIG. 1 is a flow chart of a triple quadrupole mass spectrometry auto-tuning method based on the improved PSO of example 1;
FIG. 2 is a flowchart of triple quadrupole mass spectrometry auto-tune for example 1;
FIG. 3 is a graph showing the results of the resolution auto-adjustment experiment of example 1;
FIG. 4 is a graph showing the results of the resolution adjustment experiments of the low, medium and high mass numbers of example 1;
FIG. 5 is a schematic diagram of half-width in the mass axis calibration test of example 1;
FIG. 6 is a schematic diagram of the mass axis offset in the mass axis calibration test of example 1;
FIG. 7 is a graph showing the mass numbers of tuning characteristic peaks in the mass axis calibration test of example 1;
FIG. 8 is a graph showing peak intensities in the lens parameter optimization of example 1;
FIG. 9 is a graph showing peak intensities in ion source parameter optimization of example 1;
FIG. 10 is a graph showing the results of the lens parameter optimization test in the Q3POS mode of example 1;
fig. 11 is a schematic view of an electronic device of embodiment 2;
fig. 12 is a schematic diagram of a storage medium of embodiment 3.
Detailed Description
The present invention will be further described with reference to the accompanying drawings and detailed description, wherein it is to be understood that, on the premise of no conflict, the following embodiments or technical features may be arbitrarily combined to form new embodiments.
Example 1
The triple quadrupole mass spectrometer has 8 lens parameters commonly used in the tuning process, 6 ion source parameters, 8 parameter pairs for resolution calibration, 8 parameter pairs for mass axis calibration, and about 30 tuning parameters in total. Manual tuning wastes relatively large labor and time costs and places certain demands on the experimenter's experience. Accordingly, the present invention provides a triple quadrupole mass spectrometry auto-tuning method based on improved PSO, as shown in fig. 1, comprising the steps of:
initializing tuning parameters and model parameters; wherein the tuning parameters include lens parameters, ion source parameters, resolution parameters, and mass axis parameters; the model parameters comprise a speed boundary, a maximum iteration number, a weight boundary and an initial speed;
acquiring the spectrum peak intensity, half peak width and mass number deviation corresponding to the tuning parameters;
according to the characteristics of mass spectrum peaks, setting an evaluation function in an algorithm as
wherein ,
、
、
the resolution parameter vector, the lens and ion source parameter vector and the mass axis parameter vector are respectively; i is the intensity of a MASS spectrum peak, FW and TFW are the half-peak width of the current MASS spectrum peak and the target half-peak width of the MASS spectrum peak respectively, and MASS and TMASS are the detected MASS number of the current MASS spectrum peak and the detected target MASS number of the MASS spectrum peak respectively;
、
、
Is a non-negative constant.
For a triple quadrupole mass spectrometer, the tuning process can be divided into three parts, namely mass axis calibration, resolution calibration and parameter optimization, wherein the parameter optimization comprises lens parameter optimization and ion source parameter optimization; the mass axis calibration comprises Q1POS mode mass axis calibration, Q3POS mode mass axis calibration, Q1NEG mode mass axis calibration and Q3NEG mode mass axis calibration, and the resolution calibration comprises Q1POS mode resolution calibration, Q3POS mode resolution calibration, Q1NEG mode resolution calibration and Q3NEG mode resolution calibration. Since there is a weak correlation between the mass axis calibration and the resolution calibration, as shown in fig. 2, the execution sequence is: the mass axis calibration and the resolution calibration are alternately performed, and the lens and ion source parameter optimization process is performed after the mass axis and the resolution are adjusted, and the lens and ion source parameter optimization and the resolution adjustment are alternately performed at the moment.
If the resolution parameter and the mass axis parameter meet the termination condition, outputting the resolution parameter and the mass axis parameter, and judging whether the resolution parameter and the spectrum peak intensity meet the termination condition or not; wherein the termination conditions of the resolution parameter and the mass axis parameter are as follows
FW is the half-peak width of the current MASS spectrum peak, TFW is the target half-peak width of the MASS spectrum peak, MASS is the detected MASS number of the current MASS spectrum peak, and TMASS is the target MASS number of the MASS spectrum peak;
and
Is a non-negative constant, +.>
Is the set target value.
The resolution parameter and the spectral peak intensity are terminated as follows
Wherein I is the peak intensity of mass spectrum,
is a non-negative constant, +.>
To set upA fixed target value.
Outputting a resolution parameter, a mass axis parameter, a lens parameter and an ion source parameter, wherein the resolution parameter, the mass axis parameter, the lens parameter and the ion source parameter are optimal parameters;
otherwise, updating the lens parameters and the ion source parameters through the improved PSO algorithm model, updating the resolution parameters through the improved PSO algorithm model, and jumping to the step of acquiring the spectrum peak intensity, half peak width and mass number deviation corresponding to the tuning parameters;
if the resolution parameter and the mass axis parameter do not meet the termination condition, updating the mass axis parameter, updating the resolution parameter through an improved PSO algorithm model, and jumping to the step of obtaining the spectrum peak intensity, half peak width and mass number deviation corresponding to the tuning parameter.
For the MASS axis, the MASS axis control parameter (i.e., DAC value) exhibits a linear relationship with the MASS number (i.e., MASS) (R > 0.99), assuming the form:
wherein MASS is the MASS number,
for DAC values corresponding to MASS, +.>
For slope, +>
For the intercept, the intercept should ideally be zero. The correction formula of the mass axis is as follows:
wherein ,
for the DAC value obtained for the nth iteration, < >>
For last DAC value->
Is the slope in equation (2), +.>
For the target mass number, i.e. the mass number corresponding to the tuned liquid spectrum peak, < >>
The mass number corresponding to the nth detected spectrum peak. When->
Or stopping iteration when the iteration times reach a set value.
The improved PSO algorithm model is improved on the standard PSO algorithm. The standard PSO algorithm has the advantages of easy implementation, concise parameters, low calculation complexity, high convergence speed and the like, and is widely applied to the fields of intelligent calculation, neural networks, dynamic planning and the like.
M particles exist in the standard PSO algorithm, each particle is a potential solution, and the particle senses its own historical optimal position and the global optimal position of the group in the motion process to correct the moving speed, including direction and size, and based on this, the optimal solution is approximated gradually.
The equation for modifying the movement speed of the particles j is shown as (4),
wherein ,
is inertial weight, ++>
Is a constant;
and
Is a random number +.>
For a historic optimal position of the particle j,
is the globally optimal location for all particles.
Equation (5) is an update process of the particle j position.
wherein ,
representative time->
Lower position coordinates.
However, the inertia weight in the standard PSO algorithm is constant, when the inertia weight is small, if the optimal solution is in the initial search space, the particle swarm optimization algorithm can easily find the global optimal solution, otherwise, the particle swarm optimization algorithm can not find the optimal solution. When the inertia weight is large, the particle swarm algorithm is more like a global search method, and it always searches for new areas. Of course, the particle swarm optimization algorithm at this time requires more iterations to achieve global optimization, and is more likely to fail to find a globally optimal solution. When the inertia weight is moderate, the particle swarm optimization algorithm will have a greater chance to find the globally optimal solution, but the iteration number will be greater than in the first case. Aiming at the problems, the invention provides a multi-inertia weight selection method on a random inertia weight method, namely, a plurality of inertia weights are provided, a plurality of speeds are updated in each calculation iteration, a plurality of new positions are obtained, and then the optimal positions are compared. The mathematical form of the inertial weights is shown in table 1.
TABLE 1 mathematical form of inertial weights
In connection with the analysis, the model parameters also include location boundaries. The initializing step further includes initializing a position boundary.
Updating parameters through the improved PSO algorithm model comprises the following steps:
s1, randomly generating all particle positions
Obtaining the global optimal position at the current time
;
S2, for each particle
Is->
The velocity is calculated as shown in equation (4). However, the weights are plural, and +.>
Speed under various inertial weights +.>
Respectively calculating according to the formula of
In combination with table 1, the various inertial weights in this embodiment include linear weight L, exponential weight E, power weight P, random weight R; wherein,
the formula of the linear weight is
The formula of the index weight is
The formula of the power weight is
The formula of the random weight is
wherein ,
for the maximum value in the weight boundary, +.>
N is the maximum iteration number and N is the algorithm iteration number for the minimum value in the weight boundary.
if it is
Then->
，
Is the minimum in the speed boundary;
if it is
Then->
，
Is the maximum value in the speed boundary;
s3, updating each particle
The position of (2) is represented by formula (5). According to the obtained speed under four inertial weights +.>
Update every particle->
To obtain four new positions, respectively recorded as
The location update formula is
wherein ,
representative time->
Lower position coordinates;
calculating the optimal solutions of the four new positions, wherein the formula is that
If it is
Then->
，
Is the minimum in the location boundary;
if it is
Then->
，
Is the maximum value in the location boundary;
s4, calculating a global optimal position, wherein the formula is
Then observing whether an iteration stop condition is reached, if yes, ending, otherwise returning to the step S2; wherein the iteration stop condition is that
In the application process, the standard PSO algorithm is easy to sink into local optimum, and researches show that the PSO algorithm is easy to enter in the later period of evolutionEnter a stalled state. To solve this problem, a boundary dynamic correction method is added to the standard PSO algorithm, namely, the position boundary is changed along with the increase of the iteration times. In combination with the analysis, the model parameters further include location boundary update parameters
The method comprises the steps of carrying out a first treatment on the surface of the The initialization step further comprises initializing a position boundary update parameter +.>
。
The method further comprises the following steps between the step S2 and the step S3:
dynamically updating location boundaries
The calculated formula is
The simulated annealing algorithm comprises two parts, namely a Metropolis algorithm and an annealing process, and can effectively solve the problem of local optimal solution. The simulated annealing algorithm is derived from the process of cooling the crystal, heating the solid and cooling it again if the solid is not in the lowest energy state, and as the temperature slowly drops, atoms in the solid are arranged in a certain shape to form a high-density, low-energy regular crystal corresponding to the globally optimal solution in the algorithm. The addition of the simulated annealing algorithm based on the two improvements is beneficial to improving the searching capability of the algorithm, because the probability of the particles jumping out of the local optimal solution is increased. In combination with the analysis, the model parameters also include simulated annealing parameters
The method comprises the steps of carrying out a first treatment on the surface of the The initialization step also comprises initializing simulated annealing parameters
。
S4, updating the global optimal position and the particle historical optimal position by using a simulated annealing algorithm, wherein the formula is
wherein ,
is [0,1]Random numbers generated in the uniform distribution.
The triple quadrupole mass spectrometry automatic tuning method based on the improved PSO provided by the invention is tested and experimented. First, an automatic resolution adjustment experiment was performed, in which a sample was selected as PPG, and a representative one of the peaks (theoretical mass number 906.67) was selected, and for this product, the half-peak width (resolution) was expected to be 0.7Da. For this reason, the present embodiment selects the optimal result in each iteration as the result in that iteration, instead of selecting the historical optimal result for the particle.
The experiment was repeated three times on the spectral peak (906.67) under the same parameters, the initial spectral peak half-width being around 1.3Da, ending at iteration 4, the half-width reaching the target range. The experimental results are shown in FIG. 3.
Next, this embodiment tests the resolution adjustment condition of low, medium and high mass numbers, selecting 3 mass numbers: 59.05, 906.67 and 2010.5 represent the low, medium and high mass segments, respectively, and the experimental results are shown in fig. 4. The algorithm terminates at iterations 4 and 5.
After the automatic resolution adjustment function is tested, a test of mass axis calibration is added, a representative spectrum peak 906.67 of the PPG is still selected, wherein the resolution adjustment and the mass axis calibration are alternately performed, and finally, the two values output an optimal value at the same time, namely, the output of a node S2 in a corresponding graph, and the test results are shown in fig. 5, 6 and 7. From the test results, the algorithm is terminated after 7 iterations, the initial half-peak width is 1.52Da, the mass number is offset by 3.6Da, the half-peak width is converged to 0.68Da after automatic tuning, and the mass number is offset by-0.1 Da.
The addition of optimization of the lens parameters and ion source parameters on the basis of the previous experiment did not affect the final results as the lens parameters, ion source parameters and mass axis calibration were weakly related to each other. In the Q1POS mode, after the mass axis and resolution adjustment is completed, lens parameters and ion source parameters are optimized, lens parameters to be optimized are DP and EP, ion source parameters to be optimized are CUR, GS1 and GS2, and experimental results are shown in fig. 8 and 9.
For better test algorithm performance, the lens parameters in Q3POS mode were optimized and the test results are shown in fig. 10.
The invention provides an improved PSO-based triple quadrupole mass spectrum automatic tuning method, which introduces a simulated annealing algorithm, and automatically tunes a QqQ-MS instrument by improving multi-inertia weight selection so as to optimize the mass of a spectrogram. The superiority of the method in improving the quality of spectrogram and the automation level is proved by comparing experimental results.
Example 2
An electronic device 200, as shown in FIG. 11, includes, but is not limited to: a memory 201 having program codes stored thereon; a processor 202 coupled to the memory and which when the program code is executed by the processor implements a triple quadrupole mass spectrometry auto-tuning method based on the improved PSO. For detailed description of the method, reference may be made to corresponding descriptions in the above method embodiments, and details are not repeated here.
Example 3
A computer readable storage medium having stored thereon program instructions that when executed implement a triple quadrupole mass spectrometry auto-tuning method based on improved PSO as shown in fig. 12. For detailed description of the method, reference may be made to corresponding descriptions in the above method embodiments, and details are not repeated here.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises an element.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
The foregoing is illustrative of the embodiments of the present disclosure and is not to be construed as limiting the scope of the one or more embodiments of the present disclosure. Various modifications and alterations to one or more embodiments of this description will be apparent to those skilled in the art. Any modifications, equivalent substitutions, improvements, or the like, which are within the spirit and principles of one or more embodiments of the present disclosure, are intended to be included within the scope of the claims of one or more embodiments of the present disclosure.
Claims (10)
1. The triple quadrupole mass spectrum automatic tuning method based on the improved PSO is characterized by comprising the following steps of:
initializing tuning parameters and model parameters; wherein the tuning parameters include lens parameters, ion source parameters, resolution parameters, and mass axis parameters; the model parameters comprise a speed boundary, a maximum iteration number, a weight boundary and an initial speed;
acquiring the spectrum peak intensity, half peak width and mass number deviation corresponding to the tuning parameters;
if the resolution parameter and the mass axis parameter meet the termination condition, outputting the resolution parameter and the mass axis parameter, and judging whether the resolution parameter and the spectral peak intensity meet the termination condition or not;
outputting the resolution parameter, the mass axis parameter, the lens parameter and the ion source parameter if yes;
otherwise, updating the lens parameters and the ion source parameters through an improved PSO algorithm model, updating the resolution parameters through the improved PSO algorithm model, and jumping to the step of acquiring the spectrum peak intensity, half-peak width and mass number deviation corresponding to the tuning parameters;
if the resolution parameter and the mass axis parameter do not meet the termination condition, updating the mass axis parameter, updating the resolution parameter through an improved PSO algorithm model, and jumping to the step of acquiring the spectrum peak intensity, half-peak width and mass number deviation corresponding to the tuning parameter.
2. The triple quadrupole mass spectrometry auto-tuning method based on improved PSO of claim 1, wherein: the end conditions of the resolution parameter and the quality axis parameter are that
FW is the half-peak width of the current MASS spectrum peak, TFW is the target half-peak width of the MASS spectrum peak, MASS is the detected MASS number of the current MASS spectrum peak, and TMASS is the target MASS number of the MASS spectrum peak;
and
Is a non-negative constant, +.>
Is the set target value.
3. The triple quadrupole mass spectrometry auto-tuning method based on improved PSO of claim 2, wherein: the resolution parameter and the peak intensity are terminated by
Wherein I is the peak intensity of mass spectrum,
is a non-negative constant, +.>
Is the set target value.
4. The triple quadrupole mass spectrometry auto-tuning method based on improved PSO of claim 1, wherein: the formula for updating the mass axis parameter is as follows
wherein ,
for the DAC value obtained for the nth iteration, < >>
For the last DAC value, +.>
For slope, +>
For the target mass number>
The mass number corresponding to the nth detected spectrum peak.
5. The triple quadrupole mass spectrometry auto-tuning method based on improved PSO of claim 1, wherein: the model parameters also include a location boundary;
updating parameters through the improved PSO algorithm model comprises the following steps:
s1, randomly generating all particle positions
Obtaining the global optimal position +.>
;
S2, for each particle
Speed under various inertial weights +.>
Respectively calculating according to the formula of
wherein ,
，
representing various inertial weights, +.>
and
Is a random number +.>
For the historically optimal position of particle j +.>
Global optimal positions for all particles;
if it is
Then->
，
Is the minimum in the speed boundary;
if it is
Then->
，
Is the maximum value in the speed boundary;
s3, according to the obtained speeds under various inertia weights
Update every particle->
To obtain a plurality of new positions, and the position updating formula is as follows
wherein ,
representing the position coordinates at time n+1;
calculating optimal solutions of a plurality of new positions, wherein the formula is
If it is
Then->
，
Is the minimum in the location boundary;
if it is
Then->
，
Is the maximum value in the location boundary;
s4, calculating a global optimal position, wherein the formula is
Then observing whether an iteration stop condition is reached, if yes, ending, otherwise returning to the step S2; wherein the iteration stop condition is that
wherein ,
、
、
the resolution parameter vector, the lens and ion source parameter vector and the mass axis parameter vector are respectively; i is the intensity of a MASS spectrum peak, FW and TFW are the half-peak width of the current MASS spectrum peak and the target half-peak width of the MASS spectrum peak respectively, and MASS and TMASS are the detected MASS number of the current MASS spectrum peak and the detected target MASS number of the MASS spectrum peak respectively;
、
、
Is a non-negative constant.
6. The triple quadrupole mass spectrometry auto-tuning method based on improved PSO of claim 5, wherein: the plurality of inertial weights include linear weights, exponential weights, power weights, random weights; wherein,
the formula of the linear weight is
The formula of the index weight is
The formula of the power weight is
The formula of the random weight is
wherein ,
for the maximum value in the weight boundary, +.>
N is the maximum iteration number and N is the algorithm iteration number for the minimum value in the weight boundary.
7. The triple quadrupole mass spectrometry auto-tuning method based on improved PSO of claim 5, wherein: the model parameters further include a location boundary update parameter
；
The method further comprises the following steps between the step S2 and the step S3:
dynamically updating location boundaries
The calculated formula is
8. The triple quadrupole mass spectrometry auto-tuning method based on improved PSO of claims 5 or 7, characterized by: the model parameters also include simulated annealing parameters
；
The step S4 also comprises the step of updating the global optimal position and the particle history optimal position by using a simulated annealing algorithm, wherein the formula is
wherein ,
is [0,1]Random numbers generated in the uniform distribution.
9. An electronic device, comprising: a memory having program code stored thereon; a processor coupled to the memory and which, when executed by the processor, implements the method of claim 1.
10. A computer readable storage medium, having stored thereon program instructions which, when executed, implement the method of claim 1.
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