CN115879382A - Triple quadrupole mass spectrometry 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 a medium based on improved PSO, wherein the method comprises the following steps: initializing tuning parameters and model parameters; obtaining the spectrum peak intensity, half peak width and mass number deviation corresponding to the tuning parameters; judging whether the resolution parameter and the mass axis parameter meet a termination condition, if not, 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, the half peak width and the mass number deviation; if so, 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 the parameters; otherwise, updating lens parameters, ion source parameters and resolution parameters through an improved PSO algorithm model, and jumping to the step of obtaining spectral peak intensity, half-peak width and mass number deviation; the invention introduces a simulated annealing algorithm, and automatically tunes the triple quadrupole mass spectrometer by improving the selection of multiple inertia weights, thereby realizing the optimization of the spectrogram quality.
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 has received more and more attention in scientific research and industrial production. The triple quadrupole mass spectrometry (QqQ-MS) instrument is an efficient mass spectrometry instrument, has 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 the mass spectrometer parameters to achieve optimal experimental results before sample analysis. Triple quadrupole mass spectrometers require numerous parameters to be tuned including lens parameters, ion source parameters, resolution, mass axis, etc. The 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, and the three parts are not in sequence but are performed alternately. Therefore, the qqqq-MS apparatus needs to be tuned efficiently in practical applications to ensure the accuracy and reliability of the analysis results.
Traditional mass spectrometry tuning methods often rely on manual intervention, are time consuming and prone to error. To overcome these problems, auto-tuning techniques have been developed. However, in some mass spectrometers, most of the methods used for auto-tuning are single variable tuning methods, and although the speed is high, there is a problem that the found solution is far from the optimal solution. Therefore, how to solve the problem of optimal solution in auto-tuning becomes 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) algorithm and the like.
The PSO algorithm is a heuristic optimization algorithm based on group intelligence, and has the advantages of simplicity, easiness in implementation, quickness in convergence, easiness in parallel calculation and the like. However, the conventional PSO algorithm has certain limitations in solving the optimization problem of the multi-peak function, such as poor convergence of the population of particles, poor global search capability of the algorithm, and the like.
Disclosure of Invention
To achieve the above objects and other advantages in accordance with the present invention, a first object of the present invention is to provide a triple quadrupole mass spectrometry auto-tuning method based on improved PSO, comprising the steps of:
initializing tuning parameters and model parameters; wherein the tuning parameters comprise 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;
obtaining 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;
if yes, outputting the resolution parameter, the mass axis parameter, the lens parameter and the ion source parameter;
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 obtaining the spectral peak intensity, half-peak width and mass number deviation corresponding to the tuning parameters;
and 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 skipping to the step of obtaining the spectrum peak intensity, the half peak width and the mass number deviation corresponding to the tuning parameter.
Further, the resolution parameter and the quality axis parameter are terminated by
，
Wherein FW is the current MASS spectrum peak half-peak width, TFW is the target half-peak width of the MASS spectrum peak, MASS is the MASS number of the detected current MASS spectrum peak, and TMASS is the target MASS number of the MASS spectrum peak;
and />
Is non-negative constant, is combined with a signal>
Is the set target value.
Further, the resolution parameter and the termination condition of the spectrum peak intensity are
，
Wherein I is the intensity of mass spectrum peak,
is non-negative constant, is combined with a signal>
Is a set target value.
Further, the formula for updating the mass axis parameter is
，
wherein ,
the DAC value resulting for the nth iteration->
Is the last DAC value, <' > is asserted>
Is slope->
Is a target quality number,>
the mass number corresponding to the spectral peak detected at the nth time.
Further, the model parameters also include a location boundary;
updating parameters through the improved PSO algorithm model comprises the following steps:
s1, randomly generating positions of all particles
Obtaining a global optimum position->
;
S2, for each particle
Speed under various inertial weights>
Respectively calculate according to the formula->
，
wherein ,
represents various inertial weights>
and />
Is a random number, is based on>
For a historically optimal position of particle j>
Global optimal positions for all particles;
if it is
Then>
Is the minimum in the velocity boundary;
if it is
Then->
Is the maximum value in the velocity boundary;
s3, obtaining the velocities under various inertia weights
Update each particle->
Obtaining a plurality of new positions, the position updating formula is
，/>
wherein ,
represents the position coordinate at time n +1, and>
represents the velocity vector at time n + 1;
calculating an optimal solution for the plurality of new positions, as
，
If it is
Then->
Is the minimum value in the position boundary;
if it is
Then->
Is the maximum value in the position boundary;
s4, calculating the global optimal position according to the formula
，
Then observing whether an iteration stop condition is reached, if so, ending, otherwise, returning to the step S2; wherein the iteration stop condition is
wherein ,
respectively a resolution parameter vector, a lens and ion source parameter vector and a mass axis parameter vector; i is MASS spectrum peak intensity, FW and TFW are the current MASS spectrum peak half-peak width and the target half-peak width of the MASS spectrum peak respectively, and MASS and TMASS are the MASS number of the detected current MASS spectrum peak and the target MASS number of the MASS spectrum peak respectively;
is a non-negative constant.
Further, the plurality of inertial weights include linear weights, exponential weights, power weights, random weights; wherein,
the linear weight is formulated as
；
The formula of the exponential weight is
；
The power weight is formulated as
；
The formula of the random weight is
；
wherein ,
for maxima in the weight bound>
Is the minimum value in the weight boundary, N is the maximum iteration number, and N is the algorithm iteration number.
Further, the model parameters further include a location boundary update parameter
；/>
The method also comprises the following steps between the step S2 and the step S3:
dynamically updating location boundaries
The formula of calculation is
Further, the model parameters further comprise simulated annealing parameters
；
The step S4 also comprises the step of updating the global optimal position and the particle historical optimal position by utilizing a simulated annealing algorithm, wherein the formula is
，
wherein ,
is [0,1 ]]Random numbers generated in a uniform distribution.
A second object of the present invention is to provide an electronic apparatus, comprising: a memory having program code stored thereon; a processor coupled with the memory and when the program code is executed by the processor, implementing a triple quadrupole mass spectrometry auto-tuning method based on improved PSO.
It is a third object of the present invention to provide a computer readable storage medium having stored thereon program instructions that, when executed, implement an improved PSO-based triple quadrupole mass spectrometry auto-tuning method.
Compared with the prior art, the invention has the beneficial effects that:
the traditional optimization method generally adopts a mode of fixing other parameters of single-parameter scanning and rotating scanning parameters for optimization, and the optimization mode can cause the instrument to fall into a local optimal solution and is difficult to enable the instrument to work in a better state. Aiming at the defect that the optimization process is trapped in a local optimal solution and cannot jump out due to the single parameter optimization iterative algorithm, the particle swarm algorithm is used, and the concepts of simulated annealing algorithm and inertia weight are added to the particle swarm algorithm aiming at the actual situation in use, so that the method is more suitable for the automatic tuning of triple quadrupole mass spectrometry.
The invention provides a triple quadrupole mass spectrometry automatic tuning method based on improved PSO, which is characterized in that the parameter optimization of a triple quadrupole mass spectrometer is carried out by applying the idea of particle swarm, in the process of tuning the triple quadrupole mass spectrometer, more parameters need to be optimized, the possible combinations can reach as many as ten million, and if each parameter is optimized, the triple quadrupole mass spectrometer will fall into unacceptable waiting. By applying the improved particle swarm algorithm, the method can reach the vicinity of the global optimal combination in a short time, approach or even reach the global optimal solution, namely, a relatively optimal solution (parameter combination) can be found in a short time, so that the triple quadrupole mass spectrometer can work in a better state, and a satisfactory experimental result can be obtained.
The foregoing description is only an overview of the technical solutions of the present invention, and in order to make the technical solutions of the present invention more clearly understood and to make the technical solutions of the present invention practical in accordance with the contents of the specification, the following detailed description is given of preferred embodiments of the present invention with reference to the accompanying drawings. The detailed description of the present invention is 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 embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
FIG. 1 is a flow chart of the improved PSO-based triple quadrupole mass spectrometry auto-tuning method of example 1;
FIG. 2 is an overall flow chart of the triple quadrupole mass spectrometry auto-tuning of example 1;
FIG. 3 is a graph showing the experimental results of the resolution auto-adjustment of example 1;
FIG. 4 is a graph of the results of the low, medium and high mass number resolution adjustment experiment of example 1;
FIG. 5 is a schematic diagram of the half-peak width in the mass axis calibration test of example 1;
FIG. 6 is a schematic diagram showing mass axis shift in the mass axis calibration test of example 1;
FIG. 7 is a schematic diagram of the mass number of the tuning feature peak in the mass axis calibration test of example 1;
FIG. 8 is a graph showing peak intensities in the optimization of lens parameters of example 1;
FIG. 9 is a schematic diagram of peak intensity in the optimization of ion source parameters in example 1;
FIG. 10 is a graph showing the results of the lens parameter optimization test in Q3POS mode in example 1;
FIG. 11 is a schematic view of an electronic apparatus according to embodiment 2;
FIG. 12 is a schematic view of a storage medium of embodiment 3.
Detailed Description
The present invention is further described with reference to the accompanying drawings and the detailed description, and it should be noted that, in the case of no conflict, any combination between the embodiments or technical features described below may form a new embodiment.
Example 1
The triple quadrupole mass spectrometer has 8 commonly used lens parameters, 6 ion source parameters, 8 parameter pairs for resolution calibration, 8 parameter pairs for mass axis calibration, and about 30 tuning parameters. Manual tuning can waste significant labor and time costs and can be somewhat demanding on the experiential experience of the experimenter. 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; the tuning parameters comprise 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;
obtaining the spectral peak intensity, half peak width and mass number deviation corresponding to the tuning parameters;
according to the characteristics of mass spectrum peaks, an evaluation function in the algorithm is set as
wherein ,
respectively a resolution parameter vector, a lens and ion source parameter vector and a mass axis parameter vector; i is MASS spectrum peak intensity, FW and TFW are the current MASS spectrum peak half-peak width and the target half-peak width of the MASS spectrum peak respectively, and MASS and TMASS are the MASS number of the detected current MASS spectrum peak and the target MASS number of the MASS spectrum peak respectively;
is a non-negative constant.
For a triple quadrupole mass spectrometer, a 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 quality axis calibration comprises Q1POS mode quality axis calibration, Q3POS mode quality axis calibration, Q1NEG mode quality axis calibration and Q3NEG mode quality 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, in the execution order: and the mass axis calibration and the resolution calibration are alternately carried out, the optimization process of the parameters of the lens and the ion source is carried out after the adjustment of the mass axis and the resolution is finished, and the optimization of the parameters of the lens and the ion source and the adjustment of the resolution are alternately carried out 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 resolution parameter and the quality axis parameter have termination conditions of
，
Wherein FW is the current MASS spectrum peak half-peak width, TFW is the target half-peak width of the MASS spectrum peak, MASS is the MASS number of the detected current MASS spectrum peak, and TMASS is the target MASS number of the MASS spectrum peak;
and />
Is not a negative constant, is->
Is the set target value.
The resolution parameter and the spectrum peak intensity are terminated by
，
Wherein I is the intensity of mass spectrum peak,
is not a negative constant, is->
Is the set target value.
If so, outputting a resolution parameter, a mass axis parameter, a lens parameter and an ion source parameter, which 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 obtaining the spectral peak intensity, the half-peak width and the mass number deviation corresponding to the tuning parameters;
and 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 the improved PSO algorithm model, and skipping to the step of obtaining the spectral peak intensity, the half-peak width and the mass number deviation corresponding to the tuning parameter.
For the MASS axis, the MASS axis control parameter (i.e., DAC value) and the MASS number (i.e., MASS) exhibit a linear relationship (R > 0.99) assuming the form:
wherein MASS is MASS number, DACs is DAC value corresponding to MASS,
is slope->
For the intercept, the intercept should ideally be zero. The correction formula for the mass axis is as follows:
wherein ,
the DAC value resulting for the nth iteration->
For the last DAC value,/>>
Is the slope in equation (2),. Sup.>
Is a target mass number, namely the mass number corresponding to a tuned liquid spectrum peak,
the mass number corresponding to the spectral peak detected at the nth time. When/is>
And when 0.05 or the iteration times reach a set value, stopping the iteration.
The improved PSO algorithm model is improved on the standard PSO algorithm. The standard PSO algorithm has the advantages of easiness in implementation, concise parameters, low calculation complexity, high convergence rate 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, the particles sense the historical optimal positions of the particles and the global optimal positions of the groups in the motion process to correct the moving speed, including the direction and the size, and the optimal solution is gradually approached based on the method.
The equation for the particle j to correct the moving speed is shown in equation (4),
wherein w is an inertial weight and w is a constant;
and />
Is a random number, is combined with>
For a historically optimal position of particle j>
Is the global optimal position of all particles.
Equation (5) is the update process of the particle j position.
wherein ,
and />
Respectively representing the position coordinates and velocity vector at time n + 1.
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 optimal solution cannot be found. When the inertial weight is large, the particle swarm algorithm is more like a global search method, and it always searches for a new region. Of course, the particle swarm optimization algorithm requires more iterations to achieve the global optimum, and the global optimum solution is more likely not to be found. When the inertial weight is moderate, the particle swarm optimization algorithm has a larger chance of finding a global optimal solution, but the iteration number is more than that of the first case. Aiming at the problems, the invention provides a multi-inertia weight selection method on the basis of a random inertia weight method, namely, a plurality of inertia weights are provided, a plurality of speeds are updated in each calculation iteration to obtain a plurality of new positions, and then the comparison is carried out to obtain the optimal position. The mathematical form of the inertial weights is shown in table 1.
TABLE 1 mathematical form of inertial weighting
In conjunction with the analysis, the model parameters further include a location boundary. The initializing step further comprises initializing location boundaries.
Updating parameters through the improved PSO algorithm model comprises the following steps:
s1, randomly generating positions of all particles
To obtain the global optimal position at the current moment
;
S2For each particle
Is greater than or equal to>
Calculation is carried out as shown in formula (4). But with a plurality of weights for each particle>
Speed under various inertial weights>
Respectively calculate according to the formula->
，
With reference to table 1, the plurality of inertial weights in this embodiment include a linear weight L, an exponential weight E, a power weight P, and a random weight R; wherein,
the linear weight is formulated as
；
The formula of the exponential weight is
；
The formula of the power weight is
；
The random weight is expressed by
；
wherein ,
for maxima in the weight bound>
Is the minimum value in the weight boundary, N is the maximum iteration number, and N is the algorithm iteration number.
if it is
Then->
Is the minimum in the velocity boundary;
if it is
Then->
Is the maximum value in the velocity boundary;
s3, updating each particle
The position of (3) is shown in the formula (5). Based on the resulting four inertial weights speed->
Updating the position of each particle to obtain four new positions, which are respectively recorded as
The location update formula is
，/>
wherein ,
represents the position coordinate at time n + 1, and->
Represents the velocity vector at time n + 1;
the optimal solution of the four new positions is calculated by the formula
If it is
Then->
Is the minimum value in the position boundary;
if it is
Then>
Is the maximum value in the position boundary;
s4, calculating the global optimal position by the formula
Then observing whether an iteration stop condition is reached, if so, ending, otherwise, returning to the step S2; wherein the iteration stop condition is
(8) Wherein->
。
In the application process, the standard PSO algorithm is easy to fall into local optimum, and researches show that the PSO algorithm is easy to enter a stagnation state in the later evolution stage. Aiming at the problem, a boundary dynamic correction method is added in a standard PSO algorithm, namely a position boundary is changed along with the increase of iteration times. In conjunction with the analysis, the model parameters further include a location boundary update parameter
(ii) a Also included in the initialization step is the initialization of a position boundary update parameter->
。
The method also comprises the following steps between the step S2 and the step S3:
dynamically updating location boundaries
The formula of calculation 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 crystal cooling, if the solid is not in the lowest energy state, the solid is heated and cooled again, and as the temperature slowly decreases, atoms in the solid are arranged according to a certain shape to form a high-density low-energy regular crystal, which corresponds to the globally optimal solution in the algorithm. The simulated annealing algorithm is added on the basis of the two improvements, so that the searching capability of the algorithm is favorably improved, and the probability that the particles jump out of the local optimal solution is increased.In combination with the analysis, the model parameters further include simulated annealing parameters
(ii) a The initialization step also comprises the initialization of simulated annealing parameters
。
S4, updating the global optimal position and the historical optimal position of the particles by using a simulated annealing algorithm according to the formula
wherein ,
is [0,1 ]]Random numbers generated in a uniform distribution.
The triple quadrupole mass spectrometry automatic tuning method based on the improved PSO is tested and experimented. First, a resolution auto-tuning experiment was performed, and in this example, a sample was selected as PPG, and a representative one of the spectral peaks (theoretical mass number of 906.67) was selected, for which the desired half-peak width (resolution) was 0.7Da. To this end, the present embodiment selects the optimal result in each iteration as the result in that iteration, rather than selecting the historical optimal result for the particle.
The experiment was performed on the peak (906.67) under the same parameters, repeated three times, with an initial peak half-width around 1.3Da, and terminated at iteration 4, with a half-width within the target range. The results of the experiment are shown in FIG. 3.
Next, this embodiment tests the resolution adjustment situation of low, medium, and high mass numbers, and selects 3 mass numbers: 59.05, 906.67 and 2010.5, representing the cases of low and medium high quality sections, respectively, the experimental results are shown in fig. 4. The algorithm terminates at iterations 4 and 5.
After the resolution automatic adjustment function is tested, a test of mass axis calibration is added, and a spectral peak 906.67 which is representative of the PPG is still selected, wherein the resolution adjustment and the mass axis calibration are performed alternately, and finally, the resolution adjustment and the mass axis calibration output an optimal value at the same time, namely, the output corresponding to the node S2 in the graph, and the test results are shown in fig. 5, fig. 6 and fig. 7. From the test results, it can be seen that the algorithm is terminated after 7 iterations, the initial half-peak width is 1.52Da, the mass number is shifted by 3.6Da, the half-peak width converges to 0.68Da after auto-tuning, and the mass number is shifted by-0.1 Da.
Optimization of lens parameters and ion source parameters is added on the basis of the previous experiment, and since the lens parameters, the ion source parameters and the mass axis calibration are weakly correlated with each other, the final result is not influenced by separate tests. In the Q1POS mode, after the adjustment of the mass axis and the resolution is completed, the parameters of the lens and the ion source are optimized, the parameters of the lens to be optimized are DP and EP, the parameters of the ion source to be optimized are CUR, GS1 and GS2, and the experimental results are shown in fig. 8 and 9.
For better testing of algorithm performance, lens parameter optimization in Q3POS mode was tested and the test results are shown in fig. 10.
The invention provides a triple quadrupole mass spectrometry automatic tuning method based on improved PSO, which introduces a simulated annealing algorithm, and automatically tunes a QqQ-MS instrument by improving multi-inertia weight selection, thereby realizing the optimization of spectrogram quality. The comparison experiment results prove that the method has superiority in the aspects of improving the spectrogram quality and the automation level.
Example 2
An electronic device 200, as shown in FIG. 11, includes but is not limited to: a memory 201 having program code stored thereon; a processor 202 coupled with the memory and when the program code is executed by the processor, implementing an improved PSO based triple quadrupole mass spectrometry auto-tuning method. For the detailed description of the method, reference may be made to the corresponding description in the above method embodiments, which is not repeated herein.
Example 3
A computer readable storage medium, as shown in fig. 12, having stored thereon program instructions that, when executed, implement an improved PSO-based triple quadrupole mass spectrometry auto-tuning method. For the detailed description of the method, reference may be made to the corresponding description in the above method embodiments, which is not repeated herein.
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 phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of additional identical elements in the process, method, article, or apparatus comprising the element.
All the embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from other embodiments.
The above description is intended only as an example, and not as an attempt to limit the application of the teaching to one or more embodiments. Various modifications and alterations to one or more embodiments described herein will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of the present specification should be included in the scope of claims of one or more embodiments of the present specification.
Claims (10)
1. The improved PSO-based triple quadrupole mass spectrometry automatic tuning method is characterized by comprising the following steps of:
initializing tuning parameters and model parameters; wherein the tuning parameters comprise 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;
obtaining 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;
if yes, outputting the resolution parameter, the mass axis parameter, the lens parameter and the ion source parameter;
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 obtaining the spectral peak intensity, half-peak width and mass number deviation corresponding to the tuning parameters;
and 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 skipping to the step of obtaining the spectral peak intensity, the half-peak width and the mass number deviation corresponding to the tuning parameter.
2. The improved PSO based triple quadrupole mass spectrometry auto-tuning method of claim 1, wherein: the resolution parameter and the quality axis parameter are terminated by
，
Wherein FW is the current MASS spectrum peak half-peak width, TFW is the target half-peak width of the MASS spectrum peak, MASS is the MASS number of the detected current MASS spectrum peak, and TMASS is the target MASS number of the MASS spectrum peak;
and />
Is non-negative constant, is combined with a signal>
Is the set target value.
3. As in claimThe improved PSO-based triple quadrupole mass spectrometry auto-tuning method of claim 2, characterized in that: the resolution parameter and the spectral peak intensity are terminated by
，
Wherein I is the intensity of mass spectrum peak,
is not a negative constant, is->
Is the set target value.
4. The improved PSO based triple quadrupole mass spectrometry auto-tuning method of claim 1, wherein: the formula for updating the mass axis parameters is
，
wherein ,
the DAC value resulting for the nth iteration->
Is the last DAC value, <' > is asserted>
Is slope and is based on>
Is a target quality number,>
the mass number corresponding to the spectral peak detected at the nth time.
5. The improved PSO-based triple quadrupole mass spectrometry auto-tuning method of claim 1, wherein: the model parameters further include a location boundary;
updating parameters through the improved PSO algorithm model comprises the following steps:
s1, randomly generating positions of all particles
To obtain the global optimal position at the current moment
;/>
S2, for each particle
Speed under various inertial weights>
Are calculated respectively, and the formula is->
，
wherein ,
w represents various inertial weights>
and />
Is a random number, is based on>
For a historically optimal position of particle j->
Global optimal positions for all particles;
if it is
Then->
Is the minimum in the velocity boundary;
if it is
Then->
Is the maximum value in the velocity boundary;
s3, obtaining the velocities under various inertia weights
Updating each particle>
Obtaining a plurality of new positions, the position updating formula is
，
wherein ,
represents the position coordinate at time n +1, and>
represents the velocity vector at time n + 1;
calculating an optimal solution for the plurality of new positions, as
，
If it is
Then->
Is the minimum value in the position boundary;
if it is
Then->
Is the maximum value in the position boundary;
s4, calculating the global optimal position by the formula
，
Then observing whether an iteration stop condition is reached, if so, ending, otherwise, returning to the step S2; wherein the iteration stop condition is
，
wherein ,
respectively a resolution parameter vector, a lens and ion source parameter vector and a mass axis parameter vector; i is MASS spectrum peak intensity, FW and TFW are the current MASS spectrum peak half-peak width and the target half-peak width of the MASS spectrum peak respectively, and MASS and TMASS are the MASS number of the detected current MASS spectrum peak and the target MASS number of the MASS spectrum peak respectively; />
Is a non-negative constant.
6. The improved PSO based triple quadrupole mass spectrometry auto-tuning method of claim 5, wherein: the plurality of inertial weights comprise linear weights, exponential weights, power weights, and random weights; wherein,
the linear weight is formulated as
；
The formula of the exponential weight is
；
The power weight is formulated as
；
The formula of the random weight is
；
wherein ,
is the maximum value in the weight bound->
Is the minimum value in the weight boundary, N is the maximum iteration number, and N is the algorithm iteration number.
7. The improved PSO-based triple quadrupole mass spectrometry auto-tuning method of claim 5, wherein: the model parameters further include a location boundary update parameter
；
The following steps are also included between the step S2 and the step S3:
dynamically updating location boundaries
The formula of calculation is
8. The improved PSO based triple quadrupole mass spectrometry auto-tuning method of claim 5 or claim 7, wherein: the model parameters further include simulated annealing parameters
；
The step S4 also comprises the step of 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 a uniform distribution.
9. An electronic device, comprising: a memory having program code stored thereon; a processor coupled with the memory and implementing the method of claim 1 when the program code is executed by the processor.
10. A computer-readable storage medium having stored thereon program instructions that, when executed, implement the method of claim 1.
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| CN113051771A (en) * | 2021-04-09 | 2021-06-29 | 中国科学院苏州生物医学工程技术研究所 | Particle swarm algorithm-based triple quadrupole mass spectrometer parameter optimization method and system |
| CN115078519A (en) * | 2022-05-06 | 2022-09-20 | 天津国科医工科技发展有限公司 | Spectral peak identification method, device, medium and product based on iterative algorithm |
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| CN113051771A (en) * | 2021-04-09 | 2021-06-29 | 中国科学院苏州生物医学工程技术研究所 | Particle swarm algorithm-based triple quadrupole mass spectrometer parameter optimization method and system |
| CN115078519A (en) * | 2022-05-06 | 2022-09-20 | 天津国科医工科技发展有限公司 | Spectral peak identification method, device, medium and product based on iterative algorithm |
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