CN112947332A - Triple quadrupole mass spectrometer parameter optimization method based on simulated annealing - Google Patents
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Abstract
The invention provides a triple quadrupole mass spectrometer parameter optimization method based on simulated annealing, wherein a new parameter combination is generated by disturbance of a combination, and a global optimal solution is obtained by utilizing a simulated annealing algorithm. The triple quadrupole mass spectrometer parameter optimization method based on simulated annealing can find a relatively optimal solution in a short time, so that the triple quadrupole mass spectrometer works in a better state.
Description
Technical Field
The invention belongs to the technical field of mass spectrometers, and particularly relates to a triple quadrupole mass spectrometer parameter optimization method based on simulated annealing.
Background
Triple quadrupole mass spectrometers are an important instrument currently engaged in the qualitative and quantitative analysis of target compounds by analysts. The ion generator consists of four metal rods arranged in parallel, and a magnetic field is generated by direct current voltage and radio frequency voltage applied to the rods, so that ions move along an axis between the metal rods in a spiral track. Depending on the voltage applied to the electrodes, ions of a particular m/z value will pass through the quadrupole rods, and other ions of greater or lesser m/z values will fly outward and fail to pass through the quadrupole rods. Ions with increasing m/z values can be analyzed by scanning the radio frequency voltage on the electrodes.
The primary operation before the application of the triple quadrupole mass spectrometer is tuning, namely, finding the optimal parameter combination for generating a mass spectrogram, the triple quadrupole mass spectrometer has more parameters, and finding the globally optimal parameter combination of corresponding substances is difficult. Some mass spectrometer manufacturers later also provide some automatic tuning methods, and mostly adopt a mode of adjusting a single parameter to fix other parameters and performing multiple iterations, so that the tuning time can be greatly reduced, and scientific researchers do not need to spend much effort and time on tuning, but the mass spectrometer manufacturers also have obvious defects: that is, it is difficult to find the global optimal parameter combination, but the global optimal parameter combination converges to the local optimal parameter combination, so that a better spectrogram cannot be obtained.
Disclosure of Invention
In view of the above, in order to overcome the above drawbacks, the present invention aims to provide a triple quadrupole mass spectrometer parameter optimization method based on simulated annealing.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
a triple quadrupole mass spectrometer parameter optimization method based on simulated annealing is characterized in that a new parameter combination is generated by disturbance of a combination, and a global optimal solution is obtained by utilizing a simulated annealing algorithm.
Further, iteration is carried out by utilizing a Metropolis algorithm in a simulated annealing algorithm, and the iteration is used for jumping out of a local optimal solution and converging to a global optimal solution.
Further, the specific method is as follows:
s1, initializing algorithm control parameter T, and making T equal to T0Wherein T is0The value of (a) should be chosen sufficiently large;
s2, updating the current algorithm control parameter T;
s3, for the selected current solution S1Adding perturbations to produce a new solution S2；
S4, calculating the energy increment Δ E ═ f (S)2)-f(S1) Wherein f (S) is a cost function of the system, and a current solution is generated according to the value of Delta E;
s5, judging whether the iteration number reaches 10, if so, entering the step S6, otherwise, returning to the step S3;
s6, judging whether the algorithm meets a termination condition, if so, terminating the algorithm, and outputting a current solution required by the algorithm as an optimal parameter combination of the mass spectrometer; if not, the process returns to step S2.
Further, in step S2, the update calculation formula for updating the algorithm control parameter is as follows:
T＝αT
wherein alpha is an iteration parameter, and the value range of alpha is more than or equal to 0.8 and less than or equal to 0.99.
Further, in step S3, the perturbation method specifically includes:
current solution (parameter combination) S1＝[p1,p2,…,pn]TComprising n components, assuming component piIn the range of [ pimin,pimax]From [ -1,1]Generation of random numbers xi in uniform distributioniConstant λi＝(pimax-pimin) (v) 80, new solution S2Component p of2iThe generation process is p2i＝pi+λiξiAnd p is2iThe values are as follows:
further, in step S4, the expression of the cost function is:
f(S)＝δ2[x(S)]-η·peak[x(S)]+||S-Sstd||2
wherein x (S) is the spectrum under the parameter S, δ2[x(S)]Variance of intensity difference from standard reference spectrum, peak [ x (S)]Is the peak intensity of spectrogram x (S), eta is the variation rate parameter, | | S-Sstd||2Is a regularization term of the cost function, preventing the over-fitting condition from occurring.
Further, the method for generating the current solution according to the value of Δ E is as follows:
if Δ E < 0, the new solution S generated in step S32Current solution S as required by the algorithm1Instant S1＝S2(ii) a If Delta E is more than or equal to 0, calculating S2Can be used as the current solution S required by the algorithm1Has a probability of P ═ e-ΔE/TThe method specifically comprises the following steps: from [0,1 ]]Generating random number xi in uniform distribution if e-ΔE/TAccept S > xi2Update the current solution S1If e is-ΔE/TRejecting S if is less than or equal to xi2Update the current solution S1I.e. S1＝S1。
Further, in step S6, the termination condition is: new solution S in 3 consecutive Metropolis algorithm chains2Is rejected or the set termination algorithm control parameters have been reached, the algorithm terminates.
Compared with the prior art, the triple quadrupole mass spectrometer parameter optimization method based on simulated annealing has the following advantages:
(1) the invention uses the thought of simulated annealing to optimize the parameters of the triple quadrupole mass spectrometer, and in the tuning process of 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 can be trapped in unacceptable waiting. Applying the simulated annealing algorithm can reach the vicinity of the global optimal combination in a shorter time, traversing the markov theorem proving that the simulated annealing algorithm will eventually reach the global optimal solution with probability 1. In summary, it is possible to find a relatively optimal solution (combination of parameters) in a short time to make the triple quadrupole mass spectrometer operate in a better state.
(2) The traditional optimization method generally adopts the mode of fixing other parameters of single-parameter scanning and rotating scanning parameters for optimization, and the algorithm optimization can cause the situation of falling into a local optimal solution and is difficult to enable an instrument to work in a better state. The parameter optimization based on the method of the patent can approach or even reach the global optimal solution, so that the mass spectrometer can work in a better state, and a satisfactory experimental result can be obtained.
(3) The invention provides parameters which can be configured by a user, namely algorithm control parameters, change rate parameters of a cost function, end control parameters of the algorithm and the change rate of the control parameters, and the parameters can flexibly control the running time of the algorithm and the acceptability of a final result according to the requirements of the user.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate an embodiment of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic diagram illustrating a simulated annealing algorithm according to an embodiment of the present invention to find a global optimal solution;
fig. 2 is a flow chart of a triple quadrupole mass spectrometer parameter optimization method based on simulated annealing according to an embodiment of the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
The invention introduces a new triple quadrupole mass spectrometer parameter optimization method, the method uses the basic idea of simulated annealing, the simulated annealing algorithm comprises two parts, namely a Metropolis algorithm and an annealing process, and the method can effectively solve the problem of local optimal solution. The simulated annealing algorithm is derived from the crystal cooling process, 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 global optimal solution in the algorithm. A schematic diagram in which a simulated annealing algorithm can find a globally optimal solution is shown in fig. 1.
The basic principle is as follows: a new parameter combination is generated by disturbance of a combination, iteration is carried out by combining with a Metropolis algorithm, and the core is a traversal Markov process, so that the advantage of the process is that the local optimal solution is probabilistically jumped out, and the global optimal solution is converged. The method can better assist scientific researchers in tuning the triple quadrupole mass spectrometer and obtain better experimental results.
As shown in fig. 2, the specific implementation steps are as follows:
step 1: initializing algorithm control parameter T, making T ═ T0Wherein T is0The value of (c) should be chosen sufficiently large.
Step 2: updating the current algorithm control parameter T, wherein the updating calculation formula is as follows:
T＝αT
where the iteration parameter α should be in the range 0.8 ≦ α ≦ 0.99, and the reference value α given in this patent is 0.9.
And step 3: for the selected current solution (parameter combination) S1Adding perturbations to produce a new solution S2Wherein the perturbation method is described as follows:
current solution (parameter combination) S1＝[p1,p2,…,pn]TComprising n components, assuming component piIn the range of [ pimin,pimax]From [ -1,1]Generation of random numbers xi in uniform distributioniConstant λi＝(pimax-pimin) (v) 80, new solution S2Component p of2iThe generation process is p2i＝pi+λiξiAnd p is2iThe values are as follows:
and 4, step 4: calculating the energy increment Δ E ═ f (S)2)-f(S1) Where f (S) is a cost function of the system, where the cost function is defined as:
f(S)＝δ2[x(S)]-η·peak[x(S)]+||S-Sstd||2
wherein x (S) is the spectrum under the parameter S, δ2[x(S)]Variance of intensity difference from standard reference spectrum, peak [ x (S)]Is the peak intensity of spectrogram x (S), eta is the variation rate parameter, | | S-Sstd||2Is a regularization term of the cost function, preventing the over-fitting condition from occurring.
And 5: if Δ E < 0, the new solution S generated in step 32Current solution S as required by the algorithm1Instant S1＝S2(ii) a If Delta E is more than or equal to 0, calculating S2Can be taken as the current solution S required by the algorithm1Has a probability of P ═ e-ΔE/TThe method comprises the following specific steps: from [0,1 ]]Generating random number xi in uniform distribution if e-ΔE/TAccept S > xi2Update the current solution S1If e is-ΔE/TRejecting S if is less than or equal to xi2Update the current solution S1I.e. S1＝S1。
Step 6: and (4) if the iteration number reaches 10 times, entering the step 7 if the iteration number reaches 10 times, and otherwise, returning to the step 3.
And 7: whether the algorithm meets the termination condition or not, if so, the algorithm is terminated, and the current solution S required by the algorithm is output1As an optimal parameter combination for the mass spectrometer; if not, returning to the step 2.
Wherein the termination conditions of step 7 are described as: new solution S in 3 consecutive Metropolis algorithm chains2Is rejected or the set termination algorithm control parameters have been reached, the algorithm terminates.
The technical solution of the present application is further explained with reference to specific examples.
For example, in the Q3 POS mode tuning process of triple quadrupole mass spectrometer, the basic parameters to be tuned include four parameters, DP, EP, RO2 and CXP, and the optimal combination of the four parameters is selected in the tuning process, and the four parameters form a four-dimensional space, and according to the range of the four parameters, there are 33,000,000 combinations in total, and if all experiments are performed, the waiting time is very long. An example of the optimization of the method provided by the patent is as follows:
the initialized current solution S (0) ═ dp (0), ep (0), ro2(0), cxp (0) are selected]TAnd a control parameter T, under the current control parameter, generating a new solution S (1) [ [ dp (1), ep (1), ro2(1), cxp (1) due to the disturbance]TIf Δ E < 0, S (1) ═ S (1), and if Δ E ≧ 0, the probability that S (1) can be used as the current solution required by the algorithm is calculated as P ═ E-ΔE/TWhen e is-ΔE/TWhen > ξ, S (1) ═ S (1), otherwise, S (1) ═ S (0). By analogy, a new solution S (n) [ (dp (n) ], ep (n) ], ro2(n) ], cxp (n) is generated due to disturbance]TIf Δ E < 0, s (n) ═ s (n), and if Δ E ≧ 0, the probability that s (n) can be used as the current solution required for the algorithm is calculated as P ═ E-ΔE/TWhen e is-ΔE/T(n) S (n) when > ξ, otherwise (n) S (n-1).
And after the current control parameter T is operated for 10 times, obtaining a current solution S (n), if the termination condition is not met, iteratively generating a new control parameter T which is alpha T, S (n) as an initial state of the new control parameter, and starting operation until the termination condition is met.
Those of ordinary skill in the art will appreciate that the elements and method steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of clearly illustrating the interchangeability of hardware and software. 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 several embodiments provided in the present application, it should be understood that the disclosed method and system may be implemented in other ways. For example, the above described division of elements is merely a logical division, and other divisions may be realized, for example, multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not executed. The units may or may not be physically separate, and components displayed as units may or may not be physical units, that is, 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 of the present invention.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (8)
1. A triple quadrupole mass spectrometer parameter optimization method based on simulated annealing is characterized in that: a new parameter combination is generated by the disturbance of a combination, and a global optimal solution is obtained by using a simulated annealing algorithm.
2. The simulated annealing based triple quadrupole mass spectrometer parameter optimization method according to claim 1, wherein: and iterating by using a Metropolis algorithm in the simulated annealing algorithm to jump out the local optimal solution and converge to the global optimal solution.
3. The triple quadrupole mass spectrometer parameter optimization method based on simulated annealing according to claim 1, wherein the specific method is as follows:
s1, initializing algorithm control parameter T, and making T equal to T0Wherein T is0The value of (a) should be chosen sufficiently large;
s2, updating the current algorithm control parameter T;
s3, for the selected current solution S1Adding perturbations to produce a new solution S2；
S4, calculating the energy increment Δ E ═ f (S)2)-f(S1) Wherein f (S) is a cost function of the system, and a current solution is generated according to the value of Delta E;
s5, judging whether the iteration number reaches 10, if so, entering the step S6, otherwise, returning to the step S3;
s6, judging whether the algorithm meets a termination condition, if so, terminating the algorithm, and outputting a current solution required by the algorithm as an optimal parameter combination of the mass spectrometer; if not, the process returns to step S2.
4. The simulated annealing based triple quadrupole mass spectrometer parameter optimization method according to claim 3, wherein: in step S2, the update calculation formula for updating the algorithm control parameter is:
T＝αT
wherein alpha is an iteration parameter, and the value range of alpha is more than or equal to 0.8 and less than or equal to 0.99.
5. The simulated annealing based triple quadrupole mass spectrometer parameter optimization method according to claim 3, wherein: in step S3, the perturbation method specifically includes:
current solution (parameter combination) S1＝[p1,p2,…,pn]TComprising n components, assuming component piIn the range of [ pimin,pimax]From [ -1,1]Generation of random numbers xi in uniform distributioniConstant λi＝(pimax-pimin) (v) 80, new solution S2Component p of2iThe generation process is p2i＝pi+λiξiAnd p is2iThe values are as follows:
6. the simulated annealing based triple quadrupole mass spectrometer parameter optimization method according to claim 3, wherein: in step S4, the expression of the cost function is:
f(S)＝δ2[x(S)]-η·peak[x(S)]+||S-Sstd||2
wherein x (S) is the spectrum under the parameter S, δ2[x(S)]Variance of intensity difference from standard reference spectrum, peak [ x (S)]Is the peak intensity of spectrogram x (S), eta is the variation rate parameter, | | S-Sstd||2Is a regularization term of the cost function, preventing the over-fitting condition from occurring.
7. The simulated annealing based triple quadrupole mass spectrometer parameter optimization method according to claim 3, wherein the method for generating the current solution according to the value of Δ E is as follows:
if Δ E < 0, the new solution S generated in step S32Current solution S as required by the algorithm1Instant S1＝S2(ii) a If Delta E is more than or equal to 0, calculating S2Can be used as the current solution S required by the algorithm1Has a probability of P ═ e-ΔE/TThe method specifically comprises the following steps: from [0,1 ]]Generating random number xi in uniform distribution if e-ΔE/TAccept S > xi2Update the current solution S1If e is-ΔE/TRejecting S if is less than or equal to xi2Update the current solution S1I.e. S1＝S1。
8. Simulated annealing based triple quadrupole mass spectrometer parameter according to claim 3The number optimization method is characterized in that, in step S6, the termination condition is: new solution S in 3 consecutive Metropolis algorithm chains2Is rejected or the set termination algorithm control parameters have been reached, the algorithm terminates.
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| CN115879382A (en) * | 2023-03-03 | 2023-03-31 | 中国科学院苏州生物医学工程技术研究所 | Triple quadrupole mass spectrometry automatic tuning method, equipment and medium based on improved PSO |
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