A method, device and storage medium for adaptive dynamic adjustment of mass spectrometer parameters

Abstract

The present invention relates to a method, device and storage medium for adaptively and dynamically adjusting mass spectrometer parameters. The method includes: initializing mass spectrometry parameters and a cost function under the mass spectrometry parameters; generating a set of several groups of mass spectrometry parameters to be adjusted based on the current strategy; calculating the score of the mass spectrometry parameter set; calculating the optimal strategy under the previous mass spectrometry parameters; calculating new mass spectrometry parameters through the optimal strategy under the previous mass spectrometry parameters and the current mass spectrometry parameters. It is not necessary for professionals to manually optimize the parameters. The method adopts the idea of policy iteration and improves the policy iteration on this basis, that is, calculating the corrected cost function J μ+ (x n ). Doing so can avoid the influence of accidental events to the greatest extent, because all the cost functions in the previous iterations are involved in the calculation, which can ensure that the mass spectrometry parameters move along the correct trend. This can reduce the labor cost to the greatest extent and enable the instrument to operate in good condition.

Description

Technical Field

[0001] The present invention relates to the technical field of mass spectrometers, and in particular to a method, device and storage medium for adaptive dynamic adjustment of mass spectrometer parameters. Background Art

[0002] In hospitals or third-party testing institutions, mass spectrometers usually detect the same type of samples day after day, and the method (parameters) for detecting the same type of samples is generally unchanged. However, the state of the mass spectrometer is not static. As time goes by, the electrical parameters, structural parameters, vacuum state, gas path parameters, etc. of the mass spectrometer will change slowly, and the response of the liquid mass system will also decrease, and the detection limit will increase. The direct consequence of this is that some low-concentration samples cannot be qualitatively or quantitatively analyzed. Therefore, a dedicated person is required to optimize and adjust the method every once in a while, such as adjusting the declustering voltage and collision energy of the mass spectrometer to ensure that the instrument responds to the sample high enough. Although this method can solve the problem, it also brings many new problems, such as the slow change of the mass spectrometer state and the difficulty in determining the optimization cycle, which sometimes leads to maintenance and optimization delays. Secondly, the optimization of mass spectrometry parameters requires a dedicated person, which consumes more manpower costs and also delays the experimental process. Summary of the invention

[0003] In order to achieve the above-mentioned purpose and other advantages according to the present invention, the first purpose of the present invention is to provide a method for adaptive dynamic adjustment of mass spectrometer parameters, comprising the following steps:

[0004] Initializing mass spectrometry parameters and a cost function under the mass spectrometry parameters;

[0005] Generating a plurality of groups of mass spectrometry parameters to be adjusted based on the current strategy to form a set;

[0006] calculating a score for a set of mass spectrometry parameters;

[0007] Calculate the optimal strategy under the last mass spectrometry parameters;

[0008] New mass spectrometry parameters are calculated using the optimal strategy under the previous mass spectrometry parameters and the current mass spectrometry parameters.

[0009] Furthermore, the cost function is:

[0010] J μ (x n ) = g(x n )+αJ μ (x n-1 )

[0011] g n (x n ) is the experimental result score:

[0012]

[0013] Among them, x n is the current mass spectrometry parameter, x n-1 is the last mass spectrometry parameter, α is the depreciation factor, C is a constant, A n (x n ) is the mass spectrometry parameter x n The chromatographic peak intensity of the fixed concentration sample under .

[0014] Furthermore, the initialization mass spectrum parameters and the cost function under the mass spectrum parameters include:

[0015] The initial mass spectrometer parameter x0 is the mass spectrometer factory parameter, and the initialization J μ (x0) = g(x0).

[0016] Furthermore, the optimal strategy is:

[0017]

[0018] x n =μ(x n-1 )

[0019] Among them, μ(x n-1 ) is the mass spectrometry parameter x n-1 The optimal strategy under n For the strategy μ(x n-1 )The new mass spectrometry parameters calculated.

[0020] Further, the generating of a plurality of groups of mass spectrometry parameters to be adjusted based on the current strategy to form a set includes:

[0021] Based on the current strategy μ(x n ) Generate several groups of mass spectrometry parameters to be adjusted to form a set The set of strategies is μ(x n )∈U;

[0022] The new mass spectrometry parameter is x n+ 1 = x n +μ(x n ).

[0023] Further, the calculating the score of the mass spectrometry parameter set includes:

[0024] Calculate the mass spectrometry parameter set according to the experimental result score formula Rating

[0025] Furthermore, the cost function is corrected, and the corrected cost function is:

[0026]

[0027] d n-1 =g(x n )+αJ μ (x n )-J μ (x n-1 )

[0028] J μ (x n+1 )=J μ+ (x n )

[0029] Among them, J μ+ (x n ) is the modified cost function, λ and η are the newly introduced parameters;

[0030] The optimal strategy is modified by the modified cost function, and the modified optimal strategy is:

[0031]

[0032] x n =μ(x n-1 ).

[0033] Furthermore, the initialization of mass spectrum parameters and the cost function under the mass spectrum parameters also include: initializing d -1 =0;

[0034] The optimal strategy for calculating the last mass spectrometry parameters comprises the following steps:

[0035] The mass spectrum parameter set is calculated by the modified cost function Modified cost function Update intermediate variables

[0036] The optimal μ(x n ),in,

[0037]

[0038] The calculation of new mass spectrometry parameters comprises the following steps:

[0039] By optimizing μ(x n ) and the current mass spectrometry parameters to obtain updated mass spectrometry parameters, where:

[0040] x n+1 =x n +μ(x n );

[0041] After the calculation is completed, the step of generating a plurality of mass spectrometry parameters to be adjusted based on the current strategy to form a set is returned until μ(x n )=(0,…,0) T Stop iteration, or stop after reaching the set maximum number of iterations N.

[0042] The second object of the present invention is to provide a device for adaptively and dynamically adjusting the parameters of a mass spectrometer, comprising: a memory on which a program code is stored; a processor connected to the memory, and when the program code is executed by the processor, a method for adaptively and dynamically adjusting the parameters of a mass spectrometer is implemented.

[0043] A third object of the present invention is to provide a computer-readable storage medium having program instructions stored thereon, which, when executed, implement a method for adaptive dynamic adjustment of mass spectrometer parameters.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] The present invention provides a method for adaptive dynamic adjustment of mass spectrometer parameters, which does not require manual parameter optimization by professionals. The method adopts the idea of ​​policy iteration and improves the policy iteration on this basis, that is, the modified cost function J is calculated. μ+ (x n ). This can avoid the impact of accidental events to the greatest extent, because all cost functions in previous iterations are involved in the calculation, which can ensure that the mass spectrometry parameters move along the correct trend. This can minimize labor costs and keep the instrument running in good condition.

[0046] When modifying the cost function, two parameters λ and η are introduced. Considering the marginal effect brought about by parameter setting, these two parameters are limited through calculation derivation and experiments. Preferably, 0.48≤λ≤0.52 and 0.75≤η≤0.9.

[0047] This method is different from the existing methods in that it not only focuses on the update and optimization of mass spectrometry parameters, but pays more attention to the strategy of parameter adjustment. After the algorithm is terminated, the strategy of parameter adjustment will also be obtained, which has certain reference significance for instrument research and development.

[0048] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present invention in conjunction with the accompanying drawings. The specific implementation of the present invention is given in detail by the following embodiments and their accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0050] Figure 1 A flow chart of a method for adaptive dynamic adjustment of mass spectrometer parameters;

[0051] Figure 2 Schematic diagram of a mass spectrometer parameter adaptive dynamic adjustment device in Example 2. DETAILED DESCRIPTION

[0052] The present invention is further described below in conjunction with the accompanying drawings and specific implementation methods. It should be noted that, under the premise of no conflict, the various embodiments or technical features described below can be arbitrarily combined to form a new embodiment.

[0053] Example 1

[0054] The present invention provides a method for adaptive dynamic adjustment of mass spectrometer parameters. The premise of the method is that the adjustment of mass spectrometer parameters is a Markov process, that is, the current parameter is only affected by the previous parameter and has nothing to do with the earlier parameter. In fact, the sequential adjustment of mass spectrometer parameters is indeed a Markov process, so this forms the premise of the method.

[0055] The main principle of this method is: after the experimenter completes the experiment of the day, the method will score the experimental results of today, and the score is recorded as g n (x n ), where x n It is the mass spectrometry parameter in the experiment. At the same time, the current parameters will be adjusted according to the parameter adjustment strategy. Several groups of mass spectrometry parameters to be adjusted form a set Then, the samples are injected with these parameters, the results are analyzed, and then the strategies are adjusted to obtain the next mass spectrometry parameters. The specific strategies will be described in detail below. In general, the idea of ​​dynamic programming is followed. After long-term adjustment, a stable parameter adjustment strategy is formed, based on which the mass spectrometry parameters can be adaptively optimized.

[0056] A method for adaptively and dynamically adjusting mass spectrometer parameters, such as Figure 1 As shown, the following steps are included:

[0057] S1. Initialize mass spectrometry parameters and the cost function under mass spectrometry parameters. The cost function J μ (x n )for:

[0058] J μ (x n ) = g(xn )+αJ μ (x n-1 ) (1)

[0059] g n (x n ) is the experimental result score:

[0060]

[0061] Among them, x n is the current mass spectrometry parameter, x n-1 is the last mass spectrometry parameter, α is the depreciation factor, C is a constant, A n (x n ) is the mass spectrometry parameter x n The chromatographic peak intensity of the fixed concentration sample under .

[0062] The initial mass spectrometry parameter x0 is the mass spectrometry factory parameter. The cost function under the initial mass spectrometry parameter is defined as J μ (x0) = g(x0), g(x0) is calculated by formula (2). And set the corresponding parameters, preferably, 0.6≤α≤0.8, C = 8×10 6 .

[0063] S2. Generate several groups of mass spectrometry parameters to be adjusted based on the current strategy to form a set.

[0064] The initial policy μ0 is defined as x n →x n , that is, the mass spectrometry parameter x n After the next update, it will still be x n .

[0065] The optimal strategy model is:

[0066]

[0067] x n =μ(x n-1 ) (4)

[0068] Among them, μ(x n-1 ) is the mass spectrometry parameter x n-1 The optimal strategy under n For the strategy μ(x n-1 )The new mass spectrometry parameters calculated.

[0069] Based on the current strategy μ(x n ) Generate several groups of mass spectrometry parameters to be adjusted to form a set The set of strategies is μ(x n )∈U;

[0070] The new mass spectrometry parameter is x n+1 =x n +μ(x n ).

[0071] S3. Calculate the score of the mass spectrometry parameter set; specifically, calculate the mass spectrometry parameter set according to the experimental result score formula (2): Rating

[0072] Sometimes the calculation may deviate due to accidental factors, such as a sudden decrease in intensity caused by a blockage in the chromatographic column. S4. Based on this, the model is modified by introducing two new parameters λ and η. The modified cost function is:

[0073]

[0074] d n-1 =g(x n )+αJ μ (x n )-J μ (x n-1 ) (6)

[0075] J μ (x n+1 )=J μ+ (x n ) (7)

[0076] Among them, J μ+ (x n ) is the modified cost function, λ and η are the newly introduced parameters.

[0077] The optimal strategy is corrected by the corrected cost function, and the corrected optimal strategy is:

[0078]

[0079] x n =μ(x n-1 ) (9).

[0080] Therefore, step S1 also includes: initializing d -1 =0, set parameters λ and η, preferably, 0.48≤λ≤0.52, 0.75≤η≤0.9.

[0081] The mass spectrometry parameter set is calculated by the modified cost function formulas (6) and (5) Modified cost function And update the intermediate variable according to formula (7)

[0082] S5. Calculate the optimal strategy under the last mass spectrometry parameters. Specifically, the optimal μ(x n ),in,

[0083]

[0084] S6, calculating new mass spectrometry parameters by using the optimal strategy under the previous mass spectrometry parameters and the current mass spectrometry parameters. Specifically, by using the optimal μ(x n ) and the current mass spectrometry parameters to obtain updated mass spectrometry parameters, where:

[0085] x n+1 =x n +μ(x n ).

[0086] S7, determine whether the conditions for stopping iteration are met, if yes, stop iteration, otherwise return to step S2 until μ(x n )=(0,…,0) T Stop iteration, or stop after reaching the set maximum number of iterations N.

[0087] In one embodiment, when the triple quadrupole mass spectrometer HTQ2020 is used to measure vitamin VD3, the method includes four lens parameters: DP, EP, CXP and CE, that is, the mass spectrum parameter x n The dimension of is 4. The strategy μ(x n )∈U set is as follows:

[0088]

[0089] Assuming that the initial mass spectrometry parameters of the VD3 method by default are x0=(60, 10, 15, 20), then according to the strategy set U and the method of step S2, the parameter set to be updated can be generated as follows:

[0090]

[0091] Then, according to step S3 and step S4, the corrected cost function can be calculated respectively.

[0092] According to steps S5 and S6, the strategy and updated mass spectrometry parameters under the parameter x0 = (60, 10, 15, 20) can be calculated.

[0093] In this embodiment, μ(x0)=(0,0,0,-1) is calculated, so the updated parameter x1=(60,10,15,19) can be obtained, and the next update starts with x1=(60,10,15,19). After the 7th iteration, μ(x6)=(0,0,0,0) is obtained, and the algorithm terminates at this time, and it is considered that the most reasonable parameter at this time is x6 (because x7=x6+μ(x6), so x7=x6).

[0094] The present invention provides a method for adaptive dynamic adjustment of mass spectrometry parameters, which does not require manual optimization of parameters by professionals. The method mainly adopts the idea of ​​strategy iteration, and improves the strategy iteration on this basis, that is, calculating the corrected cost function J μ+ (x n ). This can avoid the impact of accidental events to the greatest extent, because all cost functions in previous iterations are involved in the calculation, which can ensure that the mass spectrum parameters move along the correct trend.

[0095] The present invention introduces two parameters λ and η when modifying the cost function, and limits the two parameters based on practicality, preferably 0.48≤λ≤0.52 and 0.75≤η≤0.9.

[0096] The present invention not only focuses on the update and optimization of mass spectrometry parameters, but also pays more attention to the strategy of parameter adjustment μ(x n )(that is, after reaching the current parameter, μ(x n ) to which parameter should be adjusted? ), after the algorithm is terminated, the strategy for parameter adjustment will be obtained, which has a certain reference significance for instrument development.

[0097] Example 2

[0098] A mass spectrometer parameter adaptive dynamic adjustment device 200, such as Figure 2 As shown, it includes but is not limited to: a memory 201 on which program codes are stored; a processor 202, which is connected to the memory, and when the program codes are executed by the processor, a method for adaptive dynamic adjustment of mass spectrometer parameters is implemented. For a detailed description of the method, reference can be made to the corresponding description in the above method embodiment, which will not be repeated here.

[0099] Example 3

[0100] A computer-readable storage medium stores program instructions, and a method for adaptively and dynamically adjusting mass spectrometer parameters is implemented when the program instructions are executed. For a detailed description of the method, reference may be made to the corresponding description in the above method embodiment, and no further description is given here.

[0101] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0102] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0103] The above are only embodiments of this specification and are not intended to limit one or more embodiments of this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of one or more embodiments of this specification. One or more embodiments of this specification One or more embodiments of this specification One or more embodiments of this specification One or more embodiments of this specification.

Claims

1. A method for adaptive dynamic adjustment of mass spectrometer parameters, characterized in that: The steps include: Initializing mass spectrometry parameters and a cost function under the mass spectrometry parameters; Generating a plurality of groups of mass spectrometry parameters to be adjusted based on the current strategy to form a set; calculating a score for a set of mass spectrometry parameters; Calculate the optimal strategy under the last mass spectrometry parameters; Calculate new mass spectrometry parameters by using the optimal strategy under the previous mass spectrometry parameters and the current mass spectrometry parameters; The cost function is: J μ (x n )=g(x n )+αJ μ (x n-1 ) g n (x n ) is the experimental result score: Among them, x n is the current mass spectrometry parameter, x n-1 is the last mass spectrometry parameter, α is the depreciation factor, C is a constant, A n (x n ) is the mass spectrometry parameter x n The chromatographic peak intensity of the fixed concentration sample under ; The optimal strategy is: x n =μ(x n-1 ) Among them, μ(x n-1 ) is the mass spectrometry parameter x n-1 The optimal strategy under n For the strategy μ(x n-1 ) New mass spectrometry parameters calculated; The generating of a plurality of groups of mass spectrometry parameters to be adjusted based on the current strategy to form a set comprises: Based on the current strategy μ(x n ) Generate several groups of mass spectrometry parameters to be adjusted to form a set The set of strategies is The new mass spectrometry parameter is x n+1 =x n +μ(x n ).

2. A method for adaptive dynamic adjustment of mass spectrometer parameters according to claim 1, characterized in that: The initialization mass spectrum parameters and the cost function under the mass spectrum parameters include: The initial mass spectrometer parameter x0 is the mass spectrometer factory parameter, and the initialization J μ (x0) = g(x0).

3. The method for adaptive dynamic adjustment of mass spectrometer parameters according to claim 1, characterized in that: The scoring of the mass spectrometry parameter set comprises: The mass spectrometry parameter set is calculated according to the experimental result score formula Rating 4. The method for adaptively and dynamically adjusting mass spectrometer parameters according to claim 1, characterized in that: The cost function is modified, and the modified cost function is: d n-1 =g(x n )+αJ μ (x n )-J μ (x n-1 ) I μ (x n+1 )=J μ+ (x n ) Among them, J μ+ (x n ) is the modified cost function, λ and η are the newly introduced parameters; The optimal strategy is modified by the modified cost function, and the modified optimal strategy is: x n =μ(x n-1 )。 5. A method for adaptive dynamic adjustment of mass spectrometer parameters according to claim 4, characterized in that: The initialization mass spectrum parameters and the cost function under the mass spectrum parameters also include: initializing d -1 =0; The optimal strategy for calculating the last mass spectrometry parameters comprises the following steps: The mass spectrum parameter set is calculated by the modified cost function Modified cost function Update intermediate variables The optimal μ(x n ),in, The calculation of new mass spectrometry parameters comprises the following steps: By optimizing μ(x n ) and the current mass spectrometry parameters to obtain updated mass spectrometry parameters, where: x n+1 =x n +μ(x n ); After the calculation is completed, the step of generating a plurality of mass spectrometry parameters to be adjusted based on the current strategy to form a set is returned until μ(x n )=(0,…,0) T Stop iteration, or stop after reaching the set maximum number of iterations N.

6. A mass spectrometer parameter adaptive dynamic adjustment device, characterized in that: include: A memory having program code stored therein; A processor is coupled to the memory and implements the method according to any one of claims 1 to 5 when the program code is executed by the processor.

7. A computer-readable storage medium, characterized in that: Program instructions are stored thereon, and when the program instructions are executed, the method according to any one of claims 1 to 5 is implemented.

Images