Methods for determining the stability of a mass spectrometer and methods for testing the performance of a trace element analyzer

Abstract

The present disclosure provides a method for determining the stability of a mass spectrometer and a method for testing the performance of a trace element analyzer.The procedure for the determination comprises: acquiring key performance parameters of the mass spectrometer to be evaluated; controlling the mass spectrometer to be evaluated to perform a performance test in order to obtain experimental data; performing a long-term state analysis based on the experimental data to obtain the statistical long-term state and the technical long-term state of individual key performance parameters, whereby the statistical long-term state characterizes whether the key performance parameter is in a controlled state within a specified time period, and the technical long-term state characterizes whether the key performance parameter lies within a specified parameter range within the specified time period; determining, based on the statistical long-term state and the technical long-term state, whether each of the key performance parameters meets a stability requirement.The method enables an accurate and efficient evaluation of the stability of a mass spectrometer's key performance during operation from two perspectives.

Description

[0001] This disclosure claims priority from Chinese patent application No. 2023107154480 dated June 15, 2023, entitled "Method, apparatus, storage medium and electronic device for determining the stability of a mass spectrometer" and claims the entire content of Chinese patent application No. 2023107155036 dated June 15, 2023, entitled "Method, apparatus and electronic device for testing the performance of a detection limit of a trace element analyzer", which are hereby incorporated by reference. TECHNICAL AREA

[0002] This disclosure relates to the field of mass spectrometry technology and in particular to a method for determining the stability of a mass spectrometer, a method for testing the performance of a detection limit of a trace element analyzer, a device, a storage medium and an electronic device. STATE OF THE ART

[0003] Mass spectrometry is characterized by high sensitivity, high specificity, and good reproducibility, as well as advantages such as high throughput, high efficiency, and low cost. Thanks to the continuous development of clinical mass spectrometry, its applications are becoming increasingly widespread. The most common mass spectrometric technologies include gas chromatography-mass spectrometry, liquid chromatography-mass spectrometry, time-of-flight mass spectrometry, and inductively coupled plasma mass spectrometry.

[0004] Stability is the ability of a device to maintain its properties consistently over an extended period. It is a fundamental characteristic that every device must possess and a crucial indicator of its effectiveness in operation. Therefore, in addition to the usual performance requirements, mass spectrometers should also meet the stability requirements for the relevant key performance indicators, tailored to their specific application scenarios.

[0005] However, current technology does not yet offer an efficient and accurate method for assessing the stability of key mass spectrometer performance during use.

[0006] To accurately assess the stability of key performance parameters of mass spectrometers during use, the acquisition of these parameters is particularly important. Testing the detection limit of trace element analyzers is of particular significance in key performance testing. However, a standardized procedure for testing the detection limit performance of trace element analyzers is currently lacking. SUMMARY OF THE INVENTION

[0007] The main purpose of this disclosure is to provide a method for determining the stability of a mass spectrometer, a method for testing the performance of a detection limit of a trace element analyzer, a device, a storage medium and an electronic arrangement to at least solve the problem that there is no efficient and accurate method in the prior art for evaluating the stability of the key performance parameters of mass spectrometers in use.

[0008] To achieve the aforementioned task, according to one aspect of this disclosure, a method for determining the stability of a mass spectrometer is provided, comprising: acquiring key performance parameters of the mass spectrometer to be evaluated, wherein the key performance parameters include at least one of the following: a parameter affecting the safety performance of the mass spectrometer to be evaluated, a parameter affecting the mass spectrometer to be evaluated in order to achieve a function of the first importance level, and a parameter affecting the mass spectrometer to be evaluated in order to achieve a function of the second importance level; controlling the mass spectrometer to be evaluated to perform a performance test in order to obtain experimental data;Performing a long-term state analysis based on experimental data to obtain the statistical long-term state and the technical long-term state of individual key performance parameters, where the statistical long-term state characterizes whether the key performance parameter is in a controlled state within a given period, and the technical long-term state characterizes whether the key performance parameter lies within a given parameter range within a given period; Determining, based on the statistical long-term state and the technical long-term state, whether each of the key performance parameters meets a stability requirement.

[0009] According to another aspect of this disclosure, a method for testing the performance of a detection limit of a trace element analyzer is provided, comprising: performing a trigger control for the trace element analyzer, and tuning the trace element analyzer after successful triggering; selecting parameters for the trace element analyzer to obtain the target parameters, and preparing the target parameters after the tuning is performed and the trace element analyzer meets the test standards, wherein the target parameters are the same parameters required for testing the performance of a detection limit of the trace element analyzer, the preparation serving to adjust the relevant performance of the target parameters;Performing a sample correlation test for the selected and processed target parameters and analyzing the results of the sample correlation test to obtain the analysis results of the limit of detection test.

[0010] According to another aspect of this disclosure, a device for determining the stability of a mass spectrometer is provided, comprising: a first acquisition unit configured to acquire key performance parameters of the mass spectrometer to be evaluated, wherein the key performance parameters include at least one of the following: a parameter affecting the safety performance of the mass spectrometer to be evaluated, a parameter affecting the mass spectrometer to be evaluated in order to achieve a function of the first importance level, and a parameter affecting the mass spectrometer to be evaluated in order to achieve a function of the second importance level; a control unit configured to control the mass spectrometer to be evaluated in order to obtain experimental data;An analysis unit configured to perform a long-term state analysis based on experimental data to obtain the statistical long-term state and the technical long-term state of individual key performance parameters, wherein the statistical long-term state characterizes whether the key performance parameter is in a controlled state within a specified period, and the technical long-term state characterizes whether the key performance parameter lies within a specified parameter range within a specified period; a first determination unit configured to determine, based on the statistical long-term state and the technical long-term state, whether each of the key performance parameters meets a stability requirement.

[0011] According to another aspect of this disclosure, a performance testing device for a detection limit of a trace element analyzer is provided, comprising: a first control unit of the testing device, which is used to perform a trigger control for the trace element analyzer and a tuning control of the trace element analyzer after successful triggering; a first processing unit of the testing device, which is used to select parameters for the trace element analyzer to obtain the target parameters, and to process the target parameters after the tuning is performed and the trace element analyzer meets the test standards, wherein the target parameters are the same parameters required for testing the performance of a detection limit of the trace element analyzer, the processing serving to adjust the relevant performance of the target parameters;a second processing unit of the device for testing, which is used to perform a sample correlation test for the selected and processed target parameters and to analyze the results of the sample correlation test in order to obtain the analysis results of the limit of detection test.

[0012] According to another aspect of this disclosure, a computer-readable storage medium is provided which includes a stored program, wherein the device on which the computer-readable storage medium is located is controlled when the program is executed to perform one of the above-described methods for determining the stability of a mass spectrometer.

[0013] According to another aspect of this disclosure, an electronic device is provided comprising one or more processors, a memory and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and wherein the one or more programs perform one of the above-described methods for determining the stability of a mass spectrometer.

[0014] In applying the technical solution described in this disclosure, the mass spectrometer under evaluation is controlled by acquiring its key performance parameters for the performance test in order to obtain experimental data. Subsequently, a long-term analysis is performed based on this experimental data to obtain the statistical and technical long-term states of individual key performance parameters. Based on these statistical and technical long-term states, it is determined whether the individual performance parameters meet the stability requirements. In the present solution, it is determined, based on the statistical and technical long-term states of the key performance parameters, whether the individual performance parameters meet the stability requirements.This allows for an accurate and efficient evaluation of the stability of the key performance of a mass spectrometer during operation from two perspectives. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings, which form part of this disclosure, serve to enhance understanding of this disclosure. The exemplary embodiments and their descriptions serve to illustrate this disclosure and do not constitute an impermissible limitation of this disclosure. The drawings show: Fig. 1 a block diagram of the hardware structure of a mobile terminal for carrying out a method for determining the stability of a mass spectrometer according to an embodiment of the present disclosure; Fig. 2 a schematic diagram of a process for determining the stability of a mass spectrometer according to an embodiment of the present disclosure; Fig. 3 a schematic diagram of a specific procedure for determining the stability of a mass spectrometer according to an embodiment of the present disclosure; Fig. 4 a block diagram of a device for determining the stability of a mass spectrometer according to an embodiment of the present disclosure; Fig. 5 a flowchart of a method for testing the performance of a detection limit of a trace element analyzer according to an embodiment of the present disclosure; Fig. 6 a schematic representation of a first display surface according to an embodiment of the present disclosure; Fig. 7 a schematic representation of a second display surface according to an embodiment of the present disclosure; Fig.8 (a) a first schematic representation of a third display surface according to an embodiment of the present disclosure; Fig. 8 (b) a second schematic representation of a third display surface according to an embodiment of the present disclosure; Fig. 9 a schematic representation of a fourth display surface according to an embodiment of the present disclosure; Fig. 10 a schematic representation of a fifth display surface according to an embodiment of the present disclosure; Fig. 11 a schematic representation of a sixth display surface according to an embodiment of the present disclosure; Fig. 12 a schematic representation of a seventh display surface according to an embodiment of the present disclosure; Fig.13 (a) a first schematic representation of an eighth display surface according to an embodiment of the present disclosure; Fig. 13 (b) a second schematic representation of an eighth display surface according to an embodiment of the present disclosure; Fig. 14 a schematic representation of a ninth display surface according to an embodiment of the present disclosure; Fig. 15 a schematic representation of a tenth display surface according to an embodiment of the present disclosure; Fig. 16 a schematic representation of an eleventh display surface according to an embodiment of the present disclosure; Fig. 17 a schematic representation of a twelfth display surface according to an embodiment of the present disclosure; Fig.18 a schematic representation of the thirteenth display surface according to an embodiment of the present disclosure; Fig. 19 a schematic representation of the fourteenth display surface according to an embodiment of the present disclosure; Fig. 20 (a) a schematic representation of a first part of the fifteenth display surface according to an embodiment of the present disclosure; Fig. 20 (b) a schematic representation of a second part of the fifteenth display surface according to an embodiment of the present disclosure; Fig. 21 a flowchart of a specific method for testing the performance of a detection limit of a trace element analyzer according to an embodiment of the present disclosure; Fig.22 (a) a mean value control chart of the mean value range control chart of the limits of detection of element Li according to an embodiment of the present disclosure; Fig. 22 (b) a mean value control chart of the mean value range control chart of the limits of detection of element Y according to an embodiment of the present disclosure; Fig. 22 (c) a mean value control chart of the mean value range control chart of the limits of detection of element TI according to an embodiment of the present disclosure; Fig. 23 (a) a histogram of the converted detection limit data of element Li according to the embodiments of this disclosure; Fig. 23 (b) a histogram of the converted limit of detection data of element Y according to the embodiments of this disclosure; Fig. 23 (c) a histogram of the converted detection limit data of element TI according to the embodiments of this disclosure; Fig. 24 a structural block representation of a device for testing the performance of a detection limit of a trace element analyzer according to an embodiment of the present disclosure. DESCRIPTION OF THE EXECUTION FORMS

[0016] It should be noted that the embodiments and features described in this disclosure are combinable unless otherwise stated. The disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0017] To facilitate the understanding of this disclosure by those skilled in the art, the technical solutions are described in detail and in full below with reference to the accompanying drawings of the embodiments. The described embodiments naturally represent only some, not all, embodiments of this disclosure. All further embodiments that are obtained by those skilled in the art without inventive effort based on the embodiments described herein fall within the scope of protection of this disclosure.

[0018] It should be noted that the terms "first," "second," etc., in the description, claims, and associated drawings of this disclosure serve to distinguish similar objects and do not necessarily describe a specific order or sequence. It is understood that these terms are interchangeable, where appropriate, for the embodiments of this disclosure described herein. Furthermore, the terms "comprise" and "include," and all variations thereof, are to be understood as non-exclusive inclusions; for example, a process, method, system, product, or apparatus comprising a series of steps or units is not necessarily limited to the steps or units explicitly listed, but may also include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0019] To simplify the description, some of the terms or nouns used in the embodiments of this disclosure are explained below: The stability capability index of the key performance refers to the degree to which the key performance capabilities meet the requirements and the product quality standard (specification range, etc.) and also refers to the actual performance of the key performance under control within a specific period.

[0020] Statistical Process Control (SPC) uses mathematical statistics to analyze sample data from a process and determine whether variations in the production process are within acceptable limits. If necessary, process parameters are adjusted to reduce excessive deviations of product quality characteristics from target values ​​and to maintain the entire process in a stable and controlled state, influenced only by random factors. This increases process efficiency. SPC emphasizes that the process operates under controlled and efficient conditions, ensuring that products and services consistently meet customer requirements. SPC often uses statistical methods such as control charts to analyze the process or its output, enabling appropriate measures to be taken to achieve and maintain statistical control, thereby improving process capability.Control charts are visual representations with control limits used to analyze and assess the continuous state of a process. They are functional representations that differentiate between normal and abnormal fluctuations and represent important statistical tools in on-site quality management. The PPK value (Process Performance Index) in SPC is used to calculate run performance or process performance and can provide a quantitative assessment of either.

[0021] Statistical stability: The control charts for the key performance indicator parameters must meet the requirements for statistical stability;

[0022] Technical continuous state: The stability capability index value of the key performance indicator parameters must meet the specified requirements, which corresponds to the process performance index PPK.

[0023] Trace element analyzer: A trace element analyzer is a device primarily used to determine the trace element content in the human body. Electrochemical analysis, atomic absorption spectrometry, and interphase mass spectrometry (ICP-MS) are the most common methods for obtaining accurate and reliable results. Electrochemical analyzers are the most widely used and hold the largest market share. They are considered mid- to high-end products and are characterized by ease of use, accurate and reliable results, and low daily operating costs, making them popular in hospitals.

[0024] As described in the background section, there is currently no efficient and accurate method for evaluating the stability of the key performance of mass spectrometers in operation. To solve this problem, embodiments of this disclosure provide a method, a device, a storage medium, and an electronic arrangement for determining the stability of a mass spectrometer.

[0025] The technical solutions in the embodiments of this disclosure are described clearly and completely below with reference to the accompanying drawings.

[0026] The embodiments of the method described in this disclosure can be executed on a mobile device, a computer terminal or a similar computer device. Fig.Figure 1 shows a block diagram of the hardware structure of a mobile device for carrying out a method for determining the stability of a mass spectrometer according to an embodiment of the present disclosure. As in Fig. As shown in 1, the mobile device can have one or more processors 102 (in Fig. (Figure 1 shows only one) (Processor 102 can be, among other things, a microprocessor (MCU) or a programmable logic gate (FPGA)) and comprise a memory 104 for data storage. The mobile device can also comprise a transmission device 106 for communication functions and an input / output device 108. It is known to those skilled in the art that the in Fig. The structure shown in Figure 1 is merely an example and does not restrict the structure of the aforementioned mobile device. For example, the mobile device may have more or fewer components than shown. Fig. 1 shown or have a different configuration.

[0027] Memory 104 is used to store computer programs, such as application software programs and modules, like the computer program corresponding to the method for determining the stability of a mass spectrometer in this embodiment of the present disclosure. Processor 102 performs various applications and data processing by executing the program stored in memory 104, thus implementing the method described above. Memory 104 can include high-speed main memory (RAM) and non-volatile memory, such as one or more magnetic storage media, flash memory, or other non-volatile solid-state memory. In some examples, memory 104 can also include memory located remotely from processor 102 and connected to the mobile device via a network.Examples of such networks include the internet, corporate intranets, local area networks (LANs), mobile networks, and combinations thereof. The transmission device 106 is used to receive and send data over a network. Examples of such networks include wireless networks provided by the mobile device's communication provider. In one example, the transmission device 106 comprises a network interface controller (NIC) that can be connected to other network devices via a base station to communicate with the internet. In another example, the transmission device 106 could be a radio frequency module (RF module) used for wireless internet communication.

[0028] In this embodiment, a method for determining the stability of a mass spectrometer operated on a mobile device, a computer terminal, or a similar computer device is described. It should be explained that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system, for example, as a set of computer-executable instructions. Although the flowchart represents a logical sequence, the steps shown or described may, in some cases, be performed in a different order.

[0029] Fig. Figure 2 is a schematic diagram of a process for determining the stability of a mass spectrometer according to an embodiment of the present disclosure. As in Fig. As shown in section 2, the procedure comprises the following steps: Step S202: Identifying key performance parameters of the mass spectrometer to be evaluated, wherein the key performance parameters include at least one of the following: a parameter that affects the safety performance of the mass spectrometer to be evaluated, a parameter that affects the mass spectrometer to be evaluated in order to achieve a function of the first importance level, and a parameter that affects the mass spectrometer to be evaluated in order to achieve a function of the second importance level.

[0030] In particular, the mass spectrometer to be evaluated in this disclosure may be a clinical mass spectrometer. In 2021, the vast majority of adverse event reports for medical devices concerned Level II and III devices. Reports for Level II devices totaled 305,645, representing 46.97% of all reports. Most failures of clinical devices can be attributed to their performance instability. Mass spectrometers are typically used in clinical settings with long-term operation, high sample throughput, and complex sample materials, which places extremely high demands on the performance stability of the devices. The method for determining the stability of mass spectrometers described in this disclosure enables a precise determination of whether the key performance parameters of a clinical mass spectrometer meet the stability requirements.

[0031] The parameters for evaluating the safety performance of the mass spectrometer include those that directly affect safety, lead to violations of laws and regulations, and cause missing warnings, such as the water / gas circuit, which influences the operation of the entire device. A malfunction of these parameters renders the device inoperable, and the absence of warnings directly impacts safety.

[0032] The parameters for evaluating the safety performance of the mass spectrometer also include those that directly affect safety, lead to violations of laws and regulations, and trigger warning messages, such as temperature control, which affects the operation of the entire device. A malfunction of these parameters renders the device inoperable, triggers warning messages, and simultaneously poses a safety risk.

[0033] The parameters that influence the mass spectrometer's ability to achieve a first important functional level include the following: parameters that lead to the loss or degradation of basic functions, parameters that prevent the product from operating or result in reduced performance, the sensitivity / detection limit, and factors that affect overall performance. Failure of these parameters reduces measurement accuracy or even prevents detection.

[0034] The parameters that influence the mass spectrometer's ability to achieve a function of a second important functional level include the following: parameters that lead to the loss or degradation of secondary functions, the performance of interference suppression, and the impact on the detection limit of certain elements. A failure of this indicator leads to a deterioration in the measurement accuracy of certain elements or even to their inability to be detected.

[0035] In particular, the key performance parameters include at least one of the following: limit of detection, ion transport efficiency, and parameters affecting immunity. Of course, the key performance parameters may also include parameters other than limit of detection, ion transport efficiency, and parameters affecting immunity.

[0036] It should be noted that parameters affecting the user experience, such as noise / light pollution, do not affect the performance of the overall device, but do affect the user experience and therefore should not be considered key performance parameters of the mass spectrometer.

[0037] Step S204: Controlling the mass spectrometer to be evaluated to perform a performance test in order to obtain experimental data.

[0038] In other words, experimental data are obtained through performance tests. Using ICP-MS as an example, samples such as blood and urine are typically tested, particularly in clinical applications. Excessive sample injection can clog the nebulizer and cause matrix deposits at the cone tip, impairing instrument performance. To make the tests more realistic, the ICP-MS is operated for an extended period during the test. Ambient conditions are kept constant, and the ICP-MS performance is adjusted to its optimal level to simulate the actual operating environment for determining the limit of detection and interference suppression. The test procedure is as follows: After the instrument is switched on, it automatically adjusts to standard mode, thereby achieving its optimal performance level in standard mode.High-purity water with a resistivity of 18 MΩ·cm is injected, and the ion count rates at mass numbers 7, 89, and 205 are measured. Subsequently, high-purity water is injected as a test solution for the limit of detection, and the ion count rates at mass numbers 7, 89, and 205 are measured again. The limit of detection is calculated by dividing three times the standard deviation of the measurement results by the sensitivity of Li, Y, and TI. To optimize the CO / ArO ratio in the collision mode of the instrument, an automatic collision mode tuning was performed. The instrument was then kept running, and tests were repeated after four and eight hours to maintain the performance of the collision reaction cell. Using ICP-MS as an example, this study demonstrates its technical similarities to other mass spectrometry techniques.Clinical ICP-MS is suitable for the analysis of macro-, micro-, and trace elements in serum, blood, and urine. The determination of stability requirements using ICP-MS as an example is intended as a reference for other mass spectrometric products.

[0039] In other words, the performance requirements of a clinical ICP-MS, as defined by users, primarily encompass two aspects: limit of detection and immunity to interference. These two properties are therefore considered the key performance indicators of a clinical ICP-MS. The limit of detection is reflected in the minimum analyte concentration that can be detected with an appropriate level of confidence. The standard JJF1159-2006 specifies that the calibration elements for the limit of detection in the main technical indicators of an ICP-MS must include low, medium, and high levels, covering the entire mass range of the mass axis and serving as an important basis for evaluating instrument performance. The immunity of an ICP-MS is primarily achieved through a collision response cell.A collision reaction cell typically consists of a cell body with an integrated quadrupole or multipole rod, such as a hexapole rod collision reaction cell, and is usually injected with helium gas to eliminate interference from collisions. Using ArO₂ ion interference (which affects iron element detection) as a reference, the instrument parameters are adjusted to achieve the optimal CO / ArO₂ ratio. A higher CO / ArO₂ ratio indicates better interference immunity.

[0040] Step S206: Performing a long-term state analysis based on the experimental data to obtain the statistical long-term state and the technical long-term state of individual key performance parameters, where the statistical long-term state characterizes whether the key performance parameter is in a controlled state within a given period of time, and the technical long-term state characterizes whether the key performance parameter lies within a given parameter range within the given period of time; Step S208: Determine, based on the statistical long-term state and the technical long-term state, whether each of the key performance parameters meets a stability requirement.

[0041] In the method described here for determining the stability of a mass spectrometer, the mass spectrometer under evaluation is controlled for the performance test by acquiring its key performance parameters to obtain experimental data. Subsequently, the long-term state is analyzed based on this experimental data to determine the statistical and technical long-term states of individual key performance parameters. Based on these statistical and technical long-term states, it is determined whether the individual performance parameters meet the stability requirements. In the present solution, it is determined whether the individual performance parameters meet the stability requirements based on the statistical and technical long-term states of the key performance parameters.This allows for an accurate and efficient evaluation of the stability of the key performance of a mass spectrometer during operation from two perspectives.

[0042] Furthermore, the performance of a long-term state analysis based on the experimental data, in order to obtain the statistical long-term state and the technical long-term state of individual key performance parameters, includes the following in step S206: Step S2061: Determine a mean-range control chart for the individual key performance parameters based on the experimental data. The mean-range control chart belongs to the field of statistical process control.

[0043] Step S2062: Determine the characteristic parameters based on the mean-range control chart, wherein the characteristic parameters include at least one of the following: mean data points, the positional relationship between the mean data points and control limits, the positional relationship between the mean data points and a center line, and the fluctuation characteristics of the mean-range control chart.

[0044] Step S2063: Determining the statistical duration of the key performance parameters based on the characteristic parameters.

[0045] In other words, the statistical stability of the key performance parameters can be accurately determined using the mean-range control chart.

[0046] In particular, when creating a mean-range control chart, the size and number of subgroups should first be determined appropriately. Then, the mean and range of each subgroup are calculated (see Formula 1 and Formula 2). Finally, the control limits of the mean-range control chart are calculated (see Formula 3, Formula 4, Formula 5, and Formula 6). X¯=X1+X2+…+Xnn R¯=Xmax−Xmin UCL(R)=D4R¯ LCL(R)=D3R¯ UCL(X)=X¯+A2R¯ LCL(X)=X¯−A2R¯

[0047] In formulas 3, 4, and 5, D4, D3, and A2 are constants that vary with sample size and can be found in the corresponding tables. X1~Xn are the number N of values ​​of the key performance parameter, X max is the maximum value among the number N of values ​​of the key performance parameters, X minLCL is the minimum value among the number N of key performance parameters, X is the mean, R is the range, U is the upper limit, and L is the lower limit. LCL(R) represents the lower control limit of the mean-range map, UCL(R) represents the upper control limit of the mean map, and LCL(X) represents the lower control limit of the mean map.

[0048] After calculating the control limits of the mean and range control charts, a mean-range control chart can be created. Subsequently, using statistical criteria for assessing the stability of the key performance parameters, the relative positions of the mean data points in the control chart with respect to the control limits and the center line, as well as their fluctuation characteristics, can be compared and analyzed to determine whether the key performance parameters of the mass spectrometer are in a stable statistical state.

[0049] Furthermore, the performance of a long-term state analysis based on the experimental data, in order to obtain the statistical long-term state and the technical long-term state of individual key performance parameters, will include the following in step S206:

[0050] Determine, based on the experimental data, whether each set of key performance parameters conforms to a normal distribution, where the set of key performance parameters includes several key performance parameters of the same type.

[0051] Calculating a stability capability index of the corresponding key performance parameter based on the set of key performance parameters, if the set of key performance parameters conforms to the normal distribution, where the stability capability index represents the technical long-term state of the key performance parameter;

[0052] Performing a normalization transformation on the set of key performance parameters to obtain a transformed set of key performance parameters that conforms to the normal distribution if the set of key performance parameters does not conform to the normal distribution, and calculating the stability capability index (SCI) of the corresponding key performance parameter based on the transformed set of key performance parameters that conforms to the normal distribution.

[0053] This means that, based on several key performance parameters in the key performance parameter STY, the stability capability index (SCI) of the key performance parameters is determined, and the technical long-term state of the key performance parameters is determined based on the stability capability index (SCI). Table 1 shows the classification table for the stability capability index values ​​of the mass spectrometer's key performance parameters. Table 1. Classification table for the stability capability index values ​​of the key performance parameters of the mass spectrometer. SCI level Required value Condition A++ level 2.00:5SCI excellent A+ level 1.67≤SCI<2.00 very good A 1.33≤SCI<1.67 good B 1.00≤SCI<1.33 generally C 0.67≤SCI<1.00 sneaks D SCI<0.67 unacceptable

[0054] Furthermore, the step of determining, based on experimental data, whether each set of key performance parameters conforms to a normal distribution includes the following: Checking the conformity of each set of key performance parameters with the normal distribution using the Kolmogorov-Smirnov test to determine whether each set of key performance parameters conforms to the normal distribution.

[0055] Specifically, the KS test procedure was used to verify the conformity of the clinical mass spectrometer key performance indicator dataset with a normal distribution N (µ, σ). 2 ) to test.

[0056] The KS test (Kolmogorov-Smirnov test) can be used to examine the degree of conformity of the distribution of a set of sample data with a given theoretical distribution.

[0057] Let the population distribution be F(x), and let F0(x) be a known continuous distribution function. The hypothesis test problem is: H0: F(x) = F0(x). The steps of the Kolmogorov-Smirnov test are as follows: 1) For samples x(1), x(2), x(3),..., x(n) with sample size n, the order statistics are given by: x(1) ≤ x(2) ≤ x(3),... ≤ x(n). The empirical distribution function can be determined as follows: Fn(x)={0,x≤x(1)in,x(i)≤x≤x(i+1)1,x≥x(n)

[0058] Kolmogorov proposed the following statistical measure to test hypothesis H0: Dn=sup−∞ <x<+∞|Fn(x)−F0(x)|

[0059] 2) Calculating the empirical distribution function Fn(xi) and the known distribution function F0 (ti) corresponding to the individual sample data, and combining these with formulas (7) and (8) to obtain the following: δi=max{|F0x(i)−(i−1) / n|,|F0x(i)−i / n|},(i=1,2,…n)

[0060] Then the largest of δ i chosen as Dn, i.e., Dn = max {δ i}.

[0061] 3) Given a significance level α and a given sample size n, the critical value d (n,α) can be determined using the critical table of the Kolmogorov test procedure.

[0062] If D n ≤d(n,α), accept hypothesis H0; otherwise, reject hypothesis H0.

[0063] More specifically, the step of calculating the Stability and Competence Index (SCI) of the relevant key performance parameter using the set of key performance parameters comprises the following: Determining the Stability Index (SCI) of the key performance parameter based on the mean of the key performance parameter, the lower bound of the key performance parameter, and the standard deviation of the key performance parameter, if the key performance parameter is a larger-the-better parameter; Determining the Stability Index (SCI) of the key performance parameter based on the mean of the key performance parameter, the upper limit of the key performance parameter, and the standard deviation of the key performance parameter, if the key performance parameter is a smaller-the-better parameter; Determining the Stability Capability Index (SCI) of the key performance parameter based on the mean of the key performance parameter, the lower bound of the key performance parameter, the upper bound of the key performance parameter, and the standard deviation of the key performance parameter, if the key performance parameter is a nominal-the-best parameter.

[0064] Specifically: ① The Larger-the-Better property is a quality characteristic where: the bigger, the better. SCI=x¯−KL3s

[0065] 2. The Smaller-the-Better property is a quality characteristic where the rule is: the bigger, the better. SCI=KU−x¯3s

[0066] ③ For the nominal-the-best property, there exists a target value m, and it is desirable that the quality property x fluctuates around the target value m, with the fluctuation being as small as possible. Then x is called the nominal-the-best property, and the calculation formula is as follows: SCI=min(x¯−KL3s,KU−x¯3s)

[0067] In formulas 10, 11, and 12, s is the sample standard deviation of all samples. The larger-the-better property requires the highest possible value for the performance index; therefore, the lower limit of this performance parameter is K. L The smaller-the-better property requires the lowest possible value for the performance index; therefore, the upper limit of this performance parameter is defined as K. u The nominal-the-best property requires a performance index value that is neither too high nor too low; therefore, the range of values ​​for this performance parameter is defined as (KL , K u ) defined.

[0068] Furthermore, when the set of key performance parameters is not conformal to the normal distribution, performing a normalization transformation on the set of key performance parameters includes at least one of the following:

[0069] Performing a normalization transformation on the set of key performance parameters using the logarithmic transformation method if the set of key performance parameters is not conformal to the normal distribution; in particular, the logarithmic transformation represents the logarithmization of the key performance indicator X (the base-10 logarithm or the natural logarithm).

[0070] Performing a normalization transformation on the set of key performance parameters using the square root transformation method if the set of key performance parameters is not conformal to the normal distribution; in particular, the square root transformation represents taking the square root of the key performance indicator X.

[0071] Performing a normalization transformation on the set of key performance parameters using the square root arcsine transformation method if the set of key performance parameters is not conformal to the normal distribution; in particular, the square root arcsine transformation represents the calculation of the arcsine of the square root of the respective key performance indicator X.

[0072] Performing a normalization transformation on the set of key performance parameters using the square transformation method if the set of key performance parameters is not conformal to the normal distribution; in particular, the square transformation represents squaring the key performance indicator X.

[0073] Performing a normalization transformation on the set of key performance parameters using the inverse transformation method if the set of key performance parameters does not conform to the normal distribution; in particular, the inverse transformation represents taking the performance indicator index X as its reciprocal.

[0074] Performing a normalization transformation on the set of key performance parameters using the Box-Cox transformation method when the set of key performance parameters does not conform to a normal distribution; The Box-Cox transformation method is a generalized power transformation method that represents a data transformation commonly used in statistical modeling, and which applies to the dependent variable of continuous quantitative data that does not follow a normal distribution. The Box-Cox transformation transforms the dependent variable X, with the transformation of the variable X being determined by the variable parameter λ. The transformation formula is as follows: Y(λ)={Xλ−1λλ≠0Ln(X),λ=0

[0075] The Box-Cox transformation can significantly improve the normal distribution, symmetry, and homogeneity of variance of data and is applicable to many types of data.

[0076] Performing a normalization transformation on the set of key performance parameters using the normal score transformation method when the set of key performance parameters does not conform to a normal distribution. In the normal score transformation method, the data are first sorted directly, and then the data at each position are assigned new values ​​according to the standard normal distribution, thereby achieving normalization.

[0077] Furthermore, the procedure also includes the following: Determining the importance level of the key performance parameter as the fourth importance level if the key performance parameter is a parameter that influences the mass spectrometer being evaluated in such a way that it achieves the function of the second importance level; Determining the importance level of the key performance parameter as the third importance level if the key performance parameter is a parameter that influences the mass spectrometer being evaluated in such a way that it achieves the function of the first importance level; Determining the importance level of the key performance parameter as the second importance level if the key performance parameter is a parameter that affects the safety performance of the mass spectrometer being evaluated, and a warning message is present; Determining the importance level of the key performance parameter as the first importance level if the key performance parameter is a parameter that affects the safety performance of the mass spectrometer being evaluated and no warning message is present, with the importance levels being ordered in descending order as follows: the first importance level, the second importance level, the third importance level and the fourth importance level.

[0078] The details are listed in Table 2: Table 2: Ranking of the criticality level of the key features of the clinical mass spectrometer Criticality level of the key performance indicator Step description Reference example for a level assessment Level 1 This has a direct impact on safety, makes laws and regulations unenforceable, and triggers no warning. Water and gas circuits that affect the operation of the entire appliance. A failure of this index leads to the appliance malfunctioning; does not trigger any Warning issued, which directly affects safety. Level 2 This has direct implications for safety, makes laws and regulations unenforceable, and triggers a warning. Temperature control affects the operation of the entire device. A failure of this index leads to a malfunction of the device, triggers a warning, and simultaneously poses a safety risk. Level 3 The basic functions of the product are lost or its main functions are degraded, resulting in the product no longer being operational or only providing minimal performance. Sensitivity, or the detection limit, affects the performance of the entire device. A malfunction of this index impairs the measurement or even prevents it altogether. Level 4 Loss or degradation of secondary functions Noise suppression performance, which affects the testing of certain elements. A failure of this index leads to a deterioration. the measurement of some elements or the fact that these can no longer be detected. Level 5 Impact on customer experience Noise and light pollution, etc., which do not affect the performance of the entire device, but do impair the user experience.

[0079] In Table 2, level 1 represents the first level of importance, level 2 the second level of importance, level 3 the third level of importance, level 4 the fourth level of importance, and level 5 the level of non-importance.

[0080] Furthermore, the procedure also includes the following: Determine the requirement level of the Stability Capability Index (SCI) of the key performance parameter as the fourth capability level if the importance level of the key performance parameter is the fourth importance level; Determine the requirement level of the Stability Capability Index (SCI) of the key performance parameter as the third capability level if the importance level of the key performance parameter is the third importance level; Determine the requirement level of the Stability Capability Index (SCI) of the key performance parameter as the second capability level if the importance level of the key performance parameter is the second importance level; Determining the requirement level of the Stability Capability Index (SCI) of the key performance parameter as the first capability level when the importance level of the key performance parameter is the second or first importance level, with the capability levels arranged in descending order as follows: the first capability level, the second capability level, the third capability level, and the fourth capability level.

[0081] The details are listed in Table 3: Table 3. Correspondence between the importance of key performance indicators and the stability index of the clinical mass spectrometer. Importance of the Key Performance Index Corresponding SCI level Level 1 A+ or higher Level 2 A+ level Level 3 A Level 4 B Level 5 B

[0082] In Table 3, B represents the fourth skill level, A the third skill level, A+ the second skill level, and A+ and higher the first skill level.

[0083] Furthermore, the determination, based on the statistical and technical long-term states, of whether each of the key performance parameters meets a stability requirement includes the following: Determine that the key performance parameters satisfy the stability requirement to a first degree if the statistical duration state meets the statistical duration state requirement and the technical duration state meets the technical duration state requirement. Determine that the key performance parameters meet the stability requirement to a second degree if the statistical duration state does not meet the statistical duration state requirement and the technical duration state meets the technical duration state requirement; Determine that the key performance parameters do not meet the stability requirement if the technical endurance state does not meet the technical endurance state requirement.

[0084] The details are listed in Table 4: Table 4 Stability level of the key performance indicators of the clinical mass spectrometer Statistical permanent state statistically stable statistically unstable Technical durability (stability index) Meeting the requirements I III Not meeting the requirements II IV

[0085] State I: The performance is in a statistically stable state, and the stability capability index meets the requirements; this is the optimal state. The key performance parameters meet the stability requirements to the first degree.

[0086] State II: The performance is in a statistically stable state, but the stability capability index does not meet the requirements. It is rejected and a performance improvement is required.

[0087] State III: Performance is in a state of instability, but the stability capability index meets the specified requirements and is acceptable. It is recommended to analyze the abnormal causes of the statistical instability as soon as conditions allow and to implement continuous improvements.

[0088] State IV: The performance is in a persistent state, and the stability capability index does not meet the requirements. It is rejected, and a performance improvement is required.

[0089] Furthermore, the procedure, after determining that the key performance parameters meet the stability requirement to a second degree if the statistical duration does not meet the statistical duration requirement and the technical duration requirement meets the technical duration requirement, also includes the following: Determining the cause of statistical anomalies;

[0090] Improving the performance that affects the statistical stability of key performance parameters is based on identifying the cause of statistical anomalies until the statistical stability of the key performance parameters meets the requirements for statistical stability. In other words, first the cause of statistical deviations is identified, and then the performance of the statistical stability is improved to meet the requirements for statistical stability.

[0091] Furthermore, the procedure also includes the following: Determine whether the statistical durability of the key performance parameters meets the requirements for statistical durability; wherein the step of determining whether the statistical durability of the key performance parameters meets the requirements for statistical durability includes the following: Recording the number of different consecutive numerical points for the key performance parameters; Determine the maximum permissible number of external points, which corresponds to the number of distinct consecutive numerical points; Determine that the statistical duration of the key performance parameters satisfies the statistical duration requirement if the number of external points corresponding to the number of at least one of the distinct consecutive numerical points is less than the corresponding maximum allowable number of external points. For example, control charts for key performance parameters must meet statistical stability requirements. Based on statistical process control and practical experience, the requirements for the statistical stability of control charts are as follows: ① There are 25 consecutive points, and the number of outer points is d=0 ; 2 There are 35 consecutive points, and the number of outer points is d ≤ 1 ; ③ There are 100 consecutive points, and the number of outer points is d ≤ 2.

[0092] If the above conditions are not met, the device is considered statistically unstable. A statistically unstable device exhibits significant fluctuations in its actual performance, resulting in unstable performance statistics. In this case, the device may malfunction, causing further significant performance fluctuations and hindering normal, stable operation.

[0093] It should be noted that the steps shown in the flow diagram in the accompanying drawings can be carried out, for example, in a computer system using a series of computer-executable instructions, and although a logical sequence is shown in the flow diagram, the steps shown or described may in some cases be carried out in a different order than the one shown here.

[0094] This disclosure also provides a specific method for determining the stability of a mass spectrometer, as described in Fig. 3. The procedure includes: ① Determining the key performance indicator based on the technical requirements for clinical mass spectrometers; 2. Determining the stability requirements for the key performance indicator based on the importance level of the performance indicator; ③ Acquire key performance data of the clinical mass spectrometer by conducting performance tests; ④ Draw a mean-range control chart using the key performance data and assess the results of the statistical duration condition from the results of the control chart. ⑤ For key performance parameters that conform to a normal distribution, the SCI value is calculated directly; for those that do not conform to a normal distribution, it is transformed into a normal distribution and then the SCI value is calculated, and the result of the technical long-term condition is finally determined based on the SCI data value. ⑥ Determining the stability level of the key performance indicator from the results of the statistical endurance state and the technical endurance state; ⑦ Determine the final stability assessment based on the stability level of the key performance indicator.

[0095] This disclosure describes a procedure for evaluating the stability of a mass spectrometer's key performance indicators (KPIs). First, a control chart is used to analyze the statistical long-term state of the KPIs. Next, a stability capability index is applied to analyze the technical long-term state of the KPIs. Finally, the results of the statistical and technical long-term analyses are combined to determine the level of stability of the KPIs and their final outcome for the clinical mass spectrometer. This procedure not only evaluates the stability of the KPIs but also enables the early detection of instrument problems through stability assessment, thus ensuring the accuracy and effectiveness of the mass spectrometer's test results during its operation.

[0096] The proposed evaluation procedure closes a gap in the system for the stability assessment of clinical mass spectrometers, effectively promotes the development of a quality control and stability assessment system for clinical mass spectrometers, provides regulatory authorities with a basis and a method for controlling the quality and stability assessment of medical devices at market launch, and provides medical device manufacturers with an assessment basis for the continuous improvement of product stability, thereby enabling mass spectrometers to provide more stable and efficient detection in clinical applications.

[0097] In the embodiment described in this disclosure, a device for determining the stability of a mass spectrometer is also provided. It should be noted that the device for determining the stability of a mass spectrometer according to the present embodiments can be used to carry out the method for determining the stability of mass spectrometers described in this disclosure. It serves to implement the aforementioned and preferred embodiments; details already described are not repeated here. In the following, the term "module" refers to a combination of software and / or hardware by which specified functions can be performed. Although the device described in the following embodiments is preferably implemented as software, a hardware implementation or a combination of both is also possible and intended.

[0098] The following describes the device provided in the embodiments of this disclosure for determining the stability of the mass spectrometer.

[0099] Fig. Figure 4 shows a schematic representation of a device for determining the stability of a mass spectrometer according to an embodiment of the present disclosure. As in Fig. As shown in section 4, the device comprises the following: a first acquisition unit 41 configured to acquire key performance parameters of the mass spectrometer to be evaluated, wherein the key performance parameters include at least one of the following: a parameter that affects the safety performance of the mass spectrometer to be evaluated, a parameter that affects the mass spectrometer to be evaluated in order to achieve a function of the first importance level, and a parameter that affects the mass spectrometer to be evaluated in order to achieve a function of the second importance level; a control unit 42 configured to control the mass spectrometer to be evaluated in order to obtain experimental data; an analysis unit 43 configured to perform a long-term state analysis based on the experimental data in order to obtain the statistical long-term state and the technical long-term state of individual key performance parameters, wherein the statistical long-term state characterizes whether the key performance parameter is in a controlled state within a specified period of time, and the technical long-term state characterizes whether the key performance parameter is within a specified period of time of time within a specified parameter range; a first determination unit 44, which is configured to determine, based on the statistical long-term state and the technical long-term state, whether each of the key performance parameters meets a stability requirement.

[0100] In the present device for determining the stability of a mass spectrometer, the first acquisition unit acquires the key performance parameters of the mass spectrometer under evaluation. The mass spectrometer's control unit then performs performance tests to acquire experimental data. Based on this test data, the analysis unit analyzes the long-term state to determine the statistical and technical long-term state of each performance parameter. Using these statistical and technical long-term states, the first determination unit determines whether each performance parameter meets the stability requirements. This procedure determines whether each performance parameter meets the stability requirements based on the statistical and technical long-term states of the key performance parameters.This allows for a precise and efficient evaluation of the stability of the mass spectrometer's performance parameters during operation from two perspectives.

[0101] The analysis unit further comprises a first, a second, and a third determination module. The first determination module is used to create the mean-range control chart for each key performance parameter based on the experimental data. The second determination module is used to determine the characteristic values ​​based on this mean-range control chart. These characteristic values ​​include at least one of the following: data points of the mean values, the positional relationship between the data points of the mean values ​​and control limits, the positional relationship between the data points of the mean values ​​and a center line, and the fluctuation characteristics of the mean-range control chart. The third determination module is used to determine the statistical stability of the key performance parameters based on the characteristic values. This ensures a precise determination of the statistical stability of the key performance parameters.

[0102] The analysis unit further comprises a fourth determination module, a calculation module, and a processing module. The fourth determination module serves to ascertain, based on experimental data, whether the respective set of key performance parameters conforms to a normal distribution. The set of key performance parameters comprises several key performance parameters of the same type. The calculation module serves to calculate the stability capability index of the corresponding key performance parameter based on the set of key performance parameters, provided that the set of key performance parameters conforms to a normal distribution, with the stability capability index representing the technical long-term state of the key performance parameter.The processing module performs a normalization transformation on the set of key performance parameters to obtain a transformed set that conforms to the normal distribution if the set of key performance parameters does not conform to the normal distribution. It then calculates the Stability Capability Index (SCI) of the corresponding key performance parameter based on this transformed set that conforms to the normal distribution. This enables a precise determination of the technical long-term state of the key performance parameters.

[0103] Furthermore, the fourth determination module serves to test the conformity of each set of key performance parameters with the normal distribution using the Kolmogorov-Smirnov test, in order to determine whether each set of key performance parameters conforms to the normal distribution. This achieves the verification of the normal distribution.

[0104] Furthermore, the transformation module includes a first determination sub-module, a second determination sub-module, and a third determination sub-module.

[0105] The first determination submodule is used to determine the Stability Capability Index (SCI) of the key performance parameter based on the mean of the key performance parameter, the lower limit of the key performance parameter, and the standard deviation of the key performance parameter if the key performance parameter is a larger-the-better parameter.

[0106] The second determination submodule is used to determine the Stability Capability Index (SCI) of the key performance parameter based on the mean of the key performance parameter, the upper limit of the key performance parameter, and the standard deviation of the key performance parameter if the key performance parameter is a Smaller-the-Better parameter.

[0107] The third determination module is used to determine the Stability Capability Index (SCI) of the key performance parameter based on the mean of the key performance parameter, the lower limit of the key performance parameter, the upper limit of the key performance parameter, and the standard deviation of the key performance parameter if the key performance parameter is a nominal-the-best parameter.

[0108] Furthermore, the processing module includes at least one of the following: a first transformation submodule, a second transformation submodule, a third transformation submodule, a fourth transformation submodule, a fifth transformation submodule, a sixth transformation submodule, and a seventh transformation submodule.

[0109] The first transformation submodule is used to perform a normalization transformation for the set of key performance parameters using the logarithmic transformation method if the set of key performance parameters is not conformal to the normal distribution.

[0110] The second transformation submodule is used to perform a normalization transformation for the set of key performance parameters using the square root transformation method if the set of key performance parameters is not conformal to the normal distribution.

[0111] The third transformation submodule is used to perform a normalization transformation for the set of key performance parameters using the square root arcsine transformation method if the set of key performance parameters is not conformal to the normal distribution.

[0112] The fourth transformation submodule is used for a normalization transformation of the set of key performance parameters using the square transformation method if the set of key performance parameters is not conformal to the normal distribution.

[0113] The fifth transformation submodule is used to perform a normalization transformation for the set of key performance parameters using the inverse transformation method if the set of key performance parameters is not conformal to the normal distribution.

[0114] The sixth transformation submodule is used to perform a normalization transformation on the set of key performance parameters that do not follow a normal distribution, using the Box-Cox transformation method.

[0115] The seventh transformation submodule is used to perform a normalization transformation on the set of key performance parameters using the normal score transformation method when the set of key performance parameters does not conform to a normal distribution. The various methods are employed to transform the set of key performance parameters that do not conform to a normal distribution into a set of key performance parameters that do conform to a normal distribution.

[0116] Furthermore, the device also includes a second determination unit, a third determination unit, a fourth determination unit and a fifth determination unit.

[0117] The second unit of determination serves to determine the importance level of the key performance parameter as the fourth importance level if the key performance parameter is a parameter that influences the mass spectrometer being evaluated in such a way that it achieves the function of the second importance level.

[0118] The third unit of determination serves to determine the importance level of the key performance parameter as the third importance level if the key performance parameter is a parameter that influences the mass spectrometer being evaluated in such a way that it achieves the function of the first importance level.

[0119] The fourth unit of determination serves to determine the importance level of the key performance parameter as the second importance level if the key performance parameter is a parameter that influences the safety performance of the mass spectrometer being evaluated, and a warning message is present.

[0120] The fifth unit of determination serves to determine the importance level of the key performance parameter as the first importance level if the key performance parameter is a parameter that affects the safety performance of the mass spectrometer being evaluated and no warning message is present, with the importance levels being ordered in descending order as follows: the first importance level, the second importance level, the third importance level and the fourth importance level.

[0121] Furthermore, the device also includes a sixth determination unit, a seventh determination unit, an eighth determination unit and a ninth determination unit.

[0122] The sixth unit of determination serves to determine the requirement level of the Stability Capability Index (SCI) of the key performance parameter as the fourth capability level if the importance level of the key performance parameter is the fourth importance level.

[0123] The seventh unit of determination serves to determine the requirement level of the Stability Capability Index (SCI) of the key performance parameter as the third capability level if the importance level of the key performance parameter is the third importance level.

[0124] The eighth unit of determination serves to determine the requirement level of the Stability Capability Index (SCI) of the key performance parameter as the second capability level if the importance level of the key performance parameter is the second importance level.

[0125] The ninth unit of determination serves to determine the requirement level of the Stability Capability Index (SCI) of the key performance parameter as the second or first capability level when the importance level of the key performance parameter is the first importance level, with the capability levels arranged in descending order as follows: the first capability level, the second capability level, the third capability level, and the fourth capability level.

[0126] Furthermore, the first identification unit includes a fifth identification module, a sixth identification module, and a seventh identification module.

[0127] The fifth determination module serves to determine that the key performance parameters meet the stability requirement to a first degree if the statistical duration meets the requirement for the statistical duration and the technical duration meets the requirement for the technical duration.

[0128] The sixth determination module serves to determine that the key performance parameters meet the stability requirement to a second degree if the statistical duration does not meet the statistical duration requirement and the technical duration meets the technical duration requirement.

[0129] The seventh determination module serves to determine that the key performance parameters do not meet the stability requirement if the technical continuous state does not meet the requirement for the technical continuous state.

[0130] The device also includes a detection unit and an improvement unit. The detection unit is used to detect the cause of a statistical error after it has been determined that the key performance parameters provisionally meet the stability requirements of the second degree, when the statistical duration state does not meet the requirements for the statistical duration state, but the technical duration state already meets the requirements for the technical duration state. Based on the cause of the statistical error, the improvement unit improves the performance of the statistical duration state that influences the key performance parameters until the statistical duration state of the key performance parameters meets the requirements for the statistical duration state.

[0131] Furthermore, the device also includes a tenth determination unit, which serves to determine whether the statistical continuous state of the key performance parameters meets the requirements for the statistical continuous state; the tenth determination unit comprises a detection module, an eighth determination module and a ninth determination module.

[0132] The data acquisition module is used to record the number of different consecutive numerical points for the key performance parameters.

[0133] The eighth determination module is used to determine the maximum permissible number of external points, which corresponds to the number of different consecutive numerical points.

[0134] The ninth determination module serves to determine that the statistical duration of the key performance parameters meets the requirement for the statistical duration if the number of external points corresponding to the number of at least one of the different consecutive numerical points is less than the corresponding maximum permissible number of external points.

[0135] The key performance parameters include at least one of the following: limit of detection, ion transport efficiency, and parameters that affect immunity.

[0136] The device for determining the stability of a mass spectrometer comprises a processor and a memory, wherein the aforementioned first determination unit, the control unit, an analysis unit, and the first determination unit are stored as program units in the memory. The processor executes the aforementioned program units stored in memory to perform the corresponding functions. All of the aforementioned modules are located in the same processor; alternatively, they can also be located in any combination on different processors.

[0137] The processor includes a kernel that retrieves the corresponding program units from memory. One or more kernels can be provided, and adjusting the kernel parameters allows for precise determination of the mass spectrometer's stability.

[0138] The memory can include non-permanent storage in computer-readable media such as main memory (RAM) and / or non-volatile memory such as read-only memory (ROM) or flash RAM, and includes at least one memory chip.

[0139] This embodiment describes a method for verifying the performance of a detection limit of a trace element analyzer, which is executed on a mobile device, a computer terminal, or a similar computing device. It should be noted that the steps shown in the flowchart in the accompanying drawings can, for example, be executed in a computer system using a series of computer-executable instructions, and although the flowchart shows a logical sequence, the steps shown or described may, in some cases, be performed in a different order than that shown here.

[0140] Fig.Figure 5 shows a flowchart of a method for determining the detection limit performance of a trace element analyzer according to an embodiment of the present disclosure. As in Fig. As shown in Figure 5, the procedure comprises the following steps: Step S501: Performing a trigger control for the trace element analyzer and a tuning control of the trace element analyzer after successful triggering.

[0141] The goal of the trigger control for the trace element analyzer is to bring the trace element analyzer into the start-up state in order to enable the subsequent detection limit test. The goal of the tuning control is to achieve the desired signal strength and resolution by adjusting relevant parameters.

[0142] Step S502: Selecting parameters for the trace element analyzer to obtain the target parameters, and preparing the target parameters when the tuning control is performed and the trace element analyzer meets the test standards, wherein the target parameters are the same parameters required to test the performance of a detection limit of the trace element analyzer, with the preparation serving to adjust the relevant performance of the target parameters.

[0143] After successful activation of the device, an automatic vote is performed. The automatic vote is considered to meet the test criterion if the response values ​​satisfy the following conditions: Li ≥ 100,000, Co ≥ 600,000, In ≥ 1,000,000, U ≥ 1,000,000, oxidation rate ≤ 3%, and double charge rate ≤ 3%, where Li represents lithium, Co represents cobalt, In represents indium, and U represents uranium.

[0144] The limit of detection (LOD) is the lowest analyte concentration that can be detected with a sufficient level of confidence. The standard JJF1159-2006 specifies that the calibration elements for the LOD in the main technical indicators of an ICP-MS must include three elements: lithium (Li), yttrium (Y), and thallium (Tl). Li, Y, and Tl are required for detection in the human body, and their mass values ​​cover low, medium, and high levels, respectively, encompassing the entire mass range of the mass axis and serving as an important basis for evaluating instrument performance. Therefore, elements such as Li, Y, and Tl can be selected as target parameters.

[0145] The relevant performance target parameters include the following: time, peristaltic pump speed and injector inlet position, etc. Different target parameters can correspond to the same or different relevant key performance parameters.

[0146] Step S503: Perform a sample correlation test for the selected and prepared target parameters and analyze the results of the sample correlation test to obtain the analysis results of the limit of detection test.

[0147] The relevant performance test includes mean value, range, etc., so that the detection limit of each target parameter can be obtained through analysis.

[0148] This disclosure describes a method for testing the performance of a trace element analyzer's limit of detection. The triggering of the trace element analyzer is controlled. After successful triggering, the analyzer is calibrated. Once the calibration is complete and the analyzer meets the test criteria, target parameters are selected to determine the desired parameters. These target parameters are then processed. Sample-specific performance tests are performed for the selected and processed target parameters, and the results are analyzed to determine the limit of detection. The described method enables standardized testing of the limit of detection of a trace element analyzer.

[0149] In the performance testing procedure of the embodiment, the implementation of a trigger control for the trace element analyzer comprises the following: Displaying the device's user interface on the screen in response to an initial preset operation that acts on a device control widget.

[0150] A device control widget is located in the lower right corner of Fig. 6, while the device's user interface is in the middle of Fig. 6 is displayed.

[0151] Connecting the trace element analyzer, and then the appearance and opening of the vacuum opening control widget and the device triggering control widget on the display in response to a second preset operation acting on the device connection widget in the device user interface.

[0152] The second preset operation can be a single-click operation, a double-click operation, or another similar operation.

[0153] The device connection widget corresponds to the connection disconnection widget in Fig. 6. Click the disconnect widget to disconnect from the trace element analyzer, and click the disconnect widget again to disconnect the connection to the trace element analyzer. The vacuum opening control widget corresponds to the vacuum off widget in Fig. 6. The vacuum is turned off by clicking the vacuum off widget, and turned on by clicking the vacuum off widget again.

[0154] Controlling the trace element analyzer in a vacuum standby state in response to a third preset operation acting on the vacuum control widget.

[0155] The third preset operation can be a single-click operation, a double-click operation, or another type of operation.

[0156] Controlling the trace element analyzer in an operating state in response to a fourth preset operation acting on the device ignition control widget.

[0157] The fourth preset operation can be a single-click operation, a double-click operation, or another type of operation.

[0158] As shown above, the trace element analyzer is put into operation by connecting it, opening the vacuum and switching on the trigger.

[0159] In this embodiment of the performance test procedure, the calibration of the trace element analyzer after successful triggering includes the following: A manual voting widget and an automatic voting widget appear below the device control widget in response to a fifth preset operation acting on the device control widget in the display interface.

[0160] The fifth preset operation can be a single-click operation, a double-click operation, or another type of operation.

[0161] As in Fig. As shown in Figure 7, the manual voting widget and the automatic voting widget are located below the device control widget.

[0162] Displaying a list of tuning methods on the display in response to a sixth preset operation acting on the automatic tuning widget; selecting a targeted automatic tuning method from this list of automatic tuning methods in response to a seventh preset operation acting on the list of automatic tuning methods to automatically tune the trace element analyzer using that targeted automatic tuning method.

[0163] The sixth preset operation can be a single-click operation, a double-click operation, or another type of operation.

[0164] The seventh preset operation can be a single-click operation, a double-click operation, or another type of operation.

[0165] The list of automatic optimization methods is on the left. Fig.Figure 8(a) illustrates the automatic optimization methods, including: Standard Mode - Full Version - Dual Mode - Test2; Standard Mode - Simple Version - Dual Mode - Test2; Collision Mode - Full Version - Dual Mode - Test2; Collision Mode - Simple Version - Dual Mode - Test2, etc. Each tuning mode is divided into multiple tuning stages, and a stage addition widget, a stage deletion widget, a stage upshift widget, a stage downshift widget, a stage name change widget, and a tuning parameter setting widget, etc., are displayed. The key codes of the tuning parameters used to set the mode are also shown.

[0166] As in Fig.As shown in Figure 8 (b), the display interface under Standard Mode Full Version Dual Mode Test2 also displays a phase parameter widget, a device parameter widget and an element parameter widget, where the phase parameters include phases, number of iterations, fine-tuning percentage, average number of rough tunings, average number of fine tunings and sample introduction time.

[0167] Performing a sample correlation test for the selected and processed target parameters and analyzing the results of the sample correlation test to obtain the analysis results of the limit of detection test.

[0168] The eighth preset operation can be a single-click operation, a double-click operation, or another type of operation.

[0169] In this embodiment of the performance test procedure, selecting parameters for the trace element analyzer to obtain the target parameters and preparing the target parameters after tuning is performed and the trace element analyzer meets the test standards includes the following: Displaying a parameter selection and processing image on the display in response to a ninth preset operation that acts on the method processing widget in the display interface.

[0170] The ninth preset operation can be a single-click operation, a double-click operation, or another type of operation.

[0171] As in Fig. As shown in Figure 9, clicking on the method preparation widget in the display interface will show a parameter selection and preparation screen.

[0172] Appearance of a parameter display list in the parameter selection and processing screen in response to the sample parameter widget acting in the parameter selection and processing screen; and adding or deleting items and adding or deleting tunings in response to a tenth preset operation acting on the parameter display list.

[0173] The sampling parameter widget is in Fig. The first option on the left shows option 9, where the tenth preset operation can be a single click, a double click, or another action. Specifically, the single click operation can be performed by right-clicking on the right side of the field. Fig. 9 can be executed to add or remove elements.

[0174] As shown in the voting method in the lower left corner of Fig. As shown in 9, you can add or delete a vote by right-clicking on the voting interface.

[0175] The appearance of a parameter analysis list on the parameter selection and rework screen in response to the analysis parameter widget acting on the parameter selection and rework screen, and the selection of the option to be changed in response to an eleventh preset operation acting on the parameter analysis list to perform the change.

[0176] The appearance of a parameter analysis list on the parameter selection and processing screen as a reaction to the analysis parameter widget, which takes effect in the parameter selection and processing screen, is in Fig. Figure 10 is shown, where the parameter analysis list includes Scan to Add, Concentration Level, Quality Control Level, Downfill, etc.

[0177] The eleventh preset operation can be a single-click operation, a double-click operation, or another type of operation.

[0178] Appearance of a parameter rework image on the parameter selection and rework image in response to the injection parameter widget acting on the parameter selection and rework image, and rework of the parameters in response to a twelfth preset operation acting on the parameter rework widget on the parameter rework image.

[0179] The twelfth preset operation can be a single-click operation, a double-click operation, or another type of operation.

[0180] The appearance of a parameter processing image on the parameter selection and processing image as a reaction to the injection parameter widget that is active in the parameter selection and processing image, in Fig.Figure 11 shows the setting of the injection parameters as follows: the use of each individual step as well as the duration of each individual step, the rotational speed of the peristaltic pump, the position of the sample inlet, the selection and use of the dosing loop flushes.

[0181] In this embodiment of the performance testing procedure, the procedure, after the parameter rework in response to a twelfth preset operation acting on the parameter rework widget in the parameter rework image, further comprises the following: Appearance of a sample list image in the fourth preset area of ​​the parameter selection and rework image in response to the sample list widget acting in the parameter selection and rework image, and adding samples in response to a thirteenth preset operation acting on the sample list image.

[0182] The thirteenth preset operation can be a single-click operation, a double-click operation, or another type of operation.

[0183] The appearance of a sample list image in the fourth preset area of ​​the parameter selection and processing screen in response to the sample list widget that is active in the parameter selection and processing screen is in Fig. 12 is shown, where the sample list image includes sample add, sample insert, downward fill, prioritized capture, recapture, capture delete, data remove, position remove, etc.

[0184] In this embodiment of the performance testing procedure, the procedure after the calibration of the trace element analyzer further includes the performance of a sample acquisition check for the trace element analyzer. where performing a sample acquisition check for the trace element analyzer includes the following: Displaying a sample acquisition image on the display in response to a fourteenth preset operation acting on a sample acquisition widget in the display interface, and appearing a selection and acquisition template list in the sample acquisition image in response to a fifteenth preset operation acting on a creation widget in the sample acquisition image to select the target acquisition template from the selection and acquisition template list to perform sample acquisition using the target acquisition template.

[0185] The reagents required for the limit of detection test are listed in Table 5. Table 5. Reagents required for the limit of detection test number Reagent name impact 1 pure water for device testing 2 Solution for the detection limit test for device testing

[0186] The fourteenth preset operation can be a single-click operation, a double-click operation, or another type of operation.

[0187] As in Fig. 13 (a) and Fig.As shown in 13 (b), the creation widget is in Fig. 13 (b) shown. Clicking on “Create” will display the revised template. The revised template is in Fig. 13 (a) is shown. After selecting and clicking the desired template, click “OK”.

[0188] As in Fig. As shown in Figure 14, it further includes: a right-click with the mouse in the control area to access the voting, and by clicking on the voting setting, the desired voting parameters are selected, as shown in Fig. 15 shown.

[0189] After setup, click Start. During data entry, you can click the "Re-enter data" button if needed, as shown in the instructions. Fig. 16 shown.

[0190] After completing the test, click "Close". A window will appear asking if you want to save the user interface. If you click "Yes", the test file will be saved (see Fig. 17).

[0191] In this embodiment of the performance testing procedure, the analysis of the sample correlation test results includes the following: Displaying a sample collection list and an analysis widget in the display interface in response to a sixteenth preset operation that acts on the sample analysis widget in the display interface.

[0192] Performing a sample analysis in response to a seventeenth preset operation acting on the analysis widget.

[0193] Performing a sample analysis in response to a seventeenth preset operation acting on the analysis widget includes the following: Displaying an image for sample analysis in the display surface in response to a seventeenth preset operation acting on the analysis widget, Selecting a next analysis option to perform the sample analysis, in response to an eighteenth preset operation that acts on the drop-down list in the sample analysis image.

[0194] After the step of selecting the next analysis option to perform the sample analysis, in response to an eighteenth preset operation that acts on the drop-down list in the sample analysis image, the procedure further includes the following: Appearance of a list of file export methods in response to a nineteenth preset operation acting in the image for sample analysis, wherein the list of file export methods includes the export file format and file representation information.

[0195] The sixteenth and seventeenth preset operations can be a single-click operation, a double-click operation, or another type of operation.

[0196] As in Fig. As shown in Figure 18, the analysis widget is located in the lower right corner of Fig. 18; after accessing the analysis page, clicking on the small triangle on the left displays the next level of test items, as shown in Fig. 19 shown.

[0197] Right-clicking allows you to select the export format, display status, etc. (After the test, "N / A" is displayed; clicking displays "CPS"), as shown in Fig. 20 (a) shown. Fig. Figure 20(b) shows the results of the trace element analysis, the adaptation parameters of the trace elements, the standard trace elements and the trace elements to be measured.

[0198] In order to enable experts to better understand the technical solutions of this disclosure, the implementation process of the method for performance testing of the detection limit of a trace element analyzer described in this disclosure is below described in detail using specific examples.

[0199] This embodiment relates to a specific method for testing the performance of a detection limit of a trace element analyzer (see Fig. 21). It includes: triggering the device, device control, automatic calibration, process preparation, sample acquisition, and sample analysis. If the response value of the automatic calibration does not meet the standard, manual calibration is performed.

[0200] It should be noted that the steps shown in the flow diagram in the accompanying drawings can be carried out, for example, in a computer system using a series of computer-executable instructions, and although a logical sequence is shown in the flow diagram, the steps shown or described may in some cases be carried out in a different order than the one shown here. This embodiment relates to a specific key performance test, which includes the following: 1) Experimental platform

[0201] The experiment was performed using an Inspector SQ60 equipped with a highly efficient concentric atomizer and a small-volume vortex atomization chamber. This resulted in high atomization efficiency, low dead volume, and low memory effect. A self-excited solid-state RF generator operating at a frequency of 27.12 MHz was employed. A hexapole rod impact reaction cell was used, which balanced the ion focusing effect with optimal transmission efficiency across the entire mass range. 2) Experimental reagents

[0202] Self-produced voting solution (Li, Co, In, U, Ce, Ba solution); Detection limit test solution (Li, Y, TI solution); Ultrapure water with a specific resistance of 18.2 MΩ · cm, obtained using the Milli-Q ultrapure water system; Homemade diluent (for simulating blood samples). 3) Experimental methods

[0203] Optimizing the device's performance, simulating real-world operating conditions, and conducting a detection limit test. Test process: 1. After the device is triggered, automatic tuning is performed in standard mode. The device then achieves its best performance in standard mode. 2. High-purity water (18 MΩ·cm) is injected as a sample, and the ion count rates are measured at mass numbers 7, 89, and 205. Subsequently, the limit-of-detection test solution is injected with the high-purity water as a sample, and the ion count rates are measured again at mass numbers 7, 89, and 205. The residence time is 50 ms, the number of channels is 3, and the number of scans is 10. Eleven data points are acquired. The limit of detection is determined by dividing three times the standard deviation of the measurement results by the sensitivity of Li, Y, and Ti. 3. Pump in the homemade diluent to simulate the injection of the blood sample. 4. Repeat step 2 after four hours. 5. Perform regular cleaning and maintenance work.

[0204] The following results were obtained: Table 6: Mean and range of the key performance indicators for the limit of detection for each subgroup Subgroup number Mean value of the Li element subgroup Range of the Li-element subgroup Mean of the Y-element subgroup Span of the Y-element subgroup Mean of the TI element subgroup Range of the TI element subgroup 1 1,4636 0,5038 0,1283 0,1184 0,4316 0,3095 2 1,3397 1,1182 0,0846 0,1098 0,4389 0,1493 ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ 124 1,9222 2,2543 0,1790 0,2372 0,3084 0,2485 125 2,1229 1,4967 0,2452 0,1326 0,5077 0,2733 Table 7: Average values ​​of the means and average values ​​of the range of the key performance indicators for the limit of detection for each subgroup name Average of the means of the Li element subgroup Average of the means of the Y-element t-subgroup Average of the means of the TI element subgroup Average range of the Li-element subgroup Average range of the Yi element subgroup Average range of the TI element subgroup Value 1,5300 0,1417 0,4852 1,2196 0,0954 0,3251

[0205] The calculated detection limits for each element are listed in Table 8. Table 8: Detection limits and control limits for the elements Li, Y and TI Li Y Tl UCL 2,2374 0,1970 0,6738 CL 1,5300 0,1417 0,4852 LCL 0,8226 0,0864 0,2966

[0206] Based on the data in Tables 6 and 8, a mean-range control chart of the limits of detection of the three elements Li, Y and TI was created, as shown in Fig. 22 shown.

[0207] From the mean value control chart of the detection limits-mean value-range control chart for Li, Y and TI in Fig. 22 The following can be obtained: The total number of mean limits of detection for each element subgroup is 75, while the number of mean limits of detection for the Li element sample subgroup outside the control limits is 1, the number of mean limits of detection for the Y element sample subgroup outside the control limits is 8, and the number of mean limits of detection for the TI element sample subgroup outside the control limits is 6, as shown in Table 5. Table 9: Statistics of the number of outer points in the mean control chart of the limits of detection of three elements Li, Y and TI Detection limit of the element Li Detection limit of element Y Detection limit of the element TI Number of points outside the control limit of the rule card 1 8 6

[0208] Using the Box-Cox function of the software minitab17, the data set of the limit-of-detection power index was transformed and then the transformed frequency histogram was drawn (see Fig. 23). Mean and standard deviation were calculated (see Tables 10 and 11). Table 10: Mean and standard deviation of the transformed detection limit data number element Mean (µ) Standard deviation (σ) Li 0,9108 0,08405 Y 0,7833 0,04086 Tl 1,024 0,01063

[0209] The normal distribution of the transformed data set was tested using the KS test, as shown in Tables 11, 12 and 13. Table 11: Test table of the normal distribution of the transformed data for the limits of detection of element Li i Xi (i-1) / n in F0(xi) d i 1 0,6784 0,0000 0,0027 0,0028 0,0028 ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ 141 0,8911 0,3733 0,3760 0,4072 0,0340 ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ 375 1,1410 0,9973 1,0000 0,9969 0,0031

[0210] The following can be seen from Table 11: Dn = max{δi} = 0.0340.

[0211] At a significance level of α = 0.05 and a sample size of n = 375, d (375, 0,05) Calculate = 0.07023. Since Dn < d (375 , 0,05) , the null hypothesis H0 is accepted. This means that the transformed dataset of the limit of detection of the element TI of a normal distribution N[0,9108, (0,08405) 2 ] follows. Table 12: Test table of the normal distribution of the transformed data for the limits of detection of element Y i Xi (i-1) / n in F0(xi) d i 1 0,658321 0,0000 0,0027 0,0011 0,0016 ⋮ ⋮ ⋮ ⋮ ⋮ 105 0,7638 0,2773 0,2800 0,3164 0,0391 ⋮ ⋮ ⋮ ⋮ ⋮ 375 0,9145 0,9973 1,0000 0,9993 0,0020

[0212] The following can be obtained from Table 12: Dn=max{δ i}=0.0391.

[0213] At a significance level of α = 0.05 and a sample size of n = 375, d (375, 0,05) Calculate = 0.07023. Since Dn < d (375 , 0,05) , the null hypothesis H0 is accepted. This means that the transformed dataset of the limit of detection of element Y of a normal distribution N[0,7833, (0,04086)2 ] follows. Table 13: Test table of the normal distribution of the transformed data for the limits of detection of element TI i Xi (i-1) / n in F0(xi) d i 1 0,9827 0,0000 0,0027 0,0001 0,0026 ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ 266 1,0285 0,7067 0,7093 0,6653 0,0440 ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ 375 1,0585 0,9973 1,0000 0,9994 0,0021

[0214] The following can be obtained from Table 13: Dn=max{δ i}=0.0440.

[0215] At a significance level of α = 0.05 and a sample size of n = 375, d (375, 0,05) Calculate = 0.0702. Since Dn < d (375, 0,05) , the null hypothesis H0 is accepted. This means that the transformed dataset of the limit of detection of the element TI of a normal distribution N[1,024, (0,01063) 2 ] follows.

[0216] The values ​​of the Stability Index (SCI) were calculated for the dataset that conforms to a normal distribution after transformation of the Limit of Detection Performance Index. Detailed results are presented in Table 14. Table 14: Detection Limit Performance Index (SCI value) number Property name SCI value SCI level Detection limit of element Li 1,48 Level A Detection limit of element Y 4,36 A++ level Detection limit of element Tl 2,84 A++ level

[0217] Table 14 shows that the SCI value of the detection limit of element Li is A, the SCI value of the detection limit of element Y is A++, and the SCI value of the detection limit of element Tl is A++.

[0218] According to the technical assessment standard for the bonding performance in the continuous state, as shown in Table 14, the SCI detection limits of the elements Li, Y and TI are all A++, so the technical continuous state meets the requirements.

[0219] The stability results of the key performance indicators of the clinical mass spectrometer are shown in Table 15. Table 15: Stability results of the key performance indicators number Name of the performance indicator Results of the statistical long-term state Results of the technical long-term condition Results of the continuous state Detection limit of element Li Statistically stable Meets the requirements I Detection limit of element Y Statistically unstable Meets the requirements III Detection limit of element Tl Statistically unstable Meets the requirements III Name of the performance indicator

[0220] Table 15 shows that the stability of the detection limit performance indicator for element Li is in state I, indicating good stability and an ideal level. The detection limit performance of elements Y and TI is in a statistically stable state, but their technical stability capability index meets the specified requirements; that is, they are technically stable and acceptable. However, due to the statistical instability of elements Y and TI, the device was serviced. During this service, it was discovered that the power supply lines of the ICP-MS RF amplifier board had a fault in the insulation. Further analysis revealed that the RF amplifier board operates at a high current, and the rated current of the power supply line is almost equal to the operating current, leaving no current reserve.Prolonged operation of the device due to the high current led to significant heating of the cables, which caused the insulation fault and ultimately compromised the stability of the device's detection limit. After replacing the power cable with one with a higher current rating, the heat generation in the power cable was significantly reduced, the fault was rectified, and the stability of the device's detection limit was once again restored to the required level.

[0221] This disclosure also provides an apparatus for testing the performance of a detection limit of a trace element analyzer. It is explained that the apparatus for testing the performance of a detection limit of a trace element analyzer, according to the embodiments of this disclosure, can be used to carry out the method for testing the performance of a detection limit of a trace element analyzer, according to the embodiments of this disclosure. The apparatus serves to implement the aforementioned and preferred embodiments; details already described are not repeated. Hereinafter, the term "module" refers to a combination of software and / or hardware that performs a predetermined function. Although the apparatus described in the following embodiments is preferably implemented as software, a hardware implementation or a combination of both is also possible and provided for.

[0222] The following describes the device provided in the embodiments of this disclosure for testing the performance of a detection limit of the trace element analyzer.

[0223] Fig. Figure 24 shows a schematic representation of a device for testing the performance of a detection limit of a trace element analyzer according to an embodiment of the present disclosure. As in Fig. As shown in 24, the device comprises the following: a first control unit 100 of the device for testing, which is used to ensure that a trigger control for the trace element analyzer and tuning control of the trace element analyzer are carried out after successful triggering thereof; a first processing unit 200 of the testing device, which is used to select parameters for the trace element analyzer in order to obtain the target parameters, and to process the target parameters after the tuning is carried out and the trace element analyzer meets the test standards, wherein the target parameters are the same parameters required for testing the performance of a detection limit of the trace element analyzer, wherein the processing serves to adjust the relevant performance of the target parameters; a second processing unit 300 of the device for testing, which is used to perform a sample correlation test for the selected and processed target parameters and to analyze the results of the sample correlation test in order to obtain the analysis results of the limit of detection test.

[0224] This disclosure describes a device for testing the performance of a trace element analyzer's detection limit. The triggering of the trace element analyzer is controlled by a first control unit of the testing device. After successful triggering, the first processing unit of the testing device calibrates the analyzer. Once the calibration is complete and the analyzer meets the test criteria, target parameters are selected to obtain the desired results.

[0225] The target parameters are then processed. For the selected and processed target parameters, sample-specific performance tests are carried out by the second processing unit of the testing device, and the results of these tests are analyzed to obtain the performance test result for the limit of detection. The described procedure enables standardized testing of the limit of detection of a trace element analyzer.

[0226] In the present embodiments of the device, the first control unit of the testing device comprises a first, a second, a third, and a fourth response module of the testing device. The first response module of the testing device serves to display the device's user interface on the screen in response to a first preset operation acting on a device control widget. The second response module of the testing device serves to connect the trace element analyzer, and then to display and open the vacuum opening control widget and the device triggering control widget on the screen in response to a second preset operation acting on the device connection widget in the device user interface.The third response module of the testing device controls the trace element analyzer in a vacuum standby state in response to a third preset operation acting on the vacuum control widget. The fourth response module controls the trace element analyzer in an operating state in response to a fourth preset operation acting on the device ignition control widget. As described above, the trace element analyzer is placed in an operating state by connecting the device, releasing the vacuum, and triggering the device.

[0227] In the present embodiments of the device, the first control unit of the testing device additionally comprises a fifth, a sixth, and a seventh response module. The fifth response module of the testing device serves to display a manual voting widget and an automatic voting widget below the device control widget in response to a fifth preset operation acting on the device control widget in the display interface.The sixth response module of the testing device displays a list of tuning methods on the screen in response to a sixth preset operation acting on the automatic tuning widget. A seventh preset operation, acting on the same widget, selects a specific automatic tuning method from this list to automatically tune the trace element analyzer using that method. The seventh response module retrieves historical data acquired during the automatic tuning process in response to an eighth preset operation acting on the manual tuning control widget, thus performing the tuning of the trace element analyzer.

[0228] In the present embodiments of the device, the first processing unit of the testing device comprises an eighth, a ninth, a tenth, and an eleventh response module. The eighth response module of the testing device displays a parameter selection and processing screen on the display in response to a ninth preset operation acting on the method processing widget in the display interface. The ninth response module of the testing device displays a parameter display list in the parameter selection and processing screen in response to the sampling parameter widget acting on the parameter selection and processing screen; and adds or deletes items and adds or deletes tunings in response to a tenth preset operation acting on the parameter display list.The tenth response module of the testing device is used to display a parameter analysis list on the parameter selection and processing screen in response to the analysis parameter widget acting on that screen. An eleventh preset operation, acting on the parameter analysis list, selects the option to be changed to perform the change. The eleventh response module is used to display a parameter processing screen on the parameter selection and processing screen in response to the injection parameter widget acting on that screen. A twelfth preset operation, acting on the parameter processing widget, processes the parameters, thus enabling parameter processing.

[0229] In the present embodiments of the device, the device comprises a first response unit for testing. The first response unit of the device for testing serves to display a sample list image in the fourth preset area of ​​the parameter selection and processing image in response to the sample list widget acting in the parameter selection and processing image, and to add samples in response to a thirteenth preset operation acting on the sample list image after the parameters have been processed in response to a twelfth preset operation acting on the parameter processing widget in the parameter processing image. This will enable at least the addition of samples.

[0230] In the present embodiments of the device, the device further comprises a second control unit. The second control unit of the testing device serves to perform a sample acquisition check for the trace element analyzer after the calibration of the trace element analyzer.

[0231] The second control unit of the testing device includes a twelfth response module. This twelfth response module displays a sample acquisition image on the screen in response to a fourteenth preset operation acting on a sample acquisition widget in the display interface. A fifteenth preset operation, acting on a creation widget in the sample acquisition image, displays a selection and acquisition template list within the sample acquisition image. This allows the user to select the target acquisition template from the list to perform sample acquisition.

[0232] In the present embodiments of the device, the second processing unit of the testing device comprises a thirteenth and a fourteenth response module of the testing device. The thirteenth response module of the testing device is used to display a capture list and an analysis widget in the display interface in response to a sixteenth preset operation acting on the sample analysis widget in the display interface. The fourteenth response module of the testing device is used to perform a sample analysis in response to a seventeenth preset operation acting on the analysis widget.

[0233] In the present embodiments of the device, the fourteenth response module of the testing device comprises a first and a second response submodule. The first response submodule displays an image for sample analysis in the display interface in response to a seventeenth preset operation acting on the analysis widget. The second response submodule selects a subsequent analysis option to perform the sample analysis in response to an eighteenth preset operation acting on the drop-down list in the sample analysis image. This enables the sample analysis to proceed.

[0234] In the present embodiments of the device, the device also includes a second response unit for testing. This second response unit serves to display a list of file export methods in response to a nineteenth preset operation acting in the sample analysis image, after a subsequent analysis option has been selected in response to an eighteenth preset operation acting on the drop-down list in the sample analysis image, in order to perform the sample analysis. The list of file export methods includes the export file format and information on file representation. This enables the saving of the analysis results.

[0235] The device for testing the performance of a trace element analyzer's detection limit comprises a processor and a memory. The first control unit, the first processing unit, and the second processing unit of the aforementioned testing device are stored as program units in memory. The processor executes the program units stored in memory to perform the corresponding functions. All of the aforementioned modules are located in the same processor; alternatively, they can also be located in any combination on different processors.

[0236] The embodiment of the present disclosure relates to a computer-readable storage medium with a stored program, wherein, when the program is executed, the device comprising the computer-readable storage medium is controlled in order to carry out the method for determining the stability of a mass spectrometer and the method for testing the performance of a detection limit of a trace element analyzer.

[0237] The embodiment of the present disclosure provides a processor for executing a program, wherein the program in operation carries out the method for determining the stability of a mass spectrometer and the method for testing the performance of a detection limit of a trace element analyzer.

[0238] The embodiment of the present disclosure comprises an electronic device with a processor, a memory, and a program stored in the memory and executable by the processor. When the program is executed by the processor, the steps of the method for determining the stability of a mass spectrometer and the method for testing the performance of a detection limit of a trace element analyzer are implemented.

[0239] The equipment mentioned in this description may include servers, PCs, tablets, mobile phones, etc.

[0240] This disclosure also provides a computer program product which, when executed on a data processing device, is suitable for initializing a program comprising at least the steps of the method for determining the stability of a mass spectrometer and the method for verifying the performance of a detection limit of a trace element analyzer.

[0241] It is obvious to those skilled in the art that the modules or steps described above in this disclosure can be implemented using standard computers. They can be centralized on a single computer or distributed across a network of computers. They can be implemented using executable program code and thus stored on a storage medium for execution by a computer. In some cases, the steps shown or described can be executed in a different order than presented here or manufactured as separate integrated circuits. It is also possible to manufacture multiple modules or steps of these modules or steps within a single integrated circuit. This disclosure is therefore not limited to any particular hardware-software combination.

[0242] It is obvious to those skilled in the art that embodiments of this disclosure can be provided as methods, systems, or computer program products. Therefore, this disclosure can take the form of a purely hardware implementation, a purely software implementation, or an embodiment that combines software and hardware aspects. Furthermore, this disclosure can take the form of a computer program product implemented on one or more computer-readable storage media (including, but not limited to, hard disks, CD-ROMs, optical storage media, etc.) with computer-readable program code.

[0243] This disclosure is described with reference to the flowcharts and / or block diagrams of the method, the devices (systems), and the computer programs according to embodiments of this disclosure. It is understood that each flowchart / block in the flowcharts and / or block diagrams, as well as combinations of flows / blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a specialized computer, an embedded processor, or any other programmable data processing device to create a machine.The instructions, which are executed via the processor of the computer or other programmable data processing device, enable a device to implement the functions specified in one or more flowcharts and / or one or more block diagrams.

[0244] These computer program instructions can also be stored in a computer-readable storage medium that can instruct a computer or other programmable data processing device to function in a particular way, such that the instructions stored in the computer-readable storage medium produce a manufactured product comprising instructions by which the functions specified in one or more sequences in the sequence representations and / or one or more blocks in the block representations are implemented.

[0245] These computer program instructions can also be loaded onto a computer or other programmable data processing device to perform a series of operations on the computer or other programmable device, thus creating a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more sequences in the sequence diagrams and / or one or more blocks in the block diagrams.

[0246] In a typical configuration, a computer system includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0247] Memory can include non-permanent storage in computer-readable media such as main memory (RAM) and / or non-volatile memory such as read-only memory (ROM) or flash RAM. Memory can be an example of computer-readable media.

[0248] Computer-readable media encompass both permanent and non-permanent, removable and non-removable media, which can be implemented using any method or technology for storing information. The information can be computer-readable instructions, data structures, program modules, or other data.Examples of computer-readable media include, but are not limited to, phase-change memory (PRAM), static random-access memory (SRAM), dynamic random-access memory (DRAM), other types of random-access memory (RAM), read-only memory (ROM), electrically erasable and programmable read-only memory (EEPROM), flash memory or other storage technologies, read-only compact disc storage (CD-ROM), digital versatile discs (DVD) or other optical storage, magnetic cartridges, magnetic tape storage, magnetic disk storage or other magnetic storage devices, or any other non-transitory media that can be used to store information accessible by a computing device. As defined in this description, computer-readable media do not include transitory computer-readable media, such as modulated data signals and carrier waves.

[0249] It should also be noted that the term "includes," "contains," or any other variant thereof is intended to convey non-exclusive inclusion, so that a process, method, product, or device comprising a set of elements includes not only those elements but also other elements not expressly listed or elements inherent to that process, method, product, or device. Unless further limitations apply, the element limited by the phrase "includes..." does not preclude the presence of other identical elements within the process, method, product, or device comprising that element.

[0250] The embodiments described above represent only the preferred embodiments of the present disclosure and are not intended to limit the disclosure. Various modifications and variations to the disclosure are possible for those skilled in the art. All modifications, equivalent substitutions, and improvements made within the spirit and principle of the present disclosure shall be included within the scope of protection of the present disclosure. QUOTES INCLUDED IN THE DESCRIPTION

[0000] This list of documents cited by the applicant was automatically generated and is included solely for the reader's convenience. The list is not part of the German patent or utility model application. The DPMA accepts no liability for any errors or omissions. Cited non-patent literature

[0000] Application No. 2023107154480 dated June 15, 2023 entitled “Method, device, storage medium and electronic device for determining the stability of a mass spectrometer” and claims the entire content of Chinese patent application No. 2023107155036 dated June 15, 2023

[0001] Standard JJF1159-2006 [0039, 0144]

Claims

[1] Method for determining the stability of a mass spectrometer, comprising: Identifying key performance parameters of the mass spectrometer under evaluation, wherein the key performance parameters include at least one of the following: a parameter that affects the safety performance of the mass spectrometer under evaluation, a parameter that affects the mass spectrometer under evaluation in order to achieve a function of the first importance level, and a parameter that affects the mass spectrometer under evaluation in order to achieve a function of the second importance level; Controlling the mass spectrometer under evaluation to perform a performance test in order to obtain experimental data; Performing a long-term state analysis based on the experimental data to obtain the statistical long-term state and the technical long-term state of individual key performance parameters, wherein the statistical long-term state characterizes whether the key performance parameter is in a controlled state within a given period of time, and the technical long-term state characterizes whether the key performance parameter lies within a given parameter range within the given period of time; Determine, based on the statistical long-term state and the technical long-term state, whether each of the key performance parameters meets a stability requirement. [2] The method of claim 1, wherein performing a long-term state analysis based on the experimental data, in order to obtain the statistical long-term state and the technical long-term state of individual key performance parameters, comprises the following: Determining a mean-range control chart for each key performance parameter based on the experimental data; Determining the characteristic parameters based on the mean-range control chart, wherein the characteristic parameters include at least one of the following: mean data points, the positional relationship between the mean data points and control limits, the positional relationship between the mean data points and a center line, and the fluctuation characteristics of the mean-range control chart; Determining the statistical stability of the key performance parameters based on the characteristic parameters. [3] The method of claim 1, wherein performing a long-term state analysis based on the experimental data, in order to obtain the statistical long-term state and the technical long-term state of individual key performance parameters, comprises the following Determine, using experimental data, whether each set of key performance parameters conforms to a normal distribution, where the set of key performance parameters includes several key performance parameters of the same type; Calculating a stability capability index of the corresponding key performance parameter based on the set of key performance parameters, if the set of key performance parameters conforms to the normal distribution, where the stability capability index represents the technical long-term state of the key performance parameter; Performing a normalization transformation on the set of key performance parameters to obtain a transformed set of key performance parameters that conforms to the normal distribution if the set of key performance parameters does not conform to the normal distribution, and calculating the stability capability index (SCI) of the corresponding key performance parameter based on the transformed set of key performance parameters that conforms to the normal distribution. [4] Method according to claim 3, wherein determining, based on the experimental data, whether each set of key performance parameters conforms to a normal distribution comprises: Checking the conformity of each set of key performance parameters with the normal distribution using the Kolmogorov-Smirnov test to determine whether each set of key performance parameters conforms to the normal distribution. [5] Method according to claim 3, wherein calculating the Stability Capability Index (SCI) of the corresponding key performance parameter based on the set of key performance parameters comprises: Determining the Stability Index (SCI) of the key performance parameter based on the mean of the key performance parameter, the lower bound of the key performance parameter, and the standard deviation of the key performance parameter, if the key performance parameter is a larger-the-better parameter; Determining the Stability Index (SCI) of the key performance parameter based on the mean of the key performance parameter, the upper limit of the key performance parameter, and the standard deviation of the key performance parameter, if the key performance parameter is a smaller-the-better parameter; Determining the Stability Capability Index (SCI) of the key performance parameter based on the mean of the key performance parameter, the lower bound of the key performance parameter, the upper bound of the key performance parameter, and the standard deviation of the key performance parameter, if the key performance parameter is a nominal-the-best parameter. [6] Method according to claim 3, wherein performing a normalization transformation for the set of key performance parameters, if the set of key performance parameters is not conformal to the normal distribution, comprises at least one of the following: Perform a normalization transformation on the set of key performance parameters using the logarithmic transformation method if the set of key performance parameters does not conform to the normal distribution; Perform a normalization transformation on the set of key performance parameters using the square root transformation method if the set of key performance parameters is not conformal to the normal distribution; Perform a normalization transformation on the set of key performance parameters using the square root arcsine transformation method if the set of key performance parameters is not conformal to the normal distribution; Perform a normalization transformation on the set of key performance parameters using the square transformation method if the set of key performance parameters does not conform to the normal distribution; Perform a normalization transformation on the set of key performance parameters using the inverse transformation method if the set of key performance parameters does not conform to the normal distribution; Perform a normalization transformation on the set of key performance parameters using the Box-Cox transformation method if the set of key performance parameters does not conform to the normal distribution; Performing a normalization transformation on the set of key performance parameters using the normal score transformation method when the set of key performance parameters is not conformal to the normal distribution. [7] The method of claim 1, wherein the method further comprises: Determining the importance level of the key performance parameter as the fourth importance level if the key performance parameter is a parameter that influences the mass spectrometer being evaluated in such a way that it achieves the function of the second importance level; Determining the importance level of the key performance parameter as the third importance level if the key performance parameter is a parameter that influences the mass spectrometer being evaluated in such a way that it achieves the function of the first importance level; Determining the importance level of the key performance parameter as the second importance level if the key performance parameter is a parameter that affects the safety performance of the mass spectrometer being evaluated, and a warning message is present; Determining the importance level of the key performance parameter as the first importance level if the key performance parameter is a parameter that affects the safety performance of the mass spectrometer being evaluated and no warning message is present, with the importance levels being ordered in descending order as follows: the first importance level, the second importance level, the third importance level and the fourth importance level. [8] The method of claim 7, wherein the method further comprises: Determine the requirement level of the Stability Capability Index (SCI) of the key performance parameter as the fourth capability level if the importance level of the key performance parameter is the fourth importance level; Determine the requirement level of the Stability Capability Index (SCI) of the key performance parameter as the third capability level if the importance level of the key performance parameter is the third importance level; Determine the requirement level of the Stability Capability Index (SCI) of the key performance parameter as the second capability level if the importance level of the key performance parameter is the second importance level; Determining the requirement level of the Stability Capability Index (SCI) of the key performance parameter as the second or first capability level if the importance level of the key performance parameter is the first importance level, with the capability levels arranged in descending order as follows: the first capability level, the second capability level, the third capability level, and the fourth capability level. [9] Method according to claim 1, wherein determining, based on the statistical long-term state and the technical long-term state, whether each of the key performance parameters meets a stability requirement, comprises: Determine that the key performance parameters satisfy the stability requirement to a first degree if the statistical duration state meets the statistical duration state requirement and the technical duration state meets the technical duration state requirement; Determine that the key performance parameters meet the stability requirement to a second degree if the statistical duration state does not meet the statistical duration state requirement and the technical duration state meets the technical duration state requirement; Determine that the key performance parameters do not meet the stability requirement if the technical endurance state does not meet the technical endurance state requirement. [10] Method according to claim 9, wherein, after determining that the key performance parameters satisfy the stability requirement to a second degree if the statistical duration state does not satisfy the statistical duration state requirement and the technical duration state satisfies the technical duration state requirement, the method further comprises: Determining the cause of statistical anomalies; Improving performance that affects the statistical durability of key performance parameters is based on the cause of statistical anomalies until the statistical durability of the key performance parameters meets the requirements for the statistical durability. [11] The method of claim 9, wherein the method further comprises: Determine whether the statistical durability of the key performance parameters meets the requirements for statistical durability; Determining whether the statistical durability of the key performance parameters meets the requirements for statistical durability includes the following: Recording the number of different consecutive numerical points for the key performance parameters; Determine the maximum permissible number of external points, which corresponds to the number of distinct consecutive numerical points; Determine that the statistical duration of the key performance parameters satisfies the statistical duration requirement if the number of external points corresponding to the number of at least one of the distinct consecutive numerical points is less than the corresponding maximum allowable number of external points. [12] Method according to any one of claims 1 to 11, wherein the key performance parameters include at least one of the following parameters: Detection limit, ion transport efficiency and parameters that influence interference immunity. [13] Method for testing the performance of a detection limit of a trace element analyzer, wherein the trace element analyzer is a mass spectrometer, and the stability of the mass spectrometer is achieved using the method for determining the stability of a mass spectrometer according to any one of claims 1 to 12, comprising: Performing a trigger control for the trace element analyzer and a tuning control of the trace element analyzer after successful triggering; Selecting parameters for the trace element analyzer to obtain the target parameters, and preparing the target parameters when the tuning control is performed and the trace element analyzer meets the test standards, wherein the target parameters are the same parameters required for testing the performance of a detection limit of the trace element analyzer, wherein the preparation serves to adjust the relevant performance of the target parameters; Performing a sample correlation test for the selected and processed target parameters and analyzing the results of the sample correlation test to obtain the analysis results of the limit of detection test. [14] Method according to claim 13, wherein performing a trigger control for the trace element analyzer comprises: Displaying the user interface of a device on the screen in response to an initial preset operation that acts on a device control widget; Connecting the trace element analyzer, and the appearance and opening of the vacuum opening control widget and the device triggering control widget on the display in response to a second preset operation acting on the device connection widget in the device user interface; Controlling the trace element analyzer in a vacuum standby state in response to a third preset operation acting on the vacuum control widget; Controlling the trace element analyzer in an operating state in response to a fourth preset operation acting on the device ignition control widget. [15] Method according to claim 13, wherein the tuning control of the trace element analyzer after successful triggering comprises the following: Appearance of a manual voting widget and an automatic voting widget below the device control widget in response to a fifth preset operation acting on the device control widget in the display interface; Displaying a list of tuning methods on the display in response to a sixth preset operation acting on the automatic tuning widget; selecting a targeted automatic tuning method from this list of automatic tuning methods in response to a seventh preset operation acting on the list of automatic tuning methods, in order to automatically tune the trace element analyzer using this targeted automatic tuning method; Retrieving historical data obtained during the automatic tuning process in response to an eighth preset operation acting on the manual tuning control widget to perform tuning control for the trace element analyzer. [16] Method according to claim 13, wherein the step of selecting parameters for the trace element analyzer to obtain the target parameters and preparing the target parameters comprises: Displaying a parameter selection and processing image on the display in response to a ninth preset operation that acts on the method processing widget in the display interface; Appearance of a parameter display list in the parameter selection and processing screen in response to the sample parameter widget acting in the parameter selection and processing screen; and adding or deleting items and adding or deleting tuning in response to a tenth preset operation acting on the parameter display list; Appearance of a parameter analysis list on the parameter selection and rework screen in response to the analysis parameter widget acting on the parameter selection and rework screen, and selection of the option to be changed in response to an eleventh preset operation acting on the parameter analysis list to perform the change; Appearance of a parameter rework image on the parameter selection and rework image in response to the injection parameter widget acting on the parameter selection and rework image, and rework of the parameters in response to a twelfth preset operation acting on the parameter rework widget on the parameter rework image. [17] Method according to claim 16, wherein the method after processing the parameters in response to a twelfth preset operation acting on the parameter processing widget in the parameter processing image further comprises: Appearance of a sample list image in the fourth preset area of ​​the parameter selection and rework image in response to the sample list widget acting in the parameter selection and rework image, and adding samples in response to a thirteenth preset operation acting on the sample list image. [18] Method according to claim 13, wherein the method according to the tuning control of the trace element analyzer further comprises: performing a sample acquisition check for the trace element analyzer; wherein performing a sample acquisition check for the trace element analyzer comprises: Displaying a sample acquisition image on the display in response to a fourteenth preset operation acting on a sample acquisition widget in the display interface, and appearing a selection and acquisition template list in the sample acquisition image in response to a fifteenth preset operation acting on a creation widget in the sample acquisition image to select the target acquisition template from the selection and acquisition template list to perform sample acquisition using the target acquisition template. [19] Method according to claim 13, wherein analyzing the results of the sample correlation test comprises: Displaying a sample collection list and an analysis widget in the display interface in response to a sixteenth preset operation that acts on the sample analysis widget in the display interface; Performing a sample analysis in response to a seventeenth preset operation acting on the analysis widget. [20] Method according to claim 19, wherein performing a sample analysis in response to a seventeenth preset operation acting on the analysis widget comprises: Displaying an image for sample analysis in the display surface in response to a seventeenth preset operation acting on the analysis widget; Selecting a next analysis option to perform the sample analysis, in response to an eighteenth preset operation that acts on the drop-down list in the sample analysis image. [21] The method of claim 20, wherein the method, after selecting a next analysis option to perform the sample analysis in response to an eighteenth preset operation acting on the drop-down list in the sample analysis image, further comprises: Appearance of a list of file export methods in response to a nineteenth preset operation acting in the image for sample analysis, wherein the list of file export methods includes the export file format and file representation information. [22] Device for determining the stability of a mass spectrometer, comprising: a first acquisition unit configured to acquire key performance parameters of the mass spectrometer under evaluation, wherein the Key performance parameters must include at least one of the following parameters: Parameter that influences the safety performance of the mass spectrometer being evaluated, parameter that influences the mass spectrometer being evaluated in order to achieve a function of the first importance level, and parameter that influences the mass spectrometer being evaluated in order to achieve a function of the second importance level; a control unit configured to control the mass spectrometer under evaluation in order to obtain experimental data; an analysis unit configured to perform a long-term state analysis based on experimental data in order to obtain the statistical long-term state and the technical long-term state of individual key performance parameters, wherein the statistical long-term state characterizes whether the key performance parameter is in a controlled state within a specified period of time, and the technical long-term state characterizes whether the key performance parameter lies within a specified parameter range within a specified period of time; a first determination unit configured to determine, based on the statistical and technical long-term states, whether each of the key performance parameters meets a stability requirement. [23] Device for testing the performance of a detection limit of a trace element analyzer, wherein the trace element analyzer is a mass spectrometer, and the stability of the mass spectrometer is achieved using the method for determining the stability of a mass spectrometer according to any one of claims 1 to 12, comprising: a first control unit of the testing device, which is used to perform a trigger control for the trace element analyzer and a tuning control of the trace element analyzer after successful triggering; a first processing unit of the testing device, which is used to select parameters for the trace element analyzer in order to obtain the target parameters, and to process the target parameters when the tuning control is performed and the trace element analyzer meets the test standards, wherein the target parameters are the same parameters required for testing the performance of a detection limit of the trace element analyzer, wherein the processing serves to adjust the relevant performance of the target parameters; a second processing unit of the device for testing, which is used to perform a sample correlation test for the selected and processed target parameters and to analyze the results of the sample correlation test in order to obtain the analysis results of the limit of detection test. [24] Computer-readable storage medium, wherein the computer-readable storage medium comprises a stored program, wherein the device on which the computer-readable storage medium is located is controlled when the program is executed to carry out the method for determining the stability of a mass spectrometer according to any one of claims 1 to 12 or the method for testing the performance of a detection limit of a trace element analyzer according to any one of claims 13 to 21. [25] Electronic device comprising: one or more processors, a memory and one or more programs, wherein one or more programs are stored in the memory and are configured such that one or more programs are executed by one or more processors, wherein one or more programs comprise the method for determining the stability of a mass spectrometer according to any one of claims 1 to 12 or the method for testing the performance of a detection limit of a trace element analyzer according to any one of claims 13 to 21.

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