Sample injection control method and system of mass spectrometer

Abstract

The invention relates to a sample injection control method and system for a mass spectrometer, and the method comprises the steps: recognizing a to-be-detected sample, and obtaining a sample recognition result; on the basis of the sample identification result, determining sample introduction parameters of the to-be-detected sample; and performing sample injection control on the to-be-detected sample according to the sample injection parameters, and introducing the to-be-detected sample into a mass spectrometer for analysis. The technical problem that the mass spectrometry efficiency is low due to the fact that manual intervention is needed in the sample injection process of an existing mass spectrometer is solved, and the technical effects that automatic sample injection control is achieved through automatic sample recognition and intelligent parameter matching, and the overall mass spectrometry efficiency is improved are achieved.

Description

Technical Field

[0001] The present invention relates to the field of mass spectrometer sample injection control, and in particular to a mass spectrometer sample injection control method and system. Background Art

[0002] In the process of mass spectrometry analysis, the sample injection link is a key prerequisite step of the entire analysis process, and its accuracy and efficiency directly affect the quality of the subsequent mass spectrometry analysis results. In the prior art, the injection control of the mass spectrometer mainly relies on manual operation. The operator sets the injection parameters, such as injection temperature, injection rate, split ratio, etc., according to the properties and types of the sample to be tested, and then introduces the sample into the mass spectrometer for analysis. In this injection method, the operator needs to have rich professional knowledge and experience to determine the injection parameters that are most suitable for a specific sample. Frequent manual participation greatly reduces the efficiency of mass spectrometry analysis, especially in scenarios where a large number of samples need to be analyzed, causing a serious bottleneck in work efficiency. Therefore, the existing mass spectrometer has the defect that the injection process requires manual intervention, resulting in low efficiency of mass spectrometry analysis. Summary of the invention

[0003] The present invention aims to solve the technical problem that the sampling process in the existing mass spectrometer requires manual intervention, resulting in low efficiency of mass spectrometry analysis, and provides a sampling control method and system for a mass spectrometer to solve the problem.

[0004] The technical solution of the present invention to solve the above technical problems is as follows:

[0005] In a first aspect, the present invention provides a method for controlling the injection of a mass spectrometer, comprising: identifying a sample to be tested and obtaining a sample identification result; determining an injection parameter of the sample to be tested based on the sample identification result; controlling the injection of the sample to be tested according to the injection parameter, and introducing the sample to be tested into the mass spectrometer for analysis.

[0006] Optionally, identifying the sample to be tested and obtaining the sample identification result includes: performing image acquisition on the sample to be tested to obtain the image of the sample to be tested; performing image analysis on the image of the sample to be tested according to a preset feature set to obtain multiple sample features of the sample to be tested; and using the multiple sample features to be tested as the sample identification result of the sample to be tested.

[0007] Optionally, determining the injection parameters of the sample to be tested based on the sample identification result includes: constructing a measurement sample space according to archived measurement sample records and multiple feature dimensions; extracting multiple sample features of the sample to be tested in the sample identification result; based on the multiple sample features, determining multiple adjacent reference samples of the sample to be tested in the measurement sample space; and determining the injection parameters of the sample to be tested based on the multiple adjacent reference samples.

[0008] Optionally, constructing a measurement sample space based on archived measurement sample records and multiple feature dimensions includes: generating multiple feature dimensions based on the preset feature set, and generating a basic feature space based on the multiple feature dimensions; interacting with a historical measurement database, and obtaining archived measurement sample records in the historical measurement database according to a preset time period; traversing the archived measurement sample records to obtain a first archived measurement sample; extracting multiple first sample features of the first archived measurement sample, determining a first sample position in the basic feature space based on the multiple first sample features, and updating the basic feature space based on the first sample position; processing the remaining archived measurement samples in the archived measurement sample records in sequence, and continuously updating the basic feature space until all archived measurement samples are traversed to obtain a measurement sample space.

[0009] Optionally, determining multiple neighboring reference samples of the sample to be tested in the measurement sample space based on the multiple characteristics of the samples to be tested includes: determining a target position of the sample to be tested in the measurement sample space according to the multiple characteristics of the samples to be tested; determining a neighboring distance in the measurement sample space based on the target position according to a neighboring distance determination formula; constructing a neighboring space of the sample to be tested with the target position as the center and the neighboring distance as the radius; and extracting samples in the neighboring space to obtain multiple neighboring reference samples.

[0010] Optionally, the proximity distance determination formula is:

[0011]

[0012] in, is the basic reference distance, To measure the average sample density of the sample space, is the local sample density around the target location, is the preset minimum number of reference samples, is the number of samples within the basic reference distance range centered on the target position, is the smoothing factor.

[0013] Optionally, determining the injection parameters of the sample to be tested based on the multiple adjacent reference samples includes: obtaining the sample injection parameters and corresponding mass spectrometry analysis effect scores of the multiple adjacent reference samples; based on the mass spectrometry analysis effect scores, screening the sample injection parameters of the multiple adjacent reference samples, eliminating the sample injection parameters with mass spectrometry analysis effect scores lower than a preset threshold, and obtaining a set of preferred sample injection parameters; and performing weighted averaging on the sample injection parameters in the set of preferred sample injection parameters according to the corresponding mass spectrometry analysis effect scores to obtain the injection parameters of the sample to be tested.

[0014] In a second aspect, the present invention provides an injection control system for a mass spectrometer, comprising: a sample identification module, used to identify a sample to be tested and obtain a sample identification result; a parameter determination module, used to determine the injection parameters of the sample to be tested based on the sample identification result; and an injection control module, used to control the injection of the sample to be tested according to the injection parameters, and introduce the sample to be tested into the mass spectrometer for analysis.

[0015] The beneficial effects of the present invention are:

[0016] Identify the sample to be tested and obtain the sample identification result. It can automatically obtain the basic characteristic information such as the nature and type of the sample to be tested, laying the foundation for the subsequent determination of the injection parameters. Based on the sample identification result, determine the injection parameters of the sample to be tested. According to the preset sample-parameter correspondence, it can automatically determine the injection parameters that are most suitable for the current sample to be tested, such as injection temperature, injection rate, split ratio, etc., without manual intervention. According to the injection parameters, the injection of the sample to be tested is controlled, and the sample to be tested is introduced into the mass spectrometer for analysis. Through this step, the injection operation is performed according to the automatically determined parameters to ensure that the sample is introduced into the mass spectrometer for analysis under the optimal conditions.

[0017] Through the above technical scheme, the present invention realizes the automation of the whole process from sample identification to parameter determination and then to injection control, effectively solving the technical problem that the injection process in the existing mass spectrometer requires manual intervention, resulting in low efficiency of mass spectrometry analysis, and achieves the technical effect of realizing automatic injection control through automatic sample identification and intelligent parameter matching, thereby improving the overall efficiency of mass spectrometry analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic flow chart of a method for controlling a sample injection of a mass spectrometer provided by the present invention;

[0019] Figure 2 A schematic structural diagram of a sample injection control system for a mass spectrometer provided by the present invention.

[0020] In the accompanying drawings, the components represented by the reference numerals are as follows:

[0021] A sample identification module 11 , a parameter determination module 12 , and a sample injection control module 13 . DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0023] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0024] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in the present invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present invention.

[0025] Embodiment 1, as Figure 1 As shown, an embodiment of the present invention provides a sampling control method of a mass spectrometer, which is applied to a sampling control system. The method includes:

[0026] S100: Identify the sample to be tested and obtain the sample identification result;

[0027] S200: Determine the injection parameters of the sample to be tested based on the sample identification result;

[0028] S300: Controlling the injection of the sample to be tested according to the injection parameters, and introducing the sample to be tested into a mass spectrometer for analysis.

[0029] Specifically, first, the sample to be tested that enters the injection control system is subjected to feature extraction and identification analysis by automatic identification technology, so as to obtain a sample identification result that can characterize the sample to be tested. The sample identification process can be implemented in a variety of ways, such as but not limited to optical identification, barcode scanning, radio frequency identification technology, image processing and other technical means. In a preferred embodiment, the identification process uses image acquisition and analysis technology to automatically identify the appearance characteristics, color, shape, size, etc. of the sample to be tested. The sample identification result contains enough information to distinguish different samples, providing a basis for determining subsequent injection parameters. The sample identification result can be a feature vector, containing feature parameters of multiple dimensions, which together constitute the identification information of the sample to be tested. By obtaining the sample identification result, the automatic identification of the sample to be tested is realized, and the sample identification result can be obtained without manual intervention, laying the foundation for subsequent parameter matching and injection control, thereby improving the automation and efficiency of mass spectrometry.

[0030] Subsequently, the obtained sample identification results are used to determine the optimal injection parameters for the sample to be tested. The injection parameters include, but are not limited to, injection rate, injection volume, injection temperature, solvent ratio, ion source parameters, detector settings, and other key parameters that affect the mass spectrometry analysis effect. For example, a sample space-based neighboring reference matching algorithm is used to construct a multidimensional feature space to find reference samples with similar characteristics to the sample to be tested in historical measurement samples, and based on the injection parameters of these reference samples and their corresponding mass spectrometry analysis effects, the optimal injection parameters of the sample to be tested are comprehensively determined. The injection parameter determination process can automatically adjust the injection parameters according to the characteristic differences of the samples without the need for manual experience judgment or manual setting, thereby avoiding the problems of sample waste and low analysis efficiency caused by improper parameter settings in traditional mass spectrometry analysis.

[0031] Afterwards, the sample to be tested is injected and controlled according to the determined injection parameters, and the sample to be tested is accurately introduced into the mass spectrometer for analysis. The determined injection parameters are received by the control unit of the injection control system, and these parameters are converted into specific operation control signals of the injection device. The control unit accurately regulates the various execution components of the injection device, including but not limited to the syringe drive mechanism, the sample transmission channel, the temperature control device, the pressure regulator, etc., so that it strictly performs the sample injection operation according to the determined injection parameters. During the injection process, the execution status of each control parameter is monitored in real time to ensure the stability and reliability of the injection process. When the sample to be tested is successfully introduced into the mass spectrometer, the mass spectrometry analysis is automatically started, and a series of analysis processes such as ionization, mass separation and detection are performed on the sample. Through the seamless connection from parameter determination to actual injection, the injection operation link that requires manual intervention in mass spectrometry analysis is completely eliminated, and the automation, accuracy and repeatability of sample analysis are significantly improved, thereby effectively improving the overall efficiency of mass spectrometry analysis. At the same time, due to the use of the most optimized injection parameters, it can also reduce sample consumption, improve analytical sensitivity and accuracy, and provide a solid guarantee for the reliability of mass spectrometry analysis results.

[0032] The above-mentioned mass spectrometer injection control method solves the problem of low efficiency caused by manual intervention in the injection process of existing mass spectrometry analysis, realizes the automation of the whole process from sample entry to analysis completion, reduces manual operation time and errors, and improves the accuracy, repeatability and reliability of mass spectrometry analysis; at the same time, due to the automatic configuration of the optimal injection parameters for different samples, it avoids sample waste and degradation of analysis quality caused by improper parameter settings, and improves the overall efficiency and resource utilization of mass spectrometry analysis.

[0033] As an optional implementation, the identifying the sample to be tested and obtaining the sample identification result includes:

[0034] S110: Capturing an image of the sample to be tested to obtain an image of the sample to be tested;

[0035] S120: performing image analysis on the image of the sample to be tested according to a preset feature set to obtain a plurality of sample features of the sample to be tested;

[0036] S130: Using the multiple features of the samples to be tested as sample identification results of the samples to be tested.

[0037] Specifically, when identifying the sample to be tested and obtaining the sample identification result, first, the image acquisition device set in the sample injection control system is used to acquire an image of the sample to be tested to obtain a high-definition image of the sample to be tested. The image acquisition device can be, but is not limited to, a high-resolution digital camera, an industrial camera, or an optical scanner, etc., which is installed at an appropriate position where the sample enters the sample injection control system to ensure that a clear and complete image of the sample to be tested can be obtained. The image acquisition process can be carried out under different lighting conditions to obtain more comprehensive sample visual feature information.

[0038] Secondly, the collected image of the sample to be tested is analyzed according to the preset feature set. The preset feature set is a set of feature parameters that have been optimized and screened, including but not limited to the color distribution, geometric shape, size, surface texture, transparency, liquid level and other visual features that can be extracted from the image. Using image processing algorithms, such as edge detection, color segmentation, texture analysis and other technical means, multiple quantitative or qualitative features of the sample to be tested are extracted from the image of the sample to be tested, and these features together constitute a feature set that can accurately characterize the sample to be tested. Subsequently, the multiple features of the sample to be tested obtained are used as the sample identification result of the sample to be tested. The sample identification result provides a basis for the subsequent determination of the injection parameters.

[0039] By obtaining the sample identification results, automated and contactless identification of the samples to be tested is achieved, avoiding the subjective errors and inefficiencies that may exist in traditional manual identification methods, and laying a solid foundation for automated sample injection control of mass spectrometry analysis.

[0040] As an optional implementation, determining the injection parameters of the sample to be tested based on the sample identification result includes:

[0041] S210: constructing a measurement sample space according to the archived measurement sample records and multiple feature dimensions;

[0042] S220: extracting multiple features of the sample to be tested from the sample identification result;

[0043] S230: Determine a plurality of adjacent reference samples of the sample to be tested in the measurement sample space based on the plurality of features of the sample to be tested;

[0044] S240: Determine the injection parameters of the sample to be tested according to the multiple adjacent reference samples.

[0045] Specifically, first, the measurement sample space is constructed using historical analysis data. Specifically, based on the archived measurement sample records accumulated during the historical operation of the mass spectrometer, combined with multiple predefined feature dimensions, a multidimensional feature space is established to form a measurement sample space. The measurement sample space contains various types of samples that have been analyzed historically and their corresponding feature information and injection parameter information, and is the basic data structure for matching. The space can be updated in real time and is continuously expanded and optimized with the analysis of new samples. Secondly, from the obtained sample identification results, multiple feature parameters of the sample to be tested are extracted. These feature parameters are consistent with the feature dimensions used when constructing the measurement sample space, ensuring that the sample to be tested can be accurately mapped to the established measurement sample space. These feature parameters are standardized so that they meet the requirements for position positioning in the measurement sample space.

[0046] Then, based on the extracted features of the sample to be tested, the sample to be tested is mapped to a specific position in the measurement sample space, and historical samples with similar features to the sample to be tested are searched in the space to determine multiple adjacent reference samples. These adjacent reference samples are historical samples that are closest to the sample to be tested in the feature space, and the features of the adjacent reference samples are highly similar to the sample to be tested. Afterwards, the optimal injection parameters are determined for the sample to be tested based on the multiple adjacent reference samples determined and their historical injection parameters and analysis results. For example, the optimal injection parameters suitable for the current sample to be tested are generated by comprehensively considering factors such as the similarity of the adjacent reference samples, historical analysis results, and parameter sensitivity.

[0047] Through intelligent mapping from sample characteristics to injection parameters, the optimal injection parameters can be automatically determined for different types of samples without manual experience, improving the automation level and analysis efficiency of mass spectrometry. This method is highly adaptable and scalable, can handle various types of samples, and continuously optimizes the accuracy of parameter matching as historical data accumulates.

[0048] As an optional implementation manner, constructing a measurement sample space according to archived measurement sample records and multiple feature dimensions includes:

[0049] S211: generating a plurality of feature dimensions according to the preset feature set, and generating a basic feature space based on the plurality of feature dimensions;

[0050] S212: interacting with a historical measurement database, obtaining archived measurement sample records in the historical measurement database according to a preset time period;

[0051] S213: traverse the archived measurement sample records to obtain a first archived measurement sample;

[0052] S214: extracting a plurality of first sample features of the first archived measurement sample, determining a first sample position in the basic feature space according to the plurality of first sample features, and updating the basic feature space based on the first sample position;

[0053] S215: processing the remaining archived measurement samples in the archived measurement sample records in sequence, and continuously updating the basic feature space, until all archived measurement samples are traversed to obtain a measurement sample space.

[0054] Specifically, when constructing the measurement sample space, first, multiple feature dimensions are generated according to the preset feature set used. The feature dimensions in the preset feature set are the coordinate axes of the measurement sample space, and each dimension corresponds to a characteristic parameter of the sample, such as color, shape, size, etc. Secondly, a multidimensional space is initialized based on these feature dimensions to obtain the basic feature space, which is initially empty and will be gradually filled with historical sample data through subsequent steps. The number and type of dimensions of the basic feature space directly determine the accuracy and efficiency of subsequent sample matching, so features with significant distinguishing capabilities should be selected as space dimensions. Then, interact with the historical measurement database to query and obtain archived measurement sample records that meet the conditions according to the preset time period parameters. The preset time period can be a fixed historical period, such as the last month, three months or one year, or a time range that is dynamically adjusted according to the sample type. These archived measurement sample records contain key data such as the characteristic information of historical analysis samples, injection parameter settings, and analysis result evaluation.

[0055] Then, the acquired archived measurement sample records are traversed, and one archived measurement sample is acquired each time as the first archived measurement sample. Next, the first sample feature of the first archived measurement sample (i.e., multiple feature parameters of the first archived measurement sample) is extracted, and the first sample feature is mapped to the basic feature space, and the first sample position of the sample in the space (i.e., the position coordinates of the first archived measurement sample in the basic feature space) is determined. Subsequently, the basic feature space is updated based on the first sample position, and the position information of the sample and the associated injection parameters and analysis effect data are stored in the basic feature space, so as to realize the first mapping of the historical sample data to the feature space. After that, the remaining sample data in the archived measurement sample records are processed in sequence according to the same method as that for processing the first archived measurement sample, and the basic feature space is continuously updated. Each time a historical sample is processed, the feature space is expanded and optimized. After the traversal and processing of all archived measurement samples are completed, the basic feature space is transformed into a measurement sample space containing complete historical data, and each point in the space represents a historical analysis sample and its related information.

[0056] Through the automated construction of the measurement sample space, the space integrates historical analysis experience and sample feature information, providing a data basis for subsequent intelligent parameter matching. With the continued use of the mass spectrometer, the space can be continuously updated and optimized, so that the accuracy and efficiency of parameter matching can be continuously improved.

[0057] As an optional implementation manner, determining a plurality of adjacent reference samples of the sample to be tested in the measurement sample space based on the plurality of features of the sample to be tested includes:

[0058] S231: determining a target position of the sample to be tested in the measurement sample space according to the multiple features of the sample to be tested;

[0059] S232: determining a proximity distance in the measurement sample space based on the target position according to a proximity distance determination formula;

[0060] S233: constructing a neighborhood space of the sample to be tested with the target position as the center and the neighborhood distance as the radius;

[0061] S234: Extract samples in the adjacent space to obtain a plurality of adjacent reference samples.

[0062] Specifically, when obtaining multiple neighboring reference samples, first, according to the multiple features of the samples to be tested extracted from the sample identification results, the samples to be tested are mapped to specific position coordinates in the measurement sample space, that is, the target position of the samples to be tested in the multidimensional feature space is determined. The target position is the precise positioning of the sample to be tested in the feature space, reflects the characteristics of the sample to be tested in each feature dimension, and is the starting point for subsequent neighboring sample searches. The same feature mapping method as that used in constructing the measurement sample space is used to ensure that the samples to be tested are compared with the historical samples in the same coordinate system. Secondly, according to the specially designed neighbor distance determination formula, the appropriate neighbor distance is calculated in the measurement sample space based on the target position of the sample to be tested. The neighbor distance is a key parameter for finding reference samples, and its size directly affects the number and similarity of reference samples. For example, considering the overall sample distribution density of the measurement sample space and the local sample density around the target position, the optimal neighbor distance is adaptively determined to ensure that a sufficient number of reference samples are obtained and that these samples have a high similarity with the samples to be tested.

[0063] Then, with the target position of the sample to be tested as the center and the determined proximity distance as the radius, a hyperspherical proximity space is constructed in the measurement sample space. The proximity space defines the range of the reference sample to be searched, and its geometry and size are automatically adjusted according to the characteristics of the feature space and the proximity distance determination formula to adapt to the sample distribution characteristics in different regions. After that, historical samples are extracted from the constructed proximity space to obtain multiple proximity reference samples. These proximity reference samples are historical samples that are closest to the sample to be tested in the feature space, and their characteristics are highly similar to those of the sample to be tested. The location, characteristics, injection parameters, and historical analysis results of these proximity reference samples are recorded to provide data support for subsequent parameter determination.

[0064] By automatically searching for adjacent reference samples based on feature similarity in the measurement sample space, the subjectivity and uncertainty of relying on manual experience and judgment are avoided, providing an objective and reliable reference basis for determining the injection parameters of the samples to be tested.

[0065] As an optional implementation, the formula for determining the proximity distance is:

[0066]

[0067] in, is the basic reference distance, To measure the average sample density of the sample space, is the local sample density around the target location, is the preset minimum number of reference samples, is the number of samples within the basic reference distance range centered on the target position, is the smoothing factor.

[0068] Specifically, the neighbor distance determination formula comprehensively considers the global distribution characteristics and local distribution characteristics of the sample space, and can adaptively determine the optimal neighbor distance for samples to be tested at different locations.

[0069] in, The basic reference distance is a preset initial search radius, which serves as a benchmark for calculating the proximity distance. The basic reference distance can be preset based on the overall size and number of dimensions of the measurement sample space. Usually, a distance value that can contain a certain number of historical samples is selected. To measure the average sample density of the sample space, it reflects the distribution density of historical samples in the entire feature space. This parameter is obtained by calculating the total number of samples in the entire measurement sample space and dividing it by the space volume. It is a global statistical characteristic of the sample distribution. It is the local sample density around the target location, reflecting the historical sample concentration level in the area where the sample to be tested is located. This parameter is obtained by calculating the number of samples within a preset range centered on the target location and dividing it by the volume of the range, and characterizes the local characteristics of the sample distribution. The preset minimum number of reference samples is the minimum number of neighboring reference samples that one hopes to obtain. This parameter ensures that a sufficient number of reference samples are obtained to improve the reliability and stability of parameter matching. When the target position is the center, the basic reference distance The number of samples within the range reflects the number of reference samples available within the basic search range. is a smoothing factor used to avoid the situation where the divisor is zero and smooth the calculation results of the neighboring distance to improve the robustness and stability of the formula.

[0070] By using this proximity distance determination formula, adaptive calculation of proximity distance can be achieved. Item, to achieve adaptive adjustment of local sample density; when the local density is higher than the average density, the neighbor distance is reduced accordingly to avoid including too many irrelevant samples; when the local density is lower than the average density, the neighbor distance is increased accordingly to ensure that enough reference samples are obtained. The neighbor distance is appropriately increased when the number of samples in the basic range is insufficient; the neighbor distance remains basically unchanged when the number of samples in the basic range is sufficient. By comprehensively considering global and local factors, the determination of the neighbor distance takes into account both the overall sample distribution characteristics and the local sample distribution differences, so that appropriate quantity and quality of neighbor reference samples can be obtained in various complex sample distribution situations.

[0071] The above-mentioned formula for determining the proximity distance enables the search strategy to be flexibly adjusted in different sample density areas, improves the quality and relevance of the proximity reference samples, and provides a more reliable data basis for the subsequent determination of injection parameters.

[0072] As an optional implementation, determining the injection parameters of the sample to be tested according to the multiple adjacent reference samples includes:

[0073] S241: Obtaining sample injection parameters and corresponding mass spectrometry analysis effect scores of the plurality of adjacent reference samples;

[0074] S242: Based on the mass spectrometry analysis effect score, the sample injection parameters of the plurality of adjacent reference samples are screened, and the sample injection parameters whose mass spectrometry analysis effect scores are lower than a preset threshold are eliminated to obtain a set of preferred sample injection parameters;

[0075] S243: performing weighted averaging on the sample injection parameters in the preferred sample injection parameter set according to the corresponding mass spectrometry analysis effect scores to obtain the injection parameters of the sample to be tested.

[0076] Specifically, when determining the injection parameters of the sample to be tested, first, obtain the historical injection parameters of multiple adjacent reference samples and their corresponding mass spectrometry analysis effect scores. Sample injection parameters include but are not limited to injection rate, injection volume, injection temperature, solvent ratio, ion source parameters and other key parameters that affect the mass spectrometry analysis effect. The mass spectrometry analysis effect score is a comprehensive evaluation index that reflects the overall quality of mass spectrometry analysis under specific injection parameters. The score can be calculated by organizing an expert group based on multiple analysis indicators such as peak shape, signal-to-noise ratio, sensitivity, and repeatability. The injection parameter setting records of these adjacent reference samples and their corresponding analysis effect scores are extracted from the historical measurement database as the basic data for parameter determination.

[0077] Secondly, the sample injection parameters of multiple adjacent reference samples are screened and optimized based on the mass spectrometry analysis effect score. Specifically, a preset threshold is set, which represents the minimum acceptable mass spectrometry analysis effect standard. The sample injection parameters with mass spectrometry analysis effect scores lower than the preset threshold in the adjacent reference samples are eliminated, and only those sample injection parameters with higher analysis effect scores are retained, thereby forming a set of preferred sample injection parameters. This screening process ensures that only those injection parameters that perform well in historical analysis will be used for subsequent parameter calculations, effectively avoiding the negative impact of poor parameter settings on the determination of parameters of the samples to be tested. Afterwards, the sample injection parameters in the preferred sample injection parameter set are weighted averaged to obtain the final injection parameters of the samples to be tested. In the weighted average process, the mass spectrometry analysis effect scores of each adjacent reference sample are used as weight coefficients, so that reference samples with better analysis effects have greater influence in the final parameter determination. Through this weighted average method, the experience of multiple high-quality reference samples can be comprehensively considered to generate the most optimized injection parameter settings for the samples to be tested.

[0078] By introducing mass spectrometry analysis effect scores as the basis for parameter screening and weighting, it is ensured that the parameter determination process pays more attention to historical analysis results, thereby improving the reliability and effectiveness of parameter determination. By eliminating low-scoring parameters and weighted averaging high-scoring parameters, the optimization and refinement of historical experience is achieved, avoiding the adverse parameter effects that may be introduced by simple averaging, and making the final parameters more optimized. The entire parameter determination process is fully automated, without the need for manual intervention and empirical judgment, which improves the efficiency and accuracy of mass spectrometry analysis. This method can not only automatically determine the optimal injection parameters for various types of samples, but also continuously optimize the accuracy of parameter determination as historical data accumulates.

[0079] The sample injection control method of a mass spectrometer provided by an embodiment of the present invention has at least the following technical effects:

[0080] Identify the sample to be tested and obtain the sample identification results, providing basic data for subsequent parameter settings. Based on the sample identification results, determine the injection parameters of the sample to be tested, and automatically match the most suitable injection parameters for the identified sample identification results, such as injection temperature, injection rate, split ratio, etc., eliminating the need to manually set parameters based on experience and avoiding human errors. According to the injection parameters, the sample to be tested is injected and controlled, and the sample to be tested is introduced into the mass spectrometer for analysis. The injection device is controlled according to the automatically determined parameters to accurately introduce the sample into the mass spectrometer, ensuring that the sample is introduced into the analysis system under optimal conditions, improving the accuracy and repeatability of the analysis.

[0081] Through the injection control method of the mass spectrometer, automation is achieved from sample identification to parameter determination to actual injection operation, effectively eliminating the manual intervention link, which not only significantly reduces the workload and human errors of operators, but also shortens the cycle time from sample preparation to analysis. At the same time, since the injection parameters are automatically optimized and determined based on accurate sample identification results, the samples can be analyzed under optimal conditions, ensuring the stability and reliability of the analysis results, and achieving a significant improvement in the overall efficiency and accuracy of mass spectrometry analysis, especially in application scenarios where large quantities of samples need to be processed, this efficiency improvement is particularly obvious.

[0082] Embodiment 2, as Figure 2 As shown, based on the same inventive concept of a mass spectrometer injection control method provided in Embodiment 1, an embodiment of the present invention further provides a mass spectrometer injection control system, comprising:

[0083] The sample identification module 11 is used to identify the sample to be tested and obtain the sample identification result;

[0084] A parameter determination module 12, used to determine the injection parameters of the sample to be tested based on the sample identification result;

[0085] The injection control module 13 is used to control the injection of the sample to be tested according to the injection parameters, and introduce the sample to be tested into the mass spectrometer for analysis.

[0086] Furthermore, the sample identification module 11 includes the following execution steps:

[0087] Capturing an image of the sample to be tested to obtain an image of the sample to be tested;

[0088] Performing image analysis on the image of the sample to be tested according to a preset feature set to obtain a plurality of features of the sample to be tested;

[0089] The multiple features of the samples to be tested are used as sample identification results of the samples to be tested.

[0090] Furthermore, the parameter determination module 12 includes the following execution steps:

[0091] Constructing a measurement sample space based on archived measurement sample records and multiple feature dimensions;

[0092] Extracting multiple features of the sample to be tested from the sample identification result;

[0093] Based on the multiple characteristics of the samples to be tested, determining multiple neighboring reference samples of the sample to be tested in the measurement sample space;

[0094] The injection parameters of the sample to be tested are determined according to the multiple adjacent reference samples.

[0095] Furthermore, the parameter determination module 12 further includes the following execution steps:

[0096] Generating a plurality of feature dimensions according to the preset feature set, and generating a basic feature space based on the plurality of feature dimensions;

[0097] Interact with a historical measurement database, and obtain archived measurement sample records in the historical measurement database according to a preset time period;

[0098] Traversing the archived measurement sample records to obtain a first archived measurement sample;

[0099] extracting a plurality of first sample features of the first archived measurement sample, determining a first sample position in the basic feature space according to the plurality of first sample features, and updating the basic feature space based on the first sample position;

[0100] The remaining archived measurement samples in the archived measurement sample records are processed in sequence, and the basic feature space is continuously updated until all archived measurement samples are traversed to obtain a measurement sample space.

[0101] Furthermore, the parameter determination module 12 further includes the following execution steps:

[0102] Determining a target position of the sample to be tested in the measurement sample space according to the multiple characteristics of the sample to be tested;

[0103] Determine the proximity distance in the measurement sample space based on the target position according to a proximity distance determination formula;

[0104] Taking the target position as the center and the proximity distance as the radius, constructing a proximity space of the sample to be tested;

[0105] Samples in the adjacent space are extracted to obtain a plurality of adjacent reference samples.

[0106] Furthermore, the formula for determining the proximity distance is:

[0107]

[0108] in, is the basic reference distance, To measure the average sample density of the sample space, is the local sample density around the target location, is the preset minimum number of reference samples, is the number of samples within the basic reference distance range centered on the target position, is the smoothing factor.

[0109] Furthermore, the parameter determination module 12 further includes the following execution steps:

[0110] Obtaining sample injection parameters and corresponding mass spectrometry analysis effect scores of the plurality of adjacent reference samples;

[0111] Based on the mass spectrometry analysis effect score, the sample injection parameters of the multiple adjacent reference samples are screened, and the sample injection parameters whose mass spectrometry analysis effect scores are lower than a preset threshold are eliminated to obtain a set of preferred sample injection parameters;

[0112] The sample injection parameters in the preferred sample injection parameter set are weighted averaged according to the corresponding mass spectrometry analysis effect scores to obtain the injection parameters of the sample to be tested.

[0113] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0114] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0115] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0116] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0117] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0118] Although preferred embodiments of the present invention have been described, additional changes and modifications may occur to these embodiments once those skilled in the art understand the basic inventive concepts.

[0119] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention belong to the scope of the present invention and its equivalent technologies, the present invention is also intended to include these changes and variations.

Claims

1. A method for controlling the injection of a mass spectrometer, characterized in that: Applied to a sample injection control system, the method comprises: Identify the sample to be tested and obtain the sample identification result; Determining the injection parameters of the sample to be tested based on the sample identification result; The sample to be tested is injected with control according to the injection parameters, and the sample to be tested is introduced into a mass spectrometer for analysis.

2. The method according to claim 1, characterized in that The step of identifying the sample to be tested and obtaining the sample identification result includes: Capturing an image of the sample to be tested to obtain an image of the sample to be tested; Performing image analysis on the image of the sample to be tested according to a preset feature set to obtain a plurality of features of the sample to be tested; The multiple features of the samples to be tested are used as sample identification results of the samples to be tested.

3. The method according to claim 2, characterized in that The step of determining the injection parameters of the sample to be tested based on the sample identification result includes: Constructing a measurement sample space based on archived measurement sample records and multiple feature dimensions; Extracting multiple features of the sample to be tested from the sample identification result; Based on the multiple characteristics of the samples to be tested, determining multiple neighboring reference samples of the sample to be tested in the measurement sample space; The injection parameters of the sample to be tested are determined according to the multiple adjacent reference samples.

4. The method according to claim 3, characterized in that The step of constructing a measurement sample space according to the archived measurement sample records and multiple feature dimensions includes: Generating a plurality of feature dimensions according to the preset feature set, and generating a basic feature space based on the plurality of feature dimensions; Interact with a historical measurement database, and obtain archived measurement sample records in the historical measurement database according to a preset time period; Traversing the archived measurement sample records to obtain a first archived measurement sample; extracting a plurality of first sample features of the first archived measurement sample, determining a first sample position in the basic feature space according to the plurality of first sample features, and updating the basic feature space based on the first sample position; The remaining archived measurement samples in the archived measurement sample records are processed in sequence, and the basic feature space is continuously updated until all archived measurement samples are traversed to obtain a measurement sample space.

5. The method according to claim 3, characterized in that: The step of determining a plurality of adjacent reference samples of the sample to be tested in the measurement sample space based on the plurality of features of the sample to be tested comprises: Determining a target position of the sample to be tested in the measurement sample space according to the multiple characteristics of the sample to be tested; Determine the proximity distance in the measurement sample space based on the target position according to a proximity distance determination formula; Taking the target position as the center and the proximity distance as the radius, constructing a proximity space of the sample to be tested; Samples in the adjacent space are extracted to obtain a plurality of adjacent reference samples.

6. The method according to claim 5, characterized in that The formula for determining the proximity distance is: in, is the basic reference distance, To measure the average sample density of the sample space, is the local sample density around the target location, is the preset minimum number of reference samples, is the number of samples within the basic reference distance range centered on the target position, is the smoothing factor.

7. The method according to claim 3, characterized in that The step of determining the injection parameters of the sample to be tested according to the plurality of adjacent reference samples comprises: Obtaining sample injection parameters and corresponding mass spectrometry analysis effect scores of the plurality of adjacent reference samples; Based on the mass spectrometry analysis effect score, the sample injection parameters of the multiple adjacent reference samples are screened, and the sample injection parameters whose mass spectrometry analysis effect scores are lower than a preset threshold are eliminated to obtain a set of preferred sample injection parameters; The sample injection parameters in the preferred sample injection parameter set are weighted averaged according to the corresponding mass spectrometry analysis effect scores to obtain the injection parameters of the sample to be tested.

8. A sample injection control system for a mass spectrometer, characterized in that: A method for controlling the injection of a mass spectrometer according to any one of claims 1 to 7, the system comprising: The sample identification module is used to identify the sample to be tested and obtain the sample identification result; A parameter determination module, used to determine the injection parameters of the sample to be tested based on the sample identification result; The injection control module is used to control the injection of the sample to be tested according to the injection parameters, and introduce the sample to be tested into the mass spectrometer for analysis.

Images