Mass spectrum targeted secondary scanning method based on intelligent scheduling algorithm and application thereof

Abstract

The invention discloses a mass spectrum targeted secondary scanning method based on an intelligent scheduling algorithm and application thereof, and relates to the field of sample analysis, and the method comprises the following steps: carrying out primary primary mass spectrum scanning on a to-be-detected sample, and collecting mass spectrum original data through a liquid chromatography-mass spectrometry system; carrying out feature recognition on the mass spectrum original data, and recognizing the mass-to-charge ratio and retention time of the mass-to-charge ratio and retention time of the mass-to-charge ratio and retention time of the mass-to-charge ratio and retention time as target analytes; performing time sequence planning on the target analytes, and distributing the target analytes of which the retention time meets a preset condition into different secondary mass spectrometry samples; and extracting ion information corresponding to each pair according to the distributed sample injection needle number information, generating a targeted MS / MS analysis list, transmitting the targeted MS / MS analysis list to mass spectrum sample injection software, obtaining a mass spectrum analysis method corresponding to each pair, and then performing batch sample injection to complete intelligent scheduling. The target parent ions in the sample can be automatically identified, mutual interference of parent ions with close retention time is reduced, the operation complexity is reduced, and the analysis result is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of sample analysis, and more particularly to a mass spectrometry targeted secondary scanning method based on an intelligent scheduling algorithm and an application thereof. Background Art

[0002] Non-Targeted Mass Spectrometry (NT-MS) is a mass spectrometry technique that does not require pre-defined targets. It can comprehensively analyze compounds in complex samples and is widely used in metabolomics, food safety testing, and environmental analysis. In NT-MS applications, the Data-Dependent Acquisition (DDA) method can automatically acquire secondary spectra of high-intensity parent ions. This method offers significant advantages in spectral comparison and metabolite identification, making it the most common metabolite identification method.

[0003] However, the DDA method can only collect secondary spectra of the first few ions with the highest intensity in the MS1 spectrum. If the target is a low-abundance ion, the secondary spectrum cannot be obtained. In order to ensure that the secondary spectrum of the target parent ion is obtained, a targeted secondary scan is often required. However, when using the targeted secondary scan method, it is necessary to manually fill in the detailed parameters of the target ion, including the precise mass-to-charge ratio, peak time, and scan time window. This requires the analyte information in the sample to be obtained in advance, and the operator's ability requirements are high, making it difficult to apply to high-throughput detection and analysis of unknown samples. On the other hand, when performing secondary scans on multiple target parent ions at the same time, when the retention times of multiple targets overlap (RT deviation <0.5min), the synchronous triggering of the secondary scan will produce signal cross-interference, significantly reducing the quality of the spectrum.

[0004] Therefore, how to automatically identify target parent ions in samples and reduce the mutual interference of parent ions with close retention times is a technical problem that those skilled in the art urgently need to solve. Summary of the Invention

[0005] In view of this, the present invention provides a mass spectrometry targeted secondary scanning method based on an intelligent scheduling algorithm and its application, which solves the problems existing in the background technology.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] A mass spectrometry targeted secondary scanning method based on an intelligent scheduling algorithm comprises the following steps:

[0008] Perform a first-level mass spectrometry scan on the sample to be tested, and collect mass spectrometry raw data through the liquid chromatography-mass spectrometry system;

[0009] Perform feature recognition on the raw mass spectrometry data and identify the target analytes whose mass-to-charge ratio and retention time match the locally generated dynamic database;

[0010] Perform time sequence planning for target analytes and assign target analytes whose retention times meet preset conditions to different secondary mass spectrometry injections;

[0011] Based on the assigned number of injection needles, the ion information corresponding to each needle is extracted, a targeted MS / MS analysis list is generated and transmitted to the mass spectrometry injection software. After obtaining the mass spectrometry analysis method corresponding to each needle, batch injection is performed to complete intelligent scheduling.

[0012] Optionally, the method further includes: after obtaining the raw mass spectrum data, converting the raw mass spectrum data into a universal format.

[0013] Optionally, feature recognition uses a centWave algorithm to extract feature information from the raw mass spectrometry data, including mass-to-charge ratio, retention time, and peak area information.

[0014] Optionally, the method further includes: limiting the peak area information of the features, filtering out features below a set intensity threshold, and performing subsequent analyte matching only on features that meet the requirements.

[0015] Optionally, perform time sequence planning for target analytes, specifically:

[0016] The target analytes are sorted by retention time, the required number of secondary mass spectrometry injections is automatically planned according to built-in parameters, and the target analytes are allocated to each secondary mass spectrometry injection in the order of retention time, so that target analytes with the same retention time or a retention time difference of less than 0.1min are allocated to different secondary mass spectrometry injections.

[0017] Optionally, the number of injection needles for the secondary mass spectrometry is the maximum number of target analytes with a retention time difference of less than 0.1 min.

[0018] The present invention also discloses an application of the mass spectrometry targeted secondary scanning method based on the intelligent scheduling algorithm as described in any of the above items in high-throughput detection and analysis of unknown samples.

[0019] Through the above technical solution, it can be known that compared with the prior art, the present invention discloses a mass spectrometry targeted secondary scanning method based on an intelligent scheduling algorithm and its application. The sample is first subjected to a primary mass spectrometry scan (LC-MS) analysis, and then matched with a local database, and the target ion information on the mass-to-charge ratio and retention time matching is statistically analyzed. Ions with too low intensity are removed by setting an intensity threshold; Subsequently, through the intelligent scheduling algorithm, the subsequent mass spectrometry analysis method of the target ion is automatically planned, and ions with close retention times are assigned to different secondary mass spectrometry injections. Based on this design scheme, the present invention can automatically identify the target parent ions in the sample, reduce the mutual interference of parent ions with close retention times through intelligent scheduling, reduce the complexity of operation and improve the analysis results, which has important practical significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0021] Figure 1 A sample analysis flow chart provided by the present invention;

[0022] Figure 2 A schematic diagram of the effect provided by the present invention;

[0023] Figure 3 A comparison chart of the results of the targeted mass spectrometry analysis provided by the present invention and the conventional targeted secondary analysis method. DETAILED DESCRIPTION

[0024] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0025] To solve the technical problems pointed out in the background technology, the present invention wants to develop a mass spectrometry analysis method that can automatically identify the target parent ions in the sample and reduce the mutual interference of parent ions with close retention times by intelligent scheduling. Its principle is: when the sample is analyzed, a first-level mass spectrometry scan (LC-MS) is first performed, and the LC-MS data is analyzed using a local database. The local database contains the mass-to-charge ratio and retention time information of the target analyte. By matching the mass-to-charge ratio and retention time, the mass-to-charge ratio and retention time of the target parent ion in the sample are identified, and used as the basis for generating a targeted secondary scanning method. At the same time, an intelligent scheduling algorithm for the target parent ion retention time and the number of target parent ions in the sample is established. When generating the targeted secondary scanning method, the target parent ions with the same or close retention times are dispersed in multiple injections to avoid interference with each other.

[0026] Next, refer to Figure 1 , introduces a specific technical solution of a mass spectrometry targeted secondary scanning method based on an intelligent scheduling algorithm disclosed in an embodiment of the present invention, including the following steps:

[0027] Perform a first-level mass spectrometry scan on the sample to be tested, and collect mass spectrometry raw data through the liquid chromatography-mass spectrometry system;

[0028] Perform feature recognition on the raw mass spectrometry data, and identify those with mass-to-charge ratio and retention time that match the locally generated dynamic database as target analytes and include them in subsequent analysis. Features that do not match are treated as invalid data;

[0029] Perform time sequence planning for target analytes and assign target analytes whose retention times meet preset conditions to different secondary mass spectrometry injections;

[0030] Based on the assigned number of injection needles, the ion information corresponding to each needle is extracted, a targeted MS / MS analysis list is generated and transmitted to the mass spectrometry injection software. After obtaining the mass spectrometry analysis method corresponding to each needle, batch injection is performed to complete intelligent scheduling.

[0031] Furthermore, the technical solution of this embodiment also includes: after obtaining the mass spectrum raw data, converting the mass spectrum raw data into a common format, such as an mzML file.

[0032] Furthermore, in this embodiment, feature recognition uses the centWave algorithm to extract feature information from the mass spectrometry raw data (i.e., all qualified chromatographic peaks in the mass spectrometry raw data), including mass-to-charge ratio m / z, retention time, and peak area information.

[0033] Furthermore, the technical solution of this embodiment also includes: limiting the peak area information of the features, filtering out features below a set intensity threshold, and performing subsequent analyte matching only on features that meet the requirements.

[0034] Furthermore, in this embodiment, the target analytes are time-series planned, specifically:

[0035] The target analytes are sorted by retention time, and the required number of secondary mass spectrometry injections is automatically planned according to built-in parameters. The target analytes are then assigned to each secondary mass spectrometry injection in the order of retention time, so that target analytes with the same retention time or a retention time difference of less than 0.1 min are assigned to different secondary mass spectrometry injections, thereby ensuring that these analytes do not interfere with each other due to occupying the secondary mass spectrometry channels when collecting the secondary mass spectrum.

[0036] Furthermore, in this embodiment, the number of injections for the secondary mass spectrometry is the maximum number of target analytes whose retention time difference is less than 0.1 min (a built-in parameter of the intelligent scheduling algorithm core, manually input).

[0037] The mass spectrometry targeted secondary scanning method based on the intelligent scheduling algorithm disclosed in this embodiment can be practically applied in high-throughput detection and analysis of unknown samples.

[0038] Table 1 shows the secondary mass spectrometry analysis method parameters generated by the intelligent scheduling algorithm: "Name" is the name of the target substance matched in the local database, "Injection" is the number of injections to which the ion is assigned; Step2_1 represents the ion assigned to the first LC-MS / MS analysis, and Step2_2 represents the ion assigned to the second LC-MS / MS analysis; "Prec.m / z" represents the precise mass-to-charge ratio of the ion, "Ret.Time (min)" and "RT.win. (min)" represent the median time and scan duration of the secondary mass spectrometry scan; "Collision Energy" represents the secondary mass spectrometry fragmentation energy.

[0039] Table 1 Parameters of secondary mass spectrometry analysis method

[0040]

[0041]

[0042] Figure 2 The following is a schematic diagram of the effect, where the horizontal axis represents retention time and the vertical axis represents mass spectrometry signal intensity. Each peak in the figure represents a target. After passing the intensity threshold screening (Threshold), ions with intensity that meet the requirements are included in the secondary mass spectrometry scan. The parent ions scanned in each secondary mass spectrometry injection and the scan time are generated by the intelligent scheduling algorithm. For one secondary mass spectrometry scan, the secondary scan times and times of the three target ions are as follows: Figure 2 shown.

[0043] Targeted mass spectrometry analysis of glyceride components in bovine liver whole fat extract. Figure 3 As shown in the figure, compared with the conventional targeted secondary analysis method, after adding intelligent scheduling, the number of identified glycerides doubled with the same number of injections, indicating that intelligent scheduling can improve the results of mass spectrometry analysis and has important practical significance.

[0044] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0045] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A mass spectrometry targeted secondary scanning method based on an intelligent scheduling algorithm, characterized in that: The following steps are involved: Perform a first-level mass spectrometry scan on the sample to be tested, and collect mass spectrometry raw data through the liquid chromatography-mass spectrometry system; Perform feature recognition on the raw mass spectrometry data and identify the target analytes whose mass-to-charge ratio and retention time match the locally generated dynamic database; Perform time sequence planning for target analytes and assign target analytes whose retention times meet preset conditions to different secondary mass spectrometry injections; Based on the assigned number of injection needles, the ion information corresponding to each needle is extracted, a targeted MS / MS analysis list is generated and transmitted to the mass spectrometry injection software. After obtaining the mass spectrometry analysis method corresponding to each needle, batch injection is performed to complete intelligent scheduling.

2. The mass spectrometry targeted secondary scanning method based on an intelligent scheduling algorithm according to claim 1, characterized in that: Also includes: After acquiring the mass spectrum raw data, the mass spectrum raw data is converted into a common format.

3. The mass spectrometry targeted secondary scanning method based on an intelligent scheduling algorithm according to claim 1, characterized in that: Feature recognition uses the centWave algorithm to extract feature information from the raw mass spectrometry data, including mass-to-charge ratio, retention time, and peak area information.

4. The mass spectrometry targeted secondary scanning method based on an intelligent scheduling algorithm according to claim 3, characterized in that: It also includes: limiting the peak area information of the features, filtering out features below the set intensity threshold, and performing subsequent analyte matching only on features that meet the requirements.

5. The mass spectrometry targeted secondary scanning method based on an intelligent scheduling algorithm according to claim 1, characterized in that: Perform time sequence planning for target analytes, specifically: The target analytes are sorted by retention time, the required number of secondary mass spectrometry injections is automatically planned according to built-in parameters, and the target analytes are allocated to each secondary mass spectrometry injection in the order of retention time, so that target analytes with the same retention time or a retention time difference of less than 0.1min are allocated to different secondary mass spectrometry injections.

6. The mass spectrometry targeted secondary scanning method based on an intelligent scheduling algorithm according to claim 5, characterized in that: The number of injection needles for secondary mass spectrometry was the maximum number of target analytes with a retention time difference of less than 0.1 min.

7. An application of the mass spectrometry targeted secondary scanning method based on the intelligent scheduling algorithm as described in any one of claims 1 to 6 in high-throughput detection and analysis of unknown samples.

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