A rapid mass spectrometry detection method for saliva samples

Abstract

The present application discloses a method for rapid mass spectrometry detection of saliva samples, and relates to the field of material testing technology. The method comprises the following steps: obtaining first mass spectrometry data of a first saliva sample; segmenting the first mass spectrometry data to obtain a plurality of data segments; obtaining the information expression weight of the first saliva sample based on each data segment; obtaining the information expression coefficient based on the information expression weight; optimizing the principal component analysis process based on the information expression coefficient, and then obtaining the biomolecule information matching each saliva sample. Compared with the prior art, the present application adds analysis of different sample data to the analysis process, improves the weight of the proportion of saliva samples in mass-to-charge ratio data in the optimization process of principal component analysis, reduces the influence of interference information that may exist in the sample on the analysis, and increases the accuracy of mass spectrometry analysis.

Description

Technical Field

[0001] The present application relates to the technical field of material testing, and in particular to a rapid mass spectrometry detection method for saliva samples. Background Art

[0002] Mass spectrometry is an identification technology that plays a very important role in the identification of organic molecules. It can quickly and extremely accurately determine the molecular weight of biological macromolecules, allowing proteomics research to go from protein identification to advanced structural research and the interaction between various proteins. The purpose of mass spectrometry detection of saliva samples is to use its non-invasive and easy-to-obtain characteristics to deeply explore and analyze biomarkers in saliva. In the process of analyzing the biomolecular information contained in saliva, it is usually necessary to perform principal component analysis on the data in the face of massive mass spectrometry data of a large number of samples. However, in the process of direct processing through existing algorithms, it is easy to be affected by some interfering information in the sample (for example, gas molecules), and fails to highlight the data performance of important samples related to the analysis, thereby affecting the accuracy of the mass spectrometry detection results. Summary of the invention

[0003] The purpose of the present application is to provide a rapid mass spectrometry detection method for saliva samples, so as to solve the technical problem in the prior art that saliva samples are easily affected by interference information during mass spectrometry detection.

[0004] To achieve the above objectives, this application provides the following technical solutions:

[0005] A rapid mass spectrometry detection method for a saliva sample, comprising obtaining first mass spectrometry data of a first saliva sample; the first mass spectrometry data at least includes a mass-to-charge ratio and an ion intensity of the first saliva sample; the first mass spectrometry data of the first saliva sample is mass spectrometry data of any one of a plurality of saliva samples; the mass spectrometry data of the plurality of saliva samples are obtained in advance;

[0006] Segmenting the first mass spectrum data to obtain a plurality of data segments;

[0007] Based on each data segment, obtaining an information expression weight of the first saliva sample; the information expression weight is at least used to indicate the importance of the biomolecule information contained in the first saliva sample;

[0008] Based on the information expression weight, an information expression coefficient is obtained; the information expression coefficient is at least used to represent the expression intensity of the ions corresponding to each mass-to-charge ratio for the biological information;

[0009] Based on the information expression coefficient, the principal component analysis process is optimized to obtain biomolecule information matching each saliva sample.

[0010] As a specific solution in the technical solution of the present application, the first mass spectrum data is segmented to obtain multiple data segments, including:

[0011] Based on each mass spectrum data, a data matrix is ​​obtained; each row in the data matrix represents a different saliva sample; each column in the data matrix represents mass spectrum data of different mass-to-charge ratios; each element in the data matrix represents the ion intensity of the saliva sample in the corresponding row at the corresponding mass-to-charge ratio;

[0012] Based on the data matrix, obtaining a plurality of first columns, wherein each element in the first columns is 0;

[0013] The first mass spectrum data is segmented using each first column as a dividing line to obtain a plurality of data segments.

[0014] As a specific solution in the technical solution of the present application, the obtaining of the information expression weight of the first saliva sample based on each data segment includes:

[0015] Based on each data segment, obtain a first data segment; the first data segment is any one of the data segments;

[0016] Based on the first data segment, obtaining a first coefficient; the first coefficient is used to at least represent the number of non-zero elements in the first data segment;

[0017] Based on the first coefficient, obtaining a second coefficient; the second coefficient is at least used to represent the number of non-zero elements in the first saliva sample;

[0018] Based on the second coefficient, an information expression weight of the first saliva sample is obtained.

[0019] As a specific solution in the technical solution of the present application, the calculation formula for obtaining the second coefficient based on the first coefficient is as follows:

[0020] ;

[0021] in, represents the second coefficient; r represents the number of data segments; represents the first coefficient corresponding to the i-th data segment; Indicates the number of corresponding zero elements in the i-th data segment; represents the variance of non-zero elements in the i-th data segment; represents an exponential function with a natural constant as the base; t represents a first adjustment coefficient, and the first adjustment coefficient is greater than 0 and less than 1.

[0022] As a specific solution in the technical solution of the present application, the obtaining of the information expression weight of the first saliva sample based on the second coefficient includes:

[0023] Based on the data matrix, a first row of data and a second row of data are obtained; the first row of data is the row of data corresponding to the first saliva sample; the second row of data is any row of data in the data matrix except the first row of data;

[0024] Based on the first row of data and the second row of data, obtaining a mean square error;

[0025] Based on the second coefficient and the mean square error, an information expression weight of the first saliva sample is obtained.

[0026] As a specific solution in the technical solution of the present application, the calculation formula for obtaining the information expression weight of the first saliva sample based on the second coefficient and the mean square error is as follows:

[0027] ;

[0028] in, represents the information expression weight of the first saliva sample; represents the second coefficient of the first saliva sample; m is the number of rows of data in the data matrix; represents the mean square error between the row data corresponding to the first saliva sample and the i-th row data in the data matrix; represents the second coefficient of the saliva sample corresponding to the i-th row of data in the data matrix; represents a second adjustment coefficient, which is a positive integer.

[0029] As a specific solution in the technical solution of the present application, the obtaining of the information expression coefficient based on the information expression weight includes:

[0030] Based on the data matrix, obtaining a first mass-to-charge ratio; the first mass-to-charge ratio is any mass-to-charge ratio in the data matrix;

[0031] acquiring a plurality of ion intensities from each saliva sample based on the first mass-to-charge ratio;

[0032] Obtaining a third coefficient based on the information expression weight and ion intensity of each saliva sample;

[0033] Based on the third coefficient, an information expression coefficient is obtained.

[0034] As a specific solution in the technical solution of the present application, the calculation formula for obtaining the third coefficient based on the information expression weight and ion strength of each saliva sample is as follows:

[0035] ;

[0036] Wherein, Q represents the third coefficient corresponding to the first mass-to-charge ratio; m represents the number of saliva samples; represents the information expression weight corresponding to the jth saliva sample; represents the ion intensity corresponding to the first mass-to-charge ratio in the jth saliva sample; Represents a normalization function, which is used to map the values ​​in the brackets to the interval (0, 1).

[0037] As a specific solution in the technical solution of the present application, the obtaining of the information expression coefficient based on the third coefficient includes:

[0038] Based on the ion intensities corresponding to the respective mass-to-charge ratios in the data matrix, the respective mass-to-charge ratios are sorted to obtain a plurality of serial numbers;

[0039] Based on the multiple serial numbers, a first serial number is obtained; the first serial number is the serial number corresponding to the first mass-to-charge ratio;

[0040] Based on the data matrix, acquiring a plurality of first ion intensities corresponding to the first mass-to-charge ratio;

[0041] Based on each first ion intensity, obtaining a first variance;

[0042] Based on the first variance and the first sequence number, obtaining a fourth coefficient;

[0043] Based on the third coefficient and the fourth coefficient, an information expression coefficient is acquired.

[0044] As a specific solution in the technical solution of the present application, the calculation formula for obtaining the information expression coefficient based on the third coefficient and the fourth coefficient is as follows:

[0045] ;

[0046] in, represents the information expression coefficient corresponding to the first mass-to-charge ratio; represents the third coefficient corresponding to the first mass-to-charge ratio; Indicates the first serial number corresponding to the first mass-to-charge ratio; represents the first variance; Represents a normalization function, which is used to map the values ​​in brackets to the interval (0, 1); Represents the mean calculation function.

[0047] Compared with the prior art, the beneficial effects of this application are:

[0048] This application uses the mass spectrometry data of saliva samples to evaluate the amount of biomolecular information contained in saliva samples (that is, the number of non-zero elements in the mass spectrometry data); then, the information expression weight of each saliva sample mass spectrometry data is calculated by combining the information amount and the mass spectrometry data of different saliva samples; finally, the principal component analysis process is adjusted according to the information expression weight. Compared with the prior art, the analysis process adds analysis of different sample data, improves the weight of saliva samples in the mass-to-charge ratio data in the optimization process of principal component analysis, and reduces the impact of interference information that may exist in the sample on the analysis, thereby increasing the accuracy of mass spectrometry analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A schematic diagram of a process for rapid mass spectrometry detection of saliva samples proposed in an embodiment of the present application;

[0050] Figure 2 A schematic diagram of a mass spectrum of a saliva sample proposed in an embodiment of the present application. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0052] The terms "first", "second", etc. in the specification of the embodiment of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence, such as the first coefficient and the second coefficient proposed below, which belong to different coefficients. It should be understood that the coefficients used in this way can be interchanged where appropriate, so that the embodiments described here can be implemented in an order other than the content illustrated or described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device including a series of steps or modules need not be limited to those steps or modules clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices. The division of the modules that appear in the embodiment of the present application is only a logical division. There may be other division methods when implemented in practical applications, such as multiple modules can be combined or integrated into another system, or some features can be ignored, or not executed. In addition, the coupling or direct coupling or communication connection between each other shown or discussed can be through some interfaces, and the indirect coupling or communication connection between modules can be electrical or other similar forms, which are not limited in the embodiment of the present application. Furthermore, the modules or sub-modules described as separate components may or may not be physically separated, may or may not be physical modules, or may be distributed in multiple circuit modules, and some or all of the modules may be selected according to actual needs to achieve the purpose of the embodiment of the present application.

[0053] In order to solve the technical problem in the prior art that saliva samples are easily affected by interference information during mass spectrometry detection, the present application proposes an embodiment of a rapid mass spectrometry detection method for saliva samples. Specifically, Figure 1 As shown, the rapid mass spectrometry detection method for saliva samples includes steps S100 to S500.

[0054] Step S100: Acquire first mass spectrum data of a first saliva sample.

[0055] In an embodiment of the present application, the first mass spectrum data at least includes the mass-to-charge ratio and ion strength of the first saliva sample. The first mass spectrum data of the first saliva sample is the mass spectrum data of any one of the multiple saliva samples. The mass spectrum data of the multiple saliva samples are acquired in advance. It should be noted that, in an embodiment of the present application, there is no restriction on the source of the multiple saliva samples. For example: the multiple saliva samples can be multiple saliva samples of the same user; the multiple saliva samples can also be saliva samples of different users.

[0056] It should be noted that when analyzing biological molecules through saliva, it is usually necessary to perform mass spectrometry on the saliva sample. During the detection process, the mass spectrometer transfers electric charge to the molecules and converts the resulting charged ion flow into a proportional current, which is then read by the data system. The data system converts the current into digital information and presents it in the form of a mass spectrum. For saliva samples, electrospray ionization mass spectrometry can achieve relatively ideal monitoring effects. Electrospray ionization (ESI) technology applies high voltage to the eluent of the liquid chromatograph to generate ions in the aerosol. The mass spectrometer will separate, detect and measure the ions according to the mass-to-charge ratio (m / z) of the ions, draw a relationship between the relative ion flow (signal) and m / z, and generate a mass spectrum (such as Figure 2 as shown).

[0057] Generally, a special saliva collection device is used, usually including sampling strips, test kits, etc. The sampling strip is placed in the mouth near the top of the tongue for about 1 to 2 minutes to collect the saliva sample. During the collection process, it is necessary to ensure that the subject's mouth is clean before sampling to avoid interference from food and beverages; the collected saliva samples are pre-treated, for example, centrifugation (generally centrifugation at 3000 to 5000 rpm for 10 to 15 minutes) and filtration to remove impurities and cell debris and extract the components to be tested. Avoid contamination during the operation to ensure the integrity and purity of the sample.

[0058] According to the analysis requirements, set the parameters of the mass spectrometer, including the type of ion source (for saliva samples, electrospray ionization (ESI) technology is suitable for complex biological liquids to ionize saliva samples), mass spectrometry analysis mode (such as positive ion mode or negative ion mode), mass spectrometry scanning range, etc. Then inject the processed sample into the mass spectrometer for detection, usually using an automatic sampler for high-throughput analysis. Collect the data generated by mass spectrometry detection, which is usually stored in the format of raw data files (such as .mzXML, .mzML, etc.), containing information such as ion intensity (abundance), mass-to-charge ratio (m / z), etc. Figure 2 The figure is a schematic diagram of a mass spectrum composed of collected data, wherein the horizontal axis is the mass-to-charge ratio and the vertical axis is the ion intensity. It should be noted that based on the saliva sample, obtaining the mass spectrometry data of the saliva sample is a mature technology and will not be described in detail here.

[0059] It should be clear that when performing mass spectrometry on a large number of saliva samples to analyze biomolecule information, principal component analysis (PCA) is a commonly used dimensionality reduction method that can help highlight key related biomolecule information, simplify data structure, facilitate visualization and understanding, and enable researchers to focus on those variables that contribute most to the state. In order to highlight the biomolecule information in saliva samples, it is necessary to ensure that the mass spectrometry data of biomolecules can be retained to a large extent during the analysis process. However, there are many irrelevant features in the saliva mass spectrometry data, and these irrelevant features may affect the interpretability of the principal components. In order to correctly interpret the principal components in the mass spectrometry data, in an embodiment of the present application, the mass spectrometry data can be segmented through step S200.

[0060] Specifically, step S200: segment the first mass spectrum data to obtain multiple data segments.

[0061] It should be clear that in the embodiments of the present application, any suitable method may be used to segment the first mass spectrum data. Figure 2 As shown, in the mass spectrum, the mass-to-charge ratio represents the mass-to-charge ratio of the ion, reflecting the type and mass of the ion. The mass-to-charge ratio with an ion intensity of 0 usually indicates that the corresponding ion does not exist in the sample detection process. Directly performing principal component analysis on these data may make the interpretation of the components difficult. In this embodiment, the mass-to-charge ratio with an ion intensity of 0 in the first mass spectrum data can be deleted, and then the first mass spectrum data can be segmented. It should be noted that some components in the saliva sample have a low content, so that during the detection process, the ion intensity of the corresponding mass-to-charge ratio in the first mass spectrum data is 0. Deleting these data may cause the subsequent principal component interpretation to be inaccurate. In order to ensure the interpretability of the subsequent principal component analysis, in one embodiment of the present application, step S200, the first mass spectrum data is segmented to obtain multiple data segments, including steps S210 to S230.

[0062] Step S210: Acquire a data matrix based on each mass spectrum data.

[0063] It should be clear that each row in the data matrix represents a different saliva sample; each column in the data matrix represents mass spectrometry data of different mass-to-charge ratios; and each element in the data matrix represents the ion intensity of the saliva sample in the corresponding row at the mass-to-charge ratio in the corresponding row.

[0064] Step S220: Based on the data matrix, obtain a plurality of first columns.

[0065] It should be clear that all elements in the first column are 0. It should be clear that if all elements in a column of all saliva samples are 0, it means that the saliva sample is likely to not contain the substance corresponding to the mass-to-charge ratio, that is, deleting the data in this column will not affect the interpretation of the subsequent principal component analysis.

[0066] Step S230: segmenting the first mass spectrum data using each first column as a dividing line to obtain a plurality of data segments.

[0067] In this embodiment, the mass-to-charge ratio is segmented using the position of the deleted column. Different mass-to-charge ratios represent ions with different mass-to-charge ratios in the sample. Each mass-to-charge ratio corresponds to a specific ion, which can be a molecular ion, a fragment ion, an isotope ion, etc. After segmentation, each mass-to-charge ratio segment may represent a group of ions with similar characteristics, which is convenient for subsequent comparison and analysis. For example, a segment may correspond to a cluster of a certain type of metabolite or biomarker, which can improve the interpretability of subsequent principal component analysis.

[0068] Step S300: based on each data segment, obtaining the information expression weight of the first saliva sample.

[0069] It should be noted that, due to the influence of the non-uniform distribution of biomolecule information in saliva, for different saliva samples, the ion intensity that can represent the mass-to-charge ratio of biomolecule information may be abnormal, for example: some ion intensity values ​​are displayed as 0. Therefore, it is necessary to evaluate the molecular information carried in different saliva samples. In mass spectrometry data, continuous non-zero ion intensity may represent the isotope peak of a specific compound or a group of similar compounds. The segments containing more mass-to-charge ratios indicate that the ion distribution of this type of structural compound in the current saliva sample is wider, indicating that there are larger or multiple compounds within the mass-to-charge ratio range, which may have important meanings in the biomolecule analysis process; while short intervals may represent background noise or instrument noise, which is not a real compound signal. Therefore, when analyzing the data information inclusion degree of a saliva sample, it is necessary to combine the distribution length of each data segment. For this reason, it is necessary to determine the importance of the biomolecule information carried in each saliva sample (that is, the information expression weight) so as to facilitate the subsequent accurate principal component analysis. In an embodiment of the present application, the information expression weight is at least used to represent the importance of the biomolecule information contained in the first saliva sample.

[0070] In an embodiment of the present application, any suitable method can be used to obtain the information expression weight of the first saliva sample. It is easy to understand that if the first saliva sample has more non-zero elements, the more information it carries, that is, the higher the importance of the corresponding biomolecule information, and if the first saliva sample has fewer non-zero elements, the less information it carries, that is, the lower the importance of the corresponding biomolecule information. That is to say, in an embodiment of the present application, the number of non-zero elements in the mass spectrum data can be used as the information expression weight of the first saliva sample.

[0071] It should be noted that due to the differences between samples, the ion intensities of different samples at the same mass-to-charge ratio also differ. The data segment is segmented by the mass spectrum distribution of the entire sample. When a certain ion does not exist in the sample or an error occurs in the sample processing step, that is, data loss occurs in the data segment, which is manifested as an ion intensity of a certain mass-to-charge ratio of 0. Therefore, the more ion intensities are 0 in the data segment, the less information the current sample has; and the more concentrated the mass-to-charge ratio distribution of ion intensity is, according to the above analysis, the larger or more similar the missing compound information is, and the more obvious the impact on the amount of information contained in the sample is. In order to accurately obtain the information expression weight of the first saliva sample, in one embodiment of the present application, step S300, based on each data segment, obtains the information expression weight of the first saliva sample, including steps S310 to S340.

[0072] Step S310: Based on each data segment, obtain a first data segment.

[0073] In this embodiment, the first data segment is any one of the data segments.

[0074] Step S320: Obtain a first coefficient based on the first data segment.

[0075] In this embodiment, the first coefficient is used to at least represent the number of non-zero elements in the first data segment. In other embodiments of the present application, the first coefficient may also be a normalized value of the ion intensity range (i.e., the maximum ion intensity minus the minimum ion intensity) in the first data segment.

[0076] Step S330: Based on the first coefficient, obtain a second coefficient.

[0077] In this embodiment, the second coefficient is at least used to represent the number of non-zero elements in the first saliva sample. In the embodiment of the present application, step S330, based on the first coefficient, the calculation formula for obtaining the second coefficient can be as follows:

[0078] ;

[0079] in, represents the second coefficient; r represents the number of data segments; represents the first coefficient corresponding to the i-th data segment; Indicates the number of corresponding zero elements in the i-th data segment; represents the variance of non-zero elements in the i-th data segment; represents an exponential function with a natural constant as the base; t represents a first adjustment coefficient, and the first adjustment coefficient is greater than 0 and less than 1.

[0080] In this embodiment, the zero ion intensity information in the data segment is calculated, and the compound information reflected by the length of the data segment is used as a weight to adjust the calculation process. Finally, the calculation result is placed between [0,1] using an inverse exponential function. The larger the value, the less information the current sample contains.

[0081] Step S340: Based on the second coefficient, obtain the information expression weight of the first saliva sample.

[0082] As can be seen from the foregoing, in the embodiment of the present application, the second coefficient may be directly used as the information expression weight of the first saliva sample.

[0083] It should be noted that after obtaining the amount of information of different samples (that is, the second coefficient), it is necessary to evaluate the importance of analytical information expression of different mass-to-charge ratios. Since the distribution of different biological molecules in saliva is random, the ion intensity of the target mass-to-charge ratio in different samples is difficult to predict. In order to ensure the accuracy of the principal component analysis results of mass spectrometry data, it is necessary to evaluate the biological molecular information contained in different mass-to-charge ratios. For saliva containing biological information, under ideal conditions, the saliva components and biological molecular information contained in each sample should be relatively close. However, under actual conditions, it is difficult for biological molecules to achieve a completely uniform distribution. Therefore, in addition to the amount of information reflected in the ion intensity information in different samples, it is also necessary to combine the difference in ion intensity of each mass-to-charge ratio between different samples as the information uniqueness of different samples, and use it as the analysis weight (that is, the information expression weight) to evaluate the expression level of mass-to-charge ratio ion biological molecular information.

[0084] In this embodiment, step S340, obtaining the information expression weight of the first saliva sample based on the second coefficient, includes steps S341 to S343.

[0085] Step S341: Based on the data matrix, obtain the first row of data and the second row of data.

[0086] It should be clear that the first row of data is the row of data corresponding to the first saliva sample; the second row of data is any row of data in the data matrix except the first row of data. In this embodiment, when comparing saliva samples with different information amounts, it is also necessary to consider the difference in the amount of information contained in different saliva samples. When compared with saliva samples with similar information amounts, the intensity difference of ions with different mass-to-charge ratios is larger and can better reflect the specificity of the two saliva samples. In other words, in an embodiment of the present application, it is necessary to compare the mass spectrometry data in the first saliva sample with the mass spectrometry data of other saliva samples in the data matrix.

[0087] Step S342: Obtain a mean square error based on the first row of data and the second row of data.

[0088] It should be clear that obtaining the mean square error of two sets of data is a mature technology and will not be elaborated here.

[0089] Step S343: based on the second coefficient and the mean square error, obtaining the information expression weight of the first saliva sample.

[0090] In an embodiment of the present application, the average value of each mean square error in the above text can be used as the information expression weight of the first saliva sample. If the mean square error is larger, it means that the information carried by the first saliva sample is different from the information carried by most saliva samples, and the information expression weight of the first saliva sample is larger.

[0091] In one embodiment of the present application, in step S343, based on the second coefficient and the mean square error, a calculation formula for obtaining the information expression weight of the first saliva sample may be as follows:

[0092] ;

[0093] in, represents the information expression weight of the first saliva sample; represents the second coefficient of the first saliva sample; m is the number of rows of data in the data matrix; represents the mean square error between the row data corresponding to the first saliva sample and the i-th row data in the data matrix; represents the second coefficient of the saliva sample corresponding to the i-th row of data in the data matrix; Represents the second adjustment coefficient and can be any positive integer.

[0094] It should be clear that in this embodiment, the information expression weight It indicates the importance of the first saliva sample in analyzing the expression level of biomolecular information. The larger the value of , the higher the probability that the first saliva sample may provide more information when analyzing the biological information expression weights of ions with different mass-to-charge ratios.

[0095] After obtaining the information expression weights of different saliva samples, it is necessary to analyze the mass-to-charge ratio ion intensity in the mass spectrometry data of different saliva samples to determine the expressiveness of different mass-to-charge ratio ions in the current saliva sample for biological information. The ion intensity reflected in the mass-to-charge ratio of the ions constituting normal saliva in the mass spectrometry data usually accounts for the main component, while the ion intensity of biological molecules is usually relatively weak. Therefore, for the mass spectrometry data that expresses the mass-to-charge ratio of biological molecule information, its ion intensity is usually relatively low. That is to say, in the embodiment of the present application, step S400 needs to be performed.

[0096] Step S400: Based on the information expression weight, obtain the information expression coefficient.

[0097] As can be seen from the foregoing, the information expression of the saliva sample is mainly reflected by the ion intensity in the mass spectrometry data, so the information expression coefficient is at least used to represent the expression intensity of the ions corresponding to each mass-to-charge ratio for the biological information. For example: in one embodiment of the present application, the information expression coefficient can be the product of the information expression weight and the ion intensity in the mass spectrometry data.

[0098] In another embodiment of the present application, step S400, based on the information expression weight, obtains the information expression coefficient, including steps S410 to S440.

[0099] Step S410: Based on the data matrix, obtain a first mass-to-charge ratio.

[0100] It should be clear that the first mass-to-charge ratio is any mass-to-charge ratio in the data matrix.

[0101] Step S420: obtaining a plurality of ion intensities from each saliva sample based on the first mass-to-charge ratio.

[0102] It should be noted that reading the ion intensity corresponding to the first mass-to-charge ratio of each saliva sample based on the data matrix (ie, the data of a certain column in the data matrix) is a mature technology and will not be described in detail here.

[0103] Step S430: Obtaining a third coefficient based on the information expression weight and ion intensity of each saliva sample.

[0104] Specifically, in this embodiment, in step S430, based on the information expression weight and ion strength of each saliva sample, the calculation formula for obtaining the third coefficient may be as follows:

[0105] ;

[0106] Wherein, Q represents the third coefficient corresponding to the first mass-to-charge ratio; m represents the number of saliva samples; represents the information expression weight corresponding to the jth saliva sample; represents the ion intensity corresponding to the first mass-to-charge ratio in the jth saliva sample; Represents a normalization function, which is used to map the values ​​in the brackets to the interval (0, 1).

[0107] Step S440: Based on the third coefficient, obtain the information expression coefficient.

[0108] As can be seen from the foregoing, in the embodiments of the present application, the third coefficient can be directly used as the information expression coefficient.

[0109] It should be noted that since it is difficult for the biomolecule information to be completely uniform in the saliva sample, the difference between the saliva components and the biomolecule information can be reflected by calculating the variance of data with different mass-to-charge ratios. However, bubbles are inevitably contained in the saliva sample during the collection process, and the mass spectrum data may contain ion intensity information of gas molecules. Since the bubbles are also not evenly distributed in the saliva, it is necessary to optimize the variance calculation process: compared with biomolecules (proteins, peptides, etc.), the molecular weight of gas molecules is lower, and the mass-to-charge ratio of ions generated during the ionization process is relatively small, and a lower mass-to-charge ratio value will appear in the mass spectrum. Based on this, in a specific embodiment of the present application, step S440, based on the third coefficient, obtains the information expression coefficient, which may include steps S441 to S446.

[0110] Step S441: sorting the mass-to-charge ratios based on the ion intensities corresponding to the mass-to-charge ratios in the data matrix to obtain a plurality of serial numbers.

[0111] It should be clear that sorting data based on the size of the data is a mature technology and will not be described in detail here.

[0112] Step S442: Based on multiple sequence numbers, obtain a first sequence number.

[0113] In this embodiment, the first serial number is the serial number corresponding to the first mass-to-charge ratio.

[0114] Step S443: Based on the data matrix, a plurality of first ion intensities corresponding to the first mass-to-charge ratios are acquired.

[0115] It should be noted that reading the ion intensity corresponding to the first mass-to-charge ratio based on the data matrix (ie, the data of a column in the data matrix) is a mature technology and will not be described in detail here.

[0116] Step S444: Obtain a first variance based on each first ion intensity.

[0117] It should be clear that obtaining the variance of multiple data values ​​is a mature technology and will not be described in detail here.

[0118] Step S445: Obtain a fourth coefficient based on the first variance and the first sequence number.

[0119] In this embodiment, the fourth coefficient may be the product of the first variance and the first sequence number.

[0120] Step S446: Based on the third coefficient and the fourth coefficient, obtain the information expression coefficient.

[0121] It should be clear that in this embodiment, the product of the third coefficient and the fourth coefficient can be used as the information expression coefficient. In another embodiment of the present application, step S446, based on the third coefficient and the fourth coefficient, the calculation formula for obtaining the information expression coefficient is as follows:

[0122] ;

[0123] in, represents the information expression coefficient corresponding to the first mass-to-charge ratio; represents the third coefficient corresponding to the first mass-to-charge ratio; Indicates the first serial number corresponding to the first mass-to-charge ratio; represents the first variance; Represents a normalization function, which is used to map the values ​​in brackets to the interval (0, 1); Represents the mean calculation function.

[0124] Step S500: Based on the information expression coefficient, the principal component analysis process is optimized to obtain biomolecule information matching each saliva sample.

[0125] It should be clear that in this embodiment, the transposed matrix of the mass spectrometry data matrix of the saliva sample is defined as matrix A, and each row of data in the matrix is ​​called a dimension. The data of each dimension of matrix A is zero-meaned, that is, each data point in the row is subtracted from the mean of each row of data to obtain the processed matrix B. In order to retain the dimensional data features of the mass spectrometry data with different mass-to-charge ratios under the current saliva sample conditions as much as possible during data dimensionality reduction, it is necessary to analyze the dimensionality reduction matrix in combination with the optimization priority of data of different dimensions. Through the dimensionality reduction priority parameters of each dimensional data Multiply the corresponding dimension (row) data of matrix B, and use the data priority to further adjust the difference between the data to obtain the adjustment matrix . Calculate the adjustment matrix The covariance matrix C of the covariance matrix C is obtained, and the eigenvalues ​​and eigenvectors of the covariance matrix C are obtained. The eigenvectors are arranged into a matrix by row from top to bottom according to the corresponding eigenvalue size, and the first k rows are taken to form a matrix P, where the first k rows are determined by the cumulative contribution rate of the eigenvalues. In this embodiment, the cumulative contribution rate threshold is set to 80% according to the empirical value. Finally, the matrix P is multiplied by the matrix A to obtain the data after dimension reduction to k dimensions. Finally, the principal component analysis is performed on the data after dimension reduction. Since the principal component analysis technology is a mature technology, it will not be described here.

[0126] It should be noted that by performing principal component analysis on mass spectrometry data, the mass spectrometry information of molecules with abnormal mass-to-charge ratio in saliva can be screened out. Use mass spectrometry data processing software, such as Mascot, SEQUEST or MaxQuant, to parse the screened mass spectrometry information and match the compound information in the database. Commonly used databases include SalivaDB, HMDB, PeptideAtlas, MetaboLights, etc. This is a mature technology and will not be described in detail here.

[0127] It should be clear that the embodiment of the rapid mass spectrometry detection method for saliva samples proposed in the present application evaluates the amount of biomolecule information contained in the saliva sample (that is, the number of non-zero elements in the mass spectrometry data) through the mass spectrometry data of the saliva sample; then, the information expression weight of the mass spectrometry data of each saliva sample is calculated by combining the information amount and the mass spectrometry data of different saliva samples; finally, the principal component analysis process is adjusted according to the information expression weight. Compared with the prior art, the analysis process adds analysis of different sample data, improves the weight of the saliva sample's proportion of mass-to-charge ratio data in the optimization process of principal component analysis, and reduces the impact of interference information that may exist in the sample on the analysis, thereby increasing the accuracy of mass spectrometry analysis.

[0128] Although the embodiments of the present application have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles of the present application.

Claims

1. A rapid mass spectrometry detection method for saliva samples, characterized in that: Acquire first mass spectrum data of a first saliva sample; the first mass spectrum data at least includes a mass-to-charge ratio and an ion intensity of the first saliva sample; the first mass spectrum data of the first saliva sample is mass spectrum data of any one saliva sample among a plurality of saliva samples; Mass spectrometry data of multiple saliva samples were pre-acquired; Segmenting the first mass spectrum data to obtain a plurality of data segments; Based on each data segment, obtaining an information expression weight of the first saliva sample; the information expression weight is used to indicate the importance of the biomolecule information contained in the first saliva sample; Based on the information expression weight and the ion intensity, an information expression coefficient is obtained; based on the information expression coefficient, a principal component analysis process is optimized to obtain biomolecule information matching each saliva sample.

2. The rapid mass spectrometry detection method for saliva samples according to claim 1, characterized in that: The step of segmenting the first mass spectrum data to obtain a plurality of data segments includes: Based on the mass spectrometry data of each saliva sample, a data matrix is ​​obtained; each row in the data matrix represents a different saliva sample; each column in the data matrix represents mass spectrometry data of different mass-to-charge ratios; each element in the data matrix represents the ion intensity of the saliva sample in the corresponding row at the corresponding mass-to-charge ratio; Based on the data matrix, obtaining a plurality of first columns, wherein each element in the first columns is 0; The first mass spectrum data is segmented using each first column as a dividing line to obtain a plurality of data segments.

3. The rapid mass spectrometry detection method for saliva samples according to claim 2, characterized in that: The obtaining, based on each data segment, the information expression weight of the first saliva sample includes: Based on each data segment, obtain a first data segment; the first data segment is any one of the data segments; Based on the first data segment, obtaining a first coefficient; the first coefficient is used to at least represent the number of non-zero elements in the first data segment; Based on the first coefficient, obtaining a second coefficient; the second coefficient is at least used to represent the number of non-zero elements in the first saliva sample; Based on the second coefficient, an information expression weight of the first saliva sample is obtained.

4. The rapid mass spectrometry detection method for saliva samples according to claim 3, characterized in that: The calculation formula for obtaining the second coefficient based on the first coefficient is as follows: ； in, represents the second coefficient; r represents the number of data segments; represents the first coefficient corresponding to the i-th data segment; Indicates the number of corresponding zero elements in the i-th data segment; represents the variance of non-zero elements in the i-th data segment; represents an exponential function with a natural constant as the base; t represents a first adjustment coefficient, and the first adjustment coefficient is greater than 0 and less than 1.

5. The rapid mass spectrometry detection method for saliva samples according to claim 3, characterized in that: The obtaining, based on the second coefficient, the information expression weight of the first saliva sample includes: Based on the data matrix, a first row of data and a second row of data are obtained; the first row of data is the row of data corresponding to the first saliva sample; the second row of data is any row of data in the data matrix except the first row of data; Based on the first row of data and the second row of data, obtaining a mean square error; Based on the second coefficient and the mean square error, an information expression weight of the first saliva sample is obtained.

6. The rapid mass spectrometry detection method for saliva samples according to claim 5, characterized in that: The calculation formula for obtaining the information expression weight of the first saliva sample based on the second coefficient and the mean square error is as follows: ； in, represents the information expression weight of the first saliva sample; represents the second coefficient of the first saliva sample; m is the number of rows of data in the data matrix; represents the mean square error between the row data corresponding to the first saliva sample and the i-th row data in the data matrix; represents the second coefficient of the saliva sample corresponding to the i-th row of data in the data matrix; represents a second adjustment coefficient, which is a positive integer.

7. The rapid mass spectrometry detection method for saliva samples according to any one of claims 2 to 6, characterized in that: The obtaining of the information expression coefficient based on the information expression weight includes: Based on the data matrix, obtaining a first mass-to-charge ratio; the first mass-to-charge ratio is any mass-to-charge ratio in the data matrix; acquiring a plurality of ion intensities from each saliva sample based on the first mass-to-charge ratio; Obtaining a third coefficient based on the information expression weight and ion intensity of each saliva sample; Based on the third coefficient, an information expression coefficient is obtained.

8. The rapid mass spectrometry detection method for saliva samples according to claim 1, characterized in that: The calculation formula for obtaining the third coefficient based on the information expression weight and ion strength of each saliva sample is as follows: ； Wherein, Q represents the third coefficient corresponding to the first mass-to-charge ratio; m represents the number of saliva samples; represents the information expression weight corresponding to the jth saliva sample; represents the ion intensity corresponding to the first mass-to-charge ratio in the jth saliva sample; Represents a normalization function, which is used to map the values ​​in the brackets to the interval (0, 1).

9. The rapid mass spectrometry detection method for saliva samples according to claim 1, characterized in that: The obtaining of the information expression coefficient based on the third coefficient includes: Based on the ion intensities corresponding to the respective mass-to-charge ratios in the data matrix, the respective mass-to-charge ratios are sorted to obtain a plurality of serial numbers; Based on the multiple serial numbers, a first serial number is obtained; the first serial number is the serial number corresponding to the first mass-to-charge ratio; Based on the data matrix, acquiring a plurality of first ion intensities corresponding to the first mass-to-charge ratio; Based on each first ion intensity, obtaining a first variance; Based on the first variance and the first sequence number, obtaining a fourth coefficient; Based on the third coefficient and the fourth coefficient, an information expression coefficient is acquired.

10. The rapid mass spectrometry detection method for saliva samples according to claim 9, characterized in that: The calculation formula for obtaining the information expression coefficient based on the third coefficient and the fourth coefficient is as follows: ； in, represents the information expression coefficient corresponding to the first mass-to-charge ratio; represents the third coefficient corresponding to the first mass-to-charge ratio; Indicates the first serial number corresponding to the first mass-to-charge ratio; represents the first variance; Represents a normalization function, which is used to map the values ​​in brackets to the interval (0, 1); Represents the mean calculation function.

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