Substance identification device, database calling method, equipment, medium and product

Abstract

The invention discloses a substance identification device, a database calling method, equipment, a medium and a product, the substance identification device is provided with a detection module, a storage module and a data processing module, and the data processing module is configured to perform first similarity comparison on a protein fingerprint spectrum of a to-be-detected object and a standard protein fingerprint spectrum; obtaining a first matching set; verifying a second interference feature in the protein fingerprint spectrum of the to-be-detected object based on the first matching set; if the verification is passed, performing second similarity comparison on the protein fingerprint spectrum of the to-be-detected object and the standard protein fingerprint spectrum to obtain a second matching set; and determining an identification result of the to-be-detected object based on the first matching set and the second matching set. Similarity comparison is directly performed on the protein fingerprints of the to-be-detected object based on the predetermined related characteristics of the protein fingerprints, so that identification complexity is reduced while identification time consumption is shortened, and substance identification efficiency is improved.

Description

Technical Field

[0001] The present disclosure generally relates to the field of microbial detection technology, and specifically relates to a substance identification device, a database calling method, equipment, a medium and a product. Background Art

[0002] At present, in order to achieve the elimination and control of pathogenic microorganisms, we can first conduct a pathogenic examination to detect whether there are pathogens (such as bacteria, viruses, parasites, etc.) in the substance, so as to carry out targeted treatment of infectious diseases caused by pathogenic microorganisms based on the test results.

[0003] Specifically, in the above-mentioned pathological examination process, mass spectrometry analysis can be used to identify bacterial species to obtain the detection results of pathogens. Before using mass spectrometry analysis to identify bacterial species, it is usually necessary to purify and culture the bacteria of the test substance to form pure colonies on a solid culture medium for mass spectrometry identification, thereby determining the identification results of the test substance based on the mass spectrometry identification results of the colonies.

[0004] However, the purification and cultivation process of the above-mentioned bacteria tends to consume a lot of time, which results in low efficiency in the identification of substances in the prior art. Summary of the invention

[0005] In view of the above-mentioned defects or deficiencies in the prior art, it is desired to provide a substance identification device, database calling method, equipment, medium and product. The present application can directly perform similarity comparison on the protein fingerprint spectrum of the object to be tested based on the relevant characteristics of the predetermined protein fingerprint spectrum, so as to shorten the identification time and reduce the identification complexity, thereby improving the efficiency of substance identification.

[0006] In a first aspect, the present invention provides a substance identification device, which comprises a detection module for detecting an object to be detected and obtaining its protein fingerprint, a storage module for storing substance identification related data, and a data processing module for calling the data in the storage module for data processing, wherein the data processing module is configured as follows:

[0007] Perform a first similarity comparison between the protein fingerprint of the object to be tested and the standard protein fingerprint to obtain a first matching set; the first similarity comparison ignores a first interfering feature in the protein fingerprint of the object to be tested;

[0008] Verifying a second interference feature in the protein fingerprint of the object to be tested based on the first matching set;

[0009] If the verification is passed, a second similarity comparison is performed between the protein fingerprint of the object to be tested and the standard protein fingerprint to obtain a second matching set; the second similarity comparison ignores the second interfering feature in the protein fingerprint of the object to be tested;

[0010] Determine an identification result of the object to be tested based on the first matching set and the second matching set;

[0011] Among them, the first interference feature and the second interference feature are common features of the negative object protein fingerprint and the standard protein fingerprint; the mass-to-charge ratio of the first interference feature in the protein fingerprint is less than the mass-to-charge ratio of the second interference feature in the protein fingerprint, and / or the mass-to-charge ratio of the first interference feature in the protein fingerprint is less than or less than or equal to a first preset threshold, and the mass-to-charge ratio of the second interference feature in the protein fingerprint is greater than or greater than or equal to the first preset threshold.

[0012] In a second aspect, the present invention provides a database calling method, the method comprising:

[0013] Calling a urine standard atlas library; the urine standard atlas library includes multiple standard protein fingerprints;

[0014] The protein fingerprint of the sample to be tested is compared with the standard protein fingerprint to obtain a matching set; the interference feature is a common feature of the protein fingerprint of the negative urine sample and the standard protein fingerprint, and the similarity comparison ignores the interference feature in the protein fingerprint of the sample to be tested;

[0015] The identification result of the sample to be tested is determined based on the matching set.

[0016] In a third aspect, a method for identifying a substance is provided, the method comprising:

[0017] Perform a first similarity comparison between the protein fingerprint of the object to be tested and the standard protein fingerprint to obtain a first matching set; the first similarity comparison ignores a first interfering feature in the protein fingerprint of the object to be tested;

[0018] Verifying a second interference feature in the protein fingerprint of the object to be tested based on the first matching set;

[0019] If the verification is passed, a second similarity comparison is performed on the protein fingerprint of the object to be tested and the standard protein fingerprint to obtain a second matching set; the second similarity comparison ignores the second interference feature in the protein fingerprint of the object to be tested;

[0020] Determine an identification result of the object to be tested based on the first matching set and the second matching set;

[0021] Among them, the first interference feature and the second interference feature are common features of the negative object protein fingerprint and the standard protein fingerprint; the mass-to-charge ratio of the first interference feature in the protein fingerprint is less than the mass-to-charge ratio of the second interference feature in the protein fingerprint, and / or the mass-to-charge ratio of the first interference feature in the protein fingerprint is less than or less than or equal to a first preset threshold, and the mass-to-charge ratio of the second interference feature in the protein fingerprint is greater than or greater than or equal to the first preset threshold.

[0022] In a fourth aspect, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps performed by the material identification device in the first aspect, the method described in the second aspect or the third aspect are implemented.

[0023] In a fifth aspect, a computer-readable storage medium is provided, on which a computer program is stored, characterized in that when the program is executed by a processor, it implements the steps performed by the material identification device in the first aspect, the method described in the second aspect or the third aspect.

[0024] In a sixth aspect, a computer program product is provided, which includes instructions. When the instructions are executed, the steps performed by the material identification device in the first aspect and the method described in the second aspect or the third aspect are executed.

[0025] The substance identification device, database calling method, equipment, medium and product provided in the embodiments of the present application, on the one hand, compared with the prior art in which the bacteria of the test substance are purified and cultured before the identification result of the test substance is determined, the present application can directly perform a similarity comparison on the protein fingerprint spectrum of the test object based on the relevant features of the predetermined protein fingerprint spectrum, so as to shorten the identification time and reduce the identification complexity, thereby improving the substance identification efficiency; on the other hand, compared with the prior art in which the protein fingerprint spectrum of the test substance is directly compared with the standard protein fingerprint spectrum, the present application can perform a similarity comparison on the protein fingerprint spectrum of the test object and the standard protein fingerprint spectrum based on the interference features existing in both the protein fingerprint spectrum of the test object and the standard protein fingerprint spectrum, ignore the interference features during the comparison, eliminate the interference features in the identification of the protein fingerprint spectrum of the test object and the standard protein fingerprint spectrum to a certain extent, and enhance the distinctiveness of the protein fingerprint spectrum of the test object and the standard protein fingerprint spectrum as much as possible, thereby improving the identification accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0027] Figure 1The structural diagram of the substance identification device provided in this application;

[0028] Figure 2 A schematic diagram of a substance identification method provided in this application;

[0029] Figure 3 A flowchart of a method for determining interference characteristics provided in this application;

[0030] Figure 4 The protein fingerprint of the negative urine sample provided for this application;

[0031] Figure 5 A flowchart of a database calling method provided in an embodiment of the present application;

[0032] Figure 6 A set of comparative schematic diagrams of urine sample identification results provided in the embodiments of the present application;

[0033] Figure 7 Another set of comparative schematic diagrams of urine sample identification results provided in the embodiments of the present application;

[0034] Figure 8 A schematic diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0035] The present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the relevant inventions, rather than to limit the inventions. It should also be noted that, for ease of description, only the parts related to the invention are shown in the accompanying drawings.

[0036] It should be noted that, in the absence of conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments. In addition, the term "and / or" herein is merely a description of the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The terms "first" and "second" in the description and claims of the embodiments of the present application are used to distinguish different objects, rather than to describe a specific order of objects.

[0037] First, the terms involved in this application are explained as follows:

[0038] (1) Pathological examination: refers to the process of testing the pathogen that causes the disease, which may include, for example, sample collection, sample culture, observation of the presence of pathogens in the sample, identification of specific properties of the pathogen, molecular diagnosis of pathogen DNA or RNA, and detection of the presence of pathogen antibodies. The sample may be blood, urine, sputum, feces or tissue collected according to the type of disease and pathogen;

[0039] (2) Mass Spectrometry: It is an analytical technique used to identify and quantify substances present in an object by measuring the mass-to-charge ratio of ions in the object.

[0040] The specific working principle is: firstly, the object to be measured is ionized by electron bombardment, chemical ionization or matrix-assisted laser desorption ionization, etc. - then the charged ions are accelerated by electric field to obtain kinetic energy - then the ion beam is passed through the magnetic field to deflect the ions according to the mass and charge ratio of the ions - finally the number of ions is measured by the detector;

[0041] (3) Bacterial purification: used to separate specific types of bacteria from mixed bacterial flora;

[0042] (4) Bacterial culture: refers to the process of multiplying bacteria in a nutrient-rich medium under artificially controlled conditions;

[0043] (5) Double distilled water (ddH2O): water obtained by distilling water once again;

[0044] (6) Bacterial enrichment: refers to the process of isolating target microorganisms from the environment and increasing their number through a specific method.

[0045] Figure 1 Schematic diagram of the substance identification device provided in the embodiment of the present application. Figure 1 As shown, the substance identification device includes: an acquisition module 101, a detection module 102, a storage module 103 and a data processing module 104.

[0046] Exemplarily, the acquisition module 101 can be used to acquire an object to be tested that needs to be identified; the detection module 102 can be used to detect the object to be tested and obtain a protein fingerprint spectrum of the object to be tested, wherein the detection module 102 can be, for example, a mass spectrometry identification instrument; the storage module 103 can be used to store relevant data for substance identification, wherein the relevant data for substance identification can be, for example, a protein fingerprint spectrum obtained by the detection module 102; the data processing module 104 can be used to call the relevant data for substance identification in the storage module 103, and use the relevant data for data processing, wherein the data processing module 104 can be, for example, a computer device.

[0047] In a specific implementation, the object to be tested can be first acquired by the acquisition module 101 and an extraction operation can be performed on the object to be tested to increase the protein concentration in the object to be tested, thereby increasing the mass spectrometry signal intensity in the subsequent mass spectrometry detection process; then, the detection module 102 can perform mass spectrometry detection on the object to be tested after the extraction process to obtain a protein fingerprint spectrum of the object to be tested; finally, the data processing module 104 can determine the identification result of the object to be tested based on the comparison result of the protein fingerprint spectrum of the object to be tested generated by the detection module 102 and the substance identification related data stored in the storage module 103 and related data processing operations.

[0048] Exemplarily, the extraction processing method performed by the acquisition module 101 on the object to be detected may be a precipitation-redissolution extraction method (PPT-RSD for short).

[0049] For example, when the object to be tested is a urine sample, the extraction process of the urine sample may specifically include the following steps:

[0050] (1) 300 μL of a 5% saponin solution can be added to 1 mL of urine sample, vortexed for 10 seconds and thoroughly mixed, and then centrifuged for 5 minutes; wherein the centrifugal force intensity during the centrifugation can be 11000 g;

[0051] (2) removing the upper layer of liquid in the liquid mixture produced after the centrifugation in step (1), adding 500 μL of trifluoroacetic acid (C2HF3O2, also known as TFA) to resuspend the precipitate, vortexing for 10 seconds and mixing thoroughly, and then centrifuging for 5 minutes; wherein the centrifugal force intensity during the centrifugation can be 11000g;

[0052] (3) removing the upper layer of liquid in the liquid mixture produced after the centrifugation in step (2), adding 1 mL of ddH2O to resuspend the precipitate, vortexing for 10 seconds and mixing thoroughly, and then centrifuging for 5 minutes; wherein the centrifugal force intensity during the centrifugation can be 11000g;

[0053] (4) The upper layer of the liquid in the liquid mixture produced after the centrifugal treatment in step (3) is removed as much as possible, and after precipitation, 40 μL of 70% formic acid and 40 μL of 50% acetonitrile are added, mixed thoroughly, and allowed to stand at room temperature for 2 minutes before centrifugation for 5 minutes; wherein the centrifugal force intensity during the centrifugation can be 11000g.

[0054] In a possible implementation, the detection module 102 can directly identify the bacterial species of the sample to be tested by bacterial enrichment. However, when the sample to be tested is a urine sample, even if the number of pathogens in the sample to be tested can meet the identification requirements, other components in the urine sample other than microorganisms will also cause errors in the mass-to-charge ratio spectrum of the sample to be tested generated by the detection module 102, thereby causing identification errors, identification failures, or identification successes but generally low confidence.

[0055] Optionally, before the detection module 102 uses the mass spectrometry method to identify the bacterial species of the sample to be tested, the sample to be tested can be purified and cultured to form a pure colony on a solid culture medium for mass spectrometry identification, thereby determining the identification result of the sample to be tested based on the mass spectrometry identification result of the colony. However, when the sample to be tested is a urine sample, there are still a large number of interfering peaks in the bacterial species peak graph constructed by the positive urine sample and the negative urine sample, which affects the accuracy of the urine sample identification result.

[0056] Based on this, an embodiment of the present application provides a material identification device, which has a detection module, a storage module and a data processing module. The data processing module can directly perform a similarity comparison on the protein fingerprint spectrum of the object to be tested based on the relevant characteristics of the predetermined protein fingerprint spectrum, so as to shorten the identification time and reduce the identification complexity, thereby improving the efficiency of material identification.

[0057] Figure 2 is a schematic diagram of a substance identification method provided in an embodiment of the present application, such as Figure 2 As shown, the data processing module 104 is configured to perform the following steps:

[0058] Step S201, performing a first similarity comparison between the protein fingerprint of the object to be tested and the standard protein fingerprint to obtain a first matching set; the first similarity comparison ignores a first interfering feature in the protein fingerprint of the object to be tested.

[0059] In an embodiment of the present application, after obtaining the protein fingerprint of the object to be tested, the first interference feature in the protein fingerprint as an interference factor can be ignored first, so as to determine a first matching set similar to the bacterial species contained in the object to be tested based on the protein fingerprint after ignoring the first interference feature. The interference feature can correspond to an interference peak in the protein fingerprint.

[0060] In a possible implementation, a protein fingerprint of the object to be tested can be obtained by mass spectrometry analysis, wherein the object to be tested can be blood, urine, sputum, feces or tissue, etc.

[0061] For example, a protein fingerprint can be used to characterize the relationship between the mass-charge ratio (i.e., mass-to-charge ratio m / z) and ion abundance (i.e., intensity) of different ions contained in the object to be tested. Based on this, a protein fingerprint can also be called a mass spectrum.

[0062] In one possible implementation, the standard protein fingerprint is mainly used to form a control group of the protein fingerprint of the object to be tested. Based on this, the standard protein fingerprint may exist in a predetermined standard fingerprint library corresponding to the object to be tested, wherein the standard fingerprint library includes multiple standard protein fingerprints.

[0063] Exemplarily, the standard atlas library can be constructed by the protein fingerprints corresponding to the known positive samples of the object to be tested, that is, the standard protein fingerprints contained in the standard atlas library are the protein fingerprints of the known positive samples of the object to be tested. The known positive samples contain known microbial species, for example, the protein fingerprint of a urine sample containing Escherichia coli is used as the standard protein fingerprint to construct the standard atlas library.

[0064] Specifically, mass spectrometry identification can be performed on multiple known positive samples of the object to be tested to obtain protein fingerprint maps of the multiple known positive samples, thereby forming a standard spectrum library based on the protein fingerprint maps of the multiple known positive samples.

[0065] Optionally, the standard atlas library can be formed by the protein fingerprints corresponding to the subculture samples of the object to be tested, that is, the standard protein fingerprints contained in the standard atlas library are the protein fingerprints of the subculture samples of the object to be tested. The subculture samples contain known species of bacteria.

[0066] Specifically, a subculture sample of a known bacterial species of the object to be tested can be first obtained by subculture of the object to be tested, and then the subculture sample of the object to be tested can be subjected to mass spectrometry identification to obtain a protein fingerprint of the subculture sample, thereby forming a standard spectrum library based on the protein fingerprint of the subculture sample.

[0067] Exemplarily, the first interfering feature may be a feature that needs to be directly ignored in the protein fingerprint of the object to be detected.

[0068] Exemplarily, the first interference feature may be a common feature of a negative object protein fingerprint and a standard protein fingerprint; wherein the negative object protein fingerprint may be a protein fingerprint of a negative sample of the object to be tested.

[0069] Specifically, the first interference feature can be a common feature of the protein fingerprint of a negative object and a protein fingerprint of a known positive sample of the object to be tested. When the first interference feature exists in the protein fingerprint of the object to be tested, it is easy to cause the object to be tested to be identified as a negative object or a positive object incorrectly; the first interference feature can also be a common feature of the protein fingerprint of a negative object and a protein fingerprint of a subculture sample. When the first interference feature exists in the protein fingerprint of the object to be tested, it is easy to cause the identification result of the object to be tested to be unclear (i.e., the subculture sample does not distinguish between negative objects and positive objects).

[0070] In a possible implementation, after ignoring the first interfering feature in the protein fingerprint of the object to be tested, a first similarity comparison between the protein fingerprint of the object to be tested and the standard protein fingerprint can be achieved by scoring.

[0071] Exemplarily, the operation of ignoring the first interfering feature may be to directly remove the characteristic peak corresponding to the first interfering feature in the protein fingerprint of the object to be tested.

[0072] Based on this, after removing the characteristic peak corresponding to the first interference feature in the protein fingerprint, the first matching set of the object to be tested can be obtained by similarity scoring based on the new protein fingerprint of the object to be tested and the standard protein fingerprint.

[0073] Specifically, the rod-shaped peak information corresponding to the new protein fingerprint of the above-mentioned object to be tested is compared with the spectrum of the known bacterial species in the standard protein fingerprint, so that points are added when the two have the same characteristic peaks, otherwise points are subtracted; then, based on the specific scores, the first matching set corresponding to the object to be tested is obtained.

[0074] Optionally, the operation of ignoring the first interference feature may also be to set the scoring weight of the characteristic peak corresponding to the first interference feature in the protein fingerprint of the object to be tested to a value less than a third preset threshold value. For example, the scoring weight of the characteristic peak corresponding to the first interference feature may be 0.

[0075] Specifically, when the same characteristic peak appears in the protein fingerprint of the object to be tested and the standard protein fingerprint, and the characteristic peak is the characteristic peak corresponding to the first interference feature, the specific score of the characteristic peak can be determined based on the scoring weight of the characteristic peak, and then the corresponding scoring operation can be performed based on the score.

[0076] For example, when the scoring weight of the characteristic peak corresponding to the first interference feature is 0 and the protein fingerprint spectrum of the object to be tested and the standard protein fingerprint spectrum both have a characteristic peak corresponding to the first interference feature, the specific score of the characteristic peak is 0, and no additional points are given at this time.

[0077] Step S202: verifying the second interference feature in the protein fingerprint of the object to be tested based on the first matching set.

[0078] In a possible implementation, after obtaining a first matching set having similarity with the object to be tested, the first matching set obtained in step S201 may be further verified based on the existence of the second interference feature in the first matching set.

[0079] In the embodiment of the present application, by verifying whether the second interfering feature exists in the first matching set, it is possible to avoid the occurrence of errors in the identification results due to the absence of the second interfering feature in the protein fingerprint spectrum of the matching set, thereby ensuring the accuracy of the identification results of the object to be tested.

[0080] Exemplarily, the second interference feature can be a common feature of the protein fingerprint of the negative object and the protein fingerprint of a known positive sample of the object to be tested; the second interference feature can also be a common feature of the protein fingerprint of the negative object and the protein fingerprint of the subculture sample.

[0081] Exemplarily, the second interfering feature may be a feature that needs to be selectively ignored in the protein fingerprint of the object to be detected.

[0082] In a possible implementation, the first interference feature and the second interference feature may be determined based on the mass-to-charge ratio of the features in the protein fingerprint.

[0083] Exemplarily, the mass-to-charge ratio of the first interference feature in the protein fingerprint may be smaller than the mass-to-charge ratio of the second interference feature in the protein fingerprint.

[0084] Optionally, based on the first preset threshold, a feature in the protein fingerprint whose mass-to-charge ratio is less than or equal to the first preset threshold can be determined as a first interference feature, and a feature in the protein fingerprint whose mass-to-charge ratio is greater than or equal to the first preset threshold can be determined as a second interference feature.

[0085] Optionally, based on the first preset threshold, a feature in the protein fingerprint whose mass-to-charge ratio is less than or equal to the first preset threshold is first determined as a first interference feature, and then a feature in the protein fingerprint whose mass-to-charge ratio is greater than the first interference feature and whose mass-to-charge ratio in the protein fingerprint corresponds to the mass-to-charge ratio of the first interference feature and is greater than the first preset threshold is determined as a second interference feature.

[0086] Specifically, the feature corresponding to the first preset threshold in the protein fingerprint may be the first interference feature or the second interference feature.

[0087] It should be noted that, referring to the above content, based on the second interference feature being a common feature of the protein fingerprint of the negative object and the standard protein fingerprint (i.e., the second interference feature exists in the standard protein fingerprint), when the protein fingerprint in the first matching set does not have the second interference feature, it can be explained that the protein fingerprint in the first matching set may have too many deletions of characteristic peaks corresponding to the second interference feature compared with the protein fingerprint of the correct identification result of the object to be tested (i.e., the first matching set may not be the correct matching set of the object to be tested), and the next similarity comparison needs to be performed; otherwise, when the protein fingerprint in the first matching set has the second interference feature, it means that the protein fingerprint in the first matching set has a high degree of similarity with the protein fingerprint of the correct identification result of the object to be tested, and the identification result of the object to be tested can be determined based on the first matching set.

[0088] Based on this, exemplarily, when the second interference feature and the target feature within the tolerance range of the second interference feature do not exist in the first protein fingerprint map in the first matching set, it means that the first matching set may not be the correct matching set of the object to be tested, and the verification passes at this time; otherwise, when the second interference feature and the target feature within the tolerance range of the second interference feature exist in the first protein fingerprint map in the first matching set, it means that the verification fails.

[0089] Exemplarily, the target feature within the tolerance range of the second interference feature may be a feature corresponding to a mass-to-charge ratio close to the mass-to-charge ratio of the second interference feature. For example, when the second interference feature corresponds to a characteristic peak with a mass-to-charge ratio of 3711Da, the target feature within the tolerance range of the second interference feature may be a characteristic peak corresponding to a mass-to-charge ratio close to 3711Da.

[0090] Specifically, the first protein fingerprint in the first matching set may be a standard protein fingerprint whose similarity with the protein fingerprint of the object to be detected reaches a second preset threshold.

[0091] For example, when the first matching set includes multiple strains, the standard protein fingerprint of the strain whose similarity with the protein fingerprint of the object to be tested reaches a second preset threshold can be determined as the first protein fingerprint based on the standard protein fingerprint of each strain. The standard protein fingerprint reaching the second preset threshold can be the standard protein fingerprint with the highest similarity with the protein fingerprint of the object to be tested in the first matching set.

[0092] Step S203, if the verification is passed, a second similarity comparison is performed between the protein fingerprint of the object to be tested and the standard protein fingerprint to obtain a second matching set; the second similarity comparison ignores the second interference feature in the protein fingerprint of the object to be tested.

[0093] In a possible implementation, when the verification of step S202 is passed, a second matching set similar to the bacterial species contained in the object to be tested can be determined based on the protein fingerprint of the object to be tested after ignoring the second interference feature.

[0094] For example, after ignoring the second interfering feature in the protein fingerprint of the object to be tested, a second similarity comparison between the protein fingerprint of the object to be tested and the standard protein fingerprint can be achieved by scoring.

[0095] Exemplarily, the operation of ignoring the second interfering feature may be to directly remove the characteristic peak corresponding to the second interfering feature in the protein fingerprint of the object to be tested.

[0096] Based on this, after removing the characteristic peak corresponding to the second interference feature in the protein fingerprint, the second matching set of the object to be tested can be obtained by similarity scoring based on the new protein fingerprint of the object to be tested and the standard protein fingerprint.

[0097] Specifically, the rod-shaped peak information corresponding to the new protein fingerprint of the above-mentioned object to be tested is compared with the spectrum of the known bacterial species in the standard protein fingerprint, so that points are added when the same characteristic peaks exist in the two, and points are subtracted otherwise; then, based on the specific scores, a second matching set corresponding to the object to be tested is obtained.

[0098] Optionally, the operation of ignoring the second interference feature can also be to set the scoring weight of the characteristic peak corresponding to the second interference feature in the protein fingerprint of the object to be tested to a value less than the fifth preset threshold value. For example, the scoring weight of the characteristic peak corresponding to the second interference feature can be 0.

[0099] Specifically, when the same characteristic peak appears in the protein fingerprint of the object to be tested and the standard protein fingerprint, and the characteristic peak is the characteristic peak corresponding to the second interference feature, the specific score of the characteristic peak can be determined based on the scoring weight of the characteristic peak, and then the corresponding scoring operation can be performed based on the score.

[0100] For example, when the scoring weight of the characteristic peak corresponding to the second interference feature is 0 and the protein fingerprint spectrum of the object to be tested and the standard protein fingerprint spectrum both have a characteristic peak corresponding to the second interference feature, the specific score of the characteristic peak is 0, and no additional points are given at this time.

[0101] In a possible implementation, when the verification in step S202 fails, the identification result of the object to be tested may be determined based on the first matching set.

[0102] Exemplarily, the identification result of the object to be tested may be the first matching set and / or the result in the first matching set.

[0103] For example, when the first matching set includes multiple bacterial species, the identification result of the object to be tested may be at least one bacterial species in the first matching set, or may be all bacterial species corresponding to the entire set, which is not specifically limited here.

[0104] Step S204: determining an identification result of the object to be tested based on the first matching set and the second matching set.

[0105] In a possible implementation, after obtaining the first matching set and the second matching set through two similarity comparisons, the identification result of the object to be tested may be determined based on the first matching set and the second matching set.

[0106] Exemplarily, when the substance types in the first matching set are consistent with the substance types in the second matching set, the identification result of the object to be tested can be determined based on the substance types in the first matching set or the second matching set.

[0107] Specifically, the types of substances in the above-mentioned matching set can be bacterial species. Based on this, when the bacterial species in the first matching set are consistent with those in the second matching set, the identification result of the object to be tested can be determined based on the bacterial species in the first matching set; wherein, the identification result of the object to be tested can be the bacterial species result in the first matching set and / or the first matching set whose similarity with the object to be tested is higher than a preset threshold.

[0108] For example, when the first matching set includes strains A, B, and C, the identification result of the object to be tested can be all strains of strains A, B, and C, or some strains of strains A / B / C, or all strains of strain A and some strains of strains B / C. There is no specific limitation here.

[0109] Exemplarily, when the substance types in the first matching set are consistent with the substance types in the second matching set, the identification result of the object to be tested can be determined based on the substance types in the second matching set.

[0110] Specifically, the identification result of the object to be detected may be the second matching set and / or the bacterial species result in the second matching set whose similarity with the object to be detected is higher than a preset threshold.

[0111] For example, when the second matching set includes strains D, E, and F, the identification result of the object to be tested can be all strains of strains D, E, and F, or some strains of strains D / E / F, or all strains of strain D and some strains of strains E / F, without specific limitation here.

[0112] The substance identification device provided in the embodiment of the present application, on the one hand, compared with the prior art in which the bacteria of the test substance are purified and cultured before the identification result of the test substance is determined, the present application can directly perform a similarity comparison on the protein fingerprint spectrum of the test object based on the relevant features of the predetermined protein fingerprint spectrum, so as to shorten the identification time and reduce the identification complexity, thereby improving the material identification efficiency; on the other hand, compared with the prior art in which the protein fingerprint spectrum of the test substance is directly compared with the standard protein fingerprint spectrum, the present application can perform a similarity comparison on the protein fingerprint spectrum of the test object and the standard protein fingerprint spectrum based on the interference features existing in both the protein fingerprint spectrum of the test object and the standard protein fingerprint spectrum, ignore the interference features during the comparison, and eliminate the interference features to a certain extent. The identification interference of the protein fingerprint spectrum of the test object and the standard protein fingerprint spectrum, the distinctiveness of the protein fingerprint spectrum of the test object and the standard protein fingerprint spectrum is enhanced as much as possible, thereby improving the identification accuracy.

[0113] In another embodiment of the present application, a specific implementation method for obtaining the first matching set is also provided. Exemplarily, the data processing module 104 can also be configured to: after ignoring the first interfering feature of the protein fingerprint of the object to be tested, determine the common characteristic peaks of the new protein fingerprint of the object to be tested and the standard protein fingerprint; based on the common characteristic peaks, determine the target characteristic peak in the new protein fingerprint of the object to be tested, and the relative intensity of the target characteristic peak and the common characteristic peak is less than a fourth preset threshold; remove the target characteristic peak, and score the similarity between the protein fingerprint of the object to be tested after removing the target characteristic peak and the standard protein fingerprint to obtain the first matching set.

[0114] In the embodiment of the present application, after ignoring the first interfering feature of the protein fingerprint spectrum of the object to be tested, the characteristic peaks that are easily affected by errors in the similarity comparison of the object to be tested can be ignored by normalizing the protein fingerprint spectrum, thereby reducing the probability of errors in the identification results.

[0115] Exemplarily, after ignoring the first interfering feature of the protein fingerprint spectrum of the object to be tested, the characteristic peaks that exist in common between the new protein fingerprint spectrum of the object to be tested and the standard protein fingerprint spectrum can be used as a benchmark to screen out characteristic peaks whose relative intensity with the common characteristic peaks is less than a fourth preset threshold as target characteristic peaks.

[0116] Specifically, the intensity of the common characteristic peak can be preset to 1, so that the characteristic peak whose relative intensity with the common characteristic peak is less than 0.3 or 0.02 can be determined as the target characteristic peak based on the new protein fingerprint of the object to be tested and the standard protein fingerprint.

[0117] Illustratively, after the target characteristic peak is determined, the first matching set of the object to be tested can be obtained by similarity scoring based on a new protein fingerprint spectrum of the object to be tested after ignoring the target characteristic peak and a standard protein fingerprint spectrum.

[0118] Specifically, the operation of ignoring the target characteristic peak may be to directly remove the target characteristic peak in the protein fingerprint, or may be to set the scoring weight of the target characteristic peak to a third preset threshold.

[0119] In another embodiment of the present application, a specific generation method of the first interference feature and the second interference feature is also provided. Exemplarily, the data processing module 104 can also be configured to: perform mass spectrometry identification on the positive object set and the negative object set respectively, obtain the protein fingerprint of each standard positive object in the positive object set and the protein fingerprint of each standard negative object in the negative object set; perform characteristic peak statistics on the protein fingerprint of each standard positive object and the protein fingerprint of each standard negative object respectively, obtain the characteristic peak distribution law of the standard positive object and the characteristic peak distribution law of the standard negative object; determine the first interference feature and the second interference feature based on the characteristic peak distribution law of the standard positive object and the characteristic peak distribution law of the standard negative object.

[0120] In the embodiment of the present application, based on the characteristic peak distribution law of the standard positive object of the object to be tested and the characteristic peak distribution law of the standard negative object, the characteristic peaks of the protein fingerprint spectrum that exist simultaneously in the standard positive object and the standard negative object can be determined as the first interference feature and the second interference feature.

[0121] Exemplarily, the mass-to-charge ratio of the first interference feature in the protein fingerprint is less than a first preset threshold, and the mass-to-charge ratio of the second interference feature in the protein fingerprint is greater than the first preset threshold.

[0122] For example, when the object to be tested is a urine sample, the mass-to-charge ratio of the first interference feature in the protein fingerprint spectrum may be less than 3500Da, and the mass-to-charge ratio of the second interference feature in the protein fingerprint spectrum may be greater than 3500Da.

[0123] In another embodiment of the present application, a urine sample is taken as an example to introduce a specific method for determining the first interference feature and the second interference feature. Figure 3 is a flow chart of a method for determining interference features provided in an embodiment of the present application, such as Figure 3 As shown, when the object to be tested is a urine sample, the method for determining the interference feature may include the following steps:

[0124] Step S301, obtaining a protein fingerprint of a negative urine sample, and determining a characteristic peak distribution pattern of the negative urine sample based on the protein fingerprint of the negative urine sample.

[0125] Exemplarily, a mass spectrometry analysis method can be used to perform mass spectrometry analysis on a negative urine sample to obtain a protein fingerprint of the negative urine sample, and the characteristic peak distribution pattern of the negative urine sample can be determined based on the protein fingerprint of the negative urine sample.

[0126] Specifically, refer to Figure 4 The protein fingerprint spectrum of the negative urine sample shown, based on the peak distribution of different mass-to-charge ratios in the spectrum, it can be learned that the distribution pattern of the characteristic peaks is specifically as follows: there are more peaks with a mass-to-charge ratio less than 2800Da, among which the characteristic peaks with mass-to-charge ratios of 2251Da and 2791Da have higher intensities; there is a characteristic peak group in the mass-to-charge ratio range of 3300-3500Da, and the intensity of the characteristic peak group is relatively high; the intensity of the characteristic peak with a mass-to-charge ratio greater than 3500Da is usually not high, among which the main interference peaks include the characteristic peaks corresponding to mass-to-charge ratios of 3711Da, 4752Da, 4965Da, and 6192Da.

[0127] Step S302, obtaining a protein fingerprint of the positive urine sample, and determining a characteristic peak distribution pattern of the positive urine sample based on the protein fingerprint of the positive urine sample.

[0128] Exemplarily, a mass spectrometry analysis method can be used to perform mass spectrometry analysis on the positive urine sample to obtain a protein fingerprint of the positive urine sample, and the characteristic peak distribution pattern of the positive urine sample can be determined based on the protein fingerprint of the positive urine sample.

[0129] Specifically, based on the peak distribution of different mass-to-charge ratios in the protein fingerprint of the positive urine sample, it can be known that the distribution law of the characteristic peaks is as follows: the interference peaks existing in the protein fingerprint of the negative urine sample also exist in the protein fingerprint of the positive urine sample (that is, the characteristic peaks corresponding to the mass-to-charge ratios of 3711Da, 4752Da, 4965Da, and 6192Da are also the interference peaks in the protein fingerprint of the positive urine sample); within the peak range of mass-to-charge ratio less than 2800Da, when the mass-to-charge ratio is 22 When the characteristic peaks of 51Da and / or 2791Da are taken as the highest peaks, the relative intensity of the pathogen protein peak is easily reduced after intensity normalization, thus affecting the similarity comparison results; there is a characteristic peak group with higher intensity in the mass-to-charge ratio range of 3300-3500Da. When this characteristic peak group is taken as the highest peak, the relative intensity of the pathogen protein peak is easily reduced after intensity normalization, thus affecting the similarity comparison results; although the intensity of the characteristic peak with a mass-to-charge ratio greater than 3500Da is not high, it is easy to affect the identification results.

[0130] Step S303, determining an interference peak candidate set based on the characteristic peak distribution law of the negative urine sample and the characteristic peak distribution law of the positive urine sample.

[0131] Exemplarily, by comparing the distribution patterns of characteristic peaks of negative urine samples with those of positive urine samples, characteristic peaks whose peak frequencies are simultaneously within a preset range in the protein fingerprint spectra of the two urine samples can be determined as interference peak candidate sets; wherein the preset range of peak frequencies can be 60-90%.

[0132] Step S304: determining a first interference peak and a second interference peak based on the interference peak candidate set.

[0133] For example, based on the molecular weight of the known protein in the positive urine sample, characteristic peaks with a molecular weight close to or equal to that of the known protein in the interference peak candidate set can be removed to form an interference peak database.

[0134] Specifically, the interference peaks in the interference peak database can be divided into the first interference peak and the second interference peak based on the mass-to-charge ratio of 3500Da; wherein the mass-to-charge ratio of the first interference peak is less than 3500Da, and the mass-to-charge ratio of the second interference peak is greater than 3500Da. It should be noted that the first interference peak can correspond to the above-mentioned first interference feature, and the second interference peak can correspond to the above-mentioned second interference feature.

[0135] It should be noted that the reason why the mass-to-charge ratio of 3500Da is used as the standard for dividing interference peaks is that based on the protein fingerprint spectrum of known positive urine samples, it can be known that: when the characteristic peak with a mass-to-charge ratio of less than 3500Da is used as the highest peak, due to the high intensity of the highest peak, it is easy to cause the relative intensity of the pathogen protein peak in the protein fingerprint spectrum to be greatly reduced, thereby causing some pathogen characteristic peaks to fail to be successfully identified. Therefore, it is necessary to treat the interfering characteristic peak with a mass-to-charge ratio of less than 3500Da as a characteristic peak that is directly ignored (i.e., the first interference peak); and when the characteristic peak with a mass-to-charge ratio greater than 3500Da is used as the highest peak, due to the low intensity of the highest peak, although it will not cause the relative intensity of the pathogen protein peak to decrease, it may still cause errors in the identification results. Therefore, the characteristic peak with a mass-to-charge ratio greater than 3500Da can be selectively ignored. Based on this, the characteristic peak that needs to be selectively ignored is used as the second interference peak.

[0136] For example, the first interference peak may include characteristic peaks corresponding to mass-to-charge ratios of 2251Da, 2791Da, 3372Da, 3444Da and 3486Da; the second interference peak may include characteristic peaks corresponding to mass-to-charge ratios of 3711Da, 4752Da, 4965Da and 6192Da.

[0137] In another embodiment of the present application, a specific method for generating a urine standard atlas library is also provided. Exemplarily, the data processing module 104 can also be configured to: perform mass spectrometry identification on multiple positive urine samples to obtain protein fingerprints of multiple positive urine samples; construct a urine standard atlas library based on the protein fingerprints of multiple positive urine samples, and the urine standard atlas library includes multiple standard protein fingerprints; or perform mass spectrometry identification on bacterial samples of known species obtained by subculture to obtain protein fingerprints of subculture samples; construct a urine standard atlas library based on the protein fingerprints of subculture samples, and the urine standard atlas library includes multiple standard protein fingerprints.

[0138] In an embodiment of the present application, when the types of microorganisms contained in a positive urine sample are known or the types of bacteria contained in a urine sample are known, multiple standard protein fingerprint maps of the urine sample can be obtained by mass spectrometry identification of the positive urine sample or the urine sample, and a urine standard map library can be constructed based on the multiple standard protein fingerprint maps of the urine sample, thereby completing the construction of the urine standard map library.

[0139] Exemplarily, when the urine standard spectrum library is preset to be composed of protein fingerprint spectra of positive urine samples, mass spectrometry identification can be performed on multiple positive urine samples with known microorganisms to obtain protein fingerprint spectra of the multiple positive urine samples, thereby constructing a urine standard spectrum library based on the protein fingerprint spectra of the multiple positive urine samples.

[0140] Optionally, when the urine standard spectrum library is preset to be composed of protein fingerprints of urine samples from subcultures, mass spectrometry identification can be performed on positive urine samples with known bacterial species to obtain the protein fingerprints of urine samples from subcultures, thereby constructing a urine standard spectrum library based on the protein fingerprints of urine samples from subcultures.

[0141] In another embodiment of the present application, a database calling method is also provided. Figure 5 is a flowchart of a database calling method provided in an embodiment of the present application, such as Figure 5 As shown, the method comprises the following steps:

[0142] Step S501, calling a urine standard atlas library; the urine standard atlas library includes a plurality of standard protein fingerprints.

[0143] Exemplarily, the urine standard spectrum library can be a spectrum library constructed by protein fingerprints of multiple positive urine samples or protein fingerprints of subcultured urine samples in the previous embodiment.

[0144] Exemplarily, multiple positive urine samples can be subjected to mass spectrometry identification to obtain protein fingerprints of the multiple positive urine samples; and a urine standard spectrum library can be constructed based on the protein fingerprints of the multiple positive urine samples.

[0145] Specifically, mass spectrometry identification can be performed on multiple positive urine samples containing known microorganisms to obtain protein fingerprint spectra of the multiple positive urine samples, thereby constructing a urine standard spectrum library based on the protein fingerprint spectra of the multiple positive urine samples.

[0146] Step S502, perform a similarity comparison between the protein fingerprint of the sample to be tested and the standard protein fingerprint to obtain a matching set; the interference feature is a common feature between the protein fingerprint of the negative urine sample and the standard protein fingerprint, and the similarity comparison ignores the interference feature in the protein fingerprint of the sample to be tested.

[0147] Exemplarily, the interference feature may include a first interference feature with a mass-to-charge ratio less than a preset threshold and a second interference feature with a mass-to-charge ratio greater than a preset threshold; wherein the preset threshold may be 3500 Da, for example.

[0148] Exemplarily, characteristic peaks corresponding to interfering features in the protein fingerprint of the sample to be tested may be removed, and similarity scores may be performed based on the new protein fingerprint of the sample to be tested and the standard protein fingerprint to obtain a matching set.

[0149] Optionally, the scoring weight of the characteristic peak corresponding to the interference feature can be reset to a preset threshold, and the new protein fingerprint of the sample to be tested and the standard protein fingerprint are scored for similarity based on the updated scoring weight to obtain a matching set. Among them, the scoring weight of the characteristic peak corresponding to the interference feature can be reset to a value less than the sixth preset threshold.

[0150] Step S503: determining the identification result of the sample to be tested based on the matching set.

[0151] Exemplarily, the identification result of the sample to be tested can be determined based on the matching set and / or the results in the matching set.

[0152] In the embodiment of the present application, by calling the urine standard spectrum library, on the one hand, compared with the prior art of directly comparing the protein fingerprint spectrum of the substance to be tested with the standard protein fingerprint spectrum, the protein fingerprint spectrum of the object to be tested and the standard protein fingerprint spectrum can be compared for similarity based on the interference characteristics to avoid the influence of the interference characteristics on the accuracy of the identification results, thereby ensuring the accuracy of the identification results of the object to be tested; on the other hand, compared with the prior art of first purifying and culturing the bacteria of the substance to be tested before determining the identification results of the substance to be tested, the protein fingerprint spectrum of the object to be tested can be directly compared for similarity based on the relevant characteristics of the predetermined protein fingerprint spectrum, thereby reducing the purification and culturing time of the substance and improving the efficiency of substance identification.

[0153] For example, Figure 6 It is a set of comparative schematic diagrams of the urine sample identification results provided in the embodiments of the present application. The identification results of the urine samples can characterize the similarity between the protein fingerprint spectrum of the urine sample to be tested and the standard protein fingerprint spectrum of the known substance through the confidence level score, and further can represent the similarity between the substance in the urine sample to be tested and the known substance. The higher the confidence level score, the higher the similarity between the substance in the urine sample to be tested and the known substance. Optionally, the confidence level score of the above urine sample is obtained using Zhongyuan Huiji's ESX2600 mass spectrometer. Figure 6 As shown, Figure 6 (a) is used to indicate the identification result of a urine sample produced using existing identification technology, Figure 6 (b) is used to indicate the identification result of the urine sample generated based on the calling of the above urine standard spectrum library.

[0154] Specifically, refer to Figure 6, the confidence level score of the identification result of the urine sample generated by the existing identification technology is 1.73, while the confidence level score of the identification result of the urine sample generated by calling the urine standard spectrum library in this application is 2.11. It can be seen that the identification results output by the identification method and device provided by this application have a higher similarity and matching degree with the known substances, and can achieve more accurate substance identification. For example, the identification results of urine samples generated by the existing identification technology can only reach the "genus", while the identification results of urine samples generated by this application can reach the "species".

[0155] For example, Figure 7 is another set of comparative schematic diagrams of urine sample identification results provided in the embodiments of the present application, such as Figure 7 As shown, Figure 7 (a) is used to indicate the identification result of a urine sample produced using existing identification technology, Figure 7 (b) is used to indicate the identification result of the urine sample generated based on the calling of the above urine standard spectrum library.

[0156] Specifically, refer to Figure 7 , the existing identification technology did not produce an identification result for the urine sample, but by calling the urine standard atlas library in this application, it was successfully determined that the identification result of the urine sample was specifically Klebsiella pneumoniae.

[0157] Based on this, the calling of the urine standard spectrum library in this application can effectively improve the identification score of the substance and effectively improve the success rate of substance identification.

[0158] Reference below Figure 8 , Figure 8 A schematic diagram of the structure of a computer device suitable for implementing the embodiments of the present application is shown. Figure 8 As shown, the computer device 800 includes a central processing unit (CPU) 801, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 802 or the program loaded from the storage part 808 to the random access memory (RAM) 803. In the random access memory (RAM) 803, various programs and data required for the operation instructions of the system are also stored. The central processing unit (CPU) 801, the read-only memory (ROM) 802 and the random access memory (RAM) 803 are connected to each other through a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0159] The following components are connected to the input / output (I / O) interface 805: an input section 806 including a keyboard, a mouse, etc.; an output section 807 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 808 including a hard disk, etc.; and a communication section 809 including a network interface card such as a LAN card, a modem, etc. The communication section 809 performs communication processing via a network such as the Internet. A drive 810 is also connected to the input / output (I / O) interface 805 as needed. A removable medium 811, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 810 as needed so that a computer program read therefrom is installed into the storage section 808 as needed.

[0160] In particular, according to an embodiment of the present application, the above reference flow chart Figure 2 Any described process can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flow chart. In such an embodiment, the computer program includes a program code for executing the method shown in the flow chart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 809, and / or installed from the removable medium 811. When the computer program is executed by the central processing unit (CPU) 801, the above-mentioned functions defined in the system of the present application are executed.

[0161] It should be noted that the computer-readable medium shown in the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium that matches a computer-readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, device or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0162] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operating instructions of the system, method and computer program product according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the aforementioned module, program segment or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, the boxes represented by two connections can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operating instruction, or can be implemented with a combination of dedicated hardware and computer instructions.

[0163] The units or modules involved in the embodiments described in the present application may be implemented by software or by hardware. The described units or modules may also be provided in a processor, for example, may be described as: a processor including a semantic extraction unit, a weight allocation unit, and a determination unit. The names of these units or modules do not, in some cases, constitute limitations on the units or modules themselves.

[0164] As another aspect, the present application further provides a computer-readable storage medium, which may be included in the computer device described in the above embodiment, or may exist independently without being installed in the computer device. The above computer-readable storage medium stores one or more programs, and when the above programs are used by one or more processors to execute the method described in the present application. For example, it can be executed Figure 2 , Figure 3 as well as Figure 5 The steps of any of the methods shown.

[0165] The present application embodiment provides a computer program product, which includes instructions. When the instructions are executed, the method described in the present application embodiment is executed. For example, Figure 2 , Figure 3 as well as Figure 5 The steps of any of the methods shown.

[0166] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the aforementioned disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other to form a technical solution.

Claims

1. A substance identification device, characterized in that: The device comprises a detection module for detecting an object to be detected and obtaining its protein fingerprint, a storage module for storing data related to substance identification, and a data processing module for calling the data in the storage module for data processing, wherein the data processing module is configured as follows: Perform a first similarity comparison between the protein fingerprint of the object to be tested and the standard protein fingerprint to obtain a first matching set; the first similarity comparison ignores a first interfering feature in the protein fingerprint of the object to be tested; Verifying a second interference feature in the protein fingerprint of the object to be tested based on the first matching set; If the verification is passed, a second similarity comparison is performed on the protein fingerprint of the object to be tested and the standard protein fingerprint to obtain a second matching set; the second similarity comparison ignores a second interfering feature in the protein fingerprint of the object to be tested; Determine an identification result of the object to be tested based on the first matching set and the second matching set; Among them, the first interference feature and the second interference feature are common features of the negative object protein fingerprint and the standard protein fingerprint; the mass-to-charge ratio of the first interference feature in the protein fingerprint is less than the mass-to-charge ratio of the second interference feature in the protein fingerprint, and / or the mass-to-charge ratio of the first interference feature in the protein fingerprint is less than or less than or equal to a first preset threshold, and the mass-to-charge ratio of the second interference feature in the protein fingerprint is greater than or greater than or equal to the first preset threshold.

2. The device according to claim 1, characterized in that The verifying the second interference feature in the protein fingerprint of the object to be tested based on the first matching set includes: If the second interference feature and the target feature within the tolerance range of the second interference feature do not exist in the first protein fingerprint in the first matching set, the verification is passed; the first protein fingerprint is a standard protein fingerprint in the first matching set whose similarity with the protein fingerprint of the object to be tested reaches a second preset threshold; If the second interference feature and / or the target feature within the tolerance range of the second interference feature exists in the first protein fingerprint, the verification fails.

3. The device according to claim 2, characterized in that The data processing module is further configured to: If the verification fails, an identification result of the object to be tested is determined based on the first matching set.

4. The device according to any one of claims 1 to 3, characterized in that: The first similarity comparison between the protein fingerprint of the object to be tested and the standard protein fingerprint to obtain a first matching set includes: Removing the characteristic peak corresponding to the first interference feature in the protein fingerprint of the object to be tested, and scoring the similarity between the new protein fingerprint of the object to be tested and the standard protein fingerprint to obtain the first matching set; or, The scoring weight of the characteristic peak corresponding to the first interference feature is reset to a value less than a third preset threshold value, and the new protein fingerprint spectrum of the object to be tested and the standard protein fingerprint spectrum are scored for similarity based on the updated scoring weight to obtain the first matching set.

5. The device according to claim 4, characterized in that The data processing module is further configured to: After ignoring the first interfering feature of the protein fingerprint of the object to be tested, determining the common characteristic peaks of the new protein fingerprint of the object to be tested and the standard protein fingerprint; Based on the common characteristic peak, determining a target characteristic peak in a new protein fingerprint of the object to be tested, wherein the relative intensity between the target characteristic peak and the common characteristic peak is less than a fourth preset threshold; The target characteristic peak is removed, and similarity is scored between the protein fingerprint spectrum of the object to be tested after the target characteristic peak is removed and the standard protein fingerprint spectrum to obtain the first matching set.

6. The device according to any one of claims 1 to 3, characterized in that: The step of performing a second similarity comparison between the protein fingerprint of the object to be tested and the standard protein fingerprint to obtain a second matching set includes: Removing the second interfering feature in the protein fingerprint of the object to be tested, and scoring the similarity between the new protein fingerprint of the object to be tested and the standard protein fingerprint to obtain the second matching set; or, The scoring weight of the second interference feature is reset to a value less than the fifth preset threshold value, and the new protein fingerprint spectrum of the object to be tested and the standard protein fingerprint spectrum are scored for similarity based on the updated scoring weight to obtain the second matching set.

7. The device according to any one of claims 1 to 6, characterized in that: The determining the identification result of the object to be detected based on the first matching set and the second matching set includes: When the substance types corresponding to the first matching set and the second matching set are consistent, determining the identification result of the object to be tested based on the substance types in the first matching set or the second matching set; When the substance types corresponding to the first matching set and the second matching set are inconsistent, the identification result of the object to be detected is determined based on the substance types in the second matching set.

8. The device according to claim 1, characterized in that The data processing module is further configured to: Performing mass spectrometry identification on the positive object set and the negative object set respectively to obtain a protein fingerprint of each standard positive object in the positive object set and a protein fingerprint of each standard negative object in the negative object set; Performing characteristic peak statistics on the protein fingerprints of the standard positive objects and the protein fingerprints of the standard negative objects respectively, to obtain the characteristic peak distribution law of the standard positive objects and the characteristic peak distribution law of the standard negative objects; The first interference feature and the second interference feature are determined based on the characteristic peak distribution law of the standard positive object and the characteristic peak distribution law of the standard negative object.

9. The device according to any one of claims 1 to 8, characterized in that: The object to be tested is a urine sample, and the first preset threshold is a mass-to-charge ratio of 3500Da.

10. The device according to claim 9, characterized in that The characteristic peak corresponding to the first interference feature includes at least one of the characteristic peaks corresponding to a mass-to-charge ratio of 2251Da, a mass-to-charge ratio of 3372Da, and a mass-to-charge ratio of 3486Da; The characteristic peak corresponding to the second interference feature includes at least one of the characteristic peaks corresponding to a mass-to-charge ratio of 3711Da, a mass-to-charge ratio of 4752Da, and a mass-to-charge ratio of 6192Da.

11. The device according to claim 9, characterized in that The data processing module is further configured to: Performing mass spectrometry identification on multiple positive urine samples to obtain protein fingerprints of the multiple positive urine samples; the positive urine samples contain known microbial species; Constructing a urine standard map library based on the protein fingerprint maps of the plurality of positive urine samples, wherein the urine standard map library includes the plurality of the standard protein fingerprint maps; or Performing mass spectrometry identification on a bacterial sample of known species obtained by subculture to obtain a protein fingerprint of the subculture sample; The protein fingerprint of the subculture sample is used as the standard protein fingerprint to construct a urine standard fingerprint library.

12. A database calling method, characterized in that: The method comprises: Calling a urine standard atlas library; the urine standard atlas library includes a plurality of standard protein fingerprints; Performing a similarity comparison between the protein fingerprint of the sample to be tested and the standard protein fingerprint to obtain a matching set; the interference feature is a common feature between the protein fingerprint of the negative urine sample and the standard protein fingerprint, and the similarity comparison ignores the interference feature in the protein fingerprint of the sample to be tested; An identification result of the sample to be tested is determined based on the matching set.

13. The method according to claim 12, characterized in that The method further comprises: Performing mass spectrometry identification on multiple positive urine samples to obtain protein fingerprints of the multiple positive urine samples; A urine standard spectrum library is constructed based on the protein fingerprints of the multiple positive urine samples, and the urine standard spectrum library includes the multiple standard protein fingerprints.

14. The method according to claim 12 or 13, characterized in that The method of performing similarity comparison between the protein fingerprint of the sample to be tested and the standard protein fingerprint based on the interference feature to obtain a matching set includes: Removing the characteristic peak corresponding to the interference feature in the protein fingerprint of the sample to be tested, and scoring the similarity between the new protein fingerprint of the sample to be tested and the standard protein fingerprint to obtain the matching set; or, The scoring weight of the characteristic peak corresponding to the interference feature is reset to a value less than the sixth preset threshold value, and the new protein fingerprint spectrum of the sample to be tested and the standard protein fingerprint spectrum are scored for similarity based on the updated scoring weight to obtain the matching set.

15. A method for identifying a substance, characterized in that: The method comprises: Perform a first similarity comparison between the protein fingerprint of the object to be tested and the standard protein fingerprint to obtain a first matching set; the first similarity comparison ignores the first interfering feature in the protein fingerprint of the object to be tested; Verifying a second interference feature in the protein fingerprint of the object to be tested based on the first matching set; If the verification is passed, a second similarity comparison is performed on the protein fingerprint of the object to be tested and the standard protein fingerprint to obtain a second matching set; the second similarity comparison ignores the second interference feature in the protein fingerprint of the object to be tested; Determine an identification result of the object to be tested based on the first matching set and the second matching set; Among them, the first interference feature and the second interference feature are common features of the negative object protein fingerprint and the standard protein fingerprint; the mass-to-charge ratio of the first interference feature in the protein fingerprint is less than the mass-to-charge ratio of the second interference feature in the protein fingerprint, and / or the mass-to-charge ratio of the first interference feature in the protein fingerprint is less than or less than or equal to a first preset threshold, and the mass-to-charge ratio of the second interference feature in the protein fingerprint is greater than or greater than or equal to the first preset threshold.

16. A computer device, characterized in that: It comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements the steps performed by the substance identification device as described in any one of claims 1 to 11, or the method as described in any one of claims 12 to 15.

17. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the program implements the steps performed by the substance identification device as described in any one of claims 1-11, or the method as described in any one of claims 12-15.

18. A computer program product, characterized in that The computer program product comprises instructions, and when the instructions are executed, the steps performed by the substance identification device according to any one of claims 1 to 11 are performed, or the method according to any one of claims 12 to 15 is performed.

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