Lipid identification method, device and system and computer readable storage medium

Abstract

The invention relates to a lipid identification method, device and system and a computer readable storage medium, and relates to the technical field of computers. The lipid identification method comprises the following steps: acquiring an identification result of each lipid sample of a plurality of lipid samples; according to the identification result of each lipid sample, determining the unsaturation degree information and the subordinate subclass of each lipid sample; taking the retention time and the mass-to-charge ratio of each lipid sample as variables, and performing data fitting on the plurality of lipid samples based on the unsaturation degree information and the subordinate subclass; and determining the accuracy of the identification result of each lipid sample according to the data fitting result.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, and in particular to a lipid identification method, device and system, and a computer-readable storage medium. Background Art

[0002] Lipids are organic compounds composed of carbon, hydrogen and oxygen, usually containing long-chain hydrocarbons (fatty acids) and connected with polar groups (hydroxyl, phosphate, etc.). The standard representation of lipids consists of "lipid subclass, total carbon number: number of double bonds, carbon number of subchain: number of double bonds", such as PC 36:3 (PC 18:1_18:2). Due to the high cost of synthesis and the large number of lipids, there are tens of millions of different side chain combinations even for the same subclass, so there is a lack of comprehensive standards.

[0003] Mass spectrometry is an analytical method widely used in the fields of environment, biology, medicine, etc. The primary and secondary information of different substances can be obtained through mass spectrometry instruments. Among them, the primary information includes the charge-to-mass ratio, retention time, corresponding proton, etc. of the substance, and the secondary information includes the mass-to-charge ratio and response intensity of a series of fragment ions. Summary of the invention

[0004] According to a first aspect of the present disclosure, a lipid identification method is provided, comprising: obtaining an identification result of each lipid sample of a plurality of lipid samples; determining the unsaturation information and the subclass to which each lipid sample belongs according to the identification result of each lipid sample; performing data fitting on the plurality of lipid samples based on the unsaturation information and the subclass to which they belong, taking the retention time and mass-to-charge ratio of each lipid sample as variables; and determining the accuracy of the identification result of each lipid sample according to the result of the data fitting.

[0005] In some embodiments, the method uses the retention time and mass-to-charge ratio of each lipid sample as variables, based on the unsaturation information and the subclass to which it belongs, to perform data fitting on multiple lipid samples, including: mapping each lipid sample to a rectangular coordinate system using the retention time and mass-to-charge ratio as coordinates, wherein each lipid sample is represented by a sample point in the coordinate system; and performing data fitting on multiple lipid samples based on the relationship between the unsaturation information, the subclass, the retention time, and the mass-to-charge ratio.

[0006] In some embodiments, the data fitting of multiple lipid samples according to the relationship between unsaturation information, subclass, retention time and mass-to-charge ratio includes: selecting a starting point that meets the conditions from the sample points and adding it to the first set according to the relationship between the unsaturation information, subclass, retention time and mass-to-charge ratio to obtain a preliminary fitting curve; constructing a sub-coordinate system with the starting point as the origin, searching for target sample points that meet the conditions in the first and third quadrants of the sub-coordinate system corresponding to the starting point, and adding them to the first set; whenever the number of target sample points newly added to the first set reaches a threshold, re-determining the preliminary fitting curve according to the first set; and fitting the target curve according to the first set.

[0007] In some embodiments, the method of selecting a starting point that meets the conditions from the sample points and adding it to the first set based on the relationship between the unsaturation information, the subclass, the retention time and the mass-to-charge ratio to obtain a preliminary fitting curve includes: selecting a starting point that meets the conditions from the sample points and adding it to the first set to obtain a preliminary fitting curve based on the relationship between the unsaturation information, the subclass, the retention time and the mass-to-charge ratio, the mass-to-charge ratio obtained by measuring the sample points and the mass-to-charge ratio obtained by data fitting.

[0008] In some embodiments, the unsaturation information includes at least one of a total unsaturation number and a fractional unsaturation number, and the condition includes at least one of the following: the goodness of fit of a curve obtained by fitting the sample points that meet the condition exceeds a first threshold; on the curve obtained by fitting the sample points that meet the condition, for a first sample point and a second sample point with the same total unsaturation number, when the retention time of the first sample point is greater than the retention time of the second sample point, the mass-to-charge ratio of the first sample point is greater than the mass-to-charge ratio of the second sample point; on the curve obtained by fitting the sample points that meet the condition, for a third sample point and a fourth sample point with the same fractional unsaturation number, when the retention time of the third sample point is greater than the retention time of the fourth sample point, the mass-to-charge ratio of the third sample point is greater than the mass-to-charge ratio of the fourth sample point.

[0009] In some embodiments, determining the accuracy of the identification result of each lipid sample based on the result of data fitting includes: determining the accuracy of the identification result of each lipid sample based on whether the sample point is in the first set, the mass-to-charge ratio obtained by measuring the sample point and the mass-to-charge ratio obtained by data fitting.

[0010] In some embodiments, the method of determining the accuracy of the identification result of each lipid sample based on whether the sample point is in the first set and the mass-to-charge ratio obtained by measuring the sample point and the mass-to-charge ratio obtained by data fitting includes: determining the loss value of each sample point in the first set based on the mass-to-charge ratio obtained by measuring and the mass-to-charge ratio obtained by data fitting; determining the maximum loss value of the multiple sample points in the first set as the first loss value; determining the minimum loss value of the multiple sample points in the first set as the second loss value; for each sample point in the first set, determining the accuracy of the identification result of the lipid sample corresponding to the sample point based on the loss value of the sample point, the first loss value, and the second loss value.

[0011] In some embodiments, the method of determining the accuracy of the identification result of each lipid sample based on whether the sample point is in the first set and the mass-to-charge ratio obtained by measuring the sample point and the mass-to-charge ratio obtained by data fitting includes: determining the confidence interval of the target curve; for the sample point not in the first set, determining the accuracy of the identification result of the corresponding lipid sample based on whether the sample point is in the confidence interval.

[0012] In some embodiments, for the sample point that is not in the first set, determining the accuracy of the identification result of the corresponding lipid sample based on whether the sample point is in the confidence interval includes: determining the accuracy of the identification result of the lipid sample corresponding to the sample point that is not in the first set but in the confidence interval as a first value; determining the accuracy of the identification result of the lipid sample corresponding to the sample point that is not in the confidence interval and in the first set as a second value, wherein the first value is greater than the second value.

[0013] In some embodiments, the unsaturation information includes the total unsaturation number, and the retention time and mass-to-charge ratio of each lipid sample are used as variables, and data fitting is performed on multiple lipid samples based on the unsaturation information and the subclasses to which they belong, including: dividing the multiple lipid samples according to the subclasses and the total unsaturation numbers to obtain multiple second sets, wherein the lipid samples in each second set belong to the same subclass and have the same total unsaturation number; for each second set, the retention time and mass-to-charge ratio of the lipid samples therein are used as variables to perform data fitting to obtain a first fitting curve corresponding to the subclasses and total unsaturation numbers of the second set.

[0014] In some embodiments, for each second set, the retention time and mass-to-charge ratio of the lipid samples therein are used as variables to perform data fitting to obtain the first fitting curve corresponding to the subclasses and total unsaturation numbers of the second set, including: when the number of lipid samples with a first total unsaturation number of a specified subclass is less than a threshold, based on the first fitting curve of the second set corresponding to the second total unsaturation number of the specified subclass, determining the first fitting curve corresponding to the first total unsaturation number of the specified subclass, wherein the first total unsaturation number and the second total unsaturation number are different.

[0015] In some embodiments, when the number of lipid samples of the first total unsaturation number of the specified subclass is less than a threshold value, the first fitting curve corresponding to the first total unsaturation number of the specified subclass is determined according to the first fitting curve of the second set corresponding to the second total unsaturation number of the specified subclass, including: according to the first fitting curve corresponding to the second total unsaturation number of the specified subclass, a curve that passes through the lipid samples of the first total unsaturation number of the specified subclass and does not intersect with the first fitting curve of the second set corresponding to the second total unsaturation number of the specified subclass is determined as the first fitting curve corresponding to the first total unsaturation number of the specified subclass.

[0016] In some embodiments, the unsaturation information also includes unsaturation distribution, and the retention time and mass-to-charge ratio of each lipid sample are used as variables, and data fitting is performed on multiple lipid samples based on the unsaturation information and the subclasses to which they belong, including: dividing the multiple lipid samples according to subclasses, total unsaturation numbers and unsaturation distributions to obtain multiple third sets, wherein the lipid samples in each third set belong to the same subclass and have the same total unsaturation number and unsaturation distribution; for each third set, data fitting is performed with the retention time and mass-to-charge ratio of the lipid samples therein as variables to obtain a second fitting curve corresponding to the subclasses, total unsaturation numbers and unsaturation distribution of the third set.

[0017] In some embodiments, determining the accuracy of the identification result of each lipid sample based on the result of data fitting includes: determining a first accuracy based on a first fitting curve corresponding to each lipid sample; determining a second accuracy based on a second fitting curve corresponding to each lipid sample; and determining the accuracy of the identification result based on the first accuracy and the second accuracy.

[0018] In some embodiments, obtaining the identification result of each lipid sample of the multiple lipid samples includes: identifying the type of each lipid sample by comparing the spectrum of each lipid sample with a reference spectrum, as the identification result of each lipid sample.

[0019] In some embodiments, the method of identifying the type of each lipid sample by comparing the spectrum of each lipid sample with the reference spectrum as the identification result of each lipid sample includes: calculating a first score for each lipid sample according to the number of matching characteristic peaks between the spectrum of each lipid sample and the reference spectrum; calculating a second score for each lipid sample according to the importance of the matching characteristic peaks between the spectrum of each lipid sample and the reference spectrum; determining a reference spectrum that matches the spectrum of each lipid sample according to the first score and the second score of each lipid sample; and determining the type of each lipid sample according to the matched reference spectrum as the identification result of each lipid sample.

[0020] According to a second aspect of the present disclosure, a lipid identification device is provided, comprising: an acquisition module configured to acquire an identification result of each lipid sample of a plurality of lipid samples; an information determination module configured to determine the unsaturation information and the subclass to which each lipid sample belongs based on the identification result of each lipid sample; a fitting module configured to perform data fitting on the plurality of lipid samples based on the unsaturation information and the subclass to which they belong, taking the retention time and mass-to-charge ratio of each lipid sample as variables; and an accuracy determination module configured to determine the accuracy of the identification result of each lipid sample based on the result of the data fitting.

[0021] According to a third aspect of the present disclosure, a lipid identification device is provided, comprising: a memory; and a processor coupled to the memory, wherein the processor is configured to execute a lipid identification method according to some embodiments of the present disclosure based on instructions stored in the memory.

[0022] According to a fourth aspect of the present disclosure, a lipid identification system is provided, comprising: a lipid identification device according to some embodiments of the present disclosure.

[0023] According to a fifth aspect of the present disclosure, a computer-readable storage medium is provided, on which computer program instructions are stored, and when the instructions are executed by a processor, the lipid identification method according to some embodiments of the present disclosure is implemented.

[0024] According to a sixth aspect of the present disclosure, a computer program product is provided, comprising computer program instructions, which, when executed by a processor, implement the lipid identification method according to some embodiments of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The accompanying drawings, which constitute a part of the specification, illustrate embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0026] The present disclosure may be more clearly understood from the following detailed description with reference to the accompanying drawings, in which:

[0027] Figure 1 A flow chart showing a lipid identification method according to some embodiments of the present disclosure;

[0028] Figure 2 A schematic diagram showing lipid identification according to some embodiments of the present disclosure;

[0029] Figure 3 A block diagram showing a lipid identification apparatus according to some embodiments of the present disclosure;

[0030] Figure 4 A block diagram showing a lipid identification device according to other embodiments of the present disclosure;

[0031] Figure 5 A block diagram showing a computer system for implementing some embodiments of the present disclosure;

[0032] Figure 6a-6c A user interface for implementing a lipid identification system according to some embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0033] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that the relative arrangement of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present disclosure unless otherwise specifically stated.

[0034] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0035] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the present disclosure, its application, or uses.

[0036] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered as part of the specification.

[0037] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0038] It should be noted that like reference numerals and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0039] An important application of mass spectrometry is to identify substances by comparing mass spectrometry data with standard libraries and characteristic peak spectral library information. The lipid identification method in related technologies uses manual inspection of characteristic spectra and equivalent carbon number models of retention time to comprehensively identify lipids. This method is time-consuming and prone to misjudgment. It often takes 3-5 years of experienced experts several weeks to complete the identification of a mass spectrometry file.

[0040] The identification method based on similarity matching algorithm and absolute retention time ignores the actual meaning of characteristic peaks, resulting in feature redundancy. At the same time, since retention time is affected by experimental conditions, relying solely on absolute retention time for judgment can easily lead to a high false positive rate in the identification results.

[0041] The present invention provides a lipid identification method, device and system, and a computer-readable storage medium, which can improve the accuracy of lipid identification.

[0042] Figure 1 A flow chart showing a lipid identification method according to some embodiments of the present disclosure.

[0043] like Figure 1 As shown, the lipid identification method includes steps S1 to S4. In some embodiments, the lipid identification method is performed by a lipid identification device.

[0044] In step S1, the identification result of each lipid sample of multiple lipid samples is obtained. In step S2, the unsaturation information and the subclass to which each lipid sample belongs are determined according to the identification result of each lipid sample. In step S3, the retention time and mass-to-charge ratio of each lipid sample are used as variables, and data fitting is performed on multiple lipid samples based on the unsaturation information and the subclass to which they belong. In step S4, the accuracy of the identification result of each lipid sample is determined according to the result of data fitting.

[0045] The identification result is, for example, the type of lipid sample, such as oleic acid, linoleic acid, etc. After the type of lipid sample is identified, its structure can be determined, thereby determining its saturation information and subclass. The retention time and mass-to-charge ratio of the lipid sample are obtained through instrumental experiments.

[0046] There are certain rules between the retention times and mass-to-charge ratios of multiple lipid samples with the same unsaturation information and / or subclasses. If the identification result is incorrect, the lipid sample will deviate from these rules to a certain extent. Therefore, the accuracy of the identification result of each lipid sample can be determined by fitting the data of multiple lipid samples based on the relationship between retention time, mass-to-charge ratio, unsaturation information and the subclass to which it belongs.

[0047] For example, if two lipid samples have the same subclass and unsaturation information, there will be a certain pattern between their retention times and mass-to-charge ratios, such as the lipid sample with a longer retention time has a larger mass-to-charge ratio.

[0048] The lipid identification method disclosed in the present invention uses the retention time and mass-to-charge ratio of each lipid sample as variables, performs data fitting on multiple lipid samples based on unsaturation information and subclasses, thereby determining the accuracy of the identification results of each lipid sample. It can be used to identify false positive identification results, thereby improving the accuracy of lipid identification.

[0049] The following describes a process for obtaining identification results according to some embodiments of the present disclosure.

[0050] Based on the lipid characteristic spectra, a "fragment tree" hierarchical spectrum library of 166.3 million lipids can be constructed. The hierarchical spectrum library contains five levels. The first layer mainly contains the precursor ion peaks of lipids and the peaks of the head group drop of substances; the second layer is mainly the mass spectrum peaks of lipid side chains; the third layer is the mass spectrum peaks caused by physicochemical reasons such as the neutral loss of precursor ions and head groups; the fourth layer is the mass spectrum peaks caused by physicochemical reasons such as the neutral loss of lipid side chains; the fifth layer is the fingerprint spectrum generated by the reverse omics algorithm.

[0051] For example, in R language, the spectrum is iteratively calculated according to the fragmentation rules, and the spectrum is stored in the form of a list. Each element in the list is the spectrum information of a certain layer of a substance, and finally stored in the "rda" file format. The above-mentioned "fragmentation tree" hierarchical spectrum library is constructed to reduce the feature redundancy caused by the similarity algorithm. In addition, derivatized spectra are added to the spectrum library construction, which is conducive to the subsequent combination of multiple derivatization mass spectrometry methods to identify more in-depth lipid structures (such as sn position isomerism and carbon-carbon double bond position isomerism, etc.).

[0052] By deeply mining the characteristics of lipid mass spectra, a 166 million graded library can be constructed. The database is iteratively calculated, such as carbon numbers from 10-80 and unsaturation from 0-20, so that many potential lipid substances can be included, that is, non-endogenous or unreported chain compositions.

[0053] The “fragment tree” hierarchical spectral library stores reference spectra that can be used for comparison with the spectra of lipid samples.

[0054] In some embodiments, the type of each lipid sample is identified by comparing the spectrum of each lipid sample with the reference spectrum, as the identification result of each lipid sample.

[0055] For example, hash tables and binary search can be combined to optimize the data structure and compare the spectrum of lipid samples with the reference spectrum in the library. By combining hash tables and binary search, 10-100 billion spectrum matches per second can be achieved, improving the matching speed.

[0056] In some embodiments, the type of each lipid sample is identified by comparing the spectrum of each lipid sample with a reference spectrum as the identification result of each lipid sample, including: calculating a first score according to the number of matching characteristic peaks between the spectrum of each lipid sample and the reference spectrum; calculating a second score for each lipid sample according to the importance of the matching characteristic peaks between the spectrum of each lipid sample and the reference spectrum; determining a reference spectrum that matches the spectrum of each lipid sample according to the first score and the second score; and determining the type of each lipid sample according to the matched reference spectrum as the identification result of each lipid sample.

[0057] The first score is recorded as Score matched , the second score is recorded as Score ratio , the following describes how to calculate the score.

[0058] In a spectrum matching task, a reference spectrum in the library has n1 characteristic peaks, and the spectrum to be tested has n2 peaks. The formula is as follows.

[0059]

[0060] Among them, s i Represents the mass-to-charge ratio of the ith peak in the reference spectrum. s′ j Represents the mass-to-charge ratio of the jth peak in the spectrum of the lipid sample (the response of the mass spectrum peak is expressed as intensity j ). I(x) is an indicative function, which is 1 when the condition is met, otherwise it is 0. threshold ppm Indicates the acceptable mass-to-charge ratio error.

[0061] Score matched Evaluate the matching of characteristic peaks between the spectrum of lipid samples and the reference spectrum in the library. For example, a spectrum in the library has 8 characteristic peaks, and 6 peaks in the spectrum of lipid samples match these 8 characteristic peaks. matched The value is 0.75. Within the allowable mass-to-charge ratio error, if multiple peaks match the same characteristic peak, they can be counted only once.

[0062] Score ratio Evaluate whether the six matching characteristic peaks represent the main information of the entire spectrum of the lipid sample. For example, if the total response value of the six characteristic peaks is 0.7 and the total response value of the spectrum of the lipid sample is 1, then Score ratio The value of is 0.7.

[0063] Since mass spectrometers have deviations in the actual detection process of substances, the characteristic peak matching mentioned above is within the allowable mass-to-charge ratio error threshold. ppm The following comparison.

[0064] For a task with n substances to be identified, the time complexity of the matching module after using the hash table is O(mΔ), where m represents the number of spectra in the library and Δ represents the allowable error of the match.

[0065] In some embodiments, data fitting is performed on multiple lipid samples based on the unsaturation information and the subclass to which they belong, taking the retention time and mass-to-charge ratio of each lipid sample as variables, including: mapping each lipid sample to a rectangular coordinate system using the retention time and mass-to-charge ratio as coordinates, wherein each lipid sample is represented by a sample point in the coordinate system; and performing data fitting on multiple lipid samples based on the relationship between the unsaturation information, the subclass, the retention time, and the mass-to-charge ratio.

[0066] For example, in the coordinate system, the x-axis is the retention time and the y-axis is the mass-to-charge ratio, then each lipid sample can be mapped to a point in the rectangular coordinate system, and the label of the point is, for example, the specific name of the lipid determined according to the identification result. The lipid samples are divided into sets according to the unsaturation information and the subclass they belong to. For lipid samples belonging to the same set, data fitting is performed according to the retention time and mass-to-charge ratio.

[0067] The present disclosure statistically obtains three laws: equivalent carbon number model (ECN), equivalent carbon number model of chain composition (ESCN), and intra-subclass unsaturation parallel (IUP).

[0068] ECN requires that the retention time of substances within lipid subclasses with the same total unsaturation increases with the increase of mass-to-charge ratio, and the changes in their mass-to-charge ratio and retention time show a monotonically increasing quadratic function trend.

[0069] ESCN requires that the retention time of substances in lipid subclasses with the same chain unsaturation composition increases with the increase of mass-to-charge ratio, and the changes in their mass-to-charge ratio and retention time show a monotonically increasing quadratic function trend.

[0070] IUP requires that the fitted quadratic curves of different total unsaturation degrees within the same lipid subclass do not intersect in the closed interval.

[0071] After statistics of more than 100 data sets from different experimental conditions and different species, the proportion of samples that meet the above three conditions is about 90%. The above three conditions lay the foundation for the intelligent collaborative behavior of sub-individuals in the construction of the lipid swarm intelligent model. The lipid swarm intelligent model can be used to remove false positives in identification.

[0072] In some embodiments, data fitting is performed on multiple lipid samples based on the relationship between unsaturation information, subclass, retention time and mass-to-charge ratio, including: based on the relationship between unsaturation information, subclass, retention time and mass-to-charge ratio, selecting a starting point that meets the conditions from the sample points and adding it to the first set to obtain a preliminary fitting curve; constructing a sub-coordinate system with the starting point as the origin, searching for target sample points that meet the conditions in the first and fourth quadrants of the sub-coordinate system corresponding to the starting point, and adding them to the first set; whenever the number of target sample points newly added to the first set reaches a threshold, re-determining the preliminary fitting curve based on the first set; and fitting the target curve based on the first set.

[0073] For example, the lipid population intelligent model first greedily searches for the optimal combination of points as the starting point, and then conducts a global search starting from the optimal starting point combination. After the search is completed, a polynomial is used to fit the curves of all points that meet the search conditions, and the points that fall within the 95% confidence interval of the fitting curve are retrieved.

[0074] After obtaining the starting point set, a global search is performed from the points in the starting point set to the first and third quadrants, and the points that meet the loss function are extended and connected. In other words, the starting point and end point in the starting point set are the starting point and end point of the local optimal solution. There are other points that meet the conditions in the global solution that have not been taken. All points in the first quadrant with the local end point as the origin or all points in the third quadrant with the starting point as the origin are traversed, and the points in these quadrants are included in the current set one by one. After that, the loss function value is tested to determine whether the conditions are met.

[0075] For example, after determining the set of starting points, a local pruning method is used to establish a sub-coordinate system for each starting point with the starting point as the origin, and only perform a global search on the points in the first and third quadrants of the sub-coordinate system, thereby saving the complexity of the global search. Among them, if a sample point falls in the first and third quadrants, the line connecting the sample point and the starting point is an increasing function, so it can be further determined whether the sample point meets the conditions.

[0076] For the sample points in the first and third quadrants that do not fall in any sub-coordinate system, it is difficult to meet the conditions, so there is no need to use the above conditions for specific judgment.

[0077] At the same time, the same loss function is used for both the starting point judgment and the global search judgment.

[0078] The threshold value of the number of target sample points newly added to the first set may be 1, that is, each time a point is found, refitting is performed.

[0079] After the search is completed, the target curve can be fitted based on the first set.

[0080] In some embodiments, the unsaturation information includes at least one of the total unsaturation number and the fractional unsaturation number, and the data fitting condition includes at least one of the following: the goodness of fit of the curve obtained by fitting the sample points that meet the condition exceeds the first threshold; on the curve obtained by fitting the sample points that meet the condition, for the first sample point and the second sample point with the same total unsaturation number, when the retention time of the first sample point is greater than the retention time of the second sample point, the mass-to-charge ratio of the first sample point is greater than the mass-to-charge ratio of the second sample point; on the curve obtained by fitting the sample points that meet the condition, for the third sample point and the fourth sample point with the same fractional unsaturation number, when the retention time of the third sample point is greater than the retention time of the fourth sample point, the mass-to-charge ratio of the third sample point is greater than the mass-to-charge ratio of the fourth sample point. Wherein, the goodness of fit indicates the degree of fit of the fitting curve to the observed value.

[0081] Among them, "on the curve fitted according to the sample points that meet the conditions, for the first sample point and the second sample point with the same total unsaturation, when the retention time of the first sample point is greater than the retention time of the second sample point, the mass-to-charge ratio of the first sample point is greater than the mass-to-charge ratio of the second sample point; on the curve fitted according to the sample points that meet the conditions, for the third sample point and the fourth sample point with the same chain unsaturation, when the retention time of the third sample point is greater than the retention time of the fourth sample point, the mass-to-charge ratio of the third sample point is greater than the mass-to-charge ratio of the fourth sample point" namely ECN and ESCN. The identification results of samples that meet these two rules are more accurate.

[0082] The total unsaturation can be understood as the unsaturation value of the organic molecule as a whole, which reflects the total amount of all unsaturated structures (such as double bonds, triple bonds, rings, etc.) in the molecule.

[0083] The "branch unsaturation" is the unsaturation contributed by the parts (such as branches) other than the main chain (or main skeleton) in the molecule.

[0084] The corresponding loss function is expressed as follows.

[0085]

[0086] Where I() represents the indicative function, S represents the candidate set of points that meet the conditions, and n represents the number of points that meet the conditions. Indicates the goodness of fit of the fitting curve obtained by this fitting, mz i and mz j Respectively represent the mass-to-charge ratio of the i-th point and the j-th point, rt i and rt j Represent the retention time of the i-th point and the j-th point respectively. 1 , rt 2] represents a specified closed interval. i and j are positive integers.

[0087] Loss′1 requires that the retention time and charge-to-mass ratio of the candidate set of sample points have a certain functional form, and loss′2 requires that the fitting curve of the candidate set of sample points is in the closed interval [rt 1 , rt 2 ] has a monotonically increasing trend, and loss′3 requires that the fitting curve of the candidate set is in the closed interval 『rt 1 , rt 2 ] There are no jump points or discontinuities on the continuous line.

[0088] The sample points in the candidate set should simultaneously satisfy loss′1, loss′2, and loss′3 equal to 1, that is, loss′ equals 1. When loss′ equals 1, the two rules of ECN or ESCN are satisfied, that is, the changes in mass-to-charge ratio and retention time of lipid subclasses with the same total unsaturation composition show a monotonically increasing quadratic function trend; the changes in mass-to-charge ratio and retention time of lipid subclasses with the same chain unsaturation composition show a monotonically increasing quadratic function trend.

[0089] Batch calculate the loss of multiple points in matrix form i The above loss can be replaced by the following formula i The calculation formula for .

[0090]

[0091] In some embodiments, based on the unsaturation information, the subclass, the retention time and the relationship between the mass-to-charge ratio, a starting point that meets the conditions is selected from the sample points and added to the first set to obtain a preliminary fitting curve, including: based on the unsaturation information, the subclass, the relationship between the retention time and the mass-to-charge ratio, the mass-to-charge ratio obtained by measuring the sample point and the mass-to-charge ratio obtained by data fitting, a starting point that meets the conditions is selected from the sample points and added to the first set to obtain a preliminary fitting curve.

[0092] The mass-to-charge ratio obtained by measuring the sample point is used to draw the sample point in the coordinate system and perform data fitting. The “mass-to-charge ratio obtained by data fitting” refers to the mass-to-charge ratio obtained by dividing the retention time rt of the sample point by the retention time rt of the sample point. i Substitute the fitted curve and calculate the estimated value of the mass-to-charge ratio, which is the estimated value obtained after fitting the curve.

[0093] Taking a set of lipid samples of a certain subclass with a certain total unsaturation degree as an example, traverse the set and find the starting point in the set that meets the conditions according to the following loss function.

[0094]

[0095]

[0096] in, It represents the estimated value of the mass-to-charge ratio of the ith point through the fitting function, and the other parameter definitions are the same as before.

[0097] According to loss i Select the starting point, for example, select loss i The point less than the threshold k is used as the starting point, and the loss i The smallest N points are used as the starting points.

[0098] The starting point is a combination of multiple points obtained by greedy exhaustive enumeration, the number of which is, for example, 3-5, and the fitting curve is, for example, a polynomial fit.

[0099] As mentioned above, after obtaining the starting point, a global search is performed in the first and third quadrants based on the starting point. After the global search is completed, all points in the first set are fitted and the loss function loss is used. i Calculate the loss value for each point.

[0100] During the fitting process, the sample set can be divided and fitted in two ways:

[0101] The samples of the same subclass and with the same total unsaturation degree are divided into one group;

[0102] The samples of the same subcategory, with the same total unsaturation number and the same unsaturation distribution, are divided into one set.

[0103] For the above two division methods, the fitted scores can be calculated separately, that is, a sample can have two scores corresponding to the above two division methods.

[0104] The following first introduces a method of grouping samples of the same subcategory and with the same total unsaturation into one set.

[0105] In some embodiments, the unsaturation information includes the total unsaturation number, and the retention time and mass-to-charge ratio of each lipid sample are used as variables, and data fitting is performed on multiple lipid samples based on the unsaturation information and the subclasses to which they belong, including: dividing the multiple lipid samples according to the subclasses and the total unsaturation numbers to obtain multiple second sets, wherein the lipid samples in each second set of the multiple second sets belong to the same subclass and have the same total unsaturation number; for each second set, the retention time and mass-to-charge ratio of the lipid samples therein are used as variables to perform data fitting to obtain a first fitting curve corresponding to the subclasses and total unsaturation numbers of the second set.

[0106] For example, lipid samples of the same subclass and with the same total unsaturation degree belong to one set, and lipid samples of different sets are fitted separately. In other words, if there are lipid samples of multiple subclasses or multiple total unsaturation degrees, multiple curves can be fitted.

[0107] In some embodiments, for each second set, data fitting is performed with the retention time and mass-to-charge ratio of the lipid samples therein as variables to obtain a first fitting curve corresponding to the subclasses and total unsaturation numbers of the second set, including: when the number of lipid samples with a first total unsaturation number of a specified subclass is less than a threshold, determining a first fitting curve corresponding to the first total unsaturation number of the specified subclass based on the first fitting curve of the second set corresponding to the second total unsaturation number of the specified subclass, wherein the first total unsaturation number and the second total unsaturation number are different.

[0108] For example, sometimes there are too few optional sample points for a certain total unsaturation of a certain subclass, resulting in the inability to fit. In this case, the analytical equation can be solved by reversely solving the fitting results of other unsaturations belonging to the same subclass.

[0109] In some embodiments, when the number of lipid samples of the first total unsaturation number of the specified subclass is less than a threshold value, the first fitting curve corresponding to the first total unsaturation number of the specified subclass is determined according to the first fitting curve of the second set corresponding to the second total unsaturation number of the specified subclass, including: according to the first fitting curve corresponding to the second total unsaturation number of the specified subclass, a curve passing through the lipid samples of the first total unsaturation number of the specified subclass and not intersecting with the first fitting curve of the second set corresponding to the second total unsaturation number of the specified subclass is determined as the first fitting curve corresponding to the first total unsaturation number of the specified subclass.

[0110] For example, the search iteration process can be skipped by using the IUP rule. IUP requires that the fitted quadratic curves of different total unsaturation within the same lipid subclass do not intersect in the closed interval. i , mz i ), the reverse solution is as follows:

[0111]

[0112] Wherein, g(x) is the fitting equation of other unsaturation of the subclass, f(x) represents the functional relationship between mass-to-charge ratio and retention time, that is, the analytical result to be solved, that is, the trend of the point where the number of optional points is too small to fit. f(x) should pass through or infinitely approach the point and be a quotient of g(x). q(x) can be a constant divisor, such as a constant a. C is a constant term.

[0113] The following describes a method for grouping samples of the same subcategory, with the same total unsaturation number and the same unsaturation distribution, into one set.

[0114] In some embodiments, the unsaturation information also includes unsaturation distribution, and the retention time and mass-to-charge ratio of each lipid sample are used as variables, and data fitting is performed on multiple lipid samples based on the unsaturation information and the subclass to which they belong, including: dividing the multiple lipid samples according to subclasses, total unsaturation numbers and unsaturation distributions to obtain multiple third sets, wherein the lipid samples in each of the multiple third sets belong to the same subclass and have the same total unsaturation number and unsaturation distribution; for each third set, data fitting is performed with the retention time and mass-to-charge ratio of the lipid samples therein as variables to obtain a second fitting curve corresponding to the subclass, total unsaturation number and unsaturation distribution of the third set.

[0115] The methods for obtaining the first fitting curve and the second fitting curve are similar, and the main difference lies in the set division method.

[0116] In some embodiments, the accuracy of the identification result of each lipid sample is determined based on the result of data fitting, including: determining the accuracy of the identification result of each lipid sample based on whether the sample point is in the first set, the mass-to-charge ratio obtained by measuring the sample point and the mass-to-charge ratio obtained by data fitting.

[0117] For example, the accuracy of the identification results of the sample points in the first set is greater than the accuracy of the identification results of the sample points not in the first set. The closer the mass-to-charge ratio obtained by measurement of the sample point is to the mass-to-charge ratio obtained by data fitting, the higher the accuracy.

[0118] In some embodiments, the accuracy of the identification result of each lipid sample is determined based on whether the sample point is in the first set, the mass-to-charge ratio obtained by measuring the sample point and the mass-to-charge ratio obtained by data fitting, including: determining the confidence interval of the target curve; for the sample point not in the first set, the accuracy of the identification result of the corresponding lipid sample is determined based on whether the sample point is in the confidence interval. For example, the points that fall within the 95% confidence interval of the fitting curve are retrieved. Although the accuracy of the sample points that are not in the first set is less than the accuracy of the samples in the first set, the identification results of the samples in the 95% confidence interval of the fitting curve also have a certain reference value, therefore, these points are retrieved and their accuracy is calculated.

[0119] In some embodiments, for a sample point that is not in the first set, the accuracy of the identification result of the corresponding lipid sample is determined based on whether the sample point is in the confidence interval, including: determining the accuracy of the identification result of the lipid sample corresponding to the sample point that is not in the first set but in the confidence interval as a first value; determining the accuracy of the identification result of the lipid sample corresponding to the sample point that is not in the confidence interval and in the first set as a second value, wherein the first value is greater than the second value.

[0120] For example, the accuracy of the identification result of the sample point in the confidence interval is less than the accuracy of the sample point in the first set, but greater than the accuracy of the sample point that is neither in the confidence interval nor in the first set.

[0121] In some embodiments, the accuracy of the identification result of each lipid sample is determined based on whether the sample point is in the first set and the mass-to-charge ratio obtained by measuring the sample point and the mass-to-charge ratio obtained by data fitting, including: determining the loss value of each sample point in the first set based on the mass-to-charge ratio obtained by measuring and the mass-to-charge ratio obtained by data fitting; determining the maximum loss value of the multiple sample points in the first set as the first loss value; determining the minimum loss value of the multiple sample points in the first set as the second loss value; for each sample point in the first set, determining the accuracy of the identification result of the lipid sample corresponding to the sample point based on the loss value of the sample point, the first loss value, and the second loss value.

[0122] For example, to build PHS i function, unify the results, limit the score value between 0 and 1, and ensure that the smaller the loss function value, the higher the score. i The definitions are as follows.

[0123]

[0124] in:

[0125] loss max =max(loss p ), p=1,2,3......

[0126] loss min =min(loss p ), p=1,2,3......

[0127] p represents the serial number of the sample in the first set. The lipid group intelligent model uses lipid subclasses and different unsaturation compositions within subclasses as classification units, and performs the above search and calculates the score value PHS in the two classification data sets respectively. i .PHS i The value of i is, for example, 1 and 2, which respectively represent the scores obtained by fitting two different classification methods.

[0128] In some embodiments, the accuracy of the identification result of each lipid sample is determined based on the result of data fitting, including: determining a first accuracy based on a first fitting curve corresponding to each lipid sample; determining a second accuracy based on a second fitting curve corresponding to each lipid sample; and determining the accuracy of the identification result based on the first accuracy and the second accuracy.

[0129] For example, the first accuracy PHS1 and the second accuracy PHS2 are weighted averaged to obtain the accuracy PHS of the identification result. z .

[0130] You can also select only one of PHS1 and PHS2 as the accuracy PHS of the identification result. z .

[0131] According to PHS z , the accuracy of the identification result can be calculated. For example, for PHS z and Score matched , Score ratio The weighted sum is used to calculate the final accuracy score, final_score.

[0132] By comprehensively considering Score matched , Score ratio , PHS z , improving the coverage and accuracy of identification.

[0133] At the same time, based on the above search model, the judgment rules are self-learned from the retention time distribution of the data, and the dependence on the platform is small.

[0134] Figure 2 A schematic diagram showing lipid identification according to some embodiments of the present disclosure.

[0135] like Figure 2 As shown, some embodiments of the present disclosure mainly relate to the construction of hierarchical spectrogram libraries, fast matching, intelligent search of lipid groups, etc. A hash table is a common data structure that achieves fast data storage and retrieval by mapping key-value pairs to specific positions in an array. It has the advantages of efficient search, saving computing space, supporting out-of-order access, and good dynamic scalability.

[0136] The swarm intelligence model framework is the machine learning method based on the collaboration of multiple intelligent agents introduced in the previous article. It can learn the intelligent collaborative behavior of sub-individuals and has the advantages of robustness, scalability and strong interpretability.

[0137] The identification methods of some embodiments of the present disclosure can be applied to process data generated by mass spectrometers. For example, data generated by mass spectrometers such as ThermoScientific Orbitrap Exploris 240MS system, ThermoScientific Orbitrap Exploris 120MS system, Agilent 6546Q-TOF MS system, Xevo G2-XS Q-TOF MS system, SCIEX ZenoTOF 7600MS system, etc. can all be identified using the methods mentioned in the present disclosure.

[0138] In order to verify the effectiveness of the identification method of some embodiments of the present disclosure, the inventors determined the dynamic retention time and removed the results with an accuracy score of less than 2 to obtain the first 500 reliable results. In addition, the false discovery rate of the identification results was checked through review by four lipid identification experts, and the results are shown in the following table.

[0139] Table 1

[0140]

[0141] Among them, false discovery rate = number of false identifications / total number of identifications.

[0142] Taking the value "78" in the second row and second column as an example, the number of samples with a score threshold ≥ 2.8 is 78, which is consistent with the correct identification result in the next row of the column, and the false discovery rate is 0%. In other words, the identification results of samples with a score threshold ≥ 2.8 are all correct, indicating that the accuracy of the identification results calculated by the identification method according to some embodiments of the present disclosure can be used to determine whether the identification is wrong.

[0143] Figure 3 A block diagram of a lipid identification apparatus according to some embodiments of the present disclosure is shown.

[0144] like Figure 3 As shown, the lipid identification device includes an acquisition module 31, an information determination module 32, a fitting module 33, and an accuracy determination module 34.

[0145] The acquisition module 31 is configured to obtain the identification result of each lipid sample of the plurality of lipid samples, for example, by performing the following steps: Figure 1 Step S1 is shown.

[0146] The information determination module 32 is configured to determine the unsaturation information and the subclass of each lipid sample according to the identification result of each lipid sample, for example, by performing the following steps: Figure 1 Step S2 is shown.

[0147] The fitting module 33 is configured to perform data fitting on multiple lipid samples based on the retention time and mass-to-charge ratio of each lipid sample as variables and based on the unsaturation information and the subclass to which it belongs, for example, Figure 1 Step S3 shown.

[0148] The accuracy determination module 34 is configured to determine the accuracy of the identification result of each lipid sample according to the result of data fitting, for example, by performing the following steps: Figure 1 Step S4 is shown.

[0149] Figure 4 A block diagram showing a lipid identification device according to other embodiments of the present disclosure.

[0150] like Figure 4 As shown, the lipid identification device 4 includes a memory 41; and a processor 42 coupled to the memory 41, and the memory 41 is used to store instructions for executing the lipid identification method. The processor 42 is configured to execute the lipid identification method in any of some embodiments of the present disclosure based on the instructions stored in the memory 41.

[0151] Figure 5 A block diagram of a computer system for implementing some embodiments of the present disclosure is shown.

[0152] like Figure 5 As shown, the computer system 50 may be embodied in the form of a general purpose computing device. The computer system 50 includes a memory 510, a processor 520, and a bus 500 that connects the various system components.

[0153] The memory 510 may include, for example, a system memory, a non-volatile storage medium, etc. The system memory may, for example, store an operating system, an application program, a boot loader, and other programs, etc. The system memory may include a volatile storage medium, such as a random access memory (RAM) and / or a cache memory. The non-volatile storage medium may, for example, store instructions for executing the lipid identification method in any of the embodiments of the present disclosure. The non-volatile storage medium may include, but is not limited to, a disk storage, an optical storage, a flash memory, etc.

[0154] The processor 520 can be implemented by a general processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistors, etc. Discrete hardware components. Accordingly, each module such as the judgment module and the determination module can be implemented by a central processing unit (CPU) running instructions in a memory that execute corresponding steps, or can be implemented by a dedicated circuit that executes corresponding steps.

[0155] The bus 500 may use any of a variety of bus architectures, including, but not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MCA) bus, and a Peripheral Component Interconnect (PCI) bus.

[0156] The computer system 50 may also include an input / output interface 530, a network interface 540, a storage interface 550, etc. These interfaces 530, 540, 550, the memory 510, and the processor 520 may be connected via a bus 500. The input / output interface 530 may provide a connection interface for input / output devices such as a display, a mouse, and a keyboard. The network interface 540 may provide a connection interface for various networked devices. The storage interface 550 may provide a connection interface for external storage devices such as a floppy disk, a USB flash drive, and an SD card.

[0157] Here, various aspects of the present disclosure are described with reference to flowcharts and / or block diagrams of methods, devices, and computer program products according to embodiments of the present disclosure. It should be understood that each frame of the flowchart and / or block diagram and the combination of frames can be implemented by computer-readable program instructions.

[0158] These computer-readable program instructions may be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable device to produce a machine, so that the processor executes the instructions to produce means for implementing the functions specified in one or more blocks in the flowchart and / or block diagram.

[0159] These computer-readable program instructions can also be readable and stored in a computer-readable memory, which cause the computer to work in a specific manner to produce a product, including instructions for implementing the functions specified in one or more blocks in the flowchart and / or block diagram.

[0160] The present disclosure also provides a lipid identification system, comprising: a lipid identification device according to any of the embodiments of the present disclosure.

[0161] Figure 6a-6c A user interface for implementing a lipid identification system according to some embodiments of the present disclosure is shown.

[0162] Combine the following Figure 6a-6c , introduces the process of identifying lipids using an identification system according to some embodiments of the present disclosure (hereinafter referred to as LipidIN).

[0163] In order to avoid feature redundancy caused by similarity algorithms, LipidIN first constructed a "fragment tree" hierarchical spectrum library of 166.3 million lipids based on lipid characteristic spectra.

[0164] The user converts the downloaded file of the mass spectrometer into the mzML format through the MSConvert software, and then uploads the file. LipidIN will convert the mzML format and extract the spectrum information therein. The identification system of some embodiments of the present disclosure uses the standard mzML format as input and is highly universal.

[0165] Then extract the primary and secondary spectrogram information. Open the website, enter LipidIN and click the analysis option, left-click the mouse, enter the parameter name ESI under the param key, and enter the corresponding mode p under the corresponding param value. ESI can enter three modes (p represents positive ion mode, n1 represents formate mobile phase, and n2 represents acetate mobile phase). The input of this embodiment is p, continue to left-click the mouse, enter the parameter name MS2 under the param key, and enter the corresponding parameter value 0.01 under the corresponding param value, which represents the secondary spectrum peak whose secondary spectrum response intensity is lower than 1% of the highest peak. Left-click the mouse to click upload, select data.zip to upload data. Wait for the data upload to be completed, left-click the mouse to click working, wait for the operation to end, left-click the operation records to find result.zip, and left-click the mouse to download and obtain the operation results. A total of 5601 valid primary and secondary spectrum ion pairs were extracted from the example file.

[0166] Then perform a quick match. Based on the spectral information extraction, continue to process the data and name the result of the spectral information extraction data.zip. Select the upload folder by left-clicking the mouse, and left-click the mouse to add a parameter. Enter the parameter name ESI under the param key, and enter the corresponding mode p under the corresponding param value. Continue to left-click the mouse, enter the parameter name PPM1 under the param key, and enter the corresponding parameter value 10 under the corresponding param value, which means that the charge-to-mass ratio of the primary spectrum is set to allow an error of plus or minus 10ppm (threshold ppm ). Continue to left-click the mouse, enter the parameter name PPM2 under "paramkey", and enter the corresponding parameter value 30 under the corresponding param value, which means that the charge-to-mass ratio of the secondary spectrum is set to allow an error of plus or minus 30ppm (threshold ppm ). Wait for the data upload to be completed, left-click the mouse and click working, wait for the operation to end, left-click the mouse operation records to find result.zip, and left-click the mouse to download to get the operation results. In this example, 676 peaks were effectively identified and 2028 candidate identification results were obtained, of which the matching time took 0.04s.

[0167] Put the results of spectrum information extraction and quick matching in the data folder, compress it into data.zip, then left-click upload, select data.zip to upload, and wait for the upload to complete. Left-click working, wait for the operation to end, left-click operation records to find result.zip, and left-click download to get the operation results. The lipid group intelligent model generates an accuracy score for the identification results by judging the dynamic retention time.

[0168] The 166 million graded libraries of the above identification system are compatible with a variety of mass spectrometry identification software, and its identification framework is also applicable to various mzML format mass spectrometry files and MSP format spectra.

[0169] The present disclosure optimizes the selection of multiple parameters, and only three parameters (primary charge-to-mass ratio error threshold, secondary charge-to-mass ratio error threshold, and ionization mode) are required to complete the identification process, thereby reducing the impact of multiple parameters on the identification results and operation complexity.

[0170] The present disclosure can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects.

[0171] Those skilled in the art will appreciate that, in the description of the method steps in the above embodiments, the order in which the steps are actually executed is not strictly limited, and the order in which the steps are executed in actual operation may be adjusted according to functional requirements and internal logical relationships.

[0172] In the above description of various embodiments, the differences between different embodiments are emphasized, and the same points or similarities between various embodiments can be understood by reference to each other. To keep it simple, no repeated description will be given.

[0173] The lipid identification method, device and system, and computer-readable storage medium in the above embodiments improve the accuracy of lipid identification.

[0174] So far, the lipid identification method, device and system, and computer-readable storage medium according to the present disclosure have been described in detail. In order to avoid obscuring the concept of the present disclosure, some details known in the art are not described. Those skilled in the art can fully understand how to implement the technical solution disclosed here based on the above description.

Claims

1. A lipid identification method comprising: obtaining identification results of each lipid sample of a plurality of lipid samples; According to the identification results of each lipid sample, the unsaturation information and the subclass to which each lipid sample belongs are determined; Taking the retention time and mass-to-charge ratio of each lipid sample as variables, data fitting was performed on multiple lipid samples based on the unsaturation information and the subclasses to which they belonged; According to the results of data fitting, the accuracy of the identification results of each lipid sample was determined.

2. The lipid identification method according to claim 1, wherein The method uses the retention time and mass-to-charge ratio of each lipid sample as variables, performs data fitting on multiple lipid samples based on unsaturation information and subclasses, including: Using retention time and mass-to-charge ratio as coordinates, each lipid sample is mapped into a rectangular coordinate system, wherein each lipid sample is represented by a sample point in the coordinate system; Data fitting was performed for multiple lipid samples based on the relationship between unsaturation information, subclass, retention time and mass-to-charge ratio.

3. The lipid identification method according to claim 2, wherein The method of performing data fitting on multiple lipid samples according to the relationship between unsaturation information, subclass, retention time and mass-to-charge ratio includes: According to the relationship between the unsaturation information, the subclass, the retention time and the mass-to-charge ratio, starting points that meet the conditions are selected from the sample points and added to the first set to obtain a preliminary fitting curve; Construct a sub-coordinate system with the starting point as the origin, search for target sample points that meet the conditions in the first and third quadrants of the sub-coordinate system corresponding to the starting point, and add them to the first set; Whenever the number of target sample points newly added to the first set reaches a threshold, a preliminary fitting curve is re-determined based on the first set; Based on the first set, a target curve is fitted.

4. The lipid identification method according to claim 3, wherein The method of selecting a starting point that meets the conditions from the sample points and adding it to the first set according to the relationship between the unsaturation information, the subclass, the retention time and the mass-to-charge ratio to obtain a preliminary fitting curve includes: According to the unsaturation information, the subclass, the relationship between the retention time and the mass-to-charge ratio, the mass-to-charge ratio obtained by measuring the sample points and the mass-to-charge ratio obtained by data fitting, starting points that meet the conditions are selected from the sample points and added to the first set to obtain a preliminary fitting curve.

5. The lipid identification method according to claim 3, wherein The unsaturation information includes at least one of the total unsaturation and the fractional unsaturation, and the condition includes at least one of the following: The goodness of fit of the curve obtained by fitting the sample points that meet the conditions exceeds a first threshold; On a curve obtained by fitting the sample points that meet the conditions, for a first sample point and a second sample point with the same total unsaturation degree, when the retention time of the first sample point is greater than the retention time of the second sample point, the mass-to-charge ratio of the first sample point is greater than the mass-to-charge ratio of the second sample point; On the curve fitted according to the sample points that meet the conditions, for the third sample point and the fourth sample point with the same degree of chain unsaturation, when the retention time of the third sample point is greater than the retention time of the fourth sample point, the mass-to-charge ratio of the third sample point is greater than the mass-to-charge ratio of the fourth sample point.

6. The lipid identification method according to claim 3, wherein Determining the accuracy of the identification result of each lipid sample based on the result of data fitting includes: The accuracy of the identification result of each lipid sample is determined based on whether the sample point is in the first set, the mass-to-charge ratio obtained by measurement of the sample point and the mass-to-charge ratio obtained by data fitting.

7. The lipid identification method according to claim 6, wherein The method of determining the accuracy of the identification result of each lipid sample according to whether the sample point is in the first set and the mass-to-charge ratio obtained by measuring the sample point and the mass-to-charge ratio obtained by data fitting comprises: Determining a loss value of each of the plurality of sample points in the first set according to the mass-to-charge ratio obtained by measurement and the mass-to-charge ratio obtained by data fitting; Determine the maximum loss value of multiple sample points in the first set as a first loss value; Determine the minimum loss value of the plurality of sample points in the first set as the second loss value; For each sample point in the first set, the accuracy of the identification result of the lipid sample corresponding to the sample point is determined according to the loss value, the first loss value, and the second loss value of the sample point.

8. The lipid identification method according to claim 6, wherein The method of determining the accuracy of the identification result of each lipid sample according to whether the sample point is in the first set and the mass-to-charge ratio obtained by measuring the sample point and the mass-to-charge ratio obtained by data fitting comprises: Determine the confidence interval of the target curve; For a sample point that is not in the first set, the accuracy of the identification result of the corresponding lipid sample is determined according to whether the sample point is in the confidence interval.

9. The lipid identification method according to claim 8, wherein For a sample point not in the first set, determining the accuracy of the identification result of the corresponding lipid sample according to whether the sample point is in the confidence interval includes: Determine the accuracy of the identification result of the lipid sample corresponding to the sample point that is not in the first set and is in the confidence interval as a first value; The accuracy of the identification result of the lipid sample corresponding to the sample point not in the confidence interval and the first set is determined as a second value, wherein the first value is greater than the second value.

10. The lipid identification method according to claim 1, wherein The unsaturation information includes the total unsaturation number, and the retention time and mass-to-charge ratio of each lipid sample are used as variables, and data fitting is performed on multiple lipid samples based on the unsaturation information and the subclasses to which they belong, including: Dividing the plurality of lipid samples according to the subclass and the total unsaturation degree to obtain a plurality of second sets, wherein the lipid samples in each second set belong to the same subclass and have the same total unsaturation degree; For each second set, data fitting is performed with the retention time and mass-to-charge ratio of the lipid samples therein as variables to obtain a first fitting curve corresponding to the subclasses and total unsaturation degrees of the second set.

11. The lipid identification method according to claim 10, wherein For each second set, the retention time and mass-to-charge ratio of the lipid samples therein are used as variables to perform data fitting to obtain a first fitting curve corresponding to the subclasses and the total unsaturation degree of the second set, including: When the number of lipid samples of the first total unsaturation number of the specified subclass is less than a threshold value, a first fitting curve corresponding to the first total unsaturation number of the specified subclass is determined according to a first fitting curve of the second set corresponding to the second total unsaturation number of the specified subclass, wherein the first total unsaturation number is different from the second total unsaturation number.

12. The lipid identification method according to claim 11, wherein The method of determining a first fitting curve corresponding to the first total unsaturation number of the specified subclass according to a first fitting curve of a second set corresponding to a second total unsaturation number of the specified subclass when the number of lipid samples of the first total unsaturation number of the specified subclass is less than a threshold value comprises: According to the first fitting curve corresponding to the second total unsaturation number of the specified subclass, a curve that passes through the lipid sample of the first total unsaturation number of the specified subclass and does not intersect with the first fitting curve of the second set corresponding to the second total unsaturation number of the specified subclass is determined as the first fitting curve corresponding to the first total unsaturation number of the specified subclass.

13. The lipid identification method according to claim 11, wherein The unsaturation information also includes unsaturation distribution, and the retention time and mass-to-charge ratio of each lipid sample are used as variables, and data fitting is performed on multiple lipid samples based on the unsaturation information and the subclasses to which they belong, including: Dividing the plurality of lipid samples according to the subclass, the total number of unsaturations and the unsaturation distribution to obtain a plurality of third sets, wherein the lipid samples in each third set belong to the same subclass and have the same total number of unsaturations and unsaturation distribution; For each third set, data fitting is performed with the retention time and mass-to-charge ratio of the lipid samples therein as variables to obtain a second fitting curve corresponding to the subclasses, total unsaturation numbers and unsaturation distribution of the third set.

14. The lipid identification method according to claim 13, wherein Determining the accuracy of the identification result of each lipid sample based on the result of data fitting includes: Determining a first accuracy according to a first fitting curve corresponding to each lipid sample; determining a second accuracy according to a second fitting curve corresponding to each lipid sample; The accuracy of the identification result is determined based on the first accuracy and the second accuracy.

15. according to the lipid identification method of claim 1, wherein, The step of obtaining the identification result of each lipid sample of the plurality of lipid samples comprises: By comparing the spectrum of each lipid sample with the reference spectrum, the type of each lipid sample is identified as the identification result of each lipid sample.

16. according to the lipid identification method of claim 1, wherein, The method of comparing the spectrum of each lipid sample with the reference spectrum to identify the type of each lipid sample as the identification result of each lipid sample includes: Calculating a first score for each lipid sample according to the number of matching characteristic peaks between the spectrum of each lipid sample and the reference spectrum; Calculating a second score for each lipid sample according to the importance of the matching characteristic peaks of the spectrum of each lipid sample and the reference spectrum; Determining a reference spectrum matching the spectrum of each lipid sample according to the first score and the second score of each lipid sample; According to the matched reference spectrum, the type of each lipid sample is determined as the identification result of each lipid sample.

17. A lipid identification device comprising: an acquisition module, configured to acquire an identification result of each lipid sample of the plurality of lipid samples; An information determination module is configured to determine the unsaturation information and the subclass to which each lipid sample belongs based on the identification result of each lipid sample; A fitting module is configured to perform data fitting on multiple lipid samples based on unsaturation information and subclasses using the retention time and mass-to-charge ratio of each lipid sample as variables; The accuracy determination module is configured to determine the accuracy of the identification result of each lipid sample according to the result of data fitting.

18. A lipid identification device, comprising: Memory; as well as A processor coupled to the memory, the processor being configured to execute the lipid identification method according to any one of claims 1 to 16 based on instructions stored in the memory.

19. A lipid identification system comprising: A lipid identification device according to claim 17 or 18.

20. A computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, implement the lipid identification method according to any one of claims 1 to 16.

21. A computer program product, comprising computer program instructions, which, when executed by a processor, implement the lipid identification method according to any one of claims 1 to 16.

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