Method for removing false positive mass spectrum characteristics in triglyceride ion peak list based on retention time grid

Abstract

The invention discloses a method for removing false positive mass spectrum characteristics in a triglyceride ion peak list based on a retention time grid, and the method comprises the steps: screening data points according to equal DB values, and drawing a retention time grid chart through a new segmented multiple linear regression analysis formula and a fitting formula. The error of interference substances such as triglyceride can be effectively reduced. According to the improved splitting decomposition mechanism of the triglyceride in the CID mode, confirmation on charged fragments generated by free fatty acid and derivatives is increased, misjudgment on an identification result is effectively avoided, and therefore false positive caused by interfering substances is reduced. According to the method, on the basis of identifying the types of the fatty acids connected with the triglyceride, all the fatty acids are summarized, so that a list of all possible triglyceride is obtained by a mathematical exhaustion method, the triglyceride ion peak extraction process in the previous steps is rechecked according to the data, and errors caused by an analysis algorithm and interfering substances are reduced again.

Description

Technical Field

[0001] The present invention belongs to the technical field of analytical chemistry, and in particular relates to a method for removing false positive mass spectrometric features from a triglyceride ion peak list based on a retention time grid. Background Art

[0002] Triglycerides are the primary component of edible oils and fats of plant and animal origin, formed by the esterification of three fatty acids with a glycerol backbone. The triglyceride content in edible oils and fats is directly related to their quality and nutritional value. By testing triglyceride composition, we can ensure that edible oils and fats meet food safety and quality standards, avoiding quality issues that could affect consumer health. Testing triglycerides can help assess the nutritional properties of edible oils and fats, such as their unsaturated fatty acid content, helping consumers choose the right oils and fats based on their health needs. Testing triglycerides can also promptly identify potential quality issues or undesirable additives, ensuring food safety and consumer health. Therefore, testing triglycerides in edible oils and fats is crucial for quality control, nutritional assessment, and safety assurance, and is a key step in ensuring food quality and consumer health.

[0003] Liquid chromatography-high-resolution mass spectrometry combines efficient separation capabilities with precise mass spectrometry measurement capabilities for the analysis of triglycerides in oils and fats, making it one of the advanced analytical techniques currently used in scientific research and the food industry. In mass spectrometry analysis, a false positive refers to a compound or feature that is mistakenly identified as present when it is not, significantly impacting subsequent data interpretation and conclusions. Causes of false positives in mass spectrometry feature identification include background noise, instrument bias and systematic errors, data processing and analysis algorithms, experimental conditions, and sample pretreatment. Existing methods for handling false-positive features require significant manual intervention, which can lead to subjective misjudgments.

[0004] One solution is to identify false-positive mass spectrometric features based on retention time. Retention time (RT) is the time it takes for a compound to travel from the inlet to the detector. It is an important parameter in the liquid chromatography separation process. Different triglycerides have different retention times under the same elution conditions. By measuring the retention time, they can be distinguished from other false-positive compounds, thereby ensuring the accuracy and reliability of the analysis results. Existing retention time prediction methods for triglycerides include equivalent carbon number ECN = CN-2*DB, where CN is the total carbon number of the fatty acid and DB is the total number of unsaturated double bonds in the fatty acid. The smaller the ECN value, the shorter the retention time and the preferential elution. However, due to subtle differences in experimental factors such as the chromatographic column, mobile phase, temperature, sample solvent, and elution gradient, the traditional ECN formula cannot meet the retention time prediction requirements under some experimental conditions, nor can it be used to identify false-positive mass spectrometric features such as background noise, instrument deviation and systematic error, and non-triglycerides.

[0005] In this context, the present invention proposes a new formula for triglyceride structure and liquid phase elution retention time, a new strategy for constructing a liquid chromatography retention time grid, and a method for identifying false-positive mass spectrometric features in triglyceride ion peak lists, which helps to reduce subjective errors caused by manual judgment and has broad application value in the processing of triglyceride-related mass spectrometric data. Summary of the Invention

[0006] The present invention overcomes the shortcomings of existing data processing strategies. By constructing a new strategy for liquid chromatography retention time grids, it can accurately and quickly identify false positive mass spectrometric features such as background noise, instrument deviation and systematic error, and non-triglyceride ion peak tables after cleaning.

[0007] In view of the problem that the above-mentioned and / or existing methods are difficult to identify false positive mass spectral features in the triglyceride ion peak list, the present invention is proposed.

[0008] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0009] 1) Collect liquid chromatography-high-resolution mass spectrometry data; 2) Filter non-sample source signals caused by background noise (blank injection); 3) Filter signals caused by instrument deviation and systematic error (repeated injection); 4) Extract all possible triglyceride ion peaks based on MS1 data; 5) Calculate the corresponding CN and DB values based on the molecular formula, plot the relationship between RT (retention time) and CN (total carbon number of fatty acids), and screen data points; 6) Apply multivariate linear regression analysis to the above data points to fit the formula and plot a retention time grid; 7) Calculate RT 真实 and RT 预测 deviation; 8) determine whether the corresponding MS2 spectrum conforms to the triglyceride rule; 9) identify the fatty acid type of triglycerides and summarize all fatty acids; 10) based on the above fatty acids, list all possible triglycerides; 11) obtain the triglyceride ion peak table after cleaning.

[0010] 1) Collecting liquid chromatography-high-resolution mass spectrometry data: Analyze the oil sample by liquid chromatography-high-resolution mass spectrometry and collect data.

[0011] A high-resolution mass spectrometer was chosen because the m / z value accurately identified by the high-resolution instrument (with decimal points retained to 0.0001) is sufficient to calculate the exact molecular formula of the substance for subsequent structural analysis.

[0012] 2) Filtering non-sample source signals caused by background noise (blank injection: only solvent, no oil): Analyze the blank sample using liquid chromatography-high-resolution mass spectrometry and filter out non-sample source signals caused by background noise by comparing it with the real sample data.

[0013] 3) Filtering out signals caused by instrument deviation and systematic error (repeated injection): The sample was injected three times by liquid chromatography-high-resolution mass spectrometry. By comparing the repeated injections, the signals caused by instrument deviation and systematic error were filtered out.

[0014] 4) Extract all possible triglyceride ion peaks based on MS1 data: Use MS-DIAL software or similar tools to preliminarily extract all possible triglyceride ion peaks.

[0015] The extracted triglyceride ion peak should meet the following conditions: the ion adduct types of triglycerides include [M+H] + 、[M+NH4] + 、[M+Na] + and [M+K] + , and use this to calculate the molecular formula; the molecular formula of the parent ion is calculated based on the high-resolution m / z value, which satisfies C x H y O6 requirements, where x and y are both integers and x is less than y; the signal-to-noise ratio (S / N) of the parent ion is >3.

[0016] 5) Based on the molecular formula, calculate the corresponding CN and DB values, plot the relationship between RT (retention time) and CN (total carbon number of fatty acids), and screen the data points.

[0017] Calculate the triglyceride expression corresponding to the data point based on the molecular formula, such as C x H y O6 can be converted into the expression of TG CN: DB, where CN is the total carbon number of the fatty acid connected to the triglyceride, and DB is the total number of unsaturated double bonds of the fatty acid connected to the triglyceride. Note that the number of unsaturated double bonds here does not include the three ester bonds. The calculation formula is:

[0018] CN=x-3

[0019] DB=(2x+2-y) / 2-3

[0020] The process of screening data points includes: selecting points with equal DB values so that RT and CN show a significant linear relationship; removing outlying data points, requiring the linear fit R2 to be greater than 0.99; when there are less than 3 points with equal DB values, retain the data points and apply the subsequent multivariate linear regression formula for judgment.

[0021] 6) Apply the multivariate linear regression analysis method and the above data point fitting formula to draw a retention time grid diagram.

[0022] The fitting formula is specifically:

[0023] When DB=0, RT=a1*CN+c1

[0024] When DB≠0, RT=a2*ln(CN)+b2*DB+c2

[0025] Wherein, RT is the retention time, CN is the total carbon number of the fatty acid linked to the triglyceride, DB is the total number of unsaturated double bonds of the fatty acid linked to the triglyceride, and a1, c1, a2, b2, and c2 are the formula constants obtained by fitting the above-mentioned multiple linear regression analysis method.

[0026] When DB≠0, the inventors have found through long-term experiments that the original multivariate linear relationship between RT, CN and DB is not completely applicable, and should be replaced by a new formula of RT=a2*In(CN)+b2*DB+c2.

[0027] 7) Calculate RT 真实 and RT 预测 Deviation: Calculate CN and DB from the molecular formula of the substance, and then calculate RT according to the above formula 预测 , and then calculate RT 真实 and RT 预测 deviation.

[0028] The calculation formula of the deviation is (RT 真实 -RT 预测 ) / RT 真实 If the deviation value is less than 0.05, the data point meets the ion peak table requirements of triglycerides; if the deviation value is greater than or equal to 0.05, the data point is determined to be a false positive, and return to step 5) to reconfirm this data point.

[0029] 8) Determine whether the corresponding MS2 spectrum conforms to the triglyceride pattern: Determine whether the MS2 spectrum corresponding to the data point conforms to the triglyceride fragmentation pattern in CID mode. If the fragmentation pattern does not match, the data point is determined to be a false positive and the process returns to step 5) to reconfirm the data point.

[0030] The cracking rules of triglycerides in CID mode are shown in the attached Figure 6 As shown, unlike the previous principle of qualitative identification based only on diacylglycerol fragments produced by the loss of one fatty acid, the confirmation of charged fragments produced by free fatty acids and derivatives is added, which effectively avoids misjudgment of identification results and reduces false positives caused by interfering substances.

[0031] 9) Identify the fatty acid type of triglycerides and summarize all fatty acids: If the above spectrum matches the fragmentation pattern of triglycerides, identify the fatty acid type connected to the triglycerides and summarize all fatty acids.

[0032] 10) Based on the above fatty acids, list all possible triglycerides: a list of triglycerides obtained by mathematical exhaustive method, including mass-to-charge ratio m / z, CN, DB, RT 预测 Based on these data, the triglyceride ion peak extraction process in step 4) was reviewed.

[0033] The enumeration process of all triglycerides is based on the following formula:

[0034]

[0035] 11) Obtain a triglyceride ion peak table after cleaning.

[0036] The triglyceride ion peak table after cleaning is a summary of the ions successfully matched in the above steps, including m / z, CN, DB, RT 真实 , specific information of S / N.

[0037] The beneficial effects of the present invention are:

[0038] (1) Retention time is essentially dependent on the interaction between the target substance, the mobile phase, and the chromatographic column. The determination and utilization of retention time helps determine the relative hydrophilicity and separation order of the compounds. The improved data processing method of the present invention, after screening data points according to equal DB values, uses the newly developed piecewise multivariate linear regression analysis formula, fits the formula, and plots a retention time grid, effectively reducing errors in triglyceride-like interfering substances.

[0039] (2) The improved triglyceride fragmentation mechanism in the CID mode of the present invention is different from the previous principle of qualitative identification based only on the diglyceride fragments produced by the loss of one fatty acid. It increases the confirmation of charged fragments produced by free fatty acids and derivatives, effectively avoiding misjudgment of identification results, thereby reducing false positives caused by interfering substances.

[0040] (3) Based on the identification of the fatty acid types connected to triglycerides, the present invention summarizes all fatty acids and obtains a list of all possible triglycerides by mathematical exhaustive method. Based on these data, the triglyceride ion peak extraction process in the above steps is reviewed to further reduce the errors caused by the analysis algorithm and interfering substances. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them:

[0042] Figure 1 Flowchart of the method for removing false positive mass spectral features from the triglyceride ion peak list;

[0043] Figure 2 is the conversion process of triglyceride ion peak (n=430);

[0044] Figure 3 is the relationship between retention time and total carbon number of fatty acids (n=430);

[0045] Figure 4 The process of filtering data points according to the total number of unsaturated double bonds in fatty acids;

[0046] Figure 5 is the retention time grid diagram (n=151);

[0047] Figure 6 Schematic diagram of triglyceride fragmentation in improved CID mode;

[0048] Figure 7 Comparison of MS2 spectra of false positive substances and true triglycerides;

[0049] Figure 8 The triglyceride ion peak map obtained after verification (n=154). DETAILED DESCRIPTION

[0050] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with specific embodiments.

[0051] Example:

[0052] 1) Collecting liquid chromatography-high-resolution mass spectrometry data: Analyze the oil sample by liquid chromatography-high-resolution mass spectrometry and collect data.

[0053] In this case, soybean oil was used as the sample, fully dissolved in isopropanol. Analysis was performed using a SCIEX TripleTOF 6600 system. Chromatographic separation was performed at 40°C using a Phenomenex Kinetex C18 column (100 mm × 3 mm, 3 μm). Mobile phase A consisted of methanol:acetonitrile:water (1:1:1, v / v, 5 mmol / L ammonium acetate), and mobile phase B was isopropanol (containing 5 mmol / L ammonium acetate). The gradient program was as follows: 0–0.5 min, hold at 20% B; 0.5–1.5 min, linear increase in B from 20% to 40%; 1.5–3 min, linear increase in B from 40% to 60%; 3–13 min, linear increase in B from 60% to 98%; 13–14.5 min, hold at 98% B; 14.5–14.6 min, linear decrease in B from 98% to 20% B. The mobile phase flow rate was set at 0.3 mL / min. The autosampler temperature was 4°C.

[0054] For electrospray ionization, an ion source temperature of 550°C, a spray voltage of 5500 V, a curtain gas of 30 psi, a nebulizer gas of 50 psi, an auxiliary heating gas of 55 psi, and a declustering potential of 80 V were used. In TOF MS mode, an accumulation time of 250 ms was applied, and the mass range was scanned between m / z 400 and 1200. The collision energy in CID mode was 40 eV.

[0055] 2) Filtering non-sample source signals caused by background noise (blank injection): Analyze a blank sample (solvent only, no sample) using liquid chromatography-high-resolution mass spectrometry and filter out non-sample source signals caused by background noise by comparing it with the real sample data.

[0056] 3) Filtering out signals caused by instrument deviation and systematic error (repeated injection): The sample was injected three times by liquid chromatography-high-resolution mass spectrometry. By comparing the repeated injections, the signals caused by instrument deviation and systematic error were filtered out.

[0057] 4) Extract all possible triglyceride ion peaks based on MS1 data: Use MS-DIAL software or similar tools to preliminarily extract all possible triglyceride ion peaks.

[0058] The extracted triglyceride ion peak should meet the following conditions: the ion adduct types of triglycerides include [M+H] + 、[M+NH4] + 、[M+Na] + 、[M+K] + , and use this to calculate the molecular formula; the molecular formula of the parent ion is calculated based on the high-resolution m / z value, which satisfies C x H yO6 requirements, where x and y are both integers and x is less than y; the signal-to-noise ratio (S / N) of the parent ion is >3.

[0059] First, the relationship between all mass-to-charge ratios m / z and retention time RT in the sample was listed, and a total of 420 data points were obtained ( Figure 2 a); Then, the adduct mode of the parent ion is calculated based on the accurate m / z value. The triglyceride has a total of [M+H] + 、[M+NH4] + 、[M+Na] + 、[M+K] + Four types of addition ( Figure 2 b); Then, calculate the molecular formula of the original compound corresponding to all data points (note: the same compound may form multiple adducts, which are represented by the same retention time but different m / z values). The repeated data points can be removed by calculating the original molecular formula ( Figure 2 c); Finally, the triglyceride expression corresponding to the data point is calculated according to the molecular formula, such as C x H y O6 can be converted into the expression of TG CN: DB, where CN is the total carbon number of the fatty acid connected to the triglyceride, and DB is the total number of unsaturated double bonds of the fatty acid connected to the triglyceride. Note that the number of unsaturated double bonds here does not include the three ester bonds. The calculation formula is:

[0060] CN=x-3

[0061] DB=(2x+2-y) / 2-3

[0062] The conversion result is as follows Figure 2 As shown in d.

[0063] 5) According to the molecular formula, calculate the corresponding CN and DB values, and draw a relationship diagram between RT (retention time) and CN (total carbon number of fatty acids) ( Figure 3 , the data labels in the figure are the DB values calculated above). At this time, after merging the repeated data points caused by the formation of different adducts, the number of data points becomes 244.

[0064] like Figure 4 As shown in Figure 3, the process of screening data points includes: selecting points with equal DB values so that RT and CN show a significant linear or exponential relationship; removing remote data points, requiring the linear fit R2>0.99; when there are less than 3 points with equal DB values, retain the data points and apply the subsequent fitting multivariate linear regression formula for judgment.

[0065] 6) Apply the multivariate linear regression analysis method and the above data point fitting formula to draw a retention time grid diagram.

[0066] The fitting formula is specifically:

[0067] When DB=0, RT=a1*CN+c1

[0068] When DB≠0, RT=a2*ln(CN)+b2*DB+c2

[0069] Wherein, RT is the retention time, CN is the total carbon number of the fatty acid linked to the triglyceride, DB is the total number of unsaturated double bonds of the fatty acid linked to the triglyceride, and a1, c1, a2, b2, and c2 are the formula constants obtained by fitting the above-mentioned multiple linear regression analysis method.

[0070] When DB≠0, the inventors have found through long-term experiments that the original multivariate linear relationship between RT, CN and DB is not completely applicable, and should be replaced by a new formula of RT=a2*In(CN)+b2*DB+c2.

[0071] Table 1 Comparison of linear and logarithmic fitting results under different DB values

[0072]

[0073]

[0074] The results show that when DB=0, the linear fitting result is better, and when DB≠0, the logarithmic fitting result is better.

[0075] When DB = 0, RT = 0.238*CN + 2.3955

[0076] It was further found that when the DB value is greater than 7, neither the linear nor the logarithmic fitting formula is applicable. The reason is that there are too few data points or too many intervening substances. At this time, a multivariate linear regression formula should be constructed based on the existing data points to determine the data points when the DB value is greater than 7.

[0077] Using the Microsoft Excel data analysis module, a multiple linear regression analysis formula was constructed using the data points when DB=1, 2, 3, 4, 5, 6, and 7 as confirmed above. The results were:

[0078] When DB≠0, RT=11.1168*ln(CN)-0.3506*DB-33.8902

[0079] Based on the above two fitting formulas, a retention time grid suitable for triglycerides was constructed (such as Figure 5 ), 151 data points were obtained at this time.

[0080] 7) Calculate RT 真实 and RT 预测 Deviation: Calculate CN and DB from the molecular formula of the substance, and then calculate RT according to the above formula预测 , and then calculate RT 真实 and RT 预测 deviation.

[0081] The calculation formula of the deviation is (RT 真实 -RT 预测 ) / RT 真实 If the deviation value is less than 0.05, the data point meets the ion peak table requirements of triglycerides; if the deviation value is greater than or equal to 0.05, the data point is determined to be a false positive, and return to step 5) to reconfirm this data point.

[0082] 8) Determine whether the corresponding MS2 spectrum conforms to the triglyceride pattern: Determine whether the MS2 spectrum corresponding to the data point conforms to the triglyceride fragmentation pattern in CID mode. If the fragmentation pattern does not match, the data point is determined to be a false positive and the process returns to step 5) to reconfirm the data point.

[0083] The cracking rules of triglycerides in CID mode are shown in the attached Figure 6 As shown, unlike the previous principle of qualitative identification based only on diacylglycerol fragments produced by the loss of one fatty acid, the confirmation of charged fragments produced by free fatty acids and derivatives is added, which effectively avoids misjudgment of identification results and reduces false positives caused by interfering substances.

[0084] Attachment Figure 7 This is an application example of the new fragmentation mechanism to analyze MS2 spectra. Figure 7 a and 7b are highly similar interfering substances, and Figure 7 b is the true triglyceride. By applying the improved cleavage mechanism of the present invention, interfering false positive substances can be better identified.

[0085] 9) Identify the fatty acid type of triglycerides and summarize all fatty acids: If the above spectrum matches the fragmentation pattern of triglycerides, identify the fatty acid type connected to the triglycerides and summarize all fatty acids.

[0086] In this application example, 16 fatty acids including C8:0, C8:1, C9:0, C10:0, C18:0, C18:1, C18:2, and C18:3 were obtained.

[0087] 10) Based on the above fatty acids, list all possible triglycerides: a list of triglycerides obtained by mathematical exhaustive method, including mass-to-charge ratio m / z, CN, DB, RT 预测 Based on these data, the triglyceride ion peak extraction process in step 4) was reviewed.

[0088] The enumeration process of all triglycerides is based on the following formula:

[0089]

[0090] In this application example, substituting n=16 into the above formula, the result is 214, that is, 16 fatty acids can theoretically produce 214 triglycerides with different molecular weights (not considering molecular isomers). Then, the corresponding [M+H] is calculated based on the molecular weight. + 、[M+NH4] + 、[M+Na] + 、[M+K] + The precise m / z values of the four adducts were substituted into the raw data from step 4 for verification to further reduce errors caused by the analysis algorithm and interfering substances.

[0091] 11) Obtain a triglyceride ion peak table after cleaning.

[0092] The triglyceride ion peak table after cleaning is a summary of the ions successfully matched in the above steps, including m / z, CN, DB, RT 真实 , S / N specific information. Figure 8 As shown, 154 data points were obtained at this time.

[0093] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for removing false positive mass spectrometric features from a triglyceride ion peak list based on a retention time grid, characterized in that: The following steps are included: (1) Analyzing the oil sample by liquid chromatography-high resolution mass spectrometry and collecting mass spectrometry data; (2) Analyze the oil-free solvent by liquid chromatography-high-resolution mass spectrometry and filter out non-sample source signals caused by background noise by comparing the mass spectrometry data with the oil sample; (3) Repeated injection analysis of oil samples was performed using a liquid chromatography-high-resolution mass spectrometer, and the repeated injection data were compared to filter out signals caused by instrument deviation and system error; (4) extracting all possible triglyceride ion peaks based on the MS1 spectrum obtained in step (1); (5) Calculate the total carbon number and the total number of unsaturated double bonds of the fatty acids connected to the triglyceride, draw a relationship graph between the retention time and the total carbon number of the fatty acids, and filter the data points; wherein the total number of unsaturated double bonds does not include the three ester bonds of the triglyceride; (6) Applying the multivariate linear regression analysis method, the retention time grid diagram is drawn using the data point fitting formula; the fitting formula is: When DB=0, RT=a1*CN+c1 When DB≠0, RT=a2*ln(CN)+b2*DB+c2 Wherein, RT is the retention time, CN is the total carbon number of the fatty acid linked to the triglyceride, DB is the total number of unsaturated double bonds of the fatty acid linked to the triglyceride, and a1, c1, a2, b2, and c2 are the constants of the formula obtained by fitting the multiple linear regression analysis method; (7) Calculate CN and DB, and then calculate RT according to the formula in step (6) 预测 , and then calculate RT 真实 and RT 预测 Deviation; (8) Determine whether the MS2 spectrum corresponding to the data point conforms to the fragmentation pattern of triglycerides in the CID mode. If the fragmentation pattern does not match, the data point is determined to be a false positive, and return to step (5) to reconfirm the data point; (9) Identify the fatty acid types of triglycerides and summarize all fatty acids; (10) Enumerate all possible triglycerides using a mathematical exhaustive method; review the triglyceride ion peak extraction process of step (4); (11) Obtain the triglyceride ion peak table after cleaning.

2. The method for removing false positive mass spectral features in a triglyceride ion peak list based on a retention time grid according to claim 1, characterized in that: In step (4), the extracted triglyceride ion peak should meet the following conditions: the ion adduct types of triglycerides include [M+H] + 、[M+NH4] + 、[M+Na] + and [M+K] + , and calculate the molecular formula; calculate the molecular formula of the parent ion based on the high-resolution m / z value, and the molecular formula satisfies C x H y O6 requirements, where x and y are both integers and x is less than y; the signal-to-noise ratio (S / N) of the parent ion is >3.

3. The method for removing false positive mass spectral features in a triglyceride ion peak list based on a retention time grid according to claim 2, characterized in that: In step (5), the triglyceride expression corresponding to the data point is calculated according to the molecular formula, and C x H y The calculation formula for converting O6 into TG CN:DB is: CN=x-3 DB=(2x+2-y) / 2-3 Where CN represents the total carbon number of the fatty acid to which the triglyceride is attached, and DB is the total number of unsaturated double bonds of the fatty acid to which the triglyceride is attached; The screening data points include selecting points with equal DB values so that the retention time and CN show a significant linear relationship, and the R of the linear fit is required. 2 >0.99; when there are less than 3 points with equal DB values, retain the data point.

4. The method for removing false positive mass spectral features in a triglyceride ion peak list based on a retention time grid according to claim 3, characterized in that: In step (7), the calculation formula of the deviation is (RT true - RT 预测 ) / RT 真实 If the deviation value is less than 0.05, the data point meets the ion peak table requirements of triglycerides; if the deviation value is greater than or equal to 0.05, the data point is determined to be a false positive, and return to step (5) to reconfirm this data point.

5. The method for removing false positive mass spectral features in a triglyceride ion peak list based on a retention time grid according to claim 4, characterized in that: In step (8), the fragmentation pattern of triglycerides in the CID mode includes the confirmation of charged fragments produced by free fatty acids and derivatives, thereby reducing false positives caused by interfering substances.

6. The method for removing false positive mass spectral features in a triglyceride ion peak list based on a retention time grid according to claim 5, characterized in that: In step (9), if the MS2 spectrum matches the fragmentation pattern of triglycerides, the fatty acid types linked to the triglycerides are identified and all fatty acids are summarized.

7. The method for removing false positive mass spectral features in a triglyceride ion peak list based on a retention time grid according to claim 6, characterized in that: In step (10), all possible triglycerides are enumerated by mathematical exhaustive method according to the following formula: n represents the total number of fatty acids.

8. The method for removing false positive mass spectral features in a triglyceride ion peak list based on a retention time grid according to claim 7, characterized in that: In step (11), the triglyceride ion peak table includes mass-to-charge ratio m / z, CN, DB, RT 真实 , detailed information on the signal-to-noise ratio (S / N) of the parent ion.

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