Method for constructing and identifying neutral loss triggering prediction database of phytosterol ester in vegetable oil

Abstract

The invention discloses a construction and identification method for a neutral loss triggering prediction database of phytosterol ester in vegetable oil, and the method comprises the following steps: S1, constructing a simulation structure of the phytosterol ester according to a composition mechanism of a molecular structure of the phytosterol ester; s2, acquiring a phytosterol ester mass spectrum splitting signal of the training set through mass spectrum detection scanning, and decoding a phytosterol ester mass spectrum splitting rule; s3, constructing a phytosterol ester mass spectrum simulation neutral loss trigger prediction database; and S4, when the neutral loss mass of the acyl chain in the detection sample is identified, triggering neutral loss scanning, and identifying the non-targeted phytosterol ester in the vegetable oil. According to the method, the phytosterol ester component in the oil and edible oil can be rapidly analyzed, analysis workers do not need to have professional mass spectrum unfolding ability, and rapid non-targeted recognition analysis can be carried out on the sample through mass spectrum library comparison; the method has the characteristics of no dependence on standard compounds, short analysis period, high result accuracy and good result reproducibility.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of chemical analysis and detection, and particularly relates to a construction and identification method of a neutral loss trigger prediction database for phytosteryl esters in vegetable oils. BACKGROUND

[0002] Phytosterols have various physiological functions and can effectively reduce blood cholesterol levels, and play an important role in anticancer, anti-inflammatory, blood glucose reduction, immune regulation and cardiovascular protection. Phytosterol esters are hydrolyzed into sterols and fatty acids in the body, have the same or better physiological functions as free sterols, and have higher absorption and utilization rates and higher bioavailability. In addition, phytosteryl esters have stronger fat solubility and lower melting point, and can be better applied in the fields of food, medicine, health care products and cosmetics. With the rapid development of science and technology, the detection methods of phytosterols and sterol esters are becoming more diversified, and the combination of various advanced technologies also makes the analysis of phytosterol esters more convenient, fast, accurate and reliable. However, the current research objects are mainly total sterol esters, and the qualitative and quantitative detection of single sterol fatty acid esters is relatively less, and there are scientific problems such as in-situ identification difficulty and loss of fatty acid information. Therefore, it is of great significance to establish a simple and rapid in-situ identification technology for phytosterol esters, realize accurate detection of sterol esters, study the composition and content differences of phytosterol esters in different vegetable oils, and analyze the related difference mechanisms.

[0003] There are many technologies for in-situ identification of unknown compounds, including nuclear magnetic resonance spectroscopy, infrared spectroscopy, ultraviolet spectroscopy, chromatography and mass spectrometry. However, mass spectrometry is the most important means for identifying the structure information of complex compounds, and the accurate mass-to-charge ratio of characteristic ion fragments of the compound can be determined to obtain the structure information of the unknown compound, so as to analyze the cracking behavior, summarize the cracking rules, establish a corresponding neutral loss trigger prediction database of mass spectrometry, and realize the qualitative analysis and non-target screening of unknown phytosterol esters. However, there is currently no universally applicable neutral loss trigger prediction database for phytosterol esters.

[0004] Therefore, a construction and identification method of a neutral loss trigger prediction database for phytosterol esters in vegetable oils is urgently needed. SUMMARY

[0005] To solve the defects in the prior art, the present application provides a construction and identification method of a neutral loss trigger prediction database for phytosterol esters in vegetable oils.

[0006] In order to solve the above technical problems, the present application provides the following technical solutions:

[0007] The present application provides a construction and identification method of a neutral loss trigger prediction database for phytosterol esters in vegetable oils, comprising the following steps:

[0008] S1, according to the composition mechanism of the molecular structure of phytosteryl ester, a simulation structure of phytosteryl ester is constructed;

[0009] S2, a training set phytosteryl ester mass spectrum cracking signal is obtained by mass spectrum detection scanning, and the phytosteryl ester mass spectrum cracking rule is deciphered;

[0010] S3, a phytosteryl ester mass spectrum simulation neutral loss trigger prediction database is constructed;

[0011] S4, when the CID filter identifies the neutral loss mass of the acyl chain in the detection sample, the neutral loss scanning is triggered, and the non-target phytosteryl ester in the vegetable oil is identified.

[0012] Preferably, the step S1 of constructing the simulation structure of phytosteryl ester comprises the following steps:

[0013] S11, the phytosteryl ester steroidal core information and the fatty acid acyl chain molecular element composition information are extracted;

[0014] S12, the number of phytosteryl ester steroidal core information and the number of fatty acid acyl chain molecular element composition information are combined in a random one-to-one manner to construct the simulation structure of phytosteryl ester, and the number of constructed phytosteryl ester simulation structure = the number of phytosteryl ester steroidal core information × the number of fatty acid acyl chain molecular element composition information.

[0015] Preferably, the step S2 of deciphering the phytosteryl ester mass spectrum cracking rule comprises the following steps:

[0016] S21, using liquid chromatography-mass spectrometry to collect known phytosteryl ester mass spectrum cracking signals: first, detection scanning is performed, and structure analysis is performed through two modes of primary mass spectrum full scanning and data-dependent acquisition; then, composite scanning is performed, and three composite acquisition modes of primary mass spectrum full scanning, data-dependent acquisition and neutral loss trigger scanning are used to realize enhancer ion scanning, so as to obtain a high-sensitivity secondary fragment full spectrum of a low-concentration target in a complex matrix sample, that is, original mass spectrum data;

[0017] S22, in the positive ion scanning mode, phytosteryl ester generates [M+NH4] + or [M+H] + parent ion in the primary mass spectrum, the ester bond is broken after the parent ion is fragmented, the fatty acid acyl chain in the parent ion is lost, and the corresponding sterol characteristic structure fragment [M+NH4-NH3-Acid] + is generated, so as to decipher the phytosteryl ester mass spectrum cracking rule.

[0018] Preferably, the step S3 of constructing the phytosteryl ester mass spectrum simulation neutral loss trigger prediction database comprises the following steps:

[0019] S31, constructing neutral loss trigger acquisition database: acquiring original mass spectrum data in the mode of step S21;

[0020] S32, constructing neutral loss trigger identification database: combining plant sterol nucleus and fatty acid acyl chain randomly one by one to combine all AB type plant sterol esters, and then calculating accurate mass-to-charge ratios of primary parent ions and characteristic fragments and neutral loss mass according to the fragmentation rule of plant sterol esters in mass spectrum, to establish a neutral loss trigger identification database containing chemical formula, theoretical molecular weight, addition mode, accurate mass of parent ion, neutral loss mass and accurate mass of characteristic fragment of plant sterol ester.

[0021] Preferably, S41, extracting MS n accurate mass of parent ion, MS 1 accurate mass of parent ion, MS

[0022] S42, acquiring MS n accurate mass of parent ion, MS 1 accurate mass of parent ion, MS

[0023] S43, using MS 1 accurate mass of parent ion, MS 1 loss filter to obtain unique element composition as a candidate; setting mass tolerance ≤ set threshold and reference mass loss;

[0024] S44, identifying unique structure of plant sterol lipid candidate by MS 2 accurate mass of parent ion and neutral loss accurate mass exclusion strategy; specifically as follows: after plant sterol ester structure fragmentation, through neutral loss trigger identification, post-ordering prediction of fragment ions, continuously filtering predicted structure, through fragment ion filtering in positive ion mode, further identifying plant sterol ester structure;

[0025] S45, all identification results are summarized as a list to export identification results.

[0026] Compared with the prior art, the present application has the following beneficial effects:

[0027] The present application cracks the mass spectrum fragmentation characteristics of plant sterol ester, constructs a neutral loss trigger prediction database of plant sterol ester mass spectrum, and establishes a liquid chromatography mass spectrometry identification method of plant sterol ester. It can be used for identifying plant sterol ester in edible oil, and provides technical support for developing new functional plant sterol ester. BRIEF DESCRIPTION OF DRAWINGS

[0028] Figure 1 is a schematic diagram of plant sterol ester formation in the present application; Figure 2a is the primary mass spectrum of β-sitosterol oleate in positive ion mode of the present application; Figure 2b is the secondary mass spectrum of β-sitosterol oleate in positive ion mode of the present application; Figure 3 is the high-resolution mass spectrum cracking rule schematic diagram of phytosteryl ester in positive ion mode of the present application; Figure 4a is the structure diagram of phytosteryl mother nucleus β-sitosterol of the present application; Figure 4b is the structure diagram of phytosteryl mother nucleus sitosterol of the present application. DETAILED DESCRIPTION

[0029] The preferred embodiments of the present application are described below in conjunction with the accompanying drawings, and it should be understood that the preferred embodiments described herein are only used to illustrate and explain the present application, and are not used to limit the present application.

[0030] Example 1: Constructing the simulation structure of phytosteryl ester.

[0031] Phytosteryl ester is formed by dehydration condensation esterification of the hydroxyl group at C-3 position of the steroidal mother nucleus and the carboxyl group of fatty acid, and its formation principle and structure are shown in Figure 1 .

[0032] Randomly combine 21 phytosteryl mother nuclei with 56 common fatty acid acyl chains one by one, without considering various isomers, a total of 1176 phytosteryl ester molecules are simulated, and the theoretical molecular weight of the 1176 phytosteryl ester molecules and the possible primary ion accurate mass number, neutral loss accurate mass number, and mass spectrum fragment accurate mass number thereof are accurately calculated, which basically covers all possible phytosteryl esters in nature.

[0033] Example 2: Taking β-sitosterol oleate as an example, decipher the mass spectrum cracking rule of phytosteryl ester.

[0034] (1) Preparation of phytosteryl ester standard.

[0035] A certain amount of β-sitosterol oleate standard was dissolved with chloroform-methanol (2:1, V / V) to prepare a single standard stock solution of 1000 μg / mL, which was sealed, stored in a dark place at -20°C refrigerator for standby. Before use, dilute with 100% methanol to prepare a standard solution of 1000 ng / mL, and prepare it immediately before use.

[0036] (2) Collecting known phytosteryl ester mass spectrum cracking signals using liquid chromatography-mass spectrometry.

[0037] The ion source was an electrospray ionization (ESI) source, with the following source parameters: ion transport capillary temperature 200–400℃, evaporation temperature 200–400℃, spray voltage 2500–4000V(+) / 2500–4000V(-), sheath gas (N2) flow rate 30–50 arb, and auxiliary gas (N2) flow rate 2–8 arb. The full-scan parameters for primary mass spectrometry were as follows: scan range 150–1200 m / z, RF lens 40–80%, automatic gain control (AGC) 1.0e5–1.0e7, and maximum injection time 60–140 ms. The fragmentation filtration intensity threshold was set to 1.0e3–1.0e5, in data-dependent mode. The fragmentation mode adopted was high-energy collision-induced dissociation (HCD) or collision-induced dissociation (CID) or other ion fragmentation modes. The collision energy was 30±5eV, the AGC was 1.0e4–10.0e4, and the maximum injection time was 40–200ms.

[0038] First, a detection scan is performed, using a first-level full-scan mass spectrometry and data-dependent acquisition (dd-MS). 2 Structural analysis was performed using two modes; then a combined scan was conducted, employing a full-scan mass spectrometry approach and data-dependent acquisition (dd-MS). 2 The system employs three composite acquisition modes—neutral loss-triggered scanning, neutral loss-triggered scanning, and neutral loss-triggered scanning—to enhance daughter ion scanning, thereby obtaining a highly sensitive full spectrum of secondary fragments of low-concentration target analytes in complex matrix samples.

[0039] In this embodiment, β-sitosterol oleate was analyzed by liquid chromatography-mass spectrometry (LC-MS) to obtain raw mass spectrometry data. Software was used to analyze the raw mass spectrometry data, extracting the precise mass number ion root structure and the precise mass number of neutral loss of β-sitosterol oleate from the mass spectrum, and performing structural analysis of multiple ion root units.

[0040] (3) In this embodiment, the first-order mass spectra of the precise mass number ion root structure of β-sitosterol oleate in positive ion mode all show ammonium ions, producing [M+NH4]. + The parent ion, after fragmentation, loses the neutrality of the oleic acyl group (R18:1) chain, resulting in a mass-to-charge ratio of 397.38287 ([M+NH4-NH3-R). 18:1 CO2H] + β-sitosterol structural fragment ions ( Figure 2a , Figure 2b No response in negative ion mode.

[0041] Summarizing the above data and mass spectrum information, the plant sterol ester cleavage rule is summarized as shown in the table. Figure 3 In positive ion scanning mode, plant sterol esters produce [M+NH4] parent ions in the first mass spectrum, and after the parent ion fragmentation, the ester bond breaks, the fatty acid acyl chain is lost from the parent ion, and the corresponding sterol characteristic fragment ion [M+NH4-NH3-R2CO2H] is produced. + + In negative ion mode, all sterol esters are not responsive.

[0042] Example 3: Construction of plant sterol ester mass spectrometry neutral loss trigger prediction database.

[0043] Constructing a sterol ester acyl chain neutral loss database, only when the acyl chain neutral loss mass appears in the "CID filter scan" (Table 1), the fragmentation scan of the related precursor ion will be triggered. The characteristic neutral loss pattern of sterol esters greatly improves the specificity of the CID filter, reducing the number of false positive trigger scan events. The construction of the neutral loss trigger prediction database assesses potential neutral loss predictions based on sterol ester structure information and eliminates predictions that are structurally impossible, using neutral loss as trigger identification information.

[0044] Table 1: Plant sterol esters with molecular formula C 47 H 82 O2obtained from the library.

[0045]

[0046]

[0047]

[0048] The neutral loss trigger prediction database includes: according to the cleavage rule of plant sterol esters in mass spectrometry, the mass-to-charge ratio of the first parent ion and the characteristic fragment, the neutral loss mass, and the neutral loss trigger prediction database containing the chemical formula of plant sterol esters, the theoretical molecular weight, the addition mode, the accurate mass number of the parent ion, the neutral loss mass, and the accurate mass number of the characteristic fragment. The operation interface of the neutral loss trigger prediction database has a visual panel, which can add or modify each item of information in the panel, and can also be expanded.

[0049] Example 4: Identification of plant sterol esters by exclusion strategy.

[0050] According to the EU regulation, high-resolution mass spectrometry only needs to complete the matching of the parent ion and one daughter ion to realize the qualitative identification of the compound, and plant sterol esters are composed of only one sterol molecule and one fatty acid acyl chain. Therefore, when the mass-to-charge ratio of the parent ion of the compound is the same, only the mass-to-charge ratio of the fragment ion after neutral loss needs to be verified. ​

[0051] (1) Identify workflow diagram.

[0052] 1) In liquid chromatography mass spectrometry, two needles are used to first perform detection scanning, and then perform compound scanning, through three compound acquisition modes of full-scan, data-dependent acquisition (dd-MS 2 ), and neutral loss trigger scanning, to realize enhanced ion scanning. After obtaining the data, the raw file is converted into mzXML format data through MM File file conversion software or similar software platform. 2

[0053] 2) Extract MS n mass spectrum accurate mass number roots from the above data, including MS 1 mass spectrum and fragment ion mass spectrum, and neutral loss accurate mass number.

[0054] 3) Collect MS n characteristic ion accurate mass number and characteristic fragment ion accurate mass number from the MS 1 mass spectrum accurate mass number roots.

[0055] 4) Use MS 1 accurate mass number and MS 1 mass loss filtering to obtain unique element composition as a candidate.

[0056] 5) Use MS 2 accurate mass number and neutral loss accurate mass number exclusion strategy to identify the unique structure of phytosteryl ester candidate.

[0057] 6) All identification results are summarized into a list to export the identification results.

[0058] (2) MS 1 accurate mass number and MS 1 mass loss filtering to obtain unique molecular composition.

[0059] Although the MS1 mass accuracy can be better than 2ppm by optimizing the experimental conditions and better mass spectrometry mass accuracy parameters, under the condition of 2ppm, the unique element composition candidate cannot be obtained using direct accurate mass number. In positive ion mode, the first parent ion [M+NH4] + ​The mass-to-charge ratio 696.66607 Da, and the mass-to-charge ratio 696.6661 Da under different tolerances will obtain candidate elements with different compositions, such as when the tolerance is 10 ppm, 4 ppm, and 2 ppm, candidate element compositions at different mass tolerance levels can be obtained (see Table 2). The mass spectrum of the mass-to-charge ratio 696.6661 Da is attributed to [M+NH4] + Only considering the elements C, H, N, Cl, and O, there are 13, 4, and 1 possible candidate element compositions under the tolerances of 10 ppm, 4 ppm, and 2 ppm, respectively (see Table 2). Therefore, it is necessary to combine MS1 precision mass identification and reference mass defect (RMD) classification to obtain unique candidate element compositions.

[0060] Table 2: β-sitosterol oleate primary parent ion [M+NH4] + (m / z, 696.6661 Da), possible molecular formula under different mass tolerances.

[0061] Quality tolerance Serial number Molecular formula Unsaturation RBD Delta ppm 10 ppm 1 C 47 H 86 O2N]]> 5.5 1.138 2 C 30 H 80 O2N 16 ]]> -1 2.344 3 C 45 H 84 O N4]]> 6 3.065 4 C 35 H 80 N 14 ]]> 3 -3.431 5 C 28 H 78 O N 19 ]]> -0.5 4.271 6 C 44 H 88 O5]]> 1 4.985 7 C 43 H 82 N7]]> 6.5 4.992 8 C 37 H 82 O N 11 ]]> 2.5 -5.358 9 C 26 H 76 N 22 ]]> 0 6.198 10 C 42 H 86 O4N3]]> 1.5 6.912 11 C 39 H 84 O2N8]]> 2 -7.285 12 C 40 H 84 O3N6]]> 2 8.839 13 C 41 H 86 O3N5]]> 1.5 -9.212 4 ppm 1 C 47 H 86 O2N]]> 5.5 1.138 2 C 30 H 80 O2N 16 ]]> -1 2.344 3 C 45 H 84 O N4]]> 6 3.065 4 C 35 H 80 N 14 ]]> 3 -3.431 2 ppm 1 C 47 H 86 O2N]]> 5.5 1.138

[0062] By setting CH2 as a molecular structure fragment, the Conversion Mass (CM) calculation formula is formula 1:

[0063] CM = IUPAC mass * (14 / 14.01565) Formula 1

[0064] IUPAC mass—IUPAC quality

[0065] The Conversion Mass Defect (CMD) is defined as the difference between the Conversion mass and the nominal Conversion mass, as shown in formula 2:

[0066] CMD = Conversion mass - nominal Conversion mass Formula 2

[0067] The conversion formula of the calculated Reference Conversion mass defect (RCMD) is formula 3:

[0068] RCMD = (experimental CMD - reference CMD) / 0.0134 Formula 3

[0069] Experimental CMD— experimental conversion mass deficit

[0070] Reference CMD— conversion mass deficit of the reference.

[0071] Different MS 1 CMD and RCMD parameters, with which glycerophospholipids, sphingolipids, sterol lipids, alkenyl lipids, glycolipids, polyketides, diglycerides (DAGs), monoglycerides (MAGs), and monoglycerides (TAGs) are excluded.

[0072] (3) MS 2 Product ion accurate mass identifies structure.

[0073] The biggest challenge for phytosteryl ester identification is the existence of isomers due to the difference in the number of C and the position of double bond in fatty acyl chain. Meanwhile, due to the similar polarity of most phytosteryl esters, chromatographic overlap causes interference between isomer fragment ions. For example, in the neutral loss trigger prediction database, the molecular formula C 47 H 82 O2 phytosteryl ester, the neutral loss trigger prediction database results in 17 candidate isomers (Table 1). The traditional unknown identification technique relies on comparing the mass spectrum of the test substance with the known mass spectrum to obtain a matching score, and arranging multiple candidate compound structures with different scores according to the matching score, which requires manual judgment of the compliance of the results. Due to chromatographic overlap causing interference between phytosteryl ester isomer fragment ions, it is almost impossible to identify 17 candidate isomers using traditional methods.

[0074] In the molecular formula C 47 H 82 O2 phytosteryl ester MS 2 spectrum (Figure 2), by ion assignment, it can be seen that MS 2 fragment ion [M+NH4-NH3-R 18:1 CO2H] + (M / Z, 397.38303) is the neutral loss of fatty acyl chain O (R 18:1 ). Here, unlike the traditional identification method, which requires a highly consistent mass spectrum, only sufficient collision energy (CE) is needed to produce MS 2 fragment ions with a relative abundance greater than a threshold value, which can remove some false candidate isomers. Therefore, MS 2The fragment ion mass spectrum can only remove part of the false candidate isomers, i.e. phytosterol esters without any fatty acyl chain "O" substructure. Two candidate isomers (Table 3) were obtained. Among the two candidate isomers, the phytosterol parent nucleus β-sitosterol and sitosterol Figure 4a , Figure 4b ) are two cis-trans isomers, which need to be identified by other methods.

[0075] Table 3: Molecular formula C 47 H 82 O2and the fragment ion (m / z, 397.38297 Da) of the parent ion (m / z, 696.66531 Da) of the phytosterol ester obtained the structural isomers of m / z.

[0076]

[0077] Example 5: Identification of phytosterol esters in corn germ oil and rapeseed oil.

[0078] (1) Oil extraction.

[0079] Weigh 20 g of corn germ or rapeseed into a filter paper cylinder, put the filter paper cylinder into a glass bottle with a plug, pour 30-60 boiling range petroleum ether to cover the filter paper cylinder, soak for 8 h; pour the soaking liquid and filter paper cylinder into the extraction flask of a serpentine Soxhlet extraction device; connect the extraction tube and condenser tube, turn on the condenser water, set the temperature of the constant temperature water bath to 50°C, extract for 8 h, collect the petroleum ether oil mixture solution in the extraction flask; pour the mixture solution into a rotary flask, set the water bath temperature of the rotary evaporator to 50°C, rotary evaporate until the solvent in the collection bottle does not increase, and the rotary flask is the corn germ oil or rapeseed oil obtained by extraction.

[0080] (2) Sample pretreatment.

[0081] Accurately take 500 μL of corn germ oil or rapeseed oil sample into a 10 mL centrifuge tube with a plug, add 2.2 mL of methanol and mix well, then add 4.4 mL of chloroform and vortex for 5 min, ultrasonic extraction at 30°C for 30 min, add 1.65 mL of ultrapure water, vortex for 10 min, and centrifuge at 3500 r / min for 10 min to separate the layers. Take 1.0 mL of the lower liquid, filter through a 0.22 μm organic filter membrane into a sample bottle, and wait for UHPLC-HRMS analysis.

[0082] (3) Identification of phytosterol esters in corn germ oil or rapeseed oil.

[0083] The pretreated sample was subjected to data acquisition on a UHPLC-Orbitrap Fusion high-resolution mass spectrometer. After the raw data was collected by data-dependent scanning (DDA) mode, the raw data was converted into mzXML format data by MM File software or similar software platform. MS n The mass spectrometry ion tree information includes: MS 1 Mass spectrometry information and MS 2 Fragment ion mass spectrometry information. MS1 accurate mass number and MS 1 Mass loss filtering obtains unique element composition as a candidate; MS 2 Accurate mass number and exclusion strategy identify the unique structure of phytosterol ester candidate. All the identification results are summarized into a list to obtain the identification results of phytosterol esters in corn germ oil or rapeseed oil (see Table 4).

[0084] Table 4: Relative content of phytosterol esters in corn germ oil or rapeseed oil (%).

[0085]

[0086] Finally, it should be noted that the above only describes the preferred embodiments of the present application and is not intended to limit the present application. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent replacements to some technical features. Any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for constructing and identifying a database for predicting the neutral loss of phytosteryl esters in vegetable oils, characterized in that, It comprises the following steps: S1, according to the composition mechanism of the molecular structure of phytosterol ester, constructing the simulation structure of phytosterol ester; S2, obtaining the training set phytosterol ester mass spectrum cracking signal by mass spectrum detection scanning, deciphering the phytosterol ester mass spectrum cracking rule; S3, constructing the phytosterol ester mass spectrum simulation neutral loss trigger prediction database; S4, when the CID filter identifies the neutral loss mass in the acyl chain of the detection sample, triggering the neutral loss scanning, and identifying the non-target phytosterol ester in the vegetable oil.

2. The method for constructing and identifying a prediction database for neutral loss of phytosterol esters in vegetable oils according to claim 1, characterized in that, The step S1 of constructing the simulation structure of phytosterol ester comprises the following steps: S11, extracting the sterol nucleus information of phytosterol ester and the molecular element composition information of fatty acid acyl chain; S12, using random one-to-one combination to construct the simulation structure of phytosterol ester, the number of phytosterol ester simulation structure constructed = the number of phytosterol ester sterol nucleus information × the number of fatty acid acyl chain molecular element composition information.

3. The method for constructing and identifying a prediction database for neutral loss of phytosterol esters in vegetable oils according to claim 1, characterized in that, The step S2 of deciphering the phytosterol ester mass spectrum cracking rule comprises the following steps: S21, using liquid chromatography-mass spectrometry to collect the known phytosterol ester mass spectrum cracking signal: first, detection scanning, structure analysis by full scan of primary mass spectrometry and data-dependent acquisition; then, composite scanning, three kinds of composite acquisition modes of full scan of primary mass spectrometry, data-dependent acquisition and neutral loss trigger scanning are used to realize the enhancement ion scanning, and the high sensitivity secondary fragment full spectrum of low concentration target in complex matrix sample is obtained, that is, the original mass spectrum data; S22. In positive ion scanning mode, all phytosterol esters produce [M+NH4] in primary mass spectrometry. + Or [M+H] + The precursor ion breaks down, and the ester bond is broken. The precursor ion neutrally loses the fatty acid acyl chain, producing [M+NH4-NH3-Acid]. + The corresponding characteristic structural fragments of sterols were identified, thereby deciphering the mass spectrometry fragmentation pattern of phytosterol esters.

4. The method of claim 3, wherein the plant oil phytosteryl ester neutral loss trigger prediction database is constructed and identified by, The step S3 of constructing the phytosterol ester mass spectrum simulation neutral loss trigger prediction database comprises the following steps: S31, constructing the neutral loss trigger acquisition database: using the way of step S21 to collect the original mass spectrum data, and converting the original mass spectrum data into mzXML format mode data; S32, constructing the neutral loss trigger identification database: randomly combining the phytosterol nucleus and the fatty acid acyl chain one by one to combine all the AB type phytosterol esters, and then calculating the accurate mass-to-charge ratio of the primary parent ion and the characteristic fragment, the neutral loss mass, and establishing the neutral loss trigger identification database containing the chemical formula of phytosterol ester, the theoretical molecular weight, the addition mode, the accurate mass number of the parent ion, the neutral loss mass, and the accurate mass number of the characteristic fragment.

5. The method for constructing and identifying a prediction database for neutral loss of phytosterol esters in vegetable oils according to claim 4, characterized in that, The step S4 of identifying the non-target phytosterol ester in the vegetable oil comprises the following steps: S41, extracting MS from mzXML format mode data n Mass spectrum accurate mass number root, including MS 1 Mass spectrum and fragment ion mass spectrum, and neutral loss accurate mass number S42, from the MS n Mass spectrometry exact mass number of the MS 1 Characteristic ion exact mass number and characteristic fragment ion exact mass number; S43, using MS 1 Exact mass number and MS 1 Mass defect filtering takes unique elemental composition as candidate; set mass tolerance ≤ set threshold and reference mass defect; S44, by MS 2 The accurate mass number and neutral loss accurate mass number exclusion strategy identifies the unique structure of the phytosteryl ester candidate; specifically as follows: after the fragmentation of the phytosteryl ester structure, the phytosteryl ester structure is identified through neutral loss triggering identification, post-ordering prediction of fragment ions, continuous filtering of predicted structures, and further identification of the phytosteryl ester structure through filtering of predicted fragment ions in the positive ion mode. S45, all the identification results are summarized as a list to export the identification results.

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