Method for distinguishing different processed fruit and vegetable products based on quasi-targeted peptiomics

Abstract

The invention discloses a method for distinguishing different processed fruit and vegetable products based on quasi-targeted peptiomics, and relates to the technical field of analytical chemistry. The specific method for peptide analysis is constructed, the scheme is high in automation degree, batch ion pair extraction of peptide is achieved through an algorithm, and the response of quantitative ion pairs can be improved. Besides, by reducing the number of times of repeatedly reading the original data, the analysis time is greatly shortened, and precious analysis time is saved for scientific researchers. Based on the method provided by the invention, peptides in fruits and vegetables and products (especially orange juice) of the fruits and vegetables processed in different ways can be determined, and the fruits and vegetables processed in different ways and the products of the fruits and vegetables processed in different ways can be distinguished.

Description

Technical Field

[0001] The present invention relates to the technical field of analytical chemistry, and in particular to a method for distinguishing different processed fruit and vegetable products based on pseudo-targeted peptidomics. Background Art

[0002] Metabolomics focuses on the measurement of low-molecular-weight metabolites in biological systems. Liquid chromatography-mass spectrometry (LC-MS) is a key technology for metabolite analysis, with high sensitivity and strong identification capabilities. There are two main approaches to metabolomics: non-targeted and targeted strategies. Non-targeted LC-MS metabolomics uses high-resolution mass spectrometry, which helps to comprehensively analyze the metabolome and discover unknown compounds, but it has high complexity, poor reproducibility, and limited detection and quantification range. In contrast, targeted metabolomics is often used to verify and accurately quantify specific metabolites, and the multiple reaction monitoring (MRM) method of triple quadrupole mass spectrometry is often used, which has high sensitivity and specificity. In recent years, quasi-targeted methods have combined the advantages of non-targeted and targeted methods, using extensive metabolite data from non-targeted analysis and using dynamic MRM for detection to ensure data quality. Peptidomics is an important branch of metabolomics, and the quasi-targeted peptidomics workflow helps to discover and quantify peptides, but there are few related studies. In 2023, Huang et al. developed a quasi-targeted peptidomics analysis process based on the self-developed Pep-MRMer, which was applied to peptide analysis in animal drugs. However, the software is not open source and is based on C++, which poses obstacles to its use, and its applicability to fruit and vegetable samples is unclear.

[0003] Peptides have attracted much attention for their health benefits, such as anti-osteoporosis, anti-hypertension, antioxidant and antibacterial, especially in terrestrial and aquatic animals and plant foods such as soybeans. With the advancement of analytical technology, peptides in fruits and vegetables have also gradually attracted attention. In 2020, 7 tripeptides were detected in orange juice by combining non-targeted metabolomics and targeted identification. In 2022, high-throughput screening discovered the tripeptide Arg-Thr-Asp in lemons and lemon juice. In 2023, the tripeptide Glu-Val-Phe can be used as a potential marker for monitoring the storage of prickly grape juice. In 2024, four peptides, Val-Val, Glu-Leu, Gly-Tyr and Asp-Phe, were found in dried lilies, and the content of these peptides in hot air-dried dried lilies was higher than that in vacuum freeze-dried lilies.

[0004] Although the prior art CN201810584238.1 discloses an ion pair selection method for quasi-targeted metabolomics analysis, the object of this method is metabolites. When analyzing peptide substances, there are problems such as low response of the selected quantitative ion pairs and a long ion pair selection process.

[0005] In view of this, the present invention is proposed. Summary of the invention

[0006] The object of the present invention is to provide a method for differentiating different processed fruit and vegetable products based on pseudo-targeted peptidomics, so as to improve the response of quantitative ion pairs and shorten the time-consuming.

[0007] The present invention is implemented as follows:

[0008] The present invention provides a method for differentiating different processed fruit and vegetable products based on pseudo-targeted peptidomics, which includes:

[0009] S1: Construct a data acquisition method for ultra-high performance liquid chromatography-triple quadrupole tandem mass spectrometry (UPLC-QQQ MS) based on a quality control product;

[0010] The quality control product is prepared in the following manner: equal amounts of fruits and vegetables and their products processed in different ways are mixed;

[0011] The UPLC-QQQ MS data acquisition method is constructed by the following method: the quality control product is subjected to UHPLC-QTOF MS data acquisition at different collision energies; the peptides in the acquisition results are annotated based on a database; based on the peptide annotation results, the mass-to-charge ratio, retention time, abundance information at different collision energies, and the original UHPLC-QTOF MS data of the target peptides in the quality control product are obtained, and the optimal ion pairs and corresponding collision energies of the target peptides are automatically generated by code;

[0012] The method for obtaining the optimal ion pairs is as follows: (1) The minimum absolute m / z difference between the parent ion and the daughter ion is set to 2 Da to eliminate isotope ions; (2) For ions with a charge number of 1, the m / z of the daughter ion with the strongest response is determined among the ions with an m / z less than that of the parent ion, and MS2 < MS1 is set; for ions with a charge number greater than 1, the m / z of the daughter ion with the strongest response is lower or higher than that of the parent ion, and MS2 > MS1 or MS2 < MS1 is set; (3) The optimal ion pair with the strongest response and the corresponding collision energy are obtained;

[0013] Then, the retention time of the extracted target peptides is corrected for use in UPLC-QQQ MS acquisition;

[0014] S2: Evaluate the methodology of the UPLC-QQQ MS data acquisition method constructed in S1, and screen out the data acquisition methods that meet the preset criteria. The data acquisition methods that meet the preset criteria include the following information: optimal ion pairs, retention time, and collision energy information;

[0015] S3: According to the UPLC-QQQ MS data acquisition method that meets the preset criteria constructed in S2, respectively perform data acquisition on the fruits and vegetables and their products processed in different ways to be tested;

[0016] S4: Perform multivariate model analysis on the data collected by S3, and distinguish fruits and vegetables and their products with different processing methods based on the results.

[0017] The present invention has the following beneficial effects:

[0018] The present invention constructs a specific method for peptide analysis, which has a high degree of automation and uses an algorithm to achieve batch ion pair extraction of peptides, which can improve the response of quantitative ion pairs. In addition, by reducing the number of repeated readings of raw data, the analysis time is greatly shortened, saving valuable analysis time for scientific researchers. Based on the method provided by the present invention, peptides in fruits and vegetables and their products (especially orange juice) with different processing methods can be measured, and fruits and vegetables and their products with different processing methods can be distinguished. Compared with conventional non-targeted peptide analysis methods, the pseudo-targeted peptide analysis method can more accurately distinguish fruits and vegetables and their products with different processing methods, which is conducive to accurate analysis of the processing methods of fruits and vegetables and their products in the market, and facilitates market research analysis and supervision and management. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 A flow chart of a method for distinguishing fruits and vegetables processed in different ways and their products based on pseudo-targeted peptidomics provided by the present invention;

[0021] Figure 2 The principal component analysis scores of orange juice processed by different methods are shown in Figure 2 based on the pseudo-targeted peptidomics method (a) and the non-targeted peptidomics method (b).

[0022] Figure 3 This is the principal component analysis score diagram for distinguishing commercially available orange juices with different processing methods based on the pseudo-targeted peptidomics method;

[0023] Figure 4 Time statistics for peptide ion pair generation for the original and modified codes;

[0024] Figure 5 EIC plots of six peptides (a, b, c, d, e, f) obtained under the original code (blue) and the modified code (green). DETAILED DESCRIPTION

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be described clearly and completely below. For those not specified in the embodiments, conventional conditions or conditions recommended by the manufacturer are followed. For reagents or instruments not specified by the manufacturer, they are all conventional products that can be obtained through commercial purchase.

[0026] In a first aspect, the present invention provides a method for distinguishing different processed fruit and vegetable products based on pseudo-targeted peptidomics, which includes:

[0027] S1: Construct a data acquisition method for ultra-high performance liquid chromatography-triple quadrupole tandem mass spectrometry (UPLC-QQQ MS) based on quality control products;

[0028] The quality control products are prepared as follows: Equal amounts of fruits, vegetables, and their products processed in different ways are mixed;

[0029] The UPLC-QQQ MS data acquisition method is constructed by the following method: The quality control products are subjected to UHPLC-QTOF MS data acquisition at different collision energies; peptide annotation is performed on the acquisition results based on a database; based on the peptide annotation results, the mass-to-charge ratio, retention time, abundance information at different collision energies, and the original UHPLC-QTOF MS data of the target peptides in the quality control products are obtained, and the optimal ion pairs and corresponding collision energies of the target peptides are automatically generated by code;

[0030] The method for obtaining the optimal ion pairs is as follows: (1) Set the minimum absolute m / z difference between the parent ion and the daughter ion to 2 Da to eliminate isotope ions; (2) For ions with a charge number of 1, the m / z of the daughter ion with the strongest response is determined among the ions with an m / z less than that of the parent ion, and set MS2 < MS1; for ions with a charge number greater than 1, the m / z of the daughter ion with the strongest response is lower or higher than that of the parent ion, and set MS2 > MS1 or MS2 < MS1; (3) Obtain the optimal ion pair with the strongest response and the corresponding collision energy;

[0031] Then, the retention time of the extracted target peptides is corrected for use in UPLC-QQQ MS acquisition;

[0032] S2: Conduct a methodological evaluation of the UPLC-QQQ MS data acquisition method constructed in S1, and screen out the data acquisition methods that meet the preset criteria. The data acquisition methods that meet the preset criteria include the following information: optimal ion pairs, retention time, and collision energy information;

[0033] S3: According to the UPLC-QQQ MS data acquisition method that meets the preset criteria constructed in S2, respectively perform data acquisition on the fruits, vegetables, and their products processed in different ways to be tested;

[0034] S4: Perform multivariate model analysis on the data collected by S3, and distinguish fruits and vegetables and their products with different processing methods based on the results.

[0035] In order to solve the problems of low response of quantitative ion pairs and time-consuming and laborious manual determination of ion pairs, the present invention modifies the key code so that the data acquisition method is applicable to peptides, adds data acquisition in multiple charge mode (mass-to-charge ratio, retention time, charge number, abundance information), considers the situation that the m / z of the daughter ion of the peptide is greater than the m / z of the parent ion under multiple charges, thereby increasing the types and number of ion pairs that can be detected, and further increasing the possibility of screening out the characteristic ion pairs with the largest response, obtaining the optimal ion pairs that can characterize the target peptide with high accuracy, and improving the response of the quantitative ion pairs.

[0036] In addition, the data acquisition method provided by the present invention has a high degree of automation, and does not need to manually determine the optimal ion pair, thus freeing up manpower. It also reduces the number of times the raw data is read and shortens the analysis time.

[0037] In a preferred embodiment of the present invention, the retention time is corrected by the following method: first obtain the retention time of the peptide standard on UHPLC-QTOF MS and UPLC-QQQ MS, use the retention time of the confirmed peptide (standard) in the sample on UHPLC-QTOF MS and UHPLC-QQQ MS, and the retention time of the target peptide on UHPLC-QTOF MS, and output the retention time of the target peptide on UHPLC-QQQ MS through a code; the formula involved in the retention time code is as follows.

[0038] Assuming peptide "i" elutes between Peptide Standard 1 and Peptide Standard 2:

[0039]

[0040] in the formula Refers to the retention time of peptide standard 1 in UHPLC-QTOF MS, Refers to the retention time of peptide standard 1 on UHPLC-QQQ MS; Refers to the retention time of peptide standard 2 in UHPLC-QTOFMS: Refers to the retention time of peptide standard 2 on UHPLC-QQQ MS; Refers to: retention time of target peptide “i” in UHPLC-QTOF MS; Refers to: retention time of target peptide “i” in UHPLC-QQQ MS;

[0041] Assuming that the retention time of peptide "j" is smaller than the first eluting peptide standard, or larger than the last eluting peptide standard, only one peptide standard is used in the retention time calibration:

[0042]

[0043] Refers to the retention time of a peptide standard closest to the target peptide in UHPLC-QTOF MS; Refers to the retention time of a peptide standard closest to the target peptide in UPLC-QQQ MS; Refers to the retention time of peptide "j" in UHPLC-QTOF MS.

[0044] In order to make the retention time of the target peptide of UHPLC-QTOF MS match the UPLC-QQQ MS data acquisition, the inventors provided a retention time correction model to avoid the problem of differences in peptide retention time due to differences in the chromatographic systems between UHPLC-QTOF MS and UPLC-QQQ MS. The provision of the retention time correction model helps to improve the accuracy of data acquisition.

[0045] In a preferred embodiment of the present invention, the preset standard refers to: repeatability and linearity of the data acquisition method.

[0046] The repeatability and linearity of the data acquisition method were used to evaluate the quantitative performance of the method of pseudo-targeted peptidomics for distinguishing fruits, vegetables and their products with different processing methods. The repeatability and linearity were maintained at a good level, indicating that this method has a good quantitative effect on batch peptides (or targeted peptides).

[0047] In a preferred embodiment of the present invention, the repeatability of the data collection method means that at least 90% and 80% of the peptides within a day and between days, respectively, have a relative standard deviation (RSD) of ≤30%.

[0048] In a preferred embodiment of the present invention, the linearity of the data acquisition method means that at least 60% of the ion pairs have an r 2 ≥0.95, more than 80% of ion pairs have r 2 ≥0.8. Meeting the above requirements means meeting the requirements of the method for distinguishing fruits and vegetables with different processing methods and their products based on pseudo-targeted peptidomics.

[0049] If the UPLC-QQQ MS data acquisition method constructed by S1 does not meet the repeatability requirements, there may be problems such as MS source contamination, and it is necessary to clean the MS source and continue to test the repeatability; if the UPLC-QQQ MS data acquisition method constructed by S1 does not meet the linearity requirements, there may be errors in sample dilution and sample preparation, or the dilution gradient is not suitable, and it is necessary to reconfigure or select a suitable dilution gradient and continue to test the linearity.

[0050] In a preferred embodiment of the present invention, the qualitative analysis of peptides in step S1 refers to annotating the sequence, structure, and charge number of the collected data based on an existing protein database, such as using the proteomics research software ProteinPilot to annotate the sequence, structure, and charge number of the collected data.

[0051] In a preferred embodiment of the present invention, S3 includes respectively collecting UPLC-QQQ MS data for multiple fruits, vegetables and their products with known processing methods; in step S4, the fruits, vegetables and their products with different processing methods are distinguished according to the results through multivariate model analysis.

[0052] Multivariate model analysis methods include but are not limited to: multivariate analysis of variance (MANOVA), principal component analysis (PCA), factor analysis, canonical correlation analysis, cluster analysis, discriminant analysis, and multidimensional scaling analysis.

[0053] Preferably, PCA is used for multivariate model analysis.

[0054] In a preferred embodiment of the present invention, the fruits and vegetables and their products are canned fruits and vegetables, candied fruits and vegetables, dried fruits and vegetables, quick-frozen fruits and vegetables, fruit and vegetable juices, fruit wines or pickled vegetables.

[0055] Fruits and vegetables are a general term for fruits and vegetables, including: fruits, vegetables, root vegetables, leafy vegetables, edible fungi, and legumes;

[0056] Fruits include apples, bananas, pears, oranges, lemons, strawberries, citrus, pineapples, mangoes, lychees, longans, kiwis, grapes, apricots, plums, peaches, hawthorns, pomegranates, cherries, etc. Vegetables include cabbage, carrots, cucumbers, tomatoes, beans, cauliflower, pumpkins, zucchini, celery, broccoli, cabbage, etc. Root vegetables include potatoes, sweet potatoes, yams, carrots, etc. Leafy vegetables include spinach, Chinese cabbage, lettuce, mustard, etc. Edible fungi include mushrooms, bamboo fungus, white fungus, etc. Legumes include soybeans, mung beans, peas, lentils, etc.

[0057] Dried fruit and vegetable products include but are not limited to products obtained by natural air drying and oven drying of fruits and vegetables.

[0058] Fruit and vegetable juices are selected from, for example, orange juice, grape juice, peach juice, apple juice, watermelon juice, etc.

[0059] In a preferred embodiment of the present invention, when preparing quality control products, fruits and vegetables processed in different ways and their products are pre-treated to obtain samples for liquid chromatography.

[0060] For dried fruit and vegetable products, solvents are needed to dissolve or extract the dried fruit and vegetable products.

[0061] Preferably, when preparing the quality control product, the fruits and vegetables and their products processed in different ways are centrifuged respectively, the supernatant is filtered, and the filtrate is collected and then blended.

[0062] Preferably, the centrifugation condition is 6000g-10000g for 5-10min;

[0063] Preferably, the filtration is performed using a 0.22 μm filter membrane. Insoluble matter in the liquid is removed by centrifugation and filtration.

[0064] In a preferred embodiment of the present invention, the collision energy is 20 eV to 50 eV.

[0065] The features and performance of the present invention are further described in detail below in conjunction with the embodiments.

[0066] Example 1

[0067] This example provides a method for data acquisition based on quality control products using ultra-high performance liquid chromatography-triple quadrupole tandem mass spectrometry (UPLC-QQQ MS). Figure 1 shown.

[0068] (1) Freshly squeezed unpasteurized orange juice was collected directly from the factory and was denoted as C. Ultrahigh pressure (pressure gradients were set to 100, 250, 400, and 550 MPa, denoted as H-100, H-250, H-400, and H-550, respectively) and pasteurization (50, 65, 80, and 95°C, denoted as P-50°C, P-65°C, P-80°C, and P-95°C, respectively) were performed in the laboratory. The orange juices with different treatments were centrifuged at 8000 g for 10 min and filtered with a 0.22 μm filter membrane. As the test samples, equal amounts of the test orange juice samples with different processing methods were taken and mixed as quality control (QC) samples.

[0069] (2) QC samples were subjected to UHPLC-QTOF mass spectrometry acquisition at different collision energies (20, 35, 50 eV).

[0070] The autosampler was maintained at 4°C, and the injection volume and flow rate were 2 μL and 0.300 mL / min, respectively. The chromatographic column (ACQUITYUPLC HSS T3, 1.8 μm, 2.1×100 mm) was maintained at 40°C. The mobile phases A and B were 0.2% formic acid in water and acetonitrile, respectively. The solvent gradient was as follows: 0-11.50 min, 5%-30% B; 11.50-11.51 min, 30-100% B; 11.51-15.00 min, 100% B; 15.00-15.01 min, 100%-5% B; 15.01-18.00 min, 5% B. MS analysis was performed in positive ion mode using the DuoSpray ion source of a TripleTOF 6600 mass spectrometer with the following parameters: Ion Source Gas 1, 50 (arbitrary unit); Ion Source Gas 2, 50 (arbitrary unit); Curtain Gas, 25 (arbitrary unit); Temperature, 500°C; IonSpray Voltage Floating, 5.5 kV; and Declustering potential, 60 V.

[0071] IDA mode simultaneously acquired TOF MS scans (m / z 100-1000) and product ion scans (m / z 50-1000) for each sample. IDA (cycle time 545 ms) consisted of a TOF MS scan (accumulation time 50 ms; collision energy 10 eV) and 15 product ion scans (accumulation time 30 ms; collision energy set to 20 eV, 35 eV, or 50 eV), and dynamic background subtraction was performed.

[0072] (3) Perform qualitative analysis of peptides on the collected data. Based on the results of the qualitative analysis of peptides, obtain the mass-to-charge ratio, retention time, charge number, abundance information at different collision energies of the target peptide in the quality control product, as well as the original UHPLC-QTOF MS data. Convert the original UHPLC-QTOF MS data into mgf files.

[0073] (4) Automatically generate the optimal ion pairs of the target peptide and the corresponding collision energy information using an algorithm;

[0074] The method for obtaining the optimal ion pairs is as follows:

[0075] a. Set the minimum absolute m / z difference between the parent ion and the daughter ion to 2 Da; b. For ions with a charge number of 1, determine the m / z of the daughter ion with the strongest response among the ions with m / z less than that of the parent ion, and set MS2 < MS1; for ions with a charge number greater than 1, the m / z of the daughter ion with the strongest response is lower or higher than that of the parent ion, and set MS2 > MS1 or MS2 < MS1; c. Obtain the optimal ion pair with the strongest response and the corresponding collision energy.

[0076] Then correct the retention time of the extracted target peptide for UPLC-QQQ MS acquisition;

[0077] Specifically: Synthesize peptide standard substances as shown below.

[0078] DHQVPSPP, EKLPG, AAPVQ, VTVPK, ASMEN, GAVEPAK, ASGVPY, VTKPDVK, SIFGK, VWDPFR, SPLF.

[0079] Obtain the retention times of the peptide standard substances on UHPLC-QTOF MS and UPLC-QQQ MS. Using the retention times of the substances (standards) that have been confirmed in the sample on UHPLC-QTOF MS and UHPLC-QQQ MS, as well as the retention time of the target peptide on UHPLC-QTOF MS, output the retention time of the target peptide on UPLC-QQQ MS through code; the formula involved in the retention time code is as follows:

[0080] Assume that peptide "i" elutes between peptide standard substance 1 and standard substance 2:

[0081]

[0082] In the formula refers to the retention time of peptide standard substance 1 on UHPLC-QTOF MS, refers to the retention time of peptide standard substance 1 on UPLC-QQQ MS; Refers to the retention time of peptide standard 2 in UHPLC-QTOFMS: Refers to the retention time of peptide standard 2 on UHPLC-QQQ MS; Refers to: retention time of target peptide “i” in UHPLC-QTOF MS; Refers to: retention time of target peptide “i” in UHPLC-QQQ MS;

[0083] Assuming that the retention time of peptide "j" is smaller than the first eluting peptide standard, or larger than the last eluting peptide standard, only one peptide standard is used in the retention time calibration:

[0084]

[0085] Refers to: The retention time of a peptide standard closest to the target peptide in UHPLC-QTOF MS; Refers to the retention time of a peptide standard closest to the target peptide in UPLC-QQQ MS; Refers to the retention time of peptide "j" in UHPLC-QTOF MS.

[0086] (5) In order to verify whether the constructed UPLC-QQQ MS acquisition method meets the standards, the following methodological evaluation was performed:

[0087] Through methodological evaluation, data acquisition methods that meet the preset standards (linearity, repeatability) are screened out. Data acquisition methods that meet the preset standards include the following information: optimal ion pair, retention time, and collision energy information.

[0088] The constructed acquisition method was evaluated for intra-day and inter-day repeatability and linearity. In the intra-day measurement, 142, 144, and 138 ion pairs met the standard of RSD ≤ 30%. The inter-day results showed that 120 ion pairs met these standards. The linear analysis results showed that the r 2 ≥0.95, r for 141 ion pairs 2 ≥0.8, which all met the requirements for constructing pseudo-targeted peptidomics methods.

[0089] Example 2

[0090] This example provides a method for distinguishing orange juices processed in different ways based on targeted peptidomics.

[0091] Based on the UPLC-QQQ MS acquisition method constructed in Example 1, orange juices processed in different ways were measured.

[0092] The autosampler was maintained at 4°C, and the injection volume and flow rate were 2 μL and 0.300 mL / min, respectively. The chromatographic column (ACQUITYUPLC HSS T3, 1.8 μm, 2.1×100 mm) was maintained at 40°C. The mobile phases A and B were 0.2% formic acid in water and acetonitrile, respectively. The solvent gradient was as follows: 0-11.50 min, 5%-30% B; 11.50-11.51 min, 30-100% B; 11.51-15.00 min, 100% B; 15.00-15.01 min, 100%-5% B; 15.01-18.00 min, 5% B. MS analysis was performed in positive ion mode using the ion source Turbo Spray of a TripleQuad 4500 mass spectrometer, with the following parameters: Ion Source Gas 1, 50 (arbitrary unit); Ion Source Gas 2, 50 (arbitrary unit); Curtain Gas, 25 (arbitrary unit); Temperature, 500°C; Ion Spray Voltage, 5.5 kV; Collision Gas, 9 mbar; Declustering potential, 60 V; Entrance potential, 20 V. The mass spectrometry acquisition was set to MRM mode, and the optimized ion pairs and collision energy information are shown in the following table:

[0093]

[0094]

[0095]

[0096]

[0097] After counting the detected ion pairs at three collision energies (20, 35, and 50 eV) based on the code and screening out the optimal ion pairs and collision energies for each peptide, a pseudo-targeted method was established on UPLC-QQQ MS to detect the abundance of these target peptides in orange juices processed by different methods.

[0098] Compared with non-targeted peptidomics methods ( Figure 2 Compared with the targeted peptide omics method ( Figure 2 In the results of the targeted peptidomics, the increase in processing pressure and temperature led to greater differences from freshly squeezed unpasteurized orange juice. Orange juice processed at 250 MPa or pasteurized at 50 °C could be completely separated from freshly squeezed unpasteurized orange juice (C). Figure 2In contrast, the results of the non-targeted peptidomics approach showed that even in orange juice processed at 550 MPa, it was difficult to achieve complete separation from freshly squeezed unpasteurized orange juice (C). Figure 2 These results suggest that the targeted peptidomics approach can more accurately distinguish orange juices processed in different ways.

[0099] Example 3

[0100] This example provides a method for distinguishing commercially available orange juices processed in different ways based on targeted peptidomics.

[0101] The quantitative results of the pseudo-targeted peptidomics method in Example 2 were selected to distinguish orange juice processed with common process parameters on the market, namely, freshly squeezed unpasteurized orange juice, ultrahigh pressure treated orange juice (550 MPa), and pasteurized orange juice (95° C.).

[0102] The specific experimental method is as follows: Commercially available freshly squeezed unpasteurized orange juice, ultrahigh pressure treated orange juice (550 MPa), and pasteurized orange juice (95° C.) were subjected to PCA analysis according to the UPLC-QQQ MS acquisition method of Example 2 to obtain quantitative results.

[0103] The results showed that based on the pseudo-targeted peptidomics approach, good differentiation of orange juices processed by different methods can be achieved ( Figure 3 ), the cumulative contribution of PC1 and PC2 is as high as 84.7%.

[0104] Example 4

[0105] Time consuming experiment.

[0106] According to the original code, the mass-to-charge ratio, retention time, abundance information at different collision energies of the target peptide, and the original UHPLC-QTOF MS data (converted to mgf format) were imported. The time required for peptide ion pair generation was 310.4 s ( Figure 4 MRM_Ion_Pair_Finder in Example 2); according to the modified code of Example 2, the mass-to-charge ratio, retention time, abundance information at different collision energies of the target peptide, and the original UHPLC-QTOF MS data (converted to mgf format) were imported, and the time required for peptide ion pair generation was 50.3 s ( Figure 4 The analysis time was reduced by about 84%.

[0107] Example 5

[0108] Response testing experiments.

[0109] The responses of peptide substances were evaluated using the pseudo-targeted peptidomics methods established according to the original code and the modified code. Figure 5The EIC plots of 6 peptides (a FRLPDN, b DNNEPFDPFLS, c VAVAV, d VGADGGPEGR, eAEEATMSHQ, f SHEYEPS) obtained under the original code (blue) and the modified code (green) are shown. It is found that the abundance of peptides obtained by the modified code is significantly higher than that of the original code, and the linearity is better and the response effect is better.

[0110] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for distinguishing different processed fruit and vegetable products based on pseudo-targeted peptidomics, characterized in that: It includes: S1: Construct an ultra-high performance liquid chromatography-triple quadrupole tandem mass spectrometry (UPLC-QQQ MS) data acquisition method based on a quality control sample; The quality control sample is prepared in the following manner: Equal amounts of fruits, vegetables, and their products processed in different ways are mixed; The UPLC-QQQ MS data acquisition method is constructed by the following method: The quality control sample is subjected to UHPLC-QTOF MS data acquisition at different collision energies; Peptide annotation is performed on the acquisition results based on a database; Based on the peptide annotation results, the mass-to-charge ratio, retention time, abundance information at different collision energies, and the original UHPLC-QTOF MS data of the target peptides in the quality control sample are obtained, and the optimal ion pairs and corresponding collision energies of the target peptides are automatically generated using code; The method for obtaining the optimal ion pairs is as follows: (1) The minimum absolute m / z difference between the parent ion and the product ion is set to 2 Da; (2) For ions with a charge number of 1, the product ion m / z with the strongest response is determined among the ions with a m / z less than that of the parent ion, and MS2 < MS1 is set; For ions with a charge number greater than 1, the product ion m / z with the strongest response is lower or higher than that of the parent ion, and MS2 > MS1 or MS2 < MS1 is set; (3) The optimal ion pair with the strongest response and the corresponding collision energy are obtained; Then, the retention time of the extracted target peptides is corrected for use in UPLC-QQQ MS acquisition; S2: Conduct a methodological evaluation of the UPLC-QQQ MS data acquisition method constructed in S1, and screen out the data acquisition methods that meet the preset criteria. The data acquisition methods that meet the preset criteria include the following information: Optimal ion pairs, retention time, and collision energy information; S3: According to the UPLC-QQQ MS data acquisition method that meets the preset criteria constructed in S2, perform data acquisition on fruits, vegetables, and their products processed in different ways to be measured respectively; S4: Conduct a multivariate model analysis on the data collected in S3, and distinguish fruits, vegetables, and their products processed in different ways according to the results.

2. The method for distinguishing fruits and vegetables processed in different ways and their products based on pseudo-targeted peptidomics according to claim 1, characterized in that: The retention time is corrected by the following method: First, obtain the retention times of peptide standards on UHPLC-QTOF MS and UPLC-QQQ MS, and use the retention times of the peptide standards that have been confirmed in the sample on UHPLC-QTOF MS and UPLC-QQQ MS, as well as the retention time of the target peptide on UHPLC-QTOF MS, to output the retention time of the target peptide on UPLC-QQQ MS through code; The formula involved in the retention time code is as follows: Assume that peptide "i" elutes between peptide standard 1 and peptide standard 2; in the formula Refers to the retention time of peptide standard 1 in UHPLC-QTOF MS, Refers to the retention time of peptide standard 1 on UPLC-QQQ MS; Refers to the retention time of peptide standard 2 in UHPLC-QTOF MS: Refers to the retention time of peptide standard 2 on UPLC-QQQ MS; Refers to: retention time of target peptide "i" in UHPLC-QTOF MS; Refers to: retention time of target peptide "i" in UPLC-QQQ MS; Assume that the retention time of peptide "j" is smaller than that of the first eluted peptide standard or larger than that of the last eluted peptide standard, then only one peptide standard is used in the retention time calibration; Refers to the retention time of a peptide standard closest to the target peptide in UHPLC-QTOF MS; Refers to the retention time of a peptide standard closest to the target peptide in UPLC-QQQ MS; Refers to the retention time of peptide "j" in UHPLC-QTOF MS.

3. The method for distinguishing fruits and vegetables processed in different ways and their products based on pseudo-targeted peptidomics according to claim 1, characterized in that: The preset criteria refer to the repeatability and linearity of the data acquisition method.

4. The method for distinguishing fruits and vegetables processed in different ways and their products based on pseudo-targeted peptidomics according to claim 3, characterized in that: The repeatability of the data acquisition method was defined as the relative standard deviation of at least 90% and 80% of the peptides within and between days, respectively, being ≤30%.

5. The method for distinguishing fruits and vegetables processed in different ways and their products based on pseudo-targeted peptidomics according to claim 3, characterized in that: The linearity of the data acquisition method is defined as: 2 ≥0.95, more than 80% of ion pairs have r 2 ≥0.

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6. The method for distinguishing fruits and vegetables processed in different ways and their products based on pseudo-targeted peptidomics according to claim 1, characterized in that: The qualitative analysis of peptides in step S1 refers to annotating the peptide sequences of the collected data based on an existing protein database.

7. The method for distinguishing fruits and vegetables processed in different ways and their products based on pseudo-targeted peptidomics according to claim 6, characterized in that: The step S3 includes respectively collecting UPLC-QQQMS data for fruits and vegetables and their products with multiple known processing methods; in step S4, the fruits and vegetables and their products with different processing methods are distinguished according to the results through multivariate model analysis.

8. The method for distinguishing fruits and vegetables processed in different ways and their products based on pseudo-targeted peptidomics according to claim 1, characterized in that: The fruits and vegetables and their products are canned fruits and vegetables, candied fruits and vegetables, dried fruits and vegetables, quick-frozen fruits and vegetables, fruit and vegetable juices, fruit wines or pickled vegetables.

9. The method for distinguishing fruits and vegetables processed in different ways and their products based on pseudo-targeted peptidomics according to claim 8, characterized in that: When preparing the quality control product, fruits and vegetables and their products processed in different ways are pre-treated to obtain samples for liquid chromatography; Preferably, when preparing the quality control product, the fruits and vegetables and their products processed in different ways are centrifuged respectively, the supernatant is filtered, and the filtrate is collected and then blended; Preferably, the centrifugation condition is 6000g-10000g for 5-10min; Preferably, the filtration is performed using a 0.22 μm filter membrane.

10. The method for distinguishing fruits and vegetables processed in different ways and their products based on pseudo-targeted peptidomics according to claim 1, characterized in that: The collision energy is 20 eV to 50 eV.

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