A method for the analysis of fluorofocus-enhanced perfluoro / polyfluoroalkyl compounds

Abstract

The present application relates to a kind of fluorine focus enhanced perfluoro / multi-fluoro alkyl compound analysis method, comprising the following steps: sample is carried out routine data-dependent data acquisition and peak extraction processing, generate basic peak table;Subsequently, fluorine characteristic data screening is carried out based on basic peak table information, constructs the fluorine characteristic static inclusion list based on mass loss for iterative mass spectrum acquisition;Fluorine focus data acquisition method is established based on fluorine characteristic static inclusion list, obtains the liquid chromatography-mass spectrometry data of fluorine focus enhancement by multiple iterative acquisition to the same sample;Finally, fluorine focus enhanced data is carried out non-target cleaning screening and qualitative analysis.Compared with prior art, the present application significantly improves the ability of obtaining tandem mass spectrometry information and identification coverage of trace, unknown PFAS in complex matrix by iterative focusing strategy, effectively solves the problem of low-abundance target compound omission caused by the limitation of the number of PFAS tandem mass spectrometry in single acquisition in traditional non-target screening method.

Description

Technical Field

[0001] This invention relates to mass spectrometry analysis methods for detecting environmental pollutants, and in particular to an analytical method for fluorine focusing-enhanced perfluorinated / polyfluoroalkyl compounds. Background Technology

[0002] Perfluorinated and polyfluoroalkyl substances (PFAS), known as permanent chemicals, are a class of man-made chemicals widely used due to their unique surfactant properties and high thermal and chemical stability. These compounds are difficult to degrade naturally and can migrate long distances through the atmosphere and waterways, polluting global water bodies, soil, and the food chain. Studies have shown that these substances have significant biotoxicity, damaging the immune, digestive, and reproductive systems, and posing a carcinogenic risk. Given their persistence and serious health threats, substances such as perfluorooctanoic acid (PFOA) and perfluorooctane sulfonic acid (PFOS) have been banned under the Stockholm Convention. However, with the banning of some perfluorinated compounds, some shorter-chain perfluorinated compounds and some polyfluoroalkyl substances are used as industrial substitutes, but their toxicity, bioaccumulation, and effects on the immune, digestive, and reproductive systems are still significant. Currently, commonly used methods for detecting perfluorinated and polyfluoroalkyl substances include liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS). LC-MS analysis mainly includes two categories: targeted analysis methods and non-targeted analysis methods. Targeted analysis methods typically use specific ion pairs of standards for quantitative analysis of target compounds, but the number of compounds that can be analyzed is limited. Non-targeted analysis, on the other hand, collects more mass spectrometry data, but also requires significant manual time to analyze the data.

[0003] For example, the existing technology CN120314469A is a targeted screening method for PFAS results. It often requires specific standard compounds and can only obtain data results for known compounds. Some compounds that do not have standards or that undergo partial transformation in the environment to generate polyfluorinated products are often ignored.

[0004] To address the challenge of identifying unknown PFAS, non-targeted screening strategies have emerged. These methods do not rely on standards, comprehensively acquiring data through high-resolution mass spectrometry and utilizing algorithms for data mining. For example, existing technology CN118837428A discloses a method for non-targeted identification of perfluorinated and polyfluoroalkyl compounds by generating molecular networks based on spectral similarity. This method constructs a molecular network by calculating the similarity between secondary mass spectra and identifies novel compounds with similar mass spectrometric behaviors within the network, starting from known PFAS seeds. However, its effectiveness suffers from a deep pre-requisite dependency and bottleneck: the success or failure of the entire identification process is highly dependent on the completeness of the secondary mass spectra obtained in the initial data acquisition step. In conventional non-targeted data-dependent acquisition modes, the mass spectrometer must select a limited number of targets for fragmentation from thousands of ions in a complex sample matrix. For potentially novel PFAS with extremely low concentrations, low ionization efficiency, or co-eluting with high-intensity matrix ions, their primary signals may not be selected for secondary fragmentation, resulting in the failure to generate secondary spectra suitable for subsequent similarity calculations and network construction.

[0005] Therefore, a key technical challenge currently facing non-targeted PFAS screening is how to overcome the randomness and limitations of conventional single data acquisition, significantly improve the ability and coverage of acquiring secondary mass spectrometry information of trace and unknown PFAS in complex environmental samples, and provide a complete and high-quality data foundation for subsequent high-throughput and high-confidence identification. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art by providing an analytical method for fluorine focusing enhanced per- and polyfluoroalkyl compounds (PFAS) (EFF-DAA), which significantly enhances the ability to acquire and identify trace and unknown per- and polyfluoroalkyl compounds in complex matrices using secondary mass spectrometry. This method effectively solves the problem of missing low-abundance PFAS compounds caused by the limited number of tandem mass spectrometers acquired in a single acquisition in traditional non-targeted screening methods.

[0007] The objective of this invention can be achieved through the following technical solutions: This invention provides an analytical method for fluorine focusing-enhanced perfluorinated / polyfluoroalkyl compounds, comprising the following steps: S1. Sample pretreatment is performed, and preliminary data acquisition is carried out on the pretreated sample in positive / negative ion mode using liquid chromatography-high resolution mass spectrometry to obtain preliminary liquid chromatography-mass spectrometry data. S2. Process the preliminary liquid chromatography-mass data, perform peak extraction, and generate a peak table containing primary mass-to-charge ratio, retention time, and secondary fragment ion information; S3. Perform fluorine feature data filtering processing on the information in the peak table to generate a static fluorine feature inclusion list; S4. Based on the static inclusion list of fluorine characteristics, establish a fluorine focusing data acquisition method, and perform iterative data acquisition on the sample to obtain fluorine focusing enhanced liquid chromatography-mass spectrometry data; S5. For the fluorine-focused enhanced liquid chromatography-mass spectrometry data, non-targeted data cleaning and screening are performed using the fluorine feature focusing analysis method to obtain a list of fluorine feature candidate molecules. S6. For the list of fluorine-featured candidate molecules, perform non-targeted qualitative analysis using the fluorine feature focusing data analysis method. The non-targeted qualitative analysis process is a qualitative analysis based on database retrieval.

[0008] Furthermore, in S2, the peak extraction process is implemented using Python. The specific peak extraction process includes: The preliminary liquid chromatography-mass spectrometry (LC-MS) data are processed to extract chromatographic peaks from the original mass spectra and correlate them with their corresponding primary mass-to-charge ratios, retention times, and the mass-to-charge ratios and intensities of secondary fragment ions that are triggered for acquisition at those retention times, thereby generating a structured basic peak table containing primary and secondary information.

[0009] In S2, the peak extraction process is implemented using Python, including: Read the original mass spectrometry file, reconstruct the total ion current chromatogram through continuous scanning of the primary mass spectrometer, perform chromatographic peak detection and integration in the retention time dimension, and associate the secondary mass spectrometry fragment information triggered by each peak. Finally, the extracted primary mass-to-charge ratio, retention time, secondary fragment mass-to-charge ratio and intensity information are structured and stored as a pandasDataFrame or a standard format table file.

[0010] Furthermore, in S3, the specific process of screening and processing fluorine characteristic data includes: Based on the mass defect value of the first-order mass-to-charge ratio, and based on the fluorine characteristic diagnostic ions and neutral loss fragments of the second-order fragment ions, a static inclusion list of fluorine characteristics is generated.

[0011] Furthermore, in S3, the mass defect value screening based on the first-order mass-to-charge ratio includes: Calculate the mass deficit value for each first-order mass-to-charge ratio in the peak table, where the mass deficit value is equal to the difference between the exact mass and the nominal mass. The mass-to-charge ratio of the first-order fluorine feature with a mass defect value between -0.2 and 0.1 and its corresponding retention time information are included in the static inclusion list of the fluorine feature.

[0012] Furthermore, in S4, the specific process of establishing the fluorine focusing data acquisition method is as follows: In the original data acquisition method of the liquid chromatography-high resolution mass spectrometry system, the static inclusion function is invoked, and the static inclusion list of fluorine characteristics generated in S3 is used as the acquisition list. The secondary mass spectrometry information of other compounds is no longer acquired, thus constructing the fluorine focusing data acquisition method for prioritizing the acquisition of fluorine-containing characteristic compounds.

[0013] Furthermore, in S5, the process of cleaning and screening non-targeted data using the fluorine feature focusing analysis method includes: The secondary spectra in the fluorine-focused enhanced liquid chromatography-mass spectra are processed to extract a list containing the precursor ion mass-to-charge ratio, precursor ion intensity, retention time, fragment ion mass-to-charge ratio, and fragment ion peak intensity. The secondary information in the list is used to calculate fluorine characteristic diagnostic ions and neutral loss fragments, and homologue group calculations are performed to classify the compounds. Peak extraction was performed on the list of fluorine characteristic compounds obtained after screening and classification to obtain the retention time and peak area information of each candidate compound.

[0014] Furthermore, the specific method for calculating the homologous group is as follows: The calculation is performed using the formula group = (m / z)% R, where m / z is the mass-to-charge ratio of the parent ion, % is the modulo operator, and R is the mass of the repeating unit of the homologue. The calculated group values ​​are used to classify and assign fluorine-featured candidate molecules with the same or similar repeating structural units; The group value is used to classify different types of perfluorinated compounds (e.g., perfluorocarboxylic acids, perfluorosulfonic acids and their hydrogen-substituted homologues).

[0015] Furthermore, in S6, the database program includes: Experimental database: The experimental secondary mass spectra of the fluorine characteristic candidate molecule list obtained in S5 are matched and scored with local or online experimental secondary spectrum libraries (such as Massbank, HMDB, mzCloud, etc.) to determine the most likely structure.

[0016] Theoretical Database: Based on the list of compounds obtained from the open-source database that conform to the structural characteristics of perfluorinated and polyfluorinated compounds and include information such as molecular formula, InChI, and SMILES, the theoretical fragmentation fragments of candidate compounds are matched and scored with the experimental secondary mass spectra of the fluorine characteristic candidate molecule list obtained in S5 using MetFrag, CFM-ID or other theoretical fragmentation simulation algorithms to determine the most likely structure.

[0017] Suspect Screening List: Compound information is obtained from open-source databases such as PubChem and Chemspider, and entries that meet the structural characteristics of perfluorinated and polyfluorinated compounds are selected to form a dedicated suspect screening list containing molecular formula, monoisotope mass, InChI, and SMILES information. Candidate substances are matched based on the monoisotope mass of the suspect screening list, that is, the monoisotope mass of the dedicated suspect screening list is matched with the list of fluorine characteristic candidate molecules obtained in S5.

[0018] Compared with the prior art, the present invention has the following beneficial effects: 1) Breadth and depth of data collection: This invention achieves more comprehensive coverage of perfluorinated compound characteristic information.

[0019] Existing technologies (such as traditional targeted acquisition and analysis methods) can typically only detect a limited number of known perfluorinated compounds, heavily rely on standards, and are unable to handle the diverse and ever-emerging novel and unknown perfluorinated compounds. This invention optimizes the data acquisition method by integrating a fluorine focusing algorithm with mass spectrometry, significantly improving the instrument's data acquisition capabilities for characteristic perfluorinated compounds. It can also effectively capture characteristic fragment ions of short-chain, long-chain, branched isomers, and novel perfluorinated compounds with unknown structures (such as ethers and polyacids), providing effective data support for the qualitative analysis of perfluorinated compounds.

[0020] 2) Automation of data analysis: This invention constructs an efficient and accurate complete analysis process.

[0021] Existing technologies for processing non-targeted perfluorinated compound data heavily rely on manual screening and experience-based judgment by analysts, resulting in cumbersome, time-consuming, and poorly reproducible processes. This invention integrates multiple algorithm modules, including characteristic peak extraction, molecular formula matching, homologue identification (through group mass calculation), fragment ion analysis, and neutral loss analysis, into an automated analysis workflow. This invention can automatically screen all potential compounds with fluorine-containing characteristics and categorize them, reducing the targets requiring manual confirmation from massive datasets to at least a few high-confidence candidates. Attached Figure Description

[0022] Figure 1 This refers to the data acquisition and qualitative analysis results from Example 1. Figure 2 This is the result of data collection and qualitative analysis in Application Example 2. Detailed Implementation

[0023] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. Component models, material names, connection structures, circuit structures, control methods, algorithms, and other features not explicitly described in this technical solution are considered common technical features disclosed in the prior art.

[0024] Example 1 This embodiment provides an analytical method for fluorine focusing-enhanced perfluorinated / polyfluoroalkyl compounds, comprising the following steps: S1. Sample pretreatment is performed, and preliminary data acquisition is carried out on the pretreated sample in negative ion mode using liquid chromatography-high resolution mass spectrometry to obtain preliminary liquid chromatography-mass spectrometry data.

[0025] In this embodiment, the liquid chromatography conditions used were as follows: mobile phase A was 10 mM ammonium formate solution, mobile phase B was acetonitrile solution, column temperature was 45℃, analysis time was 20 min, and gradient elution was performed. Mass spectrometry conditions were as follows: negative ion mode, m / z scan range: 150-1000, normalized collision energy (NCE): 10, 20, 30.

[0026] S2. Process the preliminary liquid chromatography-mass spectrometry data, perform peak extraction, and generate a basic peak table containing primary mass-to-charge ratio, retention time, and secondary fragment ion information.

[0027] In practice, the peak extraction process in S2 is implemented using Python. The specific peak extraction process includes: The preliminary liquid chromatography-mass spectrometry (LC-MS) data are processed to extract chromatographic peaks from the original mass spectra and correlate them with their corresponding primary mass-to-charge ratios, retention times, and the mass-to-charge ratios and intensities of secondary fragment ions that are triggered for acquisition at those retention times, thereby generating a structured peak table containing primary and secondary information.

[0028] In S2, the peak extraction process is implemented using Python, including: reading the original mass spectrometry file, reconstructing the total ion current chromatogram through continuous scanning of the primary mass spectrometer, detecting and integrating chromatographic peaks along the retention time dimension, and simultaneously associating the secondary mass spectrometry fragment information triggered by each peak. Finally, the extracted primary mass-to-charge ratio, retention time, secondary fragment mass-to-charge ratio, and intensity information are structured and stored as a pandas DataFrame or a standard format table file. The above Python programming code is easily implemented by those skilled in the art based on the content described in this invention and will not be elaborated further here.

[0029] In step S2, the primary task is to analyze and structure the raw data, specifically implemented through Python programming. The program processes the initially acquired LC-MS raw data file, automatically identifying all chromatographic peaks from continuous mass spectrometry scans. For each identified peak, the program records its two core identifiers: the primary mass-to-charge ratio and its corresponding retention time. Simultaneously, the program actively searches for all secondary mass spectrometry scans automatically triggered by the mass spectrometer's data-dependent acquisition function within the peak's elution time window, extracting the precise mass-to-charge ratio and signal intensity values ​​of all fragment ions. Through this series of operations, the originally unstructured raw spectral data is transformed into a well-formatted and complete peak table. This table systematically links the primary precursor ion information of each suspected compound with its corresponding secondary fragment ion spectrum. Step S3 involves filtering the information in the peak table using fluorine characteristic data to generate a static inclusion list of fluorine characteristics.

[0030] In specific implementation, the process of screening and processing fluorine characteristic data in S3 includes: Based on the mass defect value of the first-order mass-to-charge ratio, and based on the fluorine characteristic diagnostic ions and neutral loss fragments of the second-order fragment ions, a static inclusion list of fluorine characteristics and a static exclusion list of non-fluorine characteristics are generated.

[0031] In specific implementation, in S3, the screening of mass defect values ​​based on the first-level mass-to-charge ratio includes: Calculate the mass defect value of each first-order mass-to-charge ratio in the peak table, where the mass defect value is equal to the decimal part of the mass-to-charge ratio. The mass-to-charge ratio of the first-order fluorine feature with a mass defect value between -0.2 and 0.1 and its corresponding retention time information are included in the static inclusion list of the fluorine feature.

[0032] In specific implementation, S3 involves performing fluorine characteristic data filtering processing on the information in the peak table to generate a static inclusion list of fluorine characteristics, specifically including: Mass defect calculation is performed on the mass-to-charge ratio (m / z) of the primary information in the peak table, and primary information (including m / z and retention time) with MD values ​​between -0.2 and 0.1 is recorded as fluorine characteristics and statically included in the list.

[0033] MD = m / z - round(m / z) MD: Quality Loss m / z: Mass-to-charge ratio of the parent ion round: is a mathematical function that maps the value within parentheses (i.e., the mass-to-charge ratio m / z) to the "nearest" integer.

[0034] S3 performs fluorine feature screening based on structured data. Its core logic involves running two parallel and complementary filtering algorithms to efficiently extract target signals from massive amounts of features. The first algorithm performs mass defect screening based on the first-order mass-to-charge ratio. The principle is that the specific mass number of fluorine atoms causes a characteristic shift in the mass-to-charge ratio of fluorine-containing compounds. The algorithm calculates the mass defect value for each first-order mass-to-charge ratio in the peak table, defined as the mass-to-charge ratio value minus its nearest integer value. Due to the isotopic mass characteristics of fluorine, the mass defect values ​​of fluorine-containing compounds typically concentrate in a specific range of -0.2 to 0.1. Therefore, the program automatically marks all first-order mass-to-charge ratios falling within this range and their corresponding retention time information. The second algorithm targets more direct evidence of chemical structure, namely secondary fragment ions, for diagnosis. It compares each secondary fragment ion recorded in the peak table with a pre-established reference library of known fluorine feature fragments, containing 448 diagnostic fluorine ions and 19 neutral loss fluorine feature fragments. If a compound's secondary mass spectrum contains a fragment matching the reference library, it indicates that its fragmentation produced a fluorine-containing fragment, strong evidence of its fluorine-containing nature. Through these two independent screenings, any signal and its retention time that pass the criteria of either algorithm are compiled into a static fluorine feature inclusion list. The resulting dynamic fluorine feature inclusion list provides the mass spectrometer with clear acquisition instructions.

[0035] S4. Based on the static inclusion list of fluorine characteristics, establish a fluorine focusing data acquisition method, and perform a second data acquisition on the sample to obtain fluorine focusing enhanced liquid chromatography-mass spectrometry data.

[0036] In specific implementation, the process of establishing the fluorine focusing data acquisition method in S4 is as follows: In the original data acquisition method of the liquid chromatography-high resolution mass spectrometry system, the static inclusion function is invoked, and the static inclusion list of fluorine characteristics generated in S3 is used as the acquisition list. The secondary mass spectrometry information of other compounds is no longer acquired, thus constructing the fluorine focusing data acquisition method for prioritizing the acquisition of fluorine-containing characteristic compounds.

[0037] The basic methods for liquid chromatography and mass spectrometry are consistent with those described in S1. During mass spectrometry data acquisition, a static inclusion method is selected, and the static inclusion list of fluorine features obtained through data processing in S3 is imported as the mass-to-charge ratio list for static inclusion in mass spectrometry acquisition, thereby generating a feature-focusing data acquisition method for acquiring fluorine compounds.

[0038] S5. For the fluorine-focused enhanced liquid chromatography-mass spectrometry data, non-targeted data cleaning and screening are performed using the fluorine feature-focused data analysis method to obtain a list of fluorine feature candidate molecules.

[0039] In specific implementation, the process of cleaning and screening non-targeted data using the fluorine feature-focusing data analysis method in S5 includes: The secondary spectra in the fluorine-focused enhanced liquid chromatography-mass spectra are processed to extract a list containing the precursor ion mass-to-charge ratio, precursor ion intensity, retention time, fragment ion mass-to-charge ratio, and fragment ion peak intensity. Calculations were performed on the secondary information in this list to identify fluorine-characteristic diagnostic ions and neutral loss fragments (including C2F5). - C3F7 - The compounds were classified by identifying 448 fluorine-characteristic diagnostic ions and 19 fluorine-characteristic neutral missing fragments, including [CF2] and [HF], and by performing homologue group calculations. Peak extraction was performed on the list of fluorine characteristic compounds obtained after screening and classification to obtain the retention time and peak area information of each candidate compound.

[0040] In specific implementation, the method for calculating the homologous group is as follows: The calculation is performed using the formula group = (m / z) % R, where m / z is the mass-to-charge ratio of the parent ion, % is the modulo operator, and R is the mass of the repeating unit of the homologue, for example (R CF2 = 49.9968); The calculated group values ​​are used to classify and assign fluorine-featured candidate molecules with the same or similar repeating structural units; The group value is used to classify different types of perfluorinated compounds (e.g., perfluorocarboxylic acids, perfluorosulfonic acids and their hydrogen-substituted homologues).

[0041] Specifically, the peak extraction in step S2 of this invention is for the raw preliminary liquid chromatography data obtained from the initial full scan. Its purpose is to discover and identify all possible chromatographic peaks from continuous mass spectrometry scans, and associate them with their corresponding primary mass-to-charge ratio, retention time, and secondary fragment ion information triggered by acquisition at that retention time, thereby generating a structured peak table containing information on all suspected compounds.

[0042] Peak extraction in step S5 occurs after the fluorine-enhanced LC-MS data has undergone fluorine feature screening and classification. The operation here involves a list of fluorine-characteristic compounds obtained through diagnostic ion, neutral loss, and homologue calculations. The purpose of this step is not to rediscover chromatographic peaks, but rather to extract and integrate the corresponding chromatographic peaks from the enhanced LC-MS data for each identified fluorine-characteristic candidate molecule in the list. This yields precise retention times and peak area information for each candidate compound, which can then be used for subsequent quantitative or semi-quantitative analysis.

[0043] S6. For the list of fluorine characteristic candidate molecules, perform non-targeted qualitative analysis using the fluorine characteristic focusing data analysis method. The non-targeted qualitative analysis process includes: matching the experimental secondary mass spectrometry information of the fluorine characteristic candidate molecules with the theoretical fragment ion simulation results of the local database based on a local database containing preset perfluorinated / polyfluorinated compound structural information.

[0044] In specific implementation, in S6, the process of building the local database includes: Compound information is obtained from the PubChem online database, and entries that meet the structural characteristics of perfluorinated and polyfluorinated compounds are selected to form a dedicated local database containing molecular formula, monoisotope mass, InChI, and SMILES information. This dedicated local database is used to provide candidate compound structures for theoretical fragment ion simulations.

[0045] In specific implementation, the theoretical fragment ion simulation process in S6 includes: The MetFrag algorithm is used to match and score the theoretical fragmentation fragments of candidate compounds in the local database with the experimental secondary mass spectra of the fluorine characteristic candidate molecule list obtained in S5 to determine the most likely structure. MetFrag is a publicly disclosed computational algorithm in the prior art for predicting theoretical mass spectrometry fragments based on chemical structure and performing spectrum matching.

[0046] In specific implementation, S6 involves the following steps for qualitative analysis of fluorine characteristic candidate molecules through fragment ion simulation calculations based on the local library: Construction of the local database: Information on 170 million compound molecules was downloaded from the PubChem online database, including molecular formula, single isotope molecular mass, InChI formula and SMILES formula. From this, about 15,000 compound entries that meet the structural characteristics of perfluorinated and polyfluorinated compounds were selected and used as the local database.

[0047] By constructing a local database and combining it with MetFrag, the simulated molecular fragments in the local database are matched with the secondary information of the compound to be tested, thereby determining the qualitative analysis results of the candidate.

[0048] S6 achieves precise structural identification of candidate molecules with qualitative fluorine characteristics. Its core logic is to intelligently compare and optimally match experimentally measured secondary mass spectra with the theoretical fragmentation spectra of a massive number of candidate compounds. Specifically, this method first constructs a dedicated local database of perfluorinated and polyfluorinated compounds. The data foundation comes from approximately 170 million compound molecules downloaded from the authoritative PubChem online database. The program filters out entries that meet the structural characteristics of perfluorinated and polyfluorinated compounds, ultimately forming a dedicated local library containing approximately 15,000 well-defined candidate structures. This library stores key information for each compound, such as its molecular formula, monoisotope mass, InChI, and SMILES.

[0049] In the qualitative analysis, for each member of the fluorine characteristic candidate molecule list obtained in step S5, the mass-to-charge ratio and intensity information of all fragment ions were extracted from its experimental secondary mass spectrum. Subsequently, existing mass spectrometry simulation algorithms such as MetFrag were used to simulate theoretical fragmentation rules for candidate compounds with chemically consistent structures in the local database, predicting and generating all theoretical fragment ions and their relative abundances that each candidate compound might produce in the mass spectrum.

[0050] The experimental secondary mass spectra of candidate molecules are compared one by one with the theoretical fragmentation spectra of each potential candidate structure, and the matching degree is calculated. A scientific scoring model is used to quantify the matching results. The candidate compound structure with the highest matching degree has the best match between its theoretical fragment and experimental spectrum, and is thus determined by the system to be the most likely true chemical structure corresponding to the fluorine characteristic candidate molecule, thereby completing a high-confidence non-targeted qualitative analysis.

[0051] Application Example 1: This application example demonstrates the detection and analysis of perfluorinated compounds in wastewater from the paperboard production process.

[0052] The water sample was purified by solid-phase extraction and concentrated to obtain 300 µL of the test sample.

[0053] The sample to be tested was analyzed using liquid chromatography-mass spectrometry (LC-MS) under the following conditions, and preliminary data were obtained.

[0054] Liquid chromatography conditions used: Mobile phase A was 10 mM ammonium formate solution, mobile phase B was acetonitrile solution, column temperature was 45℃, analysis time was 20 min, and gradient elution was performed. Mass spectrometry conditions: Negative ion mode was used, m / z scan range: 150-1000, normalized collision energy (NCE): 10, 20, 30.

[0055] The preliminary data were screened for fluorine characteristics, retaining data with mass defects ranging from -0.2 to 0.05. Peak extraction was performed, and the elution times of the chromatographic peaks were recorded and added to the static inclusion list. Secondary peak tables were further screened for diagnostic ions and neutral loss, and characteristic secondary peak information was added to the static inclusion list.

[0056] Based on the liquid chromatography-mass spectrometry (LC-MS) conditions in S2, static inclusion is added to the mass spectrometry analysis method to achieve fluorine focusing data acquisition.

[0057] Qualitative results were obtained by processing samples collected using fluorine focusing data analysis methods.

[0058] Table 1 shows the qualitative analysis results of the samples after processing using this method. Figure 1 This is a comparison chart of data collected using the fluorine focusing method and the normal acquisition method. Figure 1 Figure 'a' represents the result obtained from analyzing data acquired using conventional liquid chromatography-mass spectrometry (LC-MS) methods. Figure 1 Figure b is the result obtained by analyzing data acquired using the fluorine focusing data acquisition method. Figure 1 a and Figure 1 The comparison between the two datasets shows a significant increase in both the number and proportion of compounds with secondary spectra and mass defects between -0.2 and 0.05, rising from 136 to 195. This implies that, from a macroscopic perspective, the collected data is more inclined to include compounds with mass defects (polyfluorine compounds are mostly mass-defective compounds). Furthermore, from a... Figure 1 As shown in Figure c, the number of different types of compounds increased. Specifically, the number of perfluorocarboxylic acids (PFCAs) increased from 4 to 7, the number of hydrogen-substituted perfluorocarboxylic acids (ωH-PFCAs) increased from 7 to 10, and the number of perfluoroether alcohols (containing methyl-fluorinated hydroxyl groups) increased from 0 to 3. This increase is likely due to differences in ionization efficiency caused by different compound structures. For example, the number of perfluorosulfonic acids (PFSAs) detected was 3 using both conventional LC-MS and fluorine focusing data acquisition methods. This is likely because sulfuric acid compounds themselves have higher ionization efficiency, so their ion intensity reached the threshold for secondary data acquisition on the first acquisition. Meanwhile, several compounds, such as PFCAs and ωH-PFCAs, were not completely detected in conventional analysis, but their numbers increased significantly when using fluorine focusing data acquisition methods. Figure 1 In the diagram, d represents the grouping result calculated by group. Most compounds can be grouped by comparing the values ​​of group.

[0059] Table 1 Qualitative Analysis Results of Application Example 1 Application Example 2: This application example demonstrates the detection and analysis of perfluorinated compounds in cardboard.

[0060] The cardboard was broken up, purified by solid-phase extraction, and concentrated to obtain 300µL of the sample to be tested.

[0061] The sample to be tested was analyzed using liquid chromatography-mass spectrometry (LC-MS) under the following conditions, and preliminary data were obtained.

[0062] Liquid chromatography conditions used: Mobile phase A was 10 mM ammonium formate solution, mobile phase B was acetonitrile solution, column temperature was 45℃, analysis time was 20 min, and gradient elution was performed. Mass spectrometry conditions: Negative ion mode was used, m / z scan range: 150-1000, normalized collision energy (NCE): 10, 20, 30.

[0063] The preliminary data were screened for fluorine characteristics, retaining data with mass defects ranging from -0.2 to 0.1. Peak extraction was performed, and the elution times of the chromatographic peaks were recorded and added to the static inclusion list. Secondary peak tables were further screened for diagnostic ions and neutral loss, and characteristic secondary information was added to the static inclusion list.

[0064] Based on the above liquid chromatography-mass spectrometry (LC-MS) conditions, static inclusion is added to the mass spectrometry analysis method to achieve fluorine focusing data acquisition. Qualitative results were obtained by processing samples collected using fluorine focusing data analysis methods.

[0065] Table 2 shows the qualitative analysis results of the samples after processing using this method. Figure 2 This is a comparison chart of data collected using the fluorine focusing method and the normal acquisition method. Figure 2 Figure 'a' represents the result obtained from analyzing data acquired using conventional liquid chromatography-mass spectrometry (LC-MS) methods. Figure 2 Figure b is the result obtained by analyzing data acquired using the fluorine focusing data acquisition method. Figure 2 a and Figure 2 The comparison in b-column shows a significant increase in both the number and proportion of compounds with secondary spectra and mass deficit values ​​between -0.2 and 0.1, rising from 209 to 292. This implies that, from a macroscopic perspective, the collected data is more inclined to include compounds with mass deficits (polyfluorinated compounds are mostly mass-deficient compounds). Furthermore, from... Figure 2The results from the fluoropolymer (c) assay show that the number of hydrogen-substituted perfluorocarboxylic acids (ωH-PFCAs) increased from 2 to 5, and the number of perfluorosulfonic acid compounds (PFSAs) increased from 2 to 3. The number of n:2 perfluorosulfonic acid compounds (n:2FTS) increased from 0 to 1. Several compounds, including PFSAs, ωH-PFCAs, and n:2FTS, were not completely detected in conventional assays, but their numbers increased when using fluorine focusing data acquisition methods. Figure 2 In the diagram, d represents the grouping result calculated by group. Most compounds can be grouped by comparing the values ​​of group.

[0066] Table 2 Qualitative Analysis Results of Application Example 2 The above description of the embodiments is provided to enable those skilled in the art to understand and use the invention. It will be apparent to those skilled in the art that various modifications can be made to these embodiments, and the general principles described herein can be applied to other embodiments without inventive effort. Therefore, the present invention is not limited to the above embodiments, and any improvements and modifications made by those skilled in the art based on the disclosure of the present invention without departing from the scope of the invention should be within the protection scope of the present invention.

Claims

1. A method for analyzing fluorine-focused enhanced perfluorinated / polyfluoroalkyl compounds, characterized in that, Includes the following steps: S1. The sample is pretreated, and preliminary data is acquired in positive or negative ion mode using liquid chromatography-high resolution mass spectrometry to obtain preliminary liquid chromatography-mass spectrometry data. S2. Process the preliminary liquid chromatography-mass data, perform peak extraction, and generate a basic peak table containing primary mass-to-charge ratio, retention time, and secondary fragment ion information. S3. Perform fluorine characteristic data filtering on the information in the basic peak table to generate a static fluorine characteristic inclusion list; S4. Based on the static inclusion list of fluorine characteristics, establish a fluorine focusing data acquisition method, and perform iterative data acquisition on the sample to obtain fluorine focusing enhanced liquid chromatography-mass spectrometry data. S5. For the fluorine-focused enhanced liquid chromatography-mass data, non-targeted data cleaning and screening are performed using the fluorine feature focusing data analysis method to obtain a list of fluorine feature candidate molecules. S6. For the list of fluorine-featured candidate molecules, perform non-targeted qualitative analysis using the fluorine feature focusing data analysis method. The non-targeted qualitative analysis process includes searching a database based on perfluorinated / polyfluorinated compounds and performing qualitative analysis. The database includes an experimental database, a theoretical simulation database, and a suspect screening list.

2. The analytical method for fluorine-focusing enhanced perfluorinated / polyfluoroalkyl compounds according to claim 1, characterized in that, In S2, the peak extraction process is implemented using Python. The specific peak extraction process includes: The preliminary liquid chromatography-mass spectrometry (LC-MS) data are processed to extract chromatographic peaks from the original mass spectra and correlate them with their corresponding primary mass-to-charge ratios, retention times, and the mass-to-charge ratios and intensities of secondary fragment ions that are triggered for acquisition at those retention times, thereby generating a structured basic peak table containing primary and secondary information.

3. The analytical method for fluorine-focusing enhanced perfluorinated / polyfluoroalkyl compounds according to claim 2, characterized in that, In S2, the peak extraction process is implemented using Python, including: Read the original mass spectrometry file, reconstruct the total ion current chromatogram through continuous scanning of the primary mass spectrometer, perform chromatographic peak detection and integration in the retention time dimension, and associate the secondary mass spectrometry fragment information triggered by each peak. Finally, the extracted primary mass-to-charge ratio, retention time, secondary fragment mass-to-charge ratio and intensity information are structured and stored as a pandas DataFrame or a standard format table file.

4. The analytical method for fluorine-focusing enhanced perfluorinated / polyfluoroalkyl compounds according to claim 1, characterized in that, In S3, the specific process of screening and processing fluorine characteristic data includes: Based on the mass defect value of the first-level mass-to-charge ratio, a static inclusion list of fluorine characteristics is generated.

5. The analytical method for fluorine-focusing enhanced perfluorinated / polyfluoroalkyl compounds according to claim 4, characterized in that, In S3, the mass defect value screening based on the first-order mass-to-charge ratio includes: Calculate the mass deficit value of each first-order mass-to-charge ratio in the peak table, where the mass deficit value is equal to the difference between the precise mass and the nominal mass; The mass-to-charge ratio of the first-order fluorine feature with a mass defect value between -0.2 and 0.1 and its corresponding retention time information are included in the static inclusion list of the fluorine feature.

6. The analytical method for fluorine-focusing enhanced perfluorinated / polyfluoroalkyl compounds according to claim 1, characterized in that, In S4, the specific process of establishing the fluorine focusing data acquisition method is as follows: In the data acquisition method of the liquid chromatography-high resolution mass spectrometry system, the static inclusion function is invoked, and the static inclusion list of fluorine characteristics generated in S3 is used as the acquisition list. The secondary mass spectrometry information of other compounds is no longer acquired, thus constructing the fluorine focusing data acquisition method for prioritizing the acquisition of fluorine-containing characteristic compounds.

7. The analytical method for fluorine-focusing enhanced perfluorinated / polyfluoroalkyl compounds according to claim 1, characterized in that, In S5, the process of cleaning and screening non-targeted data using the fluorine feature focusing analysis method includes: The secondary spectra in the fluorine-focused enhanced liquid chromatography-mass spectra are processed to extract a list containing the precursor ion mass-to-charge ratio, precursor ion intensity, retention time, fragment ion mass-to-charge ratio, and fragment ion peak intensity. The secondary information in the list is used to calculate fluorine characteristic diagnostic ions and neutral loss fragments, and homologues are calculated to classify the compounds. Peak extraction was performed on the list of fluorine characteristic compounds obtained after screening and classification to obtain the retention time and peak area information of each candidate compound.

8. The analytical method for fluorine-focusing enhanced perfluorinated / polyfluoroalkyl compounds according to claim 7, characterized in that, The specific method for calculating the homologues is as follows: The calculation is performed using the formula group = (m / z)% R, where m / z is the mass-to-charge ratio of the parent ion, % is the modulo operator, and R is the mass of the repeating unit of the homologue. The calculated group values ​​are used to classify and assign fluorine-featured candidate molecules with the same or similar repeating structural units; The calculation of the group value is used to classify different types of perfluorinated compounds.

9. The analytical method for fluorine-focusing enhanced perfluorinated / polyfluoroalkyl compounds according to claim 1, characterized in that, In S6, one of the following processes is used for qualitative analysis based on a database search of perfluorinated / polyfluorinated compounds: Based on experimental database: The experimental secondary mass spectra of the fluorine characteristic candidate molecule list obtained in S5 are matched and scored with local or online experimental secondary spectrum libraries to determine the most likely structure; Based on theoretical simulation database: Based on the list of compounds with molecular formula, InChI, and SMILES information that conform to the structural characteristics of perfluorinated and polyfluorinated compounds obtained from the open source database, the theoretical fragmentation fragments of the candidate compounds are matched and scored with the experimental secondary mass spectra of the fluorine characteristic candidate molecule list obtained in S5 using MetFrag, CFM-ID or other theoretical fragmentation simulation algorithms to determine the most likely structure. Based on the suspect screening list: Compound information is obtained from the PubChem and Chemspider open-source databases, and entries that meet the structural characteristics of perfluorinated and polyfluorinated compounds are screened out to form a dedicated suspect screening list containing molecular formula, monoisotope mass, InChI, and SMILES information. The monoisotope mass of the dedicated suspect screening list is matched with the fluorine characteristic candidate molecule list obtained in S5 to lock in potential candidates.

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