Method and system for comprehensive detection of neonicotinoids in the environment

Abstract

This invention discloses a comprehensive detection method and system for neonicotinoids in the environment. It improves the coverage of mass spectrometry information by coupling multi-module data-dependent acquisition and multi-window data-independent acquisition modes. A suspected screening analysis database is established based on the core structure of neonicotinoids to detect compounds matching the suspected screening compounds. Furthermore, based on characteristic molecular network technology under the coupled data-dependent acquisition mode and characteristic fragment ion extraction technology under the data-independent acquisition mode, non-target analysis of neonicotinoids in the tested environmental samples is performed to deduce candidate compounds different from target compounds and suspected screening compounds. Through target analysis, suspected screening analysis, and non-target analysis, comprehensive detection of potential neonicotinoids in the tested environmental samples is achieved.

Description

Technical Field

[0001] This invention relates to the field of environmental analytical chemistry, and in particular to a comprehensive method and system for the detection of neonicotinoid compounds in the environment. Background Technology

[0002] Since their introduction in the 1990s, neonicotinoids (NEOs) have become one of the largest insecticides in the global market due to their unique mechanism of action and highly effective insecticidal activity, and are widely used in agricultural production, urban greening, and public health pest control. However, with their large-scale use, NEOs and their environmental transformation products (t-NEOs) and structural analogues (a-NEOs) have been commonly detected in various environmental media.

[0003] Current research provides a relatively in-depth understanding of neonicotinoid parent compounds (p-NEOs), but there are significant gaps in our knowledge of the more diverse t-NEOs and a-NEOs, which may have varying toxicities. This severely limits the accurate assessment of their actual environmental presence, migration and transformation behavior, and eco-health exposure risks.

[0004] While ultra-high performance liquid chromatography-high resolution mass spectrometry (UPLC-HRMS) provides precise mass numbers and fragmentation information for compounds, making it a powerful tool for screening non-target compounds in complex systems, traditional data-dependent acquisition strategies rely on preset trigger conditions in real-world environmental sample analysis. This can easily lead to missed secondary mass spectrometry acquisitions of low-abundance or unknown compounds when dealing with complex matrices, low target analyte concentrations, and wide dynamic ranges, resulting in the loss of crucial structural identification information. Therefore, improving the coverage and reliability of secondary mass spectrometry has become a core technical challenge for achieving comprehensive identification of NEOs (Neuro-Organic Oxides).

[0005] PBMT refers to a combination of chemical properties—Persistent (P), Bioaccumulation (B), Mobility (M), and Toxic (T)—used to assess the potential environmental and health risks of emerging pollutants. The large-scale emission of NEOs has already posed a significant threat to ecosystems. As persistent, bioaccumulative, mobile, and toxic compounds, their toxic effects on pollinating insects (such as bees), aquatic invertebrates, birds, and even soil organisms (such as earthworms) have been extensively studied. Furthermore, the potential chronic exposure to NEOs and its impact on human health, particularly on infant development, has raised widespread concern. Therefore, establishing an analytical method capable of systematically screening, accurately identifying, and quantitatively assessing the environmental risks of NEOs is an urgent need for implementing effective environmental regulation and developing scientific management strategies.

[0006] In conclusion, a comprehensive assessment of the environmental distribution of NEOs is the first step in implementing effective regulation, while an accurate assessment of their environmental risks is the foundation for developing scientific management strategies. Summary of the Invention

[0007] This invention provides a comprehensive method and system for detecting neonicotinoid compounds in the environment, which can screen and identify neonicotinoid compounds (including neonicotinoid conversion products and analogs) without omission from high-resolution mass spectrometry data of complex environmental matrices.

[0008] The technical solution of the present invention is as follows: A comprehensive method for detecting neonicotinoids in the environment, comprising the following steps: (1) Construct a database for suspected screening and analysis of neonicotinoid compounds; (2) Ultra-high performance liquid chromatography-high resolution mass spectrometry analysis was performed on the standards of neonicotinoid compounds and the environmental samples to be tested to obtain the chromatographic-mass spectrometry data of the standards and the environmental samples to be tested; (3) Perform target analysis, suspected screening analysis and non-target analysis on the chromatographic-mass spectrometry data of the environmental sample to be tested to obtain the standard compounds, suspected screening compounds and non-target candidate compounds present in the environmental sample to be tested; (4) Perform preliminary structural analysis on non-target candidate compounds and add them to the suspected screening analysis database to construct a retrospective suspected screening analysis database that includes non-target candidate compounds; (5) Based on the retrospective suspected screening analysis database, the chromatographic-mass spectrometry data of the environmental samples to be tested are re-screened to obtain compounds in the environmental samples to be tested that successfully match non-target candidate compounds; The compounds obtained in steps (3)-(5) are combined to obtain neonicotinoid compounds that can be comprehensively detected from the environmental samples to be tested.

[0009] In step (1), the suspected screening analysis database stores the structural information and mass spectrometry prediction information of the suspected screening compounds. The mass spectrometry prediction information includes ionization mode, adduct ion, precise mass number of adduct ion, retention time, retention time deviation range, theoretical isotope distribution pattern, and secondary mass spectrometry fragment ions.

[0010] Preferably, step (1) includes: (1-1) A suspected screening database was constructed by screening compounds with neonicotinoid core structures from publicly available chemical databases; (1-2) Using a literature data retrieval platform, search for neonicotinoid compounds and their metabolites using the names of these compounds as keywords, extract relevant compounds from the retrieved literature and add them to the suspected screening database; (1-3) Using public compound databases, the core structures of neonicotinoid compounds are used as search templates to screen compounds with relevant structures and add them to the suspected screening database; (1-4) The potential transformation products of neonicotinoid parent compounds were predicted using metabolic transformation simulation tools, and the predicted transformation products were added to the suspected screening database.

[0011] The core structure of neonicotinoids includes structural units containing chloropyridine rings, chlorothiazole rings, tetrahydrofuran rings, or trifluoromethylpyridine rings.

[0012] Step (2) includes: (2-1) Ultra-high performance liquid chromatography-high resolution mass spectrometry was used to analyze the standards of neonicotinoids. The mass spectrometry acquisition adopted the data-dependent acquisition mode to obtain the chromatographic-mass spectrometry data of the standards. (2-2) Ultra-high performance liquid chromatography-high resolution mass spectrometry analysis was performed on the environmental samples to be tested. Mass spectrometry acquisition adopted multi-module data-dependent acquisition mode and multi-window data-independent acquisition mode respectively to obtain data-dependent acquisition data and data-independent acquisition data of the environmental samples to be tested.

[0013] Both the data-dependent acquisition mode and the data-independent acquisition mode were performed under one or more ionization sources selected from atmospheric pressure chemical ionization source, electrospray ionization source or atmospheric pressure photoionization source under positive ion and negative ion conditions in ultra-high performance liquid chromatography-electrostatic field orbital trap or time-of-flight high resolution mass spectrometer, in primary ion scanning mode and secondary ion scanning mode. The data-dependent acquisition mode is a mode that selectively fragments and performs secondary mass spectrometry analysis on specific precursor ions based on preset triggering conditions. The data-independent acquisition mode is a mode that performs indiscriminate fragmentation and secondary mass spectrometry analysis on all precursor ions within a selected mass-to-charge ratio range.

[0014] Chromatographic-mass spectrometry data include chromatographic retention time, precise mass number of primary mass spectrometry, and characteristic fragmentation data of secondary mass spectrometry; characteristic fragmentation data of secondary mass spectrometry includes characteristic fragment ions and neutral lost molecules.

[0015] Preferably, the characteristic fragment ions of neonicotinoid compound standards include m / z = 126.0105, m / z = 128.0262, m / z = 144.0211, m / z = 148.0369, m / z = 78.0338, m / z = 80.0495, m / z = 90.0338, m / z = 108.0444, m / z = 71.0491, m / z = 85.0648, m / z = 131.9669, m / z = 181.0542, m / z = 56.0495, m / z = 57.0447, m / z = 113.0168, and m / z = 129.0897; neutral loss molecules include m / z = At least one of the following: 28.0308, m / z = 45.9924, m / z = 43.9893, m / z = 34.9683, m / z = 35.9761, m / z = 30.0100, m / z = 29.0386, m / z = 27.0104.

[0016] In step (2-2), the multi-module data-dependent acquisition mode includes indirect secondary mass spectrometry data acquisition under primary ion scanning with characteristic isotope ratios, indirect secondary mass spectrometry data acquisition with precise mass numbers of adduct ions in the suspected screening analysis database, and direct secondary mass spectrometry data acquisition after a full scan. Under the multi-module data-dependent acquisition mode, the primary mass spectrometry scan range is set to m / z = 100-1000; the secondary mass spectrometry scan is based on the primary mass-to-charge ratio of its parent ion and is specifically used to acquire fragment ion spectra of the target analyte.

[0017] In the multi-window data-independent acquisition mode, the first-level scan covers the full mass range of m / z 100-1000 in a segmented manner, while the second-level scan is based on a preset isolation window (such as 5 Da width). By setting a 1 Da overlap area between adjacent windows, the missed detection of ions at the window edge is effectively avoided.

[0018] In step (3), the target analysis includes: performing target analysis based on the chromatographic-mass spectrometry data of the standard and the data-dependent acquisition data of the environmental sample to be tested, and obtaining the standard compound present in the environmental sample to be tested.

[0019] The target analysis process involves systematically comparing high-resolution mass spectrometry reference data of standards with data-dependent acquisition data of the sample to be tested, including matching the precise mass number of adduct ions, chromatographic peak shape, retention time, and secondary mass spectrometry fragment ions, thereby confirming the presence of known target compounds in the environmental sample. In step (3), the suspected screening analysis includes: (3-i) Preprocess the data dependent on the acquired data; the preprocessing includes deconvolution, peak alignment, peak filling and blank signal subtraction; (3-ii) The preprocessed data depends on the mass number and isotope distribution detected in the collected data and is compared with the theoretical exact mass number and simulated isotope pattern of the compounds in the suspected screening analysis database. Automatic screening and sorting are performed based on preset multiple filtering criteria to initially screen out matching suspected precursor ions. (3-iii) Match the secondary mass spectra of suspected precursor ions with public mass spectrometry databases to screen out candidate precursor compounds whose secondary spectra match. (3-iv) Analyze the chromatographic retention behavior and mass spectrometry fragmentation pattern of the candidate precursor compounds to determine their structure, which is then identified as the suspected screening compound.

[0020] The filtering criteria include: a matching deviation of less than 5 ppm between the accurate mass number of the primary mass spectrometer and the theoretical mass in the suspected screening analysis database; successful acquisition of a secondary mass spectrum; peak intensity greater than >1.0e4; signal-to-noise ratio (S / N) >3; isotope distribution pattern matching degree >75%; and peak score >4.

[0021] The first-level screening identifies targets based on mass number and isotopic distribution; the second-level screening effectively eliminates false positive signals through chromatographic peak quality checks; and the third-level screening significantly improves the reliability of the detection results through secondary mass spectrometry matching and retention behavior analysis, ultimately achieving reliable structural detection of non-standard compounds. The suspected screening compounds do not include standard compounds.

[0022] Preferably, in step (3), the non-target analysis includes: (3-I) Based on the chromatographic-mass spectrometry data of the standards and the data-independent acquisition data, non-target analysis is performed on the characteristic fragment ions and neutral loss molecules in the data-independent acquisition data to obtain candidate compounds; (3-II) Based on the chromatographic-mass spectrometry data and data-dependent acquisition data of the standards, non-target analysis of the molecular network based on the features of the data-dependent acquisition data is performed to obtain candidate compounds; The candidate compounds obtained by combining steps (3-I) and (3-II) are the non-target candidate compounds.

[0023] Non-target candidate compounds do not include standard compounds or suspected screening compounds.

[0024] Step (3-I) includes: (3-I1) Data analysis is independent of the acquired data. Ion chromatograms corresponding to the characteristic fragment ions and neutral lost molecules of the standard are extracted one by one from each isolation window. (3-I2) Based on the extracted ion chromatogram and the first-order mass spectrometry information at the corresponding retention time, the potential precursor ions that generate these characteristic fragments are traced back and inferred. (3-I3) Compare the ion information of potential precursor ions with that of standards and suspected compounds to screen out novel candidates that do not belong to standards and suspected compounds, and determine their structures.

[0025] Step (3-II) includes: (3-II1) Combine the feature quantization table and MS / MS summary file obtained after data-dependent data preprocessing; (3-II2) Submit the characterization table and MS / MS abstract file to the Global Natural Product Molecular Network Platform to construct a molecular network; (3-II3) Based on the mass-to-charge ratio of the precursor ions, the characteristic regions of neonicotinoid compounds are located in the molecular network to obtain unknown neonicotinoid compounds with structural similarity. Step (4) includes: (4-1) Extract characteristic fragment ions from the total ion flow map of data that is independent of the data acquisition; (4-2) Based on the principle that the liquid chromatography retention times of the precursor ions of non-target candidate compounds and their characteristic fragment ions are the same, the liquid chromatography retention times of the precursor ions are marked. (4-3) By using fragmentation prediction tools and compound database searches, the molecular formulas and structures of non-target candidate compounds are preliminarily inferred to obtain their structural information and mass spectrometry prediction information; (4-4) Add the structural information and mass spectrometry prediction information of non-target candidate compounds to the suspected screening analysis database to construct a retrospective suspected screening analysis database including non-target candidate compounds.

[0026] Step (5) utilizes the retrospective suspected screening analysis database with self-learning capabilities constructed in step (4) to systematically re-mine the data collected based on data dependence. By matching and verifying the experimental data with the newly added candidate compound information, the automatic confirmation and structural correction of the previous non-target screening results are realized, which significantly improves the reliability of the detection results and the reproducibility of the method.

[0027] The present invention also provides a comprehensive detection system for neonicotinoids in the environment, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the comprehensive detection method for neonicotinoids in the environment.

[0028] This invention also provides a method for risk assessment of neonicotinoid compounds in the environment, comprising: (I) Based on the physicochemical properties of the neonicotinoids identified by the above methods, assess their persistence, bioaccumulation, migration and toxicity; (II) Ecological risk assessment of detected neonicotinoid compounds based on risk quotient model: ; ; In the formula, RQ sum To calculate the cumulative ecological risk level; Let be the measured concentration value of the i-th neonicotinoid compound; The predicted no-effect concentration for the i-th neonicotinoid compound; LC 50 The median lethal concentration (LD50) is 60%; EC50 50 The half-maximal effect concentration; AF is the evaluation factor; (III) Based on a species sensitivity distribution model, a species sensitivity distribution curve is fitted using toxicological data of neonicotinoids, and the hazardous concentration HC5 protecting 95% of aquatic species is calculated based on the species sensitivity distribution curve. This invention also provides a risk assessment system for neonicotinoids in the environment, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the risk assessment method for neonicotinoids in the environment.

[0029] Compared with the prior art, the beneficial effects of the present invention are as follows: The comprehensive detection method and system for neonicotinoids in the environment of this invention improves the coverage of mass spectrometry information by coupling multi-module data-dependent acquisition and multi-window data-independent acquisition modes. Simultaneously, a suspected screening analysis database established based on the core structure of neonicotinoids is used to detect the definite or possible structures of compounds matching the suspected screening compounds. Furthermore, based on the characteristic molecular network technology under the coupled data-dependent acquisition mode and the characteristic fragment ion extraction technology under the data-independent acquisition mode, non-target analysis is performed on neonicotinoids in the environmental samples under test to deduce the definite or possible structures of candidate compounds different from the target compounds and suspected screening compounds. Through target analysis, suspected screening analysis, and non-target analysis, comprehensive screening and detection of potential neonicotinoids in the environmental samples under test are achieved. Attached Figure Description

[0030] Figure 1 A flowchart illustrating a comprehensive detection method for neonicotinoids in the environment; Figure 2 This is a detailed flowchart of the comprehensive detection method for neonicotinoid compounds in Example 1; Figure 3 A flowchart illustrating the establishment of the suspected screening and analysis database for neonicotinoid compounds in Example 1; Figure 4 The decision tree diagram for data acquisition of neonicotinoid compounds in Example 1 is shown in a. a represents the multi-module data-dependent acquisition mode, and b represents the multi-window data-independent acquisition mode. Figure 5 This is a graph showing the matrix spiked recovery rate of NEOs in surface water environmental samples from Example 2; Figure 6 This is a graph showing the representative compound detection results from the suspected screening analysis in Example 3; Figure 7 This is a diagram showing representative compound detection results from the feature-based molecular network non-target analysis in Example 3; Figure 8 This is a flowchart illustrating the detection process for a representative compound in Example 3, which utilizes non-target analysis based on characteristic fragment ions and neutral loss molecules. Figure 9 The following is a diagram showing the risk assessment results of NEOs in surface water of the Yangtze River Basin in Example 4. In this diagram, a is the PBMT chemical characteristic assessment result of NEOs, b is the risk quotient assessment result of NEOs excluding imidacloprid, c is the risk quotient assessment result of NEOs including imidacloprid, d is the species chronic susceptibility distribution curve result of NEOs, and e is the species acute susceptibility distribution curve result of NEOs. Detailed Implementation

[0031] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be noted that the embodiments described below are intended to facilitate the understanding of the present invention and do not limit it in any way.

[0032] This invention aims to provide an integrated method for the detection and risk assessment of neonicotinoid compounds in the environment. This method organically combines target analysis, suspected screening analysis, and non-target analysis to form a complete analytical strategy. It aims to comprehensively screen and identify neonicotinoid compounds (including neonicotinoid transformation products and analogues) from complex environmental matrix high-resolution mass spectrometry data, and ultimately achieve accurate environmental risk assessment of the detected neonicotinoid compounds.

[0033] In a first aspect, the present invention provides a comprehensive method for detecting neonicotinoid compounds in the environment, comprising: A database of suspected screening analyses of neonicotinoid compounds was acquired, which was constructed to store structural information and mass spectrometry prediction information of suspected screening compounds. Ultra-high performance liquid chromatography-high resolution mass spectrometry analysis was performed on the standards in the database. The chromatographic retention time, the precise mass number of the first-stage mass spectrometry, and the characteristic fragmentation data of the second-stage mass spectrometry were obtained using FreeStyle software. The characteristic fragmentation data of the second-stage mass spectrometry included characteristic fragment ions and neutral lost molecules. Ultra-high performance liquid chromatography-high resolution mass spectrometry analysis of environmental samples to be tested is performed. The mass spectrometry acquisition adopts a strategy that combines multi-module data-dependent acquisition mode and multi-window data-independent acquisition mode to obtain data-dependent acquisition data and data-independent acquisition data of the environmental samples to be tested. Based on the mass spectrometry measurement data and data-dependent acquisition data of the standards, target analysis was performed using TraceFinder software to identify the known standards present in the environmental samples to be tested. Based on data from the suspected screening analysis database and chromatographic-mass spectrometry data of standards, suspected screening analysis is performed on data-dependent collected data and combined with the set screening and filtering standards to determine the structure of suspected screening compounds in the environmental samples to be tested that match the database; the identified compounds are non-standards, and their structures are determined or possible structures. Based on the chromatographic-mass spectrometry measured data of standards and data-independent acquisition data, non-target analysis was performed on the characteristic fragment ions and neutral loss molecules in the data-independent acquisition data to identify candidate compounds. The identified compounds were non-standards and non-suspected screening compounds, and their structures were determined or probable structures. Based on chromatographic-mass spectrometry data of standards and data-dependent acquisition data, advanced mass spectrometry analysis tools are used to perform feature-based molecular network non-target analysis to identify candidate compounds. The identified compounds are non-standards and non-suspected screening compounds, and their structures are determined or probable structures. The candidate compounds were initially analyzed for structure, and then their structural information and mass spectrometry prediction information were added to the suspected screening analysis database to construct a retrospective suspected screening analysis database that includes the candidate compounds. Based on the data from the constructed retrospective suspected screening analysis database, the collected data is re-screened according to the collected data, thereby verifying and further confirming the determination or possible structure of compounds that successfully match the candidate compounds in the environmental samples to be tested. Among them, the multi-module data-dependent acquisition mode includes indirect secondary mass spectrometry data acquisition under primary ion scanning with characteristic isotope ratios, indirect secondary mass spectrometry data acquisition with precise mass numbers of adduct ions in the suspected screening analysis database, and direct secondary mass spectrometry data acquisition after full scan. The multi-window data-independent acquisition mode includes independent secondary mass spectrometry data acquisition using nine methods (containing nine windows, corresponding to nine independent methods) under parallel data acquisition including full scan and data-independent acquisition.

[0034] Secondly, the present invention provides a risk assessment method for neonicotinoid compounds in the environment, comprising: Neonicotinic compounds were detected in the environmental samples using the comprehensive detection methods described above. a) Based on the physicochemical properties of the detected neonicotinoids, assess their persistence, bioaccumulation, migration and toxicity. Determine whether the detected neonicotinoids belong to the persistent, bioaccumulative, migratory and toxic substances by judging whether each of the persistent, bioaccumulative, migratory and toxicity criteria is met. b) An ecological risk assessment of the detected neonicotinoids is conducted based on a risk quotient model, with the risk quotient calculated using the following formula: (1); (2); In the formula, RQ sum To accumulate ecological risk levels, Let i be the measured concentration value of the i-th neonicotinoid compound. The predicted no-effect concentration and LC-1 of the i-th neonicotinoid compound. 50 The median lethal concentration (LD50) and EC50 50 The half-maximal effect concentration is AF, which is the evaluation factor (with a value of 1000).

[0035] c) Based on the species sensitivity distribution model, the species sensitivity distribution curve was fitted using the toxicological data of neonicotinoids, and the harmful concentration HC5 that protects 95% of aquatic species was calculated based on the curve.

[0036] In the course of this invention's implementation, several key technical bottlenecks that urgently need to be overcome have been systematically identified: First, at the suspected target screening level, the core challenge lies in how to construct a high-coverage, high-accuracy screening database specifically for neonicotinoid compounds. Second, in the structural analysis stage, obtaining high signal-to-noise ratio and complete secondary mass spectra is a crucial prerequisite for ensuring detection accuracy. Third, for non-target screening, the technical difficulty focuses on how to effectively identify and extract weak signals of neonicotinoid compounds from data acquired independently of complex matrix interference. Finally, in the risk assessment stage, how to select appropriate assessment models for different scenarios and obtain reliable input parameters is a decisive factor in achieving accurate risk quantification.

[0037] This invention utilizes high-coverage mass spectrometry data combining multi-module data-dependent acquisition (Multi-Module-DDA) and multi-window data-independent acquisition (Multi-Window-DIA) modes as its analytical foundation. It employs a collaborative detection strategy combining target analysis, suspected screening analysis, and non-target analysis, and integrates a multi-dimensional environmental risk assessment system encompassing PBMT attribute evaluation, risk quotient models, and species sensitivity distribution analysis. Ultimately, this achieves comprehensive detection and accurate risk assessment of neonicotinoid pollutants in complex environmental matrices. Specifically, according to some embodiments of the invention, a method for comprehensive detection and risk assessment of neonicotinoid compounds in the environment is provided, including steps S1-S9, detailed in [link to details]. Figure 1 .

[0038] Step S1: Obtain a suspected screening analysis database of neonicotinoid compounds, wherein the suspected screening analysis database is constructed to store the structural information and mass spectrometry prediction information of suspected screening compounds.

[0039] According to an embodiment of the present invention, the suspected screening analysis database is constructed through the following sub-steps S101-S104: Step S101: Screen compounds with neonicotinoid core structures from publicly available chemical databases to construct a suspected screening database; Step S102: Using a literature data retrieval platform, search for neonicotinoid compounds and their metabolites using the names of these compounds as keywords, and extract the structural information of the relevant compounds from the retrieved literature to supplement the compilation of the suspected screening database; Step S103: Using public compound databases such as PubChem, the core structure of neonicotinoid compounds is used as a search template to screen compounds with relevant structures, and their information is added to the suspected screening database. Step S104: Using metabolic transformation simulation tools such as biotransformation, enviPath, and Chemical TransformationSimulator, predict the potential transformation products of neonicotinoid parent compounds, and supplement the structural information of the predicted products into the suspected screening database.

[0040] According to embodiments of the present invention, the publicly available chemical databases include, but are not limited to, the Existing Chemical Substances List of China (IECSC), the Toxic Substances Control Act (TSCA) of the United States, the European Union's Registered, Evaluated, Authorized and Restricted Chemicals List (REACH), the Canadian Domestic Substances List (DSL), the U.S. Environmental Protection Agency's CompTox Chemicals Dashboard, the U.S. Environmental Protection Agency's Long-Term Toxicology Prediction Study Project (ToxCast), and the Norman Priority List.

[0041] According to an embodiment of the present invention, the mass spectrometry prediction information includes ionization mode, adduct ion, precise mass number of adduct ion, retention time, retention time deviation range, theoretical isotope distribution pattern, and secondary mass spectrometry fragment ions.

[0042] Specifically, in suspected case screening analysis, the core parameter used for accurate mass number matching is usually the mass number of the adduct ion of the target compound. The most common is the protonated ion ([M+H)). + The exact mass number of [[M+NH4]] may also be included, and may also include the ammonium ion ([M+NH4)). + ), plus sodium ions ([M+Na)) + These are common addition forms. It should be clarified that although the mass of the chemical formula is the basis for calculations, in actual spectral matching operations, the precise mass numbers of the aforementioned addition ions are used directly.

[0043] Step S2: Perform ultra-high performance liquid chromatography-high resolution mass spectrometry analysis on the standards in the database. Use FreeStyle software to obtain their chromatographic retention time, precise mass number of primary mass spectrometry, and characteristic fragmentation data of secondary mass spectrometry. The characteristic fragmentation data of secondary mass spectrometry includes characteristic fragment ions and neutral lost molecules.

[0044] According to an embodiment of the present invention, the ultra-high performance liquid chromatography conditions are set to a gradient elution program to effectively separate the analytes.

[0045] According to an embodiment of the present invention, high-resolution mass spectrometry analysis of NEOs standards in the database is performed in data-dependent acquisition (DDA) mode.

[0046] Furthermore, the DDA mode employs a primary ion scan and a secondary ion scan under positive ion conditions using an atmospheric pressure chemical ionization source, an electrospray ionization source, or an atmospheric pressure optical ionization source.

[0047] Furthermore, the raw chromatographic-mass spectrometry data obtained in DDA mode were used to acquire chromatographic retention time, primary mass spectrometry exact mass number, and secondary mass spectrometry characteristic fragmentation data using FreeStyle.

[0048] According to embodiments of the present invention, the acquired secondary mass spectrometry fragmentation data includes behaviors such as functional group breakage and neutral loss of compounds.

[0049] According to embodiments of the present invention, the characteristic fragment ions of NEOs obtained contain m / z = 126.0105, m / z = 128.0262, m / z = 144.0211, m / z = 148.0369, m / z = 78.0338, m / z = 80.0495, m / z = 90.0338, m / z = 108.0444, m / z = 71.0491, m / z = 85.0648, m / z = 131.9669, m / z = 181.0542, m / z = 56.0495, m / z = 57.0447, m / z = 113.0168, and m / z = 129.0897; the neutral loss molecules contain m / z = 28.0308, m / z = 45.9924, m / z = 43.9893, m / z = 34.9683, m / z = 35.9761, m / z = 30.0100, m / z = 29.0386, m / z = 27.0104.

[0050] It should be noted that the characteristic fragment ions and neutral loss molecules upon which this invention is based have a dual origin: on the one hand, they originate from the general fragmentation patterns of neonicotinoid compounds that are widely documented in the literature; on the other hand, they originate from characteristic fragments that have not yet been publicly reported and are revealed for the first time through systematic mass spectrometry experiments in this invention. Therefore, the characteristic information covered by this method is not limited to the specific examples listed herein.

[0051] Step S3: Perform ultra-high performance liquid chromatography-high resolution mass spectrometry analysis on the environmental sample to be tested. The mass spectrometry acquisition adopts a strategy that combines multi-module data-dependent acquisition mode and multi-window data-independent acquisition mode to obtain data-dependent acquisition data and data-independent acquisition data of the environmental sample to be tested.

[0052] Among them, the multi-module data-dependent acquisition mode includes indirect secondary mass spectrometry data acquisition under primary ion scanning with characteristic isotope ratios, indirect secondary mass spectrometry data acquisition with precise mass numbers of adduct ions in the suspected screening analysis database, and direct secondary mass spectrometry data acquisition after full scan.

[0053] According to embodiments of the present invention, chromatographic-mass spectrometric analysis of the environmental sample to be tested is performed in a multi-module data-dependent acquisition mode and a multi-window data-independent acquisition mode.

[0054] Furthermore, the multi-module data-dependent acquisition mode and the multi-window data-independent acquisition mode are primary and secondary ion scans under positive ion conditions using atmospheric pressure chemical ionization sources, electrospray ionization sources, or atmospheric pressure optical ionization sources.

[0055] It should be noted that the mass spectrometry ionization source used in the multi-module data-dependent acquisition mode and the multi-window data-independent acquisition mode can be the same or different, but usually the same.

[0056] Understandably, the multi-module data-dependent acquisition mode, with its preset multiple molecular feature triggering mechanisms, can acquire secondary mass spectra of analytes more accurately and efficiently, thus providing a more reliable data foundation for the confirmation of compound structures. Complementing this, the multi-window data-independent acquisition mode employs a strategy of full ion fragmentation within a set mass window, ensuring the complete acquisition of fragment information of all precursor ions without omission, greatly enhancing the ability to discover unknown or low-abundance NEOs pollutants.

[0057] Furthermore, the multi-module data-dependent acquisition mode acquires data covering both primary and secondary mass spectrometry information. The primary mass spectrometry scan range is set to m / z = 100-1000, primarily used to obtain crucial information such as the precise mass number of adduct ions in NEOs-type compounds. The secondary mass spectrometry scan is based on the primary mass-to-charge ratio of its parent ion, specifically used to acquire fragment ion spectra of the target analyte, thus providing data support for subsequent structural analysis.

[0058] According to an embodiment of the present invention, the acquired multi-module data depends on the acquired data and requires high-resolution mass spectrometry deconvolution processing. During this process, an appropriate signal intensity threshold (e.g., 1.0 × 10⁻⁶) can be set. 4 This is to filter out low-level noise and ensure the reliability of subsequent analysis data.

[0059] Furthermore, the multi-window data-independent acquisition mode also acquires primary and secondary mass spectrometry information. The primary scan covers the full mass range of m / z 100-1000 in a segmented manner (e.g., segmented by 100 Da) to estimate the molecular formula of candidate compounds; the secondary scan is based on a preset isolation window (e.g., 5 Da width). By setting a 1 Da overlap region between adjacent windows, the missed detection of ions at the window edge is effectively avoided, and the complete acquisition of characteristic fragment information of neonicotinoid pollutants in complex systems is effectively guaranteed.

[0060] Step S4: Based on the mass spectrometry measurement data and data dependency acquisition data of the standards, target analysis is performed using TraceFinder software to identify the known standards present in the environmental sample to be tested.

[0061] According to an embodiment of the present invention, this step involves target analysis; the process involves systematically comparing high-resolution mass spectrometry reference data of a standard with data-dependent acquisition data of the sample, including matching the precise mass number of adduct ions, chromatographic peak shape, retention time, and secondary mass spectrometry fragment ions, thereby identifying known target compounds present in the environmental sample. This analysis provides a basis for subsequent precise quantification of the target analyte.

[0062] Step S5: Based on the data from the suspected screening analysis database and the chromatographic-mass spectrometry data of the standards, perform suspected screening analysis on the data-dependent collection data and combine it with the set screening and filtering standards to determine the structure of the suspected screening compounds in the environmental sample to be tested that match the database; the determined compounds are non-standards, and their structures are determined or possible structures.

[0063] According to an embodiment of the present invention, step S5 is a suspected screening analysis, the purpose of which is to identify whether there are precursor compounds in the environmental samples that match the suspected screening database, and to perform structural analysis on the identified compounds, whose structures can be classified as known or possible structures; this step is specifically implemented by sub-steps S501 to S503.

[0064] Sub-step S501: The mass number and isotopic distribution of adduct ions detected in the multi-module data-dependent acquisition mode of the sample are compared with the theoretical precise mass number and simulated isotopic mode of compounds in the suspected screening database to initially screen out matching suspected precursor ions. All suspected precursor ions must simultaneously meet multiple preset chromatographic peak quality indicators, including peak shape symmetry, signal-to-noise ratio >3, isotopic mode matching degree >75%, peak shape score in Compound Discoverer software >4.0, and peak width ≤1 minute; any criterion that does not meet the requirements is excluded.

[0065] Sub-step S502: For the suspected precursor ions that have passed the initial screening, further fine match their secondary mass spectrometry fragmentation patterns with the measured secondary mass spectra of the standards, and screen out candidate precursor compounds whose secondary spectra match.

[0066] Sub-step S503: For candidate compounds that have passed the secondary spectrum verification, comprehensively analyze their chromatographic retention behavior (such as retention time, octanol-water partition coefficient, etc.) and mass spectrometry fragmentation pattern, and finally propose the definite or possible structure of the compound, completing the complete process from suspected screening to structure inference.

[0067] Through the aforementioned sub-steps S501-S503, the system achieves accurate identification of suspected neonicotinoid pollutants in environmental samples. Primary screening identifies targets using mass number and isotope distribution; secondary screening effectively eliminates false positive signals through chromatographic peak quality checks; and tertiary screening significantly improves the reliability of detection results through secondary mass spectrometry matching and retention behavior analysis, ultimately achieving reliable structural detection of non-standard compounds.

[0068] Step S6: Based on the chromatographic-mass spectrometry data of the standard and the data-independent acquisition data, non-target analysis is performed on the characteristic fragment ions and neutral loss molecules in the data-independent acquisition data to identify candidate compounds. The identified compounds are non-standards and non-suspected screening compounds, and their structures are determined or possible structures.

[0069] According to an embodiment of the present invention, step S6 is a non-target screening based on characteristic fragment ions and neutral loss molecules (CFIs & NLMs). NEOs pollutants generate common characteristic secondary fragments during mass spectrometry fragmentation, and this characteristic information provides key clues for discovering unknown similar pollutants. This step specifically screens novel candidates that are different from the standards quantified in step S4 and also different from the suspected compounds detected in step S5. By organically combining target, suspected, and non-target analysis strategies, the workflow is optimized while achieving comprehensive coverage detection of neonicotinoid pollutants in environmental samples. Step S6 specifically includes sub-steps S601 to S603.

[0070] Sub-step S601: The system parses the multi-window data in the independent acquisition mode and extracts the ion chromatograms corresponding to the standard CFIs & NLMs one by one from each isolated window.

[0071] Sub-step S602: Based on the extracted ion chromatogram and the primary mass spectrometry information at the corresponding retention time, trace back and infer the potential precursor ions that produce these characteristic fragments.

[0072] Sub-step S603: Compare the estimated precursor ion with the ion information of known standards and suspected compounds to screen out novel candidates that do not belong to the above two categories and determine their possible molecular formulas.

[0073] Through the aforementioned sub-steps S601-S603, a systematic discovery process from characteristic fragments to novel candidates was successfully achieved. The non-target screening method based on CFIs & NLMs can effectively discover unknown NEOs-like compounds, and through complementary verification with target and suspected analysis results, a complete environmental pollutant identification system was constructed.

[0074] Step S7: Based on the chromatographic-mass spectrometry measured data and data-dependent acquisition data of the standards, use advanced mass spectrometry analysis tools to perform feature-based molecular network non-target analysis to identify candidate compounds. The identified compounds are non-standards and non-suspected screening compounds, and their structures are determined or probable structures.

[0075] According to an embodiment of the present invention, step S7 is to perform feature-based non-target analysis of molecular networks based on advanced mass spectrometry analysis tools; step S7 specifically includes sub-steps S701 to S703.

[0076] Sub-step S701: Combine the feature quantization table and MS / MS summary file obtained after preprocessing the mass spectrometry data.

[0077] Sub-step S702: Submit the obtained feature quantification table and MS / MS abstract file to the Global Natural Products Social Molecular Networking (GNPS) platform to construct a molecular network.

[0078] Sub-step S703: Based on the mass-to-charge ratio of precursor ions, locate the characteristic regions of NEOs-type compounds in the network, thereby realizing the systematic extraction and detection of mass spectrometry information of unknown neonicotinoid compounds.

[0079] Through the above sub-steps S701~S703, the systematic mining of unknown NEOs-like compounds with structural similarity from massive mass spectrometry data was successfully realized. This breaks through the dependence of traditional screening methods on prior knowledge and significantly improves the ability to discover unknown t-NEOs and unknown a-NEOs.

[0080] Step S8: Perform preliminary structural analysis on candidate compounds (non-standards and non-suspected screening compounds), and then add their structural information and mass spectrometry prediction information to the suspected screening analysis database to construct a retrospective suspected screening analysis database including candidate compounds.

[0081] According to an embodiment of the present invention, step S8 includes sub-steps S801 to S804.

[0082] Sub-step S801: Extract CFIs from the total ion flow (TIC) plot of multi-window data in acquisition-independent mode.

[0083] Sub-step S802: Based on the principle that the liquid chromatography retention times of NEOs-type compound precursor ions and their characteristic fragment ions are the same, the liquid chromatography retention times of the precursor ions are marked.

[0084] Sub-step S803: Preliminary inference of the molecular formula and structure of NEOs-type compounds is made using fragmentation prediction tools (such as MetFrag, CFM-ID, MS-FINDER, MassFrontier, Molecular Structure Correlator, MS-Fragmenter, MOLGEN-MS and SIRIUS-CSI:FingerID) and compound database searches (such as PubChem and Chemspider) to obtain their structural information and mass spectrometry prediction information.

[0085] Sub-step S804: Add its structural information and mass spectrometry prediction information to the suspected screening analysis database to construct a retrospective suspected screening analysis database including candidate compounds.

[0086] Through the above sub-steps S801~S804, a retrospective screening database with self-learning and iterative optimization capabilities was successfully constructed, transforming newly discovered candidate compounds in non-targeted analysis into traceable suspected targets, significantly improving the method's ability to continuously monitor and identify unknown and newly emerging pollutants.

[0087] Step S9: Based on the data from the constructed retrospective suspected screening analysis database, the collected data is re-screened according to the collected data to verify and further confirm the determination or possible structure of the compound that successfully matches the candidate compound in the environmental sample to be tested.

[0088] According to an embodiment of the present invention, this step utilizes the self-learning retrospective database constructed in step S8 to systematically re-mine the multi-module data dependent on the acquisition pattern data that has been collected (repeating step S5). By matching and verifying the experimental data with the newly added candidate compound information, the automatic confirmation and structural correction of the previous non-target screening results are realized, which significantly improves the reliability of the detection results and the reproducibility of the method.

[0089] The compounds identified in steps S5 to S9 are combined to obtain the neonicotinoid compounds that have been comprehensively detected from the environmental samples to be tested.

[0090] As a second aspect of the present invention, a method for risk assessment of neonicotinoid compounds in the environment is provided, comprising: a) Based on the physicochemical properties of the detected neonicotinoids, assess their persistence, bioaccumulation, migration and toxicity. Determine whether the detected neonicotinoids belong to the persistent, bioaccumulative, migratory and toxic substances by judging whether each of the persistent, bioaccumulative, migratory and toxicity criteria is met. b) An ecological risk assessment of the detected neonicotinoids is conducted based on a risk quotient model, with the risk quotient calculated using the following formula: (1); (2); In the formula, RQ sum To accumulate ecological risk levels, Let i be the measured concentration value of the i-th neonicotinoid compound. The predicted no-effect concentration and LC-1 of the i-th neonicotinoid compound. 50 The median lethal concentration (LD50) and EC50 50 The half-maximal effect concentration is used, and AF is the evaluation factor. c) Based on existing species sensitivity distribution models, utilize toxicological data of neonicotinoids (including: acute toxicity of neonicotinoids (LC50, LD50)). 50 Or the half-maximal effect concentration EC 50 The species sensitivity distribution curve was fitted using data on chronic toxicity (NOEC concentration with no observed effect, exposure days ≥ 10 days) and the harmful concentration HC5 that protects 95% of aquatic species was calculated based on the curve.

[0091] According to embodiments of the present invention, the method is applicable to the comprehensive analysis of various environmental media, covering three major categories: water samples, solid samples, and biological samples. Water samples include, but are not limited to, industrial wastewater, municipal wastewater treatment plant influent and effluent, surface water (river water, lake water), seawater, groundwater, drinking water, and rainwater; solid samples include sediments, soil, wastewater treatment plant sludge, indoor dust, and atmospheric particulate matter; biological samples include human biological samples (breast milk, urine, serum), animal tissues (organs of fish, birds, sharks, and mollusks), and plant samples. Before analysis, appropriate enrichment and extraction methods should be selected based on the sample characteristics to ensure effective recovery of the target pollutants, laying the foundation for subsequent accurate detection and risk assessment.

[0092] The following detailed description provides several specific embodiments to illustrate the technical solution of the present invention. It should be noted that the specific embodiments described below are merely examples and are not intended to limit the scope of the invention.

[0093] In the following examples, some of the reagents and detection instruments are described below: Reagents: 30 NEOs standards are listed in Table 1 below.

[0094] Liquid Chromatography-Mass Spectrometry (LC-MS): Ultra-High Performance Liquid Chromatography (UHPLC) and High Resolution Mass Spectrometry (HMS) System TM 480 high resolution massspectrometry); Chromatographic column: ACQUITY UPLC Ⓡ HSS T3 column (Waters Inc., 1.8μm, 2.1mmi.d.×100mm length).

[0095] Example 1 Example 1 is a laboratory testing example, focusing on the detection of NEOs in spiked samples using an optimized UPLC-HRMS analytical method. The specific steps include (see details). Figure 2 ): Step 1: Summarize the 30 NEOs found in the environment as shown in Table 1 through text mining. These 30 traditional NEOs cover three types of NEO molecular structures, namely p-NEOs, t-NEOs, and a-NEOs.

[0096] Table 1 .

[0097] Step 2: Obtain publicly available chemical lists including those from IECSC, TSCA, REACH, DSL, US EPA CompTox ChemicalsDashboard, EPA's ToxCast chemical library, and NORMAN Suspect List Exchange, and compile all chemicals on these lists. Based on the lists obtained on October 1, 2024, a total of 341,432 chemicals were recorded. On one hand, the Tanimoto similarity algorithm was used in a Python environment to calculate compounds with a similarity greater than or equal to 0.5 to t-NEOs; on the other hand, the RDKit library in a Python environment was used to extract compounds (115 NEOs compounds) containing the core structures of t-NEOs, such as chloropyridine rings (ClC1=NC=C(C)C=C1), chlorothiazolium rings (ClC1=NC=C(C)S1), and trifluoromethylpyridine rings (FC(F)(F)C1=CC=NC=C1). Using a literature data retrieval platform, searches were conducted using the names of neonicotinoid compounds and their metabolites as keywords. Structural information of relevant compounds (308 NEOs) was extracted from the retrieved literature to supplement the aforementioned suspected screening database. Using public compound databases such as PubChem, compounds with relevant structures (211 NEOs) were screened using the core structures of neonicotinoid compounds as search templates, and their information was added to the suspected screening database. Metabolic transformation simulation tools such as biotransformation, enviPath, and Chemical Transformation Simulator were used to predict potential transformation products of neonicotinoid parent compounds (643 NEOs), and the structural information of the predicted products was supplemented to the suspected screening database (see details). Figure 3 ).

[0098] Step 3: Following Step 2, compile the final results into a new Excel file as a database for suspected NEOs screening analysis, containing the name, molecular formula, SMILES formula, and theoretical monoisotope mass of the suspected screening compounds.

[0099] Step 4: Select 30 NEOs with reference standards from the constructed suspected screening analysis database (see Table 1) and then perform UPLC-HRMS detection.

[0100] Step 5: Analyze the secondary mass spectrometry information in the Multi Module-DDA data using Xcalibur Qual Browser or FreeStyle software to obtain the mass spectrometry fragmentation modes of NEOs, with the aim of obtaining the CFIs and NLMs of NEOs (as shown in Table 2).

[0101] Table 2 ; .

[0102] Step Six: Non-target analysis based on CFIs & NLMs can be performed in MS 1 and MS 2 The scans alternate in a loop, with each loop consisting of one MS. 1 Scan, followed by five MS scans. 2 Scanning. Based on the characteristic mass spectrometry behavior of NEOs, corresponding precursor ions can be traced and labeled according to their liquid chromatography retention times and characteristic fragment information. The molecular formula of the candidate is estimated using the Seven Golden Rules, where the elemental composition is limited to C. 0-100 H 0-200 O 0-10 N 0-10 P 0-3 S 0-3 F 0-10 Cl 0-10 Br 0-10 It allows the inclusion of heteroatoms such as P, S, F, Cl, and Br. Furthermore, by combining computer-aided structural analysis algorithms with public compound databases, the molecular formula and possible structures of the target compound are systematically inferred and cross-validated.

[0103] Based on non-target analysis of characteristic molecular networks, starting with known NEOs, we systematically infer their unknown t-NEOs and a-NEOs. By analyzing the precursor ion mass-to-charge ratios shown at each node in the molecular network, we can accurately locate the characteristic regions where target NEOs are located. Then, we extract all feature groups within this sub-network that have highly similar secondary mass spectra. These feature groups are highly likely to correspond to t-NEOs and a-NEOs with unknown transformation pathways. Combining this with the list of known t-NEOs and a-NEOs established in step two, we can further screen to obtain a list of candidate t-NEOs and a-NEOs, and complete a systematic structural annotation of potential t-NEOs and a-NEOs.

[0104] Step 7: To simulate the test environment, prepare 1 mL of methanol solution containing 30 NEOs standards, each with a concentration of 100 μg / L. Then, separately test the solutions in Multi Module-DDA and Multi Window-DIA (see details). Figure 4 UPLC-HRMS detection is performed in mode ).

[0105] The UPLC conditions were as follows: column temperature was 45℃; the mobile phase consisted of an aqueous solution (A) containing 0.1% formic acid and methanol (B) containing 0.1% formic acid. The gradient elution program was as follows: first, 5% B was held for 2 min; then B was increased to 100% over 8.5 min; then 100% B was held for 4 min; then B was decreased to 5% over 0.5 min; finally, 5% B was held for 4 min; the mobile phase flow rate was 0.2 mL / min; and the injection volume was 1 μL.

[0106] HRMS conditions: General instrument parameters are set as follows: ESI source, spray voltage 3500V (ESI+), spray voltage 2500V (ESI-), ion transmission tube temperature 320℃, nebulizer gas temperature 350℃; sheath gas, auxiliary gas, and purge gas pressures are set to 30, 1, and 1 Arb, respectively. In data-dependent acquisition mode, primary mass spectrometry is acquired by an electrostatic orbital trap analyzer with a resolution of 120,000 (@m / z 200), a mass scan range of m / z 100-1000, a maximum injection time of 100 ms, automatic gain control mode set to Standard, and S-lens RF level set to 50%. Secondary mass spectrometry uses a high-energy collision dissociation mode with a resolution of 30,000 (@m / z 200) and stepped collision energies (20%, 40%, 60%); the precursor ion is isolated by a quadrupole (window width 1 Da), and the resulting fragment ions are detected by the electrostatic orbital trap. The scan range is automatically determined based on the precursor ion mass.

[0107] High-resolution mass spectrometry conditions for multi-module data-dependent acquisition mode: In indirect secondary mass spectrometry data acquisition, the target isotope ratio is the proportion of Cl characteristic isotopes, namely Cl1, Cl2, and Cl3; the target mass number is the precise mass number of adduct ions of all NEOs in the suspected screening analysis database. The deviations of the target isotope ratio and the target mass number are set to 10% and 5 ppm, respectively. The remaining settings are consistent with the general instrument parameter settings.

[0108] High-resolution mass spectrometry conditions for multi-window data-independent acquisition mode: using MS 1 With MS 2 Alternating scan cyclic acquisition mode, each complete cycle contains one full-range MS. 1 Scan and five consecutive MS 2Subscanning. To achieve full coverage analysis in the m / z range of 100-1000, nine independent DIA acquisition methods were designed, each covering a mass range of approximately 100 Da (100-200, 200-300, ..., 900-1000 Da, respectively). Each DIA method contains 20 consecutive isolation windows with a width of 5 Da, and a 1 Da overlap area between adjacent windows. This design effectively avoids the loss of ion signals due to the edge effect of the isolation windows, ensuring comprehensive capture of compounds in complex samples.

[0109] Furthermore, the NEOs detected in steps three and six can be semi-quantitatively analyzed using machine learning models that predict ionization efficiency.

[0110] Through the specific steps described above, a 100% identification rate of 30 NEOs was achieved in methanol standard solution, demonstrating the excellent accuracy of this method. Specifically, the target analysis in step one, the suspected screening analysis in step three, and the non-target analysis in step six all independently and correctly identified the target analytes. These three strategies, each with its own advantages and complementing each other, together constitute a rigorous analytical system, providing a reliable guarantee for the comprehensive detection of NEOs in complex environmental media.

[0111] Example 2 Example 2 aims to test the applicability of the detection method to real-world environmental samples. A 500 mL spiked surface water sample was selected for the experiment. By introducing a real environmental matrix, the enrichment efficiency of the sample pretreatment process for NEOs and the overall detection capability of the method under complex interference conditions were systematically evaluated.

[0112] The operation procedure of Example 2 is the same as that of Example 1, with the core difference being the pretreatment step of 500 mL of surface water environmental sample in step seven. The sample is first filtered through a 0.7 μm glass fiber membrane, and then the pH is adjusted to approximately 7.0 to ensure effective adsorption of both neutral and ionizable analytes. A 12-position solid-phase extraction device is used for extraction and enrichment. The extraction column is activated with 10 mL of methanol and equilibrated with 10 mL of ultrapure water sequentially, and then loaded at a rate of 3-5 mL / min. After loading, the extraction column is vacuum dried for 60 minutes, followed by elution with 12 mL of methanol in three fractions. The eluents are combined and concentrated to near dryness under a gentle nitrogen flow, then reconstituted with 1 mL of methanol-water solution (1:1, v / v). After centrifugation at 14000 rpm for 30 minutes, 200 μL of the supernatant is transferred to a sample vial and stored at -20 °C for instrument analysis.

[0113] Figure 5 This refers to the matrix spiked recovery rate of NEOs in the surface water environmental samples of Example 2 of the present invention. Figure 5As shown in the figure, the analysis results show that all 30 NEOs (11 p-NEOs, 11 t-NEOs, and 9 a-NEOs) were identified, indicating that this comprehensive identification method is applicable to complex environmental media and is less affected by matrix interference.

[0114] Example 3 Example 3 aims to test the comprehensive detection capability of the method of the present invention for NEOs in real environmental samples. The test samples were collected from typical surface water in the Yangtze River Basin of China, and the NEO composition was evaluated through a systematic analysis process.

[0115] This embodiment 3 follows the basic process of embodiment 1, with the core difference being the full-process treatment of the surface water sample in step seven: including glass fiber membrane filtration, pH adjustment to neutral, HLB solid-phase extraction column enrichment and purification, nitrogen blowing concentration, and solvent resolution. This method fully considers the complexity of the environmental matrix, ensuring the efficient recovery of neutral and ionizable NEOs.

[0116] Using the method of this invention, 71 neonicotinoid pollutants were detected in surface water samples from the Yangtze River Basin, including 13 structural analogs and 12 transformation products. Taking compound C8H9O2N6Cl as an example, its characteristic fragmentation behavior and structure estimation process in mass spectrometry are demonstrated.

[0117] Figures 6-8 This describes the entire process of high-resolution mass spectrometry data analysis in Embodiment 3 of the present invention. For example... Figure 6 As shown, taking denitrified imidacloprid as an example, matching features were extracted at retention time RT = 6.3 minutes in ESI+ mode, revealing a strong chromatographic peak. By comparing the mass spectrometric information of denitrified imidacloprid in standards and environmental samples, the presence of denitrified imidacloprid was finally confirmed. Figure 7 The image shows the molecular network of UPLC-HRMS data in ESI+ mode. Feature clusters are associated with denitrified imidacloprid and nicotine (a-NEOs), respectively. Each node represents a feature; the node size is related to the average intensity of the feature in each sample, while the color intensity of the edges represents the cosine similarity of the MS / MS spectra between nodes. Nodes are labeled as p-NEOs, t-NEOs, a-NEOs, isotope-labeled internal standards, unidentified compounds, or non-neonicotinic compounds. Figure 8The diagram shows a representative workflow for detecting neonicotinoid-related compounds in non-target analysis using CFIs and NLMs. (i) shows two parallel experiments set up in full scan and data-independent acquisition modes. (ii) shows the extract ion chromatograms of the two CFIs; the "blue petal" symbol indicates the presence of one CFI at this retention time. (iii) shows the precursor ion retention time (8.64 min) of the unknown neonicotinoid-related compound, labeled by the two CFIs. (iv) shows the MS at 8.64 min. 1 Mass spectrum. (v) shows the C8H content in the sample. 10 MS of ClN6O2+ (PubChem CID 89378190) 2 Mass spectrum.

[0118] This embodiment 3 demonstrates that the method can efficiently identify NEOs, filling a gap in the existing pollutant monitoring inventory.

[0119] Example 4 Example 4 aims to test the ability of the method of the present invention to comprehensively detect and accurately assess the risks of NEOs in real environmental samples. The test samples were collected from typical surface water in the Yangtze River Basin of China.

[0120] Figure 9 This is a risk assessment of NEOs in surface water of the Yangtze River basin in Embodiment 4 of the present invention. Figure 9 As shown in Figure a, the environmental risks of the 71 NEOs detected in Example 3 were calculated using open-source prediction tools, including their persistence (P), bioaccumulation (B), mobility (M), and toxicity (T), and NEOs belonging to PBT and PMT were detected. Regarding the toxicity (T) assessment criteria, this invention comprehensively considers multi-dimensional toxicity data, including aquatic ecotoxicity (covering acute toxicity to algae, fleas, and fish), endocrine disruption effects, mutagenicity, carcinogenicity, developmental toxicity, skin sensitization, and neurotoxicity. Specifically, when multiple computational toxicology tools predict a certain toxicity endpoint, the arithmetic mean is used as the consensus value for risk assessment to ensure the robustness and reliability of the evaluation results. Figure 9 As shown in Figures b and c, an additive model was used to obtain the final ecological risk assessment results for the 71 NEOs RQ values ​​detected in Example 3. For example, RQ... sum If the value is ≥ 1, the ecological risk level is high; if it is ≤ 0.1, the ecological risk level is high. sum If RQ < 1, the ecological risk level is medium; if RQ ≤ 0.01, the ecological risk level is medium. sum If the value is less than 0.1, the ecological risk level is low; RQ sum If the value is less than 0.01, the ecological risk level is no significant risk. For example... Figure 9As shown in Figures d and e, the Species Sensitivity Distribution (SSD) model was used to estimate the hazardous concentration (HC5) affecting 5% of species, i.e., the threshold that would protect 95% of aquatic organisms. Acute toxicity assessments revealed that the lower trophic level insect species *Cloeon sp.* was most sensitive to imidacloprid, with an effective concentration as low as 0.005 μg / L. Conversely, the higher trophic level fish species *Labeo rohita* showed strong tolerance to acute imidacloprid exposure, with an acute threshold concentration as high as 5.5 × 10⁻⁶. 5 μg / L. Chronic toxicity assessment revealed that the Nile tilapia (Oreochromis niloticus) was the most susceptible species to the chronic effects of imidacloprid, with adverse effects observed at a concentration of 0.01 μg / L. In contrast, Labeo rohita exhibited strong resistance under chronic exposure conditions, even at concentrations as high as 1.2 × 10⁻⁶ μg / L. 5 No significant effect was observed with μg / L. The mean HC5 values ​​calculated based on acute and chronic data were 0.050 μg / L (95% confidence interval: 0.011–0.226 μg / L) and 0.075 μg / L (95% confidence interval: 0.033–0.174 μg / L), respectively. These results highlight the significant differences in susceptibility to imidacloprid among different species, suggesting that species diversity should be fully considered in ecological risk assessments.

[0121] This invention, through testing with multiple embodiments, establishes a comprehensive analytical method for neonicotinoid pollutants in the environment based on high-resolution mass spectrometry and a multi-level risk assessment system. This method possesses the following key advantages: broad applicability, adaptable to various complex environmental matrices such as water samples, solid samples, and biological samples; precise analytical capability, achieving comprehensive structural identification of neonicotinoid pollutants and their derivatives through a three-level detection strategy of target-suspected-non-target; high-efficiency analytical characteristics, significantly improving the analytical efficiency of large-scale environmental samples by combining automated pretreatment and high-throughput mass spectrometry acquisition; and risk-oriented function, establishing a risk ranking mechanism based on physicochemical properties and ecotoxicological data to effectively screen pollutants for priority control. This method provides reliable technical support for solving the systematic monitoring and risk management of NEOs and has significant application value in environmental supervision, ecological safety assessment, and other fields.

[0122] The embodiments described above provide a detailed explanation of the technical solutions and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, additions, and equivalent substitutions made within the scope of the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for comprehensive detection of neonicotinoids in an environment, characterized by, Includes the following steps: (1) Construct a database for suspected screening and analysis of neonicotinoid compounds; include: (1-1) A suspected screening database was constructed by screening compounds with neonicotinoid core structures from publicly available chemical databases; (1-2) Using a literature data retrieval platform, search for neonicotinoid compounds and their metabolites using the names of these compounds as keywords, extract relevant compounds from the retrieved literature and add them to the suspected screening database; (1-3) Using public compound databases, the core structures of neonicotinoid compounds are used as search templates to screen compounds with relevant structures and add them to the suspected screening database; (1-4) Predict potential transformation products of neonicotinoid parent compounds using metabolic transformation simulation tools, and supplement the predicted transformation products into the suspected screening database; (2) Ultra-high performance liquid chromatography-high resolution mass spectrometry (UHPLC-HDMS) was performed on the standards and environmental samples of neonicotinoids to obtain chromatographic-mass spectrometric data of the standards and environmental samples; including: (2-1) Ultra-high performance liquid chromatography-high resolution mass spectrometry was used to analyze the standards of neonicotinoids. The mass spectrometry acquisition adopted the data-dependent acquisition mode to obtain the chromatographic-mass spectrometry data of the standards. (2-2) Ultra-high performance liquid chromatography-high resolution mass spectrometry analysis was performed on the environmental samples to be tested. Mass spectrometry acquisition adopted multi-module data-dependent acquisition mode and multi-window data-independent acquisition mode respectively to obtain data-dependent acquisition data and data-independent acquisition data of the environmental samples to be tested. The multi-module data-dependent acquisition modes are: ① indirect secondary mass spectrometry data acquisition under primary ion scanning containing characteristic isotope ratios, ② indirect secondary mass spectrometry data acquisition under precise mass numbers of adduct ions in the suspected screening analysis database, and ③ direct secondary mass spectrometry data acquisition after full scan. In the multi-window data independent acquisition mode, the first-level scan covers the full quality range of m / z 100-1000 in a segmented manner; (3) Target analysis, suspected screening analysis, and non-target analysis are performed on the chromatographic-mass spectrometry data of the environmental sample to be tested to obtain the standard compounds, suspected screening compounds, and non-target candidate compounds present in the environmental sample; the non-target analysis includes: (3-I) Based on the chromatographic-mass spectrometry data of the standards and the data-independent acquisition data, non-target analysis is performed on the characteristic fragment ions and neutral loss molecules in the data-independent acquisition data to obtain candidate compounds; (3-II) Based on the chromatographic-mass spectrometry data and data-dependent acquisition data of the standards, non-target analysis of the molecular network based on the features of the data-dependent acquisition data is performed to obtain candidate compounds; The candidate compounds obtained by combining steps (3-I) and (3-II) are the non-target candidate compounds; (4) Perform preliminary structural analysis on non-target candidate compounds and add them to the suspected screening analysis database to construct a retrospective suspected screening analysis database that includes non-target candidate compounds; (5) Based on the retrospective suspected screening analysis database, the chromatographic-mass spectrometry data of the environmental samples to be tested are re-screened to obtain compounds in the environmental samples to be tested that successfully match non-target candidate compounds; The compounds obtained in steps (3)-(5) are combined to obtain neonicotinoid compounds that can be comprehensively detected from the environmental samples to be tested.

2. The method for comprehensive detection of neonicotinoids in the environment according to claim 1, characterized in that, In step (3), the target analysis includes: performing target analysis based on the chromatographic-mass spectrometry data of the standard and the data-dependent acquisition data of the environmental sample to be tested, and obtaining the standard compound present in the environmental sample to be tested.

3. The method of claim 1, wherein the method is performed in an environment comprising a plurality of neonicotinoid compounds. In step (3), the suspected screening analysis includes: (3-i) Preprocess the data dependent on the acquired data; the preprocessing includes deconvolution, peak alignment, peak filling and blank signal subtraction; (3-ii) The preprocessed data depends on the mass number and isotope distribution detected in the collected data and is compared with the theoretical exact mass number and simulated isotope pattern of the compounds in the suspected screening analysis database. Automatic screening and sorting are performed based on preset multiple filtering criteria to initially screen out matching suspected precursor ions. (3-iii) Match the secondary mass spectra of suspected precursor ions with public mass spectrometry databases to screen out candidate precursor compounds whose secondary spectra match. (3-iv) Analyze the chromatographic retention behavior and mass spectrometry fragmentation pattern of the candidate precursor compounds to determine their structure, which is then identified as the suspected screening compound.

4. The method of claim 1, wherein the method is performed in an environment comprising a plurality of neonicotinoid compounds. Step (3-I) includes: (3-I1) Data analysis is independent of the acquired data. Ion chromatograms corresponding to the characteristic fragment ions and neutral lost molecules of the standard are extracted one by one from each isolation window. (3-I2) Based on the extracted ion chromatogram and the first-order mass spectrometry information at the corresponding retention time, the potential precursor ions that generate these characteristic fragments are traced back and inferred. (3-I3) Compare the ion information of potential precursor ions with that of standards and suspected compounds to screen out novel candidates that do not belong to standards and suspected compounds, and determine their structures.

5. The method of claim 1, wherein the method is performed in an environment comprising a plurality of neonicotinoid compounds. Step (3-II) includes: (3-II1) Combine the feature quantization table and MS / MS summary file obtained after data-dependent data preprocessing; (3-II2) Submit the characterization table and MS / MS abstract file to the Global Natural Product Molecular Network Platform to construct a molecular network; (3-II3) Based on the mass-to-charge ratio of precursor ions, the characteristic regions of neonicotinoid compounds are located in the molecular network to obtain unknown neonicotinoid compounds with structural similarity.

6. A comprehensive system for detecting neonicotinoids in the environment, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the comprehensive detection method for neonicotinoids in the environment as described in any one of claims 1-5.

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