Non-targeted analysis method for automatically screening key toxic substances based on RDA and related equipment

Abstract

The invention discloses a non-targeted analysis method and related equipment for automatically screening key toxic substances based on RDA, and the method comprises the following steps: obtaining a mixed sample through solid-phase extraction and isotope labeling; analyzing the mixed sample through high performance liquid chromatography and high resolution mass spectrum to obtain original mass spectrum data; performing chromatographic peak detection and identification on the original mass spectrum data to obtain a peak list; optimizing parameters are preset, isotope peak pair screening and matching operation is conducted on chromatographic peaks in the peak list, and a candidate pairing list is obtained; evaluating the candidate adduct pairs in the candidate pairing list to obtain a paired list after scoring; performing data cleaning on the paired list after scoring to obtain a target paired list; and performing toxic substance structure analysis on the target adduct ions in the target pairing list to obtain a target candidate structure. The method can efficiently, accurately and automatically process RDA data, improves the accuracy of toxic substance structure analysis, and can be widely applied to the technical field of substance analysis.

Description

Technical Field

[0001] This invention relates to the field of material analysis technology, and in particular to a non-targeted analysis method and related equipment for automated screening of key toxic substances based on RDA. Background Technology

[0002] Disinfection processes (such as chlorination and chlorination) can effectively kill pathogenic microorganisms in aquatic plants, but disinfectants can react with precursors such as natural organic matter and pollutants in the water to generate disinfection byproducts. Currently, over 700 disinfection byproducts have been reported, but a large number of unknown, potentially highly toxic disinfection byproducts remain unidentified, posing potential health risks. Traditional non-targeted screening techniques aim to detect all compounds in a water sample without bias, but these techniques can generate tens of thousands of mass spectrometry features in a single analysis, most of which are background interference, instrument noise, or low-toxicity substances. Furthermore, manually screening, analyzing, and identifying unknown compounds from massive amounts of data is extremely time-consuming and labor-intensive, heavily reliant on the analyst's experience. Traditional methods cannot distinguish whether detected compounds are biotoxic, resulting in a significant amount of effort being spent identifying non-toxic or low-toxic substances, while truly high-risk toxins may be missed. Therefore, reaction-directed analysis (RDA) has emerged. However, in complex HPLC-HRMS full-scan data, the signals of GSH-DBP adducts are weak and submerged among thousands of other ion signals, making direct and accurate localization difficult. Furthermore, current RDA data processing requires manual screening and pairing from tens of thousands of mass spectrometry peaks, a cumbersome, inefficient, error-prone, and reproducible process. While some bioinformatics tools exist for metabolomics isotope labeling data processing (such as X13CMS, IsoMS, and DeltaMS), they are not specifically designed for GSH-DBP adducts in RDA scenarios. Their default parameters (such as retention time windows, intensity ratio ranges, and adduct quantity assumptions) can lead to numerous false positives or false negatives when directly applied in this field. Summary of the Invention

[0003] In view of this, the main objective of the embodiments of the present invention is to provide a non-targeted analysis method and related equipment for automated screening of key toxic substances based on RDA, in order to solve at least one of the problems of the prior art. The present invention can efficiently, accurately and automatically process RDA data and improve the accuracy of toxic substance structure analysis.

[0004] To achieve the above objectives, one aspect of the present invention provides a non-targeted analysis method for automated screening of key toxic substances based on RDA, the method comprising: Mixed samples were obtained through solid-phase extraction and isotope labeling; The mixed sample was subjected to non-targeted analysis by high performance liquid chromatography and high resolution mass spectrometry to obtain raw mass spectrometry data; The raw mass spectrometry data were subjected to chromatographic peak detection and identification to obtain a peak list; Based on preset optimization parameters, isotope peak pair screening and matching operations are performed on the chromatographic peaks in the peak list to obtain a candidate pairing list; The candidate adduct pairs in the candidate pairing list are evaluated to obtain a scored pairing list; The scored pairing list is then cleaned to obtain the target pairing list. The target adduct ions in the target pairing list are subjected to toxic substance structure analysis to obtain the target candidate structures.

[0005] In some embodiments, obtaining a mixed sample through solid-phase extraction and isotope labeling includes the following steps: Obtain simulated water samples; The simulated water sample was disinfected to obtain a disinfected reaction solution; The disinfected reaction solution was subjected to a quenching reaction to obtain a quenched water sample; The quenched water sample was enriched and concentrated by solid-phase extraction to obtain a concentrated water sample. The concentrated water sample is divided into a first water sample to be labeled and a second water sample to be labeled. Add a common glutathione probe to the first water sample to be labeled to generate a water sample labeled with a common probe. The deuterated glutathione probe was added to the second water sample to be labeled to generate a deuterated probe-labeled water sample; The ordinary probe-labeled water sample and the deuterated probe-labeled water sample are mixed in equal volumes to obtain the mixed sample.

[0006] In some embodiments, the step of detecting and identifying chromatographic peaks in the raw mass spectrometry data to obtain a peak list includes the following steps: The raw mass spectrometry data is converted to a preset format file. The preset format file is analyzed and chromatographic peaks are detected and identified using an R language program to obtain the peak list.

[0007] In some embodiments, the step of performing isotope peak pair screening and matching operations on the chromatographic peaks in the peak list according to preset optimization parameters to obtain a candidate pairing list includes the following steps: The preset optimization parameters are pre-set; the preset optimization parameters include a preset mass deviation range, a preset retention time window, and a preset intensity ratio range; Based on the preset mass deviation range, the preset retention time window, and the preset intensity ratio range, the peak list is subjected to isotope peak pair filtering and matching operations to obtain the hydrogen-labeled isotope peaks and corresponding deuterium-labeled isotope peaks in the peak list. The candidate pairing list is obtained based on the hydrogen-labeled isotope peaks and the corresponding deuterium-labeled isotope peaks.

[0008] In some embodiments, evaluating candidate adduct pairs in the candidate pairing list to obtain a scored pairing list includes the following steps: The similarity between the hydrogen-labeled isotope peaks and the deuterium-labeled isotope peaks in the candidate adduct pairs was obtained by using the Pearson correlation coefficient and cross-correlation function. Obtain the first mass-to-charge ratio, first retention time, and first peak intensity of the hydrogen-labeled isotope peak; Obtain the second mass-to-charge ratio, second retention time, and second peak intensity of the deuterium-labeled isotope peak; A quality score is obtained based on the first mass-to-charge ratio and the second mass-to-charge ratio; Based on the first retention time and the second retention time, obtain a retention time score; An intensity score is obtained based on the first peak intensity and the second peak intensity; A comprehensive score is obtained based on the quality score, the retention time score, and the intensity score. The scoring pairing list is obtained based on the similarity, the overall score, and the candidate pairing list.

[0009] In some embodiments, the data cleaning operation on the scored pairing list to obtain the target pairing list includes the following steps: The target pairing list is obtained by removing source fragment interference, excluding natural isotope signals, and filtering salt adducts from the scored pairing list.

[0010] In some embodiments, the step of performing toxic substance structure analysis on the target adduct ions in the target pairing list to obtain the target candidate structure includes the following steps: The hydrogen-labeled adduct ions in the target pairing list are used as the target adduct ions; Obtain the hydrogen-labeled adduct mass number of the hydrogen-labeled adduct ion; Based on the mass number of the hydrogen-labeled adduct, the predicted molecular formula of the adduct is obtained; Based on several reaction mechanisms, the predicted adduct molecular formula, and the glutathione molecular formula, several predicted toxic substance molecular formulas were obtained. By using the database and combining the reaction mechanism, the compound structure of several predicted toxic substances was searched to obtain initial candidate structures. Obtain the secondary mass spectrum of the hydrogen-labeled adduct ion; Based on the reaction mechanism corresponding to the initial candidate structure, the initial candidate structure is combined with the glutathione structure to obtain the candidate adduct structure; The candidate adduct structure is matched and the secondary mass spectrum of the hydrogen-labeled adduct is evaluated to obtain a goodness-of-fit score, and the candidate adduct structure with the highest goodness-of-fit score is selected as the target candidate structure.

[0011] To achieve the above objectives, another aspect of the present invention proposes a non-targeted analysis device for automated screening of key toxic substances based on RDA, the device comprising: The sample preparation module is used to obtain mixed samples through solid-phase extraction and isotope labeling; The non-targeted analysis module is used to perform non-targeted analysis on the mixed sample using high performance liquid chromatography and high resolution mass spectrometry to obtain raw mass spectrometry data; The first automated data processing and adduct identification module is used to perform chromatographic peak detection and identification on the raw mass spectrometry data to obtain a peak list; The second automated data processing and adduct identification module is used to perform isotope peak pair screening and matching operations on the chromatographic peaks in the peak list according to preset optimization parameters to obtain a candidate pairing list. The third automated data processing and adduct identification module is used to evaluate the candidate adduct pairs in the candidate pairing list and obtain a scoring pairing list; The fourth automated data processing and adduct identification module is used to perform data cleaning operations on the scored pairing list to obtain the target pairing list; The structure analysis module is used to perform toxic substance structure analysis on the target adduct ions in the target pairing list to obtain the target candidate structure.

[0012] To achieve the above objectives, another aspect of the present invention provides an electronic device, the electronic device including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method described above.

[0013] To achieve the above objectives, another aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods described above.

[0014] To achieve the above objectives, another aspect of the present invention provides a computer program product or computer program that includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium and execute the computer instructions to cause the computer device to perform the aforementioned method.

[0015] The embodiments of the present invention include at least the following beneficial effects: The present invention provides a non-targeted analysis method and related equipment for automated screening of key toxic substances based on RDA. This scheme achieves efficient enrichment of target toxic substances through solid-phase extraction and uses isotope labeling to provide an internal standard reference for subsequent high-confidence non-targeted analysis, resulting in a mixed sample; non-targeted analysis of the mixed sample is performed by high-performance liquid chromatography and high-resolution mass spectrometry to obtain raw mass spectrometry data, laying a data foundation for comprehensive screening; chromatographic peak detection and identification are performed on the raw mass spectrometry data to obtain a peak list, realizing the conversion from continuous signals to discrete compound characteristics; isotope peak pairing is performed on the chromatographic peaks in the peak list according to preset optimization parameters. The matching operation automatically and efficiently identifies specific adduct signals generated by the labeling reaction from massive features, greatly narrowing the target range and obtaining a candidate pairing list. The candidate adduct pairs in the candidate pairing list are evaluated to initially distinguish between high-quality real signals and potential false positive matches, resulting in a scored pairing list. Data cleaning is performed on the scored pairing list to obtain a target pairing list for subsequent in-depth analysis of toxic substance structures. The target adduct ions in the target pairing list are then used for toxic substance structure analysis to obtain target candidate structures. This achieves efficient, accurate, and automated processing of RDA data from feature discovery to compound identification, and improves the accuracy of toxic substance structure analysis. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart of a non-targeted analysis method for automated screening of key toxic substances based on RDA provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the automated RDA process provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the data processing flow of the automated analysis program provided in an embodiment of the present invention; Figure 4This is a schematic diagram of a non-targeted analysis research route for automated screening of key toxic substances based on RDA, provided in an embodiment of the present invention. Figure 5 This is a schematic diagram illustrating the identification results of the GSH-DBP adduct formed by nucleophilic substitution according to an embodiment of the present invention; Figure 6 This is a schematic diagram showing the number and proportion of developmentally toxic and mutagenic DBPs identified in the embodiments of the present invention; Figure 7 This is a schematic diagram of the hardware structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this invention; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this invention as detailed in the appended claims.

[0019] It should be noted that although functional modules are divided in the system diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the system or the order in the flowchart. The terms "first / S100" and "second / S200" in the specification, claims, and the foregoing drawings may be used herein to describe various concepts, but unless specifically stated otherwise, these concepts are not limited by these terms. These terms are used only to distinguish one concept from another. For example, first information may also be referred to as second information without departing from the scope of the embodiments of the invention, and similarly, second information may also be referred to as first information. Depending on the context, the words "if" or "when" as used herein may be interpreted as "when," "in response to a determination," or "in the event of a determination."

[0020] The terms “at least one,” “multiple,” “each,” “any,” etc., used in this invention, “at least one” includes one, two, or more than two; “multiple” includes two or more than two; “each” refers to each of the corresponding multiple; and “any” refers to any one of the multiple.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein is for the purpose of describing embodiments of the invention only and is not intended to limit the invention.

[0022] Before providing a detailed description of the embodiments of the present invention, some of the nouns and terms involved in the embodiments of the present invention will be explained first. The nouns and terms involved in the embodiments of the present invention are subject to the following interpretations.

[0023] Reactivity-directed analysis (RDA) is a method for identifying and quantifying specific active components in a mixture by simulating or observing chemical reactions.

[0024] Glutathione (GSH) is an important antioxidant in living organisms that can protect cells from oxidative damage.

[0025] Disinfection byproducts (DBPs) are chemical substances that pose potential health risks and are generated during the disinfection process of drinking water when disinfectants react with certain components in the water.

[0026] The conventional / deuterated glutathione probe (H / D-GSH) is an analytical tool that uses conventional / deuterated glutathione as a probe molecule to capture and identify highly active intermediates or products.

[0027] High-resolution mass spectrometry (HRMS) is a mass spectrometry analysis technique that can accurately measure the mass of ions, thereby accurately inferring their elemental composition.

[0028] High-performance liquid chromatography coupled with high-resolution mass spectrometry (HPLC-HRMS) is used to efficiently separate complex mixtures, while high-resolution mass spectrometry is used to accurately determine the mass and identify the structure of the separated components.

[0029] Natural organic matter (NOM) is a complex and heterogeneous organic mixture formed by the decomposition of plant and animal remains in the natural environment, and it is a key target in water treatment.

[0030] Solid-phase extraction (SPE) is a sample pretreatment technique that uses solid adsorbents to selectively adsorb target compounds, thereby separating, purifying, and enriching the target compounds from liquid samples.

[0031] The Toxicity Estimation Software Tool (TEST) is a software tool that uses quantitative structure-activity relationship models to predict multiple toxicological endpoints of chemicals.

[0032] Disinfection processes (such as chlorination and chlorination) can effectively kill aquatic pathogens and prevent waterborne diseases such as cholera and typhoid. However, disinfectants can react with precursors in water, such as natural organic matter and pollutants, to generate a large number of disinfection byproducts with carcinogenic, teratogenic, and mutagenic effects. Currently, over 700 known DBPs have been reported, but these known DBPs (including regulated types such as trihalomethanes and haloacetic acids) only explain a small fraction (typically <30%) of the overall genotoxicity and cytotoxicity of drinking water. This means that a large number of unknown, potentially highly toxic DBPs have not yet been identified, posing a potential health risk. Among related technologies, there are traditional non-targeted screening techniques and reaction-directed analysis (RDA). Traditional non-targeted screening techniques aim to detect all compounds in a water sample without bias, but this technique generates massive and complex data, has low identification efficiency, and weak toxicity targeting. In contrast to traditional non-targeted screening, existing reaction-directed analysis techniques are based on the core concept that many toxins covalently bind to nucleophilic macromolecules in organisms (such as thiol groups and amino groups on proteins and DNA) through their electrophilic centers, thereby triggering toxic pathways. Therefore, chemical probes containing nucleophilic groups (such as -SH in GSH) are used in vitro to capture electrophilic toxins in water samples, and the resulting adducts can serve as "labels" of toxic substances. By specifically detecting these adducts, RDA can preferentially enrich and identify potentially toxic substances, greatly improving the targeting and efficiency of screening. However, existing RDA methods suffer from difficulties in adduct identification during data processing, the manual screening process is cumbersome, inefficient, error-prone, and has poor repeatability, and there is a lack of dedicated automated tools.

[0033] In view of this, this invention provides a non-targeted analysis method and related equipment for automated screening of key toxic substances based on RDA. This method uses a pair of glutathione (GSH) probes with identical chemical structures but different isotopic compositions, specifically ordinary GSH (H-GSH) and deuterated GSH (D-GSH), to capture toxic substances in water samples in parallel, forming dual-labeled adducts with a specific mass difference, thereby achieving automated and high-precision analysis of subsequent mass spectrometry data. Through peak extraction and peak selection, potential H / D pairings are automatically sought based on preset optimized parameters to obtain a candidate list of H / D-GSH-DBP adduct pairs. Then, a more reliable list of adduct pairs with scores is obtained through scoring and evaluation. The scored pairing list is then cleaned to obtain a highly reliable and finalized list of GSH-DBP adduct pairs. Finally, for each adduct pair in the determined GSH-DBP adduct pair list, DBP structure identification and reaction mechanism analysis are performed. In the structural identification of DBPs, three reaction mechanisms—nucleophilic substitution, nucleophilic addition, and thiol oxidation—were integrated. Based on the DBP molecular formula derived from GSH-DBP addition mass spectrometry data, these three reaction mechanisms were considered in parallel to deduce candidate DBP structures. The structures were then verified using database retrieval and secondary mass spectrometry analysis software to obtain the optimal candidate structure. This approach enabled efficient, accurate, and automated RDA data processing, improving the accuracy of structural analysis of toxic substances.

[0034] The embodiments of the present invention are not only applicable to the analysis of toxic substances (disinfection byproducts DBPs) in drinking water, but can also be applied to the analysis of other toxic substances that may produce toxic electrophilic chemicals, such as wastewater disinfection, swimming pool water, and food processing.

[0035] Figure 1 This is an optional flowchart of a non-targeted analysis method for automated screening of key toxic substances based on RDA provided in an embodiment of the present invention. Figure 1 The method may include, but is not limited to, steps S100 to S700: Step S100: Obtain a mixed sample through solid-phase extraction and isotope labeling; Step S200: Non-targeted analysis of the mixed sample is performed by high performance liquid chromatography and high resolution mass spectrometry to obtain raw mass spectrometry data; Step S300: Perform chromatographic peak detection and identification on the raw mass spectrometry data to obtain a peak list; Step S400: According to the preset optimization parameters, the chromatographic peaks in the peak list are screened and matched for isotope peak pairs to obtain a candidate pairing list. Step S500: Evaluate the candidate adduct pairs in the candidate pairing list to obtain a scored pairing list; Step S600: Perform data cleaning on the scored pairing list to obtain the target pairing list; Step S700: Perform toxic substance structure analysis on the target adduct ions in the target pairing list to obtain the target candidate structure.

[0036] In some embodiments, steps S100 to S700, such as Figure 2 As shown, simulated water samples were disinfected to mimic disinfected water, and key toxic substances were enriched through solid-phase extraction. Two equally divided water samples were then labeled with parallel isotopes using a glutathione probe, and the samples were merged to obtain a mixed sample, providing an internal standard reference for subsequent non-targeted analysis. High-performance liquid chromatography-high-resolution mass spectrometry (HPLC-MS / MS) was used for non-targeted analysis of the mixed sample. Through automated data processing, H / D-GSH-DBP adduct pairs were precisely screened from the generated raw mass spectrometry data. For the target adduct ion in each identified adduct pair, the toxic substance structure was identified and the reaction mechanism was analyzed, ultimately obtaining the optimal target candidate structure. This achieved efficient, accurate, and automated processing of RDA data and in-depth analysis of the structure and toxicity of the toxic substances.

[0037] In step S100 of some embodiments, a simulated disinfectant water sample is prepared, and solid-phase extraction is used to achieve efficient enrichment and concentration of toxic substances. A mixed sample is generated using isotope-labeled RDA reaction, providing an internal standard reference for subsequent high-confidence non-targeted screening and identification.

[0038] In some embodiments, step S100 may include, but is not limited to, steps S110 to S180: Step S110: Obtain a simulated water sample; Step S120: Disinfect the simulated water sample to obtain the disinfected reaction solution; Step S130: Quench the disinfected reaction solution to obtain a quenched water sample; Step S140: The quenched water sample is enriched and concentrated by solid-phase extraction to obtain a concentrated water sample. Step S150: Divide the concentrated water sample into a first water sample to be labeled and a second water sample to be labeled. Step S160: Add the ordinary glutathione probe to the first water sample to be labeled to generate an ordinary probe-labeled water sample; Step S170: Add the deuterated glutathione probe to the second water sample to be labeled to generate a deuterated probe-labeled water sample; Step S180: Mix the water sample labeled with ordinary probes and the water sample labeled with deuterated probes in equal volumes to obtain a mixed sample.

[0039] In step S110 of some embodiments, simulated source water is obtained as a simulated water sample. Optionally, an SRNOM solution is selected as the simulated water sample. The SRNOM solution refers to an aqueous solution in which the Suwannee River Natural Organic Matter standard sample is dissolved.

[0040] In steps S120 to S130 of some embodiments, the simulated water sample is disinfected. After the reaction, the residual disinfectant in the disinfected reaction solution is quenched by a quenching reaction to obtain a quenched water sample. For example, a simulated water sample is taken and disinfected by chlorination (NaClO) or chlorination (NH2Cl) to obtain a disinfected reaction solution. After the reaction, the residual disinfectant is quenched with ascorbic acid to obtain a quenched water sample. Ascorbic acid, as a strong reducing agent, can quickly and thoroughly reduce and consume the residual strong oxidizing disinfectant (available chlorine) in the system, deactivating it and thus "freezing" the chemical state of the reaction system. This ensures that the concentration and type of the target analyte (such as GSH-DBP adduct) do not change throughout the entire process from the end of the reaction to subsequent sample processing (such as solid-phase extraction) and then to non-targeted analysis (HPLC-HRMS), guaranteeing the accuracy of the analytical results.

[0041] In step S140 of some embodiments, toxic substances (such as disinfection byproducts DBPs) in the quenched water sample are enriched and concentrated by solid-phase extraction (HLB column) to obtain a concentrated water sample.

[0042] In step S150 of some embodiments, the concentrated water sample is divided into two equal parts to obtain a first water sample to be labeled and a second water sample to be labeled, so as to prepare two identical sample matrices for subsequent parallel isotope labeling experiments, ensuring that the two samples are consistent in volume and in the concentration of target intermediates and matrix components contained therein.

[0043] In step S160 of some embodiments, ordinary (¹H, lightly labeled) glutathione is used as a nucleophilic probe. The ordinary glutathione probe (H-GSH) is added to the first water sample to be labeled and reacted at 25°C for 24 hours to ensure that potentially toxic DBPs react fully with GSH, generating the corresponding H-GSH-DBP adduct, thus obtaining a water sample labeled with the ordinary probe. The ordinary glutathione probe specifically captures the electrophilic DBP intermediate generated during disinfection, forming a stable adduct suitable for mass spectrometry analysis.

[0044] In step S170 of some embodiments, a deuterated (²H or D, relabeled) glutathione probe (D-GSH) is added to a second water sample to be labeled. A completely parallel capture reaction is performed using deuterated glutathione and the reaction is carried out at 25°C for 24 hours to ensure that the potentially toxic DBPs react fully with GSH. This provides a relabeled counterpart with a fixed mass difference for each H-GSH-DBP adduct in the ordinary probe-labeled water sample, i.e., generating a D-GSH-DBP adduct, thus obtaining a deuterated probe-labeled water sample.

[0045] In some embodiments, in addition to H / D-GSH, other stable isotope-labeled thiol-containing probes, such as 13C-labeled GSH, or cysteine ​​and its isotope labels, can also be used. The core principle is to utilize mass difference for pairing and recognition. The reaction conditions of GSH and DBPs can also be adjusted within a certain range, for example, the reaction time can be between 12 and 36 hours, and the reaction temperature can be between 20°C and 30°C, while still ensuring that the reaction proceeds fully.

[0046] In step S180 of some embodiments, ordinary probe-labeled water samples and deuterated probe-labeled water samples are mixed in equal volumes before instrument analysis. This ensures that both undergo identical physicochemical processes and environments during subsequent chromatographic separation and mass spectrometry detection, thereby maximally offsetting the effects of matrix effects and instrument fluctuations. In the resulting mixed sample, each GSH-DBP adduct will be detected in the mass spectrum as an "isotope peak pair" with a fixed mass difference and identical chromatographic retention time, greatly improving the accuracy and reliability of non-targeted screening. Optionally, unsterilized NOM solution can be used instead of sterilized water samples for the same treatment as a blank control.

[0047] In step S200 of some embodiments, a high-performance liquid chromatography-high-resolution mass spectrometry (HPLC-HRMS) instrument is used to perform non-targeted analysis on the mixed sample. Chromatographic conditions are used to separate different adducts, and mass spectrometry conditions (typically positive ion mode) are used to acquire high-precision primary and secondary mass spectrometry data for each chromatographic peak. A blank solvent is used as a control. Optionally, the HPLC-HRMS instrument can be an Agilent 1290 HPLC-6545 QTOF-MS. Besides the Agilent QTOF-MS, other high-resolution mass spectrometry platforms, such as Thermo Fisher's Orbitrap series and Bruker's timsTOF series, can also be used; no limitation is made here.

[0048] In step S300 of some embodiments, through peak extraction and peak selection, the complex raw mass spectrometry data is transformed into a structured feature list containing key information such as m / z, retention time and intensity, and the data is initially reduced from tens of thousands of raw data to thousands of high-quality chromatographic peaks (e.g., from about 50,000 to about 7,000), realizing the transformation from continuous signal to discrete compound features.

[0049] In some embodiments, step S300 may include, but is not limited to, steps S310 to S320: Step S310: Convert the original mass spectrometry data to a preset format file; Step S320: Using an R language program, perform chromatographic peak detection and identification on a preset format file to obtain a peak list.

[0050] In step S310 of some embodiments, the original mass spectrometry data file is converted to an mzMl format file using MSConvert software.

[0051] In step S320 of some embodiments, such as Figure 3 As shown, the algorithm in the XCMS R package is used for chromatographic peak detection and identification. This algorithm can be the CentWave algorithm. The CentWave algorithm identifies the true chromatographic peak regions from continuous scans and can obtain a peak list containing all detected chromatographic peaks (including m / z, retention time, and peak intensity).

[0052] In step S400 of some embodiments, potential H / D pairings are automatically searched based on preset optimization parameters, and specific adduct signals generated by the labeling reaction are automatically and efficiently locked from a large number of features, greatly narrowing the target range.

[0053] In some embodiments, step S400 may include, but is not limited to, steps S410 to S430: Step S410: Preset preset optimization parameters; the preset optimization parameters include preset mass deviation range, preset retention time window, and preset strength ratio range; Step S420: Based on the preset mass deviation range, preset retention time window and preset intensity ratio range, perform isotope peak pair screening and matching operations on the peak list to obtain the hydrogen-labeled isotope peaks and corresponding deuterium-labeled isotope peaks in the peak list. Step S430: Based on the hydrogen-labeled isotope peaks and the corresponding deuterium-labeled isotope peaks, obtain a candidate pairing list.

[0054] In step S410 of some embodiments, preset optimization parameters are pre-set for the characteristics of the GSH-DBP adduct. For example... Figure 3As shown, the preset optimization parameters may include, but are not limited to: 1) Preset mass deviation range (e.g., 10ppm or 20ppm, depending on the specific situation): Δm / z = 2.0126 is selected as the preset mass deviation, corresponding to the mass difference resulting from the substitution of two hydrogen atoms with two deuterium atoms in the H-GSH and D-GSH probes. This preset mass deviation Δm / z = 2.0126 is used as the core mass fingerprint for finding paired peaks. In practical applications, the preset mass deviation has errors. A preset mass deviation range is set for this; optionally, it can be set to 10ppm or 20ppm depending on the specific situation, without limitation here.

[0055] 2) Preset retention time window (±0.3 min): This preset retention time window sets the maximum allowable retention time difference between paired peaks. Since GSH is a tripeptide molecule, its H / D isotope effect will cause a more significant change in the retention time of the adduct in reversed-phase chromatography. Compared with conventional formaldehyde labeling methods, the preset retention time window of this embodiment of the invention is relaxed. By relaxing the window, it is possible to avoid erroneously excluding true pairings due to slight differences in chromatographic behavior.

[0056] 3) Preset intensity ratio range (H / D peak intensity ratio range 0.6 to 1.4): This preset intensity ratio range specifies the allowable ratio range of the signal intensities of the hydrogen-labeled isotope peak and the deuterium-labeled isotope peak. Since the mass spectrometric responses of H-type and D-type adducts are more similar when single GSH adducts dominate, the allowable ratio range set in this embodiment of the invention is narrower and more symmetrical than conventional methods, and a stricter symmetry threshold is used, which can effectively reduce false positive matches.

[0057] In steps S420 to S430 of some embodiments, based on a preset mass deviation range, a preset retention time window, and a preset intensity ratio range, a multi-dimensional combined isotope peak pair screening and matching operation is performed on the hydrogen-labeled isotope peaks and deuterium-labeled isotope peaks in the peak list, thereby obtaining the hydrogen-labeled isotope peak and the corresponding deuterium-labeled isotope peak. A candidate pair list can be obtained based on each isotope peak pair formed by the hydrogen-labeled isotope peak and its corresponding deuterium-labeled isotope peak. For example, firstly, the mass difference between any two peaks is calculated by traversing all peaks. Only peak pairs with mass differences within the allowable error range of the preset mass deviation range are retained to form preliminary candidate pairs. Then, for each pair of peaks in the selected preliminary candidate pairs, the retention time difference (ΔRT) is calculated. Only peak pairs with ΔRT within the preset retention time window (±0.3 min) are retained. This verifies whether two peaks originate from the same chromatographic peak, i.e., whether they are the same compound. Next, the intensity ratio (H / D peak intensity ratio) of the peak pairs selected in the first two steps is calculated, and only peak pairs with ratios falling within a preset intensity ratio range (0.6 to 1.4) are retained. This further ensures the chemical rationality of the pairings and eliminates abnormal matches caused by background interference or signal suppression. Finally, peak pairs that simultaneously meet the triple criteria of preset quality deviation range, preset retention time window, and preset intensity ratio range are successfully identified as potential H / D-GSH-DBP adducts and output to the candidate pairing list (e.g., Figure 3 (as shown in the list of isotope peak pairs), thus enabling efficient and specific identification of target adduct signals from massive amounts of non-target peak data.

[0058] In step S500 of some embodiments, each candidate adduct pair is quantitatively scored using multiple indicators such as chromatographic peak shape similarity, quality score, retention time score, and intensity score. This can initially distinguish between high-quality real signals and potential false positive matches, generating a list of adduct pairs with scores and higher reliability, i.e., a scored pairing list.

[0059] In some embodiments, step S500 may include, but is not limited to, steps S510 to S580: Step S510: Obtain the similarity between the hydrogen-labeled isotope peaks and the deuterium-labeled isotope peaks in the candidate adduct pairs using the Pearson correlation coefficient and cross-correlation function. Step S520: Obtain the first mass-to-charge ratio, first retention time, and first peak intensity of the hydrogen-labeled isotope peak; Step S530: Obtain the second mass-to-charge ratio, second retention time, and second peak intensity of the deuterated isotope peak; Step S540: Obtain a mass score based on the first mass-to-charge ratio and the second mass-to-charge ratio; Step S550: Obtain a retention time score based on the first retention time and the second retention time; Step S560: Obtain an intensity score based on the intensity of the first peak and the intensity of the second peak; Step S570: Obtain a comprehensive score based on the quality score, retention time score, and strength score; Step S580: Based on similarity, comprehensive score and candidate pairing list, obtain the scored pairing list.

[0060] In step S510 of some embodiments, such as Figure 3 As shown, the complete chromatographic peak profiles of the hydrogen-labeled isotope peak and the deuterium-labeled isotope peak in the candidate adduct pair are extracted. The linear correlation between the two profiles is calculated using the Pearson correlation coefficient; the closer the value is to 1, the more consistent the peak shape trend. Further, the alignment of the two profiles on the time axis is evaluated using a cross-correlation function, generating a cross-correlation value sequence to eliminate the influence of small time shifts. A threshold is set for the cross-correlation function (e.g., >0.7). If the maximum value in the cross-correlation value sequence is greater than this threshold, it indicates that the two peaks have extremely high similarity in shape at a small time shift, and they are determined to be different isotopic forms of the same chemical substance. If the maximum value in the cross-correlation value sequence is less than or equal to the threshold, it indicates that even considering the time shift, the shapes of the two peaks are not similar, and the candidate adduct pair may be a false positive match, which will be excluded in subsequent steps.

[0061] In steps S520 to S530 of some embodiments, the first mass-to-charge ratio, first retention time, and first peak intensity of the hydrogen-labeled isotope peak are extracted from the data structure of the candidate adduct pair. Simultaneously, the second mass-to-charge ratio, second retention time, and second peak intensity of the deuterium-labeled isotope peak in the candidate adduct pair are extracted. These parameters are directly derived from the preceding peak detection results, preparing data for subsequent quantitative scoring.

[0062] In some embodiments, by combining instrument performance indicators, chromatographic theory and experimental verification data, the maximum tolerable quality error, the tolerable retention time range and the tolerable intensity ratio range can be preset, which are used in the subsequent calculation of quality score, retention time score and intensity score, respectively.

[0063] In step S540 of some embodiments, the measured mass difference is obtained based on the first mass-to-charge ratio and the second mass-to-charge ratio. ), and obtain the theoretical value ( (e.g., 2.0126Da). Calculate the absolute deviation between the measured quality difference and the theoretical value, and normalize it to a preset maximum tolerable error range. The smaller the deviation, the closer the score is to the full score of 1. For example, the formula used for quality scoring includes: ; The quality score can directly reflect the accuracy of mass spectrometry quality measurement.

[0064] In step S550 of some embodiments, according to the first retention time ( ) and the second retention period ( The absolute value of the retention time difference between the hydrogen-labeled isotope peak and the deuterium-labeled isotope peak is calculated and normalized to a preset tolerable range. The smaller the time difference, the closer the score is to 1. For example, the formula used for retention time scoring includes: ; Retention time scores can reflect the consistency of chromatographic behavior.

[0065] In step S560 of some embodiments, the measured intensity ratio and the theoretical intensity ratio are obtained based on the first peak intensity and the second peak intensity. Typically, the theoretical intensity ratio is expected to be 1 (due to equal mixing and equal volume addition). The absolute difference between the measured intensity ratio and the theoretical intensity ratio is calculated and normalized to a preset tolerable intensity ratio range. The smaller the absolute difference, the higher the score. For example, the formula used for intensity scoring includes: ; Intensity scores can reflect the comparability of labeling efficiency and mass spectrometry response.

[0066] In step S570 of some embodiments, such as Figure 3 As shown, the independent scores for the three dimensions—quality score, retention time score, and intensity score—are integrated into a single overall matching score to obtain the comprehensive score. The formula used includes: .

[0067] In step S580 of some embodiments, candidate adduct pairs in the candidate pairing list are evaluated based on similarity and comprehensive score, and a scored pairing list with higher reliability is finally output, providing a data foundation for subsequent data cleaning and structure analysis.

[0068] In some embodiments, step S600 may include, but is not limited to, step S610: Step S610: After scoring, the pairing list is processed by removing source fragment interference, excluding natural isotope signals, and filtering salt adducts to obtain the target pairing list.

[0069] In step S610 of some embodiments, such as Figure 3As shown, the scored pairing list undergoes data cleaning to further eliminate various interferences. Data cleaning may include, but is not limited to, removing source fragmentation interference: using algorithms such as ISFrag to eliminate false pairing signals generated by adduct fragmentation within the ion source; eliminating natural isotope signals: eliminating signals generated by natural heavy isotopes such as 13C contained in the molecules themselves; and filtering salt adducts: eliminating signals generated by the addition of cations such as Na+ and K+. The final output is a highly reliable and definitive list of GSH-DBP adduct pairs, i.e., the target pairing list.

[0070] In step S700 of some embodiments, the most likely chemical structure of the adduct and its corresponding parent DBP is inferred by combining the precise mass number, secondary mass spectrometry fragment information and database comparison, thus realizing the process from feature discovery to compound identification.

[0071] In some embodiments, step S700 may include, but is not limited to, steps S710 to S780: Step S710: Select the hydrogen-labeled adduct ions in the target pairing list as the target adduct ions; Step S720: Obtain the mass number of the hydrogen-labeled adduct ion; Step S730: Obtain the predicted molecular formula of the adduct based on the mass number of the hydrogen-labeled adduct; Step S740: Based on several reaction mechanisms, the predicted adduct molecular formula, and the glutathione molecular formula, obtain several predicted toxic substance molecular formulas. Step S750: Using the database and in conjunction with the reaction mechanism, perform compound structure retrieval on the molecular formulas of several predicted toxic substances to obtain initial candidate structures; Step S760: Obtain the secondary mass spectrum of the hydrogen-labeled adduct ion; Step S770: Based on the reaction mechanism corresponding to the initial candidate structure, the initial candidate structure is combined with the glutathione structure to obtain the candidate adduct structure; Step S780: Match and evaluate the fit between the candidate adduct structure and the secondary mass spectrum of the hydrogen-labeled adduct to obtain a fit score, and take the candidate adduct structure with the highest fit score as the target candidate structure.

[0072] In steps S710 to S730 of some embodiments, the hydrogen-labeled adduct ion in each identified target adduct pair is obtained, and the hydrogen-labeled adduct mass number of the hydrogen-labeled adduct ion is obtained. Based on the precise hydrogen-labeled adduct mass number and possible elemental composition of the hydrogen-labeled adduct ion, its possible adduct molecular formula can be derived, and the predicted adduct molecular formula is obtained.

[0073] In step S740 of some embodiments, based on the predicted adduct molecular formula and the glutathione molecular formula, and combined with different reaction mechanisms, the molecular formula of the toxic substance can be deduced, resulting in different predicted toxic substance molecular formulas. Exemplary reaction mechanisms include nucleophilic substitution, nucleophilic addition, and thiol oxidation. Formulas used to derive the DBP molecular formula based on different reaction mechanisms include: 1) Nucleophilic substitution: DBP molecular formula = [adduct molecular formula] - [C 10 H 17 [N3O6S] + H + Cl (or other halogens); 2) Nucleophilic addition: DBP molecular formula = [adduct molecular formula] - [C 10 H 17 [N3O6S]; 3) Thiol oxidation: DBP molecular formula = [adduct molecular formula] - [C 10 H 17 [N3O6S]+H2; In the formula, C 10 H 17 N3O6S is the molecular formula of glutathione; [adduct formula] is the predicted adduct formula.

[0074] In step S750 of some embodiments, the derived predicted toxic substance molecular formula is input into databases such as PubChem and NIST for relevant structure retrieval. Based on the database matching score, the most likely initial candidate structure under the corresponding reaction mechanism can be obtained.

[0075] In step S760 of some embodiments, a secondary mass spectrum of the hydrogen-labeled adduct ion is obtained, thus providing a data basis for subsequent structure-spectrum matching steps.

[0076] In step S770 of some embodiments, each initial candidate structure is combined with a glutathione structure and related structures (such as halogens such as H and Cl, and H2) according to their respective reaction mechanisms to obtain each candidate adduct structure.

[0077] In step S780 of some embodiments, using fragment ion information from secondary mass spectrometry, software (such as Agilent MassHunter Molecular Structure Correlator) is used to test the matching degree between the candidate adduct structure and the secondary mass spectrum of the precursor ion. That is, each candidate adduct structure is matched sequentially with the secondary mass spectrum of the hydrogen-labeled adduct, and the fit score is calculated. Based on the fit score, the candidate adduct structure with the highest fit score can be selected as the best candidate structure, i.e., the target candidate structure.

[0078] In some embodiments, a standard of the target candidate structure can be obtained, and the highest confidence level (Level 1) identification can be achieved by comparing the consistency of the toxic substances produced after the adduct formed by the reaction of the standard with GSH and dissociation (or by directly comparing the adduct) in terms of chromatographic retention time and mass spectrometry fragments.

[0079] In some embodiments, the consensus method of TEST (v5.1.2) software is used to perform quantitative structure-activity relationship predictions for multiple toxicity endpoints on all newly identified DBPs, including: Acute toxicity: 96-hour LC50 for fish (blackhead carp), 48-hour LC50 for water fleas (large fleas), and oral LD50 for rats.

[0080] Chronic toxicity: bioaccumulation factor (BCF), developmental toxicity, mutagenicity.

[0081] The predicted results were compared with the predicted toxicities of 13 common regulated DBPs to assess the potential toxicity risks of the newly discovered DBPs. The prediction results show that most of the newly identified DBPs have significant multiple toxicities, and many of the newly identified DBPs are even more toxic than regulated DBPs.

[0082] In some embodiments, taking a water sample treated with chlorination and ammoniation as an example, refer to Figure 4 The steps of the non-targeted analysis method for automated screening of key toxic substances based on RDA in this embodiment of the invention are as follows: Step 1, Sample Preparation: Prepare SRNOM simulated source water containing 3 mg / L TOC. Then, disinfect the SRNOM simulated source water by chlorination or chlorination at 25°C in the dark for 24 hours. After the reaction, add excess ascorbic acid to quench the residual chlorine. Enrich 1 L of water sample using an HLB solid-phase extraction column (500 mg / 6 mL), eluting sequentially with methanol and acetone, and concentrating to 1.2 mL DMSO by nitrogen blowing.

[0083] Step 2, Isotope-labeled RDA reaction: Prepare 3 mM H-GSH and D-GSH aqueous solutions separately. Take the concentrated water sample from step 1 and divide it into two 0.6 mL portions for labeling. Add 0.3 mL of H-GSH solution to one portion and 0.3 mL of D-GSH solution to the other portion. Vortex mix both labeled water samples and seal them in a 25°C water bath for 24 hours.

[0084] Step 3, HPLC-HRMS analysis: Following the isotope-labeled RDA reaction, 0.5 mL of each of the two labeled water samples was mixed in the same HPLC vial. Non-targeted analysis was performed using an Agilent 1290 Infinity high-performance liquid chromatography system coupled with an Agilent 6545 quadrupole-time-of-flight mass spectrometer. Non-targeted screening of hydrogen / deuterium isotope-labeled adducts was conducted in positive ion mode with an injection volume of 10 μL.

[0085] Chromatographic separation was performed using a ZORBAX RRHD Eclipse Plus C18 column (150 × 2.1 mm, 1.8 μm particle size). The mobile phase composition was: (A) an aqueous solution containing 0.1% formic acid (v / v), and (B) a methanol solution containing 0.1% formic acid (v / v). The gradient program was set as follows: from initial to 3 minutes, the proportion of phase B increased from 0% to 5%; to 30 minutes, it increased to 10%; to 65 minutes, it increased to 30%; to 100 minutes, it increased to 50%; to 135 minutes, it increased to 70%; and finally, it returned to 5% for system reequilibration. The flow rate was 0.15 mL / min, and the column oven temperature was 36°C.

[0086] The optimized mass spectrometry parameters are as follows: electrospray ionization source in positive ion mode, capillary voltage 5500 V, nozzle voltage 1000 V, pyrolysis voltage 175 V, nebulizer pressure 50 psi, drying gas temperature 320°C, sheath gas temperature 400°C, and sheath gas flow rate 11 L / min.

[0087] Step 4: Automated Data Processing The high-resolution mass spectrometry raw data obtained in step 3 were converted to mzML format using MSConvert software. A customized automated program was run based on preset optimization parameters (including preset mass deviation range, preset retention time window, and preset intensity ratio range). After approximately 5 minutes of program execution, the output results are shown in Table 1.

[0088] Table 1. Number of chromatographic peaks or paired features screened at each step of the automated procedure.

[0089] Step 5, Example of structural identification (using chloroacetic acid as an example): Taking the adduct with a detected precursor ion m / z of 366.0969 as an example, the molecular formula containing the GSH probe can be deduced to be C. 12 H 19 N3O8S (mass error = 0.69 ppm). Based on the nucleophilic substitution mechanism, the derived DBP molecular formula is C2H3ClO2; based on the nucleophilic addition mechanism, the derived DBP molecular formula may be C2H2O2. Based on the thiol oxidation mechanism, the derived DBP molecular formula may be C2H4O2.

[0090] Subsequently, compound structures of the three candidate DBP molecular formulas were searched in multiple databases (including PubChem, NIST, and SciFinder). Finally, based on the database matching score, reaction mechanism, and the fitting score between each structure and the mass spectrometry data in the software, chloroacetic acid was determined to be the best candidate structure.

[0091] Finally, chloroacetic acid standard was purchased, and the chloroacetic acid standard was reacted with GSH to form an adduct. The same mass spectrometry analysis was performed, and the secondary mass spectrum was compared with that of the precursor ion m / z 366.0969 (sample) detected in the water sample. The chromatograms, primary mass spectrometry, and secondary mass spectrometry of the GSH-chloroacetic acid standard adduct were compared. The results showed that the chromatograms, primary mass spectrometry, and secondary mass spectrometry of the GSH-chloroacetic acid adduct perfectly corresponded to those of the precursor ion m / z 366.0969, especially with excellent matching of all characteristic fragment ions in the secondary mass spectrum. Therefore, chloroacetic acid was identified as the DBP (e.g., ...) corresponding to the precursor ion m / z 366.0969. Figure 5 The chromatogram and mass spectrometry of the precursor ion m / z 366.0969 and its corresponding standard chloroacetic acid are shown.

[0092] Step 6, Toxicity Prediction: TEST prediction was performed on the identified chloroacetic acid. The results showed that its predicted oral LD50 in rats was 86.61 mg / kg, which is classified as moderately toxic and also exhibits developmental toxicity.

[0093] This invention also provides a non-targeted analysis device for automated screening of key toxic substances based on RDA, which can realize the above-mentioned non-targeted analysis method for automated screening of key toxic substances based on RDA. The device includes: The sample preparation module is used to obtain mixed samples through solid-phase extraction and isotope labeling; The non-targeted analysis module is used to perform non-targeted analysis on mixed samples using high performance liquid chromatography and high resolution mass spectrometry to obtain raw mass spectrometry data; The first automated data processing and adduct identification module is used to detect and identify chromatographic peaks in the raw mass spectrometry data to obtain a peak list; The second automated data processing and adduct identification module is used to perform isotope peak pair screening and matching operations on the chromatographic peaks in the peak list according to preset optimization parameters to obtain a candidate pairing list. The third automated data processing and adduct identification module is used to evaluate the candidate adduct pairs in the candidate pairing list and obtain a scoring pairing list; The fourth automated data processing and adduct identification module is used to perform data cleaning on the scored pairing list to obtain the target pairing list; The structure analysis module is used to perform toxic substance structure analysis on the target adduct ions in the target pairing list to obtain the target candidate structure.

[0094] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0095] This invention also provides an electronic device, which includes a processor and a memory. The memory stores a computer program, and the processor executes the computer program to implement the above-described method. This electronic device can be any smart terminal, including a tablet computer, an in-vehicle computer, or similar device.

[0096] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0097] refer to Figure 7 , Figure 7 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 801 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present invention. The memory 802 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 802 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 802 and is called and executed by the processor 801. The 803 input / output interface is used to implement information input and output. The communication interface 804 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 805 transmits information between various components of the device (e.g., processor 801, memory 802, input / output interface 803, and communication interface 804); The processor 801, memory 802, input / output interface 803, and communication interface 804 are connected to each other within the device via bus 805.

[0098] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0099] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0100] This invention also provides a computer program product or computer program that includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium and execute the computer instructions to cause the computer device to perform the aforementioned method.

[0101] In summary, the non-targeted analysis method and related equipment for automated screening of key toxic substances based on RDA according to embodiments of the present invention have the following advantages: 1. This invention shortens the data analysis process, which traditionally takes hours or even days to process manually, to within minutes. For a single water sample (containing ~50,000 raw features), the automated program can complete all processing in approximately 3-5 minutes, outputting the final list of adducts, improving efficiency by tens to hundreds of times.

[0102] 2. This invention significantly improves identification accuracy and coverage. Through optimized parameters and multiple filtering steps, it achieves highly sensitive and low false positive rate accurate identification of trace GSH-DBP adducts in highly complex matrices. In chlorinated / chloramined water samples, 126 pairs (mean) of high-confidence adducts were stably identified from >50,000 original features, with an overall retention rate of approximately 0.23%, demonstrating extremely high screening specificity. A total of 255 unique GSH-DBP adducts were ultimately identified, demonstrating broad coverage.

[0103] 3. This invention represents a breakthrough in the discovery of toxic DBPs, successfully identifying a large number (202) of potentially toxic DBPs with GSH reactivity, of which 193 are reported for the first time, greatly enriching the database of toxic DBPs. These 202 DBPs cover a wide range of compounds, including haloalkanes, haloaromatics, α,β-unsaturated carbonyl groups, nitriles, sulfonyl alkenes, styrenes, and novel thiocarbonyl and thiol compounds.

[0104] 4. This invention, through its embodiments, first discovered that DBPs form disulfide adducts with GSH via a "thiol oxidation" mechanism, leading to the discovery of new sulfur-containing DBPs (such as methanethiol). Mass spectrometry analysis and standard verification (such as with methanethiol) confirmed the existence of this new mechanism, opening a new window for discovering sulfur-containing DBPs—a previously overlooked contributor to toxicity.

[0105] 5. The embodiments of this invention, through systematic toxicity prediction, demonstrate that the DBPs identified by the method of this invention do indeed possess significant toxicity potential, and that the predicted toxicity of many substances is higher than or equivalent to that of already controlled DBPs. Predictions show that 54 DBPs are "highly toxic" to fish, 8 are "highly toxic" to daphnia, and 9 are "highly toxic" to rats. Up to 83.7% of the DBPs are predicted to have developmental toxicity (e.g.,...). Figure 6 (As shown). These data strongly demonstrate the superior ability of the method of the present invention to "focus on key targets" (i.e., highly toxic substances).

[0106] 6. This invention, through a comparison of chlorination and chlorination, reveals the formation patterns of toxic DBPs under the two processes, providing guidance for the optimization of practical water treatment processes. The embodiments of this invention show that although chlorination has a lower total organic halogen content, the proportion of halogenated GSH-reactive DBPs produced (34%) is comparable to that of chlorination (33%), suggesting that simply switching disinfectants may not effectively reduce the health risks caused by halogenated DBPs. Furthermore, chlorination uniquely produces more cyano / nitro-containing DBPs.

[0107] In some alternative embodiments, the functions / operations mentioned in the block diagrams may not occur in the order shown in the operation diagrams. For example, depending on the functions / operations involved, two consecutively shown blocks may actually be executed substantially simultaneously, or the blocks may sometimes be executed in reverse order. Furthermore, the embodiments presented and described in the flowcharts of this invention are provided by way of example to provide a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is altered and sub-operations described as part of a larger operation are executed independently.

[0108] Furthermore, although the invention has been described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the described functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the invention. Rather, given the properties, functions, and internal relationships of the various functional modules in the apparatus disclosed herein, the actual implementation of the module will be understood within the scope of conventional skill of an engineer. Therefore, those skilled in the art can implement the invention as set forth in the claims using ordinary techniques without excessive experimentation. It is also understood that the specific concepts disclosed are merely illustrative and not intended to limit the scope of the invention, which is determined by the full scope of the appended claims and their equivalents.

[0109] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0110] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0111] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0112] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0113] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0114] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

[0115] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the embodiments described. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of the present invention.

Claims

1. A non-targeted analysis method for automated screening of key toxic substances based on RDA, characterized in that, Includes the following steps: Mixed samples were obtained through solid-phase extraction and isotope labeling; The mixed sample was subjected to non-targeted analysis by high performance liquid chromatography and high resolution mass spectrometry to obtain raw mass spectrometry data; The raw mass spectrometry data were subjected to chromatographic peak detection and identification to obtain a peak list; Based on preset optimization parameters, isotope peak pair screening and matching operations are performed on the chromatographic peaks in the peak list to obtain a candidate pairing list; The candidate adduct pairs in the candidate pairing list are evaluated to obtain a scored pairing list; The scored pairing list is then cleaned to obtain the target pairing list. The target adduct ions in the target pairing list are subjected to toxic substance structure analysis to obtain the target candidate structures.

2. The method according to claim 1, characterized in that, The process of obtaining a mixed sample through solid-phase extraction and isotope labeling includes the following steps: Obtain simulated water samples; The simulated water sample was disinfected to obtain a disinfected reaction solution; The disinfected reaction solution was subjected to a quenching reaction to obtain a quenched water sample; The quenched water sample was enriched and concentrated by solid-phase extraction to obtain a concentrated water sample. The concentrated water sample is divided into a first water sample to be labeled and a second water sample to be labeled. Add a common glutathione probe to the first water sample to be labeled to generate a water sample labeled with a common probe. The deuterated glutathione probe was added to the second water sample to be labeled to generate a deuterated probe-labeled water sample; The ordinary probe-labeled water sample and the deuterated probe-labeled water sample are mixed in equal volumes to obtain the mixed sample.

3. The method according to claim 1, characterized in that, The step of detecting and identifying chromatographic peaks in the raw mass spectrometry data to obtain a peak list includes the following steps: The raw mass spectrometry data is converted to a preset format file. The preset format file is analyzed and chromatographic peaks are detected and identified using an R language program to obtain the peak list.

4. The method according to claim 1, characterized in that, The step of performing isotope peak pair screening and matching operations on the chromatographic peaks in the peak list according to preset optimization parameters to obtain a candidate pairing list includes the following steps: The preset optimization parameters are pre-set; the preset optimization parameters include a preset mass deviation range, a preset retention time window, and a preset intensity ratio range; Based on the preset mass deviation range, the preset retention time window, and the preset intensity ratio range, the peak list is subjected to isotope peak pair filtering and matching operations to obtain the hydrogen-labeled isotope peaks and corresponding deuterium-labeled isotope peaks in the peak list. The candidate pairing list is obtained based on the hydrogen-labeled isotope peaks and the corresponding deuterium-labeled isotope peaks.

5. The method according to claim 1, characterized in that, The evaluation of candidate adduct pairs in the candidate pairing list to obtain a scored pairing list includes the following steps: The similarity between the hydrogen-labeled isotope peaks and the deuterium-labeled isotope peaks in the candidate adduct pairs was obtained by using the Pearson correlation coefficient and cross-correlation function. Obtain the first mass-to-charge ratio, first retention time, and first peak intensity of the hydrogen-labeled isotope peak; Obtain the second mass-to-charge ratio, second retention time, and second peak intensity of the deuterium-labeled isotope peak; A quality score is obtained based on the first mass-to-charge ratio and the second mass-to-charge ratio; Based on the first retention time and the second retention time, obtain a retention time score; An intensity score is obtained based on the first peak intensity and the second peak intensity; A comprehensive score is obtained based on the quality score, the retention time score, and the intensity score. The scoring pairing list is obtained based on the similarity, the overall score, and the candidate pairing list.

6. The method according to claim 1, characterized in that, The process of cleaning the scored pairing list to obtain the target pairing list includes the following steps: The target pairing list is obtained by removing source fragment interference, excluding natural isotope signals, and filtering salt adducts from the scored pairing list.

7. The method according to claim 1, characterized in that, The process of performing toxic substance structure analysis on the target adduct ions in the target pairing list to obtain target candidate structures includes the following steps: The hydrogen-labeled adduct ions in the target pairing list are used as the target adduct ions; Obtain the hydrogen-labeled adduct mass number of the hydrogen-labeled adduct ion; Based on the mass number of the hydrogen-labeled adduct, the predicted molecular formula of the adduct is obtained; Based on several reaction mechanisms, the predicted adduct molecular formula, and the glutathione molecular formula, several predicted toxic substance molecular formulas were obtained. By using the database and combining the reaction mechanism, the compound structure of several predicted toxic substances was searched to obtain initial candidate structures. Obtain the secondary mass spectrum of the hydrogen-labeled adduct ion; Based on the reaction mechanism corresponding to the initial candidate structure, the initial candidate structure is combined with the glutathione structure to obtain the candidate adduct structure; The candidate adduct structure is matched and the secondary mass spectrum of the hydrogen-labeled adduct is evaluated to obtain a goodness-of-fit score, and the candidate adduct structure with the highest goodness-of-fit score is selected as the target candidate structure.

8. A non-targeted analysis device for automated screening of key toxic substances based on RDA, characterized in that, Includes the following steps: The sample preparation module is used to obtain mixed samples through solid-phase extraction and isotope labeling; The non-targeted analysis module is used to perform non-targeted analysis on the mixed sample using high performance liquid chromatography and high resolution mass spectrometry to obtain raw mass spectrometry data; The first automated data processing and adduct identification module is used to perform chromatographic peak detection and identification on the raw mass spectrometry data to obtain a peak list; The second automated data processing and adduct identification module is used to perform isotope peak pair screening and matching operations on the chromatographic peaks in the peak list according to preset optimization parameters to obtain a candidate pairing list. The third automated data processing and adduct identification module is used to evaluate the candidate adduct pairs in the candidate pairing list and obtain a scoring pairing list; The fourth automated data processing and adduct identification module is used to perform data cleaning operations on the scored pairing list to obtain the target pairing list; The structure analysis module is used to perform toxic substance structure analysis on the target adduct ions in the target pairing list to obtain the target candidate structure.

9. An electronic device, characterized in that, Including the processor and memory; The memory is used to store programs; The processor executes the program to implement the method as described in any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 7.

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