Method for accurately screening new pollutant homologues in wastewater based on spectrogram similarity

Abstract

The invention provides a method for accurately screening new pollutant homologues in wastewater based on spectrogram similarity. The method comprises the following steps: analyzing and detecting pollutants by adopting a high performance liquid chromatography-mass spectrometry method to obtain secondary spectrogram data; performing primary screening on homologues by combining the secondary spectrogram data and adopting a Kenderke mass loss and retention time increasing rule; and calculating the similarity of the spectrograms by adopting a corrected cosine algorithm to obtain a similarity matrix, setting a similarity threshold value of the corrected cosine spectrograms, performing secondary screening on the homologues by combining the obtained preliminary screening result with the similarity matrix, and outputting the result of the homologues higher than the similarity threshold value of the corrected cosine spectrograms. According to the method disclosed by the invention, high-throughput identification and identification on the new pollutant homologues in the environment are realized.

Description

Technical Field

[0001] The present invention relates to the field of screening homologues in wastewater, and in particular to a method for accurately screening homologues of new pollutants in wastewater based on spectral similarity. Background Art

[0002] With economic development, more and more new pollutants are gradually being discovered, and new pollutants with known structures are only the tip of the iceberg of the total amount of new pollutants. New pollutants such as plastic monomers, surfactants, and perfluorinated and polyfluoroalkyl substances are gradually being detected, and most of these substances are homologues with the same repeating units (RU). It is extremely important to develop a detection method for homologues of new pollutants.

[0003] High-resolution mass spectrometers can identify compounds by analyzing information such as the mass and mass-to-charge ratio of molecules. They are used in conjunction with liquid chromatography (LC-MS) to identify and characterize new pollutants in the environment. However, screening for new pollutants based on high-resolution mass spectrometry still faces many difficulties, such as the complexity of data processing and interpretation. High-resolution mass spectrometry technology can generate a large amount of data, including characteristic ion peaks under various ionization modes and reaction conditions, which require complex data processing and interpretation.

[0004] In the environmental field, non-targeted technology is widely used to screen new pollutants. Compared with traditional targeted analysis technology, non-targeted technology does not require prior knowledge of which compounds are present in the sample to be tested, and can comprehensively identify all known and unknown new pollutants. Non-targeted technology usually uses high-resolution mass spectrometers for analysis, but it often generates a large amount of data on various new pollutants. How to classify and organize the mass spectrometry data of many types of new pollutants is the focus of current research.

[0005] Detection methods for homologues, such as a method and identification system for non-targeted identification of perfluorinated compound homologues proposed in Chinese Patent No. 202311102756.2. This method uses homologue rules to extract perfluorinated and polyfluorinated compounds in sample peaks. If the mass difference between the two peaks is equal to (CF2)n or (CF2O)n or (CH2CF2)n, the two peaks are retained in the candidate list of homologues. In each homologue series, the increasing trend of m / z and retention time is calculated. As the m / z increases, the retention time increases. According to the mass loss rule of the PFAS structural unit, characteristic peaks with two decimal places greater than 0.85 or less than 0.15 are retained in the potential PFAS homologue list to identify perfluorinated compound homologues. However, this method is only used for screening perfluorinated compound homologues and cannot be used to screen homologues of other new pollutants.

[0006] In view of this, the present invention is proposed. Summary of the invention

[0007] In view of this, the present invention proposes a method for accurately screening homologues of new pollutants in wastewater based on spectral similarity, which realizes high-throughput identification and identification of homologues of new pollutants in the environment.

[0008] Specifically, the present invention is achieved through the following technical solutions:

[0009] The present invention provides a method for accurately screening homologues of new pollutants in wastewater based on spectral similarity, comprising the following steps:

[0010] The pollutants were analyzed and detected by high performance liquid chromatography-mass spectrometry to obtain secondary spectrum data;

[0011] Combined with the secondary spectrum data, the homologues were preliminarily screened using the Kendrick mass loss and retention time increment rules;

[0012] The modified cosine algorithm is used to calculate the spectral similarity to obtain a similarity matrix, and a modified cosine spectral similarity threshold is set. The initial screening results obtained above are combined with the similarity matrix to perform a re-screening of homologues, and the results of homologues above the modified cosine spectral similarity threshold are output.

[0013] Preferably, the method for accurately screening homologues of new pollutants in wastewater based on modified cosine spectrum similarity comprises the following steps:

[0014] 1) Analysis and detection: High performance liquid chromatography-high resolution mass spectrometry (HPLC-HRMS) is used to analyze and detect the samples;

[0015] 2) Data preprocessing: Use MS-Dial software to separate the chromatographic peaks obtained in the mass spectrometry analysis and detection results to obtain the mass-to-charge ratio, retention time, fragment ion and signal intensity information of each chromatographic peak;

[0016] 3) Use the MS-HS algorithm for accurate screening. The screening steps include:

[0017] a) Clean and denoise the input secondary spectrum data.

[0018] b) Analyze the secondary spectrum information and convert the two mass spectrum data sets into a two-dimensional matrix, in which each row represents a peak, the first column represents the fragment mass-to-charge ratio, and the second column represents the intensity value.

[0019] c) Calculate the spectral similarity using the modified cosine algorithm and save it as a similarity scoring matrix. For each substance in the series, select the maximum value of similarity with other substances in the series as the spectral similarity value of the substance for subsequent screening;

[0020] d) Use Kendrick's mass loss and retention time increment rules to preliminarily screen homologues;

[0021] e) Setting a modified cosine spectrum similarity threshold, combining the initial screening results with the modified cosine spectrum similarity matrix score obtained in c) for rescreening, and a score higher than the modified cosine spectrum similarity threshold indicates that the spectra are highly similar, and they are output as a group of homologues.

[0022] The protection focus of the scheme of the present invention is to combine the three methods of Kendrick mass loss, retention time increment rule and modified cosine algorithm to calculate the similarity of the spectrum to screen the homologues of new pollutants. In the prior art, only Kendrick mass loss is used to do the work of homologues. However, the simple Kendrick mass loss will obtain many homologue results for such a large data set of mass spectrometry data, but they are not necessarily true homologues. There are situations where the molecular formula looks like a homologue, but the structure is not similar at all. There is no way to accurately identify the homologues of new pollutants in wastewater. Therefore, the scheme of the present invention can achieve accurate screening. The above three methods are selected from many algorithms for organic combination, so that the homologues can be accurately screened. Therefore, there is no record of the combination of the above three methods in the prior art. And because the screening method of the present invention is relatively accurate, it can have a good analysis of the spectrum obtained by any instrumental analysis method, not limited to the spectrum of gas phase, liquid phase and mass spectrum. In the prior art, because the screening method is not accurate, it may be limited to the spectrum of gas chromatography, which is easy to analyze.

[0023] The parameter settings of MS-HS algorithm input are shown in Table 1 below:

[0024] Table 1

[0025]

[0026] Among the parameters set above, the mass-to-charge ratio range defines the minimum and maximum values ​​of the mass-to-charge ratio (m / z) used to identify homologue repeating units. Repeating units usually correspond to structural repeating parts in molecules, such as CH units. The present invention can flexibly set the mass-to-charge ratio range interval to screen homologues of different repeating unit types. The default mass-to-charge ratio range interval of the present invention is set at 1 to 100 Da, which is used to comprehensively screen all types of homologues in environmental samples.

[0027] The mass defect range is used to define the allowable deviation range between the theoretical mass and the actual observed mass, and is based on the calibration accuracy of the mass spectrometer and the data quality. The appropriate mass defect range selected by default in the present invention is -0.025 to 0.025 to balance the accuracy and sensitivity of the match.

[0028] The mass error tolerance determines the maximum mass error allowed during the homologue screening process. It is set according to the accuracy requirements of the homologue matching. The present invention defaults to setting the mass error tolerance to 0.002 for accurate matching of homologues.

[0029] According to the retention time increasing rule, the retention time of compounds with similar structures will gradually increase with the increase of molecular weight. The present invention sets whether to confirm the retention time increase, the retention time increase range interval and the retention time tolerance. The present invention defaults that whether to confirm the retention time increase parameter is set to 1, confirming that the retention time obeys the increasing rule. The retention time increase range interval is set to 0 to 15 to adapt to the actual retention time changes. The retention time tolerance is set to 0.2 to improve the accuracy of peak matching and reduce the possibility of false matching.

[0030] The modified cosine spectra similarity threshold is used to determine whether two spectra are considered similar. The present invention defaults to setting the modified cosine spectra similarity threshold to 0.3 for preliminary screening and discovery of potential homologues.

[0031] The setting of each parameter involved in the above is also the focus of the present invention. The specific values ​​of all the above parameters are set to screen out all types of homologues as much as possible while reducing the existence of false homologues detected by this method. They are all obtained after a lot of creative work. Therefore, it can be found that the present invention is not a simple superposition of the three methods, but a gradual optimization of the parameters in the whole screening process, so as to improve the accuracy of the screening.

[0032] As a further improvement of the present invention, the method also includes non-targeted screening of homologues of new pollutants: in step 2), the MS-Dial will further perform database matching to obtain compounds matching the database.

[0033] As a further improvement of the present invention, in step 3), acceleration methods such as numba acceleration and parallel computing are used to achieve rapid screening of homologues of new pollutants.

[0034] As a further improvement of the present invention, the homologues in the same series as the result of step d) should contain at least three substances.

[0035] As a further improvement of the present invention, the step 1) includes: membrane filtration, solid phase column extraction, solvent elution, concentration and dissolution, and detection.

[0036] The detection conditions are as follows:

[0037] High performance liquid chromatograph: Vanquish F;

[0038] Chromatographic column: ACQUITY UPLC BEH C18, 2.1 mm × 100 mm, 1.7 μm, Waters, USA;

[0039] Column temperature: 25°C;

[0040] Flow rate: 0.25 mL / min;

[0041] Mobile Phase:

[0042] Positive ion mode: Phase A is water with 0.1% formic acid, and phase B is methanol;

[0043] Negative ion mode: Phase A is added with 2 mmol ammonium acetate solution, and phase B is methanol;

[0044] The elution gradient is shown in Table 2 below:

[0045] Table 2

[0046]

[0047]

[0048] Mass spectrometer: Orbitrap Exploris 480, Thermo Scientific TM ;

[0049] Ion source: ESI;

[0050] Injection volume: 10 μL;

[0051] Ion mode: positive ion mode and negative ion mode;

[0052] MS scanning range: m / z 100-1000;

[0053] MS / MS scanning range: m / z 50-1000;

[0054] Ion source gas pressure: 55psi;

[0055] Ion source temperature: 413°C;

[0056] Ion source spray voltage: 3500V (positive ion mode) / -2500V (negative ion mode);

[0057] Declustering voltage: 80 V (positive ion mode), -80 V (negative ion mode);

[0058] Declustering voltage range: 20 eV;

[0059] Collision energy: 35±15 eV (positive ion mode), -35±15 eV (negative ion mode).

[0060] As a further improvement of the present invention, the solid phase extraction column is an Oasis HLB column.

[0061] As a further improvement of the present invention, the solvent is methanol.

[0062] In addition to providing a method for accurately screening homologues of new pollutants in wastewater based on spectral similarity, the present invention also provides a screening system, including:

[0063] Detection module: used to analyze and detect pollutants using high performance liquid chromatography-mass spectrometry to obtain secondary spectrum data;

[0064] Preliminary screening module: used to perform preliminary screening of homologues by using Kendrick mass loss and retention time increment rules in combination with the secondary spectrum data;

[0065] Re-screening module: used to calculate the spectrum similarity using the modified cosine algorithm to obtain a similarity matrix, and set a modified cosine spectrum similarity threshold, and re-screen the homologues based on the initial screening results obtained above and the similarity matrix, and output the results for homologues above the modified cosine spectrum similarity threshold.

[0066] In summary, the solution of the present invention has the following beneficial effects:

[0067] The mass spectra between homologues are highly similar, and these similarities are mainly reflected in the spectra, and the secondary mass spectrum information contains rich structural information. Therefore, by adding secondary mass spectrum information, homologues can be effectively identified by calculating the similarity of the spectra, reducing the false positives in the screening of homologues. The modified cosine similarity algorithm can help researchers identify and classify known and unknown homologues by matching the fragments and their neutral losses in the secondary spectra and calculating the matching similarity. Therefore, the present invention is based on the modified cosine spectrum similarity algorithm for accurately screening new pollutant homologues in wastewater.

[0068] In order to eliminate the mass difference between different compound peaks, so that compounds with similar structures have similar mass spectra, the present invention also considers applying the Kendrick mass loss method, which is specifically reflected in that the homologues of the new pollutant have the same Kendrick mass loss.

[0069] The retention time increasing rule refers to the rule that in chromatographic analysis, the retention time of compounds with similar structures will gradually increase with the increase of molecular weight. The structures and properties of pollutant homologues are very similar, and simple homologue screening often results in a large number of false positives. The retention time increasing rule can be used to clarify the retention time differences between homologues, thereby reducing the occurrence of false positives and improving the accuracy of analysis. Therefore, the present invention also considers the retention time increasing rule of new pollutant homologues.

[0070] Based on the Kendrick mass loss, retention time increment rule and modified cosine spectrum similarity, this paper develops a new pollutant homologue screening algorithm (MS-HS algorithm) using Python. It realizes accurate screening of new pollutant homologues in wastewater and achieves good results in actual wastewater applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Various other advantages and benefits will become apparent to those skilled in the art by reading the following detailed description of the preferred embodiments. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. The same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0072] Figure 1 It is the overall framework diagram of MS-HS algorithm;

[0073] Figure 2 This is a full-process non-targeted analysis paradigm diagram of the MS-HS algorithm in environmental wastewater;

[0074] Figure 3 This is the output result graph of MS-HS algorithm in environmental wastewater;

[0075] Figure 4 The algorithm outputs the mass spectra of the four substances in the CF2 series and CLASS1.

[0076] Figure 5 A flowchart of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0077] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are only examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0078] The terms used in the present disclosure are only for the purpose of describing specific embodiments and are not intended to limit the present disclosure. The singular forms "a", "said" and "the" used in the present disclosure and the appended claims are also intended to include plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0079] It should be understood that although the terms first, second, third, etc. may be used in the present disclosure to describe various information, such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present disclosure, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" used herein may be interpreted as "at the time" or "when" or "in response to determining",

[0080] Example

[0081] like Figure 1 As shown, the method provided by the present invention for accurately screening homologues of new pollutants in wastewater based on spectral similarity comprises the following steps:

[0082] The pollutants were analyzed and detected by high performance liquid chromatography-mass spectrometry to obtain secondary spectrum data;

[0083] Combined with the secondary spectrum data, the homologues were preliminarily screened using the Kendrick mass loss and retention time increment rules;

[0084] The modified cosine algorithm is used to calculate the spectral similarity to obtain a similarity matrix, and a modified cosine spectral similarity threshold is set. The initial screening results obtained above are combined with the similarity matrix to perform a re-screening of homologues, and the results of homologues above the modified cosine spectral similarity threshold are output.

[0085] The method developed by the present invention for accurately screening homologues of new pollutants in wastewater based on modified cosine spectral similarity is as follows:

[0086] (A) First, collect 1L of wastewater from the industrial sewage treatment plant and analyze it using high performance liquid chromatography-high resolution mass spectrometry (HPLC-HRMS). The analysis and detection steps are as follows:

[0087] Membrane filtration: The wastewater is filtered through a glass vacuum filter, a 1μm glass fiber filter membrane and a 0.45μm nylon filter membrane. This method can more quickly remove impurities larger than 0.45μm.

[0088] Solid phase cartridge extraction: Oasis HLB cartridges are used to enrich wastewater pollutants. Before enrichment, the Oasis HLB cartridges need to be activated with 10 mL of methanol and 10 mL of water. The flow rate of the water sample during the enrichment process is controlled at 3-5 mL / min, and the Oasis HLB cartridges are kept moist during the enrichment process.

[0089] Solvent elution: Elute the Oasis HLB column three times with 4 mL of methanol and combine the eluates;

[0090] Concentration and dissolution: Blow the eluted liquid nitrogen until it is nearly dry, then dissolve it with methanol and make up to 1 mL, filter it with a 0.45 μm PTFE filter membrane, store it in an injection vial, and place it in a -20°C refrigerator for testing.

[0091] On-machine testing: The samples were tested by high performance liquid chromatography-high resolution mass spectrometry (HPLC-HRMS), and the instrument conditions were as follows:

[0092] High performance liquid chromatograph: Vanquish F;

[0093] Chromatographic column: ACQUITY UPLC BEH C18, 2.1 mm × 100 mm, 1.7 μm, Waters, USA;

[0094] Column temperature: 25°C;

[0095] Flow rate: 0.25 mL / min;

[0096] Mobile Phase:

[0097] Positive ion mode: Phase A is water with 0.1% formic acid, and phase B is methanol;

[0098] Negative ion mode: Phase A is added with 2 mmol ammonium acetate solution, and phase B is methanol;

[0099] Elution gradient

[0100]

[0101] Mass spectrometer: Orbitrap Exploris 480, Thermo Scientific TM ;

[0102] Ion source: ESI;

[0103] Injection volume: 10 μL;

[0104] Ion mode: positive ion mode and negative ion mode;

[0105] MS scanning range: m / z 100-1000;

[0106] MS / MS scanning range: m / z 50-1000;

[0107] Ion source gas pressure: 55psi;

[0108] Ion source temperature: 413°C;

[0109] Ion source spray voltage: 3500V (positive ion mode) / -2500V (negative ion mode);

[0110] Declustering voltage: 80 V (positive ion mode), -80 V (negative ion mode);

[0111] Declustering voltage range: 20 eV;

[0112] Collision energy: 35±15 eV (positive ion mode), -35±15 eV (negative ion mode).

[0113] (B) Application of MS-HS algorithm

[0114] The MS-Dial software separates the chromatographic peaks obtained in the mass spectrometry analysis and detection results, obtains the mass-to-charge ratio, retention time, fragment ion and signal intensity information of each chromatographic peak, and organizes this information of all substances into an input file. The MS-HS algorithm is used for precise screening. The screening steps are: a) Data cleaning and denoising of the input secondary spectrum data.

[0115] b) Analyze the secondary spectrum information and convert the two mass spectrum data sets into a two-dimensional matrix, in which each row represents a peak, the first column represents the fragment mass-to-charge ratio, and the second column represents the intensity value.

[0116] c) Calculate the spectral similarity using the modified cosine algorithm and save it as a similarity scoring matrix. For each substance in the series, select the maximum value of similarity with other substances in the series as the spectral similarity value of the substance for subsequent screening;

[0117] d) Use Kendrick's mass loss and retention time increment rules to preliminarily screen homologues;

[0118] e) Setting a modified cosine spectrum similarity threshold, combining the initial screening results with the modified cosine spectrum similarity matrix score obtained in c) for rescreening, and a score higher than the modified cosine spectrum similarity threshold indicates that the spectra are highly similar, and they are output as a group of homologues.

[0119] All parameter settings in this example are the default parameters of the MS-HS algorithm, and the parameters are those in Table 1.

[0120] In the actual wastewater samples, after precise screening in the above steps, the algorithm detected homologous series containing repeating units (RU) such as CH2, CF2, CF2O, etc. The homologous series with RU of CH2 were the most in positive and negative ion modes, and there were 79 series with RU of CF2 in negative ion mode, indicating that the sample contained a large number of perfluorinated compounds.

[0121] According to the output results of the MS-HS algorithm, the CH2 series and CF2 series were selected for structural identification.

[0122] Some results of CH2 series structure identification are shown in Table 3 below.

[0123] Table 3

[0124]

[0125]

[0126] CLASS1 is mainly nonylphenols, which are usually used as surfactant intermediates to produce nonionic surfactants, such as nonylphenol ethoxylates (NPEs). These nonionic surfactants are widely used in detergents, industrial cleaners, coatings, pesticides and textile auxiliaries. CLASS2 is mainly benzoic acids, which play an important role in organic synthesis and industrial applications. CLASS3 is mainly diacids, which are used to synthesize polyester polymer materials. The synthesized materials have excellent mechanical properties, chemical stability and heat resistance, and are widely used in fibers, films and engineering plastics.

[0127] Some results of the structural identification of the CF2 series are shown in Table 4 below:

[0128] Table 4

[0129]

[0130] Class 1 is mainly perfluorinated carboxylic acids, Class 2 is mainly perfluorinated dicarboxylic acids, and Class 3 is mainly hydrogen-substituted perfluorinated carboxylic acids. These are all highly efficient industrial chemicals, but perfluorinated substances already in the environment pose a long-term threat to ecosystems and human health.

[0131] Figure 2 A specific flow chart showing the specific application of the precise screening method of the present invention is shown. Figure 3 This is the output result diagram of the MS-HS algorithm in environmental wastewater, such as Figure 3As shown in the figure, the present invention can screen out all homologues with ΔMZ in the range of 1-100Da corresponding to the repeating unit RU in the environmental wastewater sample. Similar repeating units RU include CH2, CF2, CF2O, CF2CF2, etc. The length of the line represents the number of homologue series corresponding to the repeating unit. The number of CH2 series in positive and negative ions is the largest, while there are more CF2, CF2O, and CF2CF2 series in negative ions, proving that the sample contains more perfluoroalkyl and polyfluoroalkyl compounds. Figure 4 As shown in the figure, this is the original mass spectra of the four substances corresponding to CLASS1 obtained by the MS-HS algorithm. The parent ion mass-to-charge ratios of CLASS1-1, CLASS1-2, CLASS1-3, and CLASS1-4 are 368.97629, 418.97388, 468.96970, and 518.96680, respectively, ΔMZ is 49.9968, the repeating unit RU is CF2, and the retention times are 24.838, 27.791, 29.177, and 30.470, respectively, which conforms to the retention time increasing rule. The spectral similarities are 0.86, 0.86, 0.67, and 0.79, all greater than the similarity threshold of 0.3, and have a high degree of similarity. Specifically, the original spectra of CLASS1-1, CLASS1-2, CLASS1-3, and CLASS1-4 all contain similar secondary spectrum fragments, such as characteristic fragments such as C2F5, C3F7, and C4F9. These characteristic fragments give the four substances a high degree of similarity, and the present invention can screen out these homologues with truly similar structures. Figure 4 The effectiveness of the present invention is also revealed.

[0132] The present invention is successfully applied to the detection of new pollutant homologues in environmental wastewater, and can effectively, quickly and accurately detect new pollutant homologues in environmental wastewater.

[0133] In addition, in addition to providing a method for accurately screening homologues of new pollutants in wastewater based on spectral similarity, the present invention also provides a screening system, which specifically includes:

[0134] Detection module: used to analyze and detect pollutants using high performance liquid chromatography-mass spectrometry to obtain secondary spectrum data;

[0135] Preliminary screening module: used to perform preliminary screening of homologues by using Kendrick mass loss and retention time increment rules in combination with the secondary spectrum data;

[0136] Re-screening module: used to calculate the spectrum similarity using the modified cosine algorithm to obtain a similarity matrix, and set a modified cosine spectrum similarity threshold, and re-screen the homologues based on the initial screening results obtained above and the similarity matrix, and output the results for homologues above the modified cosine spectrum similarity threshold.

[0137] In specific implementation, the above modules can be implemented as independent entities, or can be arbitrarily combined and implemented as the same or several entities. The specific implementation of the above units can refer to the previous method embodiments, which will not be repeated here.

[0138] In summary, the solution of the present invention has the following technical effects:

[0139] (1) The method developed by the present invention for accurately screening homologues of new pollutants in wastewater based on modified cosine spectral similarity is aimed at the existing technology which is limited to screening of specific types of homologues. The present method achieves comprehensive screening of all homologues of environmental samples by setting the mass-to-charge ratio range of the repeating unit.

[0140] (2) The method developed by the present invention for accurately screening homologues of new pollutants in wastewater based on modified cosine spectral similarity comprehensively considers Kendrick mass loss, retention time increment rule and modified cosine spectral similarity to develop a new pollutant homologue screening algorithm (MS-HS algorithm). This algorithm is applied to non-targeted screening of environmental wastewater, and a full-process non-targeted analysis paradigm is constructed from sample collection, sample pretreatment, instrument detection, data preprocessing, MS-HS algorithm application and homologue structure identification.

[0141] (3) The method developed in the present invention is based on the similarity of modified cosine spectra to accurately screen homologues of new pollutants in wastewater. The MS-HS algorithm is applied to the non-targeted screening of environmental wastewater, and a full-process non-targeted analysis paradigm is constructed from sample collection, sample pretreatment, instrument detection, data preprocessing, application of MS-HS algorithm to homologue structure identification.

[0142] (4) The method developed by the present invention for accurately screening homologues of new pollutants in wastewater based on modified cosine spectral similarity, while combining MS-Dial for database matching, also screens homologues of chemical substances that are not matched in the database, thus achieving rapid high-throughput screening of known and unknown homologues at the same time, with a wider screening range and higher accuracy.

[0143] (5) The method developed in the present invention for accurately screening homologues of new pollutants in wastewater based on modified cosine spectral similarity effectively classifies homologous species for the large amount of mass spectral data generated during non-targeted screening using high performance liquid chromatography-high resolution mass spectrometry (HPLC+HRMS).

[0144] Figure 5 This is a schematic diagram of the structure of a computer device disclosed in the present invention. Figure 5 As shown, the computer device 400 includes at least a memory 402 and a processor 401; the memory 402 is connected to the processor via a communication bus 403, and is used to store computer instructions executable by the processor 401, and the processor 401 is used to read computer instructions from the memory 402 to implement the steps of the method described in any of the above embodiments.

[0145] For the above-mentioned device embodiments, since they basically correspond to the method embodiments, the relevant parts can refer to the partial description of the method embodiments. The device embodiments described above are only schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the disclosed solution. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0146] Computer readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including, for example, semiconductor memory devices (e.g., EPROM, EEPROM and flash memory devices), magnetic disks (e.g., internal disks or removable disks), magneto-optical disks, and CD ROM and DVD-ROM disks. The processor and memory may be supplemented by, or incorporated in, special purpose logic circuits.

[0147] Finally, it should be noted that although this specification contains many specific implementation details, these should not be interpreted as limiting the scope of any invention or the scope of protection claimed, but are mainly used to describe the features of specific embodiments of specific inventions. Certain features described in multiple embodiments within this specification may also be implemented in combination in a single embodiment. On the other hand, the various features described in a single embodiment may also be implemented separately in multiple embodiments or in any suitable sub-combination. In addition, although features may work in certain combinations as described above and even initially claimed as such, one or more features from the claimed combination may be removed from the combination in some cases, and the claimed combination may point to a sub-combination or a variation of a sub-combination.

[0148] Similarly, although operations are depicted in a particular order in the accompanying drawings, this should not be understood as requiring that these operations be performed in the particular order shown or performed sequentially, or requiring that all illustrated operations be performed to achieve the desired results. In some cases, multitasking and parallel processing may be advantageous. In addition, the separation of various system modules and components in the above-described embodiments should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product, or packaged into multiple software products.

[0149] Thus, specific embodiments of the subject matter have been described. Other embodiments are within the scope of the appended claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the particular order or sequential order shown to achieve the desired results. In some implementations, multitasking and parallel processing may be advantageous.

[0150] The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present disclosure shall be included in the scope of protection of the present disclosure.

Claims

1. A method for accurately screening homologues of new pollutants in wastewater based on spectral similarity, characterized in that: The steps include: The pollutants were analyzed and detected by high performance liquid chromatography-mass spectrometry to obtain secondary spectrum data; Combined with the secondary spectrum data, the homologues were preliminarily screened using the Kendrick mass loss and retention time increment rules; The modified cosine algorithm is used to calculate the spectral similarity to obtain a similarity matrix, and a modified cosine spectral similarity threshold is set. The initial screening results obtained above are combined with the similarity matrix to perform a re-screening of homologues, and the results of homologues above the modified cosine spectral similarity threshold are output.

2. The method for accurately screening homologues of new pollutants in wastewater based on spectral similarity according to claim 1, characterized in that: The parameters used in the Kendrick mass loss calculation process include: mass-to-charge ratio range interval 1 to 100, mass loss range interval -0.025 to 0.025, and mass error tolerance 0 to 1.

3. The method for accurately screening homologues of new pollutants in wastewater based on spectral similarity according to claim 2, characterized in that: The parameters used in the retention time increment rule include: retention time tolerance 0 to 100, retention time increment range 0 to 15, whether to confirm the retention time increment 1 for yes, 0 for no.

4. The method for accurately screening homologues of new pollutants in wastewater based on spectral similarity according to claim 1, characterized in that: The modified cosine spectrogram similarity threshold setting allows a range between 0 and 1.

5. The method for accurately screening homologues of new pollutants in wastewater based on spectral similarity according to claim 4, characterized in that: The modified cosine spectrogram similarity threshold is set to 0.

3.

6. The method for accurately screening homologues of new pollutants in wastewater based on spectral similarity according to claim 3, characterized in that: The mass error tolerance is 0.002, and the retention time tolerance is 0.

2.

7. The method for accurately screening homologues of new pollutants in wastewater based on spectral similarity according to claim 1, characterized in that: The secondary spectra are pre-parsed to convert the two mass spectrum data sets into a two-dimensional matrix, in which each row represents a peak, the first column represents the mass-to-charge ratio of the fragments, and the second column represents the intensity value.

8. A screening system using the method for accurately screening homologues of new pollutants in wastewater based on spectral similarity according to any one of claims 1 to 7, characterized in that: include: Detection module: used to analyze and detect pollutants using high performance liquid chromatography-mass spectrometry to obtain secondary spectrum data; Preliminary screening module: used to perform preliminary screening of homologues by using Kendrick mass loss and retention time increment rules in combination with the secondary spectrum data; Re-screening module: used to calculate the spectrum similarity using the modified cosine algorithm to obtain a similarity matrix, and set a modified cosine spectrum similarity threshold, and re-screen the homologues based on the initial screening results obtained above and the similarity matrix, and output the results for homologues above the modified cosine spectrum similarity threshold.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for accurately screening homologues of new pollutants in wastewater based on spectral similarity as described in any one of claims 1 to 7 are executed.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the method for accurately screening homologues of new pollutants in wastewater based on spectral similarity as described in any one of claims 1 to 7 are implemented.

Images