Method and device for identifying iodocarbazole molecules in cold spring sediments, equipment and medium

Abstract

The application discloses a method and device for identifying iodocarbazole molecules in cold spring sediments, equipment and medium, relates to the technical field of data processing, and comprises the following steps: collecting liquid chromatography and mass spectrometry full-scan and secondary mass spectrometry of cold spring multi-end element samples to obtain a target data set; constructing an iodocarbazole candidate molecule library conforming to a target general formula; matching the target data set and the iodocarbazole candidate molecule library to obtain iodocarbazole candidates; screening iodocarbazole targets as an iodocarbazole identification list according to the matching scores of the iodocarbazole candidates; constructing a molecular fingerprint vector of the iodocarbazole targets; determining internal generation contribution and external input contribution of the cold spring surface sediment to be judged; and constructing a reaction window index according to the molecular fingerprint vector, the internal generation contribution and the external input contribution, so as to represent state information of the iodocarbazole molecules. The application can realize a whole-process scheme from data collection to mechanism identification of the cold spring surface sediment.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to methods, apparatus, equipment and media for the molecular recognition of iodocarbazole in cold seep sediments. Background Technology

[0002] Halogenated carbazoles are a class of nitrogen-containing heterocyclic aromatic compounds with carbazole as the parent nucleus, formed by substitution with halogens such as chlorine, bromine, or iodine. Current detection techniques are mostly focused on a few target halogenated carbazoles with available standards. Standard curves are typically established using internal or external standard methods, combined with gas chromatography-mass spectrometry (GC-MS), gas chromatography-tandem mass spectrometry (GC-MS), or liquid chromatography-tandem mass spectrometry (LC-MS) for qualitative and quantitative analysis. These techniques are practically valuable for determining the concentration of fixed target analytes, but they struggle to cover the large quantities of unknown iodocarbazoles and iodine-containing mixed halogenated carbazoles in the environment. Deep-sea cold seep sediments are characterized by methane seepage, a well-developed sulfate-methane transition zone, iodine ion enrichment in pore water, a strong redox gradient, and active microbial metabolism. These conditions may promote the iodination, chlorine-iodine mixed halogenation, reductive deiodination, and sedimentary preservation processes of carbazole aromatic precursors. Therefore, in cold seep sediments, iodocarbazoles may not only be introduced through external particulate matter sedimentation but may also be controlled by deep fluid migration, in-situ biohalogenation, abiotic halogenation, and microbial dehalogenation transformation.

[0003] Existing technologies employ various methods for detecting carbazole or halogenated carbazole, such as targeted detection methods for aquatic products, urine, water samples, or ordinary marine sediments, as well as methods for preparing halogenated carbazole. The core of these approaches typically involves sample pretreatment, fixation of target analyte standards, internal or external standard quantification, and GC-MS / MS or UPLC-MS / MS detection parameters. These methods address the problem of "how to accurately detect a few target compounds," but cannot identify iodocarbazole molecules in cold seep sediments. Summary of the Invention

[0004] The main objective of this application is to propose a method, apparatus, equipment, and medium for identifying iodocarbazole molecules in cold seep sediments, so as to achieve non-targeted identification, source endmember contribution analysis, and formation mechanism identification of unknown iodocarbazole in cold seep sediments.

[0005] To achieve the above objectives, one aspect of this application proposes a method comprising the following steps: The target dataset was obtained by full-scan acquisition of cold seep multi-end-member samples using liquid chromatography and high-resolution mass spectrometry, as well as secondary mass spectrometry. Construct a library of iodocarbazole candidate molecules that conform to the target general formula; The target dataset and the iodocarbazole candidate molecule library are matched to obtain iodocarbazole candidates; The iodocarbazole candidates are matched and scored, and iodocarbazole target compounds are selected from the iodocarbazole candidates based on the matching scores to form an iodocarbazole identification list. Construct the molecular fingerprint vector of the iodocarbazole target in the iodocarbazole identification list; Determine the internal generation contribution and external input contribution of the surface sediments of the cold seep to be identified; A reaction window index is constructed based on the molecular fingerprint vector, the internal generation contribution, and the external input contribution; wherein, the reaction window index is used to characterize the state information of iodocarbazole molecules in the surface sediments of the cold seep to be identified.

[0006] In some embodiments, constructing a library of iodocarbazole candidate molecules conforming to the target general formula includes the following steps: Based on the carbazole core, construct the iodocarbazole candidate molecule library that conforms to the target general formula; The target formula is: C 12 H9 (x+y+z) NCl x Br y I z ; Where x, y, and z are the substitution numbers of chlorine, bromine, and iodine, respectively, and z ≥ 1.

[0007] In some embodiments, matching the target dataset with the iodocarbazole candidate molecule library to obtain iodocarbazole candidates includes the following steps: Iodocarbazoles or iodine-containing mixed halocarbazoles containing at least one iodine atom from the iodocarbazole candidate molecule library are used as screening targets. The screening targets are matched with the target dataset. The matching type includes at least one of I-CZ, Cl / I-CZ, Br / I-CZ and Cl / Br / I-CZ.

[0008] In some embodiments, the identification and scoring of the iodocarbazole candidate includes the following steps: The matching score of the iodocarbazole candidate is calculated based on the parent ion precise mass matching score, isotope peak cluster matching score, secondary fragment matching score, cross-sample reproducibility score, and sample-blank response ratio score. The step of selecting iodocarbazole target compounds from the iodocarbazole candidates as an iodocarbazole identification list based on the matching score includes the following steps: Iodocarbazole candidates whose matching scores reach a preset score threshold are selected as iodocarbazole target compounds. The iodocarbazole identification list is constructed based on the iodocarbazole target.

[0009] In some embodiments, constructing the molecular fingerprint vector of the iodocarbazole target in the iodocarbazole identification list includes the following steps: The target variables of the iodocarbazole target analyte are determined; wherein, the target variables include total iodocarbazole concentration, relative abundance of iodocarbazole, proportion of mono- to poly-iodinated compounds, Cl / I-CZ ratio, Br / I-CZ ratio, Cl / Br / I-CZ ratio, proportion of tetrahalogenated to hexahalogenated compounds, Cl / I-CZ to I-CZ ratio, high-halogenated Cl / I-CZ to low-halogenated I-CZ ratio, and peak area ratio of characteristic homologues; At least four variables are selected from the target variables to construct the molecular fingerprint vector corresponding to the iodocarbazole target.

[0010] In some embodiments, determining the internal generation contribution and external input contribution of the surface sediments of the cold seep to be identified includes the following steps: Deep sediment endmembers were used to characterize deep fluid input or internal generation endmembers in cold seeps, and seawater particulate endmembers were used to characterize external particle input endmembers. The concentrations of halocarbazole detected in each endmember sample and surface sediment sample were normalized to obtain the endmember molecular fingerprint matrix and surface sediment molecular fingerprint vector, respectively. Determine the non-negative constrained linear relationship between the end-member molecular fingerprint matrix and the surface deposit molecular fingerprint vector; Based on the non-negative constrained linear relationship, the non-negative fitting parameters corresponding to the deep sediment endmember and the seawater particulate endmember are obtained by solving the non-negative least squares method. The internal generation contribution and the external input contribution are determined based on the non-negative fitting parameters.

[0011] In some embodiments, constructing the reaction window index based on the molecular fingerprint vector, the internal generation contribution, and the external input contribution includes the following steps: The molecular fingerprint vector, the internal generation contribution, and the external input contribution are coupled with methane, sulfate, dissolved inorganic carbon, carbon isotopes, iodide ions in pore water, total organic carbon, nutrients, halogenation functional gene parameters, and dehalogenation functional gene parameters to construct the reaction window index.

[0012] To achieve the above objectives, another aspect of this application provides a molecular recognition device for iodocarbazole in cold seep sediments, the device comprising: The sample processing unit is used to perform full-scan acquisition by liquid chromatography and high-resolution mass spectrometry, as well as secondary mass spectrometry acquisition, on cold seep multi-end-member samples to obtain the target dataset. Molecular library construction unit, used to construct iodocarbazole candidate molecular libraries that conform to the target general formula; A candidate molecule matching unit is used to match the target dataset with the iodocarbazole candidate molecule library to obtain iodocarbazole candidates; The target molecule screening unit is used to identify and score the iodocarbazole candidates, and to screen out iodocarbazole target compounds from the iodocarbazole candidates based on the matching scores as an iodocarbazole identification list. A fingerprint vector construction unit is used to construct the molecular fingerprint vector of the iodocarbazole target in the iodocarbazole identification list; The contribution determination unit is used to determine the internal generation contribution and external input contribution of the surface sediments of the cold seep to be identified. An index construction unit is used to construct a reaction window index based on the molecular fingerprint vector, the internal generation contribution, and the external input contribution; wherein the reaction window index is used to characterize the state information of iodocarbazole molecules in the surface sediments of the cold seep to be identified.

[0013] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method.

[0014] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0015] To achieve the above objectives, another aspect of this application provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0016] The embodiments of this application include at least the following beneficial effects: This application provides a method, apparatus, equipment, and medium for identifying iodocarbazole molecules in cold seep sediments. The proposed method involves obtaining a target dataset by performing full-scan liquid chromatography and high-resolution mass spectrometry (HPLC-MS / MS) and secondary mass spectrometry on multi-terminal samples from cold seeps; constructing an iodocarbazole candidate molecule library conforming to the target's general formula; matching the target dataset with the iodocarbazole candidate molecule library to obtain iodocarbazole candidates; identifying and scoring the iodocarbazole candidates; selecting iodocarbazole target compounds from the iodocarbazole candidate list based on the matching scores; constructing molecular fingerprint vectors for the iodocarbazole target compounds in the iodocarbazole identification list; determining the internal generation contribution and external input contribution of the surface sediments of the cold seep to be identified; and constructing a reaction window index based on the molecular fingerprint vector, internal generation contribution, and external input contribution. The reaction window index is used to characterize the state information of iodocarbazole molecules in the surface sediments of the cold seep to be identified. This application enables non-targeted identification of unknown iodocarbazole in cold seep sediments by matching the target dataset with a library of iodocarbazole candidate molecules. By analyzing the surface sediments of the cold seep to be identified, the internal generation contribution and external input contribution of the source endmember can be obtained. Based on the reaction window index, the state information of iodocarbazole molecules in the surface sediments of the cold seep to be identified can be determined, thereby identifying the formation mechanism. This application can realize a complete process scheme for identifying the formation mechanism of cold seep surface sediments from data acquisition to formation mechanism identification. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application, 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 this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A schematic flowchart illustrating the iodocarbazole molecule recognition method in cold seep sediments provided in this application embodiment; Figure 2 An example flowchart of the iodocarbazole molecule recognition method in cold seep sediments provided in this application embodiment; Figure 3 This is a schematic diagram illustrating the construction and multi-level screening and identification of the iodocarbazole candidate molecule library provided in the embodiments of this application; Figure 4 A schematic diagram of an end-member mixing model based on seawater particulate matter, surface sediments, and deep sediments provided for embodiments of this application; Figure 5 A comprehensive model diagram of the formation mechanism of iodocarbazole in cold seep sediments provided in the embodiments of this application; Figure 6 A schematic diagram of the structure of the iodocarbazole molecular recognition device in cold seep sediments provided in an embodiment of this application; Figure 7 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying 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 those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0020] 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 application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0021] Before providing a detailed description of the embodiments of this application, some related technologies involved in the embodiments of this application will be described first, as follows: To address the problems existing in the prior art, this application aims to establish a comprehensive method that differs from conventional target analyte detection and compound preparation. It combines high-resolution mass spectrometry non-target identification, cold seep multi-endmember sample systems, molecular fingerprint models, binary endmember contribution calculation, and geochemical-microbial reaction window identification to achieve a complete technical chain from unknown peak identification to source contribution analysis and formation mechanism identification.

[0022] The purpose of this application is to provide a scheme for constructing the molecular fingerprint of iodocarbazole in cold seep sediments, analyzing its source contribution, and identifying its formation mechanism. This scheme addresses the problems of existing technologies that mainly rely on a fixed list of target standards, can only perform concentration measurements, cannot identify a large number of unknown iodocarbazole halothions, have difficulty distinguishing between external inputs and internal cold seep generation contributions, and cannot identify the cold seep reaction window and formation mechanism.

[0023] This application provides a method, apparatus, device, and medium for recognizing iodocarbazole molecules in cold seep sediments, relating to the field of data processing technology. The method, apparatus, device, and medium provided in this application can be applied to a terminal, a server, or software running on a terminal or server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, or vehicle terminal, but is not limited to these. The server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network. The software can be an application implementing the iodocarbazole molecule recognition method in cold seep sediments, but is not limited to the above forms.

[0024] This application can be applied to numerous general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0025] Reference Figure 1 This application provides a method for recognizing iodocarbazole molecules in cold seep sediments. This method may include, but is not limited to, steps S100 to S160, as detailed below: S100: The target dataset is obtained by full-scan acquisition of cold seep multi-end-member samples by liquid chromatography and high-resolution mass spectrometry as well as secondary mass spectrometry. S110: Construct a library of iodocarbazole candidate molecules that conform to the target general formula; S120: Match the target dataset with the iodocarbazole candidate molecule library to obtain iodocarbazole candidates; S130: Identify and score the iodocarbazole candidates, and select iodocarbazole target compounds from the iodocarbazole candidates based on the matching scores as an iodocarbazole identification list. S140: Construct the molecular fingerprint vector of the iodocarbazole target in the iodocarbazole identification list; S150: Determine the internal generation contribution and external input contribution of the surface sediments of the cold seep to be identified; S160: Construct a reaction window index based on the molecular fingerprint vector, the internal generation contribution, and the external input contribution; wherein, the reaction window index is used to characterize the state information of iodocarbazole molecules in the surface sediment of the cold seep to be identified.

[0026] Specifically, embodiments of this application may include the following technical solutions: First, sediment cores were collected from the early-stage cold seep development area, the mature cold seep development area, and the local background area. Simultaneously, seawater particles, pore water, headspace, and deep sediment samples were collected to form a multi-component cold seep sample system that includes differences in development stages, exogenous particle input signals, local background signals, and deep input signals.

[0027] Secondly, full-scan and secondary mass spectrometry data were acquired using liquid chromatography-high-resolution mass spectrometry (LC-HDMS). Non-targeted identification was performed using an iodine-containing candidate molecule library, multi-isotope peak cluster matching, and iodine-containing diagnostic fragments. Unlike the detection of fixed 7, 8, 9, or 10 target halogenated carbazoles, this application uses all candidates that meet the general formula of iodinated carbazoles as screening targets, and outputs a list of iodinated carbazoles at the molecular formula or substitution type level through identification scoring and confidence grading.

[0028] Next, the identification list is converted into a molecular fingerprint vector. The molecular fingerprint includes not only concentration information but also the number of halogen substitutions, the number of iodine substitutions, mixed halogen types, the proportion of high-halogenated compounds, the Cl / I-CZ ratio, and the ratio of characteristic homologues. This fingerprint is more suitable for reflecting the compositional characteristics and source differences of iodocarbazole in cold seep systems.

[0029] Finally, a binary non-negative constrained endmember model composed of deep sediment endmembers and seawater particulate endmembers was used to calculate the internal generation contribution and the external input contribution. Local background sediments were used to compare the compositional differences between sediments at different developmental stages and background preservation signals, and to assist in determining whether a preservation or weak remodeling process had occurred. Further analysis was conducted using methane, sulfate, and δ¹⁸O₂ parameters. 13 C-DIC, pore water iodide ions, and halogenation / dehalogenation functional genes were used to construct reaction window indices, thereby identifying the potential formation, transformation, and storage mechanisms of iodocarbazole.

[0030] Optionally, the construction of the iodocarbazole candidate molecule library conforming to the target general formula includes the following steps: Based on the carbazole core, construct the iodocarbazole candidate molecule library that conforms to the target general formula; The target formula is: C 12 H9 (x+y+z) NCl x Br y I z ; Where x, y, and z are the substitution numbers of chlorine, bromine, and iodine, respectively, and z ≥ 1.

[0031] Optionally, matching the target dataset with the iodocarbazole candidate molecule library to obtain iodocarbazole candidates includes the following steps: Iodocarbazoles or iodine-containing mixed halocarbazoles containing at least one iodine atom from the iodocarbazole candidate molecule library are used as screening targets. The screening targets are matched with the target dataset. The matching type includes at least one of I-CZ, Cl / I-CZ, Br / I-CZ and Cl / Br / I-CZ.

[0032] Optionally, the identification and matching scoring of the iodocarbazole candidate includes the following steps: The matching score of the iodocarbazole candidate is calculated based on the parent ion precise mass matching score, isotope peak cluster matching score, secondary fragment matching score, cross-sample reproducibility score, and sample-blank response ratio score. The step of selecting iodocarbazole target compounds from the iodocarbazole candidates as an iodocarbazole identification list based on the matching score includes the following steps: Iodocarbazole candidates whose matching scores reach a preset score threshold are selected as iodocarbazole target compounds. The iodocarbazole identification list is constructed based on the iodocarbazole target.

[0033] Specifically, the matching score S is identified using the following calculation formula. id Sort the candidates: S id = w m ·M m + w i ·I i + w f ·F f + w r ·R r + w b ·B b ; Among them, M m To achieve accurate quality matching scores, I i For isotope peak cluster matching score, Ff R is the score for level 2 fragment matching. r For cross-sample reproducibility score, B b The sample-blank response ratio score, w m w i w f w r and w b Here are the weighting coefficients, and w m +w i +w f +w r +w b =1; when S id When the preset scoring threshold is reached, the corresponding candidate will be added to the iodocarbazole identification list.

[0034] Optionally, constructing the molecular fingerprint vector of the iodocarbazole target in the iodocarbazole identification list includes the following steps: The target variables of the iodocarbazole target analyte are determined; wherein, the target variables include total iodocarbazole concentration, relative abundance of iodocarbazole, proportion of mono- to poly-iodinated compounds, Cl / I-CZ ratio, Br / I-CZ ratio, Cl / Br / I-CZ ratio, proportion of tetrahalogenated to hexahalogenated compounds, Cl / I-CZ to I-CZ ratio, high-halogenated Cl / I-CZ to low-halogenated I-CZ ratio, and peak area ratio of characteristic homologues; At least four variables are selected from the target variables to construct the molecular fingerprint vector corresponding to the iodocarbazole target.

[0035] It is understood that, in the embodiments of this application, at least four variables can be selected from the target variables to construct the corresponding vector as the molecular fingerprint vector of the iodocarbazole target.

[0036] Optionally, determining the internal generation contribution and external input contribution of the surface sediments of the cold seep to be identified includes the following steps: Deep sediment endmembers were used to characterize deep fluid input or internal generation endmembers in cold seeps, and seawater particulate endmembers were used to characterize external particle input endmembers. The concentrations of halocarbazole detected in each endmember sample and surface sediment sample were normalized to obtain the endmember molecular fingerprint matrix and surface sediment molecular fingerprint vector, respectively. Determine the non-negative constrained linear relationship between the end-member molecular fingerprint matrix and the surface deposit molecular fingerprint vector; Based on the non-negative constrained linear relationship, the non-negative fitting parameters corresponding to the deep sediment endmember and the seawater particulate endmember are obtained by solving the non-negative least squares method. The internal generation contribution and the external input contribution are determined based on the non-negative fitting parameters.

[0037] Specifically, deep sediments and seawater particles were used as two source endmembers. Deep sediments were used to characterize the deep fluid input or internal generation endmembers of cold seeps, while seawater particles were used to characterize the external particle input endmembers. The concentrations of halocarbazole detected in each endmember sample and the surface sediment sample were normalized to obtain the endmember molecular fingerprint matrix Y and the surface sediment molecular fingerprint vector S, respectively.

[0038] Among them, the relative proportion y of the i-th halogenated carbazole in the j-th endmember i,j Calculate using the following formula: ; The relative proportion S of the i-th halocarbazole in the surface sediment i Calculate using the following formula: ; Among them, C i,j C represents the concentration of the i-th halogenated carbazole in the j-th endmember sample. s,i denoted as , where represents the concentration of the i-th type of halocarbazole in the surface sediment, and n represents the number of halocarbazole species involved in the model calculation.

[0039] The molecular fingerprint vector S of surface sediments and the endmember molecular fingerprint matrix Y satisfy the following non-negative constrained linear relationship: S = Y · X; X = lsqnonneg (Y, S); Where X = [x1, x2] x1 and x2 are the non-negative fitting parameters of the deep sediment endmember and the seawater particulate endmember obtained by the non-negative least squares method, respectively, and x1≥0 and x2≥0.

[0040] Internally generated contributions f int and external input contribution f ext Calculate using the following formulas respectively: f int = x1 / (x1+x2) × 100%; f ext = x2 / (x1+ x2) × 100%; in, f int This indicates the relative contribution of deep fluid input or internal generation in cold seeps to the halocarbazole composition of surface sediments. f ext This represents the relative contribution of external inputs of seawater particulate matter to the halocarbazole composition of surface sediments.

[0041] Optionally, constructing the reaction window index based on the molecular fingerprint vector, the internal generation contribution, and the external input contribution includes the following steps: The molecular fingerprint vector, the internal generation contribution, and the external input contribution are coupled with methane, sulfate, dissolved inorganic carbon, carbon isotopes, iodide ions in pore water, total organic carbon, nutrients, halogenation functional gene parameters, and dehalogenation functional gene parameters to construct the reaction window index.

[0042] Specifically, the response window index RWI is calculated as follows: RWI = w1·Z (CH4) + w2·Z (I - ) + w3·Z ( SO4² - ) + w4·Z ( δ¹³C-DIC) + w5·Z (G_halo) + w6·Z (P_Cl / I) ; Where Z represents the standardization treatment; CH4 is the methane concentration; I - The concentration of iodide ions in pore water; SO4² - δ¹³C-DIC represents the carbon isotope value of dissolved inorganic carbon; G_halo represents the abundance of halogenation or dehalogenation functional genes; P Cl / I is the relative proportion of chlorine-iodine mixed-substituted iodocarbazole in halocarbazole; w1 to w6 are weighting coefficients, and w1+w2+w3+w4+w5+w6=1.

[0043] Furthermore, when the RWI of a certain sedimentary layer is higher than the preset threshold of the RWI of all layers in the same core, and the Cl / I-CZ ratio, the proportion of polyiodocarbazole, or the total abundance of iodocarbazole in that layer increases simultaneously, the layer is determined to be the dominant reaction window for the formation or transformation of iodocarbazole; the preset threshold is preferably the 75th percentile of the RWI of all layers in the same core.

[0044] The following sections will provide a detailed description and explanation of some optional embodiments of this application, using specific application examples.

[0045] Specifically, this application discloses a method for constructing, analyzing the source contribution of, and identifying the formation mechanism of iodocarbazole in cold seep sediments, involving the fields of non-targeted screening of deep-sea organic pollutants, molecular recognition of halogenated organic compounds, and analysis of cold seep biogeochemical processes. This method constructs a multi-component cold seep sample system using sediments from early cold seep development, mature cold seep development, local background sediments, seawater particulate matter, and deep sediments; it acquires full-scan liquid chromatography-high-resolution mass spectrometry and secondary mass spectrometry data of the samples, constructing a system that meets the C... 12 H9 (x+y+z) NCl x Br y I z A candidate molecular library of iodinated and iodine-containing mixed halocarbazoles with z≥1 was constructed. Multiple evidence identification was performed using precise mass deviation, isotope peak clusters, iodine-containing diagnostic fragments, continuous deiodination fragments, and sample-blank response ratio. Furthermore, the concentration, relative abundance, halogen substitution number, iodine substitution number, mixed halogen type, and characteristic homologue ratio of the candidates were converted into iodinated carbazole molecular fingerprint vectors. A binary non-negative constraint end-member model composed of deep sediment end-members and seawater particulate end-members was used to calculate internal generation contributions and external input contributions, respectively. A reaction window index was constructed by combining methane, sulfate, dissolved inorganic carbon isotopes, pore water iodide ions, and halogenation / dehalogenation functional genes to identify the formation, transformation, and preservation mechanisms of iodinated carbazoles in cold seep sediments. This application's embodiments differ from conventional detection methods for fixed targets and can be used for the systematic identification, source contribution quantification, and formation mechanism analysis of unknown iodinated carbazoles in deep-sea cold seep and natural gas hydrate development areas.

[0046] This application relates to the fields of deep-sea environmental organic pollutant analysis, non-targeted screening of halogenated organic compounds, and cold seep biogeochemical process analysis. Specifically, it relates to a method for constructing molecular fingerprints of iodocarbazole in cold seep sediments, analyzing its source contribution, and identifying its formation mechanism. This method is particularly applicable to the systematic identification and mechanism analysis of unknown iodocarbazole and iodine-containing mixed halogenated carbazoles in deep-sea cold seeps, natural gas hydrate development areas, methane seepage areas, and sedimentary environments rich in iodine, strongly reducing, and with intense microbial activity.

[0047] Reference Figure 2 The embodiments of this application may include the following technical solutions: S1. Construct a multi-terminal element sample system for cold seeps: Collect sediments from the early stage of cold seep development, sediments from the mature stage of cold seep development, sediments from the local background area at a certain distance from the seepage center and not significantly affected by seepage, seawater particles from overlying water or near-bottom water, and deep sediments from the lower part of the cold seep site within the same cold seep system, and simultaneously obtain geochemical parameters of pore water and headspace. S2. Acquire high-resolution mass spectrometry data: Perform full-scan liquid chromatography-high-resolution mass spectrometry and secondary mass spectrometry acquisition on the organically extracted and purified sample to obtain the target dataset including the precise mass of the parent ion, isotope peak clusters, retention time, peak area and secondary fragments. S3. Construction of an iodocarbazole candidate library: Based on the carbazole core, construct a library that conforms to the target general formula C 12 H9 (x+y+z) NCl x Br y I z The candidate molecular library, where x, y, and z are the substitution numbers of chlorine, bromine, and iodine, respectively, and z ≥ 1; For example, Figure 3 A schematic diagram illustrating the construction and multi-level screening and identification of iodocarbazole candidate molecular libraries; S4. Perform multi-evidence non-targeted screening: Match the target dataset obtained in step S2 with the iodocarbazole candidate molecule library obtained in step S3, and perform joint screening according to precise quality deviation, isotope peak cluster matching, iodine-containing diagnostic fragments, continuous deiodinated fragments and sample-blank response ratio to obtain iodocarbazole candidates. S5. Confidence level classification: Based on whether the iodocarbazole candidates meet the requirements of standard consistency, accurate quality, isotope peak clusters, secondary fragmentation, and cross-sample reproducibility, the confidence level of the iodocarbazole candidates is classified to obtain an iodocarbazole identification list. S6. Constructing a molecular fingerprint vector: Convert the concentration or semi-quantitative response, relative abundance, total number of halogen substitutions, number of iodine substitutions, mixed halogen types, and characteristic homologue ratios in the iodocarbazole identification list into a molecular fingerprint vector. S7. Analysis of source contribution: Using the molecular fingerprint of deep sediments as the deep fluid input or internal generation end-member, and the molecular fingerprint of seawater particles as the external particle input end-member, a binary non-negative constraint end-member model is used to analyze the internal generation contribution and external input contribution in the surface sediments of the cold seep to be identified. For example, Figure 4 This is a schematic diagram of an end-member mixing model based on seawater particulate matter, surface sediments, and deep sediments; S8. Identify the formation mechanism: Couple the results of steps S6 and S7 with parameters of methane, sulfate, dissolved inorganic carbon, carbon isotopes, iodide ions in pore water, total organic carbon, nutrients, and halogenation / dehalogenation functional genes to construct a reaction window index and identify the exogenous input, deep migration, in-situ halogenation, reductive deiodination, and deposition preservation mechanisms (i.e., state information) of iodocarbazole molecules.

[0048] For example, Figure 5 A comprehensive model diagram of the formation mechanism of iodocarbazole in cold seep sediments.

[0049] Furthermore, the cold seep multi-terminal element sample system includes at least: sediments from early development zones and mature development zones used to characterize different cold seep development stages; background zone sediments far from the seepage center and not significantly affected by seepage used to characterize local background signals; overlying water or near-bottom water seawater particles used to characterize exogenous input signals; and deep sediments below the cold seep site used to characterize endogenous or deep input signals of the cold seep.

[0050] Furthermore, the high-resolution mass spectrometry data in step S2 are full-scan mass spectrometry data and data-dependent secondary mass spectrometry data obtained in negative ion mode. The method does not use the multiple reaction monitoring mode of gas chromatography-triple quadrupole mass spectrometry as a necessary condition for the identification of iodocarbazole candidates.

[0051] Furthermore, in step S3, the iodocarbazole candidate molecule library only screens iodocarbazoles or iodine-containing mixed halocarbazoles containing at least one iodine atom, and the candidate types include one or more of I-CZ, Cl / I-CZ, Br / I-CZ and Cl / Br / I-CZ.

[0052] Furthermore, in step S4, the precise mass deviation threshold is less than or equal to 5 ppm; isotope peak cluster matching is defined as the relative abundance deviation between the measured isotope peak cluster and the theoretical isotope peak cluster being less than or equal to 20%.

[0053] Furthermore, the iodine-containing diagnostic fragments in step S4 include I - [M] H I] - [M] H 2I] - and [M] H 3I] - One or more of the following; when the candidate contains chlorine or bromine, further binding [M] is performed. H HCl] - [M] H HBr] - Cl - and Br - One or more of these can be used as supporting evidence for mixed halogenation.

[0054] Furthermore, the confidence level in step S5 includes: Level 1 identification is that the candidate is consistent with the standard in terms of retention time, precise mass, isotope peak clusters, and secondary fragmentation; Level 2 identification is that there is no standard but it simultaneously meets the requirements of precise mass, isotope peak clusters, secondary fragmentation, and cross-sample reproducibility; Level 3 identification is that it meets the requirements of precise mass and isotope peak clusters but has insufficient secondary fragmentation, and is identified only at the molecular formula or substitution type level.

[0055] Furthermore, in step S5, the matching score S is identified using the following calculation formula. id Sort the candidates: S id = w m ·M m + w i ·I i + w f ·F f + w r ·R r + w b ·B b ; Among them, M m To achieve accurate quality matching scores, I i For isotope peak cluster matching score, F f R is the score for level 2 fragment matching. r For cross-sample reproducibility score, B b The sample-blank response ratio score, w m w i w f w r and w b Here are the weighting coefficients, and w m +w i +w f +w r +w b =1; when S id When the preset scoring threshold is reached, the corresponding candidate will be added to the iodocarbazole identification list.

[0056] Furthermore, the molecular fingerprint vector Y in step S6 includes at least four of the following target variables: total iodocarbazole concentration, relative abundance of iodocarbazole, ratio of monoiodine to polyiodine-substituted compounds, Cl / I-CZ ratio, Br / I-CZ ratio, Cl / Br / I-CZ ratio, ratio of tetrahalogenated to hexahalogenated compounds, Cl / I-CZ to I-CZ ratio, ratio of high-halogenated Cl / I-CZ to low-halogenated I-CZ, and peak area ratio of characteristic homologues.

[0057] Furthermore, in step S6, the molecular fingerprint vector is subjected to closed-loop normalization or logarithmic ratio transformation before source contribution analysis to reduce the impact of total concentration differences on constitutive fingerprint discrimination.

[0058] Furthermore, the binary nonnegative endmember model in step S7 is used to calculate the internal formation contribution of halocarbazole in the surface sediments of the cold seep. f int and external input contribution f ext Specifically, it includes the following steps: Deep sediments and seawater particles were used as two source endmembers, with deep sediments used to characterize deep fluid input or internal generation endmembers in cold seeps, and seawater particles used to characterize external particle input endmembers. The concentrations of halocarbazole detected in each endmember sample and the surface sediment sample were normalized to obtain the endmember molecular fingerprint matrix Y and the surface sediment molecular fingerprint vector S, respectively.

[0059] Among them, the relative proportion y of the i-th halogenated carbazole in the j-th endmember i,j Calculate using the following formula: ; The relative proportion S of the i-th halocarbazole in the surface sediment i Calculate using the following formula: ; Among them, C i,j C represents the concentration of the i-th halogenated carbazole in the j-th endmember sample. s,i denoted as , where represents the concentration of the i-th type of halocarbazole in the surface sediment, and n represents the number of halocarbazole species involved in the model calculation.

[0060] The molecular fingerprint vector S of surface sediments and the endmember molecular fingerprint matrix Y satisfy the following non-negative constrained linear relationship: S = Y · X; X = lsqnonneg (Y, S); Where X = [x1, x2] x1 and x2 are the non-negative fitting parameters of the deep sediment endmember and the seawater particulate endmember obtained by the non-negative least squares method, respectively, and x1≥0 and x2≥0.

[0061] Internally generated contributions f int and external input contribution f ext Calculate using the following formulas respectively: f int = x1 / (x1+x2) × 100%; f ext = x2 / (x1+ x2) × 100%; in, f int This indicates the relative contribution of deep fluid input or internal generation in cold seeps to the halocarbazole composition of surface sediments. f ext This represents the relative contribution of external inputs of seawater particulate matter to the halocarbazole composition of surface sediments.

[0062] Furthermore, the nonnegative fitting parameters x1 and x2 are solved using the nonnegative least squares method, where x1 is the nonnegative fitting parameter corresponding to the deep sediment endmember and x2 is the nonnegative fitting parameter corresponding to the seawater particulate endmember; and the internal generation contribution is obtained through one or more methods, such as sample resampling, variable resampling, or endmember perturbation. f int and external input contribution f ext The confidence interval.

[0063] Furthermore, the reaction window index RWI in step S8 is calculated as follows: RWI = w1·Z (CH4) + w2·Z (I - ) + w3·Z ( SO4² - ) + w4·Z ( δ¹³C-DIC) + w5·Z (G_halo) + w6·Z (P_Cl / I) ; Where Z represents the standardization treatment; CH4 is the methane concentration; I - The concentration of iodide ions in pore water; SO4² - δ¹³C-DIC represents the carbon isotope value of dissolved inorganic carbon; G_halo represents the abundance of halogenation or dehalogenation functional genes; P Cl / I is the relative proportion of chlorine-iodine mixed-substituted iodocarbazole in halocarbazole; w1 to w6 are weighting coefficients, and w1+w2+w3+w4+w5+w6=1.

[0064] Furthermore, when the RWI of a certain sedimentary layer is higher than the preset threshold of the RWI of all layers in the same core, and the Cl / I-CZ ratio, the proportion of polyiodocarbazole, or the total abundance of iodocarbazole in that layer increases simultaneously, the layer is determined to be the dominant reaction window for the formation or transformation of iodocarbazole; the preset threshold is preferably the 75th percentile of the RWI of all layers in the same core.

[0065] Furthermore, the formation mechanism identification in step S8 includes: when f ext Higher than f int Furthermore, when the molecular fingerprint approaches the end-member of seawater particulate matter, it is identified as an external particulate matter input controller; when f int Higher than f ext Furthermore, when highly halogenated Cl / I-CZ is enriched, it is identified as being carried by deep fluids or generated endogenously by cold seeps; when RWI increases and halogenation functional genes co-occur with iodocarbazole abundance, it is identified as being involved in in situ biological halogenation; when the proportion of low-iodine substitutes increases and reductive dehalogenation-related genes increase, it is identified as being involved in reductive deiodination transformation.

[0066] The embodiments of this application can be applied to the non-targeted identification, molecular fingerprinting, source contribution analysis, and formation mechanism identification of iodocarbazole and iodine-containing mixed halocarbazole in cold seeps, natural gas hydrate development areas, iodine-rich pore water deposition environments, or methane seepage deposition environments.

[0067] The embodiments of this application do not use a fixed list of known halocarbazole standards as the sole identification object, but rather use an iodine-containing candidate molecule library and high-resolution mass spectrometry multi-evidence matching results as the identification object.

[0068] The embodiments of this application do not use specific extraction media, specific surfactants, specific metal salts, specific internal standards, or specific gas chromatography temperature programs as necessary technical features for achieving source contribution analysis and formation mechanism identification.

[0069] The following embodiments of this application will describe some optional specific implementation methods.

[0070] Example 1: Construction of a multi-terminal component sample system for cold seeps.

[0071] In typical cold seep systems, early-stage cold seep development zones, mature cold seep development zones, and local background zones were selected as sediment sampling units. Mature cold seep development zones can be observed via seabed video, revealing cold seep communities, gas bubble release, carbonate crusts, or reduced sediment characteristics. Early-stage cold seep development zones are located on the periphery of active areas or are in a weak seepage stage, exhibiting weak methane or pore water anomalies. Local background zones are located at a certain distance from the seepage center and are not significantly affected by seepage. Sediment cores were collected using underwater robotic cannulation, gravity columns, or piston columns, and preserved in depth segments. Overlying or near-bottom water particles, pore water, headspace air, and deep sediments were collected simultaneously.

[0072] Among them, seawater particulate matter is used to characterize exogenous particle sedimentation input endmembers, deep sediments are used to characterize deep fluid-carried or internally generated endmembers, and local background sediments are used to characterize background preservation signals and assist in determining the degree to which samples are affected by cold seep processes. The samples to be identified are surface sediments or sediments at different depths from early-stage or mature-stage cold seep development areas.

[0073] Example 2: High-resolution mass spectrometry data acquisition and candidate molecular library construction.

[0074] Sediment and seawater particulate matter samples are extracted, purified, and concentrated using organic solvents to obtain analytes suitable for liquid chromatography-high resolution mass spectrometry analysis. The extraction and purification methods can employ conventional techniques in the art, and this application does not specify particular extraction media, surfactants, metal salts, or purification packing materials as essential technical features.

[0075] Full scan and secondary mass spectrometry acquisition were performed using liquid chromatography-high resolution mass spectrometry (LC-HDMS) to obtain precise precursor ion mass, isotope peak clusters, retention times, peak areas, and fragment ions. The carbazole precursor ion was analyzed using C0.05. 12 Based on H9N, build a C-compliant system. 12 H9 (x+y+z) NCl x Br y I z A library of iodine-containing candidate molecules, where z≥1. Theoretical exact mass, theoretical isotope peak clusters, possible deiodinated fragments, and mixed halogen fragments were calculated for each candidate.

[0076] Example 3: Identification of multiple evidences for iodocarbazole.

[0077] High-resolution mass spectrometry data were matched with a candidate molecular library. Candidates were first required to meet the following criteria: precise mass deviation of the parent ion ≤ 5 ppm; secondly, relative abundance deviation between the measured and theoretical isotope peak clusters ≤ 20%; and thirdly, the presence of Ig in the secondary mass spectrometer. - [M] H I]- [M] H 2I] - or [M] H 3I] - One or more of the following; for chlorine- or bromine-containing candidates, also pay attention to [M] H HCl] - [M] H HBr] - Cl - or Br - Fragments. Peaks appearing in the program blank need to be subtracted, and candidate peaks with a sample-blank response ratio of less than 3 will not be included in the subsequent identification list.

[0078] Recognition score S i d is used for candidate ranking. Precision quality, isotope peak clusters, secondary fragmentation, cross-sample reproducibility, and blank subtraction are scored separately and then summed according to preset weights. When S... id When a threshold is reached, the candidate is added to the identification list. This method avoids misjudgments based solely on accurate quality or a single fragment.

[0079] Example 4: Molecular fingerprint construction.

[0080] An iodocarbazole molecular fingerprint vector was established for each sample. This vector includes the total iodocarbazole concentration, the relative abundance of each candidate compound, the proportion of mono- and poly-iodinated compounds, the I-CZ ratio, the Cl / I-CZ ratio, the Br / I-CZ ratio, the Cl / Br / I-CZ ratio, the proportion of tetrahalogenated to hexahalogenated compounds, the ratio of high-halogenated Cl / I-CZ to low-halogenated I-CZ, and the peak area ratios of several characteristic homologues.

[0081] To reduce bias caused by differences in total concentration, fingerprint vectors can be subjected to closed-loop normalization or logarithmic ratio transformation. The transformed molecular fingerprint retains compositional characteristics and can be used for source endmember fitting, cluster analysis, and mechanism identification.

[0082] Example 5: Analysis of binary endmember source contributions.

[0083] The external input contribution f_ext and internal generation contribution f_int of halogenated carbazole in cold seep sediments were calculated using a binary nonnegative constraint endmember model. The model uses deep sediment endmembers and seawater particulate endmembers as two source endmembers, where the deep sediment endmember represents deep fluid input or internal generation signals, and the seawater particulate endmember represents external particle sedimentation input signals.

[0084] Let C i,jC represents the concentration of the i-th halogenated carbazole in the j-th endmember sample. s,i y represents the concentration of the i-th halocarbazole in the surface sediment, where n represents the number of halocarbazole species involved in the model calculation. y represents the relative proportion of the i-th halocarbazole in the j-th endmember. i , j The relative proportion S of the i-th halocarbazole in the surface sediment i They are respectively: ;; ; The end-member molecular fingerprint matrix Y and the surface deposit molecular fingerprint vector S are composed of the relative proportions of all halocarbazoles involved in the calculation, and their relationship is as follows: S = Y · X; X = lsqnonneg (Y, S); Where X = [x1, x2] x1 and x2 are the non-negative fitting parameters of the deep sediment endmember and the seawater particulate endmember obtained by the non-negative least squares method, respectively, and x1≥0 and x2≥0.

[0085] Internally generated contributions f int and external input contribution f ext Calculate using the following formulas respectively: f int = x1 / (x1+x2) × 100%; f ext = x2 / (x1+ x2) × 100%; when f ext Higher than f int Furthermore, when the molecular fingerprint of the sample is close to the end-member of seawater particles, the contribution of exogenous particle input is considered to be relatively high; when f int Higher than f ext Furthermore, when enriched with high-halogenated Cl / I-CZ or polyiodocarbazole, it is determined that the sediment is carried by deep fluids or generated within cold seeps, contributing significantly. Sediments from the local background area are not used as endmembers in the binary model fitting, but rather as reference samples for background preservation signals and model interpretation results.

[0086] Example 6: Reaction window index and formation mechanism identification.

[0087] By coupling molecular fingerprints with geochemical and functional genomic data, a reaction window index (RWI) is established. RWI = w1·Z (CH4) + w2·Z (I - ) + w3·Z ( SO4² - ) + w4·Z ( δ¹³C-DIC) + w5·Z (G_halo) + w6·Z (P_Cl / I) ; Where Z represents the standardization treatment; CH4 is the methane concentration; I - The concentration of iodide ions in pore water; SO4² - δ¹³C-DIC represents the carbon isotope value of dissolved inorganic carbon; G_halo represents the abundance of halogenation or dehalogenation functional genes; P Cl / I is the relative proportion of chlorine-iodine mixed-substituted iodocarbazole in halocarbazole; w1 to w6 are weighting coefficients, and w1+w2+w3+w4+w5+w6=1.

[0088] When the RWI of a certain layer is higher than the preset threshold of RWI of all layers in the same core, and the Cl / I-CZ ratio, the proportion of polyiodocarbazole, or the total abundance of iodocarbazole increases simultaneously, this layer is determined to be the dominant reaction window for the formation or transformation of iodocarbazole; the preset threshold is preferably the 75th percentile of RWI of all layers in the same core. If this window co-occurs with an increase in halogenation functional genes, in-situ biohalogenation is identified as involved; if the proportion of low-iodine substitutions increases and is accompanied by an increase in reductive dehalogenation genes, reductive deiodination transformation is identified as involved; if high-halogenated Cl / I-CZ is co-enriched in deep endmembers and surface samples, the contribution of deep fluid migration or internal generation is identified as enhanced.

[0089] In summary, the embodiments of this application differ from the prior art in at least the following ways: (1) Different targets: Existing detection methods are mostly aimed at fixed targets of halogenated carbazole in food, urine, water samples or ordinary sediments; the embodiments of this application are aimed at unknown iodocarbazole and iodine-containing mixed halogenated carbazole in cold seep sediments.

[0090] (2) Different identification strategies: Existing methods rely on standard retention time and characteristic ions; the embodiments of this application rely on high-resolution mass spectrometry for accurate mass, isotope peak clusters, iodine-containing diagnostic fragments, continuous deiodination fragments and blank subtraction for multi-evidence non-targeted screening.

[0091] (3) The core of protection is different: the core of existing methods is sample pretreatment and quantitative detection conditions; the core of the embodiments of this application is cold seep multi-terminal sample system, molecular fingerprint construction, binary non-negative end-member contribution analysis and reaction window identification.

[0092] (4) Different technical effects: Existing methods mainly output the concentration of target substances; In addition to outputting the identification list and semi-quantitative results, the embodiments of this application also output the external input contribution, internal generation contribution, dominant reaction window, and the formation and transformation mechanism of iodocarbazole.

[0093] The embodiments of this application include at least the following beneficial effects: (1) The embodiments of this application avoid the conventional detection route centered on a fixed target standard list and can identify unknown iodocarbazoles and iodine-containing mixed halocarbazoles without standards.

[0094] (2) The innovation of this application is not the specific pretreatment, internal standard, external standard or GC-MS / MS detection parameters, but the cold spring multi-terminal sample system, multi-evidence non-target identification, molecular fingerprint construction and source mechanism analysis.

[0095] (3) The embodiments of this application use a binary non-negative constraint endmember model to distinguish the external input contribution of seawater particulate matter from the internal generation contribution of deep sediments, and use local background area samples to help identify background preservation or weak remodeling processes, which can better adapt to the characteristics of multiple sources and coupled processes in cold seep sedimentary environments.

[0096] (4) The embodiments of this application link the molecular composition of iodocarbazole with methane, sulfate, carbon isotopes, iodide ions in pore water and functional genes through the reaction window index, which can identify the dominant layer for the formation and transformation of iodocarbazole in the cold seep system.

[0097] (5) The embodiments of this application can be used for environmental fate, source analysis and ecological risk assessment of novel iodine-containing halogenated organic compounds in deep-sea cold seeps, natural gas hydrate development areas and iodine-rich reducing sedimentary environments.

[0098] Reference Figure 6 This application also provides an iodocarbazole molecule recognition device in cold seep sediments, which can realize the above-mentioned iodocarbazole molecule recognition method in cold seep sediments. The device includes: The sample processing unit is used to perform full-scan acquisition by liquid chromatography and high-resolution mass spectrometry, as well as secondary mass spectrometry acquisition, on cold seep multi-end-member samples to obtain the target dataset. Molecular library construction unit, used to construct iodocarbazole candidate molecular libraries that conform to the target general formula; A candidate molecule matching unit is used to match the target dataset with the iodocarbazole candidate molecule library to obtain iodocarbazole candidates; The target molecule screening unit is used to identify and score the iodocarbazole candidates, and to screen out iodocarbazole target compounds from the iodocarbazole candidates based on the matching scores as an iodocarbazole identification list. A fingerprint vector construction unit is used to construct the molecular fingerprint vector of the iodocarbazole target in the iodocarbazole identification list; The contribution determination unit is used to determine the internal generation contribution and external input contribution of the surface sediments of the cold seep to be identified. An index construction unit is used to construct a reaction window index based on the molecular fingerprint vector, the internal generation contribution, and the external input contribution; wherein the reaction window index is used to characterize the state information of iodocarbazole molecules in the surface sediments of the cold seep to be identified.

[0099] 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.

[0100] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method of this application. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0101] It is understood that the content of the above method embodiments is applicable to the device embodiments. The specific functions implemented by the device embodiments are the same as those of the methods of this application, and the beneficial effects achieved are the same as those achieved by the methods of this application.

[0102] Figure 7 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 101 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 this application. The memory 102 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 102 can store the operating system and other applications. 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 102 and is called and executed by the processor 101. Input / output interface 103 is used to implement information input and output; The communication interface 104 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 105 transmits information between various components of the device (e.g., processor 101, memory 102, input / output interface 103, and communication interface 104); The processor 101, memory 102, input / output interface 103 and communication interface 104 are connected to each other within the device via bus 105.

[0103] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method of this application.

[0104] 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.

[0105] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0106] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0107] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0108] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0109] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0110] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0111] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0112] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0113] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0114] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0115] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0116] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part 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 multiple 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 of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0117] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for recognizing iodocarbazole molecules in cold seep sediments, characterized in that, The method includes the following steps: The target dataset was obtained by full-scan acquisition of cold seep multi-end-member samples using liquid chromatography and high-resolution mass spectrometry, as well as secondary mass spectrometry. Construct a library of iodocarbazole candidate molecules that conform to the target general formula; The target dataset and the iodocarbazole candidate molecule library are matched to obtain iodocarbazole candidates; The iodocarbazole candidates are matched and scored, and iodocarbazole target compounds are selected from the iodocarbazole candidates based on the matching scores to form an iodocarbazole identification list. Construct the molecular fingerprint vector of the iodocarbazole target in the iodocarbazole identification list; Determine the internal generation contribution and external input contribution of the surface sediments of the cold seep to be identified; A reaction window index is constructed based on the molecular fingerprint vector, the internal generation contribution, and the external input contribution; wherein, the reaction window index is used to characterize the state information of iodocarbazole molecules in the surface sediments of the cold seep to be identified.

2. The method for recognizing iodocarbazole molecules in cold seep sediments according to claim 1, characterized in that, The construction of the iodocarbazole candidate molecule library conforming to the target general formula includes the following steps: Based on the carbazole core, construct the iodocarbazole candidate molecule library that conforms to the target general formula; The target formula is: C 12 H9 (x+y+z) NCl x Br y I z ; Where x, y, and z are the substitution numbers of chlorine, bromine, and iodine, respectively, and z ≥ 1.

3. The method for recognizing iodocarbazole molecules in cold seep sediments according to claim 1, characterized in that, The step of matching the target dataset with the iodocarbazole candidate molecule library to obtain iodocarbazole candidates includes the following steps: Iodocarbazoles or iodine-containing mixed halocarbazoles containing at least one iodine atom from the iodocarbazole candidate molecule library are used as screening targets. The screening targets are matched with the target dataset. The matching type includes at least one of I-CZ, Cl / I-CZ, Br / I-CZ and Cl / Br / I-CZ.

4. The method for recognizing iodocarbazole molecules in cold seep sediments according to claim 1, characterized in that, The identification and matching scoring of the iodocarbazole candidate includes the following steps: The matching score of the iodocarbazole candidate is calculated based on the parent ion precise mass matching score, isotope peak cluster matching score, secondary fragment matching score, cross-sample reproducibility score, and sample-blank response ratio score. The step of selecting iodocarbazole target compounds from the iodocarbazole candidates as an iodocarbazole identification list based on the matching score includes the following steps: Iodocarbazole candidates whose matching scores reach a preset score threshold are selected as iodocarbazole target compounds. The iodocarbazole identification list is constructed based on the iodocarbazole target.

5. The method for recognizing iodocarbazole molecules in cold seep sediments according to claim 1, characterized in that, The process of constructing the molecular fingerprint vector of the iodocarbazole target in the iodocarbazole identification list includes the following steps: The target variables of the iodocarbazole target analyte are determined; wherein, the target variables include total iodocarbazole concentration, relative abundance of iodocarbazole, proportion of mono- to poly-iodinated compounds, Cl / I-CZ ratio, Br / I-CZ ratio, Cl / Br / I-CZ ratio, proportion of tetrahalogenated to hexahalogenated compounds, Cl / I-CZ to I-CZ ratio, high-halogenated Cl / I-CZ to low-halogenated I-CZ ratio, and peak area ratio of characteristic homologues; At least four variables are selected from the target variables to construct the molecular fingerprint vector corresponding to the iodocarbazole target.

6. The method for recognizing iodocarbazole molecules in cold seep sediments according to claim 1, characterized in that, The process of determining the internal generation contribution and external input contribution of the surface sediments of the cold seep to be identified includes the following steps: Deep sediment endmembers were used to characterize deep fluid input or internal generation endmembers in cold seeps, and seawater particulate endmembers were used to characterize external particle input endmembers. The concentrations of halocarbazole detected in each endmember sample and surface sediment sample were normalized to obtain the endmember molecular fingerprint matrix and surface sediment molecular fingerprint vector, respectively. Determine the non-negative constrained linear relationship between the end-member molecular fingerprint matrix and the surface deposit molecular fingerprint vector; Based on the non-negative constrained linear relationship, the non-negative fitting parameters corresponding to the deep sediment endmember and the seawater particulate endmember are obtained by solving the non-negative least squares method. The internal generation contribution and the external input contribution are determined based on the non-negative fitting parameters.

7. The method for recognizing iodocarbazole molecules in cold seep sediments according to any one of claims 1 to 6, characterized in that, The process of constructing the reaction window index based on the molecular fingerprint vector, the internal generation contribution, and the external input contribution includes the following steps: The molecular fingerprint vector, the internal generation contribution, and the external input contribution are coupled with methane, sulfate, dissolved inorganic carbon, carbon isotopes, iodide ions in pore water, total organic carbon, nutrients, halogenation functional gene parameters, and dehalogenation functional gene parameters to construct the reaction window index.

8. A molecular recognition device for iodocarbazole in cold seep sediments, characterized in that, The device includes: The sample processing unit is used to perform full-scan acquisition by liquid chromatography and high-resolution mass spectrometry, as well as secondary mass spectrometry acquisition, on cold seep multi-end-member samples to obtain the target dataset. Molecular library construction unit, used to construct iodocarbazole candidate molecular libraries that conform to the target general formula; A candidate molecule matching unit is used to match the target dataset with the iodocarbazole candidate molecule library to obtain iodocarbazole candidates; The target molecule screening unit is used to identify and score the iodocarbazole candidates, and to screen out iodocarbazole target compounds from the iodocarbazole candidates based on the matching scores as an iodocarbazole identification list. A fingerprint vector construction unit is used to construct the molecular fingerprint vector of the iodocarbazole target in the iodocarbazole identification list; The contribution determination unit is used to determine the internal generation contribution and external input contribution of the surface sediments of the cold seep to be identified. An index construction unit is used to construct a reaction window index based on the molecular fingerprint vector, the internal generation contribution, and the external input contribution; wherein the reaction window index is used to characterize the state information of iodocarbazole molecules in the surface sediments of the cold seep to be identified.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing 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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