Method for evaluating electron donating ability of soluble organic matter and related equipment

Abstract

The invention discloses a method for evaluating the electron donating ability of soluble organic matters and related equipment, and belongs to the technical field of environmental monitoring. The method comprises the following steps: collecting standardized three-dimensional fluorescence data and electrochemical data; performing batch preprocessing on the three-dimensional fluorescence spectrum data; realizing data annotation and visualization based on fluorescence region integral analysis; an optimal component number F is determined through segmentation semi-analysis, residual analysis and core consistency analysis, and abnormal samples are eliminated through analysis and iteration; and establishing a parallel factor analysis model based on the optimal data set and the component number F, and comparing an initialization method to output a component wavelength load and an intensity value. And constructing a linear fitting equation of the sample fluorescence parameters, the fluorescence intensity of each component of the sample and the electron donating ability of the sample, and evaluating the electron donating ability of an unknown sample. The matrix interference bottleneck of a traditional electrochemical method is broken through, a three-dimensional fluorescence data processing system is constructed based on open source ecology, and the method has lower operation cost, higher algorithm transparency and more flexible expansibility.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of environmental monitoring, and in particular to a method for evaluating the electron donating capacity of dissolved organic matter and related equipment. BACKGROUND

[0002] In recent years, water pollution incidents have occurred frequently, seriously threatening the ecological environment and public health. Water pollution detection has become a key link in environmental governance. Dissolved organic matter (DOM) is a core carrier of global carbon cycle, and its molecular structural diversity directly affects the migration and transformation of pollutants and the self-purification capacity of water systems. Therefore, accurate quantification of DOM function (especially electron donating capacity) is a key parameter for evaluating the pollution remediation potential of water bodies.

[0003] The electron donating capacity (EDC) of DOM directly determines the reduction and conversion efficiency of pollutants in environmental water bodies. Quantifying EDC is an important basic parameter for evaluating the self-purification capacity of water and soil systems. Currently, the electron donating capacity of dissolved organic matter is often quantitatively analyzed by electrochemical workstation detection methods, including mediated oxidation, constant potential coulomb method, and cyclic voltammetry indirect calculation method. The defects of these methods in specificity, anti-matrix interference, and in-situ applicability restrict the wide application of EDC as an environmental process diagnostic indicator. Therefore, there is an urgent need for an EDC analysis method that is low in cost, anti-interference, and suitable for complex environmental matrix, to break through the quantitative bottleneck of existing technology. SUMMARY

[0004] The main purpose of the embodiments of the present application is to propose a method for evaluating the electron donating capacity of dissolved organic matter by fusing parallel factor analysis (PARAFAC) and three-dimensional fluorescence spectrum (3D EEMs) and related equipment, to solve the defects of limited specificity, anti-matrix interference, and in-situ applicability in existing EDC detection technology, and the problems of low flexibility, poor expansibility, insufficient transparency, and high cost in existing fluorescence data processing technology.

[0005] To achieve the above purpose, one aspect of the embodiments of the present application proposes a method for evaluating the electron donating capacity of dissolved organic matter, which comprises:

[0006] Collecting three-dimensional fluorescence spectrum data of a plurality of dissolved organic matter samples, constructing an original three-dimensional fluorescence data set, determining the electron donating capacity of each sample, and constructing an electrochemical data set;

[0007] Pretreating the original three-dimensional fluorescence data set;

[0008] Calculating the fluorescence parameters of each sample based on fluorescence region integration (FRI);

[0009] The PARAFAC model calculation is performed on the pretreated data set, combined with segmentation half analysis, overall residual analysis and core consistency analysis to determine the optimal component number F;

[0010] Based on the determined optimal component number F, the single-sample residual structure and the leverage value are analyzed to identify and eliminate abnormal samples, and an optimized data set is obtained;

[0011] Based on the optimized data set and the optimal component number F, a PARAFAC model is established and verified; and the excitation / emission wavelength load data of each component and the sample component intensity matrix are outputted;

[0012] A linear fitting equation of the sample fluorescence parameters, the fluorescence intensity of each component of the sample, and the electron donating ability of the sample is constructed, which is used to evaluate the electron donating ability of unknown samples.

[0013] In some embodiments, the three-dimensional fluorescence spectrum data of a plurality of dissolved organic matter samples is collected, an original three-dimensional fluorescence data set is constructed, the electron donating ability of each sample is determined, and an original electrochemical data set is constructed, including:

[0014] For each EEMs data, the total organic carbon concentration is controlled to be about 1-10 mgC·L -1 ; the emission wavelength (Em) range is scanned from 200 to 700 nm with a step of 5 nm; the excitation wavelength (Ex) is varied in the range of 200 to 600 nm with a step of 5 nm; the excitation and emission slit widths are both set to 5 nm; and the blank sample is a pure water solution sample;

[0015] The electron donating ability of each sample is determined by an electrochemical workstation; for each electrochemical data, the total organic carbon concentration is controlled to be about 1-10 mgC·L -1 ; in the workstation parameters, the step potential is +0.5 V, the electrolysis time is 1800 s, and the data acquisition frequency is 1 Hz; the relative standard deviation (RSD) of continuous three repetitions is less than 5% for valid data; after deducting the capacitive current and system background drift, the trapezoidal numerical integration method is used to obtain the value of the electron donating ability; and the blank sample is a pure water solution sample.

[0016] In some embodiments, the original three-dimensional fluorescence data set is pretreated, including:

[0017] Based on the Python program, for each fluorescence data, the Rayleigh scattering signal is deducted by using the grid interpolation method, the missing values are filled by cubic spline interpolation and linear interpolation, and the three-dimensional fluorescence spectrum data after deducting the Rayleigh scattering is returned;

[0018] The obtained three-dimensional fluorescence spectrum data is deducted from the Raman scattering signal of the blank control sample, and is standardized by integrating the Raman peak at the emission wavelength of 350 nm in Raman units (R.U.).

[0019] In some embodiments, the fluorescence parameters include fluorescence index (FI), humification index (HIX), and biological source index (BIX) as basic parameters for constructing the final evaluation equation.

[0020] In some embodiments, the condition for determining the optimal component number F is that the core consistency parameter value is greater than 50%, the split half similarity is greater than 95%, and the overall residual change curve has an inflection point.

[0021] In some embodiments, the condition for identifying abnormal samples is that the leverage value of a single sample exceeds a preset value and / or the single sample residual is greater than three times the median value of the residual.

[0022] In some embodiments, the PARAFAC model is established by:

[0023] Random initialization and independent singular value decomposition are used for modeling respectively;

[0024] The model with the smallest residual sum of squares is selected as the final output;

[0025] The excitation / emission load spectrum of each component and the sample component intensity matrix are output.

[0026] In some embodiments, the linear fitting equation of the sample fluorescence parameters, the fluorescence intensity of each component of the sample, and the electron donating ability of the sample is constructed by:

[0027] A prediction equation for the electron donating ability of unknown samples is constructed for evaluating the electron donating ability of unknown samples, and the input parameters include the fluorescence parameters of the sample (such as FI, HIX, BIX), the fluorescence intensity of each independent component (such as F C1 ,F C2 ,...,F Cn ), and the measured value of the electron donating ability of the known sample;

[0028] If the data sample size is small, the fitting slope and correlation coefficient are obtained to calculate the weight factor, a comprehensive evaluation equation is constructed, and the feasibility of predicting the dissolved organic matter electron donating ability is verified;

[0029] If the data sample size is large, the LinearRegression() function is called, and the multiple linear regression training is performed by the least squares method (OLS) to make the model self-optimized based on the data weight.

[0030] In some embodiments, the fitting slope and correlation coefficient are obtained to calculate the weight factor by:

[0031] Correlations between different fluorescence parameters (e.g., FI, HIX, BIX) of the dissolved organic matter sample and the electron donating capacity are analyzed, and corresponding fitting slopes and correlation coefficients are obtained;

[0032] Correlations between fluorescence intensities (e.g., F C1 ,F C2 ,...,F Cn ) of individual components of the dissolved organic matter and the electron donating capacity are analyzed, and corresponding fitting slopes and correlation coefficients are obtained.

[0033] Weight factors are calculated according to the obtained fitting slopes and correlation coefficients.

[0034] In some embodiments, a fitting equation of different fluorescence parameters and the electron donating capacity values is established, including:

[0035] Correlations between different fluorescence intensities of the dissolved organic matter sample and the electron donating capacity are analyzed;

[0036] Fluorescence components of the dissolved organic matter are separated by using PARAFAC, and changes in fluorescence intensities of the components are analyzed, correlations between spectral characteristics and the electron donating capacity are analyzed, and feasibility of using 3DEEMs-PARAFAC to analyze the electron donating capacity of the dissolved organic matter is verified.

[0037] To achieve the above object, another aspect of the embodiments of the present application provides an electronic device, which comprises a memory and a processor, the memory stores a computer program, and the processor implements the above method when executing the computer program.

[0038] To achieve the above object, another aspect of the embodiments of the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the above method.

[0039] To achieve the above object, another aspect of the embodiments of the present application provides a computer program product, which comprises a computer program, and the computer program is executed by a processor to implement the above method.

[0040] The embodiments of the present application at least have the following beneficial effects: the present application provides a method for evaluating the electron donating capacity of dissolved organic matter, an electronic device, a storage medium and a program product, which breaks through the matrix interference bottleneck of traditional electrochemical methods, eliminates the interference of organic matter adsorption and complex environmental matrix on the electrode in electrochemical detection, adapts to environmental engineering application scenarios, and realizes accurate quantification of the electron donating capacity of DOM. In addition, a three-dimensional fluorescence data processing system is built based on the Python open source ecology, which has lower running cost, higher algorithm transparency and more flexible expandability. BRIEF DESCRIPTION OF DRAWINGS

[0041] Figure 1 is a step flow chart of a method for evaluating the electron donating ability of dissolved organic matter provided by the embodiment of the present application;

[0042] Figure 2 is a step demonstration chart of the fluorescence region integral analysis in the embodiment of the present application;

[0043] Figure 3 is a parameter analysis chart for determining the component number F in the embodiment of the present application;

[0044] Figure 4 is a split half analysis wavelength load contrast chart in the embodiment of the present application;

[0045] Figure 5 is a single sample outlier screening analysis chart in the embodiment of the present application;

[0046] Figure 6 is a component chart of the parallel factor analysis in the embodiment of the present application;

[0047] Figure 7 is a fitting chart of the electron donating ability of a small sample data set and a single parameter in the embodiment of the present application;

[0048] Figure 8 is a fitting chart of the comprehensive evaluation equation of the electron donating ability of a small sample data set in the embodiment of the present application;

[0049] Figure 9 is a fitting chart of the electron donating ability of a large sample data set and a single parameter in the embodiment of the present application;

[0050] Figure 10 is a fitting chart of the comprehensive evaluation equation of the electron donating ability of a large sample data set in the embodiment of the present application;

[0051] Figure 11 is a hardware structure schematic diagram of an electronic device provided by the embodiment of the present application. DETAILED DESCRIPTION

[0052] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application, but are only examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the appended claims.

[0053] 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 in the description herein is for describing particular embodiments only and is not intended to be limiting of the application.

[0054] Before the embodiments of the present application are explained in detail, the description of the embodiments of the present application will be described with reference to the accompanying drawings, and the terminology used in the description of the embodiments of the present application is applicable to the following explanations.

[0055] 1) PARAFAC model (Parallel Factor Analysis) is a tensor analysis method for multi-dimensional data decomposition, widely used in chemometrics, signal processing, environmental science, etc. Its core is to decompose high-dimensional data into the product of multiple low-dimensional factor matrices. PARAFAC (Parallel Factor Analysis) is a multi-linear decomposition method suitable for three-dimensional or higher dimensional data analysis. Its core idea is to decompose the original tensor into the product of multiple factor matrices, each matrix representing a specific dimension or mode of data.

[0056] In environmental water quality detection, three-dimensional fluorescence spectroscopy (3D EEMs) technology has significant advantages in identifying dissolved organic matter (DOM) due to its efficiency, high sensitivity, fast response, and rich information. This technology captures the characteristic fluorescence signals of pollutants through excitation-emission wavelength combinations, forming a unique "fluorescence fingerprint", enabling deeper analysis of DOM. Three-dimensional fluorescence spectroscopy data is essentially a three-dimensional matrix (excitation wavelength x emission wavelength x fluorescence intensity), which is inefficient and prone to errors when processed manually, making it difficult to support high-throughput analysis and deep pattern mining. Therefore, automated and intelligent data processing is the key to realizing the value of the technology. Based on Python programming, efficient batch processing, visualization, and integration of advanced algorithms (such as parallel factor analysis PARAFAC) for three-dimensional fluorescence data can significantly improve data analysis capabilities, enabling accurate identification of pollution components, quantification of concentrations, and tracing of sources. Based on this, an efficient analysis system integrating PARAFAC is developed, which can break through the limitations of existing commercial tools (such as MATLAB toolboxes), and also enable deep customization of algorithms, optimization of processes, and integration of multiple technologies.

[0057] The embodiment of the present application provides a method for evaluating the electron donating capacity of dissolved organic matter, and relates to the technical field of environmental monitoring. The method for evaluating the electron donating capacity of dissolved organic matter provided by the embodiment of the present application can be applied to a terminal, can be applied to a server, and can also be software running in the terminal or the server. In some embodiments, the terminal can be a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart speaker, a smart watch, a vehicle-mounted terminal and the like, but is not limited thereto; the server end can be configured as a stand-alone physical server, can be configured as a server cluster or a distributed system formed by multiple physical servers, can be configured as a cloud server providing basic cloud computing services such as cloud service, cloud database, cloud computing, cloud function, cloud storage, network service, cloud communication, middleware service, domain name service, security service, CDN and big data and artificial intelligence platform, and the server can also be a node server in a blockchain network; and the software can be an application for implementing the method for evaluating the electron donating capacity of dissolved organic matter and the like, but is not limited to the above forms.

[0058] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld devices or portable devices, tablet devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like. The present 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, and the like that perform specific tasks or implement specific abstract data types. The present application can also be practiced in a distributed computing environment, in which tasks are performed by remote processing devices connected by a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0059] Figure 1 is an optional flowchart of the method for evaluating the electron donating capacity of dissolved organic matter provided by the embodiment of the present application, Figure 1 The method in the embodiment of the present application can include but is not limited to steps S101 to S107.

[0060] In step S101, three-dimensional fluorescence spectrum data of a plurality of dissolved organic matter samples is collected, an original three-dimensional fluorescence data set is constructed, the electron donating capacity of each sample is determined, and an electrochemical data set is constructed.

[0061] Exemplarily, three-dimensional fluorescence spectrum data of multiple batches of samples with consistent test parameters are collected to construct an original three-dimensional fluorescence data set. The electron donating capacity of the samples is tested by an electrochemical workstation to construct an original electrochemical data set. The minimum number of samples is 20.

[0062] In some embodiments, for each EEMs data, the total organic carbon concentration is controlled to be about 1-10 mgC·L -1 The scanned emission wavelength (Em) ranges from 200 to 700 nm with a step of 5 nm. The excitation wavelength (Ex) ranges from 200 to 600 nm with a step of 5 nm. The excitation and emission slit widths are both set to 5 nm. The blank sample is a pure water solution sample.

[0063] For each electrochemical data, the total organic carbon concentration is controlled to be about 1-10 mgC·L -1 In the workstation parameters, the step potential is +0.5 V, the electrolysis time is 1800 s, and the data acquisition frequency is 1 Hz. The relative standard deviation (RSD) <5% is effective data for three consecutive repetitions. After deducting the capacitive current and system background drift, the EDC value is obtained by using the trapezoidal numerical integration method. The blank sample is a pure water solution sample.

[0064] Step S102, preprocessing the original three-dimensional fluorescence data set.

[0065] Exemplarily, the original three-dimensional fluorescence data set is batch-processed based on a Python program, including deducting the scattering signal, deducting the blank control, Raman normalization, and data denoising. In the step of deducting the scattering signal, the mask width can be adjusted to optimize the results.

[0066] In some embodiments, the batch processing method of the constructed original data set is based on a Python program, including steps S2.1-S2.2:

[0067] S2.1: For each fluorescence data, the meshgrid() function is called to deduct the Rayleigh scattering signal therein, automatically capturing all Rayleigh scattering wavelength ranges and deducting them. The missing values after deduction are filled by using the methods of cubic spline interpolation and linear interpolation, and the three-dimensional fluorescence spectrum data after deducting the Rayleigh scattering is returned.

[0068] S2.2: For the three-dimensional fluorescence spectrum data obtained in step S2.1, the Raman scattering signal is removed by using a background deduction method, specifically by subtracting the three-dimensional fluorescence spectrum data of the blank solution sample from the three-dimensional fluorescence spectrum data, and by integrating the Raman peak at Em of 350 nm as a Raman unit (R.U.) for normalization.

[0069] Step S103, calculating the fluorescence parameters of each sample based on fluorescence region integration (FRI).

[0070] Specifically, based on fluorescence regional integration (FRI) analysis, the material classification attribute of each sample data is labeled, the related fluorescence parameters are calculated, and the visualized results are output.

[0071] The fluorescence parameters include fluorescence index (FI), humification index (HIX), and biological source index (BIX).

[0072] In step S104, the pre-processed data set is subjected to PARAFAC model calculation, and the best component number F is determined by combining segmentation half analysis, overall residual analysis, and core consistency analysis.

[0073] In some embodiments, based on the pre-processed data set, the best component number F of the target data set is determined by comprehensively applying segmentation half analysis, overall residual analysis, and core consistency analysis. The conditions for determining the best component number F are that the core consistency parameter value is greater than 50%, the segmentation half similarity is greater than 95%, and the overall residual change curve has an inflection point. Further, the S4C4T2 algorithm is preferentially applied in the segmentation half analysis.

[0074] In step S105, based on the determined best component number F, the single-sample residual structure and leverage value are analyzed to identify and remove abnormal samples, and an optimized data set is obtained.

[0075] In some embodiments, based on the above best component number F, the single-sample residual structure and leverage value analysis are comprehensively applied to identify and remove abnormal samples that do not have representativeness or poor quality. Based on the new data set after removing the abnormal value samples, the above steps are repeatedly performed until no abnormal value samples are found, and the best data set for modeling is determined. Specifically, the conditions for identifying abnormal samples are that the leverage value of a single sample exceeds 30% and / or the single-sample residual is greater than three times the median residual value.

[0076] In some embodiments, the method for identifying and removing abnormal samples that do not have representativeness or poor quality includes steps S5.1-S5.2:

[0077] S5.1: Based on the pre-processed data set, a PARAFAC model is initially constructed, and the best component number F of the target data set is determined by comprehensively applying segmentation half analysis (preferentially applying the S4C4T2 method), overall residual analysis, and core consistency analysis, with the screening conditions set as: core consistency parameter value > 90%, segmentation half similarity > 95%, and overall residual inflection point positioning.

[0078] S5.2: For the optimal component number obtained in step S5.1, the single-sample residual structure and leverage value analysis are comprehensively applied, and the screening condition is that the leverage value is less than 30% and the single-sample residual is less than three times the median value, so as to identify and remove abnormal samples that do not have representativeness or poor quality, and return the optimal data set for PARAFAC modeling.

[0079] In step S106, based on the optimized data set and the optimal component number F, a PARAFAC model is established and verified; and the excitation / emission wavelength load data of each component and the component intensity matrix of the sample are output.

[0080] Exemplarily, based on the determined optimal component number F and the screened optimal data set, the final PARAFAC model is verified by cyclic iteration, and the wavelength load original data and the visualization result of each component are output, and the intensity value of each component in each sample data is extracted and output.

[0081] In some embodiments, the cyclic iteration verification method of the final PARAFAC model comprises:

[0082] Based on the optimal data set and the optimal component number F determined in the above steps, the final PARAFAC model is constructed, and the model with the minimum error is selected for analysis by comparing the random initialization (cyclic iteration for 20 times) with the local optimal method and the independent SVD initialization method, and the excitation and emission load matrix of each sample, the original data and the visualization result are output.

[0083] In step S107, a linear fitting equation of the sample fluorescence parameters, the fluorescence intensity of each component of the sample, and the electron donating ability of the sample is constructed, which is used for evaluating the electron donating ability of an unknown sample.

[0084] In some embodiments, the correlation between different fluorescence intensities and electron donating abilities of the dissolved organic matter samples is analyzed; the fluorescence components of the dissolved organic matter are separated by using the PARAFAC, and the change of the fluorescence intensity of each component is analyzed, the correlation between the spectral characteristics and the electron donating ability is analyzed, and the feasibility of using the 3DEEMs-PARAFAC to analyze the electron donating ability of the dissolved organic matter is verified.

[0085] The scheme of the embodiments of the present application is described in detail and explained with reference to the accompanying drawings and specific application examples:

[0086] The embodiments of the present application provide a method for evaluating the electron donating ability of dissolved organic matter by fusing parallel factor analysis and three-dimensional fluorescence spectrum, which comprises the following steps:

[0087] Step 1: Database construction.

[0088] Multiple batches of samples (>20) with consistent test parameters were collected to construct the original three-dimensional fluorescence data set. The specific collection conditions were as follows: for each three-dimensional fluorescence spectrum data, the total organic carbon concentration was controlled to be about 5 mgC·L -1 . The scanned emission wavelength (Em) ranged from 200 to 700 nm with a step of 5 nm. The excitation wavelength (Ex) ranged from 200 to 600 nm with a step of 5 nm. The excitation and emission slit widths were both set to 5 nm. The blank sample was a pure water solution sample.

[0089] For each electrochemical data, the total organic carbon concentration was controlled to be about 1-10 mgC·L -1 (preferably 5 mgC·L -1 ). In the workstation parameters, the step potential was set to +0.5 V, the electrolysis time was 1800 seconds, and the data acquisition frequency was 1 Hz. The relative standard deviation (RSD) was less than 5% for three consecutive repetitions, which was effective data. After deducting the capacitive current and system background drift, the EDC value was obtained by using the trapezoidal numerical integration method. The blank sample was a pure water solution sample.

[0090] Step 2: Data preprocessing.

[0091] Based on the Python program, the original data set constructed was batch processed, the mask width was adjusted to eliminate scattering to optimize the results, and the data results after each sub-step processing were output. The batch processing steps included eliminating Rayleigh scattering, eliminating Raman scattering, deducting the blank control, Raman normalization of data, and data denoising; for each fluorescence data, the meshgrid() function was called to deduct the Rayleigh scattering signal therein, all Rayleigh scattering wavelength ranges were automatically captured and deducted, the missing values after deduction were filled by using the methods of cubic spline interpolation and linear interpolation, and the three-dimensional fluorescence spectrum data after deducting Rayleigh scattering was returned. Further, the background deduction method was used to remove the Raman scattering signal therein, which was specifically achieved by subtracting the three-dimensional fluorescence spectrum data of the blank solution sample from the three-dimensional fluorescence spectrum data, and by taking the Raman peak integral standardization at Em of 350 nm as the Raman unit (R.U.).

[0092] In some embodiments, a filtering algorithm is applied to reduce noise interference. Figure 2 The data heat map after code processing of each sub-step was presented, in which the horizontal coordinate represented the excitation wavelength, the vertical coordinate represented the emission wavelength, and the value of each point represented the signal intensity.

[0093] Step 3: Data visualization.

[0094] Based on the fluorescence region integration (FRI) analysis, the material classification attribute of each sample data was labeled, and the visualization result was output (such as Figure 2The fluorescence parameters, including fluorescence index (FI), humification index (HIX) and biological index (BIX), were calculated based on the pre-processed fluorescence data sets as the basic parameters for the final evaluation equation construction. The calculation formulas of the fluorescence parameters are shown as follows:

[0095] Fluorescence index FI = F em＝450nm / F em＝500nm (measured at excitation wavelength Ex = 370 nm)

[0096] Humification index HIX = Area 435-480nm / Area 300-345nm (measured at excitation wavelength Ex = 254 nm)

[0097] Biological index BIX = F em＝380nm / F em＝430nm (measured at excitation wavelength Ex = 310 nm)

[0098] Wherein, F em＝x nm denotes the fluorescence intensity at the emission wavelength of x nm; Area x-ynm denotes the peak area in the region of x-y nm.

[0099] Step 4: Data set screening.

[0100] Based on the pre-processed data set, the best component number F of the target data set is determined by comprehensively applying split-half analysis, overall residual analysis and core consistency analysis. Based on the pre-processed data set, the PARAFAC model is initially constructed, and the best component number F of the target data set is determined by comprehensively applying split-half analysis (preferably applying S4C4T2 method), overall residual analysis and core consistency analysis, with the screening conditions set as: the core consistency parameter value is greater than 90%, the split-half similarity is greater than 95%, and the overall residual inflection point is located. Figure 3 The screening specific conditions of the best component number are presented. Among them, the S4C4T2 algorithm is preferentially applied in the split-half analysis. Figure 4 The specific wavelength data comparison of the S4C4T2 analysis method is presented. The wavelength data of the data set is randomly grouped into 4 groups, and compared in pairs, and the component number F with overall spectral similarity > 95% is retained.

[0101] Based on the above best component number F, single-sample residual structure and leverage value analysis are comprehensively applied to identify and exclude abnormal samples that do not have representativeness or poor quality. The screening conditions are that the leverage value is less than 30% and the single-sample residual is less than three times the median value, Figure 5The screening of single sample outliers is presented, and Sample #1 and Sample #5 are screened and removed as outlier samples. Based on the new data set after removing the outlier samples, the above steps are cycled until no outlier samples are found, and the best data set for modeling is determined.

[0102] Step 5: Final model verification.

[0103] Based on the best data set and the best component number F determined in the above steps, a final PARAFAC model is constructed. The method with the smallest error is selected for model construction and analysis by comparing the random initialization (cycled 20 times) method and the independent SVD initialization method, and the original wavelength loading matrix of each sample and its visualization result are output. The fluorescence intensity values of the characteristic peaks of each independent component are retrieved as the basic parameters for constructing the final evaluation equation. Figure 6 The visualization heat maps of three independent components C1, C2 and C3 obtained after the final PARAFAC model analysis of a certain type of surface water sample set are presented.

[0104] Step 6: Evaluation equation construction.

[0105] A prediction equation for the electron donating capacity (EDC) of unknown samples is constructed to evaluate the electron donating capacity of unknown samples. The input parameters include sample fluorescence parameters (FI, HIX, BIX), fluorescence intensity of each independent component (F C1 ,F C2 ,...,F Cn ) and the measured value of EDC of known samples.

[0106] If the sample size is small (number of samples < 10), based on the obtained correlation coefficient, the weight factor k i is calculated to construct a comprehensive evaluation equation to verify the feasibility of predicting the electron donating capacity of dissolved organic matter. The pre-preparation for equation construction includes: (1) analyzing the correlation between different fluorescence parameters (FI, HIX, BIX) of dissolved organic matter samples and EDC to obtain the corresponding fitting slope k i and correlation coefficient R 2 (such as obtaining the fitting slope k i and correlation coefficient R 2 of each parameter FI, HIX and BIX); (2) analyzing the correlation between the fluorescence intensity of each independent component characteristic peak of dissolved organic matter (F C1 ,F C2 ,...,F Cn ) and EDC to obtain the corresponding fitting slope k i and correlation coefficient R 2 (such as obtaining F C1 ,F C2 ,...,FCn Fitted slope k of each parameter i and correlation coefficient R 2 ). Figure 7 The correlation of EDC and each fluorescence parameter in typical DOM samples is presented, where the horizontal coordinate represents the measured value of EDC, and the vertical coordinate represents each fluorescence parameter. Each point represents a different sample. Figure 8 The comprehensive EDC evaluation equation and its fitting degree of the small sample data set are presented, where the horizontal coordinate represents the measured value of EDC, and the vertical coordinate represents each fluorescence parameter. Each point represents a different sample. The weighted comprehensive evaluation equation is used, and the specific calculation formula is as follows:

[0107] Predicted value EDC predict = K FI ·FI + K HIX ·HIX + K BIX ·BIX + K ci ·F Ci + ··· + a

[0108] Weight factor K i = k i × (R i 2 / ∑R i 2 )

[0109] where k i is the slope value of the single variable fitting of parameter i and EDC; R i 2 is the R 2 value of the single variable fitting of parameter i and EDC; the constant term a can be set to 0 (no intercept assumption), or further calibrated by fitting residuals.

[0110] If the sample size is large (sample size ≥ 10), the LinearRegression() function is called, and the multiple linear regression training is performed by the least squares method (OLS) to make the model self-optimize the weight based on the data. Figure 9 The correlation of EDC and each fluorescence parameter in typical DOM samples is presented, Figure 10 The comprehensive EDC evaluation equation and its fitting degree of the large sample data set are presented, where the horizontal coordinate represents the measured value of EDC, and the vertical coordinate represents each fluorescence parameter. Each point represents a different sample. The specific calculation formula of the evaluation equation is as follows:

[0111] Predicted value EDC predict = m FI ·FI + m HIX ·HIX + m BIX ·BIX + m ci ·F Ci + ··· + c

[0112] Where, m i Weighting factors for automatic correction of the OLS model.

[0113] The advantages of this embodiment include the following three points: (1) Improved prediction accuracy: The evaluation equation constructed by integrating multiple parameters has a goodness of fit (R²) 2 ) significantly higher than single-parameter prediction models (such as Figures 7-10 (2) Enhanced robustness: By integrating fluorescence parameters and component characteristic peak intensities, prediction fluctuations caused by single parameter detection errors are effectively reduced; (3) Improved universality: The hierarchical strategy of small sample weighting method and large sample OLS method ensures the reliability of prediction under different data scales.

[0114] In summary, compared with the prior art, the method of the present invention has at least the following advantages and beneficial effects:

[0115] (1) Anti-matrix interference: Eliminates the interference of organic adsorption and complex environmental matrix on the electrode in electrochemical detection, adapts to environmental engineering application scenarios, and realizes accurate quantification of DOM electron-donating ability.

[0116] (2) Cost optimization: Relying on the Python open source ecosystem, it completely avoids commercial software licensing fees, lowers the technical application threshold, and is easier to promote to scientific research institutions, small and medium-sized enterprises and grassroots monitoring departments.

[0117] (3) Transparent and controllable algorithm: The processing flow built on Python open source library breaks the "black box" mode of existing commercial software, realizes a high degree of transparency in the data processing process, and allows users to understand, review and modify the underlying computing logic (including the core PARAFAC algorithm), thereby improving the credibility, interpretability and customization of the results to meet specific research needs.

[0118] (4) Flexible and scalable workflow: Supports free reconstruction of preprocessing workflows, parameters, and visualization modules. Users can integrate the rich algorithms provided by libraries such as NumPy, SciPy, and Scikit-learn, or easily develop emerging machine learning models, statistical methods, and specific correction or adjustment algorithms to expand the dimensions and accuracy of analysis. In addition, users can more easily link and fuse data with other instrument analysis platforms, databases, or information systems (such as mass spectrometry analysis or online monitoring systems).

[0119] The application also provides an apparatus for evaluating the electron-donating capacity of dissolved organic matter, which can implement the above-described method. The apparatus includes:

[0120] The fluorescence spectrophotometer module is configured to acquire three-dimensional fluorescence spectral data.

[0121] an electrochemical analysis module configured to determine electron donation capacity (EDC);

[0122] a data processing module configured to execute the algorithm of steps S102-S107;

[0123] an output module configured to generate a dissolved organic matter electron donation capacity prediction report.

[0124] It can be understood that the contents in the above method embodiments are all applicable to the device embodiments, the device embodiments specifically implement the functions of the above method embodiments, and achieve the same beneficial effects as the above method embodiments.

[0125] The embodiment of the present application further provides an electronic device, which comprises a memory and a processor, the memory stores a computer program, and the processor implements the above method when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a server, etc.

[0126] It can be understood that the contents in the above method embodiments are all applicable to the device embodiments, the device embodiments specifically implement the functions of the above method embodiments, and achieve the same beneficial effects as the above method embodiments.

[0127] Please refer to Figure 11 , Figure 11 The hardware structure of the electronic device of another embodiment is illustrated, which comprises:

[0128] The processor 1101 can be implemented in the form of a general-purpose CPU (Central Processing Unit), a microprocessor, an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits, etc., for executing related programs to implement the technical solutions provided by the embodiments of the present application;

[0129] The memory 1102 can be implemented in the form of a ROM (Read Only Memory), a static storage device, a dynamic storage device, or a RAM (Random Access Memory), etc. The memory 1102 can store an operating system and other application programs, and when the technical solutions provided by the embodiments of the present application are implemented by software or firmware, the related program codes are saved in the memory 1102 and called and executed by the processor 1101 to implement the above method of the embodiments of the present application;

[0130] The input / output interface 1103 is used to realize information input and output;

[0131] The communication interface 1104 is configured to realize the communication interaction between the device and other devices. The communication can be realized through wired means (for example, a USB, a network cable, etc.) or through wireless means (for example, a mobile network, WIFI, Bluetooth, etc.).

[0132] The bus 1105 is configured to transmit information between various components (for example, the processor 1101, the memory 1102, the input / output interface 1103, and the communication interface 1104) of the device.

[0133] The processor 1101, the memory 1102, the input / output interface 1103, and the communication interface 1104 are connected to each other through the bus 1105 to realize the communication connection between the device.

[0134] The embodiment of the present application further provides a computer readable storage medium, which stores a computer program. The computer program is executed by a processor to realize the method described above.

[0135] It can be understood that the content in the above method embodiments is applicable to the storage medium embodiments. The storage medium embodiments specifically realize the functions of the above method embodiments, and achieve the same beneficial effects as the above method embodiments.

[0136] The memory is a non-transitory computer readable storage medium, which can be used to store a non-transitory software program and a non-transitory computer executable program. In addition, the memory can include a high-speed random access memory, and can further include a non-transitory memory, for example, at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state memory device. In some embodiments, the memory can optionally include a memory remotely arranged relative to the processor. These remote memories can be connected to the processor through a network. Examples of the network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0137] The embodiment of the present application further provides a computer program product, which includes a computer program. The computer program is executed by a processor to realize the method described above.

[0138] It can be understood that the contents in the above method embodiments are applicable to the program product embodiments, the program product embodiments specifically implement the functions same as those of the above method embodiments, and achieve the beneficial effects same as those of the above method embodiments. The executable computer program code or "code" for executing each embodiment can be written in a high-level programming language such as C, C++, Python, Smalltalk, Java, JavaScript, Visual Basic, Structured Query Language (for example, Transact-SQL), Perl, or in various other programming languages.

[0139] The embodiments described in the embodiments of the present application are for more clearly illustrating the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art can know that, with the evolution of technology and the appearance of new application scenarios, the technical solutions provided by the embodiments of the present application are also applicable to similar technical problems.

[0140] Those skilled in the art can understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and can include more or fewer steps than those shown in the figures, or combine certain steps, or different steps.

[0141] The device embodiments described above are merely illustrative, and the units described as separate components can or can not be physically separated, that is, can be located in one place, or can be distributed on multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiments of the present application.

[0142] Those skilled in the art can understand that all or some of the steps in the above disclosed method, the function modules / units in the system and the device can be implemented as software, firmware, hardware and their appropriate combinations.

[0143] The terms "first", "second", "third", "fourth" and the like (if any) in the specification of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily have to describe a particular order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0144] It should be understood that, in the application, "at least one" refers to one or more, and "multiple" refers to two or more. "And / or" is used to describe the association relationship of the associated objects, which means that there can be three relationships, for example, "A and / or B" can represent three cases of only A, only B, and A and B existing at the same time, wherein A and B can be singular or plural. The character " / " generally represents an "or" relationship between the associated objects before and after it. "At least one of the following" or similar expressions means any combination of these items, including any combination of single or multiple items. For example, at least one 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", wherein a, b, and c can be single or multiple.

[0145] In several embodiments provided in the application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the above units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed mutual ones can be indirect coupling or communication connection through some interfaces, devices or units, and can be electrical, mechanical or other forms.

[0146] The units described above as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on multiple network units. According to actual needs, part or all of the units can be selected to achieve the purpose of the embodiment scheme.

[0147] In addition, the functional units in each embodiment of the application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0148] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application, essentially or in other words, the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes multiple instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various program storage media.

[0149] The preferred embodiments of the embodiments of the present application are described above with reference to the accompanying drawings, and are not limited to the scope of the embodiments of the present application. Any modifications, equivalent replacements and improvements made by those skilled in the art without departing from the scope and essence of the embodiments of the present application shall be within the scope of the embodiments of the present application.

Claims

1. A method for evaluating the electron-donating capacity of soluble organic matter, characterized in that, The method includes the following steps: Three-dimensional fluorescence spectra (3D EEMs) of multiple soluble organic matter samples were collected to construct the original three-dimensional fluorescence dataset. The electron-donating ability of each sample was measured to construct the electrochemical dataset. Preprocess the original 3D fluorescence dataset; Fluorescence parameters for each sample are calculated based on fluorescence region integration. Parallel factor analysis was performed on the preprocessed dataset, and the optimal grouping number F was determined by combining splitting analysis, global residual analysis and core consistency analysis. Based on the determined optimal group number F, the single-sample residual structure and leverage value are analyzed, outlier samples are identified and removed, and an optimized dataset is obtained. Based on the optimized dataset and the optimal number of components F, a parallel factor analysis (PARAFAC) model is established; the excitation / emission wavelength load data of each component and the intensity matrix of the sample components are output. Linear fitting equations were constructed to evaluate the electron-donating ability of unknown samples by considering the sample fluorescence parameters, fluorescence intensity of each component, and electron-donating capability.

2. The method according to claim 1, characterized in that, The process involves collecting three-dimensional fluorescence spectral data from multiple dissolved organic matter samples to construct an original three-dimensional fluorescence dataset, determining the electron-donating capacity of each sample, and constructing an original electrochemical dataset, including: For each set of three-dimensional fluorescence spectral data, the total organic carbon concentration was controlled to be approximately 1-10 mg C·L⁻¹. -1 The emission wavelength range of the scan is 200 to 700 nm; the excitation wavelength varies in the range of 200 to 600 nm; the blank sample is a pure aqueous solution sample. The electron-donating capacity of each sample was measured using an electrochemical workstation; for each electrochemical dataset, the total organic carbon concentration was controlled to be approximately 1-10 mg C·L⁻¹. -1 The electron-donating capability was obtained by trapezoidal numerical integration after deducting the capacitor current and system background drift; the blank sample was a pure aqueous solution sample.

3. The method according to claim 1, characterized in that, The preprocessing of the original three-dimensional fluorescence dataset includes: Based on a Python program, for each fluorescence data, the Rayleigh scattering signal is subtracted using grid interpolation, missing values ​​are filled using cubic spline interpolation and linear interpolation, and the three-dimensional fluorescence spectrum data after subtracting Rayleigh scattering is returned. The Raman scattering signal of the blank control sample was subtracted from the obtained three-dimensional fluorescence spectral data, and Raman units were standardized by integrating the Raman peak at the emission wavelength of 350 nm.

4. The method according to claim 1, characterized in that, The fluorescence parameters include fluorescence index (FI), humification index (HIX), and biogenic index (BIX), which serve as the basic parameters for constructing the final evaluation equation. The conditions for determining the optimal group score F are: the core consistency parameter value is greater than 50%; the segment half-similarity is greater than 95%; and the overall residual change curve shows an inflection point. The criteria for identifying anomalous samples are: the leverage value of a single sample exceeds the preset value and / or the residual of a single sample is greater than three times the median value of the residuals.

5. The method according to claim 1, characterized in that, The establishment of the PARAFAC model includes: Two methods, random initialization and independent singular value decomposition, are used for modeling. The model with the smallest sum of squared residuals is selected as the final output. Output the excitation / emission load spectrum of each component and the intensity matrix of the sample components.

6. The method according to claim 1, characterized in that, The construction of linear fitting equations for sample fluorescence parameters, fluorescence intensity of each component of the sample, and electron-donating capacity of the sample includes: A predictive equation for the electron-donating capability of unknown samples is constructed to evaluate the electron-donating capability of unknown samples. The input parameters include sample fluorescence parameters, fluorescence intensity of each independent component, and measured values ​​of electron-donating capability of known samples. If the data sample size is small, obtain the fitting slope and correlation coefficient to calculate the weighting factor, construct a comprehensive evaluation equation, and verify the feasibility of predicting the electron-donating capacity of soluble organic matter. If the data sample size is large, the LinearRegression() function is called to perform multiple linear regression training using the least squares method, so that the model can self-optimize weights based on the data.

7. The method according to claim 6, characterized in that, The process of obtaining the fitting slope and correlation coefficient to calculate the weighting factor includes: The correlation between different fluorescence parameters and electron-donating capacity of soluble organic matter samples was analyzed to obtain the fitting slope and correlation coefficient for different fluorescence parameters; The correlation between the fluorescence intensity and electron-donating ability of the characteristic peaks of each independent component of soluble organic matter was analyzed, and the corresponding fitting slope and correlation coefficient were obtained. The weighting factor is calculated based on the obtained fitting slope and correlation coefficient.

8. The method according to claim 1, characterized in that, Establish fitting equations between different fluorescence parameters and electron-donating capacity values, including: The correlation between different fluorescence intensities and electron-donating capabilities of soluble organic matter samples was analyzed. The fluorescent components of soluble organic matter were separated using PARAFAC, and the changes in fluorescence intensity of each component were analyzed. The correlation between spectral characteristics and electron-donating ability was analyzed, and the feasibility of using 3DEEMs-PARAFAC to analyze the electron-donating ability of soluble organic matter was verified.

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 according to any one of claims 1 to 8.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 8.

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