Organic matter detection method and device based on in-situ three-dimensional fluorescence spectrum component analysis

Abstract

The invention discloses an organic matter detection method and device based on in-situ three-dimensional fluorescence spectrum component analysis, and relates to the field of organic matter detection. The organic matter detection method comprises the following steps: constructing an organic matter component library; obtaining a three-dimensional fluorescence spectrum of the water sample to be detected; based on the organic matter component library, performing region segmentation, mask judgment and extreme value judgment on the three-dimensional fluorescence spectrum of the to-be-detected water sample, and determining the organic matter components contained in the to-be-detected water sample; and performing multivariate linear fitting on the three-dimensional fluorescence spectrum of the to-be-detected water sample by using the typical three-dimensional fluorescence spectrum of each organic matter component contained in the to-be-detected water sample, and determining a combination coefficient of the typical three-dimensional fluorescence spectrum of each organic matter component contained in the to-be-detected water sample as the content of each organic matter component contained in the to-be-detected water sample. According to the invention, rapid analysis and detection of organic matter components are realized.

Description

Technical Field

[0001] The present application relates to the field of organic matter detection, and in particular to an organic matter detection method and device based on in-situ three-dimensional fluorescence spectral component analysis. Background Art

[0002] Dissolved organic matter (DOM) is operationally defined as any organic matter that can pass through a filter membrane (pore size is generally 0.45um). It is one of the most important forms of organic matter in natural water environments. Dissolved organic carbon (DOC) is usually used to characterize the DOM content in water bodies. DOM is the main reactant and product in biogeochemical processes and the most active component in geochemical cycles. In addition, DOM is one of the largest organic matter reservoirs on the earth's surface, and its carbon reserves are at least equivalent to atmospheric carbon reserves. Its biogeochemical processes are closely related to the global carbon cycle and have received widespread attention. In addition to natural sources, DOM has been proven to be seriously affected by human activities. Therefore, studying the content, composition, behavior, properties, and distribution of DOM has important scientific significance and economic value, and is an important topic in environmental monitoring.

[0003] DOM is a very complex organic mixture, of which only a small part has been accurately identified and analyzed. Conventional detection techniques such as gas / liquid / ion chromatography, isotope analysis, nuclear magnetic resonance spectroscopy, mass spectrometry, infrared spectroscopy, etc. are costly, time-consuming, require cumbersome sample pretreatment, and are difficult to detect on-site or in-situ. The optically active part of DOM is called chromophoric dissolved organic matter (CDOM), which can be used as a tracer of the dynamics and characteristics of the entire DOM pool. It has absorption characteristics in the ultraviolet band, and some CDOM also has a fluorescence effect. Some scholars call this part of CDOM fluorescent dissolved organic matter (FDOM). In recent years, the use of three-dimensional fluorescence spectroscopy to indirectly study the composition, structure and source of DOM has become the most commonly used analytical method.

[0004] Three-dimensional fluorescence spectra (Excitation-Emission-Matrix Spectra, EEMs) have excellent selectivity and sensitivity, can reveal more detailed characteristics of DOM, and are widely used in DOM component analysis and pollution tracing. The components of DOM in natural water environment are very complex, and its three-dimensional fluorescence spectrum also contains more complicated information accordingly, which is the superposition of fluorescence spectra of multiple substances. Therefore, the decomposition and identification of spectral components of EEMs are the top priorities of spectral analysis. The existing decomposition methods are mainly second-order correction methods, including parallel factor analysis (PARAFAC), alternating trilinear decomposition, etc. Such methods have high requirements on the number of samples, usually more than 20 different samples are required for decomposition, and manual operation is required to screen and adjust the decomposition results. Therefore, they are not suitable for rapid analysis and detection of organic components. Summary of the invention

[0005] The purpose of the present application is to provide a method and device for detecting organic matter based on in-situ three-dimensional fluorescence spectral component analysis, which can realize rapid analysis and detection of organic matter components.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides a method for detecting organic matter based on in-situ three-dimensional fluorescence spectral component analysis, comprising:

[0008] An organic component library is constructed; the organic component library includes typical three-dimensional fluorescence spectra of different organic components.

[0009] Obtain the three-dimensional fluorescence spectrum of the water sample to be tested.

[0010] Based on the organic component library, the three-dimensional fluorescence spectrum of the water sample to be tested is subjected to regional segmentation, mask discrimination and extreme value discrimination to determine the organic components contained in the water sample to be tested.

[0011] The three-dimensional fluorescence spectrum of the water sample to be tested is subjected to multivariate linear fitting using typical three-dimensional fluorescence spectra of each organic component contained in the water sample to be tested, and the combination coefficient of the typical three-dimensional fluorescence spectra of each organic component contained in the water sample to be tested is determined as the content of each organic component contained in the water sample to be tested.

[0012] In a second aspect, the present application provides an organic matter detection device based on in-situ three-dimensional fluorescence spectral component analysis, the organic matter detection device comprising: an ultraviolet LED turntable, a micro-fiber spectrometer, a control drive module and a host computer;

[0013] The control driving module is connected to the ultraviolet LED rotating disk and the micro optical fiber spectrometer respectively, and the control driving module is used to drive the ultraviolet LED rotating disk and the micro optical fiber spectrometer;

[0014] The ultraviolet LED turntable is used to excite the fluorescence of the water sample to be tested;

[0015] The micro-fiber spectrometer is connected to the host computer, and the micro-fiber spectrometer is used to scan and obtain the three-dimensional fluorescence spectrum of the water sample to be tested;

[0016] The host computer is used to determine the organic components and their contents contained in the water sample to be tested by using the above-mentioned organic matter detection method.

[0017] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0018] The present application provides an organic matter detection method and device based on in-situ three-dimensional fluorescence spectral component analysis. The present application constructs an organic matter component library, and uses typical three-dimensional fluorescence spectra of different organic matter components in the organic matter component library to fit the three-dimensional fluorescence spectrum of the water sample to be tested, thereby realizing rapid decomposition and identification of the three-dimensional fluorescence spectrum of a single sample without the need for a large number of samples, thereby realizing rapid analysis and detection of organic matter components. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1 This is a flow chart of an organic matter detection method based on in-situ three-dimensional fluorescence spectroscopy component analysis in one embodiment of the present application;

[0021] Figure 2 A schematic diagram of the structure of an organic matter detection device based on in-situ three-dimensional fluorescence spectral component analysis provided in one embodiment of the present application;

[0022] Figure 3 A flow chart of a spectral clustering algorithm based on pseudo-metric threshold provided in one embodiment of the present application;

[0023] Figure 4 A schematic diagram of a typical three-dimensional fluorescence spectrum of each organic component in an organic component library provided in an embodiment of the present application;

[0024] Figure 5 A schematic diagram of a three-dimensional fluorescence spectrum of a water sample to be tested provided in one embodiment of the present application;

[0025] Figure 6 A schematic flow chart of a method for determining renewable energy complementarity provided in one embodiment of the present application;

[0026] Figure 7 A schematic diagram of a mask of a typical three-dimensional fluorescence spectrum of each organic component in an organic component library provided in an embodiment of the present application. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0028] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0029] In an exemplary embodiment, Figure 1 As shown, a method for detecting organic matter based on in-situ three-dimensional fluorescence spectrum component analysis is provided, comprising the following steps 101 to 104. Among them:

[0030] Step 101, constructing an organic component library; the organic component library includes typical three-dimensional fluorescence spectra of different organic components.

[0031] Step 102, obtaining a three-dimensional fluorescence spectrum of the water sample to be tested.

[0032] Step 103 , based on the organic component library, performing region segmentation, mask determination and extreme value determination on the three-dimensional fluorescence spectrum of the water sample to be tested, to determine the organic components contained in the water sample to be tested.

[0033] Step 104, using the typical three-dimensional fluorescence spectra of each organic component contained in the water sample to be tested, the three-dimensional fluorescence spectrum of the water sample to be tested is subjected to multivariate linear fitting to determine the combination coefficient of the typical three-dimensional fluorescence spectra of each organic component contained in the water sample to be tested as the content of each organic component contained in the water sample to be tested.

[0034] By implementing the above-mentioned steps 101 to 104, a large number of fluorescence spectral loading data of natural water samples are clustered and analyzed to establish an organic component library. For the water sample to be tested, its three-dimensional fluorescence spectrum is compared with the characteristics of typical fluorescence components, and components that may be contained in the water sample to be tested are selected from the component library, and multivariate linear fitting is performed, thereby realizing rapid decomposition and identification of the three-dimensional fluorescence spectrum of a single sample.

[0035] In an exemplary embodiment, the above step 101 is implemented using the following steps 1 to 4.

[0036] Step 1: Cluster the fluorescence spectral load data of a large number of natural water samples collected using a spectral clustering algorithm based on a pseudo-metric threshold to obtain clustered spectral load data.

[0037] Specifically, the fluorescence spectral load data used in step 1 is the spectral load of each organic component in the water sample, which is obtained by the parallel factor method (PARAFAC). The three-dimensional fluorescence spectra of a large number of natural water samples are used as data sets, and parallel factor decomposition is performed to obtain the spectral load of each organic component, including the emission spectrum load and the excitation spectrometer load, and the data form is a one-dimensional vector.

[0038] Specifically, the spectral clustering algorithm based on pseudo-metric threshold used in step 1 includes the following steps:

[0039] (1-1) According to the congruence coefficient, input the pseudo-metric threshold, calculate the number of similar vectors for each vector (in the subsequent description of the embodiments of the present application, vectors refer to fluorescence spectral load data), and sort according to the number of similar vectors.

[0040] (1-2) Sort by the number of similar vectors and extract non-repeated initial cluster centers so that the number of similar vectors of the cluster centers is maximized and any cluster centers are not similar to each other.

[0041] (1-3) Clustering starts with the cluster center as the standard, and determines whether the congruence coefficients of the remaining vectors with the cluster center are greater than a threshold, and clustering is performed; specifically, when there are multiple cluster centers with a congruence coefficient greater than the threshold, the cluster center with the largest congruence coefficient is selected, and when there is no cluster center with a congruence coefficient greater than the threshold, the vector is treated as a scattered point and is not clustered.

[0042] (1-4) Calculate the congruence coefficients between each vector in the cluster and the remaining vectors after clustering, take the vector with the largest average congruence coefficient as the new cluster center, and update the cluster center.

[0043] (1-5) Iterate (1-3) and (1-4) until the cluster center and clustering results no longer change, then end clustering and obtain multiple clusters.

[0044] Specifically, the input threshold reference value of the congruence coefficient is 0.85, and the calculation formula is:

[0045]

[0046] Among them, S TC (x,y) is the congruence coefficient of the x-th fluorescence spectrum loading data and the y-th fluorescence spectrum loading data, xi and i are the i elements in the overlapping area of ​​the x-th fluorescence spectrum loading data and the y-th fluorescence spectrum loading data, and n is the number of elements in the overlapping area of ​​the x-th fluorescence spectrum loading data and the y-th fluorescence spectrum loading data.

[0047] When calculating the congruence coefficient, only the elements in the overlapping region between the two vectors are taken for calculation. The overlapping region is defined as the region where the wavelength ranges between different vectors are consistent.

[0048] Step 2: Perform chain transfer bi-norm normalization on the clustered spectral load data obtained in step 1.

[0049] Specifically, in step 2, the chain transfer two-norm normalization algorithm steps include:

[0050] (2-1) Initialize the jth cluster to the unnormalized set U, and initialize the normalized set N to the empty set; j = 1, 2, ..., J, where J is the number of clusters.

[0051] Calculate the overlapping area between each vector in the unnormalized set U and the remaining vectors in the set and sort them.

[0052] (2-2) The vector with the largest overlapping area is used as the initial fluorescence spectrum load data c to be normalized, and the two-norm self-normalization is performed in the full spectrum range of c. Add c' to the normalized set N, where norm(c) refers to the two-norm of c, that is, the modulus of the vector;

[0053] (2-3) Traverse the vectors in U, sort them according to the overlap with the vectors in N, select the vector with the largest overlap with the set N as the fluorescence spectrum load data a to be normalized, take the vector with the largest overlap with a in N as the normalization center b, and store a in the normalized set N after normalization;

[0054] (2-4) Iterate and repeat (2-3) until all vectors in U are added to the normalized set N, that is, the algorithm ends when U is an empty set.

[0055] Specifically, the formula for two-norm normalization is:

[0056]

[0057] Where a' is the normalized fluorescence spectrum load data obtained by normalizing the normalized fluorescence spectrum load data.

[0058] Step 3: Average the normalized spectral loads obtained in step 2 within the cluster to obtain the typical excitation spectral load and typical emission spectral load of each spectral load cluster.

[0059] Specifically, the intra-cluster averaging in step 3 is a segmented averaging method, that is, the region with the highest overlapping frequency is taken as a reference, and after segmented averaging, translation and scaling are performed toward the reference, so that the final result does not have abnormal noise points.

[0060] Step 4: Perform vector synthesis based on the typical excitation spectrum load and typical emission spectrum load obtained in step 3 to obtain the typical three-dimensional fluorescence spectrum of each cluster, regard it as the typical three-dimensional fluorescence spectrum of the organic component, and establish an organic component library.

[0061] Specifically, the vector synthesis described in step 4, that is, the excitation spectrum load vector Ex and the emission spectrum load vector Em are synthesized into a three-dimensional fluorescence spectrum Ex·Em', and the three-dimensional fluorescence spectrum is a matrix spectrum represented by the three-dimensional coordinates of excitation wavelength (x-axis)-emission wavelength (y-axis)-fluorescence intensity (z-axis).

[0062] In this embodiment, the decomposition process can only obtain the decomposed spectral load, and the specific organic component to which the spectral load belongs needs to be manually identified, and clustering needs to be performed before manual identification. For example, the fluorescence spectra of 1000 water samples, each of which decomposes 3 spectral components, have 3000 spectra. The purpose of clustering is to merge these 3000 spectra that are similar. If the final clustering average obtains 10 types of spectra, then the organic components corresponding to these 10 spectra are manually identified, thereby obtaining a typical three-dimensional fluorescence spectrum of the organic component.

[0063] In an exemplary embodiment, the above steps 102 and 103 are implemented by the following steps 5 and 6.

[0064] Step 5: Scan and obtain the three-dimensional fluorescence spectrum data of the sample to be tested, and perform conventional spectrum correction and interference elimination.

[0065] Step 6: perform region segmentation, mask discrimination, and extreme value discrimination on the three-dimensional fluorescence spectrum data obtained in step 5, exclude organic components that are impossible to be contained in the water sample to be tested from the organic component library obtained in step 4, and select organic components contained in the water sample to be tested.

[0066] Specifically, the region segmentation described in step 6 refers to segmenting the three-dimensional fluorescence spectrum obtained in step 5 according to the existing five-region division method of organic ultraviolet fluorescence, comparing the segmentation result with the typical three-dimensional fluorescence spectrum obtained in step 4, preliminarily excluding organic components that are impossible to be included, and selecting organic components that may be included to form a first candidate set.

[0067] Specifically, the mask discrimination described in step 6 refers to performing mask extraction on the three-dimensional fluorescence spectrum data obtained in step 5 and the typical three-dimensional fluorescence spectrum of the organic components in the first candidate set, selecting from them according to the mask discrimination criterion, further narrowing the range of organic components that may be included, and constructing the second candidate set. Among them, the mask discrimination criterion is: the mask of the typical three-dimensional fluorescence spectrum of any organic component in the second candidate set is a subset of the mask of the three-dimensional fluorescence spectrum of the water sample to be tested, and the sum of the masks of the typical three-dimensional fluorescence spectra of all organic components in the second candidate set is consistent with the mask of the three-dimensional fluorescence spectrum of the water sample to be tested.

[0068] Specifically, the extreme value discrimination described in step 6 refers to extracting the extreme value positions according to the derivative spectra of the three-dimensional fluorescence spectrum data obtained in step 5 and the typical three-dimensional fluorescence spectra of the organic components in the second candidate set, combining them with the classical fluorescence peak interpretation, and finally confirming the organic components contained in the water sample to be tested.

[0069] In another exemplary embodiment, an organic matter detection device based on in-situ three-dimensional fluorescence spectral component analysis is provided, comprising: an ultraviolet LED turntable, a micro-fiber spectrometer, a control drive module and a host computer; the control drive module is connected to the ultraviolet LED turntable and the micro-fiber spectrometer respectively, and the control drive module is used to drive the ultraviolet LED turntable and the micro-fiber spectrometer; the ultraviolet LED turntable is used to excite the fluorescence of a water sample to be tested; the micro-fiber spectrometer is connected to the host computer, and the micro-fiber spectrometer is used to scan and obtain a three-dimensional fluorescence spectrum of the water sample to be tested; the host computer is used to adopt the above-mentioned organic matter detection method to determine the organic components and their contents contained in the water sample to be tested.

[0070] The UV LED turntable includes 11 UV LEDs, a turntable and a stepper motor. In this embodiment, the excitation light source uses a high-current UV LED, which can ensure high light intensity output under low voltage and low power consumption conditions compared to traditional xenon lamps or deuterium lamps, and ensures good monochromaticity without the use of a monochromator. The stepper motor is used for precise alignment of the UV LED with the sample pool and the detection end.

[0071] High-current ultraviolet LEDs have good optical power and monochromaticity. The central wavelengths include 255nm, 265nm, 275nm, 285nm, 295nm, 305nm, 340nm, 365nm, 375nm, 385nm, and 395nm, covering all major fluorescence excitation wavelengths of organic matter.

[0072] The absorption spectrum scanning range of the micro-fiber spectrometer is 190 ~ 1000nm, covering all major fluorescence emission wavelengths of organic matter.

[0073] The functions of the control and drive module include drive control of the stepper motor, adjustable constant current drive of the ultraviolet LED, synchronous transmission and reception control of the ultraviolet LED and the micro-fiber spectrometer, and communication control between the device and the host computer.

[0074] Figure 2 The device includes a UV LED turntable and a sample slot for exciting sample fluorescence. The UV LED turntable includes: UV LED, turntable and stepper motor, wherein the turntable is connected to the stepper motor, and UV LEDs of 255nm, 265nm, 275nm, 285nm, 295nm, 305nm, 340nm, 365nm, 375nm, 385nm and 395nm are arranged and installed on the turntable at 0°, 28.125°, 61.875°, 90°, 118.125°, 151.875°, 180°, 208.125°, 241.875°, 270° and 298.125°, respectively. When scanning the sample to be tested, the host computer sends a command to the control drive module of the detection device to start the measurement. Under darkroom conditions, the drive control module sends a signal to the micro-fiber spectrometer to continuously measure five spectra as background spectra, then turns on the specified UV LED and continuously measures five spectra as signal spectra. The mean of the signal spectrum minus the mean of the background spectrum is used as the final result at the excitation wavelength. The drive control module then issues a command to rotate the stepper motor to the next specified position, repeating the above operation until the measurement of 11 emission spectra is completed, and then the entire system is reset, and the drive control module initiates a communication request to the host computer to upload all data.

[0075] The current adjustment range of the UV LED of the device is 0-485mA. The Switch1-12 of the control drive module is 12 outputs, which control the on and off of the UV LED. A total of 5 pins ADG1-3_EN and ADG_A0-1 control the on and off of the 12 outputs. The control method is described in Table 1.

[0076] Table 1 UV LED control methods

[0077] ADG_A0 ADG_A1 ADG1_EN ADG2_EN ADG3_EN path 0 0 1 0 0 Switch1 1 0 1 0 0 Switch2 0 1 1 0 0 Switch3 1 1 1 0 0 Switch4 0 0 0 1 0 Switch5 1 0 0 1 0 Switch6 0 1 0 1 0 Switch7 1 1 0 1 0 Switch8 0 0 0 0 1 Switch9 1 0 0 0 1 Switch10 0 1 0 0 1 Switch11 1 1 0 0 1 Switch12

[0078] In another exemplary embodiment, in order to illustrate the specific implementation of the technical solution of the above embodiment, the following example is provided:

[0079] like Figure 3As shown, this example is based on the above-mentioned organic matter detection method, and polycyclic aromatic hydrocarbons are detected based on ultraviolet fluorescence and absorption spectra, including an organic matter component library construction method and a three-dimensional fluorescence spectrum rapid decomposition and identification method (i.e., the above-mentioned step 101) of a single sample (i.e., the above-mentioned step 102-step 104). For the fluorescence spectrum load data of a large number of natural water samples collected, a spectral clustering algorithm based on a pseudo-metric threshold is used to cluster the fluorescence spectrum load data to obtain clustered fluorescence spectrum load data; then the fluorescence spectrum load data of each cluster is merged into a three-dimensional fluorescence spectrum of the component to obtain an organic component library. For a single mixed water sample to be tested, that is, a water sample to be tested, the organic matter detection device provided by the present invention is used to scan its three-dimensional fluorescence spectrum, and its three-dimensional fluorescence spectrum data is feature-matched with the organic component library, and appropriate components are selected from the component library to perform multivariate linear fitting based on the trilinear principle.

[0080] The first step of the method for constructing an organic component library is a spectral clustering algorithm based on a pseudo-metric threshold. Traditional clustering algorithms are usually based on distance metrics (such as Euclidean distance), but the similarity judgment of spectral loads is usually based on pseudo-metrics that do not satisfy the triangle inequality. Therefore, the traditional clustering algorithm has a poor clustering effect on spectral load data and is not applicable. To this end, the present application provides a spectral clustering algorithm based on a pseudo-metric threshold, and its pseudo-metric similarity parameter uses the TC (Tucker congruence) congruence coefficient.

[0081] When the similarity of two vectors (i.e., TC congruence coefficient) is greater than the threshold, the two vectors are considered similar. The algorithm flow chart is as follows: Figure 4 As shown in the figure, after clustering starts, the similarity between all vectors is calculated to obtain a similarity matrix. The number of similar vectors of each vector is further determined according to the threshold, and all vectors to be clustered are sorted according to the number of similar vectors. Non-repeating vectors are taken out in order as cluster centers, that is, cluster centers are not similar to each other. The similarity between the remaining vectors and the cluster center is calculated, and they are assigned to the most similar cluster that meets the threshold condition. The similarity between each vector in the cluster and the remaining vectors is calculated, and the vector with the largest average similarity is used as the new cluster center. A judgment box is made to determine whether the cluster label to which the sample belongs is refreshed or whether the cluster center moves in this round of clustering. If the judgment is yes, the loop is returned. If the judgment is no, the clustering is terminated.

[0082] After the spectral load clustering is completed, the clustered fluorescence spectral load data must be chain-transferred bi-norm normalized in turn. After normalization, the spectral load is averaged within the cluster to obtain the typical excitation spectral load and typical emission spectral load of each spectral load cluster; the typical excitation spectral load and the typical emission spectral load are synthesized by vector multiplication to obtain the typical three-dimensional fluorescence spectrum of each cluster, which is regarded as the typical three-dimensional fluorescence spectrum of the natural organic component to establish an organic component library.

[0083] Figure 5 Schematic diagram of the organic component library provided in this embodiment, Figure 6 The schematic diagram of the three-dimensional fluorescence spectrum of the water sample to be tested is shown in Figure 2. After the above steps, clustering, normalization and synthesis are performed. Figure 5 Schematic diagram of typical three-dimensional fluorescence spectra of 19 organic components in the water sample. After scanning the three-dimensional fluorescence spectrum of the water sample to be tested, removing scattering interference, spectral correction, and internal filter correction, the following is obtained: Figure 6 The three-dimensional fluorescence spectrum of the water sample to be tested is shown in the figure. According to the existing five-division method of organic ultraviolet fluorescence, the spectrum is segmented and compared with the organic component library. The components that are impossible to be included are initially excluded from the typical fluorescence components, and the organic components that may be included are selected. Then the mask discrimination method is used to extract the spectrum mask of the water sample to be tested and the spectrum mask of the DOM fluorescence component library, as shown in FIG. Figure 7 Assume that the mixed water sample to be tested consists of n components, then its spectral mask must satisfy the following: (1) The spectral mask P of any component i is a subset of the spectral mask M of the water sample to be tested, that is (2) The sum of the spectral masks of the n components is approximately equal to the spectral mask of the mixed sample, that is, P1∪P2∪P3...∪P n ≈M. According to these two conditions, Figure 7 The typical three-dimensional fluorescence spectrum of the organic components that meet the requirements is selected. Then, the extreme value is identified based on the derivative spectrum, and the components with matching extreme value points are selected for multivariate linear fitting.

[0084] According to the specific embodiments provided in this application, compared with the prior art, the beneficial effects of this application include:

[0085] (1) The present invention realizes three-dimensional fluorescence spectrum decomposition of a single sample, which is convenient and rapid, does not require a large amount of sample, reduces analysis costs, and is suitable for rapid in-situ analysis.

[0086] (2) The device provided by the present invention adopts discrete LED excitation to replace xenon lamp + monochromator excitation, which greatly reduces the size of the device and reduces the power consumption of the device, and is suitable for in-situ processing.

[0087] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0088] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for detecting organic matter based on in-situ three-dimensional fluorescence spectral component analysis, characterized in that: The organic matter detection method comprises: Constructing an organic component library; the organic component library includes typical three-dimensional fluorescence spectra of different organic components; Obtaining a three-dimensional fluorescence spectrum of the water sample to be tested; Based on the organic component library, performing region segmentation, mask discrimination and extreme value judgment on the three-dimensional fluorescence spectrum of the water sample to be tested, and determining the organic components contained in the water sample to be tested; The three-dimensional fluorescence spectrum of the water sample to be tested is subjected to multivariate linear fitting using typical three-dimensional fluorescence spectra of each organic component contained in the water sample to be tested, and the combination coefficient of the typical three-dimensional fluorescence spectra of each organic component contained in the water sample to be tested is determined as the content of each organic component contained in the water sample to be tested.

2. The method for detecting organic matter based on in-situ three-dimensional fluorescence spectrum component analysis according to claim 1, characterized in that: Construct an organic component library, including: Acquiring fluorescence spectrum load data of different water samples; the fluorescence spectrum load data includes emission spectrum load and laser spectrum load; The parallel factor method is used to decompose the three-dimensional fluorescence spectra of different water samples to obtain multiple fluorescence spectrum loading data of different water samples; The spectral clustering algorithm with pseudo-metric threshold is used to cluster the fluorescence spectral loading data of each water sample to obtain multiple clusters; The fluorescence spectral loading data within each cluster were normalized; Perform intra-cluster average calculation on the normalized fluorescence spectrum load data in each cluster to obtain the average fluorescence spectrum load data of each cluster; Based on the average fluorescence spectral loading data of each cluster, a typical three-dimensional fluorescence spectrum of each cluster is constructed; The organic component corresponding to each typical three-dimensional fluorescence spectrum is determined, and the organic component library is constructed.

3. The method for detecting organic matter based on in-situ three-dimensional fluorescence spectrum component analysis according to claim 2, characterized in that: The spectral clustering algorithm with pseudo-metric threshold is used to cluster the fluorescence spectral load data of each water sample to obtain multiple clusters, including: According to each fluorescence spectrum load data of each water sample, a spectrum load set is constructed; Let x=1, 2, ..., N respectively, calculate the number of similar vectors of the x-th fluorescence spectrum loading data in the spectrum loading set, and obtain the number of similar vectors of each fluorescence spectrum loading data in the spectrum loading set; wherein, the number of similar vectors of the x-th fluorescence spectrum loading data is the number of fluorescence spectrum loading data in the spectrum loading set whose congruence coefficient with the x-th fluorescence spectrum loading data is not less than the pseudo-metric threshold; wherein, N is the number of fluorescence spectrum loading data in the spectrum loading set; In the order of the number of similar vectors from large to small, multiple fluorescence spectrum load data satisfying preset conditions are selected from the spectrum load set as initial cluster centers; the preset condition is that the congruence coefficient between any two fluorescence spectrum load data is less than a preset congruence coefficient threshold; Clustering each fluorescence spectral load data in the spectral load set according to the congruence coefficient between each fluorescence spectral load data in the spectral load set and each clustering center to obtain multiple clusters; Calculate the average value of the congruence coefficients between each fluorescence spectral loading data and other fluorescence spectral loading data in the cluster, select the fluorescence spectral loading data with the largest average value of the congruence coefficient as the new cluster center of the cluster, and return to the step of "clustering each fluorescence spectral loading data in the spectral loading set according to the congruence coefficients between each fluorescence spectral loading data in the spectral loading set and each cluster center to obtain multiple clusters" until the cluster center and the clustering results no longer change.

4. The method for detecting organic matter based on in-situ three-dimensional fluorescence spectrum component analysis according to claim 3, characterized in that: The calculation formula of the congruence coefficient is: Among them, S TC (x,y) is the congruence coefficient of the x-th fluorescence spectrum loading data and the y-th fluorescence spectrum loading data, x i and i are the i elements in the overlapping area of ​​the x-th fluorescence spectrum loading data and the y-th fluorescence spectrum loading data, and n is the number of elements in the overlapping area of ​​the x-th fluorescence spectrum loading data and the y-th fluorescence spectrum loading data.

5. The method for detecting organic matter based on in-situ three-dimensional fluorescence spectrum component analysis according to claim 2, characterized in that: Normalize the fluorescence spectral loading data within each cluster, including: Initialize the jth cluster to the unnormalized set, and initialize the normalized set to the empty set; j = 1, 2, ..., J, J is the number of clusters; Let k = 1, 2, ..., K respectively j , calculate the sum of the overlapping areas of the kth fluorescence spectrum load data in the unnormalized set and each fluorescence spectrum load data in the unnormalized set except the kth fluorescence spectrum load data, as the kth fluorescence spectrum load data x in the unnormalized set j,k The total overlapping area of ​​each fluorescence spectral loading data in the unnormalized set is obtained; where K j is the number of fluorescence spectrum loading data in the jth cluster; Select the fluorescence spectrum load data with the largest total overlapping area in the unnormalized set as the initial fluorescence spectrum load data to be normalized; The initial fluorescence spectrum loading data to be normalized is deleted from the unnormalized set; Normalizing the initial fluorescence spectrum load data to be normalized, and adding the normalized fluorescence spectrum load data to the normalized set; The sum of the overlapping areas of each fluorescence spectrum load data in the unnormalized set and each normalized fluorescence spectrum load data in the normalized set is calculated respectively as the total overlapping area of ​​each fluorescence spectrum load data in the unnormalized set; Select the fluorescence spectrum loading data with the largest total overlapping area in the unnormalized set as the fluorescence spectrum loading data to be normalized; The fluorescence spectrum loading data to be normalized is deleted from the unnormalized set; Selecting the normalized fluorescence spectrum load data with the largest overlapping area with the fluorescence spectrum load data to be normalized from the normalized set as the normalization center; Using the normalized center, normalizing the fluorescence spectrum load data to be normalized, and adding the normalized fluorescence spectrum load data to the normalized set; Return to the step of "respectively calculating the sum of the overlapping areas of each fluorescence spectrum loading data in the unnormalized set and each normalized fluorescence spectrum loading data in the normalized set as the total overlapping area of ​​each fluorescence spectrum loading data in the unnormalized set" until the unnormalized set is an empty set.

6. The method for detecting organic matter based on in-situ three-dimensional fluorescence spectrum component analysis according to claim 5, characterized in that: The formula for normalizing the initial fluorescence spectrum load data to be normalized is: Wherein, c is the initial fluorescence spectrum load data to be normalized, norm() represents the two-norm function, which is used to calculate the modulus length, and c' is the normalized fluorescence spectrum load data obtained by normalizing the initial fluorescence spectrum load data to be normalized; Using the normalized center, the formula for normalizing the normalized fluorescence spectrum loading data is: Wherein, a is the fluorescence spectrum load data to be normalized, b is the normalization center, and a' is the normalized fluorescence spectrum load data obtained by normalizing the fluorescence spectrum load data to be normalized.

7. The method for detecting organic matter based on in-situ three-dimensional fluorescence spectrum component analysis according to claim 1, characterized in that: Based on the organic component library, the three-dimensional fluorescence spectrum of the water sample to be tested is subjected to regional segmentation, mask discrimination and extreme value discrimination to determine the organic components contained in the water sample to be tested, specifically including: The five-division method of organic matter ultraviolet fluorescence is used to segment the three-dimensional fluorescence spectrum of the water sample to obtain multiple segmentation results. Comparing the plurality of segmentation results with typical three-dimensional fluorescence spectra of each organic component in the organic component library, determining organic components whose overlapping areas between the typical three-dimensional fluorescence spectra in the organic component library and any segmentation result are greater than a preset overlapping area threshold, and forming a first candidate set; Selecting organic components that meet the mask discrimination criterion from the first candidate set to obtain a second candidate set; Calculating the extreme point position of the derivative spectrum of the typical three-dimensional fluorescence spectrum of each segmentation result and each organic component in the second candidate set; The organic components whose extreme point positions of the derivative spectrum of the typical three-dimensional fluorescence spectrum in the third candidate set match the extreme point positions of the derivative spectrum of any segmentation result are determined as the organic components contained in the water sample to be tested.

8. The method for detecting organic matter based on in-situ three-dimensional fluorescence spectrum component analysis according to claim 7, characterized in that: The mask discrimination criterion is: the mask of the typical three-dimensional fluorescence spectrum of any organic component in the second candidate set is a subset of the mask of the three-dimensional fluorescence spectrum of the water sample to be tested, and the sum of the masks of the typical three-dimensional fluorescence spectra of all organic components in the second candidate set is consistent with the mask of the three-dimensional fluorescence spectrum of the water sample to be tested.

9. An organic matter detection device based on in-situ three-dimensional fluorescence spectrum component analysis, characterized in that: The organic matter detection device comprises: an ultraviolet LED turntable, a micro-fiber spectrometer, a control drive module and a host computer; The control driving module is connected to the ultraviolet LED rotating disk and the micro optical fiber spectrometer respectively, and the control driving module is used to drive the ultraviolet LED rotating disk and the micro optical fiber spectrometer; The ultraviolet LED turntable is used to excite the fluorescence of the water sample to be tested; The micro-fiber spectrometer is connected to the host computer, and the micro-fiber spectrometer is used to scan and obtain the three-dimensional fluorescence spectrum of the water sample to be tested; The host computer is used to determine the organic components and their contents contained in the water sample to be tested by using the organic matter detection method described in any one of claims 1 to 8.

10. The organic matter detection device based on in-situ three-dimensional fluorescence spectrum component analysis according to claim 9, characterized in that: The ultraviolet LED turntable comprises: a turntable, a plurality of ultraviolet LED lamps arranged on the turntable, and a stepping motor connected to the turntable shaft; The control driving module is respectively connected to the driving ends of the plurality of ultraviolet LED lamps and the driving end of the stepping motor.

Images