Rapid detection method of microcystis aeruginosa

Abstract

The invention provides a rapid detection method of microcystis aeruginosa, and belongs to the technical field of environmental protection detection, and the method specifically comprises the following steps: constructing a microcystis aeruginosa-escherichia coli system, determining algae data in the microcystis aeruginosa-escherichia coli system and water quality indexes of a culture solution, and determining the microcystis aeruginosa according to the data and the determined data of the water quality indexes of the culture solution. By combining with escherichia coli, the concentration detection and treatment efficiency of the microcystis aeruginosa is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of environmental protection detection, and particularly relates to a rapid detection method for Microcystis aeruginosa. Background Art

[0002] Eutrophication is one of the most serious environmental problems affecting inland freshwater bodies. It stimulates excessive algal growth and increases suspended organic matter, thereby degrading water quality. The proliferation of harmful algal blooms (HABs) can cause odor and the death of aquatic organisms, severely disrupting water balance and impacting the economic value of the water. Microcystis aeruginosa is one of the most typical algae that cause cyanobacterial blooms, and the algal toxins it secretes pose a serious threat to human health. Therefore, measuring the growth status of Microcystis aeruginosa is particularly important for bloom monitoring and early warning.

[0003] Detecting algal biomass in water bodies is an important part of early warning of algal blooms. Detection of algal species and concentrations that cause algal blooms in eutrophic water bodies mainly focuses on the following methods: (1) Detection technology based on microalgae morphology: including microscopic examination technology (MET) and image recognition technology (IIT). This method can intuitively obtain species composition and abundance information, as well as fine features of microalgae surface morphology and internal structure. However, this method relies on the morphological structure of cells to identify algae and cannot distinguish microalgae species with highly similar morphologies. The accuracy is affected by the state of the sample and is not suitable for on-site detection.

[0004] (2) Cytochrome-based detection technology: including high-performance liquid chromatography (HPLC), absorption spectrometry analysis (ASA), and fluorescence spectrometry analysis (FSA). This method determines the concentration and type of algae by detecting the pigments contained in algae. This method can analyze a large number of samples without being affected by the sample state. However, this method requires the extraction of algae pigments, which is a complex operation process and is easily affected by changes in the composition and content of different algae pigments. It has low accuracy and is not suitable for rapid on-site detection.

[0005] (3) Immunoassay: This includes immunofluorescence assay (IFA), immunosensor assay (ISA), and enzyme-linked immunosorbent assay (ELISA). It is a species identification method based on specific antigen-antibody binding reactions. Indirect immunoassay technology is usually used to detect algae. Although it has high specificity, impurities in the sample can interfere with the detection effect and reduce the specificity of the assay.

[0006] (4) Nucleic acid detection technology: including fluorescence in situ hybridization (FISH), sandwich hybridization (SHA), polymerase chain reaction (PCR) and isothermal amplification technology (IAT). Based on the diversity of nucleic acid sequences, it identifies the target species by identifying species-specific regions of nucleic acid sequences. Although this method has the advantages of high specificity and high sensitivity, it is greatly affected by primers and has high costs.

[0007] Among traditional algae detection methods, highly sensitive and specific detection methods are usually expensive and not suitable for in situ determination; while methods suitable for rapid on-site detection usually have poor sensitivity. Summary of the Invention

[0008] To achieve the purpose of the present invention, the present invention adopts the following technical solutions: Specifically, the present application provides a method for rapid detection of Microcystis aeruginosa, which is rapid and highly specific.

[0009] To achieve the above-mentioned purpose of the invention, the present application provides a rapid detection method for Microcystis aeruginosa, comprising the following steps: S1: constructing a Microcystis aeruginosa-Escherichia coli system, and measuring algae data and culture solution water quality indicators in the Microcystis aeruginosa-Escherichia coli system; S2: constructing an array map using the algae data and the data obtained from the measurement of the water quality index of the culture medium.

[0010] Furthermore, the algae data includes algae density, algae biomass, algae chlorophyll a, and algae morphology.

[0011] Furthermore, the water quality indicators of the culture solution include total nitrogen concentration and total phosphorus concentration.

[0012] Further: constructing a Microcystis aeruginosa-Escherichia coli system, specifically including: The Microcystis aeruginosa used in the construction of the Microcystis aeruginosa-Escherichia coli system was inoculated into a liquid culture medium of target volume, wherein the concentration of the Microcystis aeruginosa algae solution was controlled at 10 5 cells / mL is the center of the preset range.

[0013] The bacterial solution of Escherichia coli with a set concentration is diluted to different concentrations and inoculated into the liquid culture medium.

[0014] Furthermore, the different concentrations are determined according to the ratio of the Escherichia coli bacterial solution concentration to the algae solution concentration, specifically including 10:1, 1:1, 1:4, and 1:10.

[0015] Furthermore, the algae density is cultured for a preset time according to the Microcystis aeruginosa-Escherichia coli system, and the number of algae cells is determined by a hemocytometer using the Microcystis aeruginosa-Escherichia coli system under the preset time.

[0016] Furthermore, the biological mass concentration is determined by culturing the Microcystis aeruginosa-Escherichia coli system for a preset time, and using the Microcystis aeruginosa-Escherichia coli system under the preset time to determine the biological mass concentration by a hemocytometer method.

[0017] Further: constructing an array diagram, specifically including: The measured data were subjected to PCoA analysis to construct an array diagram.

[0018] Furthermore, the PCoA analysis adopts one or more of the three calculation methods of Bray-Curtis, Jaccard, and Euclidean.

[0019] Compared with the prior art, the present invention has the following beneficial effects: A Microcystis aeruginosa-Escherichia coli system was constructed, and the algal characteristics and key water quality characteristics were measured and analyzed using PCoA. An algae-index array diagram was constructed, and cluster analysis was performed on algae on different days within a cycle. This method, which directly measures algal and water indicators and clusters the number of days of Microcystis aeruginosa growth, allows for rapid analysis of algal growth stages. This method is more efficient and rapid, allowing the algal growth stage to be determined from any measured Microcystis aeruginosa or water quality data, thus providing early warning of algal blooms.

[0020] Other features and advantages will be described in the following description. The objectives and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and drawings.

[0021] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The above and other features and advantages of the present invention will become more apparent by describing in detail example embodiments thereof with reference to the accompanying drawings; Figure 1 This is a flow chart of a rapid detection method for Microcystis aeruginosa; Figure 2 The Bray-Curtis calculation method was used to perform PCoA analysis to obtain the array diagram of algae responses to different environmental factors; Figure 3The Jaccard calculation method was used to perform PCoA analysis to obtain the array diagram of algae responses to different environmental factors; Figure 4 The Euclidean calculation method was used to perform PCoA analysis to obtain an array diagram of the response of algae to different environmental factors. DETAILED DESCRIPTION

[0023] To help those skilled in the art better understand the technical solutions in this specification, the following will provide a clear and complete description of the technical solutions in the embodiments of this specification, in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of this specification, not all of them. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative work should fall within the scope of protection of this specification.

[0024] Example 1 Specifically, such as Figure 1 As shown, a rapid detection method for Microcystis aeruginosa comprises the following steps: S1: constructing a Microcystis aeruginosa-Escherichia coli system, and measuring algae data and culture solution water quality indicators in the Microcystis aeruginosa-Escherichia coli system; Specifically, the following steps are involved: Step 1: inoculate Microcystis aeruginosa into 100 mL of BG-11 liquid culture medium, wherein the algae liquid concentration is 10 5 cells / mL.

[0025] Step 2, concentration is 10 8 The E. coli solution containing 10 CFU / mL was diluted to different concentrations and inoculated into 100 mL. 5 cells / mL of algal solution, the final bacteria-algae ratios were approximately 10:1, 1:1, 1:4, and 1:10, respectively.

[0026] Step 3: The number of algal cells in the Microcystis aeruginosa-Escherichia coli system was determined by hemocytometer on days 3, 6, 9, 12, 15, 18, 21, 24, 27, and 30 after inoculation.

[0027] Step 4: The Microcystis aeruginosa-Escherichia coli system was cultured for 3 days, 6 days, 9 days, 12 days, 15 days, 18 days, 21 days, 24 days, 27 days, and 30 days from the start of inoculation to determine the biomass concentration.

[0028] Step 5: culturing the Microcystis aeruginosa-Escherichia coli system to determine the chlorophyll a concentration on days 3, 6, 9, 12, 15, 18, 21, 24, 27, and 30 from the start of inoculation.

[0029] Step 6: The morphology of the algae was measured at 3, 6, 9, 12, 15, 18, 21, 24, 27, and 30 days after the inoculation of the Microcystis aeruginosa-Escherichia coli system. The area, perimeter, diameter, and roundness of the algae cells were measured using the image processing software Image-Pro Plus 6.0.

[0030] Step 7, the Microcystis aeruginosa-Escherichia coli system was cultured for 3 days, 6 days, 9 days, 12 days, 15 days, 18 days, 21 days, 24 days, 27 days, and 30 days from the start of inoculation, and the total nitrogen (TN) and total phosphorus (TP) concentrations in the culture solution were measured.

[0031] S2: constructing an array map using the algae data and the data obtained from the measurement of the water quality index of the culture medium.

[0032] Furthermore, the algae data includes algae density, algae biomass, algae chlorophyll a, and algae morphology.

[0033] Furthermore, the water quality indicators of the culture solution include total nitrogen concentration and total phosphorus concentration.

[0034] Further: constructing a Microcystis aeruginosa-Escherichia coli system, specifically including: The Microcystis aeruginosa used in the construction of the Microcystis aeruginosa-Escherichia coli system was inoculated into a liquid culture medium of target volume, wherein the concentration of the Microcystis aeruginosa algae solution was controlled at 10 5 cells / mL is the center of the preset range.

[0035] The bacterial solution of Escherichia coli with a set concentration is diluted to different concentrations and inoculated into the liquid culture medium.

[0036] Furthermore, the different concentrations are determined according to the ratio of the Escherichia coli bacterial solution concentration to the algae solution concentration, specifically including 10:1, 1:1, 1:4, and 1:10.

[0037] Furthermore, the algae density is cultured for a preset time according to the Microcystis aeruginosa-Escherichia coli system, and the number of algae cells is determined by a hemocytometer using the Microcystis aeruginosa-Escherichia coli system under the preset time.

[0038] Furthermore, the biological mass concentration is determined by culturing the Microcystis aeruginosa-Escherichia coli system for a preset time, and using the Microcystis aeruginosa-Escherichia coli system under the preset time to determine the biological mass concentration by a hemocytometer method.

[0039] Further: constructing an array diagram, specifically including: The measured data were subjected to PCoA analysis to construct an array diagram.

[0040] Furthermore, the PCoA analysis adopts one or more of the three calculation methods of Bray-Curtis, Jaccard, and Euclidean.

[0041] In one embodiment, a method for detecting Microcystis aeruginosa comprises the following steps: S11, inputting the above measured data into software capable of performing PCoA analysis.

[0042] S12, select calculation parameters, select Bray-Curtis calculation method, and select 95% confidence interval.

[0043] S13, drawing a classification array diagram of the growth days of Microcystis aeruginosa based on the obtained grouped data.

[0044] In one embodiment, a method for detecting Microcystis aeruginosa comprises the following steps: Step S21: input the above measured data into software capable of performing PCoA analysis.

[0045] Step S22: Select calculation parameters, select the Jaccard calculation method, and select 95% confidence interval.

[0046] Step S23: drawing a classification array diagram of the growth days of Microcystis aeruginosa according to the obtained grouping data.

[0047] In another embodiment, a method for detecting Microcystis aeruginosa comprises the following steps: Step S31: input the above measured data into software capable of performing PCoA analysis.

[0048] Step S32: Select calculation parameters, select the Euclidean calculation method, and select 95% confidence interval.

[0049] Step S33: drawing a classification array diagram of the growth days of Microcystis aeruginosa according to the obtained grouping data.

[0050] Example 2 In step 4, the biomass concentrations of algal cells in the bacteria-algae systems with different ratios of 10:1, 1:1, 1:4, and 1:10 increased to 3.20 g / L, 5.10 g / L, 4.75 g / L, and 4.10 g / L, respectively, after 30 days of culture.

[0051] In step 5, the chlorophyll a concentrations in the bacteria-algae systems with different ratios of 10:1, 1:1, 1:4, and 1:10 increased to 25.37 mg / L, 33.99 mg / L, 21.84 mg / L, and 20.80 mg / L, respectively, after 30 days of culture.

[0052] In step 6, the data for the measurement of algal cell area, perimeter, diameter, and circularity in the bacteria-algae systems with different ratios of 10:1, 1:1, 1:4, and 1:10 are all in pixels. The average area of ​​algal cells during 30 days of culture varied in the ranges of 110.80-572.57 pixels, 220.03-585.67 pixels, 143.43-521.50 pixels and 164.97-494.83 pixels, respectively; the average circumference varied in the ranges of 28.92-114.98 pixels, 69.12-119.94 pixels, 37.29-112.22 pixels and 49.67-145.16 pixels, respectively; the average diameter varied in the ranges of 8.26-26.83 pixels, 15.66-27.38 pixels, 9.43-25.11 pixels and 12.07-26.52 pixels, respectively; the roundness changed slightly, fluctuating in the range of 1.26-3.86 pixels.

[0053] In step 7, total nitrogen concentrations in the bacteria-algae systems at different ratios (10:1, 1:1, 1:4, and 1:10) decreased from an initial 284.00 mg / L to 137.60 mg / L, 207.00 mg / L, 203.80 mg / L, and 187.48 mg / L, respectively, on day 21. Between days 21 and 30, total nitrogen concentrations increased due to the death and lysis of some algal cells, which released their contents into the culture medium. Total phosphorus concentrations decreased from an initial 3.74 mg / L to 0.31 mg / L, 0.20 mg / L, 0.20 mg / L, and 0.22 mg / L, respectively.

[0054] PCoA is a dimensionality reduction method based on distance matrix, which is used to map sample relationships in high-dimensional data to a low-dimensional space in order to visualize the similarities or differences between samples.

[0055] Figure 2For Example 1, PCoA analysis using the Bray-Curtis calculation method yielded arrays of algae responses to different environmental factors when the bacteria-algae ratios were approximately 10:1, 1:1, 1:4, and 1:10, respectively. The graph shows the similarities between different samples (sampled on different days). The Microcystis aeruginosa-Escherichia coli system can be clearly divided into four growth phases. Algae growth stages can be distinguished by comparing the regions within which different days are fixed. When the bacteria-algae ratio was 10:1, the initial culture phase and the logarithmic growth phase overlapped in a certain region. The cluster analysis results showed that the 3d and 15d ordinate values ​​were 0.022 and 0.026, respectively, indicating a low degree of differentiation. When the bacteria-algae ratios were 1:1, 1:4, and 1:10, the relative positions of the different culture phases in the graph shifted, with the relative distances increasing compared to the 10:1 experimental group.

[0056] Figure 3 For Example 2, PCoA analysis was performed using the Jaccard calculation method to generate arrays of algal responses to different environmental factors, showing similarities between different samples (sampled on different days). At bacteria-algae ratios of 10:1, 1:1, and 1:10, the Microcystis aeruginosa-Escherichia coli system can be clearly divided into four growth phases. At a bacteria-algae ratio of 1:4, algal growth can be divided into three stages. In the experimental group with a bacteria-algae ratio of 10:1, the initial culture phase and the logarithmic growth phase intersect in a certain region. Cluster analysis results show that the ordinate values ​​for 3d, 9d, 15d, and 18d are 0.002, 0.116, 0.071, and 0.005, respectively. The degree of differentiation between 3d and 18d is relatively small, and the 15d data fall between 3d and 9d. When the bacteria-algae ratio is 1:4, the Microcystis aeruginosa-Escherichia coli system can be clearly divided into three growth stages.

[0057] Figure 4 For Example 3, when the bacteria-algae ratios were approximately 10:1, 1:1, 1:4, and 1:10, respectively, PCoA analysis was performed using the Euclidean calculation method to generate array plots of algae responses to different environmental factors. The plots show similarities between different samples (sampled on different days). The Microcystis aeruginosa-Escherichia coli system can be clearly divided into four growth phases. When the bacteria-algae ratio was 10:1, the relative distance in the PCoA plot increased compared to the other two analysis methods, indicating improved discrimination. In the experimental group with a bacteria-algae ratio of 1:1, there was an overlap between the initial culture phase and the stable growth phase. The cluster analysis results showed that the 6-day and 24-day ordinate values ​​were -30.566 and -31.029, respectively, indicating a low degree of discrimination. The experimental groups with bacteria-algae ratios of 1:4 and 1:10 were divided into four growth phases with clear discrimination, where d represents the day.

[0058] In the Microcystis aeruginosa-Escherichia coli system, when the bacteria-algae ratio was 10:1, the Euclidean calculation method was suitable for cluster analysis of algae growth stages.

[0059] In the Microcystis aeruginosa-Escherichia coli system, when the bacteria-algae ratio was 1:1, the Bray-Curtis and Jaccard calculation methods were suitable for cluster analysis of algal growth stages.

[0060] In the Microcystis aeruginosa-Escherichia coli system, when the bacteria-algae ratio was 1:4, the Bray-Curtis, Jaccard, and Euclidean calculation methods were all suitable for cluster analysis of algal growth stages.

[0061] In the Microcystis aeruginosa-Escherichia coli system, when the bacteria-algae ratio was 1:10, the Bray-Curtis and Euclidean calculation methods were suitable for cluster analysis of algal growth stages.

[0062] Optionally, the calculation strategy for the algae growth stage cluster analysis is determined based on a calculation method suitable for the algae growth stage cluster analysis under different bacteria-algae ratios.

[0063] Optionally, in the Microcystis aeruginosa-Escherichia coli system, the method for determining the calculation strategy for cluster analysis of algae growth stages is: The bacteria-algae ratio without any crossover of growth stages in different calculation methods was taken as the available bacteria-algae ratio; The matching deviation of different calculation methods is determined based on the deviation between the measured bacteria-algae ratio and the available bacteria-algae ratio under different available bacteria-algae ratios. The calculation strategy for cluster analysis of algae growth stages was determined based on the matching deviation of different calculation methods.

[0064] Furthermore, the method for determining the matching deviation amount of the calculation method is: The deviation of the bacteria-algae ratio under different available bacteria-algae ratios is determined based on the deviation between the measured bacteria-algae ratio and the available bacteria-algae ratio; Determining a deviation coefficient at different available bacteria-algae ratios based on a ratio of the bacteria-algae ratio deviation to the available bacteria-algae ratio; The matching deviation of the calculation method is determined by multiplying the product of the deviation coefficients under different available bacteria-algae ratios.

[0065] It should be noted that the calculation strategy for cluster analysis of algae growth stages is determined according to the matching deviation of different calculation methods, including: When there is a calculation method with a matching deviation less than a preset matching deviation threshold, the calculation method with the smallest matching deviation is used to determine the calculation strategy for cluster analysis of algae growth stages; When there is no calculation method with a matching deviation less than a preset matching deviation threshold, a calculation strategy for cluster analysis of algae growth stages is determined using a suitable calculation method under different available bacteria-algae ratios.

[0066] Optionally, in the Microcystis aeruginosa-Escherichia coli system, the method for determining the calculation strategy for cluster analysis of algae growth stages is: The deviation of the bacteria-algae ratio under different available bacteria-algae ratios is determined based on the deviation between the measured bacteria-algae ratio and the available bacteria-algae ratio; Specifically, in the above steps, if the deviations of the bacteria-algae ratio under different available bacteria-algae ratios do not meet the requirements, that is, they are all greater than the threshold, then all calculation methods are used to perform algae growth stage cluster analysis processing under different available bacteria-algae ratios, and only when there is an available bacteria-algae ratio whose deviation meets the requirements, proceed to the next step; Obtaining suitable calculation methods for available bacteria-algae ratios that meet the requirements for different bacteria-algae ratio deviations; determining alternative calculation methods based on the suitable calculation methods for available bacteria-algae ratios that meet the requirements for different bacteria-algae ratio deviations; using bacteria-algae ratios without crossover in growth stages from different calculation methods as available bacteria-algae ratios; and determining matching deviations for different calculation methods based on deviations between the measured bacteria-algae ratios and the available bacteria-algae ratios at different available bacteria-algae ratios; It is understandable that the above steps are divided into the following two situations: Case 1: When there is a calculation method with a suitable bacteria-algae ratio deviation that meets the required number of available bacteria-algae ratios greater than the preset threshold value of the available bacteria-algae ratio, the algae growth stage cluster analysis process is performed using the calculation method with the largest number of available bacteria-algae ratio deviations that meet the required number of available bacteria-algae ratios; Scenario 2: If there is no suitable calculation method with a bacteria-algae ratio deviation that meets the requirement and the number of available bacteria-algae ratios is greater than the preset available bacteria-algae ratio threshold: determine the matching deviations of different calculation methods based on the deviations between the measured bacteria-algae ratios and the available bacteria-algae ratios under different available bacteria-algae ratios. If there is an alternative calculation method with a matching deviation that meets the requirement, that is, a smaller matching deviation that is smaller than the threshold, then the alternative calculation method with the smallest matching deviation is used to perform clustering analysis on the algae growth stage. Only when there is no alternative calculation method with a matching deviation that meets the requirement, proceed to the next step.

[0067] The calculation strategy for cluster analysis of algae growth stages was determined based on the matching deviation of different calculation methods.

[0068] The calculation strategy for cluster analysis of algae growth stages was determined based on the matching deviation of different calculation methods.

[0069] In summary, in the Microcystis aeruginosa-Escherichia coli co-culture system, different bacterial-algal ratios resulted in distinct ecological characteristics and algal growth rates within the phycosphere. When PCoA analysis of different parameters within the phycosphere is performed using different analytical methods, each bacterial-algal system has its own specific and appropriate calculation method. Therefore, the appropriate classification method should be selected based on the actual water quality.

[0070] In the above example, a Microcystis aeruginosa-Escherichia coli system was constructed. Algal characteristics and key water quality characteristics within the system were measured and subjected to PCoA analysis using three different calculation methods. An algae-index array diagram was constructed, and cluster analysis was performed on algae on different days within a cycle. This method directly measures algal and water indicators and rapidly analyzes the algal growth stage based on cluster analysis of the number of days the Microcystis aeruginosa grows. This method is more efficient and rapid, allowing the algal growth stage to be determined using any measured Microcystis aeruginosa or water quality data, thereby providing early warning of the occurrence of algal blooms.

[0071] The various embodiments in this specification are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from the other embodiments. In particular, the device, apparatus, and non-volatile computer storage medium embodiments are generally similar to the method embodiments, so their descriptions are relatively simplified. For relevant details, refer to the descriptions of the method embodiments.

[0072] The foregoing description of this specification describes specific embodiments. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0073] The foregoing description is merely one or more embodiments of this specification and is not intended to limit this specification. It will be apparent to those skilled in the art that various modifications and variations may be made to one or more embodiments of this specification. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of one or more embodiments of this specification are intended to be within the scope of the claims of this specification.

Claims

1. A rapid detection method for Microcystis aeruginosa, characterized in that: Specifically include: S1: constructing a Microcystis aeruginosa-Escherichia coli system, and measuring algae data and culture solution water quality indicators in the Microcystis aeruginosa-Escherichia coli system; S2: constructing an array map using the algae data and the data obtained from the measurement of the water quality index of the culture medium.

2. The rapid detection method for Microcystis aeruginosa according to claim 1, wherein The algae data include algae density, algae biomass, algae chlorophyll a, and algae morphology.

3. The rapid detection method for Microcystis aeruginosa according to claim 1, wherein The water quality indicators of the culture solution include total nitrogen concentration and total phosphorus concentration.

4. The rapid detection method for Microcystis aeruginosa according to claim 1, wherein Construction of Microcystis aeruginosa-Escherichia coli system, specifically including: The Microcystis aeruginosa used in the construction of the Microcystis aeruginosa-Escherichia coli system was inoculated into a liquid culture medium of target volume, wherein the concentration of the Microcystis aeruginosa algae solution was controlled at 10 5 cells / mL is the center of the preset range. The bacterial solution of Escherichia coli with a set concentration is diluted to different concentrations and inoculated into the liquid culture medium.

5. The rapid detection method for Microcystis aeruginosa according to claim 4, wherein The different concentrations are determined according to the ratio of the E. coli bacterial solution concentration to the algae solution concentration, specifically including 10:1, 1:1, 1:4, and 1:

10.

6. The rapid detection method for Microcystis aeruginosa according to claim 1, wherein The algae density is determined by culturing the Microcystis aeruginosa-Escherichia coli system for a preset time, and the number of algae cells is determined by a hemocytometer using the Microcystis aeruginosa-Escherichia coli system under the preset time.

7. The rapid detection method for Microcystis aeruginosa according to claim 1, wherein The biological mass concentration is determined by culturing the Microcystis aeruginosa-Escherichia coli system for a preset time, and using the Microcystis aeruginosa-Escherichia coli system under the preset time to determine the biological mass concentration by a hemocytometer method.

8. The rapid detection method for Microcystis aeruginosa according to claim 1, wherein Constructing an array graph, specifically including: The measured data were subjected to PCoA analysis to construct an array diagram.

9. The rapid detection method for Microcystis aeruginosa according to claim 8, wherein: The PCoA analysis uses one or more of the three calculation methods: Bray-Curtis, Jaccard, and Euclidean.

10. The rapid detection method for Microcystis aeruginosa according to claim 8, wherein: In the Microcystis aeruginosa-Escherichia coli system, the calculation strategy for cluster analysis of algae growth stages was determined as follows: The bacteria-algae ratio without any crossover of growth stages in different calculation methods was taken as the available bacteria-algae ratio; The matching deviation of different calculation methods is determined based on the deviation between the measured bacteria-algae ratio and the available bacteria-algae ratio under different available bacteria-algae ratios. The calculation strategy for cluster analysis of algae growth stages was determined based on the matching deviation of different calculation methods.

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