Phytoplankton Sorting System

Abstract

To provide a technique for easily and rapidly discriminating phytoplankton which causes damage without requiring labor in realtime.SOLUTION: In a phytoplankton discrimination system, target particulates are irradiated with a laser radiated by a light emission device to measure intensity of scattering light by the particles and intensity of self-fluorescence, and the particles are plotted with the intensity of the scattering light and the intensity of the self-fluorescence as parameters. Thus, the kind of an organism (phytoplankton) can be classified by a plotted area. Then, the intensity of the scattering light and the intensity of the self-fluorescence are measured concerning each one of the particles, thereby to perform not only discrimination between the organism and abiosis but also obtaining the kind and generation rate of each organism. In the phytoplankton discrimination system, processing can be incorporated in a water purification process without performing an operation of dyeing or the like and without requiring a process to collect a sample. Consequently, water quality management can be rapidly performed without requiring labor.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to a phytoplankton fractionation system. [Background technology]

[0002] Due to the rapid climate change in recent years, phytoplankton has been proliferating in large numbers in lakes, reservoirs, rivers, etc., which are the raw water sources for water purification plants, causing biological problems at the plants (unpleasant tastes and odors, problems with coagulation and sedimentation treatment, filter blockages, filter leakage problems, etc.), and causing various problems in the water purification process.

[0003] Currently, detection of the occurrence of phytoplankton, which causes these problems, is carried out at water purification plants through periodic microscopic observation, which requires a great deal of effort and skilled observation techniques.In addition, due to the recent decline in water revenues due to advances in electrical appliances and increased awareness of water conservation, the number of staff operating water purification plants has been reduced, and the number of biological staff who monitor the occurrence of phytoplankton has also decreased.In some cases, local governments have difficulty responding to biological problems when they occur because they do not have staff with the knowledge and observation techniques.

[0004] Furthermore, there have been cases where excessive chemicals have been blindly used to suppress biological damage without being able to identify the cause of the damage.As such, operational control of the water purification process to prevent biological damage relies heavily on experience and technology cultivated over many years.

[0005] In this situation, further declines in water revenues are predicted in the future, and there is a need to reduce costs and increase efficiency in water purification operations (by reducing personnel, easing staff workloads, and using chemicals more efficiently in water purification treatment, etc.). At the same time, it is believed that the occurrence of biological damage due to climate change will continue to increase nationwide.

[0006] As techniques relating to such problems, techniques such as those disclosed in Patent Document 1 and Patent Document 2 are known. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] Japanese Patent Application Laid-Open No. 2017-106831 [Patent Document 2] Japanese Patent Application Laid-Open No. 2007-232382 Summary of the Invention [Problem to be solved by the invention]

[0008] If treatment such as administering an appropriate amount of chemicals at a water purification plant is not carried out, it can cause biological damage, but the chemicals used and treatment methods vary depending on the type of organism being treated and the amount of living and non-living particles. Generally, at water purification plants, staff members periodically classify the organisms using a microscope, and treatment is not carried out in real time.

[0009] The prior art technology of Patent Document 1 is a method for determining the total amount of algae, and is not capable of classifying organisms by type. Therefore, a separate determination of the organism species is required to appropriately determine the type of drug.

[0010] Furthermore, the technology in Patent Document 2 involves staining to classify organisms, but requires a process of extracting and staining samples, making it impossible to monitor purified water in real time. Furthermore, until now, there has been no system for monitoring water (purified water) in real time.

[0011] Therefore, an object of the present invention is to provide a technology that can easily and quickly distinguish damaging phytoplankton in real time without requiring human labor. [Means for solving the problem]

[0012] The present invention employs the following solutions to solve the above problems. Note that the following solutions are merely examples, and the present invention is not limited to these.

[0013] Solution 1: The phytoplankton sorting system of this solution is a phytoplankton sorting system that includes: a light-emitting means that irradiates light toward a liquid; a detection means that detects scattered light and autofluorescence emitted by phytoplankton in response to the light irradiated by the light-emitting means; and a control means that detects the intensity of the scattered light and the intensities of the autofluorescence based on the scattered light and the autofluorescence detected by the detection means, and generates sorting information for sorting the types of phytoplankton based on the intensity of the scattered light and the intensities of the autofluorescence.

[0014] As a result of extensive research, the inventors discovered that it is possible to distinguish between different types of phytoplankton based on the intensity of the scattered light and autofluorescence emitted by the phytoplankton, and thus completed this system.

[0015] According to this solution, classification information for distinguishing types of phytoplankton is generated based on the intensity of scattered light and the intensity of autofluorescence, so there is no need for staining and the type of phytoplankton can be identified by the simple task of simply checking the classification information.As a result, phytoplankton that cause damage can be distinguished easily, quickly, and in real time without requiring human labor.

[0016] Solution 2: The phytoplankton separation system of this solution is a phytoplankton separation system that is any of the solutions described above, characterized in that the control means generates a scatter plot in which the intensity of the scattered light and the intensity of the autofluorescence are plotted as parameters as the separation information.

[0017] According to this solution, a scatter diagram is generated in which the intensity of scattered light and the intensity of autofluorescence are plotted as parameters to provide separation information, making it easy to understand at a glance and reducing the burden of separation work. The technical idea of ​​plotting two parameters, the intensity of scattered light and the intensity of autofluorescence, on a scatter diagram and enabling separation of phytoplankton based on the areas on the scatter diagram is a concept that has not existed in the past.

[0018] Solution 3: The phytoplankton sorting system of this solution is a phytoplankton sorting system that is any of the solutions described above, characterized in that the control means generates a count value indicating the number of plots included in a specific range of the scatter plot as the sorting information.

[0019] According to this solution, the control means generates a count value indicating the number of plots included in a specific range of the scatter plot as classification information, so that the amount of phytoplankton present corresponding to the specific range can be grasped using a specific numerical value.

[0020] Solution 4: The phytoplankton sorting system of this solution is a phytoplankton sorting system in any of the solutions described above, characterized in that the control means determines whether or not a specific phytoplankton corresponding to the specific range is present in an amount equal to or greater than the specified value based on a predetermined specified value and the counted value.

[0021] According to this solution, the control means determines the presence of specific phytoplankton based on specified values ​​and count values, thereby automating the phytoplankton sorting process and reducing the burden of the sorting process.

[0022] Solution 5: The phytoplankton separation system of this solution is a phytoplankton separation system in any of the solutions described above, characterized in that the light-emitting means irradiates light toward water (e.g., sedimentation treatment water from a water purification plant, raw water, filtered water, etc.) containing the object to be separated (e.g., phytoplankton).

[0023] According to this solution, the light-emitting means irradiates light toward the water containing the separation target, making it easier to detect phytoplankton. Also, by introducing this system to a water purification plant, in-line measurement can be performed by incorporating it into the water purification plant line, and phytoplankton can be detected in real time.

[0024] Solution 6: The phytoplankton separation system of this solution is a phytoplankton separation system characterized in that, in any of the solutions described above, the light-emitting means irradiates ultraviolet light or visible light (e.g., light in the ultraviolet range to the blue visible light range (e.g., 330 to 460 nm)).

[0025] According to this solution, the light emitting means irradiates ultraviolet light or visible light, making it easier to detect the intensity of autofluorescence.

[0026] Solution 7: The phytoplankton sorting system of this solution is a phytoplankton sorting system characterized in that, in any of the solutions described above, it is provided with a display means for displaying the sorting information generated by the control means.

[0027] According to this solution, since a display means for displaying the sorting information is provided, the sorting information can be quickly checked on the spot. [Effects of the Invention]

[0028] According to the present invention, phytoplankton causing damage can be easily, quickly, and in real time without requiring much manpower. [Brief explanation of the drawings]

[0029] [Figure 1] 1 is a schematic configuration diagram showing a phytoplankton separation system 500 according to an embodiment. [Figure 2] FIG. 1 is a diagram showing an example of the distribution of each phytoplankton. [Figure 3] This is a scatter plot when uroglena was detected. [Figure 4] FIG. 1 shows a scatter plot of Microcystis detection. [Figure 5] FIG. 1 shows the absorption and autofluorescence wavelengths of chlorophyll a. [Figure 6] 10 is a schematic diagram showing output signals from an autofluorescence light receiving device 90 and a scattered light receiving device 110. FIG. [Figure 7] 10 is a flowchart illustrating an example of a procedure for data analysis processing. DETAILED DESCRIPTION OF THE INVENTION

[0030] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. FIG. 1 is a schematic diagram showing a phytoplankton separation system 500 according to an embodiment. The phytoplankton separation system 500 is a system to be installed in a water purification plant, and includes a light detection system 1 and a control system 2. The light detection system 1 irradiates light onto a liquid (e.g., water from a water purification plant) and detects the scattered light and autofluorescence emitted by phytoplankton. The control system 2 detects the intensity of the scattered light and the intensities of the autofluorescence based on the output signal from the light detection system 1, and generates separation information for separating types of phytoplankton based on the intensities of the scattered light and the autofluorescence. These systems then make it possible to separate phytoplankton contained in water.

[0031] [Optical detection system] The light detection system 1 comprises a light emitting device 10, an illumination optical lens system 20, a flow device 30, a first light collecting optical lens system 40, a light blocking device 50, a scattered light selecting optical device 60, a light blocking wall 65, an autofluorescence selecting optical device 70, a second light collecting optical lens system 80, an autofluorescence receiving device 90, a third light collecting optical lens system 100, and a scattered light receiving device 110. Each component will be described in detail below.

[0032] [Light-emitting device (light-emitting means)] The light emitting device 10 includes a semiconductor laser diode (such as a semiconductor LED element). The light emitting device 10 emits light (irradiation light) toward a liquid (water containing one or more types of phytoplankton). The wavelength of the laser light emitted by the light emitting device 10 is determined according to a substance capable of emitting autofluorescence (hereinafter referred to as an autofluorescent substance) present within the cells of the biological particles. The irradiation light is preferably ultraviolet or visible light.

[0033] Autofluorescent substances have excitation wavelengths that allow them to easily absorb the energy of irradiated light and become excited into an excited state. The excitation wavelengths vary depending on the substance, and the wavelengths of the autofluorescent light emitted when returning from the excited state to the ground state also vary depending on the autofluorescent substance. The light-emitting device 10 of this embodiment irradiates excitation light using a semiconductor laser diode.

[0034] [Illumination optical lens system] The irradiation optical lens system 20 includes multiple types of optical lenses, such as a collimator lens, a biconvex lens, and a cylindrical lens, which adjust the laser light emitted from the light-emitting device 10 into parallel rays and irradiate the water 33 containing phytoplankton 35 with the laser light.

[0035] [Flow Device] The flow device 30 (flow cell) has a hollow rectangular prism-shaped cylinder 32 made of synthetic quartz, sapphire, or the like, and is structured so that water 33 containing phytoplankton 35 flows from top to bottom in the figure. Laser light 31 emitted from the light-emitting device 10 is irradiated into the hollow region of the cylinder 32 where the water flows, forming a detection region.

[0036] In this detection region, the laser light 31 interacts with the phytoplankton 35 flowing within the flow device 30. The interaction between the phytoplankton 35 and the laser light 31 causes the phytoplankton 35 to emit scattered light and autofluorescence.

[0037] The intensity of the scattered light, i.e., the amount of scattered light, depends on the size of the phytoplankton 35; the larger the size, the greater the amount of light. The intensity of the autofluorescence depends on the amount of riboflavin or chlorophyll a in the cells of the phytoplankton 35. The amount of scattered light and the intensity of the autofluorescence also depend on the amount of light (intensity) of the laser light 31; increasing the laser output and irradiating the flow device 30 with more laser light 31 also increases the scattered light and autofluorescence from the phytoplankton 35.

[0038] It is preferable to carry out inline measurement by flowing water containing the separation target (for example, water after sedimentation filtration at a water purification plant (water from a filtration basin or purified water pond), sedimentation-treated water at a water purification plant, raw water, filtered water, etc.) through the flow device 30. In this case, the light emitting device 10 irradiates light toward the water containing the separation target. Note that a water purification plant is arranged, in order from upstream, with a water intake tower, a grit basin, a receiving well, a chemical mixing basin, a flocculation basin, a sedimentation basin, a filtration basin, a purified water pond, a distribution pond, etc. In addition to flowing water from a filtration basin or purified water pond, the flow device 30 can also be used to flow water from any location in the water purification plant and carry out inline measurement.

[0039] [Shading device] The shading device 50 is equipped with a laser trap. The shading device 50 blocks the laser light 31 that passes through the flow device 30 without causing any interaction. By blocking the laser light 31, the laser light 31 that passes through the flow device 30 is prevented from being reflected at various locations, which would otherwise cause scattered light by phytoplankton 35 or become noise in the detection of autofluorescence.

[0040] [First focusing optical lens system] The first focusing optical lens system 40 includes a plurality of optical lenses. The first focusing optical lens system 40 is installed at an angle of approximately 90 degrees with respect to the traveling direction (optical axis) of the laser light 31. The first focusing optical lens system 40 focuses scattered light and autofluorescence from the phytoplankton 35 in the flow device 30. Note that a large lens diameter is preferable in order to focus as much scattered light and autofluorescence from the phytoplankton 35 as possible. The position at which the first focusing optical lens system 40 is disposed is determined according to the position (distance) at which a light-receiving device that detects scattered light and autofluorescence from the phytoplankton 35 is provided.

[0041] [Scattered Light Selective Optical Device] The scattered light selecting optical device 60 includes a dichroic mirror. The scattered light selecting optical device 60 reflects the scattered light from the phytoplankton 35. The scattered light from the phytoplankton 35 is then collected by the third collecting optical lens system 100 and imaged onto the scattered light receiving device 110.

[0042] On the other hand, almost all of the autofluorescence emitted from the phytoplankton 35 flowing within the flow device 30 is transmitted through the scattered light selection optical device 60 without being reflected by the scattered light selection optical device 60. The autofluorescence from the phytoplankton 35 then travels to the autofluorescence selection optical device 70.

[0043] [Autofluorescence selective optical device] The autofluorescence selection optical device 70 includes an optical filter, which includes a long-pass filter that transmits light having a wavelength longer than a predetermined wavelength.

[0044] [Second focusing optical lens system] The second light-collecting optical lens system 80 includes a plurality of optical lenses. The second light-collecting optical lens system 80 is installed on the traveling direction (optical axis) of the light that has passed through the long-pass filter of the autofluorescence selecting optical device 70. The autofluorescence that has passed through the long-pass filter of the autofluorescence selecting optical device 70 is collected by the second light-collecting optical lens system 80, and an image is formed on the incident surface of the autofluorescence receiving device 90.

[0045] [Autofluorescence receiving device (detection means, autofluorescence detection unit)] The autofluorescence receiving device 90 detects autofluorescence emitted by phytoplankton in response to light irradiated by the light emitting device 10. The autofluorescence receiving device 90 is equipped with a semiconductor light receiving element (photodiode (PD)) or a photomultiplier tube (PMT) with higher sensitivity than a photodiode. These photodiodes and photomultiplier tubes convert the received light into an electrical element (current or voltage) and output an electrical element corresponding to the amount of light received. The magnitude of the output electrical element varies depending on the amount of light received; the greater the amount of light received, the larger the magnitude of the electrical element. The output signal output from the autofluorescence receiving device 90 is then input to the control system 2.

[0046] [Light-blocking wall] The light-shielding wall 65 is a cylindrical structure that surrounds the optical path from the transmission side of the scattered light selecting optical device 60 to the autofluorescence receiving device 90. The light-shielding wall 65 can prevent light other than autofluorescence that has passed through the scattered light selecting optical device 60 from entering the autofluorescence receiving device 90. Although not particularly shown, a light-shielding wall may also be provided in the optical path from the reflection side of the scattered light selecting optical device 60 to the scattered light receiving device 110, etc.

[0047] [Third focusing optical lens system] The third condensing optical lens system 100 includes a plurality of optical lenses. The third condensing optical lens system 100 is disposed on the traveling direction (optical axis) of the light reflected by the scattered light selecting optical device 60.

[0048] [Scattered light receiving device (detection means, scattered light detection unit)] The scattered light receiving device 110 detects scattered light emitted by phytoplankton in response to light emitted by the light emitting device 10. The scattered light receiving device 110 is equipped with a photodiode or a photomultiplier tube. The light incident on the scattered light receiving device 110 is light reflected by the scattered light selecting optical device 60, specifically, scattered light by phytoplankton 35 flowing within the flow device 30. The scattered light by the phytoplankton 35 has a greater amount of light than the autofluorescence emitted by the phytoplankton 35, and therefore can be adequately detected with an inexpensive photodiode rather than a photomultiplier tube. In this embodiment, a photodiode is provided to receive the scattered light by the phytoplankton 35 reflected by the scattered light selecting optical device 60. The light received by the photodiode is converted into an electrical signal corresponding to the amount of light, and the electrical signal is output from the photodiode. The output signal from the photodiode of the scattered light receiving device 110 is then input to the control system 2.

[0049] [Control system (control device, control circuit, control means)] The control system 2 includes a detection signal processing unit 200, a data processing unit 300, and a notification unit 400.

[0050] The detection signal processing unit 200 receives the output signals from the light detection system 1, i.e., the output signals from the autofluorescence receiving device 90 and the scattered light receiving device 110, and performs processes such as amplifying the received signals and AD converting the analog signals to digital signals.

[0051] The data processing unit 300 receives and stores the autofluorescence signals and scattered light signals that have been AD converted by the detection signal processing unit 200, generates classification information (scatter plots, count values, etc.) based on the stored autofluorescence signals and scattered light signals, and executes processing to output the generated classification information.

[0052] The notification unit 400 executes a process of displaying the classification information etc. generated by the data processing unit 300. Each component and its process will be specifically described below.

[0053] [Detection signal processing section] The detection signal processing unit 200 comprises an autofluorescence output signal processing unit 210 and a scattered light output signal processing unit 220. The autofluorescence output signal processing unit 210 comprises a first amplifier 212 and a first analog-to-digital converter 214. The scattered light output signal processing unit 220 comprises a second amplifier 222 and a second analog-to-digital converter 224.

[0054] When the autofluorescence output signal processing device 210 receives the output signal from the autofluorescence receiving device 90, the first amplifier 212 amplifies the output signal output from the autofluorescence receiving device 90. Then, the first analog-to-digital converter 214 converts the analog signal amplified by the first amplifier 212 into a digital signal.

[0055] Similarly, when the scattered light output signal processing device 220 receives the output signal from the scattered light receiving device 110, the second amplifier 222 amplifies the output signal output from the scattered light receiving device 110. Then, the second analog / digital converter 224 converts the analog signal amplified by the second amplifier 222 into a digital signal.

[0056] Thereafter, the signals converted into digital signals are output from the autofluorescence output signal processing device 210 and the scattered light output signal processing device 220 , and the output signals are input to the data processing unit 300 .

[0057] [Data Processing Section] The data processing unit 300 includes a data collection device 310, a data analysis device 320, and a result output device 330. The data collection device 310 also includes memory (such as RAM, HD (Hard Disk Drive), SSD (Solid State Drive)) for storing data.

[0058] The data processing unit 300 receives the signals output from the autofluorescence output signal processing device 210 and the scattered light output signal processing device 220. The received signals are stored directly in the memory of the data processing unit 300. Once the signals have been stored in the memory, the signals are then used to generate classification information.

[0059] [Data analysis equipment] The data analysis device 320 includes a calculation circuit (e.g., a CPU) that generates classification information based on the data (digital signals) stored in the memory of the data processing unit 300, and a memory (ROM) that pre-stores (preserves) the calculation processing contents (programs) of the calculation circuit.

[0060] [Result output device] The result output device 330 is a device that transmits the classification information generated by the data analysis device 320 to the notification unit 400 .

[0061] [Notification unit (display means)] The notification unit 400 includes a display device (liquid crystal display) 410. The display device 410 displays the classification information generated by the data analysis device 320 on a display screen.

[0062] 2 is a diagram showing an example of the distribution of each type of phytoplankton. In this figure, the vertical axis represents the autofluorescence intensity (mV), and the horizontal axis represents the scattered light intensity (mV). The area where three algae species, Urogrena, Phormidium, and Microcystis, exist is divided into fraction ranges A to C.

[0063] Uroglena is a phytoplankton that causes taste and odor disorders. Uroglenia is distributed in fractional range A in the upper left of the figure. Fractional range A has a scattered light intensity of 50 to 125 mV and an autofluorescence intensity of 400 to 2000 mV.

[0064] Phormidium is a phytoplankton that causes taste and odor disorders. Phormidium is distributed in fraction range B at the bottom left of the figure. Fraction range B has a scattered light intensity of 50 to 125 mV and an autofluorescence intensity of 100 to 399 mV.

[0065] Microcystis is a phytoplankton that causes filtration leakage problems. Microcystis is distributed in fraction range C at the bottom right of the figure. Fraction range C has a scattered light intensity of 600 to 1780 mV and an autofluorescence intensity of 50 to 520 mV. The numerical values ​​of these fraction ranges can be changed as appropriate depending on the test conditions and the number of tests performed. The number of fraction ranges can also be increased or decreased as desired depending on the test environment.

[0066] Fig. 3 shows a scatter diagram (two-dimensional map, graph) when Uroglena was detected. Fig. 4 shows a scatter diagram when Microcystis was detected. In Figs. 3 and 4, the vertical axis represents autofluorescence intensity (mV), and the horizontal axis represents scattered light intensity (mV). Figs. 3 and 4 show measurement examples of scatter diagrams of Uroglena and Microcystis detected in treated water from a water purification plant.

[0067] The scattered light intensity indicates the size of the particles. A larger scattered light intensity value indicates a larger particle, and a smaller scattered light intensity value indicates a smaller particle. On the other hand, the autofluorescence intensity indicates the strength of the fluorescence. A larger autofluorescence intensity value indicates a stronger fluorescence, and a smaller autofluorescence intensity value indicates a weaker fluorescence.

[0068] Each plot (dot) in the scatter plot represents an individual particle that was counted. The scatter plots in Figures 3 and 4 are the results of a one-minute measurement performed with this system. Measurements were performed while the sample was flowing at a flow rate of 10 mL / min, so the plots show the particles counted in 10 mL. Particles smaller than 1.0 μm in size are not plotted in the scatter plots because they are unlikely to be phytoplankton. Phytoplankton generally range in size from about 2.0 to 20.0 μm.

[0069] As shown in Figure 3, when Uroglena is detected, many plots are displayed in fraction range A. In addition, since plots are also displayed in fraction range B (see Figure 2), which corresponds to Phormidium, and fraction range C (see Figure 2), which corresponds to Microcystis, it can be determined that Phormidium and Microcystis are also present to some extent. Furthermore, from the scatter plot in Figure 3, it can be determined that Uroglena occurs most frequently among the three types of phytoplankton.

[0070] As shown in Figure 4, when Microcystis is detected, many plots are displayed in fraction range C. In addition, since plots are also displayed in fraction range A (see Figure 2) corresponding to Uroglena and fraction range B (see Figure 2) corresponding to Phormidium, it can be determined that Uroglena and Phormidium are also present to some extent. Furthermore, from the scatter plot in Figure 4, it can be determined that Microcystis occurs most frequently among the three types of phytoplankton.

[0071] Although not specifically shown, when Phormidium is detected, many plots are displayed in fraction range B (see Figure 2). In the scatter diagram, many plots are displayed in the lower left region that does not fall into any of the fractionation ranges A to C, but these are phytoplankton or other particles that are not the target of fractionation.

[0072] FIG. 5 shows the absorption and autofluorescence wavelengths of chlorophyll a. Chlorophyll a excites energy through light absorbed during photosynthesis by phytoplankton. As shown in Figure 5(A), chlorophyll a absorbs light at a wavelength with a peak at 430 nm. As shown in Figure 5(B), chlorophyll a emits autofluorescence with a wavelength distribution with a peak at 680 nm. Therefore, by using a light source with a wavelength of 405 nm, it is possible to efficiently emit autofluorescence.

[0073] 6 is a schematic diagram showing output signals from the autofluorescence receiving device 90 and the scattered light receiving device 110. In this diagram, the vertical axis represents pulse height (voltage), and the horizontal axis represents the passage of time. For example, if the value of the autofluorescence signal exceeds the threshold for determining autofluorescence and the value of the scattered light signal (particle size) indicates 1.0 μm or more, the data analysis device 320 can determine that it is phytoplankton. Furthermore, if the value of the autofluorescence signal does not exceed the threshold for determining autofluorescence and the value of the scattered light signal (particle size) indicates 1.0 μm or more, the data analysis device 320 can determine that the particle is another type of particle.

[0074] The threshold value for determining autofluorescence can be set arbitrarily, for example, to a value between 0 and 50 mV. Furthermore, although other particles are not plotted on the scatter diagram, they may be plotted on the scatter diagram.

[0075] 7 is a flowchart showing an example of the procedure for data analysis processing. This processing is executed by the data analysis device 320. This processing is called from a predetermined main loop in the program executed by the data analysis device 320. This processing is executed repeatedly while phytoplankton is being measured using this system.

[0076] Step S100: The data analysis device 320 executes a signal reception process. Specifically, the data analysis device 320 receives the autofluorescence signal and the scattered light signal that have been AD converted by the detection signal processing unit 200.

[0077] Step S102: The data analysis device 320 executes a scatter plot generation process. The data analysis device 320 detects the intensity of scattered light and the intensity of autofluorescence based on the scattered light detected by the scattered light receiving device 110 and the autofluorescence detected by the autofluorescence receiving device 90, and executes a process (control means) to generate separation information for separating types of phytoplankton based on the intensity of scattered light and the intensity of autofluorescence. Specifically, the data analysis device 320 executes a process (control means) to generate a scatter plot in which the intensity of scattered light and the intensity of autofluorescence are plotted as parameters as the separation information. In this case, it is preferable to form plots on the scatter plot based on the determination method shown in FIG. 6.

[0078] When generating a scatter plot, one plot may be added each time a particle is detected, a fixed number of plots may be added each time a fixed number of particles are detected, or plots of particles detected within a fixed time may be added each time a fixed time has elapsed.

[0079] Step S104: The data analysis device 320 executes a count value generation process. The data analysis device 320 executes a process of generating a count value indicating the number of plots included in a specific range of the scatter plot as classification information (control means). Specifically, the data analysis device 320 counts the number of plots in fractionation range A and stores it as a first count value, counts the number of plots in fractionation range B and stores it as a second count value, and counts the number of plots in fractionation range C and stores it as a third count value.

[0080] Step S106: The data analysis device 320 executes a determination process. The data analysis device 320 executes a process (control means) to determine whether or not a specific phytoplankton (Uroglena, Phormidium, Microcystis) corresponding to a specific range is present in an amount equal to or greater than a specified value, based on predetermined specified values ​​(first specified value, second specified value, third specified value) and count values ​​(first count value, second count value, third count value).

[0081] Specifically, the data analysis device 320 executes a process of determining whether the number of plots (first count value) in the fractionation range A is equal to or greater than a predetermined first specified value. If it is determined that the number of plots in the fractionation range A is equal to or greater than the predetermined first specified value, the data analysis device 320 executes a process of setting a first warning flag to ON, and if it is determined that the number of plots in the fractionation range A is less than the predetermined first specified value, the data analysis device 320 executes a process of setting the first warning flag to OFF.

[0082] The data analysis device 320 also executes a process of determining whether the number of plots in fractionation range B (second count value) is equal to or greater than a predetermined second specified value. If it is determined that the number of plots in fractionation range B is equal to or greater than the predetermined second specified value, the data analysis device 320 executes a process of setting a second warning flag to ON, and if it is determined that the number of plots in fractionation range B is less than the predetermined second specified value, the data analysis device 320 executes a process of setting the second warning flag to OFF.

[0083] Furthermore, the data analysis device 320 executes a process of determining whether the number of plots in the fractionation range C (third count value) is equal to or greater than a predetermined third specified value. If it is determined that the number of plots in the fractionation range C is equal to or greater than the predetermined third specified value, the data analysis device 320 executes a process of setting a third warning flag to ON, and if it is determined that the number of plots in the fractionation range C is less than the predetermined third specified value, the data analysis device 320 executes a process of setting the third warning flag to OFF.

[0084] Then, the result output device 330 (see FIG. 1) transmits the various information (scatter plots as classification information, information on count values ​​as classification information, and information on each warning flag) generated by the data analysis device 320 to the notification unit 400, and the display device 410 displays the various received information on the display screen.

[0085] This allows the display device 410 to display a scatter plot and count values ​​(first count value, second count value, third count value) as classification information. In this case, the display device 410 may display rectangular frames of fraction range A, fraction range B, and fraction range C superimposed on the scatter plot.

[0086] Furthermore, the display device 410 can display a predetermined warning depending on whether any one of the first warning flag, the second warning flag, or the third warning flag is set to on. For example, when the first warning flag is on, text information saying "Beware of Uroglena" can be displayed, when the second warning flag is on, text information saying "Beware of Phormidium" can be displayed, and when the third warning flag is on, text information saying "Beware of Microcystis" can be displayed.

[0087] [Summary of this system] The phytoplankton sorting system 500 is equipped with a scattered light receiving device 110 for measuring fine particles, and an autofluorescence receiving device 90 for detecting the autofluorescence emitted by phytoplankton. In the phytoplankton sorting system 500, a laser emitted by a light emitting device 10 is directed at the target fine particles to measure the intensity of the scattered light and the intensity of the autofluorescence caused by the particles, and by plotting the intensity of the scattered light and the intensity of the autofluorescence as parameters, the type of organism (phytoplankton) can be classified based on the plotted area.

[0088] 3 and 4, by measuring the intensity of scattered light and the intensity of autofluorescence of each particle, it is possible to not only distinguish between living and non-living organisms, but also to determine the type and abundance of each living organism. The phytoplankton sorting system 500 does not require any operations such as staining, nor does it require a sample collection process, and can be incorporated directly into the water purification process, allowing for rapid water quality management without manual intervention.

[0089] [System Overview] The phytoplankton sorting system 500 has the configuration shown in FIG. 1. The light source of the light-emitting device 10 uses a semiconductor laser with a wavelength of 405 nm (violet) to excite chlorophyll a, an autofluorescent pigment found in phytoplankton. The laser light is irradiated onto a flow device 30 (flow cell) through which the sample flows. When particles in the sample flowing through the flow device 30 pass through the laser light, the particles emit scattered light with the same wavelength as the wavelength of the irradiated light. The wavelength of the laser light from the light-emitting device 10 is preferably approximately 330 to 460 nm.

[0090] If the particles passing through the laser beam are phytoplankton, chlorophyll a absorbs the laser light, emitting red autofluorescence simultaneously with scattered light (FIG. 5). These lights are then received by the scattered light receiving device 110 and the autofluorescence receiving device 90, respectively, and converted into electrical signals, which output pulse signals corresponding to the scattered light intensity and autofluorescence intensity (FIG. 6). The scattered light and autofluorescence pulse signals for each particle passing through the laser beam are plotted on a scatter diagram (two-dimensional map) based on their magnitude (intensity) (FIGS. 3 and 4). The plots on this scatter diagram show different distributions depending on the type of phytoplankton (FIG. 2). By counting the number of plots within the ranges A to C on the scatter diagram corresponding to each type of phytoplankton, the types of phytoplankton can be roughly distinguished (discriminated) and counted.

[0091] As described above, the present embodiment provides the following advantages. (1) According to this embodiment, the occurrence of harmful organisms can be detected (screened) automatically and quickly in-line without manual pretreatment such as staining, making it possible to detect the occurrence of harmful organisms without specialized knowledge or skills in biological observation. As a result, it becomes easier to consider the administration of appropriate chemicals to treat the organisms. Furthermore, this system enables 24-hour in-line monitoring. Furthermore, this system can contribute to the automation of chemical administration by linking measurement and chemical administration.

[0092] (2) According to this embodiment, it is possible to differentiate and detect each of the representative phytoplankton causing damage, thereby providing a simple, labor-free detection tool for quickly identifying biological damage.

[0093] (3) According to this embodiment, a scatter diagram is generated as the separation information, in which the intensity of scattered light and the intensity of autofluorescence are plotted as parameters. This makes the separation information easy to understand at a glance, thereby reducing the burden of the separation work.

[0094] (4) According to this embodiment, the data analysis device 320 generates a count value indicating the number of plots included in a specific range of the scatter plot as classification information, so that the amount of phytoplankton present in the specific range can be grasped using a specific numerical value.

[0095] (5) According to this embodiment, the data analysis device 320 determines the presence of specific phytoplankton based on the specified value and the count value, so that the separation of phytoplankton can be automated, thereby reducing the burden of the separation work.

[0096] (6) According to this embodiment, the light emitting device 10 emits purple light (irradiation light), which makes it easier to detect the intensity of autofluorescence.

[0097] (7) According to this embodiment, the display device 410 that displays the sorting information is provided, so that the sorting information can be quickly checked on the spot.

[0098] [Modifications] The present invention is not limited to the above-described embodiment, and can be practiced in various modified forms. (1) The wavelength of the laser light that excites phytoplankton may be a wavelength that can excite other autofluorescent pigments (phycocyanin, phycoerythrin, etc.) that phytoplankton possess. The laser light may be, for example, blue (wavelength 430 to 500 μm), green (wavelength 500 to 570 μm), or yellow (wavelength 570 to 620 μm).

[0099] (2) The autofluorescence wavelength detected by the autofluorescence receiving device 90 may be that of an autofluorescent pigment (e.g., phycocyanin or phycoerythrin) possessed by phytoplankton other than chlorophyll a. The laser light may be, for example, green (wavelength 500 to 570 μm) or yellow (wavelength 570 to 620 μm).

[0100] (3) Although the example has been described in which each particle that has passed through the laser beam is plotted on a scatter diagram (two-dimensional map) based on the magnitude of the scattered light and autofluorescence pulse signal, count values ​​within a specific range (fraction range) may be output directly based on the magnitude of the scattered light and autofluorescence pulse signal without displaying a scatter diagram. That is, as the classification information, only a scatter diagram may be generated, only count values ​​may be generated, or both a scatter diagram and count values ​​may be generated.

[0101] (4) Multiple autofluorescence wavelengths may be detected. In this case, a three-dimensional scatter plot (scattered light intensity, first autofluorescence intensity, second autofluorescence intensity) may be used to distinguish the genus or species of phytoplankton. For example, the first autofluorescence intensity may be detected by autofluorescence based on chlorophyll a, and the second autofluorescence intensity may be detected by autofluorescence based on phycocyanin. In this case, one light-emitting device 10 may be installed, and multiple scattered light receiving devices 110 and autofluorescence receiving devices 90 may be installed according to the type of autofluorescence.

[0102] (5) For the distribution of plots corresponding to each particle in the scatter diagram, machine learning such as AI (artificial intelligence) may be used to derive specific characteristics due to differences in genus or species of phytoplankton. For example, each plot may be given coordinate information on the vertical and horizontal axes and stored, and information on which fractional region the coordinate information belongs to may be stored.

[0103] (6) Other water quality indicators (nutrient concentration (nitrogen, phosphorus, etc.), water temperature, hours of sunlight, pH, alkalinity, residual chlorine concentration, etc.) may be used as reference information for determining the genus and species of phytoplankton. For example, taking Uroglena as an example, if the water temperature is such that Uroglena is not likely to grow, even if Uroglena is detected, it can be prevented from being classified as Uroglena (not plotted on a scatter diagram). This can improve the accuracy of phytoplankton classification.

[0104] (7) The sorting information generated by the data analysis device 320 has been described as being displayed on the display device 410 provided in this system, but it may also be sent as data to an information processing terminal connectable to this system and displayed there, or it may be output to paper or the like without being displayed. (8) It is not necessary to incorporate all of the processes in steps S102, S104, and S106 in FIG. 7 into the present system, and it is also possible to incorporate the process relating to at least one of the steps. (9) Although the phytoplankton separation system 500 has been described as being installed in a water purification plant, it may be installed in other locations (such as a sewage treatment plant or a drinking water production facility). The liquid to be inspected by the phytoplankton separation system 500 may be a culture solution. [Explanation of symbols]

[0105] 1. Optical detection system 2. Control System 10 Light-emitting device 20. Illumination optical lens system 30 Flow Device 31 Laser Light 32 Cylinder part 33 water 35 Phytoplankton 40 First focusing optical lens system 50 Shading device 60 Scattered light selective optical device 65 Blackout Wall 70 Autofluorescence Selective Optical Device 80 Second focusing optical lens system 90 Autofluorescence receiver 100 third focusing optical lens system 110 Scattered light receiver 200 detection signal processing section 210 Autofluorescence Output Signal Processing Device 220 Scattered Light Output Signal Processing Device 300 Data Processing Unit 310 Data Collection Equipment 320 Data Analysis Equipment 330 Result Output Device 400 Information Department 410 Display device 500 Phytoplankton Sorting System A. Urogrena fraction range B. Phormidium fraction range C. Microcystis fractionation range

Claims

1. A light emitting means for irradiating light toward the liquid; a detection means for detecting scattered light and autofluorescence emitted by phytoplankton in response to light irradiated by the light emitting means; a control means for detecting the intensity of the scattered light and the intensity of the autofluorescence based on the scattered light and the autofluorescence detected by the detection means, and for generating classification information for classifying the types of phytoplankton based on the intensity of the scattered light and the intensity of the autofluorescence; a display means for displaying the sorting information generated by the control means, the control means generates, as the separation information, a scatter diagram in which the intensity of the scattered light and the intensity of the autofluorescence are plotted as parameters; the control means generates, as the classification information, a count value indicating the number of plots included in a specific range of the scatter diagram; The control means determines whether or not a specific phytoplankton corresponding to the specific range is present in an amount equal to or greater than the specified value based on a predetermined specified value and the count value; The display means displays a predetermined warning when the control means determines that a specific phytoplankton corresponding to the specific range is present at a value equal to or greater than the specified value.

2. 2. The phytoplankton fractionation system according to claim 1, The phytoplankton sorting system is characterized in that the display means displays a frame of the specific range superimposed on the scatter diagram.

3. 3. The phytoplankton sorting system according to claim 1, The phytoplankton sorting system is characterized in that the display means does not plot particles having a particle size of less than 1.0 μm in the scatter diagram.

4. 4. The phytoplankton sorting system according to claim 1, The phytoplankton sorting system is characterized in that the specific phytoplankton includes phytoplankton that causes unpleasant odors and tastes.

5. 5. The phytoplankton sorting system according to claim 1, The phytoplankton separation system is characterized in that the light emitting means irradiates light toward water containing the separation target.

6. 6. The phytoplankton sorting system according to claim 1, The phytoplankton sorting system is characterized in that the light emitting means irradiates ultraviolet light or visible light.

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