Microparticle measuring method, microparticle measuring device, and microparticle measuring system

Abstract

To provide a microparticle measurement method, a microparticle measurement device and a microparticle measurement system with which, of a simple structure though, it is possible to conduct quantitative measurement quickly.SOLUTION: The microparticle measurement method of an embodiment is a method for measuring microparticles that is executed in a microparticle measurement device comprising a light source for emitting illumination light to a liquid that contains the microparticle to be measured, an objective lens for condensing illumination light, an image-forming lens for forming the image of condensed illumination light, and a sensor for detecting illumination light formed into an image. The microparticle measurement method includes the steps of: measuring the distance from a position where the transmitted light intensity of the microparticle being measured becomes largest to the objective lens; and calculating the refractive index of the microparticle being measured, on the basis of the particle size of the microparticle and the measured distance.SELECTED DRAWING: Figure 5

Description

[Technical Field]

[0001] An embodiment of the present invention relates to a microparticle measuring method, a microparticle measuring device, and a microparticle measuring system. [Background technology]

[0002] Conventionally, a method has been proposed for measuring sludge concentration and sludge particle size by irradiating measurement light into sludge and observing the optical response to the light. A measuring device using this method includes a light source unit that irradiates measuring light, a measuring unit that includes a cell, and a detecting unit that includes a sensor that receives the measuring light.

[0003] In sludge concentration detection, the intensity of light transmitted relative to the light incident on the measurement unit is measured in the detection unit based on the Lambert-Beer law, and the concentration can be determined from the signal level obtained using a pre-measured calibration curve.

[0004] However, when measuring concentration using this method, it is necessary to create a calibration curve in advance, and because the Beer-Lambert law looks at changes in the intensity of incident and transmitted light, there is a problem in that the concentration that can be measured is limited by the sensitivity of the sensor. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Laid-Open No. 2004-317350 Summary of the Invention [Problem to be solved by the invention]

[0006] In organic wastewater treatment, various useful microorganisms are used to decompose organic matter in the wastewater and remove nitrogen and phosphorus, and in actual operation, the overall amount of microorganisms is controlled by measuring the treatment status based on sludge concentration and water quality.

[0007] Therefore, if there were a method to detect only the concentration of beneficial microorganisms, it would be possible to stabilize control and thereby improve treatment performance. However, conventional methods have had problems such as the need for expensive measuring devices, the time required for detection, and the inability to quantitatively measure minute changes in phenomena, making them impractical.

[0008] The present invention has been made in view of the above, and aims to provide a microparticle measuring method, a microparticle measuring device, and a microparticle measuring system that are simple in configuration and capable of rapid quantitative measurement. [Means for solving the problem]

[0009] The microparticle measurement method of the embodiment is a microparticle measurement method executed by a microparticle measurement device equipped with a light source that emits illumination light onto a liquid containing the microparticles to be measured, an objective lens that focuses the illumination light, an imaging lens that forms an image of the focused illumination light, and a sensor that detects the imaged illumination light, and includes the steps of measuring the distance from the position where the transmitted light intensity of the microparticles to be measured is at its maximum to the objective lens, and calculating the refractive index of the microparticles to be measured based on the particle diameter of the microparticles and the measured distance. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is a schematic diagram of a microparticle measuring device according to an embodiment. [Figure 2] FIG. 2 is an explanatory diagram of parameters in a ray tracing matrix. [Figure 3] FIG. 3 is a diagram illustrating the relationship between the difference in the actual position of the objective lens relative to the distance z between the spores (microparticles) of Bacillus strains and the objective lens when the transmitted light intensity is at its maximum, and the relative transmitted light intensity. [Figure 4]FIG. 4 is a diagram illustrating the relationship between the difference in the actual position of the objective lens relative to the distance z between an acrylic particle (microparticle) and the objective lens when the transmitted light intensity is at its maximum for an acrylic particle with a particle diameter of 30 μm, and the relative transmitted light intensity. [Figure 5] FIG. 5 is a process flowchart of the microparticle number measurement process according to the embodiment. [Figure 6] FIG. 6 is a process flowchart of a process for measuring the number of spores of Bacillus strains as microparticles. [Figure 7] FIG. 7 is a diagram illustrating the principle of this modified example. [Figure 8] FIG. 8 is a flowchart of the organic wastewater treatment. [Figure 9] Figure 9 is a flowchart of the machine learning process. [Figure 10] FIG. 10 is a diagram illustrating an example of a calibration curve used in machine learning. [Figure 11] FIG. 11 is an explanatory diagram of the results of image processing of a microscope image. DETAILED DESCRIPTION OF THE INVENTION

[0011] FIG. 1 is a schematic diagram of a microparticle measuring device according to an embodiment. The microparticle measuring device 10 includes a light source 11 that emits illumination light L, a stage 13 that supports a slide glass (preparation) 12 that holds a measurement sample SP, a stage drive unit 14 that drives the stage 13 in the vertical direction in Figure 1 along the optical axis, a laser displacement meter 15 that functions as a distance measurement unit that detects the position of the slide glass 12, an objective lens 16 that collects the illumination light L to form parallel light, an imaging lens 17 that collects the parallel light illumination light L to form an image, an image sensor 18 that captures the image formed by the imaging lens 17, and a measurement control unit 19 that functions as a measurement processing unit and controls the measurement processing and the entire microparticle measuring device 10. In the above configuration, the light source 11, the objective lens 16, and the imaging lens 17 constitute an optical system.

[0012] [1] Measurement principle First, the measurement principle of microparticles will be explained. [1.1] Refractive index and particle size When illumination light is irradiated from the rear side of a microparticle in a liquid, the illumination light is focused at a position corresponding to the particle diameter and refractive index of the microparticle due to the lens effect of the microparticle. Here, the closer to the light-focusing position, the higher the transmitted light intensity becomes, and the transmitted light intensity reaches a maximum at the light-focusing position. As the light moves away from the light-focusing position, the transmitted light intensity decreases again.

[0013] That is, the position where the transmitted light intensity is at its maximum is the light-condensing position. At this time, the focusing position can be identified by measuring the distance between the objective lens and the position where the transmitted light intensity is at its maximum. In this case, the optical path of the illumination light can be expressed by the following equation. Therefore, if the particle diameter of the microparticle is known in addition to the distance between the objective lens and the position where the transmitted light intensity is maximum, the refractive index of the microparticle can be determined by solving the equation expressed by the ray tracing matrix below.

[0014]

number

[0015] FIG. 2 is an explanatory diagram of parameters in a ray tracing matrix. In the above ray tracing matrix, the radius of the microparticle PC is r, the refractive index of the microparticle is n, the distance between the microparticle and the objective lens 16 when the transmitted light intensity of the illumination light L for the target microparticle is maximum is z, the distance from the optical axis when the illumination light L is incident on the microparticle is x0, the incident angle when the illumination light L is incident on the microparticle is u0, the distance from the optical axis when the illumination light L is incident on the image sensor 18 is x1, and the incident angle of the illumination light L when it is incident on the image sensor 18 is u1.

[0016] Furthermore, the distance between the objective lens 16 and the imaging lens 17 is l1, the distance between the imaging lens 17 and the image sensor 18 is l2, the focal length of the objective lens is f1, and the focal length of the imaging lens 17 is f2. Similarly, if the refractive index of the microparticle is known, the particle diameter of the microparticle can be determined by solving the equation expressed by the ray tracing matrix.

[0017] [1.2] Detection, counting, and concentration measurement of beneficial microorganisms Beneficial microorganisms used in organic wastewater treatment can be considered as microparticles under certain conditions. In this case, the condition is, for example, when the useful microorganism forms spores. When the useful microorganism forms spores, the shape and the like do not change, and the shape is also almost constant due to the useful microorganism.

[0018] Since spores of beneficial microorganisms have a specific size (corresponding to particle diameter) and a specific refractive index, by treating them in the same way as microparticles, it is possible to detect such beneficial microorganisms and measure their number per observation field (and therefore their concentration). In this case, when measuring the concentration, the observation position (image capturing position) is scanned along the optical axis direction, and the number of useful microorganisms in the volume corresponding to the observation field of view x scanning distance is measured, thereby making it possible to measure the concentration.

[0019] Incidentally, for spores of Bacillus strains in sludge with a known refractive index and a particle size of 1 μm or less, it was found that the position corresponding to the distance z at which the transmitted light intensity is at its maximum is located within the depth of field (effective focal position) corresponding to the focal length f1 during image acquisition. Therefore, it was found that, based on a preset transmitted light intensity threshold, portions having a light intensity equal to or greater than a threshold can be regarded as spores of Bacillus strains.

[0020] In this case, the intensity of light transmitted through the liquid containing spores of the Bacillus strain is greater than the intensity of light transmitted through the liquid not containing spores of the Bacillus strain. Therefore, by setting the threshold value of the transmitted light intensity for determining whether or not a liquid contains spores of Bacillus strains to a value slightly higher than the transmitted light intensity in a liquid that does not contain spores, spores of Bacillus strains can be reliably detected.

[0021] Furthermore, by using the set threshold value, the sample is continuously moved in the optical axis direction while sequentially capturing images, and by combining this with machine learning that uses the transmitted light intensity at each location (each pixel) obtained from the captured images and the size (particle diameter) of Bacillus spores as microparticles as judgment criteria, it is possible to detect and count Bacillus spores, and ultimately to measure the concentration of Bacillus spores with high accuracy.

[0022] In this case, machine learning involves preparing multiple samples with different concentrations of microparticles in advance, and performing supervised learning so that the manual detection results of an examiner for each sample are equal to the detection results obtained by machine learning, thereby obtaining detection or number measurement results of microparticles that correspond to the particle diameter and refractive index of the microparticles being learned.

[0023] [2] First embodiment Next, a first embodiment will be described. FIG. 3 is a diagram illustrating the relationship between the difference in the actual position of the objective lens relative to the distance z between the spores (microparticles) of Bacillus strains and the objective lens when the transmitted light intensity is at its maximum, and the relative transmitted light intensity. FIG. 4 is a diagram illustrating the relationship between the difference in the actual position of the objective lens relative to the distance z between the acrylic particle (microparticle) and the objective lens when the transmitted light intensity is at its maximum for an acrylic particle with a particle diameter of 30 μm, and the relative transmitted light intensity.

[0024] First, images of the spores of the Bacillus strain and the acrylic particles were acquired at the focal position by the image sensor 18. Then, the stage 13 was driven vertically along the optical axis direction by the stage driving unit 14, and the position difference Δz between the position of the objective lens 16 when the relative transmitted light intensity of each microparticle was at its maximum on the image sensor 18 and the actual position of the objective lens 16 was measured by the laser displacement meter 15.

[0025] Figure 3(A) shows an image captured when the relative transmitted light intensity is at its maximum in a liquid containing Bacillus spores. As shown in Figure 3(A), the relative transmitted light intensity is at its maximum at the center of the captured area. As shown in FIG. 3(B), it was calculated that in a liquid containing spores of Bacillus strains, the relative transmitted light intensity was maximized when the position difference Δz was 0 μm. In contrast, as shown in Figure 4(B), in the case of a liquid containing acrylic particles with a particle diameter of 30 μm, the relative transmitted light intensity is negative at a position difference of Δ0 μm, where the relative transmitted light intensity is maximum for Bacillus spores. In other words, this is an image captured when the transmitted light intensity is lower than the background light intensity. As shown in Figure 4(A), it can be seen that the relative transmitted light intensity is minimum around the acrylic particles. As shown in FIG. 4(B), it was calculated that in a liquid containing acrylic particles with a particle diameter of 30 μm, the relative transmitted light intensity was maximum outside the position difference Δz=±15 μm.

[0026] Figure 4(C) shows an image captured when the relative transmitted light intensity is at its maximum in a liquid containing acrylic particles with a particle diameter of 30 μm. As shown in Figure 4(C), the relative transmitted light intensity is at its maximum at the center of the captured area. As shown in FIG. 4(D), it was calculated that in a liquid containing acrylic particles with a particle diameter of 30 μm, the relative transmitted light intensity was maximized at a position difference Δz of 26 μm.

[0027] Based on these measurement results, the position difference Δz corresponding to the difference between the distance z from the Bacillus spores and 30 μm acrylic particles (microparticles with a particle diameter of 30 μm) to the objective lens 16 and the focal length of the objective lens was calculated using the ray tracing matrix described above for the Bacillus spores and 30 μm acrylic particles, which are microparticles at which the transmitted light intensity is maximized. The position difference Δz was Δz = 0.9 μm in the liquid containing the Bacillus spores and Δz = 22.5 μm in the liquid containing the 30 μm acrylic particles, which was found to be nearly consistent with the measurement results using the laser displacement meter. The distance between the objective lens 16 and the imaging lens 17 at this time was l1 = 130 mm, the distance between the imaging lens 17 and the image sensor 18 was l2 = 164.5 mm, the focal lengths of the objective lens f1 = 4.1125 mm, and the focal length of the imaging lens 17 = 164.5 mm. The spores of the Bacillus strain were set to r=1 μm and n=1.4, and the acrylic particles were set to n=1.5.

[0028] In particular, it was found that the position difference Δz = 0.9 μm in a liquid containing Bacillus spores is effectively equal to the focal length of the objective lens (within the depth of field), and the relative transmitted light intensity is maximum at the focal position. From this, it was found that in measuring spores of Bacillus strains, it is possible to detect spores of Bacillus strains by measuring the transmitted light intensity (in the embodiment, the relative transmitted light intensity) at the focal length.

[0029] That is, in a sample solution containing spores of a Bacillus strain, if the transmitted light intensity at the distance z (= the focal length of the objective lens) between the spores of the Bacillus strain and the objective lens when the transmitted light intensity is at its maximum exceeds a predetermined threshold value, the particle size of the microparticles is within the particle size range of the spores of the specified Bacillus strain, and the refractive index of the microparticles is within the refractive index range of the spores of the specified Bacillus strain, then it can be easily determined that the spores of the Bacillus strain have been detected.

[0030] Similarly, in a sample solution containing acrylic particles with a particle diameter of 30 μm, if the transmitted light intensity at the distance z + Δz between the acrylic particles and the objective lens (Δz = 26 μm in the above example) when the transmitted light intensity is at its maximum exceeds a predetermined threshold, and the particle diameter of the microparticle is within the particle diameter range of acrylic particles with a predetermined particle diameter of 30 μm, and the refractive index of the microparticle is within the refractive index range of acrylic particles with a predetermined particle diameter of 30 μm, it can be easily determined that an acrylic particle with a particle diameter of 30 μm has been detected.

[0031] By utilizing these, in a sample solution containing Bacillus spores or acrylic particles with a particle diameter of 30 μm, the focal position of the objective lens is gradually shifted along the optical axis and the (relative) light transmission intensity is detected, thereby making it possible to identify the positions of Bacillus spores or acrylic particles scattered in the sample solution within the detection field of view and count their number.Furthermore, by determining the total movement distance along the optical axis, the concentration of Bacillus spores or acrylic particles in a specified volume can be calculated.

[0032] Next, a general measurement process for microparticles will be described. FIG. 5 is a process flowchart of the microparticle number measurement process according to the embodiment. First, the optical system is used to measure the distance between the position of maximum transmitted light intensity of the microparticle and the objective lens 16 with the laser displacement meter 15 (step S11). Subsequently, an image of the microparticle is acquired by the image sensor 18, and the particle diameter of the microparticle is calculated by image recognition (step S12). Next, the ray tracing matrix is ​​solved using optical system information, such as the distance l1 between the objective lens 16 and the imaging lens 17, the distance l2 between the imaging lens and the image sensor, the focal length f1 of the objective lens 16, and the focal length f2 of the imaging lens 17, to calculate the refractive index of the microparticles (step S13). Furthermore, based on the image of the microparticles acquired in step S and the calculated refractive index of the microparticles, microparticles having the same refractive index as the microparticles to be measured are identified in the image of the microparticles, and the number of microparticles is counted by image recognition (step S14). More specifically, the number of microparticles to be measured is measured by counting the number of microparticles having the same refractive index as the microparticle to be measured from an image of one or more microparticles contained in the image.

[0033] Next, as a more specific measurement process, the measurement process of spores of Bacillus strains will be described. FIG. 6 is a process flowchart of a process for measuring the number of spores of Bacillus strains as microparticles.

[0034] In the initial state, the focal length of the optical system to be used is adjusted to a predetermined initial value. First, an optical system is used to obtain a transmission image of a spore solution of a Bacillus strain with an image sensor (step S21).

[0035] Next, it is determined whether or not the intensity of light transmitted through the spores of the Bacillus strain exceeds the background light intensity (step S22). That is, it is determined whether the relative transmitted light intensity [=(transmitted light intensity-background light intensity) / transmitted light intensity] has a positive value.

[0036] In this case, whether or not the microparticles contained in the captured image are spores of Bacillus strains is determined by measuring the particle diameter of each microparticle in the captured image, determining whether the particle diameter falls within a predetermined particle diameter range for spores of Bacillus strains, and determining whether or not the refractive index of the microparticles obtained by solving the above-mentioned ray tracing matrix expressed by the following equation based on the particle diameters of the measured microparticles falls within the predetermined refractive index range for spores of Bacillus strains.

[0037]

number

[0038] If it is determined in step S22 that the transmitted light intensity of the Bacillus spores exceeds the background light intensity (step S22; Yes), the discrimination threshold for the transmitted light intensity is determined to be a value between the transmitted light intensity of the Bacillus spores and the background light intensity (step S23). For example, the relative transmitted light intensity [=(transmitted light intensity-background light intensity) / transmitted light intensity] is set to 0.02.

[0039] Subsequently, based on the determined discrimination threshold value of the transmitted light intensity, a binarized image of the transmitted light image of the spore solution of the Bacillus strain obtained in step S21 is generated (step S24). In the generated binary image, for example, the transmitted light portion of the spore of a Bacillus strain is displayed in white ("1"), and the background light portion is displayed in black ("0"). Therefore, in this case, the white areas surrounded by black are the areas where spores of Bacillus strains exist, and the number of spores can be measured by counting the number of white areas surrounded by black in the binarized image (step S25). On the other hand, if it is determined in step S22 that the transmitted light intensity of the Bacillus spores is equal to or less than the background light intensity (step S22; No), the measurement control unit 19 controls the stage driving unit 14 to adjust the focal position of the objective lens 16 (step S26), and then returns to step S21 to acquire a transmitted image of the spore solution again (step S21).Then, the process from step S22 is repeated in the same manner until the spore count is completed. As described above, according to this embodiment, if a transmission image of a spore solution that allows a discrimination threshold to be determined can be acquired, the number of spores can be easily measured.

[0040] [2.1] Modification of the first embodiment The above explanation was for simply measuring the number of spores of Bacillus strains, but this modified example is for measuring the concentration of spores of Bacillus strains.

[0041] FIG. 7 is a diagram illustrating the principle of this modified example. When the stage 13 is scanned along the optical axis direction by the stage drive unit 14 to acquire multiple transmitted light images (or videos), the transmitted light intensity at the spores SP1 and SP2 of the Bacillus strain located at positions P1 and P2 in the scanning direction will be maximum when they are exactly located at the focal position of the objective lens.

[0042] Therefore, if the microparticle located at the position where the transmitted light intensity is greatest while scanning along the optical axis can be identified as a spore of a Bacillus strain using the above-mentioned method, it will be clear that two spores SP1 and SP2 are present within the cubic volume defined by the field of view FV and scanning distance SCL of the image sensor 18.

[0043] In this case, the area of ​​the field of view FV is AR (μm 2 ), then the volume V of a cube specified by the field of view FV and the scanning distance SCL (μm) is V = AR × SCL (μm 3 ) and the spore concentration is expressed as 2 / V.

[0044] Similarly, if there are N spores in a cube, the concentration of spores = N / V. Therefore, according to this modified example, the spore concentration, and therefore the microparticle concentration, can be easily calculated.

[0045] [3] Second embodiment Next, a second embodiment will be described in which the microparticle measuring device of the embodiment is applied to organic wastewater treatment.

[0046] FIG. 8 is a flowchart of the organic wastewater treatment. First, a sample solution is obtained from wastewater to be treated in organic wastewater treatment (step S31). Next, the sample solution is heated under predetermined conditions or pretreated with a chemical under predetermined conditions to sporulate vegetative cells of the Bacillus bacteria contained in the sample solution (step S32).

[0047] Next, the stage is driven by the stage driving unit 14 to effectively scan the focal position of the objective lens along the optical axis direction, and a plurality of transmitted light images are acquired according to the operating state (step S33).

[0048] Then, the particle diameter of the microparticles contained in the transmitted light image and the change in the transmitted light intensity are detected, and the spores are detected within the scanning range, assuming that the spores are contained at the position where the relative transmitted light intensity is maximum (step S34).

[0049] In this case, whether or not the microparticles are spores is determined based on whether the transmitted light intensity at the distance z (= objective lens focal length) between the Bacillus strain spores and the objective lens when the transmitted light intensity is at its maximum exceeds a predetermined threshold value, and whether or not the particle size of the microparticles is within the particle size range of the specified Bacillus strain spores.

[0050] As a result, the number of spores of Bacillus strains in a volume equal to the observation field of view x the operation distance can be counted using the machine learning model obtained as a result of the machine learning, and the spore concentration per unit volume is measured (step S35).

[0051] Here, we will discuss machine learning techniques. Figure 9 is a flowchart of the machine learning process. In the machine learning, first, transmitted light images of spores of Bacillus strains and sludge are acquired (step S41).

[0052] Next, based on the acquired transmitted light image, machine learning is performed using correct and incorrect instructions (step S42), and the machine learning results are stored in the measurement control unit 19, which then becomes able to automatically detect Bacillus strains in the transmitted light image.

[0053] FIG. 10 is a diagram illustrating an example of a calibration curve used in machine learning. In creating this calibration curve, the detected concentration in the device of the embodiment was determined for an adjusted concentration obtained by mixing sludge and a spore solution at a predetermined ratio.

[0054] In this example, the adjusted concentration is 5×10 3 [cells / mL], 1 × 10 4 [cells / mL], 5 × 10 4 [cells / mL], 1 × 10 5 [cells / mL], 5 × 10 5 [cells / mL], 1 × 10 6 [cells / mL], 5 × 10 6 [cells / mL], 1 × 10 7 [cells / mL], 5 × 10 7 The nine levels were [cells / mL].

[0055] The actual concentration adjustment is 5 x 10 7 The above-mentioned concentration is achieved by adjusting the solution to [cells / mL] and diluting it with sludge. The spore concentration was measured by dropping the spore solution onto a hemocytometer and counting the number of spores.

[0056] More specifically, an image of the hemocytometer was captured, and the field of view of the image obtained was 422 μm × 353 μm. In this case, since the depth of the hemocytometer was 0.1 mm, the spore concentration when one spore was contained in the field of view was 6.713 × 10 4 It can be calculated as [cells / mL]. The standard curve used for machine learning is 5 × 10 5 For the region below [cells / mL], as shown by the dashed line in Figure 10, the spore concentration is 5 × 10 5 [cells / mL] ~5 × 10 7 The calibration curve in the range of [cells / mL] is considered to be a straight line, and an extrapolated straight line is used. As a result, by using this as a calibration curve for the detected concentration by machine learning, it is possible to calculate the concentration of a spore solution of unknown concentration.

[0057] Returning to FIG. 8, it is then determined whether the measured concentration (concentration measurement result) is equal to or greater than the dominant concentration (step S36). Here, the term "dominant concentration" refers to a state in which the amount is particularly large in a biological community, and the concentration represents and determines the characteristics of the community.

[0058] That is, in this embodiment, the individual amount of spores of the Bacillus strain is large, and the concentration is such that the characteristics of the Bacillus strain are significantly exhibited.

[0059] In the determination in step S36, if the measured concentration (concentration measurement result) is less than the dominant concentration (step S36; No), the Bacillus strain is not dominant in the wastewater to be treated, and therefore an instruction is given to add a Bacillus strain to the wastewater to be treated in order to quickly perform organic wastewater treatment of the wastewater using a Bacillus strain. In response, the operator adds a Bacillus strain to the wastewater to be treated (step S37).

[0060] Next, the process returns to step S31, where a sample solution is obtained from the wastewater to be treated in the organic wastewater treatment (step S31).

[0061] Thereafter, the processes of steps S31 to S37 are repeated in the same manner until the concentration of the Bacillus strain contained in the wastewater to be treated exceeds the dominant concentration.

[0062] On the other hand, if it is determined in step S36 that the measured concentration (concentration measurement result) is equal to or greater than the dominant concentration (step S36; Yes), it is determined that the Bacillus strain is working effectively in the organic wastewater treatment and that the wastewater can be treated, and the treatment is terminated. As described above, according to the second embodiment, in organic wastewater treatment, the wastewater to be treated can be rapidly shifted to a state in which Bacillus strains are dominant, and wastewater treatment can be carried out quickly and reliably.

[0063] [4] Third embodiment Next, a third embodiment will be described. FIG. 11 is an explanatory diagram of the results of image processing of a microscope image. FIG. 11A is an explanatory diagram of a binarized image after image processing. FIG. 11(B) is a diagram showing the relationship between the relative transmitted light intensity and the threshold value for an area AR1 containing one spore.

[0064] When the focal position of the objective lens is scanned in the optical axis direction, as shown in Figure 11(B), if the relative transmitted light intensity = 0.02 is set as the discrimination threshold Ith, the relative transmitted light intensity increases sharply at the position where spores are present (position = 0 in the figure) and easily exceeds the discrimination threshold Ith. Therefore, since it is estimated that spores exist in this region, the value of the corresponding region is set to 1 and the other regions are set to 0 for binarization.

[0065] As a result, for example, the locations of spores can be indicated by black dots in the field of view, as shown in Figure 11(A). In an actual device, the black dots can be displayed in red, for example, to make them easier for the observer to recognize.

[0066] [5] Modifications of the embodiment In the above explanation, we have described a case where concentration is measured using a calibration curve obtained from a sample whose concentration has been adjusted in advance, but it is also possible to configure the system to measure concentration using the results of measuring the bacterial count concentration using conventional methods such as the microcolony method or sequencing method.

[0067] The above explanation is for the case where the microparticle measuring device is configured as a stand-alone device, but it is also possible to transfer a transmitted light image acquired by an image sensor (imaging device) on the local terminal side to a cloud server via a communication interface and a communication network, and have the cloud server identify the microparticles to be measured (e.g., spores of Bacillus strains) contained in the transmitted light image, calculate the number of microparticles (e.g., number of spores of Bacillus strains) and the microparticle concentration (e.g., spore concentration of Bacillus strains), and notify the local terminal side via the communication network.

[0068] In the above configuration, when identifying the microparticles to be measured, the cloud server identifies the detected microparticles as the microparticles to be measured if the refractive index of the detected microparticles falls within a predetermined refractive index range and the particle size of the microparticles to be measured falls within a predetermined particle size range.

[0069] Furthermore, when setting measurement standards on a cloud server, machine learning can be configured to automatically set the discrimination threshold Ith by performing supervised learning using transmitted light images and the results of manual identification of corresponding microparticles.

[0070] As described above, according to each embodiment, a transmitted light image of a solution containing the microparticles to be measured is acquired, the particle size of the microparticles is measured, and the refractive index of the microparticles is calculated based on a ray tracing matrix, thereby easily identifying the microparticles to be measured in the solution and quickly measuring the number or concentration of the microparticles.

[0071] In particular, it is possible to obtain a transmitted light image of the sludge to be treated in organic wastewater treatment as a solution, and automatically measure the spore concentration of Bacillus strains based on the learning results of machine learning. Compared to conventional detection methods such as the μ-colony method and the sequence method, this makes it possible to measure the concentration in a short time with an inexpensive device configuration.

[0072] As a result, it becomes possible to easily and continuously detect changes in the spore concentration of Bacillus strains in a short period of time, and the treatment performance of organic wastewater treatment can be easily improved.

[0073] The microparticle measuring device (measurement processing unit) of this embodiment is equipped with a control device such as a CPU, a storage device such as a ROM (Read Only Memory) or RAM, an external storage device such as a HDD or CD drive, a display device such as a display device, and input devices such as a keyboard and a mouse, and has a hardware configuration that utilizes a normal computer. The program executed by the microparticle measuring device (measurement control unit) of this embodiment is provided as a file in an installable or executable format, recorded on a computer-readable recording medium such as a semiconductor storage device such as a DVD (Digital Versatile Disk), USB memory, or SSD (Solid State Drive). The program executed by the microparticle measuring device (measurement processing unit) of this embodiment may be stored on a computer connected to a network such as the Internet and provided by being downloaded via the network. The program executed by the device of this embodiment may also be provided or distributed via a network such as the Internet. Furthermore, the program for the microparticle measuring device (measurement processing unit) of this embodiment may be provided in a state that it is pre-installed in a ROM or the like. [Explanation of symbols]

[0074] 10 Microparticle measurement device 11 Light source 12 glass slides 13 Stages 14 Stage drive unit 15 Laser displacement meter 16 Objective Lenses 17 Imaging lens 18 Image Sensor 19 Measurement control section AR1 area P1, P2 position SP1 spores f1 objective lens focal length f2 focal length of imaging lens l1 Distance between objective lens and imaging lens l2 Distance between the imaging lens and the image sensor u0 Incident angle x0 position FV field of view Ith discrimination threshold L illumination light PC microparticles SCL scanning distance SP measurement sample V volume r radius n refractive index z distance Δz position difference

Claims

1. A microparticle measurement method executed by a microparticle measurement device including a light source that emits illumination light to a liquid containing microparticles to be measured, an objective lens that condenses the illumination light, an imaging lens that forms an image of the condensed illumination light, and a sensor that detects the imaged illumination light, measuring the distance from a position where the transmitted light intensity of the microparticle to be measured is maximum to the objective lens; calculating a refractive index of the microparticle to be measured based on the particle diameter of the microparticle and the measured distance; A method for measuring microparticles comprising:

2. the sensor is an image sensor, A step of calculating the particle diameter based on an image including the microparticles captured by the sensor, The method for measuring microparticles according to claim 1 .

3. Calculating the distance between the microparticle and the objective lens when the transmitted light intensity on the sensor is at its maximum; calculating a refractive index of the observed particle by a predetermined ray tracing matrix using the distance between the objective lens and the imaging lens, the distance between the imaging lens and the sensor, the focal length of the objective lens, the focal length of the imaging lens, and the position and incident angle at which the illumination light is incident on the microparticle of radius r; The method for measuring microparticles according to claim 1 or 2, comprising:

4. the microparticles are spores of a Bacillus strain in sludge, having a known refractive index and a particle size of 1 μm or less; The step of estimating that the spores exist in an area where the transmitted light intensity at the focal length of the objective lens is equal to or greater than a predetermined transmitted light intensity threshold. The method for measuring microparticles according to any one of claims 1 to 3.

5. The transmitted light intensity in the liquid not containing spores is set as the transmitted light intensity threshold. The method for measuring microparticles according to claim 4.

6. performing machine learning in advance on the relationship between the transmitted light intensity and a transmitted light image; acquiring the transmitted light image; detecting and counting the spores in the transmitted light image by the machine learning method; 6. The method for measuring microparticles according to claim 4 or 5, comprising:

7. a light source that emits illumination light to a liquid containing microparticles to be measured; an objective lens that condenses the illumination light; an imaging lens that forms an image from the condensed illumination light; an image sensor that detects the formed illumination light and outputs a transmitted light image; a distance measuring unit for measuring the distance from the position where the transmitted light intensity of the microparticle to be measured is maximum to the objective lens; a measurement processing unit that calculates the refractive index of the microparticle to be measured based on the particle diameter of the microparticle and the measured distance; A microparticle measurement device equipped with

8. the measurement processing unit calculates the particle diameter based on an image of the microparticle included in the transmitted light image. The microparticle measuring device according to claim 7.

9. the measurement processing unit calculates the distance between the microparticle and the objective lens when the transmitted light intensity on the image sensor is at a maximum; Calculating the refractive index of the observed particle by a predetermined ray tracing matrix using the distance between the objective lens and the imaging lens, the distance between the imaging lens and the sensor, the focal length of the objective lens, the focal length of the imaging lens, and the position and incident angle at which the illumination light is incident on the microparticle of radius r; 9. The microparticle measuring device according to claim 7 or 8.

10. the microparticles are spores of a Bacillus strain in sludge, having a known refractive index and a particle size of 1 μm or less; The measurement processing unit estimates that the spores are present in an area where the transmitted light intensity at the focal length of the objective lens is equal to or greater than a predetermined transmitted light intensity threshold. The microparticle measuring device according to any one of claims 7 to 9.

11. a local terminal including a light source that emits illumination light to a liquid containing microparticles to be measured, an objective lens that condenses the illumination light, an imaging lens that forms an image of the condensed illumination light, an image sensor that detects the formed illumination light and outputs a transmitted light image, a distance measuring unit that measures the distance from the position where the transmitted light intensity of the microparticles to be measured is maximum to the objective lens, and a communication interface that transmits the transmitted light image and the distance from the position where the transmitted light intensity of the microparticles to be measured is maximum to the objective lens via a communication network; a cloud server that is communicably connected to the local terminal via the communication network, calculates the particle diameter of the microparticle based on the transmitted light image, calculates the refractive index of the microparticle to be measured based on the distance from the position where the transmitted light intensity of the microparticle to be measured is maximum to the objective lens, identifies the microparticle based on the particle diameter and the refractive index, and notifies the local terminal of the identification result; A microparticle measurement system equipped with

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