JP7770886B2 - Microparticle measuring method, microparticle measuring device, and microparticle measuring system - Google Patents

Description

Embodiments of the present invention relate to a microparticle measurement method, a microparticle measurement device, and a microparticle measurement system.

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 measurement light, a measuring unit that includes a cell, and a detecting unit that includes a sensor that receives the measurement light.

When detecting sludge concentration, the detector measures the intensity of light transmitted relative to the light incident on the measuring unit based on the Lambert-Beer law, and the concentration can be determined from the signal level obtained using a pre-measured calibration curve.

However, measuring concentration using this method requires the creation of a calibration curve in advance, and because the Beer-Lambert law looks at changes in the intensity of incident and transmitted light, there is an issue with the sensor's sensitivity limiting the concentration that can be measured.

Japanese Patent Application Laid-Open No. 2004-317350

By the way, organic wastewater treatment utilizes a variety of useful microorganisms 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.

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.

The present invention has been made in light of the above, and aims to provide a microparticle measurement method, microparticle measurement device, and microparticle measurement system that are simple in configuration and capable of rapid quantitative measurement.

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 toward a liquid containing the 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. The method includes the steps of measuring the distance from the position at which the transmitted light intensity of the microparticle to be measured is maximized to the objective lens, and calculating the refractive index of the microparticle to be measured based on the particle diameter of the microparticle and the measured distance.

FIG. 1 is a schematic diagram of a microparticle measuring device according to an embodiment. FIG. 2 is an explanatory diagram of parameters in a ray tracing matrix. 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 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 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. FIG. 5 is a process flowchart of the microparticle number measurement process according to the embodiment. FIG. 6 is a process flowchart of a process for measuring the number of spores of Bacillus strains as microparticles. FIG. 7 is a diagram illustrating the principle of this modified example. FIG. 8 is a flowchart of the organic wastewater treatment. FIG. 9 is a flowchart of the machine learning process. FIG. 10 is a diagram illustrating an example of a calibration curve used in machine learning. FIG. 11 is an explanatory diagram of the results of image processing of a microscope image.

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 up and down direction in FIG. 1 along the optical axis, a laser displacement meter 15 that functions as a distance measuring 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.

[1] Measurement Principle First, the measurement principle of microparticles will be explained.
[1.1] Refractive index and particle diameter When illumination light is irradiated from the back 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.

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.

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 .

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.

[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.

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 concentration can be measured by measuring the number of useful microorganisms in a volume corresponding to the observation field of view x scanning distance.

Incidentally, it was found that for spores of Bacillus strains in sludge with a known refractive index and a particle size of 1 μm or less, the position corresponding to the distance z at which the transmitted light intensity is maximized is located within the depth of field (effective focal position) corresponding to the focal length _f1 in 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.

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.

Furthermore, by using the set threshold value, the sample is continuously moved along the optical axis while sequentially capturing images, and by combining 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.

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 for each sample by an examiner are equivalent to the detection results obtained by machine learning, thereby obtaining detection or count results for microparticles that correspond to the particle size and refractive index of the microparticles being learned.

[2] First Embodiment Next, the 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 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.

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 on the image sensor 18 was at its maximum and the actual position of the objective lens 16 was measured by the laser displacement meter 15.

3A shows an image of a liquid containing Bacillus spores, in which the relative transmitted light intensity is at its maximum. As shown in FIG. 3A, the relative transmitted light intensity is at its maximum at the center of the imaged area.
As shown in FIG. 3B, it was calculated that in a liquid containing spores of a Bacillus strain, 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 has a negative value at a position difference of Δ0 μm, where the relative transmitted light intensity is maximum for the Bacillus spores. In other words, this is an image captured when it is clear that 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. 4B, in a liquid containing acrylic particles with a particle diameter of 30 μm, it was calculated that the relative transmitted light intensity was maximum outside the position difference Δz of ±15 μm.

4C 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 FIG. 4C, the relative transmitted light intensity is at its maximum at the center of the captured region.
As shown in FIG. 4D, in a liquid containing acrylic particles with a particle diameter of 30 μm, it was calculated that the relative transmitted light intensity was maximized when the position difference Δz was 26 μm.

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 at which the transmitted light intensity is greatest) to the objective lens 16 and the focal length of the objective lens was calculated using the above-mentioned ray tracing matrix for the Bacillus spores and 30 μm acrylic particles. 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 almost 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 l ₁ = 130 mm, the distance between the imaging lens 17 and the image sensor 18 was l ₂ = 164.5 mm, the focal length of the objective lens was f ₁ = 4.1125 mm, and the focal length of the imaging lens 17 was f ₂ = 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.

In particular, it was found that the position difference Δz = 0.9 μm in a liquid containing spores of Bacillus strains 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.

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

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 when the transmitted light intensity is at its maximum (Δz = 26 μm in the above example) 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 acrylic particles with a particle diameter of 30 μm have been detected.

Using these techniques, 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. This makes it possible to identify the positions within the detection field of view of Bacillus spores or acrylic particles scattered throughout the sample solution and count their number. Furthermore, by determining the total distance traveled along the optical axis, the concentration of Bacillus spores or acrylic particles in a given volume can be calculated.

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 microparticles is acquired by the image sensor 18, and the particle diameter of the microparticles is calculated by image recognition (step S12).
Next, the ray tracing matrix is solved using the optical system information, i.e., 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.

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.

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).

Next, it is determined whether 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.

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 the specified particle diameter range for Bacillus strain spores, 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 diameter of the measured microparticles falls within the specified refractive index range for Bacillus strain spores.

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.

Next, 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 spores of the 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 areas where spores of Bacillus strains are present, 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). The process from step S22 is then 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.

[2.1] Modification of the First Embodiment The above description was directed to simply measuring the number of spores of Bacillus strains, but this modification is directed to measuring the concentration of spores of Bacillus strains.

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 driving 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 maximized if they are located exactly at the focal position of the objective lens.

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 Bacillus spore using the method described above, it can be determined 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.

In this case, if the area of the field of view FV is AR (μm ² ), the volume V of a cube specified by the field of view FV and the scanning distance SCL (μm) is given by:
V=AR×SCL (μm ³ )
and the spore concentration is 2/V.

Similarly, when 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.

[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.

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 the vegetative cells of the Bacillus bacteria contained in the sample solution (step S32).

Next, the stage is driven by the stage driver 14 to effectively scan the focal position of the objective lens along the optical axis direction, and multiple transmitted light images are acquired depending on the operating state (step S33).

Then, changes in the particle diameter and transmitted light intensity of the microparticles contained in the transmitted light image are detected, and spores are detected within the scanning range, assuming that the position with the highest relative transmitted light intensity contains spores (step S34).

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, and whether the particle size of the microparticles is within the particle size range of the specified Bacillus strain spores.

As a result, the machine-learned model obtained from the results of machine learning can be used to count the number of Bacillus spores in a volume equal to the observation field of view x the operation distance, thereby measuring the spore concentration per unit volume (step S35).

Here, we will discuss machine learning techniques.
FIG. 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).

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

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.

In this example, the adjusted concentrations were nine levels: 5 x ¹⁰³ [cells/mL], 1 x ¹⁰⁴ [cells/mL], 5 x ¹⁰⁴ [cells/mL], 1 x ¹⁰⁵ [cells/mL], 5 x ¹⁰⁵ [cells/mL], 1 x ¹⁰⁶ [cells/mL], 5 x ¹⁰⁶ [cells/mL], 1 x ¹⁰⁷ [cells/mL], and 5 x ¹⁰⁷ [cells/mL].

The actual concentration was adjusted by preparing a solution of 5 x 10 ⁷ cells/mL and then diluting it with sludge to achieve the above-mentioned adjusted concentration.
The spore concentration was measured by dropping the spore solution onto a hemocytometer and counting the number of spores.

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 could be calculated to be 6.713 × 10 ⁴ cells/mL.
For the calibration curve used in machine learning, for the region below 5 × 10 ⁵ cells/mL, the calibration curve in the range of spore concentration = 5 × 10 ⁵ cells/mL to 5 × 10 ⁷ cells/mL is considered to be a straight line, as shown by the dashed line in Figure 10, 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.

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.

In other words, in this embodiment, the individual amount of Bacillus spores is high, and the concentration is such that the characteristics of Bacillus strains are clearly apparent.

If the measured concentration (concentration measurement result) is determined to be less than the dominant concentration in step S36 (step S36; No), Bacillus strains are not dominant in the wastewater to be treated, and an instruction is issued to add Bacillus strains to the wastewater to be treated in order to quickly treat the organic wastewater using Bacillus strains. In response, the operator adds Bacillus strains to the wastewater to be treated (step S37).

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

Then, steps S31 to S37 are repeated in a similar manner until the concentration of Bacillus strains contained in the wastewater to be treated exceeds the dominant concentration.

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 therefore 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.

[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.

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 rapidly 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.

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

[5] Modified Examples In the above explanation, concentration measurement is performed using a calibration curve obtained from a sample whose concentration has been adjusted in advance. However, it is also possible to configure the system so that concentration measurement is performed using the results of measuring the bacterial count concentration using conventional methods such as the microcolony method or the sequencing method.

The above explanation was for a standalone microparticle measurement device, but it is also possible to transfer transmitted light images acquired by an image sensor (imaging device) on the local terminal to a cloud server via a communications interface and communications network, and have the cloud server identify the microparticles to be measured (e.g., Bacillus spores) contained in the transmitted light images, calculate the number of microparticles (e.g., Bacillus spore count) and the microparticle concentration (e.g., Bacillus spore concentration), and notify the local terminal via the communications network.

In the above configuration, when identifying 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.

In addition, 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.

As explained 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. This makes it possible to easily identify the microparticles to be measured in the solution and quickly measure the number or concentration of the microparticles.

In particular, it is possible to acquire transmitted light images of sludge being treated in organic wastewater treatment as a solution, and automatically measure the spore concentration of Bacillus strains based on the results of machine learning. Compared to conventional detection methods such as the μ-colony method and sequencing method, this makes it possible to measure concentrations in a short time with a cheaper device configuration.

As a result, it is now possible to easily and quickly capture changes in the spore concentration of Bacillus strains on an ongoing basis, making it easy to improve the treatment performance of organic wastewater.

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 a RAM, an external storage device such as a HDD or a CD drive, a display device such as a display unit, 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.
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.

REFERENCE SIGNS LIST 10 Microparticle measuring device 11 Light source 12 Slide glass 13 Stage 14 Stage driving unit 15 Laser displacement meter 16 Objective lens 17 Imaging lens 18 Image sensor 19 Measurement control unit AR1 Area P1, P2 Position SP1 Spore f1 Focal length of objective lens f2 Focal length of imaging lens l1 Distance between objective lens and imaging lens l2 Distance between imaging lens and image sensor u0 Incident angle x0 Position FV Field of view Ith Discrimination threshold L Illumination light PC Microparticle SCL Scanning distance SP Measurement sample V Volume r Radius n Refractive index z Distance Δz Position difference

Claims (11)

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: 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 . 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: 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. 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. 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: 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 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. 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. 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. 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