JP2021135129A - Microparticle measuring apparatus, microparticle measuring method and microparticle measuring program - Google Patents

Abstract

To provide a microparticle measuring apparatus that can measure microparticles which are contained in treatment water in organic wastewater treatment, like Bacillus, in a short period of time and with a high degree of accuracy, and that is low cost and realizable.SOLUTION: According to an embodiment, a microparticle measuring apparatus comprises: a light source that supplies illumination light illuminating a sample; an objective lens that bundles transmission light transmitting through the sample of the illumination light; an imaging lens that condenses the transmission light bundled by the objective lens; an imaging unit that images the transmission light condensed by the imaging lens; a determining unit that determines a part corresponding to the microparticles in an image of the transmission light imaged by the imaging unit; and a counting unit that counts a part determined to be corresponding to the microparticles by the determining unit.SELECTED DRAWING: Figure 1

Description

An embodiment of the present invention relates to a fine particle measuring device for measuring fine particles contained in treated water in organic wastewater treatment, a fine particle measuring method, and a fine particle measuring program.

The activated sludge method is generally used for the treatment of organic wastewater such as urban sewage. In the activated sludge method, air is supplied to the treated water, and organic substances in the treated water are decomposed by the action of useful microorganisms.

A typical useful microorganism in activated sludge is a bacterium of the genus Bacillus (hereinafter, simply referred to as "Bacillus"). Since the enzymes and antibiotics produced by Bacillus have a lytic effect, the amount of excess sludge generated is small in the treatment facility where Bacillus is dominant. Bacillus also has a role of stopping the action of sulfate-reducing bacteria, and the amount of odor generated is small.

Therefore, it is important to know the number of bacillus in order to determine whether the treated water contains sufficient bacillus.

Patent Document 1 discloses a method of measuring sludge concentration and sludge particle size by incident light into sludge and observing the optical response to the light. In this method, the intensity of transmitted light with respect to incident light can be measured from the Lambert-Beer law, and the sludge concentration can be measured from the signal level obtained using the calibration curve.

Japanese Unexamined Patent Publication No. 2004-317350 JP-A-2018-96789 JP-A-2007-289941

However, it is unclear whether or not the sludge can be selectively measured by the technique disclosed in Patent Document 1. In addition, when measuring the sludge concentration, it is necessary to prepare a calibration curve in advance, and the Lambert-Beer law only considers the change in intensity between incident light and transmitted light, so it depends on the sensitivity of the sensor. There is a problem that the concentration that can be measured is limited.

On the other hand, in order to improve the measurement accuracy, it is possible to perform measurement by an expert using a microscope, but this measurement requires a reasonable measurement time and the measurement result that quantitatively follows changes in minute phenomena. Is difficult to obtain.

The problem to be solved by the present invention is an inexpensive and feasible fine particle measuring device capable of measuring fine particles such as Bacillus contained in treated water in organic wastewater treatment in a short time and with high accuracy. , A microparticle measurement method, and a microparticle measurement program.

The fine particle measuring device of the embodiment is a device for measuring fine particles included in a sample, and includes a light source, an objective lens, an imaging lens, an imaging unit, a determination unit, and a counting unit. The light source supplies illumination light to illuminate the sample, the objective lens bundles the transmitted light transmitted through the sample, the imaging lens collects the transmitted light bundled by the objective lens, and the imaging unit collects the transmitted light. , The transmitted light collected by the imaging lens is imaged, the determination unit determines the portion corresponding to the minute particles in the image of the transmitted light captured by the imaging unit, and the counting unit determines the minute particles by the determination unit. The part determined to correspond to is counted.

It is a general process flow diagram which shows the flow of organic wastewater treatment such as urban sewage. It is a conceptual diagram which shows the structural example of the fine particle measuring apparatus to which the fine particle measuring method of 1st Embodiment is applied. It is a conceptual diagram which shows the structural example of the microscope which realizes an optical system. It is a figure which shows a part of the image which was taken under the condition that the transmitted light intensity is the maximum, and an example of the corresponding light intensity distribution. It is a figure which shows an example of a part of the image which was taken in the state which there is no sample in a slide glass under the same condition as FIG. 4, and the corresponding light intensity distribution. It is a figure which shows an example of a part of an image image | photographed under the condition that the transmitted light intensity is the maximum in the state which there is no sample in a slide glass, and the corresponding light intensity distribution. It is a figure which shows a part of the image which was taken under the condition that the transmitted light intensity is the maximum, and an example of the corresponding light intensity distribution. It is a figure which shows a part of the image which was taken in the state which there is no sample in a slide glass, and an example of the corresponding light intensity distribution. It is a figure which shows the partial plan view obtained by the binarization process and an example of the corresponding light intensity distribution. It is a figure which shows the example which highlighted the part determined to be a fine particle in the whole 2D plane image. It is a flowchart which shows the operation example of the fine particle measuring apparatus to which the fine particle measuring method of 1st Embodiment was applied. It is a conceptual diagram which shows the structural example of the fine particle measuring apparatus to which the fine particle measuring method of 2nd Embodiment is applied.

Hereinafter, the fine particle measuring apparatus to which the fine particle measuring method of the embodiment is applied will be described with reference to the drawings.

First, an organic wastewater treatment line for urban sewage, etc. to which the fine particle measuring device is applied will be described.

FIG. 1 is a general process flow diagram showing the flow of organic wastewater treatment such as urban sewage.

The organic wastewater treatment line 60 shown in FIG. 1 is composed of a first settling basin 62, an intermediate tank 64, and a final settling basin 66.

The treated water w is first introduced into the settling basin 62. In the first settling basin 62, the solid matter k contained in the treated water w is settled, and the supernatant of the treated water w is sent to the intermediate tank 64 downstream and stays there for a predetermined time. The treated water w contains activated sludge, Bacillus. As a result, in the intermediate tank 64, the organic matter in the treated water w is decomposed by Bacillus. After that, the treated water w in the intermediate tank 64 is sent to the final settling basin 66.

In the final settling basin 66, the solid matter k produced by decomposition of the organic matter by Bacillus or the like is settled, and the supernatant of the treated water w is discharged from the final settling basin 66. As a result, the concentration of organic matter contained in the treated water w becomes equal to or less than the permissible value at the time of discharge from the final settling basin 66.

The solid material k settled in the first settling basin 62 and the final settling basin 66 is extracted and collectively reduced in volume.

Therefore, in such an organic wastewater treatment line 60, it is essential that the treated water w contains sufficient bacillus in order to promote the activated sludge treatment. In order to confirm this, it is necessary to periodically sample the treated water w from the intermediate tank 64 and count the number of bacillus.

The fine particle measuring device 10 to which the fine particle measuring method of the embodiment is applied is used for this measurement. The measurement result by the fine particle measuring device 10 is sent to the control system 68 of the organic wastewater treatment line 60, and the control system 68 controls the aeration operation of the organic wastewater treatment line 60 and the Bacillus according to the measurement result. Control the concentration and so on.

The specific configuration of the fine particle measuring device 10 will be described below.

(First Embodiment)
FIG. 2 is a conceptual diagram showing a configuration example of a fine particle measuring device to which the fine particle measuring method of the first embodiment is applied.

The fine particle measuring device 10 includes a parallel light source 12, an objective lens 14, an imaging lens 16, an image sensor 22, a determination unit 24, and a counting unit 26, and measures the fine particles contained in the samples.

The parallel light source 12, the objective lens 14, and the imaging lens 16 are arranged in this order to form an optical system 20, and the illumination light a emitted from the parallel light source 12 is a slide containing samples s containing fine particles. After passing through the glass h, it is configured to be guided to the image sensor 22 via the objective lens 14 and the imaging lens 16. In FIG. 2, the straight line c represents the optical axis. The optical system 20 can be realized by, for example, a microscope.

On the other hand, the image sensor 22, the determination unit 24, and the counting unit 26, which are components other than the optical system 20, can be realized by a computer including a processor that operates according to a dedicated program.

The sample s is, for example, the treated water w sampled from the intermediate tank 64 as described above. Microparticles are, for example, spores that produce Bacillus.

The sample s is placed on the slide glass h, fixed by a cover glass or the like, and arranged on the optical axis c between the parallel light source 12 and the objective lens 14.

The parallel light source 12 supplies an illumination light a that illuminates the slide glass h on which the sample s is fixed.

Since the microparticles such as spores that generate bacillus contained in the sample s function as a lens due to the difference in refractive index, the light passing outside the microparticles is focused on the central portion. Therefore, when the illumination light a passes through the slide glass h, it is condensed by the lens function of the fine particles contained in the sample s fixed to the slide glass h to become transmitted light t having high light intensity, and becomes the objective lens 14 Incident in.

The transmitted light t incident on the objective lens 14 is bundled by the objective lens 14 and incident on the imaging lens 16.

The transmitted light t incident on the imaging lens 16 is collected by the imaging lens 16 and sent to the image sensor 22.

The image sensor 22 functions as an imaging unit that captures the transmitted light t collected by the imaging lens 16.

The determination unit 24 determines a portion corresponding to the fine particles contained in the sample s in the image of the transmitted light t captured by the image sensor 22.

In the optical system 20 having the configuration shown in FIG. 2, the light intensity of the transmitted light t reaching the image sensor 22 is the isolation distance z _{of the sample s from the focal distance f 1 of the objective lens 14 along the optical axis c.} And the characteristics of the optical system 20 (the focal distance f ₁ of the objective lens 14, the focal distance f _{2 of the imaging lens 16, the distance l 1} between the objective lens 14 and the imaging lens 16, the imaging lens 16 and the image sensor 22). Based on the distance l ₂ ) from and the particle size r and the refractive index n of the fine particles, it is determined by performing a ray tracing matrix calculation according to the Ray transfer optics shown below.

In the above equation, x is the distance from the optical axis c, and u is the angle of incidence of light with respect to the optical axis c.

The characteristics of the optical system 20 described above are known because they are determined according to the design of the optical system 20. Further, since the fine particles to be measured are assumed in advance such as spores producing Bacillus, the particle size r (about 1 μm in the case of spores producing Bacillus) and the refractive index n (about 1.5). ) Is also known.

Therefore, the sample s has the focal length f of the objective lens 14 determined so as to maximize the light intensity of the transmitted light t reaching the image sensor 22 based on the ray tracing matrix calculation performed using the Ray trace matrix. It is arranged at a position separated from _{1 by an isolation distance z.}

Alternatively, without using the ray tracing matrix calculation, while changing the separation distance z from the focal length f ₁ of the objective lens 14 until the slide glass h, captures an image of the transmitted light t at the image sensor 22, the most light intensity The slide glass h may be arranged so that the isolation distance z when a high image is obtained is obtained.

Validity of method of determining a separation distance z while changing the separation distance z to the slide glass h from the focal length f ₁ of the objective lens 14 is verified as follows.

FIG. 3 is a conceptual diagram showing a configuration example of a microscope 21 that realizes the optical system 20 illustrated in FIG.

Microscope 21 is similar to the optical system 20, in addition to being provided with parallel light source 12, objective lens 14, an imaging lens 16, measured separation distance z from the focal length f ₁ of the objective lens 14 until the slide glass h A laser displacement meter 30 is provided for this purpose. _{The distance l 1} between the objective lens 14 and the imaging lens 16 _{is 130 mm, the distance l 2} between the imaging lens 16 and the image sensor 22 is 164.5 mm, the focal length f ₂ of the imaging lens 16 is 164.5 mm, and the objective lens. The magnification of 14 is 40 times (× 40), the numerical aperture is 0.65 (NA = 0.65), and the focal length f ₁ is 4.1125 mm.

The microparticle measuring device 10 provided with such a microscope 21 as an optical system 20 also has a determination unit 24 and a counting unit 26 in the subsequent stage of the image sensor 22, but these are not shown in FIG. There is.

4, 5 and 6, respectively, show a part of the image obtained by imaging by the image sensor 22 of the microparticle measuring device 10 provided with the microscope 21 shown in FIG. 3 as the optical system 20 and the corresponding light intensity distribution. This is an example.

FIG. 4A is a plan view illustrating a part of an image of a sample s containing spores that generate Bacillus as fine particles under the condition that the light intensity of the transmitted light t is maximized. The isolation distance z at this time is 0 μm (z = 0 μm).

FIG. 4B is a diagram showing an example of the light intensity distribution along the line AA in FIG. 4A.

The horizontal axis in FIG. 4A indicates the position x (μm) corresponding to the distance from the optical axis c (vertical direction in FIG. 2), and the vertical axis represents the position y (μm) corresponding to the distance from the optical axis c. ) (Depth direction in FIG. 2).

The horizontal axis in FIG. 4B indicates the position x (μm) corresponding to the distance from the optical axis c corresponding to FIG. 4A, and the vertical axis indicates the relative intensity of the transmitted light t.

In FIG. 4 (b), spikes are observed showing a sharp enhancement of light intensity corresponding to the spores produced by Bacillus.

FIG. 5A shows a part of the image captured without the sample s on the slide glass h under the condition that the image as shown in FIG. 4A was acquired (that is, the isolation distance z = 0 μm). It is a top view which exemplifies.

The units of the vertical axis and the horizontal axis in FIG. 5A are the same as the units of the horizontal axis and the vertical axis in FIG. 4A, and the units of the horizontal axis and the vertical axis in FIG. 5B are. It is the same as the unit of the horizontal axis and the vertical axis in FIG. 4 (b).

Here, the slide glass h contains acrylic particles having a diameter of about 30 μm (refractive index is about 1.5). Therefore, the circular portion shown in FIG. 5A corresponds to the acrylic particles.

However, according to FIG. 5 (b) showing the light intensity distribution along the line AA in FIG. 5 (a), the light intensity is reduced in the circular portion shown in FIG. 5 (a). Recognize. When the illumination light a from the parallel light source 12 has passed through the acrylic particles, the light intensity of the transmitted light t is determined by the image sensor under the condition of FIG. It can be seen that it is not the maximum at 22.

Therefore, the image sensor 22 captures an image of the transmitted light t while changing the isolation distance z, thereby searching for the isolation distance z that maximizes the light intensity of the transmitted light t in the image sensor 22. The isolation distance z is measured by the laser displacement meter 30.

As a result, as shown in FIG. 6, when the isolation distance z = 26 μm, the result was obtained that the light intensity of the transmitted light t captured by the image sensor 22 became maximum.

FIG. 6A shows a part of the image captured by the image sensor 22 under the condition that the light intensity of the transmitted light t is maximized when the slide glass h without the sample s is illuminated by the illumination light a. It is a top view which exemplifies. The portion having a diameter of about 30 μm shown in the center is considered to correspond to the acrylic particles.

FIG. 6B is a diagram showing an example of the light intensity distribution along the line AA in FIG. 6A.

The units of the vertical axis and the horizontal axis in FIG. 6A are the same as the units of the horizontal axis and the vertical axis in FIG. 4A, and the units of the horizontal axis and the vertical axis in FIG. 6B are. It is the same as the unit of the horizontal axis and the vertical axis in FIG. 4 (b).

In FIG. 6 (b), as shown in FIG. 5 (b), a portion where the light intensity is decreasing and a spike where the light intensity is increasing are observed at the same time. Since the width of the portion where the light intensity is reduced is about 30 μm, it can be seen that it corresponds to the acrylic particles constituting the slide glass h.

As described above, when measuring spores having a diameter of about 1 μm from the results of FIG. 4, the isolation distance z = 0 μm is optimal, and from the results of FIGS. 5 and 6, acrylic particles having a diameter of about 30 μm can be obtained. When measuring, it is determined that the isolation distance z = 26 μm is optimal.

In fact, when measuring spores having a diameter of about 1 μm using the microparticle measuring device 10, the optimum isolation distance z was obtained by performing a ray tracing matrix calculation according to the Ray transfer matrix described above. The result of z = 0.7 μm was obtained. Similarly, when the calculation was performed for the case of the acrylic particles having a diameter of about 30 μm, the result of z = 22.5 μm was obtained.

As described above, the isolation distance z at which the light intensity of the transmitted light t captured by the image sensor 22 is maximized is substantially the same as the theoretically calculated value.

In the image having the maximum light intensity captured by the image sensor 22 in this way, the determination unit 24, for example, a portion showing a light intensity equal to or higher than a preset threshold value is like a spore that produces Bacillus. It is judged that it corresponds to a small particle.

FIG. 7 (a) is a plan view illustrating a part of an image captured under the condition that the light intensity of the transmitted light t of the sample s is maximized, and FIG. 7 (b) is a plan view in FIG. 7 (a). It is a figure which shows an example of the light intensity distribution along the AA line.

The units of the vertical axis and the horizontal axis in FIG. 7A are the same as the units of the horizontal axis and the vertical axis in FIG. 4A, and the units of the horizontal axis and the vertical axis in FIG. 7B are. The positions x (μm) corresponding to the distance from the optical axis c corresponding to FIG. 7A and the intensities of the transmitted light t, respectively.

The light intensity distribution G shown in FIG. 7B shows spikes of light intensity SP with steep rises and falls immediately before and after. Correspondingly, in FIG. 7A, the region with high light intensity is dark, and the region with low light intensity located around the region is lightly shown.

Since the image g illustrated in FIG. 7A also includes the transmitted light due to the slide glass h itself as the background noise, it is due to the background noise whether the spike SP corresponds to fine particles. I don't know. Therefore, in order to determine this, it is necessary to subtract the contribution due to the background noise from the light intensity distribution G.

In order to grasp the contribution due to the background noise, the microparticle measuring device 10 captures an image of the transmitted light t by the image sensor 22 in a state where there is no sample s on the slide glass h.

FIG. 8A is a plan view illustrating an image taken in a state where the slide glass h does not have the sample s under the condition that FIG. 7A is obtained.

FIG. 8B is a diagram showing an example of the light intensity distribution along the line AA in FIG. 8A.

The horizontal and vertical axes in FIG. 8 (a) are the same as the horizontal and vertical axes in FIG. 7 (a), and the horizontal and vertical axes in FIG. 8 (b) are the horizontal axes in FIG. 7 (b). And the same as the vertical axis.

FIG. 8A corresponds to an image of background noise. FIG. 8B corresponds to the light intensity distribution Th of the background noise, and in this example, the light intensity shows a constant light intensity (Th = 0.02) regardless of the position x. Therefore, FIG. 8A is an image having a uniform light intensity.

The determination unit 24 uses the light intensity of such background noise as a threshold value (0.02 in the example of FIG. 8B), and the light intensity distribution G as shown in FIG. 7B. Of these, the portion showing a light intensity higher than the threshold value is determined to be transmitted light by fine particles. Such a determination method will be specifically described with reference to FIG.

9 (b) is a superposed view of FIG. 7 (b) and FIG. 8 (b). That is, in FIG. 9B, the light intensity distribution G and the light intensity distribution Th are superposed. The light intensity distribution G is a combination of the transmitted light due to the fine particles and the transmitted light due to the background noise, and the light intensity distribution Th is due to the transmitted light due to the background noise.

Therefore, as shown by the diagonal lines in FIG. 9B, the determination unit 24 has a higher light intensity than the light intensity distribution Th in the light intensity distribution G, in other words, a higher than the threshold value (0.02). The portion P having a high light intensity is determined to be transmitted light by fine particles. In this way, the determination unit 24 determines the fine particles by the binarization process based on the light intensity.

The determination unit 24 further highlights the portion P determined to be a fine particle in the two-dimensional plane image. An example thereof is shown in FIGS. 9A and 10.

FIG. 9A shows an example in which the portion P determined to be a fine particle in FIG. 9B is highlighted by a color different from the surroundings, for example, red.

Further, from the display as shown in FIG. 9A, the determination unit 24 can also grasp the particle size r of the fine particles. By this, it is confirmed whether or not the determined fine particles are the expected fine particles, or when the sample s contains a plurality of types of fine particles, they are classified by the particle size r. This makes it possible to determine the fine particles for each type.

FIG. 10 is a diagram showing an example in which a portion P determined to correspond to fine particles in the entire two-dimensional plane image captured by the image sensor 22 is highlighted.

FIG. 9A exemplifies only a portion P corresponding to one minute particle in the two-dimensional plane image captured by the image sensor 22 using an objective lens having a magnification of 40 times. It is illustrated that a large number of parts P are shown in the entire two-dimensional plane image captured by the image sensor 22 by using an objective lens having a magnification of 10 times. Also in FIG. 10, the horizontal axis indicates the position corresponding to the distance from the optical axis c (vertical direction in FIG. 2), and the vertical axis indicates the position corresponding to the distance from the optical axis c (depth direction in FIG. 2). However, it should be noted that since the image shown in FIG. 10 is acquired at a low magnification of 10 times, the field of view is larger than that of the image shown in FIG. 9 (a).

As shown in FIG. 10, the portion P determined to correspond to the fine particles by the determination unit 24 is displayed in a color different from the surroundings, such as red, in the two-dimensional plane image captured by the image sensor 22. ..

The counting unit 26 processes, for example, an image as illustrated in FIG. 10 and counts the number of portions P displayed in red. Since the counted number corresponds to the number of fine particles, the counting unit 26 can easily and surely count the number of fine particles.

Further, when the sample s contains, for example, a plurality of types of fine particles that can be discriminated by the particle size, the determination unit 24 uses different colors for each type of particles in the two-dimensional plane image as illustrated in FIG. It can also be displayed. Further, by performing image processing on this image and counting the number of partial Ps for each color, the counting unit 26 can also count the fine particles for each type.

The determination unit 24 can also calculate the refractive index n of fine particles whose refractive index is unknown by applying the ray tracing matrix calculation described above. This while changing the separation distance z from the focal length f ₁ of the objective lens 14 to the sample s, and captures an image of the transmitted light by the image sensor 22, separation distance z at which the most light intensity is high image obtained Can be substituted into the Ray transfer matrix to calculate the refractive index n of the fine particles. For this, the particle size r needs to be known, but when the particle size r is unknown, the Ray transfer using the particle size r obtained from the image shown in FIG. 9A as described above. By substituting into matrix, the refractive index n of these fine particles can be calculated.

Next, the operation of the fine particle measuring device to which the fine particle measuring method of the first embodiment is applied will be described.

FIG. 11 is a flowchart showing an operation example of the fine particle measuring device to which the fine particle measuring method of the first embodiment is applied.

In step S1, an image of transmitted light t of the sample s containing fine particles is acquired by using the optical system 20 (S1).

As shown in FIG. 2, when the slide glass h including the sample s is illuminated with the illumination light a from the parallel light source 12, the image sensor 22 captures an image of the transmitted light. It is done by doing.

When the light intensity of the transmitted light obtained by the image sensor 22 is not the maximum (S2: No), the slide glass h is installed so that the light intensity of the transmitted light obtained by the image sensor 22 is the maximum. The position is adjusted. This position, as shown in FIG. 2, corresponding to the separation distance z from the focal length f ₁ of the objective lens 14 along the optical axis c (S3).

When the light intensity of the transmitted light captured by the image sensor 22 is maximized, that is, when the optimum isolation distance z is determined (S2: Yes), the slide glass h without the sample s is arranged and parallel. The transmitted light obtained by irradiating the illumination light a from the light source 12 toward the slide glass h is imaged by the image sensor 22. This corresponds to transmitted light due to background noise as illustrated in FIG. 8B, for example. The determination unit 24 determines the threshold value for binarization from this image (S4).

After that, the determination unit 24 has the threshold value determined in step S4 (for example, FIG. 8B) of the light intensity distribution G obtained in step S1 (see, for example, FIG. 7B). The fine particles are determined by the binarization process in which the portion P (see FIG. 9B) having a light intensity higher than that of Th = 0.02) is determined to correspond to the transmitted light by the fine particles. Then, a binarized image (for example, see FIG. 10) in which the portion P determined to correspond to the fine particles is highlighted in a color different from the surroundings, such as red, is formed (S5).

After that, the counting unit 26 counts the number of the partial Ps based on the binarized image. The counted number corresponds to the number of microparticles (eg, spores that produce Bacillus). In this way, the number of fine particles is measured (S6).

As described above, according to the microparticle measuring apparatus to which the microparticle measuring method of the first embodiment is applied, fine particles such as Bacillus contained in treated water in organic wastewater treatment can be detected by a conventional detection method. It can be measured in a shorter time than the apparatus to which the micro-colony method, the sequencing method, and the fluorescent staining method are applied. Moreover, since the measurement can be realized by a simple method that does not require a calibration curve or the like, the fine particle measuring device can be realized at a low cost.

(Second Embodiment)
Next, the second embodiment will be described. In the following description, the same parts as those in the first embodiment are shown by using the same reference numerals, and duplicate explanations are avoided.

FIG. 12 is a conceptual diagram showing a configuration example of a fine particle measuring device to which the fine particle measuring method of the second embodiment is applied.

In the fine particle measuring device 10 of the first embodiment, the determination unit 24 determines the fine particles by binarization using a threshold value, but in the fine particle measuring device 11 of the second embodiment, the determination unit 24 determines the fine particles. Reference numeral 24 denotes a method for determining fine particles by binarization using a threshold value, or in combination with this method, for determining fine particles by machine learning.

In order to realize the determination of fine particles by machine learning, the fine particle measuring device 11 illustrated in FIG. 12 further includes a database 28 and a machine learning unit 29 in addition to the fine particle measuring device 10 illustrated in FIG. There is.

The database 28 is accessible from the determination unit 24.

In the database 28, the light intensity distribution information of the portion determined to correspond to the fine particles in the past by the determination unit 24 is accumulated. The light intensity distribution information may be, for example, waveform information as shown in FIG. 7B, or numerical information corresponding to this waveform information.

The machine learning unit 29 performs image analysis by machine learning on the light intensity distribution information stored in the database 28, and outputs the analysis result to the determination unit 24. The results of this analysis include waveform similarity.

Waveform similarity can be determined, for example, based on the shape characteristics of the spike SP as shown in FIG. 7 (b). That is, the spike SP shown in FIG. 7B is characterized by being accompanied _{by a spike SP 1 showing a sharp decrease in light intensity immediately before and a spike SP 2} showing a sharp decrease in light intensity immediately after. Therefore, the machine learning unit 29 pays attention to this feature, and focuses on the portion corresponding to the _{spike SP 1 which shows a sharp decrease in light intensity immediately before and the spike SP 2} which shows a sharp decrease in light intensity immediately after. , Gives high similarity.

In order to further improve the accuracy of similarity, the machine learning unit 29 states that the height of the spike SP _{is larger than the depth of the spike SP 1} , the height of the spike SP is larger than the depth _{of the spike SP 2 , and the spike SP 1} The similarity may be determined according to the characteristics that the depth of the spike SP ₂ is larger than the depth of the _{spike SP 2 and the widths of the spike SP, the spike SP 1} , and the spike SP 2 are similar.

When a new image is captured by the image sensor 22, the determination unit 24 determines that in this image, the portion showing a similarity higher than the similarity given by the machine learning unit 29 corresponds to a fine particle.

In this way, the determination unit 24 can determine the fine particles by using the result of machine learning. This can be carried out in place of the method of determining fine particles by binarization described in the first embodiment, or in combination with the method of determining fine particles by binarization.

Further, the portion P determined to correspond to the fine particles by the determination unit 24 by using the result of the machine learning in this way is also the periphery such as red in the two-dimensional plane image captured by the image sensor 22. Is displayed in a different color.

Therefore, as described in the first embodiment, the counting unit 26 can easily and surely count the number of fine particles by counting the number of the portions displayed in red, for example, by image processing. can.

The determination unit 24 further stores light intensity distribution information of a portion determined to correspond to fine particles and morphological information such as particle size, aspect ratio, roundness, and ferret length in the database 28.

As a result, the machine learning unit 29 can perform image analysis using more light intensity distribution information and morphological information, so that the determination accuracy by the determination unit 24 is further improved.

As described above, according to the microparticle measuring device 11 of the second embodiment, by applying machine learning, not only the microparticles can be determined with higher accuracy, but also a new one determined to correspond to the microparticles. Since the light intensity distribution information is accumulated in the database 28 and used for machine learning, the determination accuracy can be further improved by the learning effect.

Moreover, the method of determining fine particles by applying machine learning in this way is to be carried out in place of the method of determining fine particles by binarization using a threshold value as described in the first embodiment. Or, it can be carried out in combination.

For example, in order to further improve the determination accuracy of the portion corresponding to the fine particles, the portion determined to correspond to the fine particles by both the method of the first embodiment and the present method is determined to correspond to the fine particles. You may. The portion determined in this way has an extremely high probability of corresponding to fine particles.

As described above, the microparticle measuring apparatus to which the microparticle measuring method of the second embodiment is applied also measures microparticles such as spores that generate Bacillus in a shorter time and more simply than in the prior art. can do.

Although some embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, replacements, and changes can be made without departing from the gist of the invention. These embodiments and modifications thereof are included in the scope and gist of the invention, as well as in the scope of the invention described in the claims and the equivalent scope thereof.

10, 11 ... Fine particle measuring device, 12 ... Parallel light source, 14 ... Objective lens, 16 ... Imaging lens, 20 ... Optical system, 21 ... Microscope, 22 ... Image sensor, 24 ... Judgment Department, 26 ... Counting part, 28 ... Database, 29 ... Machine learning department, 30 ... Laser displacement meter, 60 ... Organic wastewater treatment line, 62 ... First settling pond, 64 ... Intermediate tank, 66・ ・ Final sedimentation pond, 68 ・ ・ Control system

Claims (9)

A microparticle measuring device that measures microparticles contained in a sample.
A light source that supplies illumination light to illuminate the sample,
An objective lens in which the illumination light bundles the transmitted light transmitted through the sample, and
An imaging lens that collects the transmitted light bundled by the objective lens and
An imaging unit that captures the transmitted light collected by the imaging lens, and an imaging unit.
In the image of the transmitted light captured by the imaging unit, a determination unit that determines a portion corresponding to the fine particles, and a determination unit.
A fine particle measuring device including a counting unit that counts a portion determined by the determination unit to correspond to the fine particles. The sample is arranged so as to be isolated from the focal plane of the objective lens along the optical axis at a distance determined so as to maximize the intensity of the transmitted light imaged by the imaging unit.
The fine particle measuring device according to claim 1. In the captured image of the transmitted light, the determination unit determines that a portion showing a light intensity equal to or higher than a preset threshold value is a portion corresponding to the fine particles.
The fine particle measuring device according to claim 1 or 2. The preset threshold value is determined from the light intensity in the image captured by the imaging unit in the absence of the sample under the same conditions as the image of the transmitted light is captured.
The fine particle measuring device according to claim 3. A database that stores the light intensity distribution information of the portion determined to correspond to the fine particles by the determination unit, and
It is further provided with a machine learning unit that performs image analysis by machine learning on the light intensity distribution information stored in the database and outputs the analysis result to the determination unit.
When a new image of the transmitted light is imaged by the imaging unit, the determination unit determines a portion corresponding to the fine particles in the new image based on the analysis result.
The fine particle measuring device according to claim 1 or 2. The determination unit stores the light intensity distribution information of the portion determined to correspond to the fine particles in the database.
The fine particle measuring device according to claim 5. The determination unit displays a portion determined to correspond to the fine particles in a color different from other portions in the transmitted light image.
The counting unit counts the fine particles by counting the portions displayed in different colors in the transmitted light image.
The fine particle measuring apparatus according to any one of claims 1 to 6. It is a method of measuring fine particles contained in a sample.
Illuminate the sample with the illumination light supplied by the light source.
The transmitted light transmitted by the illumination light through the sample is bundled by an objective lens.
The transmitted light bundled by the objective lens is collected by the imaging lens, and the transmitted light is collected by the imaging lens.
The transmitted light collected by the imaging lens is imaged, and the transmitted light is imaged.
In the captured image of the transmitted light, a portion corresponding to the fine particles is determined.
Counting the determined portion,
Fine particle measurement method. A program that measures fine particles contained in a sample.
A function of capturing light supplied from a light source to illuminate the sample, transmitted through the sample, bundled by an objective lens, and then focused by an imaging lens.
A function of determining a portion corresponding to the fine particles in the captured light image,
The function to count the determined part,
A fine particle measurement program to be realized in a computer.

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