WO2022168434A1

WO2022168434A1 - Particle analysis device, particle analysis method, and program for particle analysis device

Info

Publication number: WO2022168434A1
Application number: PCT/JP2021/045147
Authority: WO
Inventors: 久秋山; 浩行越川; 哲也森; 康弘立脇
Original assignee: 株式会社堀場製作所
Priority date: 2021-02-05
Filing date: 2021-12-08
Publication date: 2022-08-11
Also published as: JPWO2022168434A1

Abstract

To provide a particle analysis device that is capable of capturing particle agglomeration even if particle sizes are small and numerically evaluating the extent of agglomeration, a particle analysis device according to the present invention is made to comprise: a camera for imaging a cell accommodating therein a sample including particles; an illumination mechanism for emitting, onto a region to be illuminated of the cell, light that travels diagonally in relation to the imaging optical axis of the camera; and an agglomeration analysis unit for analyzing the agglomeration of the particles in the sample on the basis of images captured by the camera.

Description

Particle analyzer, particle analysis method, and program for particle analyzer

The present invention relates to a particle analyzer that irradiates a sample containing particles with light and analyzes the state of the particles based on the captured image.

In order to evaluate the particles contained in the sample, an index for evaluating the particle size distribution and particle dispersion state is calculated based on the image of the sample. Patent Document 1 proposes calculating entropy for each pixel of a captured image in order to evaluate the dispersed state of particles. Specifically, in the invention described in Patent Document 1, a two-dimensional array detector is arranged so as to face an illumination mechanism, and particles are detected by transmitted light while flowing heterogeneous particles on the two-dimensional array detector in one direction. image is captured.

JP 2015-520397 A

However, if entropy is used as an indicator of aggregation, it is not possible to quantify how the shape and size of particles change due to aggregation. In other words, it is difficult to say that sufficient information about aggregation of particles is obtained. In addition, for nanoparticles with a particle size on the order of 100 nm, for example, it is not known specifically under what illumination conditions changes due to aggregation of nanoparticles can be captured as an image.

The present invention has been made in view of the above-mentioned problems. An object of the present invention is to provide a particle analyzer capable of numerically evaluating changes in thickness.

That is, as a result of extensive studies by the inventors of the present application, the present invention has found that, for example, in the case of a sample with a very small particle diameter such as nanoparticles, light is irradiated from the periphery of the cell and the light scattered by the particles is captured by a camera. This is based on the discovery for the first time that images corresponding to the degree of aggregation and changes in the shape of particles due to aggregation can be obtained by imaging with .

Specifically, the particle analysis apparatus according to the present invention includes a camera that captures an image of a cell in which a sample containing particles is accommodated, and light that travels obliquely to an imaging optical axis of the camera to an irradiation target area of the cell. and an aggregation analysis unit that analyzes aggregation of particles in the sample based on the image captured by the camera.

Further, a particle analysis method according to the present invention is a particle analysis method using a camera for capturing an image of a cell in which a sample containing particles is accommodated, wherein an irradiation target area of the cell is aligned with an imaging optical axis of the camera. and analyzing aggregation of particles in the sample based on the image captured by the camera.

With such a device, by irradiating the sample with light that travels obliquely with respect to the imaging optical axis of the camera, an image can be obtained in a dark field while increasing the amount of light scattered by the particles. I can take an image. As a result, it is easier to capture changes in aggregation than in the case of imaging changes in particles due to aggregation in a bright field with transmitted illumination as in the conventional method.

In other words, with the conventional technology that uses transmitted illumination, if the density of particles within the field of view changes significantly due to agglomeration, the transmitted light intensity detected by the camera will change significantly accordingly. For this reason, if the detection sensitivity of the camera is adjusted to the state before agglutination, a saturated region will occur in the image after agglutination, and information about the particles will be lost. On the other hand, if the detection sensitivity of the camera is adjusted so that the saturated part in the image after aggregation becomes too small, the transmitted light intensity detected before aggregation becomes too small, making it impossible to calculate the particle characteristics. It's gone. Also, even if a camera with a wide luminance detection range is used, the change in transmitted light intensity is too large. Therefore, when performing image processing, the threshold value for binarization must be greatly changed according to the process of aggregation. Therefore, it is difficult to guarantee that the settings are appropriate for evaluating changes in particle shape and size due to agglomeration.

On the other hand, in the present invention, even if the particle size changes greatly due to aggregation, the scattered light intensity does not change much compared to the transmitted light intensity, so the brightness of each pixel of the image is saturated before and after aggregation. Since it is difficult for particles to become too small, the degree of aggregation of particles can be quantified and evaluated.

In order to numerically evaluate the degree of aggregation based on the captured image, the aggregation analysis unit calculates an aggregation index indicating the degree of aggregation of particles based on the luminance distribution of the image captured by the camera. Any configuration may be used as long as it is configured to calculate.

In the case where aggregation of particles progresses over time, in order to be able to numerically evaluate the aggregated portion well, the aggregation analysis unit uses the first image, which is the luminance distribution of the first image captured at the reference time point. The aggregation index may be calculated by comparing the first luminance distribution with a second luminance distribution, which is the luminance distribution of a second image captured after a predetermined time has elapsed from the reference time point. .

As a specific configuration example of the aggregation analysis unit, correction is performed by multiplying each luminance of the second luminance distribution and the first luminance distribution by a predetermined value, or by adding a predetermined value to each luminance of the first luminance distribution. The aggregation index may be calculated based on the difference luminance distribution obtained by the difference from the second luminance distribution. That is, it is expected that the particles in the second image where particles are not aggregated are in the same dispersed state as the particles in the first image captured at the reference time. Therefore, it can be said that the difference luminance distribution reduces the influence of the luminance of the portion where the particles are dispersed in the second image and emphasizes the luminance of the portion where the particles are aggregated. Therefore, with the difference luminance distribution, the aggregation index can reflect the degree of aggregation of the particles.

In order to make only the influence of the part where the particles aggregate in the image stand out, the aggregation analysis unit calculates the aggregation index based on each frequency of brightness equal to or greater than a predetermined value in the differential brightness distribution. Anything is fine.

Another aspect of the agglutination analysis unit is to generate a binarized image with a threshold value that is greater by a predetermined value than the peak with the lowest luminance in the luminance distribution of the imaged image, and generate a binarized image based on the binarized image. to calculate the aggregation index. That is, the inventors of the present application have found that when particles are dispersed, the brightness distribution of an image becomes a normal distribution having a peak at a certain brightness, whereas when aggregation occurs, the symmetry of the brightness distribution is lost and the brightness becomes high. It was found that the frequency increases on the luminance side. Therefore, by extracting peaks with brightness higher than a predetermined value from the peaks representing the dispersed state of the particles, the binarized image can reflect only the agglomerated portions. In addition, with the binarized image generated in this way, it is easy to create an agglutination index that accurately reflects the degree of agglutination.

In order for the binarized image to be such that the part where the particles are dispersed is hardly illuminated and the part where the particles are aggregated is illuminated, the threshold value is substantially It may be set based on the average value and standard deviation of the portion forming a normal distribution.

If the aggregation index is calculated based on the size of the bright spots in the binarized image, the size of aggregated particles can be numerically evaluated.

In order to evaluate the degree of aggregation of particles using a simple statistical index, the aggregation analysis unit may calculate the skewness of the brightness distribution as the aggregation index.

As a configuration capable of capturing an image reflecting the degree of aggregation of particles even when the particle size of the particles in the sample is smaller than the resolution of the image captured by the camera, the illumination mechanism includes a ring having an observation hole. and the camera is arranged to image the cell through the observation hole.

As a preferred application example of the present invention, the particles contained in the sample are nanoparticles, and the size detectable in one pixel of the image captured by the camera is larger than the particle diameter of the nanoparticles. are mentioned.

If the cell comprises a pair of light-transmitting plates spaced apart from each other by a predetermined distance, it is easy for the camera to capture an image reflecting the degree of aggregation by the light scattered by the nanoparticles.

In order to make it easier for the camera to capture the contours of the particles contained in the sample, the light emitted from the illumination mechanism includes a wavelength component that excites the fluorescence of the particles contained in the sample. If it is That is, in the present invention, since an image is captured with light scattered by particles, even light with a low intensity such as fluorescence can be captured as a difference in intensity in the image.

A rotating mechanism for rotating the cell or a portion of the cell is further provided to allow application of a shear force to the sample so that the relationship between shear force and aggregation can be evaluated. Anything is fine.

In order to change the viscosity of the dispersion medium in which the particles are dispersed so that the relationship between the change in viscosity and aggregation can be evaluated, a temperature control mechanism for controlling the temperature of the cell should be provided. Just do it.

For example, in order to numerically evaluate the degree of aggregation of nanoparticles or the like, a camera for capturing an image of a cell in which a sample containing particles is accommodated is provided on the same side of the cell as the camera. and an illumination mechanism for irradiating an irradiation target area with light from the surroundings, the program used in the particle analysis apparatus for analyzing aggregation of particles in the sample based on the image captured by the camera. A program for a particle analyzer, which is characterized by causing a computer to exhibit the mechanisms of the agglutination analysis part and the function of the particle analyzer, may be used.

The program for the particle analyzer may be electronically distributed, or may be recorded on a program recording medium such as a CD, DVD, or flash memory.

As described above, with the particle analysis apparatus according to the present invention, it is possible to obtain an image in which the brightness changes according to the degree of aggregation, even for a sample containing particles with a small particle size such as nanoparticles. It is possible to numerically evaluate the degree of aggregation based on.

BRIEF DESCRIPTION OF THE DRAWINGS The schematic diagram which shows the structure of the particle analyzer in 1st Embodiment of this invention. 2 is a functional block diagram of the particle analyzer of the first embodiment; FIG. FIG. 4 is an image diagram showing changes in an image captured by aggregation of nanoparticles captured by the particle analyzer of the first embodiment. 7 is a graph showing an example of a first luminance distribution at a reference point of time and a second luminance distribution after a predetermined period of time has elapsed; 7 is a graph showing an image of extracting a luminance distribution due to agglomeration from the first luminance distribution and the corrected second luminance distribution; 7 is a graph showing an example of threshold values for generating a binarized image in the modified example of the first embodiment; FIG. 5 is an image diagram of a binarized image generated in a modified example of the first embodiment; The schematic diagram which shows the structure of the particle analyzer in 2nd Embodiment of this invention.

A particle analyzer 100 according to the first embodiment of the present invention will be described with reference to FIGS. 1 to 4. FIG.

The particle analyzer 100 of the first embodiment analyzes aggregation of particles based on an image of a sample S containing particles. Particles to be analyzed in the first embodiment are nanoparticles having a particle diameter on the order of 100 nm, for example.

As shown in FIG. 1, the particle analysis apparatus 100 is configured as a vertical apparatus in which the imaging optical axis is set along the vertical direction. It has a side mounted camera 2 and an illumination mechanism 3 . That is, the particle analysis apparatus 100 includes an illumination mechanism 3 configured to irradiate light from the periphery of the cell 1 to an irradiation target area set in the cell 1, and an image of the particle using light scattered by the particle. It has a camera 2 for taking an image and a lens barrel 4 in which lenses and the like are accommodated.

The cell 1 can be of the type used for measuring a high-concentration sample S, for example. In the first embodiment, the cell 1 includes a pair of light-transmitting plates 11 separated from each other by a predetermined distance, and spacers 12 provided between the light-transmitting plates 11 . Each light-transmitting plate 11 is formed in the shape of a thin disc, and an annular vapor-deposited film having a thickness of about 1 μm is formed on the outer peripheral portion of the inner surface of one of the light-transmitting plates 11 . This deposited film functions as the spacer 12 described above, and the sample S containing particles is accommodated in the accommodation space 13 surrounded by the light-transmitting plates 11 and the spacers 12 . At least one of the light-transmitting plates 11 has an introduction hole (not shown) for introducing the sample S into the accommodation space 13 and a lead-out hole for leading the sample S from the accommodation space 13 to the outside. (not shown) are formed.

The illumination mechanism 3 is a ring-shaped illumination provided above the cell 1 and near the end of the lens barrel 4 on the cell 1 side. The illumination mechanism 3 is configured to irradiate the cell 1 with light traveling obliquely with respect to the imaging optical axis OA of the camera 2 from the entire circumference. More specifically, the illumination mechanism 3 includes a ring-shaped housing 31 that has an observation hole 32 in the center and holds a large number of LEDs (not shown). The optical axis of each LED provided in the ring-shaped housing 31 is directed toward the central axis of the observation hole 32, and the surroundings of the irradiation target area set in the cell 1 are irradiated so as to concentrate. In other words, the illumination system 3 is configured as ambient illumination, and light with an angle of incidence of a predetermined angular range is incident on each point of the illuminated area over 360°. In other words, when the irradiation target area is viewed from a certain point, the rays are incident so as to concentrate on that point. It should be noted that the reflected component of the light emitted from the illumination mechanism 3 is configured so as not to enter the observation hole 32 or the lens barrel 4 . Therefore, only the light scattered by the particles in the cell 1 passes through the observation hole 32 and the lens barrel 4 to enter the camera 2 .

The camera 2 is equipped with a two-dimensional array detector such as a CCD, and a field of view of a predetermined size is set in the irradiation target area of the cell 1. Also, the imaging optical axis OA of the camera 2 is configured to enter the face plate portion of the cell 1 perpendicularly. The size detectable by one pixel of the image captured by the camera 2 is larger than the particle diameter of the nanoparticles. In other words, since the resolution, which is the value obtained by dividing the area of the field of view by the number of pixels, is greater than 100 nm, it is not possible to detect the outline of a nanoparticle that fits in one pixel. Therefore, it is difficult to obtain a particle size distribution using existing methods of detecting particle contours from captured images and calculating particle sizes such as area-equivalent diameters. In addition, the camera 2 of the first embodiment can capture a color or monochrome image, and can detect the brightness of each pixel with, for example, 256 gradations (8 bits).

Furthermore, the particle analyzer 100 has a CPU, a memory, an A/D converter, a D/A converter, various input/output devices, and a computer connected to the illumination mechanism 3 and camera 2 . By executing the program for the particle analyzer 100 stored in the memory, the computer controls the operation of each device as shown in FIG. function as an aggregation analysis unit 52 that analyzes aggregation of particles.

The device control unit 51 controls each device such that the illumination mechanism 3 intermittently emits light in synchronization with the shutter timing of the camera 2, for example.

The aggregation analysis unit 52 analyzes aggregation of particles in the sample S based on the image captured by the camera 2 . In the first embodiment, the aggregation analysis unit 52 calculates an aggregation index indicating the degree of particle aggregation based on each luminance distribution in the images of the plurality of samples S captured at different times. Specifically, the aggregation analysis unit 52 analyzes the first luminance distribution, which is the luminance distribution of the first image captured at the time when the sample S is introduced into the cell 1, which is the reference time, and An aggregation index is calculated from the second luminance distribution, which is the luminance distribution of the captured second image.

Here, when the sample S containing nanoparticles is imaged by the particle analysis apparatus 100 of the first embodiment, as shown in the schematic diagram of FIG. The size and brightness of each bright spot in one image are substantially uniform, and as shown in FIG. Bright spots appear. It is considered that such a change occurs because the intensity of scattered light increases with aggregation. In addition, the number of such large bright spots increases as time elapses.

Next, Fig. 4 shows an example of changes in luminance distribution due to aggregation. As shown in the first image of FIG. 3(a), when almost no aggregation occurs at the reference point, the brightness obtained from the particles in the image varies around a certain brightness. Therefore, the histogram of the first luminance distribution forms a normal distribution having a peak at a certain luminance as shown in FIG. 4(a).

On the other hand, as shown in the second image of FIG. 3(b), a predetermined time has passed (for example, 30 minutes have passed since the reference time point), and as the aggregation progresses, the histogram of the second luminance distribution changes as shown in FIG. 4(b). forms two mountains. Here, in FIG. 4B, the peak on the low-luminance side (left side) has a shape similar to a normal distribution similar to the peak shape of FIG. 4A, and the average value is slightly lower. Therefore, the peak on the low-luminance side of the second luminance distribution in FIG. 4B is considered to be the luminance of scattered light due to particles that have not aggregated and are in a dispersed state even after the elapse of a predetermined time. In addition, since the peak on the high brightness side (right side) of the second brightness distribution in FIG. 4B did not exist at the reference time, it is considered that the peak occurred due to an increase in scattered light intensity due to aggregation.

For these reasons, the aggregation analysis unit 52 extracts only the peaks on the high brightness side of the second brightness distribution in FIG. 4(b) and calculates the aggregation index in the following procedure.

That is, as shown in FIG. 5, the aggregation analysis unit 52 sets each luminance in the first luminance distribution so that the peak of the first luminance distribution and the peak on the low luminance side of the second luminance distribution overlap within a predetermined error range. Multiplying it, the corrected first luminance distribution is calculated. Then, the aggregation analysis unit 52 calculates a difference luminance distribution obtained by, for example, the difference between the second luminance distribution and the corrected first luminance distribution. The aggregation analysis unit 52 outputs the difference luminance distribution itself calculated in this way, for example, as an aggregation index. Alternatively, the aggregation analysis unit 52 may calculate the average value of the difference luminance distribution or the like as the aggregation index.

With the particle analyzer 100 configured in this manner, an image capturing changes in aggregation of particles can be captured by capturing the scattered light of the particles obtained by the illumination mechanism 3 configured as ambient illumination. That is, even if the optical settings of the particle analyzer 100 are not changed before and after agglutination, the pixels of the image are not saturated and the brightness is insufficient for particle analysis. can be done. Therefore, changes in the shape and size of particles due to aggregation can be reflected in each pixel of the image.

In addition, by subtracting such a peak, the aggregation analysis unit 52 detects the occurrence of aggregation by utilizing the fact that the portion representing the dispersed particles in the luminance distribution of the imaged image has a substantially normal distribution. A difference luminance distribution, which is the luminance distribution of the portion where the

Furthermore, since the difference luminance distribution changes with the elapsed time and progress of aggregation, it is possible to calculate an aggregation index that quantifies the aggregation process appearing in each image based on the difference luminance distribution. That is, since it is thought that the magnitude of luminance in the differential luminance distribution correlates with the size of aggregated particles, and that each frequency correlates with the number of aggregated particles, changes in the shape and size of particles due to aggregation can be evaluated numerically.

In addition, with such an aggregation index, it is thought that the peak shifts to the high brightness side when aggregation progresses and one large particle group is formed. Therefore, it is possible to establish a correlation between the aggregation index and the particle size, and to match the interpretation of each captured image by a human being.

Next, a modified example of the first embodiment will be described. In the modified example, the configuration and operation of the aggregation analysis unit 52 are different.

Specifically, as shown in FIG. 6, the aggregation analysis unit 52 generates a binarized image by using a brightness that is greater by a predetermined value as a threshold than the peak with the lowest brightness in the brightness distribution of the captured image. An aggregation index is calculated based on the valued image. In other words, the aggregation analysis unit 52 determines that light scattered by aggregated particles is caused by a brightness higher than a predetermined value based on the brightness at the peak of the normal distribution, which is the brightness distribution of the particles in the dispersed state. . In this modified example, the threshold is set based on the average value and the standard deviation σ of the portion of the luminance distribution that substantially forms a normal distribution. In FIG. 6, the brightness that is the peak brightness of each normal distribution is used as a reference, and the brightness that is greater by 2σ is set as a threshold. Part 52 generates.

As described above, in the histogram of the luminance distribution, the unevenness that occurs in the part that deviates from the normal distribution is due to the agglomerated particles. Only the group is in the disambiguated state. For example, the first image and the second image shown in FIG. 3 can be converted into binarized images as shown in FIG. 7 in this modified example. Then, the aggregation analysis unit 52 calculates the size of the cluster of particles as an aggregation index from the binarized image.

If it is like this, it is possible to visually clarify the aggregation process by generating a binarized image of the image of the sample S captured as time passes. Also, the size of the aggregated particle group, which is an index of aggregation, can be easily evaluated using a conventional image analysis algorithm.

Next, another modified example of the first embodiment will be described.

In this modified example, the wavelength of the light emitted from the illumination mechanism 3 excites particles in the sample S to generate fluorescence. For example, if a white LED is used for the illumination mechanism 3, the possibility that the excitation wavelength is included can be increased regardless of the sample, and a fluorescence image in which particles emit fluorescence can be easily obtained. With such a device, the edge of the target particle can be highlighted in the captured image, and only the target particle can be easily extracted to evaluate its size and shape.

Here, in order to make it easier to detect only specific fluorescent particles, the wavelength of the light emitted from the illumination mechanism 3 may be limited to the excitation wavelength of the particles. For example, the wavelength of the light emitted from the illumination mechanism 3 may be limited by using a filter that passes only a specific wavelength, or the light source of the illumination mechanism 3 may emit light of a specific wavelength. Alternatively, an excitation light cut filter may be provided between the lens of the camera 2 and the cell 1 to extract only fluorescence of a specific wavelength.

Next, a particle analyzer 100 according to a second embodiment of the present invention will be described with reference to FIG. The particle analysis apparatus 100 of the second embodiment is configured to evaluate the characteristics of particles by combining the analysis result of the particle image described in the first embodiment and the analysis result of the particle by laser diffraction.

That is, the particle analyzer 100 of the second embodiment includes the first laser 6 that emits a red laser, the second laser 7 that emits a blue laser, and the particles diffracted or scattered by the particle analyzer 100 of the first embodiment. A laser detection mechanism (not shown) for detecting the emitted laser light, a rotation mechanism (not shown) for rotating the entire cell 1 or a part of the cell 1, and a camera 2 are provided on the opposite side of the cell 1. and a transmitted illumination mechanism 8 .

With the particle analysis apparatus 100 of the second embodiment, for example, one of the light-transmitting plates 11 of the cell 1 is rotated by the rotation mechanism to obtain an image of the particles while applying a shearing force to the sample S. can be done. Therefore, it becomes possible to numerically evaluate the effect of particle aggregation due to shear force.

In addition, by rotating the cell 1, the position of the field of view of the camera 2 on the cell 1 can be changed, and the characteristic evaluation regarding aggregation and the like of the entire sample S can be realized.

Based on the image of the particles obtained by the transmitted illumination mechanism 8, the particle diameter and shape may be measured, and the amount of transmitted light may be imaged by the camera 2 to calculate the concentration of the sample S and the like.

For particles with a large particle size, it is possible to measure the particle size distribution based on red laser diffraction while evaluating aggregation using the captured image. For particles with a small particle size, the particle size distribution can be measured on the basis of blue laser diffraction while the aggregation is evaluated using the captured image. For example, it is conceivable to correct the influence of aggregation from the calculated particle size distribution.

Other modifications will be explained.

In the particle analyzer of the first embodiment, the aggregation analysis unit may calculate the skewness of the luminance distribution as the aggregation index. With such a structure, the degree of agglomeration can be numerically evaluated by utilizing the fact that the luminance distribution deviates from the normal distribution.

The corrected first luminance distribution in the first embodiment is not limited to multiplying each luminance of the first luminance distribution by a predetermined value, and may be corrected by adding a predetermined value to each luminance. Further, instead of correcting the first luminance distribution, the difference between the corrected second luminance distribution obtained by correcting the second luminance distribution and the first luminance distribution may be obtained to obtain the difference luminance distribution. Also, the reference time point at which the first luminance distribution is measured is not limited to the time point when the sample is introduced into the cell, and may be any time point. That is, the first luminance distribution and the second luminance distribution may be luminance distributions obtained from images of the same sample taken at different times.

Further, in the modification of the first embodiment described above, the average value μ (peak luminance) obtained by performing fitting assuming that the peak with the lowest luminance in the luminance distribution of the captured image is a normal distribution and the standard deviation σ, the method of setting the threshold is not limited to this. For example, in the modified example, the threshold is set to a brightness that is 2σ greater than the average μ, but the threshold may be a brightness that is 3σ greater than the average μ. Alternatively, without using the standard deviation σ, an arbitrary value may be added to the average μ as the threshold. In addition, when each luminance of the luminance distribution is corrected by multiplying it by a predetermined value, the threshold value may be set by multiplying the standard deviation σ by the same magnification.

In each embodiment, the particle analysis device was configured as a vertical device with the imaging optical axis along the vertical direction, but it may be configured as a horizontal device with the imaging optical axis along the horizontal direction. Also, the cell is not limited to the high-concentration sample cell shown in each embodiment. For example, an existing rectangular parallelepiped cell, flow cell, or the like may be used.

A temperature control mechanism may be provided to control the temperature of the cell, and the relationship between temperature change and particle aggregation may be numerically evaluated based on the captured image. Here, the temperature control mechanism may include a heater for heating the sample, or may include a cooler for cooling the sample. The temperature control mechanism controls the temperature of the dispersion medium in which the particles are dispersed in the particles to adjust the viscosity of the dispersion medium. can also be done. In other words, it is also possible to evaluate the relationship between the viscosity of the dispersion medium and the aggregation of the particles.

The lighting mechanism is not limited to ring lighting, and may be, for example, dome-shaped lighting. That is, the illumination mechanism may irradiate the illumination target area of the cell with light that travels obliquely with respect to the imaging optical axis of the camera. For example, the illumination mechanism is not limited to irradiating light from the entire periphery of the imaging optical axis, and may irradiate light only from a partial range. Further, the illumination mechanism is not limited to being provided on the same side of the cell as the camera, and may be provided on the opposite side of the cell to the camera. In addition, the illumination mechanism may be configured as a dark-field illumination, and the light emitted from the illumination mechanism that is reflected by the cell or the sample or transmitted through the cell or the sample does not directly enter the camera. so that only the light scattered by the particles is directly incident on the camera. Moreover, the light source is not limited to the LED, and may be one that emits light based on other principles.

In addition, as long as it does not contradict the spirit of the present invention, various modifications of the embodiments and combinations of parts of the embodiments may be made.

According to the present invention, it is possible to obtain an image in which the brightness changes according to the degree of aggregation even for a sample containing particles with a small particle size such as nanoparticles, and based on this image, the degree of aggregation can be numerically evaluated. It is possible to provide a particle analyzer capable of evaluating

100 : Particle analyzer 1 : Cell 11 : Translucent plate 12 : Spacer 13 : Housing space 2 : Camera 3 : Illumination mechanism 31 : Housing 32 : Observation hole 4 : Lens barrel 51 : Equipment control unit 52 : Aggregation analysis unit 6 : First laser 7 : Second laser 8 : Transmitted illumination mechanism S : Sample

Claims

a camera for imaging a cell in which a sample containing particles is housed;
an illumination mechanism that irradiates the irradiation target area of the cell with light that travels obliquely with respect to the imaging optical axis of the camera;
and an aggregation analysis unit that analyzes aggregation of particles in the sample based on the image captured by the camera.
The particle analysis device according to claim 1, wherein the aggregation analysis unit is configured to calculate an aggregation index indicating the degree of aggregation of particles based on the luminance distribution of the image captured by the camera.
The aggregation analysis unit performs a first luminance distribution, which is the luminance distribution of a first image captured at a reference time point, and a second luminance distribution, which is a luminance distribution of a second image captured after a predetermined time has elapsed from the reference time point. 3. The particle analyzer according to claim 2, wherein the aggregation index is calculated by comparing the .
difference between the second luminance distribution and the corrected first luminance distribution obtained by multiplying each luminance of the first luminance distribution by a predetermined value or adding a predetermined value to each luminance of the first luminance distribution; 4. The particle analysis apparatus according to claim 3, wherein the aggregation index is calculated based on the differential luminance distribution obtained in .
The particle analysis apparatus according to claim 4, wherein the aggregation analysis unit calculates the aggregation index based on each frequency of brightness equal to or greater than a predetermined value in the differential brightness distribution.
The agglutination analysis unit generates a binarized image by using a peak having the lowest luminance as a reference in the luminance distribution of the imaged image and using a luminance that is greater by a predetermined value as a threshold, and calculates the agglutination index based on the binarized image. 3. The particle analyzer of claim 2, wherein the particle size is calculated.
The particle analyzer according to claim 6, wherein the threshold value is set based on the average value and standard deviation of a portion of the luminance distribution that substantially forms a normal distribution.
The particle analysis device according to claim 7, wherein the aggregation index is calculated based on the size of bright spots in the binarized image.
The particle analysis device according to claim 2, wherein the aggregation analysis unit calculates the skewness of the brightness distribution as the aggregation index.
The illumination mechanism is a ring-shaped illumination having an observation hole,
10. A particle analysis apparatus according to any one of claims 1 to 9, wherein said camera is arranged to image said cell through said observation hole.
the particles contained in the sample are nanoparticles;
11. The particle analyzer according to any one of claims 1 to 10, wherein a size detectable by one pixel of an image captured by said camera is larger than a particle diameter of nanoparticles.
The particle analyzer according to any one of claims 1 to 11, wherein the cell comprises a pair of transparent plates spaced apart from each other by a predetermined distance.
The particle analysis apparatus according to any one of claims 1 to 12, wherein the light emitted from the illumination mechanism contains a wavelength component that excites fluorescence in particles contained in the sample.
The particle analyzer according to any one of claims 1 to 13, further comprising a rotation mechanism for rotating the cell or part of the cell.
The particle analyzer according to any one of claims 1 to 12, further comprising a temperature control mechanism for controlling the temperature of said cell.
A particle analysis method using a camera for imaging a cell in which a sample containing particles is accommodated,
irradiating an irradiation target area of the cell with light that travels obliquely with respect to the imaging optical axis of the camera;
A particle analysis method, comprising analyzing aggregation of particles in the sample based on the image captured by the camera.
A particle analysis comprising: a camera that captures an image of a cell in which a sample containing particles is stored; and an illumination mechanism that irradiates an irradiation target area of the cell with light that travels obliquely with respect to the imaging optical axis of the camera. A program for use in a device comprising:
A program for a particle analyzer, characterized by causing a computer to exhibit a mechanism as an agglutination analysis unit that analyzes agglutination of particles in the sample based on the image captured by the camera.