Measurement system for nanobubbles

Abstract

To provide a measurement system for nanobubbles in nanobubble-containing water, which enables confirmation taking into account the size of the nanobubbles in the nanobubble-containing water.SOLUTION: A measurement system for nanobubbles comprises: a container (11) that stores nanobubble-containing water; an irradiator (12) that irradiates the container (11) with laser light to generate a light path (L) in the nanobubble-containing water; an image sensor (13) that images the light path (L) to generate image data; and an analyzer (14) that counts the number of nanobubbles in the nanobubble-containing water using the image data. The analyzer (14) has: luminance distribution data generation means (m1) for generating luminance distribution data from the image data; and counting means (m2) for counting the nanobubbles in the luminance distribution data.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a measurement system for nanobubbles contained in nanobubble-containing water.

Background Art

[0002] Nanobubble-containing water in which so-called nanobubbles are contained in water is known as water having a high cleaning effect. Therefore, various production apparatuses have been proposed, such as an apparatus capable of more easily producing nanobubble-containing water or an apparatus capable of producing more highly functional nanobubble-containing water.

[0003] Here, a nanobubble is a bubble having a diameter smaller than 1 μm, and is usually recognized as a bubble having a diameter of about 10 to 500 nm. Due to its small size, this nanobubble has a small buoyancy and can continue to float in water for several weeks to several months.

[0004] In the case of paying attention to the cleaning effect of nanobubble-containing water, it is also known that the size and amount of the contained nanobubbles are important. Therefore, it is required to confirm how many nanobubbles the produced nanobubble-containing water contains.

[0005] As a method for confirming the presence of nanobubbles in nanobubble-containing water, a method has been proposed in which nanobubble-containing water is photographed with a digital camera or the like to generate image data that enables visual recognition of the nanobubbles in the nanobubble-containing water, and the nanobubbles are counted using this image data (see, for example, Patent Document 1).

[0006] As another confirmation method, a method has been proposed in which laser light is irradiated onto nanobubble-containing water to cause diffraction or scattering of the laser light by the nanobubbles in the nanobubble-containing water, and the intensity of the diffracted or scattered light is measured (see, for example, Patent Documents 2 and 3).

[0007] However, in the method of counting nanobubbles using image data, although it is feasible for so-called microbubbles, it is extremely difficult to photograph nanobubbles at a visible size, and it is not practical. Also, in the method using diffracted light or scattered light generated by laser light, high precision of the device itself is required for stable measurement, the device becomes extremely expensive, and there is a problem that it is often not easily available.

[0008] In view of these situations, as a method for more easily confirming the presence of nanobubbles in nanobubble-containing water, a method utilizing the Tyndall phenomenon that occurs when laser light is irradiated onto nanobubble-containing water has been proposed.

[0009] In this method, the Tyndall phenomenon generates a visible optical path in the nanobubble-containing water, and image data is generated by photographing this optical path with a digital camera. The concentration of nanobubbles in the nanobubble-containing water can be evaluated from the average value of the luminance or the average value of the RGB values of this image data (see, for example, Patent Document 4). By evaluating the concentration of nanobubbles in the nanobubble-containing water through image analysis of the image data, it has become a relatively inexpensive method for confirming nanobubbles.

Prior Art Documents

Patent Documents

[0010]

Patent Document 1

Patent Document 2

Patent Document 3

Patent Document 4

Summary of the Invention

Problems to be Solved by the Invention

[0011] However, in the method of evaluating the concentration of nanobubbles in nanobubble-containing water from the average value of the luminance of the above-described image data or the average value of the RGB values, although it is possible to estimate the concentration of nanobubbles in nanobubble-containing water, it does not guarantee whether the nanobubbles of the size that causes the target cleaning effect are included.

[0012] The present inventors aim to provide a measurement system for nanobubbles in nanobubble-containing water that enables confirmation considering the size of nanobubbles in nanobubble-containing water.

Means for Solving the Problem

[0013] The nanobubble measurement system of the present invention is a nanobubble measurement system including a container for storing nanobubble-containing water, an irradiator for irradiating the container with laser light to generate an optical path in the nanobubble-containing water, an image sensor for generating image data by photographing the optical path, and an analyzer for counting the number of nanobubbles in the nanobubble-containing water using the image data. The analyzer is characterized by having luminance distribution data generation means for generating luminance distribution data from the image data and counting means for counting nanobubbles using the luminance distribution data.

[0014] Furthermore, the nanobubble measurement system of the present invention also has the following features. (1) When the analyzer counts nanobubbles by the counting means, it has frequency distribution data generation means for generating frequency distribution data of peaks by counting for each size of the peaks appearing in the luminance distribution data. (2) The first color, the second color, and the third color are each any one color selectively selected without overlap from the three primary colors of light. When the irradiator irradiates laser light of the first color, the analyzer generates luminance distribution data of the second color from the image data by the luminance distribution data generation means, and counts nanobubbles using the luminance distribution data of the second color by the counting means. (3) The parser has a luminance distribution data generation means that generates luminance distribution data of a third color from the image data, and a frequency distribution data generation means that generates frequency distribution data of the third color from the luminance distribution data of the third color. The parser has an error detection means that uses the frequency distribution data of the third color for error detection. (4) It has an average luminance value calculation means for calculating an average luminance value from the luminance distribution data.

Advantages of the Invention

[0015] According to the present invention, an analyzer for counting the number of nanobubbles in nanobubble-containing water by imaging the optical path generated by irradiating the nanobubble-containing water with a laser beam has a luminance distribution data generation means for generating luminance distribution data from the image data, and a counting means for counting nanobubbles from the luminance distribution data, thereby enabling the provision of a measurement system capable of counting the number of nanobubbles.

[0016] Thereby, it is possible not only to confirm the performance of the apparatus for producing nanobubble-containing water but also to ensure the quality of the produced nanobubble-containing water.

Brief Description of the Drawings

[0017]

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Embodiments for Carrying Out the Invention

[0018] In the nanobubble measurement system of the present invention, the number of nanobubbles in water containing nanobubbles is counted using image data.

[0019] That is, as schematically shown in FIG. 1, the nanobubble measurement system of the present invention includes a container 11 for storing water containing nanobubbles, an irradiator 12 that irradiates the container 11 with laser light to generate an optical path L in the water containing nanobubbles, an image sensor 13 that generates image data by photographing the optical path L, and an analyzer 14 that counts the number of nanobubbles in the water containing nanobubbles using the image data.

[0020] Here, the water containing nanobubbles is prepared using an appropriate nanobubble generator. In the nanobubble generator, for example, a bubble generator provided with a large number of fine pores is immersed in water, and bubbles are generated by the bubble generator to generate water containing nanobubbles.

[0021] The container 11 is preferably a prismatic quartz cell that is also used in a spectrophotometer or the like. A beaker or the like may also be used.

[0022] The irradiator 12 is preferably a laser light source capable of irradiating laser light of a predetermined single wavelength. This laser light source includes the three primary colors of a red laser light source, a green laser light source, and a blue laser light source, and may be switchable as appropriate. Alternatively, for cost reduction, it may be a single-color laser light source. For example, a green laser pointer may be used as the laser light source.

[0023] The laser light emitted from the irradiator 12 passes through a cylindrical lens 15 and is diffused light whose irradiation area expands as it travels in the irradiation direction.

[0024] When the water containing nanobubbles in the container 11 is irradiated with laser light, the Tyndall phenomenon occurs in which the laser light is scattered by the nanobubbles in the water containing nanobubbles, and the irradiation area of the laser light appears as a visible optical path L.

[0025] The image sensor 13 is provided in a direction perpendicular to the extending direction of the optical path L.

[0026] The imaging device 13 is composed of a so-called CCD (Charge Coupled Device) image sensor or a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor, and in combination with an appropriate lens or the like, it generates image data by imaging the optical path L as described later. A digital camera incorporating a CCD image sensor or a CMOS image sensor may also be used.

[0027] The analyzer 14 is a personal computer or the like. In this embodiment, a tablet-type personal computer is used. A desired program is installed in the storage device of this analyzer 14, and by the CPU of the analyzer 14 executing this program, the analyzer 14 is made to function as a luminance distribution data generation means m1, a counting means m2, a frequency distribution data generation means m3, an average luminance value calculation means m4, and an error detection means m5 as described later.

[0028] Also, the analyzer 14 controls the irradiator 12 and the imaging device 13. In particular, the image data created by the imaging device 13 is sequentially input to the analyzer 14. The irradiator 12 and the analyzer 14 are connected by a first wiring 17 via a connector 16 connected to the analyzer 14, and the imaging device 13 and the analyzer 14 are also connected by a second wiring 18 via a connector 16 connected to the analyzer 14.

[0029] The container 11, the irradiator 12 and the cylindrical lens 15, and the imaging device 13 are desirably installed at least in a dark room. In this embodiment, as shown in FIG. 2, as a nanobubble measuring device, a dark room state is realized by a predetermined housing 21.

[0030] In the housing 21 of the present embodiment, an opening / closing lid 22 is provided on the upper surface. By flipping up this opening / closing lid 22, the container 11 made of a quartz cell can be accommodated in the housing 21. After the container 11 is accommodated in the housing 21, by closing the opening / closing lid 22, the inside of the housing 21 is made into a dark room state. The irradiator 12, the cylindrical lens 15, and the imaging element 13 are fixedly mounted at predetermined positions inside the housing 21. Further, the analyzer 14 configured by a tablet-type personal computer is mounted on the upper surface of the housing 21 and is also made to function as an operation panel of the nanobubble measuring device.

[0031] Hereinafter, based on the flowchart of FIG. 3, the operation of the nanobubble measurement system using the nanobubble measuring device will be described. For convenience of explanation, unless otherwise particularly mentioned, it will be described assuming that the irradiator 12 uses a green laser light source.

[0032] First, the main power supply of the nanobubble measuring device is turned on (step S1), the operation panel is set to the activated state, and the measurement mode is selected (step S2). At this time, the irradiator 12 and the imaging element 13 are each in the power-on state, and after the completion of the initial operation, they are in the idling state.

[0033] Next, as the setting of the measurement sample, nanobubble-containing water is put into the container 11 made of a quartz cell, the water and fingerprints etc. attached to the surface of the container 11 are carefully wiped off, it is accommodated in the housing 21 of the nanobubble measuring device, and the opening / closing lid 22 is closed (step S3). The opening / closing operation of the opening / closing lid 22 is detected by a limit switch (not shown), and by detecting that the opening / closing lid 22 has become the closed state, it is configured that the irradiator 12 automatically starts irradiating laser light (step S4).

[0034] After the laser light is irradiated by the irradiator 12, the imaging element 13 performs imaging of the optical path L and generates image data (step S5). In the present embodiment, the imaging element 13 performs imaging with a shooting range of 7.0×3.7 mm and generates image data.

[0035] The image data generated by the imaging device 13 is sequentially output and input to the analyzer 14. Alternatively, the imaging device 13 may capture an image based on the input of a shutter signal from the analyzer 14, generate image data, and input the image data to the analyzer 14. In the analyzer 14, the input image data is stored in the memory as primary image data, and then the analyzer 14 functions as trimming means to generate secondary image data obtained by cutting out the portion of the optical path L from the primary image data (step S6). In the present embodiment, the secondary image data is 200×200 pixel image data. It is desirable that the secondary image data be generated by cutting it out from as close to the central portion as possible of the captured optical path L.

[0036] By executing a luminance distribution data generation program, the analyzer 14 functions as luminance distribution data generation means m1 to generate the luminance distribution data shown in FIG. 4 from the secondary image data (step S7). Specifically, the luminance of gray, which is total scattered light, is calculated at a predetermined gradation from the secondary image data, which is color image data, to obtain the luminance distribution data. In the present embodiment, each luminance is calculated with an 8-bit gradation (0 - 255), but it is not limited to an 8-bit gradation.

[0037] Next, by executing a count program, the analyzer 14 functions as counting means m2 to count the nanobubbles from the luminance distribution data (step S8).

[0038] Note that when counting the nanobubbles, a threshold value is set in the present embodiment. That is, it is considered that the nanobubbles exist at the top of the protrusion protruding upward in a mountain shape in the luminance distribution data, and this top is called a "peak". By setting a threshold value when detecting this peak, individual peaks can be detected.

[0039] Referring to FIG. 5, FIG. 5(a) shows a two-dimensional data portion of the luminance distribution data represented in three dimensions. In this case, the luminance distribution data appears as a first curve C1 in which the luminance value changes continuously as it progresses along the horizontal axis method.

[0040] As shown in FIG. 5(a), when the threshold value is set below the first curve C1, in the detection program for detecting the peak of this first curve C1, scanning is performed from the left end to the right end of the horizontal axis to identify the maximum luminance value, but only one indicated by a circle mark in FIG. 5(a) will be identified. Clearly, the peak has not been detected.

[0041] Next, as shown in FIG. 5(b), the threshold value is raised by one step. By treating the intersection of the first curve C1 in FIG. 5(a) and the straight line of the threshold value and assuming that there is no portion below the threshold value, the luminance distribution data in FIG. 5(b) will be composed of a second curve C2, a third curve C3, and a fourth curve C4.

[0042] For this second curve C2, third curve C3, and fourth curve C4, in the detection program for detecting the peak, when scanning from the left end to the right end of the horizontal axis to identify the maximum luminance value, since each curve C2, C3, C4 can be distinguished and recognized, the maximum luminance value can be identified for each of the curves C2, C3, C4, and three peaks indicated by circle marks in FIG. 5(b) will be identified.

[0043] In this way, while raising the threshold value, for the fifth curve C5, sixth curve C6, seventh curve C7, and eighth curve C8 in FIG. 5(c), for the ninth curve C9, tenth curve C10, eleventh curve C11, twelfth curve C12, and thirteenth curve C13 in FIG. 5(d), for the fourteenth curve C14, fifteenth curve C15, sixteenth curve C16, and seventeenth curve C17 in FIG. 5(e), and for the eighteenth curve C18, nineteenth curve C19, and twentieth curve C20 in FIG. 5(f), the detection program for detecting the peak scans from the left end to the right end of the horizontal axis to identify the maximum luminance value respectively, thereby enabling the identification of the peaks indicated by circle marks respectively.

[0044] In this way, since the number of peaks detected by the threshold value is different, in the present embodiment, the threshold value is sequentially increased from the smaller luminance value, and the threshold value at which the number of detected peaks becomes the maximum is set as the true threshold value. In FIG. 5, the threshold value shown in FIG. 5(d) becomes the true threshold value.

[0045] Using the threshold value set in this way, the analyzer 14 identifies the coordinates at which the luminance value becomes a peak and the magnitude of the peak, that is, the luminance value, and counts the number of identified peaks as the count of nanobubbles.

[0046] From this count result, the number of nanobubbles in the nanobubble-containing water can be estimated, and the quality of the nanobubble-containing water can be determined, and the performance of the manufacturing apparatus that manufactured this nanobubble-containing water can be confirmed.

[0047] Furthermore, the analyzer 14 functions as the frequency distribution data generation means m3 by executing a frequency distribution data generation program, and when counting nanobubbles as the counting means m2, it counts for each magnitude of the peak appearing in the luminance distribution data, thereby generating the peak frequency distribution data shown in FIG. 6 (step S9).

[0048] In actuality, the frequency distribution data generation means m3 operates in conjunction with the counting means m2, and generates the frequency distribution data in parallel with the counting in the counting means m2. By generating the frequency distribution data, the distribution state of the generated nanobubbles can be confirmed. In particular, when the nanobubble-containing water is used for washing purposes, since the size of the nanobubbles is important, it is possible to easily confirm whether nanobubbles of the target size are being generated.

[0049] Furthermore, the analyzer 14 functions as an average luminance value calculation means m4 by executing an average luminance value calculation program, and calculates the average luminance value from the luminance distribution data (step S10). In this calculation of the average luminance value, the threshold value used for peak detection is used, and the average value of the luminance values below the threshold value is calculated from the luminance distribution data shown in FIG. 4.

[0050] As a tendency of the nano-bubble-containing water, as the content of nano-bubbles smaller than 100 nm in diameter increases, the average luminance value tends to increase. Therefore, it can be presented as one of the judgment indexes for the state of the nano-bubble-containing water.

[0051] After calculating the average luminance value, the analyzer 14 displays the count result on the display of the analyzer 14, and also displays the luminance distribution data, the frequency distribution data, and the average luminance value as the output of the measurement result (step S11). When displaying the count result, it is output after converting it to the number of nano-bubbles in 1 mL. This count result, luminance distribution data, and frequency distribution data can be stored as management data in the analyzer 14.

[0052] By periodically sampling the nano-bubble-containing water and counting the number of nano-bubbles, it is possible to detect abnormalities in the nano-bubble generator and perform process management.

[0053] In the above description, the counting is performed using the luminance of the secondary image data. However, since the original data of the secondary image data, the image data, is color data, attention is paid to the components of the first color, the second color, and the third color, which are the three primary colors of light. The luminance distribution data generation means m1 of the analyzer 14 can generate the luminance distribution data of the first color, the luminance distribution data of the second color, and the luminance distribution data of the third color from the image data and use them for counting nano-bubbles.

[0054] That is, after generating the secondary image data as color image data in step S6 described above, in the luminance distribution data generation routine of step S7, the analyzer 14 functions as the luminance distribution data generation means m1 by executing a luminance distribution data generation program, and can also generate the luminance distribution data of the first color, the luminance distribution data of the second color, and the luminance distribution data of the third color.

[0055] Here, for the sake of convenience of explanation, in this embodiment, since the irradiator 12 is a green laser light source, the first color will be described as "green", the second color as "red", and the third color as "blue".

[0056] The green luminance distribution data is generated by calculating the green luminance from the secondary image data at a predetermined gradation, the red luminance distribution data is generated by calculating the red luminance from the secondary image data at a predetermined gradation, and the blue luminance distribution data is generated by calculating the blue luminance from the secondary image data at a predetermined gradation. Also in this embodiment, each luminance is calculated at an 8-bit gradation (0 - 255), but it is not limited to the 8-bit gradation. The green luminance distribution data is shown in Fig. 7(a), the red luminance distribution data is shown in Fig. 7(b), and the blue luminance distribution data is shown in Fig. 7(c).

[0057] Using these respective luminance distribution data, the frequency distribution data shown in Fig. 8 can be generated by the count routine of step S8 and the frequency distribution data generation routine of step S9 described above. The frequency distribution data generated from the green luminance distribution data is shown in Fig. 8(a), the frequency distribution data generated from the red luminance distribution data is shown in Fig. 8(b), and the frequency distribution data generated from the blue luminance distribution data is shown in Fig. 8(c).

[0058] In this embodiment, since the irradiator 12 is a green laser light source, in the green luminance distribution data which is the image data of the first color, due to being affected by the green laser light source, the average luminance value is larger compared to the case of other colors. That is, it means that the increase in luminance due to the background light has occurred. Therefore, it is difficult to specify the peak, particularly the luminance value, using the green luminance distribution data.

[0059] On the other hand, in the luminance distribution data of red, which is the luminance distribution data of the second color, and the luminance distribution data of blue, which is the luminance distribution data of the third color, the influence of the luminance increase can be eliminated.

[0060] Note that the luminance distribution data of the second color may be not only the luminance distribution data of red but also the luminance distribution data of blue. However, due to the influence of the sensitivity of the image sensor constituting the imaging element 13, as is clear from the comparison of the frequency distribution data in FIGS. 8(b) and 8(c), the number of nanobubbles detected in the blue luminance distribution data may decrease. Therefore, in the present embodiment, the red luminance distribution data in which more nanobubbles are detected is used as the image data of the second color. In particular, as shown in FIG. 7(b), in the red luminance distribution data, the peak portion has a sharp protruding shape, so it is easier to detect nanobubbles.

[0061] Also, in the frequency distribution data generated from the green luminance distribution data shown in FIG. 8(a), as the luminance increases, that is, as the horizontal axis moves to the right, not only does the frequency gradually decrease, but sometimes the frequency may increase. This is one of the effects of the above-described luminance increase, but in addition, there may be cases where nanobubbles bind to each other in the nanobubble-containing water of the subject to form a so-called lump, or cases where bubbles with a large particle size are formed. Alternatively, it may be caused by dirt on the container 11 or a defect in the imaging element 13 itself.

[0062] Influences other than the influence of the luminance increase also appear in the red frequency distribution data and the blue frequency distribution data. For example, in the blue frequency distribution data in which the detection frequency of nanobubbles tends to be small, as shown in FIG. 8(c), the frequency becomes "0" from a relatively low luminance, but on the high-luminance side, the frequency may suddenly become non-zero. For the sake of convenience of explanation, this case will be referred to as an "abnormal peak".

[0063] Abnormal peaks appearing in the frequency distribution data generated from such blue luminance distribution data are likely to be caused by effects other than the influence of increased luminance, that is, something other than nanobubbles, such as clusters of nanobubbles, large bubbles, or impurities.

[0064] Therefore, the analyzer 14 activates an error detection program to function as error detection means m5, detects abnormal peaks from the frequency distribution data generated from the blue luminance distribution data, and performs error detection to identify the coordinates of the detected abnormal peaks. Furthermore, the analyzer 14 identifies the peak at the said coordinates in the red luminance distribution data used for counting nanobubbles, and performs a decrement process on the count of the peak in the frequency distribution data generated from the red luminance distribution data. Thereby, the count result in the count routine of step S8 described above is corrected.

[0065] In this way, by performing error detection using the luminance distribution data of the third color, the measurement accuracy of the number of nanobubbles can be improved.

[0066] Specifically, the detection of abnormal peaks appearing in the frequency distribution data generated from the above-described luminance distribution data of the third color is performed by detecting that after the magnitude of the frequency continuously becomes "0" as it progresses from the low luminance side to the high luminance side in the frequency distribution data, the frequency becomes non-zero.

[0067] In the nanobubble measurement system of the present invention, by specifying the luminance of nanobubbles of a known size, the frequency distribution data can be regarded as data on the size distribution of nanobubbles.

[0068] As a method for specifying the luminance of nanobubbles of a known size, although the refractive index is slightly different, there is a method in which standard particles made of polystyrene with a diameter of 300 nm are dispersed in water to obtain dispersed water, and this dispersed water is used as water containing pseudo-nanobubbles to measure the luminance.

[0069] The luminance distribution data obtained by putting the dispersed water into the container 11 of the above-described nanobubble measuring device is shown in FIG. 9, and the frequency distribution data is shown in FIG. 10. The irradiator 12 uses a green laser light source. FIG. 9(a) shows the luminance distribution data of gray, FIG. 9(b) shows the luminance distribution data of green, FIG. 9(c) shows the luminance distribution data of red, and FIG. 9(d) shows the luminance distribution data of blue. Also, FIG. 10(a) is the frequency distribution data generated from the luminance distribution data of FIG. 9(a), FIG. 10(b) is the frequency distribution data generated from the luminance distribution data of FIG. 9(b), FIG. 10(c) is the frequency distribution data generated from the luminance distribution data of FIG. 9(c), and FIG. 10(d) is the frequency distribution data generated from the luminance distribution data of FIG. 9(d).

[0070] Compared with the luminance distribution in the case of gray in FIG. 9(a) and the case of green in FIG. 9(b), the luminance distribution in the case of red in FIG. 9(c) has a smaller peak size, that is, a smaller luminance value, but since there are local peaks, it can be seen that it is possible to identify the position and number of standard particles.

[0071] Also, since they are standard particles and have almost the same particle size, the luminance values of each peak are almost the same, and it can be used for associating the particle size with the luminance value of the peak.

[0072] Furthermore, the luminance distribution in the case of blue in FIG. 9(d) shows that it is possible to identify that there are particles of abnormal sizes such as lumps where the particles are combined and irregularly large particles.

[0073] In addition, luminance distribution data when standard particles made of polystyrene with a diameter of 300 nm are dispersed in water and using the dispersed water, and a blue laser light source is used as the irradiator 12 are shown in FIG. 11, and frequency distribution data are shown in FIG. 12. FIG. 11(a) is the luminance distribution data of gray, FIG. 11(b) is the luminance distribution data of green, FIG. 11(c) is the luminance distribution data of red, and FIG. 11(d) is the luminance distribution data of blue. Further, FIG. 12(a) is the frequency distribution data generated from the luminance distribution data of FIG. 11(a), FIG. 12(b) is the frequency distribution data generated from the luminance distribution data of FIG. 11(b), FIG. 12(c) is the frequency distribution data generated from the luminance distribution data of FIG. 11(c), and FIG. 12(d) is the frequency distribution data generated from the luminance distribution data of FIG. 11(d).

[0074] Since the output of the blue laser light source appears darker compared to the green laser light source, it is desirable to use a blue laser light source with as high an output as possible.

[0075] When using a blue laser light source, since it is difficult to detect the peak in the blue luminance distribution data, for example, the count of nanobubbles may be performed using the green luminance distribution data, and error detection may be performed using the frequency distribution data generated from the red luminance distribution data.

Explanation of Signs

[0076] L Optical path 11 Container 12 Irradiator 13 Image sensor 14 Analyzer 15 Cylindrical lens 16 Connector 17 First wiring 18 Second wiring m1 Luminance distribution data generation means m2 Count means m3 Frequency distribution data generation means m4 Average luminance value calculation means m5 Error detection means

Claims

1. A container for storing water containing nanobubbles, an irradiator that irradiates the container with laser light to create an optical path in the water containing nanobubbles, an image sensor that generates image data by photographing the optical path, and an analyzer that counts the number of nanobubbles in the water containing nanobubbles using the image data A nanobubble measurement system comprising: The analyzer, luminance distribution data generation means for generating luminance distribution data from the image data, A nanobubble measurement system having counting means for counting the nanobubbles using the luminance distribution data .

2. The analyzer according to claim 1, further comprising frequency distribution data generation means for generating frequency distribution data of peaks by counting for each peak size represented in the luminance distribution data when the counting means counts the nanobubbles.

3. The first color, the second color, and the third color are each any one color selectively selected without overlap from the three primary colors of light, When the irradiator irradiates the laser light of the first color, The analyzer generates luminance distribution data of the second color from the image data by the luminance distribution data generation means, and counts the nanobubbles using the luminance distribution data of the second color by the counting means. The nanobubble measurement system according to claim 2.

4. The analyzer generates luminance distribution data of the third color from the image data by the luminance distribution data generation means, and generates frequency distribution data of the third color from the luminance distribution data of the third color by the frequency distribution data generation means, The analyzer according to claim 3, further comprising error detection means for using the frequency distribution data of the third color for error detection.

5. The nanobubble measurement system according to any one of claims 1 to 4, further comprising average luminance value calculation means for calculating an average luminance value from the luminance distribution data.

Images