WO2017212913A1 - Environmental analysis system and environmental analysis method - Google Patents

Environmental analysis system and environmental analysis method Download PDF

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Publication number
WO2017212913A1
WO2017212913A1 PCT/JP2017/019238 JP2017019238W WO2017212913A1 WO 2017212913 A1 WO2017212913 A1 WO 2017212913A1 JP 2017019238 W JP2017019238 W JP 2017019238W WO 2017212913 A1 WO2017212913 A1 WO 2017212913A1
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Prior art keywords
particles
light
amount
classification
imaging device
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PCT/JP2017/019238
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French (fr)
Japanese (ja)
Inventor
静一郎 衣笠
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アズビル株式会社
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Publication of WO2017212913A1 publication Critical patent/WO2017212913A1/en

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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12M—APPARATUS FOR ENZYMOLOGY OR MICROBIOLOGY; APPARATUS FOR CULTURING MICROORGANISMS FOR PRODUCING BIOMASS, FOR GROWING CELLS OR FOR OBTAINING FERMENTATION OR METABOLIC PRODUCTS, i.e. BIOREACTORS OR FERMENTERS
    • C12M1/00—Apparatus for enzymology or microbiology
    • C12M1/34—Measuring or testing with condition measuring or sensing means, e.g. colony counters
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/06—Investigating concentration of particle suspensions
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/47—Scattering, i.e. diffuse reflection
    • G01N21/49—Scattering, i.e. diffuse reflection within a body or fluid
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
    • G01N21/64—Fluorescence; Phosphorescence

Definitions

  • the present invention relates to environmental technology, and relates to an environmental analysis system and an environmental analysis method.
  • a detection record of particles in the clean room may be attached to a product manufactured in the clean room as a reference material.
  • the optical particle detection device for example, sucks a gas in a clean room and irradiates the sucked gas with light. If the gas contains microbial particles or non-microbial particles, the particles irradiated with light emit fluorescence or scattered light is generated in the particles. Therefore, by detecting fluorescence or scattered light, it is possible to detect the number and size of microbial particles and non-microbial particles contained in the gas.
  • optical particle detection devices are used and are also used for detecting particles in liquids.
  • the intensity of the fluorescence emitted by the particle may vary depending on the type of particle.
  • the intensity of scattered light generated by the particles may vary depending on the type of particles. Therefore, a method for determining whether a particle is a biological particle or a non-biological particle based on the intensity of fluorescence and the intensity of scattered light has been proposed (see, for example, Patent Documents 1 to 3).
  • An object of the present invention is to provide an environmental analysis system and an environmental analysis method capable of easily analyzing an environment where particles can be generated.
  • a light source that irradiates a fluid with inspection light
  • a photodetector that detects reaction light generated in particles in the fluid irradiated with the inspection light
  • a reaction A particle classification unit for classifying particles based on light
  • a counting unit for counting the amount of classified particles
  • a threshold storage unit for storing an alarm threshold for the amount of particles for each classification
  • An environment analysis system is provided that includes an imaging device control unit that causes an imaging device to image a source when the threshold is exceeded.
  • the photographing device may photograph a moving image.
  • the environment analysis system may further include a moving image storage unit that stores a moving image.
  • the imaging device control unit may cause the imaging device to store the captured moving image in the moving image storage unit.
  • the threshold storage unit further stores a warning threshold lower than the warning threshold, and if the amount of particles exceeds the warning threshold for each classification, the imaging device control unit generates a source in the imaging device. May be taken.
  • the imaging device control unit may cause the imaging device to stop imaging of the generation source.
  • the above-described environmental analysis system may include a plurality of imaging devices, and the plurality of imaging devices may image each generation source of the classified particles.
  • an environment analysis method comprising: causing the imaging device to image the source.
  • the above-described environmental analysis method may further include causing the image capturing device to store the captured moving image in the moving image storage unit when the amount of particles exceeds an alarm threshold value for each classification.
  • the environmental analysis method further includes preparing a warning threshold value lower than the warning threshold value, and causing the imaging device to image the source when the amount of particles exceeds the warning threshold value for each classification. May be.
  • the above-described environmental analysis method may further include causing the imaging device to stop imaging of the source when the amount of particles falls below a warning threshold value for each classification.
  • a plurality of imaging devices may be prepared, and a plurality of imaging devices may be caused to image each generation source of the classified particles.
  • an environment analysis system and an environment analysis method that can easily analyze an environment in which particles can be generated.
  • FIG. 4 is a schematic graph in which an identification boundary for linearly separating a biological particle class and a non-biological particle class is added to the graph shown in FIG. 3.
  • FIG. 4 is a schematic graph in which an identification boundary for linearly separating a biological particle class and a non-biological particle class is added to the graph shown in FIG. 3.
  • FIG. 4 is a schematic graph in which an identification boundary for nonlinearly separating a class of biological particles and a class of non-biological particles is added to the graph shown in FIG. 3.
  • 6 is a schematic graph of an identification boundary in an xz two-dimensional coordinate system at an arbitrary y value according to the first embodiment of the present invention.
  • 6 is a schematic graph of an identification boundary in an xz two-dimensional coordinate system at an arbitrary y value according to the first embodiment of the present invention.
  • It is a schematic diagram of the particle
  • 1 is a schematic diagram of an environmental analysis system according to a first embodiment of the present invention. It is a schematic diagram of the particle
  • nicotinamide adenine dinucleotide (NADH) and riboflavin contained in the biological particles emit fluorescence. Even if light is applied to non-living particles, the non-living particles may emit light in the fluorescence band. For example, fluorescent particles scattered from a cleaned gown made of polyester emit fluorescence when irradiated with light. Polystyrene particles also fluoresce and reverse their color.
  • nitrogen oxide (NO x ) containing nitrogen dioxide (NO 2 ) in the gas sulfur oxide (SO x ), ozone gas (O 3 ), aluminum oxide-based gas, aluminum alloy, glass powder, and If decontamination gas for decontaminating foreign substances such as Escherichia coli and mold is included, gas-containing substances that may be smaller than these particles that cause Mie scattering receive light and emit light in the fluorescence band .
  • nitrogen dioxide when nitrogen dioxide absorbs light, it emits red-shifted light and returns to the ground state.
  • the absorption spectrum of nitrogen dioxide has a peak in the vicinity of a wavelength of 440 nm, but has a wide band of about 100 to 200 nm. Therefore, when excitation of NADH-derived fluorescence and flavin-derived fluorescence with light having a wavelength of 405 nm in the presence of nitrogen dioxide, fluorescence can be excited even in nitrogen dioxide where NADH and flavin and the absorption spectrum of excitation light overlap.
  • Nitrogen dioxide is generated by the reaction of nitrogen and oxygen in the gas when the substance burns.
  • the gas to be inspected originally does not contain nitrogen dioxide, if the gas to be inspected is irradiated with laser light having a high beam density or strong electromagnetic radiation as excitation light, the substance in the gas will burn. Nitrogen dioxide is generated, and nitrogen dioxide may fluoresce. Further, nitric oxide and ozone react to form nitrogen dioxide, which may emit fluorescence.
  • FIG. 1 shows the light intensity of the wavelength in the band of 530 nm or more on the horizontal axis for the light in the fluorescence bands emitted by each of Staphylococcus epidermidis, Bacillus subtilis spores, Escherichia coli, glass, and aluminum irradiated with excitation light. It is the graph which plotted the light intensity of the wavelength in the band around 440 nm on the vertical axis. As shown in FIG.
  • the ratio of the light intensity of the wavelength in the band of 530 nm or more to the light intensity of the wavelength in the band near 440 nm tends to be small in the non-living material and large in the microbial particle. Therefore, by measuring the intensity of light in the fluorescence band emitted by a substance for each of a plurality of wavelengths and taking a correlation between them, it is possible to identify whether the substance is a living organism or a non-living organism.
  • the intensity of the scattered light generated by the particles varies depending on the type of the particles. Therefore, as shown in FIG. 3, light is irradiated to each of a plurality of types of known biological particles and non-biological particles on a three-dimensional coordinate system including an x-axis, a y-axis, and a z-axis representing the intensity of scattered light. Plotting the measurement value of the light intensity of the fluorescence band having the first wavelength, the measurement value of the light intensity of the fluorescence band having the second wavelength, and the measurement value of the intensity of the scattered light, It is possible to define a function f (x, y, z) that gives an identification boundary with an abiotic.
  • particles that should be classified into the biological particle class are classified as non-biological particles or non-biological particles.
  • particles to be classified into the biological particle class are classified as biological particles.
  • FIG. 5 when the biological particle class and the non-biological particle class are separated nonlinearly, the particles that should be classified into the biological particle class are classified as non-biological particles. It becomes possible to reduce that the particle
  • a function f (x, y, z) that gives an identification boundary for nonlinearly separating a living thing and a non-living object is a support vector machine (SVM) that obtains an identification boundary from the learning data so that the distance to each data point is maximized.
  • SVM support vector machine
  • Nonlinear classifiers are not limited to support vector machines. For example, boosting with increased accuracy by combining many discriminators, neural networks that simulate the characteristics of brain functions on a computer, and other methods such as decision trees, nearest neighbor search, and case-based reasoning A discriminator can be used.
  • the particles irradiated with light include microbial particles, non-microbial particles having a larger particle size than microbial particles, and non-microbial particles having a smaller particle size than microbial particles, in a three-dimensional coordinate system
  • particles that give light intensity plotted in a space surrounded by a function f (x, y, z) that gives an identification boundary can be classified into a biological class.
  • particles that give light intensity plotted outside the space enclosed by the function f (x, y, z) can be classified into the non-living class.
  • a function f (x, y, z) that gives an identification boundary between a living organism and a non-living organism is defined in advance in the three-dimensional coordinate system, and then the first generated when the inspection light is irradiated to an unknown particle.
  • the measured particle is determined to be a living organism, and if the measured value is plotted outside the space enclosed by the function f (x, y, z), the measured particle is considered to be an abiotic organism. It is possible to determine.
  • the multivariable function f (x, y, z) that gives an identification boundary is a multivalent function that outputs two values of the dependent variable z for a set of independent variables (x, y)
  • FIG. 6 and FIG. 7 the first and second fluorescence band light intensities having a set of (x 1 , y 1 ) values are provided, and the first of the scattered light intensities at the discrimination boundary is given.
  • Particles that give a measure of scattered light intensity greater than the boundary value are non-biological particles.
  • the intensity of light in the first and second fluorescence bands having a set of (x 1 , y 1 ) values is given, and the intensity of scattered light is smaller than the first boundary value of the intensity of scattered light.
  • a particle that provides a measurement and also provides a measurement of the intensity of scattered light that is greater than the second boundary value of the intensity of scattered light at the identification boundary is a biological particle. Further, the scattered light is given a set of (x 1 , y 1 ) values of the first and second fluorescence band light intensity and is smaller than the second boundary value of the scattered light intensity at the identification boundary. Particles that give a strength measurement of are non-biological particles. However, the shape of the identification boundary can vary depending on the sample.
  • the particle detection device 1 included in the environmental analysis device includes a light source 10 that irradiates a fluid with inspection light, and a fluid that is irradiated with the inspection light.
  • a detector 15 for detecting reaction light generated in the particles in the particles a particle classification unit 301 for classifying particles based on the reaction light, a counting unit 302 for counting the amount of classified particles, and a particle classification unit for each classification.
  • a threshold storage unit 352 that stores an alarm threshold for the amount, and an alarm unit 303 that issues an alarm when the amount of particles exceeds the alarm threshold for each classification.
  • the particle classification unit 301, the counting unit 302, and the alarm unit 303 are included in, for example, a central processing unit (CPU) 300.
  • the threshold storage unit 352 is included in the storage device 350 connected to the CPU 300, for example.
  • the output device 401 is connected to the CPU 300.
  • the output device 401 for example, a display, a printer, an acoustic device, or the like can be used.
  • reaction light generated in the particles to be measured irradiated with inspection light include fluorescence at a plurality of wavelengths and scattered light.
  • the photodetector 15 is, for example, the intensity of the first reaction light having the first wavelength, the intensity of the second reaction light having the second wavelength, and the intensity of the third reaction light having the third wavelength. Measure the measured value.
  • the first wavelength, the second wavelength, and the third wavelength are different.
  • the “light in the fluorescence band” includes fluorescence, autofluorescence, and light that is not necessarily fluorescence, but whose wavelength band overlaps with fluorescence.
  • the first classification particles are biological particles and the second classification particles are non-biological particles will be described.
  • the storage device 350 of the particle detection device 1 further includes a boundary information storage unit 351 that stores an identification boundary that nonlinearly separates the first class particle class and the second class particle class.
  • the particle classification unit 301 classifies the particles to be measured into one of the first and second classification classes based on the first to third reaction light intensity measurements and the identification boundary.
  • the gas to be inspected by the particle detection device 1 to determine whether or not it contains particles is ejected from the nozzle 40.
  • excitation light having a broadband wavelength is emitted from the light source 10 as inspection light.
  • the light source 10 emits the inspection light having a broadband wavelength toward the flow cell or the like through which the liquid flows.
  • the fluid is a gas
  • the light source 10 for example, a light emitting diode (LED) and a laser can be used.
  • the wavelength of the inspection light is, for example, 250 to 550 nm.
  • the inspection light may be visible light or ultraviolet light.
  • the wavelength of the inspection light is, for example, in the range of 400 to 550 nm, for example, 405 nm.
  • the wavelength of the inspection light is, for example, in the range of 300 to 380 nm, for example, 340 nm.
  • the wavelength of the inspection light is not limited to these.
  • a light source driving power source 11 that supplies power to the light source 10 is connected to the light source 10.
  • a power source control device 12 that controls the power supplied to the light source 10 is connected to the light source driving power source 11.
  • the light detector 15 is included in the fluid ejected from the nozzle 40 and measures the intensity of the light in the first fluorescence band and the intensity of the light in the second fluorescence band generated by the particles to be measured irradiated with the inspection light.
  • a fluorescence intensity measuring device 102 and a scattered light measuring device 105 that measures scattered light generated by the particles to be measured irradiated with the inspection light are provided.
  • the light source 10, the fluorescence intensity measuring device 102, and the scattered light measuring device 105 are provided in the housing 30. Further, the fluorescence intensity measuring device 102 and the scattered light measuring device 105 are electrically connected to the CPU 300.
  • the fluorescence intensity measuring device 102 detects light in the fluorescence band emitted by the particle to be measured.
  • the fluorescence intensity measuring instrument 102 receives a first light receiving element 20A that receives light in the fluorescence band at the first wavelength, and a second light reception that receives light in the fluorescence band at a second wavelength different from the first wavelength. And an element 20B.
  • the first wavelength may have a band. The same applies to the second wavelength.
  • a photodiode, a phototube or the like can be used as the first light receiving element 20A and the second light receiving element 20B. When receiving light, the light energy is converted into electric energy.
  • the amplifier 21A for amplifying the current generated in the first light receiving element 20A is connected to the first light receiving element 20A.
  • An amplifier power supply 22A that supplies power to the amplifier 21A is connected to the amplifier 21A.
  • the amplifier 21A is connected to a light intensity calculating device 23A that receives the current amplified by the amplifier 21A and calculates the intensity of the light received by the first light receiving element 20A.
  • the light intensity calculation device 23A is connected to a light intensity storage device 24A that stores the light intensity calculated by the light intensity calculation device 23A.
  • the amplifier 21B that amplifies the current generated in the second light receiving element 20B is connected to the second light receiving element 20B.
  • An amplifier power supply 22B that supplies power to the amplifier 21B is connected to the amplifier 21B.
  • the amplifier 21B is connected to a light intensity calculation device 23B that receives the current amplified by the amplifier 21B and calculates the intensity of light received by the second light receiving element 20B.
  • a light intensity storage device 24B that stores the light intensity calculated by the light intensity calculation device 23B is connected to the light intensity calculation device 23B.
  • the scattered light measuring device 105 detects scattered light generated by the particles to be measured irradiated with the inspection light.
  • the scattered light measuring device 105 includes a scattered light receiving element 50 that receives scattered light.
  • a scattered light receiving element 50 a photodiode or the like can be used. When light is received, the light energy is converted into electric energy.
  • the scattered light receiving element 50 is connected to an amplifier 51 that amplifies the current generated in the scattered light receiving element 50.
  • An amplifier power supply 52 that supplies power to the amplifier 51 is connected to the amplifier 51.
  • the amplifier 51 is connected to a light intensity calculation device 53 that receives the current amplified by the amplifier 51 and calculates the intensity of scattered light received by the scattered light receiving element 50.
  • a light intensity storage device 54 that stores the intensity of scattered light calculated by the light intensity calculation device 53 is connected to the light intensity calculation device 53.
  • the identification boundary stored in the boundary information storage unit 351 is given by, for example, a multivariable function having the first fluorescent band light intensity, the second fluorescent band light intensity, and the scattered light intensity as variables. .
  • the boundary information storage unit 351 stores, for example, a three-dimensional coordinate system including a multivariable function.
  • the three-dimensional coordinate system includes an x coordinate indicating the intensity of light in the first fluorescence band, a y coordinate indicating the intensity of light in the second fluorescence band, and a z coordinate indicating the intensity of scattered light.
  • the three-dimensional coordinate system is represented by a three-dimensional table including N ⁇ N ⁇ N cells, where N is an integer. In this case, for example, an index from 0 to N-1 is assigned to cells in the x direction, and an index from 0 to N-1 is assigned to cells in the y direction. The direction cells are also indexed from 0 to N-1.
  • the three-dimensional table is composed of 256 ⁇ 256 ⁇ 256 cells
  • the cells in the x direction are indexed from 0 to 255
  • the cells in the y direction are also numbered 0 to 255. Indexes from 0 to 255 are also assigned to cells in the z direction.
  • the intensity of light is represented by a voltage signal within a range of 0 to 5 V, for example.
  • the following equation (1) is used.
  • I [N I * (S D / S M )] (1)
  • N I is the number of index, for example, 256.
  • S D is a measured value of light intensity represented by a voltage signal.
  • S M is the maximum value that the light intensity represented by the voltage signal can take.
  • the index I calculated by the equation (1) is an integer from 0 to 255.
  • Each cell included in the region of the biological particle class defined by the multivariable function that nonlinearly separates the biological particle class and the non-biological particle class is provided with an identifier of the biological particle class.
  • Each cell included in the non-biological particle region defined by the multivariable function that nonlinearly separates the biological particle class and the non-biological particle class is assigned an identifier of the non-biological particle class.
  • each cell included in a region where the fluorescence intensity is determined to be equal to or less than the predetermined threshold value determined by the predetermined threshold value of the fluorescence intensity is given an identifier of a class of non-fluorescent particles.
  • the identifier of the class of biological particles, the identifier of the class of non-biological particles, or the identifier of the class of non-fluorescent particles is identified from the specified cells. Is possible to get.
  • the particle classification unit 301 shown in FIG. 8 uses the above equation (1), for example, to measure the intensity of light in the first fluorescence band emitted from the particle to be measured and the intensity of light in the second fluorescence band.
  • a cell of coordinates (x, y, z) in the three-dimensional table stored in the boundary information storage unit 351 corresponding to the measurement value and the measurement value of the intensity of scattered light is specified. Further, when the identifier in the cell of the specified coordinates (x, y, z) is a biological particle class, the particle classification unit 301 classifies the particle to be measured into the biological particle class.
  • the particle classification unit 301 classifies the measured particle into a class of a non-biological particle. Further, the particle classification unit 301 classifies the particles to be measured into the non-fluorescent particle class when the identifier in the cell of the specified coordinates (x, y, z) is the non-fluorescent particle class.
  • the particle classification unit 301 may classify particles that are not classified as fluorescent particles as non-fluorescent particles.
  • the counting unit 302 included in the CPU 300 counts the amount of particles for each classification. For example, the counting unit 302 counts the number or concentration of particles classified into the biological particle class per unit time or operation time. The counting unit 302 counts the number or concentration of particles classified into the non-biological particle class per unit time or per operation time. Further, the counting unit 302 counts the number or concentration of particles classified into the non-fluorescent particle class per unit time or operation time. The counting unit 302 may count the amount obtained by subtracting the total amount of fluorescent particles from the total amount of particles as the amount of non-fluorescent particles. The counting unit 302 outputs the amount of particles counted for each classification from the output device 401.
  • the counting unit 302 may output the amount of particles counted for each classification as a numerical value or a graph as shown in FIG.
  • the counting unit 302 stores the amount of particles counted for each classification in the data storage unit 353 included in the storage device 350 illustrated in FIG.
  • the threshold storage unit 352 included in the storage device 350 stores an alarm threshold for the amount of particles for each classification.
  • the threshold storage unit 352 stores an alarm threshold for the number or concentration of particles classified into the biological particle class per unit time or per operation time.
  • the threshold storage unit 352 stores an alarm threshold for the number or concentration of particles classified into the non-biological particle class per unit time or per operation time.
  • the threshold storage unit 352 stores an alarm threshold for the number or concentration of particles classified into the non-fluorescent particle class per unit time or per operation time.
  • the threshold storage unit 352 may store an alarm threshold for the total amount of particles before classification.
  • the alarm threshold for the number or concentration of particles classified into the biological particle class is set low.
  • the alarm threshold for the number or concentration of particles classified into the biological particle class is set high.
  • the alarm threshold for the number or concentration of particles classified into the non-living particle class is set low.
  • the alarm threshold for the number or concentration of particles classified into the non-living particles class is set high.
  • the alarm threshold for the number or concentration of particles classified into the non-fluorescent particle class is set low.
  • the alarm threshold for the number or concentration of particles classified into the non-fluorescent particle class is set high.
  • the alarm unit 303 included in the CPU 300 issues an alarm when the amount of particles exceeds the alarm threshold for each classification. For example, as shown in FIG. 11, the alarm unit 303 outputs an output when the amount of particles classified into the biological particle class counted by the counting unit 302 exceeds the alarm threshold stored in the threshold storage unit 352. An alarm is issued via the device 401. In addition, the alarm unit 303 issues an alarm via the output device 401 when the amount of particles classified into the non-biological particle class counted by the counting unit 302 exceeds the alarm threshold stored in the threshold storage unit 352. To emit. Further, the alarm unit 303 issues an alarm via the output device 401 when the amount of particles classified into the non-fluorescent particle class counted by the counter unit 302 exceeds the alarm threshold value stored in the threshold value storage unit 352. To emit.
  • the environmental analysis apparatus captures the moving images captured by the capturing apparatuses 3A, 3B, and 3C and the capturing apparatuses 3A, 3B, and 3C that capture the moving images of the particle generation sources. And a moving image storage unit 4 to be stored.
  • the number of imaging devices included in the environmental analysis device is not particularly limited, and may be one or more.
  • the particle detection device 1, the imaging devices 3 ⁇ / b> A, 3 ⁇ / b> B, 3 ⁇ / b> C and the moving image storage unit 4 are connected via a network 2, for example.
  • the CPU 300 of the particle detection device 1 causes the imaging device control unit 304 to cause the imaging devices 3A, 3B, and 3C to image the generation source when the amount of particles exceeds the alarm threshold value for each classification. Is further provided.
  • the photographing devices 3A, 3B, and 3C are arranged in a clean room, for example.
  • the photographing devices 3A, 3B, and 3C are arranged so as to be able to photograph a moving image around the air intake port of the particle detection device 1, for example. Air taken in from the air intake port is ejected from the nozzle 40 shown in FIG.
  • the air intake port of the particle detection apparatus 1 shown in FIG. 13 is preferably a place where it is expected to be a particle generation source and its vicinity. Examples of the place where the generation source of particles is expected include a manufacturing apparatus including a conveying device and a place where an operator such as a work place or a passage frequently appears.
  • the source of particles may be predicted for each type of particle classified.
  • the imaging device control unit 304 illustrated in FIG. 8 is configured when the amount of particles counted by the counting unit 302 exceeds the alarm threshold stored in the threshold storage unit 352 for each classification. 12 and the imaging apparatuses 3A, 3B, and 3C shown in FIG. 13 start imaging of the generation source of the particles, and the captured moving image is stored in the moving image storage unit 4.
  • the imaging device control unit 304 illustrated in FIG. 8 performs the processing illustrated in FIGS. 12 and 13 when the amount of particles counted by the counting unit 302 is lower than the alarm threshold stored in the threshold storage unit 352 for each classification.
  • the imaging device control unit 304 shown in FIG. 8 determines that a predetermined time elapses when the amount of particles counted by the counting unit 302 falls below the alarm threshold stored in the threshold storage unit 352 for each classification. After that, the photographing apparatuses 3A, 3B, and 3C shown in FIGS. 12 and 13 may stop photographing the particle generation source and storing the moving image.
  • the imaging device control unit 304 illustrated in FIG. 8 causes the imaging devices 3A, 3B, and 3C illustrated in FIGS. 12 and 13 to start capturing the particle generation source, and saves the captured moving image as a moving image by multitasking. Save to Part 4.
  • the environmental analysis apparatus can capture a particle generation source when the amount of particles counted for each classification exceeds a threshold set for each classification. is there. Therefore, compared with the case where the generation source of particles is constantly monitored, for example, the capacity of the moving image storage unit 4 can be reduced, and the operation cost of the apparatus can be reduced.
  • the capacity of the moving image storage unit 4 can be reduced, and the operation cost of the apparatus can be reduced.
  • an amount of microorganisms greater than or equal to the threshold it is possible to take measures such as cleaning or sterilizing or sterilizing the clean room.
  • a non-biological particle amount exceeding the threshold it is possible to grasp the deterioration status of the device that is the source of the non-biological particle, or take measures such as performing maintenance of the device. It is.
  • the threshold storage unit 352 illustrated in FIG. 8 further stores a warning threshold lower than the alarm threshold as illustrated in FIG.
  • the imaging device control unit 304 illustrated in FIG. 8 causes the imaging devices 3A, 3B, and 3C to image the generation source when the amount of particles exceeds the warning threshold value for each classification.
  • the captured moving image is not stored in the moving image storage unit 4.
  • the image capturing device control unit 304 causes the image capturing devices 3A, 3B, and 3C to continue image capturing of the source, and further captures the captured moving image in the moving image storage unit 4. Save.
  • the imaging device control unit 304 illustrated in FIG. 8 performs the processing illustrated in FIG. 12 and FIG. 13 when the amount of particles counted by the counting unit 302 is lower than the warning threshold stored in the threshold storage unit 352 for each classification.
  • the alarm threshold for the amount of particles for each particle classification stored in the threshold storage unit 352 shown in FIG. 16 is the imaging device 3A shown in FIGS. Set for every 3B and 3C. Specifically, the alarm threshold for each particle classification for which the imaging device 3A starts to shoot moving images, the alarm threshold for each particle classification for which the imaging device 3B starts to shoot moving images, and the imaging device 3C to shoot moving images. Are stored in the threshold value storage unit 352. The same applies to the warning threshold.
  • the CPU 300 shown in FIG. 16 corrects the alarm threshold value and the warning threshold value according to the amount of particles for each classification detected in the past with the air taken in from each of the plurality of air intake ports of the particle detector 1.
  • a correction unit 305 is further provided.
  • the threshold correction unit 305 detects the air taken in at the first air intake port. The correction is made to lower the alarm threshold of the amount of particles classified into the biological particle class.
  • the threshold correction unit 305 converts the air taken in at the second air intake port
  • a correction is made to lower the alarm threshold for the amount of particles classified into the non-biological particle class.
  • the threshold correction unit 305 converts the air taken in at the third air intake port into On the other hand, a correction is made to lower the alarm threshold for the amount of particles classified into the non-fluorescent particle class.
  • the threshold value is corrected based on the past particle detection tendency, and for example, the occurrence of particles of a specific classification can be monitored more frequently. It becomes possible.
  • the environment analysis apparatus As shown in FIG. 17, the environment analysis apparatus according to the fourth embodiment records the entry / exit time of the worker in the clean room using an identification IC tag or the like worn by the worker. 18 is further provided. A record of the entry / exit time of the worker into the clean room of the worker recording unit 306 is stored in the data storage unit 353.
  • the environmental analysis apparatus further includes a merging unit 307 that generates merged data obtained by merging the data of the amount of particles generated for each classification in time series and the data of workers entering and leaving the room in time series.
  • the merging unit 307 stores the merged data in the data storage unit 353 and outputs it from the output device 401. According to the fourth embodiment, for example, based on the merged data, it is possible to grasp the classification of particles that are likely to occur when each worker enters the clean room.
  • the time series data of the work process performed in the clean room is stored in the data storage unit 353 shown in FIG.
  • the merging unit 307 generates merged data obtained by merging the data of the amount of particles generated for each classification in the time series and the data of the performed work processes in the time series. .
  • the merging unit 307 stores the merged data in the data storage unit 353 and outputs it from the output device 401.
  • the merging unit merges the data of the amount of particles generated for each classification in the time series, the data of the work process performed in the time series, and the data of the worker who entered and exited the room in the time series.
  • the merged data may be generated.
  • the first class of particles may be a kind of non-biological particle
  • the second class of particles may be another kind of non-biological particle.
  • the method of classifying the particles is arbitrary. Thus, it should be understood that the present invention includes various embodiments and the like not described herein.

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Abstract

This environmental analysis system comprises: a light source 10 for shining inspection light onto a fluid; a light detector 15 for detecting reaction light generated by particles in a fluid when the inspection light is shone thereupon; a particle classification unit 301 that classifies particles on the basis of reaction light; a counting unit that counts the quantity of classified particles; a threshold value storage unit 352 that stores the alarm threshold value for the quantity of particles for each category; an alarm unit 303 that generates an alarm if the quantity of particles exceeds an alarm threshold value for each category; an imaging unit that captures an image of the generation source of the particles; and an imaging device control unit 304 that has the imaging device capture an image of the generation source if the quantity of particles exceeds an alarm threshold value for each category.

Description

環境分析システム及び環境分析方法Environmental analysis system and environmental analysis method
　本発明は環境技術に関し、環境分析システム及び環境分析方法に関する。 The present invention relates to environmental technology, and relates to an environmental analysis system and an environmental analysis method.
　半導体製造工場、食品製造工場、飲料品製造工場、及び医薬品製造工場等のクリーンルームにおいては、粒子検出装置を用いて、飛散している微生物粒子や非微生物粒子が検出され、記録される。粒子の検出結果から、クリーンルームの空調機器の劣化具合を把握可能である。また、クリーンルームで製造された製品に、参考資料として、クリーンルーム内の粒子の検出記録が添付されることもある。 In clean rooms such as semiconductor manufacturing plants, food manufacturing factories, beverage manufacturing factories, and pharmaceutical manufacturing factories, scattered microbial particles and non-microbial particles are detected and recorded. From the particle detection result, it is possible to grasp the deterioration of the air conditioner in the clean room. In addition, a detection record of particles in the clean room may be attached to a product manufactured in the clean room as a reference material.
　光学式の粒子検出装置は、例えば、クリーンルーム中の気体を吸引し、吸引した気体に光を照射する。気体に微生物粒子や非微生物粒子が含まれていると、光を照射された粒子が蛍光を発したり、粒子において散乱光が生じたりする。そのため、蛍光や散乱光を検出することにより、気体に含まれる微生物粒子や非微生物粒子の数や大きさ等を検出することが可能となる。また、クリーンルーム以外でも、光学式の粒子検出装置は用いられており、液体中の粒子の検出にも用いられている。 The optical particle detection device, for example, sucks a gas in a clean room and irradiates the sucked gas with light. If the gas contains microbial particles or non-microbial particles, the particles irradiated with light emit fluorescence or scattered light is generated in the particles. Therefore, by detecting fluorescence or scattered light, it is possible to detect the number and size of microbial particles and non-microbial particles contained in the gas. In addition to the clean room, optical particle detection devices are used and are also used for detecting particles in liquids.
　粒子が発する蛍光の強度は、粒子の種類によって異なる場合がある。また、粒子で生じる散乱光の強度も、粒子の種類によって異なる場合がある。そのため、蛍光の強度及び散乱光の強度に基づいて、粒子が生物粒子であるか、あるいは非生物粒子であるかを判別する方法が提案されている（例えば、特許文献１から３参照。）。 The intensity of the fluorescence emitted by the particle may vary depending on the type of particle. In addition, the intensity of scattered light generated by the particles may vary depending on the type of particles. Therefore, a method for determining whether a particle is a biological particle or a non-biological particle based on the intensity of fluorescence and the intensity of scattered light has been proposed (see, for example, Patent Documents 1 to 3).
特開２０１５－１０２３５５号公報Japanese Patent Laying-Open No. 2015-102355 特開２０１６－００８９５７号公報Japanese Unexamined Patent Publication No. 2016-008957 特開２０１６－００８９５８号公報Japanese Unexamined Patent Publication No. 2016-008958
　本発明は、粒子が発生しうる環境を容易に分析可能な、環境分析システム及び環境分析方法を提供することを目的の一つとする。 An object of the present invention is to provide an environmental analysis system and an environmental analysis method capable of easily analyzing an environment where particles can be generated.
　本発明の態様によれば、（ａ）流体に検査光を照射する光源と、（ｂ）検査光を照射された流体中の粒子で生じる反応光を検出する光検出器と、（ｃ）反応光に基づき、粒子を分類する粒子分類部と、（ｄ）分類された粒子の量を計数する計数部と、（ｅ）分類ごとに粒子の量の警報閾値を保存する閾値保存部と、（ｆ）分類ごとに、粒子の量が警報閾値を超えた場合、警報を発する警報部と、（ｇ）粒子の発生源を撮影する撮影装置と、（ｈ）分類ごとに、粒子の量が警報閾値を超えた場合、撮影装置に発生源を撮影させる撮影装置制御部と、を備える、環境分析システムが提供される。 According to an aspect of the present invention, (a) a light source that irradiates a fluid with inspection light, (b) a photodetector that detects reaction light generated in particles in the fluid irradiated with the inspection light, and (c) a reaction A particle classification unit for classifying particles based on light; (d) a counting unit for counting the amount of classified particles; (e) a threshold storage unit for storing an alarm threshold for the amount of particles for each classification; f) For each classification, when the amount of particles exceeds an alarm threshold, an alarm unit that issues an alarm, (g) an imaging device that images the source of the particles, and (h) an amount of particles for each classification An environment analysis system is provided that includes an imaging device control unit that causes an imaging device to image a source when the threshold is exceeded.
　上記の環境分析システムにおいて、撮影装置が動画を撮影してもよい。また、上記の環境分析システムが、動画を保存する動画保存部をさらに備えてもよい。また、分類ごとに、粒子の量が警報閾値を超えた場合、撮影装置制御部が、撮影装置に、撮影した動画を動画保存部に保存させてもよい。 In the above environmental analysis system, the photographing device may photograph a moving image. The environment analysis system may further include a moving image storage unit that stores a moving image. In addition, for each classification, when the amount of particles exceeds the alarm threshold, the imaging device control unit may cause the imaging device to store the captured moving image in the moving image storage unit.
　上記の環境分析システムにおいて、閾値保存部が、警報閾値よりも低い警告閾値をさらに保存し、分類ごとに、粒子の量が警告閾値を超えた場合、撮影装置制御部が、撮影装置に発生源を撮影させてもよい。 In the above environmental analysis system, the threshold storage unit further stores a warning threshold lower than the warning threshold, and if the amount of particles exceeds the warning threshold for each classification, the imaging device control unit generates a source in the imaging device. May be taken.
　上記の環境分析システムにおいて、分類ごとに、粒子の量が警告閾値を下回った場合、撮影装置制御部が、撮影装置に発生源の撮影を停止させてもよい。 In the above environmental analysis system, when the amount of particles falls below the warning threshold value for each classification, the imaging device control unit may cause the imaging device to stop imaging of the generation source.
　上記の環境分析システムが、撮影装置を複数備え、複数の撮影装置が、分類された粒子のそれぞれの発生源を撮影してもよい。 The above-described environmental analysis system may include a plurality of imaging devices, and the plurality of imaging devices may image each generation source of the classified particles.
　また、本発明の態様によれば、（ａ）流体に検査光を照射することと、（ｂ）検査光を照射された流体中の粒子で生じる反応光を検出することと、（ｃ）反応光に基づき、粒子を分類することと、（ｄ）分類された粒子の量を計数することと、（ｅ）分類ごとに粒子の量の警報閾値を用意することと、（ｆ）分類ごとに、粒子の量が警報閾値を超えた場合、警報を発することと、（ｇ）粒子の発生源を撮影する撮影装置を用意することと、（ｈ）分類ごとに、粒子の量が警報閾値を超えた場合、撮影装置に発生源を撮影させることと、を備える、環境分析方法が提供される。 Moreover, according to the aspect of the present invention, (a) irradiating the fluid with inspection light, (b) detecting reaction light generated by particles in the fluid irradiated with the inspection light, and (c) reaction Classifying particles based on light; (d) counting the amount of classified particles; (e) preparing an alarm threshold for the amount of particles for each classification; and (f) for each classification. , If the amount of particles exceeds the alarm threshold, issue an alarm, (g) prepare an imaging device for imaging the source of the particles, and (h) for each classification, the amount of particles If so, an environment analysis method is provided comprising: causing the imaging device to image the source.
　上記の環境分析方法が、分類ごとに、粒子の量が警報閾値を超えた場合、撮影装置に、撮影した動画を動画保存部に保存させることをさらに備えていてもよい。 The above-described environmental analysis method may further include causing the image capturing device to store the captured moving image in the moving image storage unit when the amount of particles exceeds an alarm threshold value for each classification.
　上記の環境分析方法が、警報閾値よりも低い警告閾値を用意することと、分類ごとに、粒子の量が警告閾値を超えた場合、撮影装置に発生源を撮影させることと、をさらに備えていてもよい。 The environmental analysis method further includes preparing a warning threshold value lower than the warning threshold value, and causing the imaging device to image the source when the amount of particles exceeds the warning threshold value for each classification. May be.
　上記の環境分析方法が、分類ごとに、粒子の量が警告閾値を下回った場合、撮影装置に発生源の撮影を停止させることをさらに備えていてもよい。 The above-described environmental analysis method may further include causing the imaging device to stop imaging of the source when the amount of particles falls below a warning threshold value for each classification.
　上記の環境分析方法において、撮影装置を複数用意し、複数の撮影装置に、分類された粒子のそれぞれの発生源を撮影させてもよい。 In the environmental analysis method described above, a plurality of imaging devices may be prepared, and a plurality of imaging devices may be caused to image each generation source of the classified particles.
　本発明によれば、粒子が発生しうる環境を容易に分析可能な、環境分析システム及び環境分析方法を提供可能である。 According to the present invention, it is possible to provide an environment analysis system and an environment analysis method that can easily analyze an environment in which particles can be generated.
本発明の第１の実施の形態に係る微生物及び大気含有物質が発する光の５３０ｎｍ以上の帯域における強度に対する、４４０ｎｍ帯域における強度の関係を示すグラフである。It is a graph which shows the relationship of the intensity | strength in a 440 nm band with respect to the intensity | strength in the band of 530 nm or more of the light which the microorganisms which concern on the 1st Embodiment of this invention, and an atmospheric content substance emit. 本発明の第１の実施の形態に係る微生物及び大気含有物質が発する光の５３０ｎｍ以上の帯域における強度に対する、４４０ｎｍ帯域における強度の関係と、識別境界と、を示すグラフである。It is a graph which shows the relationship of the intensity | strength in a 440 nm band with respect to the intensity | strength in the band of 530 nm or more of the light which the microorganisms based on the 1st Embodiment of this invention, and air-containing substance emit, and an identification boundary. ｘ軸、ｙ軸、及びｚ軸を含む３次元座標系に、複数種類の既知の生物粒子及び非生物粒子のそれぞれについて、光を照射したときに生じる第１の蛍光帯域の光の強度の測定値、第２の蛍光帯域の光の強度の測定値、及び散乱光の強度の測定値をプロットした模式的なグラフである。Measurement of the intensity of light in the first fluorescence band generated when light is irradiated to each of a plurality of types of known biological particles and non-biological particles on a three-dimensional coordinate system including the x-axis, y-axis, and z-axis. It is the typical graph which plotted the value, the measured value of the intensity | strength of the light of a 2nd fluorescence zone | band, and the measured value of the intensity of scattered light. 図３に示すグラフに、生物粒子のクラスと、非生物粒子のクラスと、を線形分離する識別境界を追加した模式的なグラフである。FIG. 4 is a schematic graph in which an identification boundary for linearly separating a biological particle class and a non-biological particle class is added to the graph shown in FIG. 3. 図３に示すグラフに、生物粒子のクラスと、非生物粒子のクラスと、を非線形分離する識別境界を追加した模式的なグラフである。FIG. 4 is a schematic graph in which an identification boundary for nonlinearly separating a class of biological particles and a class of non-biological particles is added to the graph shown in FIG. 3. 本発明の第１の実施の形態に係る任意のｙの値におけるｘ－ｚ２次元座標系における識別境界の模式的なグラフである。6 is a schematic graph of an identification boundary in an xz two-dimensional coordinate system at an arbitrary y value according to the first embodiment of the present invention. 本発明の第１の実施の形態に係る任意のｙの値におけるｘ－ｚ２次元座標系における識別境界の模式的なグラフである。6 is a schematic graph of an identification boundary in an xz two-dimensional coordinate system at an arbitrary y value according to the first embodiment of the present invention. 本発明の第１の実施の形態に係る環境分析システムの粒子検出装置の模式図である。It is a schematic diagram of the particle | grain detection apparatus of the environmental analysis system which concerns on the 1st Embodiment of this invention. 本発明の第１の実施の形態に係る３次元テーブルの模式図である。It is a schematic diagram of the three-dimensional table which concerns on the 1st Embodiment of this invention. 本発明の第１の実施の形態に係る粒子の量の時間変化を示す例示的なグラフである。It is an exemplary graph which shows the time change of the quantity of the particle | grains concerning the 1st Embodiment of this invention. 本発明の第１の実施の形態に係る粒子の量の時間変化を示す例示的なグラフである。It is an exemplary graph which shows the time change of the quantity of the particle | grains concerning the 1st Embodiment of this invention. 本発明の第１の実施の形態に係る環境分析システムの模式図である。1 is a schematic diagram of an environmental analysis system according to a first embodiment of the present invention. 本発明の第１の実施の形態に係る環境分析システムの粒子検出装置と撮影装置の模式図である。It is a schematic diagram of the particle | grain detection apparatus and imaging device of the environmental analysis system which concerns on the 1st Embodiment of this invention. 本発明の第１の実施の形態に係る粒子の量の時間変化を示す例示的なグラフである。It is an exemplary graph which shows the time change of the quantity of the particle | grains concerning the 1st Embodiment of this invention. 本発明の第２の実施の形態に係る粒子の量の時間変化を示す例示的なグラフである。It is an exemplary graph which shows the time change of the quantity of the particle | grains concerning the 2nd Embodiment of this invention. 本発明の第３の実施の形態に係る環境分析システムの粒子検出装置の模式図である。It is a schematic diagram of the particle | grain detection apparatus of the environmental analysis system which concerns on the 3rd Embodiment of this invention. 本発明の第４の実施の形態に係るクリーンルームへの作業者の入退室の時間を示すデータの模式図である。It is a schematic diagram of the data which shows the time of the operator's entrance / exit to the clean room which concerns on the 4th Embodiment of this invention. 本発明の第４の実施の形態に係る環境分析システムの粒子検出装置の模式図である。It is a schematic diagram of the particle | grain detection apparatus of the environmental analysis system which concerns on the 4th Embodiment of this invention. 本発明の第５の実施の形態に係るクリーンルームで実施される作業工程の時系列データの模式図である。It is a schematic diagram of the time series data of the work process implemented in the clean room which concerns on the 5th Embodiment of this invention.
　以下に本発明の実施の形態を説明する。以下の図面の記載において、同一又は類似の部分には同一又は類似の符号で表している。但し、図面は模式的なものである。したがって、具体的な寸法等は以下の説明を照らし合わせて判断するべきものである。また、図面相互間においても互いの寸法の関係や比率が異なる部分が含まれていることは勿論である。 Embodiments of the present invention will be described below. In the following description of the drawings, the same or similar parts are denoted by the same or similar reference numerals. However, the drawings are schematic. Therefore, specific dimensions and the like should be determined in light of the following description. Moreover, it is a matter of course that portions having different dimensional relationships and ratios are included between the drawings.
　（第１の実施の形態）
　細菌等の生物粒子に光を照射すると、生物粒子において散乱光が発生する。また、金属又は樹脂からなる非生物粒子に光を照射しても、非生物粒子において散乱光が発生する。粒子で生じる散乱光の強度は、粒子の粒径に依存する傾向にある。生物粒子の粒径は、微生物の種類毎に異なる。また、非生物粒子の粒径も、種類毎に異なる。そのため、散乱光の強度から、流体に含まれる被測定粒子の種類を特定することが可能である。 (First embodiment)
When biological particles such as bacteria are irradiated with light, scattered light is generated in the biological particles. In addition, even when light is applied to non-living particles made of metal or resin, scattered light is generated in the non-living particles. The intensity of scattered light generated in the particles tends to depend on the particle size of the particles. The particle size of the biological particles varies depending on the type of microorganism. In addition, the particle size of the non-biological particles is different for each type. Therefore, it is possible to specify the type of particles to be measured contained in the fluid from the intensity of the scattered light.
　また、生物粒子に励起光を照射すると、生物粒子に含まれるニコチンアミドアデニンジヌクレオチド（ＮＡＤＨ）及びリボフラビン等が、蛍光を発する。非生物粒子に光を照射しても、非生物粒子が蛍光帯域の光を発する場合がある。例えばポリエステルからなるクリーニングしたガウンから飛散した蛍光粒子は、光を照射されると蛍光を発する。ポリスチレン粒子も蛍光を発し、その後退色する。 Further, when the biological particles are irradiated with excitation light, nicotinamide adenine dinucleotide (NADH) and riboflavin contained in the biological particles emit fluorescence. Even if light is applied to non-living particles, the non-living particles may emit light in the fluorescence band. For example, fluorescent particles scattered from a cleaned gown made of polyester emit fluorescence when irradiated with light. Polystyrene particles also fluoresce and reverse their color.
　さらに、例えば、気体中に二酸化窒素（ＮＯ2）を含む窒素酸化物（ＮＯX）、硫黄酸化物（ＳＯX）、オゾンガス（Ｏ3）、酸化アルミ系のガス、アルミ合金、ガラス粉末、並びに大腸菌及びカビ等の異物を除染するための除染ガス等が含まれていると、これらのミー散乱を起こす粒子よりも小さいこともある気体含有物質が光を受けて蛍光帯域の光を発する。 Further, for example, nitrogen oxide (NO x ) containing nitrogen dioxide (NO 2 ) in the gas, sulfur oxide (SO x ), ozone gas (O 3 ), aluminum oxide-based gas, aluminum alloy, glass powder, and If decontamination gas for decontaminating foreign substances such as Escherichia coli and mold is included, gas-containing substances that may be smaller than these particles that cause Mie scattering receive light and emit light in the fluorescence band .
　例えば、二酸化窒素は、光を吸収すると、赤方偏移した光を放出して基底状態に戻る。二酸化窒素の吸収スペクトルは、波長４４０ｎｍ付近にピークを有するが、１００から２００ｎｍ程度の広い帯域を有する。そのため、二酸化窒素の存在下、４０５ｎｍの波長を有する光でＮＡＤＨ由来の蛍光及びフラビン由来の蛍光を励起しようとすると、ＮＡＤＨ及びフラビンと励起光の吸収スペクトルが重なる二酸化窒素においても蛍光が励起されうる。また、二酸化窒素は、物質が燃焼するときに、気体中の窒素と酸素が反応して発生する。そのため、元々検査対象の気体中に二酸化窒素が含まれていなくても、検査対象の気体に励起光として高いビーム密度を有するレーザ光あるいは強力な電磁放射線を照射すると、気体中の物質が燃焼して二酸化窒素が生じ、二酸化窒素が蛍光を発することもある。さらに、一酸化窒素とオゾンが反応して二酸化窒素を形成し、蛍光を発することもある。 For example, when nitrogen dioxide absorbs light, it emits red-shifted light and returns to the ground state. The absorption spectrum of nitrogen dioxide has a peak in the vicinity of a wavelength of 440 nm, but has a wide band of about 100 to 200 nm. Therefore, when excitation of NADH-derived fluorescence and flavin-derived fluorescence with light having a wavelength of 405 nm in the presence of nitrogen dioxide, fluorescence can be excited even in nitrogen dioxide where NADH and flavin and the absorption spectrum of excitation light overlap. . Nitrogen dioxide is generated by the reaction of nitrogen and oxygen in the gas when the substance burns. Therefore, even if the gas to be inspected originally does not contain nitrogen dioxide, if the gas to be inspected is irradiated with laser light having a high beam density or strong electromagnetic radiation as excitation light, the substance in the gas will burn. Nitrogen dioxide is generated, and nitrogen dioxide may fluoresce. Further, nitric oxide and ozone react to form nitrogen dioxide, which may emit fluorescence.
　二酸化窒素については、特開２００３－１３９７０７号公報、Joel A. Thorntonら著、「Atmospheric NO2: In Situ Laser-Induced　Fluorescence Detection at Parts per Trillion Mixing Ratios」、Analytical Chemistry、Vol. 72、No. 3、February 1、2000、pp.528－539、及びS.A.Nizkorodovら著、「Time-resolved fluorescence of NO2 in a magnetic field」、Volume 215、number 6、CHEMICAL PHYSICS LETTERS、17 December 1993、pp. 662-667参照。硫黄酸化物については、特開２０１２－８６１０５号公報参照。 Regarding nitrogen dioxide, JP 2003-139707 A, Joel A. Thornton et al., “Atmospheric NO2: In Situ Laser-Induced Fluorescence Detection at Parts per Trillion Mixing Ratios”, Analytical Chemistry, Vol. 72, No. 3, February 1, 2000, pp. 528-539, and SANizkorodov et al., “Time-resolved fluorescence of NO 2 in a magnetic field”, Volume 215, number 6, CHEMICAL PHYSICS LETTERS, 17 December 1993, pp. 662-667. . For sulfur oxides, see JP2012-86105A.
　複数の波長において物質が発する蛍光帯域の光の強度を測定すると、ある波長の光の強度に対する他の波長の光の強度の相関が、物質毎に異なる。例えば、図１は、励起光を照射された表皮ブドウ球菌、枯草菌芽胞、大腸菌、ガラス、及びアルミニウムのそれぞれが発した蛍光帯域の光について、横軸に５３０ｎｍ以上の帯域における波長の光強度を、縦軸に４４０ｎｍ付近の帯域における波長の光強度をプロットしたグラフである。図１に示すように、４４０ｎｍ付近の帯域における波長の光強度に対する５３０ｎｍ以上の帯域における波長の光強度の比は、非生物において小さく、微生物粒子において大きくなる傾向にある。したがって、複数の波長毎に物質が発した蛍光帯域の光の強度を測定し、それらの相関をとることで、その物質が生物であるか非生物であるかを識別可能である。 When measuring the intensity of light in the fluorescence band emitted by a substance at a plurality of wavelengths, the correlation between the intensity of light at a certain wavelength and the intensity of light at another wavelength differs for each substance. For example, FIG. 1 shows the light intensity of the wavelength in the band of 530 nm or more on the horizontal axis for the light in the fluorescence bands emitted by each of Staphylococcus epidermidis, Bacillus subtilis spores, Escherichia coli, glass, and aluminum irradiated with excitation light. It is the graph which plotted the light intensity of the wavelength in the band around 440 nm on the vertical axis. As shown in FIG. 1, the ratio of the light intensity of the wavelength in the band of 530 nm or more to the light intensity of the wavelength in the band near 440 nm tends to be small in the non-living material and large in the microbial particle. Therefore, by measuring the intensity of light in the fluorescence band emitted by a substance for each of a plurality of wavelengths and taking a correlation between them, it is possible to identify whether the substance is a living organism or a non-living organism.
　例えば、図２において、第１の蛍光帯域の光の強度を表す横軸をｘ軸、第２の蛍光帯域の光の強度を表す縦軸をｙ軸とすると、生物と非生物との識別境界を与える関数ｙ＝ｆ（ｘ）を画定することが可能である。図２の例においては、ｙ＞ｆ（ｘ）の領域にプロットされる光強度を与える粒子は、非生物のクラスに分類することが可能であり、ｙ＜ｆ（ｘ）の領域にプロットされる光強度を与える粒子は、生物のクラスに分類することが可能である。 For example, in FIG. 2, when the horizontal axis representing the light intensity in the first fluorescence band is the x axis and the vertical axis representing the light intensity in the second fluorescence band is the y axis, the boundary between the living organism and the non-living organism is distinguished. It is possible to define a function y = f (x) that gives In the example of FIG. 2, particles giving light intensity plotted in the region of y> f (x) can be classified into the non-living class and plotted in the region of y <f (x). Particles that give a certain light intensity can be classified into a class of organisms.
　さらに、上述したように、粒子で生じる散乱光の強度は、粒子の種類によって異なる。そのため、図３に示すように、ｘ軸、ｙ軸、及び散乱光の強度を表すｚ軸を含む３次元座標系に、複数種類の既知の生物粒子及び非生物粒子のそれぞれについて、光を照射したときに生じる第１の波長を有する蛍光帯域の光の強度の測定値、第２の波長を有する蛍光帯域の光の強度の測定値、及び散乱光の強度の測定値をプロットし、生物と非生物との識別境界を与える関数ｆ（ｘ，ｙ，ｚ）を画定することが可能である。 Furthermore, as described above, the intensity of the scattered light generated by the particles varies depending on the type of the particles. Therefore, as shown in FIG. 3, light is irradiated to each of a plurality of types of known biological particles and non-biological particles on a three-dimensional coordinate system including an x-axis, a y-axis, and a z-axis representing the intensity of scattered light. Plotting the measurement value of the light intensity of the fluorescence band having the first wavelength, the measurement value of the light intensity of the fluorescence band having the second wavelength, and the measurement value of the intensity of the scattered light, It is possible to define a function f (x, y, z) that gives an identification boundary with an abiotic.
　ここで、生物粒子のクラスと、非生物粒子のクラスと、を線形分離すると、図４に示すように、本来、生物粒子のクラスに分類されるべき粒子が非生物粒子に分類されたり、非生物粒子のクラスに分類されるべき粒子が生物粒子に分類されたりする場合がある。これに対し、図５に示すように、生物粒子のクラスと、非生物粒子のクラスと、を非線形分離すると、本来、生物粒子のクラスに分類されるべき粒子が非生物粒子に分類されたり、非生物粒子のクラスに分類されるべき粒子が生物粒子に分類されたりすることを低減することが可能となる。 Here, when the biological particle class and the non-biological particle class are linearly separated, as shown in FIG. 4, particles that should be classified into the biological particle class are classified as non-biological particles or non-biological particles. In some cases, particles to be classified into the biological particle class are classified as biological particles. On the other hand, as shown in FIG. 5, when the biological particle class and the non-biological particle class are separated nonlinearly, the particles that should be classified into the biological particle class are classified as non-biological particles. It becomes possible to reduce that the particle | grains which should be classified into the class of a non-biological particle are classified into a biological particle.
　生物と非生物とを非線形分離する識別境界を与える関数ｆ（ｘ，ｙ，ｚ）は、学習データから、各データ点との距離が最大となるように識別境界を求めるサポートベクターマシン（ＳＶＭ）のような非線形な識別器によって画定可能である。なお、識別器は、分類器や判別器とも呼ばれる。非線形的な識別器はサポートベクターマシンに限られない。例えば、識別器を多数組み合わせることにより精度を高めたブースティング、脳機能に見られる特性を計算機上にシミュレートしたニューラルネットワーク、その他、決定木、最近傍探索、及び事例ベース推論等の手法を用いる識別器が使用可能である。 A function f (x, y, z) that gives an identification boundary for nonlinearly separating a living thing and a non-living object is a support vector machine (SVM) that obtains an identification boundary from the learning data so that the distance to each data point is maximized. Can be defined by a non-linear classifier such as The classifier is also called a classifier or a classifier. Nonlinear classifiers are not limited to support vector machines. For example, boosting with increased accuracy by combining many discriminators, neural networks that simulate the characteristics of brain functions on a computer, and other methods such as decision trees, nearest neighbor search, and case-based reasoning A discriminator can be used.
　例えば、光を照射される粒子に、微生物粒子、微生物粒子よりも粒径の大きい非微生物粒子、及び微生物粒子よりも粒径の小さい非微生物粒子が含まれていると、３次元座標系において、例えば図６に示すように、識別境界を与える関数ｆ（ｘ，ｙ，ｚ）で囲まれる空間にプロットされる光強度を与える粒子は、生物のクラスに分類することが可能であり、例えば図７に示すように、関数ｆ（ｘ，ｙ，ｚ）で囲まれる空間の外側にプロットされる光強度を与える粒子は、非生物のクラスに分類することが可能である。 For example, if the particles irradiated with light include microbial particles, non-microbial particles having a larger particle size than microbial particles, and non-microbial particles having a smaller particle size than microbial particles, in a three-dimensional coordinate system, For example, as shown in FIG. 6, particles that give light intensity plotted in a space surrounded by a function f (x, y, z) that gives an identification boundary can be classified into a biological class. As shown in FIG. 7, particles that give light intensity plotted outside the space enclosed by the function f (x, y, z) can be classified into the non-living class.
　この場合、予め、３次元座標系に、生物と非生物との識別境界を与える関数ｆ（ｘ，ｙ，ｚ）を画定し、その後、未知の粒子に検査光を照射したときに生じる第１の波長を有する蛍光帯域の光、第２の波長を有する蛍光帯域の光、及び散乱光の強度を測定して、測定値が関数ｆ（ｘ，ｙ，ｚ）で囲まれる空間にプロットされる場合は、測定された粒子は生物であると判定し、測定値が関数ｆ（ｘ，ｙ，ｚ）で囲まれる空間の外側にプロットされる場合は、測定された粒子は非生物であると判定することが可能である。 In this case, a function f (x, y, z) that gives an identification boundary between a living organism and a non-living organism is defined in advance in the three-dimensional coordinate system, and then the first generated when the inspection light is irradiated to an unknown particle. Measured in the space surrounded by the function f (x, y, z) by measuring the intensity of the light in the fluorescent band having the wavelength of λ, the light in the fluorescent band having the second wavelength, and the scattered light. The measured particle is determined to be a living organism, and if the measured value is plotted outside the space enclosed by the function f (x, y, z), the measured particle is considered to be an abiotic organism. It is possible to determine.
　例えば、識別境界を与える多変数関数ｆ（ｘ，ｙ，ｚ）が、一組の独立変数（ｘ，ｙ）に対し、従属変数ｚの値を２つ出力する多価関数である場合、図６及び図７を参照して、一組の（ｘ1，ｙ1）の値の第１及び第２の蛍光帯域の光の強度を与え、かつ、識別境界における散乱光の強度の第１の境界値よりも大きい散乱光の強度の測定値を与える粒子は非生物粒子である。また、一組の（ｘ1，ｙ1）の値の第１及び第２の蛍光帯域の光の強度を与え、かつ、散乱光の強度の第１の境界値よりも小さい散乱光の強度の測定値を与え、さらに、識別境界における散乱光の強度の第２の境界値よりも大きい散乱光の強度の測定値を与える粒子は生物粒子である。また、一組の（ｘ1，ｙ1）の値の第１及び第２の蛍光帯域の光の強度を与え、かつ、識別境界における散乱光の強度の第２の境界値よりも小さい散乱光の強度の測定値を与える粒子は非生物粒子である。ただし、識別境界の形状は、サンプルによって変わりうる。 For example, when the multivariable function f (x, y, z) that gives an identification boundary is a multivalent function that outputs two values of the dependent variable z for a set of independent variables (x, y), FIG. 6 and FIG. 7, the first and second fluorescence band light intensities having a set of (x 1 , y 1 ) values are provided, and the first of the scattered light intensities at the discrimination boundary is given. Particles that give a measure of scattered light intensity greater than the boundary value are non-biological particles. Further, the intensity of light in the first and second fluorescence bands having a set of (x 1 , y 1 ) values is given, and the intensity of scattered light is smaller than the first boundary value of the intensity of scattered light. A particle that provides a measurement and also provides a measurement of the intensity of scattered light that is greater than the second boundary value of the intensity of scattered light at the identification boundary is a biological particle. Further, the scattered light is given a set of (x 1 , y 1 ) values of the first and second fluorescence band light intensity and is smaller than the second boundary value of the scattered light intensity at the identification boundary. Particles that give a strength measurement of are non-biological particles. However, the shape of the identification boundary can vary depending on the sample.
　ここで、本発明の第１の実施の形態に係る環境分析装置が備える粒子検出装置１は、図８に示すように、流体に検査光を照射する光源１０と、検査光を照射された流体中の粒子で生じる反応光を検出する光検出器１５と、反応光に基づき、粒子を分類する粒子分類部３０１と、分類された粒子の量を計数する計数部３０２と、分類ごとに粒子の量の警報閾値を保存する閾値保存部３５２と、分類ごとに、粒子の量が警報閾値を超えた場合、警報を発する警報部３０３と、を備える。粒子分類部３０１、計数部３０２、及び警報部３０３は、例えば、中央演算処理装置（ＣＰＵ）３００に含まれる。閾値保存部３５２は、例えば、ＣＰＵ３００に接続された記憶装置３５０に含まれる。 Here, as shown in FIG. 8, the particle detection device 1 included in the environmental analysis device according to the first embodiment of the present invention includes a light source 10 that irradiates a fluid with inspection light, and a fluid that is irradiated with the inspection light. A detector 15 for detecting reaction light generated in the particles in the particles, a particle classification unit 301 for classifying particles based on the reaction light, a counting unit 302 for counting the amount of classified particles, and a particle classification unit for each classification. A threshold storage unit 352 that stores an alarm threshold for the amount, and an alarm unit 303 that issues an alarm when the amount of particles exceeds the alarm threshold for each classification. The particle classification unit 301, the counting unit 302, and the alarm unit 303 are included in, for example, a central processing unit (CPU) 300. The threshold storage unit 352 is included in the storage device 350 connected to the CPU 300, for example.
　ＣＰＵ３００には、出力装置４０１が接続されている。出力装置４０１としては、例えば、ディスプレイ、プリンタ、及び音響装置等が使用可能である。 The output device 401 is connected to the CPU 300. As the output device 401, for example, a display, a printer, an acoustic device, or the like can be used.
　検査光を照射された被測定粒子で生じる反応光としては、複数の波長における蛍光、及び散乱光が挙げられる。光検出器１５は、例えば、第１の波長を有する第１の反応光の強度、第２の波長を有する第２の反応光の強度、及び第３の波長を有する第３の反応光の強度の測定値を測定する。第１の波長と、第２の波長と、第３の波長と、は異なる。以下、第１及び第２の反応光が蛍光帯域の光であり、第３の反応光が散乱光である例を説明する。なお、「蛍光帯域の光」とは、蛍光、自家蛍光、及び必ずしも蛍光ではないが、波長帯域が蛍光と重なる光を含む。また、以下、第１分類の粒子が生物粒子であり、第２分類の粒子が非生物粒子である例を説明する。 Examples of reaction light generated in the particles to be measured irradiated with inspection light include fluorescence at a plurality of wavelengths and scattered light. The photodetector 15 is, for example, the intensity of the first reaction light having the first wavelength, the intensity of the second reaction light having the second wavelength, and the intensity of the third reaction light having the third wavelength. Measure the measured value. The first wavelength, the second wavelength, and the third wavelength are different. Hereinafter, an example in which the first and second reaction lights are light in the fluorescence band and the third reaction light is scattered light will be described. The “light in the fluorescence band” includes fluorescence, autofluorescence, and light that is not necessarily fluorescence, but whose wavelength band overlaps with fluorescence. Hereinafter, an example in which the first classification particles are biological particles and the second classification particles are non-biological particles will be described.
　粒子検出装置１の記憶装置３５０は、第１分類の粒子のクラスと第２分類の粒子のクラスを非線形分離する識別境界を保存する境界情報保存部３５１をさらに備える。粒子分類部３０１は、第１から第３の反応光の強度の測定値と識別境界とに基づき、被測定粒子を第１及び第２の分類の粒子のクラスのいずれかに分類する。 The storage device 350 of the particle detection device 1 further includes a boundary information storage unit 351 that stores an identification boundary that nonlinearly separates the first class particle class and the second class particle class. The particle classification unit 301 classifies the particles to be measured into one of the first and second classification classes based on the first to third reaction light intensity measurements and the identification boundary.
　粒子検出装置１によって粒子を含むか否かが検査される気体は、ノズル４０から噴出される。ノズル４０から噴出された流体に向けて、光源１０から広帯域波長の励起光が、検査光として照射される。なお、液体が検査される場合は、液体が流れるフローセル等に向けて、光源１０から広帯域波長の検査光が照射される。以下、流体が気体である例を説明する。光源１０としては、例えば、発光ダイオード（ＬＥＤ）及びレーザが使用可能である。検査光の波長は、例えば２５０ないし５５０ｎｍである。検査光は、可視光であっても、紫外光であってもよい。検査光が可視光である場合、検査光の波長は、例えば４００ないし５５０ｎｍの範囲内であり、例えば４０５ｎｍである。検査光が紫外光である場合、検査光の波長は、例えば３００ないし３８０ｎｍの範囲内であり、例えば３４０ｎｍである。ただし、検査光の波長は、これらに限定されない。光源１０には、光源１０に電力を供給する光源駆動電源１１が接続されている。光源駆動電源１１には、光源１０に供給される電力を制御する電源制御装置１２が接続されている。 The gas to be inspected by the particle detection device 1 to determine whether or not it contains particles is ejected from the nozzle 40. To the fluid ejected from the nozzle 40, excitation light having a broadband wavelength is emitted from the light source 10 as inspection light. When the liquid is inspected, the light source 10 emits the inspection light having a broadband wavelength toward the flow cell or the like through which the liquid flows. Hereinafter, an example in which the fluid is a gas will be described. As the light source 10, for example, a light emitting diode (LED) and a laser can be used. The wavelength of the inspection light is, for example, 250 to 550 nm. The inspection light may be visible light or ultraviolet light. When the inspection light is visible light, the wavelength of the inspection light is, for example, in the range of 400 to 550 nm, for example, 405 nm. When the inspection light is ultraviolet light, the wavelength of the inspection light is, for example, in the range of 300 to 380 nm, for example, 340 nm. However, the wavelength of the inspection light is not limited to these. A light source driving power source 11 that supplies power to the light source 10 is connected to the light source 10. A power source control device 12 that controls the power supplied to the light source 10 is connected to the light source driving power source 11.
　光検出器１５は、ノズル４０から噴出された流体に含まれ、検査光を照射された被測定粒子で生じる第１の蛍光帯域の光の強度及び第２の蛍光帯域の光の強度を測定する蛍光強度測定器１０２と、検査光を照射された被測定粒子で生じる散乱光を測定する散乱光測定器１０５と、を備える。光源１０と、蛍光強度測定器１０２と、散乱光測定器１０５と、は、筐体３０に設けられている。また、蛍光強度測定器１０２及び散乱光測定器１０５は、ＣＰＵ３００に電気的に接続されている。 The light detector 15 is included in the fluid ejected from the nozzle 40 and measures the intensity of the light in the first fluorescence band and the intensity of the light in the second fluorescence band generated by the particles to be measured irradiated with the inspection light. A fluorescence intensity measuring device 102 and a scattered light measuring device 105 that measures scattered light generated by the particles to be measured irradiated with the inspection light are provided. The light source 10, the fluorescence intensity measuring device 102, and the scattered light measuring device 105 are provided in the housing 30. Further, the fluorescence intensity measuring device 102 and the scattered light measuring device 105 are electrically connected to the CPU 300.
　蛍光強度測定器１０２は、被測定粒子が発する蛍光帯域の光を検出する。蛍光強度測定器１０２は、第１の波長における蛍光帯域の光を受光する第１の受光素子２０Ａと、第１の波長とは異なる第２の波長における蛍光帯域の光を受光する第２の受光素子２０Ｂと、を備える。なお、第１の波長とは、帯域を有していてもよい。第２の波長についても同様である。第１の受光素子２０Ａ及び第２の受光素子２０Ｂとしては、フォトダイオード及び光電管等が使用可能であり、光を受光すると、光エネルギーを電気エネルギーに変換する。 The fluorescence intensity measuring device 102 detects light in the fluorescence band emitted by the particle to be measured. The fluorescence intensity measuring instrument 102 receives a first light receiving element 20A that receives light in the fluorescence band at the first wavelength, and a second light reception that receives light in the fluorescence band at a second wavelength different from the first wavelength. And an element 20B. Note that the first wavelength may have a band. The same applies to the second wavelength. As the first light receiving element 20A and the second light receiving element 20B, a photodiode, a phototube or the like can be used. When receiving light, the light energy is converted into electric energy.
　第１の受光素子２０Ａには、第１の受光素子２０Ａで生じた電流を増幅する増幅器２１Ａが接続されている。増幅器２１Ａには、増幅器２１Ａに電力を供給する増幅器電源２２Ａが接続されている。また、増幅器２１Ａには、増幅器２１Ａで増幅された電流を受け取り、第１の受光素子２０Ａが受光した光の強度を算出する光強度算出装置２３Ａが接続されている。光強度算出装置２３Ａには、光強度算出装置２３Ａが算出した光の強度を保存する光強度記憶装置２４Ａが接続されている。 The amplifier 21A for amplifying the current generated in the first light receiving element 20A is connected to the first light receiving element 20A. An amplifier power supply 22A that supplies power to the amplifier 21A is connected to the amplifier 21A. The amplifier 21A is connected to a light intensity calculating device 23A that receives the current amplified by the amplifier 21A and calculates the intensity of the light received by the first light receiving element 20A. The light intensity calculation device 23A is connected to a light intensity storage device 24A that stores the light intensity calculated by the light intensity calculation device 23A.
　第２の受光素子２０Ｂには、第２の受光素子２０Ｂで生じた電流を増幅する増幅器２１Ｂが接続されている。増幅器２１Ｂには、増幅器２１Ｂに電力を供給する増幅器電源２２Ｂが接続されている。また、増幅器２１Ｂには、増幅器２１Ｂで増幅された電流を受け取り、第２の受光素子２０Ｂが受光した光の強度を算出する光強度算出装置２３Ｂが接続されている。光強度算出装置２３Ｂには、光強度算出装置２３Ｂが算出した光の強度を保存する光強度記憶装置２４Ｂが接続されている。 The amplifier 21B that amplifies the current generated in the second light receiving element 20B is connected to the second light receiving element 20B. An amplifier power supply 22B that supplies power to the amplifier 21B is connected to the amplifier 21B. The amplifier 21B is connected to a light intensity calculation device 23B that receives the current amplified by the amplifier 21B and calculates the intensity of light received by the second light receiving element 20B. A light intensity storage device 24B that stores the light intensity calculated by the light intensity calculation device 23B is connected to the light intensity calculation device 23B.
　散乱光測定器１０５は、検査光を照射された被測定粒子で生じる散乱光を検出する。散乱光測定器１０５は、散乱光を受光する散乱光受光素子５０を備える。散乱光受光素子５０としては、フォトダイオード等が使用可能であり、光を受光すると、光エネルギーを電気エネルギーに変換する。 The scattered light measuring device 105 detects scattered light generated by the particles to be measured irradiated with the inspection light. The scattered light measuring device 105 includes a scattered light receiving element 50 that receives scattered light. As the scattered light receiving element 50, a photodiode or the like can be used. When light is received, the light energy is converted into electric energy.
　散乱光受光素子５０には、散乱光受光素子５０で生じた電流を増幅する増幅器５１が接続されている。増幅器５１には、増幅器５１に電力を供給する増幅器電源５２が接続されている。また、増幅器５１には、増幅器５１で増幅された電流を受け取り、散乱光受光素子５０が受光した散乱光の強度を算出する光強度算出装置５３が接続されている。光強度算出装置５３には、光強度算出装置５３が算出した散乱光の強度を保存する光強度記憶装置５４が接続されている。 The scattered light receiving element 50 is connected to an amplifier 51 that amplifies the current generated in the scattered light receiving element 50. An amplifier power supply 52 that supplies power to the amplifier 51 is connected to the amplifier 51. The amplifier 51 is connected to a light intensity calculation device 53 that receives the current amplified by the amplifier 51 and calculates the intensity of scattered light received by the scattered light receiving element 50. A light intensity storage device 54 that stores the intensity of scattered light calculated by the light intensity calculation device 53 is connected to the light intensity calculation device 53.
　境界情報保存部３５１に保存される識別境界は、例えば、第１の蛍光帯域の光の強度、第２の蛍光帯域の光の強度、及び散乱光の強度を変数とする多変数関数で与えられる。境界情報保存部３５１は、例えば、多変数関数を含む３次元座標系を保存する。３次元座標系は、第１の蛍光帯域の光の強度を示すｘ座標と、第２の蛍光帯域の光の強度を示すｙ座標と、散乱光の強度を示すｚ座標と、からなる。３次元座標系は、例えば図９に示すように、Ｎを整数として、Ｎ×Ｎ×Ｎ個のセルからなる３次元テーブルで表現される。この場合、例えば、ｘ方向のセルには０番からＮ－１番までのインデックスが付されており、ｙ方向のセルにも０番からＮ－１番までのインデックスが付されており、ｚ方向のセルにも０番からＮ－１番までのインデックスが付されている。 The identification boundary stored in the boundary information storage unit 351 is given by, for example, a multivariable function having the first fluorescent band light intensity, the second fluorescent band light intensity, and the scattered light intensity as variables. . The boundary information storage unit 351 stores, for example, a three-dimensional coordinate system including a multivariable function. The three-dimensional coordinate system includes an x coordinate indicating the intensity of light in the first fluorescence band, a y coordinate indicating the intensity of light in the second fluorescence band, and a z coordinate indicating the intensity of scattered light. For example, as shown in FIG. 9, the three-dimensional coordinate system is represented by a three-dimensional table including N × N × N cells, where N is an integer. In this case, for example, an index from 0 to N-1 is assigned to cells in the x direction, and an index from 0 to N-1 is assigned to cells in the y direction. The direction cells are also indexed from 0 to N-1.
　例えば、３次元テーブルが、２５６×２５６×２５６個のセルからなる場合、ｘ方向のセルには０番から２５５番までのインデックスが付されており、ｙ方向のセルにも０番から２５５番までのインデックスが付されており、ｚ方向のセルにも０番から２５５番までのインデックスが付されている。 For example, if the three-dimensional table is composed of 256 × 256 × 256 cells, the cells in the x direction are indexed from 0 to 255, and the cells in the y direction are also numbered 0 to 255. Indexes from 0 to 255 are also assigned to cells in the z direction.
　光の強度は、例えば０から５Ｖ等の範囲内の電圧信号で表される。光の強度を、離散的なインデックスＩに変換するには、例えば以下の（１）式を用いる。
　　Ｉ＝［ＮI＊（ＳD／ＳM）］　　　（１）
　ここで、ＮIはインデックス数であり、例えば２５６である。ＳDは電圧信号で表された光の強度の測定値である。ＳMは電圧信号で表された光の強度がとりうる最大値である。（１）式で算出されるインデックスＩは、０から２５５までの整数となる。 The intensity of light is represented by a voltage signal within a range of 0 to 5 V, for example. In order to convert the light intensity into the discrete index I, for example, the following equation (1) is used.
I = [N I * (S D / S M )] (1)
Here, N I is the number of index, for example, 256. S D is a measured value of light intensity represented by a voltage signal. S M is the maximum value that the light intensity represented by the voltage signal can take. The index I calculated by the equation (1) is an integer from 0 to 255.
　生物粒子のクラスと非生物粒子のクラスを非線形分離する多変数関数によって画定された生物粒子のクラスの領域に含まれる各セルには、生物粒子のクラスの識別子が付されている。また、生物粒子のクラスと非生物粒子のクラスを非線形分離する多変数関数によって画定された非生物粒子の領域に含まれる各セルには、非生物粒子のクラスの識別子が付されている。さらに、蛍光強度の所定の閾値によって確定された、蛍光強度が所定の閾値以下の領域に含まれる各セルには、非蛍光粒子のクラスの識別子が付されている。したがって、３次元テーブルの（ｘ，ｙ，ｚ）座標のセルを特定すれば、特定されたセルから、生物粒子のクラスの識別子、非生物粒子のクラスの識別子、又は非蛍光粒子のクラスの識別子を取得することが可能である。 Each cell included in the region of the biological particle class defined by the multivariable function that nonlinearly separates the biological particle class and the non-biological particle class is provided with an identifier of the biological particle class. Each cell included in the non-biological particle region defined by the multivariable function that nonlinearly separates the biological particle class and the non-biological particle class is assigned an identifier of the non-biological particle class. Furthermore, each cell included in a region where the fluorescence intensity is determined to be equal to or less than the predetermined threshold value determined by the predetermined threshold value of the fluorescence intensity is given an identifier of a class of non-fluorescent particles. Therefore, if the cell of the (x, y, z) coordinate of the three-dimensional table is specified, the identifier of the class of biological particles, the identifier of the class of non-biological particles, or the identifier of the class of non-fluorescent particles is identified from the specified cells. Is possible to get.
　図８に示す粒子分類部３０１は、例えば上記（１）式を用いて、被測定粒子が発した第１の蛍光帯域の光の強度の測定値と、第２の蛍光帯域の光の強度の測定値と、散乱光の強度の測定値と、に対応する、境界情報保存部３５１に保存されている３次元テーブルにおける座標（ｘ，ｙ，ｚ）のセルを特定する。さらに、粒子分類部３０１は、特定された座標（ｘ，ｙ，ｚ）のセルにおける識別子が生物粒子のクラスであった場合は、被測定粒子を生物粒子のクラスに分類する。また、粒子分類部３０１は、特定された座標（ｘ，ｙ，ｚ）のセルにおける識別子が非生物粒子のクラスであった場合は、被測定粒子を非生物粒子のクラスに分類する。さらに、粒子分類部３０１は、特定された座標（ｘ，ｙ，ｚ）のセルにおける識別子が非蛍光粒子のクラスであった場合は、被測定粒子を非蛍光粒子のクラスに分類する。なお、粒子分類部３０１は、蛍光粒子に分類されなかった粒子を、非蛍光粒子として分類してもよい。 The particle classification unit 301 shown in FIG. 8 uses the above equation (1), for example, to measure the intensity of light in the first fluorescence band emitted from the particle to be measured and the intensity of light in the second fluorescence band. A cell of coordinates (x, y, z) in the three-dimensional table stored in the boundary information storage unit 351 corresponding to the measurement value and the measurement value of the intensity of scattered light is specified. Further, when the identifier in the cell of the specified coordinates (x, y, z) is a biological particle class, the particle classification unit 301 classifies the particle to be measured into the biological particle class. In addition, when the identifier in the specified coordinate (x, y, z) cell is a class of a non-biological particle, the particle classification unit 301 classifies the measured particle into a class of a non-biological particle. Further, the particle classification unit 301 classifies the particles to be measured into the non-fluorescent particle class when the identifier in the cell of the specified coordinates (x, y, z) is the non-fluorescent particle class. The particle classification unit 301 may classify particles that are not classified as fluorescent particles as non-fluorescent particles.
　ＣＰＵ３００に含まれる計数部３０２は、分類ごとに、粒子の量を計数する。例えば、計数部３０２は、単位時間あたり、あるいは稼働時間当たりの、生物粒子のクラスに分類された粒子の数あるいは濃度を計数する。また、計数部３０２は、単位時間あたり、あるいは稼働時間当たりの、非生物粒子のクラスに分類された粒子の数あるいは濃度を計数する。さらに、計数部３０２は、単位時間あたり、あるいは稼働時間当たりの、非蛍光粒子のクラスに分類された粒子の数あるいは濃度を計数する。なお、計数部３０２は、粒子の総量から、蛍光粒子の総量を引いた量を、非蛍光粒子の量として計数してもよい。計数部３０２は、分類ごとに計数した粒子の量を、出力装置４０１から出力する。計数部３０２は、分類ごとに計数した粒子の量を、数値で出力してもよいし、図１０に示すようにグラフで出力してもよい。また、計数部３０２は、分類ごとに計数した粒子の量を、図８に示す記憶装置３５０に含まれるデータ保存部３５３に保存する。 The counting unit 302 included in the CPU 300 counts the amount of particles for each classification. For example, the counting unit 302 counts the number or concentration of particles classified into the biological particle class per unit time or operation time. The counting unit 302 counts the number or concentration of particles classified into the non-biological particle class per unit time or per operation time. Further, the counting unit 302 counts the number or concentration of particles classified into the non-fluorescent particle class per unit time or operation time. The counting unit 302 may count the amount obtained by subtracting the total amount of fluorescent particles from the total amount of particles as the amount of non-fluorescent particles. The counting unit 302 outputs the amount of particles counted for each classification from the output device 401. The counting unit 302 may output the amount of particles counted for each classification as a numerical value or a graph as shown in FIG. The counting unit 302 stores the amount of particles counted for each classification in the data storage unit 353 included in the storage device 350 illustrated in FIG.
　記憶装置３５０に含まれる閾値保存部３５２は、分類ごとに、粒子の量の警報閾値を保存する。例えば、閾値保存部３５２は、単位時間あたり、あるいは稼働時間当たりの、生物粒子のクラスに分類された粒子の数あるいは濃度の警報閾値を保存する。また、閾値保存部３５２は、単位時間あたり、あるいは稼働時間当たりの、非生物粒子のクラスに分類された粒子の数あるいは濃度の警報閾値を保存する。さらに、閾値保存部３５２は、単位時間あたり、あるいは稼働時間当たりの、非蛍光粒子のクラスに分類された粒子の数あるいは濃度の警報閾値を保存する。なお、閾値保存部３５２は、分類される前の粒子の全量の警報閾値を保存してもよい。 The threshold storage unit 352 included in the storage device 350 stores an alarm threshold for the amount of particles for each classification. For example, the threshold storage unit 352 stores an alarm threshold for the number or concentration of particles classified into the biological particle class per unit time or per operation time. The threshold storage unit 352 stores an alarm threshold for the number or concentration of particles classified into the non-biological particle class per unit time or per operation time. Furthermore, the threshold storage unit 352 stores an alarm threshold for the number or concentration of particles classified into the non-fluorescent particle class per unit time or per operation time. The threshold storage unit 352 may store an alarm threshold for the total amount of particles before classification.
　例えば、検出対象として生物粒子を重視する場合は、生物粒子のクラスに分類された粒子の数あるいは濃度の警報閾値は、低く設定される。検出対象として生物粒子を重視しない場合は、生物粒子のクラスに分類された粒子の数あるいは濃度の警報閾値は、高く設定される。また、検出対象として非生物粒子を重視する場合は、非生物粒子のクラスに分類された粒子の数あるいは濃度の警報閾値は、低く設定される。検出対象として非生物粒子を重視しない場合は、非生物粒子のクラスに分類された粒子の数あるいは濃度の警報閾値は、高く設定される。また、検出対象として非蛍光粒子を重視する場合は、非蛍光粒子のクラスに分類された粒子の数あるいは濃度の警報閾値は、低く設定される。検出対象として非蛍光粒子を重視しない場合は、非蛍光粒子のクラスに分類された粒子の数あるいは濃度の警報閾値は、高く設定される。 For example, when importance is attached to biological particles as a detection target, the alarm threshold for the number or concentration of particles classified into the biological particle class is set low. When biological particles are not important as detection targets, the alarm threshold for the number or concentration of particles classified into the biological particle class is set high. Further, when importance is attached to non-living particles as detection targets, the alarm threshold for the number or concentration of particles classified into the non-living particle class is set low. When non-living particles are not important as detection targets, the alarm threshold for the number or concentration of particles classified into the non-living particles class is set high. Further, when importance is attached to non-fluorescent particles as a detection target, the alarm threshold for the number or concentration of particles classified into the non-fluorescent particle class is set low. When non-fluorescent particles are not important as detection targets, the alarm threshold for the number or concentration of particles classified into the non-fluorescent particle class is set high.
　ＣＰＵ３００に含まれる警報部３０３は、分類ごとに、粒子の量が警報閾値を超えた場合、警報を発する。例えば、警報部３０３は、図１１に示すように、計数部３０２が計数した生物粒子のクラスに分類された粒子の量が、閾値保存部３５２に保存されている警報閾値を超えた場合、出力装置４０１を介して警報を発する。また、警報部３０３は、計数部３０２が計数した非生物粒子のクラスに分類された粒子の量が、閾値保存部３５２に保存されている警報閾値を超えた場合、出力装置４０１を介して警報を発する。さらに、警報部３０３は、計数部３０２が計数した非蛍光粒子のクラスに分類された粒子の量が、閾値保存部３５２に保存されている警報閾値を超えた場合、出力装置４０１を介して警報を発する。 The alarm unit 303 included in the CPU 300 issues an alarm when the amount of particles exceeds the alarm threshold for each classification. For example, as shown in FIG. 11, the alarm unit 303 outputs an output when the amount of particles classified into the biological particle class counted by the counting unit 302 exceeds the alarm threshold stored in the threshold storage unit 352. An alarm is issued via the device 401. In addition, the alarm unit 303 issues an alarm via the output device 401 when the amount of particles classified into the non-biological particle class counted by the counting unit 302 exceeds the alarm threshold stored in the threshold storage unit 352. To emit. Further, the alarm unit 303 issues an alarm via the output device 401 when the amount of particles classified into the non-fluorescent particle class counted by the counter unit 302 exceeds the alarm threshold value stored in the threshold value storage unit 352. To emit.
　第１の実施の形態に係る環境分析装置は、図１２に示すように、粒子の発生源の動画を撮影する撮影装置３Ａ、３Ｂ、３Ｃと、撮影装置３Ａ、３Ｂ、３Ｃが撮影した動画を保存する動画保存部４と、をさらに備える。なお、環境分析装置が備える撮影装置の数は特に限定されず、１台であってもよいし、複数台であってもよい。粒子検出装置１と、撮影装置３Ａ、３Ｂ、３Ｃと、動画保存部４と、は、例えば、ネットワーク２で接続されている。また、図８に示すように、粒子検出装置１のＣＰＵ３００は、分類ごとに、粒子の量が警報閾値を超えた場合、撮影装置３Ａ、３Ｂ、３Ｃに発生源を撮影させる撮影装置制御部３０４をさらに備える。 As shown in FIG. 12, the environmental analysis apparatus according to the first embodiment captures the moving images captured by the capturing apparatuses 3A, 3B, and 3C and the capturing apparatuses 3A, 3B, and 3C that capture the moving images of the particle generation sources. And a moving image storage unit 4 to be stored. Note that the number of imaging devices included in the environmental analysis device is not particularly limited, and may be one or more. The particle detection device 1, the imaging devices 3 </ b> A, 3 </ b> B, 3 </ b> C and the moving image storage unit 4 are connected via a network 2, for example. As shown in FIG. 8, the CPU 300 of the particle detection device 1 causes the imaging device control unit 304 to cause the imaging devices 3A, 3B, and 3C to image the generation source when the amount of particles exceeds the alarm threshold value for each classification. Is further provided.
　図１３に示すように、撮影装置３Ａ、３Ｂ、３Ｃは、例えば、クリーンルームに配置される。撮影装置３Ａ、３Ｂ、３Ｃは、例えば、粒子検出装置１の空気取り込み口周辺の動画を撮影可能なように配置される。空気取り込み口から取り込まれた空気が、図８に示すノズル４０から噴射される。図１３に示す粒子検出装置１の空気取り込み口は、粒子の発生源と予想される場所、及びその近傍であることが好ましい。粒子の発生源と予想される場所の例としては、搬送装置等を含む製造装置、及び作業場や通路等の作業者が現れる頻度の高い場所が挙げられる。粒子の発生源は、分類された粒子の種類ごとに予想されてもよい。 As shown in FIG. 13, the photographing devices 3A, 3B, and 3C are arranged in a clean room, for example. The photographing devices 3A, 3B, and 3C are arranged so as to be able to photograph a moving image around the air intake port of the particle detection device 1, for example. Air taken in from the air intake port is ejected from the nozzle 40 shown in FIG. The air intake port of the particle detection apparatus 1 shown in FIG. 13 is preferably a place where it is expected to be a particle generation source and its vicinity. Examples of the place where the generation source of particles is expected include a manufacturing apparatus including a conveying device and a place where an operator such as a work place or a passage frequently appears. The source of particles may be predicted for each type of particle classified.
　図８に示す撮影装置制御部３０４は、図１４に示すように、分類ごとに、計数部３０２が計数した粒子の量が、閾値保存部３５２に保存されている警報閾値を超えた場合、図１２及び図１３に示す撮影装置３Ａ、３Ｂ、３Ｃに、粒子の発生源の撮影を開始させ、撮影した動画を動画保存部４に保存させる。 As illustrated in FIG. 14, the imaging device control unit 304 illustrated in FIG. 8 is configured when the amount of particles counted by the counting unit 302 exceeds the alarm threshold stored in the threshold storage unit 352 for each classification. 12 and the imaging apparatuses 3A, 3B, and 3C shown in FIG. 13 start imaging of the generation source of the particles, and the captured moving image is stored in the moving image storage unit 4.
　また、図８に示す撮影装置制御部３０４は、分類ごとに、計数部３０２が計数した粒子の量が、閾値保存部３５２に保存されている警報閾値を下回った場合、図１２及び図１３に示す撮影装置３Ａ、３Ｂ、３Ｃに、粒子の発生源の撮影及び動画の保存を停止させる。ただし、図８に示す撮影装置制御部３０４は、分類ごとに、計数部３０２が計数した粒子の量が、閾値保存部３５２に保存されている警報閾値を下回った場合、所定の時間が経過してから、図１２及び図１３に示す撮影装置３Ａ、３Ｂ、３Ｃに、粒子の発生源の撮影及び動画の保存を停止させてもよい。 In addition, the imaging device control unit 304 illustrated in FIG. 8 performs the processing illustrated in FIGS. 12 and 13 when the amount of particles counted by the counting unit 302 is lower than the alarm threshold stored in the threshold storage unit 352 for each classification. The photographing apparatuses 3A, 3B, and 3C shown in FIG. However, the imaging device control unit 304 shown in FIG. 8 determines that a predetermined time elapses when the amount of particles counted by the counting unit 302 falls below the alarm threshold stored in the threshold storage unit 352 for each classification. After that, the photographing apparatuses 3A, 3B, and 3C shown in FIGS. 12 and 13 may stop photographing the particle generation source and storing the moving image.
　なお、動画の撮影終了後に動画の保存プロセスが継続されている場合であっても、分類ごとに、計数部３０２が計数した粒子の量が、閾値保存部３５２に保存されている警報閾値を超えた場合、図８に示す撮影装置制御部３０４は、図１２及び図１３に示す撮影装置３Ａ、３Ｂ、３Ｃに、粒子の発生源の撮影を開始させ、マルチタスクにより、撮影した動画を動画保存部４に保存させる。 Even when the moving image saving process is continued after the moving image shooting is finished, the amount of particles counted by the counting unit 302 exceeds the alarm threshold value stored in the threshold value saving unit 352 for each classification. 8, the imaging device control unit 304 illustrated in FIG. 8 causes the imaging devices 3A, 3B, and 3C illustrated in FIGS. 12 and 13 to start capturing the particle generation source, and saves the captured moving image as a moving image by multitasking. Save to Part 4.
　以上説明した第１の実施の形態に係る環境分析装置は、分類ごとに計数した粒子の量が、分類ごとに設定された閾値を超えた場合に、粒子の発生源を撮影することが可能である。そのため、常時、粒子の発生源を監視する場合と比較して、例えば、動画保存部４の容量を低下させることが可能であり、装置の運用コスト等を低下させることが可能である。また、閾値以上の量の微生物が検出された場合は、クリーンルームを清掃あるいは滅菌あるいは殺菌するなどの処置をすることが可能である。また、閾値以上の量の非生物粒子が検出された場合は、非生物粒子の発生源となった装置の劣化状況を把握したり、装置のメンテナンスを行うなどの対処をしたりすることが可能である。 The environmental analysis apparatus according to the first embodiment described above can capture a particle generation source when the amount of particles counted for each classification exceeds a threshold set for each classification. is there. Therefore, compared with the case where the generation source of particles is constantly monitored, for example, the capacity of the moving image storage unit 4 can be reduced, and the operation cost of the apparatus can be reduced. In addition, when an amount of microorganisms greater than or equal to the threshold is detected, it is possible to take measures such as cleaning or sterilizing or sterilizing the clean room. In addition, when a non-biological particle amount exceeding the threshold is detected, it is possible to grasp the deterioration status of the device that is the source of the non-biological particle, or take measures such as performing maintenance of the device. It is.
　（第２の実施の形態）
　第２の実施の形態に係る環境分析システムにおいては、図８に示す閾値保存部３５２が、図１５に示すように、警報閾値よりも低い警告閾値をさらに保存する。例えば、第２の実施の形態において、図８に示す撮影装置制御部３０４は、分類ごとに、粒子の量が警告閾値を超えた場合、撮影装置３Ａ、３Ｂ、３Ｃに発生源を撮影させるが、撮影した動画を動画保存部４に保存させない。その後、分類ごとに、粒子の量が警報閾値を超えた場合、撮影装置制御部３０４は、撮影装置３Ａ、３Ｂ、３Ｃに発生源の撮影を継続させ、さらに撮影した動画を動画保存部４に保存させる。 (Second Embodiment)
In the environmental analysis system according to the second embodiment, the threshold storage unit 352 illustrated in FIG. 8 further stores a warning threshold lower than the alarm threshold as illustrated in FIG. For example, in the second embodiment, the imaging device control unit 304 illustrated in FIG. 8 causes the imaging devices 3A, 3B, and 3C to image the generation source when the amount of particles exceeds the warning threshold value for each classification. , The captured moving image is not stored in the moving image storage unit 4. Thereafter, when the amount of particles exceeds the warning threshold value for each classification, the image capturing device control unit 304 causes the image capturing devices 3A, 3B, and 3C to continue image capturing of the source, and further captures the captured moving image in the moving image storage unit 4. Save.
　また、図８に示す撮影装置制御部３０４は、分類ごとに、計数部３０２が計数した粒子の量が、閾値保存部３５２に保存されている警告閾値を下回った場合、図１２及び図１３に示す撮影装置３Ａ、３Ｂ、３Ｃに、粒子の発生源の撮影及び動画の保存を停止させる。 In addition, the imaging device control unit 304 illustrated in FIG. 8 performs the processing illustrated in FIG. 12 and FIG. 13 when the amount of particles counted by the counting unit 302 is lower than the warning threshold stored in the threshold storage unit 352 for each classification. The photographing apparatuses 3A, 3B, and 3C shown in FIG.
　以上説明した第２の実施の形態に係る環境分析装置によれば、分類ごとに、粒子の量が警報閾値を超えた時点から動画の撮影の開始までのタイムラグを抑制することが可能となる。 According to the environmental analysis apparatus according to the second embodiment described above, it is possible to suppress the time lag from the time when the amount of particles exceeds the alarm threshold to the start of moving image shooting for each classification.
　（第３の実施の形態）
　第３の実施の形態に係る環境分析装置において、図１６に示す閾値保存部３５２に保存される、粒子の分類ごとの粒子の量の警報閾値は、図１２及び図１３に示す撮影装置３Ａ、３Ｂ、３Ｃごとに設定される。具体的には、撮影装置３Ａが動画の撮影を開始する粒子の分類ごとの警報閾値と、撮影装置３Ｂが動画の撮影を開始する粒子の分類ごとの警報閾値と、撮影装置３Ｃが動画の撮影を開始する粒子の分類ごとの警報閾値と、が、閾値保存部３５２に保存される。警告閾値についても同様である。 (Third embodiment)
In the environmental analysis device according to the third embodiment, the alarm threshold for the amount of particles for each particle classification stored in the threshold storage unit 352 shown in FIG. 16 is the imaging device 3A shown in FIGS. Set for every 3B and 3C. Specifically, the alarm threshold for each particle classification for which the imaging device 3A starts to shoot moving images, the alarm threshold for each particle classification for which the imaging device 3B starts to shoot moving images, and the imaging device 3C to shoot moving images. Are stored in the threshold value storage unit 352. The same applies to the warning threshold.
　また、図１６に示すＣＰＵ３００は、粒子検出装置１の複数の空気取り込み口のそれぞれから取り込んだ空気で過去に検出された分類ごとの粒子の量に応じて、警報閾値及び警告閾値を補正する閾値補正部３０５をさらに備える。 Further, the CPU 300 shown in FIG. 16 corrects the alarm threshold value and the warning threshold value according to the amount of particles for each classification detected in the past with the air taken in from each of the plurality of air intake ports of the particle detector 1. A correction unit 305 is further provided.
　例えば、第１の空気取り込み口で取り込んだ空気において、生物粒子のクラスに分類された粒子が過去に多く検出された場合、閾値補正部３０５は、第１の空気取り込み口で取り込む空気に対して、生物粒子のクラスに分類される粒子の量の警報閾値を低くする補正をする。 For example, when a large amount of particles classified into the biological particle class has been detected in the past in the air taken in at the first air intake port, the threshold correction unit 305 detects the air taken in at the first air intake port. The correction is made to lower the alarm threshold of the amount of particles classified into the biological particle class.
　また例えば、第２の空気取り込み口で取り込んだ空気において、非生物粒子のクラスに分類された粒子が過去に多く検出された場合、閾値補正部３０５は、第２の空気取り込み口で取り込む空気に対して、非生物粒子のクラスに分類される粒子の量の警報閾値を低くする補正をする。 Further, for example, when a large amount of particles classified into the non-biological particle class has been detected in the past in the air taken in at the second air intake port, the threshold correction unit 305 converts the air taken in at the second air intake port On the other hand, a correction is made to lower the alarm threshold for the amount of particles classified into the non-biological particle class.
　さらに例えば、第３の空気取り込み口で取り込んだ空気において、非蛍光粒子のクラスに分類された粒子が過去に多く検出された場合、閾値補正部３０５は、第３の空気取り込み口で取り込む空気に対して、非蛍光粒子のクラスに分類される粒子の量の警報閾値を低くする補正をする。 Further, for example, when many particles classified into the non-fluorescent particle class have been detected in the past in the air taken in at the third air intake port, the threshold correction unit 305 converts the air taken in at the third air intake port into On the other hand, a correction is made to lower the alarm threshold for the amount of particles classified into the non-fluorescent particle class.
　以上説明した第３の実施の形態に係る環境分析装置によれば、過去の粒子の検出傾向に基づいて閾値を補正し、例えば、特定の分類の粒子の発生を、より頻繁に監視することが可能となる。 According to the environmental analysis apparatus according to the third embodiment described above, the threshold value is corrected based on the past particle detection tendency, and for example, the occurrence of particles of a specific classification can be monitored more frequently. It becomes possible.
　（第４の実施の形態）
　第４の実施の形態に係る環境分析装置は、図１７に示すように、クリーンルームへの作業者の入退室の時間を、作業者が装着している識別ＩＣタグ等を用いて記録する、図１８に示す作業者記録部３０６をさらに備える。作業者記録部３０６の、作業者のクリーンルームへの入退室の時間の記録を、データ保存部３５３に保存する。 (Fourth embodiment)
As shown in FIG. 17, the environment analysis apparatus according to the fourth embodiment records the entry / exit time of the worker in the clean room using an identification IC tag or the like worn by the worker. 18 is further provided. A record of the entry / exit time of the worker into the clean room of the worker recording unit 306 is stored in the data storage unit 353.
　環境分析装置は、時系列における、分類ごとに発生した粒子の量のデータと、時系列における、入退室した作業者のデータと、を併合した併合データを生成する併合部３０７をさらに備える。併合部３０７は、併合データをデータ保存部３５３に保存し、出力装置４０１から出力する。第４の実施の形態によれば、例えば、併合データに基づき、それぞれの作業者がクリーンルームに入室している際に発生しやすい粒子の分類を把握することが可能となる。 The environmental analysis apparatus further includes a merging unit 307 that generates merged data obtained by merging the data of the amount of particles generated for each classification in time series and the data of workers entering and leaving the room in time series. The merging unit 307 stores the merged data in the data storage unit 353 and outputs it from the output device 401. According to the fourth embodiment, for example, based on the merged data, it is possible to grasp the classification of particles that are likely to occur when each worker enters the clean room.
　（第５の実施の形態）
　第５の実施の形態においては、図１９に示すように、クリーンルームで実施される作業工程の時系列データが、図１８に示すデータ保存部３５３に保存される。第５の実施の形態において、併合部３０７は、時系列における、分類ごとに発生した粒子の量のデータと、時系列における、実施された作業工程のデータと、を併合した併合データを生成する。併合部３０７は、併合データをデータ保存部３５３に保存し、出力装置４０１から出力する。 (Fifth embodiment)
In the fifth embodiment, as shown in FIG. 19, the time series data of the work process performed in the clean room is stored in the data storage unit 353 shown in FIG. In the fifth embodiment, the merging unit 307 generates merged data obtained by merging the data of the amount of particles generated for each classification in the time series and the data of the performed work processes in the time series. . The merging unit 307 stores the merged data in the data storage unit 353 and outputs it from the output device 401.
　第５の実施の形態によれば、例えば、併合データに基づき、それぞれの作業工程が実施されている際に発生しやすい粒子の分類を把握することが可能となる。なお、併合部は、時系列における、分類ごとに発生した粒子の量のデータと、時系列における、実施された作業工程のデータと、時系列における、入退室した作業者のデータと、を併合した併合データを生成してもよい。 According to the fifth embodiment, for example, based on the merged data, it is possible to grasp the classification of particles that are likely to occur when each work process is performed. The merging unit merges the data of the amount of particles generated for each classification in the time series, the data of the work process performed in the time series, and the data of the worker who entered and exited the room in the time series. The merged data may be generated.
　（その他の実施の形態）
　上記のように本発明を実施の形態によって記載したが、この開示の一部をなす記述及び図面はこの発明を限定するものであると理解するべきではない。この開示から当業者には様々な代替実施の形態、実施例及び運用技術が明らかになるはずである。例えば、実施の形態においては、第１及び第２の光が蛍光帯域の光であり、第３の光が散乱光である例を説明したが、第１ないし第３の光は、波長が異なっていれば、任意である。また、実施の形態においては、第１分類の粒子が生物粒子であり、第２分類の粒子が非生物粒子である例を説明したが、第１分類の粒子がある種の生物粒子であり、第２分類の粒子が他種の生物粒子であってもよい。あるいは、第１分類の粒子がある種の非生物粒子であり、第２分類の粒子が他種の非生物粒子であってもよい。粒子の分類の仕方は任意である。このように、本発明はここでは記載していない様々な実施の形態等を包含するということを理解すべきである。 (Other embodiments)
Although the present invention has been described by the embodiments as described above, it should not be understood that the description and drawings constituting a part of this disclosure limit the present invention. From this disclosure, various alternative embodiments, examples and operational techniques should be apparent to those skilled in the art. For example, in the embodiment, the example in which the first and second lights are fluorescence band lights and the third light is scattered light has been described, but the first to third lights have different wavelengths. If so, it is optional. In the embodiment, the example in which the first classification particle is a biological particle and the second classification particle is a non-biological particle has been described. However, the first classification particle is a kind of biological particle, The second class of particles may be other types of biological particles. Alternatively, the first class of particles may be a kind of non-biological particle, and the second class of particles may be another kind of non-biological particle. The method of classifying the particles is arbitrary. Thus, it should be understood that the present invention includes various embodiments and the like not described herein.
１     粒子検出装置
２     ネットワーク
３Ａ、３Ｂ、３Ｃ     撮影装置
４     動画保存部
１０   光源
１１   光源駆動電源
１２   電源制御装置
１５   光検出器
２０Ａ、２０Ｂ       受光素子
２１Ａ、２１Ｂ       増幅器
２２Ａ、２２Ｂ       増幅器電源
２３Ａ、２３Ｂ       光強度算出装置
２４Ａ、２４Ｂ       光強度記憶装置
３０   筐体
４０   ノズル
５０   散乱光受光素子
５１   増幅器
５２   増幅器電源
５３   光強度算出装置
５４   光強度記憶装置
１０２ 蛍光強度測定器
１０５ 散乱光測定器
３００ 中央演算処理装置
３０１ 粒子分類部
３０２ 計数部
３０３ 警報部
３０４ 撮影装置制御部
３０５ 閾値補正部
３０６　作業者記録部
３０７　併合部
３５０ 記憶装置
３５１ 境界情報保存部
３５２ 閾値保存部
３５３ データ保存部
４０１ 出力装置
  DESCRIPTION OF SYMBOLS 1 Particle | grain detection apparatus 2 Network 3A, 3B, 3C Image pick-up apparatus 4 Movie storage part 10 Light source 11 Light source drive power supply 12 Power supply control apparatus 15 Photodetector 20A, 20B Light receiving element 21A, 21B Amplifier 22A, 22B Amplifier power supply 23A, 23B Light intensity Calculation devices 24A and 24B Light intensity storage device 30 Housing 40 Nozzle 50 Scattered light receiving element 51 Amplifier 52 Amplifier power supply 53 Light intensity calculation device 54 Light intensity storage device 102 Fluorescence intensity measurement device 105 Scattered light measurement device 300 Central processing unit 301 Particle classification unit 302 Counting unit 303 Alarm unit 304 Imaging device control unit 305 Threshold correction unit 306 Worker recording unit 307 Merger unit 350 Storage device 351 Boundary information storage unit 352 Threshold storage unit 353 Data storage unit 401 Output device

Claims (10)

  1. 　流体に検査光を照射する光源と、
    　前記検査光を照射された流体中の粒子で生じる反応光を検出する光検出器と、
    　前記反応光に基づき、前記粒子を分類する粒子分類部と、
    　前記分類された粒子の量を計数する計数部と、
    　分類ごとに前記粒子の量の警報閾値を保存する閾値保存部と、
    　前記分類ごとに、前記粒子の量が前記警報閾値を超えた場合、警報を発する警報部と、
    　前記粒子の発生源を撮影する撮影装置と、
    　前記分類ごとに、前記粒子の量が前記警報閾値を超えた場合、前記撮影装置に前記発生源を撮影させる撮影装置制御部と、
    　を備える、環境分析システム。     A light source that irradiates the fluid with inspection light;
    A photodetector for detecting reaction light generated by particles in the fluid irradiated with the inspection light;
    A particle classification unit for classifying the particles based on the reaction light;
    A counter for counting the amount of the classified particles;
    A threshold storage unit that stores an alarm threshold of the amount of particles for each classification;
    For each classification, an alarm unit that issues an alarm when the amount of particles exceeds the alarm threshold; and
    An imaging device for imaging the source of the particles;
    For each of the classifications, when the amount of the particles exceeds the alarm threshold, an imaging device controller that causes the imaging device to image the source; and
    An environmental analysis system.
  2. 　前記撮影装置が動画を撮影し、
    　前記動画を保存する動画保存部をさらに備え、
    　前記分類ごとに、前記粒子の量が前記警報閾値を超えた場合、前記撮影装置制御部が、前記撮影装置に、撮影した前記動画を前記動画保存部に保存させる、
    　請求項１に記載の環境分析システム。     The imaging device captures a video;
    A video storage unit for storing the video;
    For each classification, when the amount of the particles exceeds the alarm threshold, the imaging device control unit causes the imaging device to store the captured video in the video storage unit,
    The environmental analysis system according to claim 1.
  3. 　前記閾値保存部が、前記警報閾値よりも低い警告閾値をさらに保存し、
    　前記分類ごとに、前記粒子の量が前記警告閾値を超えた場合、前記撮影装置制御部が、前記撮影装置に前記発生源を撮影させる、
    　請求項１又は２に記載の環境分析システム。     The threshold storage unit further stores a warning threshold lower than the warning threshold;
    For each classification, when the amount of the particles exceeds the warning threshold, the imaging device controller causes the imaging device to image the source.
    The environmental analysis system according to claim 1 or 2.
  4. 　前記分類ごとに、前記粒子の量が前記警告閾値を下回った場合、前記撮影装置制御部が、前記撮影装置に前記発生源の撮影を停止させる、
    　請求項３に記載の環境分析システム。     For each classification, when the amount of the particles falls below the warning threshold, the imaging device control unit causes the imaging device to stop imaging of the source.
    The environmental analysis system according to claim 3.
  5. 　前記撮影装置を複数備え、
    　前記複数の撮影装置が、前記分類された粒子のそれぞれの発生源を撮影する、
    　請求項１から４のいずれか１項に記載の環境分析システム。     A plurality of the photographing devices are provided,
    The plurality of imaging devices image each source of the classified particles;
    The environmental analysis system of any one of Claim 1 to 4.
  6. 　流体に検査光を照射することと、
    　前記検査光を照射された流体中の粒子で生じる反応光を検出することと、
    　前記反応光に基づき、前記粒子を分類することと、
    　前記分類された粒子の量を計数することと、
    　分類ごとに前記粒子の量の警報閾値を用意することと、
    　前記分類ごとに、前記粒子の量が前記警報閾値を超えた場合、警報を発することと、
    　前記粒子の発生源を撮影する撮影装置を用意することと、
    　前記分類ごとに、前記粒子の量が前記警報閾値を超えた場合、前記撮影装置に前記発生源を撮影させることと、
    　を備える、環境分析方法。     Irradiating the fluid with inspection light;
    Detecting reaction light generated by particles in the fluid irradiated with the inspection light;
    Classifying the particles based on the reaction light;
    Counting the amount of the classified particles;
    Providing an alarm threshold for the amount of particles for each classification;
    For each of the classifications, if the amount of the particles exceeds the alarm threshold, issuing an alarm;
    Providing an imaging device for imaging the source of the particles;
    For each classification, if the amount of particles exceeds the alarm threshold, let the imaging device image the source.
    An environmental analysis method comprising:
  7. 　前記分類ごとに、前記粒子の量が前記警報閾値を超えた場合、前記撮影装置に、撮影した動画を動画保存部に保存させることをさらに備える、請求項６に記載の環境分析方法。 The environment analysis method according to claim 6, further comprising: causing the image capturing device to store a captured moving image in a moving image storage unit when the amount of the particles exceeds the alarm threshold value for each classification.
  8. 　前記警報閾値よりも低い警告閾値を用意することと、
    　前記分類ごとに、前記粒子の量が前記警告閾値を超えた場合、前記撮影装置に前記発生源を撮影させることと、
    　をさらに備える、請求項６又は７に記載の環境分析方法。     Providing a warning threshold lower than the warning threshold;
    For each classification, if the amount of particles exceeds the warning threshold, let the imaging device image the source.
    The environmental analysis method according to claim 6 or 7, further comprising:
  9. 　前記分類ごとに、前記粒子の量が前記警告閾値を下回った場合、前記撮影装置に前記発生源の撮影を停止させることをさらに備える、請求項８に記載の環境分析方法。 9. The environmental analysis method according to claim 8, further comprising: causing the imaging device to stop imaging of the source when the amount of the particles falls below the warning threshold for each classification.
  10. 　前記撮影装置を複数用意し、
    　前記複数の撮影装置に、前記分類された粒子のそれぞれの発生源を撮影させる、
    　請求項６から９のいずれか１項に記載の環境分析方法。
          Preparing a plurality of the photographing devices,
    Causing the plurality of imaging devices to image each source of the classified particles;
    The environmental analysis method according to any one of claims 6 to 9.
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