Method for simulating odor diffusion

Abstract

To provide a simulation method of odor diffusion which allows a user to highly accurately know the diffusion dynamic state of an odorous component in a space.SOLUTION: A simulation method of odor diffusion according to the present invention simulates diffusion of odorous components using a discharge device 10. A discharge unit 20 in the discharge device 10 has a discharge port that discharges mixture gas 3 containing particles. The simulation method comprises following A)-C) steps. A) The step of acquiring an image of the particles in the mixture gas discharged to a space from the discharge device over time. B) The step of converting a pixel parameter included in a unit section into the assumed density of the odorous component for each unit section made of one or more pixels for each image. C) The step of creating a simulation image in which the assumed density for each unit section is reflected as color information by using the assumed density.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a method for simulating odor diffusion using an emission device to simulate the diffusion of odor components.

Background Art

[0002] As a technique for grasping the diffusion behavior of gas in a space, a technique for visualizing the gas flow is known. For example, Patent Document 1 discloses a photographing method for visualizing human exhalation by photographing using the Schlieren method while controlling a pattern that blocks light according to a pattern projected by a projector as a background. Further, Patent Document 2 discloses a method for visualizing carbon dioxide in exhaled breath emitted from a subject for the purpose of monitoring and analyzing respiratory activities. In such a method, the carbon dioxide (CO2) distribution is visualized over time using images acquired by an infrared thermal imaging device, an infrared imaging device, and a visible light imaging device, and a three-dimensional model of the exhaled breath flow is created based on the distribution.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0004] From the viewpoint of improving the quality of life, interest in the diffusion dynamics of indoor odors is increasing. There are many types of indoor odors, such as odors from pets and air fresheners, but in recent highly sealed living environments, human bad breath can also be a cause of indoor odors. Bad breath is an odor that is mainly felt during conversation and often gives an unpleasant impression. For example, when odors from dishes containing garlic, alcoholic beverages, or coffee are emitted from a person's mouth, they can have a negative psychological effect on people around them. Therefore, people may feel anxious about whether they have bad breath or not, and whether others perceive their bad breath, and bad breath has a significant impact on people's impressions and behavior. From the viewpoint of oral hygiene, detailed analysis has been conducted on odorous components that cause bad breath, such as odorous components derived from food or beverages. However, there is insufficient knowledge on the diffusion dynamics of odorous components emitted from the human oral cavity. Accurately understanding the diffusion dynamics of odorous components that cause bad breath and other odors is important from the viewpoint of developing technology to control odors in indoor and other spaces, and detailed studies are needed.

[0005] In general, exhaled breath contains a wide variety of volatile substances in an extremely wide concentration range, from % level to ppt level in terms of gas volume ratio. Odor components are volatile substances that produce an odor (smell), and when the odor components are released into space, they diffuse while changing their concentration distribution (density distribution) in various ways. The dynamics of the concentration distribution during this diffusion differ depending on the type of odor component. Moreover, even a very small amount of odor components can be perceived. Therefore, with the techniques of Patent Documents 1 and 2, it is difficult to detect the odor components when they are diffused in very small amounts and to grasp their diffusion dynamics. In addition, the technique of Patent Document 1 uses a special tool such as a concave mirror, so the measurement area is limited, and setting and adjustment require a huge amount of time and skilled skills, making it difficult to create a diffusion model with good reproducibility. The technique of Patent Document 2 also has a narrow concentration range that can be measured, and is not capable of grasping the diffusion state of odor components in the range (space) where odors such as bad breath can be perceived.

[0006] The present invention relates to a method for simulating odor diffusion that can accurately grasp the diffusion dynamics of odor components in space.

Means for Solving the Problems

[0007] The present invention relates to a method for simulating odor diffusion that uses an emission device to simulate the diffusion of odor components. The emission device preferably includes a housing portion that houses a mixed gas containing particles and an emission portion, and the emission portion preferably includes an emission port that emits the mixed gas and a flow path that communicates the emission port with the housing portion. The simulation method preferably includes the following steps A) to C). A) A step of acquiring, over time, an image of the particles in the mixed gas emitted from the emission device into space B) For each of the individual images, a step of converting the pixel parameters included in a unit section composed of one or more pixels into an assumed concentration of the odor component for each unit section C) A step of creating a simulation image in which the assumed concentration for each unit section is reflected as color information using the assumed concentration

[0008] The present invention also relates to a method for simulating odor diffusion that uses an emission device to simulate the diffusion of odor components. The emission device preferably includes a housing portion that houses a mixed gas containing particles and an emission portion, and the emission portion preferably includes an emission port that emits the mixed gas and a flow path that communicates the emission port with the housing portion. It preferably includes the following steps A), B), D), and E). A) A step of acquiring, over time, an image of the particles in the mixed gas emitted from the emission device into space B) For each of the individual images, a step of converting the pixel parameters included in a unit section composed of one or more pixels into an assumed concentration of the odor component for each unit section D) A step of converting the assumed concentration for each unit section into an olfactory intensity of the odor component E) A step of creating a simulation image in which the olfactory intensity for each unit section is reflected as color information using the olfactory intensity

Advantages of the Invention

[0009] According to the method for simulating odor diffusion of the present invention, the diffusion behavior of odor components in space can be accurately grasped.

Brief Description of the Drawings

[0010]

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BEST MODE FOR CARRYING OUT THE INVENTION

[0011] Hereinafter, the present invention will be described with reference to the drawings based on its preferred embodiments. The odor diffusion simulation method of the present embodiment (hereinafter, also simply referred to as "simulation method") includes a "concentration simulation method" for simulating the concentration dynamics of odor components diffusing in space, and an "olfactory intensity simulation method" for simulating the distribution dynamics of the sensory intensity of the odor of odor components diffusing in space. Each of these concentration simulation methods and olfactory intensity simulation methods simulates the diffusion of odor components using the discharge device 10. The discharge device 10 includes a housing portion 11 (see FIG. 7) that houses the mixed gas 3 containing particles, and a discharge portion 20 that discharges the mixed gas 3. The discharge portion 20 includes a discharge port 21 for discharging the mixed gas 3 and a flow path 25 that communicates the discharge port 21 with the housing portion 11. The structure of the discharge device 10 will be described in detail later.

[0012] The concentration simulation method of the present embodiment will be described. The concentration simulation method of the present embodiment includes the following steps A) to C) (see FIG. 1). Also, the concentration simulation method of the present embodiment is performed in the order of steps A), B), and C). Step A): A step of acquiring over time an image 4 of particles in the mixed gas 3 discharged into space from the discharge device 10 Step B): For each individual image 4 acquired in the step A), for each unit section r composed of one or more pixels, a step of converting the pixel parameters included in the unit section r into the assumed concentration of the odor component Step C): A step of creating a first simulation image 6 in which the assumed concentration for each unit section r is reflected as color information using the assumed concentration

[0013] In step A), the mixture gas 3 is discharged from the discharge unit 20 of the discharge device 10, and an image 4 of the particles of the mixture gas 3 is acquired. The image 4 acquired in step A) is also hereinafter referred to as the "original image 4". In step A) of the present embodiment, it is preferable to capture the original image 4 while illuminating the mixture gas 3 discharged in a dark place. Specifically, the discharge device 10, the illumination, and the imaging device 31 are installed in a dark room where light is blocked, and an image 4 of the particles of the mixture gas 3 discharged and diffused in the dark room is captured. Thereby, the mixture gas 3 can be clearly imaged, and a simulation image can be created with higher accuracy. For the illumination, known illuminations such as LED lights and laser lights can be used. As the dark room, a dedicated room may be used, or a room in which light is partially blocked by a black light-shielding curtain or the like, or a space partitioned by a light-shielding partition such as a light-shielding tent may be used. The acquisition of the original image 4 is performed by the imaging device 31. The original image 4 may be a color image or a monochrome image.

[0014] The imaging environment of the original image 4 may be an environment that does not cause the influence of air flow, such as by not operating the air conditioner, or an environment in which an air flow is generated by the air conditioner assuming a living environment or a working environment. Also, an environment in which the temperature and humidity are adjusted assuming seasons such as summer and winter may be used. From the viewpoint of more easily grasping the characteristics of the diffusibility of the odor component, the imaging environment of the original image 4 is preferably an environment that does not cause the influence of air flow.

[0015] The mixture gas 3 is a gas containing particles, and is obtained by including the particles in the air. As the particles contained in the mixture gas 3, those that scatter visible light can be preferably used. Examples of such particles include droplets and powders. The particle diameters of the droplets and powders are preferably 0.1 μm or more and 100 μm or less. From the perspective of diffusibility in space, the particles contained in the mixed gas 3 are preferably droplets. The droplets are obtained by atomizing a liquid. Examples of the liquid used for generating the droplets include liquids containing water, glycols, oils, etc. Examples of the oil include vegetable oils such as olive oil, oils such as sebacic acid esters, flavorings (aromatic oils) such as limonene, and in addition, kerosene, light oil, liquid paraffin, etc. Also, as the mixed gas 3, various kinds of smoke such as dry ice, tobacco, and incense can be used. Examples of the powder include metal powder, magnesium carbonate hydroxide, silica oxide, titanium oxide, etc.

[0016] From the perspective of more stably releasing the mist-like mixed gas 3, the droplets are preferably obtained by atomizing a liquid containing glycols and water. Examples of the glycols used for generating the droplets include propylene glycol, tripropylene glycol, 1,3-butylene glycol, etc. One of these components may be used alone, or two or more of them may be used in combination. The droplets can be generated, for example, by a method of volatilizing a liquid or solid that is a raw material of the droplets by heating, or by a method of atomizing by ultrasonic vibration. For such generation, a known device such as a fog machine or a smoke machine can be used.

[0017] In step A) of this embodiment, droplets and a gas such as air are mixed to obtain the mixed gas 3. The mixed gas 3 becomes a semi-transparent or opaque white mist due to the scattering of visible light by the droplets. The mixed gas 3 containing droplets may be colored in a color other than white. Such a mixed gas 3 can be colored by adding a coloring agent to the raw material of the droplets and volatilizing or atomizing it by heating or ultrasonic vibration. The coloring agent can be used without particular limitation, either a water-soluble dye or a water-insoluble dye. Examples of the coloring agent include Red No. 2 (CI Acid Red 27), etc. When the mixed gas 3 is colored, the acquisition of the original image 4 does not have to be in a dark place.

[0018] In step A), an image 4 of the particles of the mixed gas 3 is acquired over time. That is, a plurality of original images 4 are acquired. The original image 4 is preferably acquired at intervals of 15 to 200 milliseconds, more preferably at intervals of 30 to 100 milliseconds, from the start of the release of the mixed gas 3. Thereby, the diffusion dynamics of the particles in the mixed gas 3 can be grasped with higher precision. In step A), the original image 4 may be imaged over time, or a video of the state in which the mixed gas 3 (particles) is released and diffused may be acquired, and the original image 4 may be acquired from the video over time.

[0019] Step B) of the present embodiment includes a B1) step of dividing the original image 4 for each unit section r, a B2) step of calculating pixel parameters for each unit section r, and a B3) step of converting the pixel parameters of the unit section r into an assumed concentration of an odor component. Further, step B) of the present embodiment is performed in the order of the B1) step, the B2) step, and the B3) step.

[0020] In the B1) step, each of the original images 4 acquired in the A) step is divided for each unit section r. The unit section r set in the B1) step is a continuous region composed of one or more pixels in the original image 4. The shape of the unit section r and the number of pixels constituting the unit section r can be arbitrarily set. For example, with respect to the original image 4 shown in Fig. 2(a), it may be divided into eight unit sections r as shown in Fig. 2(b), or may be divided into 32 unit sections r as shown in Fig. 2(c). Further, the unit section r may be square as shown in Figs. 2(b) and (c), or may be a rhombus, a rectangle, a triangle, a polygon with five or more sides, or the like. The unit section r set in the original image 4 may be a region composed of one pixel. That is, each pixel constituting the original image 4 may be set as the unit section r. In this case, the B2) step is performed without performing the B1) step of dividing the original image 4 for each unit section r.

[0021] From the perspective of more precisely capturing the diffusion dynamics of the particles contained in the mixed gas 3, the unit division r set in step B1) preferably consists of 1 pixel or more and 10,000 pixels or less, more preferably 4 pixels or more and 1,600 pixels or less. From the same perspective as above, in step B1), the original image 4 is preferably divided into 100 or more, more preferably 200 or more unit divisions r.

[0022] Fig. 3 shows the original image 4 obtained in step A) of the present embodiment, the pixel parameter image 5 created based on the original image 4, and the first simulation image 6 created based on the pixel parameters. The pixel parameter image 5 is an image in which the pixel parameters calculated in step B2) described later are reflected for each unit division r. Note that the first simulation image 6 can be obtained even if the pixel parameter image 5 is not created from the pixel parameters calculated in step B2), but for the sake of easy explanation, the pixel parameter image 5 is shown in Fig. 3.

[0023] In step B2), the pixel parameters of each unit division r in the original image 4 are calculated. The pixel parameters are variables of color information such as luminance, lightness, chroma, RGB values, etc. in the unit division r. The RGB values are the values of each component (red, blue, green) of the three primary colors of light for expressing full color. The variables of color information adopted as pixel parameters may be adopted alone or in combination of two or more. When the original image 4 is a monochrome image, luminance can be adopted as the pixel parameter, and when the original image 4 is a color image, any of lightness, chroma, and RGB values can be adopted as the pixel parameter. In step B2) of the present embodiment, luminance is adopted as the pixel parameter. Thereby, the mixed gas 3 (particles) in the unit division r can be captured with higher precision.

[0024] In step B2), the pixel parameters of each unit section r in the entire original image 4 are calculated. In the present embodiment, the average value of the pixel parameters of the pixels constituting the unit section r is calculated as the pixel parameter of the unit section r. When the unit section r is a region composed of one pixel, the pixel parameter of the one pixel is used as the pixel parameter of the unit section r. The pixel parameters of each unit section r are expressed in 256 gradations (8 bits) to 65,536 gradations (16 bits).

[0025] In step B2) of the present embodiment, the average value is calculated from the luminance of each pixel constituting the unit section r, and this is used as the luminance (pixel parameter) of the unit section r. Since the luminance of this unit section r is processed in 8 bits in the present embodiment, the black-and-white density (luminance) is expressed in 256 gradations (0 to 255 gradations). The image reflecting the pixel parameters calculated in this step B2) is the pixel parameter image 5 shown in FIG. 3. In such a pixel parameter image 5, the luminance of each unit section r is expressed in the black-and-white density of 256 gradations.

[0026] Step B1) and B2), or step B2) may be carried out using image analysis software. As such software, Gray-val (Ver. 3.71) manufactured by Library Co., Ltd. can be used.

[0027] In step B3), for each unit section r, the pixel parameter is converted into the assumed concentration of the odor component. More specifically, the odor component that is the target component of the simulation is set, and based on the conversion reference concentration of the odor component, the pixel parameter of each unit section r is converted into the assumed concentration of the odor component. The "assumed concentration" is a concentration that can be assumed as the concentration of the odor component released or diffused in the space, and is calculated based on the conversion reference concentration. The conversion reference concentration in the present embodiment is the concentration of the odor component based on the numerical values (concentrations) reported in research papers, publications, etc. as described later.

[0028] In step B3) of this embodiment, the odor components contained in exhaled breath are set as the target components for simulation. For example, in Example 2 described later, hydrogen sulfide and methyl mercaptan that affect halitosis are set as the target components. Odor components that affect halitosis include, in addition to hydrogen sulfide and methyl mercaptan, indole, skatole, isovaleric acid, butyric acid, dimethyl sulfide, acetaldehyde, isoprene, allyl mercaptan, diallyl disulfide, allyl methyl sulfide, allyl methyl disulfide, furfuryl mercaptan, menthol, carvone, limonene, and the like. Allyl mercaptan, diallyl disulfide, allyl methyl sulfide, and allyl methyl disulfide are odor components that affect halitosis after ingestion of garlic or onions. Furfuryl mercaptan is a key component of the aroma of coffee and is an odor component that affects halitosis after ingestion of coffee. Menthol and carvone are key components of confectionery, and limonene is a fragrance used in oral care products such as mouthwash. One or more of the above-described odor components can be used as the target component for simulation. In this embodiment, odor component i and odor component ii contained in exhaled breath are set as the target components for simulation (see Figure 3).

[0029] After setting the odor component as the target component, next, the conversion reference concentration of the odor component is set. In this embodiment, the conversion reference concentration of the odor component contained in exhaled breath is set. As this conversion reference concentration, a concentration based on research papers or publications related to halitosis can be adopted. For example, in Example 1 described later, the concentration of acetaldehyde in exhaled breath, which is known as a cause of halitosis derived from alcoholic beverages, is set as the conversion reference concentration. Such a concentration is a value obtained by adsorbing the exhaled breath of a subject 2 hours after ingesting sake into a collection tube and measuring the concentration of acetaldehyde trapped in the collection tube by GC-O / MS analysis, and can be estimated as a concentration that can actually be contained in exhaled breath. Thereby, the concentration of the odor component (acetaldehyde) that affects halitosis in exhaled breath can be reflected in the simulation.

[0030] After setting the conversion reference concentration of the odor component, based on the conversion reference concentration, the pixel parameters of each unit section r are converted into the assumed concentration. Such conversion first identifies the unit section r (hereinafter also referred to as "reference section r1") that has the maximum value among the pixel parameters of the unit section r in the entire original image 4 calculated in step B2). Next, the concentration of the odor component in this reference section r1 is set to the same value as the conversion reference concentration. In other words, the maximum concentration of the odor component in the unit section r of the entire original image 4k is set to the conversion reference concentration, and the concentration scale of the simulation in the unit section r is adjusted to the concentration (numerical value) based on research papers and publications. Then, the assumed concentration of the odor component in each unit section r is calculated by the following formula (1). R = 〔Gx / Gmax × 100(%)〕× J ···(1) R: Assumed concentration of the odor component in unit section r Gx: Tone of the pixel parameter in unit section r Gmax: Tone of the pixel parameter in reference section r1 J: Conversion reference concentration

[0031] For example, in the pixel parameter image 5 shown in FIG. 3, compared with the unit section indicated by the reference r1, the unit section indicated by the reference r2 has a lower tone of the pixel parameter (luminance). When the section indicated by the reference r1 in this pixel parameter image 5 is used as the reference section r1, the concentration of the odor component in the unit section indicated by the reference r2 shown in the figure is lower than the concentration of the odor component in the reference section r1. Specifically, the tone of the reference section r1 is 250, while the tone of the unit section r2 is 175. Therefore, using the above formula (1), the concentration of the odor component in the unit section r2 is 70% [250 / 175 × 100(%)] of the conversion reference concentration J, which is the maximum value of the assumed concentration. In this way, in step B3), the assumed concentrations of the odor components in all unit sections r in the entire original image 4 are calculated.

[0032] In this embodiment, as described above, odor component i and odor component ii are set as target components. The concentrations of these odor components i and ii in exhaled breath are different, and it has been reported that odor component i is contained in exhaled breath at a higher concentration than odor component ii (odor component i > odor component ii). Therefore, the conversion reference concentration set in step B3) has a higher value for odor component i than for odor component ii. In this case, since the concentration scales of odor component i and odor component ii are different, different first simulation images 6 will be obtained in step C) described later.

[0033] In step C), the assumed concentration of the odor component in each unit section r obtained in step B3) is converted into color information, and a first simulation image 6 in which the color information is reflected is created. Specifically, based on the assumed concentration converted in step B3), color information such as hue, lightness, and chroma is made different, and each unit section r is color-matched to create the first simulation image 6. The color matching of each unit section r can be set as appropriate. For example, when the color of the color matching of each unit section r is set to a plurality of colors (for example, 16 colors), it can be set so that the higher the assumed concentration, the warmer the color, and the lower the assumed concentration, the colder the color. Alternatively, when the color of the color matching is set to two colors (for example, black and white), it can be set so that the lower the assumed concentration, the darker one color (for example, black), and the higher the assumed concentration, the darker the other color (for example, white). Also, when reflecting the assumed concentration as color information, the color to be set is preferably associated with the olfactory intensity. For example, in the case of a concentration at which almost no odor can be perceived, it can be set to black, and in the case of a concentration at which an odor can be perceived at a predetermined intensity, it can be set to white, and it can be set to a color (shades of black and white) that changes stepwise between these.

[0034] C) For the conversion from the assumed concentration in the process to color information, an image processing program written in a programming language such as C++ or Python can be used. For example, for a dataset in a spreadsheet format that summarizes the assumed concentrations of odor components in each unit section r, by executing an image processing program, the color information of each unit section r can be calculated. Such a program can also be used for the conversion of color information in step E) described later.

[0035] In step C) of this embodiment, the first simulation images 6 of two types of odor components i and ii with different concentrations in the exhaled breath are created. In these first simulation images 6 of the odor components i and ii shown in FIG. 3, the color scheme of each unit section r is shown in gray scale shading, and it is set such that the lower the assumed concentration, the darker the black. As described above, since the conversion reference concentration of odor component i is higher than that of odor component ii, the first simulation image 6 of odor component i has a color scheme distribution with more bright (white) areas than that of odor component ii. In this way, even when using the same original image 4, simulation images with different concentration distributions can be obtained according to the target component and the conversion reference concentration of the target component.

[0036] In step C), based on the assumed concentration of the unit section r, the first simulation image 6 is created for each of all the original images 4. By creating a simulation video that displays the obtained first simulation image 6 over time, the change in the concentration distribution of the odor component can be dynamically grasped. That is, the diffusion dynamics of the odor component can be grasped. The concentration distribution of the odor components shown in the first simulation image 6 is a distribution in which the concentration scale is adjusted based on the reported values (converted reference concentrations) in research papers, presentations, etc. Therefore, the realistic concentration distribution is reflected in the simulation image 6. As a result, even if extremely trace odor components below the detection limit are not detected, the concentration distribution of the odor components in the diffusion state can be visualized (imaged). This point is particularly useful for grasping the concentration distribution when extremely trace odor components are diffused over a particularly wide range (space). In addition, the diffusion dynamics of extremely trace odor components can be simulated by a simulation video using the first simulation image 6. Thus, by using the concentration simulation method of the present embodiment, the diffusion dynamics of odor components in space can be grasped with high accuracy.

[0037] In the concentration simulation method of the present embodiment, the simulation was performed using the odor components i and ii contained in exhaled breath as target components, but the target components can be appropriately set according to the purpose of the simulation. That is, in addition to the odor components that affect bad breath, the odor components that cause odors may be set as the target components. In addition, the converted reference concentration of the target components can also be appropriately set. For example, the odor components that cause the odor of tobacco, the odor components derived from mold generated inside the air conditioner, the odor components (fragrance components) released from the aroma diffuser, etc. are set as the target components, and the concentration at which the target components can be released into the space or the concentration at which they can diffuse may be set as the converted reference concentration. As the converted reference concentration, a numerical value (concentration) based on reports such as research papers and presentations in the research field related to the odor component serving as the target component can be adopted.

[0038] Next, the olfactory intensity simulation method of the present embodiment will be described. In the following description of the olfactory intensity simulation method, the description of the same configuration as the concentration simulation method described above will be omitted. For the configuration not particularly described, the description of the concentration simulation method described above will be appropriately applied.

[0039] As shown in Fig. 1, the olfactory intensity simulation method of this embodiment comprises the following steps D) and E) instead of the step C). That is, the olfactory intensity simulation method comprises the following steps D) and E) together with the above steps A) and B). Further, the olfactory intensity simulation method of this embodiment is performed in the order of steps A), B), D), and E). Step D): A step of converting the assumed concentration for each unit section r into the olfactory intensity of the odor component Step E): A step of creating a second simulation image 7 in which the olfactory intensity for each unit section r is reflected as color information using the olfactory intensity

[0040] Fig. 4 shows the original image 4 obtained in step A) of this embodiment, the pixel parameter image 5 created based on the original image 4, and the second simulation image 7 created based on the pixel parameters of each unit section r. Note that in the simulation method of this embodiment, even if the pixel parameter image 5 is not created, the second simulation image 7 can be obtained. For ease of explanation, the pixel parameter image 5 is shown in Fig. 4. The olfactory intensity simulation method of this embodiment performs steps A) and B) in the same manner as the above-described concentration simulation method. The description of the olfactory intensity simulation method will detail steps D) and E) after step B), that is, after step B3).

[0041] In step D) of this embodiment, the assumed concentration of the odor component in each unit section r obtained in step B3) is converted into the olfactory intensity of the odor component. In step D), for each unit section r in the entire original image 4, the olfactory intensity of the odor component is calculated based on the assumed concentration of the unit section r. "Olfactory intensity" is the intensity of the odor (scent) based on the sensory amount by human olfaction for an odor component at a certain gas phase concentration (concentration).

[0042] In step D) of this embodiment, the olfactory intensity can be converted from the assumed concentration based on the curve model of the gas phase concentration - olfactory intensity of the target odor component. The curve model of gas-phase concentration-olfactory intensity can be created, for example, by the following method. First, samples of odor components with different gas-phase concentrations are prepared and obtained by sensory evaluation of the odor of the samples by panelists. Such sensory evaluation is performed, for example, by volatilizing the odor components to a predetermined gas-phase concentration in a fluororesin bag, connecting this to a discrimination dilution mixer (for example, "FDL-1" manufactured by Shimadzu Corporation), and having the panelists perform sensory evaluation of the olfactory intensity for each concentration emitted from the device. Regarding the gas-phase concentration of the odor components, for example, 0.5 mL of one type of odor component is added to a 3L polyethylene odor bag (manufactured by AS ONE Corporation), and the gas phase in the odor bag filled with odorless air and left standing for 12 hours is recovered and diluted with odorless air, whereby the odor components can be adjusted to various gas-phase concentrations.

[0043] In step D) of the present embodiment, the Labeled Magnitude Scale (LMS) is used as the scale of olfactory intensity. LMS is a sensory intensity scale developed by B. G. Green et al. (Chem. Senses., 1993, 18(6), 683-702) by combining linguistic labels and a logarithmic scale, and is used for quantifying sensory intensities such as taste, smell, and touch. LMS is a scale in which linguistic labels (Barely Detectable: 1.4, Weak: 6.1, Moderate: 17.2, Strong: 35.4, Very Strong: 53.3, Strongest Imaginable: 100) representing psychological intensity are labeled at logarithmic intervals on a 0-100 evaluation scale.

[0044] The curve model of gas-phase concentration-olfactory intensity in the present embodiment is represented by the following formula (2). The olfactory intensity in the following formula (2) is the evaluation score of LMS.

[0045]

Number

[0046] The coefficients p, q, and r in formula (2) are variables determined for each odor component, and are determined by the least squares method that minimizes the sum of the squares of the residuals between the predicted value in the gas-phase concentration-olfactory intensity curve model and the average value of the olfactory intensity obtained by the panelists' evaluation so that the predicted value approximates the average value of the olfactory intensity. For the least squares method, for example, Solver, which is an add-in program of Microsoft Excel 2010 (ver14.0), can be used.

[0047] In step D) of the present embodiment, based on the gas-phase concentration-olfactory intensity curve model of the odor component obtained by the formula (2), the olfactory intensity at the assumed concentration of each unit section r is obtained and used as the olfactory intensity of the unit section r. In this way, the olfactory intensity can be converted from the assumed concentration. Such conversion is performed for all unit sections r in the entire original image 4.

[0048] In step D), as the scale of olfactory intensity, known ones such as LMS, Visual analog scale (VAS), Magnitude estimate (ME), and Category-ratio scale (CR) can be used. Whichever scale is used, in step D), the conversion from the assumed concentration to the olfactory intensity is performed based on the gas-phase concentration-olfactory intensity curve model to which the scale is applied. From the viewpoint of making the evaluation of the olfactory intensity of ino more accurate, it is preferable to use LMS as the scale of olfactory intensity.

[0049] In this embodiment, as shown in FIG. 4, the above-described odor component i and odor component ii are set as the target components for simulation. These odor components i and ii, as described above, not only contain odor component i at a higher concentration in exhaled breath than odor component ii, but it has also been reported that odor component ii is more easily perceived as having an odor than odor component i. That is, even at the same gas-phase concentration as odor component i, odor component ii is felt to have a stronger odor than that odor component i. Therefore, the curve model of the gas-phase concentration-olfactory intensity of odor component i used in step D) is different from the curve model of the gas-phase concentration-olfactory intensity of odor component ii. In other words, since the scale of the olfactory intensity perception is different between odor component i and odor component ii, different second simulation images 7 are obtained in step E) described below.

[0050] In step E), the olfactory intensity of the odor components in each unit section r obtained in step D) is converted into color information, and a second simulation image 7 reflecting the color information is created. Specifically, based on the olfactory intensity converted in step D), color information such as hue, lightness, and chroma is made different, and each unit section r is color-matched to create the second simulation image 7. The color matching of each unit section r can be appropriately set as in step C) described above according to the degree of olfactory intensity.

[0051] In step E) of this embodiment, second simulation images 7 of two types of odor components i and ii with different olfactory intensities are created. In these second simulation images 7 of odor components i and ii shown in FIG. 4, the color matching of each unit section r is shown in shades of gray scale, and it is set such that the darker the black, the lower the olfactory intensity. As described above, since odor component ii has a higher olfactory intensity than odor component i, the second simulation image 7 of odor component ii has a higher distribution of brighter (whiter) color matching than that of odor component i. In this way, even when using the same original image 4, different simulation images can be obtained according to the target component and the olfactory intensity of the target component.

[0052] E) In the engineering, based on the olfactory intensity of the unit division r, a second simulation image 7 is created for each of the entire original images 4. By creating a simulation video that displays the obtained second simulation image 7 over time, it is possible to dynamically grasp the distribution change of the sensory intensity of the odor for the odor components. That is, it is possible to grasp the dynamics of the sensory intensity of the odor (hereinafter simply referred to as "sensory intensity") accompanying the diffusion of the odor components. Since the distribution of the sensory intensity of the odor components shown in the second simulation image 7 is based on the reported values (converted reference concentrations) in research papers, presentations, etc., the simulation image 7 reflects a realistic concentration distribution and the corresponding sensory intensity. As a result, it is possible to visualize (image) the distribution of the odor intensity of the odor components accompanying the diffusion. Also, by means of a simulation video using the second simulation image 7, it is possible to simulate the dynamics of the sensory intensity distribution of the odor accompanying the diffusion of the odor components. Thus, in the olfactory intensity simulation method of the present embodiment, it is possible to simulate the diffusion dynamics of the odor components in a form associated with the sensory intensity.

[0053] When creating a simulation video using the simulation images 6 and 7 obtained in step C) or step E), it may be carried out using image analysis software. As such software, Cosmos32 (Ver. 6.52) manufactured by Library Co., Ltd. can be used.

[0054] Next, the discharge device 10 used in both the concentration simulation method and the olfactory intensity simulation method of the present embodiment will be described with reference to FIGS. 5 to 7. In FIGS. 5 and 6, the discharge part 20 included in the discharge device 10 of the present embodiment is shown. The discharge part 20 of the present embodiment includes a flow path 25 and a discharge port 21, and discharges the mixed gas 3 into the space through the flow path 25 and the discharge port 21.

[0055] As shown in FIGS. 5 and 6, the discharge port 21 included in the discharge part of the present embodiment is formed to imitate the human lips. The flow path 25 included in the discharge part 20 of the present embodiment has a first flow path 26 continuous with the discharge port 21 and a second flow path 27 continuous with the introduction port 22 of the discharge part 20. The introduction port 22 of the discharge part 20 is an opening for introducing the air-fuel mixture 3 introduced from the accommodation part 11 into the discharge part 20. A pipe 28 connecting the discharge part 20 and the accommodation part 11 is inserted into the introduction port 22. At least a part of the flow path 25 in the discharge part 20 of the present embodiment is formed by imitating the pharyngeal cavity and oral cavity of the human body. Specifically, the first flow path 26 is formed by imitating the oral cavity, and the second flow path 27 is formed by imitating the pharyngeal cavity. As described above, by adopting the anatomical structure of the human body for the structure of the flow path when the exhaled breath is discharged in the discharge part 20 of the present embodiment, the air-fuel mixture 3 can be discharged from the discharge part 20 under conditions close to the human exhaled breath.

[0056] The discharge part 20 of the present embodiment can be manufactured using a 3D printer or the like. For example, 3D data (three-dimensional coordinate information) generated from MRI data of the human head is input into a 3D printer, and a three-dimensional object that reproduces the inside (oral cavity and pharyngeal cavity) of the head is manufactured. In the flow path 25 included in such a discharge part 20, a part imitating the speech and voice organs including the nasal cavity, oral cavity, pharyngeal cavity, and pharynx through which the exhaled breath flows may be formed. Further, a part imitating the respiratory organs including the lungs and trachea may be formed in the flow path 25. The part imitating the speech and voice organs or the respiratory organs in the flow path 25 may reproduce the internal structure when a human speaks. From the viewpoint of improving the reproducibility of bad breath diffusion during conversation, the flow path 25 in the discharge part 20 is preferably formed by imitating the three-dimensional structure of the speech and voice organs or the respiratory organs during speech. For example, in addition to the nasal cavity, oral cavity, pharyngeal cavity, larynx, trachea, lungs, etc., it is preferably formed by imitating the three-dimensional structure of the internal organs of the human body such as the tongue, dental arch, and nose, particularly those related to voice production. Usually, since the nasopharyngeal cavity is closed during speech, the nasopharyngeal cavity formed as a part of the flow path 25 of the discharge part 20 can be in a closed state. From the same viewpoint as above, the discharge port 21 of the present embodiment is preferably formed by imitating the shape of the lips imitating the time of speech.

[0057] As described above, the discharge device 10 includes a housing portion 11 that houses the mixed gas 3. The housing portion 11 of the present embodiment is connected to the discharge portion 20 via a pipe 28 as shown in FIG. 7, and is configured such that the mixed gas 3 can be introduced into the discharge portion 20 through the pipe 28. Specifically, the housing portion 11 includes a housing bag 13 that houses the mixed gas 3, a housing body 12 that houses the housing bag 13, and a pressure fluctuation portion 14 that varies the pressure inside the housing body 12. The pressure fluctuation portion 14 includes a pump 17 and a flow rate controller 15, and is configured such that air taken in from the outside by the pump 17 can be introduced into the housing body 12 through a pipe. By introducing air from this pump 17 to increase the pressure inside the housing body 12, the housing bag 13 is contracted, and the mixed gas 3 is sent from the housing bag 13 to the discharge portion 20. As the pump 17 in the present embodiment, a known one such as a diaphragm pump can be used.

[0058] The human body discharges the air in the lungs by contracting the volume of the lungs due to, for example, the upward movement of the diaphragm and the contraction of the internal intercostal muscles in the rib portion. The housing portion 11 of the present embodiment has a configuration that mimics the human lungs, thoracic cavity, and diaphragm by including the housing bag 13, the housing body 12, and the pump 17. For example, the housing bag 13 corresponds to the lungs and the housing body 12 corresponds to the thoracic cavity. The housing body 12 may be configured by a container made of synthetic resin. Also, the pipe 28 that connects the discharge portion 20 and the housing portion 11 may be configured by a silicon tube that mimics the trachea. Further, the amount and number of introductions (cycles) of the air introduced into the housing body 12 by the pressure fluctuation portion 14 are adjusted so that the amount of the mixed gas 3 sent from the housing bag 13 to the discharge portion 20 is the same as the human exhalation volume. Specifically, the flow rate controller 15 controls the amount and number of introductions (cycles) of the air introduced into the housing body 12. Thereby, the human exhalation is reproduced. The amount and number of introductions of the air by the pressure fluctuation portion 14 may be adjusted according to the exhalation of the human age, gender, etc.

[0059] Next, a simulation device 30 that executes the simulation method shown in FIG. 1 will be described with reference to the drawings. The simulation device 30 of the present embodiment is suitably used for the above-described simulation method. FIG. 8 shows a block diagram of the simulation device of the present embodiment.

[0060] The simulation device 30 of the present embodiment includes an imaging device 31 and a main body processing unit 32 (see FIG. 8). The imaging device 31 and the main body processing unit 32 are communicably connected to each other. As the imaging device 31, a digital still camera, a digital video camera, or the like can be used. The imaging element (image sensor) of the imaging device 31 may be a CCD or a C-MOS.

[0061] A known general-purpose computer can be used for the main body processing unit 32. The general-purpose computer includes a CPU, a ROM, a RAM, an HDD (Hard Disk Drive), and the like. The processing performed by the main body processing unit 32 is realized by the CPU expanding a program stored in the ROM or a disk into the RAM and executing it. The processing may be realized by a GPU (Graphics Processing Unit), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or a combination of an ASIC and an FPGA.

[0062] In addition, the main body processing unit 32 can use SaaS (Software as a Service), PaaS (Platform as a Service), and IaaS (Infrastructure as a Service) provided by a cloud server without providing an OS (Operating System) such as dedicated software, hardware, an on-premises server configuration, etc. With such a configuration, the simulation images 6 and 7 created by the main body processing unit 32 and the simulation video created based on them can be provided to the user via a general-purpose web browser. For example, the user may operate separate information terminals P1 and P2 such as a smartphone owned by the user, and the exchange of information between the information terminals P1 and P2 and the simulation device 30 may be performed via the web browser.

[0063] The main body processing unit 32 includes a communication unit 33, a storage unit 34, and a simulation processing unit 35 (see FIG. 8). The main body processing unit 32 of the present embodiment is communicably connected to the information terminals P1 and P2 owned by the user via the network N. The communication unit 33 receives access information and stores the information in the storage unit 34. The access information is information used for access from each of the imaging device 31 and the information terminals P1 and P2 owned by the user, and includes information required for various processes such as acquisition of data of the original image 4, acquisition of data of various conditions (target components and their conversion reference concentrations) for creating the simulation images 6 and 7 and the simulation video, and arithmetic processing and processing such as update. For example, it includes input information such as information on various conditions of the simulation input by each user (for example, target components), information on terminal operations performed by the user or others to create the information, and other personal information input by the user or others. In addition, the communication unit 33 transmits each piece of information (simulation images 6 and 7) generated or calculated by the main body processing unit 32 to the information terminals P1 and P2 owned by the user in response to the user's terminal operation.

[0064] The storage unit 34 stores data of the original image 4 acquired by the imaging device 31, and various programs, data, parameters, etc. necessary for the simulation processing unit 35 to perform calculation and processing under the control of the communication unit 33 and the simulation processing unit 35 respectively. The storage unit 34 stores, in addition to the above-described access information, output information transmitted to the information terminals P1 and P2 via the communication unit 33, etc.

[0065] The simulation device 30 of the present embodiment stores the data of the original image 4 acquired in advance in the storage unit 34, and reads the data of the original image 4 that conforms to the conditions according to the simulation conditions input by the user. Therefore, in the flowchart shown in FIG. 9 described later, the process corresponding to the step A) of acquiring the original image 4 is not executed. When storing the data of the original image 4, the storage unit 34 associates the original image 4 with its acquisition time (timestamp), and stores a plurality of original images 4 in chronological order. When the original image 4 is acquired from a moving image, the original image 4 is stored in association with the moving image and the elapsed time from the start of playback of the moving image. The process for acquiring the original image 4 from the moving image is executed by the image conversion unit 36 (simulation processing unit 35) described later, and the acquired original image 4 is stored in the storage unit 34.

[0066] In addition, the storage unit 34 can store various odor components in association with their conversion reference concentrations, curve models of gas phase concentration - olfactory intensity, and odor qualities. A database system or a file system may be used for such a storage unit 34. The storage unit 34 is composed of various recording media such as a main storage device composed of a ROM and a RAM, an auxiliary storage device composed of a non-volatile memory, etc., an HDD, an SSD (Solid State Drive), and a flash memory.

[0067] The simulation processing unit 35 includes an image conversion unit 36, an assumed concentration conversion unit 37, an olfactory intensity conversion unit 38, and a simulation image creation unit 39 (see FIG. 8). The image conversion unit 36 reads the data of the simulation conditions set by the user from the storage unit 34, identifies the original image 4 to be used in the simulation based on the conditions, and performs a process of dividing the original image 4 into units r. The shape of the unit r and the number of pixels included in the unit r are preset by the user of the simulation device 30. Also, the original image 4 to be used in the simulation is selected according to the conditions preset by the user. For example, the user can select, according to the purpose of the simulation, the original image 4 of a moving image in which a mixture gas 3 corresponding to the exhalation volume according to age, gender, etc. is emitted, and set this as the original image 4 to be used in the simulation.

[0068] Each unit r set in the original image 4 is stored in the storage unit 34 together with the area information of the unit in the original image 4 and the information of the pixel parameters in the unit r. For example, as shown in Table 1 below, the unit r set for each original image 4 is stored in association with the area information of the unit r and the information of the pixel parameters (luminance) of the unit r.

[0069] The assumed concentration conversion unit 37 reads the odor component (target component) set by the user from the storage unit 34, and performs a process of converting the pixel parameters of each unit r in all the original images 4 into assumed concentrations based on the conversion reference concentration J of the odor component. Also, the assumed concentration conversion unit 37 stores the converted assumed concentrations in the storage unit 34 in association with the unit r as shown in Table 1 below.

[0070] The olfactory intensity conversion unit 38 reads the data of the curve model of the odor component and its gas-phase concentration - olfactory intensity from the storage unit 34, and performs a process of converting the assumed concentration of each unit r in all the original images 4 into olfactory intensity. Also, the olfactory intensity conversion unit 38 stores the converted olfactory intensity in the storage unit 34 in association with the unit r as shown in Table 1 below. Table 1 below is an example of a data set of the processing results derived by the image conversion unit 36, the assumed concentration conversion unit 37, the olfactory intensity conversion unit 38, and the simulation image creation unit 39 (to be described later) in the present embodiment.

[0071]

Table 1

[0072] The simulation image creation unit 39 reads the assumed density in each unit section r of the dataset, converts the assumed density into color information, and creates the first simulation image 6. The simulation image creation unit 39 records the color information (Color Information 1 in Table 1) converted from the assumed density in the dataset shown in Table 1, and stores it in the storage unit 34 in association with the corresponding unit section r. Further, the first simulation image 6 is stored in the storage unit 34 in association with the chronological sequence order of the original image 4 serving as the basis or the elapsed time from the start of video playback and the simulation conditions. Furthermore, the simulation image creation unit 39 creates a density simulation video formed by arranging the first simulation images 6 in chronological order, and stores it in the storage unit 34 in association with the simulation conditions.

[0073] Also, the simulation image creation unit 39 reads the olfactory intensity in each unit section r of the dataset, converts the olfactory intensity into color information, and creates the second simulation image 7. The simulation image creation unit 39 records the color information (Color Information 2 in Table 1) converted from the olfactory intensity in the dataset shown in Table 1, and stores it in the storage unit 34 in association with the corresponding unit section r. Further, the second simulation image 7 is stored in the storage unit 34 in association with the chronological sequence order of the original image 4 serving as the basis or the elapsed time from the start of video playback. Furthermore, the simulation image creation unit 39 creates an olfactory intensity simulation video formed by arranging the second simulation images 7 in chronological order, and stores it in the storage unit 34 in association with the simulation conditions.

[0074] The simulation image creation unit 39 may read the pixel parameters (luminance) in each unit section r of the dataset and create a pixel parameter image 5 that reflects the pixel parameters. In this case, the simulation image creation unit 39 stores the pixel parameter image 5 in the storage unit 34 in association with the corresponding original image 4. The pixel parameter image 5 can be created by converting the pixel parameters of each unit section r into color information.

[0075] Next, a simulation method using the simulation device 30 of the present embodiment will be described. FIG. 9 shows a flowchart indicating the flow of the simulation method.

[0076] In the simulation method of the present embodiment, first, a user inputs an odor component that is the target component of the simulation by operating the terminal (step S1). Further, the user inputs simulation conditions by operating the terminal (step S2). In steps S1 and S2, the simulation device 30 receives information on the odor component and the simulation conditions transmitted from the information terminals P1 and P2. The input of information in steps S1 and S2 may utilize the character input function provided by the OS of the information terminals P1 and P2, or may utilize voice input.

[0077] The simulation conditions input in step S2 are conditions for selecting the original image 4 used in the simulation such as the emission conditions of the mixed gas 3, and setting conditions for the unit section r such as the number of pixels constituting the unit section r. In the subsequent step S3, the simulation processing unit 35 (image conversion unit 36) selects the original image 4 used in the simulation according to such simulation conditions. For example, when gender and age are input as simulation conditions, the original image 4 of a moving image in which the mixed gas 3 corresponding to the exhalation volume is emitted is selected.

[0078] In the subsequent step S4, a simulation method is selected. Specifically, according to the user's terminal operation, a concentration simulation method or an olfactory intensity simulation method is selected.

[0079] When the density simulation method is selected in step S4, the process proceeds to step S5. In step S5, for all the original images 4 used in the simulation, the pixel parameters for each unit section r are converted into the assumed density. In such step S5, the image conversion unit 36 and the assumed density conversion unit 37 execute the above-described step B). In the subsequent step S6, the assumed density for each unit section converted in step S5 is converted into color information. Then, in step S7, the color information for each unit section r converted in step S6 is reflected in the unit section r to create the first simulation image 6. In such steps S6 and S7, the simulation image creation unit 39 executes the above-described step C). In step S7, after creating the first simulation image 6 for all the original images 4 used in the simulation, the process proceeds to step S12.

[0080] When the olfactory intensity simulation method is selected in step S4, the process proceeds to step S8. In step S8, for all the original images 4 used in the simulation, the pixel parameters for each unit section r are converted into the assumed density. In such step S8, the image conversion unit 36 and the assumed density conversion unit 37 execute the above-described step B). In the subsequent step S9, the assumed density for each unit section converted in step S8 is converted into the olfactory intensity. In such step S9, the olfactory intensity conversion unit 38 executes the above-described step D). In the subsequent step S10, the olfactory intensity for each unit section r converted in step S9 is converted into color information. Then, in step S11, the color information for each unit section r converted in step S10 is reflected in the unit section r to create the second simulation image 7. In such steps S10 and S11, the simulation image creation unit 39 executes the above-described step E). In step S11, after creating the second simulation image 7 for all the original images 4 used in the simulation, the process proceeds to step S12.

[0081] In step S12, simulation images 6 and 7 created in step S7 or step S11 are arranged in chronological order to create a simulation video. In the subsequent step S13, the data of the simulation image obtained in step S7 or step S11 or the simulation video obtained in step S12 is transmitted to information terminals P1 and P2 owned by the user, and the process shown in FIG. 9 is terminated.

[0082] The simulation device 30 of the above-described embodiment and the simulation method using the same may be used, for example, in a medical institution having a halitosis outpatient clinic via the network N. In this case, a user such as a doctor operates an information terminal installed in the medical institution to utilize the simulation device 30 of the above-described embodiment and the simulation method using the same.

[0083] As described above, the present invention has been described based on its preferred embodiments, but the present invention is not limited to the above-described embodiments. The discharge device 10 of the above-described embodiment had a portion in which the flow path 25 in the discharge unit 20 was formed to mimic the human oral cavity and pharyngeal cavity. However, the shape and configuration of the flow path 25 may be appropriately changed according to the simulation purpose. For example, the flow path 25 may have a portion formed to mimic the oral cavity and pharyngeal cavity of an animal other than a human, such as a dog or a cat, and the discharge port may be formed to mimic the lips of the animal. In this case, the exhalation of an animal other than a human can be reproduced. Further, the discharge unit 20 may have the same structure as that of a device that generates a gas or an air current, such as a commercially available air conditioner or an aroma diffuser. In this case, the diffusion behavior of the air containing the odor component discharged by the device can be reproduced. Further, the structure of the discharge unit 20 in the discharge device 10 may be replaced with a tube provided with a discharge port 21 to have a simple structure. The simulation method of the above-described embodiment created the first simulation image 6 or the second simulation image 7 that reflected the concentration distribution or the sensory intensity distribution of one type of odor component. However, the concentration distribution or the sensory intensity distribution of two or more types of odor components may be reflected in the simulation image. In this case, the color information reflecting the concentration distribution or the sensory intensity distribution of the odor components may be imaged while being made different for each odor component.

Example

[0084] Hereinafter, the present invention will be described more specifically with reference to examples, but the present invention is not limited to such examples.

[0085] 〔Acquisition of original image〕 Using the discharge device 10 shown in FIG. 7, the mixed gas 3 was discharged from the discharge part 20, and the state in which the mixed gas 3 diffused was photographed. The video was photographed in a dark room. A light capable of wide-angle irradiation equipped with a large number of LEDs was installed behind the discharge part 20, and the mixed gas 3 discharged from the discharge part 20 was irradiated with light by irradiating light forward, and the mixed gas 3 was diffused. As described above, the discharge part 20 included in the discharge device 10 has the first flow path 26 formed to simulate the oral cavity of the human body and the second flow path 27 formed to simulate the pharyngeal cavity of the human body, and the discharge port 21 is formed to simulate the lips of the human body. Such a discharge part 20 is composed of a three-dimensional shaped object of a 3D printer based on an MRI image showing the vocal tract, nasal cavity, dental arch part, lips, etc. during the pronunciation of the vowel / a / by an adult male in his 30s. The MRI image used was the one publicly available on the support page of "Computational Model and Visualization of Speech Generation" (edited by Tokihiko Kamiki) by Corona Co., Ltd. Usually, since the nasal cavity and the pharyngeal cavity are blocked during speech, the portion corresponding to the nasopharyngeal cavity in the discharge part 20 was closed. A silicon tube with an inner diameter of 15 mm was used for the tube 28 connecting the discharge part 20 and the housing part 11. A plastic container with a volume of 16 L was used for the housing body 12 included in the housing part 11. A housing bag 13 containing 12 L of the mixed gas 3 was housed in this housing body 12. Air was intermittently introduced into the housing main body 12 by a pump 17 (diaphragm pump) provided in the pressure fluctuation unit 14. Specifically, control was performed such that 1000 mL of air was introduced over 6 seconds, and then the introduction of air was stopped for 6 seconds, repeating such a cycle. Such control was performed using a flow controller 15. In this way, the mixed gas 3 was sent from the housing part 11 to the discharge part 20, and the mixed gas 3 was discharged from the discharge port 21 of the discharge part 20. The mixed gas 3 used was one containing droplets generated by a fog machine (Antari Z - 800II) using a liquid containing polyethylene glycol and water as particles.

[0086] In order to simulate the diffusion dynamics until the odor component reaches the nose of the conversation partner, an observation subject S who is the conversation partner was placed at a position 1 m in front of the above - mentioned discharge part 20, and in that state, the mixed gas 3 was discharged from the discharge device 10. The mixed gas 3 diffused through the space between the discharge part 20 and the observation subject S and reached the observation subject S. Thereafter, it was visually observed that the mixed gas 3 rose along the upward airflow due to the body temperature of the observation subject S and reached the nose of the observation subject S. Also, the state of diffusion of such mixed gas 3 was photographed with a digital video camera.

[0087] 〔Example 1〕 Regarding the video data obtained by photographing the state of diffusion of the above - mentioned mixed gas 3, frame images at 2 seconds from the start of playback were extracted, and these were used as the original images 4 of the mixed gas 3 "immediately after discharge" [see Fig. 10(a)]. In addition to this, frame images at 8 seconds from the start of playback were extracted, and these were used as the original images 4 of the mixed gas 3 "at the time of arrival" when it reached the position of the observation subject S [see Fig. 10(a)].

[0088] For each of the original images 4 shown in Fig. 10(a), the above - described concentration simulation method and olfactory intensity simulation method were performed. Acetaldehyde was set as the odor component that is the target component. Acetaldehyde is known as a causative component of bad breath derived from drinking. The unit section r was set as a square area consisting of 400 pixels. Also, the unit section showing the highest luminance in the original image shown in Fig. 10(a) was set as the reference section r1. The conversion reference concentration of acetaldehyde was set as the concentration of acetaldehyde in exhaled breath (700 ng / L) described in "Components Involved in Unpleasant Odor of Exhaled Breath after Drinking" (Author: Hiroaki Negoro, Journal of the Society for Odor and Flavor Environment, Vol. 46, No. 5, 2015). Next, for each of the original images 4 shown in Fig. 10(a), the luminance of each unit section r was converted into an assumed concentration. The above formula (1) was used for such conversion. Then, the assumed concentration of the unit section r was converted into color information, and a first simulation image 6 reflecting the color information was created [see Fig. 10(b)]. Also, the assumed concentration in each unit section r was converted into olfactory intensity. The scale of olfactory intensity was the above-described LMS, and based on the curve model of the gas-phase concentration - olfactory intensity of acetaldehyde, the assumed concentration of the unit section r was converted into olfactory intensity. Then, the olfactory intensity of the unit section r was converted into color information, and a second simulation image 7 reflecting the color information was created [see Fig. 10(c)]. The obtained first and second simulation images were superimposed on the images obtained by imaging the emission unit and the observation subject S [see Figs. 10(b) and (c)]. The first simulation image also shows an intensity index representing the relationship between the assumed concentration and the color information (maximum value of the assumed concentration: 1864 ng / L) [see Fig. 10(b)]. The second simulation image also shows an intensity index representing the relationship between the olfactory intensity and the color information (maximum value of LMS: 17.2) [see Fig. 10(c)]. The first simulation image and the second simulation image of this example were created by setting them to be colored in black and white shades such that the lower the assumed concentration or the olfactory intensity, the darker the black, and the higher the assumed concentration, the darker the white.

[0089] [Example 2] Regarding the video data used in Example 1, frame images were extracted at 1-second intervals from the video from 2 seconds after the start of playback to 5 seconds after the start of playback, and these were used as the original images 4. That is, the frame images at 2 seconds, 3 seconds, 4 seconds, and 5 seconds after the start of playback were obtained as the original images 4. For each of these original images 4, the above-described olfactory intensity simulation method was performed. As odor components that are the target components, hydrogen sulfide and methyl mercaptan were each set. The unit section that showed the highest luminance in the original images at 2 seconds, 3 seconds, 4 seconds, and 5 seconds after the start of playback was set as the reference section r1. The conversion reference concentration of hydrogen sulfide was set as the average value (29.4 ng / L) of the concentration of hydrogen sulfide contained in the exhaled breath of the subjects in whom halitosis was observed in the literature of Tangerman A., Winkel E.G. Intra- and extra-oral halitosis: Finding of a new form of extra-oral blood-borne halitosis caused by dimethyl sulphide. J. Clin. Periodontol. 2007;34:748-755. Similarly, the conversion reference concentration of methyl mercaptan was set as the average value (23.2 ng / L) of the concentration of methyl mercaptan contained in the exhaled breath of the subjects in whom halitosis was observed in the above literature. Next, for each of the original images 4 at 2 seconds, 3 seconds, 4 seconds, and 5 seconds after the start of playback, the luminance of each unit section r was converted into an assumed concentration. The above formula (1) was used for such conversion. Then, the assumed concentration of the unit section r was converted into color information, and a first simulation image 6 reflecting the color information was created. Also, the assumed concentration in each unit section r was converted into olfactory intensity. The scale of olfactory intensity was the LMS described above. Based on the curve model of gas-phase concentration-olfactory intensity of hydrogen sulfide or methyl mercaptan, the assumed concentration of unit section r was converted into olfactory intensity. Then, the olfactory intensity of unit section r was converted into color information, and the second simulation image 7 reflecting the color information was created. The first simulation image of this example is shown in FIG. 11, and the second simulation image is shown in FIG. 12. The first simulation image and the second simulation image of this example were created by setting the color scheme with black becoming darker as the assumed concentration or olfactory intensity is lower and white becoming darker as the assumed concentration is higher. In FIG. 11, for each first simulation image of hydrogen sulfide and methyl mercaptan, the scales of the intensity indicators (maximum value of assumed concentration: 50 ng / L) representing the relationship between the assumed concentration and the color information are shown to be the same. Also, in FIG. 12, for each second simulation image of hydrogen sulfide and methyl mercaptan, the scales of the intensity indicators (maximum value of olfactory intensity: 17.2) representing the relationship between the olfactory intensity and the color information are shown to be the same.

[0090] From the first simulation image shown in FIG. 10(b), the concentration distribution of diffused acetaldehyde immediately after release and at the time of arrival can be grasped at an actually assumable concentration. Also, from the second simulation image shown in FIG. 10(c), the distribution of the sensory intensity of the smell of diffused acetaldehyde can also be grasped. According to the second simulation image shown in FIG. 10(c), it is simulated that acetaldehyde reaches the nose of the observation subject S in a perceivable state. Also, from the simulation images shown in FIGS. 11 and 12, it is possible to grasp the concentration distribution in the diffusion state of different odor components and the distribution of the sensory intensity of the odor. For example, in the first simulation images of hydrogen sulfide and methyl mercaptan, there is no significant difference in the concentration distribution (see FIG. 11), but in the second simulation images, the sensory intensity of the odor of hydrogen sulfide is shown in a low distribution, while the sensory intensity of the odor of methyl mercaptan is shown in a high distribution. From such results, it is possible to visually grasp that methyl mercaptan contributes more significantly to bad breath in the diffusion dynamics of exhaled breath than hydrogen sulfide. Thus, it has been shown that the simulation method of the present invention can accurately grasp the diffusion dynamics of odor components in space.

Explanation of Reference Numerals

[0091] 3 Mixed gas 4 Original image 5 Pixel parameter image 6 First simulation image 7 Second simulation image 10 Discharge device 11 Storage part 12 Storage body 13 Storage bag 14 Pressure fluctuation part 15 Flow rate controller 17 Pump 20 Discharge part 21 Discharge port 22 Inlet 25 Flow path 26 First flow path 27 Second flow path 28 Pipe 30 Simulation device 31 Imaging device 32 Main body processing part 33 Communication part 34 Storage part 35 Simulation processing part 36 Image conversion part 37 Assumed concentration conversion part 38 Olfactory intensity conversion part 39 Simulation Image Creation Unit P1, P2 Information Terminals r Unit Division r1 Reference Division

Claims

1. A method for simulating odor diffusion using an emission device, the method comprising: The emission device includes a storage unit for storing a mixed gas containing particles and an emission unit. The emission unit includes an emission port for emitting the mixed gas and a flow path communicating the emission port with the storage unit. The method includes the following steps A) to C): A) A step of acquiring, over time, an image of the particles in the mixed gas emitted from the emission device into a space. B) For each individual image, for each unit section consisting of one or more pixels, a step of converting the pixel parameters included in the unit section into an assumed concentration of the odor component. C) A step of creating a simulation image in which the assumed concentration for each unit section is reflected as color information using the assumed concentration. A simulation method, wherein the concentration of the odor component contained in exhaled breath is used for the conversion of the assumed concentration.

2. A method for simulating odor diffusion using an emission device, the method comprising: The emission device includes a storage unit for storing a mixed gas containing particles and an emission unit. The emission unit includes an emission port for emitting the mixed gas and a flow path communicating the emission port with the storage unit. The method includes the following steps A), B), D), and E): A) A step of acquiring, over time, an image of the particles in the mixed gas emitted from the emission device into a space. B) For each individual image, for each unit section consisting of one or more pixels, a step of converting the pixel parameters included in the unit section into an assumed concentration of the odor component. D) A step of converting the assumed concentration for each unit section into an olfactory intensity of the odor component. E) A step of creating a simulation image in which the olfactory intensity for each unit section is reflected as color information using the olfactory intensity. A simulation method, wherein the concentration of the odor component contained in exhaled breath is used for the conversion of the assumed concentration.

3. The simulation method according to claim 2, wherein the scale of the olfactory intensity is any one selected from the group consisting of Labeled Magnitude Scale (LMS), Visual analog scale (VAS), Magnitude estimate (ME), and Category-ratio scale (CR).

4. The simulation method according to any one of claims 1 to 3, wherein the pixel parameter is any one selected from the group consisting of luminance, lightness, chroma, and RGB values.

5. The simulation method according to any one of claims 1 to 4, wherein the particles are droplets.

6. The simulation method according to claim 5, wherein the droplets are obtained by atomizing a liquid containing glycols and water or a liquid containing oil.

7. The simulation method according to claim 6, wherein the glycols are at least one or more selected from the group consisting of propylene glycol, tripropylene glycol, and 1,3-butylene glycol.

8. The discharge part in the discharge device includes a flow path and a discharge port. The simulation method according to any one of claims 1 to 7, wherein at least a part of the flow path is formed by imitating the pharyngeal cavity and oral cavity of the human body, and the discharge port is formed by imitating the lips of the human body.

9. The housing part includes a housing bag for housing the mixed gas, a housing body for housing the housing bag, and a pressure fluctuation part for fluctuating the pressure inside the housing body. The simulation method according to any one of claims 1 to 8, wherein the pressure fluctuation part raises the pressure inside the housing body by a pump to contract the housing bag and send the mixed gas from the housing bag to the discharge part.

10. The simulation method according to any one of claims 1 to 9, wherein the odor component is at least one or more selected from the group consisting of indole, skatole, isovaleric acid, butyric acid, hydrogen sulfide, methyl mercaptan, dimethyl sulfide, allyl mercaptan, diallyl disulfide, allyl methyl sulfide, allyl methyl disulfide, acetaldehyde, isoprene, furfuryl mercaptan, menthol, carvone, and limonene.

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