Method for generating base data of odor image

Abstract

A method for generating base data of an odor image is a method for generating base data for expressing an odor of a sample containing an odor substance with an image. The method includes: a measurement result acquisition step of acquiring each measurement result determined by each of a plurality of sensor elements (11) of an odor sensor (10) with respect to an odor substance contained in a sample; and a data processing step of processing the acquired measurement results individually to generate base data for expressing an odor of the sample with an image. The detection characteristics of the plurality of sensor elements (11) with respect to the odor substance are different from each other. In a case where each of the base data is expressed with a small image (2), the odor of the sample is expressed as an odor image (1) in a given display mode with a set of a plurality of small images (2), and each small image (2) is changed according to the magnitude of the value of the base data.

Description

[0001] This application is a divisional application of the application filed on October 30, 2019, application number 201780090188.5, and titled "Method for generating basis data of odor image". TECHNICAL FIELD

[0002] The present application relates to a method for generating basis data of an odor image. Specifically, it relates to a method for generating basis data for expressing an odor of a sample containing an odor substance with an image. BACKGROUND

[0003] In order to measure the odor of air, a sensor provided with a crystal oscillator that specifically adsorbs odor substances in air is known (see Patent Document 1).

[0004] Prior Art Documents

[0005] Patent Documents

[0006] Patent Document 1: Japanese Patent Application Publication No. 5-187986 SUMMARY

[0007] Problems to be Solved by the Invention

[0008] However, if only a sensor is used to measure the odor of air and the measurement result is saved, it is difficult to directly grasp what kind of odor it is.

[0009] The present application has been made in view of the above circumstances, and it is an exemplary problem to provide a method for generating basis data for expressing an odor with an image in order to easily visually grasp the measurement result of an odor measured with an odor sensor.

[0010] Technical Solution for Solving the Problem

[0011] In order to solve the above problem, the present application has the following configuration.

[0012] (1) A method of generating basic data of an odor image, the basic data being used to express an odor of a sample containing an odor substance with an image, the method of generating basic data of an odor image comprising: a measurement result acquisition step of acquiring, using an odor sensor provided with a plurality of sensor elements, each measurement result determined at each of the plurality of sensor elements with respect to the odor substance contained in the sample in association with a state of each of the plurality of sensor elements; and a data processing step of processing the acquired measurement results respectively to generate basic data for expressing the odor of the sample with an image, the basic data being associated with each of the plurality of sensor elements, detection characteristics of the plurality of sensor elements with respect to the odor substance being different from each other, in the data processing step, in a case where each of the basic data is expressed with a small image corresponding to each of the sensor elements, the basic data is generated in such a manner that the odor of the sample is expressed as an image in a given display manner with a set of a plurality of the small images, and each of the small images is changed in accordance with a magnitude of a value of the basic data.

[0013] Further objects or other features of the present application will become apparent from the following preferred embodiments described with reference to the accompanying drawings.

[0014] Effects of the Invention

[0015] According to the present application, it is possible to provide a method of generating basic data for expressing an odor with an image for the purpose of easily visually grasping a measurement result of an odor determined with an odor sensor. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 is a database Dl of measurement results acquired in the measurement result acquisition step of Embodiment 1.

[0017] Figure 2 is a graph representing the measurement results acquired in the measurement result acquisition step of Embodiment 1.

[0018] Figure 3 is a diagram explaining an outline of the data processing step S2 of Embodiment 1.

[0019] Figure 4 is an example of an image expressed based on the basic data generated in the data processing step S2 of Embodiment 1.

[0020] Figure 5 is a schematic plan view of the odor sensor 10 in Embodiment 1.

[0021] Figure 6 is a schematic plan view of the odor sensor 10 in Embodiment 1. Figure 5a cross-sectional view of the A-A' section in FIG. 1.

[0022] Figure 7 is an explanatory diagram schematically showing a mechanism of odor measurement of Embodiment 1. DETAILED DESCRIPTION

[0023] [Embodiment 1]

[0024] Hereinafter, a method of generating basic data of an odor image related to Embodiment 1 will be described. The method of generating basic data of an odor image related to Embodiment 1 is a method of generating basic data for expressing an odor of a sample containing an odor substance by an image. The method of generating basic data of an odor image related to Embodiment 1 has a measurement result acquisition step, a data processing step.

[0025] In Embodiment 1, "odor" refers to a substance that can be acquired as olfactory information with respect to a human or a living being including a human, and the concept includes a molecular monomer or a substance in which a molecular group composed of different molecules is collected with respective concentrations.

[0026] In Embodiment 1, a molecular monomer or a substance in which a molecular group composed of different molecules is collected with respective concentrations, which constitutes the above odor, is referred to as an "odor substance". However, in a broad sense, an odor substance sometimes broadly indicates a substance that can be adsorbed to a substance adsorption film of an odor sensor 10 described later. That is, there are many cases in which a plurality of odor substances that become causes are included in "odor", and in addition, since there can be a substance that is not recognized as an odor substance or an unknown odor substance, a substance that is not normally a cause substance of odor can also be included.

[0027] <Measurement result acquisition step S1>

[0028] In the measurement result acquisition step S1, using the odor sensor 10, at each of the plurality of sensor elements 11 included in the odor sensor 10, each measurement result measured with respect to an odor substance included in a sample is acquired. The detection characteristics of the plurality of sensor elements 11 with respect to an odor substance are each different. The specific structure of the odor sensor 10 will be described below.

[0029] Each measurement result is acquired in association with each of the plurality of sensor elements 11. Specifically, each sensor element 11 and a measurement result database in which each measurement result measured at each sensor element 11 is saved in association with each other can be acquired.

[0030] Figure 1The measurement result database D1 is obtained in the measurement result acquisition step of Embodiment 1. In the measurement result database D1, each sensor element 11 and the measurement results measured by each sensor element 11 are stored in a state that is correlated with each other. Figure 1 The measurement result database D1 shown stores the measurement results for 35 sensor elements 11, from 11-01 to 11-35, establishing and saving the status of each element. Furthermore, in... Figure 1 For ease of explanation, details regarding sensor elements 11-08 to 11-34 are omitted.

[0031] Specifically, the measurement results are the raw data detected by each sensor element 11. In the case where the odor sensor 10 is, for example, a crystal oscillator sensor (QCM), the raw data generated by the sensor element 11 can be set as the time-varying resonant frequency of the crystal oscillator. That is, the measurement results based on the sensor element 11 can be set as the resonant frequencies at multiple moments elapsed since the start of operation of the odor sensor 10. For example, such as... Figure 1 As shown, based on the resonant frequency 0 seconds after the start of operation of the odor sensor 10, the resonant frequency measured at sensor element 11-01 is "9.3" after 14 seconds and "-11.0" after 16 seconds. Furthermore, based on the resonant frequency 0 seconds after the start of operation of the odor sensor 10, the resonant frequency measured at sensor element 11-02 is "10.7" after 14 seconds and "-11.7" after 16 seconds. Moreover, the time interval for recording the measurement results is not particularly limited; for example, it can be set to a 1-second interval.

[0032] The measurement based on the odor sensor 10 is preferably performed multiple times, and the average value of the raw data from the multiple measurements is taken as the measurement result. There is no particular limitation on the number of measurements; for example, it can be set to three times. The average value can be obtained by using the arithmetic mean (summary average).

[0033] <Data Processing Step S2>

[0034] In the data processing step S2, the measurement results obtained in the measurement result obtaining step S1 are respectively processed to generate the basis data for expressing the odor of the sample with the odor image 1, that is, to generate the basis data associated with each of the plurality of sensor elements 11. In addition, in the data processing step S2, in the case where each of the basis data is expressed with the small image 2 corresponding to each of the sensor elements 11, the odor of the sample is expressed as the odor image 1 in the given display manner with the set of the plurality of small images 2, and the basis data is generated in such a manner that each of the small images 2 changes according to the magnitude of the value of the basis data.

[0035] The data processing step S2 can also have the respective sub-steps of the difference calculation sub-step S2-1, the logarithmic operation sub-step S2-2, the value classification sub-step S2-3, and the basis data generation sub-step S2-4.

[0036] DIFFERENCE CALCULATION SUB-STEP S2-1

[0037] In the difference calculation sub-step S2-1, for each of the measurement results obtained in the measurement result obtaining step S1, the difference (difference) between the maximum value and the first minimum value after the maximum value (hereinafter, also referred to as "minimum value after the maximum value") is calculated. Then, in the case where a plurality of differences (maximum value and minimum value after the maximum value) exist, the value of the difference having the largest value is taken as the difference of the measurement result. In this way, for each of the measurement results, the difference associated with each of the plurality of sensor elements 11 is obtained.

[0038] Figure 2 is a graph showing the measurement result obtained in the measurement result obtaining step of Embodiment 1. In Figure 2 , the vertical axis is the displacement amount "Hz" of the resonance frequency measured after a given time in the case where the resonance frequency after 0 seconds from the start of the operation of the odor sensor 10 is taken as the reference, and the horizontal axis is the elapsed time "seconds" after the start of the operation of the odor sensor 10. In Figure 2 , the measurement results with respect to the sensor elements 11-01, 11-02, and 11-03 among the measurement results shown in the measurement result database D1 are shown. In Figure 2 , the measurement result of the sensor element 11-01 is shown with a solid line, the measurement result of the sensor element 11-02 is shown with a broken line, and the measurement result of the sensor element 11-03 is shown with a dot-and-dash line. With respect to the other sensor elements 11-04 to 11-35, of course, the graph can be similarly made. In Figure 2In the case of the sensor element 11-01, the difference in the measurement result is "22.6 Hz". That is, in the measurement result with respect to the sensor element 11-01, the difference between the maximum value "9.3 Hz" at 14 seconds after the start of the operation of the odor sensor 10 and the minimum value "-13.3 Hz" at 17 seconds after the start of the operation of the odor sensor 10.

[0039] When calculating the difference, the range of the elapsed time after the start of the operation of the odor sensor 10 can also be limited. For example, in a case where the measurement of the odor of the sample is started 15 seconds after the start of the operation of the odor sensor 10 and ended 20 seconds after the start of the operation of the odor sensor 10, the range of the elapsed time in which the difference is calculated can be set to a period from 14 seconds to 25 seconds after the start of the operation of the odor sensor 10. Further, the range of the elapsed time can also be arbitrarily set.

[0040] < Logarithm operation sub-step S2-2 >

[0041] In the logarithm operation sub-step S2-2, with respect to each of the differences calculated in the difference calculation sub-step S2-1, a logarithm operation is performed to obtain a logarithmic value associated with each of the plurality of sensor elements 11. In the logarithm operation, the base number is not particularly limited, and can be set to 2, for example. Further, the difference is the difference between the maximum value and the minimum value, and is a positive value (real number).

[0042] < Value classification sub-step S2-3 >

[0043] In the value classification sub-step S2-3, each of the logarithmic values obtained in the logarithm operation sub-step S2-2 is classified into a plurality of regions according to the magnitude of the value. The number of regions for classification is not particularly limited, and can be set to 3 to 5 regions, for example. Hereinafter, a case where the classification is performed into three regions will be described.

[0044] In the value classification sub-step S2-3, first, the maximum logarithmic value and the minimum logarithmic value among the plurality of logarithmic values with respect to each sample obtained in the logarithm operation sub-step S2-2 are determined. Next, the quotient in a case where the difference between the maximum logarithmic value and the minimum logarithmic value is divided by 3 is calculated. The range of values between the maximum logarithmic value and the minimum logarithmic value can be divided into three equal regions using the quotient thus obtained. That is, the range can be divided into three equal regions: a region from the minimum logarithmic value to a value obtained by adding the minimum logarithmic value to the quotient, a region from the minimum logarithmic value to a value obtained by adding twice the minimum logarithmic value to the quotient, and a region from the value obtained by adding twice the minimum logarithmic value to the quotient to the maximum logarithmic value.

[0045] Next, each of the logarithmic values associated with each of the sensor elements 11 is classified into any one of the three regions. As to each of the logarithmic values, a flag for identifying the classified region can also be set. For example, for the three regions after the trisection, the flag can be set as (1), (2), (3) from the region of the small value. Thereby, the measurement result associated with each of the sensor elements 11 can be classified into three stages according to the magnitude of the value.

[0046] As to the above-described data processing step S2, the following is used. Figure 3 Further description is made. Figure 3 is a diagram that explains the outline of the data processing step S2 of Embodiment 1. Figure 3 In Table (A), the difference obtained in the sensor element 11-01 is "38.7", and the difference obtained in the sensor element 11-02 is "27.0". Further, for the sake of explanation, the values of the sensor elements 11-11 to 11-34 are omitted (the same as Tables (B) and (E) described later).

[0047] Next, the difference with respect to each of the sensor elements 11 is subjected to logarithmic operation processing by the logarithmic operation sub-step S2-2. The logarithmic operation here is expressed by the following formula (1). That is, by setting the base number as 2, the logarithmic operation is performed on the absolute value of the difference value, and the logarithmic value is found.

[0048] [Logarithmic value] = log2[|difference|]... Formula (1)

[0049] Table (B) is a table that shows the logarithmic value with respect to each of the sensor elements 11 found by the logarithmic operation sub-step S2-2. For example, in Table (B), the logarithmic value calculated based on the difference obtained in the sensor element 11-01 is "5.3", and the logarithmic value calculated based on the difference obtained in the sensor element 11-02 is "4.8".

[0050] Next, by the value classification sub-step S2-3, the logarithmic value with respect to each of the sensor elements 11 is classified into three regions based on the obtained logarithmic value. Specifically, first, in the sample in the measurement, the maximum logarithmic value (maximum value) and the minimum logarithmic value (minimum value) among the logarithmic values with respect to each of the sensor elements 11 are determined. Then, the quotient in the case where 3 is divided by the difference between the maximum value and the minimum value is calculated. These determined maximum value, minimum value, and calculated quotient are shown in Table (C). In Table (C), the determined maximum value is "6.7", the determined minimum value is "3.1", and the calculated quotient is "1.2".

[0051] Based on these determined maximum values, minimum values, and calculated quotients, the logarithmic values with respect to the respective sensor elements 11 are classified into three stages. At the time of classification, classification is performed based on the classification rule shown in Table (D) like this. Specifically, classification is performed based on the classification rule that the region of the smallest logarithmic value (Region 1) is the range of 3.1 ≤ [logarithmic value] ≤ 4.3, the region of the second smallest logarithmic value (Region 2) is the range of 4.3 < [logarithmic value] ≤ 5.5, and the region of the largest logarithmic value (Region 3) is the range of 5.5 < [logarithmic value] ≤ 6.7.

[0052] Next, based on the result of the classification, a flag is assigned with respect to each sensor element 11. The result after the flag is assigned with respect to each sensor element 11 is shown in Table (E). With respect to the sensor element 11 that obtained a logarithmic value belonging to Region 1, a flag (1) is assigned, with respect to the sensor element 11 that obtained a logarithmic value belonging to Region 2, a flag (2) is assigned, and with respect to the sensor element 11 that obtained a logarithmic value belonging to Region 3, a flag (3) is assigned. For example, in Table (E), with respect to the sensor element 11-01, a flag (2) is assigned, with respect to the sensor element 11-30, a flag (1) is assigned, and with respect to the sensor element 11-09, a flag (3) is assigned.

[0053] <Basic data generating sub-step S2-4>

[0054] In the basic data generating sub-step S2-4, based on the logarithmic values (measurement results) classified in the value classifying sub-step S2-3, that is, the logarithmic values (measurement results) corresponding to each sensor element 11, basic data is generated. The basic data has a value corresponding to each sensor element 11, respectively.

[0055] The basic data refers to data that becomes the basis of the image data for expressing the odor of the sample with the odor image 1. The basic data is not data (pixel data) indicating information such as the color or position of each pixel forming the odor image 1, but is data indicating the position, size, color, shape, and the like of the small image 2. The odor image 1 generated based on the basic data is an image including a plurality of small images 2 expressed by the basic data corresponding to each sensor element 11. The odor image 1 as a collection of these plurality of small images 2 can be expressed by a given display method. Each small image 2 can be changed according to the size of the value of the corresponding basic data. Specifically, the size, color, shape, and the like of the small image 2 can be changed according to the size of the value of the corresponding basic data. That is, in the basic data generating sub-step S2-4, the basic data is generated in a manner that the odor of the sample is expressed as the odor image 1 by a given display method. In addition, in the basic data generating sub-step S2-4, the basic data is generated in a manner that each small image 2 is changed according to the size of the value of the basic data.

[0056] Figure 4 is an example of an image expressed based on the base data generated in the data processing step S2 of Embodiment 1. Figure 4 The odor image 1 shown is composed of 35 small images 2, each of which is in the shape of a circle. The small images 2 are arranged in order from the upper left in correspondence with the sensor elements 11-01 to 11-35. Specifically, in the example shown in Figure 4 , the two small images 2 in the first row from the top are in order from the left in correspondence with the sensor elements 11-01 and 11-02, and the five small images 2 in the second row from the top are in order from the left in correspondence with the sensor elements 11-03 to 11-07, respectively. In addition, the small image 2 in correspondence with the sensor element 11-03 (marker (1)) is represented by a small circle, the small image 2 in correspondence with the sensor element 11-09 (marker (3)) is represented by a large circle, and the small image 2 in correspondence with the sensor element 11-01 (marker (2)) is represented by a larger circle than the small circle and the large circle.

[0057] In the example shown in Figure 4 , the shapes of all the small images 2 are represented by circles, but the shapes of the small images 2 are not limited to circles, and can be squares, rectangles, diamonds, other irregular shapes, or the like. In addition, the shapes of all the small images 2 do not have to be uniform, and the small images 2 can have different shapes. In the example shown in Figure 4 , the colors of the small images 2 are represented by black, but the colors of the small images 2 are not limited to black, and can be represented by any color. In addition, the colors of all the small images 2 do not have to be uniform, and the small images 2 can be represented by different colors.

[0058] In the example shown in Figure 4 , the small images 2 are represented in a manner that differs in size according to the magnitude of the value of the corresponding base data. Specifically, if the value of the base data is large, the small image 2 is represented large, and if the value of the base data is small, the small image 2 is represented small. Here, the size of each small image 2 can also be classified into a plurality of stages according to the stage classified in the value classification sub-step S2-3. That is, in the case where the value classification sub-step S2-3 classifies into three stages of markers (1), (2), and (3), the size of the small image 2 can also be classified into three stages and represented.

[0059] In the given display, the intervals between the small images 2 are preferably constant (equidistant arrangement). In addition, in the given display, the positions of the small images 2 (the centers or the centers of gravity of the small images 2) are preferably constant (do not change depending on the values of the underlying data). By thus making the positions and the intervals of the small images 2 constant, in the case where the sizes or the shapes of the small images 2 change depending on the magnitude of the values of the underlying data, it is visually easy to grasp the small images 2 after the change by comparing the odor image 1 before and after the change. Furthermore, the intervals between the small images 2 are not limited to be constant (equidistant), and the odor image 1 can also be an image obtained by combining a plurality of small images 2 of different shapes.

[0060] <Odor sensor 10>

[0061] Figure 5 is a schematic plan view of the odor sensor 10 of Embodiment 1. Figure 6 is a schematic cross-sectional view of the A-A' section in Figure 5 . The odor sensor 10 is provided with a plurality of sensor elements 11. The sensor elements 11 each have a substance adsorption film 13 that adsorbs odor substances, and a detector 15 that detects the adsorption state of the odor substances to the substance adsorption film 13.

[0062] As shown in Figure 6 , the sensor element 11 is configured to include the detector 15 and the substance adsorption film 13 provided on the surface of the detector 15. The substance adsorption film 13 preferably covers the entire surface of the detector 15. That is, the size of the detector 15 is preferably the same as or smaller than the formation range of the substance adsorption film 13. Furthermore, a plurality of detectors 15 can also be provided within the formation range of one substance adsorption film 13.

[0063] On the sensor substrate 17, a plurality of sensor elements 11 are provided, and are arranged in a lattice shape of 3 rows by 3 columns as shown in Figure 5 . At this time, the substance adsorption films 13 of the adjacent sensor elements 11 do not contact or are insulated from each other. Furthermore, the sensor elements 11 do not necessarily need to be arranged on the sensor substrate 17, and can be provided randomly, or arranged in a form other than 3 rows by 3 columns. Furthermore, in order to generate the odor image 1 having 35 small images 2 as shown in Figure 4 , 35 sensor elements 11 corresponding to the respective small images 2 are preferably used. In this case, all of the sensor elements 11 do not need to be provided on one sensor substrate 17, and different sensor elements 11 can be provided on a plurality of sensor substrates 17.

[0064] The properties of the respective substance adsorbing films 13 of the plurality of sensor elements 11 provided on the sensor substrate 17 are different from each other. Specifically, it is preferable that all of the plurality of sensor elements 11 are composed of substance adsorbing films 13 having different compositions, and there are no substance adsorbing films 13 having the same property. Here, the property of the substance adsorbing film 13 can also be referred to as the adsorption characteristic of the odor substance to the substance adsorbing film 13. That is, even if the same odor substance (or aggregate thereof) is present, different adsorption characteristics are exhibited in substance adsorbing films 13 having different properties. In Figure 5 and Figure 6 In the above, all of the substance adsorbing films 13 are expressed identically for convenience, but in fact, the properties thereof are different from each other. Furthermore, the adsorption characteristics of the substance adsorbing film 13 of each sensor element 11 do not necessarily need to be all different, and a sensor element 11 provided with a substance adsorbing film 13 having the same adsorption characteristic can also be provided.

[0065] As the material of the substance adsorbing film 13, a thin film formed of a π electron conjugated polymer can be used. In the thin film, at least one of an inorganic acid, an organic acid, or an ionic liquid can be contained as a dopant. By changing the kind or content of the dopant, the property of the substance adsorbing film 13 can be changed.

[0066] As the π electron conjugated polymer, there is no particular limitation, but a polymer having a π electron conjugated polymer such as polypyrryl and derivatives thereof, polyaniline and derivatives thereof, polythiophene and derivatives thereof, polyacetylene and derivatives thereof, polyazulene and derivatives thereof, or the like as a backbone is preferable.

[0067] In the case where the π electron conjugated polymer is in an oxidized state and the backbone polymer itself is a cation, by containing an anion as a dopant, conductivity can be found. Furthermore, in the present application, a neutral π electron conjugated polymer not containing a dopant can also be used as the substance adsorbing film 13.

[0068] As specific examples of the dopant, inorganic ions such as chloride ions, oxychloride ions, bromide ions, sulfate ions, nitrate ions, borate ions, organic acid anions such as alkyl sulfonic acids, benzene sulfonic acids, carboxylic acids, and high molecular acid anions such as polyacrylic acids and polystyrene sulfonic acids can be listed.

[0069] In addition, by coexisting a salt such as common salt or an ionic compound including both a cation and an anion such as an ionic liquid in a neutral π electron conjugated polymer, a method of doping by chemical equilibrium can also be used.

[0070] In a case where the state of each two repeating units constituting the π-electron conjugated polymer into one dopant unit (ion) is set to 1, the content of the dopant in the π-electron conjugated polymer is in a range of 0.01 to 5, and preferably is adjusted to a range of 0.1 to 2. By setting the content of the dopant to be higher than the lower limit of the range, disappearance of the characteristics of the substance adsorption film 13 as a substance can be suppressed. In addition, by setting the content of the dopant to be lower than the upper limit of the range, the effect of the adsorption characteristics of the π-electron conjugated polymer itself can be suppressed from being reduced and the substance adsorption film 13 having desired adsorption characteristics can be difficult to produce. In addition, since the film in which the dopant as a low molecular weight substance is dominant is generally obtained, a significant decrease in the durability of the substance adsorption film 13 can be suppressed. Therefore, by setting the content of the dopant to be in the above range, the detection sensitivity of the odor substance can be appropriately maintained.

[0071] In the plurality of sensor elements 11, different kinds of π-electron conjugated polymers can be used in order to change the adsorption characteristics of the substance adsorption film 13, respectively. In addition, different adsorption characteristics can be found by changing the kind or content of the dopant by using the same kind of π-electron conjugated polymer. For example, by changing the kind of the π-electron conjugated polymer or the kind, content, or the like of the dopant, the hydrophobic or hydrophilic properties of the substance adsorption film 13 can be changed.

[0072] The thickness of the substance adsorption film 13 can be appropriately selected in accordance with the characteristics of the odor substance as an adsorption object. For example, the thickness of the substance adsorption film 13 can be set to a range of 10 nm to 10 μm, and preferably is set to 50 nm to 800 nm. When the thickness of the substance adsorption film 13 is less than 10 nm, sufficient sensitivity can not be obtained in some cases. In addition, when the thickness of the substance adsorption film 13 exceeds 10 μm, the upper limit of the weight that can be detected by the detector 15 can be exceeded in some cases.

[0073] The detector 15 measures a change in the physical, chemical, or electrical characteristics of the substance adsorption film 13 caused by the odor substance adsorbed to the surface of the substance adsorption film 13, and has a function as a signal conversion section (converter) that outputs the measured data as, for example, an electrical signal. That is, the detector 15 detects the adsorption state of the odor substance to the surface of the substance adsorption film 13. As the signal output as the measured data by the detector 15, physical information such as an electrical signal, luminescence, a change in resistance, a change in vibration frequency, or the like can be cited.

[0074] As for the detector 15, there are no particular limitations as long as it is a sensor that measures changes in the physical, chemical, or electrical properties of the adsorption membrane 13. Various sensors can be appropriately used. Specifically, examples of detectors 15 include crystal oscillator sensors (QCM), surface acoustic wave sensors, field-effect transistor (FET) sensors, charge-coupled device (CCD) sensors, MOS field-effect transistor sensors, metal oxide semiconductor sensors, organic conductive polymer sensors, and electrochemical sensors.

[0075] Furthermore, when a crystal oscillator sensor is used as detector 15, although not shown, electrodes can be provided on both sides of the crystal oscillator as excitation electrodes. Alternatively, a single-sided electrode can be provided to detect high Q values. The excitation electrode can also be provided on the sensor substrate 17 side of the crystal oscillator, separated from the sensor substrate 17. The excitation electrode can also be formed of any conductive material. Specifically, materials for the excitation electrode include inorganic materials such as gold, silver, platinum, chromium, titanium, aluminum, nickel, nickel-based alloys, silicon, carbon, and carbon nanotubes, as well as organic materials such as conductive polymers like polypyrrole and polyaniline.

[0076] like Figure 6 As shown, the detector 15 can be shaped into a flat plate. (As indicated...) Figure 5 As shown, the shape of the flat plate can be a quadrilateral or a square, but it can also be a circle or an ellipse, or various other shapes. In addition, the shape of the detector 15 is not limited to a flat plate shape, and its thickness can also be varied, and it can also be formed with concave or convex parts.

[0077] When the detector 15 is a detector using an oscillator, as described above, the influence (crosstalk) from other oscillators coexisting on the same sensor substrate 17 can be reduced by changing the resonant frequency of each oscillator of the multiple sensor elements 11. For each oscillator on the same sensor substrate 17, the resonant frequency can be arbitrarily designed to exhibit different sensitivities for a given vibration frequency. For example, the resonant frequency can be changed by adjusting the thickness of the oscillator or the material adsorption film 13.

[0078] As the sensor substrate 17, silicon substrates, substrates made of crystal, printed wiring substrates, ceramic substrates, resin substrates, etc., can be used. In addition, the substrate is a multilayer wiring substrate such as an interlayer substrate, and the excitation electrodes for vibrating the crystal substrate and the mounting wires and electrodes for energizing are arranged at arbitrary positions.

[0079] By forming such a structure as described above, the odor sensor 10 having a plurality of sensor elements 11 each having a material adsorption film 13 having different adsorption characteristics for odor materials can be obtained. Thus, in the case where the odor of air containing a certain odor material or a composition thereof is measured using the odor sensor 10, similarly, the odor material or the composition thereof comes into contact with the material adsorption film 13 of each sensor element 11, but the odor material is adsorbed to each material adsorption film 13 in a different form. That is, the amount of adsorption of the odor material differs at each material adsorption film 13. Therefore, the detection result of the detector 15 differs at each sensor element 11. Thus, for a certain odor material or a composition thereof, the number of sensor elements 11 (material adsorption films 13) provided in the odor sensor 10 corresponds to the generation of the measurement data generated by the detector 15.

[0080] By measuring for a certain odor material or a composition thereof, the group of measurement data (hereinafter referred to as odor data) generated by the odor sensor 10 is generally specific (unique) to a specific odor material or a composition of odor materials. Therefore, by measuring the odor data by the odor sensor 10, the odor can be identified as an odor material or a composition (mixture) of odor materials alone.

[0081] Next, the structure of an odor data acquisition unit that acquires odor data using the odor sensor 10 will be described. Figure 7 is a diagram schematically showing the mechanism of odor measurement according to Embodiment 1. Odor measurement can be performed, for example, using an odor measurement device. The odor measurement device has the odor sensor 10, an arithmetic processing device 51 connected to the odor sensor 10, and a storage device 52 connected to the arithmetic processing device 51. The measurement result measured by the odor sensor 10 can be processed in the arithmetic processing device 51 and stored in the storage device 52. The odor measurement unit M1 that realizes the odor measurement result acquisition step S1 can cause the odor sensor 10 to function as an odor measurement unit by storing a program P1 in the storage device 52 and causing the arithmetic processing device 51 to execute the program. In addition, the acquisition of odor data can be performed by other structures independently of the execution based on the arithmetic processing device 51.

[0082] The odor measurement unit (program) one-to-one corresponds the measurement result obtained from each of the sensor elements 11 of the odor sensor 10 to each of the small images 2 of the odor image 1. At this time, the arrangement of each of the sensor elements 11 on the odor sensor 10 can or can not have a correlation with the arrangement of each of the small images 2 on the odor image 1 (i.e., can be random). For example, the small image 2 associated with the sensor element 11 having a similar substance adsorption characteristic to the substance adsorption film 13 provided to the sensor element 11 can be arranged at a similar position on the odor image 1. In this way, by having a given correlation between the kind of the substance adsorption film 13 of each of the sensor elements 11 and the arrangement of each of the small images 2, even if a third party who does not know the information of the correlation acquires the odor sensor 10, the third party cannot use the information of the correlation. Therefore, such a third party cannot generate the basis data based on the correlation and the odor image 1.

[0083] <Usage example of odor image 1>

[0084] The odor image obtained based on the basis data generated by the basis data generation method of the odor image according to Embodiment 1 is an odor image capable of expressing the odor of a sample with the odor image 1. That is, the odor image is an odor image capable of visually expressing the odor of a sample. For example, it is an odor image capable of visually expressing the odor of a food or air (atmosphere) or the like.

[0085] For example, the odor image obtained based on the basis data generated by taking a beverage such as wine or coffee, black tea, or the like as a sample is expressed on the package or label of these beverages, and even if the container of the beverage is not opened, the information of the odor can be visually obtained by referring to the odor image. Of course, as the sample, it is not limited to a beverage, and as long as a gas (atmosphere) containing an odor substance is used, it is not particularly limited and can be used as a sample.

[0086] The basis data generation method of the odor image according to Embodiment 1 has been described above, but the present application is not limited to Embodiment 1. For example, in the data processing step S2, the value classification sub-step S2-3 is not necessarily a process and can be omitted. In this case, the odor image 1 can not change the size, color, shape of the small image 2 into a plurality of stages according to the size of the value of the basis data.

[0087] The preferred embodiments of the present application have been described above, but the present application is not limited to these embodiments and various modifications or changes can be made within the scope of the gist thereof. For example, the present application is a technical solution including the following concept.

[0088] (Concept 1) As a concept of a method of generating basis data of an odor image, a method of generating basis data of an odor image for expressing an odor of a sample containing an odor substance with an image, includes: a measurement result acquisition step of acquiring, using an odor sensor provided with a plurality of sensor elements, each measurement result determined in each of the plurality of sensor elements with respect to the odor substance contained in the sample, in a state associated with each of the plurality of sensor elements; and a data processing step of processing the acquired measurement results respectively to generate basis data for expressing the odor of the sample with an image, the basis data being associated with each of the plurality of sensor elements, the plurality of sensor elements each having a different detection characteristic for the odor substance, in which, in the data processing step, in a case where each of the basis data is expressed with a small image corresponding to each of the sensor elements, the basis data is generated in such a manner that the odor of the sample is expressed as an image in a given display manner of a set of a plurality of small images, and each of the small images is changed according to a magnitude of a value of the basis data.

[0089] Thus, in order to make it easy to visually grasp the measurement result of the odor determined with the odor sensor, the odor that can be expressed with an image.

[0090] (Concept 2) The given display manner can be a display manner in which a plurality of small images corresponding to each of the basis data have a given interval from each other and are represented by a given size, color, and shape.

[0091] (Concept 3) In the data processing step, the basis data can be classified into a plurality of stages according to a value of the basis data, and at least one of a size, a color, and a shape of the small image can be changed according to each of the plurality of stages after the classification.

[0092] (Concept 4) The plurality of sensor elements can each have a substance adsorption film that adsorbs the odor substance, and a detector that detects an adsorption state of the odor substance to the substance adsorption film, and an adsorption characteristic of the odor substance to the substance adsorption film can be different for each of the plurality of sensor elements.

[0093] (Concept 5) A method of generating an odor image expressed by the basis data generated by the method of generating basis data of an odor image according to any one of concepts 1 to 4 is a concept.

[0094] Symbol explanation

[0095] 1: odor image 2: small image

[0096] 10: odor sensor 11: sensor element

[0097] 13: substance adsorption film 15: detector

[0098] 17: sensor substrate 19: sensor surface

[0099] 20: imaging device 21: lens portion

[0100] 51: arithmetic processing device 52: storage device

[0101] D1: measurement result database

Claims

1. A method for generating basic data for an odor image, comprising generating basic data used to represent the odor of a sample containing odor substances using an image, the method comprising: The measurement result acquisition step involves using an odor sensor equipped with multiple sensor elements to acquire the measurement results obtained by each of the multiple sensor elements for the odor substance contained in the sample in a state associated with each of the multiple sensor elements. as well as The data processing step involves processing the acquired measurement results to generate basic data for representing the odor of the sample using images. This basic data is associated with each of the multiple sensor elements. The odor sensor outputs the measurement result by taking the change in the physical or chemical properties of each of the plurality of sensor elements caused by the adsorption of the odor substance onto each of the sensor elements. The plurality of sensor elements have different detection characteristics for the odor substance, and each sensor element comprises a detector and a substance adsorption film disposed on the surface of the detector. The detector measures the changes in the physical or chemical properties of the adsorption membrane caused by odor substances adsorbed on its surface, and outputs the detector data as an electrical signal. The data processing step includes a difference calculation sub-step that calculates the difference between a given extreme value (i.e., the first extreme value) in the measurement result and the initial extreme value (i.e., the second extreme value) of the measurement result after passing the first extreme value. In the data processing step, when the basic data is represented by small images corresponding to each of the sensor elements, the basic data is generated in the following manner: the odor of the sample is represented as an image in a set of multiple small images, i.e., a given display method, and each of the small images varies according to the magnitude of the value of the basic data.

2. The method for generating basic data for odor images according to claim 1, wherein, The given display method is as follows: multiple small images corresponding to each of the basic data are represented with a given interval between them and with a given size, color and shape.

3. The method for generating basic data for odor images according to claim 2, wherein, In the data processing step, Based on the values ​​of the basic data, the basic data is categorized into multiple stages. Based on each of the multiple stages after classification, at least one change is made to the size, color, and shape of the small image.

4. The method for generating basic data for an odor image according to any one of claims 1 to 3, wherein, The plurality of sensor elements each have: A substance adsorption membrane that adsorbs the odor substance; as well as A detector that detects the adsorption state of the odorant onto the adsorption membrane. The adsorption characteristics of the substance adsorption membrane for the odor substance are different in the various sensor elements.

5. A method for generating an odor image, representing the odor image based on the basic data generated by the odor image basic data generation method according to any one of claims 1 to 4.

Images