Patent Description:
In order to measure an odor of an atmosphere, a sensor including a quartz oscillator that specifically adsorbs an odor substance in the atmosphere is known from (for example) <CIT>.

<CIT> discloses a data structure includes a main data storage area, and an odor data storage area. The odor data is based on a measurement result of an odor in air measured by an odor sensor.

<CIT> discloses an odor sensing system comprising a sensor cell including a plurality of quartz resonator sensors aligned therein to detect odor by variation of resonant frequencies resulting from weight loading on the surfaces thereof.

<CIT> discloses an artificial olfactory system and associated method for rapidly identifying an object by its aroma. The artificial olfactory system includes a testing chamber in which is disposed an array of gas sensors. The object to be identified is placed in close proximity to the testing chamber. The air pressure within the testing chamber is then lowered below ambient, thereby causing ambient air to flow past the object being identified and into the testing chamber. As air flows past the object being identified, the aroma of the object becomes mixed with the air and is carried into the testing chamber. Once within the testing chamber, the aroma/air mixture is exposed to the array of gas sensors. The gas sensors detect the levels of various gases comprising the aroma/air mixture and produce a sensor pattern capable of being identified using pattern recognition techniques.

<NPL>) presents applications of differential electronic noses in dynamic (on-line) volatile measurement. They compare the classical nose employing only one sensor array and its extension to a differential form containing two sensor arrays working in differential mode, and show that differential nose performs better at changing environmental conditions, especially temperature, and performs well in the dynamic mode of operation.

<CIT> discloses a method for detecting the composition of an unknown gas mixture through an electronic nose equipped with a measuring chamber that houses at least one sensor. The sensor is calibrated by feeding a known gas mixture into the measuring chamber, feeding the unknown gas mixture into the chamber while keeping the unknown gas mixture in a predetermined desired state, and detecting the composition of the unknown gas mixture by means of the sensor.

<CIT> discloses a method for preparing an odor image. Each of a plurality of sensor elements in an odor sensor has different detection properties with respect to the odor substance. In a case where each of the original data items is represented in a small image, the odor of the sample is represented in an odor image in a predetermined display mode in which a plurality of small images are assembled, and each of the small images is varied in accordance with the magnitude of the value of each original data item.

<CIT> provides a fragrance presentation system. After the generation of a first fragrance, a second fragrance containing components of a desired fragrance is generated to present the desired fragrance similarly. In this process, the components of the first fragrance are obtained by eliminating the components of the desired fragrance from the components of the second fragrance.

<CIT> provides a method for coding, processing and decoding odor.

<CIT> proposes an odor measuring apparatus. An m-dimensional space is created from detection signals of m pieces of odor sensors, and a standard odor vector representing the result of measurement of a standard odor and another vector representing the result of measurement of an unknown sample are drawn in the space. Then, from the angle between the two vectors, a degree of similarity is determined within the range from <NUM> to <NUM>%, where the degree of similarity is corrected taking account of the difference in sensitivity between the odor sensor and the human nose.

By using such a sensor, the odor of an atmosphere can be detected, and the detected signals are converted into numerical values to obtain an odor information of the respective atmospheres. However, it is difficult to determine what kind of odor is obtained when a specific odor and other odor are mixed. In addition, it is difficult to determine what kind of odor should be mixed with a specific odor in order to obtain a target odor.

The present invention has been made in view of the above-mentioned circumstances, and it is an exemplary problem of the present invention to provide an odor exploration method and an odor exploration system which are able to explore an odor obtained when different odors are mixed with each other or an odor mixed with a particular odor in order to obtain a desired odor is mixed with each other.

In order to attain the object described above, the present invention provides an odor exploration method as set out in claim <NUM>, and an odor exploration system as set out in claim <NUM>.

Further objects or other characteristics of the present invention will be apparent by preferred embodiments described below with reference to the attached drawings.

According to the present invention, it is possible to provide an odor exploration method and an odor exploration system which are able to explore an odor obtained when different odors are mixed with each other or an odor mixed with a particular odor in order to obtain a desired odor is mixed with each other.

Hereinafter, respective steps of an odor exploration method according to Embodiment <NUM> will be described in order by referring to <FIG> is a flow chart showing the steps of odor exploration method according to Embodiment <NUM>.

In Embodiment <NUM>, the "odor" can be acquired by a human or living things including the human as olfactory information and corresponds to a concept including a molecular simple substance or a group of molecules made of different molecules gathered with respective concentrations.

In Embodiment <NUM>, the molecular simple substance or the group of molecules made of different molecules gathered with respective concentrations included in the odor is referred to as an "odor substance". However, in a broad sense, the odor substance may broadly mean a substance which can be adsorbed on a substance adsorbing membrane of an odor sensor, which will be described below. That is, since the "odor" contains a plurality of odor substances responsible for the odor in many cases, and a substance not recognized as the odor substance or an unknown odor substance may be present, a substance generally not regarded as an odor causing substance may be contained.

In the odor exploration method according to Embodiment <NUM>, the respective steps of a detection step S1, a generation step S2, a preparation step S3, and a calculation step S4 are performed based on a plurality of odor informations generated using a plurality of sensor elements, and an arithmetic unit. The odor exploration method according to Embodiment <NUM> may further include adjustment step S5, extraction step S6, similarity calculation step S7, and selection step. Each step will be described below.

In the detection step S1, a first gas is detected by a plurality of sensor elements <NUM> (see <FIG>) (S101 of <FIG>). The first gas, like any other gas, is a gas containing a plurality of odor substances. In the detection step S1, in addition to the first gas, other gases may be detected, such as, for example, a second gas (S103 of <FIG>). The first gas and the other gases are not detected simultaneously, but are detected individually.

The sensor element <NUM> is an element constituting an odor sensor <NUM> (refer to <FIG>), and outputs a detection signal according to its state of adsorption by indicating an adsorption reaction unique to each odor substance. That is, the sensor element <NUM> shows an unique adsorption reaction for each of the many kinds of odor substances contained in the first gas. By detecting the first gas using a plurality of sensor elements indicating unique adsorption reactions to the first gas, unique detection signals can be outputted from each of the plurality of sensor elements <NUM>. Of course, when different odor substance gases are detected, different detection signals are outputted from each of the plurality of sensor elements <NUM> than when the first gas was detected. The specific configuration of the sensor element <NUM> will be described later.

<FIG> is a detection signal database D1 detected in the detection step S1. In the detection signal database D1, the sensor element <NUM> and the detection signal detected in each of sensor elements <NUM> are shown in association with the respective other. In the detection signal database D1 shown in <FIG>, the respective detection signals are stored in a manner associated with a total of <NUM> sensor elements <NUM> from sensor elements <NUM>-<NUM> to <NUM>-<NUM>. Incidentally, in <FIG>, for the convenience of the description, the description of the sensor elements <NUM>-<NUM> to <NUM>-<NUM> is omitted.

Detection signal is specifically raw data detected by the respective sensor elements <NUM>. In a case where the odor sensor <NUM>, for example, is a quartz oscillator sensor (QCM), a temporal change in a resonance frequency of a quartz oscillator can be the raw data that is generated by the sensor element <NUM>. That is, a detection signal from the sensor element <NUM> may be a resonance frequency at a plurality of time points having different elapse times from an operation start of the odor sensor <NUM>. For example, as shown in <FIG>, the detection signal detected in the sensor element <NUM>-<NUM> has a resonant frequency of "<NUM>" detected at <NUM> seconds and a resonant frequency of "-<NUM>" detected at <NUM> seconds, when the resonant frequency at <NUM> seconds after operation start of the odor sensor <NUM>. Further, the detection signal detected in the sensor element <NUM>-<NUM> has a resonance frequency of "<NUM>" detected at <NUM> seconds after operation start of the odor sensor <NUM> and a resonance frequency of "-<NUM>" detected at <NUM> seconds, when the resonance frequency at <NUM> seconds after operatoin start of odor sensor <NUM>. The time interval for recording detection signal is not particularly limited, but may be, for example, a time interval of <NUM> second.

The detection by the sensor element <NUM> is preferably performed a plurality of times, and an average value of the raw data of the detection performed a plurality of times is preferably used as detection signal. The number of times of detection is not particularly limited, and for example, can be three times. An average value according to an arithmetic average (an arithmetic means) can be adopted as the average value.

In the generation step S2, a first odor information group is generated based on the detection signal of the first gas detected in detection step S1 (S102 to <FIG>). From the detection signal of the first gas, an odor information is generated by an arithmetic unit. That is, the detection signal outputted from each sensor elements by detecting the first gas is quantified to numerical values by the arithmetic unit, and a plurality of odor informations corresponding to the respective sensor element is generated. From a detection signal outputted by a predetermined sensor element, an odor information corresponding one-to-one to a predetermined sensor element is generated by an arithmetic unit. A specific configuration of the arithmetic unit will be described later.

An odor information is a numerical value obtained by quantifying a detection signal outputted by a sensor element by an arithmetic unit. In other words, an odor information is a numerical value obtained by quantifying the odor detected by the respective sensor elements. When the first gas is detected using n sensor element, n odor informations are obtained. The n odor informations obtained by detecting the first gas are collectively referred to as a first odor information group. The quantification by the arithmetic unit is preferably a quantification such that the numerical value is <NUM> (zero) or a positive value, and the odor information is preferably <NUM> (zero) or a positive value. An odor can be evaluated more accurately by an odor information of <NUM> (zero) or positive values instead of negative value. It is also advantageous to determine or compare a sum of or a difference of the odor informations by an odor information of <NUM> (zero) or positive value.

The generation step S2 can be executed by, for example, sub-steps of a difference value calculation sub-step S2-<NUM>, a logarithmic arithmetic sub-step S2-<NUM>, a value classifying sub-step S2-<NUM>, and an index generating sub-step S2-<NUM>.

In the difference value calculation sub-step S2-<NUM>, for each of the detection signals detected in the detection step S1, the difference (difference value) between the maximal value and the first minimal value after passing through the maximal value (hereinafter also referred to as "minimal value immediately after the maximal value") is calculated. Then, in a case where there are a plurality of difference values (between the maximal value and the minimal value immediately after the maximal value), the difference value having the largest value is adopted as the difference value of the measurement result. In this manner, for each measurement result, a difference value associated with each of the plurality of sensor element <NUM> is obtained. Further, by dividing the difference value by the time until it changes from the maximal value to the minimal value immediately after the maximal value, it is possible to obtain the slope for the respective detection signal.

<FIG> is a graph showing a detection signal detected in detection step S1. In <FIG>, the vertical axis denotes a displacement amount [Hz] of a resonance frequency which is detected after a predetermined time, on the basis of the resonance frequency after <NUM> seconds from the operation start of the odor sensor <NUM>, and the horizontal axis denotes an elapse time [second] from the operation start of the odor sensor <NUM>. In <FIG>, among detection signal shown in detection signal database D1, detection signal for sensor element <NUM>-<NUM>, <NUM>-<NUM>, <NUM>-<NUM> is shown. In <FIG>, sensor element <NUM>-<NUM> shows detection signal as a solid line, sensor element <NUM>-<NUM> shows detection signal as a broken line, and sensor element <NUM>-<NUM> shows the as a dashed-dotted line. It is obvious that graphs can also be prepared with respect to other sensor elements <NUM>-<NUM> to <NUM>-<NUM>, similarly. In <FIG>, for sensor element <NUM>-<NUM>, the difference value of the detection signals is "<NUM>". That is, in the detection signals for the sensor element <NUM>-<NUM>, the difference value between the maximal value "<NUM>" at elapse time <NUM> seconds after the start of operation of the odor sensor <NUM> and the minimal value "-<NUM>" at elapse time <NUM> seconds after the start of operation of the odor sensor <NUM>.

In the calculation of the difference values, the scope of elapse time after operation of the odor sensor <NUM> is started may be limited. For example, in a case where the measurement of the odor of the sample is started after <NUM> seconds from the operation start of the odor sensor <NUM>, and the measurement of the odor of the sample is ended after <NUM> seconds from the operation start of the odor sensor <NUM>, the range of the elapse time for calculating the difference value can be set to an elapse time of <NUM> seconds to <NUM> seconds from the operation start of the odor sensor <NUM>. Incidentally, the range of the elapse time can be arbitrarily set.

In the logarithmic arithmetic sub-step S2-<NUM>, a logarithmic arithmetic operation is performed with respect to each of the difference values calculated in the difference value calculation sub-step S2-<NUM>, and thus, a logarithmic value associated with each of the plurality of sensor elements <NUM> is obtained. In the logarithmic arithmetic operation, the base is not particularly limited, and for example, can be <NUM>. Incidentally, the difference value is a difference between the maximal value and the minimal value and is a positive value (real number). In the same manner as the difference values, the logarithmic value can be obtained for the slope.

In the value classifying sub-step S2-<NUM>, each of the logarithmic values obtained in the logarithmic arithmetic sub-step S2-<NUM> is classified into ranges in accordance with the magnitude of the value. The number of classified ranges is not particularly limited, and can be three ranges to five ranges, and the like, for example. Hereinafter, a case where the value is classified into three ranges will be described.

In the value classifying sub-step S2-<NUM>, first, among the plurality of logarithmic values of the respective samples which are obtained in the logarithmic arithmetic sub-step S2-<NUM>, a maximum logarithmic value and a minimum logarithmic value are identified. Next, a quotient in a case where a difference between the maximum logarithmic value and the minimum logarithmic value is divided by <NUM> is calculated. A numerical range between the maximum logarithmic value and the minimum logarithmic value can be partitioned into trisected ranges by using the quotient obtained as described above. That is, the numerical range can be trisected into a range from the minimum logarithmic value to a value in which the quotient is added to the minimum logarithmic value, a range from the value in which the quotient is added to the minimum logarithmic value to a value in which twice the quotient is added to the minimum logarithmic value, and a range from the value in which twice the quotient is added to the minimum logarithmic value to the maximum logarithmic value.

Next, each of the logarithmic values associated with each of the sensor elements <NUM> is classified into any range of three ranges. To each of the logarithmic values, a flag for identifying the classified range may be provided. For example, for the three trisected ranges, flags such as (<NUM>), (<NUM>), and (<NUM>) in an increasing order can be provided. Accordingly, the detection signal associated with each of the sensor elements <NUM> can be classified into three stages in accordance with the magnitude of the value.

The generation step S2 described above will be described in more detail by using <FIG> is a diagram illustrating an outline of the generation step S2 of Embodiment <NUM>. In <FIG>, Table (A) is a table showing the difference values calculated in difference value calculation sub-step S2-<NUM> for certain samples. The value of each of the difference values of each of the sensor elements <NUM>-<NUM> to <NUM>-<NUM> is shown. For example, in Table (A), the difference value obtained in sensor element <NUM>-<NUM> is "<NUM>", and the difference value obtained in sensor element <NUM>-<NUM> is "<NUM>". Incidentally, for the convenience of the description, indication of the values of the sensor elements <NUM>-<NUM> to <NUM>-<NUM> will be omitted (the same applies to Table (B) and Table (E) described below).

Next, according to the logarithmic arithmetic sub-step S2-<NUM>, the difference values of each of the sensor elements <NUM> are subjected to logarithmic arithmetic processing. Here, the logarithmic arithmetic operation is represented by Formula (<NUM>) described below. That is, an absolute value of the value of the difference value is subjected to logarithmic arithmetic operation by setting the base to <NUM>, and thus, the logarithmic value is obtained.

Table (B) is a table showing the logarithmic values of each of the sensor elements <NUM> which are obtained in the logarithmic arithmetic sub-step S2-<NUM>. For example, in Table (B), the logarithmic value calculated based on the difference value obtained in sensor element <NUM>-<NUM> is "<NUM>", the logarithmic value calculated based on the difference value obtained in sensor element <NUM>-<NUM> is "<NUM>".

Next, according to the value classifying sub-step S2-<NUM>, the logarithmic values of each of the sensor elements <NUM> are classified into three ranges on the basis of the obtained logarithmic value. Specifically, first, in the sample being measured, in the logarithmic values of the respective sensor elements <NUM>, the maximum logarithmic value (maximum value) and the minimum logarithmic value (minimum value) are identified. Then, a quotient in a case where the difference between the maximum value and the minimum value is divided by <NUM> is calculated. The identified maximum value and minimum value, and the calculated quotient are shown in Table (C). In Table (C), the identified maximum value is "<NUM>", the identified minimum value is "<NUM>", and the calculated quotient is "<NUM>".

The logarithmic values of each of the sensor elements <NUM> are classified into three levels on the basis of the identified maximum value and minimum value, and the calculated quotient. The classification is performed on the basis of a classification rule as shown in Table (D). Specifically, the classification is performed on the basis of a classification rule in which a range of the smallest logarithmic values (range <NUM>) is a range of <NUM> ≦ [Logarithmic Value] ≦ <NUM>, a range of the second smallest logarithmic values (range <NUM>) is a range of <NUM> < [Logarithmic Value] ≦ <NUM>, and a range of the largest logarithmic values (range <NUM>) is a range of <NUM> < [Logarithmic Value] ≦ <NUM>.

Next, flags are applied to each of the sensor elements <NUM> on the basis of the classification result. The result of applying the flag to each of the sensor elements <NUM> is shown in Table (E). Flags (<NUM>) are applied to the sensor elements <NUM> in which the logarithmic value corresponding to range <NUM> is obtained, flags (<NUM>) are applied to the sensor elements <NUM> in which the logarithmic value corresponding to range <NUM> is obtained, and flags (<NUM>) are applied to the sensor elements <NUM> in which the logarithmic value corresponding to range <NUM> is obtained. For example, in Table (E), a flag (<NUM>) is applied to the sensor element <NUM>-<NUM>, a flag (<NUM>) is applied to the sensor element <NUM>-<NUM>, and a flag (<NUM>) is applied to the sensor element <NUM>-<NUM>.

In the index generating sub-step S2-<NUM>, a logarithmic value (measured result) classified in the value classifying sub-step S2-<NUM>, based on the logarithmic value (detected result) corresponding to each sensor element <NUM>, an index is generated. The index has a value corresponding to the the respective sensor element <NUM>.

The index is a data which provide the basis of image data for representing the odor of the samples in an odor image <NUM> (refer to <FIG>). The index is a data indicating the position or the size, the color, the shape, and the like of small images <NUM>, but not a data (pixel data) indicating information of the color, the position, and the like of each pixel forming the odor image <NUM>. The odor image <NUM> prepared on the basis of the indices includes a plurality of small images <NUM> represented by indices corresponding to each of the sensor elements <NUM>. The odor image <NUM> can be represented in the predetermined display mode, as an assembly of the plurality of small images <NUM>. Each of the small images <NUM> can be varied in accordance with the magnitude of the values of the corresponding indices. Specifically, the size, the color, and the shape of the small images <NUM> can be varied in accordance with the magnitude of the values of the corresponding indices. That is, in the index generating sub-step S2-<NUM>, the index is generated such that the odor of the sample is represented in the odor image <NUM> in the predetermined display mode. In the index generating sub-step S2-<NUM>, the indices are generated so that each small images <NUM> changes according to the magnitude of values of the indices.

<FIG> is an example of an image represented based on the indices generated in generation step S2 of Embodiment <NUM>. The odor image <NUM> illustrated in <FIG> includes <NUM> small images <NUM>, and each of the small images <NUM> is in the shape of a circle. Each of the small images <NUM> corresponds to each of the sensor elements <NUM>-<NUM> to <NUM>-<NUM>, and is aligned in order from the upper left side. Specifically, in <FIG>, two small images <NUM> in the first row from the upper side respectively correspond to the sensor elements <NUM>-<NUM> and <NUM>-<NUM> in order from the left side, and five small images <NUM> in the second row from the upper side respectively correspond to the sensor elements <NUM>-<NUM> to <NUM>-<NUM> in order from the left side. In addition, the small image <NUM> corresponding to the sensor element <NUM>-<NUM> (flag (<NUM>)) is represented by a small circle, the small image <NUM> corresponding to the sensor element <NUM>-<NUM> (flag (<NUM>)) is represented by a large circle, and the small image <NUM> corresponding to the sensor element <NUM>-<NUM> (flag (<NUM>)) is represented by a circle having a size between the small circle and the large circle.

In <FIG>, the shape of all of the small images <NUM> is represented by a circle, but the shape of each of the small images <NUM> is not limited to a circle, and may be a square, a rectangle, a rhomboid, other indefinite shapes, and the like. In addition, it is not necessary that the shapes of all of the small images <NUM> are coincident with each other, and the small images <NUM> may respectively have different shapes. In <FIG>, the color of each of the small images <NUM> is represented in black, but the color of each of the small images <NUM> is not limited to black, and may be represented by an arbitrary color. In addition, it is not necessary that the colors of all of the small images <NUM> are coincident with each other, and the small images <NUM> may be respectively represented by different colors.

In <FIG>, each of the small images <NUM> is represented such that the size is different in accordance with the magnitude of the value of the corresponding index. Specifically, the small image <NUM> is displayed large when the value of the index increases, and the small image <NUM> is displayed small when the value of the index decreases. Here, the size of each of the small images <NUM> to be displayed may be classified into a plurality of levels in accordance with the level classified in the value classifying sub-step S2-<NUM>. That is, in the value classifying sub-step S2-<NUM>, in a case where the classification is performed into three levels of flags (<NUM>), (<NUM>), and (<NUM>), the size of the small image <NUM> can also be classified and displayed in three levels.

In the predetermined display mode, it is preferable that an interval between the small images <NUM> is constant (an equal interval). In addition, in the predetermined display mode, it is preferable that the position of each of the small images <NUM> (the center or the gravity center of each of the small images <NUM>) is constant (not changed in accordance with the value of the index). As described above, the position or the interval of each of the small images <NUM> is constant, and thus, in a case where the size or the shape of each of the small images <NUM> is changed in accordance with the size of the value of the index, it is possible to easily visually grasp the changed small image <NUM> by comparing the odor images <NUM> before and after being changed with each other. Incidentally, the interval between the small images <NUM> is not limited to being constant (the equal interval), and the odor image <NUM> may be a combination of a plurality of small images <NUM> having different shapes.

In the preparation step S3, a second gas is detected by a sensor element and quantified into numerical value by an arithmetic unit to generate a second odor information group, similarly to the first gas (S104 of <FIG>).

In the calculation step S4, a sum of or a difference between the first odor information group and the second odor information group is calculated and a third odor information group as a result is generated (S105 in <FIG>). The sum of or the difference between the first odor information group and the second odor information group is calculated based on the sum of or the difference between the odor informations generated using the same sensor element.

When the sum between the odor informations is obtained, one odor information as it is may be added to the other odor information. Alternatively, one odor information may be multiplied by an arbitrary coefficient and then added to the other odor information, or both odor informations may be multiplied by an arbitrary coefficient and then added together. When the difference between the odor informations is obtained, one odor information as it is may be subtracted from the other odor information. Alternatively, one odor information may be multiplied by an arbitrary coefficient and then subtracted from the other odor information, or both odor informations may be multiplied by an arbitrary coefficient and then subtracted from the other. Although there is no particular limitation on the coefficient for multiplying odor information, an odor coefficient can be determined by considering the difference in the detection intensity between the odors due to the difference in the adsorption amount of odor substance to the substance adsorbing membrane or the like.

As used herein, "using the same sensor element" may refer to a single sensor element for detection of both the first gas and the second gas, or separate sensor elements with the same configuration for detection of the first gas and the second gas, respectively. It is assumed that the same detection signal can be obtained when a sensor element having the same configuration is used to detect gases under a fixed condition.

The plurality of sensor element for detecting the first gas and the plurality of sensor element for detecting the second gas may be all of the same sensor element, or may be partially different sensor elements. It is preferable that a plurality of sensor elements for detecting the first gas and a plurality of sensor elements for detecting the second gas are all of the same sensor element. The more identical sensor element used for detection, the more numerical values are obtained, and thus the resulting odor can be more accurately represented.

In calculation step S4, some of the odor informations included in the third odor information group as a result of difference between the second odor information group and the first odor information group may be a negative value. Since an odor information is preferably a positive value, if some odor information is a negative value, a fourth odor information group can be generated by adjusting some odor information (S106 in <FIG>).

For example, the following adjustment steps S5-A to S5-D can be used as adjustment step S5 for adjusting odor information that has a negative value in the third odor information group. The adjustment step S5 is not limited to these adjustment steps S5-A to S5-D, and odor information having a negative value can be adjusted by other methods.

The adjustment step S5-A is a step excluding a particular odor information from the third odor information group when the particular odor information contained in the third odor information group has a negative value. The third odor information group wherein the odor information which has a negative value is excluded, may be defined as a fourth odor information group (<NUM>). The odor information contained in the fourth odor information group (<NUM>) does not contain odor information which has a negative value, but has only odor information with <NUM> (zero) or positive values.

The adjustment step S5-B is a step replacing a numerical value of the particular odor information with <NUM> (zero) when the particular odor information contained in the third odor information group has a negative value. The third odor information group wherein the numerical value of odor information which has a negative value is replaced with <NUM> (zero) can be defined as a fourth odor information group (<NUM>). The odor information contained in the fourth odor information group (<NUM>) does not contain odor information which has a negative value, but has only odor information with <NUM> (zero) or positive values.

The adjustment step S5-C is a step adding an absolute value of a particular odor information to every odor information which the third odor information group has when the particular odor information contained in the third odor information group has a negative value. When there are a plurality of particular odor informations which have negative values, it is preferable to add an absolute value of the particular odor information having the largest absolute value of negative value. The third odor information group wherein the largest absolute value of a particular odor information is added to every odor information of the third odor information group having a particular odor information of negative value can be defined as a fourth odor information group (<NUM>). The odor information contained in the fourth odor information group (<NUM>) does not contain odor information which has a negative value, but has only odor information with <NUM> (zero) or positive values.

The adjustment step S5-D is a step repeatedly adding the first odor information group or the second odor information group to the third odor information group when a particular odor information contained in the third odor information group has a negative value until the particular odor information become <NUM> (zero) or a positive value. That is, for each odor information of the third odor information group, each odor information of the first odor information group or the second odor information group are repeatedly added so that the particular odor information which has a negative value become <NUM> (zero) or a positive value. At this time, when the third odor information group is a calculation result (difference) when the second odor information group is subtracted from the first odor information group, it is the first odor information group that is added to the third odor information group. When the third odor information group is a calculation result (difference) when the first odor information group is subtracted from the second odor information group, it is the second odor information group that is added to the third odor information group. The third odor information group wherein the first odor information group or the second odor information group is repeatedly added to the third odor information group until the particular odor information that had a negative value become <NUM> (zero) or a positive value can be defined as a fourth odor information group (<NUM>). The odor information contained in the fourth odor information group (<NUM>) does not contain odor information which has a negative value, but has only odor information with <NUM> (zero) or positive values.

In the extraction step S6, part or all of the plurality of the odor information groups <NUM> is extracted from the previously created odor information database D2 (S107). <FIG> shows an exemplary odor information database D2. As shown in <FIG>, the odor information database D2 contains a plurality of odor informations <NUM> associated with a particular odor and stored as odor information group <NUM>. The odor information database D2 contains a plurality of odor information groups <NUM>. In <FIG>, the difference values calculated for each sensor element <NUM> with respect to odor a and odor b are shown. For odor a, the difference value calculated using sensor element <NUM>-<NUM> is "<NUM>" and the difference value calculated using sensor element <NUM>-<NUM> is "<NUM>". The difference values (odor information) calculated by using these sensor element are combined to obtain odor information group <NUM> for the odor a. In <FIG>, for convenience, only the difference values (odor information) of sensor elements <NUM>-<NUM> to <NUM>-<NUM> are shown.

In the similarity calculation step S7, a degree of similarity between the odor information group <NUM> extracted from the database D1 in extraction step S6 and the third odor information group or the fourth odor information group is calculated (S108). That is, each odor information <NUM> included in the odor information group <NUM> extracted from the odor information database D2 in extraction step S6 is compared with each odor information <NUM> included in the third odor information group or the fourth odor information group by the corresponding odor information <NUM>, and a degree of similarity is calculated. The third odor information group is compared when no adjustments were made in adjustment step S5. It is to be noted that "odor informations <NUM> corresponding to each other" means odor informations <NUM> detected using the same sensor element.

The calculation of degree of similarity can be calculated as degree of similarity between vectors when odor information group <NUM> is regarded as a vector whose coordinate is odor information <NUM> included in odor information group <NUM>. When the odor information group <NUM> contains n odor informations <NUM>, the vector is a n-dimensional vector.

Specifically, the degree of similarity can be obtained by calculating various indices such as cosine similarity, cosine similarity (score value) for a value (θ/π) obtained by dividing an angle θ formed by both vectors in cosine similarity by π, inter-vector distances, Pearson correlation coefficients, and deviation pattern similarity. As degree of similarity, score value, cosine similarity, Pearson correlation coefficient, deviation pattern similarity is higher in degree of similarity when the score value is closer to <NUM>, and the inter-vector distance is higher in degree of similarity when the score value is smaller.

In the selection step, a specific odor information group <NUM> is selected from the plurality of odor information group <NUM> extracted from the database D1 in extraction step S6 based on degree of similarity calculated in similarity calculation step S7. For example, an odor information group <NUM> whose degree of similarity with the third odor information group or the fourth odor information group is higher than a predetermined threshold may be selected from a plurality of odor information groups <NUM> extracted from the database D1, or one or more odor information group <NUM> whose degree of similarity is the highest may be selected.

Next, an odor exploration system <NUM> according to Embodiment <NUM> will be described referring to Drawings. <FIG> is a schematic diagram showing an outline of the odor exploration system <NUM>. The odor exploration system <NUM> has an odor sensor <NUM>, a generation means, a preparation means, and a calculation means. The generation means generates a plurality of odor informations <NUM> which are numerical values by respectively quantifying each detection signal from a plurality of sensor elements, and generates a first odor information group consisting of a plurality of odor informations <NUM> generated based on detected result of a first gas. The preparation means prepares a second odor information group consisting of a plurality of odor informations <NUM> generated on the basis of a detected result in which a second gas differing from the first gas is detected by a plurality of sensor elements. The calculation means calculates a sum of or a difference between the second odor information group and the first odor information group based on the sum of or the difference between odor information <NUM> generated using the same sensor element.

The odor exploration system <NUM> may further include an extracting means of extracting part or all of odor information group <NUM> from an odor information database D2 in which odor information group <NUM> including a plurality of third odor informations is stored, a similarity calculation means calculating the degree of similarity between part or all of odor information group <NUM> from a plurality of odor information groups <NUM> extracted by the extracting means and third odor information group, and a selecting means selecting specific odor information group <NUM> from a plurality of odor information groups <NUM> based on a degree of similarity calculated by the similarity calculation means.

An arithmetic processing device <NUM> as an arithmetic unit can realize functions such as generation means, preparation means, calculation means, extracting means, similarity calculation means, and selection means. The odor exploration system <NUM> can implement the odor exploration method described above.

<FIG> is a plan view schematically illustrating the odor sensor <NUM>. <FIG> is a cross-sectional view schematically illustrating the A-A' cross section of <FIG>. The odor sensor <NUM> includes a plurality of sensor elements <NUM>. Each of the sensor elements <NUM> includes the substance adsorbing membrane <NUM> that adsorbs the odor substance and a detector <NUM> that detects an adsorption state of the odor substance with respect to the substance adsorbing membrane <NUM>.

As shown in <FIG> and <FIG>, the sensor element <NUM> includes the detector <NUM> and the substance adsorbing membrane <NUM> provided on the surface of the detector <NUM>. It is preferable that the substance adsorbing membrane <NUM> covers the entire surface of the detector <NUM>. That is, the size of the detector <NUM> is preferably the same as the formation range of the substance adsorbing membrane <NUM>, or smaller than the formation range of the substance adsorbing membrane <NUM>. Incidentally, a plurality of detectors <NUM> may be provided within the formation range of one substance adsorbing membrane <NUM>.

A plurality of sensor elements <NUM> is disposed on a sensor substrate <NUM>, and six sensor elements <NUM> as shown in <FIG> may be aligned so as to draw an equilateral triangle. In this instance, substance adsorbing membranes <NUM> of adjacent sensor elements <NUM> are not in contact with each other or are insulated. Incidentally, the sensor elements <NUM> may not be aligned on the sensor substrate <NUM> and may be randomly arranged or aligned in any form. Incidentally, it is not necessary that a sensor element <NUM> corresponding to all of odor information <NUM> for constituting the one odor information group <NUM> is provided on one sensor substrate <NUM>, and different sensor elements <NUM> may be provided on a plurality of sensor substrates <NUM>.

It is preferable that, in the plurality of sensor elements <NUM> arranged on the sensor substrate <NUM>, properties of the respective substance adsorbing membranes <NUM> are different from each other. Specifically, it is preferable that all the plurality of sensor elements <NUM> have the substance adsorbing membranes <NUM> of different compositions, and that substance adsorbing membranes <NUM> of the same property do not exist. Here, the property of the substance adsorbing membrane <NUM> can be referred to as the adsorption characteristic of the odor substance with respect to the substance adsorbing membrane <NUM>. That is, one same odor substance (or an aggregate thereof) can exhibit different adsorption characteristics if the substance adsorbing membrane <NUM> has different property. In <FIG> and <FIG>, for the sake of convenience, all the substance adsorbing membranes <NUM> are illustrated in the same manner. However, in practice, properties thereof are different from each other. Incidentally, it is not necessary that the adsorption characteristics of all of the substance adsorbing membranes <NUM> of each of the sensor elements <NUM> are different from each other, and among them, the sensor elements <NUM> provided with the substance adsorbing membranes <NUM> having the same adsorption properties may be provided.

As a material of the substance adsorbing membrane <NUM>, it is possible to use a thin film formed of a π-electron conjugated polymer. This thin film can contain at least one of an inorganic acid, an organic acid, or an ionic liquid as a dopant. By changing the type or content of the dopant, it is possible to change the property of the substance adsorbing membrane <NUM>.

Examples of the π-electron conjugated polymer preferably include, but are not limited to, a polymer having the π-electron conjugated polymer as a skeleton such as polypyrrole and a derivative thereof, polyaniline and a derivative thereof, polythiophene and a derivative thereof, polyacetylene and a derivative thereof, or polyazulene and a derivative thereof.

In a case in which the π-electron conjugated polymer is in an oxidized state and the skeleton polymer itself is a cation, conductivity can be developed by containing an anion as a dopant. Incidentally, in the invention, a neutral π-electron conjugated polymer not containing a dopant can be adopted as the substance adsorbing membrane <NUM>.

Specific examples of the dopant can include inorganic ions such as chlorine ion, chlorine oxide ion, bromine ion, sulfate ion, nitrate ion, and borate ion, organic acid anions such as alkylsulfonic acid, benzenesulfonic acid, and carboxylic acid, and polymer acid anions such as polyacrylic acid and polystyrene sulfonic acid.

In addition, it is possible to use a method of performing chemical equilibrium doping by allowing salt such as table salt or an ionic compound containing both a cation and an anion such as an ionic liquid to coexist with the neutral π-electron conjugated polymer.

In a case in which a state in which one dopant unit (ion) enters per two repeating units included in the π-electron conjugated polymer is set to <NUM>, the content of the dopant in the π-electron conjugated polymer may be adjusted in a range of <NUM> to <NUM>, preferably in a range of <NUM> to <NUM>. When the content of the dopant is set to be greater than or equal to the minimum value of this range, it is possible to inhibit disappearance of the characteristic of the substance adsorbing membrane <NUM>. In addition, when the content of the dopant is set to be less than or equal to the maximum value of this range, it is possible to inhibit a decrease in effect of the adsorption characteristic of the π-electron conjugated polymer itself, which makes it difficult to produce the substance adsorbing membrane <NUM> having a desirable adsorption characteristic. In addition, it is possible to inhibit a significant decrease in durability of the substance adsorbing membrane <NUM> due to the dopant, which is a low molecular weight substance, when predominant in the membrane. Therefore, by setting the content of the dopant in the above-mentioned range, it is possible to suitably maintain detection sensitivity of the odor substance.

In the plurality of sensor elements <NUM>, different types of π-electron conjugated polymers can be used to vary the respective adsorption characteristics of the substance adsorbing membranes <NUM>. In addition, respective adsorption characteristics may be developed by changing the type or the content of the dopant while using the same kind of π-electron conjugated polymer. For example, hydrophobic/hydrophilic properties of the substance adsorbing membrane <NUM> can be changed by changing the type of the π-electron conjugated polymer, the type and the content of the dopant, etc..

A thickness of the substance adsorbing membrane <NUM> can be appropriately selected according to the characteristic of the odor substance to be adsorbed. For example, the thickness of the substance adsorbing membrane <NUM> can be in a range of <NUM> to <NUM>, preferably <NUM> to <NUM>. When the thickness of the substance adsorbing membrane <NUM> is less than <NUM>, sufficient sensitivity may not be obtained in some cases. In addition, when the thickness of the substance adsorbing membrane <NUM> exceeds <NUM>, an upper limit of the weight detectable by the detector <NUM> may be exceeded.

The detector <NUM> has a function as a signal converter (transducer) which measures a change in physical, chemical, or electrical characteristic of the substance adsorbing membrane <NUM> due to the odor substance adsorbed on the surface of the substance adsorbing membrane <NUM> and outputs measurement data thereof as, for example, an electric signal. That is, the detector <NUM> detects an adsorption state of the odor substance on the surface of the substance adsorbing membrane <NUM>. Examples of the signal output as the measurement data by the detector <NUM> include physical information such as an electric signal, light emission, a change in electric resistance, or a change in vibration frequency.

The detector <NUM> is not particularly limited as long as the detector <NUM> is a sensor which measures the change in physical, chemical, or electrical characteristic of the substance adsorbing membrane <NUM>, and various sensors can be appropriately used. Specific examples of the detector <NUM> include a quartz oscillator sensor (QCM), a surface elastic wave sensor, a field effect transistor (FET) sensor, a charge coupled device sensor, an MOS field effect transistor sensor, a metal oxide semiconductor sensor, an organic conductive polymer sensor, an electrochemical sensor.

Incidentally, in the case of using the quartz oscillator sensor as the detector <NUM>, although not illustrated, as an excitation electrode, electrodes may be provided on both sides of the crystal oscillator or a separated electrode may be provided on one side to detect a high Q value. In addition, the excitation electrode may be provided on the sensor substrate <NUM> side of the quartz oscillator with the sensor substrate <NUM> interposed therebetween. The excitation electrode can be formed of an arbitrary conductive material. Specific examples of the material of the excitation electrode include inorganic materials such as gold, silver, platinum, chromium, titanium, aluminum, nickel, nickel alloy, silicon, carbon, and carbon nanotube, and organic materials such as conductive polymers such as polypyrrole and polyaniline.

As illustrated in <FIG> and <FIG>, the detector <NUM> can have a flat-plate shape. As illustrated in <FIG>, a shape of the flat plate of the flat-plate shape can be can be circular, but can also be of various shapes such as quadrilateral, square, elliptical, etc. However, the shape can be of various shapes such as a circle or an ellipse. Further, the shape of the detector <NUM> is not limited to the flat plate shape. A thickness thereof may be altered, and a concave portion or a convex portion may be formed.

In a case in which the detector <NUM> uses an oscillator as the quartz oscillator sensor described above, it is possible to reduce the influence (crosstalk) received from another oscillator coexisting on the same sensor substrate <NUM> by changing resonance frequencies of respective oscillators in the plurality of sensor elements <NUM>. It is possible to arbitrarily design the resonance frequencies so that the respective oscillators on the same sensor substrate <NUM> exhibit different sensitivities with respect to a certain frequency. The resonance frequency can be changed, for example, by adjusting the thickness of the oscillator or the substance adsorbing membrane <NUM>.

As the sensor substrate <NUM>, it is possible to use a silicon substrate, a substrate made of quartz crystal, a printed wiring substrate, a ceramic substrate, a resin substrate, etc. In addition, the substrate is a multilayer wiring substrate such as an interposer substrate, and an excitation electrode for oscillating the quartz substrate, mounting wirings, and an electrode for energizing are disposed at arbitrary positions.

By adopting the configuration as described above, it is possible to obtain the odor sensor <NUM> including the plurality of sensor elements <NUM> having the substance adsorbing membranes <NUM> whose adsorption characteristics of the odor substance are different from each other. As a result, in a case in which an odor of air containing a certain odor substance or a composition thereof is measured by the odor sensor <NUM>, the odor substance or the composition thereof comes into contact with the substance adsorbing membrane <NUM> of each sensor element <NUM> in the same manner. However, the odor substance is adsorbed to the respective substance adsorbing membranes <NUM> in different modes. That is, an adsorption amount of the odor substance is different between the respective substance adsorbing membranes <NUM>. For this reason, a detection result of the detector <NUM> is different between the respective sensor elements <NUM>. Therefore, pieces of measurement data by the detector <NUM> corresponding to the number of sensor elements <NUM> (substance adsorbing membranes <NUM>) included in the odor sensor <NUM> are generated for the certain odor substance or the composition thereof.

A set of measurement data generated by the odor sensor <NUM> by measuring the certain odor substance or the composition thereof is usually specific (unique) to a specific odor substance or a composition of the odor substance. For this reason, by measuring data using the odor sensor <NUM>, it is possible to identify the odor as an odor substance alone or as a composition (mixture) of odor substances.

The generation means, the preparation means, and the calculation means of the odor exploration system <NUM> can be realized by an arithmetic processing device <NUM> as an arithmetic unit. As shown in <FIG>, the arithmetic processing device <NUM> is communicatively connected to each of the sensor elements <NUM> of the odor sensor <NUM> and can read detected result detected by each of the sensor elements <NUM> by the arithmetic processing device <NUM> and calculate based on it. The arithmetic processing device <NUM> may be connected to a storage device <NUM>. The storage device <NUM> can store a program P1 which realizes various means such as the generation means, the preparation means, and the calculation means. In addition, the storage device <NUM> can store detection signal database D1 and odor information database D2.

It is known that the odor of lemon, the odor of lime, and the odor of cinnamon give the odor of cola when they are combined. Based on such finding, this was verified using the odor exploration system <NUM> of Embodiment <NUM>.

Sensory tests were carried out using odor samples of lemon, lime, and cinnamon provided by Aromaster. Approximately 10µl of each odor sample was dropped onto perfume test papers, respectively, and each perfume test paper was enclosed in sample bottles. After <NUM> minutes of standing, the sample bottles were opened and subjected to a sensory test by human testers. As a result of the sensory test, <NUM> out of <NUM> persons were evaluated as having an odor close to cola.

An odor sample of lemon and an odor sample of cinnamon provided by Aromaster were premixed, and a first odor information group containing a difference value of detection signal was generated for the mixed odor sample. A second odor information group containing difference values of detection signal was generated regarding an odor sample of Lyme provided by Aromaster. The sum of the first odor information group and the second odor information group was calculated to generate the third odor information group.

The degree of similarity between the plurality of odor information groups extracted from the odor information database D2 and the third odor information group was calculated, and the extracted plurality of odor information groups were sorted in descending order by the degree of similarity, and the top <NUM> odor information groups were selected. The degree of similarity was calculated using cosine similarity and sorted based on this. The various requirements of Example <NUM>-<NUM> are shown in Table <NUM>. The selection results of Example <NUM>-<NUM> are shown in Table <NUM>. In the odor information database D2, the odor information group generated by using odor sensor <NUM> for odors of various samples is stored in association with labels of the samples. A detailed description of each sample used in the Examples is given in Table <NUM>.

The measurement method of each sample was carried out depending on its state. When the condition of the sample was liquid, <NUM> of sample was poured into a vial bottle having a volume of <NUM>, and measurement was performed by bringing the opening of the vial bottle close to the odor sensor <NUM>. When the samples were aroma oil, they were measured by bringing the opening of the aroma oil container close to the odor sensor <NUM>. When the state of the sample was liquid, a sample obtained by dropping a few drops of the sample onto a mueth (perfume paper) was placed in a sample bottle, and measurement was performed by bringing the opening of the sample bottle close to odor sensor <NUM>. When the sample was solid, the sample was placed in a vial bottle of <NUM> and measurement was performed by bringing the opening of the vial bottle close to the odor sensor <NUM>. When the sample cannot be contained in a vial bottle with a volume of <NUM>, it was crushed and pulverized to such an extent that it can enter the vial bottle as appropriate. When the condition of the sample was gaseous, a cloth exposed to the sample was placed in a sample bottle and measurement was performed by bringing the opening of the sample bottle close to the odor sensor <NUM>. When the sample was a smoker's breath, a fabric was exposed to the sample by blowing the smoker's breath during smoking onto the fabric.

For the odor measurements, an odor measurement apparatus having an odor sensor <NUM> inside and an introduction port for introducing external air onto the odor sensor <NUM> was used. The odor of the samples was measured by bringing the opening of the vial bottles or sample bottles close to about <NUM> in the vicinity of the introduction port and holding the vial bottles or sample bottles at that position for about <NUM> seconds.

As shown in Table <NUM>, in Example <NUM>-<NUM>, the odor information group having the highest degree of similarity with the third odor information group (highest degree of similarity) was the odor information group generated for Cola Drink A (Pepsi ® Cola manufactured by Suntory Food International Co. The odor information group of second highest degree of similarity was the odor information group generated for black tea A (using Earl Gray tea leaves manufactured by Janat) and the odor information group of third highest degree of similarity was the odor information group generated for aroma oil D (using "tomato" aroma oil from among the Aromaster wine aroma kits (<NUM> aromas)).

The odor information group was selected in the same manner as in Example <NUM>-<NUM>, except that the condition shown in Table <NUM> was used. For Examples <NUM>-<NUM> to <NUM>-<NUM>, when the odor information contained in the third odor information group has a negative value, a fourth odor information group was generated excluding odor information which had negative value, and the degree of similarity was calculated based on the fourth odor information group. The selection results for Examples <NUM>-<NUM> to <NUM>-<NUM> are shown in Table <NUM>.

It is known that the odor of isovaleric acid combined with the odor of vanillin gives the odor of chocolate. Based on such finding, this was verified using odor exploration system <NUM> of Embodiment <NUM>.

Sensory tests were carried out using a <NUM>-fold diluted aqueous solution of isovaleric acid and an odor sample of vanillin manufactured by Aromaster. A cotton swab onto which a <NUM>-fold diluted aqueous solution of isovaleric acid was added in trace drops and a cotton swab onto which about <NUM> of an odor sample of vanillin was added dropwise were enclosed in a sample bottle. After <NUM> minutes of standing, the sample bottles were opened and subjected to a sensory test by human testers. As a result of the sensory test, <NUM> out of <NUM> persons have evaluated as having a scent close to chocolate.

The odor information group was selected in the same manner as in Example <NUM>-<NUM>, except that the condition shown in Table <NUM> was used. For Examples <NUM>-<NUM> to <NUM>-<NUM>, when the odor information contained in the third odor information group has a negative value, a fourth odor information group was generated excluding odor information which had negative value, and the degree of similarity was calculated based on the fourth odor information group. The selection results of Examples <NUM>-<NUM>, <NUM>-<NUM> to <NUM>-<NUM> are shown in Table <NUM>. A detailed description of each sample used in the Examples is given in Table <NUM>.

Claim 1:
An odor exploration method exploring odor based on a plurality of odor informations generated by
(<NUM>) a plurality of sensor elements (<NUM>) each outputting a detection signal according to the state of adsorption by indicating an adsorption reaction unique to each odor substance, and
(<NUM>) an arithmetic unit (<NUM>) generating the plurality of odor informations which are numerical values, by quantifying each detection signal outputted from the plurality of sensor elements;
wherein the odor exploration method comprises:
a detection step detecting a first gas containing a plurality of odor substances with the plurality of sensor elements;
a generation step (S2) generating a first odor information group consisting of the plurality of odor informations generated based on a detection result of the first gas;
a preparation step (S3) preparing a second odor information group consisting of a plurality of odor informations generated based on a detection of a second gas different from the first gas with the plurality of sensor elements,
a calculation step (S4) calculating a sum of or a difference between the second odor information group and the first odor information group based on a sum of or difference between the odor informations generated using the same sensor element; characterized by
deriving a third odor information group, being a result of the difference between the second odor information group and the first odor information group; and
an adjustment step (S5) wherein, when a particular odor information in the plurality of odor information contained in the third odor information group has a negative value, the particular odor information is adjusted by one of:
i. excluding that particular odor information from the third odor information group,
ii. replacing the numerical value of that particular odor information with <NUM>,
iii. adding an absolute value of the particular odor information to every odor information which the third odor information group contains, or
iv. repeatedly adding the second odor information group to the third odor information group until that particular odor information becomes greater than or equal to <NUM>.