Formulation setting method and formulation setting system

Abstract

The present invention provides a formulation setting method that makes it easy to set the formulation of a food product. This formulation setting method is for setting the formulation of ingredients for a food product on the basis of a user's request. The method includes: a selection step for selecting a desired base formulation from a plurality of base formulations preset for a predetermined food product; a change input step for inputting a change to a value related to a functional item of the selected base formulation; and a formulation setting step for setting the ingredient formulation on the basis of the changed value related to the functional item.

Description

Mixture determination method and mix determination system

[0001] The present invention relates to a formulation determination method and a formulation determination system.

[0002] In recent years, food products have been prepared according to user requests (see, for example, Non-Patent Document 1). When preparing food products according to user requests, multiple base formulations containing pre-mixed ingredients are prepared, and the user selects one base formulation and requests changes to the flavor, etc. of that base formulation.

[0003] To meet user requests, developers manually changed the ingredient mix from the base mix to create food with the ingredient mix that met the user's needs.

[0004] https: / / shouse.jp / oem / index.html

[0005] However, it took a long time to create because the developers had to manually change the ingredient mix from the base mix.

[0006] The present invention aims to provide a formulation determination method and formulation determination system that can easily determine the formulation of foods.

[0007] [1] A method for determining the ingredient mix of a food product based on a user's request, comprising: a selection step in which a desired base mix is ​​selected from a plurality of base mixes preset for a given food product; a change input step in which changes to values ​​related to functional items of the selected base mix are input; and a combination determination step in which the ingredient mix is ​​determined based on the values ​​related to the changed functional items.

[0008] [2] The formulation determination method described in [1], wherein a sensory evaluation value is predetermined for each functional item of the base formulation by sensory evaluation, and the change input step inputs a change to the sensory evaluation value.

[0009] [3] The formulation determination step includes: a conversion process for converting a change in the sensory evaluation value into a change in score based on the relationship between a score set according to the materials contained in the base formulation for each functional item of the base formulation and the sensory evaluation value in a plurality of the base formulations; and a formulation determination process for determining the formulation of the materials according to the change in score. [2] The formulation determination method described in [2].

[0010] [4] The method for determining a formulation described in [3], wherein a sensory evaluation value is determined in advance for each functional item of the ingredients through sensory evaluation, and the score of the base formulation is set based on the sensory evaluation value, formulation amount, and potency of the ingredients.

[0011] [5] The change in the score is expressed as a difference vector from a current position based on the sensory evaluation value of the base formulation to a target position based on a target score corresponding to the changed sensory evaluation value, and the formulation determination step sets a higher probability of selecting the material when the material is added to or subtracted from the base formulation, the more similar the direction of movement from the current position based on the change in the score is to the direction of the difference vector. [3] The formulation determination method described in [3].

[0012] [6] The formulation determination method described in [5], wherein the formulation determination step determines the amount to add or subtract from the selected material according to the set probability using a probability distribution centered on the amount that matches the target position.

[0013] [7] The formulation determination method described in [6], wherein the formulation determination step sets the current position to a position based on the score of the formulation changed by adding or subtracting the determined amount of the selected material to or from the base formulation, and the probability of selecting the material is set higher the higher the similarity between the direction of the difference vector from the current position to the target position and the direction of movement from the current position when the material is added to or subtracted from the changed formulation.

[0014] [8] The composition determination step determines the amount of the material selected according to the set probability to be added or subtracted using a probability distribution centered on the amount that matches the target position, sets the current position to a position based on the score of the composition changed by adding or subtracting the determined amount of the selected material to or from the changed composition, and sets a higher probability of selecting the material the higher the similarity between the direction of the difference vector from the current position to the target position and the direction of movement from the current position when the material is added to or subtracted from the changed composition, repeating this process a predetermined number of times to determine the composition of the materials.

[0015] [9] The method for determining a blending ratio according to any one of [3] to [8], further comprising a display step of displaying the determined blending ratios of the plurality of ingredients in order of similarity to the target score corresponding to the changed sensory evaluation value.

[0016]

[10] The method for determining a blending composition according to any one of [3] to [9], wherein the relationship is updated using a sensory evaluation value determined by a sensory evaluation for the functional items of the blending of ingredients determined by the blending determination step, and a score set according to the ingredients included in the blending of ingredients determined.

[11] The method for determining a blending composition according to any one of [1] to

[10] , wherein the functional items include at least one selected from sweetness, saltiness, sourness, bitterness, umami, and pungency.

[0017]

[12] A formulation determination method according to any of [1] to

[11] , further comprising a cost setting step in which a desired cost value for the food is set, a cost value being set for each of the ingredients, and the formulation determination step determining the formulation of ingredients based on the value related to the changed function and the cost value.

[0018]

[13] A formulation determination system that determines the formulation of food ingredients based on a user's requests, comprising: an input unit into which a desired base formulation is input from a plurality of base formulations preset for a specific food product and changes to values ​​related to the selected functional items of the base formulation; and a formulation determination unit that determines the formulation of ingredients based on the values ​​related to the changed functional items.

[0019] This specification includes the disclosure of Japanese Patent Application No. 2024-046642, from which the present application claims priority. All publications, patents, and patent applications cited in this specification are incorporated herein by reference in their entirety.

[0020] According to the present invention, it is possible to provide a composition determination method and a composition determination system that can easily determine the composition of foods.

[0021] 1 is a block diagram showing the configuration of a formulation determination system according to an embodiment of the present invention. (a) A diagram showing a table of ingredients and their formulation amounts contained in each of a plurality of base formulations stored in a memory unit of the formulation determination system, (b) A diagram showing an example of the table of FIG. 2(a). (a) A diagram showing a table of sensory evaluation values ​​set for functional items in each of a plurality of base formulations, (b) A diagram showing an example of the table of FIG. 3(a). (a) A diagram showing a table of sensory evaluation values ​​and titers set for functional items for each ingredient, (b) A diagram showing an example of the table of FIG. 4(a). (a) A diagram showing a table of sensory evaluation values ​​and scores for functional items for each base formulation, (b) A diagram showing an example of the table of FIG. 5(a). (a) A diagram showing an example of a graph for calculating a sweetness coefficient, (b) A diagram showing an example of a graph for calculating a saltiness coefficient. A schematic diagram for explaining cosine similarity used when calculating the probability of selecting an ingredient. A schematic diagram for explaining determination of formulation amounts of selected ingredients. A schematic diagram for explaining a process for determining a formulation of ingredients so as to approach a target position. A diagram showing an example of a display of a formulation determined by the formulation determination system according to an embodiment of the present invention. It is a block diagram showing the configuration of a combination determination system according to a modified example of the embodiment of the present invention.

[0022] Hereinafter, a formulation determination system and a formulation determination method according to an embodiment of the present invention will be described with reference to the drawings.

[0023] (Overview of the Mixture Determination System 1) The mix determination system 1 of this embodiment determines the mix of ingredients for food based on the user's request. Examples of food include liquid foods such as curry or soup, and seasonings (powdered seasonings).

[0024] 1 is a block diagram showing the configuration of a blending determination system 1. The blending determination system 1 includes a terminal device 10 and a cloud server 20. The terminal device 10 may be, for example, a personal computer, a tablet, a smartphone, or the like.

[0025] The terminal device 10 includes a processor and a storage device. The processor is, for example, a central processing unit (CPU). Alternatively, the processor may be a processor different from the CPU. The processor executes processing for formulation determination according to a program stored in the storage device. The storage device includes a non-volatile memory such as a read-only memory (ROM) and a volatile memory such as a random access memory (RAM). The storage device may include an auxiliary storage device such as a hard disk or a solid-state drive (SSD). The storage device is an example of a non-transitory computer-readable recording medium.

[0026] The terminal device 10 has an input unit 11, a display unit 12, and a transmission / reception unit 13. The terminal device 10 realizes the functions of the input unit 11, the display unit 12, and the transmission / reception unit 13 by executing a program stored in a storage device.

[0027] The operator operates the input unit 11 of the terminal device 10 to select a desired base formulation from a plurality of base formulations preset for a specific food product in response to the user's (client's) request. The input unit 11 is operated by the operator to input changes to the values ​​related to the functional items of the selected base formulation in response to the user's (client's) request. The input unit 11 may be a touch panel, keyboard, mouse, switch, or the like.

[0028] The display unit 12 displays an input screen when the operator operates the input unit 11. An example of the display unit 12 is a display. If the input unit 11 is a touch panel, it may also serve as the display unit 12.

[0029] The transmission / reception unit 13 communicates signals with the transmission / reception unit 22 of the cloud server 20. The transmission / reception unit 13 transmits information regarding the selected base formulation input into the input unit 11 to the cloud server 20 as a selection signal. The transmission / reception unit 13 transmits information regarding changes to values ​​related to the function items of the selected base formulation input into the input unit 11 as a change signal to the cloud server 20. The transmission / reception unit 13 receives information regarding the formulation determined by the cloud server 20 from the cloud server 20 as a formulation signal. Communication between the transmission / reception unit 13 of the terminal device 10 and the transmission / reception unit 22 of the cloud server 20 may be, for example, via the Internet, Bluetooth (registered trademark), a LAN, a telephone line, an in-house network, Wi-Fi (Wireless Fidelity), or other communication lines, or a combination of these, and may be wired or wireless.

[0030] The cloud server 20 determines the ingredient combination based on the values ​​of the changed function items. The terminal device 10 displays the combination determined by the cloud server 20.

[0031] Although FIG. 1 shows a configuration in which one terminal device 10 communicates with the cloud server 20 , a plurality of terminal devices 10 may communicate with the cloud server 20 .

[0032] (Cloud Server 20) The cloud server 20 includes a processor and a storage device. The processor is, for example, a CPU (Central Processing Unit). Alternatively, the processor may be a processor different from the CPU. The processor executes processing for formulation determination according to a program stored in the storage device. The storage device includes a non-volatile memory such as a ROM (Read Only Memory) and a volatile memory such as a RAM (Random Access Memory). The storage device may include an auxiliary storage device such as a hard disk or an SSD (Solid State Drive). The storage device is an example of a non-transitory computer-readable recording medium.

[0033] The cloud server 20 has a memory unit 21, a transmission / reception unit 22, and a combination determination unit 23. The memory unit 21 is included in the storage device. The cloud server 20 realizes the functions of the transmission / reception unit 22 and the combination determination unit 23 by executing a program stored in the storage device.

[0034] (Storage unit 21) First, we will explain the data stored in the storage unit 21 of the cloud server 20. The storage unit 21 stores the compounding amounts of ingredients in a plurality of base formulations, the sensory evaluation values ​​of the functional items in the base formulations, the sensory evaluation values ​​and potencies of ingredients used to determine the formulations, and the coefficients of the functional items.

[0035] (Base formulation) A plurality of base formulations are set in advance. A base formulation is composed of a plurality of materials, and each material is contained in a predetermined blend amount. If the number of base formulations is n, then in FIG. 1, １ ~A ｎ are stored in advance in the storage unit 21. The storage unit 21 stores the ingredients contained in each of the plurality of base formulations and the amounts of ingredients included.

[0036] 2(a) is a table showing the ingredients and their amounts contained in each of a plurality of base formulations. The top row shows n base formulations A ｉ (1≦i≦n). The leftmost column indicates m materials Bｊ (1≦j≦m) Base Formulation A ｉ Material B included in ｊ The amount of α ＡｉＢｊ For example, base formulation A １ Material B ２ The amount of α Ａ１Ｂ２ The blending amount α ＡｉＢｊ includes the value zero.

[0037] For example, if the food is curry, base composition A １ Material B ｊ The ingredients include roux, salt, sugar, curry powder, water, and adjusted lard. ２ Although multiple ingredients B are also included in the base formulation A, １ For example, base formulation A １ The amount of sugar in the base blend A is ２ By increasing the amount of base compound A １ In this way, a curry with a strong sweetness can be made. １ ~A ｎ is set in advance and stored in the storage unit 21.

[0038] Figure 2(b) is a table of the specific example of Figure 2(a). The materials shown in Figure 2(b) are listed for clarity of explanation. In Figure 2(b), for example, Base Formulation A １ is composed of 84 parts roux, 12 parts sauce, 6 parts curry powder, 10 parts salt, 19 parts sugar, 6 parts MSG (monosodium glutamate), and 2 parts beef extract mixed with water. The amounts are expressed, for example, in parts per thousand.

[0039] (Sensory evaluation value of functional items in base formulation) Each base formulation (A １ ~A ｎ The sensory evaluation has been carried out in advance by an evaluator for each base formulation (A), and a sensory evaluation value has been set for each function item. １ ~A ｎ) function items. FIG. 3(a) is a diagram showing a table of the sensory evaluation values ​​set for the function items for each base formulation. The top row shows the q function items C ｋ (1≦k≦q). The leftmost column shows n base formulations A ｉ (1≦i≦n).

[0040] Function item C １ ~C ｑ Examples of the flavors include sweetness, saltiness, sourness, bitterness, umami, spiciness, oiliness, beef flavor, buttery flavor, fruity flavor, spicy flavor, and herb flavor.

[0041] Base formulation A ｉ Function item C in ｋ The sensory evaluation value of ＡｉＣｋ Base formulation A １ Function item C ２ The sensory evaluation value of Ａ１Ｃ２ The sensory evaluation value β ＡｉＣｋ is a numerical value set between 0 and 5, for example, and is determined by an evaluator. For example, in this embodiment, the sensory evaluation value is set in increments of 0.5.

[0042] Figure 3(b) is a table showing a specific example of Figure 3(a). Figure 3(b) illustrates examples of functional categories, such as sweetness, saltiness, sourness, bitterness, umami, and spiciness. Base Blend A １ The (sweetness, saltiness, sourness, bitterness, umami, and spiciness) in the above formula are evaluated and set as (sweetness 2, saltiness 4, sourness 2.5, bitterness 1.5, umami 3, and spiciness 1.5). ２ The functional items (sweetness, saltiness, sourness, bitterness, umami, spiciness) in the table are evaluated and set as (sweetness 2, saltiness 2.5, sourness 1.5, bitterness 1.5, umami 3.5, spiciness 2).

[0043] (Sensory evaluation values ​​and potency of materials used to determine the formulation) Material B described above １ ~B ｍ The storage unit 21 stores the sensory evaluation values ​​for the functional items of the material B. １ ~B ｍThe sensory evaluation values ​​of the functional items set for each of the materials are stored. １ ~B ｍ The table below shows the sensory evaluation values ​​set for each of the functional items for each of the materials. ｊ Function item C in ｋ The sensory evaluation value of γ ＢｉＣｋ For example, material B １ Function item C ２ The sensory evaluation value of γ Ｂ１Ｃ２ The sensory evaluation value γ ＢｉＣｋ is the sensory evaluation value β ＡｉＣｋ Similarly, the sensory evaluation value is a value indicated by a number between 0 and 5, for example, and is determined by an evaluator. For example, the sensory evaluation value is evaluated in increments of 0.5.

[0044] In FIG. 4(a), material B １ ~B ｍ The storage unit 21 stores the potency of each of the materials B. １ ~B ｍ The potency for each of the materials is recorded. ｊ The titer of δ Ｂｊ The potency is a value based on the concentration when setting the sensory evaluation value. The lower the concentration of the material for the sensory evaluation value, the higher the potency is set. For example, the potency can be expressed as 100 / (concentration at the time of sensory evaluation). For example, a material diluted to 1% for sensory evaluation has a potency of 100, and a material diluted to 0.5% has a potency of 200.

[0045] Figure 4(b) is a diagram showing a specific example of Figure 4(a). In Figure 4(b), for example, the roux is set to (sweetness 0, saltiness 0, sourness 0, bitterness 0, umami 1, spiciness 0) and has a potency of 7. Also, the curry powder is set to (sweetness 0, saltiness 0, sourness 0, bitterness 4, umami 0, spiciness 3) and has a potency of 33.

[0046] (Function item coefficient) The function item coefficient is function item C. ｋ Base formulation A １ ~A ｎ Sensory evaluation value β ＡｉＣｋ And material B １ ~B ｍ Sensory evaluation value γＢｉＣｋ The score is calculated in advance from the relationship between the score set from and the score set from, and is stored in the storage unit 21. １ ~A ｎ Function item C for each of ｋ Score ε ＡｉＣｋ For example, base formulation A ｉ Function item C ｋ Score ε ＡｉＣｋ is base formulation A ｉ Material B １ ~B ｍ The results are calculated from the sensory evaluation values, blend amounts, and power factors of the above. ｉ Function item C ｋ Score ε ＡｉＣｋ is calculated from the following formula:

[0047] In FIG. 5(a), the coefficients ζ of the function items are shown. Ｃｋ The coefficients of the function items are shown. Ｃｋ is the slope of the sensory evaluation value relative to the score. Ｃｋ is the score / 1000 on the horizontal axis and the sensory evaluation value on the vertical axis, (β Ａ１Ｃｋ , ε Ａ１ＣＫ / 1000), (β Ａ２Ｃｋ , ε Ａ２ＣＫ / 1000), (β Ａ３Ｃｋ , ε Ａ３ＣＫ / 1000), (β Ａ４Ｃｋ , ε Ａ４ＣＫ / 1000), ... (β ＡｋＣｋ , ε ＡｋＣＫ / 1000), ... (β ＡｎＣｋ , ε ＡｎＣＫ / 1000) and linearly approximate it using, for example, the least squares method.

[0048] Fig. 5(b) is a diagram showing a specific example of Fig. 5(a), in which examples are shown for six types of functional items (sweetness, saltiness, sourness, bitterness, umami, and spiciness).

[0049] Base formulation A １ As shown in FIG. 2(b), Base Formulation A is composed of the ingredients of roux, sauce, curry powder, salt, sugar, MSG, and beef extract.１ From the table in Figure 4(b), it can be seen that the only ingredient that contributes to sweetness among the ingredients is sugar, and the sugar titer is 10. Also, from Figure 2(b), it can be seen that the base formulation A １ The sugar content in the base blend is 19, so １ The sweetness score is 5 (sensory evaluation value of ingredients) x 10 (potency) x 19 (amount blended) = 950.

[0050] Base formulation A １ In the base blend A, the ingredients that contribute to the salty taste are sauce, salt, and beef extract, as shown in the table of FIG. １ The saltiness score of this dish is {1 (sensory evaluation value of sauce) x 50 (potency of sauce) x 12 (amount of sauce) + 5 (sensory evaluation value of salt) x 67 (potency of salt) x 10 (amount of salt) + 1 (sensory evaluation value of beef extract) x 50 (potency of beef extract) x 2 (amount of beef extract)} = 600 + 3350 + 100 = 4050.

[0051] As shown in FIG. 6(a), the sensory evaluation value is plotted on the vertical axis, and (score / 1000) is plotted on the horizontal axis. １ ~A ６ The relationship between the sensory evaluation value and the score is plotted. The slope of this approximate line, 1.8648, is the coefficient of sweetness. As shown in Figure 6(b), the sensory evaluation value is set on the vertical axis, and the score is set on the horizontal axis. １ ~A ６ The relationship between the sensory evaluation value and the score is plotted. The slope of this approximate line, 1.2447, is the coefficient for saltiness. Similarly, examples of coefficients for sourness, bitterness, umami, and spiciness are shown in the table in Figure 5(b). The approximate line can be determined by the least squares method or the like.

[0052] (Transmitter / receiver 22) The transmitter / receiver 22 of the cloud server 20 receives, as a selection signal, information on the base composition selected by the operator operating the input unit 11. For example, when the operator operates the input unit 11 to select base composition A, １ ~A ｎ Base composition A ２ If you select Base Mixture A ２The transmitting / receiving unit 22 receives information indicating that the user has selected the above.

[0053] The transceiver 22 of the cloud server 20 receives, as a change signal, information on a change to the value of the function item of the base formulation selected by the operator operating the input unit 11. For example, when the operator operates the input unit 11 to select the base formulation A, ２ For example, if sweetness 3 and umami 4 are input, the transmitting / receiving unit 22 receives information indicating that sweetness 3 and umami 4 have been input as a change signal. The transmitting / receiving unit 22 transmits information regarding the ingredient combination determined by the combination determination unit 23 to the terminal device 10 as a combination signal.

[0054] (Formulation determination unit 23) The formulation determination unit 23 determines the formulation of ingredients based on the formulation amounts of multiple base formulation ingredients stored in the memory unit 21, the sensory evaluation values ​​of the functional items in the base formulation, the sensory evaluation values ​​and potencies of the ingredients used in formulation determination, and the coefficients of the functional items, so as to match the sensory evaluation value input in the input unit 11.

[0055] The composition determination unit 23 includes a conversion unit 31, a material selection unit 32, a composition amount determination unit 33, and a transfer unit 34.

[0056] The conversion unit 31 converts the change in the sensory evaluation value based on the change signal received from the input unit 11 into a change in the score. ｉ Function item C ｋ Sensory evaluation value β ＡｉＣｋ and the entered function item C ｋ From the sensory evaluation value of ＡｉＣｋ The change amount η ＡｉＣｋ For example, the selected base formulation A ２ For the above, if you enter sweetness 3 and umami 4, the base blend A ２ The sensory evaluation values ​​of sweetness and umami are 2 and 3.5, respectively, so the amounts of change are 1 for sweetness and 0.5 for umami.

[0057] Base formulation A ｉ Function item C ｋ Sensory evaluation value β ＡｉＣｋ The change amount η ＡｉＣｋ the coefficient ζ ＣｋDivide by and multiply by 1000 to get the score change λ Ｃｋ =(η ＡｉＣｋ / ζ Ｃｋ ) × 1000. The reason for multiplying by 1000 is that the score was divided by 1000 when calculating the coefficient of the function item. Ｃ１、 λ Ｃ２、・・・ λ Ｃｋ , ..., λ Ｃｑ ) is base formulation A ｉ is expressed as a difference vector from the current position vector based on the sensory evaluation value of the vehicle to the target position vector based on the sensory evaluation value changed by the input unit 11. Note that the change amount η ＡｉＣｋ Since λ includes positive, negative, and zero values, Ｃｋ The difference vector can be expressed by the following equation (2):

[0058]

[0059] Note that the cloud server 20 does not need to specifically calculate the current position vector and the target position vector, but only calculates the difference vector. However, for ease of understanding, the current position vector and the target position vector are shown below.

[0060] Base formulation A ｉ The current position vector for the material can be expressed by the value obtained by multiplying the score shown in FIG. 5(a) by the coefficient of the function, as in the following formula (3).

[0061] The target position vector can be expressed as a value obtained by adding the amount of change in score to the current position, as shown in the following equation (4).

[0062]

[0063] ​For example, if only sweetness, saltiness, sourness, bitterness, umami, and spiciness are used among the function items as described above, the sensory evaluation value of sweetness has changed from 2 to 3, so the conversion unit 31 calculates a change amount of 1 and a score change amount of (1 / 1.865)×1000=536. Furthermore, the sensory evaluation value of umami has changed from 3.5 to 4, so the conversion unit 31 calculates a change amount of 0.5 and a score change amount of (0.5 / 0.7391)×1000=676.

[0064] Therefore, the conversion unit 31 obtains the difference vector as (536, 0, 0, 0, 676, 0). The current position vector is expressed as (950×1.865, 4050×1.2447, 600×1.1286, 200×0.8149, 2288×0.7391, 594×0.7148), and the target position vector is expressed as (950×1.865+536, 4050×1.2447, 600×1.1286, 200×0.8149, 2288×0.7391+676, 594×0.7148).

[0065] The material selection unit 32 sets a higher probability of selecting a material with a high similarity the more similar the direction of movement from the current position based on the change in score when a material is added to or subtracted from the base formulation is to the direction of movement of the difference vector.

[0066] The material selection unit 32 selects all the materials B based on the difference vector. １ ~B ｍ The combination determination unit 23 calculates the probability of selecting material B. １ ~B ｍ Any one of materials B ａ When the compounding amount is changed, the material change vector and the difference vector form an angle of cosθ ａ Calculate the probability based on the cosine similarity. a is calculated from the vector inner product equation using the following (Equation 5).

[0067]

[0068] The blending determination unit 23 determines all the materials B １ ~B ｍCalculate cosθ for each of the materials B ａ The probability of selecting P ａ is calculated by the following formula (6). ａ The denominator indicates the sum of the cosine similarities of all materials.

[0069] 7 is a schematic diagram for explaining the cosine similarity. In FIG. 7, ｃ and the target position is V ｔ For example, as described above, base formulation A １ The following describes the case where the sensory evaluation value for sweetness is increased by 1 and the sensory evaluation value for umami is increased by 0.5. As described above, the difference vector is (536, 0, 0, 0, 676, 0). For example, when salt is added, the material change vector when 1 amount of salt is added can be expressed as (0, 335, 0, 0, 0, 0) from the table in Figure 4(b). As shown in Figure 7, the material change vector due to the addition of salt forms a right angle with the difference vector, and the dot product of the material change vector due to the addition of salt and the difference vector is 0, resulting in cosθ of salt = 0.

[0070] On the other hand, when MSG is added, the material change vector when MSG is added in a blending amount of 1 can be expressed as (0, 0, 0, 0, 250, 0) from the table in Fig. 4(b). The angle formed by the material change vector and the difference vector when MSG is added in a blending amount of 1 is expressed as θa, and cosθa has a value other than 0, and the probability that MSG will be selected is calculated.

[0071] As described above, the composition determination unit 23 determines the composition of material B １ ~B ｍ Calculate cosθ for each of the above and find the probability Pa for each material.

[0072] The material selection unit 32 selects one material based on the determined probability. Note that a material with a high probability is simply more likely to be selected, and the material with the highest probability is not necessarily selected. This is because, in this embodiment, in order to be able to determine multiple types of combinations as described below, it is necessary to avoid selecting only the same material.

[0073] The blend amount determination unit 33 determines the blend amounts of the materials selected by the material selection unit 32. Here, the amount of movement due to an increase or decrease in the selected material can be determined linearly, so the amount of increase or decrease that most closely approaches the target position can be found by calculation. However, in this embodiment, the blend amount determination unit 33 determines the blend amounts of the selected materials using a probability distribution so that multiple types of blends can be determined.

[0074] 8 is a schematic diagram for explaining how to determine the blending amount of the selected material. As shown in FIG. 8, ｃ When the material is added from the target position V ｔ Position V closest to ｄ The destination is determined by a normal probability distribution in units of 1 / 1000 (0.01) based on the distance from the current position V. ｃ From position V ｄ By blending the selected material, the target position V ｔ This position V ｄ The destination is determined by the probability of normal distribution in the direction of increasing or decreasing the blend amount based on the position V ｄ Current position V ｃ Side position V ｄ When the compounding amount is determined so that the liquid moves to position V ｄ Current position V ｃ Position V on the opposite side of ｄ In some cases, the compounding amounts are determined so that the material moves to "". As will be described later, in order to be able to determine multiple types of compounding, the compounding amounts of the selected materials are set according to probability.

[0075] The moving unit 34 is a current position V ｃ The material selected from the above is moved to a position where the material is increased or decreased by the determined amount. This position movement is in the direction of the material change vector of the selected material by the determined amount. The material selection unit 32 described above sets the moved position as the current position V ｃ As the target position V ｔ The difference vector up to the target position V ｔ is the current position V ｃThe moved position is expressed as a relative position from the current position V ｃ Therefore, the distance between the moved position and the target position V can be expressed as a relative position from the target position V without specifically calculating the current position vector and the target position vector as explained in (Equation 3) and (Equation 4). ｔ The difference vector between these two can be calculated.

[0076] Then, the material selection unit 32 determines the current position V ｃ and target position V ｔ Based on the difference vector with １ ~B ｍ The mixing amount determination unit 33 determines the mixing amount of the selected material. The movement unit 34 moves the current position V ｃ The selected material is moved to a position where the determined amount of material has been added.

[0077] The combination determination unit 23 determines the moved position as the current position V ｃ As above, the selection probability of all materials is calculated, materials are selected, the blending amounts of the selected materials are determined, and the current position V ｃ The number of repetitions Q can be set by the input unit 11, for example.

[0078] 9 is a schematic diagram for explaining the process of determining the material composition so as to approach the target position. ｃ0 Let the current position after the first movement be V ｃ1 The current position after the second movement is V ｃ２ Then, set it sequentially, and the current position after Q times is V ｃＱ As shown in FIG. 9, by repeating the above control, the current position V ｃ０ From the target position V ｔ It will gradually approach towards.

[0079] The combination determination unit 23 determines the position V after repeating the process for the set number of times Q. ｃＱ The amount of change in the composition of each material in the base composition A ｉThe blending amounts of the ingredients are changed from those in the formula 1 and the blending amounts are determined. This allows the user to determine a blend that has the desired function.

[0080] If the number of types of blend amounts determined by the blend determination unit 23 is R, the determination of the blend amounts of the materials as described above is repeated R times. This allows the blend determination unit 23 to determine R blends that achieve the desired blend that is modified from the base blend. The transceiver unit 22 transmits information about the R blends determined by the blend determination unit 23 to the display unit 12 as a display command signal.

[0081] The display unit 12 displays R types of combinations determined by the cloud server 20 based on the display command signal. Fig. 10 is a diagram showing the combination amounts displayed on the display unit 12. In Fig. 10, R = 10 types of combinations are displayed. In Fig. 10, the "distance" is shown for each of the 10 types of combinations, and the order is displayed in ascending order of distance. The "distance" is the distance from the current position V after Q times. ｃＱ and target position V ｔ The "distance" is the distance between the initial difference vector and the position V after moving Q times. ｃＱ The closer the distance, the closer the function to the target, so the earlier it is displayed in the ranking.

[0082] Based on the recipe displayed on the display unit 12, ingredients can be mixed to create food that meets the user's needs.

[0083] (Operation of the Mixing Determination System) Next, the operation of the mixing determination system will be described. Fig. 11 is a flow chart showing the operation of the mixing determination system.

[0084] First, in step S11, the operator uses the input unit 11 to input the base composition A. １ ~A ｎ The input unit 11 selects a base composition from the selected base composition A. ｉ The information relating to the above is transmitted as a selection signal to the cloud server 20, and the cloud server 20 receives the selection signal. Step S11 corresponds to an example of a selection step.

[0085] Next, in step S12, the operator uses the input unit 11 to input the function item C ｋ Sensory evaluation value β ＡｉＣｋ to a desired value. The input unit 11 transmits information on the changed sensory evaluation value as a change signal to the cloud server 20, and the cloud server 20 receives the change signal. Step S11 corresponds to an example of a change input step.

[0086] Next, in step S13, the conversion unit 31 of the cloud server 20 converts the sensory evaluation value β ＡｉＣｋ The change amount η ＡｉＣｋ Calculate the change amount η ＡｉＣｋ The score change amount λ Ｃｋ Step S13 corresponds to an example of a conversion step.

[0087] Next, in step S14, the formulation determination unit 23 starts up a new formulation. In steps S15 to S19 following step S14, one type of formulation is determined.

[0088] Next, in step S15, the material selection unit 32 of the cloud server 20 converts the sensory evaluation value into a score change amount λ Ｃｋ The initial difference vector obtained from １ ~B ｍ The probability P of selection for each material is calculated from the material change vector when the blending amount of each material is changed. ａ The material selection unit 32 selects one material based on the calculated probability.

[0089] Next, in step S16, the blending amount determination unit 33 determines the blending amount of the selected material based on the probability of the normal distribution shown in FIG.

[0090] Next, in step S17, the movement unit 34 changes the selected material by the determined blending amount, thereby moving the position from the current position by the blending amount, and sets the moved position as the current position.

[0091] Next, in step S18, the combination determination unit 23 determines whether the number of times of positional movement has reached the set number Q. If it is determined in step S18 that the number of times Q has not been reached, control returns to step S15, and steps S15, S16, and S17 are repeated until the number of times of positional movement reaches the number Q.

[0092] In step S18, if it is determined that the movement of the position has been repeated Q times, in step S19, the combination determination unit 23 determines the position V ｃＱ The amount of change in the composition of each material in the base composition A ｉ The compounding amount of the ingredients is changed from that in the target position V ｔ and position V ｃＱ The distance between the first and second electrodes is also calculated. Steps S14 to S18 correspond to an example of a combination determination process. Steps S13 to S18 correspond to an example of a combination determination step.

[0093] Next, in step S20, the combination determination unit 23 determines whether or not R types of combinations have been determined. The combination determination unit 23 determines that R types of combinations have been determined if the launch of a new combination in step S14 has been performed R times. Note that the combination determination unit 23 may also determine that R types of combinations have been determined if the combination determination in step S19 has been performed R times.

[0094] Next, in step S21, the transmitter / receiver 22 of the cloud server 20 transmits information about the determined combination of the R types as a combination signal to the terminal device 10. The terminal device 10 receives the combination signal transmitted from the cloud server 120 at the transmitter / receiver 13.

[0095] Next, in step S22, the display unit 12 displays the determined combination of R types, as shown in Fig. 10. Step S22 corresponds to an example of a display step.

[0096] The formulation determination method of this embodiment is １ ~A ｎ From the desired base formulation Aｉ Select the base formulation A ｉ Function item C ｋ The value of β ＡｉＣｋ The changes to the function are entered, and the value β for the item of the changed function is ＡｉＣｋ +η ＡｉＣｋ The material composition is determined based on the above.

[0097] (Other Embodiments) Although one embodiment of the present invention has been described above, the present invention is not limited to the above embodiment, and various modifications are possible without departing from the gist of the invention.

[0098] (A) In the above embodiment, as shown in FIG. 2(a), material B used in the blending １ ~B ｍ is base formulation A １ ~A ｎ However, it is not limited to these, and the material B used in the formulation １ ~B ｍ is base formulation A １ ~A ｎ It may contain materials that are not included in any of the above.

[0099] (B) In the above embodiment, base formulation A １ ~A ｎ Although there is no limit to the amount of change in the base ingredients of the base recipe A, a limit may be imposed. For example, as shown in FIG. 2(b), １ ~A ６ In the case of the base material, the increase or decrease of the amount of change may be limited to a range of 80% to 120%. The limit range may be input by the operator using the input unit 11.

[0100] (C) In the above embodiment, the formulation determination unit 23 does not impose a limit on the number of times the same material is repeatedly selected during the Q repetitions, but a limit may be imposed on the number of times the material is selected. For example, if the number of repetitions Q is set to 20, the number of times MSG in FIG. 2(b) is selected may be limited to, for example, 6 times. This limit on the number of times the material is selected may be configured to be input by the operator using the input unit 11.

[0101] (D) In ​​the above embodiment, no reference is made to cost when determining the composition, but the composition of materials may be determined taking cost into consideration. In this case, the storage unit 21 stores the material B １ ~B ｍ The cost values ​​of each of the above are stored. In addition to steps S11 and S12, a cost setting step is provided, in which a desired cost value is input using the input unit 11. The desired cost value can be, for example, an upper limit value of the cost or a target value of the cost. In this case, the composition determination unit 23 determines the composition of materials taking the upper limit value into consideration. It may also be possible to set a weight for exceeding the desired cost value.

[0102] In the above embodiment, in step S16, the blending amount determination unit 33 determines the blending amount according to a normal distribution, based on the blending amount that minimizes the distance to the target position. This distance can be considered a flavor-related distance that approaches the target position by blending the ingredients. However, when considering the cost of ingredients, the distance is set based on the distance related to the flavor as well as the distance related to the ingredient cost. The distance related to the ingredient cost is the difference between the ingredient cost before adding the ingredient selected in step S15 and the set desired cost value. Furthermore, it is preferable to set this difference as, for example, the excess over the set desired cost value. The blending amount determination unit 33 calculates the Euclidean distance to the target position from the distance related to the flavor and the distance related to the ingredient cost, and determines the blending amount according to a normal distribution, based on the blending amount that minimizes the Euclidean distance.

[0103] For example, if sweetness is "1" away from the target and sourness is "1" away from the target, and the cost is also 1 (yen / kg), the distance to the target position taking raw material cost into consideration is the Euclidean distance (=√3). In step S16, the blending amounts of the ingredients selected in step S15 are determined according to a normal distribution based on the position where this Euclidean distance (√3) is shortest. The raw material cost is calculated based on the selected ingredients and the determined blending amounts.

[0104] Furthermore, in the above example, a deviation of 1 in flavor distance is equivalent to an excess of 1 yen / kg in raw material cost, but "weighting" can be used to change the value of an excess of 1 yen / kg in raw material cost relative to a deviation of 1 yen / kg in flavor distance. For example, when "weighting" is set to 0.01, as described above, if sweetness is 1 yen away from the target, sourness is 1 yen away from the target, and cost is also 1 yen / kg away, the cost distance will be 0.01, which is the Euclidean distance (≒√2). The blending amounts of ingredients are then determined according to a normal distribution based on the position where this Euclidean distance is shortest.

[0105] If the weighting is set high, it becomes difficult to generate a recipe that exceeds the cost price. On the other hand, if the weighting is set low, it is possible to generate a recipe that meets the requirements, even if the cost price exceeds the upper limit. In this way, the weighting of raw material costs can be changed depending on whether the cost requirements are strict or not, depending on the user.

[0106] The method of determining the material composition taking cost into consideration is not limited to the above. For example, if the composition determined in step S19 exceeds a set cost value, it may be discarded, and the flow from step S14 to step S20 may be repeated until a composition within the set cost value is determined.

[0107] (E) In the above embodiment, the blend amount and distance are displayed as shown in Fig. 10, but the difference from the sensory evaluation value desired by the user may also be displayed for each type of blend. For example, if the sweetness desired by the user is 3, the sweetness score may be calculated from the ingredients of a predetermined blend (e.g., the blend ranked first) determined by the blend determination method of this embodiment, and the score may be converted to a sensory evaluation value using the relationship in Fig. 6(a), and the difference between the converted sensory evaluation value and the sensory evaluation value of 3 for the desired sweetness may be displayed.

[0108] (F) In the above embodiment, multiple types of determined combinations are displayed, but it is also possible to determine and display only one type of combination.

[0109] (G) In the above embodiment, the formulation determination system 1 includes a terminal device 10 and a cloud server 20, but may also be configured as a standalone device such as a personal computer or tablet. For example, the formulation determination system of the present application may be configured as the formulation determination device 100 of FIG. 12. The formulation determination device 100 includes the above-mentioned input unit 11, display unit 13, memory unit 21, and formulation determination unit 23.

[0110] (H) In the above embodiment, the coefficient ζ of the function item Ｃｋ is a fixed numerical value stored in advance in the storage unit 21, but may be updatable. For example, a prototype is actually made of the composition displayed on the display unit 12 as shown in FIG. 10, and a sensory evaluation is performed on the prototype. This makes it possible to determine the sensory evaluation value for each functional item as shown in FIGS. 3(a) and 3(b) for the prototype. The sensory evaluation value for each functional item is input using the input unit 11 and transmitted to the cloud server 20. The cloud server 20 also determines a score for each functional item from the composition of the prototype. This determines the sensory evaluation value and score for the functional item for the composition of the prototype. For example, the storage unit 21 of the cloud server 20 stores the sensory evaluation values ​​and scores for the functional items for each of the base compositions shown in FIG. 5(a). The cloud server 20 adds the sensory evaluation values ​​and scores for the functional items for the composition of the prototype to the sensory evaluation values ​​and scores, and calculates a new functional item coefficient ζ for each functional item. Ｃｋ The cloud server 20 calculates the coefficient ζ of the calculated function item. Ｃｋ is stored in the storage unit 21, and the coefficients of the previous function items are updated. As a result, the coefficients of the updated function items can be used when determining the next combination.

[0111] For example, referring to FIG. 5(b), if the sensory evaluation value of the sweetness of a prototype is 3.5 and the score is 1500, the sensory evaluation value of 3.5 and the score of 1500 are added to the sensory evaluation value and score data of base formulations A1 to A6, and the sensory evaluation value is plotted against score / 1000 as shown in FIG. 6(a), and the slope of the approximate line is calculated. This allows the coefficient for the sweetness function item to be updated to 1.969. In this way, new plots may be added and the coefficient may be corrected.

[0112] (I) In the above embodiment, a cloud server 20 is used as the server used in the formulation determination system 1, but it is not limited to a virtual server such as a cloud server, and may also be a physical server.

[0113] According to the method for determining a composition of the present invention, it is possible to easily determine the composition of a food product, and the method can be used to determine the composition of, for example, liquid foods such as curry or soup, or seasonings (powdered seasonings).

[0114] 11: Input unit 12: Controller 13: Display unit 21: Memory unit 22: Transmitting / receiving unit 23: Blending determination unit 31: Conversion unit 32: Material selection unit 33: Blending amount determination unit 34: Movement unit

Claims

1. A method for determining the ingredient composition of a food product based on a user's request, comprising: a selection step for selecting a desired base composition from a plurality of base compositions preset for a given food product; a change input step for inputting changes to the values ​​of the functional items of the selected base composition; and a composition determination step for determining the ingredient composition based on the values ​​of the functional items that have been changed.

2. A formulation determination method as described in claim 1, wherein a sensory evaluation value is predetermined for each functional item of the base formulation by sensory evaluation, and the change input step involves inputting changes to the sensory evaluation value.

3. The formulation determination method of claim 2, wherein the formulation determination step includes: a conversion step for converting a change in the sensory evaluation value into a change in score based on the relationship between a score set according to the ingredients contained in the base formulation for each functional item of the base formulation and the sensory evaluation value in a plurality of the base formulations; and a formulation determination step for determining the formulation of the ingredients according to the change in score.

4. A method for determining a formulation as described in claim 3, wherein a sensory evaluation value is predetermined for each functional item of the ingredients through sensory evaluation, and the score of the base formulation is set based on the sensory evaluation value, formulation amount, and potency of the ingredients.

5. The formulation determination method described in claim 3, wherein the change in score is expressed as a difference vector from a current position based on the sensory evaluation value of the base formulation to a target position based on a target score corresponding to the changed sensory evaluation value, and the formulation determination step sets a higher probability of selecting an ingredient the more similar the direction of movement from the current position based on the change in score when the ingredient is added to or subtracted from the base formulation is to the direction of the difference vector.

6. The composition determination method according to claim 5, wherein the composition determination step determines the amount of the selected material to be added or subtracted according to the set probability using a probability distribution centered on the amount that corresponds to the target position.

7. The formulation determination method described in claim 6, wherein the formulation determination step sets the current position to a position based on the score of the formulation changed by adding or subtracting the determined amount of the selected material from the base formulation, and the probability of selecting the material is set higher the higher the similarity between the direction of the difference vector from the current position to the target position and the direction of movement from the current position when the material is added to or subtracted from the changed formulation.

8. The composition determination method according to claim 7, wherein the composition determination step determines the amount of the ingredient selected according to the set probability to be added or subtracted using a probability distribution centered on the amount that matches the target position, sets the current position to a position based on the score of the composition changed by adding or subtracting the determined amount of the selected ingredient to or from the changed composition, and sets a higher probability of selecting the ingredient the higher the similarity between the direction of the difference vector from the current position to the target position and the direction of movement from the current position when the ingredient is added to or subtracted from the changed composition, by repeating this process a predetermined number of times to determine the composition of the ingredients.

9. A formulation determination method according to any one of claims 3 to 8, further comprising a display step of displaying the determined formulations of the plurality of ingredients in order of similarity to the target score corresponding to the changed sensory evaluation value.

10. A method for determining a blending composition according to any one of claims 3 to 9, wherein the relationship is updated using a sensory evaluation value determined by sensory evaluation for each functional item of the blending composition of ingredients determined by the blending determination step, and a score set according to the ingredients included in the determined blending composition of ingredients.

11. The method for determining a blend according to any one of claims 1 to 10, wherein the functional items include at least one selected from sweetness, saltiness, sourness, bitterness, umami, and spiciness.

12. A formulation determination method according to any one of claims 1 to 11, further comprising a cost setting step in which a desired cost value for the food is set, a cost value being set for each of the ingredients, and the formulation determination step determining the formulation of ingredients based on the value related to the changed function and the cost value.

13. A formulation determination system that determines the formulation of food ingredients based on a user's requests, comprising: an input unit for inputting a desired base formulation from a plurality of base formulations preset for a specified food product and changes to values ​​related to the selected functional items of the base formulation; and a formulation determination unit that determines the formulation of ingredients based on the values ​​related to the changed functional items.

Images