CN111950721A - Flavor identification method based on smell-taste joint perception model - Google Patents

Flavor identification method based on smell-taste joint perception model Download PDF

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CN111950721A
CN111950721A CN202011070975.3A CN202011070975A CN111950721A CN 111950721 A CN111950721 A CN 111950721A CN 202011070975 A CN202011070975 A CN 202011070975A CN 111950721 A CN111950721 A CN 111950721A
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门洪
郑文博
石岩
英宇翔
刘晶晶
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Northeast Electric Power University
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Abstract

The invention discloses a flavor recognition method based on a smell-taste joint perception model, which relates to the technical field of flavor recognition methods and comprises the steps of establishing the smell-taste joint perception model, inputting flavor information of different samples acquired by a smell-taste sensor into the smell-taste joint perception model, extracting the characteristics of the smell-taste joint perception model under the condition of inputting the flavor information by a wavelet packet transformation method, and inputting the characteristics into a GS-SVM model to realize qualitative classification of the different samples. The construction of the olfactory-gustatory synesthesia perception model is based on olfactory-gustatory synesthesia nerve conduction path of human body, which comprises construction of olfactory-gustatory synaptitus solitary nucleus module, construction of olfactory-gustatory synaptitus thalamus ventral posterior medial nucleus module and construction of olfactory-gustatory connection structure module. The flavor identification method can be used for carrying out qualitative analysis on different flavor information more accurately.

Description

Flavor identification method based on smell-taste joint perception model
Technical Field
The invention relates to the technical field of flavor recognition methods, in particular to a flavor recognition method based on a smell-taste joint perception model.
Background
At present, research aiming at olfactory-gustatory nerve conduction mechanisms is mostly connected with human sensory tests, relevant scholars have researched olfactory-gustatory synaesthesia mechanisms of a cerebral cortex central nervous system through means of physiological anatomy, electrophysiological experiments, an electromagnetic imaging technology, a brain imaging technology and the like, and documents disclose that the smell of food exposed in the air can have a remarkable influence on the appetite of a testee, and things which make people feel pleasurable can improve the hunger of the testee so as to increase the appetite; it is stated in the literature that food is a product category in which olfactory stimuli greatly contribute to the overall taste experience, and most of the food-related sensory pleasure and evaluation of taste is dependent on olfactory perception, as evidenced by studies on the taste perception of patients with olfactory impairment; the information synaesthesia ability of the olfactory and gustatory systems is found by people to form food perception and favorable feeling, and is verified by experiments, and the result shows that the primary olfactory cortex is identified as a possible site playing an important role in forming the food perception and favorable feeling. There are papers and patents which propose smell and taste perception models respectively, but the sensory experience and sensory evaluation of foods are usually the sensory mixing phenomenon after the synergistic action of human taste and smell, and both models start from the perspective of a single sensory channel and do not consider the sensory mixing phenomenon between smell and taste.
The above research aiming at the olfactory-olfactory synesthesia mechanism is mainly the research result obtained by physiological experiments or a single olfactory/gustatory system, but the overall olfactory-olfactory synesthesia mechanism and the perception capability thereof are not researched, and high bionics cannot be realized.
Disclosure of Invention
The invention mainly aims to provide a flavor identification method based on a smell-taste joint perception model.
The technical scheme adopted by the invention is as follows: a flavor identification method based on a smell-taste joint perception model comprises the following steps:
s1: establishing a topological structure and dynamic characteristic description model of an orphan beam kernel module in a smell-taste joint perception model;
s2: establishing a topological structure and dynamic characteristic description model of a thalamus ventral posterior medial nucleus module in the olfactory-olfactory synaptism perception model;
s3: establishing a topological structure and dynamic characteristic description model of a connection structure module in a smell-taste joint perception model;
s4: constructing an olfactory-gustatory synaesthesia perception model by combining the modules in the steps S1-S3 and the cranial nerve module, the trigeminal nerve module, the islet cortex module, the pericyte module, the olfactory bulb module, the anterior olfactory nucleus module, the anterior piriformis cortex module, the exocortical layer module and the olfactory-gustatory feedback module;
s5: acquiring flavor information of different samples through a smell-taste sensor and inputting the flavor information into the smell-taste joint perception model in the step S4;
s6: the characteristic of the smell-taste joint perception model under the condition of inputting flavor information of different samples is extracted through a wavelet packet transformation method, and the characteristic is input into a GS-SVM model, so that qualitative classification of the different samples is realized.
Furthermore, the topological structure of the solitary bundle nucleus module is in a multi-input multi-output structure mode, the solitary bundle nucleus module receives the outputs of the facial nerve module, the glossopharyngeal nerve module, the vagus nerve module and the trigeminal nerve module and the feedback information of the islet cortex module, and outputs taste information to the thalamus retroperitoneal inner nucleus module and the islet cortex module, wherein the topological structure of the trigeminal nerve module is in a single-output structure mode; the trigeminal module outputs information to the solitary bundle nucleus module, and is provided with a node J5；
The isolated beam nuclear module is provided with four nodes J1、J2、K1And K2The dynamics description models are differential equations of formula (1) to formula (4), respectively:
Figure DEST_PATH_IMAGE001
（1）
Figure 972904DEST_PATH_IMAGE002
（2）
Figure DEST_PATH_IMAGE003
（3）
Figure 850861DEST_PATH_IMAGE004
（4）
wherein, a and b1All are differential equation coefficients, and n is the number of channels of the taste perception model;
J1i(t）、J2i(t)、K1i(t)、K2i(t) are respectively the ith passage J1、J2、K1、K2Potential state of the node (i =1, …, n);
Q(J1i(t))，Q(J2i(t))，Q(K1i(t))，Q(K2i(t))，Q(E1i(t))，Q(E2i(t))，Q(E3i(t)) and Q (J)5i(t)) is the ith channel J1， J2，K1，K2，E1，E2，E3，J5An output of the node;
E1，E2，E3respectively are nodes of a facial nerve module, a glossopharyngeal nerve module and a vagus nerve module in the cranial nerve module in the taste perception model;
Q(J1j(t)) is the jth channel J1The output of the node (j =1, …, n); q (K)1j(t)) is the jth channel K1An output of the node;
D6(t) is the potential state of the feedback link from the islet cortex to the solitary nucleus in the gustatory nerve conduction pathway;
Wjjfor all channels J1The connection coefficient between the nodes;
Wj1j2，Wj2j1is the same channel J1And J2Connection coefficient of nodes, all channels J1And J2The connection coefficient values of the nodes are consistent;
Wj1k1，Wk1j1is the same channel J1And K1Connection coefficient of nodes, all channels J1And K1The connection coefficient values of the nodes are consistent;
Wj1k2，Wk2j1is the same channel J1And K2Connection coefficient of nodes, all channels J1And K2The connection coefficient values of the nodes are consistent;
Wj1e1is the same channel J1And E1Connection coefficient of nodes, all channels J1And E1The connection coefficient values of the nodes are consistent;
Wj1e2is the same channel J1And E2Connection coefficient of nodes, all channels J1And E2The connection coefficient values of the nodes are consistent;
Wj1e3is the same channel J1And E3Connection coefficient of nodes, all channels J1And E3The connection coefficient values of the nodes are consistent;
Wj1j5is the same channel J1And J5Connection coefficient of nodes, all channels J1And J5The connection coefficient values of the nodes are consistent;
Wj2k1，Wk1j2is the same channel J2And K1Connection coefficient of nodes, all channels J2And K1The connection coefficient values of the nodes are consistent;
Wkkfor all channels K1The connection coefficient between the nodes;
Wk1k2，Wk2k1is the same channel K1And K2Connection coefficient of nodes, all channels K1And K2The connection coefficient values of the nodes are consistent;
Kd6is J1Node and feedback link D6Is connected withA coefficient; the values of i and j are all 1 to n, i is not equal to j, and n is the number of channels of the taste perception model.
Furthermore, the topological structure of the thalamus ventral posterior medial nucleus module is a multi-input single-output structure mode, namely the thalamus ventral posterior medial nucleus module receives the gustatory information of the solitary fasciculus nucleus module and the insular cortex module and the olfactory information of the front piriformis cortex module and outputs the gustatory information to the insular cortex module, and the thalamus ventral posterior medial nucleus module is provided with four nodes which are J nodes respectively3、J4、K3、K4The dynamics description models are differential equations of equations (8) to (11), respectively:
Figure DEST_PATH_IMAGE005
（8）
Figure 826776DEST_PATH_IMAGE006
（9）
Figure DEST_PATH_IMAGE007
（10）
Figure 410948DEST_PATH_IMAGE008
（11）
wherein, a and b1All are differential equation coefficients, and n is the number of channels of the taste perception model;
J3(t)，J4(t)，K3(t)， K4(t) is J3，J4，K3，K4The potential state of the node;
Q(J3(t))，Q(J4(t))，Q(K3(t))，Q(K4(t))，Q(B3(t)) is J3，J4，K3，K4，B3An output of the node;
B3the nodes are front piriform cortex module nodes;
Q(J1j(t)) is the jth channel J1The output of the node, wherein j takes the value of 1 to n;
D5(t) is the potential state of the feedback link from the islet cortex to the retrothalamic medial nucleus in the taste transduction pathway;
Wj3j1for all channels J1Node and J3The connection coefficient of the node;
Wj3j4，Wj4j3is J3And J4The connection coefficient of the node;
Wj3k3，Wk3j3is J3And K3The connection coefficient of the node;
Wj3k4，Wk4j3is J3And K4The connection coefficient of the node;
Wj4k3，Wk3j4is J4And K3The connection coefficient of the node;
Wk3k4，Wk4k3is K3And K4The connection coefficient of the node;
Kd5is J3Node and feedback link D5The connection coefficient between;
Figure DEST_PATH_IMAGE009
is the potential state of the central noise of the taste perception model.
Furthermore, the topological structure of the linkage structure module is a multi-input structure mode, the linkage structure module receives the taste information of the island cortex module of the taste nervous system and the posterior medial nucleus module of the thalamus abdomen and the smell information of the outer cortex module of the smell nervous system, and the dynamic characteristic description model of the N node of the smell-taste linkage structure module is a differential equation of a formula (22):
Figure 59098DEST_PATH_IMAGE010
（22）
wherein, a and b1All are differential equation coefficients, and s is the number of channels of the taste perception model minus 1;
Nl(t) is the potential state of the nth node of the l channel, l =1,2, …, N; n is the number of channels of the taste perception model;
Q(K3(t)), Q (M (t)), Q (C (t)) is K3The output of node M, C;
Q(Nj(t)) is the output of the N node of the j channel, wherein l takes on the value from 1 to s;
Wnnconnecting coefficients among N nodes of all channels;
Wnk3is K3And the connection coefficient of the N node;
Wnmthe connection coefficient of the N node and the M node is obtained;
Wncthe connection coefficient of the N node and the C node is obtained;
q (C (t)) is the output of the skin layer module, which is represented by the C node;
q (M (t)) is the output of the island cortex module, which is represented by the M node.
Further, in the olfactory-olfactory joint perception model, trigeminal module J5Node and isolated beam nucleus module J1Node association, J5The node outputs the excitability information to J1A node;
b of front piriform cortex module3J for outputting inhibitory olfactory information to posterior medial nucleus module of thalamus abdomen3A node, the fiber link connecting the sniff-gustatory perception model;
olfactory information from the cortex module and gustatory information from the islet cortex module are input to the N nodes of the olfactory-gustatory linkage module in a linear superposition, and furthermore, the N nodes receive information from the thalamus ventral medial nucleus module K3An output of the node;
lateral connection exists among N nodes of all channels of the olfactory-olfactory joint perception model, and each channel N node receives excitatory output from other channel N nodes.
The invention has the advantages that:
the flavor recognition method provided by the invention describes the processing process of human flavor information from the aspect of the olfaction-flavor nerve conduction dynamic characteristic, is close to the olfaction-flavor joint perception experience of human, and improves the bionics degree of a flavor recognition system.
The flavor identification method of the invention can accurately identify the flavor information of wines or other liquid beverages, and make qualitative classification, and can be widely applied to the production and identification of wines and other beverages.
In addition to the objects, features and advantages described above, other objects, features and advantages of the present invention are also provided. The present invention will be described in further detail below with reference to the drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention.
FIG. 1 is a topological structure of an orphan bundle kernel module of a flavor identification method based on a smell-taste joint perception model of the invention;
FIG. 2 is a topological structure of a trigeminal nerve module of a flavor identification method based on a smell-taste joint perception model of the invention;
FIG. 3 is a topological structure of a thalamus retroventral medial nucleus module of a flavor recognition method based on a smell-taste joint perception model of the invention;
FIG. 4 is a topological structure of a front piriformis layer module of the flavor identification method based on a smell-taste joint perception model;
FIG. 5 is a topological structure of a connection structure module of a flavor identification method based on a smell-taste joint perception model of the invention;
FIG. 6 is a topological structure of an outer skin layer module of a flavor identification method based on a smell-taste joint perception model of the invention;
FIG. 7 is a topological structure of an island cortex module of a flavor identification method based on a smell-taste joint perception model of the invention;
FIG. 8 is a topological structure of an overall olfactory-olfactory simultaneous perception model of the olfactory-olfactory simultaneous perception model-based flavor recognition method of the present invention;
FIG. 9 is a flow chart of a flavor recognition method based on a smell-taste joint perception model according to the present invention;
FIG. 10 is a radar chart of flavor information of beer of different brands based on a flavor recognition method of a smell-taste joint perception model of the invention;
FIG. 11 is a parameter search process of GS-SVM in the flavor recognition method based on the olfactory-olfactory joint perception model of the present invention;
fig. 12 shows the classification result of the GS-SVM in the flavor recognition method based on the olfactory-olfactory joint perception model according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1 to 12, a flavor recognition method based on a smell-taste joint perception model includes the following steps:
s1: establishing a topological structure and dynamic characteristic description model of an orphan beam kernel module in a smell-taste joint perception model;
s2: establishing a topological structure and dynamic characteristic description model of a thalamus ventral posterior medial nucleus module in the olfactory-olfactory synaptism perception model;
s3: establishing a topological structure and dynamic characteristic description model of a connection structure module in a smell-taste joint perception model;
s4: constructing an olfactory-gustatory synaesthesia perception model by combining the modules in the steps S1-S3 and the cranial nerve module, the trigeminal nerve module, the islet cortex module, the pericyte module, the olfactory bulb module, the anterior olfactory nucleus module, the anterior piriformis cortex module, the exocortical layer module and the olfactory-gustatory feedback module;
s5: acquiring flavor information of different samples through a smell-taste sensor and inputting the flavor information into the smell-taste joint perception model in the step S4;
s6: the characteristic of the smell-taste joint perception model under the condition of inputting flavor information of different samples is extracted through a wavelet packet transformation method, and the characteristic is input into a GS-SVM model, so that qualitative classification of the different samples is realized.
In this embodiment, the topological structure of the solitary bundle nucleus module is in a multi-input multi-output structural mode, the solitary bundle nucleus module receives the outputs of the facial nerve module, the glossopharyngeal nerve module, the vagus nerve module and the trigeminal nerve module and the feedback information of the islet cortex module, and outputs taste information to the thalamus retroperitoneal inner nucleus module and the islet cortex module, wherein the topological structure of the trigeminal nerve module is in a single-output structural mode; as shown in FIG. 2, the trigeminal module outputs information to the solitary bundle nucleus module, which is provided with a node J5；
The isolated beam nuclear module is provided with four nodes J1、J2、K1And K2The dynamics description models are differential equations of formula (1) to formula (4), respectively:
Figure 255593DEST_PATH_IMAGE012
（1）
Figure 633485DEST_PATH_IMAGE014
（2）
Figure 437493DEST_PATH_IMAGE016
（3）
Figure 385989DEST_PATH_IMAGE018
（4）
wherein, a and b1Are coefficients of differential equations, n is taste perceptionThe number of channels of the model;
J1i(t）、J2i(t)、K1i(t)、K2i(t) are respectively the ith passage J1、J2、K1、K2Potential state of the node (i =1, …, n);
Q(J1i(t))，Q(J2i(t))，Q(K1i(t))，Q(K2i(t))，Q(E1i(t))，Q(E2i(t))，Q(E3i(t)) and Q (J)5i(t)) is the ith channel J1， J2，K1，K2，E1，E2，E3，J5An output of the node;
E1，E2，E3respectively are nodes of a facial nerve module, a glossopharyngeal nerve module and a vagus nerve module in the cranial nerve module in the taste perception model;
Q(J1j(t)) is the jth channel J1The output of the node (j =1, …, n); q (K)1j(t)) is the jth channel K1An output of the node;
D6(t) is the potential state of the feedback link from the islet cortex to the solitary nucleus in the gustatory nerve conduction pathway;
Wjjfor all channels J1The connection coefficient between the nodes;
Wj1j2，Wj2j1is the same channel J1And J2Connection coefficient of nodes, all channels J1And J2The connection coefficient values of the nodes are consistent;
Wj1k1，Wk1j1is the same channel J1And K1Connection coefficient of nodes, all channels J1And K1The connection coefficient values of the nodes are consistent;
Wj1k2，Wk2j1is the same channel J1And K2Connection coefficient of nodes, all channels J1And K2The connection coefficient values of the nodes are consistent;
Wj1e1is the same channel J1And E1Connection coefficient of nodes, all channels J1And E1The connection coefficient values of the nodes are consistent;
Wj1e2is the same channel J1And E2Connection coefficient of nodes, all channels J1And E2The connection coefficient values of the nodes are consistent;
Wj1e3is the same channel J1And E3Connection coefficient of nodes, all channels J1And E3The connection coefficient values of the nodes are consistent;
Wj1j5is the same channel J1And J5Connection coefficient of nodes, all channels J1And J5The connection coefficient values of the nodes are consistent;
Wj2k1，Wk1j2is the same channel J2And K1Connection coefficient of nodes, all channels J2And K1The connection coefficient values of the nodes are consistent;
Wkkfor all channels K1The connection coefficient between the nodes;
Wk1k2，Wk2k1is the same channel K1And K2Connection coefficient of nodes, all channels K1And K2The connection coefficient values of the nodes are consistent;
Kd6is J1Node and feedback link D6The connection coefficient between; the values of i and j are all 1 to n, i is not equal to j, and n is the number of channels of the taste perception model.
In this example, the cranial nerve model in the taste perception model (E)1，E2，E3) The dynamic characterization model of (1):
Figure DEST_PATH_IMAGE020
(5)
Figure DEST_PATH_IMAGE022
(6)
Figure 589437DEST_PATH_IMAGE024
(7)
E1i(t）、E2i(t)、E3i(t) are respectively the ith channel E1、E2、E3The potential state of the node; i =1,2, …, n, n is the number of channels of the taste perception model;
Wr1，Wr2，Wr3is the same channel E1，E2And E3The connection coefficient of the nodes and the RG is consistent with the connection coefficient of all channels;
We3e2is the same channel E2And E3The connection coefficient between the nodes is consistent with the connection coefficient values of all the channels;
NGp(t) is the potential state of the peripheral noise in the taste perception model; a and b1Is the differential equation coefficient; RG is the external stimulus in the taste perception model; RG (route group)iAnd (t) is the potential state of the external stimulus of the ith channel taste perception model.
In this embodiment, the topology of the thalamocortical nucleus module is a multi-input single-output structure mode, as shown in fig. 3, that is, the thalamocortical nucleus module receives the gustatory information of the solitary nucleus module, the islet cortex module, and the olfactory information of the anterior piriformis cortex module, and outputs the received information to the islet cortex module, and the thalamocortical nucleus module is provided with four nodes, J being respectively3、J4、K3、K4The dynamics description models are differential equations of equations (8) to (11), respectively:
Figure 759518DEST_PATH_IMAGE026
（8）
Figure 265586DEST_PATH_IMAGE028
（9）
Figure 630271DEST_PATH_IMAGE030
（10）
Figure DEST_PATH_IMAGE032
（11）
wherein, a and b1All are differential equation coefficients, and n is the number of channels of the taste perception model;
J3(t)，J4(t)，K3(t)， K4(t) is J3，J4，K3，K4The potential state of the node;
Q(J3(t))，Q(J4(t))，Q(K3(t))，Q(K4(t))，Q(B3(t)) is J3，J4，K3，K4，B3An output of the node;
Q(J1j(t)) is the jth channel J1The output of the node, wherein j takes the value of 1 to n;
D5(t) is the potential state of the feedback link from the islet cortex to the retrothalamic medial nucleus in the taste transduction pathway;
Wj3j1for all channels J1Node and J3The connection coefficient of the node;
Wj3j4，Wj4j3is J3And J4The connection coefficient of the node;
Wj3k3，Wk3j3is J3And K3The connection coefficient of the node;
Wj3k4，Wk4j3is J3And K4The connection coefficient of the node;
Wj4k3，Wk3j4is J4And K3The connection coefficient of the node;
Wk3k4，Wk4k3is K3And K4The connection coefficient of the node;
Kd5is J3Node and feedback link D5The connection coefficient between;
Figure DEST_PATH_IMAGE034
is the potential state of the central noise of the taste perception model.
In this example, Q (B)3(t)) is B3The output of the node is the output of a front piriformis layer module in the olfactory perception model; the topological structure of the front piriformis layer module is in a multi-input and multi-output structure mode, as shown in fig. 4, the front piriformis layer module receives information of the olfactory bulb module and the exocortical layer module and outputs the information to the exocortical layer module, the thalamus abdomen posterior medial nucleus module and the front olfactory nucleus module;
the front piriform cortex module is provided with four nodes A1、A2、B3、B4The dynamics description models are differential equations of equations (12) to (15), respectively:
Figure 637411DEST_PATH_IMAGE036
（12）
Figure 724315DEST_PATH_IMAGE038
（13）
Figure 135705DEST_PATH_IMAGE040
（14）
Figure 996476DEST_PATH_IMAGE042
（15）
wherein, a and b2All are differential equation coefficients, and m is the number of channels of the olfactory perception model; a. the1(t）、A2(t)、B3(t)、B4(t) are each A1、A2、B3、B4The potential state of the node;
Q(A1(t))，Q(A2(t))， Q(B3(t))，Q(B4(t)), Q (C (t)) is A1，A2，B3，B4Outputting a C node, wherein the C node is a topological form of an exodermis layer module in the olfactory perception model;
Q(M1k(t)) is the k-th channel M1Output of node, M1The nodes are key nodes of the olfactory bulb in the olfactory transduction model (k =1, …, m);
Wb3cis B3And the connection coefficient between the C nodes;
Wa1mfor all channels M1Node A and node B1The connection coefficient between the nodes;
Wa1a2，Wa2a1is A1And A2The connection coefficient of the node;
Wa1b3，Wb3a1is A1And B3The connection coefficient of the node;
Wa1b4，Wb4a1is A1And B4The connection coefficient of the node;
Wa2b3and Wb3a2Is A2And B3The connection coefficient of the node;
Wb3b4，Wb4b3is B3And B4The connection coefficient of the node.
In this embodiment, the olfactory perception model includes an olfactory bulb module (M)1，M2，G1，G2) The dynamic characteristic description model of (1) is as follows:
Figure 885935DEST_PATH_IMAGE044
（16）
Figure 92925DEST_PATH_IMAGE046
（17）
Figure 393325DEST_PATH_IMAGE048
（18）
Figure 787397DEST_PATH_IMAGE050
（19）
wherein M is1l(t）、M2l(t)、G1l(t)、G2l(t) are the first channels M, respectively1、M2、G1、G2The potential state of the node (l =1, …, m), where m is the number of channels of the olfactory perception model; a and b2Are all differential equation coefficients;
Q(M1l(t))，Q(M2l(t))，Q(G1l(t))，Q(G2l(t))，Q(P1l(t)) is the l-th channel M1，M2，G1，G2，P1Output of node, where P1The nodes are pericentral cell module nodes in the olfactory perception model;
Q(M1j(t)) is the jth channel M1The output of the node (j =1, …, m); q (G)1j(t)) is the jth channel G1An output of the node;
D1(t) is the potential state of the feedback link from the anterior olfactory nucleus to the olfactory bulb in the olfactory nerve conduction pathway;
D4(t) is the potential state of the feedback link from the exodermis to the olfactory bulb in the olfactory nerve conduction pathway;
Figure 431612DEST_PATH_IMAGE052
for all channels M1，G1The connection coefficient between the nodes;
Wm1g1，Wg1m1is the same channel M1And G1Connection of nodesConnecting coefficients, all channels M1And G1The connection coefficient values of the nodes are consistent;
Wm1g2，Wg2m1is the same channel M1And G2Connection coefficient of nodes, all channels M1And G2The connection coefficient values of the nodes are consistent;
Wm1p1is the same channel M1And P1Connection coefficient of nodes, all channels M1And P1The connection coefficient values of the nodes are consistent;
Wm1ris the same channel M1And ROlAll channels M1And ROlThe values of the connection coefficients are consistent; ROlThe external stimulation of the first channel is consistent with that of all channels; ROl(t) is the potential state of the first channel external stimulation;
Wm2g1，Wg1m2is the same channel M2And G1Connection coefficient of nodes, all channels M2And G1The connection coefficient values of the nodes are consistent;
Wg1g2is the same channel G1And G2Connection coefficient of nodes, all channels G1And G2The connection coefficient values of the nodes are consistent;
Wm1m2is the same channel M1And M2Connection coefficient of nodes, all channels M1And M2The connection coefficient values of the nodes are consistent.
In this example, the pericyte module (P) in the olfactory perception model1，P2) The dynamic characteristic description model of (1) is as follows:
Figure 961950DEST_PATH_IMAGE054
（20）
Figure 980722DEST_PATH_IMAGE056
（21）
wherein, P1l(t）、P2l(t) is the first channel P1、P2The potential state of the node (l =1, …, m), where m is the number of channels of the olfactory perception model; a and b2Are all differential equation coefficients;
Q(P1l(t)) is the first channel P1An output of the node;
Q(P1j(t)) is the jth channel P1The output of the node (j =1, …, m);
D2(t) is the potential state of the feedback link from the anterior olfactory nucleus to the pericyte in the olfactory nerve conduction pathway;
Wppfor all channels P1The connection coefficient between the nodes;
Wp1p2is the same channel P1And P2Connection coefficient of nodes, all channels P1And P2The connection coefficient values of the nodes are consistent.
In this embodiment, the topology of the connection structure module is a multi-input structure mode, as shown in fig. 5, the connection structure module receives gustatory information of the gustatory nervous system island cortex module and the thalamus ventral posterior medial nucleus module and olfactory information of the olfactory nervous system cortex module, and the dynamics characteristic description model of the node N of the olfactory-gustatory connection structure module is a differential equation of formula (22):
Figure 862090DEST_PATH_IMAGE058
（22）
wherein, a and b1All are differential equation coefficients, and s is the number of channels of the taste perception model minus 1;
Nl(t) is the potential state of the nth node of the l channel, l =1,2, …, N; n is the number of channels of the taste perception model;
Q(K3(t)), Q (M (t)), Q (C (t)) is K3The output of node M, C;
Q(Nj(t)) is the output of the N node of the j channel, wherein l takes on the value from 1 to s;
Wnnconnecting coefficients among N nodes of all channels;
Wnk3is K3And the connection coefficient of the N node;
Wnmthe connection coefficient of the N node and the M node is obtained;
Wncthe connection coefficient of the N node and the C node is obtained;
q (c (t)) is the output of the cortex module, the topological structure of the cortex module is a single-input multiple-output structural mode, as shown in fig. 6, the cortex module receives olfactory information of the front piriformis module and outputs the information to the olfactory bulb module, the front piriformis module and the connection structural module; the cortex module is represented by a C node;
q (m (t)) is an output of the island cortex module, and the topological structure of the island cortex module is a multi-input multi-output structural mode, as shown in fig. 7, the island cortex module receives information of the solitary fasciculation nucleus module and the thalamus ventral posterior medial nucleus module, and outputs information to the solitary fasciculation nucleus module, the thalamus ventral posterior medial nucleus module and the connection structural module; the island cortex modules are represented by M nodes.
In this embodiment, in the olfactory-olfactory joint perception model, as shown in fig. 8, the trigeminal module J5Node and isolated beam nucleus module J1Node association, J5The node outputs the excitability information to J1A node;
J1also received is module E from cranial nerves1、E2、E3Excitability output information of the node;
front piriform cortex module A1The node accepts all M's from the sniffer ball module1Olfactory processing information of the node and outputting the information to I of the pre-olfactory nucleus module1A node;
b of front piriform cortex module3J for outputting inhibitory olfactory information to posterior medial nucleus module of thalamus abdomen3A node, the fiber link connecting the sniff-gustatory perception model;
the olfactory information from the cortex module and the gustatory information from the islet cortex module are input to the N node of the olfactory-gustatory linkage module in a linear superposition manner, and furthermore, the N node receives the olfactory information from the posterior-medial ventral thalamusNuclear Module K3An output of the node;
lateral connection exists among N nodes of all channels of the olfactory-olfactory joint perception model, and each channel N node receives excitatory output from other channel N nodes.
And (3) experimental verification:
in the present invention, the flavor information recognition process and qualitative classification of the beer of different brands by the flavor recognition method of the present invention will be described in detail by taking beer of different brands as an example, but the present invention is not limited to beer in practical use, and flavor information of other alcoholic beverages and liquid beverages may be accurately and qualitatively classified.
(I) Experimental Equipment and Material
The different samples can be 5 kinds of beer with different brands, and the specific parameters are shown in the table 1:
serial number Brand Alcohol content (% vol) Concentration of raw wheat juice (1)。P） Raw materials Producing area
1 Blue belt ≥4.3 11 Water, malt, rice, hop Guangdong (Chinese character of Guangdong)Zhaoqing
2 Snow flake ≥3.3 9 Water, malt, rice, hop Jilin Changchun
3 Baiwei medicine ≥3.6 9.7 Water, malt, wheat, hop Tangshan mountain of Hebei river
4 Harbin ≥3.6 9.1 Water, malt, rice, hop Wuhan Hubei
5 Qingdao (Qingdao) ≥4.3 11 Water, malt, rice, hop Shandong Qingdao
TABLE 1 parameter information for different brands of beer
The olfactory sensor can be a PEN3 electronic nose, and a sensor array of the electronic nose comprises 10 metal oxide sensors, so that cross-sensitive detection of odor information can be realized.
The taste sensor can be an SA-402B electronic tongue, the sensor array of which consists of 2 reference electrodes and 5 basic taste sensors; the basic taste sensor can realize the sensory information detection of 5 basic tastes including sour, fresh, salty, bitter and astringent tastes of a sample to be detected.
(II) a specific process for acquiring flavor information of beer of different brands:
1. olfactory information acquisition of beer of different brands
(1) An experimenter puts a 5ml beer sample into a 50ml sample bottle, screws down a bottle cap and keeps for 10 minutes to ensure that the gas at the top of the sealed bottle reaches a saturated state;
(2) prior to gas detection, the experimenter needs to clean and calibrate the sensor gas cell. The specific operation is as follows: introducing clean gas dried by active carbon with the flow rate of 300ml/min into the air chamber, and keeping the flow rate for 60 s;
(3) after the correction is completed, the detection is started. Each set of samples was tested for 100s to allow the sensor response to reach steady state. The sensor response value is G/G0（G0G), where G is the conductance of the sensor when the gas to be measured enters the chamber, G0Is the conductance of the sensor when pure air enters the air chamber.
(4) For each beer 18 parallel samples were prepared and for 5 beers 90 sets of data were obtained. The conductivity value at 60s of the sensor response curve was taken as a characteristic value for data analysis. The beer smell-taste information acquisition experiment conditions are consistent: the room temperature is 20 +/-0.5 ℃, and the relative humidity is 65 +/-2% RH.
2. Taste information acquisition for different brands of beer
(1) Placing a beer sample, a reference solution and a positive and negative electrode cleaning solution;
the reference solution is a solution containing 0.3 mmol/L tartaric acid and 30mmol/L potassium chloride; the preparation process of the anode cleaning solution comprises the following steps: adding 300mL of 95% ethanol into about 500mL of distilled water, fully stirring, adding 100mL of 1M hydrochloric acid solution, transferring the solution into a 1000mL volumetric flask for constant volume to obtain a positive electrode cleaning solution; the preparation process of the negative electrode cleaning solution comprises the following steps: after 7.46g of potassium chloride and 500mL of 95% ethanol were added to about 500mL of distilled water and stirred uniformly, 10mL of 1M potassium hydroxide solution was added and the mixture was transferred to a 1000mL volumetric flask for constant volume to obtain a negative electrode cleaning solution.
(2) Before the test starts, the positive electrode sensor array is placed into positive electrode cleaning solution, the negative electrode sensor array is placed into negative electrode cleaning solution and cleaned for 90s, after the test is finished, the positive electrode sensor array and the negative electrode sensor array are respectively placed into two containers containing reference solution and cleaned for 120s, the reference solution is replaced and cleaned continuously for 120s, and then the reference solution is replaced to enable the balance of the sensors to return to zero for 30s so as to ensure that output signals are stable;
(3) after the response output of the sensor reaches balance, starting to acquire taste information of the beer, wherein the detection time of each type of beer is 30s, after the measurement is finished, quickly cleaning the beer in a reference solution for 2 times, detecting the aftertaste value of the basic taste information in the replaced reference solution, finishing the measurement once, repeating the step (2), and cleaning and calibrating the sensor;
(4) 3 parallel samples are prepared for the beer of different brands, each group of samples are repeatedly detected for 6 times by setting system parameters, namely 18 groups of taste information data are intelligently obtained for the beer of each brand, 90 groups of taste data are obtained after the experiment is finished, and the voltage value of the 30 th s of the sensor response curve is taken as a characteristic value for data analysis.
And performing characteristic fusion and selection on the olfactory information and the gustatory information of the beer to finally obtain the flavor information of the beer. FIG. 10 shows radar charts of flavor information of beer of different brands (wherein C00: bitterness sensor; AE 1: astringency sensor; CA 0: acidity sensor; Cpa (COO): bitterness sensor aftertaste value detection; AAE: umami sensor; W1C, W3S, W3C, W5C, W1W, W2S, W1S are electronic nose metal oxide sensors).
Parameter value setting of differential equations of different modules of (II) smell-taste joint perception model
Setting parameter values according to a computer numerical simulation result of the smell-taste joint perception model:
TABLE 2 parameter values of the differential equations of all modules of the olfactory-olfactory Joint perception model
Figure DEST_PATH_IMAGE060
The method comprises the steps of extracting features of an olfactory-olfactory joint perception model under the condition that flavor information of different samples is input through a Wavelet Packet Transform (WPT) method, and inputting the features into a GS-SVM (grid search-support vector machine) model to realize qualitative classification of the different samples.
Referring to fig. 9 to 12, 5 different brands of beer flavor data are input into the olfactory-olfactory simultaneous perception model, and wavelet packet variances of N node output sequences of 5 channels OFC layers of the olfactory-olfactory simultaneous perception model are obtained by a WPT method.
The method divides 90 groups of characteristic data into 60 groups of training set data and 30 groups of test set data, selects a Radial Basis Function (RBF) as a kernel function, and optimizes a penalty factor in a support vector machine model by utilizing a grid search algorithm
Figure DEST_PATH_IMAGE062
And kernel parameters
Figure DEST_PATH_IMAGE064
. In this connection, it is possible to use,
Figure 968718DEST_PATH_IMAGE062
in the range of [0.25, 16 ]]，
Figure 619142DEST_PATH_IMAGE064
In the range of [0.0625, 16]. In the parameter searching process, the cross validation classification error rate is adopted as a fitness function, and the parameter with the maximum cross validation accuracy rate
Figure 277656DEST_PATH_IMAGE062
And
Figure 646321DEST_PATH_IMAGE064
and returning and establishing the model.
The method is used for detecting the smell-taste synaesthesia perception modelThe wave packet variance feature data set is processed, the parameter searching process is shown in FIG. 11, and the optimal parameter is found by searching the curve chart
Figure DEST_PATH_IMAGE066
And
Figure DEST_PATH_IMAGE068
and the GS-SVM cross validation accuracy is 93.33%, the classification accuracy is 96.67%, and the numerical result shows that the pattern recognition method provided by the invention can realize qualitative classification of beer samples of different brands.
The invention relates to a flavor identification method based on a smell-taste joint perception model, which has the working principle that: by using a smell-taste sensor, which can be an electronic nose-electronic tongue, the flavor information of different samples is obtained as external stimulation
Figure DEST_PATH_IMAGE070
Inputting the olfactory sensation to the olfactory-olfactory simultaneous perception model constructed by the invention in combination with external noise; and extracting the wavelet packet variance characteristics of the olfactory-olfactory joint perception model under the input stimulation, inputting the characteristics into a GS-SVM model, and verifying the effectiveness of the mode recognition method according to the qualitative classification result of the GS-SVM on flavor information of different samples.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (5)

1. A flavor recognition method based on a smell-taste joint perception model is characterized by comprising the following steps of
The following steps:
s1: establishing a topological structure and dynamic characteristic description model of an orphan beam kernel module in a smell-taste joint perception model;
s2: establishing a topological structure and dynamic characteristic description model of a thalamus ventral posterior medial nucleus module in the olfactory-olfactory synaptism perception model;
s3: establishing a topological structure and dynamic characteristic description model of a connection structure module in a smell-taste joint perception model;
s4: constructing an olfactory-gustatory synaesthesia perception model by combining the modules in the steps S1-S3 and the cranial nerve module, the trigeminal nerve module, the islet cortex module, the pericyte module, the olfactory bulb module, the anterior olfactory nucleus module, the anterior piriformis cortex module, the exocortical layer module and the olfactory-gustatory feedback module;
s5: acquiring flavor information of different samples through a smell-taste sensor and inputting the flavor information into the smell-taste joint perception model in the step S4;
s6: the characteristic of the smell-taste joint perception model under the condition of inputting flavor information of different samples is extracted through a wavelet packet transformation method, and the characteristic is input into a GS-SVM model, so that qualitative classification of the different samples is realized.
2. The method for flavor recognition based on olfactory-taste synaptic perception model according to claim 1, wherein the method comprises
Is characterized in that the topological structure of the solitary fasciculation nucleus module is a multi-input multi-output structure mode, the solitary fasciculation nucleus module receives the output of the facial nerve module, the glossopharyngeal nerve module, the vagus nerve module and the trigeminal nerve module and the feedback information of the islet cortex module and outputs taste information to the thalamus retroperitoneal inner side nucleus module and the islet cortex module, wherein the topological structure of the trigeminal nerve module is a single-output structure mode; the trigeminal module outputs information to the solitary bundle nucleus module, and is provided with a node J5；
The isolated beam nuclear module is provided with four nodes J1、J2、K1And K2The dynamics description models are differential equations of formula (1) to formula (4), respectively:
Figure 425778DEST_PATH_IMAGE002
（1）
Figure 670814DEST_PATH_IMAGE004
（2）
Figure 919393DEST_PATH_IMAGE006
（3）
Figure 908077DEST_PATH_IMAGE008
（4）
wherein, a and b1All are differential equation coefficients, and n is the number of channels of the taste perception model;
J1i(t）、J2i(t)、K1i(t)、K2i(t) are respectively the ith passage J1、J2、K1、K2Potential state of the node (i =1, …, n);
Q(J1i(t))，Q(J2i(t))，Q(K1i(t))，Q(K2i(t))，Q(E1i(t))，Q(E2i(t))，Q(E3i(t)) and Q (J)5i(t)) is the ith channel J1， J2，K1，K2，E1，E2，E3，J5An output of the node;
E1，E2，E3respectively are nodes of a facial nerve module, a glossopharyngeal nerve module and a vagus nerve module in the cranial nerve module in the taste perception model;
Q(J1j(t)) is the jth channel J1The output of the node (j =1, …, n); q (K)1j(t)) is the jth channel K1An output of the node;
D6(t) is the potential state of the feedback link from the islet cortex to the solitary nucleus in the gustatory nerve conduction pathway;
Wjjfor all channels J1The connection coefficient between the nodes;
Wj1j2，Wj2j1is the same channel J1And J2Connection coefficient of nodes, all channels J1And J2The connection coefficient values of the nodes are consistent;
Wj1k1，Wk1j1is the same channel J1And K1Connection coefficient of nodes, all channels J1And K1The connection coefficient values of the nodes are consistent;
Wj1k2，Wk2j1is the same channel J1And K2Connection coefficient of nodes, all channels J1And K2The connection coefficient values of the nodes are consistent;
Wj1e1is the same channel J1And E1Connection coefficient of nodes, all channels J1And E1The connection coefficient values of the nodes are consistent;
Wj1e2is the same channel J1And E2Connection coefficient of nodes, all channels J1And E2The connection coefficient values of the nodes are consistent;
Wj1e3is the same channel J1And E3Connection coefficient of nodes, all channels J1And E3The connection coefficient values of the nodes are consistent;
Wj1j5is the same channel J1And J5Connection coefficient of nodes, all channels J1And J5The connection coefficient values of the nodes are consistent;
Wj2k1，Wk1j2is the same channel J2And K1Connection coefficient of nodes, all channels J2And K1The connection coefficient values of the nodes are consistent;
Wkkfor all channels K1The connection coefficient between the nodes;
Wk1k2，Wk2k1is the same channel K1And K2Connection coefficient of nodes, all channels K1And K2The connection coefficient values of the nodes are consistent;
Kd6is J1Node and feedback link D6The connection coefficient between; the values of i and j are all 1 to n, i is not equal to j, and n is taste senseThe number of model channels is known.
3. The method for flavor recognition based on olfactory-taste synaptic perception model according to claim 1, wherein the method comprises
Is characterized in that the topological structure of the thalamus ventral posterior medial nucleus module is a multi-input single-output structure mode, namely the thalamus ventral posterior medial nucleus module receives the gustatory information of the solitary fasciculation nucleus module and the islet cortex module and the olfactory information of the front piriformis cortex module and outputs the gustatory information to the islet cortex module, the thalamus ventral posterior medial nucleus module is provided with four nodes which are J nodes respectively3、J4、K3、K4The dynamics description models are differential equations of equations (8) to (11), respectively:
Figure 191291DEST_PATH_IMAGE010
（8）
Figure 108694DEST_PATH_IMAGE012
（9）
Figure 641306DEST_PATH_IMAGE014
（10）
Figure 309048DEST_PATH_IMAGE016
（11）
wherein, a and b1All are differential equation coefficients, and n is the number of channels of the taste perception model;
J3(t)，J4(t)，K3(t)， K4(t) is J3，J4，K3，K4The potential state of the node;
Q(J3(t))，Q(J4(t))，Q(K3(t))，Q(K4(t))，Q(B3(t)) is J3，J4，K3，K4，B3An output of the node;
B3the nodes are front piriform cortex module nodes;
Q(J1j(t)) is the jth channel J1The output of the node, wherein j takes the value of 1 to n;
D5(t) is the potential state of the feedback link from the islet cortex to the retrothalamic medial nucleus in the taste transduction pathway;
Wj3j1for all channels J1Node and J3The connection coefficient of the node;
Wj3j4，Wj4j3is J3And J4The connection coefficient of the node;
Wj3k3，Wk3j3is J3And K3The connection coefficient of the node;
Wj3k4，Wk4j3is J3And K4The connection coefficient of the node;
Wj4k3，Wk3j4is J4And K3The connection coefficient of the node;
Wk3k4，Wk4k3is K3And K4The connection coefficient of the node;
Kd5is J3Node and feedback link D5The connection coefficient between;
Figure 571402DEST_PATH_IMAGE018
is the potential state of the central noise of the taste perception model.
4. The method for flavor recognition based on olfactory-taste synaptic perception model according to claim 1, wherein the method comprises
Characterized in that the topological structure of the connection structure module is a multi-input structure mode, the connection structure module receives taste information of an island cortex module of a taste nervous system and an island cortex module of a thalamus ventral posterior medial nucleus module and smell information of an outer cortex module of an olfactory nervous system, and a dynamic characteristic description model of an N node of the smell-taste connection structure module is a differential equation of a formula (22):
Figure 361504DEST_PATH_IMAGE020
（22）
wherein, a and b1All are differential equation coefficients, and s is the number of channels of the taste perception model minus 1;
Nl(t) is the potential state of the nth node of the l channel, l =1,2, …, N; n is the number of channels of the taste perception model;
Q(K3(t)), Q (M (t)), Q (C (t)) is K3The output of node M, C;
Q(Nj(t)) is the output of the N node of the j channel, wherein l takes on the value from 1 to s;
Wnnconnecting coefficients among N nodes of all channels;
Wnk3is K3And the connection coefficient of the N node;
Wnmthe connection coefficient of the N node and the M node is obtained;
Wncthe connection coefficient of the N node and the C node is obtained;
q (C (t)) is the output of the skin layer module, which is represented by the C node;
q (M (t)) is the output of the island cortex module, which is represented by the M node.
5. The method for flavor recognition based on olfactory-taste synaptic perception model according to claim 1, wherein the method comprises
Characterized in that in the olfactory-olfactory synaesthesia perception model, the trigeminal nerve module J5Node and isolated beam nucleus module J1Node association, J5The node outputs the excitability information to J1A node;
b of front piriform cortex module3J for outputting inhibitory olfactory information to posterior medial nucleus module of thalamus abdomen3A node, the fiber link connecting the sniff-gustatory perception model;
fromOlfactory information of the cortex module and gustatory information of the islet cortex module are input to the N node of the olfactory-gustatory linkage structure module in a linear superposition manner, and the N node receives olfactory information from the thalamus retroventral nucleus module K3An output of the node;
lateral connection exists among N nodes of all channels of the olfactory-olfactory joint perception model, and each channel N node receives excitatory output from other channel N nodes.
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