CN111175298A - Sweet taste solution detection device and method - Google Patents
Sweet taste solution detection device and method Download PDFInfo
- Publication number
- CN111175298A CN111175298A CN201911162573.3A CN201911162573A CN111175298A CN 111175298 A CN111175298 A CN 111175298A CN 201911162573 A CN201911162573 A CN 201911162573A CN 111175298 A CN111175298 A CN 111175298A
- Authority
- CN
- China
- Prior art keywords
- cell
- sweet taste
- solution
- value
- calculating
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
Abstract
The invention discloses a sweet taste solution detection device and method. The device includes computer, phase contrast microscope, infusion equipment and is used for placing taste receptor cell place the module, it includes the slide to place the module, the slide upper surface is equipped with the standing groove that is used for placing taste receptor cell, slide upper surface both sides are equipped with inlet and liquid outlet respectively, the slide upper surface still is equipped with the first guiding gutter of intercommunication inlet and standing groove, the second guiding gutter of intercommunication liquid outlet and standing groove, the inlet passes through pipeline and infusion equipment intercommunication, the liquid outlet passes through the tube coupling waste liquid collector, phase contrast microscope and computer electricity are connected. The invention utilizes the cell imaging method, does not contact with the detected cell, greatly reduces the interference brought by the outside, eliminates the interference factors influencing the instantaneous physiological state of the cell, and has quick detection aging and high detection accuracy.
Description
Technical Field
The invention relates to the technical field of solution detection, in particular to a sweet taste solution detection device and method.
Background
Taste cell sensors are one type of cell sensor that uses taste receptor cells as molecular recognition elements to determine the presence and concentration of certain taste types (e.g., sweet, bitter, salty, sour, etc.) for qualitative and quantitative analysis and real-time, rapid and non-destructive intelligent detection.
The current detection methods include patch clamp method, bionic taste detection method, and electrochemical cell sensor method. Although the patch clamp method can detect the ion channel response signal of a single cell, the instrument is inconvenient to use and operate and can only be used in a laboratory. Although the bionic taste detection technology can realize the classification of taste substances to a certain extent, the repeatability and the accuracy of the bionic taste detection technology are limited. Although the electrochemical cell sensor method can achieve the detection target of taste substances to some extent, its reproducibility is also limited.
Disclosure of Invention
In order to solve the technical problems, the invention provides a sweet taste solution detection device and a method, which utilize a cell imaging method, do not contact with a cell to be detected, greatly reduce the interference brought by the outside, eliminate interference factors influencing the instantaneous physiological state of the cell, and have the advantages of quick detection time effect and high detection accuracy.
In order to solve the problems, the invention adopts the following technical scheme:
the sweet taste solution detection device comprises a computer, a phase contrast microscope, infusion equipment and a placing module for placing taste receptor cells, wherein the placing module comprises a glass slide, a placing groove for placing the taste receptor cells is formed in the upper surface of the glass slide, a liquid inlet and a liquid outlet are respectively formed in two sides of the upper surface of the glass slide, a first flow guide groove for communicating the liquid inlet with the placing groove and a second flow guide groove for communicating the liquid outlet with the placing groove are further formed in the upper surface of the glass slide, the liquid inlet is communicated with the infusion equipment through a pipeline, the liquid outlet is connected with a waste liquid collector through a pipeline, and the phase contrast microscope is electrically connected with the computer.
In the scheme, during detection, taste receptor cells are placed in a placing groove of a glass slide, the glass slide is fixed on a stage of a phase difference microscope, pbs buffer solution is input into the placing groove from a liquid inlet by a transfusion device to clean the taste receptor cells, then sweet solution to be detected is input into the placing groove from the liquid inlet by the transfusion device, the pbs buffer solution is discharged from a liquid outlet, after a set amount of sweet solution to be detected is input by the transfusion device, the taste receptor cells are wrapped by the sweet solution to be detected, at the moment, the phase difference microscope collects morphological characteristics, color characteristics and texture characteristics of the taste receptor cells and conveys the morphological characteristics, the color characteristics and the texture characteristics to a computer, the steps are repeated for many times, and the computer comprehensively analyzes the morphological characteristics, the color characteristics and the change condition of the texture characteristics of the taste receptor cells to judge which sweet solution the.
During the detection, the temperature was maintained at 37 ℃. + -. 0.2 ℃. The taste receptor cell is LM3 liver cancer cell.
Preferably, the infusion device comprises a micro peristaltic pump.
Preferably, the axis of the first guide groove and the axis of the second guide groove are located on the same straight line.
Preferably, the axis of the first flow guide groove and the axis of the second flow guide groove both pass through the center of the placing groove.
The invention discloses a sweet taste solution detection method, which is used for the sweet taste solution detection device and comprises the following steps:
s1: the pbs buffer solution is conveyed into the placing groove by the micro peristaltic pump to clean the taste receptor cells, then the sweet taste solution to be tested is conveyed to the taste receptor cells in the placing groove by the micro peristaltic pump to be stimulated, the morphological characteristics, the color characteristics and the texture characteristics of the taste receptor cells are collected by a phase-contrast microscope and conveyed to a computer, and the computer calculates an evaluation index KC1 and an evaluation index KC 2;
s2: the step of repeatedly executing is S1N times, and N evaluation indexes KC1 and N evaluation indexes KC2 are obtained;
s3: inputting N evaluation indexes KC1 as input data KC1(t) into a layer of nonlinear dynamic model:
wherein V (x, t) is a potential function, x (t) is a Brownian motion particle motion trajectory function, a, b and c are set constants, ξ (t) is excitation noise, D is the intensity of the excitation noise,
is a periodic sinusoidal signal, a is the signal amplitude, f is the signal frequency, t is the motion time,
for phase, set
The first and second derivatives of V (x, t) for x are calculated and the equation is made equal to 0, resulting in a two-layer nonlinear dynamical model:
the noise intensity D is set to 0,
KCl (t) ═ 0; calculating to obtain a critical value of A
Substituting the critical value of A into a layer of nonlinear dynamics model, and setting x0(t)＝0，sn0And (3) solving a layer of nonlinear dynamic model by adopting a fourth-order long lattice Kutta algorithm to obtain:
and calculating:
wherein x isn(t) is the nth derivative of x (t), snn-1Is the value of the nth-1 derivative of S (t) at t ═ 0, snn+1Is the value of the nth +1 order derivative of S (t) at t-0, N-0, 1, …, N-1; can obtain x1(t)，x2(t)，…，xn+1(t) value;
for x1(t)，x2(t)，…，xn+1(t) integrating to obtain x (t), and calculating the maximum value x (t) of the absolute value of x (t)mMean value of x (t)
Using formulas
Calculating the signal-to-noise ratio SNR of the output of the second-order nonlinear dynamical model, wherein, Delta U is 3a3/20bc2，
Establishing a rectangular coordinate system by taking the excitation noise intensity D as an X axis and the signal-to-noise ratio SNR as a Y axis, drawing a signal-to-noise ratio SNR curve, and finding out an abscissa value X of a characteristic peak in the signal-to-noise ratio SNR curveeAnd the horizontal coordinate value xeComparing with the predetermined range of the abscissa value of the characteristic peak corresponding to each sweet taste solution, if x iseThe sweet taste solution to be detected is the corresponding sweet taste solution when the sweet taste solution is positioned in the range of the abscissa value of the characteristic peak corresponding to the sweet taste solution;
s4: and averaging the N evaluation indexes KC2 to obtain an average value as a y 'value, substituting the average value into a detection model corresponding to the sweet taste solution, wherein y' ═ hx '+ k, h and k are constants, and x' is the solution concentration, and calculating the concentration of the sweet taste solution to be detected.
Acquiring the range of the abscissa value of the characteristic peak corresponding to sucrose, glucose, sorbitol, sucralose and steviolbiose in advance, and determining which of the range of the abscissa value of the characteristic peak corresponding to the sweet solution to be tested is located, so that the sweet solution to be tested is the corresponding sweet solution, and the qualitative analysis of the sweet solution to be tested is realized.
Preferably, the phase contrast microscope collects morphological characteristics, color characteristics and texture characteristics of taste receptor cells and transmits the morphological characteristics, color characteristics and texture characteristics to the computer, and the computer calculates the evaluation indexes KC1 and KC2 by the following methods:
m1: extracting the cell area A', cell perimeter PS, cell eccentricity ECR and cell roundness RCR of the taste receptor cells by a phase contrast microscope;
m2: extracting the mean value MVS, standard deviation SDS, smoothness EVS, third-order moment TMS, consistency CSS and entropy ENS of the cell pixel value spatial distribution of the taste receptor cells by a phase-contrast microscope;
m3: calculating cell morphological characteristic factor
Calculating cell color characteristic factor
Calculating cell texture characteristic factor
M4: calculating an evaluation index KC1 and an evaluation index KC 2:
the area a ═ Σ f (x, y) is obtained by counting the number of pixels satisfying the condition f (x, y) ═ 1.
Perimeter PS: the sum of all the pixels occupied by the cell boundary is represented by calculating the sum of the distances between adjacent pixels on the cell region boundary, assuming that the boundary chain code of the cell region is { a }1a2…anAnd each code segment aiLength of (a) liExpressed, then the perimeter is expressed as:
wherein n isuIs the number of even codes in the chain code, nsThe number of odd codes is found for the chain code.
Eccentricity ECR: used for calculating the eccentric position of the cell nucleus in the cell, is the eccentricity with the equivalent standard second-order center distance in the cell range,
wherein c is the half-focal length in the cell range and q is the half-major axis distance in the cell range.
Cell roundness RCR:
assuming that z represents a random amount of gray levels, the corresponding histogram is: p (z)i) I-0, 1, 2 …, L-1, L representing the number of gray levels,
mean value
Standard deviation of
Smoothness of the surface
Third moment
Consistency
Entropy of the entropy
The evaluation index KC1 is used for highlighting the influence of the form and color on cells in the detection process, and simultaneously, the distance enlarging effect of the cell texture index on the cell detection effect is used in a differentiation mode, and is mainly reflected in a KC1 definition formula, the product of the form and the texture is added, and the product of the color and the texture in a denominator is subtracted, so that the differentiation of cell response signals under the stimulation of different kinds of solutions can be further enlarged, and the differentiation and the judgment are facilitated.
The evaluation index KC2 is a sphere-like system under three coordinates constructed by comprehensively utilizing the cell shape, color and texture detection parameters, and the change of the cell shape, color and texture is mapped to the external shape characteristics of the sphere-like system. Meanwhile, under the condition of single index, the cell texture index is used in a differentiation mode to enlarge the cell detection effect, namely the texture quality in the denominator is reduced, so that the differentiation of the cell morphology, the color and the texture response signal mapping sphere-like system under the stimulation of different kinds of solutions can be further increased, and the distinguishing and distinguishing effect is enhanced.
Preferably, the method for obtaining a model for assaying a certain sweet taste solution comprises the steps of:
the method comprises the steps of respectively and independently conveying g sweet taste solutions with different concentrations to taste receptor cells for stimulation, obtaining corresponding evaluation indexes KC2 by stimulating the taste receptor cells each time, establishing a rectangular coordinate system by taking the concentration of the sweet taste solution as an x axis and the evaluation index KC2 as a y axis, marking a point formed by the concentration of each sweet taste solution and the corresponding evaluation index KC2 in the rectangular coordinate system, and obtaining a detection model y '═ hx' + k of the sweet taste solution through linear fitting.
The invention has the beneficial effects that: (1) by using the cell imaging method, the detection system is not in contact with the detected cell, so that the cell is nondestructive and non-contact, the interference caused by the outside is greatly reduced for cell imaging, and the interference factors influencing the instantaneous physiological state of the cell are eliminated. (2) The detection time is good, the intracellular physiological state of the cells can be calibrated almost instantaneously, and a certain response time is needed by a method such as patch clamp or electrochemistry, so that the method provided by the invention has high detection time. (3) According to the obtained cell images, the characteristic quantities of the plurality of cell images are comprehensively summarized into three main indexes of form, color and texture, so that the comprehensive physiological change of the cells in the process of being stimulated by taste substances can be better reflected, and the detection accuracy is very high.
Drawings
FIG. 1 is a schematic view of a slide;
FIG. 2 is a flow chart of an embodiment;
FIG. 3 is an image of taste receptor cells;
FIG. 4 is a graph of signal-to-noise ratios obtained by stimulating taste receptor cells with multiple solutions.
In the figure: 1. slide, 2, standing groove, 3, inlet, 4, liquid outlet, 5, first guiding gutter, 6, second guiding gutter.
Detailed Description
The technical scheme of the invention is further specifically described by the following embodiments and the accompanying drawings.
Example (b): the sweet taste solution detection device of this embodiment, as shown in fig. 1, including the computer, it is microscope to differ, infusion equipment and the module of placing that is used for placing taste receptor cell, it includes slide 1 to place the module, 1 upper surface of slide is equipped with standing groove 2 that is used for placing taste receptor cell, 1 upper surface both sides of slide are equipped with inlet 3 and liquid outlet 4 respectively, 1 upper surface of slide still is equipped with the first guiding gutter 5 of intercommunication inlet 3 and standing groove 2, the second guiding gutter 6 of intercommunication liquid outlet 4 and standing groove 2, inlet 3 passes through pipeline and infusion equipment intercommunication, liquid outlet 4 passes through tube coupling waste liquid collector, it is connected with the computer electricity to differ microscope.
The infusion device includes a micro peristaltic pump. The axis of the first diversion trench and the axis of the second diversion trench are located on the same straight line. The standing groove is circular, and the axis of first guiding gutter and the axis of second guiding gutter all pass the center of standing groove.
In the scheme, during detection, taste receptor cells are placed in a placing groove of a glass slide, the glass slide is fixed on a stage of a phase difference microscope, pbs buffer solution is input into the placing groove from a liquid inlet by a transfusion device to clean the taste receptor cells, then sweet solution to be detected is input into the placing groove from the liquid inlet by the transfusion device, the pbs buffer solution is discharged from a liquid outlet, after a set amount of sweet solution to be detected is input by the transfusion device, the taste receptor cells are wrapped by the sweet solution to be detected, at the moment, the phase difference microscope collects morphological characteristics, color characteristics and texture characteristics of the taste receptor cells and conveys the morphological characteristics, the color characteristics and the texture characteristics to a computer, the steps are repeated for many times, and the computer comprehensively analyzes the morphological characteristics, the color characteristics and the change condition of the texture characteristics of the taste receptor cells to judge which sweet solution the.
During the detection, the temperature was maintained at 37 ℃. + -. 0.2 ℃. The taste receptor cell is LM3 liver cancer cell.
The method for detecting a sweet taste solution according to this embodiment is applied to the above sweet taste solution detection device, as shown in fig. 2, and includes the following steps:
s1: the pbs buffer solution is conveyed into the placing groove by the micro peristaltic pump to clean the taste receptor cells, then the sweet taste solution to be tested is conveyed to the taste receptor cells in the placing groove by the micro peristaltic pump to be stimulated, the morphological characteristics, the color characteristics and the texture characteristics of the taste receptor cells are collected by a phase-contrast microscope and conveyed to a computer, and the computer calculates an evaluation index KC1 and an evaluation index KC 2;
the morphological characteristics, color characteristics and texture characteristics of the taste receptor cells are collected by a phase-contrast microscope and transmitted to a computer, and the computer calculates the evaluation indexes KC1 and KC2 as follows:
m1: extracting the cell area A', cell perimeter PS, cell eccentricity ECR and cell roundness RCR of the taste receptor cells by a phase contrast microscope;
the area a ═ Σ f (x, y), that is, the number of pixels satisfying the condition f (x, y) ═ 1 is counted;
perimeter PS: the sum of all the pixels occupied by the cell boundary is represented by calculating the sum of the distances between adjacent pixels on the cell region boundary, assuming that the boundary chain code of the cell region is { a }1a2…anAnd each code segment aiLength of (a) liExpressed, then the perimeter is expressed as:
wherein n isuIs the number of even codes in the chain code, nsFinding the number of odd codes for the chain code;
eccentricity ECR: used for calculating the eccentric position of the cell nucleus in the cell, is the eccentricity with the equivalent standard second-order center distance in the cell range,
wherein c is the half-focal length in the cell range and q is the half-major axis distance in the cell range;
cell roundness RCR:
m2: extracting the mean value MVS, standard deviation SDS, smoothness EVS, third-order moment TMS, consistency CSS and entropy ENS of the cell pixel value spatial distribution of the taste receptor cells by a phase-contrast microscope;
assuming that z represents a random amount of gray levels, the corresponding histogram is: p (z)i) I-0, 1, 2 …, L-1, L representing the number of gray levels,
mean value
Standard deviation of
Smoothness of the surface
Third moment
Consistency
Entropy of the entropy
M3: calculating cell morphological characteristic factor
Calculating the cellsColor characteristic factor
Calculating cell texture characteristic factor
M4: calculating an evaluation index KC1 and an evaluation index KC 2:
s2: the step of repeatedly executing is S1N times, and N evaluation indexes KC1 and N evaluation indexes KC2 are obtained;
s3: inputting N evaluation indexes KC1 as input data KC1(t) into a layer of nonlinear dynamic model:
wherein V (x, t) is a potential function, x (t) is a Brownian motion particle motion trajectory function, a, b and c are set constants, ξ (t) is excitation noise, D is the intensity of the excitation noise,
is a periodic sinusoidal signal, a is the signal amplitude, f is the signal frequency, t is the motion time,
for phase, set
The first and second derivatives of V (x, t) for x are calculated and the equation is made equal to 0, resulting in a two-layer nonlinear dynamical model:
the noise intensity D is set to 0,
KC1(t) ═ 0; calculating to obtain a critical value of A
Substituting the critical value of A into a layer of nonlinear dynamics model, and setting x0(t)＝0，sn0And (3) solving a layer of nonlinear dynamic model by adopting a fourth-order long lattice Kutta algorithm to obtain:
and calculating:
wherein x isn(t) is the nth derivative of x (t), snn-1Is the value of the nth-1 derivative of S (t) at t ═ 0, snn+1Is the value of the nth +1 order derivative of S (t) at t-0, N-0, 1, …, N-1; can obtain x1(t)，x2(t)，…，xn+1(t) value;
for x1(t)，x2(t)，…，xn+1(t) integrating to obtain x (t), calculating the absolute value of x (t)For maximum value x (t)mMean value of x (t)
Using formulas
Calculating the signal-to-noise ratio SNR of the output of the second-order nonlinear dynamical model, wherein, Delta U is 3a3/20bc2，
Establishing a rectangular coordinate system by taking the excitation noise intensity D as an X axis and the signal-to-noise ratio SNR as a Y axis, drawing a signal-to-noise ratio SNR curve, and finding out an abscissa value X of a characteristic peak in the signal-to-noise ratio SNR curveeAnd the horizontal coordinate value xeComparing with the predetermined range of the abscissa value of the characteristic peak corresponding to each sweet taste solution, if x iseThe sweet taste solution to be detected is the corresponding sweet taste solution when the sweet taste solution is positioned in the range of the abscissa value of the characteristic peak corresponding to the sweet taste solution;
s3: and averaging the N evaluation indexes KC2 to obtain an average value as a y 'value, substituting the average value into a detection model corresponding to the sweet taste solution, wherein y' ═ hx '+ k, h and k are constants, and x' is the solution concentration, and calculating the concentration of the sweet taste solution to be detected.
The method for obtaining a model for testing a sweet taste solution comprises the following steps:
the method comprises the steps of respectively and independently conveying g sweet taste solutions with different concentrations to taste receptor cells for stimulation, obtaining corresponding evaluation indexes KC2 by stimulating the taste receptor cells each time, establishing a rectangular coordinate system by taking the concentration of the sweet taste solution as an x axis and the evaluation index KC2 as a y axis, marking a point formed by the concentration of each sweet taste solution and the corresponding evaluation index KC2 in the rectangular coordinate system, and obtaining a detection model y '═ hx' + k of the sweet taste solution through linear fitting.
In this protocol, an image of taste receptor cells is shown in FIG. 3. The taste receptor cell is LM3 liver cancer cell, and can distinguish sucrose, glucose, sorbitol, sucralose and stevia. Acquiring the range of the abscissa value of the characteristic peak corresponding to sucrose, glucose, sorbitol, sucralose and steviolbiose in advance, and determining which of the range of the abscissa value of the characteristic peak corresponding to the sweet solution to be tested is located, so that the sweet solution to be tested is the corresponding sweet solution, and the qualitative analysis of the sweet solution to be tested is realized.
The range of the abscissa value of the characteristic peak corresponding to the sucrose solution is as follows: [63.2, 64.1 ];
the characteristic peak abscissa value range corresponding to the glucose solution is as follows: [77.0, 80.9 ];
the range of the abscissa value of the characteristic peak corresponding to sorbitol is as follows: [68.5, 70.4 ];
the range of the abscissa value of the characteristic peak corresponding to sucralose is as follows: [60.3, 61.6 ];
the range of the abscissa value of the characteristic peak corresponding to the steviolbiose is as follows: [84.9, 85.5].
The evaluation index KC1 is used for highlighting the influence of the form and color on cells in the detection process, and simultaneously, the distance enlarging effect of the cell texture index on the cell detection effect is used in a differentiation mode, and is mainly reflected in a KC1 definition formula, the product of the form and the texture is added, and the product of the color and the texture in a denominator is subtracted, so that the differentiation of cell response signals under the stimulation of different kinds of solutions can be further enlarged, and the differentiation and the judgment are facilitated.
The evaluation index KC2 is a sphere-like system under three coordinates constructed by comprehensively utilizing the cell shape, color and texture detection parameters, and the change of the cell shape, color and texture is mapped to the external shape characteristics of the sphere-like system. Meanwhile, under the condition of single index, the cell texture index is used in a differentiation mode to enlarge the cell detection effect, namely the texture quality in the denominator is reduced, so that the differentiation of the cell morphology, the color and the texture response signal mapping sphere-like system under the stimulation of different kinds of solutions can be further increased, and the distinguishing and distinguishing effect is enhanced.
For example: taking 7 sucrose, glucose, sorbitol, sucralose and stevia disaccharide solutions with different concentrations in advance, as shown in the table I,
The results of the assays obtained with the first Con1 concentration in each of the above solutions to stimulate taste receptor cells are shown in Table II,
watch two
Using the method of the examples to detect that each concentration of solution stimulates taste receptor cells to obtain the corresponding evaluation index KC2, the linear fitting obtained each sweet taste solution detection model as follows: sucrose solution concentration detection model: y ═ 1.85 x' +3.45, R2＝0.89591。
Glucose solution concentration detection model: y ═ 1.63 x' +2.69, R2＝0.97848。
Sorbitol solution concentration detection model: y ═ 1.32 x' +1.72, R2＝0.87141。
A sucralose solution concentration detection model: y ═ 1.73 x' +1139.2, R2＝0.8698。
A stevia rebaudiana disaccharide solution concentration detection model: y ═ 1.86 x' +1608.5, R2＝0.83679。
The first Con1 concentration for each solution was used to stimulate taste receptor cells to obtain SNR curves, as shown in fig. 4, with excitation noise intensity D on the abscissa and signal to noise ratio SNR on the ordinate, and the peak of each SNR curve is the corresponding characteristic peak.
Claims (7)
1. The utility model provides a sweet taste solution detection device, its characterized in that includes computer, phase contrast microscope, infusion equipment and is used for placing taste receptor cell's the module of placing, it includes slide (1) to place the module, slide (1) upper surface is equipped with standing groove (2) that are used for placing taste receptor cell, slide (1) upper surface both sides are equipped with inlet (3) and liquid outlet (4) respectively, slide (1) upper surface still is equipped with first guiding gutter (5), the second guiding gutter (6) of intercommunication liquid outlet (4) and standing groove (2) of intercommunication inlet (3) and standing groove (2), inlet (3) communicate with infusion equipment through the pipeline, liquid outlet (4) are through tube coupling waste liquid collector, phase contrast microscope is connected with the computer electricity.
2. The apparatus of claim 1, wherein the infusion device comprises a peristaltic pump.
3. The device for testing sweet taste solution according to claim 1, wherein the axis of the first guide groove (5) and the axis of the second guide groove (6) are located on the same straight line.
4. A device for testing a sweet taste solution according to claim 3, wherein the axis of the first guide groove (5) and the axis of the second guide groove (6) both pass through the center of the placement groove (2).
5. A method for testing a sweet taste solution, which is used in the device for testing a sweet taste solution according to claim 2, comprising the steps of:
s1: the pbs buffer solution is conveyed into the placing groove by the micro peristaltic pump to clean the taste receptor cells, then the sweet taste solution to be tested is conveyed to the taste receptor cells in the placing groove by the micro peristaltic pump to be stimulated, the morphological characteristics, the color characteristics and the texture characteristics of the taste receptor cells are collected by a phase-contrast microscope and conveyed to a computer, and the computer calculates an evaluation index KC1 and an evaluation index KC 2;
s2: the step of repeatedly executing is S1N times, and N evaluation indexes KC1 and N evaluation indexes KC2 are obtained;
s3: inputting N evaluation indexes KC1 as input data KC1(t) into a layer of nonlinear dynamic model:
wherein V (x, t) is a potential function, x (t) is a Brownian motion particle motion trail function, ab and c are set constants, ξ (t) is excitation noise, D is excitation noise intensity,
is a periodic sinusoidal signal, a is the signal amplitude, f is the signal frequency, t is the motion time,
for phase, set
The first and second derivatives of V (x, t) for x are calculated and the equation is made equal to 0, resulting in a two-layer nonlinear dynamical model:
the noise intensity D is set to 0,
KC1(t) ═ 0; calculating to obtain a critical value of A
Substituting the critical value of A into a layer of nonlinear dynamics model, and setting x0(t)＝0，sn0And (3) solving a layer of nonlinear dynamic model by adopting a fourth-order long lattice Kutta algorithm to obtain:
and calculating:
wherein x isn(t) is the nth derivative of x (t), snn-1Is the value of the nth-1 derivative of S (t) at t ═ 0, snn+1Is the value of the nth +1 order derivative of S (t) at t-0, N-0, 1, …, N-1; can obtain x1(t)，x2(t)，…，xn+1(t) value;
for x1(t)，x2(t)，…，xn+1(t) integrating to obtain x (t), and calculating the maximum value x (t) of the absolute value of x (t)mMean value of x (t)
Using formulas
Calculating the signal-to-noise ratio SNR of the output of the second-order nonlinear dynamical model, wherein, Delta U is 3a3/20bc2Establishing a rectangular coordinate system by taking the excitation noise intensity D as an X axis and the signal-to-noise ratio SNR as a Y axis, drawing a signal-to-noise ratio SNR curve, and finding out an abscissa value X of a characteristic peak in the signal-to-noise ratio SNR curveeAnd the horizontal coordinate value xeComparing with the predetermined range of the abscissa value of the characteristic peak corresponding to each sweet taste solution, if x iseThe sweet taste solution to be detected is the corresponding sweet taste solution when the sweet taste solution is positioned in the range of the abscissa value of the characteristic peak corresponding to the sweet taste solution;
s4: and averaging the N evaluation indexes KC2 to obtain an average value as a y 'value, substituting the average value into a detection model corresponding to the sweet taste solution, wherein y' ═ hx '+ k, h and k are constants, and x' is the solution concentration, and calculating the concentration of the sweet taste solution to be detected.
6. The method of claim 5, wherein the phase contrast microscope collects morphological, color and texture characteristics of taste receptor cells and sends them to the computer, and the computer calculates evaluation index KC1 and evaluation index KC2 by the following method:
m1: extracting the cell area A', cell perimeter PS, cell eccentricity ECR and cell roundness RCR of the taste receptor cells by a phase contrast microscope;
m2: extracting the mean value MVS, standard deviation SDS, smoothness EVS, third-order moment TMS, consistency CSS and entropy ENS of the cell pixel value spatial distribution of the taste receptor cells by a phase-contrast microscope;
m3: calculating cell morphological characteristic factor
Calculating cell color characteristic factor
Calculating cell texture characteristic factor
M4: calculating an evaluation index KC1 and an evaluation index KC 2:
7. the method of claim 5, wherein the step of obtaining a model for the detection of a sweet taste solution comprises the steps of:
the method comprises the steps of respectively and independently conveying g sweet taste solutions with different concentrations to taste receptor cells for stimulation, obtaining corresponding evaluation indexes KC2 by stimulating the taste receptor cells each time, establishing a rectangular coordinate system by taking the concentration of the sweet taste solution as an x axis and the evaluation index KC2 as a y axis, marking a point formed by the concentration of each sweet taste solution and the corresponding evaluation index KC2 in the rectangular coordinate system, and obtaining a detection model y '═ hx' + k of the sweet taste solution through linear fitting.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911162573.3A CN111175298B (en) | 2019-11-22 | 2019-11-22 | Sweet taste solution detection device and method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911162573.3A CN111175298B (en) | 2019-11-22 | 2019-11-22 | Sweet taste solution detection device and method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111175298A true CN111175298A (en) | 2020-05-19 |
| CN111175298B CN111175298B (en) | 2022-05-31 |
Family
ID=70648774
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911162573.3A Active CN111175298B (en) | 2019-11-22 | 2019-11-22 | Sweet taste solution detection device and method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111175298B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030231791A1 (en) * | 2002-06-12 | 2003-12-18 | Torre-Bueno Jose De La | Automated system for combining bright field and fluorescent microscopy |
| US20060194261A1 (en) * | 2004-12-22 | 2006-08-31 | Kim Chin H P | Methods for determining cellular response to stimuli |
| CN105973820A (en) * | 2016-06-07 | 2016-09-28 | 厦门华厦学院 | Rapid total-phosphorus micro-detection system |
| CN208383685U (en) * | 2018-05-29 | 2019-01-15 | 广州中医药大学第一附属医院 | A kind of Calcium imaging cell perfusion slot |
| CN110320268A (en) * | 2019-08-15 | 2019-10-11 | 杭州市中医院 | The method for characterizing the taste substance in solution by sense of taste detection system |
-
2019
- 2019-11-22 CN CN201911162573.3A patent/CN111175298B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030231791A1 (en) * | 2002-06-12 | 2003-12-18 | Torre-Bueno Jose De La | Automated system for combining bright field and fluorescent microscopy |
| US20060194261A1 (en) * | 2004-12-22 | 2006-08-31 | Kim Chin H P | Methods for determining cellular response to stimuli |
| CN105973820A (en) * | 2016-06-07 | 2016-09-28 | 厦门华厦学院 | Rapid total-phosphorus micro-detection system |
| CN208383685U (en) * | 2018-05-29 | 2019-01-15 | 广州中医药大学第一附属医院 | A kind of Calcium imaging cell perfusion slot |
| CN110320268A (en) * | 2019-08-15 | 2019-10-11 | 杭州市中医院 | The method for characterizing the taste substance in solution by sense of taste detection system |
Non-Patent Citations (1)
| Title |
|---|
| WEI ZHANG ET AL.,: ""A biomimetic bioelectronic tongue: A switch for On- and Off- response of acid sensations"", 《BIOSENSORS AND BIOELECTRONICS》 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111175298B (en) | 2022-05-31 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109581546B (en) | Rainfall type identification method based on microwave link attenuation and polarization information | |
| Ciosek et al. | Electronic tongue for flow-through analysis of beverages | |
| CN104374738B (en) | A kind of method for qualitative analysis improving identification result based on near-infrared | |
| CN106197612B (en) | A kind of transparent bottled liquid-level detecting method based on machine vision | |
| EP3611495A1 (en) | Method for detecting quality of cell culture fluid based on raman spectral measurement | |
| CN108876860A (en) | A kind of image calibration method for pipe bubble offset measurement | |
| CN108592948A (en) | A kind of pipe bubble offset method for automatic measurement | |
| CN105466921A (en) | Simultaneous detection method of many samples | |
| CN109187366A (en) | Polarised light fluidic chip cancer cell device for fast detecting and method | |
| Yang et al. | Evaluation of monosodium glutamate, disodium inosinate and guanylate umami taste by an electronic tongue | |
| CN113567326A (en) | High-throughput real-time single-cell electrical intrinsic parameter measurement system and method | |
| CN110186963A (en) | A method of NaCl concentration of aqueous solution is judged based on dielectric property measurement | |
| CN111175299B (en) | Sweet taste solution detection method based on cell image recognition | |
| CN107300562B (en) | X-ray nondestructive testing method for measuring contact distance of finished relay product | |
| CN102360503A (en) | SAR (Specific Absorption Rate) image change detection method based on space approach degree and pixel similarity | |
| CN111175298B (en) | Sweet taste solution detection device and method | |
| CN105372204B (en) | A kind of method for online detecting near infrared spectrum of Etimicin Sulfate column separation process | |
| US9304096B2 (en) | Method of measuring a capacitance | |
| Anthony et al. | Electronic honey quality analyser | |
| CN114383528A (en) | Counting pool depth calibration method and system, intelligent terminal and storage medium | |
| CN110954584B (en) | Taste testing method and verification method based on electronic tongue | |
| CN109272484A (en) | A kind of rainfall detection method based on video image | |
| CN107527362A (en) | A kind of apple mouthfeel qualitative identification method based on image texture characteristic index | |
| CN105651739B (en) | Contents of many kinds of heavy metal ion nanocomposite optical detection device and method based on Stripping Voltammetry | |
| CN111175482B (en) | Glucose solution detection device and method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |