CN111175482B - Glucose solution detection device and method - Google Patents
Glucose solution detection device and method Download PDFInfo
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- 238000001514 detection method Methods 0.000 title claims abstract description 101
- WQZGKKKJIJFFOK-GASJEMHNSA-N Glucose Natural products OC[C@H]1OC(O)[C@H](O)[C@@H](O)[C@@H]1O WQZGKKKJIJFFOK-GASJEMHNSA-N 0.000 title claims abstract description 65
- 239000008103 glucose Substances 0.000 title claims abstract description 65
- 238000000034 method Methods 0.000 title claims description 20
- 239000000243 solution Substances 0.000 claims abstract description 116
- 239000007788 liquid Substances 0.000 claims abstract description 34
- 239000007853 buffer solution Substances 0.000 claims abstract description 16
- 210000004027 cell Anatomy 0.000 claims description 171
- 238000011156 evaluation Methods 0.000 claims description 42
- 238000001802 infusion Methods 0.000 claims description 42
- 230000000877 morphologic effect Effects 0.000 claims description 25
- 108091005708 gustatory receptors Proteins 0.000 claims description 20
- 210000003370 receptor cell Anatomy 0.000 claims description 20
- 238000005312 nonlinear dynamic Methods 0.000 claims description 15
- 230000005284 excitation Effects 0.000 claims description 10
- 230000000638 stimulation Effects 0.000 claims description 9
- 230000002572 peristaltic effect Effects 0.000 claims description 8
- 238000012360 testing method Methods 0.000 claims description 7
- 230000033001 locomotion Effects 0.000 claims description 6
- 239000000284 extract Substances 0.000 claims description 5
- 238000012935 Averaging Methods 0.000 claims description 3
- 230000005653 Brownian motion process Effects 0.000 claims description 3
- 238000005537 brownian motion Methods 0.000 claims description 3
- 239000002245 particle Substances 0.000 claims description 3
- 230000000737 periodic effect Effects 0.000 claims description 3
- 239000002699 waste material Substances 0.000 claims description 3
- 238000003384 imaging method Methods 0.000 abstract description 4
- 230000035790 physiological processes and functions Effects 0.000 abstract description 4
- 239000000872 buffer Substances 0.000 abstract description 3
- 230000005611 electricity Effects 0.000 abstract 1
- 210000001519 tissue Anatomy 0.000 description 10
- 230000000694 effects Effects 0.000 description 8
- 230000001965 increasing effect Effects 0.000 description 8
- 230000004069 differentiation Effects 0.000 description 6
- 230000004044 response Effects 0.000 description 6
- 230000008859 change Effects 0.000 description 5
- 238000010586 diagram Methods 0.000 description 3
- 201000007270 liver cancer Diseases 0.000 description 3
- 208000014018 liver neoplasm Diseases 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 239000000126 substance Substances 0.000 description 3
- 230000024245 cell differentiation Effects 0.000 description 2
- 210000003855 cell nucleus Anatomy 0.000 description 2
- 239000011664 nicotinic acid Substances 0.000 description 2
- 238000000275 quality assurance Methods 0.000 description 2
- 102000004310 Ion Channels Human genes 0.000 description 1
- 101100168115 Neurospora crassa (strain ATCC 24698 / 74-OR23-1A / CBS 708.71 / DSM 1257 / FGSC 987) con-6 gene Proteins 0.000 description 1
- 230000032683 aging Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000002848 electrochemical method Methods 0.000 description 1
- 230000001339 gustatory effect Effects 0.000 description 1
- 230000003834 intracellular effect Effects 0.000 description 1
- 230000004936 stimulating effect Effects 0.000 description 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/5005—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells
- G01N33/5008—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells for testing or evaluating the effect of chemical or biological compounds, e.g. drugs, cosmetics
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
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- G01N15/01—
Abstract
The invention discloses a glucose solution detection device and a glucose solution detection method. The device includes computer, cell image acquisition module, solution transport module and cell placement module, solution transport module includes the transfer pump, a plurality of is used for placing the detection container of solution to be tested and the buffer solution container that is used for placing the abs buffer, it is equipped with first transfer line to detect the container bottom, the front end of first transfer line is equipped with first solenoid valve, buffer solution container bottom is equipped with the second transfer line, the front end of second transfer line is equipped with the second solenoid valve, the feed liquor pipeline and the first transfer line of transfer pump, the second transfer line intercommunication, the play liquid pipeline and the cell placement module intercommunication of transfer pump, the computer is connected with cell image acquisition module respectively, the transfer pump, first solenoid valve, the second solenoid valve electricity. The invention uses the cell imaging method, does not contact the detected cells, reduces the interference caused by the outside, eliminates the interference factors affecting the instantaneous physiological state of the cells, and has high detection accuracy.
Description
Technical Field
The invention relates to the technical field of solution detection, in particular to a glucose solution detection device and method.
Background
The existing glucose solution concentration detection technical 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 single cells, the instrument is inconvenient to use and operate and can only be used in a laboratory. Although the bionic taste detection technology can realize classification of taste substances to a certain extent, the repeatability and the accuracy of the technology are limited. Electrochemical cell sensor methods, although capable of achieving the detection of taste substances to some extent, have limitations in reproducibility.
Disclosure of Invention
The invention aims to solve the technical problems, and provides a glucose solution detection device and a glucose solution detection method, which utilize a cell imaging method, are not in contact with detected cells, greatly reduce interference caused by the outside, eliminate interference factors affecting the instantaneous physiological state of the cells, and have the advantages of quick detection aging and high detection accuracy.
In order to solve the problems, the invention is realized by adopting the following technical scheme:
the invention relates to a glucose solution detection device which comprises a computer, a cell image acquisition module, a solution conveying module and a cell placement module, wherein the solution conveying module comprises an infusion pump, a plurality of detection containers for placing solutions to be detected and buffer solution containers for placing a pbs buffer solution, a first infusion tube is arranged at the bottom of the detection containers, a first electromagnetic valve is arranged at the front end of the first infusion tube, a second infusion tube is arranged at the bottom of the buffer solution containers, a second electromagnetic valve is arranged at the front end of the second infusion tube, a liquid inlet pipeline of the infusion pump is communicated with the first infusion tube and the second infusion tube, a liquid outlet pipeline of the infusion pump is communicated with the cell placement module, and the computer is respectively and electrically connected with the cell image acquisition module, the infusion pump, the first electromagnetic valve and the second electromagnetic valve.
In the scheme, during detection, taste receptor cell tissues are placed on a cell placement module, a solution to be detected is poured into a detection container, and a cell image acquisition module acquires the taste receptor cell tissues, and selects a taste receptor cell which has clear outline and does not overlap with other cells as a detection cell.
Firstly, a second electromagnetic valve is opened, an infusion pump conveys a pbs buffer solution to a cell placement module to clean detection cells, then, the second electromagnetic valve is closed, a first electromagnetic valve of a detection container containing a solution to be detected is opened, a micro peristaltic pump conveys the solution to be detected to the detection cells on the cell placement module to stimulate, a cell image acquisition module acquires morphological characteristics, color characteristics and texture characteristics of the detection cells and conveys the morphological characteristics, the color characteristics and the texture characteristics of the detection cells to a computer, the steps are repeated for a plurality of times, and the computer comprehensively analyzes the change conditions of the morphological characteristics, the color characteristics and the texture characteristics of the detection cells to judge whether the solution to be detected is glucose solution, and if the change conditions are glucose solution, the concentration of the solution to be detected is calculated. During the detection, the temperature was maintained at 37 ℃ + -0.2 ℃. The taste receptor cell is LM3 liver cancer cell.
Preferably, the cell placement module comprises a placement table, the upper surface of the placement table is provided with a placement groove, two sides of the upper surface of the placement table are respectively provided with a liquid inlet and a liquid outlet, the upper surface of the placement table is also provided with a first diversion trench communicated with the liquid inlet and the placement groove, a second diversion trench communicated with the liquid outlet and the placement groove, the liquid inlet is communicated with a liquid outlet pipeline of the infusion pump through a pipeline, and the liquid outlet is connected with a waste liquid collector through a pipeline.
Preferably, the infusion pump is a micro peristaltic pump.
The invention discloses a glucose solution detection method, which is used for the glucose solution detection device and comprises the following steps:
s1: placing taste receptor cell tissues in a placing groove, and pouring a solution to be detected into a detection container;
s2: the cell image acquisition module acquires a taste receptor cell tissue image, and selects a taste receptor cell which has clear outline and does not overlap with other cells from the taste receptor cell tissue image as a detection cell;
s3: the second electromagnetic valve is opened, the infusion pump conveys the pbs buffer solution into the placing groove to clean the detection cells, then the second electromagnetic valve is closed, the first electromagnetic valve of the detection container for accommodating the solution to be detected is opened, the micro peristaltic pump conveys the solution to be detected onto the detection cells in the placing groove to stimulate, the cell image acquisition module acquires morphological characteristics, color characteristics and texture characteristics of the detection cells and conveys the morphological characteristics, the color characteristics and the texture characteristics of the detection cells to the computer, and the computer calculates an evaluation index KC1 and an evaluation index KC2;
s4: the step of repeatedly executing is that S3N times are carried out to obtain N evaluation indexes KC1 and N evaluation indexes KC2;
s5: n evaluation indexes KC1 are input into a layer of nonlinear dynamics model as input data KC1 (t):
wherein V (x, t) is a potential function, x (t) is a Brownian motion particle motion track function, a, b, c are set constants, ζ (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 movement time,/->For the phase, set->
Calculating the first and second derivatives of V (x, t) for x and equating the equation to 0, yielding a two-layer nonlinear dynamics model:
the noise intensity d=0 is set,KC1 (t) =0; calculating to obtain critical value of A asSubstituting the critical value of A into a layer of nonlinear dynamics model, and setting x 0 (t)＝0，sn 0 =0, solving a layer of nonlinear dynamics model by adopting a fourth-order long lattice tower algorithm to obtain:
and calculating:
wherein x is n (t) is the nth derivative of x (t), sn n-1 For the value of the n-1 th derivative of S (t) at t=0, sn n+1 N=0, 1, …, N-1, which is the value of the n+1th derivative of S (t) at t=0; can obtain x 1 (t)，x 2 (t)，…，x n+1 A value of (t);
for x 1 (t)，x 2 (t)，…，x n+1 (t) integrating to obtain x (t), and calculating the maximum value x (t) of the absolute value of x (t) m Mean value of x (t)
Using the formulaCalculating a signal-to-noise ratio SNR of the output of the second order nonlinear dynamics model, wherein Δu=3a 3 /20bc 2 ，
Establishing a rectangular coordinate system by taking excitation noise intensity D as an X axis and 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 curve e And will have an abscissa value x e Comparing the characteristic peak abscissa value range corresponding to the glucose solution obtained in advance, if x e If the measured value is within the range of the abscissa value of the characteristic peak corresponding to the glucose solution, the solution to be measured is the glucose solution, and the step S6 is executed, otherwise, the solution to be measured is not the glucose solution, and the detection is finished;
s6: and (3) averaging the N evaluation indexes KC2, substituting the obtained average value as a y 'value into a detection model of the corresponding glucose solution, wherein y' =hx '+k, h and k are constants, and x' is the solution concentration, so as to calculate the concentration of the solution to be detected.
Preferably, the cell image acquisition module acquires and detects morphological features, color features and texture features of cells and transmits the morphological features, the color features and the texture features to a computer, and the computer calculates an evaluation index KC1 and an evaluation index KC2 by the method comprising the following steps:
m1: the cell image acquisition module extracts and detects cell area A', cell perimeter PS, cell eccentricity ECR and cell roundness RCR of cells;
m2: the cell image acquisition module extracts the mean MVS, standard deviation SDS, smoothness EVS, third-order moment TMS, consistency CSS and entropy ENS of the spatial distribution of cell pixel values of the detected cells;
m3: calculating cell morphological feature factors
Calculating cell color characteristic factors
Calculating cell texture characteristic factors
M4: calculating an evaluation index KC1 and an evaluation index KC2:
area a' = Σf (x, y), i.e., the number of pixel points 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 boundary of the cell region, assuming that the boundary chain code of the cell region is { a } 1 a 2 …a n And each code segment a i Length al of (2) i Expressed, the expression for the perimeter is:
wherein n is u N is the number of even numbers in the chain code s The number of that odd number is found for the chain code.
Eccentricity ECR: is 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,where c is the half focal length in the cell range and q is the half long wheelbase in the cell range.
Cell roundness RCR:
assuming that z represents a random amount of gray level, the corresponding histogram is: p (z) i ) I=0, 1,2 …, L-1, L represents the number of gray levels,
mean value of
Standard deviation of
Smoothness degree
Third order moment
Consistency of
Entropy of
The evaluation index KC1 is used for highlighting the influence of morphology and color on cells in the detection process, and meanwhile, the differentiation uses the distance increasing effect of the cell texture index on the cell detection effect, and is mainly characterized in that in the KC1 definition formula, the product of morphology and texture is added, and the product of color and texture in denominator is subtracted, so that the differentiation of cell response signals under the stimulation of different types of solutions can be further increased, and the differentiation and discrimination are facilitated.
The evaluation index KC2 is characterized in that the morphological, color and texture detection parameters of the cells are comprehensively utilized, a spheroid-like system under three coordinates is constructed by adopting the three parameters in a molecular approximation mode, and the morphological, color and texture changes of the cells are mapped into the external shape characteristics of the spheroid-like system. Meanwhile, under the condition of single index, the differentiation uses the effect of increasing the cell texture index on the cell detection effect, namely the texture quality assurance in denominator is subtracted, so that the distinction of a spheroid system mapped by cell morphology, color and texture response signals under the stimulation of different types of solutions can be further increased, and the distinction and distinguishing effects are enhanced.
Preferably, the method for obtaining a test model of a glucose solution comprises the steps of:
and g glucose solutions with different concentrations are respectively and independently conveyed to detection cells for stimulation, the detection cells are stimulated each time to obtain corresponding evaluation indexes KC2, the concentration of the glucose solutions is taken as an x axis, the evaluation indexes KC2 are taken as a y axis, a rectangular coordinate system is established, points formed by the concentration of each glucose solution and the corresponding evaluation indexes KC2 are marked in the rectangular coordinate system, and a detection model y '=hx' +k of the glucose solution is obtained through linear fitting.
The beneficial effects of the invention are as follows: (1) By using the cell imaging method, the detection system is not contacted with the detected cells, so that the detected cells are nondestructive and noncontact, interference brought by the outside is greatly reduced for cell imaging, and interference factors affecting the instantaneous physiological state of the cells are eliminated. (2) The detection time is good, the intracellular physiological state of the cells can be calibrated almost instantaneously, and certain response time is needed by a patch clamp or an electrochemical method, so the method provided by the invention has high detection time. (3) According to the acquired cell image, the characteristic quantities of a plurality of cell images are comprehensively reduced into three main indexes of morphology, color and texture, so that the omnibearing physiological change of the cells in the process of receiving gustatory substances stimulation can be well reflected, and the detection accuracy is very high.
Drawings
FIG. 1 is a schematic circuit connection block diagram of an embodiment;
FIG. 2 is a schematic diagram of the solution delivery module;
FIG. 3 is a schematic diagram of the structure of a cell placement module;
FIG. 4 is an image of a test cell;
FIG. 5 is a graph of signal to noise ratio obtained by stimulation of test cells with a glucose solution.
In the figure: 1. the cell image acquisition device comprises a computer, a cell image acquisition module, an infusion pump, a first electromagnetic valve, a second electromagnetic valve, a placing table, a placing groove, a placing table, a liquid inlet, a liquid outlet, a first diversion trench, a liquid storage device, a buffer container and a buffer container.
Detailed Description
The technical scheme of the invention is further specifically described below through examples and with reference to the accompanying drawings.
Examples: the embodiment of the glucose solution detection device, as shown in fig. 1,2 and 3, comprises a computer 1, a cell image acquisition module 2, a solution conveying module and a cell placement module, wherein the solution conveying module comprises an infusion pump 3, a plurality of detection containers 13 for placing solutions to be detected and a buffer solution container 14 for placing the pbs buffer solution, a first infusion tube is arranged at the bottom of the detection container 13, a first electromagnetic valve 4 is arranged at the front end of the first infusion tube, a second infusion tube is arranged at the bottom of the buffer solution container 14, a second electromagnetic valve 5 is arranged at the front end of the second infusion tube, a liquid inlet pipeline of the infusion pump 3 is communicated with the first infusion tube and the second infusion tube, a liquid outlet pipeline of the infusion pump 3 is communicated with the cell placement module, and the computer 1 is respectively electrically connected with the cell image acquisition module 2, the infusion pump 3, the first electromagnetic valve 4 and the second electromagnetic valve 5, and the infusion pump is a micro peristaltic pump.
The cell placement module comprises a placement table 6, a placement groove 7 is formed in the upper surface of the placement table 6, a liquid inlet 8 and a liquid outlet 9 are formed in two sides of the upper surface of the placement table 6 respectively, a first diversion trench 10 which is communicated with the liquid inlet 8 and the placement groove 7 and a second diversion trench 11 which is communicated with the liquid outlet 9 and the placement groove 7 are further formed in the upper surface of the placement table 6, the liquid inlet 8 is communicated with a liquid outlet pipeline of the infusion pump 3 through a pipeline, the liquid outlet 9 is connected with a waste liquid collector through a pipeline, the axis of the first diversion trench 10 and the axis of the second diversion trench 11 are located in the same straight line, and the axis of the first diversion trench 10 and the axis of the second diversion trench 11 all penetrate through the center of the placement groove 7.
In the scheme, during detection, taste receptor cell tissues are placed on a cell placement module, a solution to be detected is poured into a detection container, and a cell image acquisition module acquires the taste receptor cell tissues, and selects a taste receptor cell which has clear outline and does not overlap with other cells as a detection cell.
Firstly, a second electromagnetic valve is opened, an infusion pump conveys a pbs buffer solution to a cell placement module to clean detection cells, then, the second electromagnetic valve is closed, a first electromagnetic valve of a detection container containing a solution to be detected is opened, a micro peristaltic pump conveys the solution to be detected to the detection cells on the cell placement module to stimulate, a cell image acquisition module acquires morphological characteristics, color characteristics and texture characteristics of the detection cells and conveys the morphological characteristics, the color characteristics and the texture characteristics of the detection cells to a computer, the steps are repeated for a plurality of times, and the computer comprehensively analyzes the change conditions of the morphological characteristics, the color characteristics and the texture characteristics of the detection cells to judge whether the solution to be detected is glucose solution, and if the change conditions are glucose solution, the concentration of the solution to be detected is calculated. During the detection, the temperature was maintained at 37 ℃ + -0.2 ℃. The taste receptor cell is LM3 liver cancer cell.
The glucose solution detection method of the present embodiment is used in the glucose solution detection device, and includes the following steps:
s1: placing taste receptor cell tissues in a placing groove, and pouring a solution to be detected into a detection container;
s2: the cell image acquisition module acquires a taste receptor cell tissue image, and selects a taste receptor cell which has clear outline and does not overlap with other cells from the taste receptor cell tissue image as a detection cell;
s3: the second electromagnetic valve is opened, the infusion pump conveys the pbs buffer solution into the placing groove to clean the detection cells, then the second electromagnetic valve is closed, the first electromagnetic valve of the detection container for accommodating the solution to be detected is opened, the micro peristaltic pump conveys the solution to be detected onto the detection cells in the placing groove to stimulate, the cell image acquisition module acquires morphological characteristics, color characteristics and texture characteristics of the detection cells and conveys the morphological characteristics, the color characteristics and the texture characteristics of the detection cells to the computer, and the computer calculates an evaluation index KC1 and an evaluation index KC2;
s4: the step of repeatedly executing is that S3N times are carried out to obtain N evaluation indexes KC1 and N evaluation indexes KC2;
s5: n evaluation indexes KC1 are input into a layer of nonlinear dynamics model as input data KC1 (t):
wherein V (x, t) is a potential function, x (t) is a Brownian motion particle motion track function, a, b, c are set constants, ζ (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 movement time,/->For the phase, set->
Calculating the first and second derivatives of V (x, t) for x and equating the equation to 0, yielding a two-layer nonlinear dynamics model:
the noise intensity d=0 is set,KC1 (t) =0; calculating to obtain critical value of A asSubstituting the critical value of A into a layer of nonlinear dynamics model, and setting x 0 (t)＝0，sn 0 =0, solving a layer of nonlinear dynamics model by adopting a fourth-order long lattice tower algorithm to obtain:
and calculating:
wherein x is n (t) is the nth derivative of x (t), sn n-1 For the value of the n-1 th derivative of S (t) at t=0, sn n+1 N=0, 1, …, N-1, which is the value of the n+1th derivative of S (t) at t=0; can obtain x 1 (t)，x 2 (t)，…，x n+1 A value of (t);
for x 1 (t)，x 2 (t)，…，x n+1 (t) integrating to obtain x (t), and calculating the maximum value x (t) of the absolute value of x (t) m Mean value of x (t)
Using the formulaCalculating a signal-to-noise ratio SNR of the output of the second order nonlinear dynamics model, wherein Δu=3a 3 /20bc 2 ，
Establishing a rectangular coordinate system by taking excitation noise intensity D as an X axis and 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 curve e And will have an abscissa value x e Comparing the characteristic peak abscissa value range corresponding to the glucose solution obtained in advance, if x e If the measured value is within the range of the abscissa value of the characteristic peak corresponding to the glucose solution, the solution to be measured is the glucose solution, and the step S6 is executed, otherwise, the solution to be measured is not the glucose solution, and the detection is finished;
s6: and (3) averaging the N evaluation indexes KC2, substituting the obtained average value as a y 'value into a detection model of the corresponding glucose solution, wherein y' =hx '+k, h and k are constants, and x' is the solution concentration, so as to calculate the concentration of the solution to be detected.
The method for acquiring the morphological characteristics, the color characteristics and the texture characteristics of the detected cells by the cell image acquisition module and transmitting the morphological characteristics, the color characteristics and the texture characteristics to a computer, and calculating the evaluation indexes KC1 and KC2 by the computer comprises the following steps:
m1: the cell image acquisition module extracts and detects cell area A', cell perimeter PS, cell eccentricity ECR and cell roundness RCR of cells;
m2: the cell image acquisition module extracts the mean MVS, standard deviation SDS, smoothness EVS, third-order moment TMS, consistency CSS and entropy ENS of the spatial distribution of cell pixel values of the detected cells;
m3: calculating cell morphological feature factors
Calculating cell color characteristic factors
Calculating cell texture characteristic factors
M4: calculating an evaluation index KC1 and an evaluation index KC2:
area a' = Σf (x, y), i.e., the number of pixel points 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 boundary of the cell region, assuming that the boundary chain code of the cell region is { a } 1 a 2 …a n And each code segment a i Length al of (2) i Expressed, the expression for the perimeter is:
wherein n is u N is the number of even numbers in the chain code s The number of that odd number is found for the chain code.
Eccentricity ECR: is 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,where c is the half focal length in the cell range and q is the half long wheelbase in the cell range.
Cell roundness RCR:
assuming that z represents a random amount of gray level, the corresponding histogram is: p (z) i ) I=0, 1,2 …, L-1, L represents the number of gray levels,
mean value of
Standard deviation of
Smoothness degree
Third order moment
Consistency of
Entropy of
The evaluation index KC1 is used for highlighting the influence of morphology and color on cells in the detection process, and meanwhile, the differentiation uses the distance increasing effect of the cell texture index on the cell detection effect, and is mainly characterized in that in the KC1 definition formula, the product of morphology and texture is added, and the product of color and texture in denominator is subtracted, so that the differentiation of cell response signals under the stimulation of different types of solutions can be further increased, and the differentiation and discrimination are facilitated.
The evaluation index KC2 is characterized in that the morphological, color and texture detection parameters of the cells are comprehensively utilized, a spheroid-like system under three coordinates is constructed by adopting the three parameters in a molecular approximation mode, and the morphological, color and texture changes of the cells are mapped into the external shape characteristics of the spheroid-like system. Meanwhile, under the condition of single index, the differentiation uses the effect of increasing the cell texture index on the cell detection effect, namely the texture quality assurance in denominator is subtracted, so that the distinction of a spheroid system mapped by cell morphology, color and texture response signals under the stimulation of different types of solutions can be further increased, and the distinction and distinguishing effects are enhanced.
The method for obtaining the detection model of the glucose solution comprises the following steps:
and g glucose solutions with different concentrations are respectively and independently conveyed to detection cells for stimulation, the detection cells are stimulated each time to obtain corresponding evaluation indexes KC2, the concentration of the glucose solutions is taken as an x axis, the evaluation indexes KC2 are taken as a y axis, a rectangular coordinate system is established, points formed by the concentration of each glucose solution and the corresponding evaluation indexes KC2 are marked in the rectangular coordinate system, and a detection model y '=hx' +k of the glucose solution is obtained through linear fitting.
In this embodiment, an image of the detected cells is shown in FIG. 4. The detection cells are LM3 liver cancer cells, and can distinguish glucose solutions. And acquiring a characteristic peak abscissa value range corresponding to the glucose solution in advance, determining whether the measured characteristic peak abscissa value range corresponding to the solution to be measured is within the characteristic peak abscissa value range corresponding to the glucose solution, if so, determining that the solution to be measured is the glucose solution, and calculating the concentration, otherwise, determining that the solution to be measured is not the glucose solution. The range of the abscissa value of the characteristic peak corresponding to the glucose solution: [77.0, 80.9].
For example: 7 glucose solutions of different concentrations were pre-taken, as shown in table one,
| Con1 | Con2 | Con3 | Con4 | Con5 | Con6 | Con7 | |
| glucose | 0.24e-1 | 0.36e-1 | 0.50e-1 | 0.70e-1 | 1.01e-1 | 1.43e-1 | 2.68e-1 |
List one
The results of the first Con1 concentration of the glucose solution stimulated the cells to be assayed are shown in Table II,
watch II
The method of the example was used to detect each concentration of solution stimulusDetecting cells to obtain a corresponding evaluation index KC2, and linearly fitting to obtain a detection model of glucose solution: y '=1.63 x' +2.69, r 2 ＝0.97848。
The signal-to-noise ratio SNR curve obtained by stimulating the test cells with the first Con1 concentration of the glucose solution is shown in fig. 5, the abscissa is the excitation noise intensity D, the ordinate is the signal-to-noise ratio SNR, and the peak value of the signal-to-noise ratio SNR curve is the corresponding characteristic peak.
Claims (4)
1. The glucose solution detection device is characterized by comprising a computer (1), a cell image acquisition module (2), a solution conveying module and a cell placement module, wherein the solution conveying module comprises an infusion pump (3), a plurality of detection containers (13) for placing solutions to be detected and buffer solution containers (14) for placing the pbs buffer solution, a first infusion tube is arranged at the bottom of each detection container (13), a first electromagnetic valve (4) is arranged at the front end of each first infusion tube, a second infusion tube is arranged at the bottom of each buffer solution container (14), a second electromagnetic valve (5) is arranged at the front end of each second infusion tube, a liquid inlet pipeline of each infusion pump (3) is communicated with the corresponding first infusion tube and the corresponding second infusion tube, a liquid outlet pipeline of each infusion pump (3) is communicated with the corresponding cell placement module, and the computer (1) is respectively electrically connected with the cell image acquisition module (2), the corresponding infusion pump (3), the corresponding first electromagnetic valve (4) and the corresponding second electromagnetic valve (5); the cell placement module comprises a placement table (6), a placement groove (7) is formed in the upper surface of the placement table (6), a liquid inlet (8) and a liquid outlet (9) are formed in two sides of the upper surface of the placement table (6), a first diversion groove (10) communicated with the liquid inlet (8) and the placement groove (7), a second diversion groove (11) communicated with the liquid outlet (9) and the placement groove (7) are further formed in the upper surface of the placement table (6), the liquid inlet (8) is communicated with a liquid outlet pipeline of the infusion pump (3) through a pipeline, and the liquid outlet (9) is connected with a waste liquid collector through a pipeline.
2. The glucose solution testing device according to claim 1, wherein the infusion pump (3) is a micro peristaltic pump.
3. A glucose solution testing method for use in a glucose solution testing apparatus as set forth in claim 2, comprising the steps of:
s1: placing taste receptor cell tissues in a placing groove, and pouring a solution to be detected into a detection container;
s2: the cell image acquisition module acquires a taste receptor cell tissue image, and selects a taste receptor cell which has clear outline and does not overlap with other cells from the taste receptor cell tissue image as a detection cell;
s3: the second electromagnetic valve is opened, the infusion pump conveys the pbs buffer solution into the placing groove to clean the detection cells, then the second electromagnetic valve is closed, the first electromagnetic valve of the detection container for accommodating the solution to be detected is opened, the micro peristaltic pump conveys the solution to be detected onto the detection cells in the placing groove to stimulate, the cell image acquisition module acquires morphological characteristics, color characteristics and texture characteristics of the detection cells and conveys the morphological characteristics, the color characteristics and the texture characteristics of the detection cells to the computer, and the computer calculates an evaluation index KC1 and an evaluation index KC2;
s4: the step of repeatedly executing is that S3N times are carried out to obtain N evaluation indexes KC1 and N evaluation indexes KC2;
s5: n evaluation indexes KC1 are input into a layer of nonlinear dynamics model as input data KC1 (t):
wherein V (x, t) is a potential function, x (t) is a Brownian motion particle motion track function, a, b, c are set constants, ζ (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 movement time,/->For the phase, set->
Calculating the first and second derivatives of V (x, t) for x and equating the equation to 0, yielding a two-layer nonlinear dynamics model:
the noise intensity d=0 is set,KC1 (t) =0; the critical value of A is calculated to be +.>Substituting the critical value of A into a layer of nonlinear dynamics model, and setting x 0 (t)＝0，sn 0 =0, solving a layer of nonlinear dynamics model by adopting a fourth-order long lattice tower algorithm to obtain:
and calculating:
wherein x is n (t) is the nth derivative of x (t), sn n-1 For the value of the n-1 th derivative of S (t) at t=0, sn n+1 N=1, 2, …, N, being the value of the n+1st derivative of S (t) at t=0; can obtain x 1 (t),x 2 (t),,x n+1 A value of (t);
for x 1 (t),x 2 (t),,x n+1 (t) integrating to obtain x (t), and calculating the maximum value x (t) of the absolute value of x (t) m Mean value of x (t)
Using the formulaCalculating a signal-to-noise ratio SNR of the output of the second order nonlinear dynamics model, wherein Δu=3a 3 /20bc 2 Establishing a rectangular coordinate system by taking excitation noise intensity D as an X axis and 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 curve e And will have an abscissa value x e Comparing the characteristic peak abscissa value range corresponding to the glucose solution obtained in advance, if x e If the measured value is within the range of the abscissa value of the characteristic peak corresponding to the glucose solution, the solution to be measured is the glucose solution, and the step S6 is executed, otherwise, the solution to be measured is not the glucose solution, and the detection is finished;
s6: averaging the N evaluation indexes KC2 to obtain an average value, substituting the average value as a y 'value into a detection model of a corresponding glucose solution, wherein y' =hx '+k, h and k are constants, and x' is the solution concentration, so as to calculate the concentration of the solution to be detected;
the method for acquiring the morphological characteristics, the color characteristics and the texture characteristics of the detected cells by the cell image acquisition module and transmitting the morphological characteristics, the color characteristics and the texture characteristics to a computer, and calculating the evaluation indexes KC1 and KC2 by the computer comprises the following steps:
m1: the cell image acquisition module extracts and detects cell area A', cell perimeter PS, cell eccentricity ECR and cell roundness RCR of cells;
m2, extracting a mean value MVS, a standard deviation SDS, a smoothness EVS, a third-order moment TMS, a consistency CSS and an entropy ENS of the spatial distribution of cell pixel values of detected cells by a cell image acquisition module;
m3: calculating cell morphological feature factors
Calculating cell color characteristic factors
Calculating cell texture characteristic factors
M4: calculating an evaluation index KC1 and an evaluation index KC2:
4. a method for detecting a glucose solution according to claim 3, wherein the method for obtaining a detection model of the glucose solution comprises the steps of:
and g glucose solutions with different concentrations are respectively and independently conveyed to detection cells for stimulation, the detection cells are stimulated each time to obtain corresponding evaluation indexes KC2, the concentration of the glucose solutions is taken as an x axis, the evaluation indexes KC2 are taken as a y axis, a rectangular coordinate system is established, points formed by the concentration of each glucose solution and the corresponding evaluation indexes KC2 are marked in the rectangular coordinate system, and a detection model y '=hx' +k of the glucose solution is obtained through linear fitting.
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