A colloidal gold detection method and system for an antigen kit

Abstract

This application relates to a colloidal gold detection method and system for an antigen kit. The method includes the following steps: obtaining an image of the colloidal gold to be tested; obtaining the particle size range of the colloidal gold to be tested based on the image of the colloidal gold to be tested; making a numerical comparison between the particle size range of the colloidal gold to be tested and the standard particle size to obtain a preliminary test result of the colloidal gold to be tested; in response to the qualification of the preliminary test result of the colloidal gold to be tested, performing a secondary test on the colloidal gold to be tested; the secondary test includes: performing DLS particle size analysis, Zeta potential detection and electron microscopy imaging on the colloidal gold to be tested respectively to obtain the particle size distribution uniformity, Zeta potential and particle size morphology regularity of the colloidal gold to be tested, and further obtaining the test result of the colloidal gold to be tested. The system and equipment are used to execute the above method. This application can reduce the subjective influence of humans in the quality inspection process of colloidal gold, improve the accuracy and reliability of the quality inspection process of colloidal gold, and further improve the product quality of the antigen kit.

Description

Technical Field

[0001] This application relates to the technical field of antigen reagent detection, specifically to a colloidal gold detection method and system for an antigen reagent kit. Background Technology

[0002] Colloidal gold, also known as gold sol, is a stable, uniform, and singly dispersed suspension of gold particles in a liquid formed after gold salts are reduced to elemental gold. It is also a component of antigen kits.

[0003] The properties of colloidal gold, such as particle size, distribution, and morphology, affect the accuracy of the final detection results. Currently, there is no unified process or standard for quality inspection during the production of colloidal gold. Most manufacturers use a sampling inspection model, where quality inspectors visually inspect the solution color and observe particle distribution under an electron microscope. This method relies heavily on the experience of the quality inspectors and is significantly influenced by their subjectivity. This can lead to inconsistencies in the colloidal gold standards of the antigen kits leaving the factory, and some colloidal gold may be substandard, resulting in false detections. This not only affects the reliability of the antigen kit products but may also endanger the health and lives of those being tested. Therefore, how to conduct more objective and accurate quality inspection of colloidal gold is a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0004] To address the problems existing in the prior art, this application aims to provide a colloidal gold detection method and system for antigen kits. This application can reduce the influence of human subjectivity in the colloidal gold quality inspection process, improve the accuracy and reliability of the colloidal gold quality inspection process, and thus improve the product quality of the antigen kit.

[0005] The colloidal gold detection method for an antigen kit described in this application includes the following steps:

[0006] S01. Obtain an image of the colloidal gold to be tested;

[0007] S02. Obtain the particle size range of the colloidal gold to be tested based on the image of the colloidal gold to be tested;

[0008] S03. Compare the particle size range of the colloidal gold to be tested with the standard particle size to obtain the preliminary test result of the colloidal gold to be tested.

[0009] S04. If the initial test result of the colloidal gold to be tested is qualified, a second test shall be performed on the colloidal gold to be tested.

[0010] The secondary detection includes:

[0011] Perform DLS particle size analysis, Zeta potential detection, and electron microscopy imaging on the colloidal gold to be tested, and obtain the particle size distribution uniformity, Zeta potential, and particle size morphology regularity of the colloidal gold to be tested;

[0012] Based on the particle size distribution uniformity, Zeta potential, and particle size morphology regularity of the colloidal gold to be tested obtained, obtain the test result of the colloidal gold to be tested.

[0013] Preferably, step S02 includes:

[0014] Input the image of the colloidal gold to be tested into the particle size range recognition model to obtain the particle size range of the colloidal gold to be tested;

[0015] The particle size range recognition model is obtained through the following steps:

[0016] Collect images of colloidal gold with multiple different particle sizes, and establish a mapping database of particle size - colloidal gold images as sample data;

[0017] Input the sample data into the classification model to train the classification model, and obtain the particle size range recognition model.

[0018] Preferably, in the secondary detection, performing DLS particle size analysis on the colloidal gold to be tested includes:

[0019] Obtain the average particle size Particle size standard deviation σ and polydispersity index PDI of the colloidal gold to be tested, and calculate the particle size distribution uniformity result Uni of the colloidal gold to be tested according to the following formula:

[0020]

[0021] where w1, w2, and w3 respectively represent the initial first weight, second weight, and third weight, satisfying w1 < w2 ≤ w3, and d' represents the standard particle size;

[0022] Preset a standard deviation threshold thr σ for the particle size standard deviation σ PDI and a polydispersity threshold thr σ for the polydispersity index PDI. In response to the particle size standard deviation σ being not less than the standard deviation threshold thr PDI and / or the polydispersity index PDI being not less than the polydispersity threshold thr, decrease the first weight and increase the second weight and / or the third weight.

[0023] Preferably, in the secondary detection, performing Zeta potential detection on the colloidal gold to be tested includes:

[0024] Preset standard range for Zeta potential c = (V min V max The zeta potential of the colloidal gold to be tested is obtained.

[0025] If the Zeta potential of the colloidal gold to be tested does not belong to the standard interval c, then the detection result of the Zeta potential of the colloidal gold to be tested is set to 0. If the Zeta potential of the colloidal gold to be tested belongs to the standard interval c, then the Zeta potential of the colloidal gold to be tested is normalized based on the standard interval c to obtain the detection result V of the Zeta potential of the colloidal gold to be tested. Zeta .

[0026] Preferably, the secondary detection, including electron microscopy imaging analysis of the colloidal gold to be tested, includes:

[0027] Obtain an electron microscope image of the colloidal gold to be tested, and based on the electron microscope image, obtain the major axis L of the particles in the colloidal gold to be tested. l and short axis L s Calculate the aspect ratio AR of each particle in the colloidal gold to be tested:

[0028] AR = L l / L s ;

[0029] Based on the aspect ratio AR of each particle, the average aspect ratio of the particles in the colloidal gold to be tested was calculated.

[0030]

[0031] Wherein, n represents the number of particles in the colloidal gold to be tested, and i represents the particle number;

[0032] Extreme Aspect Ratio AR max :

[0033] AR max =max(AR1, AR2...AR) n ),

[0034] Irregularity percentage P irr :

[0035]

[0036] Where, n irr This indicates the number of particles in the colloidal gold sample with an aspect ratio greater than the aspect ratio threshold.

[0037] Based on the obtained average aspect ratio Extreme Aspect Ratio AR max and the proportion of irregular shapes P irrThe morphological regularity of the colloidal gold under test was calculated using MRI.

[0038]

[0039] Preferably, the detection results of the colloidal gold to be tested, based on the uniformity of particle size distribution, zeta potential, and regularity of particle morphology, include:

[0040] Calculate the detection index DR of the colloidal gold to be tested:

[0041] DR = w4*Uni + w5*(1-V) Zeta) +w6*(1-MRI),

[0042] Where w4, w5, and w6 represent the fourth, fifth, and sixth weights, respectively.

[0043] If the particle size distribution uniformity, Zeta potential, and particle morphology regularity of the colloidal gold to be tested all meet the standards, and the detection result DR of the colloidal gold to be tested is less than the preset detection result threshold thr DR If the colloidal gold to be tested is qualified, then the colloidal gold to be tested is qualified; otherwise, the colloidal gold to be tested is unqualified.

[0044] Preferably, calculating the detection index DR of the colloidal gold to be tested further includes:

[0045] Construct a genetic algorithm for the fourth, fifth, and sixth weights;

[0046] When calculating the detection index DR of the colloidal gold to be tested, the optimal solutions of the fourth weight, fifth weight and sixth weight corresponding to the particle size range of the colloidal gold to be tested are obtained and updated through the genetic algorithm based on the particle size range of the colloidal gold to be tested.

[0047] This application discloses a colloidal gold detection system for an antigen reagent kit, comprising:

[0048] The imaging module is used to acquire images of the colloidal gold to be tested;

[0049] A particle size identification module is used to obtain the particle size range of the colloidal gold to be tested based on the image of the colloidal gold to be tested;

[0050] The initial inspection module is used to compare the particle size range of the colloidal gold to be tested with the standard particle size to obtain the initial inspection result of the colloidal gold to be tested.

[0051] A secondary detection module is used to perform a secondary detection on the colloidal gold under test in response to the initial detection result of the colloidal gold under test being qualified.

[0052] The secondary detection includes:

[0053] The colloidal gold under test was subjected to DLS particle size analysis, Zeta potential detection and electron microscopy imaging to obtain the uniformity of particle size distribution, Zeta potential and regularity of particle size morphology.

[0054] The detection results of the colloidal gold under test are obtained based on the uniformity of particle size distribution, zeta potential, and regularity of particle size morphology.

[0055] A computer device according to this application includes a processor and a memory connected by a signal, characterized in that the memory stores at least one instruction or at least one program, which, when loaded by the processor, executes the colloidal gold detection method of the antigen kit as described above.

[0056] This application discloses a computer-readable storage medium storing at least one instruction or at least one program, characterized in that, when the at least one instruction or the at least one program is loaded by a processor, it executes the colloidal gold detection method of the antigen kit as described above.

[0057] The colloidal gold detection method and system for an antigen kit described in this application have the advantage that the application uses machine vision to perform initial inspection of the colloidal gold to be tested, and performs secondary inspection on the colloidal gold to be tested that passes the initial inspection. The secondary inspection includes DLS particle size analysis, zeta potential detection and electron microscopy imaging, and calculates the test results after quantifying and normalizing each test result. This can reduce the subjective influence of human factors in the colloidal gold quality inspection process, improve the accuracy and reliability of the colloidal gold quality inspection process, and thus improve the product quality of the antigen kit. Attached Figure Description

[0058] Figure 1 This is a flowchart of the steps of the colloidal gold detection method of the antigen kit described in this embodiment;

[0059] Figure 2 This is a schematic diagram of the structure of the computer device described in this embodiment;

[0060] Figure 3 This is an exemplary electron microscope image of colloidal gold.

[0061] Figure labeling: 101 - Processor, 102 - Memory. Detailed Implementation

[0062] like Figure 1 As shown, the colloidal gold detection method of the antigen kit described in this application includes the following steps:

[0063] S01. Obtain an image of the colloidal gold to be tested;

[0064] S02. Obtain the particle size range of the colloidal gold to be tested based on the image of the colloidal gold to be tested;

[0065] The color of colloidal gold solutions is directly related to their particle size, which is determined by surface plasmon resonance and light scattering properties.

[0066] Generally speaking, colloidal gold solutions with small particle sizes (2-20 nm) are orange-yellow to wine-red.

[0067] Colloidal gold solutions with medium particle size (20-50 nm) are deep red to purplish red.

[0068] Colloidal gold solutions with large particle sizes (50-80 nm) appear blue-purple.

[0069] The above is the basis for judging the colloidal gold solution to be tested using machine vision.

[0070] To acquire an image of the colloidal gold to be tested, specifically, a multi-channel light source (such as the 400-800nm ​​band) is used to provide illumination, and an industrial camera is used to acquire the solution image. Preferably, short-wave infrared and ultraviolet light can be used to irradiate the colloidal gold to be tested to enhance the color resolution of the image.

[0071] The particle size range of the colloidal gold to be tested is obtained from the image of the colloidal gold to be tested, specifically including:

[0072] The image of the colloidal gold to be tested is input into the particle size range recognition model to obtain the particle size range of the colloidal gold to be tested;

[0073] The particle size range identification model is obtained through the following steps:

[0074] Multiple sets of colloidal gold images with different particle sizes were collected, and a mapping database of particle size and colloidal gold images was established as sample data.

[0075] The sample data is input into the classification model to train the classification model and obtain the particle size range recognition model.

[0076] For example, the classification model uses the YOLOv8-lite lightweight model, which is commonly used in image classification and has high sensitivity to images.

[0077] Collect multiple sets of colloidal gold images with different particle sizes. For example, by preparing colloidal gold solutions with different particle sizes and imaging them in the same imaging environment, multiple sets of colloidal gold solution images with different particle sizes can be obtained. Each set of colloidal gold solution images is labeled with the corresponding particle size range information, such as 10-15 nm.

[0078] Color features, such as the intensity ratio of red and blue channels, are extracted from images using existing image processing algorithms. Based on the intensity ratio and the labeled grain size, a correspondence between the red and blue channel intensity ratio and the grain size is established. For example, the intensity of red in an image is generally negatively correlated with the grain size. A mapping database of image color features and grain size is established based on multiple sets of correspondences. Preferably, sample augmentation, such as generating image variants using CycleGAN, can further increase the amount of sample data. A portion of the data, for example, 80%, is used as training data, and the remaining 20% ​​is used as test data.

[0079] The YOLOv8-lite model is trained using training data. Specifically, the YOLOv8-lite model is trained with color features extracted from images as input and particle size as output, so that the YOLOv8-lite model has the ability to classify particle size based on image color features.

[0080] By iterating multiple times until the required number of iterations is met, the classification accuracy of the model is verified using test data. The particle size recognition result of the model is compared with the actual particle size corresponding to the image to determine the particle size recognition accuracy. When the accuracy requirement is met, such as the recognition accuracy reaching 90%, the model is considered to meet the accuracy requirement. If it is not met, the number of training iterations is increased and the model constraints are adjusted.

[0081] The above exemplary steps can be used to obtain a particle size range recognition model that takes an image of colloidal gold solution as input and the particle size in the colloidal gold solution as output. In actual testing, after acquiring an image of the colloidal gold solution to be tested through an imaging device, the image is input into the particle size range recognition model to obtain the particle size range of the colloidal gold to be tested.

[0082] S03. Compare the particle size range of the colloidal gold to be tested with the standard particle size to obtain the preliminary inspection result of the colloidal gold to be tested; for example, for a certain batch of colloidal gold solution, the required standard particle size range is 10-20nm. If the particle size range identification model is used to obtain that the colloidal gold solution to be tested is 10-15nm or 15-20nm, then the preliminary inspection result of the colloidal gold solution to be tested is qualified.

[0083] S04. In response to the initial test result of the colloidal gold to be tested being qualified, a second test is performed on the colloidal gold to be tested;

[0084] The secondary detection includes:

[0085] The colloidal gold under test was subjected to DLS particle size analysis, Zeta potential detection and electron microscopy imaging to obtain the uniformity of particle size distribution, Zeta potential and regularity of particle size morphology.

[0086] Based on the uniformity of the particle size distribution, the Zeta potential, and the regularity of the particle size morphology of the colloidal gold to be measured, the detection result of the colloidal gold to be measured is obtained.

[0087] Specifically, the DLS particle size analysis of the colloidal gold to be measured includes:

[0088] Use a Nicomp Z3000 device to perform DLS particle size analysis on the colloidal gold to be measured. Through this device, the average particle size particle size standard deviation σ, and polydispersity index PDI of the colloidal gold to be measured can be obtained. Specifically:

[0089] Average particle size

[0090]

[0091] where n represents the number of particles, and d i represents the particle size of the particle numbered i.

[0092] Particle size standard deviation σ:

[0093]

[0094] [[ID=~]]Polydispersity index PDI:

[0095]

[0096] Calculate the particle size distribution uniformity result Uni of the colloidal gold to be measured according to the following formula:

[0097]

[0098] where w1, w2, and w3 respectively represent the initial first weight, second weight, and third weight. The applicant has found through actual verification that for the special object of colloidal gold solution, the average particle size is easily affected by abnormal particle sizes, and its characteristic of particle size distribution uniformity is relatively poor compared to the other two parameters. Therefore, the weight ratio is adjusted so that the weight ratio satisfies w1 < w2 ≤ w3 to make the particle size distribution uniformity result more accurate. d' represents the standard particle size. For example, for a colloidal gold solution with a standard particle size range of 10 - 20 nm, the standard particle size can be taken as the median 15 nm.

[0099] The applicant further found that in the colloidal gold solution of the polydisperse system, the performance of the average particle size for the particle size distribution uniformity will be further weakened. At this time, more attention should be paid to the two indicators of standard deviation and polydispersity index. Therefore, in this embodiment, in order to make the calculation result of the particle size distribution uniformity more accurate, the weights of the three indicators are dynamically adjusted as follows:

[0100] A preset standard deviation threshold thr for the particle size standard deviation σ is provided. σ And the multidispersion threshold thr for the multidispersion index PDI PDI In response to the particle size standard deviation σ being not less than the standard deviation threshold thr σ And / or the polydispersity index (PDI) is not less than the polydispersity threshold (thr). PDI The first weight is decreased, and the second weight and / or the third weight is increased.

[0101] For example, using the standard deviation of particle size If the standard deviation is a passing standard, then the standard deviation threshold can be set to thr. σ for Using a polydispersity index (PDI) ≤ 0.25 as the standard for a satisfactory polydispersity index, the polydispersity index threshold thr can be set as follows: PDI Set it to 0.2.

[0102] When calculating the uniformity of particle size distribution Uni, if the calculated standard deviation of particle size σ is greater than or equal to the standard deviation threshold, and / or the polydispersity index PDI is greater than or equal to the polydispersity threshold thr PDI The result indicates that the colloidal gold to be tested has obvious polydispersity characteristics. For this type of colloidal gold, the average particle size ratio is easily affected by irregularly shaped particles, which weakens the performance of particle size distribution uniformity. Therefore, the first weight corresponding to the average particle size ratio is reduced, and the second and third weights corresponding to the standard deviation and polydispersity index are increased, so that the final particle size distribution uniformity Uni calculation result is more accurate and targeted.

[0103] The secondary detection includes performing Zeta potential detection on the colloidal gold to be tested, which includes:

[0104] Preset standard range for Zeta potential c = [V min V max ], to obtain the Zeta potential of the colloidal gold to be tested;

[0105] If the Zeta potential of the colloidal gold to be tested does not belong to the standard interval c, then the detection result of the Zeta potential of the colloidal gold to be tested is set to 0. If the Zeta potential of the colloidal gold to be tested belongs to the standard interval c, then the Zeta potential of the colloidal gold to be tested is normalized based on the standard interval c to obtain the detection result V of the Zeta potential of the colloidal gold to be tested. Zeta .

[0106] For example, the preset standard interval c = (V min V maxIf the zeta potential of the colloidal gold to be tested is -10mV, then the zeta potential detection result is directly set to 0. If the zeta potential of the colloidal gold to be tested is -40mV, then normalization is performed to obtain the potential detection result.

[0107]

[0108] The normalized detection result of the Zeta potential of the colloidal gold to be tested can be calculated by the above steps. The closer the normalized detection result is to 1, the better the stability of the colloidal gold to be tested.

[0109] The secondary detection process includes electron microscopy imaging analysis of the colloidal gold sample, which includes:

[0110] Obtain an electron microscope image of the colloidal gold to be tested, and based on the electron microscope image, obtain the major axis L of the particles in the colloidal gold to be tested. l and short axis L s Calculate the aspect ratio AR of each particle in the colloidal gold to be tested:

[0111] AR = L l / L s ;

[0112] Based on the aspect ratio AR of each particle, the average aspect ratio of the particles in the colloidal gold to be tested was calculated.

[0113]

[0114] Wherein, n represents the number of particles in the colloidal gold to be tested, and i represents the particle number;

[0115] Extreme Aspect Ratio AR max :

[0116] AR max =max(AR1, AR2...AR) n ),

[0117] Irregularity percentage P irr :

[0118]

[0119] Where, n irr This indicates the number of particles in the colloidal gold to be tested whose aspect ratio is greater than the aspect ratio threshold. For example, the aspect ratio threshold can be set to 1.2. When the aspect ratio of a particle is greater than 1.2, it is determined that the particle may be a long rod-shaped or elliptical irregular shape.

[0120] Based on the obtained average aspect ratio Extreme Aspect Ratio AR max and the proportion of irregular shapes Pirr The morphological regularity of the colloidal gold under test was calculated using MRI.

[0121]

[0122] Specifically, the colloidal gold solution to be tested is dropped onto a silicon wafer or copper mesh, allowed to dry naturally, and then sputtered with gold. The resulting image is then obtained using a scanning electron microscope (SEM). For example, such as... Figure 3 As shown.

[0123] After acquiring the electron microscope image, the operator measured the major axis L of the particles in the colloidal gold to be tested. l and short axis L s The aspect ratio of each particle is then calculated. The aspect ratio is used to measure the roundness of the particles. For particles in colloidal gold solutions, high roundness is required to avoid rod-shaped, elliptical, or other similar shapes. Therefore, the aspect ratio of each particle is used as one of the detection indicators. Preferably, the aspect ratio of the particles can be automatically calculated using image processing algorithms.

[0124] An exemplary embodiment is as follows:

[0125] Histogram equalization was used to optimize the distinction between particles and background in electron microscopy images, and Gaussian filtering was used to eliminate image noise. An adaptive thresholding algorithm was employed to convert grayscale images into binary images to distinguish particles from the background. A watershed algorithm was used to separate contacting particles through distance transformation and local extremum detection. Morphological operations were applied to erosion-dilation of adherent regions, and the particles in the image were segmented using skeleton extraction.

[0126] The Feret axial length method is used to calculate the lengths of the major and minor axes of particles, which enables automated calculation of the major and minor axes of particles and improves detection efficiency.

[0127] After calculating the major axis length and minor axis length of each particle, the aspect ratio of each particle can be calculated, and then the average aspect ratio, the extreme value of the aspect ratio and the proportion of irregularities can be obtained. In this way, the morphological regularity result MRI can be calculated. The closer this value is to 1, the more regular the particle morphology in the colloidal gold to be tested is.

[0128] Based on the uniformity of particle size distribution, zeta potential, and regularity of particle morphology of the colloidal gold to be tested, the detection results of the colloidal gold to be tested include:

[0129] Calculate the detection index DR of the colloidal gold to be tested:

[0130] DR = w4*Uni + w5*(1-V) Zeta )+w6*(1-MRI),

[0131] Where w4, w5, and w6 represent the fourth, fifth, and sixth weights, respectively.

[0132] If the particle size distribution uniformity, Zeta potential, and particle morphology regularity of the colloidal gold to be tested all meet the standards, and the detection result DR of the colloidal gold to be tested is less than the preset detection result threshold thr DR If the colloidal gold to be tested is qualified, then the colloidal gold to be tested is qualified; otherwise, the colloidal gold to be tested is unqualified.

[0133] After calculating the particle size distribution uniformity, zeta potential, and particle morphology regularity of the colloidal gold to be tested, since these three indicators are interrelated and influence each other, in addition to judging whether each of the three indicators is qualified, it is also necessary to judge the overall performance of the colloidal gold in terms of stability and uniformity. Therefore, the overall detection index of the colloidal gold to be tested is calculated using the above formula. By reasonably allocating the weights and adjusting the influence of the three different indicators on the final detection index, the accuracy of the final detection results can be further improved.

[0134] During the actual testing process, the applicant found that for colloidal gold solutions with different particle sizes, their application scenarios may differ. For example, diagnostic colloidal gold solutions typically have a particle size of 20-40 nm, while labeling colloidal gold solutions typically have a particle size of 5-15 nm. The significant difference in particle size results in varying sensitivities to various detection indicators. For instance, small-particle-size colloidal gold solutions exhibit stronger surface plasmon resonance and more active Brownian motion, making them more sensitive to zeta potentials. Therefore, the weight of zeta potential can be appropriately increased. Conversely, large-particle-size solutions, due to the steric hindrance effect of their particles, respond less strongly to zeta potentials compared to small-particle-size solutions, and the weight of zeta potential can be appropriately reduced.

[0135] Therefore, in order to improve the applicability and accuracy of the method in this embodiment under different particle sizes and different scenarios, the fourth, fifth, and sixth weights can be dynamically adjusted using a genetic algorithm. An exemplary process is as follows:

[0136] Weighted correlation of colloidal gold properties:

[0137] Particle size distribution uniformity: Small particle size (e.g., 5-20 nm) colloidal gold is more sensitive to particle size distribution uniformity and its weight needs to be increased to control the impact of polydispersity on optical performance; large particle size allows for a higher tolerance for particle size distribution uniformity.

[0138] Zeta potential: Small particle size is more sensitive to zeta potential, so the weight of zeta potential can be appropriately increased. However, due to the steric hindrance effect of the particles, the large particle size solution is less sensitive to zeta potential than the small particle size solution, so the weight of zeta potential can be appropriately reduced.

[0139] Morphological regularity: The morphology of large-particle colloidal gold is easily affected by the preparation process (such as agglomeration), and the weight needs to be increased to suppress the interference of irregular particles on the detection signal.

[0140] The fourth, fifth, and sixth weights are used as chromosomes for the genetic algorithm, satisfying the constraint that the sum of the weights equals 1, and real-number encoding is used to map the solution space.

[0141] Natural selection mechanism: The fitness function comprehensively detects the error of the index and eliminates weight combinations that violate physical laws (such as small particle size but correspondingly low weight).

[0142] Encoding and Population Initialization:

[0143] The weights are directly represented by real numbers (e.g., 0.6, 0.2, 0.2). The initial population randomly generates N sets of weights (e.g., 50-200 sets). Normalization ensures that the sum of the weights in each set is equal to 1.

[0144] Constrained embedding: Illegal solutions are handled through a penalty function. For example, for small-particle-size samples, the fitness of the weight combination is deducted when the fourth weight corresponding to the uniformity of particle size distribution is less than 0.5.

[0145] Fitness function design:

[0146] Error standardization was performed on three indicators of the sample data: uniformity of particle size distribution, Zeta potential, and regularity of particle size morphology.

[0147]

[0148] The target value and allowable deviation are set by the company according to industry standards and product requirements.

[0149] Fitness calculation:

[0150] f(w)=w4*(1-E4)+w5*(1-E5)+w6*(1-E6),

[0151] By setting a fitness threshold, weight combinations that meet the fitness criteria are selected.

[0152] Genetic manipulation:

[0153] Selection: Use a tournament mode to retain highly fit individuals while taking into account diversity, for example, select 3 individuals each time to compete for the best.

[0154] Crossover: The offspring weights are generated using an arithmetic crossover formula.

[0155] child=a*parent1+(1-a)*parent2,

[0156] a∈[0.2,0.8], randomly generated.

[0157] Mutation: Gaussian perturbation mutation is used to add N(0,0.05) noise to the random weights, and then normalization is performed after mutation.

[0158] Dynamic parameter adjustment:

[0159] Adaptive mutation rate: As the number of iterations increases, the mutation rate decreases linearly from 0.1 to 0.01.

[0160] Elite retention: The best 5% of individuals are retained in each generation to prevent the loss of high-quality solutions.

[0161] Through the above steps, the optimal genetic solution for the weight combination under different particle sizes can be obtained. When the particle size range of the colloidal gold to be tested is obtained in actual detection, the median of the particle size range can be input into the above genetic algorithm to obtain the optimal solution for the weight combination corresponding to the colloidal gold to be tested and update it, so that the final calculated detection index DR is more accurate.

[0162] This embodiment also provides a colloidal gold detection system for an antigen reagent kit, including:

[0163] The imaging module is used to acquire images of the colloidal gold to be tested;

[0164] A particle size identification module is used to obtain the particle size range of the colloidal gold to be tested based on the image of the colloidal gold to be tested;

[0165] The initial inspection module is used to compare the particle size range of the colloidal gold to be tested with the standard particle size to obtain the initial inspection result of the colloidal gold to be tested.

[0166] A secondary detection module is used to perform a secondary detection on the colloidal gold under test in response to the initial detection result of the colloidal gold under test being qualified.

[0167] The secondary detection includes:

[0168] The colloidal gold under test was subjected to DLS particle size analysis, Zeta potential detection and electron microscopy imaging to obtain the uniformity of particle size distribution, Zeta potential and regularity of particle size morphology.

[0169] The detection results of the colloidal gold under test are obtained based on the uniformity of particle size distribution, zeta potential, and regularity of particle size morphology.

[0170] The system in this embodiment and the method described above belong to the same inventive concept and can be understood with reference to the above text, and will not be repeated here.

[0171] like Figure 2As shown, this embodiment also provides a computer device, including a processor 101 and a memory 102 connected via a bus signal. The memory 102 stores at least one instruction or at least one program segment. When the at least one instruction or the at least one program segment is loaded by the processor 101, it executes the colloidal gold detection method of the antigen kit described above. The memory 102 can be used to store software programs and modules. The processor 101 executes various functional applications by running the software programs and modules stored in the memory 102. The memory 102 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for the functions, etc.; the data storage area may store data created according to the use of the device, etc. In addition, the memory 102 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 102 may also include a memory controller to provide the processor 101 with access to the memory 102.

[0172] The methods and embodiments provided in this application can be executed in a computer terminal, server, or similar computing device; that is, the aforementioned computer device may include a computer terminal, server, or similar computing device. The internal structure of the computer device may include, but is not limited to, a processor, a network interface, and memory. The processor, network interface, and memory within the computer device can be connected via a bus or other means.

[0173] The processor 101 (or CPU, Central Processing Unit) is the computing and control core of the computer device. The network interface may optionally include a standard wired interface or a wireless interface (such as Wi-Fi, mobile communication interface, etc.). The memory 102 is the storage device in the computer device used to store programs and data. It is understood that the memory 102 here can be a high-speed RAM storage device, or a non-volatile storage device, such as at least one disk storage device; optionally, it can also be at least one storage device located remotely from the processor 101. The memory 102 provides storage space that stores the operating system of the electronic device, which may include, but is not limited to: Windows (an operating system), Linux (an operating system), Android (a mobile operating system), iOS (a mobile operating system), etc., and this application does not limit this; furthermore, the storage space also stores one or more instructions suitable for being loaded and executed by the processor 101, which may be one or more computer programs (including program code). In the embodiments of this specification, the processor 101 loads and executes one or more instructions stored in the memory 102 to implement the colloidal gold detection method of the antigen kit described in the above method embodiments.

[0174] This application also provides a computer-readable storage medium storing at least one instruction or at least one program segment, which, when loaded by processor 101, executes the colloidal gold detection method of the antigen kit described above. The aforementioned computer-readable storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of this application.

[0175] According to embodiments of this application, the computer-readable storage medium can be a non-volatile computer-readable storage medium. Examples include, but are not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0176] In the description of this application, it should be understood that the orientation or positional relationship indicated by directional terms such as "front, back, up, down, left, right", "horizontal, vertical, horizontal" and "top, bottom" is usually based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing this application and simplifying the description. Unless otherwise stated, these directional terms do not indicate or imply that the device or element referred to must have a specific orientation or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on the scope of protection of this application.

[0177] For those skilled in the art, various other corresponding changes and modifications can be made based on the technical solutions and concepts described above, and all such changes and modifications should fall within the protection scope of the claims of this application.

Claims

1. A colloidal gold detection method for an antigen kit, characterized in that, Includes the following steps: S01. Obtain an image of the colloidal gold to be tested; S02. Obtain the particle size range of the colloidal gold to be tested based on the image of the colloidal gold to be tested; S03. Compare the particle size range of the colloidal gold to be tested with the standard particle size to obtain the preliminary test result of the colloidal gold to be tested. S04. If the initial test result of the colloidal gold to be tested is qualified, a second test shall be performed on the colloidal gold to be tested. The secondary detection includes: The colloidal gold under test was subjected to DLS particle size analysis, Zeta potential detection and electron microscopy imaging to obtain the uniformity of particle size distribution, Zeta potential and regularity of particle size morphology. The detection results of the colloidal gold under test are obtained based on the uniformity of particle size distribution, zeta potential, and regularity of particle size morphology.

2. The colloidal gold detection method of the antigen kit according to claim 1, characterized in that, Step S02 includes: The image of the colloidal gold to be tested is input into the particle size range recognition model to obtain the particle size range of the colloidal gold to be tested; The particle size range identification model is obtained through the following steps: Multiple sets of colloidal gold images with different particle sizes were collected, and a mapping database of particle size and colloidal gold images was established as sample data. The sample data is input into the classification model to train the classification model and obtain the particle size range recognition model.

3. The colloidal gold detection method of the antigen kit according to claim 1, characterized in that, The secondary detection process includes DLS particle size analysis of the colloidal gold sample, which includes: The average particle size of the colloidal gold under test was obtained by DLS particle size analysis. The particle size standard deviation σ and polydispersity index PDI were used to calculate the particle size distribution uniformity result Uni of the colloidal gold to be tested, according to the following formula: Where w1, w2, and w3 represent the initial first weight, second weight, and third weight, respectively, satisfying w1 <w2≤w3,d ' Indicates standard particle size; A preset standard deviation threshold thr for the particle size standard deviation σ is provided. σ And the multidispersion threshold thr for the multidispersion index PDI PDI In response to the particle size standard deviation σ being not less than the standard deviation threshold thr σ And / or the polydispersity index (PDI) is not less than the polydispersity threshold (thr). PDI The first weight is decreased, and the second weight and / or the third weight is increased.

4. The colloidal gold detection method of the antigen kit according to claim 3, characterized in that, The secondary detection includes performing Zeta potential detection on the colloidal gold to be tested, which includes: Preset standard range for Zeta potential c = (V min V max The zeta potential of the colloidal gold to be tested is obtained. If the Zeta potential of the colloidal gold to be tested does not belong to the standard interval c, then the detection result of the Zeta potential of the colloidal gold to be tested is set to 0. If the Zeta potential of the colloidal gold to be tested belongs to the standard interval c, then the Zeta potential of the colloidal gold to be tested is normalized based on the standard interval c to obtain the detection result V of the Zeta potential of the colloidal gold to be tested. Zeta .

5. The colloidal gold detection method of the antigen kit according to claim 4, characterized in that, The secondary detection process includes electron microscopy imaging analysis of the colloidal gold sample, which includes: Obtain an electron microscope image of the colloidal gold to be tested, and based on the electron microscope image, obtain the major axis L of the particles in the colloidal gold to be tested. l and short axis L s Calculate the aspect ratio AR of each particle in the colloidal gold to be tested: AR=L l / L s ; Based on the aspect ratio AR of each particle, the average aspect ratio of the particles in the colloidal gold to be tested was calculated. Wherein, n represents the number of particles in the colloidal gold to be tested, and i represents the particle number; Extreme Aspect Ratio AR max : AR max =max(AR1、AR2...AR n ), Irregularity percentage P irr : Where, n irr This indicates the number of particles in the colloidal gold sample with an aspect ratio greater than the aspect ratio threshold. Based on the obtained average aspect ratio Extreme Aspect Ratio AR max and the proportion of irregular shapes P irr The morphological regularity of the colloidal gold under test was calculated using MRI.

6. The colloidal gold detection method of the antigen kit according to claim 5, characterized in that, Based on the uniformity of particle size distribution, zeta potential, and regularity of particle morphology of the colloidal gold to be tested, the detection results of the colloidal gold to be tested include: Calculate the detection index DR of the colloidal gold to be tested: DR=w4*Uni+w5*(1-V Zeta )+w6*(1-MRI), Where w4, w5, and w6 represent the fourth, fifth, and sixth weights, respectively. If the particle size distribution uniformity, Zeta potential, and particle morphology regularity of the colloidal gold to be tested all meet the standards, and the detection result DR of the colloidal gold to be tested is less than the preset detection result threshold thr DR If the colloidal gold to be tested is qualified, then the colloidal gold to be tested is qualified; otherwise, the colloidal gold to be tested is unqualified.

7. The colloidal gold detection method of the antigen kit according to claim 6, characterized in that, The calculation of the detection index DR of the colloidal gold to be tested also includes: Construct a genetic algorithm for the fourth, fifth, and sixth weights; When calculating the detection index DR of the colloidal gold to be tested, the optimal solutions of the fourth weight, fifth weight and sixth weight corresponding to the particle size range of the colloidal gold to be tested are obtained and updated through the genetic algorithm based on the particle size range of the colloidal gold to be tested.

8. A colloidal gold detection system for an antigen reagent kit, characterized in that, include: The imaging module is used to acquire images of the colloidal gold to be tested; A particle size identification module is used to obtain the particle size range of the colloidal gold to be tested based on the image of the colloidal gold to be tested; The initial inspection module is used to compare the particle size range of the colloidal gold to be tested with the standard particle size to obtain the initial inspection result of the colloidal gold to be tested. A secondary detection module is used to perform a secondary detection on the colloidal gold to be tested if the initial detection result of the colloidal gold to be tested is qualified. The secondary detection includes: The colloidal gold under test was subjected to DLS particle size analysis, Zeta potential detection and electron microscopy imaging to obtain the uniformity of particle size distribution, Zeta potential and regularity of particle size morphology. The detection results of the colloidal gold under test are obtained based on the uniformity of particle size distribution, zeta potential, and regularity of particle size morphology.

9. A computer device comprising a processor and a memory connected by signals, characterized in that, The memory stores at least one instruction or at least one program, which, when loaded by the processor, executes the colloidal gold detection method of the antigen kit as described in any one of claims 1-7.

10. A computer-readable storage medium having stored thereon at least one instruction or at least one program, characterized in that, When the at least one instruction or the at least one program segment is loaded by the processor, the colloidal gold detection method of the antigen kit as described in any one of claims 1-7 is executed.

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