HBsAg Detection Method and Related Equipment Based on Multiple Wavelengths

Abstract

The present invention provides a multi-wavelength-based HBsAg detection method, including: detecting the optical absorption value of a sample to be detected for HBsAg by a sandwich immunoassay using a multi-wavelength microplate reader to obtain the optical absorption data of the sample to be detected for HBsAg at multiple wavelengths; determining the OD value of the sample, the OD value of the negative control, and the OD value of the positive control at two wavelengths based on the optical absorption data at 450 nm and 630 nm wavelengths; determining whether the first detection result is valid within the preset concentration range of HBsAg based on the OD value of the negative control and the OD value of the positive control; if the first detection result is invalid within the preset concentration range of HBsAg, determining the second detection result of the sample to be detected for HBsAg based on the mapping relationship between the optical absorption data at multiple wavelengths and the detection result of chemiluminescence method. The present invention improves the sensitivity, accuracy, and reliability of the detection of hepatitis B surface antigen at low concentrations.

Description

Technical Field

[0001] The present invention relates to the technical field of immunoassay for hepatitis B surface antigen, and in particular to a method for detecting HBsAg based on multi-wavelength and related equipment. Background Art

[0002] Hepatitis B surface antigen (HBsAg) is an important marker for hepatitis B virus infection, and its immunoassay is of great significance for the diagnosis, treatment and prevention of hepatitis B. At present, the immunoassay methods for hepatitis B surface antigen mainly include enzyme-linked immunosorbent assay (ELISA), chemiluminescence method, radioimmunoassay, etc. Among them, the double antibody sandwich method, as a commonly used ELISA technique, is widely used because of its simple operation and low cost.

[0003] However, in practical applications, the double antibody sandwich method has some problems, such as insufficient sensitivity and susceptibility to interference factors, resulting in inaccurate test results sometimes. Especially in the detection of low-concentration HBsAg samples, due to the presence of interfering substances or minor changes in operating conditions, it may cause deviations in test results, and false negative or false positive results may occur, affecting the accuracy of the detection. Therefore, there is an urgent need for a solution to improve the detection accuracy of hepatitis B surface antigen at low concentrations. Summary of the Invention

[0004] An embodiment of the present invention provides a method for detecting HBsAg based on multi-wavelength, aiming to improve the detection accuracy of hepatitis B surface antigen at low concentrations. The light absorption value of the HBsAg sample to be tested by the double antibody sandwich method can be detected by a multi-wavelength microplate reader. Based on the light absorption data at two wavelengths (450 nm and 630 nm), a first test result is determined. Then, the OD value of the negative control and the OD value of the positive control are used to verify whether the test result is valid within the preset concentration range of HBsAg. When the first test result is invalid within the preset concentration range of HBsAg, based on the mapping relationship between the light absorption data at multi-wavelength and the chemiluminescence method test result, a second test result of the sample to be tested is determined. By using multi-wavelength detection, a double verification mechanism and a calibration method of chemiluminescence method result mapping, the problems existing in the immunoassay of hepatitis B surface antigen by the traditional double antibody sandwich method are effectively solved, and the sensitivity, accuracy and reliability of the detection of hepatitis B surface antigen at low concentrations are improved.

[0005] In a first aspect, an embodiment of the present invention provides a method for detecting HBsAg based on multi-wavelength, and the method for detecting HBsAg based on multi-wavelength includes:

[0006] The light absorption value of the HBsAg sample to be measured by the double antibody sandwich method is detected by a multi-wavelength microplate reader, and the light absorption data of the HBsAg sample to be measured at multiple wavelengths are obtained. The multiple wavelengths include multiple wavelengths between 450 nm and 630 nm;

[0007] Based on the light absorption data at the 450 nm wavelength and the 630 nm wavelength, the sample OD values, negative control OD values, and positive control OD values at the 450 nm wavelength and the 630 nm wavelength are determined;

[0008] Based on the sample OD values, negative control OD values, and positive control OD values at the 450 nm wavelength and the 630 nm wavelength, the first test result of the HBsAg sample to be measured is determined, and based on the negative control OD value and the positive control OD value, it is determined whether the first test result is valid within the preset concentration range of HBsAg;

[0009] If the first test result is invalid within the preset concentration range of HBsAg, then based on the mapping relationship between the light absorption data at multiple wavelengths and the chemiluminescence method test result, the second test result of the HBsAg sample to be measured is determined.

[0010] Optionally, the number of negative control OD values is multiple, the number of positive control OD values is 1, and the step of determining whether the first test result is valid within the preset concentration range of HBsAg based on the negative control OD value and the positive control OD value includes:

[0011] Obtain the first critical value at the 450 nm wavelength within the preset concentration range of HBsAg, and obtain the second critical value at the 630 nm wavelength within the preset concentration range of HBsAg;

[0012] Based on the sample OD value, the positive control OD value, the first critical value, and the second critical value, it is determined whether the first test result is valid within the preset concentration range of HBsAg.

[0013] Optionally, the step of determining whether the first test result is valid within the preset concentration range of HBsAg based on the sample OD value, the positive control OD value, the first critical value, and the second critical value includes:

[0014] Determine whether the first critical value is less than the preset first critical value threshold, and determine whether the second critical value is less than the preset second critical value threshold. The first critical value threshold is greater than the second critical value threshold;

[0015] If the first critical value is less than a preset first critical value threshold, and the second critical value is less than a preset second critical value threshold, it is determined that the negative control OD value is valid within the preset concentration range of HBsAg; otherwise, it is determined that the negative control OD value is invalid within the preset concentration range of HBsAg.

[0016] Determine whether the positive control OD value is greater than a preset positive value threshold.

[0017] If the positive control OD value is greater than the preset positive value threshold, it is determined that the positive control OD value is valid within the preset concentration range of HBsAg; otherwise, it is determined that the positive control OD value is invalid within the preset concentration range of HBsAg.

[0018] If the negative control OD value is valid within the preset concentration range of HBsAg and the positive control OD value is valid within the preset concentration range of HBsAg, it is determined that the first test result is valid within the preset concentration range of HBsAg; otherwise, it is determined that the first test result is invalid within the preset concentration range of HBsAg.

[0019] Optionally, the step of determining the second test result of the sample to be tested based on the light absorption data at multiple wavelengths includes:

[0020] Determine the deviation value of the first test result within the preset concentration range of HBsAg.

[0021] Obtain the light absorption data at multiple wavelengths, and based on the light absorption data at multiple wavelengths, determine a light absorption value sequence sorted in ascending order of wavelength.

[0022] Based on the deviation value and the light absorption value sequence, determine the second test result of the HBsAg sample to be tested.

[0023] Optionally, the step of determining the deviation value of the first test result within the preset concentration range of HBsAg includes:

[0024] Based on the difference between the first critical value and the first critical value threshold, determine the first negative deviation value of the first test result within the preset concentration range of HBsAg.

[0025] Based on the difference between the second critical value and the second critical value threshold, determine the second negative deviation value of the first test result within the preset concentration range of HBsAg.

[0026] Based on the difference between the positive control OD value and the positive value threshold, determine the positive deviation value of the first test result within the preset concentration range of HBsAg.

[0027] Based on the first negative deviation value, the second negative deviation value, and the positive deviation value, determine the deviation value of the first test result within the preset concentration range of HBsAg.

[0028] Optionally, the determining the second test result of the HBsAg sample to be tested based on the deviation value and the light absorption value sequence includes:

[0029] Input the deviation value and the light absorption value sequence into a pre-trained prediction model. The prediction model includes a first feature extraction network, a second feature extraction network, a feature fusion network, and an output network. The output ends of the first feature extraction network and the second feature extraction network are both connected to the input end of the feature fusion network, and the output end of the feature fusion network is connected to the input end of the output network;

[0030] Perform a deconvolution operation on the deviation value through the first feature extraction network to obtain the deviation feature corresponding to the deviation value;

[0031] Perform a convolution operation on the light absorption value sequence through the second feature extraction network to obtain the light absorption feature of the light absorption value sequence. The deviation feature and the light absorption feature have the same resolution size;

[0032] Through the fusion network, obtain the fusion feature from the deviation feature and the light absorption feature;

[0033] Perform a prediction process on the fusion feature through the output network to obtain a prediction result as the second test result of the HBsAg sample to be tested.

[0034] Optionally, before the step of inputting the deviation value and the light absorption value sequence into the pre-trained prediction model, the method further includes:

[0035] Obtain the first sample test result and the second sample test result of the same HBsAg sample. The first sample test result is the test result of the double antibody sandwich method, and the second sample test result is the test result of the chemiluminescence method. The sample double antibody sandwich method test result is the light absorption data at multiple wavelengths corresponding to the invalid state of the first test result. The HBsAg sample is an HBsAg sample within the preset concentration range of HBsAg;

[0036] Based on the first sample test result, determine the sample deviation value corresponding to the first sample test result, and determine the first sample test result and the sample deviation value as the sample data of a binary group;

[0037] Determine the second sample test result as the label data of the sample data;

[0038] Construct a training data set based on the sample data and the label data;

[0039] Obtain a pre-trained model, and train the pre-trained model with the training data set. After the training is completed, a trained prediction model is obtained.

[0040] In a second aspect, an embodiment of the present invention further provides a multi-wavelength based HBsAg detection device, and the multi-wavelength based HBsAg detection device includes:

[0041] A detection module, configured to detect the optical absorption value of the HBsAg sample to be measured by a sandwich method through a multi-wavelength microplate reader, and obtain the optical absorption data of the HBsAg sample to be measured at multiple wavelengths, where the multiple wavelengths include multiple wavelengths between 450 nm and 630 nm;

[0042] A first processing module, configured to determine the sample OD value, the negative control OD value, and the positive control OD value at the 450 nm wavelength and the 630 nm wavelength based on the optical absorption data at the 450 nm wavelength and the 630 nm wavelength;

[0043] A second processing module, configured to determine a first detection result of the HBsAg sample to be measured based on the sample OD value, the negative control OD value, and the positive control OD value at the 450 nm wavelength and the 630 nm wavelength, and determine whether the first detection result is valid within a preset concentration range of HBsAg based on the negative control OD value and the positive control OD value;

[0044] A third processing module, configured to, if the first detection result is invalid within the preset concentration range of HBsAg, determine a second detection result of the HBsAg sample to be measured based on the mapping relationship between the optical absorption data at the multiple wavelengths and the chemiluminescence detection result.

[0045] The present invention further provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the steps in the multi-wavelength based HBsAg detection method according to any one of the embodiments of the present invention are implemented.

[0046] The present invention further provides a computer storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps in the multi-wavelength based HBsAg detection method according to any one of the embodiments of the present invention are implemented.

[0047] By using the multi-wavelength-based HBsAg detection method of the present invention, the optical absorption value of the HBsAg sample to be tested in the double antibody sandwich method can be detected by a multi-wavelength microplate reader. The first detection result is determined based on the optical absorption data at two wavelengths (450 nm and 630 nm). Then, the OD value of the negative control and the OD value of the positive control are used to verify whether the detection result is valid within the preset concentration range of HBsAg. When the first detection result is invalid within the preset concentration range of HBsAg, based on the mapping relationship between the optical absorption data at multiple wavelengths and the detection result of the chemiluminescence method, the second detection result of the sample to be tested is determined. By adopting multi-wavelength detection, a double verification mechanism, and a calibration method of mapping the chemiluminescence method results, the problems existing in the traditional double antibody sandwich method for HBsAg immunoassay are effectively solved, and the sensitivity, accuracy, and reliability of HBsAg detection at low concentrations are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts.

[0049] Figure 1 is a schematic flowchart of a multi-wavelength-based HBsAg detection provided by an embodiment of the present invention;

[0050] Figure 2 is a schematic structural diagram of a multi-wavelength-based HBsAg detection device provided by an embodiment of the present invention;

[0051] Figure 3 is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0052] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, rather than all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments of the present invention belong to the scope of protection of the present invention.

[0053] As Figure 1 shown, Figure 1 is a schematic flowchart of a multi-wavelength-based HBsAg detection method provided by an embodiment of the present invention. It is used for the result determination of HBsAg (hepatitis B surface antigen). The multi-wavelength-based HBsAg detection method includes:

[0054] 101. Detect the optical absorption value of the HBsAg sample to be tested by the double antibody sandwich method using a multi-wavelength microplate reader, and obtain the optical absorption data of the HBsAg sample to be tested at multiple wavelengths.

[0055] In the embodiment of the present invention, the optical absorption value of the HBsAg sample to be tested by the double antibody sandwich method is detected using a multi-wavelength microplate reader. The multi-wavelength microplate reader can simultaneously detect the optical absorption at multiple wavelengths. In this embodiment, the selected wavelength range is from 450 nm to 630 nm. The selection of this wavelength range is based on the optical property changes generated after the binding of HBsAg and antibodies. The optical absorption values at different wavelengths can reflect the concentration differences of HBsAg in the sample.

[0056] During the detection process, the sample to be tested reacts with the microplate coated with specific antibodies to form an antigen-antibody complex. Subsequently, an enzyme-labeled antibody is added to form a double antibody sandwich structure. After adding the substrate, the enzyme catalyzes the substrate to produce a color reaction, and its optical absorption value is proportional to the concentration of HBsAg in the sample. Specifically, an anti-HBs-coated reaction plate is used. After adding the sample to be tested and incubating, anti-HBS-HRP is added. When HBsAg exists in the sample, this HBsAg binds to the coated anti-HBs and then binds to anti-HBs-HRP to form an anti-HBs-HBsAg-anti-HBsHRP complex. Adding the TMB substrate produces a color reaction, otherwise there is no color reaction.

[0057] Detect the optical absorption values of this color reaction at different wavelengths using a multi-wavelength microplate reader, and then the optical absorption data of the sample to be tested at multiple wavelengths can be obtained. By adjusting the filter of the multi-wavelength microplate reader, the acquisition of optical absorption data at different wavelengths by the multi-wavelength microplate reader is adjusted, and different filters correspond to different wavelengths.

[0058] Specifically, the process of obtaining the optical absorption data of the HBsAg sample to be tested by the double antibody sandwich method is as follows:

[0059] 1. Prepare the working concentration washing solution (taking 25-fold dilution with purified water as an example: measure 480 ml of purified water, add 20 ml of washing solution, and mix well to form 500 ml of working concentration washing solution for later use).

[0060] 2. Select a certain number of reaction plate strips according to the experimental requirements.

[0061] 3. Add 75 μL of the sample to be tested and negative and positive controls into the reaction wells (reserving 3 wells for negative control, 1 well for positive control, and it is recommended to reserve 1 well for blank control).

[0062] 4. Cover the reaction plate with a cover slip and incubate the reaction plate at 37 °C for 60 minutes.

[0063] 5. Take out the reaction plate, remove the cover sheet, and add 50 μL of enzyme conjugate to the wells that have been added with the test sample and negative and positive controls.

[0064] 6. Shake on a microplate shaker for 10 seconds, or gently shake by hand for 10 seconds.

[0065] 7. After covering the reaction plate with a cover sheet, incubate the reaction plate at 37 °C for 30 minutes.

[0066] 8. Take out the reaction plate, remove the cover sheet, and wash the reaction plate 5 times. Manual plate washing: Discard the liquid in the wells, fill each well with the working concentration washing solution prepared in step 1, let stand for 30 - 60 seconds, centrifuge dry and repeat 5 times, then pat dry on a clean absorbent paper. Plate washer plate washing: Select the program for washing 5 times, fill each well with the working concentration washing solution prepared in step 1, and the residence time of the working concentration washing solution in the microplate wells of the reaction plate is 30 - 60 seconds each time, and ensure that each time it is completely aspirated without residue, and pat dry on a clean absorbent paper after washing.

[0067] 9. Immediately after washing, add 50 μL of chromogen A and chromogen B to all wells and mix well.

[0068] 10. Shake on a microplate shaker for 10 seconds, or gently shake by hand for 10 seconds.

[0069] 11. After covering the reaction plate with a cover sheet, incubate the reaction plate at 37 °C for 30 minutes.

[0070] 12. Add 50 μL of stop solution to all wells and shake the reaction plate for 5 seconds to mix well.

[0071] 13. Read with a multi - wavelength microplate reader, perform continuous readings from a wavelength of 450 nm to 630 nm. If it is necessary to deduct the chromogen blank, first zero with the chromogen blank control well, and then read the OD value (optical density value) of each well.

[0072] 102. Based on the light absorption data at wavelengths of 450 nm and 630 nm, determine the sample OD value, negative control OD value, and positive control OD value at wavelengths of 450 nm and the said 630 nm.

[0073] In the embodiments of the present invention, based on the light absorption data at wavelengths of 450 nm and 630 nm, the sample OD values S, negative control OD values NCn, and positive control OD values PC at wavelengths of 450 nm and 630 nm are determined. The OD value is a representation of the light absorption value, and its magnitude is proportional to the concentration of the substance to be measured in the sample. Negative control and positive control are two control methods designed in the enzyme-linked immunosorbent assay (ELISA) to verify the accuracy and reliability of the enzyme-linked immunosorbent assay. The negative control is usually a sample without HBsAg, and the negative control OD value is used to determine the background noise and detection limit; the positive control is a sample known to contain the substance to be measured, and the positive control OD value is used to verify the sensitivity and specificity of the experiment.

[0074] Through the readings of a multi-wavelength microplate reader, the following index data can be obtained:

[0075] NCn: Negative control OD value, where n can be equal to 3.

[0076] PC: Positive control OD value.

[0077] In this embodiment, by comparing the relationship between the sample OD value, the negative control OD value, and the positive control OD value, it can be preliminarily determined whether the concentration of HBsAg in the sample to be measured is within the preset concentration range.

[0078] It can be understood that among the above wavelengths of 450 nm and 630 nm, the 450 nm wavelength is the wavelength for chromogenic absorption, and the 630 nm wavelength is the wavelength that is not sensitive to specific chromogenesis. Further, the light absorption data obtained at the 630 nm wavelength is non-specific and comes from absorption caused by fingerprints, dust, dirt, etc. on the plate wells.

[0079] 103. Based on the sample OD values, negative control OD values, and positive control OD values at wavelengths of 450 nm and 630 nm, determine the first test result of the HBsAg sample to be measured, and based on the negative control OD value and the positive control OD value, determine whether the first test result is valid within the preset concentration range of HBsAg.

[0080] In the embodiments of the present invention, the above first test result is determined by the light absorption data at wavelengths of 450 nm and 630 nm. The specific steps for determining the first test result of the HBsAg sample to be measured through the light absorption data at wavelengths of 450 nm and 630 nm are as follows:

[0081] NCx: Negative control average OD value.

[0082] NCn: Negative control OD value, where n can be equal to 3.

[0083] PC: OD value of the positive control.

[0084] Calculate NCx:

[0085] NCx = (NC1 + NC2 + …… + NCn) ÷ n. If NCx < 0, then calculate it as 0.

[0086] Calculate COV (Cut - Off Value reference value): COV = NCx + a, where a can be equal to 0.100.

[0087] S: OD value of the sample to be tested.

[0088] S / COV: The ratio of the sample to be tested and COV. When S / COV > b, it indicates that the HBsAg result of the sample to be tested is positive, that is, the first test result is positive, indicating the presence of hepatitis B surface antigen. b can be equal to 1.0.

[0089] When S / COV < b, it indicates that the HBsAg result of the sample to be tested is negative, that is, the first test result is negative, indicating the absence of hepatitis B surface antigen.

[0090] The detection validity of the first test result within the preset concentration range is used to illustrate whether the HBsAg of the corresponding sample to be tested may be a low - concentration HBsAg sample (since the HBsAg of the sample to be tested cannot be quantitatively detected in advance, the first test result obtained by the double - antibody sandwich method may have false negative or false positive results).

[0091] If NCx ≤ e and PC > f, the test result is valid. Conversely, if NCx > e and PC ≤ f, the test result is invalid. Among them, e and f can be determined according to the empirical values of the preset concentration range. For example, e can be equal to 0.100 and f can be equal to 1.000.

[0092] Color reagent blank:

[0093] In the double - wavelength reading of a multi - wavelength microplate reader, if the color reagent blank ≤ c, where c can be equal to 0.040, the test result is valid.

[0094] In a possible embodiment, a machine learning model can be used to predict the OD value of the sample, the OD value of the negative control, and the OD value of the positive control, and predict whether the first test result is valid within the preset concentration range of HBsAg.

[0095] The above - mentioned preset concentration range is the concentration range within which the multi - wavelength microplate reader can determine valid test results. For example, it can be the concentration range corresponding to within 10 to the power of 2 of the hepatitis B virus quantity.

[0096] 104. If the first test result is invalid within the preset concentration range of HBsAg, then based on the mapping relationship between the light absorption data at multiple wavelengths and the test result of the chemiluminescence method, the second test result of the sample to be tested is determined.

[0097] In the embodiment of the present invention, when the first test result is invalid within the preset concentration range of HBsAg, that is, the OD value of the sample to be tested exceeds the range of the negative control and the positive control, it indicates that the concentration of HBsAg in the sample may be too low to be accurately measured directly by the double antibody sandwich method. At this time, based on the mapping relationship between the light absorption data at multiple wavelengths and the test result of the chemiluminescence method, the second test result of the sample to be tested is determined.

[0098] The chemiluminescence method is a highly sensitive detection method. Its principle is to use the light energy generated by a chemical reaction to detect the concentration of the substance to be tested. It has high sensitivity and can quantitatively detect HBsAg, but the detection process is more complex and requires a longer time. In this embodiment, through the pre-established mapping relationship between the light absorption data at multiple wavelengths and the test result of the chemiluminescence method, the light absorption data at multiple wavelengths of the sample to be tested can be converted into the test result of the chemiluminescence method, so as to achieve accurate determination of low-concentration samples. The purpose of this step is to make up for the deficiency of the double antibody sandwich method in the low-concentration detection range through the high sensitivity of the chemiluminescence method, and improve the accuracy and reliability of the detection.

[0099] It should be noted that in this embodiment, multiple settings of the OD values of the negative control and the positive control and the judgment of the critical value are also involved. The number of OD values of the negative control is multiple, which is to more accurately determine the range of background noise and detection limit; the number of OD values of the positive control is 1, which is to verify the sensitivity and specificity of the experiment. When judging whether the first test result is valid within the preset concentration range of HBsAg, it is necessary to obtain the critical values at the wavelengths of 450 nm and 630 nm within the preset concentration range of HBsAg and compare them with the sample OD value. At the same time, it is also necessary to judge whether the OD values of the negative control and the positive control are within the preset threshold range to ensure the accuracy and reliability of the experimental results.

[0100] It should be noted that in this embodiment, a prediction model is also introduced to correct and predict the deviation value. When the first test result is invalid, the introduction of the prediction model can further improve the accuracy and reliability of the detection, especially for those samples that cannot be accurately determined in the first test result.

[0101] During the training process of the prediction model, it is necessary to obtain the first sample test result and the second sample test result of the same HBsAg sample. The first sample test result is the test result of the double antibody sandwich method, and the second sample test result is the test result of the chemiluminescence method mapped by the light absorption data at multiple wavelengths. By constructing a training dataset and training the pre-trained model, a trained prediction model can be obtained. Through machine learning methods, the detection accuracy and generalization ability are improved, enabling the prediction model to better adapt to the changes of different samples and experimental conditions.

[0102] The HBsAg detection method based on multiple wavelengths combines the advantages of the double antibody sandwich method and the chemiluminescence method. By detecting the light absorption values of the sample to be tested at different wavelengths with a multi-wavelength microplate reader and combining the prediction model to correct and predict the deviation values, the accurate determination of HBsAg is achieved. This method has the advantages of high sensitivity, strong specificity, and simple operation.

[0103] Embodiment In the embodiment of the present invention, the light absorption value of the HBsAg sample to be tested by the double antibody sandwich method can be detected with a multi-wavelength microplate reader. Based on the light absorption data at two wavelengths (450 nm and 630 nm), the first test result is determined. Then, the negative control OD value and the positive control OD value are used to verify whether the test result is valid within the preset concentration range of HBsAg. When the first test result is invalid within the preset concentration range of HBsAg, based on the mapping relationship between the light absorption data at multiple wavelengths and the chemiluminescence method test result, the second test result of the sample to be tested is determined. By adopting multi-wavelength detection, a double verification mechanism, and a correction means of chemiluminescence method result mapping, the problems existing in the traditional double antibody sandwich method in the immunoassay of hepatitis B surface antigen are effectively solved, and the sensitivity, accuracy, and reliability of the hepatitis B surface antigen detection at low concentrations are improved.

[0104] Optionally, the number of negative control OD values is multiple, and the number of positive control OD values is 1. The steps of determining whether the first test result is valid within the preset concentration range of HBsAg based on the negative control OD value and the positive control OD value include: obtaining the first critical value at the 450 nm wavelength within the preset concentration range of HBsAg, and obtaining the second critical value at the 630 nm wavelength within the preset concentration range of HBsAg; based on the sample OD value, the positive control OD value, the first critical value, and the second critical value, determining whether the first test result is valid within the preset concentration range of HBsAg.

[0105] In the embodiments of the present invention, it should be noted that in immunological assays, negative controls and positive controls are the basis for evaluating the validity of experimental results. A negative control is typically a sample without the target antigen, used to determine the background signal level; while a positive control is a sample known to contain the target antigen, used to verify the sensitivity and accuracy of the detection system. In this embodiment, the settings of the OD values of the negative control and the positive control are aimed at establishing a reference framework for accurately interpreting the OD value of the sample to be tested for HBsAg.

[0106] There are multiple OD values for the negative control, while there is 1 OD value for the positive control. This design is based on statistical principles. By having multiple negative controls, the influence of accidental errors can be reduced, improving the stability and reliability of the results. The multiple OD values of the negative control can form a distribution, thus better estimating the range and average value of the background noise. In contrast, as a known standard, the positive control has a high degree of consistency, so only one is needed to meet the verification requirements.

[0107] Specifically, in this embodiment, to determine whether the OD value of the sample to be tested falls within the preset concentration range of HBsAg, the first critical value and the second critical value at wavelengths of 450 nm and 630 nm within the preset concentration range of HBsAg can be determined first. The selection of these two wavelengths is based on the principle of spectral analysis. 450 nm is usually in the blue region of visible light, while 630 nm is in the red region. They have different absorption characteristics for different types of chemical bonds and molecular structures, so they can provide more information for distinguishing the components in the sample. The above critical values can be the ratio PC / NCx between the OD value of the positive control and the average OD value of the negative control. The above first critical value and second critical value are only used to represent the judgment conditions in this double antibody sandwich method and are only related to the readings of the multi-wavelength microplate reader in this double antibody sandwich method.

[0108] The effectiveness of the negative control and the positive control can be evaluated by comparing the actually measured OD value with the preset critical values and thresholds. Specifically, it can be determined whether the first critical value is less than the preset first critical value threshold, and whether the second critical value is less than the preset second critical value threshold. Here, the first critical value threshold and the second critical value threshold are pre-set judgment criteria. The first critical value threshold and the second critical value threshold represent the maximum allowable value at which the OD value of the negative control can be considered valid at this wavelength. Since the light absorption characteristics are different for different wavelengths, different thresholds are set.

[0109] If both the first critical value and the second critical value are less than the corresponding thresholds, it indicates that the OD value of the negative control is valid within the preset concentration range of HBsAg, that is, the background noise is at an acceptable level. Otherwise, it indicates that the negative control is invalid, which may be caused by unstable experimental conditions, instrument errors, or sample contamination, etc.

[0110] For the positive control, it is necessary to determine whether its OD value is greater than the preset positive value threshold. This threshold is the criterion for judging whether the positive control can be correctly identified. If the OD value of the positive control is greater than the positive value threshold, it indicates that the detection system can accurately identify the sample containing HBsAg, and the positive control is valid. Otherwise, it indicates that there may be a problem with the insufficient sensitivity of the detection system.

[0111] Based on the effectiveness of the negative control and the positive control, determine whether the first test result is valid within the preset concentration range of HBsAg. Only when both the negative control and the positive control are valid can the first test result be considered reliable. Otherwise, the first test result cannot be considered reliable.

[0112] Optionally, the step of determining whether the first test result is valid within the preset concentration range of HBsAg based on the OD value of the sample, the OD value of the positive control, the first critical value, and the second critical value includes: determining whether the first critical value is less than the preset first critical value threshold, and determining whether the second critical value is less than the preset second critical value threshold, where the first critical value threshold is greater than the second critical value threshold; if the first critical value is less than the preset first critical value threshold and the second critical value is less than the preset second critical value threshold, then determine that the OD value of the negative control is valid within the preset concentration range of HBsAg, otherwise, determine that the OD value of the negative control is invalid within the preset concentration range of HBsAg; determine whether the OD value of the positive control is greater than the preset positive value threshold; if the OD value of the positive control is greater than the preset positive value threshold, then determine that the OD value of the positive control is valid within the preset concentration range of HBsAg, otherwise, determine that the OD value of the positive control is invalid within the preset concentration range of HBsAg; if the OD value of the negative control is valid within the preset concentration range of HBsAg and the OD value of the positive control is valid within the preset concentration range of HBsAg, then determine that the first test result is valid within the preset concentration range of HBsAg, otherwise, determine that the first test result is invalid within the preset concentration range of HBsAg.

[0113] In the embodiment of the present invention, the light absorption data at two wavelengths of 450 nm and 630 nm are obtained, which are obtained by detecting the HBsAg sample to be tested by the double antibody sandwich method with a multi-wavelength microplate reader. Based on the light absorption data at two wavelengths of 450 nm and 630 nm, the multi-wavelength microplate reader can calculate the OD value of the sample, the OD value of the negative control, and the OD value of the positive control as readings. Among them, the number of OD values of the negative control is multiple, which is used to reflect the level of non-specific reaction during the detection process, while the number of OD values of the positive control is 1, which is used to verify the effectiveness of the detection system.

[0114] Furthermore, the above critical value can be the ratio PC / NCx between the OD value of the positive control and the average OD value of the negative control. The above first critical value and second critical value are only used to represent the judgment conditions in this double antibody sandwich method and are only related to the readings of the multi-wavelength microplate reader in this double antibody sandwich method. Determining whether the first test result is valid within the preset concentration range of HBsAg mainly depends on the setting of two critical value thresholds: the first critical value threshold corresponds to a wavelength of 450 nm, and the second critical value threshold corresponds to a wavelength of 630 nm. These two critical value thresholds are obtained through statistical analysis of a large amount of experimental data and represent the standard of the light absorption value limit when the HBsAg concentration reaches the preset range at different wavelengths.

[0115] After determining the critical value, a comparison operation can be performed. Check whether the first critical value is less than the preset first critical value threshold and whether the second critical value is less than the preset second critical value threshold. It should be noted that the first critical value threshold is set to be greater than the second critical value threshold. At different wavelengths, the change law and sensitivity of the light absorption value may vary. By setting different thresholds, the accuracy of the test result can be more precisely controlled.

[0116] If both of the two critical values, the first critical value and the second critical value, are less than their respective corresponding thresholds, then it can be determined that the OD value of the negative control is valid within the preset concentration range of HBsAg. Furthermore, it shows that the non-specific reaction during the detection process is controlled within an acceptable range and will not have a significant impact on the accuracy of the test result. On the contrary, if any one of the critical values is greater than or equal to the corresponding threshold, then it can be determined that the OD value of the negative control is invalid, which may be caused by excessive non-specific reaction or other interfering factors during the detection process.

[0117] Furthermore, the validity of the OD value of the positive control can be checked. Specifically, it can be determined whether the OD value of the positive control is greater than the preset positive value threshold. The positive value threshold is also obtained through statistical analysis of experimental data and represents the minimum light absorption value of the positive control in the effective case. If the OD value of the positive control is greater than this positive value threshold, it indicates that the detection can correctly identify the positive sample, that is, the first test result is valid. On the contrary, if the OD value of the positive control is less than or equal to the positive value threshold, it may mean that there is a problem or failure in the first test result.

[0118] Even further, the validity of the first test result can be judged by comprehensively considering the validity of the OD value of the negative control and the OD value of the positive control. Only when the OD values of both the negative control and the positive control are valid at the same time, will it be determined that the first test result is valid within the preset concentration range of HBsAg. Through the double verification mechanism, the accuracy and reliability of the test result can be ensured, and the risk of false positives or false negatives can be reduced.

[0119] If the first test result is determined to be invalid, further measures can be taken to determine the second test result of the sample to be tested. Based on the mapping relationship between the light absorption data at multiple wavelengths and the chemiluminescence test result, a relatively accurate second test result can still be obtained using the light absorption data at multiple wavelengths when the first test result is invalid.

[0120] In this embodiment, by reasonably setting the critical value and threshold, and comprehensively judging in combination with the OD value of the sample, the OD value of the negative control, and the OD value of the positive control, the accuracy and reliability of the test result can be effectively improved.

[0121] Optionally, the steps of determining the second test result of the sample to be tested based on the light absorption data at multiple wavelengths include: determining the deviation value of the first test result within the preset concentration range of HBsAg; obtaining the light absorption data at multiple wavelengths, and based on the light absorption data at multiple wavelengths, determining a light absorption value sequence sorted in ascending order of wavelength; and determining the second test result of the sample to be tested based on the deviation value and the light absorption value sequence.

[0122] In the embodiment of the present invention, the deviation value of the first test result within the preset concentration range of HBsAg can be determined. The above deviation value reflects the degree of difference between the first test result and the preset concentration range, and can be used to determine the second test result subsequently. To calculate this deviation value, the first critical value, the second critical value, and the difference between the OD value of the positive control and its respective threshold can be comprehensively considered, so as to obtain a value that comprehensively reflects the deviation degree of the test result.

[0123] The light absorption data at multiple wavelengths can be obtained. The light absorption data at multiple wavelengths is obtained by detecting the HBsAg sample to be tested with a multi-wavelength microplate reader, covering multiple wavelengths between 450 nm and 630 nm. The light absorption data at multiple wavelengths can reflect the light absorption characteristics of the HBsAg sample to be tested at different wavelengths.

[0124] After obtaining the light absorption data at multiple wavelengths, the light absorption data at multiple wavelengths can be processed to determine a light absorption value sequence sorted in ascending order of wavelength. The construction of the above light absorption value sequence is for facilitating subsequent data analysis and feature extraction, and ensuring the consistency and comparability of the data.

[0125] After obtaining the deviation value and the light absorption value sequence, the second test result of the sample to be tested can be further determined. Specifically, the deviation value and the light absorption value sequence can be input into a pre-trained prediction model for prediction, so as to obtain the second test result of the HBsAg sample to be tested. The above prediction model can be constructed by a convolutional neural network (CNN), a recurrent neural network (RNN), etc.

[0126] In this embodiment, the advantages of light absorption data under multiple wavelengths and deep learning technology are fully utilized to improve the accuracy and reliability of detection.

[0127] Optionally, the step of determining the deviation value of the first test result within the preset concentration range of HBsAg includes: determining the first negative deviation value of the first test result within the preset concentration range of HBsAg based on the difference between the first critical value and the first critical value threshold; determining the second negative deviation value of the first test result within the preset concentration range of HBsAg based on the difference between the second critical value and the second critical value threshold; determining the positive deviation value of the first test result within the preset concentration range of HBsAg based on the difference between the positive control OD value and the positive value threshold; and determining the deviation value of the first test result within the preset concentration range of HBsAg based on the first negative deviation value, the second negative deviation value, and the positive deviation value.

[0128] In the embodiment of the present invention, the difference between the first critical value and the first critical value threshold can be calculated first, and this difference is the first negative deviation value. The first critical value is a boundary value of the preset concentration range of HBsAg at a wavelength of 450 nm, while the first critical value threshold is a preset standard of this boundary value. By comparing the difference between the two, the deviation degree of the first test result from the expected concentration range at a wavelength of 450 nm can be quantified.

[0129] At the same time, calculate the difference between the second critical value and the second critical value threshold to obtain the second negative deviation value. Similar to the first negative deviation value, the second negative deviation value reflects the deviation of the first test result from the preset concentration range at a wavelength of 630 nm. The second critical value and the second critical value threshold are respectively the boundary value of the preset concentration range of HBsAg at a wavelength of 630 nm and its preset standard.

[0130] At the same time, calculate the difference between the positive control OD value and the positive value threshold, and this difference is the positive deviation value. The positive control OD value is the test result of a known concentration of HBsAg sample set during the experiment, while the positive value threshold is the standard for judging whether this test result is valid. By calculating the positive deviation value, the compliance degree of the positive control part in the first test result with the expected value can be evaluated.

[0131] After obtaining the first negative deviation value, the second negative deviation value, and the positive deviation value, the total deviation value of the first test result within the preset concentration range of HBsAg can be determined by synthesizing these three deviation values. The above total deviation value is a comprehensive indicator that integrates the information of the negative control and the positive control under dual wavelengths and can comprehensively reflect the overall deviation between the first test result and the expected concentration range.

[0132] The above total deviation value not only quantitatively evaluates the validity of the first test result but also provides a key input for determining the second test result based on the light absorption data under multiple wavelengths. In some possible embodiments, by accurately calculating the deviation value, it is possible to more accurately identify those test samples whose results may deviate from the expected range due to various reasons (such as too low sample concentration, experimental operation errors, etc.) in the first test.

[0133] Let the first negative deviation value be , the second negative deviation value be , the positive deviation value be , be the average value of the first negative deviation value, the second negative deviation value, and the positive deviation value, and the influence factor of the preset concentration range be ( is a constant determined according to factors such as experimental conditions and sample characteristics and is used to adjust the calculation of the deviation value). Then the deviation value of the first test result within the preset concentration range of HBsAg can be calculated as:

[0134]

[0135] where represents taking the logarithm after adding 1 to each deviation value, which can handle the case where the deviation value is 0 and at the same time makes the increase in the deviation value produce a non-linear effect in the formula, improving the non-linear response ability of the deviation value within the preset concentration range of HBsAg. represents taking the ratio of the three deviation values to the average value as the input of the exponential function, and then the influence of the deviation value can be dynamically amplified or reduced, depending on the average level of the deviation value. As the influence factor of the preset concentration range.

[0136] In a possible embodiment, in addition to the above method based on the difference between the critical value and the threshold, other statistical methods or machine learning algorithms can also be considered to evaluate the difference between the first test result and the expected value. For example, statistical quantities such as the standard deviation and the coefficient of variation can be used to measure the dispersion degree of the test results; or a special machine learning model can be trained to predict and evaluate the deviation value. Of course, the selection of these methods should be determined according to the specific application scenario, data characteristics, and actual requirements.

[0137] In this embodiment, the method of determining the deviation value based on the difference between the critical value and the threshold value has its unique advantages. It is simple and easy to implement, has high computational efficiency, and can intuitively reflect the deviation degree between the detection result and the expected concentration range.

[0138] Optionally, determining the second detection result of the sample to be tested based on the deviation value and the light absorption value sequence includes: inputting the deviation value and the light absorption value sequence into a pre-trained prediction model. The prediction model includes a first feature extraction network, a second feature extraction network, a feature fusion network, and an output network. The output ends of the first feature extraction network and the second feature extraction network are both connected to the input end of the feature fusion network, and the output end of the feature fusion network is connected to the input end of the output network; performing a deconvolution operation on the deviation value through the first feature extraction network to obtain the deviation feature corresponding to the deviation value; performing a convolution operation on the light absorption value sequence through the second feature extraction network to obtain the light absorption feature of the light absorption value sequence. The deviation feature and the light absorption feature have the same resolution size; obtaining a fusion feature by fusing the deviation feature and the light absorption feature through the fusion network; and performing a prediction process on the fusion feature through the output network to obtain the prediction result as the second detection result of the sample to be tested.

[0139] In the embodiment of the present invention, the system needs to use the previously calculated deviation value and the light absorption value sequence sorted from small to large by wavelength as input data and input them into a pre-trained prediction model. The above prediction model can be a deep learning model, and the above prediction model can include a first feature extraction network, a second feature extraction network, a feature fusion network, and an output network. The above prediction model aims to fully extract and fuse the feature information in the deviation value and the light absorption value sequence to improve the accuracy of the second detection result.

[0140] The first feature extraction network will perform a deconvolution operation on the deviation value. The deconvolution operation is a commonly used upsampling technique that can upsample the input data through a learned filter to obtain a higher-resolution feature map. In this embodiment, through the deconvolution operation, the deviation feature corresponding to the deviation value can be obtained, and the deviation feature reflects the spatial distribution and change of the deviation value.

[0141] Meanwhile, the second feature extraction network performs a convolution operation on the light absorption value sequence. The convolution operation is one of the most commonly used feature extraction methods in deep learning, which can extract local features of the input data by means of a sliding window. In this embodiment, through the convolution operation, the light absorption features in the light absorption value sequence can be extracted, and the light absorption features reflect the variation law and internal relationship of the light absorption values at different wavelengths. It should be noted that in order to ensure the effective fusion of the deviation features and the light absorption features in the subsequent steps, it is necessary to ensure that the deviation features and the light absorption features have the same resolution size. When the resolution sizes of the deviation features and the light absorption features are different, the deviation features and / or the light absorption features can be linearly transformed according to a linear matrix to obtain the deviation features and the light absorption features with the same resolution size.

[0142] The deviation features and the light absorption features are fused through a feature fusion network. Feature fusion can fuse features from different sources or different levels to obtain a more comprehensive and rich feature representation. Feature fusion can be splicing fusion or stacking fusion. In this embodiment, through the feature fusion operation, the useful information in the deviation features and the light absorption features is integrated to obtain fused features. The fused features contain both the spatial distribution and variation of the deviation values, and the variation law and internal relationship of the light absorption values at different wavelengths, so they have stronger expression ability and generalization ability.

[0143] The fused features are processed through an output network for prediction to obtain a prediction result as the second detection result of the sample to be tested. This second detection result is a continuous numerical value or a classification label, and the second detection result reflects the concentration or presence state of HBsAg in the sample to be tested. In order to obtain an accurate second detection result, the output network usually uses a fully connected layer or a classifier to implement the mapping and transformation of the fused features. At the same time, in order to improve the robustness and generalization ability of the model, techniques such as regularization and batch normalization can also be used to optimize the training process of the model.

[0144] In this embodiment, the second detection result of the sample to be tested is determined based on the prediction method of the deep learning model. By fully extracting and fusing the feature information in the deviation values and the light absorption value sequence, the accuracy and reliability of the second detection result are improved.

[0145] Optionally, before the step of inputting the deviation value and the light absorption value sequence into the pre-trained prediction model, it further includes: obtaining the first sample detection result and the second sample detection result of the same HBsAg sample, where the first sample detection result is the detection result of the double antibody sandwich method, and the second sample detection result is the detection result of the chemiluminescence method. The sample double antibody sandwich method detection result is the light absorption data at multiple wavelengths corresponding to the invalid state of the first detection result. The above-mentioned HBsAg sample is an HBsAg sample within the preset concentration range of HBsAg; based on the first sample detection result, determining the sample deviation value corresponding to the first sample detection result, and determining the first sample detection result and the sample deviation value as the sample data of a binary group; determining the second sample detection result as the label data of the sample data; constructing a training data set based on the sample data and the label data; obtaining a pre-trained model, and training the pre-trained model through the training data set, and obtaining a trained prediction model after the training is completed.

[0146] In the embodiment of the present invention, two different detection results of the same HBsAg sample can be obtained first, that is, the first sample detection result and the second sample detection result. In this embodiment, for the HBsAg samples collected from the same human body at the same time, the double antibody sandwich method and the chemiluminescence method are respectively used for detection to obtain the corresponding first sample detection result and second sample detection result. The first sample detection result is obtained by the double antibody sandwich method, and the second sample detection result is obtained by the chemiluminescence method.

[0147] It should be noted that these HBsAg samples are within the preset concentration range of HBsAg (within the low concentration range of HBsAg), and the first detection results (that is, the results of the double antibody sandwich method) of these HBsAg samples are invalid. This means that these HBsAg samples may not obtain accurate results due to certain reasons (such as too low concentration, interfering substances, etc.) in the conventional double antibody sandwich method detection. Therefore, it is necessary to use these special samples, combined with the light absorption data at multiple wavelengths corresponding to them, to train the prediction model to improve the accuracy and robustness of the model when dealing with similar complex situations.

[0148] According to the first sample detection result (that is, the invalid double antibody sandwich method result), determine the sample deviation value corresponding to these results. The calculation method of the above-mentioned deviation value is the same as the calculation method of the above-mentioned deviation value, which is used to reflect the gap between the actual result and the expected result of the sample.

[0149] Combine the first sample detection result with its corresponding sample deviation value into the sample data of a binary group. The organization form of the sample data of the binary group helps the model to simultaneously pay attention to the original detection data of the sample and its deviation situation during the learning process.

[0150] Determine the second sample test result (i.e., the result obtained by chemiluminescence method) as the labeled data corresponding to the binary sample data. Since the chemiluminescence method usually has higher sensitivity and specificity, its result can be regarded as the true value or standard value of these samples. Using the result of the chemiluminescence method as the labeled data can guide the prediction model to gradually approach these true values during the learning process, thereby improving the prediction accuracy of the model.

[0151] After completing data preparation and label determination, a training dataset can be constructed based on the above sample data and corresponding labeled data. The above training dataset will contain multiple binary sample data and their corresponding labeled data.

[0152] A pre-trained model will be obtained and the pre-trained model will be further trained with the above constructed training dataset. The pre-trained model may be a deep learning model that has been preliminarily trained on a large amount of data and has certain feature extraction and representation learning capabilities. By further training on the training dataset of the current specific task (i.e., multi-wavelength based HBsAg detection), the model can learn more features and patterns related to the current task, thereby improving its performance in handling similar tasks.

[0153] During the training process, various optimization algorithms and learning strategies can be adopted to adjust the parameters and structure of the model to minimize the difference between the model prediction result and the true label. For example, the gradient descent algorithm can be used to update the weights and bias terms of the model; regularization techniques can be used to prevent the model from overfitting; and the learning rate decay strategy can be used to dynamically adjust the learning step size, etc. Furthermore, ensure that the model can converge stably during the training process and achieve high prediction accuracy.

[0154] After the training is completed, a trained prediction model can be obtained. The trained prediction model has fully learned the features and patterns in the multi-wavelength based HBsAg detection task and has the ability to accurately predict new samples. In subsequent practical applications, this trained model can be directly used to determine the second test result of the sample to be tested, thereby improving the accuracy and reliability of the entire detection method.

[0155] It should be noted that the above second test result is closer to the result of the chemiluminescence method and can be used to verify whether the first test result is accurate. For example, if the first test result is negative and the second test result is also negative, it can be determined that the first test result is accurate; if the first test result is negative and the second test result is positive, it can be determined that the first test result is a false negative, and further it can be determined that the first test result is inaccurate.

[0156] It should also be noted that since the second test result is obtained only when the first test result is invalid within the preset concentration range of HBsAg, for test results outside the preset concentration range, it does not affect the test speed. At the same time, because the mapping relationship between the light absorption data at multiple wavelengths and the chemiluminescence method test result is utilized, in actual use, there is no chemiluminescence method test process, no need to collect additional HBsAg samples to be tested, and no need to prepare a chemiluminescence method test environment. Only an ELISA reader that supports multiple wavelengths is required on the device. Therefore, a higher sensitivity HBsAg test result can be obtained at a lower cost.

[0157] As Figure 2 shown, Figure 2 FIG. is a schematic structural diagram of a multi-wavelength based HBsAg detection device provided by an embodiment of the present invention. The multi-wavelength based HBsAg detection device includes:

[0158] A detection module 201, configured to detect the light absorption value of the HBsAg sample to be tested in the double antibody sandwich method through an ELISA reader with multiple wavelengths, and obtain the light absorption data of the HBsAg sample to be tested at multiple wavelengths, where the multiple wavelengths include multiple wavelengths between 450 nm and 630 nm;

[0159] A first processing module 202, configured to determine the sample OD value, negative control OD value, and positive control OD value at the 450 nm wavelength and the 630 nm wavelength based on the light absorption data at the 450 nm wavelength and the 630 nm wavelength;

[0160] A second processing module 203, configured to determine the first test result of the HBsAg sample to be tested based on the sample OD value, negative control OD value, and positive control OD value at the 450 nm wavelength and the 630 nm wavelength, and determine whether the first test result is valid within the preset concentration range of HBsAg based on the negative control OD value and the positive control OD value;

[0161] A third processing module 204, configured to, if the first test result is invalid within the preset concentration range of HBsAg, determine the second test result of the sample to be tested based on the mapping relationship between the light absorption data at multiple wavelengths and the chemiluminescence method test result.

[0162] The multi-wavelength based HBsAg detection device provided by the embodiment of the present invention can implement each process implemented by the multi-wavelength based HBsAg detection method in the above method embodiment, and can achieve the same beneficial effects. To avoid repetition, it will not be elaborated here.

[0163] See Figure 3 , Figure 3It is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. As Figure 3 shown, it includes: a memory 302, a processor 301, and a computer program of a multi-wavelength-based HBsAg detection method stored on the memory 302 and executable on the processor 301, wherein:

[0164] The processor 301 is configured to call the computer program stored in the memory 302 and execute the following steps:

[0165] Detect the light absorption value of the HBsAg sample to be tested by the sandwich method with double antibodies through a multi-wavelength microplate reader to obtain the light absorption data of the HBsAg sample to be tested at multiple wavelengths, and the multiple wavelengths include multiple wavelengths between 450 nm and 630 nm;

[0166] Based on the light absorption data at the 450 nm wavelength and the 630 nm wavelength, determine the sample OD value, negative control OD value, and positive control OD value at the 450 nm wavelength and the 630 nm wavelength;

[0167] Based on the sample OD value, negative control OD value, and positive control OD value at the 450 nm wavelength and the 630 nm wavelength, determine the first test result of the HBsAg sample to be tested, and based on the negative control OD value and the positive control OD value, determine whether the first test result is valid within the preset concentration range of HBsAg;

[0168] If the first test result is invalid within the preset concentration range of HBsAg, then based on the mapping relationship between the light absorption data at multiple wavelengths and the chemiluminescence method test result, determine the second test result of the HBsAg sample to be tested.

[0169] The electronic device provided by the embodiment of the present invention can implement each process implemented by the multi-wavelength-based HBsAg detection method in the above method embodiment, and can achieve the same beneficial effects. To avoid repetition, it will not be elaborated here.

[0170] The embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements each process of the multi-wavelength-based HBsAg detection method provided by the embodiment of the present invention, and can achieve the same technical effects. To avoid repetition, it will not be elaborated here.

[0171] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above various methods. Among them, the computer-readable storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc.

[0172] The above-disclosed are only the preferred embodiments of the present invention. Of course, the scope of the rights of the present invention cannot be limited thereby. Therefore, equivalent changes made according to the claims of the present invention still fall within the scope covered by the present invention.

Claims

1. A multi-wavelength based HBsAg detection method, characterized in that, The multi-wavelength based HBsAg detection method includes: Detecting the optical absorption value of the HBsAg sample to be tested detected by the double antibody sandwich method of enzyme-linked immunosorbent assay through a multi-wavelength microplate reader, obtaining the optical absorption data of the HBsAg sample to be tested at multiple wavelengths, where the multiple wavelengths include multiple wavelengths between 450 nm and 630 nm; Based on the optical absorption data at the 450 nm wavelength and the 630 nm wavelength, determining the sample OD value, negative control OD value, and positive control OD value at the 450 nm wavelength and the 630 nm wavelength; Based on the sample OD value, negative control OD value, and positive control OD value at the 450 nm wavelength and the 630 nm wavelength, determining the first detection result of the HBsAg sample to be tested, and based on the negative control OD value and the positive control OD value, determining whether the first detection result is valid within the preset concentration range of HBsAg; If the first detection result is invalid within the preset concentration range of HBsAg, then based on the mapping relationship between the optical absorption data at multiple wavelengths and the chemiluminescence detection result, determining the second detection result of the HBsAg sample to be tested; the step of determining the second detection result of the HBsAg sample to be tested based on the mapping relationship between the optical absorption data at multiple wavelengths and the chemiluminescence detection result includes: Determining the deviation value of the first detection result within the preset concentration range of HBsAg; Obtaining the optical absorption data at multiple wavelengths, and based on the optical absorption data at multiple wavelengths, determining the optical absorption value sequence sorted in ascending order of wavelength; Based on the deviation value and the optical absorption value sequence, determining the second detection result of the HBsAg sample to be tested; the step of determining the second detection result of the HBsAg sample to be tested based on the deviation value and the optical absorption value sequence includes: inputting the deviation value and the optical absorption value sequence into a pre-trained prediction model, and obtaining the prediction result as the second detection result of the HBsAg sample to be tested; the HBsAg detection method is for non-diagnostic purposes.

2. The multi-wavelength based HBsAg detection method according to claim 1, wherein The number of negative control OD values is multiple, and the number of positive control OD values is 1. The step of determining whether the first detection result is valid within the preset concentration range of HBsAg based on the negative control OD value and the positive control OD value includes: Obtaining the first critical value at the 450 nm wavelength within the preset concentration range of HBsAg, and obtaining the second critical value at the 630 nm wavelength within the preset concentration range of HBsAg; Based on the sample OD value, the positive control OD value, the first critical value, and the second critical value, determining whether the first detection result is valid within the preset concentration range of HBsAg.

3. The multi-wavelength based HBsAg detection method according to claim 2, wherein, The step of determining whether the first detection result is valid within the preset concentration range of HBsAg based on the sample OD value, the positive control OD value, the first critical value, and the second critical value includes: Determine whether the first critical value is less than a preset first critical value threshold, and determine whether the second critical value is less than a preset second critical value threshold, where the first critical value threshold is greater than the second critical value threshold; If the first critical value is less than the preset first critical value threshold and the second critical value is less than the preset second critical value threshold, determine that the negative control OD value is valid within the preset concentration range of HBsAg; otherwise, determine that the negative control OD value is invalid within the preset concentration range of HBsAg; Determine whether the positive control OD value is greater than a preset positive value threshold; If the positive control OD value is greater than the preset positive value threshold, determine that the positive control OD value is valid within the preset concentration range of HBsAg; otherwise, determine that the positive control OD value is invalid within the preset concentration range of HBsAg; If the negative control OD value is valid within the preset concentration range of HBsAg and the positive control OD value is valid within the preset concentration range of HBsAg, determine that the first test result is valid within the preset concentration range of HBsAg; otherwise, determine that the first test result is invalid within the preset concentration range of HBsAg.

4. The multi-wavelength based HBsAg detection method according to claim 3, wherein The step of determining the deviation value of the first test result within the preset concentration range of HBsAg includes: Based on the difference between the first critical value and the first critical value threshold, determine the first negative deviation value of the first test result within the preset concentration range of HBsAg; Based on the difference between the second critical value and the second critical value threshold, determine the second negative deviation value of the first test result within the preset concentration range of HBsAg; Based on the difference between the positive control OD value and the positive value threshold, determine the positive deviation value of the first test result within the preset concentration range of HBsAg; Based on the first negative deviation value, the second negative deviation value, and the positive deviation value, determine the deviation value of the first test result within the preset concentration range of HBsAg.

5. The multi-wavelength based HBsAg detection method according to claim 1 or 4, characterized in that, The step of determining the second test result of the HBsAg sample to be tested based on the deviation value and the light absorption value sequence includes: Input the deviation value and the light absorption value sequence into a pre-trained prediction model, where the prediction model includes a first feature extraction network, a second feature extraction network, a feature fusion network, and an output network. The output ends of the first feature extraction network and the second feature extraction network are both connected to the input end of the feature fusion network, and the output end of the feature fusion network is connected to the input end of the output network; Perform a deconvolution operation on the deviation value through the first feature extraction network to obtain the deviation feature corresponding to the deviation value; Perform a convolution operation on the light absorption value sequence through the second feature extraction network to obtain the light absorption feature of the light absorption value sequence, where the deviation feature and the light absorption feature have the same resolution size; Through the fusion network, obtain the fusion feature from the deviation feature and the light absorption feature; The predicted result is obtained by performing prediction processing on the fused feature through the output network, and is used as the second detection result of the HBsAg sample to be detected.

6. The multi-wavelength based HBsAg detection method according to claim 5, wherein Before the step of inputting the deviation value and the sequence of light absorption values into the pre-trained prediction model, the method further includes: obtaining a first sample detection result and a second sample detection result of the same HBsAg sample, where the first sample detection result is the detection result of the double antibody sandwich method, the second sample detection result is the detection result of the chemiluminescence method, the detection result of the double antibody sandwich method of the HBsAg sample is the light absorption data at multiple wavelengths corresponding to the invalid state of the first detection result, and the HBsAg sample is an HBsAg sample within the preset concentration range of HBsAg; determining a sample deviation value corresponding to the first sample detection result based on the first sample detection result, and determining the first sample detection result and the sample deviation value as sample data of a binary group; determining the second sample detection result as the label data of the sample data; constructing a training data set based on the sample data and the label data; obtaining a pre-trained model, and training the pre-trained model through the training data set, and obtaining a trained prediction model after the training is completed.

7. An HBsAg detection device based on multiple wavelengths, characterized in that, The HBsAg detection device based on multiple wavelengths includes: a detection module configured to detect the light absorption value of the HBsAg sample to be detected in the enzyme-linked immunosorbent assay double antibody sandwich method through a multi-wavelength microplate reader, so as to obtain the light absorption data of the HBsAg sample to be detected at multiple wavelengths, where the multiple wavelengths include multiple wavelengths between 450 nm and 630 nm; a first processing module configured to determine the sample OD value, the negative control OD value, and the positive control OD value at the 450 nm wavelength and the 630 nm wavelength based on the light absorption data at the 450 nm wavelength and the 630 nm wavelength; a second processing module configured to determine the first detection result of the HBsAg sample to be detected based on the sample OD value, the negative control OD value, and the positive control OD value at the 450 nm wavelength and the 630 nm wavelength, and determine whether the first detection result is valid within the preset concentration range of HBsAg based on the negative control OD value and the positive control OD value; A third processing module, configured to determine a second test result of the HBsAg sample to be tested based on the mapping relationship between the light absorption data at multiple wavelengths and the chemiluminescence test result if the first test result is invalid within the preset concentration range of HBsAg; the step of determining the second test result of the HBsAg sample to be tested based on the mapping relationship between the light absorption data at multiple wavelengths and the chemiluminescence test result includes: determining the deviation value of the first test result within the preset concentration range of HBsAg; obtaining the light absorption data at multiple wavelengths, and determining a light absorption value sequence sorted in ascending order of wavelength based on the light absorption data at multiple wavelengths; determining the second test result of the HBsAg sample to be tested based on the deviation value and the light absorption value sequence; the determining the second test result of the HBsAg sample to be tested based on the deviation value and the light absorption value sequence includes: inputting the deviation value and the light absorption value sequence into a pre-trained prediction model, and obtaining a prediction result as the second test result of the HBsAg sample to be tested.

8. An electronic device, characterized in that, Comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps in the multi-wavelength based HBsAg detection method according to any one of claims 1 to 6 when executing the computer program.

9. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, and when the computer program is executed by the processor, the steps in the multi-wavelength based HBsAg detection method according to any one of claims 1 to 6 are implemented.

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