Multi-signal fusion immunochromatography analysis method and quantitative detection equipment

Abstract

The invention relates to the technical field of biological detection instruments, in particular to a multi-signal fusion immunochromatography analysis method and quantitative detection equipment, and the multi-signal fusion immunochromatography analysis method comprises the following steps: obtaining a visible light detection result, a fluorescence detection result and an infrared detection result of a calibration sample group with known concentration; respectively establishing fitting curves according to a visible light detection result, a fluorescence detection result and an infrared detection result; establishing a comprehensive concentration equation about the fitted curve; and calculating the comprehensive concentration value of the to-be-detected sample by using the comprehensive concentration equation. The defects that a traditional immunochromatography detection method and device are single in collected signal and limited in sensitivity are overcome, multi-signal detection fitting calculation enables recognition of a to-be-detected sample to be more sensitive, and due to multi-mode detection and data fitting processing, experimental results are more reliable and accurate.

Description

Technical Field

[0001] This application relates to the technical field of biological detection instruments, and particularly to a multi-signal fusion immunochromatographic analysis method and a quantitative detection device. Background Art

[0002] Immunochromatography assay (ICA) is a rapid diagnostic technique commonly used to detect specific antigens or antibodies. The principle of immunochromatography is to first immobilize a specific antibody on a certain zone of a nitrocellulose membrane. When one end of the dry nitrocellulose is immersed in a sample to be tested, due to capillary action, the sample to be tested will move forward along the membrane. When it moves to the area where the antibody is immobilized, the corresponding antigen in the sample to be tested will specifically bind to the antibody. If immunogold or immunoenzyme staining is used, a certain color can be shown in this area, thus realizing specific immunodiagnosis. This method combines the advantages of immunological techniques and chromatography techniques, and can provide results within a few minutes, making it very suitable for point-of-care testing.

[0003] With the increasing demand for fast and convenient medical services, immunochromatographic detection devices, as rapid diagnostic methods that can be carried out outside medical institutions, are widely used in the fields of food safety and medical detection due to their rapid detection and simple operation. However, traditional immunochromatographic detection devices mostly detect in a single visible light colorimetric mode. For example, an enzyme-linked immunosorbent assay analyzer converts the light signal into an electrical signal through monochromatic light absorption and a photodetector, and analyzes the concentration of the sample to be tested in combination with reagents. Or a fluorescence immunoassay analyzer uses fluorescence signal conversion technology and combines with appropriate reagents to detect specific molecules in the sample to be tested. Due to the single signal collected by traditional immunochromatographic detection methods and devices, the sensitivity is limited. Only colorimetric signals can be obtained, and the detection sensitivity is relatively low. It is particularly difficult to accurately detect low-concentration target substances. The colorimetric signal is easily interfered by the matrix of the sample to be tested and ambient light, with poor stability and weak anti-interference ability. Summary of the Invention

[0004] Based on this, the purpose of the present invention is to overcome the deficiencies of traditional immunochromatographic detection methods and devices due to single signal collection and limited sensitivity, and provide a multi-signal fusion immunochromatographic analysis method and a quantitative detection device. The multi-signal detection and fitting calculation of the present invention make the recognition of the sample to be tested more sensitive, and have more advantages in the quantitative analysis of low-concentration samples to be tested. In the quantitative analysis of samples to be tested at normal concentrations, due to multi-modal detection and data fitting processing, the experimental results are more reliable and accurate.

[0005] To solve the above technical problems, the present invention provides a multi-signal fusion immunochromatographic analysis method, which specifically includes the following steps:

[0006] Irradiate a calibration sample group with a known concentration using visible light, capture the color development strips of the calibration sample group to collect the colorimetric signals of the calibration sample group, and obtain visible light detection results;

[0007] Irradiate a calibration sample group with a known concentration using an excitation light source with a specific wavelength, filter the light, and collect the fluorescence labeling reaction intensity of the calibration sample group to obtain fluorescence detection results;

[0008] Heat a calibration sample group with a known concentration using a laser, perform pseudo-color imaging on the photothermal response in the calibration sample group, collect the thermal signal output of the calibration sample group, and obtain infrared detection results;

[0009] Establish fitting curves through visible light detection results, fluorescence detection results, and infrared detection results respectively;

[0010] According to the fitting curves of visible light detection results, fluorescence detection results, and infrared detection results, establish a comprehensive concentration equation for the fitting curves;

[0011] Collect visible light detection results, fluorescence detection results, and infrared detection results of a test sample with an unknown concentration, and use the comprehensive concentration equation to calculate the comprehensive concentration value of the test sample.

[0012] The present invention conducts analysis and identification by covering three signals, combines three analysis and detection modes of visible light, fluorescence, and photothermal signals to comprehensively achieve quantitative analysis. In addition to basic visible light determination, the double spectral overlap of fluorescence signals improves the sensitivity of fluorescence signal detection. The photothermal response provides a stable infrared signal output. The data are mutually compared and verified with each other. At the same time, there is an infrared photothermal signal as a third comparison means, further increasing the comparison samples, significantly improving the accuracy of quantitative detection. When setting three signal identifications, the present invention establishes fitting curves through data fitting, and simultaneously processes data based on the three fitting curves, establishes a comprehensive concentration equation for the test sample regarding the fitting curves, and calculates a more accurate comprehensive concentration value. Through fitting and calculation, the human error and operation complexity are significantly reduced. The multi-signal detection fitting calculation makes the identification of the test sample more sensitive, especially having advantages in the quantitative analysis of test samples with low concentrations. In the quantitative analysis of test samples with normal concentrations, due to multi-modal detection and data fitting processing, the experimental results are more reliable and accurate, and it is particularly suitable for scenarios where the actual sample concentration distribution is wide and the response is uneven.

[0013] Further, the method for obtaining visible light detection results is as follows:

[0014] Irradiate the test sample with visible light, collect the image information of the color development strip of the test sample through an industrial camera to obtain colorimetric signals, convert the RGB image of the colorimetric signals to the CIE Lab uniform color space through color space conversion, and construct a color difference gray distribution matrix;

[0015] Specifically, taking standard black (L*=0, a*=0, b*=0) as the reference color, the color difference value △E00 between each pixel in the detection area and the reference color is calculated pixel by pixel based on the color difference formula △E=[(△L*)^2+(△a*)^2+(△b*)^2], and a color difference grayscale distribution matrix is constructed, in which the color difference value is positively correlated with the grayscale value.

[0016] Performing projection integration on the color difference grayscale matrix along the horizontal direction to obtain a lateral cumulative color difference distribution curve of the sample to be tested;

[0017] According to the difference in optical properties between the sample reaction area and the substrate material, the local minimum point of the cumulative color difference distribution curve is detected to achieve accurate positioning and quantitative analysis of the test line and the control line to obtain visible light detection results.

[0018] Since the detection line and control line (T / C line) in the test paper reaction area have different optical properties from the base material, that is, the normal base area has a large cumulative color difference due to its high reflectivity, while the immune reaction line area has a significantly reduced cumulative color difference due to the light absorption enhancement effect produced by the aggregation of nanomaterials. The present invention realizes the precise positioning and quantitative analysis of the detection line and the control line by detecting the local minimum point of the cumulative color difference curve, which effectively improves the objectivity and repeatability of the test paper strip detection of the sample to be tested, and provides a reliable image processing solution for instant detection equipment.

[0019] Furthermore, the method for obtaining the fluorescence detection result is:

[0020] An excitation light source with a specific wavelength is used to illuminate the sample to be tested, a filter is used to filter out short-wavelength stray light, and an industrial camera is used to collect the intensity of the fluorescent marker reaction to obtain a fluorescence distribution image of the sample to be tested;

[0021] Specifically, a 365-370nm ultraviolet light source is used, and a 450nm long-pass filter is used to effectively filter out short-wavelength stray light, retaining the fluorescence emission signal >450nm. Image acquisition can use an industrial camera in conjunction with an optical imaging component to obtain the fluorescence distribution image of the test strip of the sample to be tested.

[0022] The fluorescence distribution image is converted into RGB color space. Based on the spectral response characteristics of the fluorescence signal in the long wavelength region, the R channel data is preferentially extracted to generate a grayscale distribution matrix. The grayscale distribution matrix is projected and integrated in the horizontal direction to construct the axial fluorescence intensity distribution curve of the sample. The extreme value detection algorithm is used to accurately locate the characteristic peaks of the sample reaction area detection line and the control line, and quantitative analysis is performed to obtain the fluorescence detection results.

[0023] The present invention significantly improves the signal-to-noise ratio and positioning accuracy of weak fluorescence signals through a strategy combining spectral filtering and spatial integration.

[0024] Further, the method for obtaining the infrared detection result is as follows:

[0025] Use a laser to perform spatially selective preheating on the sample to be measured and achieve targeted thermal excitation. After thermal equilibrium, use thermal imaging technology to collect panoramic thermal distribution data of the color-developing area of the sample to be measured, and obtain a two-dimensional temperature field matrix;

[0026] Implement multi-scale extreme value search within the color-developing characteristic area, and use the dynamic threshold segmentation method to delimit the effective detection area;

[0027] Locate the extreme value points of the temperature field through the gradient tracking method to obtain the infrared detection result.

[0028] Further, the method for establishing the comprehensive concentration equation is as follows:

[0029] Fit the visible light detection result, fluorescence detection result, and infrared detection result with a least squares linear function to obtain the corresponding fitting curves f1(x), f2(x), and f3(x) respectively. Use the visible light gray-scale integral value as the variable value of the visible light detection result fitting curve f1(x), use the fluorescence excitation gray-scale integral value as the variable value of the fluorescence detection result fitting curve f2(x), use the temperature as the variable value of the infrared detection result fitting curve f3(x), and combine the proportionality coefficient magnitudes corresponding to f1(x), f2(x), and f3(x) of the sample to be measured to establish a comprehensive concentration equation:

[0030] n = w1 * f1(I L ) + w2 * f1(I U ) + w3 * f1(T);

[0031] Where I L is the visible light gray-scale integral value, I U is the fluorescence excitation gray-scale integral value, T is the temperature, and w1, w2, and w3 are the proportionality coefficients of the visible light detection result, fluorescence detection result, and infrared detection result respectively.

[0032] The calculation method of the proportionality coefficient is as follows:

[0033] Calculate the concentration prediction value of the calibration sample group according to the fitting curve of the calibration sample group;

[0034] Calculate the standard deviation v between the concentration prediction value of the calibration sample group and the true concentration of the calibration sample group, take the reciprocal of the standard deviation v and normalize it, and use it as the proportionality coefficient magnitudes corresponding to f1(I L ), f2(I U ), f3(T).

[0035] The above data fusion processing solution can improve the accuracy of the final output concentration data, automatically complete data analysis and result output, avoid the professional knowledge and cumbersome steps required for traditional manual drawing and regression fitting, cross-validate between various acquired data modalities, improve detection reliability, and still output stable and consistent detection results in complex sample matrices or on-site environments.

[0036] The present invention also provides a quantitative detection device applying the multi-signal fusion immunochromatographic analysis method as described above, including a detection chamber, a sample tray disposed in the detection chamber, a laser generator, a filter system, an image acquisition device, and a thermal imaging system disposed in the detection chamber above the sample tray, and a control processor respectively connected to the laser generator, the filter system, the image acquisition device, and the thermal imaging system.

[0037] It should be noted that by setting the fitting algorithm of the above multi-signal fusion immunochromatographic analysis method in the control processor device, the analysis and result output of the three acquired data can be automatically completed, avoiding the professional knowledge and cumbersome steps required for traditional manual drawing and regression fitting.

[0038] Furthermore, it further includes a rotating motor with an output end connected to the sample tray, and the rotating motor is connected to the control processor; a plurality of test strip sample slots are evenly distributed in a circumferentially divergent manner on the sample tray.

[0039] Furthermore, the laser generator is a laser generator with adjustable wavelength; specifically, an 808nm semiconductor linear array laser can be used to perform spatially selective preheating on the test strip of the sample to be measured, and targeted thermal excitation is achieved by optimizing the laser power density (X W / cm 2 ) and irradiation time (Y s).

[0040] Furthermore, the image acquisition device is an industrial camera; a high-sensitivity CMOS industrial camera is used for image acquisition.

[0041] Furthermore, the filter system is a filter, and the filter is disposed on the lens of the image acquisition device;

[0042] Furthermore, the detection chamber includes a dark box with one end open, and a closed door detachably connected to the opening of the dark box, and the sample tray is correspondingly disposed at the opening position of the dark box. The detection chamber body adopts a fully enclosed dark box structure to isolate external light source interference, improve the stability of visible light and fluorescence signals, and the combination of the enclosed dark box structure and infrared response makes the infrared thermal imaging response unaffected by environmental temperature fluctuations, enabling the device to still output stable and consistent detection results in complex sample matrices or on-site environments, and having high anti-interference and repeatability.

[0043] The present invention also provides an operation procedure for the quantitative detection device as described above, including the following steps:

[0044] Place the sample to be tested on the sample tray;

[0045] The control processor controls the laser generator to emit a visible light source to irradiate the sample to be tested, and the image acquisition device captures the colorimetric strip of the test strip to complete the collection of colorimetric signals; the control processor controls the laser generator to emit a fluorescence wavelength source to irradiate the sample to be tested, and the image acquisition device filters and then collects the fluorescence labeling reaction intensity of the sample to be tested; the control processor controls the laser generator to emit an infrared light source to irradiate the sample to be tested, and the thermal imaging system collects the photothermal response for pseudo-color imaging and outputs the thermal signal of the sample to be tested.

[0046] The control processor executes a preset data processing program to obtain and output concentration data;

[0047] Take out the sample to be tested to complete a full-automatic immunochromatographic quantitative detection.

[0048] Compared with the prior art, the beneficial effects of the present invention are:

[0049] (1) By covering three signals for analysis and recognition, the present invention combines three analysis and detection modes of visible light, fluorescence and photothermal signals to comprehensively achieve quantitative analysis. In addition to the basic visible light determination, the double spectral overlap of the fluorescence signal improves the sensitivity of the fluorescence signal detection, and the photothermal response provides a stable infrared signal output. The data are mutually compared and demonstrated, and at the same time, the infrared photothermal signal is used as a third comparison means to further increase the comparison samples, significantly improving the accuracy of quantitative detection.

[0050] (2) While setting three signal recognitions, the present invention establishes a fitting curve through data fitting, and at the same time processes data based on the three fitting curves, establishes a comprehensive concentration equation of the sample to be tested with respect to the fitting curve, and calculates a more accurate comprehensive concentration value. Through fitting and calculation, the human error and operation complexity are significantly reduced. The multi-signal detection fitting calculation makes the recognition of the sample to be tested more sensitive, especially in the quantitative analysis of samples to be tested with low concentrations. In the quantitative analysis of samples to be tested with normal concentrations, due to multi-modal detection and data fitting processing, the experimental results are more reliable and accurate, and it is especially suitable for scenarios where the actual sample concentration distribution is wide and the response is uneven. Description of the Drawings

[0051] Figure 1 It is a flow schematic diagram of a multi-signal fusion immunochromatographic analysis method in an embodiment;

[0052] Figure 2 It is a perspective view of a quantitative detection device in an embodiment;

[0053] Figure 3 Exploded view of the quantitative detection device in one embodiment;

[0054] Figure 4 Structural schematic diagram of the sample tray in one embodiment.

[0055] 1 - Detection chamber, 11 - Dark box, 12 - Sealing door, 2 - Sample tray, 21 - Sample slot, 3 - Laser generator, 4 - Filter system, 5 - Image acquisition device, 6 - Thermal imaging system, 7 - Control processor, 8 - Rotating motor. Detailed implementation manners

[0056] The present invention will be further described below in conjunction with the detailed implementation manners. Among them, the attached drawings are only for illustrative purposes, showing only schematic diagrams, rather than physical diagrams, and should not be construed as a limitation of this patent; in order to better illustrate the embodiments of the present invention, some components in the attached drawings will be omitted, enlarged or reduced, which do not represent the actual size of the product; for those skilled in the art, it is understandable that some well-known structures and their descriptions in the attached drawings may be omitted.

[0057] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the attached drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0058] In the attached drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components; in the description of the present invention, it should be understood that if there are terms such as "upper", "lower", "left", "right", etc. indicating the orientation or positional relationship, they are based on the orientation or positional relationship shown in the attached drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, the terms describing the positional relationship in the attached drawings are only for illustrative purposes and should not be construed as a limitation of this patent. For those of ordinary skill in the art, the specific meanings of the above terms can be understood according to specific circumstances.

[0059] Embodiment 1

[0060] As Figure 1 shown in the first embodiment of the present invention, a multi-signal fusion immunochromatographic analysis method specifically includes the following steps:

[0061] Irradiate a calibration sample group with known concentration by visible light, capture the color development strips of the calibration sample group to collect the colorimetric signals of the calibration sample group, and obtain the visible light detection result;

[0062] Irradiate a calibration sample group with known concentration by an excitation light source with a specific wavelength, filter and then collect the fluorescence labeling reaction intensity of the calibration sample group to obtain the fluorescence detection result;

[0063] Use a laser to heat a calibration sample group with a known concentration, perform false-color imaging on the photothermal response in the calibration sample group, collect the thermal signal output of the calibration sample group, and obtain an infrared detection result;

[0064] Respectively establish fitting curves through visible light detection results, fluorescence detection results, and infrared detection results;

[0065] According to the fitting curves of the visible light detection result, the fluorescence detection result, and the infrared detection result, establish a comprehensive concentration equation for the fitting curves;

[0066] Collect the visible light detection result, fluorescence detection result, and infrared detection result of a test sample with an unknown concentration, and use the comprehensive concentration equation to calculate the comprehensive concentration value of the test sample.

[0067] In this embodiment, the method for obtaining the visible light detection result is as follows:

[0068] Irradiate the test sample with visible light, collect the image information of the color development strip of the test sample through an industrial camera to obtain a colorimetric signal, convert the RGB image of the colorimetric signal to the CIE Lab uniform color space through color space conversion, and construct a color difference gray-scale distribution matrix;

[0069] Specifically, taking the standard black (L* = 0, a* = 0, b* = 0) as the reference color, calculate the color difference value △E00 between each pixel point in the detection area and the reference color pixel by pixel based on the color difference formula △E = [(△L*)^2 + (△a*)^2 + (△b*)^2], and construct a color difference gray-scale distribution matrix, where the color difference value is positively correlated with the gray value.

[0070] Perform projection integration on the color difference gray-scale matrix in the horizontal direction to obtain the horizontal cumulative color difference distribution curve of the test sample;

[0071] According to the optical property differences between the sample reaction area and the substrate material, detect the local minimum points of the cumulative color difference distribution curve to achieve precise positioning and quantitative analysis of the test line and the control line, and obtain the visible light detection result.

[0072] Since there are optical property differences between the test line and the control line T / C line in the test paper reaction area and the substrate material, that is, the normal substrate area has a large cumulative color difference due to its high reflection characteristics, while the immune reaction line area has a significant reduction in cumulative color difference due to the light absorption enhancement effect caused by the aggregation of nanomaterials. In this application, precise positioning and quantitative analysis of the test line and the control line are achieved by detecting the local minimum points of the cumulative color difference curve, effectively improving the objectivity and repeatability of the test strip detection of the test sample, and providing a reliable image processing solution for point-of-care testing devices.

[0073] In this embodiment, the method for obtaining the fluorescence detection result is as follows:

[0074] Irradiate the sample to be measured with an excitation light source of a specific wavelength, filter out short-wavelength stray light using a filter, collect the fluorescence labeling reaction intensity through an industrial camera, and obtain the fluorescence distribution image of the sample to be measured;

[0075] Specifically, a UV light source in the 365 - 370 nm band is used, and short-wavelength stray light is effectively filtered out through a 450 nm long-pass filter, retaining the fluorescence emission signal with a wavelength > 450 nm. For image acquisition, an industrial camera can be used in combination with an optical imaging component to obtain the fluorescence distribution image of the test strip of the sample to be measured.

[0076] Convert the fluorescence distribution image to the RGB color space. Based on the spectral response characteristics of the fluorescence signal in the long-wavelength region, preferentially extract the R-channel data to generate a gray-scale distribution matrix. Perform projection integration on the gray-scale distribution matrix along the horizontal direction to construct the axial fluorescence intensity distribution curve of the sample. Use the extreme value detection algorithm to accurately locate the characteristic peak positions of the detection line and the control line in the sample reaction area, and perform quantitative analysis to obtain the fluorescence detection result.

[0077] In this application, by combining the strategies of spectral filtering and spatial integration, the signal-to-noise ratio and positioning accuracy of weak fluorescence signals are significantly improved.

[0078] In this embodiment, the method for obtaining the infrared detection result is as follows:

[0079] Use a laser to perform spatially selective preheating on the sample to be measured and achieve targeted thermal excitation. After thermal equilibrium, use thermal imaging technology to collect the panoramic thermal distribution data of the color-developing area of the sample to be measured, and obtain a two-dimensional temperature field matrix;

[0080] Perform multi-scale extreme value search within the color-developing characteristic region, and use the dynamic threshold segmentation method to delimit the effective detection region;

[0081] Locate the extreme points of the temperature field through the gradient tracking method to obtain the infrared detection result.

[0082] Specifically, set 5 groups of calibration sample groups with known concentrations, and obtain the following visible light detection results, fluorescence detection results, and infrared detection results through the above methods:

[0083] Table 1 Detection Results of Calibration Sample Groups

[0084]

[0085] In this embodiment, the method for establishing the comprehensive concentration equation is as follows:

[0086] Fit the visible light detection result, fluorescence detection result, and infrared detection result with the least squares linear function f(x) = kx + b to obtain the corresponding fitting curves f1(x), f2(x), and f3(x) respectively, and calculate to obtain:

[0087] It can be seen that the cursor curve f1(x) = 295.25x-77.87;

[0088] Fluorescence standard curve f2(x)=464.26x-209.26;

[0089] Photothermal calibration curve f3(x)=32.71x-984.49;

[0090] Specifically, the visible light detection results, fluorescence detection results and infrared detection results of the unknown concentration of the sample to be tested are collected according to the above method:

[0091] Table 2 Test results of samples to be tested

[0092]

[0093] The visible light grayscale integral value is used as the variable value of the visible light detection result fitting curve f1(x), the fluorescence excitation grayscale integral value is used as the variable value of the fluorescence detection result fitting curve f2(x), and the temperature is used as the variable value of the infrared detection result fitting curve f3(x). Combined with the proportional coefficients corresponding to the tested samples f1(x), f2(x), and f3(x), a comprehensive concentration equation is established:

[0094] n=w1*f1(I L )+w2*f1(I U )+w3*f1(T);

[0095] Among them I L is the visible light grayscale integral value, I U is the fluorescence excitation grayscale integral value, T is the temperature, w1, w2 and w3 are the proportional coefficients of visible light detection results, fluorescence detection results and infrared detection results respectively.

[0096] The proportionality factor is calculated as:

[0097] Calculate the concentration prediction value of the calibration sample group according to the fitting curve of the calibration sample group;

[0098] Calculate the standard deviation v between the predicted concentration of the calibration sample group and the actual concentration of the calibration sample group, take the inverse of the standard deviation v and normalize it as f1(I L )、f2(I U ), the size of the proportional coefficient corresponding to f3(T).

[0099] Specifically, the proportionality coefficient is calculated as follows:

[0100] First, calculate the standard deviation: Substitute the visible light T / C line ratios of the calibration sample group, 0.4536, 0.5346, 0.6425, 0.7572, and 0.8953, into f1(x) to obtain 56.0458, 79.9706, 111.7311, 145.7933, and 186.4763. Calculate the standard deviation to be 5.74;

[0101] Similarly, the standard deviations of the fluorescence group and the photothermal group after calculation are 3.61 and 9.88 respectively;

[0102] Then, perform normalization processing:

[0103] Take the reciprocals of 5.74, 3.61, and 9.88, and perform normalization calculation to obtain w1 = 0.3153, w2 = 0.5015, and w3 = 0.1832.

[0104] Substitute the proportionality coefficients into the comprehensive concentration equation to calculate the unknown concentration of the sample to be measured, and calculate that the concentration of the sample to be measured is 31.6288 + 53.9601 + 17.9922 = 103.5811.

[0105] The above data fusion processing scheme can improve the accuracy of the final output concentration data, automatically complete data analysis and result output, avoid the professional knowledge and cumbersome steps required for traditional manual drawing and regression fitting. The data between each acquisition modality can be cross-validated to improve the detection reliability, and stable and consistent detection results can still be output in complex sample matrices or on-site environments.

[0106] The advantages of this embodiment are as follows: By covering three signals for analysis and identification, it combines three analytical detection modes of visible light, fluorescence, and photothermal signals to comprehensively achieve quantitative analysis. In addition to the basic visible light determination, the double spectral overlap of the fluorescence signal improves the sensitivity of fluorescence signal detection. The photothermal response provides a stable infrared signal output. The data are mutually compared and verified with each other. At the same time, there is also an infrared photothermal signal as a third comparison means to further increase the comparison samples, significantly improving the accuracy of quantitative detection; while setting three signal identifications in this application, through data fitting, a fitting curve is established, and at the same time, data processing is performed based on the three fitting curves to establish a comprehensive concentration equation of the sample to be measured with respect to the fitting curve, and a more accurate comprehensive concentration value is calculated. Through fitting and calculation, the human error and operation complexity are significantly reduced. The multi-signal detection and fitting calculation make the identification of the sample to be measured more sensitive, especially in the quantitative analysis of low-concentration samples to be measured. In the quantitative analysis of samples to be measured with normal concentrations, due to multi-modal detection and data fitting processing, the experimental results are more reliable and accurate, which is especially suitable for scenarios where the actual sample concentration distribution is wide and the response is uneven.

[0107] Example 2

[0108] A quantitative detection device applying the multi-signal fusion immunochromatographic analysis method described in the above embodiment 1 in this embodiment:

[0109] As Figure 2 and Figure 3 shown, it includes a detection chamber 1, a sample tray 2 arranged in the detection chamber 1, a laser generator 3, a filter system 4, an image acquisition device 5 and a thermal imaging system 6 arranged in the detection chamber 1 and above the sample tray 2, and a control processor 7 respectively connected to the laser generator 3, the filter system 4, the image acquisition device 5 and the thermal imaging system 6.

[0110] It should be noted that by setting the fitting algorithm of the above multi-signal fusion immunochromatographic analysis method in the control processor 7 device, the analysis and result output of the three types of acquired data can be automatically completed, avoiding the professional knowledge and cumbersome steps required for traditional manual drawing and regression fitting.

[0111] In this embodiment, test strip sample slots 21 are distributed on the sample tray 2.

[0112] In this embodiment, the laser generator 3 is a laser generator with adjustable wavelength; specifically, an 808nm semiconductor linear array laser can be used to perform spatial selective preheating on the test strip of the sample to be measured, and targeted thermal excitation is achieved by optimizing the laser power density X W / cm 2 and the irradiation time Y s.

[0113] In this embodiment, the image acquisition device 5 is an industrial camera; a high-sensitivity CMOS industrial camera is used for image acquisition.

[0114] In this embodiment, the filter system 4 is a filter, and the filter is arranged on the lens of the image acquisition device 5;

[0115] In this embodiment, the thermal imaging system 6 can adopt a DIY-Thermocam V3 thermal imaging system 6 based on a LEPTON3.5 long-wave infrared detection module, with a working band of 8-14μm for infrared thermal radiation detection. The thermal detector of this thermal imaging system 6 has a spatial resolution of 160×120 pixels, can detect a temperature range of -10°C to +450°C, and the temperature sensitivity reaches 50mKΔT < 0.05°C.

[0116] As Figure 3 shown, the detection chamber 1 includes a dark box 11 with an opening at one end, and a closing door 12 detachably connected to the opening of the dark box 11. The sample tray 2 is correspondingly arranged at the opening position of the dark box 11.

[0117] The detection cabin 1 adopts a fully enclosed dark box 11 structure to isolate interference from external light sources and improve the stability of visible light and fluorescence signals. The enclosed dark box 11 structure is combined with infrared response, and the infrared thermal imaging response is not affected by ambient temperature fluctuations, so that the equipment can still output stable and consistent test results in complex sample matrices or on-site environments, with high anti-interference and repeatability.

[0118] The operation steps of this embodiment specifically include the following steps:

[0119] Place the sample to be tested on sample tray 2;

[0120] The control processor 7 controls the laser generator 3 to emit a visible light source to illuminate the sample to be tested, and the image acquisition device 5 captures the color strip of the test paper to complete the colorimetric signal acquisition; the control processor 7 controls the laser generator 3 to emit a fluorescent wavelength light source to illuminate the sample to be tested, and the image acquisition device 5 collects the fluorescence labeling reaction intensity of the sample to be tested after filtering; the control processor 7 controls the laser generator 3 to emit an infrared light source to illuminate the sample to be tested, and the thermal imaging system 6 collects the photothermal response to perform pseudo-color imaging, and collects the thermal signal output of the sample to be tested;

[0121] The control processor 7 executes a preset data processing program to obtain concentration data output;

[0122] Take out the sample to be tested and complete a fully automatic immunochromatographic quantitative test.

[0123] Example 3

[0124] This embodiment is similar to Embodiment 2, except that, in this embodiment:

[0125] like Figure 2 and Figure 3 As shown, it also includes a rotating motor 8 whose output end is connected to the sample tray 2, and the rotating motor 8 is connected to the control processor 7;

[0126] like Figure 4 As shown, 24 test paper sample slots 21 are evenly distributed along the circumferential direction on the sample plate 2 .

[0127] The control processor 7 controls the rotating motor 8 to drive the sample tray 2 to rotate, so that the sample tray 2 can be moved to the bottom of the corresponding collection device during different data collection. It can also switch to the next sample for collection after completing one sample collection, realizing automatic collection and automatic switching, greatly improving the collection efficiency and the detection rate.

[0128] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, rather than limitations on the implementation manners of the present invention. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation manners here. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the claims of the present invention.

Claims

1. A multi-signal fusion immunochromatographic analysis method, characterized in that Specifically, it includes the following steps: Irradiate a calibration sample group with known concentration using visible light, capture the color development strips of the calibration sample group to collect the colorimetric signals of the calibration sample group, and obtain the visible light detection result; Irradiate a calibration sample group with known concentration using an excitation light source with a specific wavelength, filter the light, and collect the fluorescence labeling reaction intensity of the calibration sample group to obtain the fluorescence detection result; Heat a calibration sample group with known concentration using a laser, perform false color imaging on the photothermal response in the calibration sample group, and collect the thermal signal output of the calibration sample group to obtain the infrared detection result; Respectively establish fitting curves through the visible light detection result, the fluorescence detection result, and the infrared detection result; According to the fitting curves of the visible light detection result, the fluorescence detection result, and the infrared detection result, establish a comprehensive concentration equation for the fitting curves; Collect the visible light detection result, the fluorescence detection result, and the infrared detection result of a sample to be measured with unknown concentration, and use the comprehensive concentration equation to calculate the comprehensive concentration value of the sample to be measured.

2. The multi-signal fusion immunochromatographic analysis method according to claim 1, wherein The method for obtaining the visible light detection result is as follows: Irradiate the sample with visible light, collect the image information of the color development strip of the sample through an industrial camera to obtain the colorimetric signal, convert the RGB image of the colorimetric signal to the CIE Lab uniform color space through color space conversion, and construct a color difference gray distribution matrix; Perform projection integration on the color difference gray matrix in the horizontal direction to obtain the horizontal cumulative color difference distribution curve of the sample; According to the optical property differences between the sample reaction area and the substrate material, detect the local minimum points of the cumulative color difference distribution curve, realize the precise positioning and quantitative analysis of the detection line and the control line, and obtain the visible light detection result.

3. A multi-signal fusion immunochromatographic analysis method according to claim 1, characterized in that, The method for obtaining the fluorescence detection result is as follows: Irradiate the sample with an excitation light source with a specific wavelength, use a filter to filter out short-wavelength stray light, collect the fluorescence labeling reaction intensity through an industrial camera, and obtain the fluorescence distribution image of the sample; Convert the fluorescence distribution image to the RGB color space, based on the spectral response characteristics of the fluorescence signal in the long-wavelength region, preferentially extract the R-channel data to generate a gray distribution matrix, perform projection integration on the gray distribution matrix in the horizontal direction, construct the axial fluorescence intensity distribution curve of the sample, use the extreme value detection algorithm to precisely locate the characteristic peak positions of the detection line and the control line in the sample reaction area, and perform quantitative analysis to obtain the fluorescence detection result.

4. A multi-signal fusion immunochromatographic analysis method according to claim 1, characterized in that, The method for obtaining the infrared detection result is as follows: Use a laser to perform spatially selective preheating on the sample and achieve targeted thermal excitation. After thermal equilibrium, use thermal imaging technology to collect the panoramic thermal distribution data of the color development area of the sample to obtain a two-dimensional temperature field matrix; Implement multi-scale extreme value search within the color development characteristic area, and use the dynamic threshold segmentation method to delimit the effective detection area; Locate the extreme value points of the temperature field through the gradient tracking method to obtain the infrared detection result.

5. A multi-signal fusion immunochromatographic analysis method according to claim 1, characterized in that The method for establishing the fitting curve is as follows: Use the linear function to perform least squares fitting on the visible light detection result, the fluorescence detection result, and the infrared detection result, and obtain the corresponding fitting curves f1(x), f2(x), and f3(x) respectively.

6. A multi-signal fusion immunochromatographic analysis method according to claim 5, characterized in that, The method for establishing the comprehensive concentration equation is as follows: Taking the visible light gray integral value as the variable value of the fitting curve f1(x) of the visible light detection result, taking the fluorescence excitation gray integral value as the variable value of the fitting curve f2(x) of the fluorescence detection result, and taking the temperature as the variable value of the fitting curve f3(x) of the infrared detection result, a comprehensive concentration equation is established: n = w1*f1(I L ) + w2*f2(I U ) + w3*f3(T); Where I L is the visible light gray scale integral value, I U is the fluorescence excitation gray scale integral value, T is the temperature, and w1, w2, and w3 are the proportionality coefficients of the visible light detection result, the fluorescence detection result, and the infrared detection result, respectively.

7. The multi-signal fusion immunochromatographic analysis method according to claim 6, characterized in that, The calculation method of the proportionality coefficient is as follows: Calculating the concentration prediction value of the calibration sample group according to the fitting curve of the calibration sample group; Calculate the standard deviation v between the predicted concentration values of the calibration sample group and the true concentration of the calibration sample group. Take the reciprocal of the standard deviation v and normalize it to be used as the proportionality coefficient magnitudes corresponding to f1(I L ) and f2(I U ) and f3(T).

8. A quantitative detection device applying the multi-signal fusion immunochromatographic analysis method according to any one of claims 1-7, characterized in that, It includes a detection chamber (1), a sample tray (2) provided in the detection chamber (1), a laser generator (3), a filter system (4), an image acquisition device (5) and a thermal imaging system (6) provided in the detection chamber (1) and above the sample tray (2), and a control processor (7) respectively connected to the laser generator (3), the filter system (4), the image acquisition device (5) and the thermal imaging system (6).

9. The quantitative detection device according to claim 8, characterized in that It further includes a rotation motor (8) with an output end connected to the sample tray (2), and the rotation motor (8) is connected to the control processor (7); a plurality of test paper sample slots (21) are evenly distributed in a circumferentially divergent manner on the sample tray (2).

10. The quantitative detection device according to claim 8, characterized in that, The laser generator (3) is a laser generator with adjustable wavelength, the image acquisition device (5) is an industrial camera, the filter system (4) is a filter, and the filter is provided on the lens of the image acquisition device (5); the detection chamber (1) includes a dark box (11) with one end open, and a closing door (12) detachably connected to the opening of the dark box (11), and the sample tray (2) is correspondingly arranged at the opening position of the dark box (11).

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