Method and system for testing analyte, medium, and device

Abstract

The invention provides an analyte detection method, system, medium and equipment, and belongs to the field of optical analysis, and the method comprises the following steps: an imaging step: irradiating a first region by infrared light in a first wavelength range and imaging the first region, and irradiating the first region by ultraviolet light in a second wavelength range and imaging the first region; a spectrum acquisition step: selecting a detection point and a reference point from the second image to obtain information of an analyte in the imaging area, the information of the analyte including information associated with spectrum data of the analyte; and a compensation step: compensating the information of the analyte according to the gray difference between the detection point and the reference point in the first image. Compared with a mode of analyzing only through spectral data of different areas, the method has the advantage that the detection result is more accurate.

Description

(19) State Intellectual Property Office (12) Invention Patent Application (10) Application Publication Number (43) Application Publication Date (21) Application Number 202410951460.6 (22) Application Date 2024.07.16 (71) Applicant Guangzhou Ruixin Microelectronics Co., Ltd. Address Room 1703, No. 188, Kaitai Avenue, Huangpu District, Guangzhou City, Guangdong Province 510535 (72) Inventor Zheng Hongzhi (74) Patent Agency Shanghai Duan & Duan Law Firm 31334 Patent Attorney Niu Shan (51) Int.Cl. A61B 5 / 1455 (2006.01) A61B 5 / 145 (2006.01) A61B 5 / 00 (2006.01) G06V 40 / 145 (2022.01) G06V 10 / 143 (2022.01) G06T 7 / 00 (2017.01) (54) Invention Title: Method, System, Medium, and Device for Detecting Analytes (57) Abstract: This invention provides a method, system, medium, and device for detecting analytes, belonging to the field of optical analysis, including: Imaging step: irradiating a first region with infrared light within a first wavelength range and imaging the first region, and irradiating the first region with ultraviolet light within a second wavelength range and imaging the first region; Spectrum acquisition step: selecting a detection point and a reference point from the second image to obtain information about the analyte in the imaging region, wherein the information about the analyte includes information related to the analyte and spectral data; Compensation step: compensating for the information about the analyte based on the grayscale difference between the detection point and the reference point in the first image. Compared with the method of analyzing only the spectral data of different regions, the detection results of this application are more accurate. Claims (2 pages), Description (12 pages), Drawings (7 pages), CN 121337336 A 2026.01.16 CN 1 21 33 73 36 A 1. A method for detecting an analyte, characterized in that it comprises: an imaging step: irradiating a first region with infrared light within a first wavelength range and imaging the first region to obtain a first image of the imaging region; irradiating the first region with ultraviolet light within a second wavelength range and imaging the first region to obtain a second image of the imaging region; the first image includes data reflecting the grayscale distribution of the reflected signal generated by the analyte under infrared light irradiation in the imaging region; the second image includes spectral data reflecting the fluorescence radiation signal excited by the analyte under ultraviolet light irradiation in the imaging region; a spectral acquisition step: selecting a detection point and a reference point from the second image to obtain information about the analyte in the imaging region, the information about the analyte including information related to the analyte and the spectral data; a compensation step: compensating for the information about the analyte based on the grayscale difference between the detection point and the reference point in the first image.2. The method for detecting an analyte according to claim 1, characterized in that the spectral acquisition step includes: dividing the imaging region into a candidate region for detection points and a candidate region for reference points according to the grayscale distribution of pixels in the first image; selecting a detection point from the candidate region for detection points and a reference point from the candidate region for reference points; and acquiring the spectral data of the detection point and the spectral data of the reference point from the second image respectively; selecting a pixel whose grayscale value meets a preset requirement as a detection point or a combination of the pixel and its adjacent pixels as a detection point according to the grayscale value of pixels in the second image; selecting a pixel as a reference point or a combination of the pixel and its multiple adjacent pixels as a reference point from the candidate region for reference points; and calculating the fluorescence spectral data of the detection point and the fluorescence spectral data of the reference point. 3. The method for detecting an analyte according to claim 1, characterized in that the second wavelength range is 300-390 nanometers, and the second wavelength range is outside the effective response range of the imaging spectral detection device; the light in the second wavelength range can excite a fluorescence radiation signal from the analyte, and the main peak of the fluorescence spectrum of the fluorescence radiation signal is within the effective response range of the imaging spectral detection device. 4. The method for detecting an analyte according to claim 1, characterized in that the analysis step includes inputting the grayscale difference and the information of the analyte into a trained compensation model, and outputting the compensated information of the analyte. 5. An analyte detection system, characterized in that it comprises: an imaging module: irradiating a first region with infrared light within a first wavelength range and imaging the first region to obtain a first image of the imaging region; irradiating the first region with ultraviolet light within a second wavelength range and imaging the first region to obtain a second image of the imaging region; the first image includes data reflecting the grayscale distribution of the reflected signal generated by the analyte under infrared light irradiation in the imaging region; the second image includes spectral data reflecting the fluorescence radiation signal excited by the analyte under ultraviolet light irradiation in the imaging region; a spectral acquisition module: selecting a detection point and a reference point from the second image to obtain information of the analyte in the imaging region, the information of the analyte including information related to the analyte and the spectral data; and a compensation module: compensating for the information of the analyte based on the grayscale difference between the detection point and the reference point. 6. The analyte detection system according to claim 5, characterized in that the spectral acquisition module comprises: dividing the imaging region into a detection point candidate region and a reference point candidate region according to the grayscale distribution of pixels in the first image; selecting a detection point from the detection point candidate region; and selecting a reference point from the reference point candidate region.The system detects an analyte by obtaining spectral data of the detection point and spectral data of the reference point from the second image, respectively. Based on the grayscale value of the pixels in the second image, a pixel with a grayscale value meeting a preset requirement is selected from the candidate region of the detection point as the detection point, or a combination of that pixel and its adjacent pixels is selected as the detection point. Similarly, a pixel is selected from the candidate region of the reference point as the reference point, or a combination of that pixel and multiple adjacent pixels is selected as the reference point. The fluorescence spectral data of the detection point and the fluorescence spectral data of the reference point are then calculated. 7. The analyte detection system according to claim 5, wherein the second wavelength range is 300-390 nanometers, and the second wavelength range is outside the effective response range of the imaging spectral detection device; the light in the second wavelength range can excite fluorescence radiation signals from the analyte, and the main peak of the fluorescence spectrum of the fluorescence radiation signal is within the effective response range of the imaging spectral detection device. 8. The analyte detection system according to claim 5, wherein the analysis module includes inputting the grayscale difference and the information of the analyte into a trained compensation model, and outputting the compensated information of the analyte. 9. A computer-readable storage medium storing a computer program, characterized in that, when the computer program is executed by a processor, it implements the steps of the method for detecting the analyte according to any one of claims 1 to 4. 10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, when the computer program is executed by the processor, it implements the steps of the method for detecting the analyte according to any one of claims 1 to 4. Claims 2 / 2 Page 3 CN 121337336 A Method, System, Medium and Device for Detecting Analytes Technical Field

[0001] This invention relates to the field of optical analysis, specifically to a method, system, medium and device for detecting analytes. Background Art

[0002] In the detection technology for analytes, taking the detection of glucose in the human body as an example: Patent document US20160287147A1 discloses a device for non-invasive in vivo measurement by Raman spectroscopy, using Raman spectroscopy to measure in vivo blood glucose concentration. The advantage of this approach is that it has higher accuracy compared to the electrochemical method in US20100065441A1. However, its disadvantage is that it currently requires a laboratory-grade Raman spectroscopy system, which is bulky and expensive.

[0003] In addition, patent document CN118078277A discloses a non-invasive blood glucose detection method based on hyperspectral data analysis, achieving non-invasive detection. This document uses absorption spectroscopy, and the spectral signals collected and analyzed not only include the spectral signals of blood glucose, but also the spectral signals of components such as skin tissue. The spectral signals of different wavelengths are superimposed, resulting in fine analysis.It is difficult to separate and extract spectral signals related to blood glucose. At the same time, factors such as the excitation light source, human skin color and epidermal thickness differences will also affect the intensity of the spectral signal, resulting in differences in the intensity of the spectral signal. The final acquired spectral signal is easily affected and cannot be strongly correlated with blood glucose concentration, affecting the accurate measurement of blood glucose concentration. Similarly, patent document CN108542402A also has the same problem.

[0004] Patent document CN117503123A discloses a non-invasive blood glucose detection system and method based on multi-wavelength near-infrared, which adopts multiple sensors and modules to simultaneously acquire multiple biological signals such as fingertip infrared information, facial infrared information and forehead temperature information, and integrates multiple information for analysis and judgment. Its drawback is that too many biological signals need to be collected, and the locations are different. Blood glucose concentrations also vary in different body parts. The cost is high and it cannot achieve portable real-time detection effect. At the same time, too much information makes the detection algorithm complex. Summary of the Invention

[0005] In view of the defects in the prior art, the purpose of the present invention is to provide a detection method, system, medium and device for analytes.

[0006] A method for detecting an analyte according to the present invention includes:

[0007] An imaging step: irradiating a first region with infrared light within a first wavelength range and imaging the first region to obtain a first image of the imaging region; irradiating the first region with ultraviolet light within a second wavelength range and imaging the first region to obtain a second image of the imaging region;

[0008] The first image includes data reflecting the gray-scale distribution of the reflected signal generated by the analyte under infrared light irradiation in the imaging region;

[0009] The second image includes spectral data reflecting the fluorescence radiation signal excited by the analyte under ultraviolet light irradiation in the imaging region;

[0010] A spectral acquisition step: selecting a detection point and a reference point from the second image to obtain information about the analyte in the imaging region, wherein the information about the analyte includes information related to the analyte and the spectral data; Specification 1 / 12 page 4 CN 121337336 A

[0011] A compensation step: compensating for the information about the analyte based on the gray-scale difference between the detection point and the reference point in the first image.

[0012] Preferably, the spectral acquisition step includes:

[0013] Dividing the imaging area into a detection point candidate area and a reference point candidate area according to the grayscale distribution of pixels in the first image, selecting a detection point from the detection point candidate area, selecting a reference point from the reference point candidate area, and acquiring the spectral data of the detection point and the spectral data of the reference point from the second image respectively;

[0014] Based on the grayscale value of pixels in the second image, selecting a pixel whose grayscale value meets a preset requirement as a detection point or a combination of the pixel and its adjacent pixels as a detection point from the detection point candidate area, and acquiring the spectral data of the detection point and the reference point from the reference point candidate area.A pixel is selected as a reference point, or a combination of the pixel and multiple adjacent pixels is selected as a reference point, and the fluorescence spectrum data of the detection point and the fluorescence spectrum data of the reference point are calculated.

[0015] Preferably, the second wavelength range is 300-390 nanometers, and the second wavelength range is outside the effective response range of the imaging spectral detection device;

[0016] The light in the second wavelength range can excite the analyte to emit a fluorescence radiation signal, and the main peak of the fluorescence spectrum of the fluorescence radiation signal is within the effective response range of the imaging spectral detection device.

[0017] Preferably, the analysis step includes inputting the gray difference and the information of the analyte into a trained compensation model, and outputting the compensated information of the analyte.

[0018] A detection system for an analyte according to the present invention includes:

[0019] an imaging module: irradiating a first region with infrared light within a first wavelength range and imaging the first region to obtain a first image of the imaging region; irradiating the first region with ultraviolet light within a second wavelength range and imaging the first region to obtain a second image of the imaging region;

[0020] the first image includes data reflecting the grayscale distribution of the reflected signal generated by the analyte under infrared light irradiation in the imaging region;

[0021] the second image includes spectral data reflecting the fluorescence radiation signal excited by the analyte under ultraviolet light irradiation in the imaging region;

[0022] a spectral acquisition module: selecting a detection point and a reference point from the second image to obtain information about the analyte in the imaging region, wherein the information about the analyte includes information related to the analyte and the spectral data;

[0023] a compensation module: compensating for the information about the analyte based on the grayscale difference between the detection point and the reference point.

[0024] Preferably, the spectral acquisition module includes:

[0025] dividing the imaging area into a candidate detection point area and a candidate reference point area according to the grayscale distribution of pixels in the first image, selecting a detection point from the candidate detection point area, selecting a reference point from the candidate reference point area, and acquiring the spectral data of the detection point and the spectral data of the reference point from the second image respectively;

[0026] selecting a pixel whose grayscale value meets a preset requirement as a detection point or a combination of the pixel and its neighboring pixels as a detection point according to the grayscale value of pixels in the second image, selecting a pixel as a reference point or a combination of the pixel and its neighboring pixels as a reference point from the candidate detection point area, and calculating the fluorescence spectral data of the detection point and the fluorescence spectral data of the reference point.

[0027] Preferably, the second wavelength range is 300-390 nanometers, and the second wavelength range is outside the effective response range of the imaging spectral detection device;

[0028] The light in the second wavelength range can excite the analyte to emit a fluorescence radiation signal, and the main peak of the fluorescence spectrum of the fluorescence radiation signal is located within the effective response range of the imaging spectral detection device. Specification 2 / 12 Page 5 CN 121337336 A

[0029] Preferably, the analysis module includes inputting the gray difference and the information of the analyte into a trained compensation model and outputting the compensated information of the analyte.

[0030] According to the present invention, a computer-readable storage medium storing a computer program is provided, wherein when the computer program is executed by a processor, the steps of the method for detecting the analyte are implemented.

[0031] According to the present invention, an electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the steps of the method for detecting the analyte are implemented.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] 1. The technical solution of this application does not require electrochemical reaction with the analyte, and the detection method is more convenient, especially when detecting analytes in living organisms, it can achieve the purpose of non-invasive detection.

[0034] 2. This application utilizes the non-uniform distribution of analytes in the imaging region to obtain spectral data from different regions. Since the distribution of other components besides the analytes in the imaging region is relatively uniform, the differences in spectral data from different regions can directly reflect the information related to the analytes and spectral data after essentially excluding the influence of non-analytes, such as the concentration of the analytes. At the same time, this application uses the grayscale difference between different regions as a depth reference for measuring the analytes to compensate for the information of the analytes. This compensates for the influence of the analytes at different depths on the spectral data. Compared with the method of analyzing only the spectral data from different regions, the detection results of this application are more accurate.

[0035] 3. This application uses fluorescence spectroscopy for detection, avoiding the traditional method of measuring analytes using Raman spectroscopy, thereby achieving low cost and miniaturization of the detection system and achieving the purpose of real-time detection.

[0036] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0037] FIG1 is a flowchart of Embodiment 1;

[0038] FIG2 is a schematic diagram of a first image acquired in Embodiment 2;

[0039] FIG3 is a schematic diagram of a second image acquired in Embodiment 2;

[0040] FIG4 is a schematic diagram of the detection model of Embodiment 2;

[0041] FIG5 is a schematic diagram of the detection point-reference point spectral data obtained in Embodiment 2;

[0042] FIG6 shows the experimental results of the accuracy of the analysis results of the analysis model;

[0043] FIG7 is a structural schematic diagram of an analyte detection device provided in Embodiment 5;

[0044] Figure 8 is a structural schematic diagram of the electronic device provided in Embodiment 6;

[0045] Figure 9 is a structural schematic diagram of an analytical substance detection watch provided in Embodiment 5;

[0046] Figure 10 is a schematic diagram of the back of the analytical substance detection watch;

[0047] Figure 11 is an exploded view of the analytical substance detection watch;

[0048] Figure 12 is a schematic diagram of the analytical substance detection watch in use;

[0049] Figure 13 is a schematic diagram of another detection model in Embodiment 2.

[0050] In the figure:

[0051] 100: Imaging area; 200: Detection device;

[0052] 201: Light source; 202: Imaging spectrum detection device;

[0053] 203: Controller; 204: First bandpass filter; Specification 3 / 12 page 6 CN 121337336 A

[0054] 205: Lens; 206: Second bandpass filter;

[0055] 207: Circuit board; 501: Processor;

[0056] 502: Memory. Detailed Description

[0057] The present invention will be described in detail below with reference to specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but do not limit the present invention in any way. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all fall within the protection scope of the present invention.

[0058] Example 1

[0059] Figure 1 is a flowchart of this embodiment. This embodiment is a method for detecting an analyte, including:

[0060] Imaging step: Light within a preset wavelength range is provided by a light source to illuminate a first region, and an imaging spectral detection device is used to image the first region to obtain an image of the imaging region. By illuminating the first region with light within a preset wavelength range, the image can reflect the distribution data and spectral data of the reflection signal or excitation signal generated by the analyte under light irradiation in the imaging region. The first region can be a certain area on the surface of human skin. In order to avoid the influence of external light such as ambient light on the detection, the acquisition window of the imaging spectral detection device needs to be tightly attached to the surface of human skin in the first region, and the imaging region refers to the area within the lens range of the imaging spectral detection device. In general, the imaging region can be a part of the first region, or it can be the same region as the first region.

[0061] Since different wavelength ranges of light are needed to irradiate the analyte to obtain the distribution data and spectral data of the analyte, there are two ways to achieve this: the light is light with a larger wavelength range provided by a light source or light with a smaller wavelength range provided by two light sources respectively. When there is only one light source, the wavelength range of the light provided by that light source needs to simultaneously cover the wavelength range that can acquire data on the distribution of analytes and the wavelength range that can acquire spectral data of analytes.Surrounding. When there are two light sources, the two light sources provide different light. The wavelength of one light covers the wavelength range that can acquire the distribution data of the analyte, and the wavelength of the other light covers the wavelength range that can acquire the spectral data of the analyte. At the same time, when there is one light source, the image is one, and when there are two light sources, the image is two. For ease of processing, the imaging areas of the two images are required to be the same, that is, the acquisition window of the imaging spectral detection device does not move on the surface of human skin.

[0062] In this application, the analyte can be glucose, ketone, alcohol, lactate, oxygen, hemoglobin A1C, acetylcholine, amylase, bilirubin, cholesterol, human chorionic gonadotropin, creatine kinase (e.g., CK-MB), creatine, creatinine, DNA, fructosamine, glutamine, growth hormone, hormone, peroxide, prostate-specific antigen, prothrombin, RNA, thyroid-stimulating hormone or troponin in blood vessels, or drugs such as antibiotics (e.g., gentamicin, vancomycin, etc.), digitalis, digoxin, abused drugs, theophylline or warfarin. In embodiments that detect two or more analytes, the analytes can be monitored at the same or different times. In other embodiments, the analytes can also be other substances within the body surface, and non-invasive detection can be achieved through this invention.

[0063] Spectral acquisition step: Spectral data reflecting the uneven distribution of reflection or excitation signals generated by the analytes when irradiated by light in the imaging area is acquired from the image using an imaging spectral detection device. Specifically, the imaging area can be partitioned according to different distribution data to facilitate the selection of locations from different partitions for acquiring spectral data.

[0064] Analysis step: Information about the analytes in the imaging area is obtained based on the acquired spectral data. The information about the analytes includes information related to the analytes and spectral data. Since the distribution of analytes in different partitions is different, the reflection or excitation signals generated by the analytes when irradiated by light will also be different. Taking human skin as an example, it is divided into three parts: epidermis, dermis, and subcutaneous tissue. Veins and other blood vessels are located in the subcutaneous tissue. Ultraviolet light irradiation can yield corresponding spectral data for both vascular and avascular skin areas, or for skin areas with thicker and thinner blood vessels. The difference between these two spectral data can reflect the correlation between the analyte in the blood vessel and the spectral data, such as the degree of influence of the analyte on the spectral data, for further analysis, or directly obtain information such as the concentration of the analyte through an analytical model.

[0065] Compensation step: Based on the grayscale difference between the detection point and the reference point, the information of the analyte is compensated. The grayscale difference between the detection point and the reference point reflects the depth and thickness of the vein to a certain extent, reducing the impact of the depth of the vein on the analyte's concentration.Different degrees of detection error.

[0066] Example 2

[0067] This example is based on Example 1, taking the detection of glucose in human blood vessels as an example, and provides a non-invasive glucose detection method, including:

[0068] Imaging step: Irradiate the skin at the location of the vein, such as the wrist or back of the hand, with infrared light in the first wavelength range of 800-1000 nm, and collect the first image of the imaging area. The first wavelength range is preferably the near-infrared band. And irradiate the same location with ultraviolet light in the second wavelength range of 300-390 nm, and collect the second image of the imaging area.

[0069] As shown in Figure 2, the horizontal axis is the horizontal coordinate of the first image, the vertical axis is the vertical coordinate of the first image, the white box represents the selected detection point pixel block on the vein, and the black box represents the selected reference point pixel block on the surrounding skin. In the first image, some infrared light penetrates the human skin and some is absorbed by the skin. Simultaneously, it is also heavily absorbed by the veins, resulting in lower pixel grayscale values ​​in vein areas and higher grayscale values ​​in non-vein areas. This allows for easy division of the imaging area into vein and non-vein regions.

[0070] As shown in Figure 3, the horizontal axis represents the horizontal coordinate of the second image, and the vertical axis represents the vertical coordinate. White boxes represent selected detection point pixels on the veins, and black boxes represent selected reference point pixels on the surrounding skin. In the second image, it is difficult to distinguish between vein and non-vein areas, thus requiring the first image for differentiation. Excitation light in the second wavelength range of 300-390 nm is used to obtain high-quality effective fluorescence spectral signals. This is because the main response band of the imaging spectral detection device is located in the 400-800 nm range. When the wavelength of the excitation light used is less than 300 nm, the main peak of the fluorescence spectrum of the excited fluorescence radiation signal is located in the <400 nm band, and the imaging spectral detection device has difficulty obtaining a high-quality effective fluorescence spectrum signal. When the wavelength of the excitation light used is >390 nm, the excitation light itself is also visible light, and the spectral signal of the excitation light is superimposed with the fluorescence spectrum signal, making it difficult to eliminate the interference of the spectral signal of the excitation light and extract the effective fluorescence spectrum signal. After absorbing ultraviolet light in the wavelength range of 300-390 nm, glucose in the vein can emit a fluorescence radiation signal in the visible light band of 400-800 nm. This band is within the effective response range of the imaging spectral detection device, and the characteristic spectral intensity of this fluorescence radiation signal is positively correlated with the concentration of glucose, and has a high fluorescence excitation efficiency.

[0071] Spectral acquisition step: The imaging area is divided into veins according to the gray distribution of the pixels in the first image.In both the venous and non-venous regions, detection points are selected from the locations in the second image corresponding to the venous regions, and reference points are selected from the locations in the second image corresponding to the non-venous regions. Spectral data for the detection points and reference points are then acquired in the second image. Specifically, based on the grayscale values ​​of pixels in the second image, a pixel with a grayscale value meeting preset requirements is selected from the venous region as a detection point, or a combination of that pixel and its adjacent pixels is selected as a detection point. Similarly, a pixel with a grayscale value within a preset deviation range from the selected detection point is selected from the non-venous region as a reference point, or a combination of that pixel and multiple adjacent pixels is selected as a reference point. The fluorescence spectral data for the detection points and the reference points are then calculated in the second image. The spectral data is taken from a single pixel of the detection point or reference point, or an average of multiple pixels, which can be appropriately selected based on the vessel width. Averaging multiple pixels can improve the signal-to-noise ratio, but is limited by the vessel width, avoiding areas outside the vessel. Selecting a single pixel offers high spatial resolution and is suitable for thinner vessels, but has a lower signal-to-noise ratio. The preset requirement for grayscale values ​​can be to select the point with the smallest grayscale value as the detection point, but this application does not impose this restriction. The calculation results are shown in Figure 5, where the horizontal axis represents wavelength (in nm), the vertical axis represents relative radiance (in W / nm), the solid line represents the spectral data of the detection point, and the dashed line represents the spectral data of the reference point. The reason why the grayscale value of the reference point and the grayscale value of the selected detection point are within the preset deviation range is that the skin in the imaging area may have influencing factors such as skin color, spots, and cosmetics, which will directly affect the spectral data of the reference point. The first image cannot distinguish the areas with these influencing factors. By setting the preset deviation range of grayscale values, these influencing factors can be effectively eliminated. In addition, the fact that the grayscale value and the grayscale value of the selected detection point are within the preset deviation range can ensure that the selection of the reference point is close to the detection point. For example, selecting it at the edge of a vein can ensure that the color, thickness, and other parameters of the epidermis, dermis, and subcutaneous tissue, other than the blood vessel, are as close as possible, so that the deviation between the spectral data of the detection point and the spectral data of the reference point can minimize the influence of non-analytes.

[0072] Besides using spectral reconstruction algorithms, spectral data can also be obtained by generating radiometric calibration coefficients through prior radiometric calibration, and then calculating the spectral lines by multiplying the grayscale value by the radiometric calibration coefficients.

[0073] When selecting a combination of multiple pixels as the detection point, the fluorescence spectral data of that detection point can be the average of the fluorescence spectral data of these pixels. Simultaneously, the number of detection points and reference points can be one or more. When there are multiple detection points and reference points, the average of the fluorescence spectral data of all detection points and the average of the fluorescence spectral data of all reference points can be calculated separately.

[0074] Analysis Steps: As shown in Figure 13, the spectral data of the acquired detection points and the spectral data of the reference points are input into the detection model to obtain information about the analytes in the imaging area. The information about the analytes includes information related to the analytes and the spectral data. The gray-level difference between the acquired detection points and the reference points in the first image, as well as the spectral data of the acquired detection points and the spectral data of the reference points, are preprocessed and input into the trained detection model to output the glucose concentration or intermediate results related to the glucose and spectral data.

[0075] Compensation Steps: The gray-level difference between the detection points and the reference points in the first image, as well as the information about the analytes, are input into the second detection model (compensation model) to obtain the compensated information about the analytes. The gray-level difference reflects the depth and thickness of the blood vessels. The deeper the blood vessels, the greater the gray level (the lighter the color), and the thicker the blood vessels, the smaller the gray level (the darker the color). For the same person, the spectral data collected from blood vessels of different depths and thicknesses are different. Taking the gray-level difference into account can make the detection results more accurate.

[0076] During training, the detection model needs to simultaneously acquire the spectral data of the tested object and the accurate test results, such as blood test results. The spectral data is used as the input of the detection model, and the blood test results are used as the output of the detection model for training. Similarly, during training, the compensation model needs to simultaneously acquire the spectral data of the tested object, the accurate test results, and the grayscale difference between the detection point and the reference point.

[0077] The detection model and the compensation model can adopt a convolutional neural network model, which sequentially includes an input layer, at least two convolutional layers, at least two activation function layers, a Flatten layer, a fully connected layer, and an output layer. The convolutional layers and the activation function layers are distributed alternately; the activation function used in the activation function layers is the ReLU function.

[0078] In the convolutional neural network model, the kernel size of each layer is 1. The number of kernels in the first convolutional layer is 32 (page 6 / 12, CN 121337336 A, manual), and the number of kernels in the second layer is 64. Both are used to extract blood glucose features, and the output of the convolutional layer is nonlinearly transformed through the activation function. The Flatten layer flattens the output of the convolutional layer into a one-dimensional vector, which is convenient for connecting subsequent fully connected layers. The final output dimension is 1. During the model training process, the Adam optimizer is used for model training, and the mean squared error is used as the loss function. At the same time, the mean absolute error is calculated as the performance index for model evaluation.

[0079] When the output result of the detection model and the compensation model is glucose concentration, if the error between the output result and the measured standard glucose concentration value meets the preset condition, the training is stopped to obtain the detection model and the compensation model. When the output result of the detection model and the compensation model is an intermediate result of glucose and spectral data association, such as the result of an intermediate neuron, if the outputIf the error between the result and the intermediate neuron result meets the preset condition, training stops and the detection model and compensation model are obtained. Further model correction processing is performed on the intermediate neuron result to obtain the glucose concentration.

[0080] As shown in Figure 4, the Input layer is the spectral data input layer, obtained after preprocessing the original spectral data. The Hidden layer is the intermediate hidden layer, which performs deep learning through convolution operations, combines features, and outputs the final predicted blood glucose concentration value. Alternatively, deep learning through convolution operations can be used to combine features and output a neuron Output1 as an intermediate result value. The intermediate result value Output1 and two infrared (IR) feature brightness values ​​are then used for model training again to further correct the blood glucose prediction error and output the final predicted blood glucose concentration value Output2. The training level of the detection model needs to be set with different parameters as required. The extracted glucose feature values ​​will continuously learn according to the different parameter settings until the error between the output result and the standard glucose value of the above label value meets the requirements, at which point training stops and the detection model is obtained.

[0081] Through multiple iterative training, neurons learn the corresponding change patterns between different glucose concentrations and glucose spectral characteristics of different samplers, thereby improving the universality of the detection model and enabling the prediction of glucose concentrations for different users.

[0082] The entire glucose detection process does not require puncturing the skin to collect blood or implanting a needle. It obtains the spectral information of the test subject based on fluorescence spectroscopy and obtains the glucose detection result of the test subject based on the spectral information, avoiding pain and discomfort and improving the comfort and convenience of the detection. This method can finely distinguish the spectral signals of blood vessels and skin, providing the possibility for accurate extraction of glucose signals in the future. At the same time, it also makes the spectral signal strongly correlated with the glucose concentration, realizing accurate measurement of glucose concentration, making the detection results more accurate and the processing more convenient.

[0083] Figure 6 shows a schematic diagram of the experimental effect of the detection model after training. The horizontal axis is the reference blood glucose concentration collected by the blood glucose meter (unit: mmol / L), and the vertical axis is the blood glucose concentration predicted by the method of this patent (unit: mmol / L). The total sample size of the test subjects was 2037, including 1537 training set samples and 500 prediction set samples. The distribution of the detection results of the detection model can be seen from the figure. The MARD value of the predicted samples was 11.32%, with the vast majority of samples falling into areas A and B. Specifically, 87.03% of the samples fell into area A, and 12.77% fell into area B, indicating that the detection model has high accuracy.

[0084] Example 3

[0085] This example is based on Example 2, replacing infrared light with visible light to provide another non-invasive glucose detection method, including:

[0086] Imaging steps: The skin at the location of the vein, such as the wrist or back of the hand, is irradiated with visible light to acquire a first image of the imaging area. The same location is then irradiated with ultraviolet light in the second wavelength range of 300-390 nm to acquire a second image of the imaging area.

[0087] In the first image, the color of the area containing the vein differs from that of the non-vein area, making it easy to divide the imaging area into the vein area and the non-vein area.

[0088] In the second image, it is difficult to distinguish between the vein area and the non-vein area, thus requiring the first image for differentiation. The excitation light in the second wavelength range of 300-390 nm is used to obtain a high-quality effective fluorescence spectral signal. This is because the main response band of the imaging spectral detection device is located in the 400-800 nm range. When the wavelength of the excitation light used is less than 300 nm, the main peak of the fluorescence spectrum of the excited fluorescence radiation signal is located in the <400 nm band, making it difficult for the imaging spectral detection device to obtain a high-quality effective fluorescence spectral signal. When the wavelength of the excitation light used is > 390 nm, the excitation light itself is also visible light. The spectral signal of the excitation light is superimposed with the fluorescence spectral signal, making it difficult to eliminate the interference of the spectral signal of the excitation light and extract the effective fluorescence spectral signal. After absorbing ultraviolet light in the wavelength range of 300-390 nm, glucose in the vein can emit fluorescence radiation signals in the visible light band of 400-800 nm. This band is within the effective response range of the imaging spectral detection device. The characteristic spectral intensity of this fluorescence radiation signal is positively correlated with the concentration of glucose and has high fluorescence excitation efficiency.

[0089] Spectral acquisition steps: According to the gray distribution of the pixels in the first image, the imaging area is divided into the area where the vein is located and the area where the non-vein is located. Detection points are selected from the area where the vein is located and reference points are selected from the area where the non-vein is located. The spectral data of the detection points and the spectral data of the reference points are acquired respectively. Specifically, based on the grayscale values ​​of pixels in the second image, a pixel with a grayscale value meeting preset requirements, or a combination of that pixel and its neighboring pixels, is selected from the region where veins are located as a detection point. A pixel with a grayscale value within a preset deviation range from the selected detection point, or a combination of that pixel and several neighboring pixels, is selected from the region where veins are not located as a reference point. The fluorescence spectral data of the detection point and the reference point are then calculated. The reason the grayscale value of the reference point is within the preset deviation range from the grayscale value of the selected detection point is that skin in the imaging area may have influencing factors such as skin color, pigmentation, and cosmetics, which directly affect the spectral data of the reference point. The first image cannot simultaneously distinguish these factors.By setting a preset deviation range for grayscale values, all influencing factors can be effectively excluded from the region.

[0090] When selecting a combination of multiple pixels at the detection point, the fluorescence spectrum data of the detection point can be the average value of the fluorescence spectrum data of these pixels. Simultaneously, the number of detection points and reference points can be one or more. When there are multiple detection points and reference points, the average value of the fluorescence spectrum data of all detection points and the average value of the fluorescence spectrum data of all reference points can be calculated separately.

[0091] Analysis steps: After preprocessing, the acquired spectral data of the detection points and reference points are input into the trained detection model, which outputs the glucose concentration. During training, the detection model needs to simultaneously acquire the spectral data of the tested object and accurate test results, such as blood test results. The spectral data is used as the input to the detection model, and the blood test results are used as the output to train the detection model.

[0092] The detection model can adopt a convolutional neural network model, which sequentially includes an input layer, at least two convolutional layers, at least two activation function layers, a flatten layer, a fully connected layer, and an output layer. The convolutional layers and the activation function layers are distributed alternately. The activation function used in the activation function layer is the ReLU function.

[0093] In the convolutional neural network model, the kernel size of each convolutional layer is 1. The number of kernels in the first convolutional layer is 32, and the number of kernels in the second convolutional layer is 64. Both are used to extract blood glucose features, and the output of the convolutional layer is nonlinearly transformed by the activation function. The flatten layer flattens the output of the convolutional layer into a one-dimensional vector, which is convenient for connecting to the subsequent fully connected layers. The final output dimension is 1. During the model training process, the Adam optimizer is used for model training, and the mean squared error is used as the loss function. At the same time, the mean absolute error is calculated as the performance index for model evaluation.

[0094] If the error between the output result of the detection model and the standard glucose value meets the preset condition, the training is stopped and the detection model is obtained.

[0095] The training level of the detection model needs to be set with different parameters as required. The extracted glucose feature values ​​will continuously learn according to the different parameter settings, until the error between the output result and the standard glucose value of the above label value meets the requirements, then the training stops and the detection model is obtained.

[0096] Through multiple iterative training, the neurons learn the corresponding change law between different glucose concentrations and glucose spectral characteristics of different samplers, thereby improving the universality of the detection model and enabling it to predict the glucose concentration of different users.

[0097] The entire glucose detection process does not require blood collection or skin puncture. It obtains the spectral information of the test subject based on fluorescence spectroscopy and obtains the glucose detection result of the test subject based on the spectral information, avoiding pain and discomfort and improving the detection efficiency.Comfort and convenience of measurement. This method can finely distinguish the spectral signals of blood vessels and skin, providing the possibility for accurate extraction of glucose signals in the future. At the same time, it also makes the spectral signal strongly correlated with glucose concentration, realizing accurate measurement of glucose concentration, making the detection results more accurate and the processing more convenient.

[0098] Example 4

[0099] This embodiment provides an analyte detection system. The analyte detection system can be implemented by executing the process steps of the analyte detection method. That is, those skilled in the art can understand the analyte detection method as a preferred embodiment of the analyte detection system. The analyte detection system includes:

[0100] Imaging module: The light source provides light within a preset wavelength range to irradiate the first area, and the imaging spectral detection device images the first area to obtain an image of the imaging area. By irradiating the first area with light within a preset wavelength range, the image can reflect the distribution data and spectral data of the reflection signal or excitation signal generated by the analyte under light irradiation in the imaging area. Since different wavelength ranges are required to obtain the distribution data and spectral data of the analyte, the light can be two corresponding wavelength ranges, or it can be a light with a larger wavelength range that covers both required wavelength ranges. When there are two types of light, two images are obtained. For ease of processing, it is usually required that the imaging areas of the two images are the same.

[0101] In this application, the analyte can be glucose, ketones, alcohols, lactate, oxygen, hemoglobin A1C, acetylcholine, amylase, bilirubin, cholesterol, human chorionic gonadotropin, creatine kinase (e.g., CK-MB), creatine, creatinine, DNA, fructosamine, glutamine, growth hormone, hormones, peroxides, prostate-specific antigen, prothrombin, RNA, thyroid-stimulating hormone, troponin, or drugs such as antibiotics (e.g., gentamicin, vancomycin, etc.), digitalis, digoxin, abused drugs, theophylline, and warfarin. In embodiments that detect more than one analyte, the analyte can be monitored at the same or different times. In other embodiments, the analyte can also be other substances in a liquid.

[0102] Spectrum Acquisition Module: Acquires spectral data from the image using an imaging spectral detection device, reflecting the uneven distribution of reflection or excitation signals generated by the analyte under light irradiation within the imaging region. Specifically, the imaging region can be partitioned according to different distribution patterns to facilitate the selection of locations from different partitions for acquiring spectral data.

[0103] Analysis Module: Obtains information about the analyte in the imaging region based on the acquired spectral data. This information includes the association between the analyte and the spectral data. Since the distribution of the analyte differs in different partitions, the analyte...The reflected or excitation signals generated by the light exposure of the organisms will also differ. Utilizing this characteristic, the difference in spectral data between the two can be obtained, thereby accurately reflecting the information related to the analyte and the spectral data, such as the concentration of the analyte.

[0104] Those skilled in the art know that, besides implementing the system and its various devices, modules, and units provided by this invention in a purely computer-readable program code manner, the same functions can be achieved entirely by logically programming the method steps to make the system and its various devices, modules, and units of the present invention (pages 9 / 12 of CN 121337336 A) in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, the system and its various devices, modules, and units provided by this invention can be considered a hardware component, and the devices, modules, and units included therein for implementing various functions can also be considered as structures within the hardware component; the devices, modules, and units for implementing various functions can also be considered as both software modules implementing the method and structures within the hardware component.

[0105] Example 5

[0106] Figure 7 shows an electronic device of this embodiment, specifically an analyte detection device 200. The detection device 200 is a portable, non-invasive detection device for the human body. It can be a standalone detection device or integrated into a watch or mobile phone, thereby enabling convenient and quick detection of analytes on the body surface.

[0107] The detection device 200 includes: a light source 201, an imaging spectral detection device 202, a controller 203, a first bandpass filter 204, a second bandpass filter 206, and a lens 205. The controller 203 establishes an electrical connection or a communication connection with the light source 201 and the imaging spectral detection device 202, respectively.

[0108] The light source 201 can provide light within a preset wavelength range. Since different wavelength ranges of light are needed to irradiate the analyte to obtain distribution data and spectral data, there are two implementation methods: one is a light source that can provide light with a larger wavelength range; or, two light sources that each provide light with a smaller wavelength range. When there is only one type of light source, the wavelength range of the light provided by the light source needs to simultaneously cover the wavelength range that can acquire data on the distribution of analytes and the wavelength range that can acquire spectral data of analytes, such as a halogen lamp. When there are two types of light sources, the two light sources provide different light, one light whose wavelength covers the wavelength range that can acquire data on the distribution of analytes, and the other light whose wavelength covers the wavelength range that can acquire spectral data of analytes, such as an infrared lamp combined with an ultraviolet lamp, or a visible light lamp combined with an ultraviolet lamp.

[0109] To ensure uniform illumination of the imaging area 100, a ring light source can be used, which has multiple light-emitting modules evenly distributed on the same circumference. When there are two types of light sources, the light-emitting modules of the two types of light sources are arranged alternately.

[0110] The imaging spectral detection device 202 is capable of imaging the imaging area 100 according to instructions to obtain a corresponding image, and is also capable of obtaining corresponding spectral data according to instructions. The imaging spectral detection device 202 includes a sensor and a periodic pixel-level filter structure disposed on the sensor surface. The periodic pixel-level filter structure is used to spectrally modulate the incoming light signal so that the sensor can generate an image containing the spectral information to be measured.

[0111] The periodic pixel-level filter structure includes multiple filter pixel channels with different shapes. The multiple filter pixel channels have the same size and are uniformly arranged, and their length and width are integer multiples of the pixel size in the image sensor. The filter pixel channels of different shapes correspond to different spectral filtering coefficients, and the pixel-level filter structures with different spectral filtering coefficients are periodically arranged after being combined in a fixed order. The sensor modulates the received first detection light through the periodic pixel-level filter structure disposed on its surface to form a mosaic image containing spectral information, and then reconstructs the spectral data using an algorithm.

[0112] The controller 203 is configured to control the light source to provide light within a preset wavelength range to illuminate the first region, and to control the imaging spectral detection device to image the first region to obtain an image of the imaging region. The controller also controls the imaging spectral detection device to acquire spectral data from the image reflecting the uneven distribution of reflection or excitation signals generated by the analyte under light illumination within the imaging region. Information about the analyte in the imaging region is obtained based on the grayscale difference between the detection point and the reference point in the first image, as well as the acquired spectral data of the detection point and the reference point. This information includes information related to the analyte and the spectral data. When there is only one type of light source 201, one image is formed; when there are two types of light sources 201, two images are formed. When the first type of light source is on, the second type of light source is off; similarly, when the second type of light source is on, the first type of light source is off, and the two do not interfere with each other. Instruction Manual, Pages 10 / 12, CN 121337336 A

[0113] The first bandpass filter 204 is located between the light source 201 and the imaging area 100. Its function is to allow light within a preset wavelength range to pass through, while blocking light outside the preset wavelength range, thereby reducing the influence of other external light on the detection results.

[0114] The second bandpass filter 206 is located between the imaging spectral detection device 202 and the lens 205. Its function is to allow light within the wavelength range of the reflected signal or excitation signal generated by the analyte being irradiated to pass through, while blocking light within other wavelength ranges, thereby reducing the influence of the reflected signal or excitation signal of non-analytes on the detection results.

[0115] The lens 205 can be used for fixed focusing to obtain a clear image. In other embodiments, the second bandpass filter 206 can also be located on the side of the lens 205 away from the imaging spectral detection device 202, and the present invention does not limit this.

[0116] As described above, Figure 9 shows an analytical analyte detection watch provided in this embodiment. The front of the watch is a display, as shown in Figure 10. The back of the watch has a light-transmitting window and houses the detection device 200. As shown in Figure 11, both the light source 201 and the first bandpass filter 204 are annular structures. The light-emitting modules of the light source 201 are arranged in a ring. The emitted light is filtered by the first bandpass filter 204 and outputs light with a wavelength that meets the requirements, which then shines onto the human body through the light-transmitting window on the back of the watch. The reflected signal or excitation signal from the human body enters the light-transmitting window, passes through the hollowed-out portion in the middle of the light source 201 and the first bandpass filter 204, passes through the lens 205, and enters the second bandpass filter 206. After being filtered by the second bandpass filter 206, it enters the imaging spectral detection device 202. The imaging spectral detection device 202 is mounted on a circuit board 207. Simultaneously, the controller 203 (not shown in the figure) of the detection device 200 is also mounted on the circuit board 207. As shown in Figure 12, in order to more accurately identify the location of veins, the watch can be worn on the inside of the wrist.

[0117] Embodiment 6

[0118] Figure 8 is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. As shown in Figure 8, it includes at least one processor 501; and a memory 502 communicatively connected to at least one processor 501; wherein, the memory 502 stores instructions that can be executed by at least one processor 501, and the instructions are executed by at least one processor 501 to enable at least one processor 501 to perform the above-mentioned method for detecting analytes.

[0119] Wherein, the memory 502 and the processor 501 are connected by a bus. The bus can include any number of interconnected buses and bridges, and the bus connects various circuits of one or more processors 501 and memory 502 together. The bus can also connect various other circuits such as peripheral devices, voltage regulators and power management circuits, which are well known in the art, and therefore, the present invention will not describe them further. The bus interface provides an interface between the bus and the transceiver. A transceiver can be a single component or multiple components, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by processor 501 is transmitted over a wireless medium via an antenna, which further receives data and transmits it to processor 501.

[0120] Processor 501 is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces,

[0121] voltage regulation, power management, and other control functions. Memory 502 can be used to store data used by processor 501 during operation.

[0122] The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for detecting analytes.

[0123] That is, those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. The program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes: USB flash drive, mobile hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, and other media that can store program code.

[0124] Those skilled in the art will understand that the above embodiments are specific embodiments of the present invention. In practical applications, various changes can be made to them in form and detail without departing from the spirit and scope of the present invention.

[0125] The specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above. Those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the substantive content of the present invention. Where there is no conflict, the embodiments and features in the embodiments of this application can be combined with each other arbitrarily. Instruction Manual 12 / 12 Page 15 CN 121337336 A Figure 1 Figure 2 Instruction Manual Drawings 1 / 7 Page 16 CN 121337336 A Figure 3 Figure 4 Instruction Manual Drawings 2 / 7 Page 17 CN 121337336 A Figure 5 Figure 6 Instruction Manual Drawings 3 / 7 Page 18 CN 121337336 A Figure 7 Figure 8 Instruction Manual Drawings 4 / 7 Page 19 CN 121337336 A Figure 9 Figure 10 Instruction Manual Drawings 5 / 7 Page 20 CN 121337336 A Figure 11 Figure 12 Instruction Manual Drawings 6 / 7 Page 21 CN 121337336 A Figure 13 Instruction Manual Drawings 7 / 7 Page 22 CN 121337336 A Abstract The present invention relates to the field of optical analysis, and provides a method and a system for testing an analyzer, a medium, and a device. The method includes: imaging: irradiating a firstarea by infrared light within a first wavelength range and imaging the first area; and irradiating the first area by ultraviolet light within a second wavelength range and imaging the first area; spectral obtaining: selecting a testing point and a reference point from the second image to obtain information about the analyte in the imaging area, where the information about the analyte includes information about the analyte correlated to the spectral data; and compensation: compensating the information about the analyte based on a grayscale difference between the testing point and the reference point in the first image. In this application, spectral data in different areas is analyzed, to provide a more accurate test result.

Claims

1. A method of detecting an analyte, characterized by, The method comprises the following steps: An imaging step: a first region is irradiated by infrared light in a first wavelength range and imaged to obtain a first image of the imaging region, and the first region is irradiated by ultraviolet light in a second wavelength range and imaged to obtain a second image of the imaging region; The first image comprises data reflecting the grayscale distribution of the reflection signal of the analyte generated by the irradiation of infrared light in the imaging region; The second image comprises spectral data reflecting the fluorescence radiation signal of the analyte excited by the irradiation of ultraviolet light in the imaging region; A spectral acquisition step: a detection point and a reference point are selected from the second image to obtain information of the analyte in the imaging region, wherein the information of the analyte comprises information associated with the spectral data of the analyte; A compensation step: the information of the analyte is compensated according to the grayscale difference between the detection point and the reference point in the first image.

2. The method of claim 1, wherein The spectral acquisition step comprises: The imaging region is divided into a detection point candidate region and a reference point candidate region according to the grayscale distribution of the pixel points in the first image, a detection point is selected from the detection point candidate region, a reference point is selected from the reference point candidate region, and spectral data of the detection point and spectral data of the reference point are acquired from the second image respectively; According to the grayscale value of the pixel points in the second image, a pixel point with a grayscale value meeting a preset requirement is selected from the detection point candidate region as a detection point or a combination of the pixel point and adjacent pixel points is selected as a detection point, a pixel point is selected from the reference point candidate region as a reference point or a combination of the pixel point and multiple adjacent pixel points is selected as a reference point, and fluorescence spectral data of the detection point and fluorescence spectral data of the reference point are calculated.

3. The method of claim 1, wherein The second wavelength range is 300-390 nm, and the second wavelength range is located outside the effective response range of the imaging spectral detection device; The light in the second wavelength range can excite the analyte to generate a fluorescence radiation signal, and a main peak of the fluorescence spectrum of the fluorescence radiation signal is located within the effective response range of the imaging spectral detection device.

4. The method of claim 1, wherein The analysis step comprises inputting the grayscale difference and the information of the analyte into a trained compensation model to output the information of the compensated analyte.

5. A system for detecting an analyte, characterized by, The method comprises the following steps: An imaging module: a first region is irradiated by infrared light in a first wavelength range and imaged to obtain a first image of the imaging region, and the first region is irradiated by ultraviolet light in a second wavelength range and imaged to obtain a second image of the imaging region; The first image comprises data reflecting the grayscale distribution of the reflection signal of the analyte generated by the irradiation of infrared light in the imaging region; The second image comprises spectral data reflecting the fluorescence radiation signal of the analyte excited by the irradiation of ultraviolet light in the imaging region; A spectral acquisition module: a detection point and a reference point are selected from the second image to obtain information of the analyte in the imaging region, wherein the information of the analyte comprises information associated with the spectral data of the analyte; A compensation module: the information of the analyte is compensated according to the grayscale difference between the detection point and the reference point.

6. The analyte detection system of claim 5, wherein, The spectral acquisition module comprises: The imaging region is divided into a detection point candidate region and a reference point candidate region according to the gray scale distribution of the pixels in the first image, a detection point is selected from the detection point candidate region, a reference point is selected from the reference point candidate region, and spectral data of the detection point and spectral data of the reference point are obtained from the second image respectively; According to the gray scale values of the pixels in the second image, one pixel point with a gray scale value meeting a preset requirement is selected as a detection point or a combination of the pixel point and adjacent pixel points is selected as a detection point from the detection point candidate region, one pixel point is selected as a reference point or a combination of the pixel point and multiple adjacent pixel points is selected as a reference point from the reference point candidate region, and fluorescence spectral data of the detection point and fluorescence spectral data of the reference point are calculated.

7. The analyte detection system of claim 5, wherein, The second wavelength range is 300-390 nm, and the second wavelength range is located outside the effective response range of the imaging spectral detection device. The light in the second wavelength range can excite the analyte to emit a fluorescence radiation signal, and a main peak of a fluorescence spectrum of the fluorescence radiation signal is located within the effective response range of the imaging spectral detection device.

8. The analyte detection system of claim 5, wherein, The analysis module comprises inputting the gray scale difference and information of the analyte into a trained compensation model and outputting compensated information of the analyte.

9. A computer readable storage medium storing a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the detection method of the analyte in any one of claims 1 to 4.

10. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The computer program is executed by the processor to realize the steps of the detection method of the analyte in any one of claims 1 to 4.

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