Method and system for testing analyte, medium, and device

Abstract

The invention provides an analyte detection method, system, medium and equipment, and the method comprises the following steps: an imaging step: irradiating a first region through wide-spectrum visible light or near-infrared light or visible-near infrared light in a first wavelength range, and carrying out imaging to obtain a first image of an imaging region; a spectrum acquisition step: acquiring color or gray level distribution condition data reflecting non-uniform distribution of the analyte in the imaging area from the first image; according to the color or gray level distribution condition data, acquiring reflection spectrum data reflecting non-uniform distribution of the analyte in the imaging area at a required position from the first image; and an analysis step: obtaining the information of the analyte in the imaging area according to the obtained reflection spectrum data. According to the method, the region where the blood vessel is located and the region where the non-blood vessel is located are distinguished through wide-spectrum visible light or near-infrared light or visible-near infrared light, accurate selection of the detection point and the reference point is further achieved, and 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 202410951457.4 (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 Guo Guozhong (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) G06V 10 / 25 (2022.01) G06V 10 / 56 (2022.01) G06V 10 / 60 (2022.01) G06T 7 / 00 (2017.01) (54) Invention Title: Method, System, Medium and Device for Detection of Analytes (57) Abstract: This invention provides a method, system, medium and device for detecting analytes, comprising: an imaging step: irradiating a first region with broadband visible light or near-infrared light or visible-near-infrared light within a first wavelength range and imaging it to obtain a first image of the imaging region; a spectral acquisition step: acquiring color or grayscale distribution data reflecting the non-uniform distribution of the analyte in the imaging region from the first image; acquiring reflectance spectral data reflecting the non-uniform distribution of the analyte in the imaging region at a desired location from the first image based on the color or grayscale distribution data; and an analysis step: obtaining information about the analyte in the imaging region based on the acquired reflectance spectral data. This application uses broad-spectrum visible light, near-infrared light, or visible-near-infrared light to distinguish between areas containing blood vessels and areas not containing blood vessels, further enabling accurate selection of detection points and reference points, resulting in more accurate detection results. Claims: 2 pages; Description: 12 pages; Drawings: 6 pages. CN 121337334 A 2026.01.16 CN 1 21 33 73 34 A 1. A method for detecting an analyte, characterized by comprising: an imaging step: irradiating a first region with broad-spectrum visible light, near-infrared light, or visible-near-infrared light within a first wavelength range and imaging the first region to obtain a first image of the imaging region; a spectral acquisition step: acquiring color or grayscale distribution data reflecting the non-uniform distribution of the analyte in the imaging region from the first image; and, based on the color or grayscale distribution data, acquiring reflectance spectral data reflecting the non-uniform distribution of the analyte in the imaging region at a desired location from the first image;Analysis Steps: 1. Obtain information about the analyte in the imaging region based on the acquired reflectance spectral data. The information about the analyte includes information related to the reflectance spectral data. 2. The method for detecting the analyte according to claim 1, wherein the spectral acquisition step includes: dividing the imaging region into a candidate region for detection points and a candidate region for reference points based on the color or grayscale distribution of pixels in the first image; selecting a detection point from the position corresponding to the candidate region for detection points in the first image; selecting a reference point from the position corresponding to the candidate region for reference points in the first image; and acquiring the reflectance spectral data of the detection point and the reflectance spectral data of the reference point, respectively. 3. The method for detecting the analyte according to claim 2, wherein the spectral acquisition step includes: selecting a pixel whose grayscale value meets a preset requirement from the position corresponding to the candidate region for detection points in the first image, or a combination of the pixel and its adjacent pixels, based on the grayscale value of pixels in the first image; selecting a pixel whose grayscale value is within a preset deviation range from the selected detection point from the position corresponding to the candidate region for reference points in the first image, or a combination of the pixel and multiple adjacent pixels, as a reference point; and calculating the reflectance spectral data of the detection point and the reflectance spectral data of the reference point. 4. The method for detecting an analyte according to claim 3, characterized in that the analysis step includes inputting the calculated reflectance spectral data of the detection point and the reflectance spectral data of the reference point into a trained analyte detection model, and outputting information about the analyte. 5. An analyte detection system, characterized in that it comprises: an imaging module: irradiating a first region with broadband visible light or near-infrared light or visible-near-infrared light within a first wavelength range and imaging the first region to obtain a first image of the imaging region; a spectral acquisition module: acquiring color or grayscale distribution data reflecting the non-uniform distribution of the analyte in the imaging region from the first image; and acquiring reflectance spectral data reflecting the non-uniform distribution of the analyte in the imaging region at a desired location from the first image based on the color or grayscale distribution data; and an analysis module: obtaining information about the analyte in the imaging region based on the acquired reflectance spectral data, wherein the information about the analyte includes information relating the analyte to the reflectance spectral data. 6. The analyte detection system according to claim 5, wherein the spectral acquisition module comprises: dividing the imaging region into a candidate region for detection points and a candidate region for reference points according to the color or grayscale distribution of pixels in the first image; selecting a detection point from the position of the candidate region for detection points corresponding to the position of the first image; selecting a reference point from the position of the candidate region for reference points corresponding to the position of the first image; and acquiring the reflectance spectral data of the detection point and the reflectance spectral data of the reference point respectively.7. The analyte detection system according to claim 6, wherein the spectral acquisition module comprises: selecting a pixel whose gray value meets a preset requirement as a detection point or a combination of the pixel and adjacent pixels as a detection point from the position of the candidate detection point region corresponding to the first image based on the gray value of the pixel in the first image; selecting a pixel whose gray value is within a preset deviation range from the selected detection point as a reference point or a combination of the pixel and multiple adjacent pixels as a reference point from the position of the candidate reference point region corresponding to the first image; and calculating the reflectance spectral data of the detection point and the reflectance spectral data of the reference point. 8. The analyte detection system according to claim 7, wherein the analysis module comprises inputting the calculated reflectance spectral data of the detection point and the reflectance spectral data of the reference point into a trained analyte detection model, and outputting information about the analyte. 9. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the analyte detection method 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 executed by the processor, the computer program 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 121337334 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, commonly used detection technologies include electrochemical methods and optical methods. Taking the detection of glucose in the human body as an example: Patent document US20100065441A1 discloses an analyte monitoring system, device, and method, in which a sensor is implanted under the skin of the human body to undergo an electrochemical reaction with glucose under the skin, thereby obtaining the glucose level. The advantage of this approach is that glucose data can be collected in real time throughout the day as needed, without the need for multiple punctures to collect blood, which greatly facilitates user use. However, its disadvantage is that it uses an invasive method to implant the sensor under the skin of the human body. Patent document US20160287147A1 discloses a device for non-invasive in vivo measurement using Raman spectroscopy, specifically for measuring in vivo blood glucose concentration. This approach offers higher accuracy compared to the electrochemical method in US20100065441A1; however, 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 include not only 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, making it difficult to finely separate and extract the spectral signals related to blood glucose. At the same time, factors such as the excitation light source, human skin color, and differences in epidermal layer thickness will also affect the intensity of the spectral signals, resulting in differences in the intensity of the spectral signals. The final collected spectral signals are 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] Products using electrochemical methods for detection have been developed for more than 10 years, while Raman spectroscopy is still difficult to industrialize because it is difficult to achieve the purpose of real-time detection by portability like patent document US20100065441A1.

[0005] Patent document CN117503123A discloses a non-invasive blood glucose detection system and method based on multi-wavelength near-infrared radiation. It employs multiple sensors and modules to simultaneously acquire various biological signals such as fingertip infrared information, facial infrared information, and forehead temperature information, fusing these signals for analysis and judgment. Its drawback is that it requires collecting too many biological signals at different locations, and blood glucose concentrations vary across different body parts. This results in high costs and an inability to achieve portable, real-time detection. Furthermore, the excessive information complicates the detection algorithm. Summary of the Invention

[0006] To address the deficiencies in the prior art, the purpose of this invention is to provide a method, system, medium, and device for detecting analytes.

[0007] A method for detecting an analyte according to the present invention includes:

[0008] An imaging step: irradiating a first region with broadband visible light or near-infrared light or visible-near-infrared light within a first wavelength range and imaging the first region to obtain a first image of the imaging region;

[0009] A spectral acquisition step: acquiring color or grayscale distribution data reflecting the non-uniform distribution of the analyte in the imaging region from the first image; and acquiring reflectance spectral data reflecting the non-uniform distribution of the analyte in the imaging region at a desired location from the first image based on the color or grayscale distribution data;

[0010] An analysis step: obtaining information about the analyte in the imaging region based on the acquired reflectance spectral data, wherein the information about the analyte includes information related to the reflectance spectral data.

[0011] Further, the spectral acquisition step includes:

[0012] dividing the imaging region into detection points based on the color or grayscale distribution of pixels in the first image.Select a region and a candidate region for reference points, select a detection point from the candidate region for detection points, select a reference point from the candidate region for reference points, and obtain the reflectance spectral data of the detection point and the reflectance spectral data of the reference point respectively.

[0013] Further, the spectral acquisition step includes:

[0014] Based on the gray value of the pixel in the first image, select a pixel whose gray value meets the preset requirements from the candidate region for detection points as a detection point or a combination of the pixel and its neighboring pixels as a detection point; select a pixel whose gray value is within the preset deviation range of the selected detection point from the candidate region for reference points as a reference point or a combination of the pixel and its multiple neighboring pixels as a reference point, and calculate the reflectance spectral data of the detection point and the reflectance spectral data of the reference point.

[0015] Further, the analysis step includes inputting the calculated reflectance spectral data of the detection point and the reflectance spectral data of the reference point into the trained analytical object detection model, and outputting the analytical object information.

[0016] A detection system for an analyte according to the present invention includes:

[0017] an imaging module: irradiating a first region with broadband visible light or near-infrared light or visible-near-infrared light within a first wavelength range and imaging the first region to obtain a first image of the imaging region;

[0018] a spectrum acquisition module: acquiring color or grayscale distribution data reflecting the non-uniform distribution of the analyte in the imaging region from the first image; and acquiring reflectance spectral data reflecting the non-uniform distribution of the analyte in the imaging region at a desired location from the first image based on the color or grayscale distribution data;

[0019] an analysis module: obtaining information about the analyte in the imaging region based on the acquired reflectance spectral data, wherein the information about the analyte includes information relating the analyte to the reflectance spectral data.

[0020] Further, the spectrum acquisition module includes:

[0021] dividing the imaging area into a detection point candidate area and a reference point candidate area according to the color or 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 reflectance spectral data of the detection point and the reflectance spectral data of the reference point respectively.

[0022] Further, the spectrum acquisition module includes:

[0023] 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 from the detection point candidate area according to the grayscale value of pixels in the first image, selecting a pixel whose grayscale value is within a preset deviation range from the selected detection point as a reference point or a combination of the pixel and its multiple neighboring pixels as a reference point from the reference point candidate area, and calculating the reflectance spectral data of the detection point and the reflectance spectral data of the reference point.

[0024] Further, the analysis module includes inputting the calculated reflectance spectral data of the detection point and the reflectance spectral data of the reference point into the trained analyte detection model, and outputting the analyte information.

[0025] 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 analyte detection method are implemented.

[0026] According to the present invention, an electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when the computer program is executed by the processor, the steps of the analyte detection method are implemented.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] 1. The present application uses broadband visible light or near-infrared light or visible-near-infrared light to distinguish between areas where blood vessels are located and areas where non-blood vessels are located, and further enables accurate selection of detection points and reference points, making the detection results more accurate.

[0029] 2. The technical solution of this application does not require electrochemical reaction with the analyte, making the detection method more convenient, especially when detecting analytes in living organisms, achieving the purpose of non-invasive detection.

[0030] 3. This application utilizes the non-uniform distribution of the analyte in the imaging region to obtain spectral data from different regions. Since the distribution of other components besides the analyte in the imaging region is relatively uniform, the differences in spectral data from different regions can directly reflect the information related to the analyte and spectral data after basically excluding the influence of non-analytes, such as the concentration of the analyte.

[0031] 4. 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.

[0032] 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:

[0033] FIG1 is a flowchart of Embodiment 2;

[0034] FIG2 is a schematic diagram of the first image acquired in Embodiment 4;

[0035] FIG3 is a schematic diagram of the second image acquired in Embodiment 4;

[0036] FIG4 is a schematic diagram of the detection model of Embodiment 4;

[0037] FIG5 is a schematic diagram of the detection point-reference point spectral data obtained in Embodiment 4;

[0038] FIG6 is the experimental results of the accuracy of the analysis results of the analysis model;

[0039] FIG7 is a structural schematic diagram of an analyte detection device provided in Embodiment 7;

[0040] FIG8 is a structural schematic diagram of an electronic device provided in Embodiment 8;

[0041] FIG9 is a structural schematic diagram of an analyte detection watch provided in Embodiment 6;

[0042] Figure 10 is a schematic diagram of the back of the analytical substance detection watch;

[0043] Figure 11 is an exploded view of the analytical substance detection watch;

[0044] Figure 12 is a schematic diagram of the analytical substance detection watch in use.

[0045] In the figures:

[0046] 100: Imaging area; 200: Detection device;

[0047] 201: Light source; 202: Imaging spectral detection device;

[0048] 203: Controller; 204: First bandpass filter;

[0049] 205: Lens; 206: Second bandpass filter;

[0050] 207: Circuit board; 501: Processor;

[0051] 502: Memory. Detailed Description

[0052] 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, for those skilled in the art, several changes and improvements can be made without departing from the concept of the present invention. These all fall within the protection scope of the present invention.

[0053] Example 1

[0054] This example provides a method for detecting an analyte, including:

[0055] Imaging step: Irradiating a first region with broadband visible light or near-infrared light or visible-near-infrared light within a first wavelength range and imaging the first region to obtain a first image of the imaging region;

[0056] Spectral acquisition step: Acquiring color or grayscale distribution data reflecting the non-uniform distribution of the analyte in the imaging region from the first image; Based on the color or grayscale distribution data, acquiring reflectance spectral data reflecting the non-uniform distribution of the analyte in the imaging region at the desired location from the first image;

[0057] Analysis step: Obtaining information about the analyte in the imaging region based on the acquired reflectance spectral data, wherein the information about the analyte includes information related to the reflectance spectral data.

[0058] Example 2

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

[0060] Imaging step: Irradiating a first region with light within a preset wavelength range provided by a light source, and imaging the first region with an imaging spectral detection device to obtain an image of the imaging region. Irradiation with light within a preset wavelength range allows the image to 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. 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. The imaging region refers to the area within the lens range of the imaging spectral detection device. In general, the imaging region...The domain 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 a light with a larger wavelength range provided by one light source or light with a smaller wavelength range provided by two light sources. When there is one light source, the wavelength range of the light provided by the light source needs to cover both the wavelength range that can be used to obtain the distribution data of the analyte and the wavelength range that can be used to obtain the spectral data of the analyte. When there are two light sources, the two light sources provide different light, one light whose wavelength covers the wavelength range that can be used to obtain the distribution data of the analyte, and the other light whose wavelength covers the wavelength range that can be used to obtain 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 the human skin.

[0062] In this application, the analyte may 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, or troponin, or it may be a drug 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 may be monitored at the same or different times. In other embodiments, the analytes may 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 reflected or excitation signals generated by the analyte under light irradiation in the imaging area is acquired from the image using an imaging spectral detection device. Specifically, according to the distribution data on page 4 / 12 of the instruction manual (CN 121337334 A), the imaging area can be divided into partitions to facilitate the selection of locations from different partitions for obtaining spectral data.

[0064] Analysis steps: Based on the acquired spectral data, information about the analytes in the imaging area is obtained. The information about the analytes includes information related to the analytes and the spectral data. Since the distribution of analytes in different partitions is different, the reflected 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 can be used to irradiate skin areas with blood vessels and skin areas without blood vessels to obtain corresponding spectral data, or skin areas with thicker blood vessels and skin areas with thinner blood vessels.Even fine skin areas can yield corresponding spectral data. The difference between two spectral data can reflect information about 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 the analysis model.

[0065] Example 3

[0066] This example is based on Example 2, using broadband visible light or near-infrared light or visible-near-infrared light as the light source, and provides a method for detecting an analyte, including:

[0067] Imaging step: Irradiating a first region with broadband visible light or near-infrared light or visible-near-infrared light within a first wavelength range and imaging the first region to obtain a first image of the imaging region;

[0068] Spectral acquisition step: Acquiring color or grayscale distribution data reflecting the non-uniform distribution of the analyte in the imaging region from the first image; Based on the color or grayscale distribution data, acquiring reflectance spectral data reflecting the non-uniform distribution of the analyte in the imaging region at the desired location from the first image;

[0069] Analysis step: Obtaining information about the analyte in the imaging region based on the acquired reflectance spectral data, wherein the information about the analyte includes information related to the reflectance spectral data.

[0070] The spectral acquisition step includes:

[0071] Dividing the imaging area into a candidate detection point area and a candidate reference point area according to the color or grayscale distribution of the 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 reflectance spectral data of the detection point and the reflectance spectral data of the reference point respectively.

[0072] The spectral acquisition step includes:

[0073] Based on the grayscale value of the pixels in the first image, selecting a pixel whose grayscale value meets a preset requirement from the candidate detection point area as a detection point or a combination of the pixel and its neighboring pixels as a detection point, selecting a pixel whose grayscale value is within a preset deviation range from the selected detection point from the candidate reference point area as a reference point or a combination of the pixel and its multiple neighboring pixels as a reference point, and calculating the reflectance spectral data of the detection point and the reflectance spectral data of the reference point.

[0074] The analysis step includes inputting the calculated reflectance spectral data of the detection point and the reflectance spectral data of the reference point into a trained analyte detection model, and outputting the analyte information.

[0075] The imaging step includes: acquiring a first image under broadband visible light, near-infrared light, or visible-near-infrared light irradiation;

[0076] The first image includes data reflecting the color or grayscale distribution of the reflected signal generated by the analyte being irradiated by broadband visible light, near-infrared light, or visible-near-infrared light in the imaging area;

[0077] The spectral acquisition step includes: dividing the imaging area into a candidate region for detection points of a first color and a candidate region for reference points of a second color based on color or grayscale distribution data; or dividing the imaging area into a candidate region for detection points with smaller grayscale values ​​and a candidate region for reference points with larger grayscale values;

[0078] Selecting a pixel with a grayscale value that meets preset requirements from the first image based on the candidate region for detection points, or a combination of the pixel and its adjacent pixels as a detection point; selecting a pixel with a grayscale value within a preset deviation range from the grayscale value of the detection point from the first image based on the candidate region for reference points, or a combination of the pixel and multiple adjacent pixels as a reference point;

[0079] Substituting the grayscale value of the detection point in the first image into the spectral reconstruction algorithm to obtain the reflectance spectral data of the detection point; substituting the grayscale value of the reference point in the first image into the spectral reconstruction algorithm to obtain the reflectance spectral data of the reference point;

[0080] The analysis steps include: inputting the reflectance spectral data of the detection point and the reflectance spectral data of the reference point into the trained analyte detection model, and outputting information on the association between the analyte and the reflectance spectral data;

[0081] The method of training the analyte detection model includes: simultaneously acquiring the reflectance spectral data of the tested object and the accurate test result, using the reflectance spectral data as the input of the detection model and the accurate test result as the output of the detection model, and training the detection model.

[0082] Example 4

[0083] This example is based on Example 2, taking the detection of glucose in human blood vessels as an example, and provides a non-invasive glucose detection method, including:

[0084] Imaging step: irradiating 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 nanometers, and acquiring a first image of the imaging area, preferably in the near-infrared band. And irradiating the same location with ultraviolet light in the second wavelength range of 300-390 nanometers, and acquiring a second image of the imaging area.

[0085] As shown in Figure 2, the horizontal axis represents the horizontal coordinate of the first image, and the vertical axis represents 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, part of the infrared light penetrates the human skin and part is absorbed by the human skin. At the same time, it is also largely absorbed by the vein in the vein area. Therefore, the pixel gray value in the vein area is smaller, and the pixel gray value in the non-vein area is larger. This makes it easy to divide the imaging area into the vein area and the non-vein area.

[0086] As shown in Figure 3, the horizontal axis represents the horizontal coordinate of the second image, and the vertical axis represents the vertical coordinate of the second image.The white boxes represent the selected detection point pixels on the vein, and the black boxes represent the selected reference point pixels on the surrounding skin. In the second image, it is difficult to distinguish between areas containing veins and areas not containing veins; therefore, the first image is needed for differentiation. An excitation light in the 300-390 nm wavelength range 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 excitation light wavelength is less than 300 nm, the main peak of the excited fluorescence radiation signal is located in the <400 nm band, making it difficult for the imaging spectral detection device to obtain high-quality effective fluorescence spectral signals. When the excitation light wavelength is greater than 390 nm, the excitation light itself is also visible light, and the spectral signal of the excitation light is superimposed on the fluorescence spectral signal, making it difficult to eliminate the interference of the excitation light's spectral signal and extract the effective fluorescence spectral signal. After absorbing ultraviolet light in the wavelength range of 300-390 nm, glucose in veins 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.

[0087] Spectral acquisition steps: According to the gray-scale distribution of pixels in the first image, the imaging area is divided into the area where veins are located and the area where non-vein vessels are located. Detection points are selected from the positions in the second image corresponding to the areas where veins are located, and reference points are selected from the positions in the second image corresponding to the areas where non-vein vessels are located. The spectral data of the detection points and the spectral data of the reference points in the second image are acquired respectively. 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 region of the vein vessel as a detection point, or a combination of that pixel and its adjacent pixels as a detection point. A pixel with a grayscale value within a preset deviation range from the selected detection point is selected from the non-vein vessel region as a reference point, or a combination of that pixel and multiple adjacent pixels as a reference point. The fluorescence spectral data of the detection point and the fluorescence spectral data of the reference point 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 provides high spatial resolution, suitable for thinner vessels, but with a lower signal-to-noise ratio. The preset requirement for grayscale value can be to select the point with the smallest grayscale value as the detection point, but this application does not impose this limitation. The calculation results are shown in Figure 5, where the horizontal axis represents wavelength (unit: wavelength).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 gray value of the reference point and the gray 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 the gray value, these influencing factors can be effectively eliminated. In addition, the gray value and the gray value of the selected detection point are within the preset deviation range, which can ensure that the selection of the reference point is close to the detection point. For example, if it is selected at the edge of the vein, it can ensure that the color, thickness and other parameters of the epidermis, dermis and subcutaneous tissue are most similar, 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.

[0088] In addition to the spectral reconstruction algorithm, the spectral data can also be obtained by forming a radiometric calibration coefficient through the previous radiometric calibration, and the spectral line is obtained by calculating the gray value * the radiometric calibration coefficient.

[0089] When selecting a combination of multiple pixels at the detection point, the fluorescence spectral data of the detection point can be the average value 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 value of the fluorescence spectral data of all detection points and the average value of the fluorescence spectral data of all reference points can be calculated separately.

[0090] Analysis steps: After preprocessing, the acquired spectral data of the detection points and reference points are input into the trained detection model, outputting the glucose concentration or intermediate results relating glucose to the spectral data. 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.

[0091] 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.

[0092] In the convolutional neural network model, each convolutional kernel has a size of 1. The first convolutional layer has 32 kernels, and the second convolutional layer has 64 kernels, both used to extract blood glucose features. The output of the convolutional layer is non-linearly transformed by an activation function. The Flatten layer flattens the output of the convolutional layer into a one-dimensional vector, which facilitates connection to subsequent fully connected layers. The final output dimension is 1. During model training, 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.

[0093] When the output of the detection model is glucose concentration, if the error between the output and the measured standard glucose concentration value meets the preset condition, then training is stopped to obtain the detection model. When the output of the detection model is an intermediate result of glucose and spectral data association, such as the result of an intermediate neuron, if the error between the output and the result of the intermediate neuron meets the preset condition, then training is stopped to obtain the detection model. Further model correction processing is performed on the result of the intermediate neuron to obtain the glucose concentration. Specification 7 / 12 pages 10 CN 121337334 A

[0094] As shown in Figure 4, the Input layer is the spectral data input layer, which is obtained after preprocessing the original spectral data. The Hidden layer is the intermediate hidden layer, which performs deep learning through convolution operation, and outputs the final predicted blood glucose concentration value after feature combination. Alternatively, deep learning through convolution operation can be used to output a neuron Output1 as an intermediate result value after feature combination. The intermediate result value Output1 and two infrared IR feature brightness values ​​are 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 needed. 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.

[0095] Through multiple iterations of training, the neurons learn the corresponding change rules between different glucose concentrations and glucose spectral features of different samplers, thereby improving the universality of the detection model and enabling the prediction of glucose concentrations of different users.

[0096] The entire glucose detection process does not require puncturing the skin to collect blood or puncturing the skin for implantation. It obtains the spectral information of the subject based on fluorescence spectroscopy and obtains the glucose detection result of the 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 subsequent glucose signals. 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.

[0097] Figure 6 shows a schematic diagram of the experimental results of the trained detection model. The horizontal axis represents the reference blood glucose concentration (in mmol / L) collected by the blood glucose meter, and the vertical axis represents the blood glucose concentration (in mmol / L) predicted using the method of this patent. The total sample size of the test subjects was 2037, of which 1537 were training samples and 500 were prediction samples. As can be seen from the figure, the distribution of the detection results of the detection model shows that the MARD value of the predicted samples was 11.32%, and the vast majority of samples fell into regions A and B.Of these, 87.03% of the samples fell into area A and 12.77% fell into area B, indicating that the detection model has high accuracy.

[0098] Example 5

[0099] This example is based on Example 4, replacing infrared light with visible light to provide another non-invasive glucose detection method, including:

[0100] Imaging steps: Irradiating the skin at the location of the vein, such as the wrist or back of the hand, with visible light to acquire a first image of the imaging area. And irradiating the same location with ultraviolet light in the second wavelength range of 300-390 nm to acquire a second image of the imaging area.

[0101] In the first image, since the color of the area where the vein is located differs from that of the area where the non-vein is located, the imaging area can be easily divided into the area where the vein is located and the area where the non-vein is located.

[0102] In the second image, since it is difficult to distinguish the area where the vein is located and the area where the non-vein is located, the first image is needed for differentiation. The excitation light in the second wavelength range of 300-390 nm is used to obtain a high-quality effective fluorescence spectrum signal. 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 spectrum signal. When the wavelength of the excitation light used is greater than 390 nm, the excitation light itself is also visible light, and the spectral signal of the excitation light is superimposed on 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 300-390 nm wavelength range, glucose in venous blood vessels 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, exhibiting high fluorescence excitation efficiency.

[0103] Spectral acquisition steps: Based on the grayscale distribution of pixels in the first image, the imaging area is divided into areas where veins are located and areas where non-vein vessels are located. Detection points are selected from the areas where veins are located, and reference points are selected from the areas where non-vein vessels are 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 that meets a preset requirement or a combination of that pixel and its adjacent pixels is selected from the areas 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 multiple adjacent pixels is selected from the areas where non-vein vessels are located as a reference point.The fluorescence spectral data of the detection point and the fluorescence spectral data of the reference point are calculated. The reason why the gray value of the reference point is within a preset deviation range from the gray value of the selected detection point is that the skin in the imaging area may have influencing factors such as skin color, pigmentation, and cosmetics, which will directly affect the spectral data of the reference point. The first image cannot simultaneously distinguish all influencing factors. By setting a preset deviation range for the gray value, these influencing factors can be effectively eliminated.

[0104] 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 value of the fluorescence spectral data of all detection points and the average value of the fluorescence spectral data of all reference points can be calculated separately.

[0105] Analysis steps: After preprocessing, the obtained spectral data of the detection points and reference points are input into the trained detection model, and the glucose concentration is output. 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 of the detection model, and the blood test results are used as the output of the detection model to train the detection model.

[0106] 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.

[0107] In the convolutional neural network model, the kernel size of each convolutional layer is 1. The number of convolutional kernels in the first convolutional layer is 32, and the number of convolutional 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. The Adam optimizer is used for model training during the model training process, and the mean squared error is used as the loss function. The mean absolute error is calculated as the performance metric for model evaluation.

[0108] If the error between the output of the detection model and the standard glucose value meets the preset conditions, training is stopped to obtain the detection model.

[0109] The training degree 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 and the standard glucose value of the above label value meets the requirements, at which point training is stopped to obtain the detection model.

[0110] Through multiple iterative training, the neurons learn the corresponding change patterns between different glucose concentrations and the 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.

[0111] The entire glucose detection process does not require blood collection or skin puncture. It obtains the spectral information of the subject based on fluorescence spectroscopy and obtains the glucose detection result of the 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 the accurate measurement of glucose concentration (see page 9 / 12 of CN 121337334 A). The detection results are more accurate and the processing is more convenient.

[0112] Example 6

[0113] This example 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:

[0114] Imaging module: The light source provides light within a preset wavelength range to illuminate the first area, and the imaging spectral detection device images the first area to obtain an image of the imaging area. By illuminating the image with light within a preset wavelength range, the image can reflect the distribution and spectral data of the reflected or excitation signals generated by the analyte under light illumination within the imaging area. Since different wavelength ranges are required to obtain the analyte distribution and spectral data, the light source can be two corresponding wavelength ranges, or a single light source with a larger wavelength range covering both required wavelengths. When two types of light are used, two images are obtained. For ease of processing, it is usually required that the imaging areas of the two images are identical.

[0115] In this application, the analyte may 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 analytes may be monitored at the same or different times. In other embodiments, the analyte may also be other substances in a liquid.

[0116] Spectral acquisition module: Acquires spectral data from an image using an imaging spectral detection device, reflecting the uneven distribution of the reflected or excitation signals generated by the analyte under light irradiation in the imaging area. Specifically, the imaging area can be divided into partitions based on different data distributions, so that the location for obtaining spectral data can be selected from different partitions.

[0117] Analysis Module: Based on the acquired spectral data, information about the analytes in the imaging region is obtained. The information about the analytes includes information related to the analytes and the spectral data. Since the distribution of analytes in different zones is different, the reflected or excitation signals generated by the analytes when irradiated by light will also be different. Utilizing this characteristic, the difference in spectral data between the two can be obtained, thereby accurately reflecting the information related to the analytes and the spectral data, such as the concentration of the analytes.

[0118] Those skilled in the art know that, in addition to implementing the system and its various devices, modules, and units provided by the present invention in a purely computer-readable program code manner, the same functions can be achieved by logically programming the method steps, making the system and its various devices, modules, and units provided by the present invention implement the same functions 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 the present invention can be considered as 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 for implementing the method and structures within the hardware component.

[0119] Example 7

[0120] 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. Specification 10 / 12 pages 13 CN 121337334 A

[0121] 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.

[0122] The light source 201 is capable of providing light within a preset wavelength range. Since obtaining analyte distribution and spectral data requires illumination with light of different wavelength ranges, there are two possible implementation methods: one light source that provides a wide range of wavelengths; or two light sources that each provide a narrower range of wavelengths. When using only one light source, the wavelength range of the light provided must simultaneously cover the wavelength range required to obtain analyte distribution data and the wavelength range required to obtain analyte spectral data, such as a halogen lamp. When using two light sources, the two sources provide different wavelengths, one covering the wavelength range required to obtain analyte distribution data and the other covering the wavelength range required to obtain analyte spectral data, such as an infrared lamp combined with an ultraviolet lamp, or a visible light lamp combined with an ultraviolet lamp.

[0123] To ensure uniform illumination of the imaging area 100, a ring light source can be used. The light source has multiple light-emitting modules, which are 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.

[0124] The imaging spectral detection device 202 can image the imaging area 100 according to instructions to obtain the corresponding image, and can obtain the 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 perform spectral modulation on the incoming light signal so that the sensor can generate an image containing the spectral information to be measured.

[0125] 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 evenly arranged. Their length and width are integer multiples of the pixel size in the image sensor. The filter pixel channels with different shapes correspond to different spectral filtering coefficients. 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 a periodic pixel-level filter structure set on its surface to form a mosaic image containing spectral information. Then, an algorithm is used to reconstruct a grayscale image containing the spectral information to be measured.

[0126] 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, obtaining 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 in the imaging region. Information about the analyte in the imaging region is obtained based on the acquired spectral data. This information includes information related to the analyte and the spectral data. When there is 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.

[0127] 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.

[0128] 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 when illuminated, while blocking light in other wavelength ranges, thereby reducing the influence of the reflected signal or excitation signal of non-analytes on the detection results.

[0129] 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 lens 205 away from imaging spectral detection device 202, and the present invention does not limit this.

[0130] According to the above description, FIG9 shows an analytical substance detection watch provided in this embodiment. The front of the watch is a display, as shown in FIG10, and the back of the watch has a light-transmitting window, which houses the detection device 200. As shown in FIG11, the light source 201 and the first bandpass filter 204 are both annular structures. The light-emitting modules of the light source 201 are arranged in a ring, and the emitted light is filtered by the first bandpass filter 204 and outputs light with the required wavelength, which is irradiated onto the human body through the light-transmitting window on the back of the watch. The reflected signal or excitation signal of the human body enters the light-transmitting window, passes through the hollow part in the middle of the light source 201 and the first bandpass filter 204, passes through lens 205 and enters the second bandpass filter 206, and enters the imaging spectral detection device 202 after being filtered by the second bandpass filter 206. The imaging spectral detection device 202 is mounted on the circuit board 207, and 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.

[0131] Embodiment 8

[0132] 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 detection method of analytes.

[0133] Wherein, the memory 502 and the processor 501 are connected by a bus, and 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 will not be described further in this invention. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, 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.

[0134] Processor 501 is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interface, voltage regulation, power management, and other control functions. Memory 502 can be used to store data used by processor 501 during operation.

[0135] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-described method for detecting the analyte.

[0136] 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 various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0137] Those skilled in the art will understand that the above embodiments are specific embodiments of the present invention, and 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.

[0138] 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, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features described in these embodiments can be arbitrarily combined with each other. Instruction Manual 12 / 12 Page 15 CN 121337334 A Figure 1 Figure 2 Instruction Manual Drawings 1 / 6 Page 16 CN 121337334 A Figure 3 Figure 4 Instruction Manual Drawings 2 / 6 Page 17 CN 121337334 A Figure 5 Figure 6 Instruction Manual Drawings 3 / 6 Page 18 CN 121337334 A Figure 7 Figure 8 Instruction Manual Drawings 4 / 6 Page 19 CN 121337334 A Figure 9 Figure 10 Instruction Manual Drawings 5 / 6 Page 20 CN 121337334 A Figure 11 Figure 12 Instruction Manual Drawings 6 / 6 Page 21 CN 121337334 A Abstract The present invention provides a method and a system for testing an analyte, a medium, and a device, including: irradiation: irradiating a first area by broad-spectrum visible light, infraredlight, or visible-near-infrared light within a first wavelength range, and imaging the first area, to obtain an image of an imaging area; spectral obtaining: obtaining, from the first image, color or grayscale distribution data that demonstrates uneven distribution of the analyte in the imaging area; and based on the color or grayscale distribution data, obtaining, at a required position from the first image, reflection spectral data that demonstrates uneven distribution of the analyte in the imaging area; and analysis: obtaining information about the analyte in the imaging area based on the obtained reflection spectral data. According to this application, distinction between an area at which a blood vessel is located and an area at which a blood vessel is not located can be implemented through the broad-spectrum visible light, infrared light, or visible-near-infrared light, which further implements accurate selection of a testing point and reference point, and achieve a more accuratetest result.

Claims

1. A method of detecting an analyte, characterized by, The method comprises: an imaging step of irradiating a first region with broad spectrum visible light or near infrared light or visible-near infrared light in a first wavelength range and imaging the first region to obtain a first image of the imaged region; a spectrum acquisition step of acquiring color or grayscale distribution data reflecting non-uniform distribution of an analyte in the imaged region from the first image, and acquiring reflection spectrum data reflecting non-uniform distribution of the analyte in the imaged region at a required position from the first image according to the color or grayscale distribution data; an analysis step of obtaining information of the analyte in the imaged region according to the acquired reflection spectrum data, wherein the information of the analyte comprises information associated with the reflection spectrum data.

2. The method of claim 1, wherein The spectrum acquisition step comprises: dividing the imaged region into a detection point candidate region and a reference point candidate region according to color or grayscale distribution of pixel points in the first image, selecting a detection point from a position of the first image corresponding to the detection point candidate region, selecting a reference point from a position of the first image corresponding to the reference point candidate region, and respectively acquiring reflection spectrum data of the detection point and reflection spectrum data of the reference point.

3. The method of claim 2, wherein the analyte is a protein. The spectrum acquisition step comprises: selecting a pixel point with a grayscale value meeting a preset requirement as a detection point or a combination of the pixel point and adjacent pixel points as a detection point from a position of the first image corresponding to the detection point candidate region, selecting a pixel point with a grayscale value within a preset deviation range from the grayscale value of the selected detection point as a reference point or a combination of the pixel point and multiple adjacent pixel points as a reference point from a position of the first image corresponding to the reference point candidate region, and calculating reflection spectrum data of the detection point and reflection spectrum data of the reference point.

4. The method of claim 3, wherein the analyte is a protein. The analysis step comprises inputting the calculated reflection spectrum data of the detection point and the reflection spectrum data of the reference point into a trained analyte detection model, and outputting information of the analyte.

5. A system for detecting an analyte, characterized by, The method comprises: an imaging module of irradiating a first region with broad spectrum visible light or near infrared light or visible-near infrared light in a first wavelength range and imaging the first region to obtain a first image of the imaged region; a spectrum acquisition module of acquiring color or grayscale distribution data reflecting non-uniform distribution of an analyte in the imaged region from the first image, and acquiring reflection spectrum data reflecting non-uniform distribution of the analyte in the imaged region at a required position from the first image according to the color or grayscale distribution data; an analysis module of obtaining information of the analyte in the imaged region according to the acquired reflection spectrum data, wherein the information of the analyte comprises information associated with the reflection spectrum data.

6. The analyte detection system of claim 5, wherein The spectrum acquisition module comprises: dividing the imaged region into a detection point candidate region and a reference point candidate region according to color or grayscale distribution of pixel points in the first image, selecting a detection point from a position of the first image corresponding to the detection point candidate region, selecting a reference point from a position of the first image corresponding to the reference point candidate region, and respectively acquiring reflection spectrum data of the detection point and reflection spectrum data of the reference point.

7. The system for detecting an analyte according to claim 6, wherein The spectrum acquisition module comprises: According to a gray value of a pixel in the first image, a pixel with a gray value meeting a preset requirement is selected as a detection point or a combination of the pixel and adjacent pixels as the detection point from a position of a detection point candidate region corresponding to the first image, a pixel with a gray value within a preset deviation range of the selected detection point is selected as a reference point or a combination of the pixel and multiple adjacent pixels as the reference point from a position of a reference point candidate region corresponding to the first image, and reflection spectrum data of the detection point and reflection spectrum data of the reference point are calculated.

8. The system for detecting an analyte according to claim 7, wherein The analysis module comprises inputting the calculated reflection spectrum data of the detection point and the reflection spectrum data of the reference point into a trained analyte detection model, and outputting information of an analyte.

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

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