A method, a system, a medium, and a device for testing an analyte

Abstract

The invention provides an analyte detection method and system, a medium and equipment, and belongs to the field of optical analys.The method comprises the steps that imaging is conducted, specifically, a first area is irradiated through light within a preset wavelength range, imaging is conducted on the first area, and an image of an imaging area is obtained; a spectrum acquisition step: acquiring spectrum data reflecting non-uniform distribution of reflected signals or excitation signals generated by the analyte under light irradiation in an imaging area from the image; and an analysis step: obtaining the information of the analyte in the imaging area according to the obtained spectral data, wherein the information of the analyte comprises the information of the analyte associated with the spectral data. The spectral data of different areas are obtained by utilizing the characteristic that the analyte is not uniformly distributed in the imaging area, and since the distribution of other components except the analyte in the imaging area is relatively uniform, the spectral data difference of different areas can directly reflect the information associated with the analyte and the spectral data after the influence of non-analyte is eliminated.

Description

(19) State Intellectual Property Office (12) Invention Patent Application (10) Application Publication Number (43) Application Publication Date (21) Application Number 202410951440.9 (22) Application Date 2024.07.16 (71) Applicant Guangzhou Ruixin Microelectronics Co., Ltd. Address 510535, Room 1703, No. 188, Kaitai Avenue, Huangpu District, Guangzhou, Guangdong Province (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) 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, belonging to the field of optical analysis. The method includes: an imaging step: irradiating a first region with light within a preset wavelength range and imaging the first region to obtain an image of the imaging region; a spectral acquisition step: acquiring spectral data from the image that reflects the uneven distribution of the reflection signal or excitation signal generated by the analyte under light irradiation in the imaging region; and an analysis step: obtaining information about the analyte in the imaging region based on the acquired spectral data, wherein the information about the analyte includes information related to the analyte and the spectral data. This application utilizes the characteristic of uneven distribution of analytes in the imaging region to obtain spectral data of different regions. Since the distribution of other components besides the analyte in the imaging region is relatively uniform, the difference in spectral data of different regions can directly reflect the information related to the analyte and the spectral data after excluding the influence of non-analytes. Claims 3 pages, Description 13 pages, Drawings 6 pages, CN 121337329 A 2026.01.16 CN 1 21 33 73 29 A 1. A method for detecting an analyte, characterized in that it comprises: an imaging step: illuminating a first region with light within a preset wavelength range and imaging the first region to obtain an image of the imaging region; a spectral acquisition step: acquiring spectral data from the image reflecting the uneven distribution of reflection or excitation signals generated by the analyte under light irradiation in the imaging region; an analysis step: obtaining information about the analyte in the imaging region based on the acquired spectral data, wherein the information about the analyte includes information relating the analyte to the spectral data. 2. The method for detecting an analyte according to claim 1, characterized in that the imaging step comprises: acquiring a first image under illumination of light within a first wavelength range, and acquiring a second image under illumination of light within a second wavelength range;The first image includes data reflecting the distribution of reflected or excited signals generated by the analyte under light illumination in the imaging area; the second image includes spectral data reflecting the reflected or excited signals generated by the analyte under light illumination in the imaging area; the spectral acquisition step includes: acquiring spectral data of the desired location from the second image based on the distribution data. 3. The analyte detection method according to claim 2, wherein the light in the first wavelength range includes infrared light, and the spectral acquisition step includes: dividing the imaging area into a candidate detection point area and a candidate reference point area based on the grayscale distribution of pixels in the first image, selecting a detection point from the position of the candidate detection point area corresponding to the position of the second image, selecting a reference point from the position of the candidate reference point area corresponding to the position of the second image, and acquiring the spectral data of the detection point and the spectral data of the reference point from the second image respectively. 4. The method for detecting an analyte according to claim 3, wherein the light in the second wavelength range includes ultraviolet light, and the spectral acquisition step includes: selecting a pixel whose gray value meets a preset requirement from the position of the candidate detection point region corresponding to the position of the second image, or a combination of the pixel and its adjacent pixels, as a detection point based on the gray value of the pixels in the second image; selecting a pixel from the position of the candidate reference point region corresponding to the position of the second image, or a combination of the pixel and its multiple adjacent pixels, as a reference point; and calculating the fluorescence spectral data of the detection point and the fluorescence spectral data of the reference point. 5. The method for detecting an analyte according to claim 4, 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 cause 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. 6. The method for detecting an analyte according to claim 1, wherein the analysis step includes inputting the acquired spectral data into a trained analyte detection model and outputting information about the analyte. 7. The method for detecting an analyte according to claim 1, characterized in that the imaging step includes: acquiring a first image under infrared light irradiation in the wavelength range of 800-1000 nanometers, and acquiring a second image under ultraviolet light irradiation in the wavelength range of 300-390 nanometers; the first image includes grayscale distribution data reflecting 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.The spectral acquisition step includes: dividing the imaging area into candidate regions for detection points with smaller gray values ​​and candidate regions for reference points with larger gray values ​​based on gray-level distribution data; selecting a pixel in the second image whose gray value meets preset requirements as a detection point or a combination of that pixel and its neighboring pixels as a detection point based on the candidate regions for detection points; selecting a pixel in the second image as a reference point or a combination of that pixel and multiple neighboring pixels as a reference point based on the candidate regions for reference points; substituting the gray values ​​of the detection points in the second image into the spectral reconstruction algorithm to obtain the spectral data of the detection points; substituting the gray values ​​of the reference points in the second image into the spectral reconstruction algorithm to obtain the spectral data of the reference points; the analysis step involves inputting the spectral data of the detection points and the spectral data of the reference points into a trained analyte detection model, and outputting information on the association between the analyte and the spectral data; the method of training the analyte detection model includes: simultaneously acquiring the spectral data of the tested object and the accurate test results; using the spectral data as the input of the detection model and the accurate test results as the output of the detection model to train the detection model. 8. A detection system for an analyte, characterized in that it comprises: an imaging module: irradiating a first region with light within a preset wavelength range and imaging the first region to obtain an image of the imaging region; a spectral acquisition module: acquiring spectral data from the image reflecting the non-uniform distribution of reflection or excitation signals generated by the analyte under light irradiation in the imaging region; and an analysis module: obtaining information about the analyte in the imaging region based on the acquired spectral data, wherein the information about the analyte includes information relating the analyte to the spectral data. 9. The detection system for an analyte according to claim 8, characterized in that the imaging module comprises: acquiring a first image under light irradiation within a first wavelength range, and acquiring a second image under light irradiation within a second wavelength range; the first image includes data reflecting the distribution of reflection or excitation signals generated by the analyte under light irradiation in the imaging region; the first image includes spectral data reflecting the reflection or excitation signals generated by the analyte under light irradiation in the imaging region; and the spectral acquisition module includes: acquiring spectral data at a desired location from the second image based on the distribution data. 10. The analyte detection system according to claim 9, wherein the light in the first wavelength range includes infrared light, and the spectral acquisition module comprises: 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 position of the candidate detection point area corresponding to the position of the second image; selecting a reference point from the position of the candidate reference point area corresponding to the position of the second image; and acquiring the spectral data of the detection point and the spectral data of the reference point from the second image respectively.11. The analyte detection system according to claim 10, wherein the light in the second wavelength range includes ultraviolet light, and the spectral acquisition module comprises: (Claims 2 / 3, page 3, CN 121337329 A) Based on the grayscale value of pixels in the second image, selecting a pixel whose grayscale value meets a preset requirement from the position of the candidate detection point corresponding to the second image as a detection point, or a combination of the pixel and adjacent pixels as a detection point; selecting a pixel from the position of the candidate reference point corresponding to the second image as a reference point, or a combination of the pixel and multiple adjacent pixels as a reference point; and calculating the fluorescence spectral data of the detection point and the fluorescence spectral data of the reference point. 12. The analyte detection system according to claim 11, 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 cause 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. 13. The analyte detection system according to claim 8, wherein the analysis module includes inputting acquired spectral data into a trained analyte detection model and outputting analyte information. 14. The analyte detection system according to claim 8, characterized in that the imaging module comprises: acquiring a first image under infrared light irradiation in the wavelength range of 800-1000 nanometers, and acquiring a second image under ultraviolet light irradiation in the wavelength range of 300-390 nanometers; the first image includes grayscale distribution data reflecting the reflection signal generated by the analyte under infrared light irradiation in the imaging area; the second image includes spectral data reflecting the fluorescence radiation signal excited by the analyte under ultraviolet light irradiation in the imaging area; the spectral acquisition module comprises: 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 ​​according to the grayscale distribution data; selecting a pixel with a grayscale value that meets a preset requirement as a detection point or a combination of the pixel and adjacent pixels as a detection point from the second image according to the candidate region for detection points; and selecting a pixel as a reference point or a combination of the pixel and multiple adjacent pixels as a reference point from the second image according to the candidate region for reference points. The gray values ​​of the detection points in the second image are substituted into the spectral reconstruction algorithm to obtain the spectral data of the detection points. Similarly, the gray values ​​of the reference points in the second image are substituted into the spectral reconstruction algorithm to obtain the spectral data of the reference points. The analysis module inputs the spectral data of the detection points and the spectral data of the reference points into the trained analyte detection model and outputs information about the association between the analyte and the spectral data. The methods for training the analyte detection model include simultaneously acquiring the spectral data of the tested object and the accurate test results.The detection model is trained by using spectral data as input and accurate test results as output. 15. 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 7. 16. An electronic device, comprising a memory, a processor, and a computer program stored on 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 7. Claims 3 / 3 Page 4 CN 121337329 A Method, System, Medium and Device for Detection of 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 in the subcutaneous tissue, thereby obtaining the glucose level. The advantage of this approach is that glucose data can be collected in real time as needed throughout the day without the need for multiple punctures to collect blood, which greatly facilitates the user. However, its disadvantage is that it is an invasive method for the human body, implanting the sensor under the skin. Patent document US20160287147A1 discloses a device for non-invasive in vivo measurement using Raman spectroscopy, which uses Raman spectroscopy to measure in vivo blood glucose concentration. The advantage of this approach is that it has higher accuracy compared with the electrochemical method of US20100065441A1, but 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, which realizes non-invasive detection. This document uses absorption spectroscopy, and the collected and analyzed spectral signals include not only blood glucose signals but also spectral signals from components such as skin tissue. The overlapping of spectral signals of different wavelengths makes it difficult to accurately separate and extract blood glucose-related spectral signals. Furthermore, factors such as the excitation light source, skin color, and epidermal thickness can also affect the intensity of the spectral signals, resulting in variations in intensity. Consequently, the 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 suffers from the same problem.

[0004] Products using electrochemical methods for detection have been developed for over 10 years, while Raman spectroscopy remains difficult to industrialize due to its lack of portability and real-time detection capabilities, unlike patent document US20100065441A1.

[0005] Patent document CN117503123A discloses a non-invasive blood glucose detection system and method based on multi-wavelength near-infrared spectroscopy, employing multiple sensors and modules to simultaneously acquire various biological signals such as fingertip infrared information, facial infrared information, and forehead temperature information, and then integrating these multiple information signals for analysis and judgment. Its drawback is that it requires the collection of too many biological signals at different locations, and the blood glucose concentration varies in different body parts, resulting in high costs and an inability to achieve portable real-time detection. Furthermore, the excessive information leads to complex detection algorithms. 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 light within a preset wavelength range and imaging the first region to obtain an image of the imaging region;

[0009] a spectral acquisition step: acquiring spectral data from the image that reflects the non-uniform distribution of the reflection signal or excitation signal generated by the analyte under light irradiation in the imaging region;

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

[0011] Further, the imaging step includes: acquiring a first image under illumination of light within a first wavelength range, and acquiring a second image under illumination of light within a second wavelength range;

[0012] The first image includes data reflecting the distribution of reflection signals or excitation signals generated by the analyte under illumination in the imaging area;

[0013] The second image includes spectral data reflecting the reflection signals or excitation signals generated by the analyte under illumination in the imaging area;

[0014] The spectral acquisition step includes: acquiring spectral data of a desired location from the second image based on the distribution data.

[0015] Further, the light within the first wavelength range includes infrared light, and the spectral acquisition step includes:

[0016] dividing the imaging area into a candidate detection point area and a candidate reference point area based on 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.

[0017] Further, the light within the second wavelength range includes ultraviolet light, and the spectral acquisition step includes:

[0018] Based on the grayscale value of the pixels in the second image, a pixel whose grayscale value meets the preset requirements is selected from the candidate detection point region as a detection point, or a combination of the pixel and its neighboring pixels is selected as a detection point. A pixel is selected from the candidate reference point region as a reference point, or a combination of the pixel and its multiple neighboring pixels is selected as a reference point. The fluorescence spectral data of the detection point and the fluorescence spectral data of the reference point are calculated.

[0019] Further, the second wavelength range is 300-390 nanometers, which is outside the effective response range of the imaging spectral detection device.

[0020] 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.

[0021] Further, the analysis step includes inputting the acquired spectral data into the trained analyte detection model after preprocessing, and outputting the information of the analyte.

[0022] Further, the imaging step includes: acquiring a first image under infrared light irradiation in the wavelength range of 800-1000 nanometers, and acquiring a second image under ultraviolet light irradiation in the wavelength range of 300-390 nanometers;

[0023] The first image includes grayscale distribution data reflecting the reflected signal generated by the analyte under infrared light irradiation in the imaging area;

[0024] The second image includes spectral data reflecting the fluorescence radiation signal excited by the analyte under ultraviolet light irradiation in the imaging area;

[0025] The spectral acquisition step includes: 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 ​​according to the grayscale distribution data; selecting a pixel with a grayscale value that meets a preset requirement from the second image as a detection point or a combination of the pixel and adjacent pixels as a detection point according to the candidate region for detection points; selecting a pixel from the second image as a reference point or a combination of the pixel and multiple adjacent pixels as a reference point according to the candidate region for reference points;

[0026] Substitute the gray values ​​of the detection points in the second image into the spectral reconstruction algorithm to obtain the spectral data of the detection points. Substitute the gray values ​​of the reference points in the second image into the spectral reconstruction algorithm to obtain the spectral data of the reference points.

[0027] Analysis steps: After preprocessing the spectral data of the detection points and the spectral data of the reference points, input them into the trained analyte detection model, and output the information related to the analyte and the spectral data.

[0028] The method of training the analyte detection model includes: simultaneously acquiring the spectral data of the tested object and the accurate test results, using the preprocessed spectral data as the input of the detection model and the accurate test results as the output of the detection model, and training the detection model.

[0029] According to the present invention, an analyte detection system includes:

[0030] an imaging module: irradiating a first region with light within a preset wavelength range and imaging the first region to obtain an image of the imaging region;

[0031] a spectral acquisition module: acquiring spectral data from the image reflecting the uneven distribution of reflection or excitation signals generated by the analyte under light irradiation in the imaging region;

[0032] an analysis module: obtaining information about the analyte in the imaging region based on the acquired spectral data, wherein the information about the analyte includes information related to the spectral data.

[0033] Further, the imaging module includes: acquiring a first image under light irradiation within a first wavelength range, and acquiring a second image under light irradiation within a second wavelength range;

[0034] the first image includes data reflecting the distribution of reflection or excitation signals generated by the analyte under light irradiation in the imaging region;

[0035] the first image includes spectral data reflecting the reflection or excitation signals generated by the analyte under light irradiation in the imaging region;

[0036] the spectral acquisition module includes: acquiring spectral data at a desired location from the second image based on the distribution data.

[0037] Further, the light in the first wavelength range includes infrared light, and the spectrum acquisition module includes:

[0038] 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.

[0039] Further, the light in the second wavelength range includes ultraviolet light, and the spectrum acquisition module includes:

[0040] 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 detection point candidate area, and calculating the fluorescence spectral data of the detection point and the fluorescence spectral data of the reference point.

[0041] Further, the second wavelength range is 300-390 nanometers, which is outside the effective response range of the imaging spectral detection device;

[0042] 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.

[0043] Further, the analysis module includes preprocessing the acquired spectral data and inputting it into a trained analyte detection model, outputting information about the analyte.

[0044] Further, the imaging module includes: acquiring a first image under infrared light irradiation in the wavelength range of 800-1000 nanometers, and acquiring a second image under ultraviolet light irradiation in the wavelength range of 300-390 nanometers;

[0045] The first image includes gray-scale distribution data reflecting the reflected signal generated by the analyte under infrared light irradiation in the imaging area; Specification 3 / 13 Page 7 CN 121337329 A

[0046] The second image includes spectral data reflecting the fluorescence radiation signal excited by the analyte under ultraviolet light irradiation in the imaging area;

[0047] The spectral acquisition module includes: dividing the imaging area into a candidate region for detection points with smaller gray-scale values ​​and a candidate region for reference points with larger gray-scale values ​​according to the gray-scale distribution data; selecting a pixel point whose gray-scale value meets the preset requirements from the second image as a detection point or a combination of the pixel point and adjacent pixels as a detection point according to the candidate region for detection points; selecting a pixel point from the second image as a reference point or a combination of the pixel point and multiple adjacent pixels as a reference point according to the candidate region for reference points;

[0048] Substitute the gray values ​​of the detection points in the second image into the spectral reconstruction algorithm to obtain the spectral data of the detection points, and substitute the gray values ​​of the reference points in the second image into the spectral reconstruction algorithm to obtain the spectral data of the reference points;

[0049] Analysis module: Input the spectral data of the detection points and the spectral data of the reference points into the trained analyte detection model after preprocessing, and output the information related to the analyte and the spectral data;

[0050] The method of training the analyte detection model includes: simultaneously acquiring the spectral data of the tested object and the accurate test result, using the preprocessed 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.

[0051] 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.

[0052] 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 analyte detection method are implemented.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] 1. 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, it can achieve the purpose of non-invasive detection.

[0055] 2. By utilizing the non-uniform distribution of the analyte in the imaging region, this application can obtain spectral data of different regions. Since the distribution of other components besides the analyte in the imaging region is relatively uniform, the spectral data of different regions...The difference can directly reflect the information of the correlation between the analyte and the spectral data after the influence of non-analytes has been basically excluded, such as the concentration of the analyte.

[0056] 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.

[0057] 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:

[0058] FIG1 is a flowchart of Embodiment 1;

[0059] FIG2 is a schematic diagram of the first image acquired in Embodiment 2;

[0060] FIG3 is a schematic diagram of the second image acquired in Embodiment 2;

[0061] FIG4 is a schematic diagram of the detection model of Embodiment 2;

[0062] FIG5 is a schematic diagram of the detection point-reference point spectral data obtained in Embodiment 2;

[0063] FIG6 is the experimental results of the accuracy of the analysis results of the analysis model;

[0064] FIG7 is a structural schematic diagram of an analyte detection device provided in Embodiment 5; Specification 4 / 13 pages 8 CN 121337329 A

[0065] FIG8 is a structural schematic diagram of an electronic device provided in Embodiment 6;

[0066] FIG9 is a structural schematic diagram of an analyte detection watch provided in Embodiment 5;

[0067] FIG10 is a schematic diagram of the back of the analyte detection watch;

[0068] Figure 11 is an exploded view of the analyte detection watch;

[0069] Figure 12 is a schematic diagram of the analyte detection watch in use.

[0070] In the figures:

[0071] 100: Imaging area; 200: Detection device;

[0072] 201: Light source; 202: Imaging spectral detection device;

[0073] 203: Controller; 204: First bandpass filter;

[0074] 205: Lens; 206: Second bandpass filter;

[0075] 207: Circuit board; 501: Processor;

[0076] 502: Memory. Detailed Description

[0077] 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.

[0078] Example 1

[0079] Figure 1 is a flowchart of this embodiment. This embodiment provides a method for detecting an analyte, including:

[0080] Imaging step: Irradiating a first region with light within a preset wavelength range provided by a light source, and imaging the first region using an imaging spectral detection device to obtain an image of the imaging region. Irradiation with light within a preset wavelength range allows...The image can reflect the distribution data and spectral data of the reflected or excitation signals generated by the analyte under light irradiation in the imaging area. The first area can be a region 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 area. The imaging area refers to the area within the lens range of the imaging spectral detection device. In general, the imaging area can be a part of the first area or the same area as the first area.

[0081] Since different wavelength ranges of light are needed to irradiate the analyte to obtain the distribution data and spectral data, there are two ways to achieve this: the light is a light with a large wavelength range provided by one light source or light with a small 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 and the wavelength range that can be used to obtain the spectral data. 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 and the other light whose wavelength covers the wavelength range that can be used to obtain the spectral data. Meanwhile, when there is only one light source, the image is one; when there are two light sources, the image is two. For ease of processing, the imaging areas of the two images must be the same, that is, the acquisition window of the imaging spectral detection device must not move on the surface of the human skin.

[0082] In this application, the analytes 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 hormone, or troponin, 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 analyte can also be other substances within the body surface, enabling non-invasive detection through this invention.

[0083] Spectral acquisition step: Spectral data reflecting the uneven distribution of reflection 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, the imaging area can be partitioned according to different distribution data to facilitate the selection of locations from different partitions for acquiring spectral data.

[0084] Analysis step: Information about the analyte in the imaging area is obtained based on the acquired spectral data, including the analyte information package.This includes information related to the analyte and spectral data. Since the distribution of analytes in different regions is different, the reflected or excitation signals generated by the analyte when exposed to 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 to obtain corresponding spectral data. The difference between the two spectral data can reflect the information related to the analyte in the blood vessels and the spectral data, such as the degree of influence of the analyte on the spectral data, etc., for further analysis, or directly obtain information such as the concentration of the analyte through the analysis model.

[0085] Example 2

[0086] 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:

[0087] 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 nanometers, and acquire the first image of the imaging area. The first wavelength range is preferably the near-infrared band. And by irradiating the same location with ultraviolet light in the second wavelength range of 300-390 nanometers, a second image of the imaging area is acquired.

[0088] 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, part of the infrared light will penetrate the human skin and part will be absorbed by the human skin. At the same time, it will also be absorbed in large quantities by the vein in the vein area. Therefore, the pixel gray value in the vein area is small and the pixel gray value in the non-vein area is large. In this way, the imaging area can be easily divided into the vein area and the non-vein area.

[0089] As shown in Figure 3, the horizontal axis is the horizontal coordinate of the second image, the vertical axis is the vertical coordinate of the second 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 second image, it is difficult to distinguish between areas containing veins and areas not containing veins; therefore, the first image is needed for differentiation. Using excitation light in the second wavelength range of 300-390 nm is 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 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 wavelength of the excitation light used is >At 390nm, 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 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.

[0090] Spectral acquisition steps: According to the gray 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 where the vein is located as a detection point, or a combination of that pixel and its neighboring pixels is selected as a detection point. A pixel with a grayscale value within a preset deviation range from the selected detection point is selected from the region where the vein is not located as a reference point, or a combination of that pixel and multiple neighboring pixels is selected as a reference point. The fluorescence spectral data of the detection point and the fluorescence spectral data of the reference point are 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 width of the blood vessel. Averaging multiple pixels can improve the signal-to-noise ratio, but is limited by the width of the blood vessel, avoiding the capture of areas outside the blood vessel. Selecting a single pixel has high spatial resolution and is suitable for thinner blood vessels, but the signal-to-noise ratio is lower. 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 (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 gray values ​​of the reference point and the selected detection point are within a 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 areas with these influencing factors. By setting a preset deviation range for the gray values, these influencing factors can be effectively eliminated. In addition, the fact that the gray values ​​of the reference point and the selected detection point are within the preset deviation range ensures that the selection of the reference point is close to the detection point. For example, selecting it at the edge of a vein ensures that, apart from the blood vessel, the color, thickness, and other parameters of the epidermis, dermis, and subcutaneous tissue are as close as possible, making the spectral data of the detection point consistent with the reference point.The deviation of the spectral data of the test points should be minimized to exclude the influence of non-analytes.

[0091] In addition to the spectral reconstruction algorithm, the spectral data can also be obtained by forming a radiometric calibration coefficient through previous radiometric calibration, and the spectral line is obtained by calculating the gray value * the radiometric calibration coefficient.

[0092] When a combination of multiple pixels is selected as the detection point, the fluorescence spectral data of the detection point can be the average value of the fluorescence spectral data of these pixels. At the same time, 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.

[0093] Analysis steps: After preprocessing, the spectral data of the acquired detection points and reference points are input into the trained detection model, and the glucose concentration or the intermediate result of the correlation between glucose and spectral data is output. When the detection model is trained, it is necessary to simultaneously acquire the spectral data of the test object and the accurate test result, such as the blood test result. The spectral data is used as the input of the detection model and the blood test result is used as the output of the detection model to train the detection model.

[0094] The detection model can employ a convolutional neural network (CNN) 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 activation function layers are spaced apart. The activation function used in the activation function layers is the ReLU function.

[0095] In the CNN 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 layers is non-linearly transformed using the activation function. The flatten layer flattens the output of the convolutional layers into a one-dimensional vector, facilitating 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. The mean absolute error is also calculated as the performance metric for model evaluation. Instruction manual, page 7 / 13, 11 CN 121337329 A

[0096] When the output result of the detection model is glucose concentration, if the error between the output result and the measured standard glucose concentration value meets the preset condition, then training is stopped to obtain the detection model. When the output result of the detection model is an intermediate result of glucose and spectral data association, such as the result of an interneuron, if the error between the output result and the result of the interneuron meets the preset condition, then training is stopped to obtain the detection model. Further model correction processing is performed on the interneuron result to obtain the glucose concentration.

[0097] As shown in Figure 4, the Input layer is the spectral data input layer, which is obtained after preprocessing the original spectral data. Hiddenlayer is an intermediate hidden layer. Through deep learning with convolutional operations, it combines features and outputs the final predicted blood glucose concentration value. Output layer can also be used to combine features with convolutional operations and output a neuron as an intermediate result value. The intermediate result value Output1 and two infrared (IR) feature brightness values ​​are used to train the model again to further correct the blood glucose prediction error and output the final predicted blood glucose concentration value Output2. The training degree of the detection model needs to be set with different parameters as needed. The extracted glucose feature values ​​will learn continuously 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.

[0098] 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 it to predict the glucose concentration of different users.

[0099] 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 based on this spectral information, avoiding pain and discomfort and improving the comfort and convenience of the test. 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.

[0100] Figure 6 shows a schematic diagram of the experimental effect of the trained detection model. 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, of which 1537 were training set samples and 500 were prediction set samples. As can be seen from the figure, the distribution of the detection results of the detection model is as follows: the MARD value of the predicted samples is 11.32%, and the vast majority of samples fall into areas A and B. Among them, the proportion of samples falling into area A is 87.03%, and the proportion of samples falling into area B is 12.77%, indicating that the detection model has high detection accuracy.

[0101] Example 3

[0102] This example is based on Example 2, replacing infrared light with visible light to provide another non-invasive glucose detection method, including:

[0103] 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 nanometers to acquire a second image of the imaging area.

[0104] In the first image, since the color of the area where the vein is located differs from the color of the area where the non-vein is located,The difference allows for easy division of the imaging area into regions containing veins and regions not containing veins.

[0105] In the second image, since it is difficult to distinguish between regions containing veins and regions not containing veins, 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. 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 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 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 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.

[0106] Spectral acquisition steps: According to the gray value distribution of pixels in the first image, the imaging area is divided into the vein area and the non-vein area. Detection points are selected from the vein area and reference points are selected from the non-vein area. The spectral data of the detection points and the spectral data of the reference points are acquired respectively. Specifically, according to the gray value of pixels in the second image, a pixel with a gray value that meets the preset requirements or a combination of the pixel and its adjacent pixels is selected from the vein area as a detection point. A pixel with a gray value within the preset deviation range of the selected detection point or a combination of the pixel and its multiple adjacent pixels is selected from the non-vein area 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 values ​​of the reference point and the selected detection point are within a 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 simultaneously distinguish the areas of all influencing factors. By setting a preset deviation range for the gray values, these influencing factors can be effectively eliminated.

[0107] When a combination of multiple pixels is selected as the detection point, the fluorescence spectrum data of the detection point can be the average value of the fluorescence spectrum data of these pixels. At the same time, the number of detection points and reference points can be one or more.When there are multiple test points, the average value of the fluorescence spectral data of all test points and the average value of the fluorescence spectral data of all reference points can be calculated separately.

[0108] Analysis steps: After preprocessing, the spectral data of the obtained test points and reference points are input into the trained detection model, and the glucose concentration is output. When the detection model is trained, it is necessary to simultaneously obtain the spectral data of the test object and the accurate test results, such as the 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.

[0109] 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 by the activation function layer is the ReLU function.

[0110] 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, 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.

[0111] 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.

[0112] The training degree of the detection model needs to be set with different parameters as needed. The extracted glucose feature values ​​will continuously learn according to the setting of different parameters until the error between the output result and the standard glucose value of the above label value meets the requirements. Then the training is stopped and the detection model is obtained.

[0113] 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 it to predict the glucose concentration of different users.

[0114] 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 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.

[0115] Example 4

[0116] This embodiment provides an analyte detection system. The analyte detection system can be implemented by executing the 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:

[0117] An imaging module: A light source provides light within a preset wavelength range to illuminate a first region, and an imaging spectral detection device images the first region to obtain an image of the imaging region. Through illumination with light within the preset wavelength range, the image reflects the distribution data and spectral data of the reflected or excited signals generated by the analyte under light illumination in the imaging region. Since different wavelength ranges are required to obtain the analyte distribution data and spectral data, the light can be two corresponding wavelength ranges, or it can be a light with a larger wavelength range covering 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.

[0118] 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.

[0119] 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 partitioned according to different distribution data to facilitate the selection of locations from different partitions for acquiring spectral data.

[0120] Analysis module: 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 reflection signals 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.

[0121] Those skilled in the art will know that, in addition to implementing the system provided by the present invention in a purely computer-readable program code manner...In addition to its various devices, modules, and units, the system and its various devices, modules, and units provided by the present invention can be made to achieve the same function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. 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 regarded as structures within the hardware component; the devices, modules, and units for implementing various functions can also be regarded as both software modules for implementing the method and structures within the hardware component.

[0122] Embodiment 5

[0123] 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 realizing convenient and quick detection of analytes on the body surface.

[0124] 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.

[0125] The light source 201 is capable of providing light within a preset wavelength range. Since different wavelength ranges of light are required to irradiate the analyte to obtain distribution data and spectral data, there are two possible implementation methods: one light source capable of providing light with a large 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 capable of obtaining distribution data of the analyte and the wavelength range capable of obtaining spectral data of the analyte, such as a halogen lamp. When there are two light sources, the two light sources provide different light. One light source covers the wavelength range that can acquire data on the distribution of analytes, while the other light source covers the wavelength range that can acquire spectral data of analytes. For example, an infrared lamp combined with an ultraviolet lamp, or a visible light lamp combined with an ultraviolet lamp.

[0126] To ensure uniform illumination of the imaging area 100, a ring light source can be used. The light source has multiple light-emitting modules that are evenly distributed on the same circumference. When there are two light sources, the light-emitting modules of the two light sources are arranged alternately.

[0127] 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 for spectral modulation of the incoming light signal.To facilitate the sensor in generating an image containing the spectral information to be measured.

[0128] 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. 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 the periodic pixel-level filter structure set on its surface to form a mosaic image containing spectral information. Then, the spectral data is reconstructed using an algorithm.

[0129] The controller 203 is configured to control the light source to provide light within a preset wavelength range to illuminate the first area, and to control the imaging spectral detection device to image the first area to obtain an image of the imaging area. The controller controls the imaging spectral detection device to obtain spectral data from the image that reflects the uneven distribution of the reflection signal or excitation signal generated by the analyte under light illumination in the imaging area. The information of the analyte in the imaging area is obtained based on the obtained spectral data. The information of the analyte 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 turned on, the second type of light source is turned off, and similarly, when the second type of light source is turned on, the first type of light source is turned off, and the two do not interfere with each other.

[0130] 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 light outside the preset wavelength range is blocked, thereby reducing the influence of other external light on the detection results.

[0131] 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 it is irradiated to pass through, while light within other wavelength ranges is blocked, thereby reducing the influence of the reflected signal or excitation signal of non-analyte on the detection results.

[0132] The lens 205 can be used for fixed focusing in order to obtain a clear image. In other embodiments, the second bandpass filter 206 may 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.

[0133] According to the above description, FIG9 shows an analyte 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 passes through the first bandpass filter 204.The light output from the bandpass filter 204, after being filtered by the filter, has a wavelength that meets the requirements and shines onto the human body through the light-transmitting window on the back of the watch. The reflected or excitation signal from the human body enters the light-transmitting window, passes through the light source 201 and the hollowed-out portion in the middle of 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, the light enters the imaging spectral detection device 202. 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, to more accurately identify the location of veins, the watch can be worn on the inside of the wrist.

[0134] Example 6

[0135] FIG8 is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. As shown in FIG8, 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 executable 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-described method for detecting the analyte.

[0136] Wherein, the memory 502 and the processor 501 are connected by a bus. The bus may include any number of interconnected buses and bridges, and the bus connects various circuits of one or more processors 501 and the memory 502 together. The bus may 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 further described in this invention. The bus interface provides an interface between the bus and the transceiver. The transceiver may 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. Furthermore, the antenna also receives data and transmits it to processor 501.

[0137] Processor 501 is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces,

[0138] voltage regulation, power management, and other control functions. Memory 502 can be used to store data used by processor 501 during operation.

[0139] 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 analytes.

[0140] 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. This 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 the methods described in the various embodiments of this application.All or part of the steps of the method. 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.

[0141] 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.

[0142] 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 on pages 12 / 13 of the above specification, CN 121337329 A. 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. In the absence of conflict, the embodiments and features in the embodiments of this application can be arbitrarily combined with each other. Instruction Manual 13 / 13 Page 17 CN 121337329 A Figure 1 Figure 2 Instruction Manual Drawings 1 / 6 Page 18 CN 121337329 A Figure 3 Figure 4 Instruction Manual Drawings 2 / 6 Page 19 CN 121337329 A Figure 5 Figure 6 Instruction Manual Drawings 3 / 6 Page 20 CN 121337329 A Figure 7 Figure 8 Instruction Manual Drawings 4 / 6 Page 21 CN 121337329 A Figure 9 Figure 10 Instruction Manual Drawings 5 / 6 Page 22 CN 121337329 A Figure 11 Figure 12 Instruction Manual Drawings 6 / 6 Page 23 CN 121337329 A Abstract The present invention provides a method, a system, a medium, and a device for testing an analysis which relates to the field of optical analysis. The method comprises: imaging: irradiating a first area by light within a preset wavelength range, and imaging the first area to obtain an image of an imaging area; spectrum obtaining: obtaining, from the image,spectral data that indicate uneven distribution in the imaging area of a reflection signal or an excitation signal generated by the analyte when irradiated by light; and analyzing step: obtaining information about the analyte in the imaging area based on the obtained spectral data, wherein the information about the analyte comprises information about the analyte correlated to the spectral data. The present application utilizes the characteristic of uneven distribution of the analyte in the imaging area to obtain spectral data of different areas. Since the distribution of components other than the analyte in the imaging area is relatively uniform, differences in spectral data among different areas can directly reflect the information about the analyte correlated to the spectral data, after eliminating the influence of non- analyte components.

Claims

1. A method of detecting an analyte, characterized by, The method comprises the following steps: An imaging step: imaging the first region under light in a preset wavelength range to obtain an image of the imaging region; A spectrum acquisition step: acquiring spectrum data from the image, the spectrum data reflecting non-uniform distribution of reflection signals or excitation signals generated by the analyte under light in the imaging region; An analysis step: obtaining information of the analyte in the imaging region according to the acquired spectrum data, the information of the analyte including information associated with the spectrum data.

2. The method of claim 1, wherein The imaging step comprises: acquiring a first image under light in a first wavelength range, and acquiring a second image under light in a second wavelength range; The first image comprises distribution data of reflection signals or excitation signals generated by the analyte under light in the imaging region; The second image comprises spectrum data of reflection signals or excitation signals generated by the analyte under light in the imaging region; The spectrum acquisition step comprises: acquiring spectrum data of a required position from the second image according to the distribution data.

3. The method of claim 2, wherein the analyte is a protein. The light in the first wavelength range comprises infrared light, and the spectrum acquisition step comprises: According to the gray scale distribution of pixel points in the first image, the imaging region is divided into a detection point candidate region and a reference point candidate region, a detection point is selected from a position of the second image corresponding to the detection point candidate region, a reference point is selected from a position of the second image corresponding to the reference point candidate region, and spectrum data of the detection point and spectrum data of the reference point are acquired from the second image, respectively.

4. The method of claim 3, wherein The light in the second wavelength range comprises ultraviolet light, and the spectrum acquisition step comprises: According to the gray scale value of pixel points in the second image, a 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 a position of the second image corresponding to the detection point candidate region, a 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 a position of the second image corresponding to the reference point candidate region, and fluorescence spectrum data of the detection point and fluorescence spectrum data of the reference point are calculated.

5. The method of claim 4, wherein the analyte is a protein. The second wavelength range is 300-390 nm, and the second wavelength range is located outside an effective response range of an imaging spectrum detection device; The light in the second wavelength range can excite the analyte to generate fluorescence radiation signals, and a main peak of a fluorescence spectrum of the fluorescence radiation signals is located within the effective response range of the imaging spectrum detection device.

6. The method of claim 1, wherein The analysis step comprises inputting the acquired spectrum data into a trained analyte detection model to output information of the analyte.

7. The method of claim 1, wherein The imaging step comprises: acquiring a first image under infrared light in a wavelength range of 800-1000 nm, and acquiring a second image under ultraviolet light in a wavelength range of 300-390 nm; The first image comprises gray scale distribution data of reflection signals generated by the analyte under infrared light in the imaging region; The second image comprises spectrum data of fluorescence radiation signals excited by the analyte under ultraviolet light in the imaging region; The spectrum acquisition step comprises: dividing the imaging area into a detection point candidate area with smaller gray value and a reference point candidate area with larger gray value according to the gray distribution data; selecting a pixel point with gray value meeting preset requirements from the second image as a detection point or a combination of the pixel point and adjacent pixel points as a detection point according to the detection point candidate area; and selecting a pixel point from the second image as a reference point or a combination of the pixel point and multiple adjacent pixel points as a reference point according to the reference point candidate area; The gray value of the detection point in the second image is substituted into the spectrum reconstruction algorithm to obtain spectrum data of the detection point, and the gray value of the reference point in the second image is substituted into the spectrum reconstruction algorithm to obtain spectrum data of the reference point; The analysis step comprises: inputting the spectrum data of the detection point and the spectrum data of the reference point into the trained analyte detection model to output information associated with the spectrum data of the analyte; The training method of the analyte detection model comprises: simultaneously acquiring spectrum data of the measured object and accurate test results, taking the spectrum data as the input of the detection model, taking the accurate test results as the output of the detection model, and training the detection model.

8. A system for detecting an analyte, characterized by Comprise: An imaging module: irradiating light in a preset wavelength range to a first area and imaging the first area to obtain an image of the imaging area; A spectrum acquisition module: acquiring spectrum data reflecting non-uniform distribution of reflection signals or excitation signals generated by the analyte under light irradiation in the imaging area from the image; An analysis module: obtaining information of the analyte in the imaging area according to the acquired spectrum data, wherein the information of the analyte comprises information associated with the spectrum data of the analyte.

9. The system for detection of an analyte according to claim 8, wherein, The imaging module comprises: collecting a first image under irradiation of light in a first wavelength range, and collecting a second image under irradiation of light in a second wavelength range; The first image comprises distribution data reflecting reflection signals or excitation signals generated by the analyte under light irradiation in the imaging area; The first image comprises spectrum data reflecting reflection signals or excitation signals generated by the analyte under light irradiation in the imaging area; The spectrum acquisition module comprises: acquiring spectrum data of a required position from the second image according to the distribution data.

10. The system for detection of an analyte according to claim 9, wherein, The light in the first wavelength range comprises infrared light, and the spectrum acquisition module comprises: According to the gray distribution of the pixel points in the first image, the imaging area is divided into a detection point candidate area and a reference point candidate area, a detection point is selected from a position of the second image corresponding to the detection point candidate area, a reference point is selected from a position of the second image corresponding to the reference point candidate area, and spectrum data of the detection point and spectrum data of the reference point are acquired from the second image, respectively.

11. The system for detecting an analyte according to claim 10, wherein, The light in the second wavelength range comprises ultraviolet light, and the spectrum acquisition module comprises: According to the gray value of the pixel point in the second image, a pixel point with a gray value meeting preset requirements is selected as a detection point or a combination of the pixel point and adjacent pixel points as the detection point from a position of the second image corresponding to the detection point candidate area, a pixel point is selected as a reference point or a combination of the pixel point and multiple adjacent pixel points as the reference point from a position of the second image corresponding to the reference point candidate area, and fluorescence spectral data of the detection point and fluorescence spectral data of the reference point are calculated.

12. The system for detection of an analyte according to claim 11, wherein, The second wavelength range is 300-390 nm, 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 the analyte to emit a fluorescence radiation signal, and a fluorescence spectral main peak of the fluorescence radiation signal is within the effective response range of the imaging spectral detection device.

13. The analyte detection system of claim 8, wherein, The analysis module includes inputting the obtained spectral data into a trained analyte detection model to output information of the analyte.

14. The analyte detection system of claim 8, wherein, The imaging module includes: collecting a first image under irradiation of infrared light in a wavelength range of 800-1000 nm, and collecting a second image under irradiation of ultraviolet light in a wavelength range of 300-390 nm; The first image includes gray distribution data reflecting a reflection signal generated by the analyte under irradiation of infrared light in the imaging area; The second image includes spectral data reflecting a fluorescence radiation signal excited by the analyte under irradiation of ultraviolet light in the imaging area; The spectral acquisition module includes: dividing the imaging area into a detection point candidate area with a smaller gray value and a reference point candidate area with a larger gray value according to the gray distribution data, selecting a pixel point with a gray value meeting preset requirements as a detection point or a combination of the pixel point and adjacent pixel points as the detection point from the second image according to the detection point candidate area, and selecting a pixel point as a reference point or a combination of the pixel point and multiple adjacent pixel points as the reference point from the second image according to the reference point candidate area; The gray value of the detection point in the second image is substituted into a spectral reconstruction algorithm to obtain spectral data of the detection point, and the gray value of the reference point in the second image is substituted into the spectral reconstruction algorithm to obtain spectral data of the reference point; The analysis module: inputting the spectral data of the detection point and the spectral data of the reference point into a trained analyte detection model to output information associated with the spectral data of the analyte; The way of training the analyte detection model includes: simultaneously acquiring spectral data of the measured object and accurate test results, taking the spectral data as the input of the detection model, taking the accurate test results as the output of the detection model, and training the detection model.

15. 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 analyte detection method in any one of claims 1-7.

16. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein, The computer program is executed by the processor to realize the steps of the analyte detection method in any one of claims 1-7.

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