Illumination and detection system for an object to be analyzed

Abstract

To provide an illumination system and a detection system for an object to be analyzed.SOLUTION: The device includes a case and a light source, wherein the light source is capable of providing light within a preset wavelength range, the case is capable of being in close contact with a to-be-detected part of a to-be-detected person to form a shooting area at the contact part, and the light source is configured to illuminate the shooting area. In the present application, the wavelength range of the light provided by the light source can simultaneously cover the wavelength range in which the distribution data of the analyte can be obtained, and the wavelength range of the spectral data of the analyte can be obtained, and the light source is arranged as an annular light source, so that on one hand, stable and uniform illumination can be provided to the imaging area, and no electrochemical reaction with the analyte is required during detection, and on the other hand, the detection method is simpler, and the purpose of non-invasive detection can be achieved, especially when detecting the analyte in vivo.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to the field of optical analysis, and in particular to illumination and detection systems for analytical objects. [Background technology]

[0002] Non-invasive measurement usually refers to non-invasive measurement. In contrast to invasive measurement, it is also called indirect measurement because it indirectly induces or senses physiological and biochemical parameters of a living body by, for example, contacting a measuring instrument with the skin of the subject. Currently, commonly used non-invasive measurement techniques include electrochemical methods and optical methods.

[0003] Taking the detection of glucose in the human body as an example, a prior patent document, publication number CN118078277A, discloses a non-invasive blood glucose detection method based on hyperspectral data analysis. A light source is used to provide ambient light to a hyperspectral meter. In a darkroom environment, the light source is used to illuminate the vascular area on the back of the hand. The hyperspectral meter then captures multiple images of the vascular area on the skin of the target human body to obtain hyperspectral images with different intensities. The captured hyperspectral images are then sent to a detection device, which aggregates them to obtain a hyperspectral image dataset consisting of two-dimensional spatial data and one-dimensional spectral data. Obtaining detection results requires high environmental requirements, and optical interference in the environment affects detection accuracy, making it difficult to apply to miniaturized scenarios.

[0004] Furthermore, patent document US6424849B1 discloses an independent method for determining blood glucose levels using an IR beam reflected from the skin surface. This device has a housing for pressing a part of the body against an ATR plate, and a selected skin surface contacts the ATR plate during measurement. The IR beam is irradiated onto the surface of the human skin, and the reference wavelength component and the measurement wavelength component in the reflected IR beam are detected and quantified. During the measurement process, ambient light enters the housing through an opening, making the measurement results susceptible to interference by the ambient light and affecting the detection accuracy. Summary of the Invention [Problem to be solved by the invention]

[0005] SUMMARY OF THE INVENTION It is an object of the present invention to provide an illumination and detection system for an analyte in view of the shortcomings of the prior art. [Means for solving the problem]

[0006] The illumination system for an analysis object provided by the present invention includes a case and a light source, the light source being capable of providing light rays within a preset wavelength range, the case being in close contact with the detection site of the subject to form an imaging area at the close contact area, and the light source irradiating the imaging area.

[0007] Furthermore, the light source includes a single light source, and the wavelength range of the light beam provided by the single light source simultaneously covers the wavelength range capable of obtaining distribution status data of the analyte and the wavelength range capable of obtaining spectral data of the analyte.

[0008] Furthermore, the light source includes a plurality of light emitting modules uniformly distributed on the same circumference.

[0009] Furthermore, the light source includes two types of light sources, the wavelength range of the light beam provided by one of the light sources covers the wavelength range in which distribution status data of the analyte can be obtained, and the wavelength range of the light beam provided by the other light source covers the wavelength range in which spectral data of the analyte can be obtained.

[0010] Furthermore, each of the two types of light sources includes a plurality of light emitting modules, and the plurality of light emitting modules of the two types of light sources are alternately arranged on the same circumference, and the light emitting modules are uniformly distributed on the same circumference.

[0011] Furthermore, one of the light sources provides infrared light in the wavelength range of 800 nanometers to 1000 nanometers, and the other of the light sources provides ultraviolet light in the wavelength range of 300 nanometers to 390 nanometers.

[0012] Furthermore, a first bandpass filter is further provided within the case, the first bandpass filter being located between the light source and the imaging area, and the first bandpass filter passing light within a predetermined wavelength range and blocking light outside the predetermined wavelength range.

[0013] Furthermore, the shape and material of the case allow the case to fit closely to the skin surrounding the detection site of the subject.

[0014] The detection system provided by the present invention employs an illumination system for the analyte.

[0015] Further included is a portable device, wherein the illumination system for the analyte is integrated into the portable device. [Effects of the Invention]

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. In this application, the wavelength range of light provided by the light source simultaneously covers the wavelength range capable of obtaining distribution data of the analyte and the wavelength range capable of obtaining spectral data of the analyte, and the light source is arranged as an annular light source, thereby providing stable and uniform light irradiation in the imaging area, eliminating the need for electrochemical reaction with the analyte during detection, while providing a simpler detection method, and achieving the purpose of non-invasive detection, especially when detecting an analyte in vivo. 2. The present application utilizes the uneven distribution of the analyte in the imaging area to obtain spectral data in different areas, and since the distribution of components other than the analyte in the imaging area is relatively uniform, the difference in spectral data in different areas can directly reflect information about the analyte, such as the concentration of the analyte, which is correlated with the spectral data after the influence of non-analytes is substantially eliminated. 3. This application avoids the traditional method of using Raman spectroscopy to measure analytes, and instead uses fluorescence spectroscopy for detection, thereby achieving low-cost, compact detection systems and achieving real-time detection objectives. Other features, objects and advantages of the present invention will become more apparent through the detailed description given below, given by way of non-limiting example with reference to the drawings in which: [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a structural schematic diagram of an analyte detection device provided by Example 1. FIG. [Figure 2] 1 is a structural schematic diagram of a wristwatch provided by Example 1. FIG. [Figure 3] 1 is a schematic exploded view of the internal structure of a wristwatch provided by Example 1. FIG. [Figure 4] 1 is a schematic diagram of the wearing structure of the wristwatch provided by Example 1. FIG. [Figure 5] 1 is a schematic diagram of the back structure of a wristwatch provided by Example 1. FIG. [Figure 6] 10 is a flowchart of a second embodiment. [Figure 7] FIG. 1 is a schematic diagram of a first image collected in Example 3. [Figure 8] FIG. 10 is a schematic diagram of a second image collected in Example 3. [Figure 9] FIG. 10 is a diagram illustrating the principle of a detection model according to a third embodiment. [Figure 10] FIG. 1 is a schematic diagram of detection point-reference point spectral data obtained in Example 3. [Figure 11]These are experimental results of the accuracy of the analytical results of the analytical model. [Figure 12] 1 is a structural schematic diagram of an electronic device provided by Example 6. 100: Imaging area DETAILED DESCRIPTION OF THE INVENTION

[0018] The present invention will be described in detail below with reference to specific examples. The following examples will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any way. It should be noted that those skilled in the art may make some modifications and improvements without departing from the concept of the present invention, all of which fall within the protection scope of the present invention.

[0019] Example 1 1 is a structural diagram of this embodiment, which provides an illumination system for an object to be analyzed, including a case and a light source 201. The light source 201 can provide light within a preset wavelength range. The case is brought into close contact with the detection site of the subject, and an imaging area 100 can be formed at the contact area. The light source 201 illuminates the imaging area 100.

[0020] The light source 201 includes a single light source 201, and the wavelength range of the light beam provided by the single light source 201 simultaneously covers the wavelength range for obtaining distribution data of the analyte and the wavelength range for obtaining spectral data of the analyte. The light source 201 includes a plurality of light emitting modules uniformly distributed on the same circumference.

[0021] The light source 201 includes two types of light sources 201, where the wavelength range of the light beam provided by one of the light sources 201 covers a wavelength range capable of acquiring distribution data of the analyte, and the wavelength range of the light beam provided by the other light source 201 covers a wavelength range capable of acquiring spectral data of the analyte. Each of the two types of light sources 201 includes a plurality of light-emitting modules, which are alternately arranged on the same circumference and are uniformly distributed on the same circumference. Here, one of the light sources 201 provides infrared light in the wavelength range of 800 nanometers to 1000 nanometers, and the other light source 201 provides ultraviolet light in the wavelength range of 300 nanometers to 390 nanometers.

[0022] The shape and material of the case allow the case to fit closely to the skin around the detection site of the subject. A first bandpass filter 204 is further provided within the case, and the first bandpass filter 204 is located between the light source 201 and the imaging region 100. The first bandpass filter 204 passes light within a preset wavelength range and blocks light outside the preset wavelength range.

[0023] Specifically, the light source 201 is part of the detection device 200, which further includes an imaging spectrum detector, a controller 203, a first bandpass filter 204, a second bandpass filter 206, and a lens 205. The controller 203 establishes electrical or communication connections with the light source 201 and the imaging spectrum detector, respectively.

[0024] The light source 201 can provide light within a predetermined wavelength range. Since light of different wavelength ranges is required to obtain distribution data and spectral data of an analyte, two methods can be used: one light source 201 capable of providing light with a wider wavelength range, or two light sources 201 capable of providing light with narrower wavelength ranges. When there is one light source 201, the wavelength range of the light provided by the light source 201 must 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 201, the two light sources 201 provide different light, with one light wavelength covering the wavelength range capable of obtaining distribution data of the analyte and the other light wavelength covering the wavelength range capable of obtaining spectral data of the analyte, such as a combination of an infrared lamp and an ultraviolet lamp, or a combination of a visible light lamp and an ultraviolet lamp.

[0025] To uniformly illuminate the imaging area 100, a ring-shaped light source 201 can be used, which has multiple light-emitting modules uniformly distributed on the same circumference. When there are two types of light source 201, the light-emitting modules of the two types of light source 201 are arranged relative to each other.

[0026] The imaging spectral detector can image the imaging region 100 to obtain a corresponding image in response to an instruction, and can also obtain corresponding spectral data in response to an instruction. The imaging spectral detector includes a sensor and a periodic pixel-level optical filter structure disposed on a surface of the sensor. The periodic pixel-level optical filter structure is used to perform spectral modulation on an incident optical signal, thereby enabling the sensor to generate an image containing the spectral information of interest.

[0027] The periodic pixel-level optical filter structure includes a plurality of optical filter pixel channels with different shapes of pixel-level structures, each having the same specifications and size, uniformly arranged, and each having a length and width that is an integer multiple of the pixel point size of the pixel sensor. The optical filter pixel channels of the different shapes of the pixel-level optical filter structures correspond to different spectral wave filter coefficients, and the pixel-level optical filter structures with different spectral optical filter coefficients are periodically arranged after being combined in a fixed order. The sensor modulates the received first detection light through the periodic pixel-level optical filter structures arranged on its surface to form a mosaic image containing spectral information, and then uses an algorithm to reconstruct a grayscale image containing the measured spectral information.

[0028] The controller 203 is configured to control the light source 201 to provide light within a predetermined wavelength range to illuminate a first region, and to control the imaging spectral detector to image the first region to acquire an image of the imaging region 100, and to control the imaging spectral detector to acquire spectral data from the image, the spectral data reflecting the non-uniform distribution of reflected or excited signals generated by an analyte when the light is irradiated in the imaging region 100. Based on the acquired spectral data, information about the analyte in the imaging region 100 is acquired, where the information about the analyte includes information about spectral data correlated with the analyte. When there is one type of light source 201, one image is acquired, and when there are two types of light source 201, two images are acquired. When the first light source 201 is turned on, the second light source 201 is turned off; similarly, when the second light source 201 is turned on, the first light source 201 is turned off, so that the two light sources do not interfere with each other.

[0029] The first bandpass filter 204 is located between the light source 201 and the imaging region 100, and its function is to pass light rays within a preset wavelength range and block light rays outside the preset wavelength range, thereby reducing the influence of other external light rays on the detection results. In this embodiment, an annular first bandpass filter 204 is preferably used, and the annular shape of the first bandpass filter 204 matches the annular light source 201, thereby ensuring that the light rays emitted from the light source 201 pass through the first bandpass filter 204 and enter the imaging region 100.

[0030] The second bandpass filter 206 is located between the imaging spectrum detector 202 and the lens 205, and its function is to pass light in the wavelength range in which the reflection signal or excitation signal generated by the analyte when irradiated with light is located, and to block light in other wavelength ranges, thereby reducing the influence of reflection signals or excitation signals other than those of the analyte on the detection results.

[0031] The lens 205 can be used for focusing to obtain a clear image. In another embodiment, the second bandpass filter 206 can be located on one side of the lens 205, away from the imaging spectrum detector 202, but the present invention is not limited thereto.

[0032] The lighting system of this embodiment can be integrated into a detection system and a detection device 200, which is a portable, non-invasive detection device 200 for the human body, and can be a separate detection device 200, or can be integrated into a wristwatch or a mobile phone, which realizes convenient and rapid detection of analytes on the body surface.

[0033] As shown in Figures 2, 3, 4, and 5, the detection device 200 in this embodiment is a wristwatch. The detection system is integrated into the wristwatch, and the lens 205, second bandpass filter 206, light source 201, and first bandpass filter 204 are sequentially arranged from the inside to the outside. The controller 203 is integrated into the wristwatch's circuit board 207, and an opening area that comes into contact with the skin of the user's arm is formed on the back case of the wristwatch. During detection, the portion of the opening area on the back of the wristwatch that comes into contact with the skin of the user's arm becomes the imaging area 100. The reflected or excited signal from the human body enters the light-transmitting window, passes through the hollow space between the light source 201 and the first bandpass filter 204, and enters the second bandpass filter 206 through the lens 205. After being optically filtered by the second bandpass filter 206, the signal enters the imaging spectrum detector 202.

[0034] Example 2 This embodiment provides a method for detecting an analyte based on the embodiment 1, and FIG. 6 is a flow chart of this embodiment, which includes the following steps:

[0035] Imaging step: Irradiating a first region with light within a predetermined wavelength range through a light source, and capturing an image of the first region through an imaging spectrum detector to obtain an image of the imaging region. By irradiating a first region with light within the predetermined wavelength range, distribution data and spectral data of the reflected or excited signals generated by the analyte when the light is irradiated can be reflected in the image. The first region may be a specific region on the surface of human skin. To prevent external light such as ambient light from affecting the detection, the lens of the imaging spectrum detector must be tightly attached to the surface of the human skin in the first region. The imaging region refers to the region within the lens range of the imaging spectrum detector. Generally, the imaging region may be a part of the first region or the same region as the first region.

[0036] Since light rays with different wavelength ranges are required to obtain analyte distribution data and spectral data, two methods can be used: light rays with a wider wavelength range provided by one light source, or light rays with narrower wavelength ranges provided by two light sources. When one light source is used, the wavelength range of the light rays provided by the light source must simultaneously cover the wavelength range for obtaining analyte distribution data and the wavelength range for obtaining analyte spectral data. When two light sources are used, the two light sources provide different light rays, with the wavelength of one light ray covering the wavelength range for obtaining analyte distribution data and the wavelength of the other light ray covering the wavelength range for obtaining analyte spectral data. At the same time, when one light source is used, only one image is captured, and when two light sources are used, two images are captured. For ease of processing, the imaging area of ​​the two images must be the same, i.e., the lens of the imaging spectrum detection device does not move on the surface of human skin.

[0037] In the present application, the analyte may be vascular glucose, ketones, alcohol, lactate, oxygen, hemoglobin A1C, acetylcholine, amylase, bilirubin, cholesterol, 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 may be a drug such as an antibiotic (e.g., gentamicin, vancomycin, etc.), digitoxin, digoxin, a drug of abuse, theophylline, or warfarin. In embodiments where more than one analyte is detected, the analytes may be monitored at the same or different times. In other embodiments, the analyte may be any other substance within a body surface that can be noninvasively detected using the present invention.

[0038] Spectral acquisition step: Acquire spectral data from the image, which reflects the non-uniform distribution of reflected or excited signals generated by the object to be analyzed when the light beam is irradiated in the imaging region via the imaging spectral detection device. Specifically, the imaging region can be divided based on different distribution status data, and positions for acquiring spectral data from different sections can be selected.

[0039] Analysis stage: Based on the acquired spectral data, information about the analyte in the imaging area is acquired, including information about the analyte correlated with the spectral data. Because the distribution of the analyte in different sections is different, the reflected or excited signals generated by the analyte when irradiated with light also differ. Taking human skin as an example, it is divided into three parts: the epidermis, the dermis, and the subcutaneous tissue, and blood vessels such as veins are located in the subcutaneous tissue. UV light can be used to irradiate skin areas with blood vessels and skin areas without blood vessels to acquire corresponding spectral data, or to irradiate skin areas with thick blood vessels and skin areas with thin blood vessels to acquire corresponding spectral data. The difference between the two spectral data can reflect information about the analyte correlated with the spectral data in the blood vessels. Intermediate information, such as data about the degree of impact of the analyte on the spectral data, can be acquired for further analysis, or information such as the concentration of the analyte can be directly acquired through an analytical model.

[0040] Example 3 This embodiment is based on the second embodiment and takes glucose detection in human blood vessels as an example to provide a non-invasive glucose detection method, which includes the following steps:

[0041] Imaging step: Irradiate the skin where veins are located on the wrist, back of the hand, etc. with infrared light in a first wavelength range of 800 to 1000 nanometers, preferably in the near-infrared band, to capture a first image of the imaging area, and then irradiate the same location with ultraviolet light in a second wavelength range of 300 to 390 nanometers to capture a second image of the imaging area.

[0042] 7, where 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 pixel point block of the detection point selected on the venous blood vessel, and the black box represents the pixel point block of the reference point selected on the surrounding skin. In the first image, some of the infrared light penetrates the human skin, and some of it is absorbed by the human skin. At the same time, the area where the venous blood vessel is located is absorbed in large amounts by the venous blood vessel, so the grayscale value of the pixel in the area where the venous blood vessel is located is smaller, and the grayscale value of the pixel in the area where the non-venous blood vessel is located is larger, which makes it easy to divide the imaging area into the area where the venous blood vessel is located and the area where the non-venous blood vessel is located.

[0043] As shown in Figure 8, the horizontal axis represents the horizontal coordinate of the second image, the vertical axis represents the vertical coordinate of the second image, the white box represents the pixel point block of the detection point selected on the venous blood vessel, and the black box represents the pixel point block of the reference point selected on the surrounding skin. Because it is difficult to distinguish between venous blood vessels and non-venous blood vessels in the second image, it is necessary to distinguish between them in the first image. The purpose of using excitation light in the second wavelength range of 300-390 nanometers is to obtain a high-quality effective fluorescence spectrum signal. This is because the main response band of the imaging spectrum detection device is between 400 and 800 nm. If the wavelength of the excitation light used is less than 300 nm, the main peak of the fluorescence spectrum of the excited fluorescence emission signal is located in the <400 nm band, making it difficult for the imaging spectrum detection device to obtain a high-quality effective fluorescence spectrum signal. If the wavelength of the excitation light used exceeds 390 nm, the excitation light itself will be visible light, and the spectral signal of the excitation light will be superimposed on the fluorescence spectral signal, making it difficult to extract an effective fluorescence spectral signal without interference from the excitation light. Glucose in venous blood vessels absorbs ultraviolet light in the wavelength range of 300-390 nanometers and then emits a fluorescent emission signal in the visible light band of 400-800 nm, which is within the effective response range of the imaging spectral detector. The characteristic spectral intensity of this fluorescent emission signal is positively correlated with the glucose concentration, resulting in higher fluorescence excitation efficiency.

[0044] Spectrum acquisition step: based on the grayscale distribution of the pixel points in the first image, divide the imaging area into areas where venous blood vessels are located and areas where non-venous blood vessels are located, select a detection point from the position of the area where venous blood vessels are located corresponding to the second image, select a reference point from the position of the area where non-venous blood vessels are located corresponding to the second image, and respectively acquire spectral data of the detection point and the reference point. Specifically, based on the grayscale values ​​of the pixel points in the second image, select one pixel point having a grayscale value that meets a predetermined requirement as the detection point from the area where venous blood vessels are located, or select a combination of this pixel point and adjacent pixel points as the detection point, and select one pixel point having a grayscale value within a predetermined deviation range from the grayscale value of the selected detection point as the reference point from the area where non-venous blood vessels are located, or select a combination of this pixel point and multiple adjacent pixel points as reference points, and calculate and acquire fluorescence spectral data of the detection point and the reference point in the second image. The spectral data can be selected from a single pixel point of the detection point, the reference point, or an average of a combination of multiple pixel points, and can be appropriately selected based on the width of the blood vessel. Averaging a combination of multiple pixel points improves the signal-to-noise ratio but is limited by the width of the blood vessel and avoids acquiring data from areas outside the blood vessel. Selecting a single pixel point provides high spatial resolution and is suitable for situations with thin blood vessels, but the signal-to-noise ratio is lower. As a preset requirement for the grayscale value, it is possible to use the point with the smallest grayscale value as the detection point, but this application is not limited to this. The calculation results are shown in Figure 10, where the horizontal axis is wavelength (unit: nm) and the vertical axis is relative radiance (unit: W / nm), the solid line is the spectral data of the detection point, and the dotted line is the spectral data of the reference point.Here, the reason why the grayscale value of the reference point and the grayscale value of the selected detection point are within a predetermined deviation range is that the skin in the imaging area has influencing factors such as skin color, blemishes, and cosmetics, which may directly affect the spectral data of the reference point. However, since the first image does not distinguish the areas of these influencing factors, setting a predetermined deviation range for the grayscale value makes it possible to effectively exclude these influencing factors. Furthermore, because the grayscale value and the grayscale value of the selected detection point are within the predetermined deviation range, it is guaranteed that a reference point close to the detection point, such as the edge of a venous blood vessel, will be selected. This ensures that, excluding the blood vessels, the parameters of the remaining epidermis, dermis, and subcutaneous tissue, such as color and thickness, are closest. The deviation between the spectral data of the detection point and the spectral data of the reference point makes it possible to exclude as much as possible the influence of non-analyte objects.

[0045] In addition to the spectral reconstruction algorithm, the method of obtaining the spectral data is to form the radiation calibration coefficient through pre-radiation calibration, and then calculate the grayscale value * radiation calibration coefficient to obtain the spectral line.

[0046] When a combination of multiple pixel points is selected from the detection point, the fluorescence spectrum data of the detection point can be the average value of the fluorescence spectrum data of these pixel points. At the same time, the number of detection points and reference points can be one or more. When the number of detection points and reference points is multiple, the average value of the fluorescence spectrum data of all the detection points and the average value of the fluorescence spectrum data of all the reference points can be calculated respectively.

[0047] Analysis stage: The acquired spectral data of the detection points and reference points is preprocessed and then input into a trained detection model, which outputs the glucose concentration or an intermediate result showing the correlation between glucose and the spectral data. When training the detection model, it is necessary to simultaneously acquire the subject's spectral data and accurate test results such as blood test results, and use the spectral data as the input of the detection model and the blood test results as the output of the detection model to train the detection model.

[0048] The detection model may employ a convolutional neural network model, which sequentially includes an input layer, at least two convolutional layers, at least two activation function layers, a flattened layer, a fully connected layer, and an output layer, where the convolutional layers and the activation function layers are spaced apart, and the activation function used in the activation function layer is a Relu function.

[0049] Here, the size of the convolution kernel of each layer in the convolutional neural network model is 1, the number of convolution kernels in the first convolutional layer is 32, and the number of convolution kernels in the second layer is 64. Both are used to extract blood glucose features and nonlinearly transform the output of the convolutional layer through an activation function. The Flatten layer flattens the output of the convolutional layer into a one-dimensional vector to facilitate connection to the subsequent fully connected layer, and the final output dimension is 1. In the model training process, the Adam optimizer is used to train the model, and the mean squared error is used as the loss function, and the mean absolute error is simultaneously calculated as the performance indicator for model evaluation.

[0050] If the output result of the detection model is a glucose concentration, the training for obtaining the detection model is stopped when the error between the output result and the measured standard glucose concentration value satisfies a preset condition. If the output result of the detection model is an intermediate result correlating glucose with spectral data, such as an intermediate neuron result, the training for obtaining the detection model is stopped when the error between the output result and the intermediate neuron result satisfies a preset condition. The intermediate neuron result is further processed by model correction to obtain the glucose concentration.

[0051] As shown in Figure 9, the input layer is a spectral data input layer obtained by preprocessing the original spectral data. The hidden layer is an intermediate hidden layer that uses deep learning convolutional operations to combine features and output the final predicted blood glucose concentration value as an output layer. Deep learning convolutional operations can also output one neuron Output1 as an intermediate result after combining features. The intermediate result Output1 and the two infrared IR feature intensity values ​​are then used for further model training to further correct the blood glucose prediction error and output the final predicted blood glucose concentration value Output2. The degree of training of the detection model can be determined by setting different parameters as needed. The extracted multiple glucose feature values ​​are continuously trained according to different parameter settings. When the error between the output result and the standard glucose value of the above label value meets the requirements, the training for obtaining the detection model is stopped.

[0052] Through multiple repeated training, the neurons can learn the corresponding change rules between different glucose concentrations and glucose spectrum features of different samplers, thereby improving the generality of the detection model and achieving the goal of predicting the glucose concentrations of different users.

[0053] The entire glucose detection process does not require blood sampling or skin puncture or skin implantation, but rather obtains the subject's spectral information based on the fluorescence spectrum, and then obtains the subject's glucose detection result based on the spectral information, thereby avoiding pain and discomfort and improving the discomfort and convenience of detection. This method allows for detailed differentiation of the spectral signals from the blood vessel site and the skin site, allowing for accurate extraction of the subsequent glucose signal, and at the same time, the intensity of the spectral signal and the glucose concentration are closely correlated, achieving accurate measurement of the glucose concentration, resulting in more accurate results and easier processing.

[0054] Figure 11 shows a schematic diagram of the experimental results of the trained detection model. The horizontal axis represents the reference blood glucose concentration (unit: mmol / L) collected by the blood glucose meter, and the vertical axis represents the blood glucose concentration (unit: mmol / L) predicted by the patented method. The total number of samples collected by the subjects was 2,037, with 1,537 samples in the training set and 500 samples in the prediction set. The figure shows the distribution of the detection results of the detection model. The MARD value of the predicted samples was 11.32%, and most of the samples were classified into regions A and B. The samples in region A accounted for 87.03%, and the samples in region B accounted for 12.77%, indicating that the detection accuracy of the detection model is relatively high.

[0055] Example 4 This example is based on Example 3, and replaces infrared light with visible light to provide another non-invasive glucose detection method, which includes the following steps:

[0056] Imaging step: Visible light is irradiated onto the skin where veins are located, such as on the wrist or back of the hand, to capture a first image of the imaging area, and ultraviolet light within a second wavelength range of 300 to 390 nanometers is irradiated onto the same location to capture a second image of the imaging area.

[0057] In the first image, the color of the area where venous blood vessels are located is different from the color of the area where non-venous blood vessels are located, so the imaged area can be easily divided into areas where venous blood vessels are located and areas where non-venous blood vessels are located.

[0058] Because it is difficult to distinguish between venous and non-venous blood vessels in the second image, it is necessary to distinguish between the first and second images. The purpose of using excitation light in the second wavelength range of 300-390 nanometers is to obtain a high-quality effective fluorescence spectral signal. This is because the main response band of the imaging spectral detection device is 400-800 nm. If the wavelength of the excitation light used is less than 300 nm, the main peak of the fluorescence spectrum of the excited fluorescence emission signal is located in the <400 nm band, making it difficult for the imaging spectral detection device to obtain a high-quality effective fluorescence spectral signal. If the wavelength of the excitation light used is greater than 390 nm, the excitation light itself is visible light, and the spectral signal of the excitation light is superimposed on the fluorescence spectral signal, making it difficult to extract the effective fluorescence spectral signal without interference from the spectral signal of the excitation light. Glucose in the venous blood vessels absorbs ultraviolet light in the wavelength range of 300 to 390 nanometers, and then emits fluorescent radiation signals in the visible light band of 400 to 800 nm, which is located within the effective response range of the imaging spectrum detection device. The characteristic spectral intensity of the fluorescent radiation signals is positively correlated with the glucose concentration and has higher fluorescence excitation efficiency.

[0059] Spectrum acquisition step: based on the grayscale distribution of pixel points in the first image, divide the imaging area into areas where venous blood vessels are located and areas where non-venous blood vessels are located, select detection points from the areas where venous blood vessels are located corresponding to the second image, select detection points from the areas where non-venous blood vessels are located corresponding to the second image, and obtain spectral data of the detection points and spectral data of reference points, respectively. Specifically, based on the grayscale values ​​of the pixel points in the second image, select one pixel point having a grayscale value that satisfies a predetermined requirement or a combination of the pixel point and adjacent pixel points as the detection point from the areas where non-venous blood vessels are located, select one pixel point having a grayscale value within a predetermined deviation range from the grayscale value of the selected detection point or a combination of the pixel point and multiple adjacent pixel points as the reference point, and calculate and obtain fluorescence spectral data of the detection points and fluorescence spectral data of the reference points. Here, the reason why the grayscale value of the reference point and the grayscale value of the selected detection point are within a preset deviation range is that the skin in the imaging area has influencing factors such as skin color, blemishes, cosmetics, etc., which may directly affect the spectral data of the reference point. However, in the first image, it is not possible to distinguish the areas of all influencing factors at the same time. By setting a preset deviation range of the grayscale value, these influencing factors can be effectively excluded.

[0060] When a combination of multiple pixel points is selected from the detection point, the fluorescence spectrum data of the detection point can be the average value of the fluorescence spectrum data of these pixel points. At the same time, the number of detection points and reference points can be one or more. When the number of detection points and reference points is multiple, the average value of the fluorescence spectrum data of all the detection points and the average value of the fluorescence spectrum data of all the reference points can be calculated respectively.

[0061] Analysis stage: The acquired spectral data of the detection point and reference point are preprocessed and then input into a trained detection model to output the glucose concentration. When training the detection model, it is necessary to simultaneously acquire the subject's spectral data and accurate test results such as blood test results, use the spectral data as the input of the detection model, and use the blood test results as the output of the detection model to train the detection model.

[0062] The detection model may employ a convolutional neural network model, which sequentially includes an input layer, at least two convolutional layers, at least two activation function layers, a flattened layer, a fully connected layer, and an output layer, where the convolutional layers and the activation function layers are spaced apart, and the activation function used in the activation function layer is a Relu function.

[0063] Here, the size of the convolution kernel of each layer in the convolutional neural network model is 1, the number of convolution kernels in the first convolutional layer is 32, and the number of convolution kernels in the second layer is 64. Both are used to extract blood glucose features and nonlinearly transform the output of the convolutional layer through an activation function. The Flatten layer flattens the output of the convolutional layer into a one-dimensional vector to facilitate connection to the subsequent fully connected layer, and the final output dimension is 1. In the model training process, the Adam optimizer is used to train the model, and the mean squared error is used as the loss function, and the mean absolute error is simultaneously calculated as the performance indicator for model evaluation.

[0064] When the error between the output result of the detection model and the standard glucose value satisfies a preset condition, the training for obtaining the detection model is stopped.

[0065] The degree of training of the detection model needs to set different parameters as needed, and the extracted multiple glucose feature values ​​are continuously learned according to different parameter settings. When the error between the output result and the standard glucose value of the above label value meets the requirement, the training to obtain the detection model is stopped.

[0066] Through multiple repeated training, the neurons can learn the corresponding change rules between different glucose concentrations and glucose spectrum features of different samplers, thereby improving the generality of the detection model and achieving the goal of predicting the glucose concentrations of different users.

[0067] The entire glucose detection process does not require blood sampling or skin puncture, but rather obtains the subject's spectral information based on the fluorescence spectrum, and then obtains the subject's glucose detection result based on the spectral information, thereby avoiding pain and discomfort and improving the discomfort and convenience of detection. This method allows for the detailed distinction of the spectral signals from the blood vessel site and the skin site, allowing for the accurate extraction of the subsequent glucose signal, and at the same time, the intensity of the spectral signal and the glucose concentration are closely correlated, achieving accurate measurement of the glucose concentration, resulting in more accurate results and easier processing.

[0068] Example 5 This example provides an analyte detection system based on Example 1, which can be realized by performing the process steps of the analyte detection method, i.e., 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:

[0069] The imaging module includes a light source that provides light within a predetermined wavelength range to illuminate a first region, and an imaging spectrum detector that captures an image of the first region to capture the image of the region. By irradiating the light within the predetermined wavelength range, the image can reflect distribution data and spectral data of the reflected or excited signal generated by the analyte when the light is irradiated. Because different wavelength ranges are required to capture the distribution data and spectral data of the analyte, the light may be light of two corresponding wavelength ranges, or one wavelength range may be wider and cover the two required wavelength ranges. When two types of light are used, two images are obtained, and for ease of processing, the imaging areas of the two images typically need to be the same.

[0070] In the present application, the analyte may be glucose, ketones, alcohol, lactate, oxygen, hemoglobin A1C, acetylcholine, amylase, bilirubin, cholesterol, chorionic gonadotropin, creatine kinase (e.g., CK-MB), creatine, creatinine, DNA, fructosamine, glutamine, growth hormone, hormones, peroxide, prostate-specific antigen, prothrombin, RNA, thyroid-stimulating hormone, troponin, or drugs such as antibiotics (e.g., gentamicin, vancomycin, etc.), digitoxin, digoxin, drugs of abuse, theophylline, and warfarin in an animal's blood vessels. In embodiments where more than one analyte is detected, the analytes can be monitored at the same or different times. In other embodiments, the analyte may be other substances in the fluid.

[0071] Spectral acquisition module: Acquires spectral data from the image, which reflects the non-uniform distribution of reflected or excited signals generated by the object to be analyzed when the imaging area is irradiated with light via the imaging spectrum detection device. Specifically, the imaging area can be divided based on different distribution status data, and positions for acquiring spectral data from different sections can be selected.

[0072] Analysis module: Based on the acquired spectral data, the analysis module acquires information about the analyte in the imaging area, including information about the analyte correlated with the spectral data. Because the distribution of the analyte in different sections is different, the reflected or excited signals generated by the analyte when irradiated with light are also different. By utilizing this characteristic, the difference between the spectral data can be obtained, and the information about the analyte correlated with the spectral data, such as the concentration of the analyte, can be accurately reflected.

[0073] Those skilled in the art will recognize that in addition to realizing the system provided by the present invention and its respective devices, modules, and units purely in the form of computer-readable program codes, the system provided by the present invention and its respective devices, modules, and units can also realize similar functions in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. by performing logic programming in method steps. Therefore, the system provided by the present invention and its various devices, modules, and units can be considered as hardware components, and the devices, modules, and units included therein that realize various functions can also be considered as structures within the hardware components, and the devices, modules, and units for realizing various functions can also be considered as both software modules for realizing methods or structures within the hardware components.

[0074] Example 6 This example is a structural schematic diagram of an electronic device provided based on Example 1, which includes at least one processor 501 and a memory 502 communicatively connected to the at least one processor 501, as shown in FIG. 12, wherein the memory 502 stores instructions executable by the at least one processor 501, the instructions are executed by the at least one processor 501, and the at least one processor 501 is capable of performing the above-mentioned method for detecting an analyte.

[0075] Here, the memory 502 and the processor 501 are connected in a bus manner, which may include any number of interconnected buses and bridges, connecting various circuits of one or more processors 501 and memories 502. 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 will not be further described in the present invention. A bus interface provides an interface between the bus and a transceiver. The transceiver may be a single component or multiple components, for example, multiple receivers and transmitters, providing a unit for communicating with various other devices via a transmission medium. Data processed by the processor 501 is transmitted over a wireless medium via an antenna, which receives data and transmits data to the processor 501.

[0076] The processor 501 is responsible for bus management and general processing, and may also provide a variety of functions including timing, peripheral interfaces, voltage regulation, power management, and other control functions. The memory 502 may be used to store data used by the processor 501 when performing operations.

[0077] The present invention further provides a computer-readable storage medium having stored thereon a computer program, which, when executed by a processor, implements the above-described method for detecting an analyte.

[0078] That is, those skilled in the art will understand that all or some of the steps of implementing the methods of the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a storage medium containing some instructions that enable a device (which may be a single-chip microcomputer, chip, etc.) or a processor to execute all or some of the steps of the methods described in each embodiment of the present application. The storage medium includes various media that can store program code, such as a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0079] Those skilled in the art will understand that the above embodiments are specific examples for realizing the present invention, and that in actual applications, various changes in form and details are possible without departing from the spirit and scope of the present invention.

[0080] In addition, in the description of this application, the orientations or positional relationships indicated by terms such as "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer" are based on the orientations or positional relationships shown in the drawings, and are intended to facilitate and simplify the description of this application. They do not indicate or imply that the indicated devices or elements must have a specific orientation, or be configured or operate in a specific orientation, and cannot be understood as limiting this application.

[0081] The above describes specific examples of the present invention. The present invention is not limited to the above specific embodiments, and it should be understood that those skilled in the art can make various changes or modifications within the scope of the claims without affecting the essential content of the present invention. Unless inconsistent, the examples and features of the examples in this application can be combined with each other in any way. [Explanation of symbols]

[0082] 200:Detection device 201: Light source 202: Imaging spectrum detector 203: Controller 204: 1st bandpass filter 205: Lens 206: Second bandpass filter 207: Circuit board 501: Processor 502: Memory

Claims

1. 1. An illumination system for an analyte, comprising: a case; and a light source (201), the light source (201) being capable of providing light within a preset wavelength range; The illumination system for an object to be analyzed is characterized in that the case can be brought into close contact with the detection site of the subject to form an imaging area (100) at the close contact site, and the light source (201) irradiates the imaging area (100).

2. The light source (201) includes one type of light source (201), and the wavelength range of the light beam emitted by the one type of light source (201) simultaneously covers a wavelength range capable of obtaining distribution status data of the analyte and a wavelength range capable of obtaining spectral data of the analyte.

10. The illumination system for analysis of claim 1.

3. The light source (201) is characterized in that it includes a plurality of light-emitting modules uniformly distributed on the same circumference.

3. The illumination system for analysis of claim 2.

4. The light source (201) includes two types of light sources (201), and the wavelength range of the light beam provided by one of the light sources (201) covers a wavelength range in which distribution status data of the analyte can be obtained, and the wavelength range of the light beam provided by the other light source (201) covers a wavelength range in which spectral data of the analyte can be obtained.

10. The illumination system for analysis of claim 1.

5. The two types of light sources (201) each include a plurality of light emitting modules, and the plurality of light emitting modules of the two types of light sources (201) are alternately arranged on the same circumference, and the light emitting modules are uniformly distributed on the same circumference.

5. The illumination system for analysis of claim 4.

6. One of the light sources (201) provides infrared light in the wavelength range of 800 nanometers to 1000 nanometers; The other light source (201) is characterized by providing ultraviolet light in the wavelength range of 300 nanometers to 390 nanometers.

5. The illumination system for analysis of claim 4.

7. A first band-pass filter (204) is further provided in the case, and the first band-pass filter (204) is located between the light source (201) and the imaging region (100); The first bandpass filter (204) is characterized in that it passes light within a predetermined wavelength range and blocks light outside the predetermined wavelength range.

10. The illumination system for analysis of claim 1.

8. The shape and material of the case are such that the case can be fitted closely to the skin around the detection site of the subject.

10. The illumination system for analysis of claim 1.

9. 1. An analyte detection system comprising: The illumination system for an object to be analyzed according to any one of claims 1 to 8 is adopted. Analyte detection system.

10. 1. An analyte detection system comprising: The method further includes a portable device, wherein the illumination system for the analysis target is integrated into the portable device. Analyte detection system.

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