Method and device for detecting creatinine content information of blood

Abstract

The invention discloses a blood creatinine content information detection method and system, and the method comprises the steps: obtaining a face video image, determining the absorbance data of different components in blood based on the face video image, and determining the spectral information of different components based on the absorbance data; preprocessing the spectral information to obtain a blood component spectrogram; and inputting the spectrogram into a discrimination model, and outputting creatinine content information in the blood. The purpose of non-contact measurement of creatinine content information is achieved.

Description

Technical Field

[0001] The invention relates to the technical field of near infrared detection, and in particular to a method for detecting blood creatinine content information. Background Art

[0002] Traditional creatinine testing methods include testing for serum creatinine in blood and testing for urine creatinine in urine, etc. Serum creatinine testing is usually done by contact testing, which is relatively inefficient. Summary of the invention

[0003] The main purpose of the present invention is to provide a data processing method and device to solve the deficiencies existing in the related art.

[0004] In order to achieve the above-mentioned purpose, according to a first aspect of the present invention, there is provided a method for acquiring a face video image, and determining absorbance data of different components in the blood based on the face video image, and determining spectral information of the different components based on the absorbance data; preprocessing the spectral information to obtain a blood component spectrum; inputting the spectrum into a discriminant model, and outputting creatinine content information in the blood.

[0005] Optionally, when constructing the discriminant model, the method includes: obtaining a facial video image sample, and determining a spectral information sample of different blood components corresponding to the facial video image sample; preprocessing the spectral information sample to obtain a blood spectrogram sample; and constructing a discriminant model based on the blood spectrogram sample and the corresponding known blood component content data, and using a least squares regression method.

[0006] Optionally, based on the change of absorbance with concentration at different wave numbers of the blood spectrum sample, the wave number with a larger change rate value is selected to participate in the construction of the discrimination model.

[0007] Optionally, the output creatinine content information in the blood is compared with a standard value; and different comparison result data are stored in different databases.

[0008] According to a second aspect of the present invention, there is provided a device for detecting blood creatinine content information, comprising: an acquisition unit for acquiring a face video image, and determining absorbance data of different components in the blood based on the face video image, and determining spectral information of the different components based on the absorbance data; a processing unit for preprocessing the spectral information to obtain a blood component spectrum; and a content determination unit for inputting the spectrum into a discrimination model to output creatinine content information in the blood.

[0009] Optionally, when constructing the discriminant model, the method includes: obtaining a facial video image sample, and determining a spectral information sample of different blood components corresponding to the facial video image sample; preprocessing the spectral information sample to obtain a blood spectrogram sample; and constructing a discriminant model based on the blood spectrogram sample and the corresponding known blood component content data, and using a least squares regression method.

[0010] Optionally, based on the change of absorbance with concentration at different wave numbers of the blood spectrum sample, the wave number with a larger change rate value is selected to participate in the construction of the discrimination model.

[0011] Optionally, based on the change of absorbance with concentration at different wave numbers of the blood spectrum sample, the wave number with a larger change rate value is selected to participate in the construction of the discrimination model.

[0012] According to a third aspect of the present invention, there is provided a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, wherein the computer instructions are used to cause the computer to execute any one of the methods described in the first aspect.

[0013] According to a fourth aspect of the present invention, there is provided an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor executes the method described in any one implementation of the first aspect.

[0014] The present embodiment provides a method and system for detecting blood creatinine content information, wherein the method comprises obtaining a face video image, and determining absorbance data of different components in the blood based on the face video image, and determining spectral information of different components based on the absorbance data; preprocessing the spectral information to obtain a blood component spectrum; inputting the spectrum into a discrimination model, and outputting creatinine content information in the blood. The purpose of non-contact measurement of creatinine content information is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 is a flow chart of a method for detecting blood creatinine content information according to an embodiment of the present invention;

[0017] Figure 2 is a schematic diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0019] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so as to describe the embodiments of the present invention described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0020] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0021] According to an embodiment of the present invention, a method for detecting blood creatinine content information is provided, such as Figure 1 As shown, it includes the following steps 101 to 103:

[0022] Step 101: Acquire a face video image, and determine absorbance data of different components in blood based on the face video image, and determine spectral information of the different components based on the absorbance data.

[0023] In this step, a near-infrared camera is used to collect a video image of the user's face, and the facial video image is spatially decomposed, temporally filtered, and processed in real time to obtain a dynamic spectrum of different components such as blood creatinine content.

[0024] The target area to be measured is tracked in real time to determine the ROI area; the RGB three-channel separation is performed on each frame of the image sequence of the ROI area, and the sum and average are taken to obtain the time series waveform within a period of time; the above time series waveform is detrended and normalized to obtain the time series signal; the normalized signal is denoised using the empirical mode decomposition method to obtain a three-channel time domain signal with a high signal-to-noise ratio; finally, the peaks and troughs of the time domain signal are obtained to obtain the dynamic spectrum of the content of each component in the blood.

[0025] Specifically, since the human face has abundant blood flow and is convenient for sampling, this embodiment utilizes a portable acquisition device that has both near-infrared spectroscopy and imaging functions, and this embodiment is not limited. Taking a portable near-infrared camera as an example, before performing face acquisition, the camera is calibrated and the faces of the tested people are uniformly scanned at least once. During the scan, the spectrometer excites several near-infrared lights of different wavelengths to illuminate the face of the person being tested. In this embodiment, the cheek and forehead areas of the face are framed to effectively avoid motion noise at the eyes and mouth. The spectrometer receives the photoelectric pulse wave corresponding to the outgoing light of each wavelength, and the photoelectric conversion device and the analog-to-digital converter realize the conversion of optical-electrical-digital signals to obtain the data of the changing outgoing light intensity. The absorbance data of each component in the blood is calculated based on the data of the changing outgoing light intensity, and the absorbance data includes the absorbance data of creatinine; the spectra of different components of the blood are obtained based on the absorbance data.

[0026] Step 102: pre-process the spectral information to obtain a blood component spectrum.

[0027] In this step, the spectral sample is pre-processed to obtain a blood component spectrogram. Due to the use of near-infrared spectral camera device to obtain the data signal, in addition to the measured sample to be measured component information, also includes the noise of the equipment, such as high-frequency random noise, baseline drift, stray information, sample background background and other irrelevant information. Therefore, before data analysis, first of all, the specific signal measurement and sample system should be reasonably processed to reduce or even eliminate the impact of various non-target factors on the detection signal information, in order to establish a stable and reliable mathematical model to lay the foundation. Commonly used data pre-processing methods include data standardization (mean centering, normalization, standard normal transformation, etc.), high-frequency noise filtering (convolution smoothing, Fourier transform, wavelet transform, etc.), signal differential derivation and baseline correction, etc.

[0028] Specifically include:

[0029] 1): Use Euclidean distance to determine the discrete points of the spectrum and eliminate invalid spectra; that is, by calculating the coordinates of each spectral data point, and then calculating the Euclidean distance between each spectral data point and all other spectral data points, after obtaining the Euclidean distance, determine the current spectral sample discrete points according to the thresholds set for different blood components, and eliminate the spectrum corresponding to the above discrete points.

[0030] 2): Use wavelet transform denoising method to remove the interference noise of the spectrum. Wavelet transform (Wave l etTransform): WT decomposes the chemical signal into multiple scale components according to the different frequencies, and adopts corresponding coarse and fine sampling steps for different scale components, so as to focus on any part of the signal and achieve complete extraction of signal data. In essence, it uses the signal x to project onto the wavelet to obtain the wavelet coefficients which are relatively easy to process. The final signal is selected according to different needs. Different processing of the wavelet coefficients is selected, and then the signal is obtained by inverse transformation;

[0031] 3) Remove the spectrum baseline of the spectrum by using one or more of the peak-valley point leveling, offset subtraction, differential processing and baseline tilt methods. When the peak-valley point leveling method is used, each new spectrum is subtracted from the average value of the absorbance at the peak and valley of the spectrum.

[0032] 4): Use Fourier Transform to decompose the synthetic waveform in the time domain into countless sub-waves with constant periods. The amplitude height of the sub-wave is the height in the frequency domain, and different frequency values ​​are different sub-waves. In spectral information processing, FT as a signal processing technology can not only decompose the spectral signal into the superposition of many sine waves of different frequencies, but also smooth, interpolate, filter, fit and improve the resolution of the original spectral data, as well as perform smoothing and denoising, data compression and information extraction.

[0033] 5): The preprocessed spectrum is normalized to obtain a preprocessed blood component spectrum.

[0034] Step 103: input the spectrum into the discrimination model, and output the creatinine content information in the blood.

[0035] In this step, the above-mentioned judgment model is used to identify the input blood component spectrum, and the corresponding creatinine content in the blood is obtained, the creatinine content value is compared with the control standard, and the judgment result is output as a normal value, an increased value, or a decreased value. According to the judgment result, the blood spectra corresponding to different creatinines are stored in a normal value sub-database, an increased value sub-database, or a decreased value sub-database.

[0036] As an optional implementation of this embodiment, when constructing the discriminant model, the method includes: obtaining a facial video image sample, and determining a spectral information sample of different blood components corresponding to the facial video image sample; preprocessing the spectral information sample to obtain a blood spectrogram sample; based on the blood spectrogram sample and the corresponding known blood component content data, and using a least squares regression method to construct a discriminant model.

[0037] In this optional implementation, since blood is an opaque turbid medium with strong absorption and scattering characteristics, it is impossible to detect the material components using the Lambert-Beer law. Therefore, this embodiment uses a chemometric algorithm to establish a functional relationship between the spectral sample and the content of its components, that is, by matching the known sample spectrum with the content data one by one, selecting a chemometric algorithm to pre-process and optimize the spectral sample, and establishing a blood spectrum graph with a high correlation between the spectral sample and the component content to construct a discrimination model, and then using the discrimination model to achieve rapid prediction of related data such as the content or properties of the unknown sample components.

[0038] For the construction method of the above-mentioned discriminant model, the optional methods include principal component analysis (PCA), MLR principle regression analysis, partial least squares regression analysis, and BP artificial neural network (BP-ANN). This embodiment is described by taking partial least squares regression analysis as an example. That is, the data dimension reduction operation is performed on the above-mentioned pre-processed blood spectrogram and blood component content data by partial least squares regression analysis.

[0039] Partial least squares regression is mainly used to solve many-to-many linear regression analysis problems, especially when there are multiple correlations between variables, many variables but small sample size, heteroscedasticity and other problems, the use of least squares regression has advantages that classical linear regression cannot match. The analysis process of partial least squares regression concentrates the characteristics of multiple methods such as principal component analysis, canonical correlation analysis, and linear regression analysis, so the analysis of the problem is more in-depth, the information provided is richer, and the results obtained are more reasonable.

[0040] Here, the characteristics of partial least squares regression are:

[0041] (1) It can perform regression modeling when there are serious multiple correlations between independent variables;

[0042] (2) It allows regression modeling when the number of sample points is less than the number of variables;

[0043] (3) Partial least squares regression will include all original independent variables in the final model;

[0044] (4) The partial least squares regression model is easier to identify system information and noise (even some non-random noise);

[0045] (5) In the partial least squares regression model, the regression coefficient of each independent variable will be easier to interpret.

[0046] Specific modeling method:

[0047] Suppose there are q dependent variables and p independent variables. In order to study the statistical relationship between the dependent variable and the independent variable, n sample points are observed, thus forming the data tables X and Y of the independent variable and the dependent variable. Partial least squares regression extracts t and u from X and Y respectively, with the following requirements: (1) t and u should carry as much variation information as possible in their respective data tables; (2) the correlation between t and u can be maximized. After the first component is extracted, partial least squares regression implements the regression of X on t and the regression of Y on t respectively. If the regression equation has reached a satisfactory accuracy, the algorithm terminates; otherwise, the residual information of X explained by t and the residual information of Y explained by t will be used for the second round of component extraction. This process is repeated until a satisfactory accuracy can be achieved. If multiple components are finally extracted for X, partial least squares regression will regress yk on these components of X, and then express it as a regression equation of yk on the original independent variable.

[0048] The partial least squares regression analysis method used in this embodiment. In partial least squares regression, the original independent variables are converted into a set of new comprehensive variables (called principal components or latent variables), and then these principal components are used for regression analysis, thereby reducing the collinearity between the independent variables, improving the stability and predictive ability of the model, solving the multicollinearity problem in the spectral matrix, removing the redundant components in the spectral data, and greatly improving the speed and efficiency of the operation.

[0049] As an optional implementation of this embodiment, the output creatinine content information in the blood is compared with the standard value; and different comparison result data are stored in different databases.

[0050] In this optional implementation, blood component content data and its corresponding blood spectra are obtained; a chemometric algorithm is selected to preprocess the blood spectra; and a blood creatinine discrimination model is constructed based on the blood component content data and the preprocessed blood spectra using partial least squares regression analysis. Here, the variation law of the absorbance of the sample spectrum at different wavenumbers with concentration is analyzed, and the wavenumber point with a larger change rate value (Vmean value) is selected to participate in the near-infrared spectroscopy blood creatinine content modeling.

[0051] As an optional implementation of this embodiment, the output creatinine content information in the blood is compared with the standard value; and different comparison result data are stored in different databases.

[0052] In this optional implementation, the blood creatinine content value is compared with the control standard, and the judgment result is output as a normal value, an increased value, or a decreased value; according to the judgment result, the blood spectra corresponding to different creatinines are stored in a normal value sub-database, an increased value sub-database, or a decreased value sub-database.

[0053] In order to uniformly manage and call it, a spectral database is constructed. At the same time, according to the existing blood creatinine control standard, a normal value sub-database, an increased value sub-database and a decreased value sub-database are constructed in the spectral database to store the blood component spectral samples after obtaining the accurate creatinine content. In this embodiment, the division standards of the above sub-databases are exemplified as follows: normal value of blood creatinine (SCr) (53-106umol / L for men; 44-97umol / L for women); increased blood creatinine (SCr) (greater than 106umol / L for men; greater than 97umol / L for women); decreased blood creatinine (SCr) (less than 53umol / L for men; less than 44umol / L for women). Generally speaking, the normal value range of creatinine is different for men and women, but there is no difference in age.

[0054] By using the chemometric algorithm and the partial least squares regression analysis method to construct a discriminant model for the known blood component content data and its corresponding blood spectra, a functional relationship, namely the discriminant model, is established between the spectral samples and the blood component content data. The collected blood component spectral samples are input into the discriminant model to obtain the current creatinine detection in the blood. Since it uses non-invasive and non-contact detection methods, real-time detection of the population can be achieved, and the analysis speed is fast and the signal-to-noise ratio is high. It is particularly suitable for scenarios that require rapid detection, such as online detection and analysis.

[0055] This embodiment uses a chemometric algorithm to construct a discriminant model for known blood component content data and its corresponding blood spectra in conjunction with a partial least squares regression analysis method, and establishes a functional relationship between the spectral sample and the blood component content data, namely, the discriminant model. The collected blood component spectral samples are input into the discriminant model to obtain the current detection of creatinine in the blood. Since it uses a non-invasive and non-contact detection method, real-time detection of the population can be achieved, and the analysis speed is fast and the signal-to-noise ratio is high. It is particularly suitable for scenarios that require rapid detection, such as online detection and analysis.

[0056] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0057] According to an embodiment of the present invention, a device for detecting blood creatinine content information is also provided, including an acquisition unit for acquiring a face video image, and determining absorbance data of different components in the blood based on the face video image, and determining spectral information of the different components based on the absorbance data; a processing unit for preprocessing the spectral information to obtain a blood component spectrum; and a content determination unit for inputting the spectrum into a discriminant model to output creatinine content information in the blood.

[0058] As an optional implementation of this embodiment, when constructing the discriminant model, the method includes: obtaining a facial video image sample, and determining a spectral information sample of different blood components corresponding to the facial video image sample; preprocessing the spectral information sample to obtain a blood spectrogram sample; based on the blood spectrogram sample and the corresponding known blood component content data, and using a least squares regression method to construct a discriminant model.

[0059] As an optional implementation of this embodiment, based on the change of absorbance with concentration at different wave numbers of the blood spectrum sample, the wave number with a larger change rate value is selected to participate in the construction of the discrimination model.

[0060] As an optional implementation of this embodiment, the output creatinine content information in the blood is compared with the standard value; and different comparison result data are stored in different databases.

[0061] According to an embodiment of the present invention, the present invention also provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can implement the method described in any of the above embodiments when executing.

[0062] According to an embodiment of the present invention, the present invention further provides a readable storage medium, which stores computer instructions, and the computer instructions are used to enable a computer to implement the method described in any of the above embodiments when executed.

[0063] According to an embodiment of the present invention, the present invention further provides a computer program product, which can implement the method described in any of the above embodiments when executed by a processor.

[0064] Figure 2A schematic block diagram of an example electronic device 300 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices.

[0065] like Figure 2 As shown, the electronic device 300 includes a computing unit 301, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 302 or a computer program loaded from a storage unit 308 into a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 can also be stored. The computing unit 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0066] A number of components in the electronic device 300 are connected to the I / O interface 305, including: an input unit 306, such as a keyboard, a mouse, etc.; an output unit 307, such as various types of displays, speakers, etc.; a storage unit 308, such as a disk, an optical disk, etc.; and a communication unit 309, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 309 allows the electronic device 300 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0067] The computing unit 301 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 301 performs the various methods and processes described above, such as the object matching method. For example, in some embodiments, the object matching method may be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as a storage unit 308. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 300 via the ROM 302 and / or the communication unit 309. When the computer program is loaded into the RAM 303 and executed by the computing unit 301, one or more steps of the method described above may be performed.

[0068] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs, which may be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general programmable processor, which may receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0069] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0070] In the context of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

Claims

1. A method for detecting blood creatinine content information, characterized in that: include: Acquire a face video image, and determine absorbance data of different components in the blood based on the face video image, and determine spectral information of the different components based on the absorbance data; Preprocessing the spectral information to obtain a blood component spectrum; The spectrum is input into the discrimination model, and the creatinine content information in the blood is output.

2. The method for detecting blood creatinine content information according to claim 1, characterized in that: When constructing the discriminant model, the method includes: Acquire a face video image sample, and determine a spectrum information sample of different blood components corresponding to the face video image sample; Preprocessing the spectrum information sample to obtain a blood spectrum sample; Based on the blood spectrogram samples and the corresponding known blood component content data, a discrimination model is constructed using a least squares regression method.

3. The method for detecting blood creatinine content information according to claim 2, characterized in that: Based on the change of absorbance with concentration at different wave numbers of blood spectrum samples, the wave number with larger change rate value is selected to participate in the construction of the discrimination model.

4. The method for detecting blood creatinine content information according to claim 1, characterized in that: Compare the outputted creatinine content information in the blood with the standard value; Different comparison result data are stored in different databases.

5. A device for detecting blood creatinine content information, characterized in that: include: An acquisition unit, used for acquiring a face video image, and determining absorbance data of different components in the blood based on the face video image, and determining spectral information of the different components based on the absorbance data; A processing unit, used for preprocessing the spectral information to obtain a blood component spectrum; The content determination unit is used to input the spectrum into the discrimination model and output the creatinine content information in the blood.

6. The blood creatinine content information detection system according to claim 5, characterized in that: When constructing the discriminant model, the method includes: Acquire a face video image sample, and determine a spectrum information sample of different blood components corresponding to the face video image sample; Preprocessing the spectrum information sample to obtain a blood spectrum sample; Based on the blood spectrogram samples and the corresponding known blood component content data, a discrimination model is constructed using a least squares regression method.

7. The blood creatinine content information detection system according to claim 6, characterized in that: Based on the change of absorbance with concentration at different wave numbers of blood spectrum samples, the wave number with larger change rate value is selected to participate in the construction of the discrimination model.

8. The blood creatinine content information detection system according to claim 5, characterized in that: The output creatinine content information in the blood is compared with the standard value; different comparison result data are stored in different databases.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the method according to any one of claims 1 to 4.

10. An electronic device, characterized in that: include: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor executes the method described in any one of claims 1 to 4.

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