A method and device for detecting drugs and their metabolites in hair

Abstract

The present invention provides a method and a detection device for detecting drugs and their metabolites in hair, which relates to the technical field of drug detection. The present invention collects hair samples, prepares samples with known drug concentration values, collects spectral data through a Raman spectrometer, inputs the spectral data matrix and the response variable matrix, analyzes them, generates a relationship matrix reflecting the correlation between the spectral data matrix and the response variable matrix, and further processes it to generate a weight vector reflecting the weight coefficients of the detection sample concentration at corresponding wavelengths. By analyzing the correlation between the weight vector and the spectral data matrix, a confidence vector that maximally reflects the directional relationship between the response variable matrix Y and the spectral data matrix X is generated, a back-projection matrix is generated after back-projection, and a confidence index is output. By comparing the confidence index with a confidence threshold, it is output whether the concentration detected by the Raman spectrometer in the current environment is valid.

Description

Technical Field

[0001] The present invention relates to the technical field of drug detection, and specifically provides a method and device for detecting drugs and their metabolites in hair. Background Art

[0002] Drug 2C-B is a synthetic hallucinogenic drug belonging to the class of phenethylamine compounds. 2C-B produces hallucinogenic effects and a few physical effects, usually in the form of tablets or capsules, and sometimes also used in powder form. 2C-B can cause altered perception, visual and auditory hallucinations, enhanced perception of colors and shapes, etc. Users may experience amplified emotions, including feelings of pleasure, excitement or anxiety, and may also include increased heart rate, sweating, mild nausea, etc. At the same time, the intensity and nature of the effects brought by 2C-B are highly related to the dose, and higher doses may lead to more intense hallucinations and physical and mental reactions.

[0003] When detecting drug 2C-B, a Raman spectrometer is often used for auxiliary detection. Raman spectroscopy is based on the Raman scattering phenomenon, that is, when a laser irradiates a sample, most photons will undergo Rayleigh scattering, but a small fraction will change energy due to interaction with the vibrational or rotational energy levels of the molecules, which is Raman scattering. By measuring the frequency shift of the Raman scattered light, information about molecular vibrations can be obtained to identify chemical components. During detection, different temperatures and different light intensities will affect the signal intensity and quality, resulting in inconsistent detection results and affecting the accuracy of the detection results.

[0004] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present disclosure, and thus it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0005] The purpose of the present invention is to provide a method and device for detecting drugs and their metabolites in hair to solve the problems raised in the above background art.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] A method for detecting drugs and their metabolites in hair, the specific steps including:

[0008] Step 1, collect sample hair and process the sample hair;

[0009] Step 2, collect spectral data through a Raman spectrometer and generate a spectral data matrix X and a response variable matrix Y;

[0010] Step 3: Conduct a correlation analysis on the spectral data matrix X and the response variable matrix Y to generate a relationship matrix C, where the relationship matrix C is used to reflect the correlation between the spectral data matrix X and the response variable matrix Y;

[0011] Step 4: Analyze the relationship matrix C to generate a weight vector V, where the weight vector is used to reflect the weight coefficient of the concentration of the test sample at the corresponding wavelength;

[0012] Step 5: Conduct a correlation analysis on the weight vector V and the spectral data matrix X to generate a confidence vector, where the confidence vector is used to maximize the reflection of the directional relationship between the response variable matrix Y and the spectral data matrix X;

[0013] Step 6: Conduct a correlation analysis on the confidence vector with the response variable matrix Y and the spectral data matrix X respectively to generate a response variable projection vector Q and a spectral data variable projection vector P, and combine the response variable projection vector Q, the spectral data variable projection vector P, and the weight vector V for correlation analysis to generate a mapping matrix B, where the mapping matrix B is used to map the spectral data matrix X back to the response variable matrix Y;

[0014] Step 7: Conduct a correlation analysis on the mapping matrix B with the spectral data matrix X and the response variable matrix Y to generate a confidence index ZZ, where the confidence index ZZ is used to reflect the confidence level of the accuracy of the detected concentration. Set a confidence threshold, compare the confidence index ZZ with the confidence threshold, and output whether the detected concentration is valid.

[0015] Further, in S1, rinse the sample hair with deionized water, remove the surface dirt, put the hair sample into an ultrasonic cleaner, set the frequency to 40 kHz, clean for ten minutes, remove the remaining cleaning agent, dry at 60 degrees Celsius for one hour, freeze in liquid nitrogen for five minutes, use a cryogenic grinder to grind the sample hair, add different doses of 2CB drugs to each sample hair respectively, record the known concentration values, and detect the detected concentration values of each hair sample through a Raman spectrometer.

[0016] Further, the spectral data matrix X is a matrix with m rows and n columns, where m is the number of samples and n is the number of spectral wavelengths. Each item in the spectral data matrix X corresponds to the detected concentration value of the sample at different spectral wavelengths. The response variable matrix Y is a matrix with m rows and 1 column, where each item is the known concentration value of the corresponding sample.

[0017] Further, the formula based on which the correlation analysis is conducted on the spectral data matrix X and the response variable matrix Y to generate the relationship matrix C is:

[0018] C = X T Y

[0019] Among them, X T is the transpose of the spectral data matrix X, and the relationship matrix C is used to reflect the correlation between the spectral data matrix X and the response variable matrix Y.

[0020] Furthermore, a correlation analysis is performed on the relationship matrix C to generate eigenvalues, and the formula is as follows:

[0021] det(C - εI) = 0

[0022] where I is the identity matrix, ε is the eigenvalue, and the maximum value ε of the eigenvalues max is substituted into the formula (C - ε max I)V = 0, and V is output, where V is the weight vector, and each term is the weight coefficient of the concentration of the detection sample at the corresponding wavelength;

[0023] A correlation analysis is performed on the weight vector V and the spectral data matrix X to generate a confidence vector t, and the formula is as follows:

[0024] t = XV

[0025] where the confidence vector is used to maximize the reflection of the directional relationship between the response variable matrix Y and the spectral data matrix X;

[0026] A correlation analysis is performed on the confidence vector t and the response variable matrix Y to generate a response variable projection vector Q, and the formula is as follows:

[0027]

[0028] The response variable projection vector Q is used to reflect the projection of the known concentration value in the direction of the confidence vector t;

[0029] A correlation analysis is performed on the confidence vector t and the spectral data matrix X to generate a spectral data variable projection vector P, and the formula is as follows:

[0030]

[0031] The spectral data variable projection vector P is used to reflect the projection of the known concentration value in the direction of the confidence vector t.

[0032] Furthermore, a correlation analysis is performed on the spectral data variable projection vector P, the response variable projection vector Q, and the weight vector V to generate a back-projection matrix B, and the formula is as follows:

[0033] B = (P T V) -1 IQ T

[0034] The back-projection matrix B is used to map the spectral data matrix X back to the response variable matrix Y.

[0035] Further, a correlation analysis is performed on the back-projection matrix B and the spectral data matrix X to generate a prediction matrix The formula is as follows:

[0036]

[0037] For the prediction matrix and the response variable matrix Y, a correlation analysis is performed to generate a confidence index ZZ. The formula is as follows:

[0038]

[0039] where where is the average value of the known concentration, and the confidence index ZZ is used to reflect the confidence level of the accuracy of the detected concentration.

[0040] Further, the confidence threshold θ is 0.67. The confidence index ZZ is compared with the confidence threshold θ. When ZZ ≥ θ, it indicates that the detected concentration can be used as a valid value. When ZZ < θ, it indicates that the detected concentration cannot be used as a valid value.

[0041] The present invention also provides a detection device for drugs and their metabolites in hair, which is used to execute a detection method for drugs and their metabolites in hair, including:

[0042] A sample collection module for collecting sample hair and processing the sample hair;

[0043] A detection module for collecting spectral data through a Raman spectrometer and generating a spectral data matrix X and a response variable matrix Y;

[0044] A relationship matrix analysis module for performing a correlation analysis on the spectral data matrix X and the response variable matrix Y to generate a relationship matrix C;

[0045] A weight analysis module for analyzing the relationship matrix C to generate a weight vector V;

[0046] A confidence analysis module for performing a correlation analysis on the weight vector V and the spectral data matrix X to generate a confidence vector;

[0047] A back-projection module for performing a correlation analysis on the confidence vector with the response variable matrix Y and the spectral data matrix X respectively to generate a response variable projection vector Q and a spectral data variable projection vector P, and performing a correlation analysis by combining the response variable projection vector Q, the spectral data variable projection vector P, and the weight vector V to generate a back-projection matrix B;

[0048] A comprehensive analysis module is used to perform correlation analysis on the response matrix B, the spectral data matrix X, and the response variable matrix Y to generate a confidence index ZZ. The confidence index ZZ is used to reflect the confidence level of the accuracy of the detected concentration. A confidence threshold is set, and the confidence index ZZ is compared with the confidence threshold to output whether the detected concentration is valid.

[0049] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0050] In the present invention, a hair sample is collected, and a sample with a known drug concentration value is modulated. The spectral data is collected by a Raman spectrometer and entered into the spectral data matrix and the response variable matrix. Then, the data is analyzed to generate a relationship matrix reflecting the correlation between the spectral data matrix and the response variable matrix, and further processed to generate a weight vector reflecting the weight coefficient of the detected sample concentration at the corresponding wavelength. By analyzing the correlation between the weight vector and the spectral data matrix, a confidence vector that maximally reflects the directional relationship between the response variable matrix Y and the spectral data matrix X is generated. After back-projection, a back-projection matrix is generated, and a confidence index is output. By comparing the confidence index with the confidence threshold, it is output whether the concentration detected by the Raman spectrometer in the current environment is valid. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a schematic diagram of the overall method flow of the present invention;

[0052] Figure 2 It is a schematic diagram of the system flow of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0053] To make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to specific embodiments.

[0054] It should be noted that unless otherwise defined, the technical terms or scientific terms used in the present invention should have the ordinary meanings understood by those with ordinary skills in the field to which the present invention belongs. The "first", "second", and similar terms used in the present invention do not indicate any order, quantity, or importance, but are only used to distinguish different components. The terms "including" or "comprising" and the like mean that the elements or objects appearing before the term cover the elements or objects listed after the term and their equivalents, without excluding other elements or objects. The terms "connected" or "coupled" and the like are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The terms "upper", "lower", "left", "right", etc. are only used to represent relative positional relationships, and when the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0055] Example:

[0056] Please refer to Figure 1 , the present invention provides a technical solution:

[0057] A method for detecting drugs and their metabolites in hair. When detecting drug 2C-B, a Raman spectrometer is often used for auxiliary detection. Raman spectroscopy is based on the Raman scattering phenomenon, that is, when a laser irradiates a sample, most photons will undergo Rayleigh scattering, but a small part will change energy due to interaction with the vibrational or rotational energy levels of molecules, which is Raman scattering. By measuring the frequency shift of the Raman scattered light, information about molecular vibrations can be obtained, thereby identifying chemical components. During detection, due to different light illumination and temperature, the signal intensity and quality will be affected, resulting in inconsistent detection results at different temperatures and different light intensities, affecting the accuracy of the detection results. Therefore, the present invention performs an analysis and detection before each detection and outputs an execution index reflecting the detection accuracy. The specific steps include:

[0058] Step 1: Collect sample hair and process the sample hair;

[0059] Rinse the sample hair with deionized water and remove the surface dirt. Put the hair sample into an ultrasonic cleaner, set the frequency to 40 kHz, clean for ten minutes, remove the residual cleaning agent, dry at 60 degrees Celsius for one hour, freeze in liquid nitrogen for five minutes, use a cryogenic grinder to grind the sample hair, and add different doses of 2CB drugs to each sample hair respectively. Record the known concentration values, and detect the detected concentration values of each hair sample through a Raman spectrometer.

[0060] Step 2: Collect spectral data through a Raman spectrometer and generate a spectral data matrix X and a response variable matrix Y;

[0061] The spectral data matrix X is a matrix with m rows and n columns, where m is the number of samples and n is the number of spectral wavelengths. Each item in the spectral data matrix X corresponds to the detected concentration value of the sample at different spectral wavelengths. The response variable matrix Y is a matrix with m rows and 1 column, where each item is the known concentration value of the corresponding sample.

[0062] Step 3: Perform a correlation analysis on the spectral data matrix X and the response variable matrix Y to generate a relationship matrix C, and the relationship matrix C is used to reflect the correlation between the spectral data matrix X and the response variable matrix Y;

[0063] Performing a correlation analysis on the spectral data matrix X and the response variable matrix Y to generate a relationship matrix C, and the formula based on is:

[0064] C = X T Y

[0065] where, X Tis the transpose of the spectral data matrix X, and the relationship matrix C is used to reflect the correlation between the spectral data matrix X and the response variable matrix Y.

[0066] Step 4: Analyze the relationship matrix C to generate a weight vector V, where the weight vector is used to reflect the weight coefficient of the concentration of the test sample at the corresponding wavelength;

[0067] Perform a correlation analysis on the relationship matrix C to generate eigenvalues, and the formula is:

[0068] det(C - εI) = 0

[0069] where I is the identity matrix, ε is the eigenvalue, and substitute the maximum value ε of the eigenvalues max into the formula (C - ε max I)V = 0, and output V, where V is the weight vector, and each term is the weight coefficient of the concentration of the test sample at the corresponding wavelength;

[0070] Step 5: Perform a correlation analysis on the weight vector V and the spectral data matrix X to generate a confidence vector, and the confidence vector is used to maximize the reflection of the directional relationship between the response variable matrix Y and the spectral data matrix X;

[0071] Perform a correlation analysis on the weight vector V and the spectral data matrix X to generate a confidence vector t, and the formula is:

[0072] t = XV

[0073] where the confidence vector is used to maximize the reflection of the directional relationship between the response variable matrix Y and the spectral data matrix X;

[0074] Step 6: Perform a correlation analysis on the confidence vector with the response variable matrix Y and the spectral data matrix X respectively to generate a response variable projection vector Q and a spectral data variable projection vector P, and perform a correlation analysis in combination with the response variable projection vector Q, the spectral data variable projection vector P, and the weight vector V to generate a back-projection matrix B, and the back-projection matrix B is used to map the spectral data matrix X back to the response variable matrix Y;

[0075] Perform a correlation analysis on the confidence vector t and the response variable matrix Y to generate a response variable projection vector Q, and the formula is:

[0076]

[0077] The response variable projection vector Q is used to reflect the projection of the known concentration value in the direction of the confidence vector t;

[0078] Perform a correlation analysis on the confidence vector t and the spectral data matrix X to generate a spectral data variable projection vector P, and the formula is:

[0079]

[0080] The spectral data variable projection vector P is used to reflect the projection of the known concentration value in the direction of the confidence vector t.

[0081] Perform a correlation analysis on the spectral data variable projection vector P, the response variable projection vector Q, and the weight vector V to generate the mapping matrix B. The formula is as follows:

[0082] B = (P T V) -1 IQ T

[0083] The mapping matrix B is used to map the spectral data matrix X back to the response variable matrix Y.

[0084] Step 7: Perform a correlation analysis on the mapping matrix B, the spectral data matrix X, and the response variable matrix Y to generate the confidence index ZZ. The confidence index ZZ is used to reflect the confidence level of the accuracy of the detected concentration. Set a confidence threshold, compare the confidence index ZZ with the confidence threshold, and output whether the detected concentration is valid.

[0085] Perform a correlation analysis on the mapping matrix B and the spectral data matrix X to generate the prediction matrix The formula is as follows:

[0086]

[0087] Perform a correlation analysis on the prediction matrix and the response variable matrix Y to generate the confidence index ZZ. The formula is as follows:

[0088]

[0089] Where Where is the average value of the known concentration. The confidence index ZZ is used to reflect the confidence level of the accuracy of the detected concentration.

[0090] The confidence threshold θ is 0.67. Compare the confidence index ZZ with the confidence threshold θ. When ZZ ≥ θ, it means that the detected concentration can be used as a valid value. When ZZ < θ, it means that the detected concentration cannot be used as a valid value.

[0091] Referring to Figure 2 , the present invention also provides a detection device for drugs and their metabolites in hair, which is used to execute a detection method for drugs and their metabolites in hair, including:

[0092] A sample collection module, which is used to collect sample hair and process the sample hair;

[0093] A detection module, configured to collect spectral data through a Raman spectrometer and generate a spectral data matrix X and a response variable matrix Y;

[0094] A relationship matrix analysis module, configured to perform a correlation analysis on the spectral data matrix X and the response variable matrix Y to generate a relationship matrix C;

[0095] A weight analysis module, configured to analyze the relationship matrix C to generate a weight vector V;

[0096] A confidence analysis module, configured to perform a correlation analysis on the weight vector V and the spectral data matrix X to generate a confidence vector;

[0097] A back-projection module, configured to perform a correlation analysis on the confidence vector with the response variable matrix Y and the spectral data matrix X respectively to generate a response variable projection vector Q and a spectral data variable projection vector P, and perform a correlation analysis in combination with the response variable projection vector Q, the spectral data variable projection vector P, and the weight vector V to generate a back-projection matrix B;

[0098] A comprehensive analysis module, configured to perform a correlation analysis on the back-projection matrix B, the spectral data matrix X, and the response variable matrix Y to generate a confidence index ZZ. The confidence index ZZ is used to reflect the confidence level of the accuracy of the detected concentration. A confidence threshold is set, and the confidence index ZZ is compared with the confidence threshold to output whether the detected concentration is valid.

[0099] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to obtain a formula that is closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0100] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art can realize that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0101] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units. They may be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0102] The above are only specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application.

Claims

1. A method for detecting drugs and their metabolites in hair, characterized in that: The specific steps include: S1. Collecting sample hair and processing the sample hair; S2, collecting spectral data through a Raman spectrometer, and generating a spectral data matrix X and a response variable matrix Y; S3, performing correlation analysis on the spectral data matrix X and the response variable matrix Y to generate a relationship matrix C, wherein the relationship matrix C is used to reflect the correlation between the spectral data matrix X and the response variable matrix Y; S4, analyzing the relationship matrix C to generate a weight vector V, wherein the weight vector is used to reflect the weight coefficient of the concentration of the detected sample at the corresponding wavelength; S5, performing correlation analysis on the weight vector V and the spectral data matrix X to generate a confidence vector, where the confidence vector is used to maximize the directional relationship between the response variable matrix Y and the spectral data matrix X; S6, performing correlation analysis on the confidence vector, the response variable matrix Y and the spectral data matrix X, respectively, to generate a response variable projection vector Q and a spectral data variable projection vector P, and performing correlation analysis on the response variable projection vector Q, the spectral data variable projection vector P, and the weight vector V, to generate a reflection matrix B, which is used to map the spectral data matrix X back to the response variable matrix Y; S7, performing correlation analysis on the reflection matrix B, the spectral data matrix X, and the response variable matrix Y, generating a confidence index ZZ, the confidence index ZZ is used to reflect the confidence level of the accuracy of the detected concentration, setting a confidence threshold, comparing the confidence index ZZ with the confidence threshold, and outputting whether the detected concentration is valid; In S1, the hair samples are rinsed with deionized water to remove surface dirt, the hair samples are placed in an ultrasonic cleaner, the frequency is set to 40kHz, the cleaning time is ten minutes, the residual cleaning agent is removed, the hair samples are dried at 60 degrees Celsius for one hour, and frozen in liquid nitrogen for five minutes. The hair samples are ground using a cryo-grinder, and different doses of 2C-B drugs are added to each hair sample, the known concentration values ​​are recorded, and the detection concentration values ​​of each hair sample are detected by a Raman spectrometer; The spectral data matrix X is a matrix of m rows and n columns, where m is the number of samples and n is the number of spectral wavelengths. Each item in the spectral data matrix X corresponds to the detected concentration value of the sample under different spectral wavelengths. The response variable matrix Y is a matrix of m rows and 1 column, where each item is the known concentration value of the corresponding sample. The confidence threshold θ is 0.

67. The confidence index ZZ is compared with the confidence threshold θ. When ZZ ≥ θ, it means that the detected concentration can be used as a valid value. When ZZ < θ, it means that the detected concentration cannot be used as a valid value.

2. The method for detecting drugs and their metabolites in hair according to claim 1, characterized in that: Correlation analysis is performed on the spectral data matrix X and the response variable matrix Y to generate the relationship matrix C, based on the formula: C=X T Y Among them, X T is the transpose of the spectral data matrix X, and the relationship matrix C is used to reflect the correlation between the spectral data matrix X and the response variable matrix Y.

3. The method for detecting drugs and their metabolites in hair according to claim 2, characterized in that: The correlation analysis of the relationship matrix C is performed to generate the eigenvalue based on the formula: det(C-εI)=0 Where I is the unit matrix, ε is the eigenvalue, and the maximum value ε among the eigenvalues ​​is max Substituting into the formula (C-ε max I) V = 0, output V, where V is a weight vector, each of which is a weight coefficient of the concentration of the detected sample at the corresponding wavelength; The weight vector V and the spectral data matrix X are subjected to correlation analysis to generate the confidence vector t, based on the formula: t=XV Among them, the confidence vector is used to maximize the directional relationship between the response variable matrix Y and the spectral data matrix X; Perform correlation analysis on the confidence vector t and the response variable matrix Y to generate the response variable projection vector Q. The formula is as follows: The response variable projection vector Q is used to reflect the projection of the known concentration value in the direction of the confidence vector t; The confidence vector t and the spectral data matrix X are subjected to correlation analysis to generate the spectral data variable projection vector P, based on the following formula: The spectral data variable projection vector P is used to reflect the projection of the known concentration value in the direction of the confidence vector t.

4. The method for detecting drugs and their metabolites in hair according to claim 3, characterized in that: The correlation analysis is performed on the spectral data variable projection vector P, the response variable projection vector Q, and the weight vector V to generate the reflection matrix B. The formula is as follows: B=(P T V) -1 IQ T The back-mapping matrix B is used to map the spectral data matrix X back to the response variable matrix Y.

5. The method for detecting drugs and their metabolites in hair according to claim 4, characterized in that: Perform correlation analysis on the reflection matrix B and the spectral data matrix X to generate the prediction matrix The formula is: For the prediction matrix Perform correlation analysis with the response variable matrix Y to generate the confidence index ZZ, based on the formula: in, in is the average value of known concentrations, and the confidence index ZZ is used to reflect the confidence level of the accuracy of the detected concentration.

6. A device for detecting drugs and their metabolites in hair, used for executing the method for detecting drugs and their metabolites in hair according to claim 1, characterized in that: include: A sample collection module, used for collecting sample hair and processing the sample hair; A detection module is used to collect spectral data through a Raman spectrometer and generate a spectral data matrix X and a response variable matrix Y; The relationship matrix analysis module is used to perform correlation analysis on the spectral data matrix X and the response variable matrix Y to generate the relationship matrix C; The weight analysis module is used to analyze the relationship matrix C and generate the weight vector V; A confidence analysis module is used to perform correlation analysis on the weight vector V and the spectral data matrix X to generate a confidence vector; The echo module is used to perform correlation analysis on the confidence vector, the response variable matrix Y and the spectral data matrix X, respectively, to generate the response variable projection vector Q and the spectral data variable projection vector P, and to perform correlation analysis on the response variable projection vector Q, the spectral data variable projection vector P and the weight vector V, to generate the echo matrix B; The comprehensive analysis module is used to perform correlation analysis on the reflection matrix B, the spectral data matrix X, and the response variable matrix Y, and generate a confidence index ZZ. The confidence index ZZ is used to reflect the confidence level of the accuracy of the detection concentration, set a confidence threshold, compare the confidence index ZZ with the confidence threshold, and output whether the detection concentration is valid.

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