CN115791742A - Correlation detection method and system for weak signal analysis - Google Patents
Correlation detection method and system for weak signal analysis Download PDFInfo
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Abstract
The invention provides a correlation detection method and a correlation detection system for weak signal analysis, which comprises the following steps of 1, preprocessing a substance to be detected, collecting characteristic peak signals of substance samples to be detected with different gradient concentrations, and dividing the characteristic peak signals into test signals and reference signals; step 2, establishing a correlation function model of the test signal and the reference signal and obtaining a maximum value of the correlation function; and 3, performing linear fitting on the maximum values of the correlation functions of the samples to be measured with different concentrations and the corresponding sample concentrations to obtain a fitting function for enhancing the characteristic signal intensity and improving the signal-to-noise ratio. By means of the mode that the inner product of the multichannel to-be-detected signal and the reference signal are obtained, the correlation function is obtained, the detection sensitivity of trace substances is improved, the characteristic signal intensity is amplified, and the signal to noise ratio is improved. And quantitative analysis is realized by fitting the relation between the peak value of the correlation function and the concentration of the substance, and the detection limit is far higher than that of the traditional quantitative method.
Description
Technical Field
The invention belongs to the technical field of substance detection, and particularly relates to a related detection method and system for weak signal analysis.
Background
The detection of trace substances is widely realized by adopting technologies such as chromatography, mass spectrometry, spectroscopy and the like in the detection field. The detection limit of a substance, which is an important index of detection ability, has been a property of most interest in theoretical and industrial applications. In the existing detection analysis method, three times of signal-to-noise ratio is generally used as the detection limit of detection, and when the intensity of a characteristic signal is less than three times of a noise signal, the signal is considered to be undetectable. The invention defines the signal as weak signal, improves the detection sensitivity by a correlation detection method and realizes the analysis of the weak signal.
The trace substance detection limit is limited by a hardware detection platform, and the detection of trace substances with extremely low concentration is difficult to realize. It is highly desirable to realize weak signal identification in detection signals through data analysis algorithms.
Prior art document 1 (202210494342.8) discloses a method for detecting methanol dissolved in transformer oil based on a ZnO/Ag composite substrate, which includes collecting an aqueous solution of a sample to be detected by using a raman spectrometer to perform raman detection, and substituting a least square quantitative detection model into raman characteristic peak intensity at a position 1019cm-1 of methanol to obtain the concentration of methanol in the sample to be detected. The prior art document 1 has a disadvantage that it combines the least square method with its corresponding concentration to perform linear fitting for the raman characteristic peak intensity at the methanol 1019cm-1, which is a conventional quantitative analysis method. It is considered that the detection limit of the detection is three times the signal-to-noise ratio, and a signal having a characteristic signal intensity lower than three times the noise signal cannot be detected. In addition, the method for improving the detection accuracy of the weak signal in the prior art is mostly suitable for the periodic signal, and due to the particularity of the periodic signal, the analysis method applied to the periodic signal cannot be generally applied to the non-periodic signal, so that the reference significance is lacked. Therefore, a detection method is needed to improve the detection sensitivity so as to realize the detection of weak signals with the signal to noise ratio below three times.
Disclosure of Invention
In order to solve the defects in the prior art, the present invention aims to provide a correlation detection method and system for weak signal analysis.
The invention adopts the following technical scheme.
A correlation detection method for weak signal analysis, comprising the steps of:
step 1, preprocessing a substance to be detected, collecting characteristic peak signals of the substance sample to be detected with different gradient concentrations, and dividing the characteristic peak signals into a test signal and a reference signal;
step 2, establishing a correlation function model of the test signal and the reference signal and obtaining the maximum value of the correlation function;
and 3, performing linear fitting on the maximum values of the correlation functions of the samples to be measured with different concentrations and the corresponding sample concentrations to obtain a fitting function for enhancing the characteristic signal intensity and improving the signal-to-noise ratio.
The pretreatment comprises the following steps: dissolving the substance to be detected in a solvent to prepare samples to be detected with different gradient concentrations.
The reference signal is the highest intensity characteristic peak signal of the saturation concentration of the substance to be detected in the transformer oil.
The test signal is the highest intensity characteristic peak signal of the sample to be tested with gradient concentration.
And step 2, sequentially testing signals in the correlation function model to obtain a correlation function value of the highest-intensity characteristic peak signal of the gradient concentration sample, wherein the test signals are signals obtained by measuring the same characteristic peak of the substance to be tested for multiple times or signals obtained by measuring the same characteristic peak of the substance to be tested through multiple sensors.
And (3) the samples to be detected with different gradient concentrations in the step 1 are more than or equal to 5.
The correlation detection method further comprises: and (3) calculating the maximum value of the correlation function of the substance to be analyzed, and obtaining the concentration of the substance to be analyzed based on the fitting function in the step (3).
The correlation detection system for weak signal analysis based on the correlation detection method for weak signal analysis comprises a data acquisition module, a signal analysis module, a logic calculation module and a correlation function model module;
the data acquisition module is used for acquiring the spectrum of the sample with gradient concentration and acquiring characteristic peak signals of the sample to be detected with different gradient concentrations;
the signal analysis module is used for establishing a correlation function relation between the test signal and the reference signal;
the logic calculation module is used for calculating the maximum value of the correlation function and calculating the concentration of the substance to be detected;
the correlation function model module is used for establishing a correlation function model of the test signal and the reference signal and establishing a fitting function according to the maximum value of the correlation function of the substances to be tested with different concentrations and the corresponding sample concentration.
Compared with the prior art, the quantitative method in the traditional detection field fits the characteristic signal intensity with the substance concentration, and determines the detection limit according to the triple signal-to-noise ratio. The method improves the detection sensitivity of trace substances, amplifies the intensity of characteristic signals and improves the signal-to-noise ratio by solving the inner product of a multi-channel signal to be detected and then solving the correlation function with a reference signal. And quantitative analysis is realized by fitting the relation between the peak value of the correlation function and the concentration of the substance, and the detection limit is far higher than that of the traditional quantitative method. The invention realizes the further amplification of the signal through the inner product of the multi-channel signal on the basis of the cross-correlation algorithm; the invention is suitable for spectra, mass spectra and the like without periodic signals.
Drawings
FIG. 1 is a Raman spectrum of samples containing different concentrations of DBDS transformer oil;
FIG. 2 is a linear fitting relationship between the characteristic peak area and the concentration of a gradient sample;
FIG. 3 is a correlation function for samples of transformer oil containing different concentrations of DBDS;
FIG. 4 is a linear fit of the maximum value of the correlation function of the gradient sample to the concentration;
fig. 5 is a flowchart of a correlation detection method for weak signal analysis according to an embodiment of the present invention.
Detailed Description
The present application is further described below with reference to the accompanying drawings. The following examples are only used to illustrate the technical solutions of the present invention more clearly, and the protection scope of the present application is not limited thereby.
Step 1, preprocessing a substance to be detected, collecting characteristic peak signals of the substance sample to be detected with different gradient concentrations, and dividing the characteristic peak signals into a test signal and a reference signal; the pretreatment comprises the following steps: dissolving the substance to be detected in a solvent to prepare samples to be detected with different gradient concentrations.
The reference signal is the highest intensity characteristic peak signal of the saturation concentration of the substance to be detected in the transformer oil.
The test signal is the highest intensity characteristic peak signal of the sample to be tested with gradient concentration. Wherein the samples to be detected with different gradient concentrations are more than or equal to 5.
Step 2, establishing a correlation function model of the test signal and the reference signal and obtaining the maximum value of the correlation function; and sequentially testing signals in the correlation function model to obtain a correlation function value of the highest-intensity characteristic peak signal of the gradient concentration sample, wherein the test signals are signals obtained by measuring the same characteristic peak of the substance to be tested for multiple times or signals obtained by measuring the same characteristic peak of the substance to be tested through a plurality of sensors.
And 3, performing linear fitting on the maximum values of the correlation functions of the samples to be measured with different concentrations and the corresponding sample concentrations to obtain a fitting function for enhancing the characteristic signal intensity and improving the signal-to-noise ratio.
The correlation detection method further comprises: and (3) calculating the maximum value of the correlation function of the substance to be analyzed, and obtaining the concentration of the substance to be analyzed based on the fitting function in the step (3).
The method solves the correlation function of the test signal and the reference signal through a correlation detection theory, and realizes the analysis of the weak signal according to the correlation function. A weak signal is a signal with a signal strength three times lower than the noise signal, i.e. a signal to noise ratio of less than 3. Wherein the correlation function model is as follows:
wherein τ is the amount of displacement, expressed as time delays in the time series signal, expressed as frequency differences in the spectral signal, expressed as time differences in the chromatographic signal, which is essentially the interval between discrete data points, and R (τ) is a function of the amount of displacement τ;
g is a reference signal, specifically, the reference signal is the peak value of the peak of the highest intensity characteristic of the saturated concentration of the substance to be detected in the transformer oil;
f i is the characteristic peak value of the sample to be detected;
n is the number of input signals;
and x is discrete data of spectral characteristic peak signals.
Example 1: the application of the related detection method for weak signal analysis is illustrated by taking raman spectroscopy for measuring dibenzyl disulfide (DBDS) in transformer oil as an example, and it should be noted that the method is not limited to dibenzyl disulfide detection in transformer oil, and the method is applicable to any detection based on peak signal intensity, such as: raman spectroscopy, infrared spectroscopy, chromatography, mass spectrometry, and the like. Other application scenarios are exemplified by: the method is based on gas chromatography-mass spectrometry combination to monitor melamine in milk, and based on infrared spectroscopy to detect CO, CO2 gas and the like.
Step 1, firstly dissolving DBDS in transformer oil, preparing DBDS samples with different concentration gradients, injecting the samples into a quartz cuvette, and measuring the Raman spectrum of the sample with the gradient concentration by a confocal Raman spectrometer, wherein a characteristic peak at 1002cm & lt-1 & gt with the strongest intensity is taken as the basis of quantitative analysis as shown in figure 1. The characteristic peak is selected in a mode of selecting the characteristic peak with the strongest signal of the characteristic substance so as to realize the detection of the low-concentration substance. In the raman spectrum detection, the intensity of a characteristic peak linearly decreases with the decrease of the concentration of a substance, and therefore the characteristic peak is selected as the basis of analysis. The reference signal needs to have obvious peak type characteristics, and a characteristic substance characteristic peak signal of saturated concentration is usually selected.
In the conventional quantitative analysis, the relationship between the intensity of the characteristic peak and the concentration is calculated, and the quantitative analysis is performed according to the linear fitting relationship, as shown in fig. 2, the lower detection limit is 338.51ppm according to the detection signal-to-noise ratio.
Step 2, establishing a correlation function model of the test signal and the reference signal and obtaining the maximum value of the correlation function;
in particular, the amount of the solvent to be used,
r (tau) in the model is a function of the spectral signal frequency difference tau;
tau is the frequency difference of the spectral signal;
g is a reference signal, and specifically, the reference signal is the peak value of the highest intensity characteristic of the saturation concentration of the substance to be detected in the transformer oil;
f i is the peak value of the characteristic peak of the sample to be detected with gradient concentration;
n is the number of test signals input; the input test signal is the signal of the strongest characteristic peak of the sample to be tested with gradient concentration, and the input test signal can be the signal obtained by measuring the same characteristic peak of the same substance for multiple times or the signal obtained by measuring the same characteristic peak of the same substance through multiple sensors; the number of the test signals is positively correlated with the detection capability;
and x is discrete data of spectral characteristic peak signals.
And substituting the test signal and the reference signal into the formula, wherein the reference signal is a 1002cm & lt-1 & gt characteristic peak signal of the saturated concentration DBDS, the test signal is a 1002cm & lt-1 & gt characteristic peak signal of the DBDS of the gradient concentration sample, the number of the test signals is a positive integer, multiple groups of signals can be obtained through multiple measurements or multiple sensors, the more the number of the test signals is, the stronger the detection capability is, the lower the detection limit is, and the number of the test signals required by specific calculation is determined according to the requirement on the detection limit in an actual situation and the test cost.
The calculation mode substituted into the formula is as follows: selecting different spectrum signal frequency differences tau, carrying out frequency shift on a reference signal g according to g (x + tau), and carrying out frequency shift on the reference signal g after frequency shift and a multi-channel input signal f i And (3) firstly obtaining the inner product and then summing to obtain a correlation function R (tau) under the frequency difference tau of the spectrum signal, and transforming the value of the tau to obtain a function of the correlation function R (tau) relative to the tau.
And 3, performing linear fitting on the maximum value of the correlation function of the substances to be measured with different concentrations and the corresponding sample concentration to obtain a fitting function.
Taking the number of the test signals as 1 as an example, the correlation function obtained by the above formula at different concentrations is shown in fig. 3, the maximum value of the correlation function is obtained and is linearly fitted with the sample concentration, and the fitting function is shown in fig. 4, wherein the degree of fitting R is shown in fig. 4 2 Is 0.99917, the lower detection limit is 178.30ppm according to the detection signal-to-noise ratio, which is improved by 1.90 times compared with the traditional method. When the number of the test signals is 2 and 5, the lower detection limit can reach 115.62ppm and 63.19ppm respectively, and the lower detection limit is improved by 2.93 and 5.36 times compared with the traditional mode respectively.
And calculating a correlation function of the reference signal and the signal to be detected by adopting the formula, and identifying the characteristic signal according to the correlation function.
Example 2: according to the method for monitoring melamine in milk based on gas chromatography-mass spectrometry, firstly, milk is used for preparing a melamine saturated solution, attention is paid that the measuring range of a mass spectrometer is not exceeded, and a peak of melamine characteristic fragment ions in a mass spectrum is selected as a reference peak. And simultaneously configuring a series of concentration gradient samples, and selecting the characteristic peak of the same fragment ion to substitute into a correlation function model:
The embodiment also provides a correlation detection system for weak signal analysis, which is based on a correlation detection method for weak signal analysis, and comprises a data acquisition module, a signal analysis module, a logic calculation module and a correlation function model module.
The data acquisition module is used for acquiring a Raman spectrum of a sample with gradient concentration and acquiring characteristic peak signals of samples to be detected with different gradient concentrations through a confocal Raman spectrometer;
the signal analysis module is used for establishing a correlation function relation between the test signal and the reference signal;
the logic calculation module is used for calculating the maximum value of the correlation function and calculating the concentration of the substance to be detected;
the correlation function model module is used for establishing a correlation function model of the test signal and the reference signal and establishing a fitting function according to the maximum value of the correlation function of the substances to be tested with different concentrations and the corresponding sample concentration.
Compared with the prior art, the quantitative method in the traditional detection field fits the characteristic signal intensity with the substance concentration, and determines the detection limit according to the triple signal-to-noise ratio. The method improves the detection sensitivity of trace substances, amplifies the strength of characteristic signals and improves the signal-to-noise ratio by solving the inner product of a multi-channel signal to be detected and then solving a correlation function with a reference signal. And quantitative analysis is realized by fitting the relation between the peak value of the correlation function and the concentration of the substance, and the detection limit is far higher than that of the traditional quantitative method. The invention realizes further amplification of signals through the inner product of multi-channel signals on the basis of a cross-correlation algorithm; the invention is suitable for spectra, mass spectra and the like without periodic signals.
The present disclosure may be systems, methods, and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement various aspects of the present disclosure.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
Computer program instructions for carrying out operations of the present disclosure may be assembler instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).
Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (10)
1. A correlation detection method for weak signal analysis, comprising the steps of:
step 1, preprocessing a substance to be detected, collecting characteristic peak signals of the substance sample to be detected with different gradient concentrations, and dividing the characteristic peak signals into a test signal and a reference signal;
step 2, establishing a correlation function model of the test signal and the reference signal and obtaining the maximum value of the correlation function;
and 3, performing linear fitting on the maximum value of the correlation function of the samples to be tested with different concentrations and the corresponding sample concentrations to obtain a fitting function for enhancing the characteristic signal intensity and improving the signal-to-noise ratio.
2. A correlation detection method for weak signal analysis according to claim 1, characterized in that:
the pretreatment comprises the following steps: dissolving the substance to be detected in a solvent to prepare samples to be detected with different gradient concentrations.
3. A correlation detection method for weak signal analysis according to claim 2, characterized in that:
the reference signal is the highest intensity characteristic peak signal of the saturation concentration of the substance to be detected in the transformer oil.
4. A correlation detection method for weak signal analysis according to claim 2, characterized in that:
the test signal is the highest intensity characteristic peak signal of the sample to be tested with gradient concentration.
5. The correlation detection method for weak signal analysis according to claim 4, wherein:
and step 2, sequentially testing signals in the correlation function model to obtain a correlation function value of the highest-intensity characteristic peak signal of the gradient concentration sample, wherein the test signals are signals obtained by measuring the same characteristic peak of the substance to be tested for multiple times or signals obtained by measuring the same characteristic peak of the substance to be tested through multiple sensors.
6. The correlation detection method for weak signal analysis according to claim 5, wherein:
and (3) the samples to be detected with different gradient concentrations in the step 1 are more than or equal to 5.
7. The correlation detection method for weak signal analysis according to claim 6, wherein:
the correlation detection method further comprises: and (3) calculating the maximum value of the correlation function of the substance to be analyzed, and obtaining the concentration of the substance to be analyzed based on the fitting function in the step (3).
8. The correlation detection system for weak signal analysis based on the correlation detection method for weak signal analysis of any one of claims 1 to 7, wherein the system comprises a data acquisition module, a signal analysis module, a logic calculation module, and a correlation function model module;
the data acquisition module is used for acquiring the spectrum of the sample with gradient concentration and acquiring characteristic peak signals of the sample to be detected with different gradient concentrations;
the signal analysis module is used for establishing a correlation function relation between the test signal and the reference signal;
the logic calculation module is used for calculating the maximum value of the correlation function and calculating the concentration of the substance to be detected;
the correlation function model module is used for establishing a correlation function model of the test signal and the reference signal and establishing a fitting function according to the maximum value of the correlation function of the substances to be tested with different concentrations and the corresponding sample concentration.
9. A terminal comprising a processor and a storage medium; the method is characterized in that:
the storage medium is to store instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to any one of claims 1 to 8.
10. Computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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