CN113624710A - Terahertz information identification method for liquid substance component change and application thereof - Google Patents
Terahertz information identification method for liquid substance component change and application thereof Download PDFInfo
- Publication number
- CN113624710A CN113624710A CN202111086796.3A CN202111086796A CN113624710A CN 113624710 A CN113624710 A CN 113624710A CN 202111086796 A CN202111086796 A CN 202111086796A CN 113624710 A CN113624710 A CN 113624710A
- Authority
- CN
- China
- Prior art keywords
- terahertz
- entropy
- weighted
- signals
- composite
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 239000000126 substance Substances 0.000 title claims abstract description 36
- 238000000034 method Methods 0.000 title claims abstract description 29
- 230000008859 change Effects 0.000 title claims abstract description 24
- 239000007788 liquid Substances 0.000 title claims abstract description 22
- 239000002131 composite material Substances 0.000 claims abstract description 45
- 239000012530 fluid Substances 0.000 claims abstract description 6
- 238000001514 detection method Methods 0.000 claims abstract description 4
- 239000000203 mixture Substances 0.000 claims abstract description 3
- 239000000758 substrate Substances 0.000 claims description 20
- 230000006870 function Effects 0.000 claims description 16
- 239000010453 quartz Substances 0.000 claims description 16
- VYPSYNLAJGMNEJ-UHFFFAOYSA-N silicon dioxide Inorganic materials O=[Si]=O VYPSYNLAJGMNEJ-UHFFFAOYSA-N 0.000 claims description 16
- 239000000463 material Substances 0.000 claims description 15
- 238000000354 decomposition reaction Methods 0.000 claims description 8
- 238000012935 Averaging Methods 0.000 claims description 4
- 239000010432 diamond Substances 0.000 claims description 3
- 229910003460 diamond Inorganic materials 0.000 claims description 3
- 230000008569 process Effects 0.000 claims description 3
- 239000002210 silicon-based material Substances 0.000 claims description 3
- 230000007613 environmental effect Effects 0.000 claims description 2
- 230000001678 irradiating effect Effects 0.000 claims description 2
- 102000004169 proteins and genes Human genes 0.000 claims description 2
- 108090000623 proteins and genes Proteins 0.000 claims description 2
- 230000010365 information processing Effects 0.000 abstract description 2
- 230000008447 perception Effects 0.000 abstract description 2
- 230000000975 bioactive effect Effects 0.000 abstract 1
- 239000003550 marker Substances 0.000 abstract 1
- 238000012544 monitoring process Methods 0.000 abstract 1
- 239000002904 solvent Substances 0.000 abstract 1
- 239000000243 solution Substances 0.000 description 26
- 108010074051 C-Reactive Protein Proteins 0.000 description 10
- 102100032752 C-reactive protein Human genes 0.000 description 10
- 238000004364 calculation method Methods 0.000 description 5
- 230000003993 interaction Effects 0.000 description 5
- 238000012360 testing method Methods 0.000 description 5
- 230000005855 radiation Effects 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 229920002521 macromolecule Polymers 0.000 description 2
- 230000010355 oscillation Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000012353 t test Methods 0.000 description 2
- 238000002834 transmittance Methods 0.000 description 2
- LFQSCWFLJHTTHZ-UHFFFAOYSA-N Ethanol Chemical compound CCO LFQSCWFLJHTTHZ-UHFFFAOYSA-N 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000000969 carrier Substances 0.000 description 1
- 230000007797 corrosion Effects 0.000 description 1
- 238000005260 corrosion Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 239000003814 drug Substances 0.000 description 1
- 230000005484 gravity Effects 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000000691 measurement method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 239000012460 protein solution Substances 0.000 description 1
- 238000002310 reflectometry Methods 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 230000008054 signal transmission Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/3577—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing liquids, e.g. polluted water
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/3581—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using far infrared light; using Terahertz radiation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
Landscapes
- Physics & Mathematics (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Toxicology (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The invention relates to the technical field of intelligent perception information processing, in particular to an information identification method for liquid substance component change without contact and ionization damage, which comprises the following steps: the terahertz time-domain signal of the substance to be identified is obtained based on a vertical upward reflection type terahertz detection method, the composite weighting scale entropy of the terahertz time-domain signal is further obtained, and the substance to be identified is distinguished according to the difference of the composite weighting scale entropy. The invention provides a non-contact and non-ionization damage-free fluid substance component change information acquisition and identification method, which is simple and convenient to operate, good in signal repeatability, high in identification accuracy, wide in application range and capable of being used for identifying solutions with different concentrations, bioactive solvent marker identification or liquid mixture stability monitoring and the like.
Description
Technical Field
The invention relates to the technical field of intelligent perception information processing, in particular to a terahertz information identification method for liquid substance component change and application thereof.
Background
Terahertz (THz) waves are electromagnetic waves between millimeter waves and infrared, the frequency of the THz waves is approximately within the range of 0.1-10 THz, the vibration energy levels of a plurality of macromolecules fall in a THz frequency band, and the THz waves have better one-to-one correspondence by researching the interaction rule of the THz signals and macromolecular substances. THz wave radiation energy is low, and is probably in millielectron volt magnitude, so that the THz wave radiation energy can not damage a measured substance, and the THz wave radiation energy is an ideal nondestructive detection means. Accordingly, in recent years, more and more researchers use terahertz waves to detect and identify substances, particularly in the fields of biology, chemistry, medicine, and the like. Fluid or solution-like samples are generally not well characterized and are not readily distinguishable directly from the original signal.
The phase stability of a terahertz irradiation mode in the prior art is poor, for example, a mode that a liquid pool is conventionally adopted has a plurality of interfaces, the liquid level thickness of the liquid pool is usually small so that terahertz signals can effectively penetrate through, multi-wave interference superposition which cannot be filtered can occur, the thickness of the liquid pool is usually 1mm or less, the liquid pool is relatively close to the wavelength range of terahertz, and a plurality of echo oscillations are generated behind the plurality of interfaces. Some also adopt a downward irradiation mode, and at this time, when the liquid to be measured is replaced, the accurate consistency of the liquid level cannot be ensured, and the slight change causes the signal change caused by the fact that the micro-disturbance of terahertz possibly submerges the trace components in the liquid.
Entropy is a fundamental physical quantity in nature and can be used to characterize the complexity of a system. For a solution system or a solution-like system, a complex integral system is formed by the interaction of a large number of molecules, and information of the system is carried when the terahertz signal interacts with the system. The sample entropy is the complexity of determining a time sequence by calculating the negative logarithm of the conditional probability, can be used for feature extraction of a time signal, breaks through the limitation of conventional feature extraction, but the calculation result of the sample entropy only has one numerical value (namely, is established on a single scale), and sometimes cannot correctly represent the change rule of a long-time signal. Composite multi-scale entropy quantifies the complexity of the signal by considering sample entropy at multiple scales, but defaults to the fact that signals at different scales react equally to the sample, still limiting the effectiveness of the recognition.
Disclosure of Invention
In order to solve the problems in the prior art and application, the invention provides a terahertz information identification method for fluid substance component change without contact and ionization damage and application thereof.
When the micro change of the components in the substance causes the change of the whole macroscopic system, thereby causing the change of the observation signal, the invention obtains the change of the macroscopic quantity corresponding to the microscopic change through analysis, thereby effectively judging the microscopic information. The first step is a signal testing mode with high phase precision. A high-purity fused isotropic quartz substrate is used as a bearing body of a measured object; the method comprises the following steps of selecting quartz as a first-selected carrier substance by integrating various demand factors, screening and comparing a large number of substances, considering the transmissivity, reflectivity, material cost, stability, corrosion resistance, hardness, multispectral light transmittance, visibility and the like of terahertz electromagnetic waves, and selecting diamond and high-resistance silicon materials as carriers under certain extreme conditions; the measured object is placed on the upper surface of the substrate by means of natural gravity, and reaches a certain thickness and coverage area; terahertz signals are incident from the lower surface of the substrate to the upper surface through the lower surface, interact with a loaded object on the upper surface, and are collected to be used for data analysis. The second step is signal preprocessing. Obtaining terahertz time-domain signals of a plurality of substances to be identified; and obtaining wavelet signals of the signals with different frequency domain scales or different time domain scales. And thirdly, characterizing the signal. Obtaining a composite weighted scale entropy of the wavelet signal; distinguishing the plurality of substances to be identified through the difference of the composite weighting scale entropy; the composite weighted scale entropy is obtained by taking the amplitude change of the Etherz signal as weight and calculating sample entropy after weighted averaging the signal.
Based on the principle, the invention provides an information identification method for liquid substance component change, which comprises the following steps:
placing the substance to be identified on a substrate, irradiating terahertz waves from the bottom of the substrate in a direction perpendicular to the plane of the substrate, wherein the terahertz waves penetrate through the substrate, contact the liquid to be detected and reflect back to a detector to obtain terahertz signals of the liquid;
the substrate is made of one or more of isotropic quartz, diamond or high-resistance silicon materials, and isotropic quartz is preferred.
Further, the thickness of the substrate is more than 0.5cm, and preferably 2-3 cm. The avoidance of the oscillation of the signal on the lower surface of the quartz is generally required, and the avoidance is better as the thickness is thicker, but the negative effects such as increased signal attenuation, overlong signal transmission path, improved beam deformation influence and the like are also enhanced.
Further, the acquiring the composite weighted scale entropy of the terahertz time-domain signal includes:
carrying out variational mode decomposition on the terahertz time-domain signal to obtain a plurality of groups of mode functions; and selecting a group of mode functions as the wavelet signals of the terahertz time-domain signals, and acquiring the composite weighting scale entropy of the wavelet signals as the composite weighting scale entropy of the terahertz time-domain signals.
Further, the selecting a set of mode functions as the wavelet signal of the terahertz time-domain signal comprises:
selecting a group of mode functions with the largest difference by combining the material state prior information of the material to be identified based on the frequency domain of the mode functions and the coverage range of the terahertz time-domain signal;
the material state prior information comprises: predicted concentration range, composition of the solution, stability of environmental conditions, stability of equipment conditions.
Further, in the process of obtaining the composite weighting scale entropy of the wavelet signal, after obtaining a plurality of weighting coarse graining sequences, calculating the sample entropies of all the weighting coarse graining sequences of each substance to be identified, and then taking the average value of the sum of the corresponding weighting scales as the composite weighting scale entropy of each substance to be identified.
Further, the secondary penalty factor α used in the variational mode decomposition is obtained by:
(1) optimizing the secondary penalty factor alpha within the range of 200-2200, and sequentially increasing 10 from 200 to 2200;
(2) for each secondary penalty factor alpha, carrying out variational mode decomposition, combining material state prior information, selecting a group of mode functions as wavelet signals, dividing the terahertz time-domain signals of the material to be identified into a plurality of non-overlapping windows according to a set scale s, calculating the average value of the terahertz time-domain signals in each window as coarse graining signals, and obtaining s coarse graining sequences;
dividing each coarse grain sequence into a plurality of non-overlapping windows according to a set weighting scale factor ss, obtaining the amplitude change rate of adjacent signal points in each window as the weight coefficient omega of the window (the weight is set to be 1 because the first point in the window can not obtain amplitude change), carrying out weighted average according to the weight coefficient of each coarse grain signal to obtain weighted coarse grain signals, and obtaining ss weighted coarse grain sequences aiming at each coarse grain sequence;
after calculating the sample entropy of each weighted coarse-grained signal, averaging the sample entropies of ss weighted coarse-grained sequences according to the sum of corresponding weighted scales to obtain a composite weighted scale entropy;
(3) and determining the difference of sample entropy values of the weighting scales under all scales, and obtaining a secondary penalty factor alpha with difference of entropy values under at least one weighting scale.
Further, in the step (2), s is more than or equal to 2 and less than or equal to 20, and/or s is more than or equal to 2 and less than or equal to 20;
further, in step (2), after calculating the sample entropy of each weighted coarse-grained signal, the composite weighted scale entropy may be calculated by the following composite weighted scale entropy formula:
wherein the content of the first and second substances,
m is embedding dimension, r is similarity threshold, omega (k, j) is weight coefficient, and is less than or equal to 1k≤s，1≤kk≤ss。
Further, in step (2), the selecting a set of modulus functions as the wavelet signal is: and selecting a group of mode functions with the maximum difference by combining the material state prior information of the material to be identified based on the frequency domain of the mode functions and the coverage range of the terahertz time-domain signal.
Further, in the step (3), after the secondary penalty factors alpha with different entropy values under at least one weighting scale are obtained, the number of the weighting scales with different sample entropy values is compared, and the corresponding secondary penalty factor alpha with the largest number is the optimal secondary penalty factor alpha.
Further, in the step (3), the difference of the sample entropy values under each weighting scale is determined by binarizing the image.
Further, the identification of the substances to be identified according to the difference of the composite weighting scale entropy is to determine the statistical difference of different substances to be identified on the time scale according to t test so as to distinguish different substances to be identified.
The invention further provides the application of the information identification method in identification of fluid or solution samples.
Further, the solution samples are alloprotein samples with different concentrations.
Further, the protein sample is a C-reactive protein.
The invention provides a method for acquiring a high-consistency terahertz original signal by upward vertical incidence, which enables a high-complexity fluid or solution system to be analyzed finely. When the interaction between the upper interface of the substrate material and the target to be detected can be ensured only by adopting upward vertical incidence, the phase consistency of the time-domain terahertz signal is very high, and the phase stability of the time-domain terahertz signal cannot be ensured by other terahertz irradiation modes.
In addition, the invention not only considers the coarse graining processing of the signals (namely, averaging the number of points in each divided interval to obtain a corresponding sequence) when calculating the composite scale entropy, but also further adds a weighted average processing process on the basis of the coarse graining, provides a composite weighted scale entropy algorithm, distinguishes the terahertz signals of substances with similar properties according to the sample entropies under different scales and weighted scales, and effectively improves the reliability of identification. After a highly stable original signal is obtained, the original signal is transformed into wavelets of different time domain components or frequency domain components by a method of variational mode decomposition, and efficient interaction intervals are usually different according to different measured targets, so that the wavelets of proper components in a selected area can better identify the fine influence of trace substances on a system. The decomposed wavelets are influenced by the value of the parameter alpha, and after a specific wavelet is extracted through optimization, the composite weighting scale entropy of the wavelet signals is calculated and used for distinguishing the terahertz signals of substances with similar properties.
The invention has the following beneficial effects:
the invention uses a vertical upward reflection type terahertz measurement method based on an isotropic quartz substrate to obtain a high-quality original signal, obtains an optimized wavelet signal through mode analysis on the basis of composite multi-scale entropy of an original time sequence aiming at the difference of terahertz time-domain signals of different substances, calculates the composite weighting scale entropy, distinguishes different substances through calculating the composite weighting scale entropy, and applies a weighting coefficient to combine the change of amplitude and time delay characteristics of the terahertz signal and increase the corresponding weighting scale, thereby obtaining more sample entropies and improving the identification effect of micro-difference, which has important significance in the field of substance identification, such as distinguishing protein solutions with different concentrations.
Drawings
Fig. 1 is a schematic diagram of a vertical upward reflection type terahertz measurement device and method based on an isotropic quartz substrate used in the invention.
FIG. 2 is a terahertz time-domain signal of C-reactive protein calibrator solutions with different concentrations provided in example 1 of the present invention; wherein a is the terahertz time-domain signal difference of the C-reactive protein calibrator solution with the concentration of 19.7mg/L and 52.2mg/L, b is the terahertz time-domain signal difference of the C-reactive protein calibrator solution with the concentrations of 19.7mg/L and 143mg/L, and C is the C-reactive protein calibrator solution with the concentrations of 19.7mg/L and 281 mg/L.
Fig. 3 is a schematic diagram of weighting the coarse grained sequence by amplitude variation according to example 1 of the present invention.
FIG. 4 is the calculation results of the composite scale entropy and the composite weighted scale entropy of the C-reactive protein calibrator solution with the concentration of 19.7mg/L and 52.2mg/L provided in example 1 of the present invention; wherein a is a binary image of composite weighted scale entropy p-value distribution of the first modality wavelet signal when alpha is 139; b is a composite multi-scale entropy curve of the first mode wavelet signal at the 2 nd scale when alpha is 139.
FIG. 5 is the calculation result of the composite weighted scale entropy of the C-reactive protein calibrator solution with a concentration of 19.7mg/L and 143mg/L provided in example 1 of the present invention; when a is a composite weighting scale entropy p value distribution binary image of the first modality wavelet signal when alpha is 9; and b is a composite multi-scale entropy curve of the first mode wavelet signal under the 3 rd scale when the alpha is 9.
FIG. 6 is the calculation result of the composite weighted scale entropy of the C-reactive protein calibrator solution with a concentration of 19.7 mg/L281 mg/L provided in example 1 of the present invention; wherein a is a composite weighting scale entropy p-value distribution binary image of the first modality wavelet signal when alpha is 169; and b is a composite multi-scale entropy curve of the first-mode wavelet signal under the 1 st scale when the alpha is 169.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
The embodiment provides a method for identifying C-reactive protein calibrator solutions with different concentrations based on terahertz signal composite weighted scale entropy, which specifically comprises the following steps:
1. a first set of two concentrations (19.7 and 52.2mg/L) of C-reactive protein calibrator solutions (available from Texas diagnostics systems, Inc.) were selected for testing.
2. For the 19.7mg/L solution, 1ml is extracted by using a disposable syringe, is gently dropped on an isotropic quartz plate, and a terahertz time-domain signal is collected. After collection, the isotropic quartz plate was washed with alcohol and clean water. Then, 1ml of the solution was sequentially extracted using different syringes to collect corresponding signals. A total of 3 tests were performed. The same procedure was followed for 52.2mg/L solution.
The step can be specifically shown in the flow shown in fig. 1, and specifically, the liquid to be detected can be dropped on an isotropic quartz substrate, and the isotropic quartz has a high transmittance for terahertz waves. The terahertz waves vertically penetrate through the isotropic quartz upwards, contact the liquid to be detected and are reflected back to the detector, and terahertz signals of the liquid are obtained. When the interaction between the upper interface of the substrate material and the target to be detected can be ensured only by adopting upward vertical incidence, the phase consistency of the time-domain terahertz signal is very high, and the phase stability of the time-domain terahertz signal cannot be ensured by other terahertz irradiation modes. The thickness of the isotropic quartz is 2cm, and the interference of the reflection signal of the lower surface of the isotropic quartz on the reflection signal of the sample can be effectively removed.
3. The solutions of the second group (52.2 and 281mg/L) and the third group (143 and 281mg/L) were sequentially selected and tested according to the procedure (2), and FIG. 2 shows the average signal of each concentration solution.
4. The magnitude of the composite weighted scale entropy of the above signals is calculated.
(1) Decomposing the terahertz time-domain signals of the sample by utilizing variational mode decomposition, taking a first mode function as wavelet signals of a terahertz signal frequency domain or a terahertz time domain, optimizing a secondary penalty factor alpha within a range of 200-2200, sequentially increasing 10 from 200 and traversing to 2200, and decomposing 201 wavelet signals in total for each sample signal.
(2) Decomposing the wavelet signal { x according to a set scale siDividing the window into a plurality of non-overlapping windows (the number of the windows is determined by the size number s), and calculating points in the windowsThe average of the numbers, the sequence of averages within each window (coarse grained sequence) is:
(2) dividing the sequence into a plurality of non-overlapping windows (the number of the windows depends on the set weighting scale), taking the amplitude change rate between two adjacent points in the window as a weighting coefficient omega (the first point in the window cannot obtain the amplitude change, so the weight is set to be 1), and obtaining the sequence with the weight:
Figure 3 shows a description of the algorithm weight scale sequence (scale and weighting scale both take 2).
(3) And calculating sample entropy for the sequence according to the following formula to obtain a composite weighting scale entropy.
Where m is the embedding dimension, r is the similarity threshold, and in general, m is 2, r is 0.15 σ, and σ is the standard deviation of the time signal.
The end result is an entropy value at different scales and different weighted scales.
5. According to t test, the statistical difference of sample entropy values of each two groups of concentration solutions under corresponding scales is determined, a secondary punishment factor alpha with larger entropy value difference under a weighted scale is obtained, and entropy values with difference are obtained, so that the aim of identification is fulfilled.
And (3) testing each concentration solution in the steps (2) and (3) for 3 times and collecting corresponding signals, wherein the required testing amount is 1 ml.
And (4) parameter setting of the composite weighting scale entropy: the scale s and the weighting scale ss are both set to 20, the embedding dimension is 2, the threshold r is 0.15 σ, and σ is the standard deviation of the time signal.
In step (5), for the composite weighted scale entropy, the present embodiment uses a binarized image to represent the recognition result (with p being 0.05 as a threshold). For the representation of the one-dimensional curve result, a certain scale with the most different entropy values is selected in this embodiment, and entropy value distribution curves of different weighting scales under the scale are obtained.
From the calculation results of fig. 4-6, the composite weighted scale entropy has entropy values with more statistical differences. Therefore, the method can effectively distinguish substances with similar terahertz signals, and the method has important value in identifying similar substances in related fields.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A terahertz information identification method for liquid substance component change is characterized by comprising the following steps:
obtaining a terahertz time-domain signal of a substance to be identified based on a vertical upward reflection type terahertz detection method; and acquiring a composite weighting scale entropy of the terahertz time-domain signal, and identifying the substance to be identified according to the difference of the composite weighting scale entropy.
2. The terahertz information identification method according to claim 2, wherein the vertical upward reflection type terahertz detection method is:
placing the substance to be identified on a substrate, irradiating terahertz waves from the bottom of the substrate in a direction perpendicular to the plane of the substrate, wherein the terahertz waves penetrate through the substrate, contact the liquid to be detected and reflect back to a detector to obtain terahertz signals of the liquid;
the substrate is made of one or more of isotropic quartz, diamond or high-resistance silicon materials, and isotropic quartz is preferred.
3. The terahertz information identification method as claimed in claim 1 or 2, wherein the substrate has a thickness greater than 0.5cm, preferably 2-3 cm.
4. The terahertz information identification method according to any one of claims 1 to 3, wherein the obtaining of the composite weighted scale entropy of the terahertz time-domain signal comprises:
carrying out variational mode decomposition on the terahertz time-domain signal to obtain a plurality of groups of mode functions; and selecting a group of mode functions as the wavelet signals of the terahertz time-domain signals, and acquiring the composite weighting scale entropy of the wavelet signals as the composite weighting scale entropy of the terahertz time-domain signals.
5. The terahertz information identification method of claim 4, wherein the selecting a set of mode functions as the wavelet signals of the terahertz time-domain signal comprises:
selecting a group of mode functions with the largest difference by combining the material state prior information of the material to be identified based on the frequency domain of the mode functions and the coverage range of the terahertz time-domain signal;
the material state prior information comprises: predicted concentration range, composition of the solution, stability of environmental conditions, stability of equipment conditions.
6. The terahertz information identification method according to any one of claims 4 or 5, wherein in the process of obtaining the composite weighted scale entropy of the wavelet signals, after obtaining a plurality of weighted coarse grained sequences, for each substance to be identified, sample entropies of all weighted coarse grained sequences are calculated, and then an average value of the sum of corresponding weighted scales is used as the composite weighted scale entropy.
7. The terahertz information identification method according to any one of claims 4 to 6, wherein a secondary penalty factor α used in the variational mode decomposition is obtained by:
(1) optimizing the secondary penalty factor alpha within the range of 200-2200, and sequentially increasing 10 from 200 to 2200;
(2) for each secondary penalty factor alpha, carrying out variational mode decomposition, combining material state prior information, selecting a group of mode functions as wavelet signals, dividing the terahertz time-domain signals of the material to be identified into a plurality of non-overlapping windows according to a set scale s, calculating the average value of the terahertz time-domain signals in each window as coarse graining signals, and obtaining s coarse graining sequences;
dividing each coarse grain sequence into a plurality of non-overlapping windows according to a set weighting scale factor ss, obtaining the amplitude change rate of adjacent signal points in each window as the weight coefficient omega of the window (the weight is set to be 1 because the first point in the window can not obtain amplitude change), carrying out weighted average according to the weight coefficient of each coarse grain signal to obtain weighted coarse grain signals, and obtaining ss weighted coarse grain sequences aiming at each coarse grain sequence;
after calculating the sample entropy of each weighted coarse-grained signal, averaging the sample entropies of ss weighted coarse-grained sequences according to the sum of corresponding weighted scales to obtain a composite weighted scale entropy;
(3) and determining the difference of sample entropy values of the weighting scales under all scales, and obtaining a secondary penalty factor alpha with difference of entropy values under at least one weighting scale.
8. The terahertz information identifying method according to claim 7,
in the step (2), s is more than or equal to 2 and less than or equal to 20, and/or s is more than or equal to 2 and less than or equal to 20; and/or the presence of a gas in the gas,
in the step (3), after the secondary penalty factors alpha with different entropy values under at least one weighting scale are obtained, the number of the weighting scales with different sample entropy values is compared, and the corresponding secondary penalty factor alpha with the largest number is the optimal secondary penalty factor alpha.
9. Use of the terahertz information identification method of any one of claims 1 to 8 for identifying a fluid or solution sample.
10. The use according to claim 9, wherein the solution samples are samples of the same protein at different concentrations.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111086796.3A CN113624710A (en) | 2021-09-16 | 2021-09-16 | Terahertz information identification method for liquid substance component change and application thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111086796.3A CN113624710A (en) | 2021-09-16 | 2021-09-16 | Terahertz information identification method for liquid substance component change and application thereof |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113624710A true CN113624710A (en) | 2021-11-09 |
Family
ID=78390216
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111086796.3A Pending CN113624710A (en) | 2021-09-16 | 2021-09-16 | Terahertz information identification method for liquid substance component change and application thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113624710A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116908148A (en) * | 2023-09-12 | 2023-10-20 | 中国科学技术大学 | Spectrum detection control system and method based on complexity analysis |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070073115A1 (en) * | 2005-09-23 | 2007-03-29 | Samsung Electronics Co., Ltd. | Apparatus and method for measuring concentration of blood component using terahertz |
| JP2009019925A (en) * | 2007-07-10 | 2009-01-29 | Iwate Prefectural Univ | Spectrometric sample, spectrometric substrate, and spectrometry method |
| CN104215776A (en) * | 2014-09-15 | 2014-12-17 | 深圳大学 | Terahertz time-domain spectroscopy-based unmarked hemagglutinin detection method |
| US20150193376A1 (en) * | 2014-01-06 | 2015-07-09 | National Central University | Method of multi-scales intrinsic entropy analysis |
| CN107468250A (en) * | 2017-08-21 | 2017-12-15 | 北京理工大学 | Biological tissue's terahertz imaging method, system and equipment based on multi-scale entropy |
| CN113155773A (en) * | 2021-04-07 | 2021-07-23 | 中国科学院重庆绿色智能技术研究院 | System for detecting marker protein in liquid by utilizing terahertz spectrum technology |
-
2021
- 2021-09-16 CN CN202111086796.3A patent/CN113624710A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070073115A1 (en) * | 2005-09-23 | 2007-03-29 | Samsung Electronics Co., Ltd. | Apparatus and method for measuring concentration of blood component using terahertz |
| JP2009019925A (en) * | 2007-07-10 | 2009-01-29 | Iwate Prefectural Univ | Spectrometric sample, spectrometric substrate, and spectrometry method |
| US20150193376A1 (en) * | 2014-01-06 | 2015-07-09 | National Central University | Method of multi-scales intrinsic entropy analysis |
| CN104215776A (en) * | 2014-09-15 | 2014-12-17 | 深圳大学 | Terahertz time-domain spectroscopy-based unmarked hemagglutinin detection method |
| CN107468250A (en) * | 2017-08-21 | 2017-12-15 | 北京理工大学 | Biological tissue's terahertz imaging method, system and equipment based on multi-scale entropy |
| CN113155773A (en) * | 2021-04-07 | 2021-07-23 | 中国科学院重庆绿色智能技术研究院 | System for detecting marker protein in liquid by utilizing terahertz spectrum technology |
Non-Patent Citations (2)
| Title |
|---|
| HAISHUN LIU等: "Diagnosis of hepatocellular carcinoma based on a terahertz signal and VMD-CWSE", 《BIOMEDICAL OPTICS EXPRESS》, vol. 11, no. 9, pages 1 - 10 * |
| 徐德刚等: "《光学太赫兹辐射源及其生物医学应用》", 30 April 2021, 华东理工大学出版社, pages: 6 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116908148A (en) * | 2023-09-12 | 2023-10-20 | 中国科学技术大学 | Spectrum detection control system and method based on complexity analysis |
| CN116908148B (en) * | 2023-09-12 | 2024-01-09 | 中国科学技术大学 | Spectrum detection control system and method based on complexity analysis |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US6421121B1 (en) | Method and apparatus for rapid particle identification utilizing scattered light histograms | |
| US7072038B2 (en) | Particle ID with narrow angle detectors | |
| US10379027B2 (en) | Method for identifying blood particles using a photodetector | |
| CN109581546B (en) | Rainfall type identification method based on microwave link attenuation and polarization information | |
| EP2373957B1 (en) | Methods for determining a liquid level in a container using imaging | |
| US10337975B2 (en) | Method and system for characterizing particles using a flow cytometer | |
| Li et al. | Determination of non-spherical particle size distribution from chord length measurements. Part 2: Experimental validation | |
| Suzuki et al. | The evaluation of macroscopic and microscopic textures of sand grains using elliptic Fourier and principal component analysis: implications for the discrimination of sedimentary environments | |
| CN101981446A (en) | Method and system for analysis of flow cytometry data using support vector machines | |
| Richardson et al. | Bragg scatter detection by the WSR-88D. Part I: Algorithm development | |
| CN113624710A (en) | Terahertz information identification method for liquid substance component change and application thereof | |
| CN107300562A (en) | A kind of X-ray lossless detection method of measuring relay finished product contact spacing | |
| CN111366600B (en) | Method, device and system for detecting sweetness of fruit and storage medium | |
| CN116399850B (en) | Spectrum detection and identification system for optical signal processing and detection method thereof | |
| CN111339986B (en) | Device frequency law mining method and system based on time domain/frequency domain analysis | |
| CN114578040B (en) | Sample detection method, sample analyzer and control device thereof | |
| CN110567900A (en) | method and device for judging antigen excess in sample reaction and optical detection system | |
| EP3244191A1 (en) | Method and system for characterizing particles using a flow cytometer | |
| Wang et al. | Nondestructive detecting cracks of preserved eggshell using polarization technology and cluster analysis | |
| CN114088817A (en) | Deep learning flat ceramic membrane ultrasonic defect detection method based on deep features | |
| Maffezzoli et al. | Detection of ice core particles via deep neural networks | |
| CN113624759A (en) | Apple nondestructive testing method based on machine learning | |
| CN1469111A (en) | Method and equipment for fast distinguishing particles utilizing with scattered light histogram | |
| CN114720541B (en) | Method for improving accuracy of classifying spicy intensity of red peppers | |
| Wu et al. | Analysis of echo characteristics of dual-polarization Doppler weather radar in lightning weather |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |