CN113450883B - Solution ion concentration detection method based on multispectral fusion - Google Patents
Solution ion concentration detection method based on multispectral fusion Download PDFInfo
- Publication number
- CN113450883B CN113450883B CN202110710863.8A CN202110710863A CN113450883B CN 113450883 B CN113450883 B CN 113450883B CN 202110710863 A CN202110710863 A CN 202110710863A CN 113450883 B CN113450883 B CN 113450883B
- Authority
- CN
- China
- Prior art keywords
- spectral
- curve
- optimal
- subinterval
- standard
- 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.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/90—Programming languages; Computing architectures; Database systems; Data warehousing
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/70—Machine learning, data mining or chemometrics
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Physics (AREA)
- Computational Mathematics (AREA)
- Databases & Information Systems (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Optimization (AREA)
- Mathematical Analysis (AREA)
- Life Sciences & Earth Sciences (AREA)
- Computing Systems (AREA)
- Software Systems (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Chemical & Material Sciences (AREA)
- Health & Medical Sciences (AREA)
- General Engineering & Computer Science (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Algebra (AREA)
- Crystallography & Structural Chemistry (AREA)
- General Health & Medical Sciences (AREA)
- Evolutionary Biology (AREA)
- Analytical Chemistry (AREA)
- Artificial Intelligence (AREA)
- Pathology (AREA)
- Immunology (AREA)
- Operations Research (AREA)
- Probability & Statistics with Applications (AREA)
- Biochemistry (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Medical Informatics (AREA)
- Evolutionary Computation (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The invention belongs to the field of spectral curve matching and fusion, and particularly relates to a solution ion concentration detection method based on multi-spectral line fusion. The method comprises the following steps: constructing a standard absorption spectrum curve library, and detecting a sample to be detected at different positions to obtain a plurality of spectrum curves to be detected; calculating the MSGA values of the multiple spectral curves to be measured and the standard spectral curve, and screening the spectral curves to be measured; performing band division on the screened spectrum curve to be tested and the standard spectrum curve, and screening according to a preset optimal subinterval screening method to obtain optimal subintervals of the plurality of spectrum curves to be tested; and performing series fusion on the screened optimal subinterval spectral curves to obtain an optimal to-be-detected spectral curve, and determining the ion concentration of the to-be-detected solution according to the optimal spectral curve. The method eliminates the interference of suspended microparticles in the solution, lays a data foundation for establishing a stable prediction model of the solution ion concentration, and thus accurately determines the concentration of the solution to be measured.
Description
Technical Field
The invention belongs to the field of spectral curve matching and fusion, and particularly relates to a solution ion concentration detection method based on multi-spectral line fusion.
Background
The metal ion concentration detection based on the Lambert beer law is usually in a wavelength range of 200nm-780nm, ion light absorption information is required to be accurately acquired, and suspended micron-sized particles in a solution interfere with detection light transmission to cause scattering and diffraction, so that a detection spectral line is nonlinearly lifted, and the spectral line not only contains ion light absorption information, but also contains light loss information caused by particles. Therefore, conventional spectroscopy with a single beam is not suitable for accurate detection of the ion concentration of a solution.
The single-beam spectrometer is characterized in that a light source is fixed, the intensity of a light beam is easily influenced by the outside, a plurality of dispersed pure areas without particles necessarily exist in a solution due to the uniformity and the continuity of the distribution of the solution, and how to use the single-beam spectrometer to detect the pure areas can obtain a basically interference-free spectral curve, and the single-beam spectrometer is an idea for eliminating interference spectral lines of suspended particles. In addition, after a plurality of spectral lines are obtained, how to perform multi-spectral line matching and output an optimal spectral line in a fusion manner is performed, and there are many spectral similarity measurement methods in the prior art, such as Spectral Angle Mapping (SAM), mahalanobis Distance (MD), spectral Gradient Angle (SGA), spectral information divergence and the like. The methods have definite physical significance and simple calculation, are widely applied to the field of spectrum matching, but only one difference (shape difference, amplitude difference or information difference and the like) among different spectra is usually considered, and the utilization of spectral information is limited, so the application effect of the methods is to be improved.
Disclosure of Invention
Based on the technical problem, the invention aims at the technical problem that multiple spectral lines detected at different positions of a light source are firstly subjected to single spectral line matching with a standard spectral curve to screen a batch of relatively pure spectral lines to be tested, then the matching of the optimal band subintervals of the multiple spectral lines is carried out according to the absorption characteristics of the spectral curves to screen the optimal band subintervals of the multiple spectral lines, finally the optimal band subintervals of the multiple spectral lines are connected in series and fused to output an optimal spectral curve, and an ion concentration analytical model is established to predict the ion concentration of the optimal spectral curve.
The invention provides a solution ion concentration detection method based on multispectral fusion, which specifically comprises the following steps:
s1, constructing a standard absorption spectrum curve library, and detecting a sample to be detected by a movable light source type spectrometer to obtain a plurality of spectrum curves to be detected;
s2, calculating MSGA values of the multiple spectral curves to be tested and the standard spectral curve, selecting the spectral curves to be tested with the MSGA values smaller than a set threshold value, and obtaining multiple screened spectral curves to be tested;
s3, dividing the bands of the screened spectrum curve to be measured and the standard spectrum curve according to a method that the subinterval contains at most one absorption characteristic to obtain a plurality of subarea spectrum curves to be measured and subarea standard spectrum curves;
s4, screening according to the plurality of subarea spectral curves to be tested and the subarea standard spectral curves and a preset optimal subinterval screening method to obtain optimal subintervals of the plurality of spectral curves to be tested;
and S5, performing series fusion on the screened optimal subinterval spectral curves to obtain an optimal to-be-detected spectral curve, and determining the ion concentration of the to-be-detected solution according to the optimal spectral curve.
Further, the step S1 specifically includes:
preparing a standard solution with a concentration gradient, detecting the standard sample solution by adopting a single-beam spectrometer with a fixed light source to obtain a standard spectral line and construct a standard curve library;
and detecting the sample to be detected at a plurality of detection positions by adopting a movable light source type spectrometer to obtain a plurality of spectral curves to be detected.
Further, the step S2 specifically includes the following steps:
setting the absorbance value of the spectrum curve to be measured to form a line vector X = [ X ] 1 ,x 2 ,…,x n ]The absorbance value of the standard spectrum curve forms a line vector Y = [ Y ] 1 ,y 2 ,…,y n ]According to the formula
Calculating the MSGA value of the spectral curve to be measured and the standard spectral curve; in the formula D M (X, Y) is the Mahalanobis distance between the spectral curve X to be measured and the standard spectral curve Y, SGA (grad (X), grad (Y)) is the cosine value of the gradient angle between the spectral curve X to be measured and the standard spectral curve Y, sigmoid () is a nonlinear activation function, and an independent variable is mapped to a (0, 1) interval;
and comparing the obtained MSGA value with a threshold value epsilon, and screening out the spectrum curve to be tested, wherein the MSGA value is not more than epsilon.
Further, the step S3 specifically includes:
the method for dividing the standard spectrum curve into the sub-intervals of the wave bands specifically comprises the following steps: obtaining absorption wave crest and wave trough on standard absorption spectrum curve, and identifying other absorption characteristics according to relative error identification method to [ lambda ] m -α,λ m +α]Dividing the wave band containing the absorption characteristics as a subinterval, and dividing the wave band without the absorption characteristics into subintervals continuously and evenly according to 6 times of the length of the subinterval with the absorption characteristics;
and dividing the screened spectrum curves to be detected into sub-interval ranges of wave bands according to a standard curve to obtain a plurality of partitioned spectrum curves to be detected.
Further, the method for identifying the relative error specifically comprises the following steps:
calculating the relative error between the average value of the absorbance values in the alpha neighborhood of the maximum/minimum value point of the absorption peak valley and the average value of the absorbance values in the tangent alpha neighborhood according to a relative error calculation formula, and identifying the absorption feature with the relative error larger than 20%;
the relative error is calculated by the formula
In the formula
Is the average of the absorbance values in the alpha neighborhood of the maximum (small) value point,
is the average of the absorbance values in the left-cut alpha neighborhood,
is the average of the absorbance values in the right-cut alpha neighborhood.
Further, the method for screening the optimal subinterval preset in step S4 specifically includes:
for the subintervals with single absorption characteristics, respectively calculating the SAI values of the spectral curve to be measured and the standard spectral curve of the corresponding interval, and screening out the subinterval of the spectral curve to be measured, which is closest to the SAI value of the standard spectral curve, as the first-class optimal subinterval;
and for the subintervals without the absorption characteristics, respectively calculating the MSGA values of the spectral curve to be detected and the standard spectral curve of the corresponding interval, and screening out the second type of optimal subintervals according to the minimum MSGA value principle.
Further, the step of obtaining the first-class optimal subinterval specifically includes:
respectively calculating SAI values of subintervals of the standard spectral curve and the ith spectral curve to be detected, and respectively marking the SAI values as SAI _ s and SAI _ i; the calculation formula is as follows:
W＝λ 2 -λ 1
in the formula, λ 1 ,λ 2 ,λ m Are respectively a point S 1 ，S 2 M, the wavelength corresponding to M; a. The 1 ，A 2 ，A m Are respectively a point S 1 ，S 2 The absorbance value corresponding to M; w is the width of the absorption characteristic peak, d is the symmetry of the absorption characteristic peak, and SAI is the spectral absorption index of the absorption characteristic of the subinterval;
calculating the difference value of the SAI of the ith spectrum curve to be measured and the standard spectrum curve on the subinterval, and taking the subinterval of the spectrum curve to be measured with the smallest difference value as a first-class optimal subinterval according to the principle that the smaller the difference value is, the stronger the similarity of the two spectra in the subinterval is, wherein the first-class optimal subinterval selection formula is as follows:
in the formula, k p Means that in m subintervals with the number p, the k-th spectrumThe subinterval numbered p on the curve is the first-type optimal subinterval.
Further, the step of obtaining the second type of optimal subinterval specifically includes:
calculating the MSGA value of the ith spectral curve to be measured and the standard spectral curve;
and screening out a spectrum curve to be measured with the minimum MSGA value, and taking a subinterval with the serial number i in the spectrum curve to be measured as a second-class optimal subinterval.
Further, the step S5 specifically includes:
performing series fusion on the obtained first and second optimal subintervals according to the wavelength, and outputting an optimal spectrum curve to be measured;
dividing full-spectrum bands into sub-intervals of set values at equal intervals, taking spectral lines in a standard spectrum curve library as a correction set, establishing a PLS model on each sub-interval, taking a cross validation root mean square error RMSECV value as a precision measurement index of each sub-interval model, comparing the RMSECV value of each sub-interval of ions to be tested, screening out an optimal sub-interval set, and establishing an iPLS ion concentration analysis model by using an optimal interval combination;
in the formula, is y i The actual value of the ith sample is,
is the predicted value of the ith sample, and n is the number of correction set samples;
and predicting the ion concentration of the optimal spectrum curve to be measured according to the iPLS ion concentration analysis model, and outputting the ion concentration of the solution.
Further, the movable light source type spectrometer specifically includes: the device comprises a cuvette, a cuvette cover, a bottom plate, an optical fiber base, a connecting plate, a shading cover and a shading body;
the optical fiber seat is characterized in that grooves penetrating through two side edges are formed in two sides of the light-shading body, the optical fiber seat is U-shaped, lugs matched with the grooves are arranged on two inner sides of the light-shading body, the optical fiber seat and the light-shading body are slidably connected through the grooves and the lugs, the length of the two side edges of the light-shading body is smaller than that of the two side edges of the optical fiber seat, and the connecting plate is connected with the optical fiber seat in a detachable connection mode.
Has the advantages that:
(1) The multispectral matching and fusing method provided by the invention is formed by nonlinear fusion of Mahalanobis distance and spectral gradient angle matching, integrates the advantages of two similarity measurement methods of Mahalanobis distance and spectral gradient angle, and can realize high-precision matching of a spectral curve to be measured and a standard spectral curve.
(2) The invention divides the wave band subintervals by the absorption characteristics, selects the spectral absorption index or the multi-spectral line fusion method for the optimal wave band subinterval if the wave band subinterval has a single absorption characteristic, realizes the characteristic level fusion of the optimal wave band subinterval of the multi-spectral line on the characteristic level, outputs an optimal spectrum curve to be measured, eliminates the interference of suspended microparticles in the solution, and lays a data foundation for establishing a steady prediction model of the solution ion concentration.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a method for detecting solution ion concentration based on multiline fusion according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a moving light source type fiber spectrometer according to an embodiment of the present invention;
FIG. 3 is a simplified diagram of the Mahalanobis distance and spectral gradient angle principle provided by an embodiment of the present invention;
FIG. 4 is a schematic diagram of the principle of Spectral Absorption Index (SAI) provided by an embodiment of the present invention;
201-shading cover; 202-clear hole; 203-optical fiber seat; 204-a bottom plate; 205-connecting plate; 206 light shield.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As shown in fig. 1, in the embodiment of the present invention, a flow chart of a solution ion concentration detection method based on multiline fusion is provided, which specifically includes the following steps:
s1, constructing a standard absorption spectrum curve library, and detecting a sample to be detected by a movable light source type spectrometer to obtain a plurality of spectrum curves to be detected.
In the embodiment of the invention, a series of standard sample liquids with concentration gradients are manually prepared in a laboratory environment, an ATP2000 fiber spectrometer is used for detecting the standard sample liquids to obtain standard spectral lines with certain concentration gradients, and a standard spectral curve library is constructed. Then refer to fig. 2 the structure of a movable light source type optical fiber spectrometer specifically comprises a cuvette, a cuvette cover, a bottom plate 204, an optical fiber base 203, a connecting plate 205, a light shielding cover 201 and a light shielding body 206, wherein grooves penetrating through two side edges are arranged on two sides of the light shielding body 206, the optical fiber base 203 is in a U shape, two inner sides of the light shielding body 206 are provided with convex blocks matched with the grooves, the optical fiber base 203 and the light shielding body 206 are slidably connected with the convex blocks through the grooves, the length of the two side edges of the light shielding body 206 is smaller than the length of the two side edges of the optical fiber base, so that the change of the measuring position realizes the multi-spectral line measurement, the light shielding body 206 and the bottom plate 204 are fixed through bolts, and the connecting plate 205 and the optical fiber base are connected through bolts. Be provided with the cell in the shading cover, during the detection, deposit the appearance liquid that awaits measuring in the cell to install at 206 shade, cover shading cover 201, through stably moving optic fibre seat 203 for ultraviolet visible light source emission single beam passes light hole 202 and shines on the cell, realizes detecting same leachate in the contrast cell in different positions, thereby obtains n spectral curve that awaits measuring.
Step S2; and calculating MSGA values of the multiple spectral curves to be tested and the standard spectral curve, selecting the spectral curve to be tested with the MSGA value smaller than the set threshold value, and obtaining multiple screened spectral curves to be tested.
In the embodiment of the invention, the absorbance value of the spectral curve to be measured is set to form a line vector X = [ X ] 1 ,x 2 ,…,x n ]The absorbance value of the standard spectrum curve forms a line vector Y = [ Y ] 1 ,y 2 ,…,y n ](ii) a The MSGA value formula for calculating the spectrum curve to be measured and the standard spectrum curve is shown as (1): the mahalanobis distance and the spectral gradient angle are fused non-linearly. The smaller the MSGA value, the more similar the measured spectrum curve is to the standard spectrum curve, i.e. the spectrum curve is not interfered by the micro-particles.
In the formula D M (X, Y) is the Mahalanobis distance between the spectral curve X to be measured and the standard spectral curve Y, SGA (grad (X), grad (Y)) is the cosine of the gradient angle between the spectral curve X to be measured and the standard spectral curve Y, sigmoid () is a nonlinear activation function, and the independent variable is mapped to the interval (0, 1).
As shown in FIG. 3 (a), the Mahalanobis distance D between the spectrum curve X to be measured and the standard spectrum curve Y M The formula for the calculation of (X, Y) is:
as shown in fig. 3 (b), the mahalanobis distance is creatively merged into the spectral gradient angle based on the concept of the triangular cosine value, and the spectral gradient angle of the gradient vector of the spectral curve X to be measured and the standard spectral curve Y is:
grad(X)＝[x 2 -x 1 ,x 3 -x 2 ,…,x n -x n-1 ]
grad(Y)＝[y 2 -y 1 ,y 3 -y 2 ,…,y n -y n-1 ]
in the formula, the gradient vectors of the spectrum curve X to be measured and the standard spectrum curve Y are grad (X), grad (Y) and D respectively M (grad (X), grad (Y)) are mahalanobis distances between gradient vectors grad (X), grad (Y).
And S3, dividing the bands of the screened spectrum curve to be measured and the standard spectrum curve according to a method that the subinterval contains at most one absorption characteristic, and obtaining a plurality of subarea spectrum curves to be measured and subarea standard spectrum curves.
In the embodiment of the invention, the standard spectral curve is divided into the sub-intervals of the wave bands. The absorption characteristic of the spectral curve refers to a relatively obvious absorption peak and an absorption trough on the curve. Mathematically, the peak and valley points are the maximum point and minimum point respectively, and the first derivative values are all 0. And [ lambda ] centered between the wavelengths of the maximum (small) points m -α,λ m +α]In the interval, the first derivative value of the point on the left side of the maximum (small) value point is positive (negative), and the first derivative value of the point on the right side is negative (positive). To identify more pronounced absorption characteristics, the relative error of the average of the absorbance values in the alpha neighborhood of the maximum (small) value point and the average of the absorbance values in the tangent alpha neighborhood is also considered. Will relatively errorGreater than 20% are identified as absorption characteristics and [ lambda ] is m -α,λ m +α]As one subinterval, it is referred to as a first-type subinterval. The wavelength band without absorption characteristics is divided into sub-intervals in 6 times of the sub-interval length with absorption characteristics, and the sub-intervals are called as a second type of sub-intervals. The relative error is calculated as:
in the formula (I), the compound is shown in the specification,
is the average of the absorbance values in the alpha neighborhood of the maximum (small) value point,
is the average of the absorbance values in the left-cut alpha neighborhood,
is the average of the absorbance values in the right-cut alpha neighborhood.
And dividing the m to-be-detected spectral curves screened in the step S2 according to the wave band subinterval range of the standard spectral curve.
S4, screening according to the plurality of subarea spectral curves to be tested and the subarea standard spectral curves and a preset optimal subinterval screening method to obtain optimal subintervals of the plurality of spectral curves to be tested;
in the embodiment of the present invention, the preset optimal subinterval screening method specifically includes:
for the subintervals (first type subintervals) with single absorption characteristics, respectively calculating SAI values (spectral absorption indexes) of m to-be-measured spectral curves and standard spectral curves in the subintervals, screening out the subintervals of a certain curve which is most similar to the standard SAI value, and taking the subintervals as the optimal subintervals; as shown in fig. 4, calculating the SAI value of the standard spectral curve subinterval, which is denoted as SAI _ s; and calculating the SAI value of the ith spectral curve subinterval to be detected, and marking as SAI _ i. The spectral absorption index SAI is a spectral similarity measurement method for concerned absorption characteristics, and the calculation formula is as follows:
W＝λ 2 -λ 1
in the formula, λ 1 ，λ 2 ，λ m The wavelengths corresponding to the points S1, S2, M, respectively; a1 A2 and Am are absorbance values corresponding to the points S1, S2 and M respectively; w is the width of the characteristic absorption peak, d is the symmetry of the characteristic absorption peak, and SAI is the spectral absorption index of the absorption characteristic of the subinterval.
Calculating the difference value of the SAI of the ith spectrum curve to be measured and the standard spectrum curve in the subinterval, and according to the principle that the smaller the difference value is, the stronger the similarity of the two spectra in the subinterval is, the difference value is the smallest
This sub-interval of the spectral curve serves as an optimal sub-interval. The optimal subinterval selection formula is as follows:
in the formula, k p The method refers to that in m subintervals with the number p, the subinterval with the number p on the kth spectral curve is the first optimal subinterval.
For the subintervals without absorption characteristics (second-class subintervals), respectively calculating the MSGA values of the m spectral curves to be measured and the standard spectral curves in the subintervals, and screening out the optimal subintervals according to the minimum MSGA value principle; calculating the MSGA value of the ith spectral curve to be measured and the standard spectral curve;
and screening the optimal subinterval in the m spectral curves to be tested according to the principle that the smaller the MSGA value is, the stronger the similarity of the two spectra in the subinterval is. And selecting the subinterval of the jth curve as the optimal subinterval.
And S5, performing series fusion on the screened optimal subinterval spectral curves to obtain an optimal to-be-detected spectral curve, and determining the ion concentration of the to-be-detected solution according to the optimal spectral curve.
In the embodiment of the invention, the method specifically comprises the steps of carrying out series fusion on the optimal subinterval spectral curves screened in the steps according to the wavelength, and outputting an optimal spectral curve to be tested. The full spectrum wave band is divided into sub-intervals of set values at equal intervals, spectral lines in a standard spectrum curve library are used as a correction set, a PLS model is established on each sub-interval, a cross validation root mean square error RMSECV value is used as an accuracy measurement index of each sub-interval model, and the smaller the RMSECV value is, the higher the accuracy of the model is. Therefore, an optimal subinterval set is screened out by comparing the RMSECV value of each subinterval of each ion to be detected, and an iPLS ion concentration analysis model is established by using the optimal interval combination;
and predicting the ion concentration of the optimal spectrum curve to be measured by adopting the iPLS ion concentration analytical model, and outputting the ion concentration of the solution.
The multispectral matching and fusing method provided by the invention divides the waveband subintervals through the absorption characteristics, selects the spectral absorption index or the multispectral fusing method for the optimal waveband subinterval according to whether the waveband subinterval has a single absorption characteristic or not, realizes the characteristic level fusion of the optimal waveband subinterval of the multispectral at the characteristic level, outputs an optimal spectrum curve, eliminates the interference of suspended microparticles in the solution, lays a data foundation for establishing a steady prediction model of the ion concentration of the solution, and thus accurately determines the concentration of the solution to be measured.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice in the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It should be understood that, although the steps in the flowcharts of the embodiments of the present invention are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in various embodiments may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above may be implemented by a computer program, which may be stored in a non-volatile computer readable storage medium, and when executed, may include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), rambus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
Claims (9)
1. A solution ion concentration detection method based on multispectral fusion is characterized by specifically comprising the following steps:
s1, constructing a standard absorption spectrum curve library, and detecting a sample to be detected by a movable light source type spectrometer to obtain a plurality of spectrum curves to be detected;
s2, calculating MSGA values of the multiple spectral curves to be tested and the standard spectral curve, selecting the spectral curves to be tested with the MSGA values smaller than a set threshold value, and obtaining multiple screened spectral curves to be tested;
the calculation formula of the MSGA value is as follows:
in the formula D M (X, Y) is the Mahalanobis distance between the spectral curve X to be measured and the standard spectral curve Y, SGA (grad (X),grad (Y)) is a cosine value of a gradient angle between the spectral curve X to be measured and the standard spectral curve Y, sigmoid () is a nonlinear activation function, and an independent variable is mapped to a (0, 1) interval;
s3, dividing the bands of the screened spectrum curve to be tested and the standard spectrum curve according to a method that the subinterval at most contains one absorption characteristic to obtain a plurality of subarea spectrum curves to be tested and subarea standard spectrum curves;
s4, screening according to the multiple subarea spectral curves to be tested and the subarea standard spectral curves and a preset optimal subinterval screening method to obtain optimal subintervals of the multiple spectral curves to be tested;
and S5, performing series fusion on the screened optimal subinterval spectral curves to obtain an optimal to-be-detected spectral curve, and determining the ion concentration of the to-be-detected solution according to the optimal spectral curve.
2. The method for detecting the ion concentration of the solution based on the multiline fusion as claimed in claim 1, wherein the step S1 specifically includes:
preparing a standard solution with a concentration gradient, detecting the standard sample solution by adopting a single-beam spectrometer with a fixed light source to obtain a standard spectral line and construct a standard curve library;
and detecting the sample to be detected at a plurality of detection positions by adopting a movable light source type spectrometer to obtain a plurality of spectral curves to be detected.
3. The method for detecting the ion concentration of the solution based on the multiline fusion according to claim 1, wherein the step S3 specifically comprises:
the method for dividing the standard spectrum curve into the sub-intervals of the wave bands specifically comprises the following steps: acquiring absorption peaks and troughs on a standard absorption spectrum curve, and identifying other absorption characteristics according to a relative error identification method to [ lambda ] m -α,λ m +α]Dividing the wave band containing the absorption characteristics as a subinterval, and dividing the wave band without the absorption characteristics into subintervals continuously and evenly according to 6 times of the length of the subinterval with the absorption characteristics; wherein λ m Is a maximum or minimum pointA corresponding wavelength; alpha is a constant;
and dividing the screened spectrum curves to be detected into sub-interval ranges of wave bands according to a standard curve to obtain a plurality of partitioned spectrum curves to be detected.
4. The method for detecting the ion concentration of the solution based on the multiline fusion as claimed in claim 3, wherein the relative error identification method is specifically as follows:
calculating the relative error between the average value of the absorbance values in the alpha neighborhood of the maximum/minimum value point of the absorption peak valley and the average value of the absorbance values in the tangent alpha neighborhood according to a relative error calculation formula, and identifying the absorption feature with the relative error larger than 20%;
the relative error is calculated by the formula
In the formula
Is the average of the absorbance values in the alpha neighborhood of the maximum or minimum point,
is the average of the absorbance values in the left tangent alpha neighborhood,
is the average of the absorbance values in the right-cut alpha neighborhood.
5. The method for detecting the ion concentration of the solution based on the multiline fusion as claimed in claim 1, wherein the method for screening the optimal subinterval preset in the step S4 specifically includes:
for the subintervals with single absorption characteristics, respectively calculating the SAI values of the spectral curve to be measured and the standard spectral curve of the corresponding interval, and screening out the subinterval of the spectral curve to be measured, which is closest to the SAI value of the standard spectral curve, as the first-class optimal subinterval;
and for the subintervals without the absorption characteristics, respectively calculating the MSGA values of the spectral curve to be measured and the standard spectral curve of the corresponding interval, and screening out the second type of optimal subintervals according to the minimum MSGA value principle.
6. The solution ion concentration detection method based on multiline fusion according to claim 5, wherein the step of obtaining the first-class optimal subinterval specifically includes:
respectively calculating SAI values of subintervals of the standard spectral curve and the ith spectral curve to be detected, and respectively marking the SAI values as SAI _ s and SAI _ i; the calculation formula is as follows:
W＝λ 2 -λ 1
in the formula, λ 1 ,λ 2 ,λ m Are respectively a point S 1 ，S 2 M, the wavelength corresponding to M; a. The 1 ，A 2 ，A m Are respectively a point S 1 ，S 2 The absorbance value corresponding to M; w is the width of the absorption characteristic peak, d is the symmetry parameter of the absorption characteristic peak, and SAI is the spectral absorption index of the absorption characteristic of the subinterval;
calculating the difference value of the SAI of the ith spectrum curve to be measured and the standard spectrum curve on the subinterval, and taking the subinterval of the spectrum curve to be measured with the minimum difference value as a first-class optimal subinterval according to the principle that the smaller the difference value is, the stronger the similarity of the two spectra in the subinterval, wherein the first-class optimal subinterval selection formula is as follows:
in the formula, k p The method means that the subinterval numbered p on the kth spectral curve is the optimal subinterval of the first type in the m subintervals numbered p.
7. The method for detecting the ion concentration of the solution based on the multiline fusion as claimed in claim 5, wherein the step of obtaining the second optimal subinterval specifically comprises:
calculating the MSGA value of the ith spectral curve to be measured and the standard spectral curve;
and screening out a spectrum curve to be measured with the minimum MSGA value, and taking a subinterval with the serial number i in the spectrum curve to be measured as a second-class optimal subinterval.
8. The method for detecting the ion concentration of the solution based on the multiline fusion according to claim 5, wherein the step S5 specifically comprises:
performing series fusion on the obtained first and second optimal subintervals according to the wavelength, and outputting an optimal spectrum curve to be measured;
dividing full-spectrum bands into sub-intervals of set values at equal intervals, taking spectral lines in a standard spectrum curve library as a correction set, establishing a PLS model on each sub-interval, taking a cross validation root mean square error RMSECV value as a precision measurement index of each sub-interval model, comparing the RMSECV value of each sub-interval of ions to be tested, screening out an optimal sub-interval set, and establishing an iPLS ion concentration analysis model by using an optimal interval combination;
in the formula, is y i The actual value of the ith sample is,
is the predicted value of the ith sample, and n is the number of correction set samples;
and predicting the ion concentration of the optimal spectrum curve to be measured according to the iPLS ion concentration analytic model, and outputting the ion concentration of the solution.
9. The method for detecting the ion concentration of the solution based on the multiline fusion as claimed in claim 1, wherein the moving source spectrometer specifically comprises: the device comprises a cuvette, a cuvette cover, a bottom plate, a spectrometer detection tool, an optical fiber seat, a connecting plate, a shading cover and a shading body;
the optical fiber seat is characterized in that grooves penetrating through two side edges are formed in two sides of the light-shading body, the optical fiber seat is U-shaped, lugs matched with the grooves are arranged on two inner sides of the light-shading body, the optical fiber seat and the light-shading body are slidably connected through the grooves and the lugs, the length of the two side edges of the light-shading body is smaller than that of the two side edges of the optical fiber seat, and the connecting plate is connected with the optical fiber seat in a detachable connection mode.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110710863.8A CN113450883B (en) | 2021-06-25 | 2021-06-25 | Solution ion concentration detection method based on multispectral fusion |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110710863.8A CN113450883B (en) | 2021-06-25 | 2021-06-25 | Solution ion concentration detection method based on multispectral fusion |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113450883A CN113450883A (en) | 2021-09-28 |
| CN113450883B true CN113450883B (en) | 2023-03-21 |
Family
ID=77812826
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110710863.8A Active CN113450883B (en) | 2021-06-25 | 2021-06-25 | Solution ion concentration detection method based on multispectral fusion |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113450883B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114166768B (en) * | 2022-02-14 | 2022-05-20 | 四川大学华西医院 | Conversion method and device for detecting homogeneity of same index by different equipment and electronic equipment |
| CN114486799A (en) * | 2022-04-15 | 2022-05-13 | 广东省农业科学院动物科学研究所 | Detection method and system for clostridium butyricum in poultry intestinal tract |
| CN115639168B (en) * | 2022-12-21 | 2023-04-07 | 杭州泽天春来科技有限公司 | Gas detection method, system and medium for gas analyzer |
| CN116223398B (en) * | 2023-02-01 | 2023-11-03 | 广州华立学院 | Water, fertilizer and pesticide integrated concentration proportioning method based on spectral analysis |
| CN118213011A (en) * | 2024-05-22 | 2024-06-18 | 之江实验室 | Star cloud spectrum molecule identification method, electronic device and storage medium |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE69800414T2 (en) * | 1998-04-21 | 2001-04-12 | Hewlett Packard Co | Detection and determination of noble gases |
| US7562057B2 (en) * | 1999-04-09 | 2009-07-14 | Plain Sight Systems, Inc. | System and method for hyper-spectral analysis |
| CN109211806A (en) * | 2018-09-04 | 2019-01-15 | 北京航空航天大学 | A kind of spectral line choosing method for laser absorption spectrum tomography |
| CN109187392B (en) * | 2018-09-26 | 2021-11-19 | 中南大学 | Zinc liquid trace metal ion concentration prediction method based on partition modeling |
| CN109799207A (en) * | 2019-01-15 | 2019-05-24 | 上海交通大学 | The quantitative detecting method of talcum powder is mixed in the root of Dahurain angelica based on near-infrared spectrum analysis |
| CN110441244A (en) * | 2019-07-26 | 2019-11-12 | 北京建筑大学 | A kind of spectrum segmentation coloring earth recognition methods for taking Absorption Characteristics into account |
| CN111521577B (en) * | 2020-04-29 | 2023-06-27 | 清华大学合肥公共安全研究院 | Infrared spectrum quantitative analysis method taking carbon dioxide peak area as reference |
| CN112700822A (en) * | 2020-12-03 | 2021-04-23 | 西南交通大学 | Laser-induced breakdown spectroscopy concentration extraction method for online monitoring of trace gas impurities |
-
2021
- 2021-06-25 CN CN202110710863.8A patent/CN113450883B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN113450883A (en) | 2021-09-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113450883B (en) | Solution ion concentration detection method based on multispectral fusion | |
| EP2710353B1 (en) | SPECTROSCOPIC APPARATUS AND METHOD of DETERMINING COMPONENTS PRESENT IN A SAMPLE | |
| US8352205B2 (en) | Multivariate optical elements for nonlinear calibration | |
| Wang et al. | High precision wide range online chemical oxygen demand measurement method based on ultraviolet absorption spectroscopy and full-spectrum data analysis | |
| US10684388B2 (en) | Discrimination analysis used with optical computing devices | |
| CN104089911A (en) | Spectral model transmission method based on unary linear regression | |
| CN108037084B (en) | Anti-interference measuring method suitable for photometric principle water quality automatic analyzer | |
| WO2008002192A1 (en) | Method for producing multidimensional calibrating patterns | |
| CN108956583A (en) | Characteristic spectral line automatic selecting method for laser induced breakdown spectroscopy analysis | |
| CN109564199A (en) | Analyze data processing method and analysis data processing equipment | |
| CN106018331B (en) | The method for estimating stability and pretreatment optimization method of multi-channel spectral system | |
| CN117146977A (en) | Peak searching method and device for monochromator spectrum and electronic equipment | |
| Liu et al. | Study on the effect of spectral difference coefficient on the precision of quantitative spectral analysis | |
| CN105466885B (en) | Based on the near infrared online measuring method without measuring point temperature-compensating mechanism | |
| CN112630180B (en) | Ultraviolet/visible light absorption spectrum model for detecting concentration of organophosphorus pesticide in water body | |
| Duncker et al. | Detection and classification of Norway spruce compression wood in reflected light by means of hyperspectral image analysis | |
| CN110887798B (en) | Nonlinear full-spectrum water turbidity quantitative analysis method based on extreme random tree | |
| CN104020124A (en) | Spectral wavelength screening method based on preferential absorptivity | |
| Pessoa et al. | Development of ant colony optimization (aco) algorithms based on statistical analysis and hypothesis testing for variable selection | |
| CN107356556A (en) | A kind of double integrated modelling approach of Near-Infrared Spectra for Quantitative Analysis | |
| Jakubek | A new model for the calculation of homogeneously broadened Raman spectral bandwidths and bandshapes | |
| US20230194416A1 (en) | Preparation method for preparing spectrometric determinations of at least one measurand in a target application | |
| Moreira et al. | Evaluation of the influence of sample variability on the calibration of a NIR photometer for quantification of ethanol in gasoline | |
| CN114235716B (en) | Water optical classification and quality control method and computer readable storage medium | |
| CN112986180B (en) | Spectrum type gas sensing data processing method and system |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |