CN111289446A - Method and system for detecting component concentration of complex solution - Google Patents
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- 238000004458 analytical method Methods 0.000 claims abstract description 29
- 238000007781 pre-processing Methods 0.000 claims abstract description 18
- 230000000007 visual effect Effects 0.000 claims abstract description 16
- 238000012937 correction Methods 0.000 claims description 12
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- 238000009499 grossing Methods 0.000 claims description 6
- 238000003062 neural network model Methods 0.000 claims description 6
- 238000010238 partial least squares regression Methods 0.000 claims description 6
- 238000000513 principal component analysis Methods 0.000 claims description 6
- 230000009466 transformation Effects 0.000 claims description 6
- 238000001514 detection method Methods 0.000 abstract description 12
- 239000000243 solution Substances 0.000 description 37
- 239000000126 substance Substances 0.000 description 5
- 230000000694 effects Effects 0.000 description 3
- 239000002960 lipid emulsion Substances 0.000 description 3
- 230000003595 spectral effect Effects 0.000 description 3
- 238000002474 experimental method Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000004445 quantitative analysis Methods 0.000 description 2
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- 238000002329 infrared spectrum Methods 0.000 description 1
- 239000007788 liquid Substances 0.000 description 1
- 239000008267 milk Substances 0.000 description 1
- 210000004080 milk Anatomy 0.000 description 1
- 235000013336 milk Nutrition 0.000 description 1
- 239000011259 mixed solution Substances 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
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- 238000001228 spectrum Methods 0.000 description 1
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- G06T2207/10036—Multispectral image; Hyperspectral image
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Abstract
The invention relates to a method and a system for detecting the component concentration of a complex solution, wherein the method comprises the following steps: acquiring a hyperspectral image of a solution to be detected; preprocessing the hyperspectral image to obtain a clear hyperspectral image; respectively selecting a set number of pixel curves on each wave band in the clear hyperspectral image; combining the pixel curves from small to large to form a visual light intensity distribution image; and establishing a concentration component analysis model according to the visual light intensity distribution image, and obtaining the concentration of each component in the solution to be detected according to the concentration component analysis model. The method greatly reduces the workload and improves the detection precision.
Description
Technical Field
The invention relates to the field of solution component concentration detection, in particular to a method and a system for detecting the component concentration of a complex solution.
Background
The visible near infrared spectrum technology is widely applied to quantitative analysis of complex solutions such as milk, blood, urine, semen, bacteria liquid and the like due to the characteristics of rapidness, no damage, no pollution and the like. Due to the influence of scattering substances contained in the complex solution, the detection precision is not high due to nonlinear information caused by scattering effect, and the practicability of the model is poor. Therefore, the precision of the spectral quantitative analysis of the components of complex solutions is receiving increasing attention from researchers.
In order to improve the accuracy of the content detection of the components of the complex solution, many researchers have tried to establish a correlation between the scattering information in the complex solution and the concentration of the components of the substance using the scattering information of the medium, and perform concentration analysis on the chemical components using the scattering information of the complex solution.
In the prior art, when the concentration of chemical components is analyzed by using scattering information of a complex solution, the model data volume of the analysis method is large, the calculation efficiency and the prediction precision of the model cannot be effectively improved, and the effective chemical component concentration analysis of the complex solution is difficult to realize.
Disclosure of Invention
The invention aims to provide a method and a system for detecting the concentration of a complex solution component with low workload and high precision.
In order to achieve the purpose, the invention provides the following scheme:
a method for detecting the component concentration of a complex solution comprises the following steps:
acquiring a hyperspectral image of a solution to be detected;
preprocessing the hyperspectral image to obtain a clear hyperspectral image;
respectively selecting a set number of pixel curves on each wave band in the clear hyperspectral image;
combining the pixel curves according to the wave bands from small to large to form a visual light intensity distribution image;
and establishing a concentration component analysis model according to the visual light intensity distribution image, and obtaining the concentration of each component in the solution to be detected according to the concentration component analysis model.
Preferably, the preprocessing is at least one of smoothing filtering, multivariate scatter correction, orthonormal transformation, orthogonal signal correction, and principal component analysis.
Preferably, the concentration component analysis model is at least one of a partial least squares regression model, a stacked self-coding network model, and a BP neural network model.
Preferably, the detection method further comprises:
and selecting an interested area of the clear hyperspectral image, and respectively selecting a set number of pixel curves on each wave band in the interested area.
Preferably, the set number is an integer of 1 or more and 10 or less.
The invention also provides a detection system for the component concentration of the complex solution, which comprises:
the original image acquisition module is used for acquiring a hyperspectral image of the solution to be detected;
the preprocessing module is used for preprocessing the hyperspectral image to obtain a clear hyperspectral image;
the pixel curve selection module is used for selecting a set number of pixel curves on each wave band in the clear hyperspectral image respectively;
the light intensity image acquisition module is used for combining the pixel curve wave bands from small to large to form a visualized light intensity distribution image;
and the component analysis module is used for establishing a concentration component analysis model according to the visual light intensity distribution image and obtaining the concentration of each component in the solution to be detected according to the concentration component analysis model.
Preferably, the preprocessing is at least one of smoothing filtering, multivariate scatter correction, orthonormal transformation, orthogonal signal correction, and principal component analysis.
Preferably, the concentration component analysis model is at least one of a partial least squares regression model, a stacked self-coding network model, and a BP neural network model.
Preferably, the detection method further comprises:
and selecting an interested area of the clear hyperspectral image, and respectively selecting a set number of pixel curves on each wave band in the interested area.
Preferably, the set number is an integer of 1 or more and 10 or less.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention relates to a method and a system for detecting the component concentration of a complex solution, wherein the method comprises the following steps: acquiring a hyperspectral image of a solution to be detected; preprocessing the hyperspectral image to obtain a clear hyperspectral image; respectively selecting a set number of pixel curves on each wave band in the clear hyperspectral image; combining the pixel curves from small to large to form a visual light intensity distribution image; and establishing a concentration component analysis model according to the visual light intensity distribution image, and obtaining the concentration of each component in the solution to be detected according to the concentration component analysis model. The method greatly reduces the workload and improves the detection precision.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a flow chart of the method for detecting the concentration of a component in a complex solution according to the present invention;
FIG. 2 is a visualized light intensity distribution image;
FIG. 3 is a graph comparing the concentration analysis modeling and the prediction effect of the complex solution components.
Detailed Description
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 only a part of the embodiments of the present invention, and not all of the embodiments. 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.
The invention aims to provide a method and a system for detecting the concentration of a complex solution component with less workload and high precision.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
As shown in fig. 1, the present invention provides a method for detecting the concentration of a component in a complex solution, comprising:
step S1: and acquiring a hyperspectral image of the solution to be detected.
Step S2: and preprocessing the hyperspectral image to obtain a clear hyperspectral image.
Step S3: and respectively selecting a set number of pixel curves on each wave band in the clear hyperspectral image.
Step S4: and combining the pixel curves according to the wave bands from small to large to form a visual light intensity distribution image.
Step S5: and establishing a concentration component analysis model according to the visual light intensity distribution image, and obtaining the concentration of each component in the solution to be detected according to the concentration component analysis model.
The invention collects the hyperspectral image by a Charge Coupled Device (CCD) image sensor.
As an alternative embodiment, the preprocessing according to the present invention is at least one of smoothing filtering, multivariate scatter correction, orthonormal transformation, orthogonal signal correction, and principal component analysis.
As an optional embodiment, the concentration component analysis model of the present invention is at least one of a partial least squares regression model, a stacked self-coding network model, and a BP neural network model.
As an optional implementation manner, the detection method of the present invention further includes:
and selecting an interested area of the clear hyperspectral image, and respectively selecting a set number of pixel curves on each wave band in the interested area.
As an alternative embodiment, the set number is an integer of 1 or more and 10 or less.
The invention also provides a detection system for the component concentration of the complex solution, which comprises:
and the original image acquisition module is used for acquiring a hyperspectral image of the solution to be detected.
And the preprocessing module is used for preprocessing the hyperspectral image to obtain a clear hyperspectral image.
And the pixel curve selection module is used for selecting a set number of pixel curves on each wave band in the clear hyperspectral image respectively.
And the light intensity image acquisition module is used for combining the pixel curves from small to large according to the wave bands to form a visual light intensity distribution image.
And the component analysis module is used for establishing a concentration component analysis model according to the visual light intensity distribution image and obtaining the concentration of each component in the solution to be detected according to the concentration component analysis model.
As an alternative embodiment, the preprocessing according to the present invention is at least one of smoothing filtering, multivariate scatter correction, orthonormal transformation, orthogonal signal correction, and principal component analysis.
As an optional embodiment, the concentration component analysis model of the present invention is at least one of a partial least squares regression model, a stacked self-coding network model, and a BP neural network model.
As an optional implementation manner, the detection method of the present invention further includes:
and selecting an interested area of the clear hyperspectral image, and respectively selecting a set number of pixel curves on each wave band in the interested area.
As an alternative embodiment, the set number is an integer of 1 or more and 10 or less.
The specific implementation mode is as follows:
firstly, a complex solution sample is prepared, a hyperspectral instrument of Headwall Photonics is used for acquiring a hyperspectral image of the complex solution sample, the hyperspectral image is used as a hyperspectral image acquisition system, the wavelength range is 550-1700 nm, the spectral resolution is 4.775nm, and the wavelength number is 244. The experiment used a mixed solution of indian ink and fat emulsion solution as the sample of the complex solution to be tested. Fixing the concentration of India ink to be 0.0006%, changing the concentration of fat emulsion from 0.25% -1.6% at intervals of 0.03%, dividing the concentration of the fat emulsion into 46 groups of samples from small to large, collecting hyperspectral images of 46 groups of complex solution samples by using a hyperspectral meter, respectively carrying out 10 times of repeated experiments on each group of samples, averaging, and obtaining image information of 630 × 320 pixels from each group of averaged samples.
As shown in FIG. 2, the light intensity of 550-700 nm is higher than the light intensity of other wavelength ranges, so the wavelengths in this range are selected for further modeling and processing. Meanwhile, according to the spectral resolution of a high-resolution spectrometer, the spectrum has 32 wave bands in the range of 550-700 nm. By preprocessing a hyperspectral image obtained by a sample between 550 and 700nm, as shown in fig. 2(a) to 2(d), curves of 10 pixels in total are selected in front of and behind a curve passing through a maximum light intensity point in the transverse direction in each wave band, and 320 curves selected from 32 wave bands form a visual light intensity distribution image according to the sequence of the wavelengths from small to large.
In order to reduce interference of irrelevant information and ensure high signal-to-noise ratio of the detection system, each visualized light intensity distribution image is respectively extracted by defining an interested region, as shown in fig. 2(f), as the light intensity of 550-650 nm is obviously higher than that of other wavelength ranges, a rectangular region with the size of 220 x 120 pixels is selected as the interested region, and further modeling and processing are carried out to obtain the concentration information of each component.
As shown in FIG. 3, the correlation coefficient is improved by 1.29% and the mean square error is reduced by 3.07% by the method.
The invention reduces the data volume of the model and further improves the prediction precision of the model.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (10)
1. A method for detecting the concentration of a component in a complex solution is characterized by comprising the following steps:
acquiring a hyperspectral image of a solution to be detected;
preprocessing the hyperspectral image to obtain a clear hyperspectral image;
respectively selecting a set number of pixel curves on each wave band in the clear hyperspectral image;
combining the pixel curves according to the wave bands from small to large to form a visual light intensity distribution image;
and establishing a concentration component analysis model according to the visual light intensity distribution image, and obtaining the concentration of each component in the solution to be detected according to the concentration component analysis model.
2. The method of claim 1, wherein the pre-processing is at least one of smoothing filtering, multivariate scatter correction, orthonormal transformation, orthogonal signal correction, and principal component analysis.
3. The method according to claim 1, wherein the concentration component analysis model is at least one of a partial least squares regression model, a stacked self-coding network model and a BP neural network model.
4. The method for detecting the concentration of a component in a complex solution according to claim 1, further comprising:
and selecting an interested area of the clear hyperspectral image, and respectively selecting a set number of pixel curves on each wave band in the interested area.
5. The method according to claim 1, wherein the predetermined number is an integer of 1 or more and 10 or less.
6. A system for detecting the concentration of a component in a complex solution, comprising:
the original image acquisition module is used for acquiring a hyperspectral image of the solution to be detected;
the preprocessing module is used for preprocessing the hyperspectral image to obtain a clear hyperspectral image;
the pixel curve selection module is used for selecting a set number of pixel curves on each wave band in the clear hyperspectral image respectively;
the light intensity image acquisition module is used for combining the pixel curves from small to large according to wave bands to form a visual light intensity distribution image;
and the component analysis module is used for establishing a concentration component analysis model according to the visual light intensity distribution image and obtaining the concentration of each component in the solution to be detected according to the concentration component analysis model.
7. The system according to claim 6, wherein the pre-processing is at least one of smoothing filtering, multivariate scatter correction, orthonormal transformation, orthogonal signal correction, and principal component analysis.
8. The system for detecting the concentration of components in a complex solution according to claim 6, wherein the concentration component analysis model is at least one of a partial least squares regression model, a stacked self-coding network model and a BP neural network model.
9. The system for detecting the concentration of components in a complex solution according to claim 6, wherein the method further comprises:
and selecting an interested area of the clear hyperspectral image, and respectively selecting a set number of pixel curves on each wave band in the interested area.
10. The system for detecting the concentration of components in a complex solution according to claim 6, wherein the predetermined number is an integer of 1 or more and 10 or less.
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| CN114047187A (en) * | 2021-11-04 | 2022-02-15 | 温州医科大学 | Method for measuring concentration of colored solution substance by using RAW image |
| CN115128014A (en) * | 2022-09-01 | 2022-09-30 | 北京智麟科技有限公司 | Hyperspectral image acquisition system and analysis method |
| CN115184281A (en) * | 2022-09-05 | 2022-10-14 | 北京智麟科技有限公司 | Method and system for determining concentration of solution components based on two-dimensional spectrum |
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| CN111916156A (en) * | 2020-06-23 | 2020-11-10 | 宁波大学 | Real-time tail gas sulfur-containing substance concentration prediction method based on stacked self-encoder |
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| CN115128014A (en) * | 2022-09-01 | 2022-09-30 | 北京智麟科技有限公司 | Hyperspectral image acquisition system and analysis method |
| CN115184281A (en) * | 2022-09-05 | 2022-10-14 | 北京智麟科技有限公司 | Method and system for determining concentration of solution components based on two-dimensional spectrum |
| CN115184281B (en) * | 2022-09-05 | 2022-12-09 | 北京智麟科技有限公司 | Method and system for determining concentration of solution components based on two-dimensional spectrum |
| CN117274236A (en) * | 2023-11-10 | 2023-12-22 | 山东第一医科大学第一附属医院(山东省千佛山医院) | Urine component abnormality detection method and system based on hyperspectral image |
| CN117274236B (en) * | 2023-11-10 | 2024-03-08 | 山东第一医科大学第一附属医院(山东省千佛山医院) | Urine component abnormality detection method and system based on hyperspectral image |
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Application publication date: 20200616 Assignee: TIANJIN LIANTU TECHNOLOGY Co.,Ltd. Assignor: TIANJIN POLYTECHNIC University Contract record no.: X2024980001900 Denomination of invention: A detection method and system for the concentration of complex solution components Granted publication date: 20210629 License type: Common License Record date: 20240202 |