CN117848963A - Colorimetric quantification-based method and system for detecting component content of object to be detected - Google Patents
Colorimetric quantification-based method and system for detecting component content of object to be detected Download PDFInfo
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Abstract
The invention relates to the technical field of colorimetric quantification, in particular to a method and a system for detecting the component content of an object to be detected based on colorimetric quantification, wherein the method comprises the steps of constructing a standard curve between the content of a target component of the object to be detected and a chromaticity component in a standard environment; selecting a reference object; acquiring a first image containing a reference object under a standard environment, and acquiring standard chromaticity of the reference object in the first image; acquiring a second image containing a reference object at a target detection site, and acquiring the actual chromaticity of the reference object in the second image; if the chromaticity deviation between the actual chromaticity and the standard chromaticity is larger than the set value, constructing a chromaticity correction model, and acquiring a conversion relation between the actual chromaticity and the standard chromaticity; acquiring the field chromaticity component of a target component of an object to be detected at a target detection field; and determining the content of the target component of the object to be detected corresponding to the field chromaticity component by using the conversion relation and the standard curve. The method realizes the accurate detection of the component content of the object to be detected.
Description
Technical Field
The invention relates to the technical field of colorimetric quantification, in particular to a method and a system for detecting the component content of an object to be detected based on colorimetric quantification.
Background
Colorimetric (colorimetry) is a method for determining the content of a component to be detected by comparing or measuring the color depth of a colored substance, and is widely applied to the fields of water quality detection, gas sensing, biochemical analysis and the like, and relates to various aspects of industrial production, medical health, daily life and the like. The traditional colorimetric analysis method mainly depends on visual inspection of human eyes, has low test efficiency and is difficult to accurately quantify, and compared with the colorimetric method, the colorimetric analysis method has the advantages of small equipment volume, low test cost, short detection time, small reagent consumption, visual test result and the like.
With the rapid development of technologies such as photography and photography, hardware conditions for quantitative detection are basically provided, and platforms such as smartphones have more and more advanced computing and image processing capabilities. However, the important reason for restricting the large-scale application of the colorimetric quantitative method at present is that chromaticity deviation caused by different on-site test conditions, such as equipment hardware, camera setting, on-site light source conditions, shooting angles/distances and the like, can cause far-reaching shooting results on the same system, and the accuracy and the application range of the shooting quantitative method are greatly limited. Aiming at the difficult problem, most researchers think of fixing shooting conditions, so that errors caused by test conditions can be reduced to a certain extent, but hardware equipment and cost are greatly increased, and the requirements on the test conditions are very strict.
Therefore, a method and a system for detecting the component content of an analyte based on colorimetric quantification are needed. Through developing the detection technology which does not depend on fixed hardware conditions, selecting a reference object with known color composition, analyzing the image of the reference object at a test site, thereby obtaining the characteristic rules of different test conditions, or correcting and restoring chromaticity based on the characteristic rules, so that the test conditions at different sites are all calibrated back to specific standard conditions, and the component content of the object to be tested can be more accurately and quantitatively analyzed.
Disclosure of Invention
The invention provides a method and a system for detecting the component content of an object to be detected based on colorimetric quantification, which are used for solving the problem that the colorimetric deviation is large due to different on-site test conditions existing based on colorimetric quantification in the prior art, so that the content of the target component of the object to be detected cannot be accurately and quantitatively detected.
In order to achieve the above object, the first aspect of the present invention provides a method for detecting the component content of an analyte based on colorimetric quantification, the method comprising:
constructing a standard curve between the content of a target component of the object to be detected and the chromaticity component in a standard environment;
selecting a reference object according to the target component of the object to be detected and the material and color development rule of the detection system;
determining the number of sample color blocks contained in the standard object and a adopted chromaticity system, wherein the percentage of the total span interval of each chromaticity component in the chromaticity system, which covers the total span interval of the chromaticity component detection limit of the standard curve, is not less than a first set value;
acquiring a first image containing the reference object under a standard environment, and acquiring standard chromaticity of the reference object in the first image;
acquiring a second image containing the reference object at a target detection site, and acquiring the actual chromaticity of the reference object in the second image;
if the chromaticity deviation between the actual chromaticity of the reference object in the second image and the standard chromaticity of the reference object in the first image is larger than a second set value, constructing a chromaticity correction model, and acquiring a conversion relation between the actual chromaticity of the reference object in the second image and the standard chromaticity of the reference object in the first image;
acquiring the field chromaticity component of a target component of an object to be detected at a target detection field;
and determining the content of the target component of the object to be detected corresponding to the on-site chromaticity component by utilizing the conversion relation and the standard curve.
Preferably, the selecting the reference object according to the target component of the object to be detected specifically includes:
and selecting a reference object according to the target component of the object to be detected by a subjective weighting method or an objective weighting method.
Preferably, the subjective weighting method comprises an analytic hierarchy process, a weight factor judging table process, a Delphi process and a fuzzy analysis process, and the objective weighting method comprises a principal component analysis process, a factor analysis process, an entropy value process and a regression analysis process.
Preferably, the reference object is a planar structure.
Preferably, the reference substance includes a standard color chart, a color developing device, and a paper chip.
Preferably, before acquiring the actual chromaticity of the reference object in the second image, the method includes:
a pre-evaluation is made of the second image,
and under the condition that the pre-evaluation result of the second image meets the requirement, acquiring the actual chromaticity of the reference object in the second image.
Preferably, the pre-evaluating the second image specifically includes:
the second image is pre-evaluated for pixels, sharpness, contrast, signal-to-noise ratio, inclination, color temperature, noise, illumination intensity, and light uniformity.
Preferably, the chromaticity correction model is constructed by a neural network algorithm or a multivariate nonlinear fitting method.
Preferably, the determining the target component content of the object to be detected corresponding to the in-situ chromaticity component by using the conversion relation and the standard curve specifically includes:
converting the field chrominance component into a standard chrominance component using the conversion relationship;
searching the target component content of the object to be detected corresponding to the standard chromaticity component on the standard curve, and determining the target component content of the object to be detected.
Preferably, the method further comprises:
if the chromaticity deviation between the actual chromaticity of the reference object in the second image and the standard chromaticity of the reference object in the first image is not larger than a second set value;
and determining the content of the target component of the object to be detected corresponding to the field chromaticity component according to the standard curve.
Preferably, the chromaticity system includes RGB, HSV, CMYK and LAB.
Preferably, the standard environment is a laboratory environment.
In order to achieve the above object, a second aspect of the present invention provides a colorimetric quantitative-based component content detection system for an analyte, which is applied to the method, the system comprising:
the standard curve construction module is used for constructing a standard curve between the content of the target component of the object to be detected and the chromaticity component in a standard environment;
the reference object selecting module is used for selecting a reference object according to the target component of the object to be detected, the material of the detection system and the color development rule;
a reference object setting module, configured to determine the number of sample color blocks included in the reference object and a chromaticity system adopted, where a percentage of a total span interval of each chromaticity component in the chromaticity system covering a total span interval of a chromaticity component detection limit of the standard curve is not less than a first set value;
the standard chromaticity acquisition module is used for acquiring a first image containing the reference object under a standard environment and acquiring the standard chromaticity of the reference object in the first image;
the actual chromaticity acquisition module is used for acquiring a second image containing the reference object at the target detection site and acquiring the actual chromaticity of the reference object in the second image;
the conversion relation acquisition module is used for constructing a chromaticity correction model if the chromaticity deviation between the actual chromaticity of the reference object in the second image and the standard chromaticity of the reference object in the first image is larger than a second set value, and acquiring a conversion relation between the actual chromaticity of the reference object in the second image and the standard chromaticity of the reference object in the first image;
the on-site chromaticity component acquisition module is used for acquiring on-site chromaticity components of target components of the to-be-detected object in the target detection site;
and the target component content determining module is used for determining the target component content of the object to be detected corresponding to the field chromaticity component by utilizing the conversion relation and the standard curve.
According to the technical scheme, based on the method, the standard chromaticity of the reference object in the first image and the actual chromaticity of the reference object in the second image of the target detection site are obtained through systematically and strictly selecting the reference object from multiple sides, so that the accuracy of the constructed chromaticity correction model is effectively improved, and the accurate detection of the target component content of the object to be detected is realized. Meanwhile, the method does not depend on specific shooting conditions, has good applicability to different shooting equipment, light sources and shooting methods, does not need to add additional equipment such as light sources and fixing devices, reduces the requirements and cost of the shooting method on hardware facilities, finishes a plurality of analysis and verification works by image processing analysis programs, and has the advantages of simplicity in operation, high testing speed and wide application range.
Drawings
FIG. 1 is a flow chart of a method for detecting the component content of a test substance based on colorimetric quantification;
FIG. 2 is a schematic diagram of a colorimetric quantification-based assay component content detection system;
FIG. 3 is a standard curve of chromium ions in example 1;
FIG. 4 is a reference set up in example 1;
FIG. 5 is a graph showing the comparison between the result of detecting the concentration of chromium ions in water by the method of the present invention and the result of detecting the concentration of chromium ions in water by the national standard method in example 1;
FIG. 6 is a reference established in example 2;
FIG. 7 is a graph of the correction effect based on the neural network algorithm under the RGB color system in example 2;
FIG. 8 is a graph showing the comparison between the results of the detection of nickel ions in water by the method of the present invention and the results of the detection of nickel ions in water by the national standard method in example 2;
FIG. 9 is a diagram of the standard color chart standard selected in example 3;
FIG. 10 is a graph showing the comparison between the result of detecting the concentration of chromium ions in water by the method of the present invention and the result of detecting the concentration of chromium ions in water by the national standard method in example 3;
FIG. 11 is a graph showing the relationship between the concentration of hydrogen gas and the chromaticity component in the air in example 4;
fig. 12 is an effect diagram of RGB chrominance components before and after correction in embodiment 4.
Detailed Description
The following describes the detailed implementation of the embodiments of the present invention with reference to the drawings. It should be understood that the detailed description and specific examples, while indicating and illustrating the invention, are not intended to limit the invention.
In the description of the present application, the terms "first", "second" are used for descriptive purposes only and are not to be construed as indicating relative importance or implicitly indicating the number of technical features indicated. Thus, unless otherwise indicated, features defining "first", "second" may include one or more such features either explicitly or implicitly; the meaning of "plurality" is two or more. The term "comprises," "comprising," and any variations thereof, are intended to cover a non-exclusive inclusion, a possible presence or addition of one or more other features, elements, components, and/or combinations thereof.
Furthermore, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; either directly or indirectly through intermediaries, or in communication with each other. The specific meaning of the terms in this application will be understood by those of ordinary skill in the art as the case may be.
The first aspect of the present invention provides a method for detecting the component content of an analyte based on colorimetric quantification, as shown in fig. 1, the method comprising the steps of:
s1, constructing a standard curve between the content of a target component of an object to be detected and a chromaticity component in a standard environment;
s2, selecting a reference object according to the target component of the object to be detected and the material and color development rule of the detection system;
s3, determining the number of sample color blocks contained in the standard object and a chromaticity system adopted, wherein the percentage of the total span interval of each chromaticity component in the chromaticity system covering the total span interval of the chromaticity component detection limit of the standard curve is not smaller than a first set value;
s4, acquiring a first image containing the reference object under a standard environment, and acquiring standard chromaticity of the reference object in the first image;
s5, acquiring a second image containing the reference object at a target detection site, and acquiring the actual chromaticity of the reference object in the second image;
s6, if the chromaticity deviation between the actual chromaticity of the reference object in the second image and the standard chromaticity of the reference object in the first image is larger than a second set value, constructing a chromaticity correction model, and acquiring a conversion relation between the actual chromaticity of the reference object in the second image and the standard chromaticity of the reference object in the first image;
s7, acquiring the field chromaticity component of the target component of the object to be detected in the target detection field;
s8, determining the content of the target component of the object to be detected corresponding to the field chromaticity component by utilizing the conversion relation and the standard curve.
According to the technical scheme, based on the method, the standard chromaticity of the reference object in the first image and the actual chromaticity of the reference object in the second image of the target detection site are obtained through systematically and strictly selecting the reference object from multiple sides, so that the accuracy of the constructed chromaticity correction model is effectively improved, the accurate detection of the content of the target component of the object to be detected is realized, and the method has the advantages of simplicity in operation, high test speed and wide application range.
In the colorimetric quantification-based method for detecting the component content of the analyte in the present invention, in step S1, the standard environment may be a laboratory environment or any environment that meets the conditions. In a specific embodiment, in a laboratory environment, chromaticity components containing the contents of target components of different test objects are detected according to the target components of the test objects to be detected, so that a standard curve between the contents of the target components and the chromaticity components of the test objects is constructed.
In the method for detecting the component content of the to-be-detected object based on colorimetric quantification, in the step S2, in a specific embodiment, a reference object is selected according to the target component of the to-be-detected object and the material and color development rule of a detection system, and specifically, the reference object is selected by a subjective weighting method or an objective weighting method according to the target component of the to-be-detected object and the material and color development rule of the detection system. Specifically, the subjective weighting method comprises an analytic hierarchy process, a weight factor judgment table process, a Delphi process and a fuzzy analysis process; the objective weighting method comprises a principal component analysis method, a factor analysis method, an entropy value method and a regression analysis method. The method is characterized in that the method comprises the steps of selecting a standard object, wherein a plurality of factors are required to be considered, and different standard objects are required to be comprehensively considered in different aspects in the actual application process according to specific requirements of different test scenes, so that variables which are required to be comprehensively analyzed when the standard object is selected through the subjective weighting method or the objective weighting method more specifically comprise target components of the object to be detected, materials and color development rules of a detection system, cost of the standard object, service life of the standard object, reduction degree of the detection system, color types contained in the standard object, availability of the standard object and use convenience of the standard object, and accuracy of standard chromaticity acquired in a laboratory environment and actual chromaticity acquired in a target detection site are ensured. In a preferred embodiment, the reference object selected by the subjective weighting method or the objective weighting method is a planar structure or a slightly curved surface structure, so that an imaging error caused by irregular reflection of light due to surface irregularities of the reference object is prevented. The standard substance selected by the subjective weighting method or the objective weighting method comprises a standard color card, a color development device and a paper chip, and further comprises a micro-fluidic chip, a testing device body for substances with colors such as dyes or added dyes, and other substances which are easy to cover key chromaticity characteristics possibly involved or are close to a system involved in experiments or contain enough color information.
In the method for detecting the component content of the to-be-detected object based on colorimetric quantification, in the step S3, in a specific embodiment, an optional colorimetric system comprises RGB, HSV, CMYK and LAB. In a more specific embodiment, the number of sample color blocks included in the reference object may be determined according to actual needs, and the type of the chrominance component included in each sample color block is determined by a selected chrominance system, for example, an RGB chrominance system, which includes three chrominance components, R, G and B; the percentage of the total span interval of each chromaticity component in the RGB chromaticity system covering the total span interval of the chromaticity component detection limit of the standard curve is not less than 20% (i.e. the first set value) so as to ensure the accuracy of the subsequent data. Generally, the more kinds of sensitive chrominance components in the reference object are, the more favorable the subsequent chrominance analysis processing precision is, but the calculation amount is increased, the reference object is more complex, and the like, and the method can be flexibly adjusted according to specific test requirements in the actual use process.
In the method for detecting the component content of the object to be detected based on colorimetric quantification according to the present invention, in step S4, in a specific embodiment, a photographing device is used to photograph the reference object in a laboratory environment, so as to obtain a first image containing the reference object, and obtain a standard chromaticity of the reference object in the first image. The shooting equipment comprises various equipment capable of obtaining color information, such as cameras, mobile phones, cameras, scanners, monitoring and the like.
In the method for detecting the component content of the object to be detected based on colorimetric quantification according to the present invention, in step S5, in a specific embodiment, a photographing device is used to photograph the reference object on a target detection site, so as to obtain a second image containing the reference object, and obtain the actual chromaticity of the reference object in the second image. In a preferred embodiment, the method further comprises pre-evaluating the second image before acquiring the actual chromaticity of the reference object in the second image, and acquiring the actual chromaticity of the reference object in the second image if the pre-evaluation result of the second image meets the requirement. In a specific embodiment, the pre-evaluating the second image specifically includes pre-evaluating pixels, sharpness, contrast, signal-to-noise ratio, inclination, color temperature, noise, illumination intensity, and light uniformity of the second image, so that various parameter values thereof are within a set range, and various parameter values are prevented from deviating greatly, so that the acquired second image containing the reference object is not representative, and the accuracy of the actual chromaticity of the reference object in the acquired second image is improved.
In the method for detecting the component content of the object to be detected based on colorimetric quantification according to the present invention, in the steps S6 to S8, in a specific embodiment, if the chromaticity deviation between the actual chromaticity of the reference object in the second image and the standard chromaticity of the reference object in the first image is greater than 5% (i.e., the second set value), preferably, the chromaticity correction model is constructed by a neural network algorithm or a multiple nonlinear fitting method, so as to obtain a conversion relationship between the actual chromaticity of the reference object in the second image and the standard chromaticity of the reference object in the first image; further, the on-site chromaticity component of the target component of the object to be detected in the target detection site is obtained, firstly, the on-site chromaticity component is converted into a standard chromaticity component by utilizing the conversion relation, and then the content of the target component of the object to be detected corresponding to the standard chromaticity component is searched on the standard curve, so that the content of the target component of the object to be detected is determined. If the chromaticity deviation between the actual chromaticity of the reference object in the second image and the standard chromaticity of the reference object in the first image is not more than 5%, the target component content of the object to be detected corresponding to the on-site chromaticity component can be directly determined according to the standard curve. And if the chromaticity deviation between the actual chromaticity of the reference object in the second image and the standard chromaticity of the reference object in the first image is not more than 5%, the environment of the target detection site is approximately the same as the laboratory environment. Wherein, when constructing the chromaticity correction model, attention is paid to: the fitting can be performed in the form of common equations including polynomial, linear/nonlinear, power function, exponential function and the like, the best fitting form is selected according to the characteristics of the system, regression can be performed in a plurality of fitting forms if necessary, and the form with the minimum average error is finally selected; when the neural network training is performed, as the number of samples is smaller than that of the traditional neural network training conditions (still needs not less than 10 groups), main index thresholds such as performance values, gradients and the like of the neural network training are required to be set, and then the training is performed for multiple times, and the condition meeting the threshold requirement is selected to have the smallest average error. Further, the processing such as image transformation, key region selection, edge detection, noise reduction, smoothing, chroma enhancement and the like can be performed according to the characteristics of the second image containing the reference object and the first image containing the reference object.
In a second aspect, the present invention provides a colorimetric quantification-based component content detection system, as shown in fig. 2, for use in the method, the system comprising:
the standard curve construction module is used for constructing a standard curve between the content of the target component of the object to be detected and the chromaticity component in a standard environment;
the reference object selecting module is used for selecting a reference object according to the target component of the object to be detected, the material of the detection system and the color development rule;
a reference object setting module, configured to determine the number of sample color blocks included in the reference object and a chromaticity system adopted, where a percentage of a total span interval of each chromaticity component in the chromaticity system covering a total span interval of a chromaticity component detection limit of the standard curve is not less than a first set value;
the standard chromaticity acquisition module is used for acquiring a first image containing the reference object under a standard environment and acquiring the standard chromaticity of the reference object in the first image;
the actual chromaticity acquisition module is used for acquiring a second image containing the reference object at the target detection site and acquiring the actual chromaticity of the reference object in the second image;
the conversion relation acquisition module is used for constructing a chromaticity correction model if the chromaticity deviation between the actual chromaticity of the reference object in the second image and the standard chromaticity of the reference object in the first image is larger than a second set value, and acquiring a conversion relation between the actual chromaticity of the reference object in the second image and the standard chromaticity of the reference object in the first image;
the on-site chromaticity component acquisition module is used for acquiring on-site chromaticity components of target components of the to-be-detected object in the target detection site;
and the target component content determining module is used for determining the target component content of the object to be detected corresponding to the field chromaticity component by utilizing the conversion relation and the standard curve.
In the colorimetric quantitative-based component content detection system for the object to be detected, the standard chromaticity of the reference object in the first image and the actual chromaticity of the reference object in the second image of the target detection site are obtained by systematically and strictly selecting the reference object from multiple sides, so that the accuracy of a constructed chromaticity correction model is effectively improved, and the accurate detection of the content of the target component of the object to be detected is realized.
The present invention will be described in detail by way of examples, but the scope of the present invention is not limited thereto.
Example 1
Referring to fig. 3, taking the concentration of the target component of the object to be measured as the chromium ion in water as an example, a paper chip is used for testing the property of the chromium ion in water (a compound reagent mainly composed of dibenzoyl dihydrazide is preset in a detection tank of the paper chip, and can perform a specific color reaction with chromium), a water sample containing chromium is added into the paper chip from a sample adding area during detection, and the sample flows to the detection tank along a channel and reacts with the reagent to generate a pink substance. And (3) performing correlation fitting of the chromium ion concentration and the chromaticity distance under laboratory conditions to obtain standard curve basic data, wherein the chromaticity distance is in direct proportion to the chromium ion concentration in a detection linear interval for the system.
Further, according to the method, the target component of the object to be detected, the material and color development rule of the detection system, the cost of the reference object, the service life of the reference object, the reduction degree of the detection system, the color type contained in the reference object, the availability of the reference object and the use convenience of the reference object are comprehensively analyzed by adopting an analytic hierarchy process, a blank chip which is the same as a paper chip in material is selected to be added with dyes with different colors as the reference object, as shown in fig. 4, and the number of sample color blocks contained in the reference object is further determined to be 30, and the adopted chromaticity system is RGB, wherein the type of chromaticity components contained in each sample color block is 3, and the total span interval of each chromaticity component in RGB covers the total span interval of the chromaticity component detection limit of the standard curve, wherein the percentage of the total span interval of the chromaticity component detection limit in the standard curve is not less than 50%.
Further, photographing the reference object by adopting a camera in a laboratory environment for acquiring a standard curve so as to acquire a first image containing the reference object, and acquiring standard chromaticity of the reference object in the first image; acquiring a second image containing the reference object on a water quality detection site containing chromium ions, and acquiring the actual chromaticity of the reference object in the second image under the condition that the pixel, definition, contrast, signal to noise ratio, gradient, color temperature, noise, illumination intensity and light uniformity of the second image are pre-evaluated and the pre-evaluation result meets the requirements; if the chromaticity deviation between the actual chromaticity of the reference object in the second image and the standard chromaticity of the reference object in the first image is larger than 5%, constructing a chromaticity correction model by adopting a multi-element nonlinear fitting method, and acquiring the conversion relation between the actual chromaticity of the reference object in the second image and the standard chromaticity of the reference object in the first image. Wherein the fitting formula of the multi-element nonlinear fitting method is Wherein R, G, B is a colorimetric value obtained by field shooting, and R ', G ' and B ' are corrected colorimetric values.
Further, the on-site chromaticity component of the chromium ions in the water is obtained at the water quality detection site containing the chromium ions, the on-site chromaticity component is converted into the standard chromaticity component by utilizing the conversion relation, and the concentration of the chromium ions in the water corresponding to the standard chromaticity component is searched on the standard curve, so that the concentration of the chromium ions in the water can be determined.
Through detection, referring to fig. 5, the concentration of chromium ions in water obtained by the method is compared with that of the national standard method, the average deviation is less than 5%, the reliability of the method is verified, and meanwhile, the method has obvious advantages in the aspects of detection time, convenience and the like.
Example 2
Taking the concentration of nickel ions in water as an example of the target component content of an object to be measured, selecting a paper chip for testing aiming at the property of the nickel ions in water, wherein the core part of the chip is filter paper subjected to hydrophobic modification (a sample inlet, a channel and a detection pool are hydrophilic, the rest area is hydrophobic, a compound reagent taking dimethylglyoxime as a main substance is preset in the detection pool and can perform specific color reaction with nickel), a nickel-containing water sample is added into the paper chip from a sample adding area during detection, and the sample flows to the detection pool along the channel and reacts with the reagent to generate pink substances. And (3) performing correlation fitting of the nickel ion concentration and the chromaticity distance under laboratory conditions to obtain standard curve basic data, wherein the chromaticity distance is in direct proportion to the nickel ion concentration in a detection linear interval for the system.
Further, according to the nickel ions in water, comprehensively analyzing the target components of the to-be-detected object, the material and color development rule of the detection system, the cost of the reference object, the service life of the reference object, the reduction degree of the detection system, the color types contained in the reference object, the availability of the reference object and the use convenience of the reference object by adopting a fuzzy analysis method, selecting blank chips which are made of the same material as paper chips, adding dyes with different colors as the reference object, as shown in fig. 6, and further determining that the number of sample color blocks contained in the reference object is 20 and the adopted chromaticity system is RGB, wherein the types of chromaticity components contained in each sample color block are 3, and the total span interval of each chromaticity component in RGB covers the total span interval of the chromaticity component detection limit of the standard curve, wherein the percentage of the total span interval is not less than 80%.
Further, photographing the reference object by adopting a camera in a laboratory environment for acquiring a standard curve so as to acquire a first image containing the reference object, and acquiring standard chromaticity of the reference object in the first image; acquiring a second image containing the reference object on a water quality detection site containing nickel ions, and acquiring the actual chromaticity of the reference object in the second image under the conditions that the pixel, the definition, the contrast, the signal to noise ratio, the gradient, the color temperature, the noise, the illumination intensity and the light uniformity of the second image are pre-evaluated and the pre-evaluation result meets the requirements; if the chromaticity deviation between the actual chromaticity of the reference object in the second image and the standard chromaticity of the reference object in the first image is larger than 5%, a chromaticity correction model is constructed by adopting a neural network algorithm, and the conversion relation between the actual chromaticity of the reference object in the second image and the standard chromaticity of the reference object in the first image is obtained. The method for constructing the chromaticity correction model by adopting the neural network algorithm specifically comprises the following steps: and (3) correlating the actual chromaticity of the reference object in the second image with the standard chromaticity of the reference object in the first image by adopting a BP neural network algorithm, setting regression thresholds such as performance values, gradients and the like of the neural network training to be smaller than 0.001, then training for multiple times, and selecting the one with the smallest average error from the conditions meeting the threshold requirements. The effect of using BP neural network algorithm before and after correction under RGB chromaticity system is shown in figure 7.
Further, the on-site chromaticity component of the nickel ions in the water is obtained on the water quality detection site containing the nickel ions, the on-site chromaticity component is converted into the standard chromaticity component by utilizing the conversion relation, and the concentration of the nickel ions in the water corresponding to the standard chromaticity component is searched on the standard curve, so that the concentration of the nickel ions in the water can be determined.
Through detection, referring to fig. 8, the concentration of nickel ions in water obtained by the method is compared with that of the national standard method, the average deviation is less than 4%, the reliability of the method is verified, and meanwhile, the method has obvious advantages in the aspects of detection time, convenience and the like.
Example 3
Taking the concentration of chromium ions in water as an example of the target component content of an object to be measured, aiming at the property of the chromium ions in water, testing by adopting a microfluidic chip, taking organic glass (PMMA) as a basic material of the chip, and preparing the chip by adopting a cnc engraving machine machining or injection molding machining mode. And constructing structures such as a channel, a detection pool and the like on the chip bottom sheet, and then packaging by using a cover sheet to finally form a closed space. And presetting a certain amount of reagent in the detection tank, injecting the liquid to be detected into the chip from the central sample inlet, and mixing and reacting the liquid to the detection tank with the reagent to generate pink substances. And (3) performing correlation fitting of the chromium ion concentration and the chromaticity distance under laboratory conditions to obtain standard curve basic data, wherein the chromaticity distance is in direct proportion to the chromium ion concentration in a detection linear interval for the system.
Further, according to the method of comprehensively analyzing the target components of the to-be-detected object, the material and color development rule of the detection system, the cost of the reference object, the service life of the reference object, the reduction degree of the detection system, the color types contained in the reference object, the availability of the reference object and the use convenience of the reference object by adopting an entropy method, selecting a standard color card, adding dyes with different colors as the reference object, as shown in fig. 9, and further determining that the number of sample color blocks contained in the reference object is 15 and the adopted chromaticity system is HSV, wherein the types of chromaticity components contained in each sample color block are 3, and the percentage of the total span interval of each chromaticity component in the HSV to the total span interval of the chromaticity component detection limit of the standard curve is not less than 90%.
Further, photographing the reference object by adopting a camera in a laboratory environment for acquiring a standard curve so as to acquire a first image containing the reference object, and acquiring standard chromaticity of the reference object in the first image; acquiring a second image containing the reference object on a water quality detection site containing chromium ions, and acquiring the actual chromaticity of the reference object in the second image under the condition that the pixel, definition, contrast, signal to noise ratio, gradient, color temperature, noise, illumination intensity and light uniformity of the second image are pre-evaluated and the pre-evaluation result meets the requirements; if the chromaticity deviation between the actual chromaticity of the reference object in the second image and the standard chromaticity of the reference object in the first image is larger than 5%, constructing a chromaticity correction model by adopting a multi-element nonlinear fitting method, and acquiring the conversion relation between the actual chromaticity of the reference object in the second image and the standard chromaticity of the reference object in the first image.
Further, the on-site chromaticity component of the chromium ions in the water is obtained at the water quality detection site containing the chromium ions, the on-site chromaticity component is converted into the standard chromaticity component by utilizing the conversion relation, and the concentration of the chromium ions in the water corresponding to the standard chromaticity component is searched on the standard curve, so that the concentration of the chromium ions in the water can be determined.
Through detection, referring to fig. 10, the concentration of chromium ions in water obtained by the method is compared with that of the national standard method, the average deviation is less than 7%, the reliability of the method is verified, and meanwhile, the method has obvious advantages in the aspects of detection time, convenience and the like.
Example 4
Taking the concentration of the target component of the object to be measured as hydrogen in the air as an example, aiming at the property of the hydrogen in the air, adopting a metal oxide material as a detection reagent, and preparing metal powder by a precipitation method and a hydrothermal method, wherein the powder is milky white and gradually turns into deep blue along with the contact with the hydrogen. And (3) performing correlation fitting of the chromium ion concentration and the chromaticity distance under laboratory conditions to obtain standard curve basic data, wherein the chromaticity distance is in direct proportion to the concentration of hydrogen in air in a detection linear interval for the system.
Further, according to the principle component analysis method adopted by the hydrogen in the air, comprehensively analyzing the target component of the object to be detected, the material and color development rule of the detection system, the cost of the reference object, the service life of the reference object, the reduction degree of the detection system, the color type contained in the reference object, the availability of the reference object and the use convenience of the reference object, selecting a standard color card, adding dyes with different colors as the reference object, further determining that the number of sample color blocks contained in the reference object is 20 and the adopted chromaticity system is RGB, and determining that the type of chromaticity components contained in each sample color block is 3, wherein the total span interval of each chromaticity component in RGB covers the percentage of the total span interval of the chromaticity component detection limit of the standard curve.
Further, photographing the reference object by adopting a camera in a laboratory environment for acquiring a standard curve so as to acquire a first image containing the reference object, and acquiring standard chromaticity of the reference object in the first image; acquiring a second image containing the reference object in an air detection site containing hydrogen, and acquiring the actual chromaticity of the reference object in the second image under the condition that the pixels, definition, contrast, signal to noise ratio, gradient, color temperature, noise, illumination intensity and light uniformity of the second image are pre-evaluated and the pre-evaluation result meets the requirement; if the chromaticity deviation between the actual chromaticity of the reference object in the second image and the standard chromaticity of the reference object in the first image is larger than 5%, a chromaticity correction model is constructed by adopting a neural network algorithm, and the conversion relation between the actual chromaticity of the reference object in the second image and the standard chromaticity of the reference object in the first image is obtained.
Further, referring to fig. 11-12, the configured 1%, 2%, 4%, 6%, 10% concentration hydrogen is tested to obtain each chromaticity component value, and the field chromaticity component photographed on site is matched with the standard curve to obtain the calculated hydrogen concentration, and the deviations from the true values are 8%, 6%, 3%, 4%, 1%, respectively, so that the reliability of the method of the invention is verified.
According to the colorimetric quantitative-based method and system for detecting the component content of the object to be detected, which are provided by the invention, the standard chromaticity of the reference object in the first image and the actual chromaticity of the reference object in the second image of the target detection site are obtained by systematically and strictly selecting the reference object from multiple sides, so that the accuracy of the constructed chromaticity correction model is effectively improved, the accurate detection of the content of the target component of the object to be detected is realized, and the colorimetric quantitative-based method and system for detecting the component content of the object to be detected have the advantages of simplicity in operation, high test speed and wide application range.
The preferred embodiments of the present invention have been described in detail above with reference to the accompanying drawings, but the present invention is not limited thereto. Within the scope of the technical idea of the invention, a plurality of simple variants can be made to the technical proposal of the invention, and in order to avoid unnecessary repetition, the invention does not need to be additionally described for various possible combinations. Such simple variations and combinations are likewise to be regarded as being within the scope of the present disclosure.
Claims (13)
1. The method for detecting the component content of the object to be detected based on colorimetric quantification is characterized by comprising the following steps:
constructing a standard curve between the content of a target component of the object to be detected and the chromaticity component in a standard environment;
selecting a reference object according to the target component of the object to be detected and the material and color development rule of the detection system;
determining the number of sample color blocks contained in the standard object and a adopted chromaticity system, wherein the percentage of the total span interval of each chromaticity component in the chromaticity system, which covers the total span interval of the chromaticity component detection limit of the standard curve, is not less than a first set value;
acquiring a first image containing the reference object under a standard environment, and acquiring standard chromaticity of the reference object in the first image;
acquiring a second image containing the reference object at a target detection site, and acquiring the actual chromaticity of the reference object in the second image;
if the chromaticity deviation between the actual chromaticity of the reference object in the second image and the standard chromaticity of the reference object in the first image is larger than a second set value, constructing a chromaticity correction model, and acquiring a conversion relation between the actual chromaticity of the reference object in the second image and the standard chromaticity of the reference object in the first image;
acquiring the field chromaticity component of a target component of an object to be detected at a target detection field;
and determining the content of the target component of the object to be detected corresponding to the on-site chromaticity component by utilizing the conversion relation and the standard curve.
2. The method according to claim 1, wherein the selecting the reference object according to the target component of the object to be detected, the material and the color development rule of the detection system specifically comprises:
and selecting a reference object by a subjective weighting method or an objective weighting method according to the target component of the object to be detected and the material and color development rule of the detection system.
3. The method according to claim 2, wherein the subjective weighting method includes a hierarchical analysis method, a weight factor judgment table method, a delphire method, and a fuzzy analysis method, and the objective weighting method includes a principal component analysis method, a factor analysis method, an entropy value method, and a regression analysis method.
4. The method of claim 2, wherein the fiducial is a planar structure.
5. The method of claim 2, wherein the fiducials comprise standard color cards, color development equipment, and paper chips.
6. The method of claim 1, comprising, prior to acquiring the actual chromaticity of the fiducial in the second image:
a pre-evaluation is made of the second image,
and under the condition that the pre-evaluation result of the second image meets the requirement, acquiring the actual chromaticity of the reference object in the second image.
7. The method according to claim 6, wherein the pre-evaluating the second image specifically comprises:
the second image is pre-evaluated for pixels, sharpness, contrast, signal-to-noise ratio, inclination, color temperature, noise, illumination intensity, and light uniformity.
8. The method of claim 1, wherein the chromaticity correction model is constructed by a neural network algorithm or a multivariate nonlinear fitting method.
9. The method according to claim 1, wherein determining the target component content of the object to be measured corresponding to the in-situ chromaticity component using the conversion relationship and the standard curve specifically comprises:
converting the field chrominance component into a standard chrominance component using the conversion relationship;
searching the target component content of the object to be detected corresponding to the standard chromaticity component on the standard curve, and determining the target component content of the object to be detected.
10. The method as recited in claim 1, further comprising:
if the chromaticity deviation between the actual chromaticity of the reference object in the second image and the standard chromaticity of the reference object in the first image is not larger than a second set value;
and determining the content of the target component of the object to be detected corresponding to the field chromaticity component according to the standard curve.
11. The method of claim 1, the chromaticity system including RGB, HSV, CMYK and LAB.
12. The method of claim 1, wherein the standard environment is a laboratory environment.
13. A colorimetric quantification-based assay component content detection system, characterized in that it is applied to the method according to any one of claims 1 to 12, comprising:
the standard curve construction module is used for constructing a standard curve between the content of the target component of the object to be detected and the chromaticity component in a standard environment;
the reference object selecting module is used for selecting a reference object according to the target component of the object to be detected, the material of the detection system and the color development rule;
a reference object setting module, configured to determine the number of sample color blocks included in the reference object and a chromaticity system adopted, where a percentage of a total span interval of each chromaticity component in the chromaticity system covering a total span interval of a chromaticity component detection limit of the standard curve is not less than a first set value;
the standard chromaticity acquisition module is used for acquiring a first image containing the reference object under a standard environment and acquiring the standard chromaticity of the reference object in the first image;
the actual chromaticity acquisition module is used for acquiring a second image containing the reference object at the target detection site and acquiring the actual chromaticity of the reference object in the second image;
the conversion relation acquisition module is used for constructing a chromaticity correction model if the chromaticity deviation between the actual chromaticity of the reference object in the second image and the standard chromaticity of the reference object in the first image is larger than a second set value, and acquiring a conversion relation between the actual chromaticity of the reference object in the second image and the standard chromaticity of the reference object in the first image;
the on-site chromaticity component acquisition module is used for acquiring on-site chromaticity components of target components of the to-be-detected object in the target detection site;
and the target component content determining module is used for determining the target component content of the object to be detected corresponding to the field chromaticity component by utilizing the conversion relation and the standard curve.
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