System for detecting cadmium content in glass clarifying agent

Abstract

The invention relates to the technical field of detection, and particularly discloses a system for detecting the content of cadmium in a glass clarifying agent, which comprises a sample processing module, a sensor module, an optical detection module, a data processing module and a regeneration and maintenance module. The sample processing module is used for removing particle impurities and non-soluble components interfering with detection in a clarifying agent sample; functional ligands in the sensor module are specifically combined with cadmium ions to trigger color change, so that the cadmium ions are quantitatively detected; the optical detection module is used for measuring color change caused by a cadmium ion complexing reaction in the sensor module through an optical technology; the data processing module is used for converting the optical signal into a concentration result and analyzing and outputting the concentration result; the regeneration and maintenance module prolongs the service life of the sensor. According to the invention, functions of sample processing, sensing detection, optical analysis, data processing, regeneration maintenance and the like are integrated through modular design, the detection process is simplified, and dependence on professional operators is reduced.

Description

Technical Field

[0001] The invention relates to the field of detection technology, and more specifically to a system for detecting cadmium content in a glass clarifier. Background Art

[0002] At present, the methods for detecting cadmium content mainly rely on precision instruments and complex processes, such as atomic absorption spectroscopy, inductively coupled plasma mass spectrometry, and high performance liquid chromatography. Although these methods have high sensitivity and a wide detection range, they also have significant limitations: their operation is complicated, requiring expensive experimental equipment and professional technicians, and are not suitable for rapid detection or on-site use, especially when detecting cadmium content in complex matrix samples such as glass clarifiers. The detection process is even more cumbersome. In addition, these methods are easily interfered by other metal ions during the sample pretreatment stage, resulting in errors in the test results, and may even produce false positives or false negatives. In addition, some methods have high requirements for the sample environment, such as requiring specific temperature, humidity or chemical conditions, which further limits the convenience of their practical application.

[0003] In the existing public document 1 (Research on Detection of Heavy Metal Cadmium Content in Rapeseed Leaves Based on Hyperspectral Imaging Technology, 2023), a rapeseed leaf cadmium content detection model based on spectral and image fusion features is proposed. The model selects rapeseed leaves under different cadmium concentration gradients as experimental samples, and obtains the spectral information and image information of the samples through a hyperspectral imaging system. Three feature extraction methods are used to screen out the wavelength variables most relevant to the cadmium content from the full spectral information, extract color features and texture features from the hyperspectral image, and screen out key image feature variables by the same method. The spectral features and image features are normalized and then fused to construct a fused feature set. By comparing the modeling results of single spectral features and single image features, it is found that the model based on fused features has higher prediction accuracy and stability; although the model uses a feature extraction method, there is still redundant information that has not been completely removed, which will affect the complexity and prediction accuracy of the model. In the existing open document 2 (Analysis of cadmium content in rice and separation and purification of cadmium-binding protein, 2014), a quantitative prediction model for simultaneously detecting lead and cadmium in crayfish by combining D405 resin adsorption with infrared spectroscopy was established. The prediction model collected near-infrared spectra of the adsorbed resin, optimized the spectra by different pretreatment methods, selected the best modeling band by using competitive adaptive reweighted sampling method, and established and verified the prediction model by combining partial least squares method. The results showed that the adsorption rates of lead and cadmium under the best adsorption conditions were 99.5% and 99.7%, respectively. The prediction performance of the model established after wavelet transform pretreatment combined with characteristic band selection was good, and the average recovery rates of lead and cadmium were 103.7% and 103.5%, respectively. However, when the lead and cadmium content in crayfish was detected by near-infrared spectroscopy, other interfering components existed in the spectral information, resulting in relatively poor detection repeatability. Although the predicted content of the model was well correlated with the actual content, there was a certain deviation in the prediction of lead and cadmium from the scatter distribution of low-content to high-content samples.

[0004] Therefore, how to improve the anti-interference ability and accuracy of the detection system while simplifying the detection process has become an important technical problem that needs to be solved urgently in the current field. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention provides a system for detecting the cadmium content in a glass clarifier, which makes the calculation of the cadmium ion concentration more accurate by introducing a high-precision ultraviolet-visible spectrometer and a nonlinear calibration model, and enhances the signal quality by using technologies such as signal acquisition and wavelet denoising, and uses acid elution and vacuum drying to achieve the recycling of the sensor, which not only prolongs the service life of the sensor but also reduces the detection cost, so as to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A cadmium content detection system in a glass clarifier comprises a sample processing module, a sensor module, an optical detection module, a data processing module and a regeneration and maintenance module; the sample processing module is used to remove particulate impurities and insoluble components interfering with detection in a clarifier sample; the functional ligand in the sensor module specifically binds to cadmium ions and triggers color change, thereby quantitatively detecting cadmium ions; the optical detection module measures the color change caused by the complexation reaction of cadmium ions in the sensor module by optical technology; the data processing module converts the optical signal into a cadmium ion concentration result and analyzes and outputs it; the regeneration and maintenance module extends the service life of the sensor; the data analysis unit in the data processing module calibrates the collected signal by a calibration model to improve the detection accuracy, and the steps of the calibration model are as follows:

[0008] Step S1, the spectral signal is X∈R n×m , n is the number of wavelengths, m is the number of samples, and the absorbance value of each wavelength i is standardized: Among them, μ i is the mean value of wavelength i; σ i is the standard deviation of wavelength i; Z is the standardized signal matrix; X is the original spectral signal matrix; j is the index of the sample.

[0009] Step S2: Use discrete wavelet transform to remove noise and obtain the denoised signal Among them, L is the number of layers of wavelet decomposition, w k is the wavelet coefficient of the kth layer, φ k (t) is the wavelet basis function; there is a nonlinear relationship between the cadmium ion concentration y and the absorbance, and the calculation formula is: β k is the fitting coefficient, p is the order of the polynomial, ε is the residual term, is the Chebyshev polynomial, capturing the nonlinear characteristics of the signal. satisfy T0(x)=1,T1(x)=x.

[0010] Step S3, introduce regularization term to prevent overfitting: Where λ is the regularization coefficient; the expectation maximization algorithm is used to optimize the model parameters: 1. Step E: Estimate the noise distribution: Q(θ|θ (t) )=E[logp(y,z|θ|y,θ (t) ], where θ is the model parameter, z is the noise variable, and t is the number of iterations; 2.M step: maximize the log-likelihood function:

[0011] Step S4: In the optimized model, input the detection signal The cadmium ion concentration was predicted using the following formula:

[0012] As a further solution of the present invention, the sample processing module is used to remove particulate impurities and insoluble components that interfere with detection in the clarifier sample, including the following specific contents: the sample processing unit includes a sample pretreatment unit, a pH adjustment unit and a mixing unit. The sample processing unit performs preliminary filtration on the liquid sample through filter paper or a microporous filtration device, and further separates fine particles and colloidal impurities in combination with a 12,000rpm high-speed centrifuge to obtain a clarified sample liquid. The treated sample enters the pH adjustment unit, which adjusts the pH of the sample solution in real time through a buffer solution and a diluted hydrochloric acid or sodium hydroxide solution. The concentration of the buffer solution is 0.1M to ensure that the solution has a stable pH value during the detection process. The pH-adjusted sample is transferred to the mixing unit, which is equipped with a mechanical stirrer. The speed adjustment range of the mechanical stirrer is 0-300rpm, and the user can select a suitable stirring speed according to the sample viscosity and solution volume. The stirring time is controlled within 10 minutes to ensure that the sample is fully contacted and reacted with the sensing material without causing oxidation of the sample or temperature increase due to excessive stirring. To further improve the mixing effect, the mixing unit can also be equipped with an ultrasonic auxiliary device to accelerate the uniform distribution of reactants through the cavitation effect. During this process, the sample volume is controlled at 10mL. The sample processing module has a built-in temperature control function, and a constant temperature water bath or a built-in heating device is used to control the sample temperature at 25°C. This temperature is the best condition for the cadmium ion complex reaction, which can not only maintain the stability of the sensor, but also avoid the damage of high temperature to other components in the sample.

[0013] As a further solution of the present invention, the functional ligand in the sensor module specifically binds to the cadmium ions and triggers a color change, thereby quantitatively detecting the cadmium ions, including the following specific contents: the sensor module is mainly composed of a solid-state sensor, including a sensing material unit, a temperature control unit and an oscillating device. The sensing material unit uses a porous silicon substrate to fix the functional ligand. The preparation process of the porous silicon material includes using tetramethoxysilane (TMOS) and a triblock copolymer F108 to form a uniform sol-gel system, and then inducing it to assemble into a liquid crystal phase through an acidified solution and heat-treating it to generate a regular porous structure. The functional ligand is selected from 4-(hexyloxy)phenylazobenzene-1,3-diphenol (DPDB), which has extremely high selectivity and sensitivity and can form a stable complex with cadmium ions through its hydroxyl and azo groups. In order to fix the functional ligand on the porous silicon surface, the sensor module adopts a vacuum adsorption method, dissolving the DPDB ligand in an ethanol solution and evenly coating it on the porous silicon substrate, and ensuring its stable adsorption by constant temperature drying (30°C). To further enhance the fixation effect, chemical modification technology is used to introduce hydroxyl or amino groups on the surface of the silicon substrate to increase the binding sites of the ligand, thereby improving its loading capacity and stability. After the sample solution comes into contact with the solid-state sensor, the cadmium ions react with the DPDB ligand and induce a significant color change, gradually changing from yellow to red. The temperature control unit maintains the reaction temperature at 25°C, and the oscillating device promotes full contact and binding between the cadmium ions and the ligand at a constant stirring speed (110rpm).

[0014] As a further solution of the present invention, the optical detection module measures the color change caused by the complexation reaction of cadmium ions in the sensor module through optical technology, including the following specific contents: the optical detection module includes a spectral analysis unit. The spectral analysis unit adopts a UV-visible spectrometer with a detection wavelength range of 200-700nm, which can cover the key absorption peaks in the complexation reaction of cadmium ions. The light source of the spectral analysis unit uses a combination of deuterium lamps and tungsten lamps with high stability and low noise to ensure high sensitivity and low background noise of the spectral signal. At the same time, a high-resolution monochromator and grating system are configured to improve the accuracy of wavelength selection. During detection, the sample solution is introduced from the sensor module into the spectral analysis unit through an automated sampling system. After the light beam passes through the sample cell, the detector records the absorbance change to form an absorption spectrum.

[0015] As a further solution of the present invention, the data processing module converts the optical signal into a concentration result and analyzes and outputs it, including the following specific contents: the data processing module includes a signal acquisition unit, a data analysis unit and a result output unit. The signal acquisition unit acquires absorbance signal data from the optical detection module in real time, and has a built-in high-sensitivity photoelectric detector that can capture spectral information at a nanosecond response speed. The data processing module records the absorbance data in a digital form, converts the analog spectral signal into a processable digital signal through a high-precision analog-to-digital converter, and stores it in a system cache in a matrix form.

[0016] The result output unit is used to organize, display and store the concentration data generated by the data analysis unit, providing users with intuitive and easy-to-understand test results. The concentration results are displayed in real time in digital form through a high-resolution LCD screen or an integrated computer interface, and are accompanied by a visual spectrum chart to help users intuitively understand the cadmium ion concentration and its corresponding optical properties.

[0017] As a further solution of the present invention, a regeneration and maintenance module extends the service life of the sensor, including the following specific contents: the regeneration and maintenance module includes an elution unit, a drying unit and a repeated detection unit. The main function of the elution unit is to remove the cadmium ions adsorbed on the surface of the sensing material to restore the activity of the sensor. The sensor is exposed to an acidic eluent, and the cadmium ions complexed with the functional ligand are dissolved into the solution by immersion and stirring. This process relies on the destruction of the stability of the complex by the acidic environment, and is carried out at room temperature of 25°C for 5-10 minutes to ensure that the elution is complete and does not damage the chemical structure of the sensing material. In order to improve the elution efficiency, the elution unit is equipped with a micro-liquid injection pump to control the flow rate and number of cycles of the eluent, thereby optimizing the elution conditions.

[0018] The function of the drying unit is to remove residual moisture or eluent on the sensing material to ensure that the sensor has good physical properties and chemical stability when it is used next time. The drying unit adopts a combination of low-temperature heating and vacuum drying, taking into account both drying speed and material protection. The sensing material is placed in a vacuum drying oven, and the temperature is controlled at 45°C to avoid decomposition of functional ligands or destruction of the porous silicon substrate structure due to high temperature. The vacuum environment can lower the boiling point of water, accelerate the volatilization of the liquid, and prevent the occurrence of adverse reactions such as oxidation. The entire drying process lasts about 1 hour, and the drying status is monitored in real time by a built-in humidity sensor, and it stops automatically when the humidity is lower than 5%.

[0019] The main task of the repeated detection unit is to ensure that the detection ability of the sensor remains stable after multiple cycles. The sensor that has been eluted and dried will first be transferred to a dedicated performance evaluation device, detected by a standard cadmium ion solution, and the absorbance change will be recorded and compared with the previous calibration curve to calculate key indicators such as detection sensitivity, selectivity and repeatability. The results of the performance verification are automatically evaluated by a built-in algorithm. When the detection sensitivity decreases by less than 5% and the repeatability error is less than 2%, the sensor is marked as qualified and can continue to be put into use. If the performance indicator exceeds the preset threshold, the system will issue a warning prompt, requiring the user to recheck the elution and drying steps or replace the sensing material.

[0020] The technical effects and advantages of the cadmium content detection system in a glass clarifier of the present invention: the detection system of the present invention integrates functions such as sample processing, sensor detection, optical analysis, data processing and regeneration maintenance through modular design, simplifies the detection process, reduces the dependence on professional operators, and is suitable for rapid detection and field application. Secondly, the detection system adopts the specific binding of functional ligands and cadmium ions and the innovative design of porous silicon-based materials to improve the sensitivity and selectivity of detection and avoid the interference of other metal ions on the results. The optical detection module introduces a high-precision ultraviolet-visible spectrometer and a nonlinear calibration model to make the calculation of cadmium ion concentration more accurate and stable. The data processing module enhances the signal quality through technologies such as signal acquisition and wavelet denoising, and further improves the anti-interference ability and accuracy through dynamic calibration models and regularization algorithms. The regeneration and maintenance module realizes the recycling of sensors through acid elution, vacuum drying and performance verification, which not only prolongs the service life of the sensor, but also reduces the detection cost. In addition, the overall operation of the detection system is efficient, the sensitivity can reach 0.88μg / L, the reusability is excellent, and it meets the detection needs of complex matrix samples. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 The present invention is a schematic structural diagram of a system for detecting cadmium content in a glass clarifier.

[0022] Figure 2 This is a graph showing the prediction results of a rapeseed leaf cadmium content detection model based on spectral and image fusion features proposed in the prior art.

[0023] Figure 3 This is a correlation diagram of reference values ​​and predicted values ​​for lead and cadmium content in calibration set samples of a quantitative prediction model for simultaneously detecting lead and cadmium in crayfish by combining D405 resin adsorption with infrared spectroscopy proposed in the prior art.

[0024] Figure 4 This is a sensitivity result diagram of the sensor in the present invention in the process of detecting cadmium ions. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] Example 1

[0027] See also Figure 1 As shown in the schematic diagram, an embodiment of the present invention provides a system for detecting cadmium content in a glass clarifier, which includes a sample processing module, a sensor module, an optical detection module, a data processing module, and a regeneration and maintenance module. The sample processing module is used to remove particulate impurities and insoluble components that interfere with detection in the clarifier sample; the functional ligand in the sensor module specifically binds to cadmium ions to trigger color changes, thereby quantitatively detecting cadmium ions; the optical detection module measures the color changes caused by the complexation reaction of cadmium ions in the sensor module through optical technology; the data processing module converts the optical signal into a cadmium ion concentration result and analyzes and outputs it; the regeneration and maintenance module extends the service life of the sensor.

[0028] In this embodiment, see Figure 2 As shown in the prediction result diagram, in the existing public document 1 (Study on Detection of Heavy Metal Cadmium Content in Rapeseed Leaves Based on Hyperspectral Imaging Technology, 2023), a rapeseed leaf cadmium content detection model based on spectral and image fusion features is proposed. The model selects rapeseed leaves under different cadmium concentration gradients as experimental samples, and obtains the spectral information and image information of the samples through a hyperspectral imaging system. Three feature extraction methods are used to screen out the wavelength variables most relevant to the cadmium content from the full spectral information, extract color features and texture features from the hyperspectral image, and screen out key image feature variables by the same method. The spectral features and image features are normalized and fused to construct a fusion feature set. By comparing the modeling results of single spectral features and single image features, it is found that the model based on fusion features has higher prediction accuracy and stability. Although the model uses a feature extraction method, there is still redundant information that has not been completely removed, which will affect the complexity and prediction accuracy of the model. Figure 3The reference value and predicted value correlation diagram shown in the figure, in the existing public document 2 (Analysis of cadmium content in rice and separation and purification of cadmium binding protein, 2014), a quantitative prediction model for the simultaneous detection of lead and cadmium in crayfish by combining D405 resin adsorption with infrared spectroscopy was established. The prediction model collected the near-infrared spectrum of the adsorption resin, optimized the spectrum by different pretreatment methods, selected the best modeling band by competitive adaptive reweighted sampling method, and established and verified the prediction model by combining partial least squares method; the results showed that the adsorption rates of lead and cadmium under the best adsorption conditions were 99.5% and 99.7%, respectively, and the prediction performance of the model established after wavelet transform pretreatment combined with characteristic band selection was good, and the average recovery rates of lead and cadmium were 103.7% and 103.5%, respectively; however, when the near-infrared spectroscopy method was used to detect the content of lead and cadmium in crayfish, other interfering components existed in the spectral information, resulting in relatively poor detection repeatability; although the predicted content of the model was well correlated with the actual content, there was a certain deviation in the prediction of lead and cadmium from the scatter distribution of low-content to high-content samples.

[0029] Further, the sample processing module is used to remove particulate impurities and insoluble components that interfere with the detection in the clarifier sample, including: the sample processing unit includes a sample pretreatment unit, a pH adjustment unit and a mixing unit. The sample processing unit performs preliminary filtration on the liquid sample through filter paper or a microporous filtration device, and further separates fine particles and colloidal impurities in combination with a 12,000rpm high-speed centrifuge to obtain a clarified sample liquid. The treated sample enters the pH adjustment unit, which adjusts the pH of the sample solution in real time through a buffer solution (such as 3-morpholinepropanesulfonic acid, MOPS) and a diluted hydrochloric acid or sodium hydroxide solution. The concentration of the buffer solution is 0.1M to ensure that the solution has a stable pH value during the detection process. The pH-adjusted sample is transferred to the mixing unit, which is equipped with a mechanical stirrer. The speed adjustment range of the mechanical stirrer is 0-300rpm, and the user can select a suitable stirring speed according to the sample viscosity and solution volume. The stirring time is controlled within 10 minutes to ensure that the sample is fully contacted and reacted with the sensing material without causing oxidation of the sample or temperature increase due to excessive stirring. To further improve the mixing effect, the mixing unit can also be equipped with an ultrasonic auxiliary device to accelerate the uniform distribution of reactants through the cavitation effect. During this process, the sample volume is controlled at 10mL. The sample processing module has a built-in temperature control function, and a constant temperature water bath or a built-in heating device is used to control the sample temperature at 25°C. This temperature is the best condition for the cadmium ion complex reaction, which can not only maintain the stability of the sensor, but also avoid the damage of high temperature to other components in the sample.

[0030] Furthermore, the functional ligand in the sensor module specifically binds to the cadmium ions and triggers a color change, thereby quantitatively detecting the cadmium ions, including: the sensor module is mainly composed of a solid-state sensor, including a sensing material unit, a temperature control unit and an oscillating device. The sensing material unit uses a porous silicon substrate to fix the functional ligand. The preparation process of the porous silicon material includes using tetramethoxysilane (TMOS) and a triblock copolymer F108 to form a uniform sol-gel system, and then inducing it to assemble into a liquid crystal phase through an acidified solution (such as HCl at pH 1.3) and heat treatment to generate a regular porous structure. The functional ligand is selected from 4-(hexyloxy)phenylazobenzene-1,3-diphenol (DPDB), which has extremely high selectivity and sensitivity and can form a stable complex with cadmium ions through its hydroxyl and azo groups. In order to fix the functional ligand on the porous silicon surface, the sensor module adopts a vacuum adsorption method, dissolving the DPDB ligand in an ethanol solution and evenly coating it on the porous silicon substrate, and ensuring its stable adsorption by constant temperature drying (30°C). To further enhance the fixation effect, chemical modification technology is used to introduce hydroxyl or amino groups on the surface of the silicon substrate to increase the binding sites of the ligand, thereby improving its loading capacity and stability. After the sample solution comes into contact with the solid-state sensor, the cadmium ions react with the DPDB ligand and induce a significant color change, gradually changing from yellow to red. The temperature control unit maintains the reaction temperature at 25°C, and the oscillating device promotes full contact and binding between the cadmium ions and the ligand at a constant stirring speed (110rpm).

[0031] Furthermore, the optical detection module measures the color change caused by the cadmium ion complexation reaction in the sensor module through optical technology, including: the optical detection module includes a spectral analysis unit. The spectral analysis unit adopts a UV-visible spectrometer with a detection wavelength range of 200-700nm, which can cover the key absorption peaks in the cadmium ion complexation reaction. The light source of the spectral analysis unit uses a combination of deuterium lamps and tungsten lamps with high stability and low noise to ensure high sensitivity and low background noise of the spectral signal. At the same time, a high-resolution monochromator and grating system are configured to improve the accuracy of wavelength selection. During detection, the sample solution is introduced from the sensor module into the spectral analysis unit through an automated sampling system. After the light beam passes through the sample cell, the detector records the absorbance change to form an absorption spectrum.

[0032] Furthermore, the data processing module converts the optical signal into a concentration result and analyzes and outputs it, including: the data processing module includes a signal acquisition unit, a data analysis unit and a result output unit. The signal acquisition unit acquires absorbance signal data from the optical detection module in real time, and has a built-in high-sensitivity photodetector that can capture spectral information at a nanosecond response speed. The data processing module records the absorbance data in a digital form, converts the analog spectral signal into a processable digital signal through a high-precision analog-to-digital converter, and stores it in a system cache in a matrix form.

[0033] The data analysis unit calibrates the collected signals through the calibration model to improve the anti-interference ability and accuracy of the detection. The specific steps of the calibration model are as follows:

[0034] Step S1, the spectral signal is X∈R n×m , where n is the number of wavelengths and m is the number of samples, the absorbance value of each wavelength i is standardized: Among them, μ i is the mean value of wavelength i; σ i is the standard deviation of wavelength i; Z is the standardized signal matrix, which removes the influence of baseline drift and amplitude inconsistency; X is the original spectral signal matrix, which contains the absorbance values ​​of different wavelengths; j is the index of the sample;

[0035] Step S2: Use discrete wavelet transform to remove noise and obtain the denoised signal Among them, L is the number of layers of wavelet decomposition, w k is the wavelet coefficient of the kth layer, φ k (t) is the wavelet basis function; there is a nonlinear relationship between the cadmium ion concentration y and the absorbance, and the calculation formula is: β k is the fitting coefficient, p is the order of the polynomial, ε is the residual term, is the Chebyshev polynomial, capturing the nonlinear characteristics of the signal. satisfy T0(x)=1,T1(x)=x.

[0036] Step S3, introduce regularization terms to control the complexity of the model during the fitting process and prevent overfitting: Where λ is the regularization coefficient; the expectation maximization algorithm is used to optimize the model parameters: 1. Step E: Estimate the noise distribution: Q(θ| (t) )=E[logp(y,z|θ)|y,θ (t)], where θ is the model parameter, z is the noise variable, and t is the number of iterations; 2.M step: maximize the log-likelihood function:

[0037] Step S4: In the optimized model, input the detection signal The cadmium ion concentration was predicted using the following formula:

[0038] The result output unit is used to organize, display and store the concentration data generated by the data analysis unit, providing users with intuitive and easy-to-understand test results. The concentration results are displayed in real time in digital form through a high-resolution LCD screen or an integrated computer interface, and are accompanied by a visual spectrum chart to help users intuitively understand the cadmium ion concentration and its corresponding optical properties.

[0039] In this embodiment, in order to verify the accuracy of the Chebyshev polynomial fitting of the cadmium ion concentration in the data processing module, the absorbance data of the known cadmium ion standard solution is used to perform fitting calculations and evaluate its error; the experimental instruments include an ultraviolet-visible spectrophotometer with a detection wavelength of 450nm, a standard cadmium ion solution, a concentration range of 0.5-30.0μg / L, and a quartz cuvette with an optical path of 1cm. The experimental steps are: 1. Prepare standard cadmium ion solutions of different concentrations (0.5, 1.0, 2.0, 5.0, 10.0, 15.0, 20.0, 30.0μg / L); 2. Use an ultraviolet-visible spectrophotometer to measure absorbance; 3. Use the Chebyshev polynomial fitting method to calculate the cadmium ion concentration; 4. Calculate the absolute error and relative error to evaluate the accuracy of the model. The following table shows the experimentally measured cadmium ion absorbance, the concentration value calculated by fitting, and the error analysis:

[0040] Table 1 Cadmium ion absorbance, fitted concentration values, and error analysis

[0041]

[0042] Furthermore, the regeneration and maintenance module extends the service life of the sensor, including the following specific contents: the regeneration and maintenance module includes an elution unit, a drying unit and a repeated detection unit. The main function of the elution unit is to remove the cadmium ions adsorbed on the surface of the sensing material to restore the activity of the sensor. The sensor is exposed to an acidic eluent (such as a 0.20M hydrochloric acid solution), and the cadmium ions complexed with the functional ligand are dissolved into the solution by immersion and stirring. This process relies on the destruction of the stability of the complex by the acidic environment, and is carried out at room temperature of 25°C for 5-10 minutes to ensure that the elution is complete and does not damage the chemical structure of the sensing material. In order to improve the elution efficiency, the elution unit is equipped with a micro-liquid injection pump to control the flow rate of the eluent (such as 1mL / min) and the number of cycles, thereby optimizing the elution conditions.

[0043] The function of the drying unit is to remove residual moisture or eluent on the sensing material to ensure that the sensor has good physical properties and chemical stability when it is used next time. The drying unit adopts a combination of low-temperature heating and vacuum drying, taking into account both drying speed and material protection. The sensing material is placed in a vacuum drying oven, and the temperature is controlled at 45°C to avoid decomposition of functional ligands or destruction of the porous silicon substrate structure due to high temperature. The vacuum environment can lower the boiling point of water, accelerate the volatilization of the liquid, and prevent the occurrence of adverse reactions such as oxidation. The entire drying process lasts about 1 hour, and the drying status is monitored in real time by a built-in humidity sensor, and it stops automatically when the humidity is lower than 5%.

[0044] The main task of the repeated detection unit is to ensure that the detection ability of the sensor remains stable after multiple cycles. The sensor after elution and drying will first be transferred to a dedicated performance evaluation device and then tested by a standard cadmium ion solution (such as a 10 μg / L Cd 2 + solution) for detection, record the absorbance change and compare it with the previous calibration curve, and calculate key indicators such as detection sensitivity, selectivity and repeatability. Figure 4 The result graph shown is the sensitivity of the sensor in the process of detecting cadmium ions. The results of the performance verification are automatically evaluated by the built-in algorithm. When the detection sensitivity decreases by less than 5% and the repeatability error is less than 2%, the sensor is marked as qualified and can continue to be put into use. If the performance index exceeds the preset threshold, the system will issue a warning prompt, requiring the user to recheck the elution and drying process or replace the sensing material.

[0045] The detection system of the present invention integrates functions such as sample processing, sensor detection, optical analysis, data processing and regeneration maintenance through modular design, simplifies the detection process, reduces the dependence on professional operators, and is suitable for rapid detection and field application. Secondly, this detection system adopts the innovative design of specific binding of functional ligands and cadmium ions and porous silicon-based materials, which improves the sensitivity and selectivity of detection and avoids the interference of other metal ions on the results. The optical detection module introduces a high-precision UV-visible spectrometer and a nonlinear calibration model to make the calculation of cadmium ion concentration more accurate and stable. The data processing module enhances the signal quality through technologies such as signal acquisition and wavelet denoising, and further improves the anti-interference ability and accuracy through dynamic calibration models and regularization algorithms. The regeneration and maintenance module realizes the recycling of sensors through acid elution, vacuum drying and performance verification, which not only prolongs the service life of the sensor, but also reduces the detection cost. In addition, the overall operation of this detection system is efficient, the sensitivity can reach 0.88μg / L, the reusability is excellent, and it adapts to the detection needs of complex matrix samples.

[0046] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0047] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A system for detecting cadmium content in a glass clarifier, characterized in that: It includes a sample processing module, a sensor module, an optical detection module, a data processing module and a regeneration and maintenance module; the data processing module converts the optical signal into a cadmium ion concentration result and analyzes and outputs it; the data analysis unit in the data processing module calibrates the collected signal through a calibration model to improve the detection accuracy, and the steps of the calibration model are as follows: Step S1, the spectral signal is X∈R n×m , n is the number of wavelengths, m is the number of samples, and the absorbance value of each wavelength i is standardized: Among them, μ i is the mean value of wavelength i; σ i is the standard deviation of wavelength i; Z is the standardized signal matrix; X is the original spectral signal matrix; j is the index of the sample; Step S2: Use discrete wavelet transform to remove noise and obtain the denoised signal Among them, L is the number of layers of wavelet decomposition, w k is the wavelet coefficient of the kth layer, φ k (t) is the wavelet basis function; there is a nonlinear relationship between the cadmium ion concentration y and the absorbance, and the calculation formula is: β k is the fitting coefficient, p is the order of the polynomial, and ε is the residual term; Step S3, introduce regularization term to prevent overfitting: Where λ is the regularization coefficient; the expectation maximization algorithm is used to optimize the model parameters:

1. Step E: Estimate the noise distribution: Q(θ|θ (t) )=E[logp(y,z|θ|y,θ (t) ], where θ is the model parameter, z is the noise variable, and t is the number of iterations; 2.M step: maximize the log-likelihood function: Step S4: In the optimized model, input the detection signal The cadmium ion concentration was predicted using the following formula:

2. A system for detecting cadmium content in a glass clarifier according to claim 1, characterized in that , in the step S2, is the Chebyshev polynomial, capturing the nonlinear characteristics of the signal. satisfy T0(x)=1,T1(x)=x.

3. A system for detecting cadmium content in a glass clarifier according to claim 1, characterized in that The sample processing module is used to remove particulate impurities and insoluble components that interfere with detection in the clarifier sample; the sample processing module includes a sample pretreatment unit, a pH adjustment unit and a mixing unit.

4. A system for detecting cadmium content in a glass clarifier according to claim 3, characterized in that: The sample pretreatment unit preliminarily filters the liquid sample through a microporous filtration device combined with a 12,000 rpm centrifuge, the pH adjustment unit adjusts the pH with a 0.1 M buffer solution and a diluted sodium hydroxide solution, and the mixing unit is equipped with a mechanical stirrer with a speed adjustment range of 0-300 rpm, the stirring time is controlled within 10 minutes, and the sample volume is controlled within 10 mL.

5. The system for detecting cadmium content in a glass clarifier according to claim 1, characterized in that: The functional ligand in the sensor module specifically binds to the cadmium ions, triggering a color change, thereby quantitatively detecting the cadmium ions; the sensor module is based on a solid-state sensor, and includes a sensing material unit, a temperature control unit, and an oscillating device.

6. A system for detecting cadmium content in a glass clarifier according to claim 5, characterized in that: The sensing material unit uses a silicon substrate to fix the functional ligand. The silicon material is formed by mixing tetramethoxysilane and triblock copolymer F108 to form a sol-gel system, which is induced to assemble into a liquid crystal phase by acidified solution and generated by heat treatment. The functional ligand is fixed to the silicon surface by vacuum adsorption, and the fixation is strengthened by constant temperature drying and chemical modification technology. The temperature control unit maintains the reaction temperature at 25°C, and the stirring speed of the oscillating device is 110rpm.

7. The system for detecting cadmium content in a glass clarifier according to claim 1, characterized in that: The optical detection module measures the color change caused by the complexation reaction of cadmium ions in the sensor module through optical technology; the optical detection module includes a spectral analysis unit, which adopts an ultraviolet-visible spectrometer with a detection wavelength range of 200-700nm. The light source uses a combination of a deuterium lamp and a tungsten lamp, is equipped with a high-resolution monochromator and a grating system, introduces the sample solution through an automatic sampling system, and records the absorbance change to form an absorption spectrum.

8. The system for detecting cadmium content in glass clarifier according to claim 1, characterized in that: The regeneration and maintenance module prolongs the service life of the sensor; the regeneration and maintenance module includes an elution unit, a drying unit and a repeated detection unit.

9. A system for detecting cadmium content in a glass clarifier according to claim 8, characterized in that: The elution unit uses a 0.20M hydrochloric acid solution to elute the cadmium ions by soaking and stirring at room temperature of 25°C for 5-10 minutes, and is equipped with a micro-liquid injection pump to control the eluent flow rate and the number of cycles; the drying unit uses a combination of low-temperature heating and vacuum drying, dries for 1 hour, and monitors through a built-in humidity sensor, and automatically stops when the humidity is lower than 5%; the repeated detection unit detects the treated sensor with a standard cadmium ion solution, calculates the key indicators of detection sensitivity, selectivity and repeatability, and when the detection sensitivity decreases by less than 5% and the repeatability error is less than 2%, the sensor is judged to be qualified.

10. The system for detecting cadmium content in glass clarifier according to claim 1, characterized in that: The sample processing module sends the processed sample into the sensor module through the sample tube. After the sensor module completes the cadmium ion complexation and color change, the signal is transmitted to the optical detection module. The optical detection module transmits the spectral signal to the data processing module through the data interface. After the detection is completed, the sensor material is transferred to the regeneration and maintenance module for cleaning and regeneration, and then returned to the sensor module for recycling.

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