A food hazard factor detection method and system based on biomimetic hydrolytic enzyme catalytic bioluminescence

Abstract

The present application relates to the technical field of food hazard factor detection, in particular to a food hazard factor detection method and system based on biomimetic hydrolytic enzyme catalytic bioluminescence; comprising: S1, obtaining the turbidity characteristic value, background fluorescence intensity and fluorescence quenching coefficient of the sample solution to be measured; S2, based on the turbidity characteristic value and the fluorescence quenching coefficient, the robustness index is obtained; S3, according to the comparison result of the robustness index and the preset qualified threshold, the working gain for total fluorescence intensity signal acquisition is determined through the preset closed-loop feedback control logic; S4, through the dynamic signal correction processing containing gain normalization, the normalized apparent fluorescence signal is obtained; S5, based on the normalized apparent fluorescence signal, the apparent catalytic rate is calculated; S6, the concentration of the target hazard factor is determined by the apparent catalytic rate, the turbidity characteristic value and the fluorescence quenching coefficient. The present application significantly improves the accuracy and reliability of detecting the target hazard factor in complex matrix.

Description

Technical Field

[0001] This invention belongs to the field of food hazard factor detection technology, specifically a food hazard factor detection method and system based on biomimetic hydrolytic enzyme catalytic fluorescence. Background Technology

[0002] In the field of food hazard factor detection, especially when using biomimetic enzyme-catalyzed fluorescence generation methods, the detection environment, namely the complex and variable food matrix, can introduce significant interference. These interferences mainly originate from the physical and optical properties of the sample matrix, such as physical occlusion caused by turbidity, and signal loss caused by background fluorescence and chemical quenching substances.

[0003] In existing detection methods, these matrix interference factors cause severe attenuation of fluorescence signals and directly affect the accurate measurement of apparent catalytic rates. This signal distortion reduces the signal-to-noise ratio of the detection, which severely limits the accuracy and robustness of quantitative detection in complex samples.

[0004] Therefore, how to construct a detection mechanism that can quantify and actively and dynamically correct and compensate for matrix interference in real time throughout the entire chain of signal acquisition, processing and concentration calculation, so as to achieve highly accurate and robust quantitative detection of hazardous factors, has become a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0005] The purpose of this invention is to propose a method and system for detecting food hazard factors based on biomimetic hydrolytic enzyme catalysis and fluorescence, so as to improve the accuracy and reliability of detecting target hazard factors in complex matrices.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows:

[0007] A method for detecting food hazard factors based on biomimetic hydrolytic enzyme-catalyzed fluorescence includes:

[0008] S1, obtain the turbidity characteristic value, background fluorescence intensity and fluorescence quenching coefficient of the sample solution to be tested;

[0009] S2, based on turbidity characteristic value and fluorescence quenching coefficient, yields the robustness index;

[0010] S3, based on the comparison result between the robustness index and the preset qualified threshold, determines the working gain used for total fluorescence intensity signal acquisition through the preset closed-loop feedback control logic;

[0011] S4 combines the real-time collected total fluorescence intensity with the working gain and background fluorescence intensity, and obtains the normalized apparent fluorescence signal through dynamic signal correction processing including gain normalization.

[0012] S5, based on the normalized apparent fluorescence signal, the apparent catalytic rate was calculated;

[0013] S6. Substitute the apparent catalytic rate, turbidity characteristic value and fluorescence quenching coefficient into the preset matrix immune signal transduction model for inverse solution to determine the concentration of the target hazard factor.

[0014] Preferably, S2 includes:

[0015] The normalized turbidity characteristic value and the fluorescence quenching coefficient are linearly combined using preset weighting parameters to calculate the matrix attenuation factor.

[0016] Matrix attenuation factor

[0017] in, Turbidity characteristic value after normalization The weight parameters, Fluorescence quenching coefficient Weight parameters;

[0018] The robustness index is determined by subtracting the matrix attenuation factor from the preset initial value:

[0019]

[0020] in, It is the robustness index.

[0021] Preferably, S3 includes:

[0022] If the robustness index is not higher than the preset minimum positive threshold, the signal is determined to be too weak to be compensated, and the preset maximum gain is used as the working gain or the detection is stopped and a prompt is given.

[0023] If the robustness index is lower than the preset qualified threshold but greater than the preset minimum positive threshold, then based on the robustness index, nonlinear adjustment is performed using a preset power-law control rule to generate the working gain.

[0024]

[0025] in: For work gain, The preset standard gain, The robustness index This is a preset dimensionless compensation index;

[0026] If the robustness index is not lower than the preset qualified threshold, then the preset standard gain is determined as the working gain.

[0027] Preferably, the preset minimum positive threshold and the preset dimensionless compensation index are determined as follows:

[0028] Preset minimum positive threshold To ensure the system operates at the preset maximum gain The robustness index corresponding to when the minimum signal-to-noise ratio requirement still cannot be met, or through The inverse solution yields the result;

[0029] The process of determining the value is as follows: Select a series of robustness indices with different robustness indices. The calibration samples were used to evaluate the signal-to-noise ratio of the output signal by scanning different gain settings in each sample. Determine the signal-to-noise ratio Maximize the optimal working gain Obtain the calibration dataset Based on the calibration dataset Through nonlinear fitting relationship Solve for the optimal value.

[0030] Preferably, S4 includes:

[0031] The background fluorescence intensity is separated from the total fluorescence intensity to obtain the difference signal;

[0032] Divide the difference signal by the working gain and normalize the signal amplitude to obtain the normalized apparent fluorescence signal.

[0033] Preferably, S5 includes:

[0034] Obtain the time series curve of the normalized apparent fluorescence signal;

[0035] The normalized fluorescence signal growth rate is obtained by calculating the initial slope of the time series curve.

[0036] Using a preset system conversion constant, the normalized fluorescence signal growth rate is converted into an apparent catalytic rate.

[0037] The preferred method for calibrating the system conversion constant is as follows:

[0038] For a known concentration of The initial growth rate of the fluorescence signal was measured using a fluorescent standard solution under ideal conditions free from matrix interference. ;

[0039] Through formula Calculate the system transformation constants ,in It is the rate of concentration change known based on the stoichiometric relationship of the reaction. It is the change in the normalized apparent fluorescence signal.

[0040] The preferred, pre-defined matrix immune signal transduction model is the Michaelis-Menten enzyme-catalyzed reaction kinetic equation that incorporates a dimensionless attenuation factor with matrix interference:

[0041]

[0042] in, For apparent catalytic rate, For the maximum catalytic rate, The concentration of the target hazard factor. It is the Michaelis constant and the maximum catalytic rate. and Mi constant These are constants pre-calibrated through standard experiments. It is the robustness index.

[0043] Preferred weight parameters and The calculation is as follows:

[0044] Prepare a series of normalized turbidity values and fluorescence quenching coefficient value The standard matrix sample was used to experimentally measure the signal loss fraction of the standard matrix sample compared to the pure buffer solution. Obtain the calibration dataset ;

[0045] Based on the calibration dataset The least squares method is used to analyze the linear relationship. Perform fitting and solve for the optimal solution. and value.

[0046] A food hazard detection system based on biomimetic hydrolytic enzyme-catalyzed fluorescence, implemented based on any one of the above-mentioned methods for detecting food hazard factors based on biomimetic hydrolytic enzyme-catalyzed fluorescence, comprising:

[0047] The parameter acquisition module is used to acquire the turbidity characteristic value, background fluorescence intensity, and fluorescence quenching coefficient of the sample solution to be tested;

[0048] The state assessment module is used to calculate the robustness index based on turbidity eigenvalues ​​and fluorescence quenching coefficients.

[0049] The gain control module is used to determine the working gain for total fluorescence intensity signal acquisition based on the comparison result between the robustness index and the preset qualified threshold, through preset closed-loop feedback control logic.

[0050] The signal processing module is used to combine the real-time acquired total fluorescence intensity with the working gain and background fluorescence intensity, and then obtain a normalized apparent fluorescence signal through dynamic signal correction processing including gain normalization.

[0051] The rate calculation module is used to calculate the apparent catalytic rate by normalizing the apparent fluorescence signal.

[0052] The concentration calculation module is used to substitute the apparent catalytic rate, turbidity characteristic value and fluorescence quenching coefficient into the preset matrix immune signal transduction model for inverse solution to determine the concentration of the target hazard factor.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] 1. This invention acquires the turbidity characteristic value, background fluorescence intensity, and fluorescence quenching coefficient of the sample in real time and uses these parameters to evaluate matrix interference. By combining a Michaelis-Menten enzyme-catalyzed reaction kinetic model incorporating a matrix interference attenuation factor for inverse solving, it effectively overcomes the quenching and attenuation effects of complex matrices such as pigments and suspended matter in food samples on the fluorescence signal, significantly improving the accuracy and reliability of detecting target hazardous factors in complex matrices.

[0055] 2. This invention innovatively establishes a robustness index to quantify the overall interference level of the sample matrix. Based on this index, the working gain of signal acquisition is nonlinearly adaptively adjusted through closed-loop feedback control logic and a preset power-law control rule. This design ensures that stable, high-quality fluorescence signals can still be obtained under different matrix interference levels, especially in samples with strong interference, greatly enhancing the environmental adaptability and stability of the detection system.

[0056] 3. This invention employs a dynamic signal correction processing technique that includes gain normalization. This technique, after removing background fluorescence interference, uses adaptively adjusted working gain to normalize the signal amplitude, eliminating the signal scale inconsistencies caused by gain differences under different detection conditions. This ensures the authenticity and comparability of the normalized apparent fluorescence signal, providing accurate data with a high signal-to-noise ratio for subsequent calculations of the apparent catalytic rate.

[0057] 4. This invention constructs a complete closed-loop control and correction process from state assessment to signal processing and concentration calculation. Through the collaborative work of the parameter acquisition module, state assessment module, and gain control module, dynamic optimization of the detection process is achieved. Finally, using a matrix-immune signal transduction model, multi-dimensional information such as apparent catalytic rate, turbidity, and quenching coefficient is integrated for inverse solution, achieving precise quantification of the target hazardous factor with high anti-interference capability. Attached Figure Description

[0058] Figure 1 This is a flowchart of the method of the present invention;

[0059] Figure 2 This is a system structure diagram of the present invention. Detailed Implementation

[0060] The technical solution of the present invention will now be described in detail with reference to the accompanying drawings.

[0061] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0062] Example 1:

[0063] Please see Figure 1 This embodiment provides a method for detecting food hazard factors based on biomimetic hydrolytic enzyme-catalyzed fluorescence, including:

[0064] S1, obtain the turbidity characteristic value, background fluorescence intensity and fluorescence quenching coefficient of the sample solution to be tested;

[0065] S2, based on turbidity characteristic value and fluorescence quenching coefficient, yields the robustness index;

[0066] S3, based on the comparison result between the robustness index and the preset qualified threshold, determines the working gain used for signal acquisition through the preset closed-loop feedback control logic;

[0067] S4 combines the real-time collected total fluorescence intensity with the working gain and background fluorescence intensity, and obtains the normalized apparent fluorescence signal through dynamic signal correction processing including gain normalization.

[0068] S5, based on the normalized apparent fluorescence signal, the apparent catalytic rate was calculated;

[0069] S6. Substitute the apparent catalytic rate, turbidity characteristic value and fluorescence quenching coefficient into the preset matrix immune signal transduction model for inverse solution to determine the concentration of the target hazard factor.

[0070] The food hazard factor detection method provided in this embodiment constructs a closed-loop feedback control system by parameterizing the interference factors of the detection environment, i.e., the food matrix, in real time. In the entire chain of signal acquisition, processing and concentration calculation, the matrix interference is actively and dynamically corrected and compensated, thereby achieving highly robust and accurate quantitative detection of hazard factors in complex and variable food samples.

[0071] S1, acquire the turbidity characteristic value, background fluorescence intensity, and fluorescence quenching coefficient of the sample solution to be tested; this step aims to accurately quantify the physical and optical characteristics of the matrix microenvironment in which the current detection occurs, providing the basic input for all subsequent correction and compensation algorithms; in this embodiment, after the sample solution to be tested is injected into the detection system, the built-in micro-spectral sensing unit of the system performs optical scanning on the sample; by measuring the attenuation of transmitted light intensity at a specific wavelength, the turbidity characteristic value is obtained. The background fluorescence intensity is obtained by exciting and measuring the autofluorescence of the sample before the catalytic reaction occurs. The fluorescence quenching coefficient was determined through experimental calibration methods or based on a sample type database. ;

[0072] In this invention, the fluorescence quenching coefficient is defined as a dimensionless parameter that characterizes the fraction of fluorescence signal loss caused by the quencher present in the sample matrix. The coefficient is determined by adding an interfering substance of the same type and concentration as the sample matrix to a pure buffer system without the target analyte, and measuring the fluorescence intensity of a standard fluorescent substance before and after the addition of the interfering substance.

[0073] The formula for calculating the fluorescence quenching coefficient is: , and These represent the fluorescence intensity after the addition of the interfering agent and before the addition of the interfering agent, respectively; from this definition, it can be seen that... The value ranges from 0 to 1; the larger the value, the stronger the chemical quenching effect of the matrix. In some embodiments, an empirical value can also be obtained directly from a preset database based on the sample type. Values; This database can be used to systematically calibrate a large number of typical food matrix samples of different types and sources, following the experimental method in this step, namely, by adding standard fluorescent substances, and measuring and recording their corresponding values. Values ​​are pre-established;

[0074] S2, based on turbidity eigenvalues ​​and fluorescence quenching coefficients, calculates the robustness index; this step aims to integrate multi-dimensional interference parameters into a single, physically meaningful evaluation index to assess the reliability of the current detection environment in real time; robustness index The solution is based on a model that reflects the signal loss mechanism, which directly quantifies the effective residual proportion of the signal after transmission in the current matrix;

[0075] S3, based on the comparison between the robustness index and the preset acceptable threshold, the working gain for signal acquisition is determined through a preset closed-loop feedback control logic. This step embodies the closed-loop feedback control of this invention, and its purpose is to actively adjust the instrument's sensitivity to counteract signal attenuation caused by the matrix based on the evaluation results of the detection environment. The system presets an acceptable threshold, derived from statistical analysis of a large amount of sample experimental data covering different interference levels, which ensures the lowest signal-to-noise ratio of the detection results. When the robustness index is calculated When the value is below this threshold, it indicates severe matrix interference, and the system will activate the sensitivity compensation mechanism to generate a working gain higher than the standard gain. Conversely, standard gain is used.

[0076] S4, combining the real-time acquired total fluorescence intensity with the working gain and background fluorescence intensity, and performing dynamic signal correction processing including gain normalization to obtain a normalized apparent fluorescence signal; this step aims to accurately decouple the true fluorescence signal generated by the target catalytic reaction from the distorted original mixed signal; the photoelectric detection unit uses the working gain determined in the previous step. Real-time acquisition of total fluorescence intensity during the reaction process The signal processing module performs dynamic signal correction, which subtracts the background fluorescence intensity. and utilize working gain The signal amplitude is normalized to eliminate the influence of gain adjustment on the absolute value of the signal, and finally the normalized apparent fluorescence signal is obtained. ;

[0077] S5, based on the normalized apparent fluorescence signal, calculates the apparent catalytic rate; this step aims to convert fluorescence growth information in the signal domain into reaction rate information in the chemical kinetics domain; the rate calculation module calculates the normalized apparent fluorescence signal... The time series curve was analyzed, and the normalized fluorescence signal growth rate was obtained by calculating the slope of the curve in the initial stage of the reaction; a preset system conversion constant was used. This converts the fluorescence growth rate into an apparent catalytic rate with a clear chemical meaning. ;

[0078] S6, substituting the apparent catalytic rate, turbidity characteristic value, and fluorescence quenching coefficient into a pre-defined matrix-immune signal transduction model for inverse calculation, determines the concentration of the target hazardous factor; this step is the final solution stage, utilizing a kinetic model with built-in matrix interference correction to determine the concentration of the target hazardous factor from the apparent catalytic rate. Accurately reconstructing target analyte concentrations; the matrix-immune signal transduction model uses real-time measured turbidity characteristic values. and fluorescence quenching coefficient As input parameters, the influence of matrix interference on the catalytic reaction rate is directly quantified and offset at the kinetic equation level; the apparent catalytic rate obtained from S5 is used as the input parameter. By substituting the values ​​into the model, the concentration of the target hazard can be calculated in reverse. .

[0079] Example 2:

[0080] This embodiment, based on embodiment 1, specifically defines the calculation method of the robustness index in step S2;

[0081] The turbidity characteristic value and the fluorescence quenching coefficient are linearly combined using preset weighting parameters to calculate the matrix attenuation factor. The matrix attenuation factor refers to the total proportion of signal loss caused by both physical shielding and chemical quenching effects. Before this, the turbidity characteristic value needs to be... Normalization is performed to make it a dimensionless quantity, for example, by dividing by a preset turbidity reference value; this reference value can be, for example, set as a turbidity reference value representing the maximum acceptable level of interference. Or the turbidity value of a standard turbidity solution ,For example, It can be set to the turbidity characteristic value measured by a 100 NTU formalin turbidity solution under a specific optical path in this system;

[0082] Due to the normalized turbidity characteristic value and fluorescence quenching coefficient All are dimensionless parameters, therefore their weighting parameters and Also a dimensionless pure number; the calculation logic for the matrix attenuation factor is as follows: matrix attenuation factor The determination of these two weighting parameters is as follows: A series of standard matrix samples with known normalized turbidity values ​​are prepared. and fluorescence quenching coefficient value The signal loss fraction compared to pure buffer solution was measured experimentally. Based on this calibration dataset The least squares method is used to analyze the linear relationship. By performing a fitting process, the optimal solution can be obtained. and The value is then embedded into the system algorithm;

[0083] The robustness index is determined by subtracting the matrix attenuation factor from a preset initial value of 1; the robustness index The calculation formula is clearly defined as:

[0084]

[0085] Among them, due to This represents the signal fraction lost due to matrix effects, therefore It directly represents the effective residual signal fraction after the signal has undergone matrix attenuation, and its value range is determined by... The function is limited to between;

[0086] Example 3:

[0087] This embodiment, based on embodiment 1, specifically defines the closed-loop feedback control logic in step S3;

[0088] If robustness index Below the preset qualified threshold And greater than a very small positive threshold ,Right now ,in To ensure the system operates at the preset maximum gain The robustness index corresponding to when the minimum signal-to-noise ratio requirement still cannot be met, or through The inverse solution yields the working gain, which is then generated through nonlinear adjustment based on the robustness index using a preset power-law control rule. If the robustness index... That is, excessive interference or If the signal is too weak to be compensated, the system can use the preset maximum gain. Or stop the detection and display a prompt; when hour:

[0089]

[0090] in, It is the system's standard gain, the system's factory calibration value, which is usually set to 1; It is a robustness index calculated in real time; It is a preset dimensionless compensation index; The process for determining the value is as follows: Select a series of values ​​with different matrix interference levels, i.e., different The calibration samples were used to evaluate the signal-to-noise ratio of the output signal by scanning different gain settings in each sample. , find Maximize the optimal working gain Based on this calibration dataset Through nonlinear fitting relationship Solve for the optimal value;

[0091] If the robustness index is not lower than the preset qualified threshold, then the preset standard gain is determined as the working gain; when At that time, the control logic command .

[0092] Example 4:

[0093] This embodiment, based on embodiment 1, specifically defines the internal implementation steps of the dynamic signal correction processing, including gain normalization, in step S4; the calculation formula for this dynamic signal correction processing is:

[0094]

[0095] The processing flow of this formula can be broken down into two collaborative steps:

[0096] The background fluorescence intensity is separated from the total fluorescence intensity to obtain the difference signal; this step corresponds to the numerator of the formula. ;

[0097] Divide the difference signal by the operating gain to normalize the signal amplitude; this step corresponds to the division by the gain in the formula. ;because and All are controlled by photoelectric detection units at operating gain The measurements show that this normalization process reduces the signal amplitude from dynamically changing values. Domain, uniformly converted back to standard gain This domain eliminates the influence of gain adjustment on the absolute value of the signal.

[0098] Example 5:

[0099] This embodiment, based on Embodiment 1, specifically defines the method for calculating the apparent catalytic rate in step S5;

[0100] Obtain the time series curve of the normalized apparent fluorescence signal obtained after step S4. ;

[0101] Through calculation The curve in the initial stage of the reaction, i.e. slope at time The normalized fluorescence signal growth rate was obtained.

[0102] Using a preset system conversion constant, the normalized fluorescence signal growth rate is converted into an apparent catalytic rate; the apparent catalytic rate The final calculation formula is:

[0103]

[0104] Wherein, the system conversion constant Its function is to convert the rate of increase in unit fluorescence intensity into the rate of change in molar concentration, and its dimensions are... ; The calibration method for the value is as follows: using a known concentration... The initial growth rate of the fluorescence signal of the fluorescent standard solution was measured under ideal conditions without matrix interference. ; Through formula The calculation shows that, among which It is the rate of concentration change known based on the stoichiometric relationship of the reaction. It is the change in the normalized apparent fluorescence signal.

[0105] Example 6:

[0106] Based on Example 1, this embodiment explicitly defines the mathematical essence of the matrix immune signal transduction model preset in step S6; the model is a Michaelis-Menten enzyme-catalyzed reaction kinetic equation that incorporates a dimensionless attenuation factor with matrix interference; the modified model is expressed as follows:

[0107]

[0108] in, For apparent catalytic rate, For the maximum catalytic rate, The concentration of the target hazard factor. It is the Michaelis constant. The robustness index is obtained by solving in step S2;

[0109] Main part of the model The formula represents the classic Michaelis-Menten equation, indicating the theoretical catalytic rate under undisturbed conditions; the maximum catalytic rate is... and Mi constant These are the inherent biochemical characteristics of specific biomimetic enzyme and substrate combinations, which originate from constants that are pre-calibrated and stored in the system through standard experiments;

[0110] The core improvement lies in the introduction of a robustness index. As a multiplicative correction factor; according to the definition in Example 2. This index comprehensively quantifies the effective residual proportion of the signal caused by both physical masking and chemical quenching; by... By placing the molecular position as a multiplier, the model intuitively expresses how matrix interference leads to the observed apparent catalytic rate. Compared to the physical fact that the theoretical rate decays; when performing the reverse solution in step S6, if If the value is not zero, the influence of the matrix can be accurately eliminated, and the true concentration can be obtained. .

[0111] Example 7:

[0112] Please see Figure 2 This embodiment provides a food hazard factor detection system based on biomimetic hydrolytic enzyme-catalyzed fluorescence, which is a modular implementation of the detection method in terms of hardware and functions; the system includes:

[0113] The parameter acquisition module is used to obtain the turbidity characteristic values ​​of the sample solution to be tested. Background fluorescence intensity and fluorescence quenching coefficient ;

[0114] The status assessment module is used to receive data from the parameter acquisition module. and The robustness index is calculated based on preset weight parameters. ;

[0115] Gain control module, used based on robustness index Compared with the preset qualified threshold The comparison results are used to determine the operating gain for signal acquisition through preset closed-loop feedback control logic. ;

[0116] The signal processing module is used to combine the real-time acquired total fluorescence intensity with the working gain and background fluorescence intensity, and obtain a normalized apparent fluorescence signal through dynamic signal correction processing including gain normalization.

[0117] Rate calculation module for normalized apparent fluorescence signal The apparent catalytic rate was calculated. ;

[0118] The concentration calculation module is used to calculate the apparent catalytic rate. Turbidity characteristic value and fluorescence quenching coefficient The concentration of the target hazardous factor is determined by substituting the input into a pre-defined matrix immune signal transduction model and performing a reverse solution. .

[0119] Example 8:

[0120] This embodiment further defines the specific functional implementation of the gain control module based on embodiment 7;

[0121] The internal firmware logic of the gain control module executes a branch judgment and control flow:

[0122] If the robustness index of the received satisfy ,in If the positive threshold is extremely small, the module determines that the current matrix interference is severe and activates the power-law control algorithm based on the received data. Value and preset standard gain and compensation index Through calculation Generates a working gain that has been nonlinearly amplified. Control signal; if If the signal is too weak, the module can generate a preset maximum gain. Control signals or issue error alarms;

[0123] If robustness index Not lower than the preset qualified threshold If the module determines that the current substrate environment is good, it will directly call the preset standard gain. Value, generate equal to working gain Control signals;

[0124] This module will generate The control signal is output to the gain control pin of the photoelectric detection unit to complete the real-time dynamic setting of the system hardware sensitivity.

[0125] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for detecting food hazard factors based on biomimetic hydrolytic enzyme-catalyzed fluorescence, characterized in that, include: S1, obtain the turbidity characteristic value, background fluorescence intensity and fluorescence quenching coefficient of the sample solution to be tested; S2, based on turbidity characteristic value and fluorescence quenching coefficient, yields the robustness index; S3, based on the comparison result between the robustness index and the preset qualified threshold, determines the working gain used for total fluorescence intensity signal acquisition through the preset closed-loop feedback control logic; S4 combines the real-time collected total fluorescence intensity with the working gain and background fluorescence intensity, and obtains the normalized apparent fluorescence signal through dynamic signal correction processing including gain normalization. S5, based on the normalized apparent fluorescence signal, the apparent catalytic rate was calculated; S6. Substitute the apparent catalytic rate and robustness index into the preset matrix immune signal transduction model for inverse solution to determine the concentration of the target hazard factor. S2 includes: The normalized turbidity characteristic value and the fluorescence quenching coefficient are linearly combined using preset weighting parameters to calculate the matrix attenuation factor. Matrix attenuation factor in, Turbidity characteristic value after normalization The weight parameters, Fluorescence quenching coefficient Weight parameters; The robustness index is determined by subtracting the matrix attenuation factor from the preset initial value: in, The robustness index; S3 includes: If the robustness index is not higher than the preset minimum positive threshold, the signal is determined to be too weak to be compensated, and the preset maximum gain is used as the working gain or the detection is stopped and a prompt is given. If the robustness index is lower than the preset qualified threshold but greater than the preset minimum positive threshold, then based on the robustness index, nonlinear adjustment is performed using a preset power-law control rule to generate the working gain. in: For work gain, The preset standard gain, The robustness index This is a preset dimensionless compensation index; If the robustness index is not lower than the preset qualified threshold, then the preset standard gain is determined as the working gain. The pre-defined matrix immune signal transduction model is based on the Michaelis-Menten enzyme kinetic equation, which incorporates a dimensionless attenuation factor to introduce matrix interference. in, For apparent catalytic rate, For the maximum catalytic rate, The concentration of the target hazard factor. It is the Michaelis constant and the maximum catalytic rate. and Mi constant It is a constant that has been pre-calibrated through standard experiments. It is the robustness index.

2. The method for detecting food hazard factors based on biomimetic hydrolytic enzyme-catalyzed fluorescence as described in claim 1, characterized in that, The preset minimum positive threshold and the preset dimensionless compensation exponent are determined as follows: Preset minimum positive threshold To ensure the system operates at the preset maximum gain The robustness index corresponding to when the minimum signal-to-noise ratio requirement still cannot be met, or through The inverse solution yields the result; The process of determining the value is as follows: Select a series of robustness indices with different robustness indices. The calibration samples were used to evaluate the signal-to-noise ratio of the output signal by scanning different gain settings in each sample. Determine the signal-to-noise ratio Maximize the optimal working gain Obtain the calibration dataset Based on the calibration dataset Through nonlinear fitting relationship Solve for the optimal value.

3. The method for detecting food hazard factors based on biomimetic hydrolytic enzyme-catalyzed fluorescence as described in claim 1, characterized in that, S4 includes: The background fluorescence intensity is separated from the total fluorescence intensity to obtain the difference signal; Divide the difference signal by the working gain and normalize the signal amplitude to obtain the normalized apparent fluorescence signal.

4. The method for detecting food hazard factors based on biomimetic hydrolytic enzyme-catalyzed fluorescence as described in claim 1, characterized in that, S5 include: Obtain the time series curve of the normalized apparent fluorescence signal; The normalized fluorescence signal growth rate is obtained by calculating the initial slope of the time series curve. Using a preset system conversion constant, the normalized fluorescence signal growth rate is converted into an apparent catalytic rate.

5. The method for detecting food hazard factors based on biomimetic hydrolytic enzyme-catalyzed fluorescence as described in claim 4, characterized in that, The calibration method for the system conversion constant is as follows: For a known concentration of The initial growth rate of the fluorescence signal was measured using a fluorescent standard solution under ideal conditions free from matrix interference. ; Through formula Calculate the system transformation constants ,in It is the rate of concentration change known based on the stoichiometric relationship of the reaction. It is the change in the normalized apparent fluorescence signal.

6. The method for detecting food hazard factors based on biomimetic hydrolytic enzyme-catalyzed fluorescence as described in claim 1, characterized in that, Weight parameters and The specific calculation is as follows: Prepare a series of normalized turbidity values and fluorescence quenching coefficient value The standard matrix sample was used to experimentally measure the signal loss fraction of the standard matrix sample compared to the pure buffer solution. Obtain the calibration dataset ; Based on the calibration dataset The least squares method is used to analyze the linear relationship. Perform fitting and solve for the optimal solution. and value.

7. A food hazard factor detection system based on biomimetic hydrolytic enzyme-catalyzed fluorescence, implemented based on the food hazard factor detection method based on biomimetic hydrolytic enzyme-catalyzed fluorescence as described in any one of claims 1-6, characterized in that, include: The parameter acquisition module is used to acquire the turbidity characteristic value, background fluorescence intensity, and fluorescence quenching coefficient of the sample solution to be tested; The state assessment module is used to calculate the robustness index based on turbidity eigenvalues ​​and fluorescence quenching coefficients. The gain control module is used to determine the working gain for total fluorescence intensity signal acquisition based on the comparison result between the robustness index and the preset qualified threshold, through preset closed-loop feedback control logic. The signal processing module is used to combine the real-time acquired total fluorescence intensity with the working gain and background fluorescence intensity, and then obtain a normalized apparent fluorescence signal through dynamic signal correction processing including gain normalization. The rate calculation module is used to calculate the apparent catalytic rate by normalizing the apparent fluorescence signal. The concentration calculation module is used to substitute the apparent catalytic rate and robustness index into a preset matrix immune signal transduction model for inverse solution to determine the concentration of the target hazard factor.

Images