A fluorescence probe signal calibration method and system for rapid detection of multiple residues in complex matrices

Abstract

This invention provides a fluorescent probe signal calibration method and system for rapid detection of multiple residues in complex matrices, comprising: acquiring the fluorescence signal of a sample to be tested, and resolving the original concentration, average fluorescence lifetime, and peak fluorescence emission wavelength of the sample; calculating the lifetime deviation between the average fluorescence lifetime and a preset reference lifetime, and the wavelength deviation between the peak fluorescence emission wavelength and the preset reference wavelength; calculating a signal fidelity index based on the lifetime deviation and wavelength deviation; determining a corresponding calibration strategy according to a preset threshold range into which the signal fidelity index falls, wherein the calibration strategy is one of no calibration, calibration execution, or invalidation; when the calibration strategy is calibration execution, calculating a total calibration factor based on the lifetime deviation and wavelength deviation, calibrating the original concentration using the total calibration factor, and outputting the calibrated concentration.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of signal processing and calibration for analysis and detection, and particularly relates to a fluorescence probe signal calibration method and system for rapid detection of multiple residues in a complex matrix. BACKGROUND

[0002] With the wide application of biomimetic enzyme fluorescent probes in the field of rapid detection, they have become the core technology support for food quality and safety screening, environmental pollutant monitoring, etc. due to their advantages of high sensitivity, specificity and rapid response. From pesticide residue detection in tea, fruits and vegetables, to on-site screening of harmful pollutants in water and soil, this kind of probe technology is gradually replacing traditional detection methods to meet the urgent needs of grassroots supervision and on-site rapid analysis. However, the complexity of the actual detection environment far exceeds the ideal conditions in the laboratory. Proteins, lipids and natural pigments (such as chlorophyll and carotenoids) in food matrix, humic acid and heavy metal ions in environmental samples, and reducing substances in biological samples will all interact with biomimetic enzyme fluorescent probes, causing significant interference with the detection signal.

[0003] Such matrix interference is not a single effect, but a combined effect caused by multiple physical and chemical processes. For example, polyphenols in plant-derived samples may change the conformation of the probe through non-specific binding, causing a shift in the fluorescence emission wavelength; heavy metal ions in environmental water samples easily cause fluorescence quenching, reducing signal intensity; and natural pigments in food produce background fluorescence, which overlaps with the probe signal, and even exacerbates signal distortion through fluorescence resonance energy transfer (FRET) mechanism. More challenging is that these interferences often exist simultaneously and superimpose on each other, making the change in fluorescence signal no longer simply reflect the target concentration, but a comprehensive result mixed with multiple matrix effects, thus breaking the traditional detection logic that relies on the linear relationship between signal and concentration.

[0004] To address the above problems, the industry has developed various conventional calibration strategies, but all have obvious technical limitations. The most common approach is to calibrate based on a single fluorescence intensity parameter, by comparing the intensity difference between the blank control and the sample to correct the results, but this method cannot distinguish whether the signal change is due to the fluctuation of target concentration or the superposition or quenching effect of background fluorescence, which is prone to misjudgment. Another common solution is to use a fixed calibration coefficient for compensation, by pre-determining the interference level of a specific matrix to establish a calibration library, but the matrix composition of actual samples varies greatly, even for the same type of food, different varieties, origins or processing methods will cause significant differences in interference components, so the fixed coefficient cannot adapt to the dynamic changes in the interference scene, and almost loses its calibration value in unknown matrices.

[0005] In recent years, some improved methods attempt to introduce a single dimension of signal parameters to assist calibration, such as distinguishing quenching-induced signal attenuation by fluorescence lifetime, or avoiding the absorption interference of pigments by using near-infrared spectroscopy, but these methods still cannot break through the "single parameter limitation": the former cannot cope with the intensity shift caused by background fluorescence, and the latter is completely ineffective for non-pigment interference (such as protein adsorption, ion quenching). More importantly, the existing technology generally lacks the ability to accurately identify the type of interference, and cannot quantify the contribution weight of different physical and chemical processes to the signal, resulting in the calibration behavior being reduced to "blind compensation". In complex samples with significant matrix effects, the relative error of the detection results often exceeds the acceptable range, seriously restricting the practical application value of the biomimetic enzyme fluorescent probe technology.

[0006] Therefore, how to break through the inherent defects of traditional calibration methods, realize the accurate identification and dynamic adaptation of multiple interferences in complex matrix, and establish a signal calibration system that can match the actual detection scene, has become a key bottleneck for promoting the biomimetic enzyme fluorescent probe technology to scale application, and is also a core technical problem urgently needed to be solved in the field. SUMMARY

[0007] In view of the defects and deficiencies of the prior art, the present application provides a fluorescent probe signal calibration method and system for rapid detection of multiple residues in complex matrix, aiming to solve the technical problems of fluorescence detection signal distortion caused by complex interference in complex matrix and the difficulty of accurate adaptation of conventional calibration methods.

[0008] The method first collects the fluorescence signal of the to-be-tested sample, and resolves two types of key fluorescence physical parameters, i.e., an original concentration, a fluorescence average lifetime and a fluorescence peak emission wavelength from the fluorescence signal; then, a lifetime deviation of the fluorescence average lifetime from a preset reference lifetime and a wavelength deviation of the fluorescence peak emission wavelength from a preset reference wavelength are calculated; a weighted sum is performed based on the square of the ratio of the two types of deviations to the corresponding reference values, and a signal fidelity index is obtained by combining a preset reference value, and the index is boundary processed to ensure that it is in a reasonable interval, and the index can quantitatively reflect the degree of signal interference; then, a calibration strategy is determined according to the preset threshold interval (including a high-confidence threshold and a calibration lower limit threshold) into which the signal fidelity index falls, and the calibration strategy is specifically that the signal is not calibrated when the signal confidence is extremely high, the calibration is performed when the signal is moderately distorted, and the signal is marked as invalid when the signal is severely distorted, wherein the high-confidence threshold is determined based on the signal fidelity index distribution statistics of the non-interference standard sample, the calibration lower limit threshold is determined in combination with the relative error after calibration, and a preset minimum tolerance is used when the thresholds are compared to ensure numerical stability; when the calibration is performed, the wavelength deviation is multiplied by a preset wavelength calibration coefficient, the lifetime deviation is multiplied by a preset lifetime calibration coefficient, and then the two types of calibration values are added to obtain a total calibration factor, the wavelength calibration coefficient is calibrated by adding an interference substance that only affects the wavelength in the standard sample and performing linear regression, and the lifetime calibration coefficient is calibrated by adding an interference substance that only affects the lifetime, and finally the calibration is completed based on the correlation between the original concentration and the total calibration factor and the calibrated concentration is output.

[0009] The system corresponds to the above method and is provided with a signal acquisition module, a deviation calculation module, a fidelity index module, a calibration strategy module and a concentration calibration module, and can be further integrated with an in-situ isolation sensing unit, which is provided with an in-situ isolation micro-reactor model containing an interface film characteristic permeability coefficient and an interference substance repulsion coefficient, and can establish a mapping relationship between the external sample concentration and the effective concentration in the micro-reactor to reduce the influence of the interference substance from the physical layer. In addition, the present application also provides a computer device and a non-transitory computer readable storage medium for implementing the above method, which are matched by a hardware carrier and software storage to ensure that the calibration method can be stably implemented.

[0010] The present application breaks through the limitation of traditional single parameter calibration by using the technical architecture of "two-dimensional fluorescence parameter acquisition-signal fidelity quantitative evaluation-hierarchical closed-loop calibration-physical isolation cooperation", realizes accurate identification and directional compensation of complex interference in complex matrix, and significantly improves the accuracy and reliability of multi-residue rapid detection.

[0011] The technical scheme specifically adopted by the present application to solve the technical problems is:

[0012] A fluorescence probe signal calibration method for multi-residue rapid detection in a complex matrix, comprising:

[0013] The fluorescence signal of the sample to be tested is collected, and the original concentration, average fluorescence lifetime and fluorescence peak emission wavelength of the sample are extracted from it.

[0014] Calculate the lifetime deviation between the average fluorescence lifetime and the preset reference lifetime, and the wavelength deviation between the peak fluorescence emission wavelength and the preset reference wavelength;

[0015] Based on the lifetime deviation and wavelength deviation, the signal fidelity index is calculated.

[0016] Based on the preset threshold range into which the signal fidelity index falls, a corresponding calibration strategy is determined, wherein the calibration strategy is one of no calibration, performing calibration, or marking as invalid.

[0017] When the calibration strategy is to perform calibration, a total calibration factor is calculated based on the lifetime deviation and wavelength deviation, and the original concentration is calibrated using the total calibration factor to output the calibrated concentration.

[0018] Furthermore, when acquiring the fluorescence signal, time-correlated single-photon counting technology is used to obtain the average fluorescence lifetime and the peak emission wavelength of fluorescence, and the original concentration is obtained by analyzing the fluorescence intensity signal acquired by the high-sensitivity fluorescence detector; the lifetime deviation is the difference between the average fluorescence lifetime and the preset reference lifetime, and the wavelength deviation is the difference between the peak emission wavelength of fluorescence and the preset reference wavelength.

[0019] Furthermore, the method for calculating the signal fidelity index based on the lifetime deviation and wavelength deviation specifically includes: squaring the ratio of the wavelength deviation to a preset reference wavelength to obtain a wavelength normalized squared deviation; squaring the ratio of the lifetime deviation to a preset reference lifetime to obtain a lifetime normalized squared deviation; weighted summing the two normalized squared deviations to generate a weighted deviation sum; subtracting the weighted deviation sum from a preset reference value of 1 to obtain the signal fidelity index; after calculating the signal fidelity index, if it is greater than 1, it is adjusted to 1, and if it is less than 0, it is adjusted to 0, so that the index is in the 0-1 range.

[0020] Furthermore, the preset threshold range includes a high confidence threshold and a lower correction threshold, wherein: when the signal fidelity index is not lower than the high confidence threshold, the calibration strategy is no calibration; when the signal fidelity index is not lower than the lower correction threshold but lower than the high confidence threshold, the calibration strategy is to perform calibration; when the signal fidelity index is lower than the lower correction threshold, the calibration strategy is to mark it as invalid; the high confidence threshold is determined based on the statistical distribution of the signal fidelity index of the interference-free standard sample, and the lower correction threshold is determined based on whether the relative error after calibration exceeds a preset range, and a preset minimum tolerance judgment is used when comparing thresholds to ensure numerical stability.

[0021] Further, the method for calculating the total calibration factor based on the lifetime deviation and wavelength deviation specifically includes: multiplying the wavelength deviation by a preset wavelength calibration coefficient to obtain a wavelength calibration value, multiplying the lifetime deviation by a preset lifetime calibration coefficient to obtain a lifetime calibration value, and adding the two calibration values ​​to obtain the total calibration factor; the wavelength calibration coefficient is determined by adding an interfering substance that only causes a shift in the emission wavelength of the fluorescence peak to the standard sample and performing linear regression on the normalized concentration deviation and the normalized wavelength deviation; the lifetime calibration coefficient is determined by adding an interfering substance that only affects the average lifetime of fluorescence to the standard sample and performing linear regression in the same way; when calibrating the original concentration using the total calibration factor, the calibrated concentration is calculated based on the correlation between the original concentration and the total calibration factor.

[0022] Furthermore, the method is applied to an in-situ isolated sensing system based on a pre-defined in-situ isolated microreactor model. The in-situ isolated microreactor model includes the characteristic permeability coefficient of the interface membrane to the target analyte and the repulsion coefficient of various major interfering substances in the sample. This model is used to establish a mapping relationship between the external sample concentration and the effective concentration inside the microreactor in order to reduce the influence of interfering substances.

[0023] And, a fluorescent probe signal calibration system for rapid detection of multiple residues in complex matrices, comprising:

[0024] The signal acquisition module is used to acquire the fluorescence signal of the sample to be tested and to extract the original concentration, average fluorescence lifetime and fluorescence peak emission wavelength of the sample to be tested.

[0025] The deviation calculation module is used to calculate the lifetime deviation between the average fluorescence lifetime and the preset reference lifetime, and the wavelength deviation between the fluorescence peak emission wavelength and the preset reference wavelength.

[0026] The fidelity index module is used to calculate the signal fidelity index based on the lifetime deviation and wavelength deviation.

[0027] The calibration strategy module is used to determine the corresponding calibration strategy based on the preset threshold range into which the signal fidelity index falls. The calibration strategy is one of no calibration, performing calibration, or marking as invalid.

[0028] The concentration calibration module is used to calculate the total calibration factor based on the lifetime deviation and wavelength deviation when the calibration strategy is to perform calibration, and to calibrate the original concentration using the total calibration factor, and output the calibrated concentration.

[0029] Furthermore, it also includes an in-situ isolation sensing unit, which is pre-set with an in-situ isolation microreactor model. The in-situ isolation microreactor model includes the characteristic permeability coefficient of the interface membrane to the target analyte and the repulsion coefficient of various major interfering substances in the sample, which is used to establish a mapping relationship between the external sample concentration and the effective concentration inside the microreactor to reduce the influence of interfering substances on the fluorescence signal.

[0030] And a computer device including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the method described above.

[0031] A non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described above.

[0032] Compared with the prior art, the present invention and its preferred embodiments have at least the following beneficial effects:

[0033] Compared with the prior art, the present invention and its preferred embodiments have at least the following beneficial effects:

[0034] Breaking through the limitations of traditional calibration methods that rely on a single fluorescence intensity parameter, this method simultaneously collects and analyzes two specific fluorescence physical dimension parameters: average fluorescence lifetime and peak fluorescence emission wavelength. Combined with deviation calculation and signal fidelity index evaluation, it can quantitatively identify complex interferences caused by different physicochemical processes such as quenching and background fluorescence. It no longer judges the concentration of the target substance based solely on a single signal change, providing a reliable basis for distinguishing interferences for subsequent accurate calibration and avoiding the blind calibration caused by the inability of traditional methods to identify the type of interference.

[0035] A hierarchical closed-loop calibration strategy was constructed, performing differentiated calibration operations based on the threshold range (high confidence, moderate distortion, severe distortion) of the signal fidelity index: no calibration is performed for scenarios with extremely high signal confidence to avoid introducing additional errors; targeted calibration is initiated for scenarios with moderate distortion to ensure the effectiveness of correction; and scenarios with severe distortion are marked as invalid, indicating the need for sample processing. This intelligent decision-making mechanism effectively prevents the problems of "overcalibration" or "undercalibration" under traditional fixed-coefficient calibration or uniform calibration modes, improving the robustness of the detection system.

[0036] During calibration, the total calibration factor is not calculated using a general compensation value. Instead, it is based on the fluorescence mean lifetime deviation and fluorescence peak emission wavelength deviation, and is weighted and summed by pre-set calibration coefficients calibrated by specific interfering substances. This allows for targeted compensation of the effects of different types of interference on the detection signal, achieving precise decomposition and targeted correction of composite interference, rather than the "one-size-fits-all" compensation of traditional methods, thus further improving the accuracy of detection results in complex matrices.

[0037] By combining signal calibration methods with in-situ isolated sensing systems, a dual anti-interference system of "physical isolation + signal calibration" is formed: the in-situ isolated microreactor model uses the characteristic permeability coefficient and interference repulsion coefficient of the interface membrane to reduce the entry of interference into the sensing area from a physical level and purify the detection microenvironment; the signal adaptive calibration logic accurately corrects the residual interference that penetrates the physical barrier. The two work together to significantly enhance the system's anti-interference capability against complex matrices compared to technologies that rely solely on signal calibration or physical isolation.

[0038] The system provides a fluorescent probe system corresponding to the calibration method, as well as computer equipment and non-transitory computer-readable storage media for implementing the method. Through the integrated design of functional modules and the matching of hardware and software carriers, the calibration method can be stably implemented, avoiding the problem that the technical solution remains only at the theoretical level and is difficult to apply in practice. It provides an operable and complete technical solution for rapid detection of multiple residues. Attached Figure Description

[0039] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments:

[0040] Figure 1 This is a flowchart illustrating the implementation of the method in an embodiment of the present invention.

[0041] Figure 2 This is a system structure diagram of an embodiment of the present invention. Detailed Implementation

[0042] To make the features and advantages of the present invention more apparent and understandable, specific embodiments are described below in detail:

[0043] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0044] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0045] To address the problems of existing technologies, this invention provides a fluorescent probe signal calibration scheme for rapid detection of multiple residues in complex matrices, along with a corresponding biomimetic enzyme fluorescent probe system and its construction method. This scheme overcomes the limitations of traditional reliance on a single fluorescence intensity, innovatively analyzing two physical dimensions of the fluorescence signal simultaneously: average lifetime and peak emission wavelength. Its core lies in acquiring the original concentration, fluorescence lifetime, and wavelength of the sample to be tested, and calculating their directional deviation from a preset reference value. Based on this deviation, the signal reliability is quantitatively assessed using a signal fidelity model, and a signal fidelity index is calculated. Finally, based on this index, a hierarchical closed-loop calibration strategy is adopted to determine the total calibration factor, and adaptive closed-loop calibration is performed on the original concentration. This method can deeply understand and correct complex interferences caused by quenching, background fluorescence, etc., providing a decision-making basis for achieving accurate, reliable, and rapid detection.

[0046] Please see Figure 1 First, the core solution process of this invention is provided, namely, a fluorescent probe signal calibration method for rapid detection of multiple residues in complex matrices, including:

[0047] The fluorescence signal of the sample to be tested is collected, and the original concentration, average fluorescence lifetime and fluorescence peak emission wavelength of the sample are extracted from it.

[0048] Calculate the lifetime deviation between the average fluorescence lifetime and the preset reference lifetime, and the wavelength deviation between the peak fluorescence emission wavelength and the preset reference wavelength;

[0049] Based on the lifetime deviation and wavelength deviation, the signal fidelity index is calculated.

[0050] Based on the preset threshold range into which the signal fidelity index falls, a corresponding calibration strategy is determined, wherein the calibration strategy is one of no calibration, performing calibration, or marking as invalid.

[0051] When the calibration strategy is to perform calibration, a total calibration factor is calculated based on the lifetime deviation and wavelength deviation, and the original concentration is calibrated using the total calibration factor to output the calibrated concentration.

[0052] Based on this, to make the objectives, technical solutions, and advantages of the present invention clearer, the following describes in detail the specific implementation schemes, design, and construction of the present invention in conjunction with several specific embodiments:

[0053] Example 1:

[0054] This embodiment provides a scheme for constructing a biomimetic enzyme fluorescent probe for rapid detection of multiple residues, including:

[0055] The original concentration, average lifetime of fluorescence signal, and peak emission wavelength of the sample to be tested were collected.

[0056] Based on the average lifetime collected and the preset reference lifetime, the lifetime orientation deviation is determined;

[0057] Based on the collected peak emission wavelength and the preset reference wavelength, the wavelength orientation deviation is determined;

[0058] By combining lifetime orientation deviation and wavelength orientation deviation, the signal fidelity index is calculated through signal fidelity model prediction.

[0059] Based on the signal fidelity index, lifetime orientation deviation, and wavelength orientation deviation, and in response to a preset correction threshold, the total calibration factor is determined through a hierarchical closed-loop calibration strategy.

[0060] By combining the original concentration and the total calibration factor, the calibrated concentration is output through adaptive closed-loop calibration logic based on sensor signals.

[0061] This invention provides an adaptive calibration method for biomimetic enzyme fluorescent probe signals for rapid detection of multiple residues. This method constructs a complete adaptive closed-loop calibration process, aiming to solve the problem of detection signal distortion caused by complex matrix interference. This method constitutes a technical closed loop from signal acquisition, distortion assessment, graded calibration to final concentration output.

[0062] In a specific implementation scenario, such as detecting pesticide residues in fruit and vegetable juices, the execution flow of this method is as follows:

[0063] The raw signal of the sample to be tested is collected, and the raw concentration is calculated. The original concentration here This refers to the target concentration calculated directly based on raw signals such as fluorescence intensity before any calibration; simultaneously, using time-correlated single-photon counting technology, two key signal characteristic dimensions are acquired: the average lifetime of the fluorescence signal. and peak emission wavelength .

[0064] Based on the collected average lifespan and preset reference lifespan Determine lifetime orientation deviation Based on the collected peak emission wavelength and the preset reference wavelength Determine wavelength orientation deviation Reference lifespan here With reference wavelength In an ideal buffer solution free from any interference, reference values ​​from a pre-established database of reference signals based on a series of standard samples are measured. The aim is to provide a vector basis with clear physical meaning for subsequent signal quality assessment and precise calibration. Combining lifetime orientation deviation and wavelength orientation deviation, a signal fidelity index is calculated using a signal fidelity model. The signal fidelity index here It is a real-time quantitative assessment of the purity or reliability of the current detected signal; based on the signal fidelity index. The lifetime orientation deviation and wavelength orientation deviation are calculated, and in response to a preset correction threshold, the total calibration factor is determined through a hierarchical closed-loop calibration strategy. The total calibration factor here It is determined by the signal fidelity index Triggered and driven by specific lifetime orientation deviations and wavelength orientation deviations; combined with the original concentration With total calibration factor Through adaptive closed-loop calibration logic based on sensor signals, the calibrated concentration is output. Its calculation logic is as follows: .

[0065] This invention achieves adaptive calibration of detection signals by constructing a complete closed loop from multidimensional signal acquisition, fidelity assessment, graded calibration to concentration output. Compared with existing technologies that rely on a single signal intensity or use fixed coefficients for calibration, this invention can identify and quantify composite interference caused by different sources such as quenching and background fluorescence in real time and dynamically, and adopt differentiated calibration strategies according to the reliability of the signal, which greatly improves the accuracy and reliability of detection results in complex matrices.

[0066] Example 2:

[0067] Based on the above embodiments, this embodiment provides a specific design for signal fidelity model prediction, including:

[0068] Based on the wavelength orientation deviation and the reference wavelength, calculate the normalized squared wavelength deviation;

[0069] Based on lifetime orientation bias and reference lifetime, calculate lifetime normalized squared bias;

[0070] The normalized squared deviation of wavelength and the normalized squared deviation of lifetime are weighted and summed to generate a weighted deviation sum;

[0071] The signal fidelity index is obtained by subtracting the weighted deviation from the preset baseline value of 1.

[0072] This embodiment is a specific implementation of the signal fidelity model prediction in Embodiment 1. Its purpose is to provide an accurate and robust mathematical model for calculating the signal fidelity index. The model's construction draws on the concept of distance measurement in multidimensional space, using real-time measured signal feature points. With reference point The degree of deviation in the normalized feature space is used as a measure of signal distortion.

[0073] The specific calculation process of this model is as follows: Based on the wavelength orientation deviation and the reference wavelength, calculate the normalized squared wavelength deviation. Normalization aims to eliminate the influence of dimensions across different wavelength ranges, making wavelength deviations of different detection items comparable; squaring aims to amplify the effect of the deviation and ensure it remains positive; similarly, based on the lifetime orientation deviation and the reference lifetime, the normalized squared lifetime deviation is calculated. The normalized squared deviation of wavelength and the normalized squared deviation of lifetime are weighted and summed to generate a weighted deviation sum:

[0074]

[0075] in, and These are two dimensionless weighting coefficients, whose function is to adjust the contribution of the two-dimensional biases in the final evaluation based on the differences in the probe system's sensitivity to different types of interference, and satisfying the following conditions: These two weighting coefficients are not arbitrarily set, but are determined by performing multiple linear regression analysis on a calibration dataset containing various known types and concentrations of interfering substances, thereby quantifying the relative impact of wavelength and lifetime variations on the accuracy of concentration measurements.

[0076] The specific steps are as follows: Prepare at least 20 sets of standard samples containing different types and concentration gradients of interfering substances, and determine the true concentration of each set of samples. It is known that the measurements of each sample group are as follows: , and Calculate the normalized concentration deviation Normalized wavelength deviation and normalized lifetime bias Establish a multiple linear regression model Solve for the coefficients using the least squares method. and The regression coefficients were normalized to obtain... and ,make sure ; Verify the goodness of fit of the regression model It should be greater than 0.85; otherwise, the sample size needs to be increased or the experimental conditions optimized for recalibration.

[0077] The signal fidelity index is obtained by subtracting the weighted deviation from the preset reference value of 1. The final mathematical expression is:

[0078]

[0079] Based on the fundamental signal fidelity model concept defined in Example 1, this example provides a specific implementation method based on the idea of ​​normalized Euclidean distance. This model eliminates the influence of dimensions and bias directionality through normalization and squaring, making the evaluation more objective. Furthermore, it introduces calibrable weighting coefficients. and This gives the model the flexibility to be optimized according to specific application scenarios.

[0080] To ensure To determine the physical meaning and effectiveness of subsequent hierarchical calibration strategies, boundary processing is performed on the calculation results: when the calculated results... When the value is greater than 1, it is truncated to 1, indicating that the signal quality exceeds expectations and is still processed as a high-quality signal; when the calculated value is greater than 1, it is truncated to 1. When the value is less than 0, it is truncated to 0, indicating severe signal distortion, and will be deemed invalid in subsequent graded calibration strategies; after boundary processing, Strictly between 0 and 1, it provides a highly reliable and quantitatively accurate basis for subsequent graded calibration strategies.

[0081] Example 3:

[0082] Based on the above embodiments, this embodiment provides a specific design for a hierarchical closed-loop calibration strategy, including:

[0083] If the signal fidelity index is greater than or equal to a preset high confidence threshold, the total calibration factor is determined to be zero.

[0084] When the signal fidelity index is less than the high confidence threshold and greater than or equal to the preset lower limit of the correction threshold, the total calibration factor is composed of the weighted sum of the wavelength orientation deviation and the lifetime orientation deviation.

[0085] If the signal fidelity index is less than the lower limit of the correction threshold, an invalid flag is output.

[0086] This embodiment is a specific implementation of the hierarchical closed-loop calibration strategy of Embodiment 1. Its purpose is to establish an intelligent decision-making logic to avoid calibrating all signals using a uniform mode, thereby improving the accuracy and reliability of the calibration results. This strategy is based on the signal fidelity index. Different operations are performed depending on the location of the interval.

[0087] The execution logic of this strategy involves two key preset thresholds: a high-confidence threshold. and correction lower limit threshold The values ​​of these two thresholds were not set based on experience, but were determined through statistical analysis of a large amount of historical experimental data containing samples with different levels of interference; specifically, the high-confidence threshold... The determination method is as follows: select at least 30 sets of interference-free standard samples and calculate their signal fidelity index. The distribution is taken as the 95% confidence lower bound of the distribution. Value, typically ranging from 0.85 to 0.95; lower limit threshold for correction. The method for determining the interference concentration is as follows: prepare a series of samples with known interference concentrations, and calculate their respective concentrations. The value and the relative error after calibration; when the relative error exceeds the preset acceptable range, the corresponding... The value is The typical value range is 0.50-0.65.

[0088] For example, high confidence threshold It can be set to cover 95% of the non-interference standard samples. The lower confidence limit of the value distribution is used to ensure that statistically normal systematic fluctuations are not misjudged as disturbances; the correction lower limit threshold is also used. This can be set to occur when the prediction error of the calibration model begins to exceed a preset acceptable range, such as 10%. A value below this is considered to indicate that signal distortion can no longer be reliably corrected.

[0089] The response is when the signal fidelity index is greater than or equal to a preset high-confidence threshold, i.e., when When the system determines that the current signal quality is extremely high and almost unaffected by interference, it sets the total calibration factor to zero. ; In response to a signal fidelity index being less than a high confidence threshold and greater than or equal to a preset lower correction limit threshold, i.e., when When the system determines that the signal has been subjected to moderate interference and requires calibration, the total calibration factor is composed of the weighted sum of wavelength orientation deviation and lifetime orientation deviation. The calibration factor is determined when the signal fidelity index is less than the lower calibration threshold. If the system determines that the signal is severely distorted, it will output an invalid flag and prompt the user to take measures such as sample pretreatment.

[0090] In practice, considering the precision issues of floating-point arithmetic, a tolerance-based approach is used for threshold comparison: a very small tolerance value is set. ,when At that time, it is considered It falls into the high confidence interval; when At that time, it is considered The tolerance is included in the correction interval; this tolerance processing ensures the numerical stability of the algorithm.

[0091] This embodiment introduces a hierarchical calibration logic, elevating the calibration process from a single calculation step to an intelligent decision-making process. It achieves an intelligent response of no calibration for high reliability, fine calibration for moderate distortion, and early warning for severe contamination, greatly enhancing the robustness of the system and effectively preventing erroneous calibration.

[0092] Example 4:

[0093] Based on the above embodiments, this embodiment provides a specific design for constructing a total calibration factor by weighted sum of wavelength orientation deviation and lifetime orientation deviation, including:

[0094] Multiply the wavelength orientation deviation by the preset wavelength calibration coefficient to obtain the wavelength calibration value;

[0095] Multiply the lifetime orientation deviation by the preset lifetime calibration coefficient to obtain the lifetime calibration value;

[0096] The total calibration factor is obtained by adding the wavelength calibration value to the lifetime calibration value.

[0097] This embodiment is based on Embodiment 3, in the correction area. The specific implementation of the technical feature of the total calibration factor consisting of the weighted sum of wavelength orientation deviation and lifetime orientation deviation aims to provide a calibration factor calculation method that can trace the source and accurately eliminate different types of interference.

[0098] Multiplying the wavelength orientation deviation by a preset wavelength calibration coefficient yields the wavelength calibration value; here, the wavelength calibration coefficient... It is a dimensionless parameter determined through calibration experiments; the calibration method is as follows: Add interfering substances, such as background fluorescent agents, that primarily cause wavelength shift to the standard sample, and set a series of concentration gradients; the normalized concentration deviation is then measured... Deviation from normalized wavelength A linear regression analysis is performed on the relationship between them, and the slope of the resulting straight line is... Similarly, the lifetime orientation deviation is multiplied by a preset lifetime calibration coefficient to obtain the lifetime calibration value; here, the lifetime calibration coefficient is... The calibration process and Similarly, the difference lies in the addition of interfering substances that primarily affect fluorescence lifetime, such as quenchers; the total calibration factor is obtained by adding the wavelength calibration value to the lifetime calibration value. The final mathematical expression is:

[0099]

[0100] The model assumes that the measurement bias effects caused by different physicochemical processes are linearly independent, and the total calibration factor can be approximated by a first-order sum of the weighted components of each bias. The embodiment proposed in this example... The innovative aspect of the calculation formula lies in the fact that calibration is no longer based on a general signal distortion, but on a directional deviation with a clearly traceable physical origin; this is achieved by introducing decoupled calibration coefficients. and This allows the calibration model to distinguish and compensate for measurement biases caused by different physicochemical processes; for example, when quenching... and background fluorescence When both exist simultaneously, this model can accurately calculate the total calibration direction and amplitude based on the magnitude of their respective deviations and calibration coefficients, thus achieving precise and directional calibration of composite interference.

[0101] Example 5:

[0102] Based on the above embodiments, this embodiment provides a method applied to the specific design of an in-situ isolated sensing system, wherein the system is pre-set to an in-situ isolated microreactor model;

[0103] An in-situ isolated microreactor model is used to establish the mathematical relationship between the external sample concentration and the effective concentration inside the microreactor.

[0104] In-situ isolated microreactor model, including:

[0105] The characteristic permeability coefficient of the interface membrane to the target analyte;

[0106] The repulsion coefficient of the interface membrane against multiple major interfering substances in the sample;

[0107] The internal effective concentration is determined by the external sample concentration, the characteristic permeability coefficient, and the concentrations and repulsion coefficients of all major interfering substances.

[0108] This embodiment illustrates the application of the aforementioned adaptive signal calibration method to a specific physical system, namely an in-situ isolated sensing system. The application of this method is based on a designed sensing microenvironment. For this purpose, the system is pre-set with an in-situ isolated microreactor model.

[0109] The purpose of the in-situ isolation microreactor model is to pre-process and quantify the fundamental impact of matrix interference on target analyte detection from both physical and mathematical perspectives. The core of this model is to construct a smart-response interface membrane on the probe's sensing surface, forming a microreactor, thereby achieving in-situ isolation of the target analyte from most interfering substances at the molecular level. This model is used to establish external sample concentrations. With the effective concentration inside the microreactor The mathematical relationship between them;

[0110] This in-situ isolated microreactor model concretizes its core components, and its mathematical expression draws on the competitive inhibition model of enzyme-catalyzed reactions in biochemistry:

[0111]

[0112] in, This refers to the effective concentration of the target substance inside the microreactor that can actually interact with the biomimetic enzyme probe; This refers to the actual concentration of the target analyte in the original sample to be tested, which can be obtained through standard analytical methods. It is the characteristic permeability coefficient of the interfacial membrane to the target analyte. It is a dimensionless parameter that is calibrated by using a permeation experiment with a pure target analyte standard solution that does not interfere with the test. It characterizes the maximum transport efficiency of the interfacial membrane to the target analyte. It is the interface membrane on the sample. The repulsion coefficient of the main interfering substances, in units of Its value is determined by competitive permeation experiments in a solution containing a single interfering agent, reflecting the inhibitory strength of a specific interfering agent on the transport of the target analyte; It refers to the first in the sample The concentrations of the main interfering substances, their types and concentration ranges, were obtained during the design phase through pre-analysis using standard analytical methods such as high performance liquid chromatography-mass spectrometry.

[0113] This model is based on the following physical assumptions and applicable conditions: the transport of target and interfering substances through the interfacial membrane follows a passive diffusion mechanism, conforming to Fick's first law; interfering substances and target substances have competitive binding sites at the membrane interface, and the binding of interfering substances reduces the effective permeability of the target substance; the system is in steady state, i.e., the concentration gradient across the membrane is constant; this model is applicable to interfering substance concentrations. If the concentration of a certain interfering substance is extremely high and does not exceed the membrane saturation concentration, a modified Langmuir adsorption model should be used. The model assumes that the inhibition effects of each interfering substance are independent and can be linearly superimposed. This assumption holds true in most real samples, but for special systems with synergistic interference effects, cross terms need to be introduced for correction.

[0114] By combining signal calibration logic with a physical isolation model, a dual anti-interference system of physical isolation and signal correction is constructed. The in-situ isolated microreactor at the physical level serves as the first line of defense, purifying the sensing microenvironment at its source. The signal adaptive closed-loop calibration logic at the mathematical level serves as the second line of defense, accurately identifying and correcting signal distortion caused by residual interference that has penetrated the physical barrier. This hardware and software combined design produces synergistic anti-interference capabilities, and its performance significantly surpasses any single method.

[0115] Example 6:

[0116] Based on the above embodiments, this embodiment provides a specific design scheme for evaluating the anti-interference capability of the in-situ isolated sensing system using a composite evaluation model of probe system detection performance:

[0117] The evaluation model introduces an isolation synergy factor to characterize the performance leap brought about by the in-situ isolated sensing system. The value of the isolation synergy factor is determined by the microscopic physical characteristics of the in-situ isolated microreactor.

[0118] After constructing the in-situ isolation sensing system, to quantitatively evaluate its superior anti-interference capability, this embodiment further introduces a composite evaluation model for probe system detection performance. This evaluation model is mainly used in the system design and optimization stages, and specific application scenarios include: in the research and development stage, comparing the isolation synergy factors of different interface film materials. Values ​​guide the selection and optimization of membrane materials; during the system validation phase, measurements are used to... The evaluation assesses whether the overall system performance meets the design specifications; during the quality control phase, the performance of standard samples is measured periodically. The value is used to monitor whether the system performance is stable. This evaluation model does not participate in the real-time calibration calculation in the daily testing process, but serves as a macro-level evaluation tool for system performance. Its purpose is to break the limitation of the separation between sensitivity and robustness in traditional evaluation and establish a single indicator that can comprehensively reflect the synergistic improvement of the two.

[0119] The core innovation of this evaluation model lies in the introduction of an isolation synergy factor. This is used to macroscopically and quantitatively characterize the performance leap brought about by in-situ isolated sensing systems; isolation synergy factor. The value is directly determined by the microscopic physical properties of the in-situ isolated microreactor, that is, by the permeability of the interfacial membrane to the target analyte. and repulsion of interfering substances Jointly determined; its inherent logical relationship can be expressed as better isolation performance, i.e., higher... and This will lead to The value approaches 0; for conventional probes that do not possess the isolation structure of this invention, its The value approaches 1.

[0120] The mathematical expression for this performance composite evaluation model is:

[0121]

[0122] in, The performance synergy index is a dimensionless comprehensive index. The higher the value, the stronger the anti-interference capability of the probe system while maintaining high sensitivity. The ideal signal-to-noise ratio is measured in a pure buffer solution free from any interference and represents the theoretical upper limit of the probe system's performance. The matrix interference coefficient is a dimensionless parameter between 0 and 1, used to quantify the inherent interference intensity of a specific sample matrix. Its value is assigned according to the complexity of the sample being tested. The isolation synergy factor, in its physical sense, characterizes the ability of an isolation system to suppress matrix interference. The larger the value, the better the isolation effect and the stronger the suppression of interference; for traditional probes that do not have the isolation structure of this invention, Approaching 0, at this point The model degenerates into Performance decreases linearly with disturbance; for systems with good isolation performance, The value is relatively large, typically ranging from 2 to 5. Significantly reduced, making it possible even at larger scales Value below, It remains small, thus It remains at a high level.

[0123] In practical implementation, its accurate value is determined by various known interference coefficients. Experimental measurements were performed in the matrix, and the experimental data points were analyzed. Perform nonlinear fitting, the fitting function is The solution obtained by the least squares method is Value; goodness of fit It should be greater than 0.90 to ensure model reliability.

[0124] The physical rationality of this model is reflected in: when That is, when there is no interference, This meets the ideal situation; when and That is, when there is extremely strong interference and no isolation, This meets the criteria for complete failure; when When the interference level is increased, at the same level of interference The significant improvement quantitatively reflects the performance leap brought about by isolation.

[0125] This evaluation model provides a novel scientific method for quantitatively evaluating the overall performance of a probe system; it achieves this by isolating synergistic factors. This core parameter, for the first time, reveals the microscopic properties of membrane materials. , Macroscopic system anti-interference performance Directly related, it not only demonstrates the superiority of the present invention's system over traditional systems, but also serves as a research and development tool, enabling quantitative comparisons of the performance of different interfacial membrane materials. The value is used to guide and optimize the design of in-situ isolated microreactors.

[0126] Example 7:

[0127] Please see Figure 2 Based on the method design of the above embodiments, this embodiment further provides a systematic implementation scheme, including:

[0128] The signal acquisition module is used to acquire the original concentration of the sample to be tested, the average lifetime of the fluorescence signal, and the peak emission wavelength.

[0129] The deviation determination module is used to determine the lifetime orientation deviation and wavelength orientation deviation based on the acquired average lifetime and peak emission wavelength, combined with the preset reference lifetime and reference wavelength.

[0130] The fidelity index calculation module is used to calculate the signal fidelity index by combining lifetime orientation deviation and wavelength orientation deviation.

[0131] The calibration factor determination module is used to determine the total calibration factor based on the signal fidelity index, lifetime orientation deviation, and wavelength orientation deviation, and in response to a preset correction threshold.

[0132] The concentration output module is used to combine the original concentration with the total calibration factor to output the calibrated concentration.

[0133] The system provided in this embodiment of the invention is a physical carrier for implementing the above-described method, and integrates multiple functional modules that work together to achieve high-precision adaptive detection.

[0134] The system includes a signal acquisition module, a deviation determination module, a fidelity index calculation module, a calibration factor determination module, and a concentration output module.

[0135] The signal acquisition module consists of a high-sensitivity fluorescence detector and a time-correlated single-photon counting controller. It is responsible for performing optical measurements on the sample and resolving the data to calculate the original concentration. Required fluorescence intensity, average lifetime of fluorescence signal and peak emission wavelength .

[0136] The deviation determination module has a built-in reference lifespan. and reference wavelength The reference database receives signals from the signal acquisition module. and The lifetime orientation deviation and wavelength orientation deviation are determined by performing subtraction operations.

[0137] The fidelity index calculation module is a core processor that embeds a signal fidelity model algorithm. It takes lifetime orientation deviation and wavelength orientation deviation as inputs and calls preset weighting coefficients. and , calculate the signal fidelity index .

[0138] The calibration factor determination module is also implemented in the core processor, embedding a hierarchical closed-loop calibration strategy and a total calibration factor calculation formula, based on the signal fidelity index. and in response to a preset high confidence threshold. and correction lower limit threshold To determine the total calibration factor .

[0139] The concentration output module receives the raw concentration. and total calibration factor Perform the final calibration calculation. and the calibrated concentration Output to a display interface or data recording system.

[0140] This invention provides a highly integrated and automated detection system. By embedding complex calibration algorithms and decision logic into different functional modules, it achieves full automation from sample measurement to result output. Compared with traditional equipment that requires manual interpretation of spectra and manual data correction, this system can provide real-time, objective and intelligently calibrated detection results, significantly improving detection efficiency and result reliability. It is particularly suitable for application scenarios such as rapid on-site detection.

[0141] Compared with the prior art, the present invention has the following beneficial effects:

[0142] 1. Breaking through the limitations of traditional reliance on a single fluorescence intensity, a signal fidelity model is constructed by simultaneously analyzing two additional physical dimensions: fluorescence lifetime and peak emission wavelength. This model can evaluate the reliability of the detection signal in real time and quantitatively, and gain a deep understanding of the complex interference caused by different physicochemical processes such as quenching and background fluorescence, providing a reliable decision-making basis for subsequent accurate calibration.

[0143] 2. Based on the signal fidelity index, this invention establishes an intelligent hierarchical closed-loop calibration strategy. This strategy can take differentiated measures according to the degree of signal distortion: no calibration is performed for high-quality signals to avoid introducing errors; fine calibration is initiated for moderately distorted signals; and early warning is issued for heavily polluted signals. This intelligent decision-making mechanism greatly improves the robustness of the system and effectively prevents erroneous calibration.

[0144] 3. When calibration is required, the total calibration factor of this invention is not a general compensation value, but is calculated by weighting lifetime deviation and wavelength deviation together with their respective independent calibration coefficients. These calibration coefficients are obtained through calibration experiments that introduce specific types of interference, so that the calibration can be traced back to the specific physical interference source, thereby achieving accurate identification and targeted compensation of multiple interferences in complex matrices.

[0145] 4. This invention combines an intelligent signal calibration algorithm with the physical sensing system of an in-situ isolated microreactor; physical isolation, as the first line of defense, purifies the sensing microenvironment from the source and significantly reduces the influence of interfering substances; signal adaptive calibration logic, as the second line of defense, accurately corrects residual interference that penetrates the physical barrier; this dual anti-interference design combining hardware and software produces a synergistic effect, significantly improving the accuracy and reliability of detection in complex samples.

[0146] Based on the same inventive concept, this invention also provides a computer device, comprising: one or more processors, and a memory for storing one or more computer programs; the programs include program instructions, and the processor executes the program instructions stored in the memory. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, used to implement one or more instructions, specifically for loading and executing one or more instructions stored in a computer storage medium to implement the above-described method.

[0147] It should be further explained that, based on the same inventive concept, the present invention also provides a computer storage medium storing a computer program, which, when executed by a processor, performs the above-described method. This storage medium can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0148] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0149] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

[0150] This invention is not limited to the preferred embodiment described above. Anyone inspired by this invention can derive other forms of fluorescent probe signal calibration methods and systems for rapid detection of multiple residues in complex matrices. All equivalent variations and modifications made within the scope of the claims of this invention shall fall within the scope of this invention.

Claims

1. A method for calibrating fluorescent probe signals for rapid detection of multiple residues in complex matrices, characterized in that, include: The fluorescence signal of the sample to be tested is collected, and the original concentration, average fluorescence lifetime and fluorescence peak emission wavelength of the sample are extracted from it. Calculate the lifetime deviation between the average fluorescence lifetime and the preset reference lifetime, and the wavelength deviation between the peak fluorescence emission wavelength and the preset reference wavelength; Based on the lifetime deviation and wavelength deviation, the signal fidelity index is calculated. Based on the preset threshold range into which the signal fidelity index falls, a corresponding calibration strategy is determined, wherein the calibration strategy is one of no calibration, performing calibration, or marking as invalid. When the calibration strategy is to perform calibration, a total calibration factor is calculated based on the lifetime deviation and wavelength deviation, the original concentration is calibrated using the total calibration factor, and the calibrated concentration is output. The method for calculating the signal fidelity index specifically includes: squaring the ratio of wavelength deviation to a preset reference wavelength to obtain a wavelength normalized squared deviation; squaring the ratio of lifetime deviation to a preset reference lifetime to obtain a lifetime normalized squared deviation; weighting and summing the two normalized squared deviations to generate a weighted deviation sum; subtracting the weighted deviation sum from a preset reference value of 1 to obtain the signal fidelity index; after calculating the signal fidelity index, if it is greater than 1, it is adjusted to 1, and if it is less than 0, it is adjusted to 0, so that the index is in the range of 0-1. The method for calculating the total calibration factor specifically includes: multiplying the wavelength deviation by a preset wavelength calibration coefficient to obtain the wavelength calibration value, multiplying the lifetime deviation by a preset lifetime calibration coefficient to obtain the lifetime calibration value, and adding the two calibration values ​​to obtain the total calibration factor; the wavelength calibration coefficient is determined by adding an interfering substance that only causes a shift in the emission wavelength of the fluorescence peak to the standard sample and performing linear regression on the normalized concentration deviation and the normalized wavelength deviation; the lifetime calibration coefficient is determined by adding an interfering substance that only affects the average lifetime of fluorescence to the standard sample and performing linear regression in the same way; when calibrating the original concentration using the total calibration factor, the calibrated concentration is calculated based on the correlation between the original concentration and the total calibration factor.

2. The fluorescent probe signal calibration method for rapid detection of multiple residues in complex matrices according to claim 1, characterized in that: When acquiring the fluorescence signal, time-correlated single-photon counting technology is used to obtain the average fluorescence lifetime and the peak emission wavelength of fluorescence. The original concentration is obtained by analyzing the fluorescence intensity signal acquired by the high-sensitivity fluorescence detector. The lifetime deviation is the difference between the average fluorescence lifetime and the preset reference lifetime, and the wavelength deviation is the difference between the peak emission wavelength of fluorescence and the preset reference wavelength.

3. The fluorescent probe signal calibration method for rapid detection of multiple residues in complex matrices according to claim 1, characterized in that: The preset threshold range includes a high confidence threshold and a lower correction threshold, wherein: when the signal fidelity index is not lower than the high confidence threshold, the calibration strategy is no calibration; when the signal fidelity index is not lower than the lower correction threshold but lower than the high confidence threshold, the calibration strategy is to perform calibration; when the signal fidelity index is lower than the lower correction threshold, the calibration strategy is to mark it as invalid; the high confidence threshold is determined based on the statistical distribution of the signal fidelity index of interference-free standard samples, and the lower correction threshold is determined based on whether the relative error after calibration exceeds a preset range, and a preset minimum tolerance judgment is used when comparing thresholds to ensure numerical stability.

4. The fluorescent probe signal calibration method for rapid detection of multiple residues in complex matrices according to claim 1, characterized in that: The method is applied to an in-situ isolation sensing system based on a pre-defined in-situ isolation microreactor model. The in-situ isolation microreactor model includes the characteristic permeability coefficient of the interface membrane to the target analyte and the repulsion coefficient of various major interfering substances in the sample. This model is used to establish a mapping relationship between the external sample concentration and the effective concentration inside the microreactor in order to reduce the influence of interfering substances.

5. A fluorescent probe signal calibration system for rapid detection of multiple residues in complex matrices, used to implement the method as described in claim 1, characterized in that, include: The signal acquisition module is used to acquire the fluorescence signal of the sample to be tested and to extract the original concentration, average fluorescence lifetime and fluorescence peak emission wavelength of the sample to be tested. The deviation calculation module is used to calculate the lifetime deviation between the average fluorescence lifetime and the preset reference lifetime, and the wavelength deviation between the fluorescence peak emission wavelength and the preset reference wavelength. The fidelity index module is used to calculate the signal fidelity index based on the lifetime deviation and wavelength deviation. The calibration strategy module is used to determine the corresponding calibration strategy based on the preset threshold range into which the signal fidelity index falls. The calibration strategy is one of no calibration, performing calibration, or marking as invalid. The concentration calibration module is used to calculate the total calibration factor based on the lifetime deviation and wavelength deviation when the calibration strategy is to perform calibration, and to calibrate the original concentration using the total calibration factor, and output the calibrated concentration.

6. The fluorescent probe signal calibration system for rapid detection of multiple residues in complex matrices according to claim 5, characterized in that: It also includes an in-situ isolation sensing unit, which is based on an in-situ isolation microreactor model. The in-situ isolation microreactor model includes the characteristic permeability coefficient of the interface membrane to the target and the repulsion coefficient of various major interfering substances in the sample. It is used to establish a mapping relationship between the external sample concentration and the effective concentration inside the microreactor to reduce the influence of interfering substances on the fluorescence signal.

7. A computer device, characterized in that, It includes a processor and a memory, the memory storing a computer program, and the processor, when executing the computer program, implements the method of any one of claims 1-4.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1-4.

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