Detection method based on double-tagging time-resolved fluorescence immunoassay technology

Abstract

The invention relates to the technical field of immunoassay detection, and discloses a detection method based on a double-tagging time-resolved fluorescence immunoassay technology. The method comprises the following steps: executing double-labeled time-resolved fluorescence signal acquisition, and obtaining a historical first labeled fluorescence signal data set and a historical second labeled fluorescence signal data set of a target analyte in a preset historical time window; traversing the two historical data sets to carry out data stability analysis, and determining a first labeled fluorescence stability factor and a second labeled fluorescence stability factor; secondly, searching in a fluorescence detection configuration space in a centralized manner by taking the two stability factors as indexes, and determining target detection integral time; and finally, performing double-tagging time-resolved fluorescence immunoassay detection on the target analyte based on the target detection integral time to obtain a periodic detection data set, and identifying the data set by using a fluorescence detection identifier to obtain a target analyte detection result.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of immunoassay detection technology, in particular to a detection method based on double-labeled time-resolved fluorescence immunoassay technology. BACKGROUND

[0002] In the fields of modern medical diagnosis, environmental monitoring, and food safety detection, immunoassay technology has become an indispensable detection means due to its high specificity and sensitivity. Among them, time-resolved fluorescence immunoassay technology has been widely used in quantitative and qualitative detection of various target analytes because it can effectively eliminate non-specific fluorescence interference and further improve detection accuracy. With the continuous upgrading of detection needs, single-labeled time-resolved fluorescence immunoassay technology gradually shows limitations. For example, in complex sample matrix, single-labeled signal is easily disturbed by background interference, leading to decreased detection accuracy, and it is difficult to realize simultaneous detection of multiple target analytes, limiting the improvement of detection efficiency. To break through this limitation, double-labeled time-resolved fluorescence immunoassay technology has emerged. This technology introduces two different labels, which can theoretically achieve simultaneous detection of two target analytes and reduce the interference risk of single-labeled signal. However, in actual application, double-labeled time-resolved fluorescence immunoassay technology still faces many challenges. First, the fluorescence signals generated by the two labels are prone to signal overlap or unstable signal intensity during acquisition. Especially in a long detection period, fluorescence signals are affected by factors such as temperature, sample concentration changes, and instrument drift, leading to large differences in the stability of historical fluorescence signal data sets. If detection parameters are set directly based on these data, detection errors are likely to occur. Second, the current processing of double-labeled fluorescence signals mostly uses fixed detection integration time, without considering the stability differences of different labeled fluorescence signals, and cannot dynamically adjust the integration time according to the actual signal characteristics, leading to insufficient fluorescence signal acquisition in some time periods and excessive signal acquisition in other time periods, further affecting the reliability of detection data. In addition, when identifying periodic detection data sets, existing identification methods often fail to fully combine the synergistic characteristics of double-labeled signals, and only analyze single signals, making it difficult to effectively distinguish specific signals from non-specific interference signals, resulting in difficulty in meeting the accuracy and repeatability of target analyte detection results. The existence of these problems seriously restricts the promotion and application of double-labeled time-resolved fluorescence immunoassay technology, and an detection method that can solve the above problems is urgently needed. SUMMARY

[0003] The purpose of the present application is to provide a detection method based on double-labeled time-resolved fluorescence immunoassay technology to solve the problems raised in the background art.

[0004] To achieve the above object, the application provides a detection method based on double-labeled time-resolved fluorescence immunoassay technology, which comprises the following steps: Performing double-labeled time-resolved fluorescence signal acquisition to obtain a historical first-labeled fluorescence signal data set and a historical second-labeled fluorescence signal data set for monitoring a target analyte within a preset historical time window; Performing data stability analysis on the historical first-labeled fluorescence signal data set and the historical second-labeled fluorescence signal data set to determine a first-labeled fluorescence stability factor and a second-labeled fluorescence stability factor; Using the first-labeled fluorescence stability factor and the second-labeled fluorescence stability factor as indexes, performing centralized search in a fluorescence detection configuration space to determine a target detection integration time; Based on the target detection integration time, performing double-labeled time-resolved fluorescence immunoassay detection on the target analyte to obtain a periodic detection data set, and using a fluorescence detection identifier to identify the periodic detection data set to obtain a target analyte detection result.

[0005] Preferably, the step of performing double-labeled time-resolved fluorescence signal acquisition further comprises: Obtaining a background interference signal data set of the target analyte within the preset historical time window; Synchronously collecting the historical first-labeled fluorescence signal data set, the historical second-labeled fluorescence signal data set and the background interference signal data set to construct a double-labeled fluorescence interference data set.

[0006] Preferably, the step of performing data stability analysis on the historical first-labeled fluorescence signal data set and the historical second-labeled fluorescence signal data set further comprises: Performing time-frequency analysis on the double-labeled fluorescence interference data set to extract a first-labeled dynamic feature and a second-labeled dynamic feature; Based on the first-labeled dynamic feature and the second-labeled dynamic feature, calculating the mean absolute deviation to obtain the first-labeled fluorescence stability factor and the second-labeled fluorescence stability factor.

[0007] Preferably, the step of using the first-labeled fluorescence stability factor and the second-labeled fluorescence stability factor as indexes to perform centralized search in the fluorescence detection configuration space further comprises: Obtaining a plurality of sample first-labeled fluorescence stability factors and a plurality of sample second-labeled fluorescence stability factors, and a plurality of corresponding sample detection integration times as construction data; pre-constructing a two-dimensional space, wherein a coordinate origin of the two-dimensional space is a reference point, a horizontal axis is a first marker fluorescence stability factor, and a vertical axis is a second marker fluorescence stability factor; inputting the construction data into the two-dimensional space to obtain a plurality of sample space points, and identifying the plurality of sample space points by using a plurality of sample detection integration times to obtain the fluorescence detection configuration space.

[0008] Preferably, the concentrated searching in the fluorescence detection configuration space to determine the target detection integration time further comprises: extracting a line passing through the first marker fluorescence stability factor and parallel to the vertical axis in the fluorescence detection configuration space as a first reference line; extracting a line passing through the second marker fluorescence stability factor and parallel to the horizontal axis in the fluorescence detection configuration space as a second reference line; taking an intersection of the first reference line and the second reference line as a central reference point, constructing a neighborhood space with the central reference point as a starting point and according to a preset search radius, wherein the neighborhood space comprises a plurality of neighborhood sample space points; concentrated searching the plurality of neighborhood sample space points to determine a target sample space point, and taking a sample detection integration time corresponding to the target sample space point as the target detection integration time.

[0009] Preferably, the concentrated searching the plurality of neighborhood sample space points further comprises: calculating a neighborhood density of the neighborhood space; randomly selecting a neighborhood sample space point from an edge of the neighborhood space as a current search point and calculating a search density of the current search point; judging whether the search density is greater than or equal to the neighborhood density, if yes, updating the current search point as the starting point and continuing the concentrated searching until a preset search number is satisfied, and taking a search point obtained in the last search as the target sample space point.

[0010] Preferably, the judging whether the search density is greater than or equal to the neighborhood density further comprises: if no, updating a failure count with an initial value of zero to one and randomly selecting a neighborhood sample space point from an edge of the neighborhood space as a current search point for search analysis, and when the failure count is greater than a preset maximum failure count, taking the central reference point as the target sample space point.

[0011] Preferably, the dual-label time-resolved fluorescence immunoassay detection of the target analyte based on the target detection integration time further comprises: Start the dual-label time-resolved fluorescence detection device, collect real-time first label fluorescence signal data and real-time second label fluorescence signal data; Based on the real-time first label fluorescence signal data and the real-time second label fluorescence signal data, determine the background noise interference amount; From the background noise interference amount and the correlation strength between the real-time first label fluorescence signal data and the real-time second label fluorescence signal data, determine the fluorescence detection robustness.

[0012] Preferably, the identification of the periodic detection data set by the fluorescence detection identifier further comprises: Through the background noise interference amount and the fluorescence detection robustness, signal drift prediction is performed on the periodic detection data set to obtain a signal drift deviation; Based on the signal drift deviation, adjust the identification threshold of the fluorescence detection identifier to obtain an adjusted identification threshold; Using the adjusted identification threshold to identify the periodic detection data set, the target analyte detection result is obtained.

[0013] Preferably, the signal drift prediction of the periodic detection data set by the background noise interference amount and the fluorescence detection robustness further comprises: Combine the historical first label fluorescence signal data set and the historical second label fluorescence signal data set to construct a dynamic signal trajectory; Based on the gradient characteristics of the dynamic signal trajectory, filter a plurality of signal lag characteristics, and convert the plurality of signal lag characteristics into a signal lag amount; Fuse the signal lag amount and the fluorescence detection robustness to predict the signal drift deviation.

[0014] Compared with the prior art, the beneficial effects of the present application are: The detection method based on the dual-label time-resolved fluorescence immunoassay technology acquires the historical fluorescence signal data set of two labels within a preset historical time window through the execution of dual-label time-resolved fluorescence signal acquisition, which provides a comprehensive signal basis for subsequent detection parameter optimization. Compared with the traditional method which only relies on a small amount of historical data or fixed parameters, this method can fully utilize long-time sequence historical signal data and more comprehensively reflect the change characteristics of the fluorescence signals of two labels, avoiding parameter setting deviation caused by insufficient data sample amount. In the data processing link, the method performs data stability analysis by traversing the historical first marker fluorescence signal data set and the historical second marker fluorescence signal data set, determines the fluorescence stability factors of the two markers respectively, and can accurately capture the stability difference of the fluorescence signals of different markers. This way of evaluating the stability of different markers separately breaks the limitation of using a unified stability standard for double-labeled signals in traditional methods, making the subsequent parameter adjustment more targeted. For example, when there is a significant difference in the fluorescence stability of the two markers, the difference can be clearly identified through the stability factor, providing a clear basis for the dynamic adjustment of the integration time. The traditional method does not make such a distinction and often uses an averaging method, which cannot take into account the signal characteristics of both markers and easily leads to poor signal acquisition for one of the markers. In the detection integration time determination aspect, the method uses the fluorescence stability factors of the two markers as an index to perform a concentrated search in the fluorescence detection configuration space and determine the target detection integration time. This way of dynamically adjusting the integration time based on signal stability can match the optimal integration time for the fluorescence signals of the two markers according to their actual stability. For example, for a marker with high stability, the integration time can be shortened appropriately to ensure sufficient signal acquisition while improving detection efficiency; for a marker with low stability, the integration time can be extended appropriately to ensure that sufficient effective signals can be acquired and reduce interference caused by signal fluctuations. Compared with the fixed integration time mode in traditional methods, this method can adaptively optimize the integration time in different detection scenarios, avoiding incomplete signal acquisition due to too short integration time and preventing low detection efficiency and signal redundancy due to too long integration time, effectively balancing detection efficiency and detection data quality. In the detection execution and result identification link, the method performs double-labeled time-resolved fluorescence immunoassay detection based on target detection integration time, obtains a periodic detection data set, and identifies the data set by using a fluorescence detection identifier. Since the target detection integration time is determined based on the stability characteristics of the two labels, the periodic detection data set collected can more accurately reflect the true information of the target analyte, reducing the influence of non-specific interference signals. At the same time, in the identification process, the fluorescence detection identifier can fully combine the synergistic characteristics of the double-labeled signals instead of single signal characteristics, and can more accurately distinguish specific signals from interference signals, avoiding the misjudgment caused by ignoring the correlation of double-labeled signals in the traditional identification method. In addition, by periodically collecting detection data, the method can monitor the dynamic changes of the target analyte in real time, compared with the traditional single detection mode, it can provide more abundant detection information, which is helpful to more comprehensively understand the existence state and concentration change trend of the target analyte, and further improves the reliability and practicability of the detection result. Whether in the detection of trace biomarkers in medical diagnosis or in the screening of harmful residues in food safety detection, the method can provide more effective technical support for actual detection work due to its excellent performance. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 A working principle diagram of the detection method based on the double-labeled time-resolved fluorescence immunoassay technology described in the application; Figure 2 A flowchart of double-labeled time-resolved fluorescence signal collection; Figure 3 A flowchart of constructing a fluorescence detection configuration space; Figure 4 A flowchart of neighborhood sample space point set search. DETAILED DESCRIPTION

[0016] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0017] Please refer to Figure 1 The application provides a detection method based on double-labeled time-resolved fluorescence immunoassay technology, which comprises: The dual-labeled time-resolved fluorescence signal acquisition is performed to obtain a historical first-labeled fluorescence signal data set and a historical second-labeled fluorescence signal data set for monitoring the target analyte in a preset historical time window. The historical first-labeled fluorescence signal data set and the historical second-labeled fluorescence signal data set are traversed for data stability analysis to determine a first-labeled fluorescence stability factor and a second-labeled fluorescence stability factor. The first-labeled fluorescence stability factor and the second-labeled fluorescence stability factor are used as indexes to perform a centralized search in a fluorescence detection configuration space to determine a target detection integration time. The target analyte is subjected to dual-labeled time-resolved fluorescence immunoassay detection based on the target detection integration time to obtain a periodic detection data set, and the periodic detection data set is identified by using a fluorescence detection identifier to obtain a target analyte detection result.

[0018] Embodiment 1: refer to Figure 2 When performing the dual-labeled time-resolved fluorescence signal acquisition, the setting of the preset historical time window is the basis. The length of the window needs to cover enough signal periods to capture the fluorescence response characteristics of the target analyte under possible changing conditions. For example, for an in-vitro diagnostic detection project, the window can be set as a monitoring period lasting for several hours, and data is collected once per second, thereby accumulating thousands of data points to form the historical first-labeled fluorescence signal data set and the historical second-labeled fluorescence signal data set. The two sets correspond to the specific fluorescence signals generated by the two different rare earth element-labeled antibodies or antigens, and the time-resolved characteristics allow measurement after a delay excitation to eliminate the interference of short-lived background fluorescence. The acquisition of the background interference signal data set of the target analyte is a key step performed synchronously. The background interference signal is not the fluorescence from the target analyte itself, but is caused by factors such as sample matrix, non-specific binding, instrument dark current, or environmental stray light. The collection of the background signal is usually performed in a blank control sample not containing the target analyte or in a detection channel shielding a specific fluorescence label within the same time window. The collected background signal is strictly aligned with the two labeled signal sets in the time dimension, and each labeled signal data point corresponds to a background interference data point with the same time stamp.

[0019] The three sets: the historical first-labeled fluorescence signal data set, the historical second-labeled fluorescence signal data set, and the background interference signal data set, are synchronously collected to construct a dual-labeled fluorescence interference data set. The synchronization is ensured by a unified system clock and triggering mechanism to ensure that the time axes of all data streams are completely consistent. The data set is a multi-dimensional time sequence, in which each time point contains three values: the first-labeled signal intensity, the second-labeled signal intensity, and the background noise level. The construction of this composite data set provides a complete information basis for the subsequent analysis of the behavior of the signal in the real noise environment.

[0020] After construction, the dual-labeled fluorescence interference dataset is subjected to time-frequency analysis, which is a tool that can reveal how the frequency components of a signal change over time. In this embodiment, the method applied is the short-time Fourier transform with sliding windows applied to the entire dataset. The analysis process first divides the long time series data into a series of overlapping shorter time segments (windows). For the first labeled signal data subset within each window, the Fourier transform is calculated to obtain the frequency spectrum of the signal within that time period. The same operation is applied to the second labeled signal data subset and the background interference data subset. From these frequency spectra, features are extracted that characterize the dynamic behavior of the signals. For the first labeled signal, the first labeled dynamic features can include the sequence of amplitudes of its dominant frequency peak over time, the trajectory of the center frequency shift, and the fluctuations of the energy in a specific frequency band (e.g., the signal characteristic emission band). The extraction of the second labeled dynamic features follows the same logic but focuses on its own unique frequency characteristics. The spectral features of the background interference signal are also extracted, mainly for understanding the frequency domain characteristics of the noise.

[0021] Based on the extracted first labeled dynamic features and second labeled dynamic features, the mean absolute deviation is calculated to obtain the stability factor. Take the calculation of the first labeled fluorescence stability factor as an example: the first labeled dynamic features obtained from time-frequency analysis are, for example, a sequence containing N amplitude values at different time points. First, the arithmetic mean of all amplitude values in the sequence is calculated. Then, the absolute difference between each amplitude value in the sequence and the mean value is calculated. Finally, the sum of these absolute differences is divided by N to obtain the mean absolute deviation of the feature sequence. This value intuitively reflects the average fluctuation of the signal amplitude around its average level. The smaller the mean absolute deviation value, the more stable the signal is in that feature dimension. In order to obtain the final first labeled fluorescence stability factor, the inverse of the mean absolute deviation is usually taken or a normalization to a specific interval is adopted, so that a higher value represents better stability. The second labeled fluorescence stability factor is obtained through the same calculation process, but its calculation basis is the second labeled dynamic feature sequence.

[0022] The construction of the dual-labeled fluorescence interference dataset puts the marker signal and the background noise into the same analytical framework. The time-frequency analysis goes beyond the simple time-domain or frequency-domain analysis, revealing the law of the evolution of signal frequency components over time, which is crucial for capturing transient changes or slow drifts that may be overlooked in fixed time point or fixed frequency analysis. The mean absolute deviation, as a robust measure of dispersion, is less sensitive to extreme values in the data, and can better reflect the concentration trend of most data points than variance or standard deviation, so that the calculated first marker fluorescence stability factor and second marker fluorescence stability factor can more reliably represent the overall stability performance of the signal in the monitoring period. These factors, as key input parameters for configuring the search space in the subsequent steps, the accuracy and representativeness of their calculation directly affect the rationality of the final determination of the target detection integration time, and thus affect the performance of the entire detection method.

[0023] Embodiment 2: Refer to Figure 3 The data comes from a long-term accumulated experimental database, containing a large number of records of historical detection samples. Each record consists of three key data elements: a sample first marker fluorescence stability factor, a sample second marker fluorescence stability factor, and a sample detection integration time that has been proven to be effective under the condition of the stability factors. For example, a record may show that when the first marker stability factor is calculated to be 0.85 and the second marker stability factor is 0.72, the corresponding optimal detection integration time is 350 milliseconds. These historical data are pre-processed before being stored in the database, including removing obvious outliers, normalizing the stability factors to make all values fall within the interval of 0 to 1, and verifying the effectiveness of the integration time to ensure that it matches the corresponding signal quality. These cleaned and standardized data elements form the basis for subsequent space construction.

[0024] The pre-constructed two-dimensional space is the core step, which is an abstract mathematical representation used to establish the mapping relationship between the stability factors and the integration time. The origin of the coordinates is set as a reference point, usually representing the theoretical minimum stability state. The horizontal axis is defined as the first marker fluorescence stability factor, with a linear scale from 0 (completely unstable) to 1 (completely stable). Similarly, the vertical axis is defined as the second marker fluorescence stability factor, with a scale range consistent with the horizontal axis. Each point on this two-dimensional plane corresponds to a pair of possible stability factor values. The creation of this space provides a framework for visualizing and mathematically processing multi-parameter optimization problems.

[0025] All the construction data are input into this two-dimensional space, each of which is determined by the first two values in the historical data triplets (first factor, second factor, integration time), namely a pair of stability factors, to determine the plane coordinates of a point. This point is called a sample space point. The third value in the triplet, namely the sample detection integration time, is associated with the sample space point as a label attribute or "value". After all historical data records are mapped into the space, a large number of sample space points with integration time labels are formed. The distribution of these points in the space may be uneven, with some areas having a dense point set representing common stability combinations, and some areas having a sparse point set representing special working conditions. This set composed of sample points and their labels is the fluorescence detection configuration space. It is essentially a discrete query table based on historical experience, but its structure allows for proximity search and interpolation rather than simple exact matching.

[0026] When a new target analyte needs to be detected, the system first calculates the current first labeled fluorescence stability factor and the second labeled fluorescence stability factor according to the real-time collected historical signal data. With these two calculated factor values as indexes, the search process in the configuration space begins. First, find all the points in the two-dimensional space whose horizontal coordinates are equal to the current first labeled fluorescence stability factor. All these points form a straight line perpendicular to the horizontal axis, which is extracted as the first reference line. Similarly, find all the points whose vertical coordinates are equal to the current second labeled fluorescence stability factor. They form a straight line perpendicular to the vertical axis, which is extracted as the second reference line. The intersection point of the two reference lines in the space, which is determined by the current two factor values, is defined as the center reference point. This point represents the theoretical position of the current system stability state in the configuration space.

[0027] Since the configuration space is discrete, the probability that the center reference point coincides with a historical sample point is very low. Therefore, the search needs to be carried out around the center point. With the center reference point as the center, a preset search radius is set to construct a circular neighborhood space. The size of the radius can be adaptively adjusted according to the overall density of the sample points in the configuration space, with the goal of including a certain number of neighborhood sample space points. These sample points falling within the circle, their carried integration time information is the candidate set of the current search operation. A centralized search is performed on multiple neighborhood sample space points, the purpose is to find a most suitable point from these candidate points, and determine its label value, namely the sample detection integration time, as the final target detection integration time. The search strategy can be diverse, for example, calculating the average or median of the integration times of all points in the neighborhood, or using a more complex weighted average, with the weight inversely proportional to the distance from the center point. The closer the sample point to the center point, the more similar its stability state to the current system, so the reference weight of its integration time value is higher.

[0028] Embodiment 3: refer to Figure 4 The space is a circular region with a center reference point as the geometric center and a preset search radius R. The radius is not fixed but dynamically adjusted according to the global distribution density of sample points in the entire fluorescence detection configuration space. The basic principle is: in the macro area with sparse sample point distribution, a larger search radius is used to include a sufficient number of candidate points; in the area with dense sample point distribution, a smaller search radius is used to achieve more precise local search. All sample space points falling within this circular region constitute the candidate set of this search, referred to as neighborhood sample space points.

[0029] The neighborhood density of the neighborhood space is calculated, which is a macro indicator reflecting the degree of aggregation of candidate points in the local area. The calculation formula is as follows: Wherein: represents the calculated neighborhood density. represents the total number of sample space points falling within the current circular neighborhood space. represents the preset search radius currently used. is the area of the circular neighborhood space. The physical meaning of the density is the number of candidate points per unit area. The higher the value, the more historical experience data the area has, and the higher the probability of finding high-quality candidate points theoretically.

[0030] The search iteration is started in a random manner. A sample space point is randomly selected from the edge zone of the current neighborhood space as the current search point. The introduction of randomization avoids the deviation that may be caused by starting the search from a fixed direction, increasing the diversity of exploration. A sub-neighborhood space is constructed again using the same search radius R as before with the newly selected current search point as the center. The density of the sub-neighborhood space, i.e. the search density , is calculated in the same way as the calculation of : the number of points falling within the sub-neighborhood is counted and divided by the circular area.

[0031] The system compares the two density values and judges whether the density of the sub-area where the current search point is located ( ) is greater than or equal to the average density of the entire original neighborhood ( ), which usually means that we have randomly jumped to a "rich" area with more data points and possibly better information. At this point, the search is considered successful. The system updates the center point of this new, denser area, i.e. the current search point, as the starting center point for the next iteration. Meanwhile, the failure count is reset to zero. The search process will center on this new starting point, rebuild the neighborhood, calculate the neighborhood density, and start a new round of random selection and density comparison. This iteration process will continue until a preset upper limit of search times is reached. At this point, the sample space point reached by the last successful iteration will be selected as the target sample space point. This mechanism makes the search have a tendency to move to denser and more information-rich areas.

[0032] If the calculated search density is less than the original neighborhood density , it indicates that this random jump has reached a relatively sparse area with weak historical data support, and this search attempt is considered a failure. The system will not move the search center, but will increment the value of a failure counter that is specifically used to record the number of consecutive failures. Then, the system will randomly select another sample space point from the edge of the original neighborhood space as the new current search point, and repeat the steps of search density calculation and comparison. If such consecutive failures continue to occur, causing the value of the failure counter to accumulate beyond another preset threshold: maximum failure count, it means that there may be no significantly better dense area than the center reference point in the current initial neighborhood space, or the random search is difficult to hit effectively for some reason. In order to avoid endless ineffective attempts, the search algorithm will actively terminate the iteration and take a conservative strategy: directly determine the original center reference point itself as the target sample space point. This means that the integral time scheme ultimately adopted by the system is based on the direct mapping result of the current real-time stability factor in the configuration space, and fails to find a better alternative in its neighborhood area that is verified by historical data.

[0033] Embodiment 4: Start the dual-label time-resolved fluorescence detection device. This operation is not simply turning on the device, but contains a series of fine initialization steps. The optical module of the device first performs self-check and calibration. The intensity of the excitation light source is set to the preset working point. The gain and bias of the photomultiplier tube or other detector are set according to the expected signal intensity of the target analyte. Most importantly, the target detection integration time previously determined by the search is loaded into the timing control unit, which will strictly define the length of time for each fluorescence signal acquisition. After the device is started, real-time signal acquisition of the current sample to be tested begins. The system captures the fluorescence signals from the two different wavelength channels simultaneously according to the set integration time window, and records them as real-time first label fluorescence signal data and real-time second label fluorescence signal data, respectively. These data are discrete intensity value sequences that change over time, reflecting the fluorescence decay process of the labels within a certain integration time.

[0034] Based on these real-time signal data, the background noise interference quantity is determined. This interference quantity is not directly measured, but derived through calculation and analysis. The analysis process usually selects the tail stage of the signal decay curve, at which stage the specific fluorescence of the target analyte has been basically decayed, and the remaining fluctuations are mainly caused by background interference. The system calculates the standard deviation of the intensity in this signal interval, or uses a more complex algorithm such as fitting a noise model to estimate its amplitude. This calculated value is quantified as the background noise interference quantity, which objectively reflects the overall level of non-specific signals in the current detection environment.

[0035] The system needs to evaluate the relationship between the two signal channels, which is achieved by calculating the correlation intensity between the real-time first label fluorescence signal data and the real-time second label fluorescence signal data. Correlation intensity describes the consistency of the two signal sequences in terms of trend. The calculation is not a simple comparison of absolute intensity, but an analysis of the relative fluctuation pattern. A common method is to calculate the correlation coefficient of the intensity values of the two signal sequences at the same time point. A higher positive correlation coefficient indicates that the two label signals are affected by similar interference factors and their changes are coordinated; while a lower coefficient may indicate that the interference sources are independent or that an abnormality has occurred in one channel. Finally, the fluorescence detection robustness is determined by the background noise interference quantity and the correlation intensity. Robustness is a comprehensive indicator, and its calculation logic is: under the same background noise level, the more coordinated the performance of the two signals (the higher the correlation intensity), the better the robustness of the entire detection system against random interference and the identification of true signals. Therefore, robustness is usually positively correlated with correlation intensity and negatively correlated with background noise interference quantity. A simplified quantification method can be to divide the correlation intensity value by the background noise interference quantity and then perform appropriate normalization to obtain a dimensionless scalar value to represent robustness.

[0036] Table 1: Real-time signal acquisition and background noise analysis data. Referring to Table 1, a real-time signal data segment acquired within a target detection integration time (e.g., 1000 ms) is shown. The time point column shows the time since the start of excitation. The real-time first label signal intensity and real-time second label signal intensity columns record the intensity information such as photon counts or voltage values detected by the two channels at the corresponding time points. The signal decay tail interval identification column indicates which time points are identified by the system as the signal decay tail and used for the calculation of the background noise disturbance. For example, starting from 400 ms, the signal intensity has dropped to a lower and slowly changing level, and the data in this interval is used to calculate the background noise disturbance. The system can calculate the standard deviation of the first label signal intensity in this identified interval as 8.5 counts, and the standard deviation of the second label signal intensity as 7.2 counts, and finally obtain a background noise disturbance. At the same time, the system can calculate the correlation intensity of the two signal sequences within the entire integration time, and can obtain a correlation coefficient as high as 0.98, indicating that the two signals have good coordination. Combined with the high correlation intensity and low noise disturbance, the system finally calculates a high fluorescence detection robustness value, which indicates that the quality of the environment for this detection is good, and the subsequent identification steps can obtain more reliable results. The entire process embodies the complete closed loop from parameter setting to real-time data acquisition, and then to real-time quality evaluation.

[0037] Example 5: Signal drift prediction on a periodic detection data set, which is a series of first label and second label fluorescence signal intensity readings obtained by a plurality of detection cycles within a target detection integration time. Signal drift prediction is not a simple time series extrapolation, but a multi-factor fusion analysis process. First, the system will call the historical first label fluorescence signal data set and the historical second label fluorescence signal data set. These historical data provide the behavior baseline of the signal over a long period of time. The system aligns and superimposes the data of the current cycle with these historical data to construct a dynamic signal trajectory spanning the past and present. This trajectory clearly shows the overall path and trend of signal intensity change over time.

[0038] Based on the constructed dynamic signal trajectory, the system further analyzes its gradient characteristics. Gradient calculation reveals the rate and direction of signal change. By identifying key points in the trajectory where the rate of change significantly changes, the system can filter out multiple signal lag features. These features can manifest as: after a certain time point, the rate of increase or decrease in signal intensity significantly slows down; or the rate of change of two marker signals begins to differentiate. These phenomena often indicate that the system response may have delayed or some degree of desynchronization. These qualitative characteristics are further converted into quantitative signal lag quantities. The lag quantity is a comprehensive indicator that may quantify the delay time of the rate of change, or the percentage amplitude of the rate of decrease.

[0039] The implementation process enters the key fusion stage, fusing the calculated signal lag quantity with the previously determined fluorescence detection robustness. The robustness index reflects the overall anti-interference ability of the detection system, while the signal lag quantity specifically indicates abnormal behavior in the time dimension. The fusion algorithm gives different weights to these two indicators. Generally speaking, in the case of low robustness itself, even a small signal lag may be given a higher weight, as it is more likely to indicate a serious drift. Conversely, in the context of high robustness, the system's tolerance for lag is correspondingly increased. Through this weighted fusion calculation, the system finally outputs a quantitative prediction value, i.e., the signal drift bias. This value quantitatively estimates the degree and direction of the signal's deviation from its expected ideal position.

[0040] Based on the predicted signal drift bias, the system begins to adjust the recognition threshold of the fluorescence detection recognizer. The fluorescence detection recognizer is essentially a decision function, and its built-in recognition threshold is the key boundary for distinguishing between positive and negative, or quantifying the concentration level. In the ideal case of no drift, using a fixed threshold is feasible. However, when a signal drift bias is predicted, continuing to use a fixed threshold may lead to misjudgment. Therefore, the adjustment mechanism will shift or scale the original threshold according to the value and sign of the signal drift bias. For example, if the prediction shows that the signal is drifting positively as a whole (i.e., the overall intensity is too high), the system will adjust the recognition threshold proportionally upwards to avoid misjudging background noise as a weak positive signal. This adjustment process is dynamic and adaptive, ensuring that the decision boundary always maintains a relatively consistent positional relationship with the true signal distribution. The final recognition of the periodic detection data set is performed using the adjusted recognition threshold. The signal intensity readings of each detection period are compared with this dynamically adjusted threshold. The recognition logic depends on the specific detection type: for qualitative detection, if the intensity is higher than the threshold, it is determined to be positive, otherwise it is negative; for quantitative detection, the specific concentration of the target analyte is calculated according to the difference between the intensity value and the threshold, with reference to the standard curve.

[0041] It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting; it is not intended to exclude myriad other embodiments of the present application that other inventors can develop based on the same general inventive concepts embodied by the described embodiments. That is, although the present application is described in terms of particular embodiments and illustrative figures, it should be apparent that the scope of the present application is not limited to these specific embodiments.

[0042] While the embodiments of the application have been shown and described herein, it will be understood by those skilled in the art that many changes, modifications, substitutions and alterations to these embodiments can be made without departing from the principles and spirits of the application, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A detection method based on dual-label time-resolved fluorescence immunoassay, characterized in that, Includes the following steps: Perform dual-label time-resolved fluorescence signal acquisition to obtain a set of historical first-label fluorescence signal data and a set of historical second-label fluorescence signal data for monitoring the target analyte within a preset historical time window; Data stability analysis is performed by traversing the historical first-label fluorescence signal data set and the historical second-label fluorescence signal data set to determine the first-label fluorescence stability factor and the second-label fluorescence stability factor. Using the first and second labeled fluorescence stability factors as indexes, a centralized search is performed in the fluorescence detection configuration space to determine the target detection integration time; Based on the target detection integration time, the target analyte is detected by dual-label time-resolved fluorescence immunoassay to obtain a periodic detection data set. The periodic detection data set is then identified using a fluorescence detection identifier to obtain the target analyte detection result.

2. The detection method based on dual-label time-resolved fluorescence immunoassay as described in claim 1, characterized in that, The process of performing dual-label time-resolved fluorescence signal acquisition also includes: Obtain the set of background interference signal data of the target analyte within the preset historical time window; The historical first-label fluorescence signal data set, the historical second-label fluorescence signal data set, and the background interference signal data set are simultaneously collected to construct a dual-label fluorescence interference dataset.

3. The detection method based on dual-label time-resolved fluorescence immunoassay as described in claim 2, characterized in that, The step of performing data stability analysis by traversing the historical first-label fluorescence signal data set and the historical second-label fluorescence signal data set also includes: Time-frequency analysis was performed on the dual-labeled fluorescence interference dataset to extract the dynamic features of the first label and the dynamic features of the second label. Based on the first label dynamic characteristics and the second label dynamic characteristics, the mean absolute deviation is calculated to obtain the first label fluorescence stability factor and the second label fluorescence stability factor.

4. The detection method based on dual-label time-resolved fluorescence immunoassay as described in claim 3, characterized in that, The step of performing a centralized search in the fluorescence detection configuration space using the first labeled fluorescence stability factor and the second labeled fluorescence stability factor as indexes also includes: The fluorescence stability factors of the first label and the fluorescence stability factors of the second label of multiple samples, as well as the corresponding detection integration times of multiple samples, were obtained as the construction data. A pre-constructed two-dimensional space is provided, wherein the origin of the coordinate system of the two-dimensional space is a reference point, the horizontal axis is the first label fluorescence stability factor, and the vertical axis is the second label fluorescence stability factor. The constructed data is input into the two-dimensional space to obtain multiple sample space points, and the multiple sample space points are marked using the multiple sample detection integration time to obtain the fluorescence detection configuration space.

5. The detection method based on dual-label time-resolved fluorescence immunoassay as described in claim 4, characterized in that, The step of performing a centralized search within the fluorescence detection configuration space to determine the target detection integration time also includes: The line in the fluorescence detection configuration space that passes through the first labeled fluorescence stability factor and is parallel to the vertical axis is extracted as the first reference line; The line in the fluorescence detection configuration space that has been marked with the second fluorescence stability factor and is parallel to the horizontal axis is extracted as the second reference line; The intersection of the first reference line and the second reference line is taken as the center reference point. Starting from the center reference point, a neighborhood space is constructed according to a preset search radius. The neighborhood space includes multiple neighborhood sample space points. A concentrated search is performed on the multiple neighborhood sample spatial points to determine the target sample spatial point, and the sample detection integration time corresponding to the target sample spatial point is used as the target detection integration time.

6. The detection method based on dual-label time-resolved fluorescence immunoassay as described in claim 5, characterized in that, The centralized search of the multiple neighborhood sample spatial points also includes: Calculate the neighborhood density of the neighborhood space; Randomly select a neighborhood sample space point from the edge of the neighborhood space as the current search point, and calculate the search density of the current search point; Determine whether the search density is greater than or equal to the neighborhood density. If so, update the current search point to the starting point and continue the concentrated search until the preset number of searches is met. Use the search point obtained in the last search as the target sample space point.

7. The detection method based on dual-label time-resolved fluorescence immunoassay as described in claim 6, characterized in that, The step of determining whether the search density is greater than or equal to the neighborhood density also includes: If not, the failure count, which was initially zero, is updated to one, and a neighborhood sample space point is randomly selected from the edge of the neighborhood space as the current search point for search analysis. When the failure count is greater than the preset maximum failure count, the central reference point is used as the target sample space point.

8. The detection method based on dual-label time-resolved fluorescence immunoassay as described in claim 1, characterized in that, The dual-label time-resolved fluorescence immunoassay detection of the target analyte based on the target detection integration time further includes: Start the dual-label time-resolved fluorescence detection device to collect real-time fluorescence signal data of the first label and real-time fluorescence signal data of the second label; Based on the real-time first label fluorescence signal data and the real-time second label fluorescence signal data, the background noise interference amount is determined; The robustness of fluorescence detection is determined by the amount of background noise interference and the correlation strength between the real-time first-label fluorescence signal data and the real-time second-label fluorescence signal data.

9. The detection method based on dual-label time-resolved fluorescence immunoassay as described in claim 8, characterized in that, The step of using a fluorescence detection identifier to identify the periodic detection data set also includes: Signal drift bias is obtained by predicting the signal drift using the background noise interference and the fluorescence detection robustness of the periodic detection dataset. The recognition threshold of the fluorescence detector is adjusted based on the signal drift deviation to obtain the adjusted recognition threshold. The adjusted identification threshold is used to identify the periodic detection data set to obtain the detection result of the target analyte.

10. The detection method based on dual-label time-resolved fluorescence immunoassay as described in claim 9, characterized in that, The method of predicting signal drift in the periodic detection dataset based on the background noise interference and the fluorescence detection robustness further includes: By combining the historical first-label fluorescence signal data set and the historical second-label fluorescence signal data set, a dynamic signal trajectory is constructed; Based on the gradient characteristics of the dynamic signal trajectory, multiple signal hysteresis features are filtered out, and the multiple signal hysteresis features are converted into signal hysteresis quantities; By combining the signal hysteresis and the fluorescence detection robustness, the signal drift bias is predicted.

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