Rapid detection method for microplasma samples for pharmacokinetic studies

Abstract

This invention relates to the fields of analytical detection and online solid-phase extraction technology, specifically to a rapid detection method for trace plasma samples used in pharmacokinetic studies. The method first measures the empty baseline pressure; after injection, it calculates the net sample pressure and total resistance integral in real time, and identifies the transition point from injection to cleaning to lock the fluid dynamics boundary parameters; during the cleaning phase, it calculates the measured pressure decay rate and extrapolates the theoretical pressure decay rate under ideal emptying conditions, uses the total resistance integral to compensate for viscosity at the measured rate, and then obtains the flow hysteresis ratio characterizing the sample matrix retention effect based on both; finally, it uses the flow hysteresis ratio to adaptively correct a standard threshold to obtain a target threshold, and performs flow path switching when the measured rate reaches the target. This invention effectively decouples sample viscosity from adsorption characteristics, enabling adaptive adjustment of the cleaning time based on real-time sample kinetic feedback, completely eliminating matrix residue contamination while ensuring the recovery rate of polar drugs.

Description

Technical Field

[0001] This invention relates to the fields of analytical detection and online solid-phase extraction technology, specifically to a rapid detection method for trace plasma samples used in pharmacokinetic studies. Background Technology

[0002] In fields such as pharmacokinetic studies and clinical newborn screening for inherited metabolic diseases, researchers can usually only obtain trace amounts of plasma samples. In order to perform high-throughput and automated drug concentration analysis on such trace samples, online solid-phase extraction technology is widely used. This technology uses a sample pump to push the plasma sample into a micro-collection column, removes matrix interferences such as proteins and salts by washing with the mobile phase, and then elutes the target components to the chromatographic column for separation and detection by switching the flow path.

[0003] However, the physical properties of actual clinical plasma samples vary significantly from person to person. For example, high-lipid plasma or hemolyzed plasma usually has a high bulk viscosity, resulting in greater linear resistance when flowing through the collection column. At the same time, hydrophobic proteins in the sample are prone to non-specific adsorption on the tube wall and packing surface, resulting in non-linear flow hysteresis effects. In micro-injection mode, existing washing endpoint control methods set a fixed washing time. If the washing time is set too short, high-viscosity or strongly adsorbed samples may not be completely eluted from the matrix, resulting in residual contamination and damage to the analytical column. If the washing time is set too long, for low-viscosity samples without adsorption, excessive mobile phase rinsing can cause highly polar target drugs to penetrate and be lost, reducing the detection recovery rate. In addition, although existing liquid chromatography systems are equipped with pressure sensors, it is unreliable to determine the washing endpoint solely by monitoring whether the absolute pressure value has dropped to a certain point. This is because the slow pressure decay caused by high viscosity and the slow pressure decay caused by strong adsorption are two completely different physical mechanisms. The former does not require a significant extension of the washing time, while the latter must be extended. Current control algorithms cannot decouple and distinguish between these two physical characteristics in real time, making it difficult for the system to adaptively adjust the washing window for clinical samples with different characteristics.

[0004] Therefore, existing cleaning endpoint control methods cannot effectively decouple and identify the bulk viscosity and non-specific adsorption characteristics of trace samples, resulting in the inability to adaptively adjust the cleaning time based on real-time kinetic feedback of samples with different physical properties, making it difficult to completely eliminate matrix residual contamination while ensuring the recovery rate of polar drugs. Summary of the Invention

[0005] To address the technical problem that existing washing endpoint control methods cannot identify physical differences in samples in real time and adaptively adjust the washing time, thus making it difficult to balance matrix washing effectiveness and target component recovery, the present invention aims to provide a rapid detection method for trace plasma samples for pharmacokinetic studies. The specific technical solution adopted is as follows: In a first aspect, one embodiment of the present invention provides a rapid detection method for trace plasma samples for pharmacokinetic studies, the method comprising the following steps: Measure the baseline pressure before injection; After the injection begins, the net pressure of the sample is calculated in real time based on the baseline pressure and integrated over time to obtain the total resistance integral of the sample, which characterizes the bulk viscosity of the sample; based on the change in the net pressure of the sample, the transition time from injection to cleaning is identified; based on the net pressure of the sample and the rate of change of pressure at the transition time, the boundary parameters of the fluid dynamics are determined. After the cleaning begins, the measured pressure decay rate is calculated in real time. Based on the boundary parameters, the theoretical pressure decay rate under ideal evacuation conditions is deduced. The viscosity is compensated for the measured pressure decay rate by integrating the total sample resistance at the transition time. Based on the theoretical pressure decay rate and the compensated measured pressure decay rate, the flow hysteresis ratio characterizing the sample matrix retention effect is obtained. The target threshold is obtained by adaptive correction using the flow hysteresis comparison to the preset standard threshold. When the measured pressure decay rate is less than the target threshold, the cleaning is determined to be completed and the flow path is switched.

[0006] Furthermore, the method for obtaining the baseline pressure is as follows: During the equilibrium phase when the injection valve is in the injection position and the injection pump delivers the mobile phase at a constant flow rate, the real-time pressure data collected in front of the column within a preset time window is defined as the first pressure data. The arithmetic mean of the first pressure data is used as the baseline pressure before sample injection without loading. If the arithmetic mean exceeds a preset empirical range or the standard deviation of the first pressure data exceeds a preset stability threshold, the current baseline pressure is determined to be invalid and a second measurement is performed. If the second measurement still fails the verification, the baseline pressure of the most recently successfully measured pressure will be retrieved from the memory.

[0007] Furthermore, the method for obtaining the net pressure of the sample is as follows: The real-time pre-column pressure data obtained after the start of injection is defined as the second pressure data. For any second pressure data at any sampling time, the difference between the second pressure data and the baseline pressure is compared with the preset minimum pressure data, and the maximum value is taken as the net sample pressure at that sampling time.

[0008] Furthermore, the method for obtaining the transition time is as follows: After the injection action is completed and a preset valve switching delay is passed, the first-order differential rate of change of the sample net pressure is calculated in real time. When the first-order differential rate of change of the sample is negative for a first preset number of consecutive sampling times, and the cumulative pressure drop within the first preset number of consecutive sampling times exceeds the preset noise tolerance, the current sampling time is determined to be the transition time from injection to cleaning.

[0009] Furthermore, the method for obtaining the boundary parameters is as follows: The sample net pressure at the transition time is compared with the preset minimum effective potential energy, and the maximum value is taken as the net pressure at the transition point. The net pressure of the sample is obtained at the transition time and the second preset number of consecutive sampling times thereafter, and the absolute value of the slope is calculated as the initial decay rate through linear regression. If the initial attenuation rate is less than the preset minimum effective slope, then the absolute value of the net pressure difference between the sample at the switching time and the adjacent sampling time thereafter is taken as the attenuation rate at the switching point. If the calculated transition point decay rate is less than the minimum slope constant, then the minimum slope constant is taken as the transition point decay rate. If the initial attenuation rate is greater than or equal to the preset minimum effective slope, then the initial attenuation rate is used as the attenuation rate at the transition point. The net pressure at the transition point and the decay rate at the transition point are used as boundary parameters for fluid dynamics.

[0010] Furthermore, the method for obtaining the measured pressure decay rate is as follows: Select the sample net pressure sequence with the current sampling time as the end point and the starting point no earlier than the transition time; If the number of sampling times between the current sampling time and the transition time is greater than the third preset number, then the net pressure sequence of the samples is a sequence that includes the current sampling time and its preceding consecutive third preset number of sampling times; If the number of sampling times between the current sampling time and the transition time is less than or equal to the third preset number, then the net pressure sequence of the samples is the sequence from the transition time to the current sampling time. The net pressure sequence of the sample was linearly fitted using the least squares method, and the absolute value of the slope of the fitted line was taken as the measured pressure decay rate at the current sampling time.

[0011] Furthermore, the method for obtaining the theoretical pressure decay rate is as follows: After the cleaning begins, the ratio of the net pressure of the sample at the current sampling time to the net pressure at the transition point is taken as the first value; The product of the attenuation rate at the conversion point and the first value is taken as the theoretical pressure attenuation rate at the current sampling time.

[0012] Furthermore, the method for viscosity compensation of the measured pressure decay rate using the integral of the total sample resistance at the transition time is as follows: The result of normalizing the sample total resistance integral at the transition time and performing positive correlation mapping is used as the viscosity weighting coefficient. The product of the viscosity weighting coefficient and the measured pressure decay rate at each sampling time is used as the compensated measured pressure decay rate at each sampling time.

[0013] Furthermore, the method for obtaining the flow hysteresis ratio is as follows: For any sampling time, the ratio of the theoretical pressure decay rate at that sampling time to the sum of the compensated measured pressure decay rate at that sampling time and a preset minimum positive number is taken as the instantaneous lag ratio at that sampling time. The weighted sum of the instantaneous lag ratio at the current sampling time and the flow lag ratio at the previous adjacent time is used as the flow lag ratio at the current sampling time.

[0014] Furthermore, the method of adaptively correcting the target threshold using the flow hysteresis ratio against a preset standard threshold, and determining the end of cleaning and performing flow path switching when the measured pressure decay rate is less than the target threshold, is as follows: The flow lag ratio at the current sampling time is compared with the preset correction parameter, and the maximum value is defined as the correction coefficient. The ratio of the preset standard threshold to the correction coefficient is compared with the preset minimum safety threshold, and the maximum value is defined as the target threshold at the current sampling time. If the duration of the cleaning phase exceeds the preset minimum cleaning time, and the measured pressure decay rate is less than the target threshold within a continuous preset confirmation period, then the cleaning is determined to be completed and the flow path is switched.

[0015] Secondly, another embodiment of the present invention provides a rapid detection system for micro-volume plasma samples for pharmacokinetic studies. The system includes: a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any of the above methods.

[0016] The present invention has the following beneficial effects: This invention first measures the baseline pressure before sample injection, which helps eliminate the interference of system background resistance on the true fluid characteristics of the sample. After sample injection begins, the net pressure of the sample is calculated in real time based on the baseline pressure and integrated over time to obtain the total resistance integral characterizing the bulk viscosity of the sample, which can quantify the bulk resistance characteristics of the sample during the injection phase. To accurately distinguish between the injection and cleaning phases, the transition time from injection to cleaning is identified based on the change in the net pressure of the sample, establishing the logical origin of the adaptive cleaning model. To establish a dynamic theoretical reference frame for the cleaning phase, the boundary parameters of fluid dynamics are determined based on the net pressure of the sample and the rate of pressure change at the transition time, locking the initial fluid kinetic energy of the sample. After cleaning begins, the measured pressure decay rate is calculated in real time to capture the true evacuation kinetic energy of the sample under the combined effects of viscosity, adsorption, and other factors under the current flow path conditions. Based on the boundary parameters, the theoretical pressure decay rate under ideal evacuation conditions is derived. The ideal decay trajectory of a specific sample under hydraulic potential energy alone is predicted. Furthermore, the integral of the total sample resistance at the transition moment is used to compensate for the viscosity of the measured pressure decay rate, decoupling the simple viscosity effect from the pipe wall adsorption effect in the computational dimension. This avoids over-cleaning due to viscosity misjudgment. The theoretical pressure decay rate and the compensated measured pressure decay rate are then compared to obtain the flow hysteresis ratio, which characterizes the sample matrix retention effect, accurately quantifying the dragging effect of pipe wall specific adsorption on the fluid. To achieve adaptive control where stronger retention leads to longer cleaning, the flow hysteresis ratio is used to adaptively correct a preset standard threshold to obtain a target threshold, which is then used to lower the cleaning completion standard. When the measured pressure decay rate is less than the target threshold, the cleaning is accurately determined to be complete, and a flow path switch is executed. This ensures that while thoroughly eliminating matrix residues in strongly retained samples, cleaning is quickly terminated for non-retained samples to guarantee a high recovery rate of polar drugs. Attached Figure Description

[0017] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic flowchart illustrating a rapid detection method for micro-plasma samples used in pharmacokinetic studies, provided as an embodiment of the present invention. Figure 2 This is a structural diagram of a rapid detection system for micro-plasma samples used in pharmacokinetic studies, provided in one embodiment of the present invention. Figure 3 This is a schematic diagram of a computer device provided according to an embodiment of the present invention. Detailed Implementation

[0019] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of the rapid detection method for micro-plasma samples for pharmacokinetic studies proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0021] The following description, in conjunction with the accompanying drawings, details the specific scheme of the rapid detection method for micro-plasma samples used in pharmacokinetic studies provided by this invention.

[0022] Example 1: This invention proposes a rapid detection method for trace plasma samples in pharmacokinetic studies. Please refer to [link to relevant documentation]. Figure 1 The diagram illustrates a schematic flowchart of a rapid detection method for trace plasma samples in pharmacokinetic studies according to an embodiment of the present invention. The method includes the following steps: Step S1: Measure the baseline pressure of the empty sample before injection.

[0023] Specifically, before analyzing trace amounts of plasma samples, it is necessary to first establish the system's environmental baseline to ensure the accuracy and reliability of subsequent sample characteristic calculations. It is known that the tubing, valve interfaces, and solid-phase extraction columns of a liquid chromatography system possess inherent fluid resistance under pure mobile phase conditions. This resistance manifests as background pressure. To extract the pressure increment introduced solely by the sample matrix and to eliminate the masking effect of inherent system resistance on the true fluid dynamics of the sample, it is essential to accurately determine the system's background state before the sample enters the flow path.

[0024] Therefore, during the equilibrium phase when the injection valve is in the injection position and the injection pump delivers pure mobile phase at a constant flow rate, the system initiates a pressure acquisition program to collect real-time pre-column pressure data within a preset time window, which is defined as the first pressure data. In this embodiment, the pressure data acquisition frequency is set to 50Hz to ensure the capture of high-frequency pump pulsation details and improve subsequent filtering accuracy. The preset time window duration is set to 30 seconds to ensure sufficient sample volume for statistical analysis. Implementers can flexibly set the acquisition frequency and preset time window duration according to the pump pulsation cycle and pressure sensor resolution of the specific liquid chromatography system; no limitation is imposed here.

[0025] To eliminate the interference of pump periodic pulsation on baseline determination, the system calculates the arithmetic mean of the first pressure data and uses it as the baseline pressure before injection. Considering that air bubbles in the tubing, pump valve leakage, or sensor drift may distort the first pressure data, and to prevent the subsequent decoupling calculation of viscosity and adsorption characteristics from completely failing due to abnormal baseline pressure, the system performs real-time verification of the arithmetic mean to determine whether it is within a preset empirical range; simultaneously, it calculates the standard deviation of the first pressure data to determine whether it exceeds a preset stability threshold. If the arithmetic mean exceeds the preset empirical range or the standard deviation of the first pressure data exceeds the preset stability threshold, it indicates that the current flow path environment has not reached a stable state, and the current baseline pressure is deemed invalid. In this embodiment, the preset empirical range is set to 5 bar to 20 bar to ensure that the baseline pressure is within the healthy operating range of a conventional liquid chromatography system; the preset stability threshold is set to 0.2 bar to ensure that there are no severe air bubbles or cavitation fluctuations in the flow path that affect flow rate determination. Implementers can set the preset empirical range and preset stability threshold according to the specific liquid chromatography pump model, trapping column specifications, and mobile phase viscosity; these settings are not limited here.

[0026] To ensure the automated operation of the testing system and the continuity of high-throughput analysis, when the baseline pressure is determined to be invalid, the system first initiates a secondary measurement procedure, i.e., re-executes the above steps to obtain the baseline pressure. If the secondary measurement still fails the verification, to prevent prolonged shutdown of the high-throughput production line due to minor environmental fluctuations, the system will perform a circuit breaker degradation operation. This involves automatically retrieving the most recently successfully measured baseline pressure from the memory, using it as a temporary baseline pressure for subsequent calculations, and simultaneously recording an anomaly log for later manual review.

[0027] Step S2: After the injection begins, the net pressure of the sample is calculated in real time based on the baseline pressure and integrated over time to obtain the total resistance integral of the sample, which characterizes the bulk viscosity of the sample; based on the change in the net pressure of the sample, the transition time from injection to cleaning is identified; based on the net pressure of the sample and the rate of change of pressure at the transition time, the boundary parameters of the fluid dynamics are determined.

[0028] Specifically, when the sample enters the solid-phase extraction column with the mobile phase, the flow path pressure dynamically responds to the physical properties of the sample matrix. This embodiment achieves quantification of sample viscosity characteristics and precise locking of the initial boundary of the cleaning stage by deeply analyzing the pressure signal during the injection stage. To accurately separate the fluid resistance increment introduced by the sample matrix and eliminate the masking effect of the inherent background pressure of the system pipeline and the trapping column, the net sample pressure at each sampling moment is first calculated in real time based on the baseline pressure after the injection begins, so that subsequent calculations focus only on the physical feedback of the sample itself. To quantify the macroscopic fluid resistance characteristics of the sample in the flow path, the net sample pressure from the start of injection to the current moment is integrated over time to obtain the total sample resistance integral characterizing the bulk viscosity of the sample, accurately reflecting the total energy consumed by the sample to overcome viscous friction during the flow through the solid-phase extraction column. The larger the total sample resistance integral, the higher the bulk viscosity of the sample (e.g., high-lipid plasma or high-protein samples), the greater the flow resistance of the fluid in the micro-packing pores, and the slower the natural pressure decay rate should be in the subsequent cleaning stage compared to low-viscosity samples.

[0029] As the injection process ends, the high-viscosity sample plug in the flow path is gradually replaced by the low-viscosity mobile phase, and the system pressure will transition from a relatively stable plateau phase to a downward trend. In order to accurately distinguish the kinetic boundary between the injection and cleaning phases, and to ensure that the subsequent theoretical pressure decay rate calculation has a physically rigorous starting point, we can avoid misjudging the small fluctuations of the injection plateau as decaying flow rate. Based on the real-time changes in the net sample pressure, we can identify the transition moment from injection to cleaning. This moment marks the transition of the fluid from a blocked injection state to an exponential decay state, which is the logical origin for establishing the adaptive cleaning model.

[0030] To establish a theoretical evolution benchmark under ideal evacuation conditions during the cleaning phase, and to provide a quantifiable ideal reference system, it is beneficial to accurately separate the nonlinear velocity hysteresis caused by pipe wall-specific adsorption from complex viscous friction. Furthermore, based on the sample net pressure and pressure change rate locked at the transition moment, the boundary parameters of fluid dynamics can be determined. Specifically, the sample net pressure locked at the transition moment is used as the initial pressure potential energy, and the pressure change rate locked at the transition moment is used as the initial decay rate. These two parameters represent a standard kinetic snapshot of the specific sample before it is significantly dragged by surface adsorption effects. Using these two parameters, the system can deduce the ideal pressure decay curve of the sample under viscosity-only influence, providing a dynamic benchmark for adaptive cleaning.

[0031] Preferably, in one feasible embodiment of this method, the method for obtaining the net sample pressure is as follows: First, the real-time pre-column pressure data after the start of sample injection is obtained and defined as the second pressure data; for the second pressure data at any sampling time, the second pressure data includes the background resistance inherent in the system pipeline and the resistance increment introduced by the sample matrix. In order to accurately extract the pressure feedback caused purely by the physical characteristics of the sample, the second pressure data is subtracted from the baseline pressure to determine the pressure difference that only characterizes the fluid properties of the sample; considering that sensor electrical noise or small fluctuations in the flow path may cause the above pressure difference to be negative, in order to prevent physically unreasonable negative pressure values ​​from interfering with subsequent viscosity accumulation integrals and normalized logarithm calculations, causing program crashes, the difference between the second pressure data and the baseline pressure is compared with a preset minimum pressure data, and the maximum value is taken as the net sample pressure at that sampling time. In this embodiment, the preset minimum pressure data is set as follows: The bar provides a safety baseline for mathematical operations. Implementers can set a preset minimum pressure data according to the data processing accuracy of the specific system, without any restrictions here.

[0032] Preferably, in one feasible embodiment, the method for obtaining the transition time is as follows: In order to accurately capture the physical characteristic inflection point of the transition from the high-pressure injection platform to the cleaning pressure attenuation and avoid misjudgment caused by local signal fluctuations, this embodiment calculates the first-order differential rate of change of the sample net pressure in real time after the injection action is completed and after a preset valve switching delay, which is used to characterize the local evolution trend of the pressure signal; when the first-order differential rate of change of a first preset number of consecutive sampling times are all negative, and the cumulative pressure drop within a first preset number of consecutive sampling times exceeds the preset noise tolerance, it indicates that the resistance balance in the fluid channel has been fundamentally changed, and the fluid has changed from the blocked injection state to the exponential decay state, and thus the current sampling time is determined to be the transition time from injection to cleaning. In this embodiment, the first preset quantity is set to 5 to ensure coverage of at least one complete pump pulsation cycle, thereby effectively filtering out negative pressure pulsation interference caused by the periodic reciprocating motion of the plunger pump and preventing false positive triggering. The implementer can set the first preset quantity according to the operating frequency of the injection pump and the sampling frequency of the system; this is not limited here. The preset noise tolerance amplitude is set to 0.5 bar to ensure that the identified pressure drop is a decay trend with macroscopic physical significance rather than high-frequency white noise from the sensor or minor environmental disturbances. The implementer can set the preset noise tolerance amplitude according to the system's baseline background noise and the sensitivity of the pressure sensor; this is not limited here. The method for obtaining the first-order differential rate of change is well-known and will not be described further.

[0033] Preferably, in one feasible method of this embodiment, the boundary parameters are obtained as follows: Considering that the net sample pressure at the transition time will be used as the denominator variable when subsequently extrapolating the theoretical pressure decay rate, if the net sample pressure at the transition time is extremely small due to a high system bottom pressure or abnormal data, it will cause a division-to-zero error or data miscalculation. Therefore, this embodiment compares the net sample pressure at the transition time with the preset minimum effective potential energy and uses the maximum value as the net pressure at the transition point to ensure that the calculation denominator of the theoretical pressure decay rate model has a safe numerical baseline. In this embodiment, the preset minimum effective potential energy is set to 1.0 bar to ensure the safety of numerical calculation and to conform to the physical common sense of the minimum effective resistance of a micro-liquid chromatography system. The implementer can set the preset minimum effective potential energy according to the dead volume resistance of the system pipeline, which is not limited here. To accurately extract the initial kinetic energy characteristics of the fluid at the start of the cleaning phase and eliminate random electrical noise interference that may exist in single-point sampling, the net pressure of the sample is obtained at the transition moment and the next second preset number of consecutive sampling moments. The absolute value of the slope is calculated by linear regression as the initial decay rate. In this embodiment, the second preset number is set to 5 to ensure that the regression calculation can smooth high-frequency pulsating noise and accurately reflect the true average decreasing trend in the early stage of decay, avoiding the nonlinear decay region in the later part of the pressure curve caused by taking too many points. The implementer can set the size of the second preset number according to the sampling frequency of the pressure sensor and the working flow rate of the pump, which is not limited here. The slope of the linear regression calculation is a well-known fact and will not be described in detail here. Considering that when facing extremely high viscosity samples or when the system has a local response lag, the pressure change within the linear regression window may be too gradual, causing the slope to approach zero. To prevent this mathematical distortion from causing subsequent adaptive control to fall into a deadlock state that never reaches the threshold, if the initial decay rate is less than the preset minimum effective slope, the absolute value of the net pressure difference between the sample at the transition time and the adjacent sampling time is taken as the decay rate at the transition point. The instantaneous real pressure drop between the two adjacent points is used as a fallback mechanism to force the system to have a physically effective initial kinetic energy. It should be noted that if the calculated decay rate at the transition point is less than the minimum slope constant, the minimum slope constant is taken as the decay rate at the transition point. In this embodiment, the minimum slope constant is set to 0.01 bar / s to ensure that the kinetic reference during the cleaning stage always has a non-zero initial kinetic energy, thus completely eliminating the risk of division-by-zero deadlock. If the initial decay rate is greater than or equal to the preset minimum effective slope, the initial decay rate is used as the transition point decay rate to fully utilize the smoothing advantage of the regression algorithm. In this embodiment, the preset minimum effective slope is 0.01 bar / s, ensuring that even when the pressure drop is extremely slow due to extremely high viscosity samples, the subsequently extrapolated theoretical pressure decay rate still has an initial value that can be distinguished by the hardware. This ensures that the adaptive cleaning control logic can be triggered normally and smoothly enter the closed-loop judgment process, avoiding computational crashes. Implementers can set the preset minimum effective slope according to the lower limit of the pressure sensor resolution, the background noise level of the sampling circuit, and the pre-measured minimum pressure drop rate of extremely high viscosity samples such as high-lipid plasma. No limitation is imposed here. In order to provide accurate calibration parameters for the theoretical pressure decay rate decay model under the ideal evacuation state during the cleaning stage, the net pressure at the transition point and the decay rate at the transition point are used as boundary parameters of fluid dynamics.

[0034] Step S3: After the cleaning begins, the measured pressure decay rate is calculated in real time. Based on the boundary parameters, the theoretical pressure decay rate under ideal evacuation conditions is deduced. The measured pressure decay rate is compensated for viscosity using the integral of the total sample resistance at the transition time. Based on the theoretical pressure decay rate and the compensated measured pressure decay rate, the flow hysteresis ratio characterizing the sample matrix retention effect is obtained.

[0035] Specifically, once the transition time is determined, the system enters the cleaning control phase. This embodiment constructs a dynamic theoretical evolution reference frame during the cleaning process and compares it with the real-time feedback of the fluid state. This isolates the nonlinear retention characteristics of the sample matrix from the complex fluid dynamics signal, enabling the system to independently and continuously quantify the additional resistance generated by pipe wall adsorption in the flow path. This provides reliable feedback variables for subsequent adaptive dynamic adjustment of the cleaning duration.

[0036] Therefore, once the cleaning phase begins, the system calculates the measured pressure decay rate at each sampling moment in real time. This helps capture the true venting kinetic energy of the sample under the combined effects of viscosity, adsorption, and other factors in the current flow path conditions, providing an objective physical basis for comparative analysis. To establish an ideal fluid motion reference frame, the system can predict the ideal venting trajectory when driven solely by hydraulic potential energy without retention resistance. Based on boundary parameters, it can then deduce the theoretical pressure decay rate under ideal venting conditions, accurately reflecting the expected pressure drop rate of a specific sample under standard kinetic conditions. Considering the different base viscosities of different samples, the pressure drop of high-viscosity samples will naturally be slower than that of low-viscosity samples. To avoid misinterpreting the simple bulk viscosity effect as matrix retention and blindly prolonging the cleaning time, the system uses the integral of the total sample resistance at the transition moment to compensate for the measured pressure decay rate based on viscosity. By introducing a compensation gain determined by viscosity characteristics into the measured rate in the calculation dimension, the measured pressure decay rate can offset the natural flow hysteresis caused by bulk viscosity.

[0037] Subsequently, based on the theoretical pressure decay rate and the compensated measured pressure decay rate, the flow hysteresis ratio, characterizing the sample matrix retention effect, was obtained. This index accurately eliminates viscosity interference and only quantifies the degree of specific drag on the sample components by the inner surface of the pipeline and the packing of the trapping column. A larger flow hysteresis ratio indicates that, after eliminating the influence of viscosity, the measured evacuation rate is still significantly lower than the theoretical expectation, meaning that the sample components have a strong matrix retention effect, and the cleaning time needs to be adaptively extended. If the flow hysteresis ratio is close to 1, it indicates that the current decay hysteresis is entirely caused by the sample viscosity, and there is no significant retention, so the system does not require additional cleaning. Through this feature decoupling, this embodiment achieves accurate perception of the cleaning window depth.

[0038] Preferably, in one feasible embodiment of this invention, the method for obtaining the measured pressure decay rate is as follows: Select a sample net pressure sequence with the current sampling time as the endpoint and the starting point no earlier than the conversion time, ensuring that the calculation process falls entirely within the cleaning stage and avoiding severe interference from residual high-pressure signals during the injection stage; to balance the smoothing effect of the pressure signal with the real-time performance of the system response, this embodiment sets the third preset number to 10, ensuring that while obtaining an effective statistical trend, there is no calculation lag due to too many sampling points. The implementer can set the third preset number according to the system sampling frequency and pump pulsation cycle, which is not limited here; if the current sampling time and the conversion time... If the number of sampling times between two points is greater than the third preset number, it indicates that the cleaning process has entered a stable decay period. In order to extract the current local steady-state decay rate and effectively filter out high-frequency pulsation noise, the sample net pressure sequence is set to a sequence containing the current sampling time and its preceding consecutive third preset number of sampling times. If the number of sampling times between the current sampling time and the transition time is less than or equal to the third preset number, it indicates that the system is in the very early stage of just crossing the transition time. In order to maximize the use of existing cleaning stage sampling points under the premise of strictly shielding the data of the injection stage, the sample net pressure sequence is set to a sequence from the transition time to the current sampling time. To robustly quantify the evolution intensity of fluid pressure from a physical perspective, a linear fit was performed using the least squares method with time as the independent variable and the net pressure corresponding to each point in the sample net pressure sequence as the dependent variable. The absolute value of the slope of the fitted line was taken as the measured pressure decay rate at the current sampling moment. The least squares method for linear fitting is a well-known technique and will not be elaborated further.

[0039] Preferably, in one feasible method of this embodiment, the theoretical pressure decay rate is obtained as follows: after the cleaning begins, the ratio of the net pressure of the sample at the current sampling moment to the net pressure at the transition point is taken as the first value through the linear evolution logic of the flow velocity and pressure in fluid dynamics, so as to accurately reflect the residual relative pressure potential energy in the flow path at the current moment; then the product of the decay rate at the transition point and the first value is taken as the theoretical pressure decay rate at the current sampling moment, so as to lock the ideal decay trajectory of the specific sample under the drive of only viscous friction based on the initial kinetic energy at the moment of cleaning.

[0040] Preferably, in one feasible method of this embodiment, the method of using the sample total resistance integral at the transition time to compensate for viscosity of the measured pressure decay rate is as follows: considering that different plasma samples have different bulk viscosities due to individual differences (such as high-lipid plasma), which will cause the pressure decay rate to slow down naturally during the washing stage, the sample total resistance integral at the transition time is normalized and then positively correlated and mapped as the viscosity weighting coefficient. This accurately reflects the background component in the sample flow resistance characteristics caused only by viscous friction, which is beneficial to decoupling the simple viscosity effect from the tube wall adsorption effect in the calculation dimension. Among them, the larger the viscosity weighting coefficient, the higher the bulk viscosity of the sample, and the higher the theoretical expected tolerance of the system to the slow decrease in flow rate. Specifically, the formula for calculating the viscosity weighting factor is as follows: In the formula, The viscosity weighting factor; is the preset empirical adjustment factor; ln is the logarithmic function with the natural constant as the base; max is the maximum value function; Z is the integral of the total sample resistance at the conversion time; A preset total resistance integral benchmark is set. In this embodiment, a preset empirical adjustment factor is set to 0.1 to ensure that the system has appropriate compensation sensitivity to viscosity differences, which can both offset viscous flow resistance and retain necessary adsorption feedback. The implementer can set the preset empirical adjustment factor according to the packing pore size of different specifications of the trapping column and the pump flow rate, which is not limited here. A preset total resistance integral benchmark is set to 1.0 bar·s, which can be obtained by pre-measuring pure mobile phase or standard blank plasma samples with the same injection volume. It is used to eliminate viscosity calculation deviations caused by dead volumes in different equipment pipelines, which is not limited here. It should be noted that, through To achieve dimensionless processing of the drag integral, and to prevent logarithmic calculation crashes caused by a minimum negative value in the total sample drag integral at the transition time due to baseline fluctuations or sensor drift, a method is used... Provide mandatory nonnegative physical constraints for mathematical models; To obtain a pure adsorption feedback variable after eliminating viscosity interference, the product of the viscosity weighting coefficient and the measured pressure decay rate at each sampling time is used as the compensated measured pressure decay rate at each sampling time. In this case, the viscosity weighting coefficient serves as a compensation gain, amplifying the apparent value of the measured rate to offset the natural decay hysteresis caused by bulk viscosity.

[0041] Preferably, in one feasible embodiment, the flow lag ratio is obtained as follows: For any sampling moment, in order to quantify the real-time influence of the pipe wall's specific adsorption resistance on the cleaning process, the ratio of the theoretical pressure decay rate at that sampling moment to the sum of the compensated measured pressure decay rate at that sampling moment and a preset minimum positive number is used as the instantaneous lag ratio at that sampling moment. This accurately reflects the lag factor of the actual evacuation rate relative to the ideal evacuation rate after eliminating viscosity interference. The larger the instantaneous lag ratio, the slower the measured rate is relative to the theoretical expectation under the same pressure potential energy, and the stronger the retention and dragging effect of the pipe wall and packing surface on the fluid. In this embodiment, the preset minimum positive number is set as... bar / s, to prevent the calculation from crashing due to a zero denominator. Implementers can set a preset minimum positive number according to the data processing precision, which is not limited here; Considering the high-frequency electrical noise inherent in the pressure sensor and the local fluctuations caused by pump pulsation, to avoid severe oscillations in the control system due to single-point instantaneous anomalies, the instantaneous lag ratio at the current sampling moment is weighted and summed with the flow lag ratio at the previous adjacent moment, and this sum is used as the flow lag ratio at the current sampling moment. The formula for calculating the flow lag ratio is as follows: In the formula, Let be the flow hysteresis ratio at the t-th sampling time; Let be the instantaneous lag ratio at the t-th sampling time; The flow hysteresis ratio at the (t-1)th sampling time; Preset reference weights; this embodiment sets A value of 0.1 ensures a significant smoothing effect, allowing the output curve to reflect both the adsorption trend and shield against glitch noise. The implementer can set this value according to the sampling frequency and noise characteristics. No specific restrictions are imposed here. It should be noted that at the initial sampling time of the cleaning phase, the initial value of the flow lag ratio of the previous adjacent time is set to 1, which means that the standard state with no lag is assumed at the moment the cleaning begins.

[0042] Step S4: Use the flow hysteresis ratio to adaptively correct the preset standard threshold to obtain the target threshold. When the measured pressure decay rate is less than the target threshold, the cleaning is determined to be over and the flow path is switched.

[0043] Specifically, it is known that the adsorption intensity of different samples on the tube wall varies significantly. Using a single fixed time or fixed threshold for control will result in incomplete cleaning of samples with strong adsorption, or excessive cleaning of samples without adsorption, leading to loss of the target drug. In order to maximize analytical efficiency while ensuring the recovery rate of the target analyte, this embodiment uses the flow hysteresis ratio to adaptively correct the preset standard threshold to obtain the target threshold, thereby achieving closed-loop intelligent control where the stronger the adsorption, the lower the threshold and the longer the cleaning time.

[0044] The target threshold is obtained as follows: Considering that when channeling or air bubbles occur in the pipeline, causing the measured flow rate to be faster than the theoretical pressure decay rate, the flow hysteresis ratio may be less than 1. If it is directly used as a divisor, it will abnormally raise the target threshold and cause premature valve shut-off. Therefore, the flow hysteresis ratio at the current sampling moment is compared with the preset correction parameter, and the maximum value is defined as the correction coefficient to ensure that the target threshold will not be higher than the preset standard threshold under any circumstances. The larger the correction coefficient, the stronger the adsorption drag effect of the current sample. In order to reverse the pressure and reduce the cleaning completion standard, the dynamic threshold expectation for the current sample is determined by the ratio of the preset standard threshold to the correction coefficient. Considering that the flow hysteresis ratio is extremely large under extremely strong adsorption samples, the above ratio may infinitely approach zero, causing the measured decay rate to never reach the target condition and deadlock. In order to prevent the program from waiting indefinitely, the ratio of the preset standard threshold to the correction coefficient is compared with the preset minimum safety threshold, and the maximum value is defined as the target threshold at the current sampling moment to ensure that the calculated target threshold is always higher than the hardware detection limit. In this embodiment, a preset standard threshold is set to 0.05 bar / s to ensure that the preset standard threshold represents the typical evacuation flow rate of a sample without adsorption. The implementer can set the preset standard threshold according to the operating baseline noise of the pump, which is not limited here. A preset correction parameter is set to 1 to ensure unidirectional correction limitation in the absence of adsorption or negative hysteresis state. The implementer can set the preset correction parameter according to the theoretical error tolerance of the system, which is not limited here. A preset minimum safety threshold is set to 0.01 bar / s to ensure that it is slightly higher than the detection limit of the pressure sensor. The implementer can set the preset minimum safety threshold according to the resolution of the pressure sensor, which is not limited here.

[0045] Considering the drastic changes in fluid state during the initial few seconds of cleaning and the fact that the flow hysteresis ratio has not yet stabilized and converged, this embodiment sets a preset minimum cleaning time of 5 seconds to ensure that the non-steady-state transition zone in the initial stage of cleaning is avoided. This prevents premature termination of cleaning due to transient disturbances causing the measured pressure decay rate to occasionally fall below the target threshold. The implementer can set the preset minimum cleaning time according to the dead volume of the solid phase extraction column, which is not limited here. At the same time, a preset confirmation time period of 2 seconds is set to ensure that this duration covers at least one complete liquid pump pulse cycle. This allows the endpoint determination logic to effectively filter out interference from local flow rate troughs caused by the periodic suction and discharge actions of the pump. The implementer can set the preset confirmation time period according to the reciprocating frequency of the pump, which is not limited here. If the cleaning phase lasts longer than the preset minimum cleaning time, and the measured pressure decay rate is less than the target threshold for a continuous preset confirmation period, it indicates that the adsorbed residue in the current pipeline has been fully eluted and the flow rate has stabilized below the target level. In this case, cleaning is considered complete, and a flow path switch is executed, connecting the solid-phase extraction column to the analytical flow path. This facilitates the non-destructive transfer of the target drug from the trapping column to the chromatographic column for separation and detection, ensuring high-precision quantification of the chromatographic analysis system and long-term instrument cleanliness. It should be noted that if the cumulative cleaning time exceeds the preset maximum time (e.g., 120 seconds), a forced flow path switch will be executed, and an abnormal alarm will be output.

[0046] In summary, this embodiment first measures the empty baseline pressure; after sample injection, it calculates the net sample pressure and total resistance integral in real time, and identifies the transition time from injection to cleaning to lock the fluid dynamics boundary parameters; during the cleaning phase, it calculates the measured pressure decay rate and extrapolates the theoretical pressure decay rate under ideal evacuation conditions, uses the total resistance integral to compensate for viscosity at the measured rate, and then obtains the flow hysteresis ratio characterizing the sample matrix retention effect based on both; finally, it uses the flow hysteresis ratio to adaptively correct the standard threshold to obtain the target threshold, and performs flow path switching when the measured rate reaches the target. This invention effectively decouples sample viscosity and adsorption characteristics, realizes adaptive adjustment of cleaning time based on real-time sample kinetic feedback, and completely eliminates matrix residual contamination while ensuring the recovery rate of polar drugs.

[0047] Example 2: This invention also proposes a rapid detection system for trace plasma samples for pharmacokinetic studies; please refer to [link to relevant documentation]. Figure 2 The diagram shows a structural diagram of a rapid detection system for micro-plasma samples for pharmacokinetic studies provided by an embodiment of the present invention. The system includes: a baseline pressure acquisition module 10, a boundary parameter acquisition module 20, a flow hysteresis ratio acquisition module 30, and a decision analysis module 40.

[0048] The baseline pressure acquisition module 10 is used to determine the baseline pressure before injection.

[0049] The boundary parameter acquisition module 20 is used to calculate the sample net pressure in real time based on the baseline pressure after the injection starts and perform time integration to obtain the sample total resistance integral characterizing the sample bulk viscosity; to identify the transition time from injection to cleaning based on the change in sample net pressure; and to determine the fluid dynamics boundary parameters based on the sample net pressure and pressure change rate at the transition time.

[0050] The flow hysteresis ratio acquisition module 30 is used to calculate the measured pressure decay rate in real time after the cleaning starts, deduce the theoretical pressure decay rate under ideal evacuation conditions based on the boundary parameters, use the sample total resistance integral at the transition time to perform viscosity compensation on the measured pressure decay rate, and obtain the flow hysteresis ratio characterizing the sample matrix retention effect based on the theoretical pressure decay rate and the compensated measured pressure decay rate.

[0051] The judgment and analysis module 40 is used to adaptively correct the preset standard threshold by using the flow hysteresis ratio to obtain the target threshold. When the measured pressure decay rate is less than the target threshold, the cleaning is determined to be over and the flow path is switched.

[0052] It should be noted that the system provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above. In addition, the rapid detection system for micro-plasma samples for pharmacokinetic studies and the rapid detection method for micro-plasma samples for pharmacokinetic studies provided in the above embodiments belong to the same concept. The specific implementation process is detailed in the method embodiments and will not be repeated here.

[0053] Example 3: This invention also proposes a rapid detection device for trace plasma samples in pharmacokinetic studies. The device includes a memory and a processor. The memory stores executable program code, and the processor calls and executes this executable program code to perform a rapid detection method for trace plasma samples in pharmacokinetic studies provided in the embodiments of this application. Specifically, the device may be a chip, component, or module. The chip may include a connected processor and memory; the memory stores instructions, and when the processor calls and executes the instructions, the chip can perform the rapid detection method for trace plasma samples in pharmacokinetic studies provided in the above embodiments.

[0054] In addition, this embodiment also protects a computer device; please refer to [link to relevant documentation]. Figure 3The computer device includes a memory 401, a processor 402, and a computer program 403 stored in the memory 401 and running on the processor 402. When the processor 402 executes the computer program 403, the computer device can execute any of the aforementioned rapid detection methods for micro-plasma samples used in pharmacokinetic studies.

[0055] Example 4: The present invention also provides a computer-readable storage medium storing computer program code, which, when executed on a computer, causes the computer to perform the aforementioned method steps to implement the rapid detection method for micro-plasma samples for pharmacokinetic studies provided in the above embodiments.

[0056] Example 5: The present invention also provides a computer program product that, when run on a computer, causes the computer to perform the aforementioned related steps to realize the rapid detection method for micro-plasma samples for pharmacokinetic studies provided in the above embodiments.

[0057] In this embodiment, the device, computer-readable storage medium, computer program product, or chip are all used to execute the corresponding methods provided above. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods provided above, and will not be repeated here.

[0058] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0059] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A rapid detection method for trace plasma samples used in pharmacokinetic studies, characterized in that, The method includes the following steps: Measure the baseline pressure before injection; After the injection begins, the net pressure of the sample is calculated in real time based on the baseline pressure and integrated over time to obtain the total resistance integral of the sample, which characterizes the bulk viscosity of the sample; based on the change in the net pressure of the sample, the transition time from injection to cleaning is identified; based on the net pressure of the sample and the rate of change of pressure at the transition time, the boundary parameters of the fluid dynamics are determined. After the cleaning begins, the measured pressure decay rate is calculated in real time. Based on the boundary parameters, the theoretical pressure decay rate under ideal evacuation conditions is deduced. The viscosity is compensated for the measured pressure decay rate by integrating the total sample resistance at the transition time. Based on the theoretical pressure decay rate and the compensated measured pressure decay rate, the flow hysteresis ratio characterizing the sample matrix retention effect is obtained. The target threshold is obtained by adaptive correction using the flow hysteresis comparison to the preset standard threshold. When the measured pressure decay rate is less than the target threshold, the cleaning is determined to be completed and the flow path is switched.

2. The rapid detection method for trace plasma samples for pharmacokinetic studies as described in claim 1, characterized in that, The method for obtaining the baseline pressure is as follows: During the equilibrium phase when the injection valve is in the injection position and the injection pump delivers the mobile phase at a constant flow rate, the real-time pressure data collected in front of the column within a preset time window is defined as the first pressure data. The arithmetic mean of the first pressure data is used as the baseline pressure before sample injection without loading. If the arithmetic mean exceeds a preset empirical range or the standard deviation of the first pressure data exceeds a preset stability threshold, the current baseline pressure is determined to be invalid and a second measurement is performed. If the second measurement still fails the verification, the baseline pressure of the most recently successfully measured pressure will be retrieved from the memory.

3. The rapid detection method for trace plasma samples for pharmacokinetic studies as described in claim 1, characterized in that, The method for obtaining the net pressure of the sample is as follows: The real-time pre-column pressure data obtained after the start of injection is defined as the second pressure data. For any second pressure data at any sampling time, the difference between the second pressure data and the baseline pressure is compared with the preset minimum pressure data, and the maximum value is taken as the net sample pressure at that sampling time.

4. The rapid detection method for trace plasma samples for pharmacokinetic studies as described in claim 1, characterized in that, The method for obtaining the transition time is as follows: After the injection action is completed and a preset valve switching delay is passed, the first-order differential rate of change of the sample net pressure is calculated in real time. When the first-order differential rate of change of the sample is negative for a first preset number of consecutive sampling times, and the cumulative pressure drop within the first preset number of consecutive sampling times exceeds the preset noise tolerance, the current sampling time is determined to be the transition time from injection to cleaning.

5. The rapid detection method for trace plasma samples for pharmacokinetic studies as described in claim 1, characterized in that, The method for obtaining the boundary parameters is as follows: The sample net pressure at the transition time is compared with the preset minimum effective potential energy, and the maximum value is taken as the net pressure at the transition point. The net pressure of the sample is obtained at the transition time and the second preset number of consecutive sampling times thereafter, and the absolute value of the slope is calculated as the initial decay rate through linear regression. If the initial attenuation rate is less than the preset minimum effective slope, then the absolute value of the net pressure difference between the sample at the switching time and the adjacent sampling time thereafter is taken as the attenuation rate at the switching point. If the calculated transition point decay rate is less than the minimum slope constant, then the minimum slope constant is taken as the transition point decay rate. If the initial attenuation rate is greater than or equal to the preset minimum effective slope, then the initial attenuation rate is used as the attenuation rate at the transition point. The net pressure at the transition point and the decay rate at the transition point are used as boundary parameters of the fluid dynamics.

6. The rapid detection method for trace plasma samples for pharmacokinetic studies as described in claim 1, characterized in that, The method for obtaining the measured pressure decay rate is as follows: Select the sample net pressure sequence with the current sampling time as the end point and the starting point no earlier than the transition time; If the number of sampling times between the current sampling time and the transition time is greater than the third preset number, then the net pressure sequence of the samples is a sequence that includes the current sampling time and its preceding consecutive third preset number of sampling times; If the number of sampling times between the current sampling time and the transition time is less than or equal to the third preset number, then the net pressure sequence of the samples is the sequence from the transition time to the current sampling time. The net pressure sequence of the sample was linearly fitted using the least squares method, and the absolute value of the slope of the fitted line was taken as the measured pressure decay rate at the current sampling time.

7. The rapid detection method for trace plasma samples for pharmacokinetic studies as described in claim 5, characterized in that, The method for obtaining the theoretical pressure decay rate is as follows: After the cleaning begins, the ratio of the net pressure of the sample at the current sampling time to the net pressure at the transition point is taken as the first value; The product of the attenuation rate at the conversion point and the first value is taken as the theoretical pressure attenuation rate at the current sampling time.

8. The rapid detection method for trace plasma samples in pharmacokinetic studies as described in claim 1, characterized in that, The method for viscosity compensation of the measured pressure decay rate using the integral of the total sample resistance at the transition time is as follows: The result of normalizing the sample total resistance integral at the transition time and performing positive correlation mapping is used as the viscosity weighting coefficient. The product of the viscosity weighting coefficient and the measured pressure decay rate at each sampling time is used as the compensated measured pressure decay rate at each sampling time.

9. The rapid detection method for trace plasma samples in pharmacokinetic studies as described in claim 1, characterized in that, The method for obtaining the flow lag ratio is as follows: For any sampling time, the ratio of the theoretical pressure decay rate at that sampling time to the sum of the compensated measured pressure decay rate at that sampling time and a preset minimum positive number is taken as the instantaneous lag ratio at that sampling time. The weighted sum of the instantaneous lag ratio at the current sampling time and the flow lag ratio at the previous adjacent time is used as the flow lag ratio at the current sampling time.

10. The rapid detection method for trace plasma samples in pharmacokinetic studies as described in claim 1, characterized in that, The method for obtaining a target threshold by adaptively correcting a preset standard threshold using a flow hysteresis ratio, and determining the end of cleaning and performing flow path switching when the measured pressure decay rate is less than the target threshold, is as follows: The flow lag ratio at the current sampling time is compared with the preset correction parameter, and the maximum value is defined as the correction coefficient. The ratio of the preset standard threshold to the correction coefficient is compared with the preset minimum safety threshold, and the maximum value is defined as the target threshold at the current sampling time. If the duration of the cleaning phase exceeds the preset minimum cleaning time, and the measured pressure decay rate is less than the target threshold within a continuous preset confirmation period, then the cleaning is determined to be completed and the flow path is switched.

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