Micro-fluidic chip detection system and method

Abstract

The invention discloses a micro-fluidic chip detection system and method, and relates to the technical field of micro-fluidic chip detection systems, and the method comprises the following steps: S1, system preparation and calibration; the method comprises the following steps: (1) pretreating and assembling a micro-fluidic chip; (2) calibrating and debugging the system; s2, treating and loading a sample; s3, data acquisition and analysis; and S4, carrying out system maintenance and troubleshooting. According to the micro-fluidic chip detection system and method disclosed by the invention, pump speed and flow parameters in the micro-fluidic chip detection system are accurately set, so that stable flowing of fluid in an experiment process is ensured; the light source intensity and detector sensitivity parameters in the optical detection module are optimized to improve the detection performance and accuracy of the system, and the index moving average method is adopted to eliminate random noise, so that the influence of the noise can be effectively reduced, the processed data are smoother, and the trend and mode of the data can be better analyzed.

Description

Technical Field

[0001] The present invention relates to the technical field of microfluidic chip detection systems, and in particular to a microfluidic chip detection system and method. Background Art

[0002] Microfluidic chip technology integrates the basic operating units of sample preparation, reaction, separation, detection, etc. in the biological, chemical, and medical analysis process into a micrometer-scale chip to automatically complete the entire analysis process. Due to its huge potential in the fields of biology, chemistry, and medicine, it has developed into a new research field that intersects biology, chemistry, medicine, fluids, electronics, materials, and mechanics. Microfluidic chips are currently a hot area in the development of micro-total analysis systems.

[0003] Microfluidic chip analysis uses chips as operating platforms, analytical chemistry as its basis, micro-electromechanical processing technology as its support, micro-pipeline networks as its structural features, and life sciences as its main application objects. It is the focus of the current development of the field of micro-total analysis systems. Its goal is to integrate the functions of the entire laboratory, including sampling, dilution, reagent addition, reaction, separation, detection, etc., on a microchip, and it can be used multiple times. At present, in the process of detection through a microfluidic chip detection system, the steps of calibrating and debugging the microfluidic chip detection system are often ignored, which seriously affects the detection performance of the microfluidic chip detection system and the accuracy of the detection results. In addition, the existing microfluidic chip technology only uses a simple moving average method to reduce the noise of the data when processing the data, which makes it impossible to quickly reduce the noise and filter the data, resulting in a certain degree of unreality in the final data. In view of this, the present application proposes a microfluidic chip detection system and method. Summary of the invention

[0004] The invention discloses a microfluidic chip detection system and method, aiming to solve the technical problems raised in the background technology.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A microfluidic chip detection method comprises the following steps:

[0007] S1. System preparation and calibration;

[0008] (1) Pretreatment and assembly of microfluidic chips: The microfluidic chips are pretreated before system preparation. The pretreatment process includes cleaning, drying and packaging of the microfluidic chips.

[0009] (2) System calibration and debugging. During the calibration process, the pump speed and flow rate parameters are first accurately set to ensure the stable flow of the fluid during the experiment; then the light source intensity and detector sensitivity parameters in the optical detection module are optimized to improve the detection performance and accuracy of the system; finally, the overall system debugging is carried out through standard samples to verify the detection performance and accuracy of the system. During the calibration process, it is necessary to pay close attention to the response time and stability of the system to ensure that the system is in the best working state;

[0010] S2, sample processing and loading;

[0011] (1) Sample preparation and pretreatment: pretreat samples according to different testing requirements and sample characteristics;

[0012] (2) Sample loading and flow control. During the sample loading stage, the pre-treated sample is accurately injected into the chip through the inlet of the microfluidic chip with the help of a precision syringe pump and pressure source;

[0013] S3, data collection and analysis;

[0014] (1) Data acquisition method: optical method, electrochemical detection or mass spectrometry analysis can be used to acquire data according to the detection target and the working principle of the microfluidic chip;

[0015] (2) Data analysis and processing: remove invalid data, fill in missing values, and perform standardization operations on the collected raw data to ensure the integrity and reliability of the data set.

[0016] Data filtering and noise reduction: To further improve data quality, digital signal processing technology is used to filter and reduce noise on the data. At the same time, physical models or chemical principles are combined to correct and compensate the data, so that the final data is closer to the true value. Statistical methods and machine learning algorithms are used to deeply mine and analyze the data;

[0017] S4. System maintenance and troubleshooting;

[0018] (1) Daily maintenance and care;

[0019] Clean the chip. During the cleaning process, use appropriate solvents and cleaning methods, strictly follow the operating procedures, and avoid using overly intense or rough cleaning methods to effectively prevent damage to the chip surface and microstructure. Regular replacement of consumables is also an important step in maintaining system stability, including but not limited to flow path seals, buffer bottles and other consumables. These consumables may wear or age after long-term use, affecting the system's sealing performance and fluid control accuracy. Therefore, they need to be replaced in a timely manner according to the system's consumption and the manufacturer's recommendations. In addition, equipment calibration ensures that the system's performance parameters remain within the allowable error range by detecting key indicators such as sensitivity, resolution, and baseline stability.

[0020] (2) Troubleshooting and repair.

[0021] In a preferred solution, in step S1, when cleaning the microfluidic chip, the microfluidic chip is repeatedly rinsed with deionized water solvent to ensure that the chip surface is clean and free of impurities.

[0022] In a preferred embodiment, in step S1, when drying the microfluidic chip, a dedicated industrial oven is used for drying to remove excess water and residual solvent.

[0023] In a preferred solution, in step S1, a special fixture is used to accurately assemble the microfluidic chip with the fluid control system and the detection system when packaging the microfluidic chip, so as to ensure the sealing and stability of the chip during the experiment and ensure good docking between the components.

[0024] In a preferred solution, in step S2, during the data analysis and processing, the exponential moving average method is used to eliminate random noise when performing data filtering and noise reduction. The exponential moving average method suppresses noise and fluctuations in the data by calculating the weighted average of a series of data points, and the weight decays exponentially. Specifically, EMA assigns different weights to each data point. As time goes by, the weight of the earlier data point gradually decreases, while the weight of the newer data point is larger, which can better reflect the current trends and characteristics.

[0025] In a preferred embodiment, the principle formula of the exponential moving average method is as follows:

[0026] [EMA_t=\alpha\cdot x_t+(1-\alpha)\cdot EMA_{t-1}]

[0027] Among them, (\alpha)(0<(\alpha)<1) is the smoothing coefficient, (x_t) is the data point at the current moment, and (EMA_{t-1}) is the EMA value at the previous moment. This formula shows that the EMA value at the current moment is obtained by weighting the current data point and the EMA value at the previous moment, with weights of (\alpha) and ((1-\alpha)) respectively.

[0028] A microfluidic chip detection system includes the following modules:

[0029] Microfluidic chip, which is composed of two layers of substrates, the microfluidic system is constructed by micro-electromechanical processing technology, and the microfluidic chip is provided with structural units such as microchannels, microstructures, injection ports and detection windows, which are used to control and guide the flow of fluids to achieve sample mixing, reaction and detection;

[0030] An optical detection module, which uses laser-induced fluorescence to detect samples, can analyze samples quickly and accurately, and is suitable for high-throughput analysis needs;

[0031] The electrical impedance detection module analyzes the properties of the sample by measuring the change in the electrical impedance of the sample in the electric field. This method does not require complex immunolabeling pretreatment, is suitable for cell detection, and can improve the accuracy and sensitivity of detection.

[0032] The processor is responsible for controlling the operation of the entire system, including data processing and analysis. The processor works in conjunction with the microfluidic chip, the optical detection module, and the electrical impedance detection module to achieve an automated and high-throughput detection process.

[0033] As can be seen from the above. A microfluidic chip detection system and method provided by the present invention has the following technical effects. The present invention accurately sets the pump speed and flow rate parameters in the microfluidic chip detection system to ensure the stable flow of the fluid during the experiment; then optimizes the light source intensity and detector sensitivity parameters in the optical detection module to improve the detection performance and accuracy of the system; finally, the overall debugging of the system is carried out through standard samples to verify the detection performance and accuracy of the system, aiming to improve the overall performance of the detection system and achieve higher sensitivity and resolution measurement. At the same time, the present invention adopts the exponential moving average method to eliminate random noise, by giving a higher weight to the most recent data point and a lower weight to the earlier data point, so that the changes in recent data can be better reflected, thereby reducing the interference of historical data and improving the real-time and accuracy of data. Moreover, the calculation is relatively simple, and there is no need to store a large amount of historical data like the moving average method. Only the most recent data points need to be stored for calculation, which makes the exponential moving average method more efficient in calculation, and can be used for various types of time series data, whether it is stable or non-stationary, and can be better processed. By giving a higher weight to recent data, the influence of noise can be effectively reduced, making the processed data smoother, which helps to better analyze the trend and pattern of data. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 A schematic diagram of the steps of a microfluidic chip detection method proposed in the present invention.

[0035] Figure 2 This is a schematic diagram of the composition of a microfluidic chip detection system proposed in the present invention. DETAILED DESCRIPTION

[0036] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0037] Reference Figure 1 and Figure 2 , a microfluidic chip detection method, comprising the following steps:

[0038] S1. System preparation and calibration;

[0039] (1) Pretreatment and assembly of microfluidic chips: The microfluidic chips are pretreated before system preparation. The pretreatment process includes cleaning, drying and packaging of the microfluidic chips.

[0040] Specifically, when cleaning the microfluidic chip, the microfluidic chip is repeatedly rinsed with deionized water solvent to ensure that the chip surface is clean and free of impurities;

[0041] Specifically, when drying the microfluidic chip, a dedicated industrial oven is used for drying to remove excess water and residual solvent;

[0042] Specifically, when packaging the microfluidic chip, a special fixture is used to accurately assemble the microfluidic chip with the fluid control system and the detection system to ensure the sealing and stability of the chip during the experiment, while ensuring good docking between the various components.

[0043] Among them, the operation table for pretreatment of the microfluidic chip is as follows:

[0044]

[0045]

[0046] (2) System calibration and debugging. During the calibration process, the pump speed and flow rate parameters are first accurately set to ensure the stable flow of the fluid during the experiment; then the light source intensity and detector sensitivity parameters in the optical detection module are optimized to improve the detection performance and accuracy of the system; finally, the overall system debugging is carried out through standard samples to verify the detection performance and accuracy of the system. During the calibration process, it is necessary to pay close attention to the response time and stability of the system to ensure that the system is in the best working state;

[0047] Specifically, the calibration process is as follows:

[0048]

[0049]

[0050] S2, sample processing and loading;

[0051] (1) Sample preparation and pretreatment are aimed at removing impurities and useless components that may interfere with the test results, thereby improving the accuracy and sensitivity of the test. Samples are pretreated according to different test requirements and sample characteristics. The pretreatment methods are diverse and flexible, mainly including but not limited to dilution, filtration, concentration, etc. The dilution operation can effectively adjust the sample concentration to adapt it to the requirements of the microfluidic chip detection system; filtration can remove particulate matter, bacteria or other tiny impurities, and reduce the detection error caused by clogging the chip microchannel; and the concentration operation is aimed at low-concentration targets, and its concentration is increased by special methods to enhance the detection signal. In addition, in order to ensure the reliability of the test results, the uniformity and stability of the sample are also crucial. During the preparation process, measures must be taken to ensure that the sample is fully mixed and maintained in a stable state before testing to avoid precipitation, coagulation, etc. For complex samples, such as biological fluids, tissue extracts, etc., further separation, purification and other steps may be required. For example, through centrifugation, chromatography, extraction and other technologies, the target analyte is effectively separated from other components and interfering substances are removed, so as to obtain more accurate and clear test results;

[0052] (2) Sample loading and flow control. During the sample loading stage, the pre-treated sample is accurately injected into the chip through the inlet of the microfluidic chip with the help of a precise syringe pump and a pressure source. During this process, the sample injection volume must be strictly controlled to ensure that there is enough sample volume to complete the entire analysis process, while avoiding excessive injection that causes sample overflow or blockage of the microchannel. At the same time, the injection speed must also be precisely controlled, because too fast a speed may cause the sample to fail to fully contact the inner wall of the chip and affect the detection effect, while too slow a speed may prolong the analysis time and reduce the detection efficiency. In order to achieve uniform distribution and efficient analysis of the sample in the chip, flow control is particularly important. Microfluidic chip detection systems are usually equipped with advanced fluid control systems that can perform fine operations on the flow of samples in the chip. This includes setting key parameters such as flow direction and adjusting flow speed. A reasonable flow control strategy can ensure that the sample flows smoothly and orderly in the chip microchannel according to the preset path and achieves an ideal distribution state, thereby ensuring the efficiency and accuracy of the analysis process.

[0053] S3, data collection and analysis;

[0054] (1) Data acquisition method. Data acquisition plays a vital role in the operation of the microfluidic chip detection system. It is the cornerstone of the entire analysis process. According to the detection target and the working principle of the microfluidic chip, optical methods, electrochemical detection or mass spectrometry are used to acquire data. The optical method is a common data acquisition method. The changes in optical signals are captured by optical detection devices such as photodiodes and photomultiplier tubes, and then converted into electrical signals to realize data reading. For electrochemical detection, the electrode array on the microfluidic chip is used to undergo redox reactions with the substance to be tested, and the required data is obtained by measuring changes in parameters such as current, potential or conductivity. In addition, mass spectrometry also plays an important role in microfluidic chip detection. It can accurately analyze and identify the chemical components in the sample. When collecting data in real time, it is necessary to ensure the stability and accuracy of the acquisition equipment. Any noise interference and error may have a significant impact on the final result. Therefore, effective shielding and filtering measures should be taken to reduce the impact of environmental noise and other electromagnetic interference. For different detection needs, the selection of sampling frequency is crucial. Too high a frequency may lead to data redundancy, while too low a frequency may lose key information. Proper sampling time is also critical to ensure that enough data points are obtained as the sample flows through the detection area.

[0055] (2) Data analysis and processing: Data analysis and processing is one of the core steps in the microfluidic chip detection system. Its purpose is to extract valuable information from massive and complex data to support application decisions in the fields of scientific research and engineering technology. The collected raw data is processed to remove invalid data, fill in missing values, and standardize to ensure the integrity and reliability of the data set.

[0056] Data filtering and noise reduction,In order to further improve the data quality, digital signal processing technology is used to filter and reduce the noise of the data.

[0057] Specifically, the exponential moving average method is used to eliminate random noise. The exponential moving average method suppresses noise and fluctuations in the data by calculating the weighted average of a series of data points, and the weight decays exponentially. Specifically, EMA assigns different weights to each data point. As time goes by, the weight of older data points gradually decreases, while the weight of newer data points increases, which can better reflect current trends and characteristics;

[0058] The principle formula of the exponential moving average method is as follows:

[0059] [EMA_t=\alpha\cdot x_t+(1-\alpha)\cdot EMA_{t-1}]

[0060] Among them, (\alpha)(0<(\alpha)<1) is the smoothing coefficient, (x_t) is the data point at the current moment, and (EMA_{t-1}) is the EMA value at the previous moment. This formula shows that the EMA value at the current moment is obtained by weighting the current data point and the EMA value at the previous moment, with weights of (\alpha) and ((1-\alpha)) respectively.

[0061] At the same time, the data is corrected and compensated in combination with physical models or chemical principles, so that the final data is closer to the real value. Statistical methods and machine learning algorithms are used to deeply mine and analyze the data. Statistical analysis can intuitively display the distribution characteristics, trend changes and correlations between different variables of the data: machine learning models can automatically discover the laws and patterns hidden behind complex data, such as using support vector machines, neural networks, decision trees and other classifiers to predict and classify unknown samples; in addition, cluster analysis, association rule mining and other technologies can be used to deeply cluster data or discover association rules.

[0062] S4. System maintenance and troubleshooting;

[0063] (1) Daily maintenance and care;

[0064] In order to ensure the long-term stable operation of the microfluidic chip detection system, a series of daily maintenance and care work is required. These measures are essential to maintain the performance of the system, extend its service life, and ensure the accuracy of the experimental results. First, clean the chip. During the cleaning process, use appropriate solvents and cleaning methods, strictly follow the operating procedures, and avoid using overly intense or rough cleaning methods, so as to effectively prevent damage to the chip surface and microstructure; regular replacement of consumables is also an important step in maintaining the stability of the system, including but not limited to flow path seals, buffer bottles and other consumables. These consumables may wear or age after long-term use, affecting the sealing performance and fluid control accuracy of the system. Therefore, they need to be replaced in time according to the consumption of the system and the manufacturer's recommendations. In addition, equipment calibration ensures that the various performance parameters of the system remain within the allowable error range by detecting key indicators such as sensitivity, resolution, and baseline stability. During the calibration process, the calibration procedure should be strictly followed, and all calibration data should be recorded to trace and evaluate the historical performance of the system performance. In daily work, it is also necessary to conduct a comprehensive inspection of the operating status of the equipment regularly. This includes but is not limited to confirming whether the various components of the equipment are operating normally, observing the indicator lights and alarm information on the instrument panel, and checking the status of the internal components of the equipment. Once any abnormal signs or performance degradation are found, measures must be taken immediately to investigate and resolve them to prevent potential failures from developing into serious problems and affecting the experimental progress and data quality.

[0065] (2) Troubleshooting and repair;

[0066] In the process of using the microfluidic chip detection system, it is inevitable to encounter various types of faults. In the face of this situation, it is necessary to conduct systematic troubleshooting and repair work. When a fault occurs, the first task is to observe and analyze the fault phenomenon in detail, and clarify the specific manifestations, characteristics and possible causes of the fault. This usually requires a comprehensive judgment based on the equipment's usage records, operating manuals and related technical information. Effective troubleshooting can quickly locate the problem and take targeted repair measures. For hardware damage, such as physical damage such as chip cracking and sensor failure, new parts need to be replaced to ensure the normal operation of the system; for software or system setting problems, they can be solved by adjusting device parameters, updating software programs, etc. In the whole process of troubleshooting and repair, safety operating procedures must always be strictly followed to avoid safety accidents caused by improper operation. At the same time, detailed records should be kept for each fault phenomenon troubleshooting and repair process. This not only helps to summarize experience and lessons in a timely manner, but also provides a valuable reference for subsequent maintenance work, improving the overall efficiency and effectiveness of system maintenance.

[0067] A microfluidic chip detection system includes the following modules:

[0068] Microfluidic chip, which is composed of two layers of substrates, the microfluidic system is constructed by micro-electromechanical processing technology, and the microfluidic chip is provided with structural units such as microchannels, microstructures, injection ports and detection windows, which are used to control and guide the flow of fluids to achieve sample mixing, reaction and detection;

[0069] An optical detection module, which uses laser-induced fluorescence to detect samples, can analyze samples quickly and accurately, and is suitable for high-throughput analysis needs;

[0070] The electrical impedance detection module analyzes the properties of the sample by measuring the change in the electrical impedance of the sample in the electric field. This method does not require complex immunolabeling pretreatment, is suitable for cell detection, and can improve the accuracy and sensitivity of detection.

[0071] The processor is responsible for controlling the operation of the entire system, including data processing and analysis. The processor works in conjunction with the microfluidic chip, the optical detection module, and the electrical impedance detection module to achieve an automated and high-throughput detection process.

[0072] In summary, the present invention accurately sets the pump speed and flow rate parameters in the microfluidic chip detection system to ensure the stable flow of the fluid during the experiment; then optimizes the light source intensity and detector sensitivity parameters in the optical detection module to improve the detection performance and accuracy of the system; finally, the overall debugging of the system is carried out through standard samples to verify the detection performance and accuracy of the system, aiming to improve the overall performance of the detection system and achieve higher sensitivity and resolution measurement. At the same time, the present invention adopts the exponential moving average method to eliminate random noise, by giving a higher weight to the most recent data point and a lower weight to the earlier data point, so that the changes in recent data can be better reflected, thereby reducing the interference of historical data, improving the real-time and accuracy of the data, and the calculation is relatively simple, and there is no need to store a large amount of historical data like the moving average method, only the most recent data points need to be stored for calculation, which makes the exponential moving average method more efficient in calculation, and can be used for various types of time series data, whether it is stationary or non-stationary, It can be better processed, and by giving a higher weight to recent data, the influence of noise can be effectively reduced, making the processed data smoother, which helps to better analyze the trend and pattern of the data.

[0073] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. The replacement may be a replacement of a part of the structure, device, method step, or a complete technical solution. Any equivalent replacement or change according to the technical solution and the inventive concept of the present invention shall be covered within the protection scope of the present invention.

Claims

1. A microfluidic chip detection method, characterized in that: The following steps are involved: S1. System preparation and calibration; (1) Pretreatment and assembly of microfluidic chips: The microfluidic chips are pretreated before system preparation. The pretreatment process includes cleaning, drying and packaging of the microfluidic chips. (2) System calibration and debugging. During the calibration process, the pump speed and flow rate parameters are first accurately set, and then the light source intensity and detector sensitivity parameters in the optical detection module are optimized. Finally, the overall system debugging is performed using standard samples to verify the system's detection performance and accuracy. S2, sample processing and loading; (1) Sample preparation and pretreatment: pretreat samples according to different testing requirements and sample characteristics; (2) Sample loading and flow control. During the sample loading stage, the pre-treated sample is accurately injected into the chip through the inlet of the microfluidic chip with the help of a precision syringe pump and pressure source; S3, data collection and analysis; (1) Data acquisition method: optical method, electrochemical detection or mass spectrometry analysis can be used to acquire data according to the detection target and the working principle of the microfluidic chip; (2) Data analysis and processing: removing invalid data, filling missing values, and standardizing the collected raw data; Data filtering and noise reduction: using digital signal processing technology to filter and reduce noise on data, while combining physical models or chemical principles to correct and compensate data, and using statistical methods and machine learning algorithms to conduct in-depth data mining and analysis; S4. System maintenance and troubleshooting; (1) Daily maintenance and care; Clean the chip. During the cleaning process, use appropriate solvents and cleaning methods, strictly follow the operating procedures, and avoid using overly intense or rough cleaning methods to effectively prevent damage to the chip surface and microstructure; regularly replace consumables; calibrate the equipment to ensure that the system's performance parameters remain within the allowable error range by testing key indicators such as sensitivity, resolution, and baseline stability; (2) Troubleshooting and repair.

2. A microfluidic chip detection method according to claim 1, characterized in that: In step S1, when cleaning the microfluidic chip, the microfluidic chip is repeatedly rinsed with deionized water solvent.

3. A microfluidic chip detection method according to claim 1, characterized in that: In step S1, when drying the microfluidic chip, a dedicated industrial oven is used for drying.

4. A microfluidic chip detection method according to claim 1, characterized in that: In step S1, when packaging the microfluidic chip, a special fixture is used to accurately assemble the microfluidic chip, the fluid control system, and the detection system.

5. A microfluidic chip detection method according to claim 1, characterized in that: In step S2, during the data analysis and processing, when performing data filtering and noise reduction processing, the exponential moving average method is used to eliminate random noise. The exponential moving average method suppresses noise and fluctuations in the data by calculating the weighted average of a series of data points, and the weight decays exponentially.

6. A microfluidic chip detection method according to claim 5, characterized in that: The principle formula of the exponential moving average method is as follows: [EMA_t=\alpha\cdot x_t+(1-\alpha)\cdot EMA_{t-1}] Among them, (\alpha)(0<(\alpha)<1) is the smoothing coefficient, (x_t) is the data point at the current moment, and (EMA_{t-1}) is the EMA value at the previous moment. This formula shows that the EMA value at the current moment is obtained by weighting the current data point and the EMA value at the previous moment, with weights of (\alpha) and ((1-\alpha)) respectively.

7. A microfluidic chip detection system, characterized in that: Includes the following modules: Microfluidic chip, which is composed of two layers of substrates, the microfluidic system is constructed by micro-electromechanical processing technology, and the microfluidic chip is provided with structural units such as microchannels, microstructures, injection ports and detection windows, which are used to control and guide the flow of fluids to achieve sample mixing, reaction and detection; An optical detection module, wherein the optical detection module detects the sample using laser-induced fluorescence; An electrical impedance detection module, which analyzes the properties of the sample by measuring the change of the electrical impedance of the sample in an electric field; The processor is responsible for controlling the operation of the entire system, including data processing and analysis.

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