Portable gas chromatography-mass spectrometry system and method

Abstract

The invention relates to a portable expiration analysis device which is specially used for rapidly detecting various respiratory pathogens, such as new coronavirus and influenza virus. According to the equipment, different pathogens are identified and distinguished by analyzing volatile organic compounds (VOCs) in exhaled air by utilizing a gas chromatography-mass spectrometry (GC-MS) technology. The main innovation points of the invention are as follows: a novel small chromatographic column with low heat capacity and an asymmetric linear ion trap mass analyzer are developed, and the detection speed and sensitivity are improved; a set of data processing and matching algorithm based on a neural network is realized, so that the recognition capability of complex samples is enhanced; a user-friendly intelligent operation system is designed, the operation process is simplified, and the equipment can be easily used in various environments such as hospitals, airports and schools.

Description

Technical Field

[0001] The present invention belongs to the technical field of medical diagnosis, and particularly relates to a portable gas chromatography-mass spectrometry combined system and method. Background Art

[0002] Traditional methods for detecting respiratory pathogens are usually polymerase chain reaction (PCR) and antigen detection. Although this technology is relatively mature at present, there are also some obvious limitations, including long detection cycles, high costs, strong dependence on equipment, etc. These methods usually need to be carried out in a professional laboratory environment, involving complex sample processing and long waiting times, which are particularly insufficient when rapid diagnosis and response to public health emergencies are required. Moreover, the processing of the detection has a certain degree of professionalism, requiring complex laboratory equipment and professional personnel to operate. In the face of some emergency environments, it is not possible to detect respiratory diseases well through this method. Therefore, a portable detection system and method are needed to solve the above-mentioned problems. Summary of the Invention

[0003] The purpose of the present invention is to provide a portable gas chromatography-mass spectrometry combined system and method to solve one or more technical problems existing in the prior art, and at least provide a beneficial choice or create conditions.

[0004] Currently, the detection of respiratory pathogens in the market mostly relies on PCR technology and antigen detection. Although these methods are relatively mature, they have limitations such as long detection cycles, high costs, and strong dependence on equipment. For example, PCR detection requires laboratory conditions and professional personnel to operate, and the result feedback time is relatively slow. Although antigen detection is fast, its sensitivity and specificity are inferior to those of PCR. These limitations affect their practicality in rapid and large-scale screening of virus infections. In addition, existing equipment generally lacks flexibility and cannot meet the needs of simultaneous detection of multiple pathogens, which is particularly obvious during the flu season or the COVID-19 pandemic.

[0005] The technical solution provided by the present invention is a portable gas chromatography-mass spectrometry combined detection method, and the method includes: sample collection, sample transportation, gas chromatography-mass spectrometry combined detection, data processing and interpretation, result display and output.

[0006] Further, in the sample collection, the patient directly exhales a gas sample through a special breathing mask, and the special breathing mask can obtain the breathing gas sample, and the obtained breathing gas sample in the special breathing mask is detected by gas chromatography-mass spectrometry; in the sample transportation, the exhaled gas sample obtained by the sample volume weight is transmitted to the analysis part of the device through a closed system.

[0007] Preferably, the dedicated breathing mask is designed with more layers of filter cotton cores than ordinary medical masks, so that the compounds in the exhaled gas of the patient can be retained in the dedicated breathing mask to a greater extent, and it is designed to be non-invasive in the sample collection step, improving the comfort and acceptance of the tested person. The sample transportation step can ensure that the sample is not contaminated by the external environment and maintain the integrity and representativeness of the sample.

[0008] Furthermore, the gas chromatography-mass spectrometry detection includes gas chromatography separation and mass spectrometry analysis. In gas chromatography separation, various chemical substances in the exhaled gas sample are separated. Among the separated compounds, there are volatile organic compounds VOCs in the exhaled gas. The high-resolution mass spectrometry technology is used to identify the compounds associated with specific diseases in the volatile organic compounds VOCs to determine potential biomarkers. The high-resolution mass spectrometry technology is a low-heat-capacity small chromatographic column, which is used to improve the response speed and sample processing speed of the equipment. The low-heat-capacity small chromatographic column can be quickly heated and cooled, thereby reducing the residence time of the sample in the chromatographic column and accelerating the whole analysis process.

[0009] Furthermore, the mass spectrometry analysis includes the integration of an asymmetric linear ion trap mass analyzer and microfluidic technology. The asymmetric linear ion trap mass analyzer enhances the analysis ability of the mass spectrometry through an improved ion trap design, improving the sensitivity and resolution for complex samples and accurately identifying the compounds associated with specific diseases in the trace VOCs in the exhaled gas. The integration function of the microfluidic technology is to achieve precise manipulation and processing of the detection sample.

[0010] Furthermore, after the exhaled gas sample is detected by gas chromatography-mass spectrometry, data is output. In the data processing and interpretation, the deep learning algorithm of the Transformer model is used to process and analyze the data output after gas chromatography-mass spectrometry detection; the key of the Transformer model lies in the self-attention mechanism, which can be expressed as:

[0011]

[0012] Among them, Q represents the query, K represents the key, and V represents the value matrix. The specific values of Q, K, and V are obtained by converting the input data through different weight matrices. d k is the dimension of the key, which is used to adjust the scaling ratio to help the stability of the model.

[0013] Furthermore, in exhaled gas analysis, the Transformer model will be trained to identify disease-specific VOC patterns from the data generated by the portable gas chromatography-mass spectrometry detection method. Through the training of a large number of known samples, the model learns how to associate specific VOC configurations with specific diseases.

[0014] A portable gas chromatography-mass spectrometry system, the system comprising a deep learning algorithm, microfluidics technology, and an intelligent operating system for a low thermal mass chromatographic column, the deep learning algorithm, microfluidics technology, and an intelligent operating system for a low thermal mass chromatographic column are integrated into a complete portable detection system, and the system can run any of the portable gas chromatography-mass spectrometry detection methods described above.

[0015] Preferably, the data processing and interpretation mainly includes: the data collected from the portable gas chromatography-mass spectrometry system is input into a built-in data processing unit, where a preset algorithm or machine learning model is used to analyze the data, identify and compare known pathogen markers in the database, and thus obtain the test results.

[0016] The test results are presented directly to the operator through the device's display interface and can be transmitted wirelessly or wired to a central medical system or personal electronic device for further analysis and recording by medical staff.

[0017] This series of steps constitutes the core technical content of the present invention, which aims to provide a rapid and accurate respiratory pathogen detection solution through a portable device, which is suitable for various medical and public health environments.

[0018] The beneficial effects of the present invention are as follows: the developed portable GC-MS device is not only for the new coronavirus, but also can detect other respiratory infectious diseases such as influenza, providing a versatile detection platform that can adapt to a variety of public health emergency scenarios. The portability and rapid detection capabilities of the device enable it to be used directly in a variety of occasions outside the hospital, such as airports, schools or community centers, greatly improving the flexibility and efficiency of responding to public health emergencies. Microfluidic technology is integrated into the portable GC-MS device to achieve precise manipulation and processing of test samples, effectively reducing reagent consumption and improving the sensitivity and repeatability of detection. Combined with a self-built mass spectrometry database and a neural network-based matching algorithm, the speed and accuracy of pathogen identification are improved, especially when dealing with complex or unknown biomarkers. The GC-MS device adopts a modular design and is easy to maintain and upgrade. Users can replace or upgrade specific modules as needed without having to replace the entire system, extending the service life of the equipment and reducing long-term costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The above and other features of the present invention will become more obvious by describing in detail the embodiments shown in the accompanying drawings. The same reference numerals in the accompanying drawings of the present invention represent the same or similar elements. It is obvious that 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 work.

[0020] In the figure:

[0021] Figure 1 The figure shows a schematic flowchart of the principle of a portable gas chromatography - mass spectrometry detection method. Specific embodiments

[0022] The following will clearly and completely describe the concept, specific structure and technical effects of the present invention in combination with embodiments and drawings, so as to fully understand the purpose, scheme and effects of the present invention. It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0023] The technical solution provided by the present invention is a portable gas chromatography - mass spectrometry combined detection method, and the method includes: sample collection, sample transportation, gas chromatography - mass spectrometry combined detection, data processing and interpretation, result display and output.

[0024] Further, in the sample collection, the patient directly exhales a gas sample through a special breathing mask, and the special breathing mask can obtain the breathing gas sample, and the breathing gas sample obtained in the special breathing mask is detected by gas chromatography - mass spectrometry; in the sample transportation, the exhaled gas sample obtained by obtaining the sample volume weight is transmitted to the analysis part of the device through a closed system.

[0025] Preferably, the special breathing mask has more filter cotton cores than the ordinary medical mask in design, so that the compounds in the patient's exhaled gas can be retained to a greater extent in the special breathing mask, and it is designed to be non - invasive in the sample collection step, improving the comfort and acceptance of the detected person. The sample transportation step can ensure that the sample is not polluted by the external environment and maintain the integrity and representativeness of the sample.

[0026] Further, the gas chromatography - mass spectrometry combined detection includes gas chromatography separation and mass spectrometry analysis. Among them, various chemical substances in the exhaled gas sample are separated by gas chromatography separation. Among the separated compounds, there are volatile organic compounds VOCs in the exhaled gas. The compounds associated with specific diseases are identified through high - resolution mass spectrometry technology to determine potential biomarkers. The high - resolution mass spectrometry technology is a low - heat - capacity small chromatographic column. The low - heat - capacity small chromatographic column is used to improve the response speed and sample processing speed of the device. The low - heat - capacity small chromatographic column can be quickly heated and cooled, thereby reducing the residence time of the sample in the chromatographic column and accelerating the whole analysis process.

[0027] Furthermore, the mass spectrometry analysis includes the integration of an asymmetric linear ion trap mass analyzer and microfluidic technology. The asymmetric linear ion trap mass analyzer enhances the analysis ability of the mass spectrometry through an improved ion trap design, improves the sensitivity and resolution for complex samples, and accurately identifies compounds associated with specific diseases in trace VOCs in exhaled breath. The integration of the microfluidic technology serves to achieve precise manipulation and processing of the detection samples.

[0028] Furthermore, the exhaled gas sample outputs data after gas chromatography-mass spectrometry (GC-MS) detection. In the data processing and interpretation, a deep learning algorithm of the Transformer model is used to process and analyze the data output from the GC-MS detection; the key of the Transformer model lies in the self-attention mechanism, which can be expressed as:

[0029]

[0030] where Q represents the query, K represents the key, and V represents the value matrix. The specific values of Q, K, and V are obtained by converting the input data through different weight matrices, and d k is the dimension of the key, which is used to adjust the scaling ratio to help the stability of the model.

[0031] Furthermore, in the exhaled breath analysis, the Transformer model will be trained to identify disease-specific VOC patterns from the data generated by the portable gas chromatography-mass spectrometry detection method. Through the training of a large number of known samples, the model learns how to associate specific VOC configurations with specific diseases.

[0032] A portable gas chromatography-mass spectrometry system, which includes a deep learning algorithm, microfluidic technology, and an intelligent operating system for a low thermal mass chromatographic column. The deep learning algorithm, microfluidic technology, and intelligent operating system for the low thermal mass chromatographic column are integrated into a complete portable detection system, and the system can run any of the above portable gas chromatography-mass spectrometry detection methods.

[0033] Preferably, the data processing and interpretation mainly involve: the data collected from the portable gas chromatography-mass spectrometry system are input into the built-in data processing unit. Here, preset algorithms or machine learning models are used to analyze the data, identify and compare known pathogen markers in the database, so as to obtain the detection results.

[0034] The detection results are directly presented to the operator through the display interface of the device, and at the same time, they can be transmitted to the central medical system or personal electronic devices wirelessly or wiredly, facilitating further analysis and recording by medical staff.

[0035] This series of steps constitutes the core technical content of the present invention, aiming to provide a fast and accurate respiratory pathogen detection solution through a portable device, which is applicable to various medical and public health environments.

[0036] As Figure 1 shown, the technical route design of this project aims to develop an efficient portable rapid respiratory pathogen detection system by integrating advanced chromatography technology, mass spectrometry technology, microfluidics technology, and deep learning algorithms. The following is a detailed breakdown of the technical route:

[0037] Chromatography technology development:

[0038] Develop a low-heat-capacity small chromatography column: To improve the response speed and sample processing speed of the device, the project will develop a new type of low-heat-capacity chromatography column that can quickly heat and cool, thereby reducing the residence time of the sample in the chromatography column and accelerating the entire analysis process.

[0039] Mass spectrometry technology optimization:

[0040] Development of an asymmetric linear ion trap mass analyzer: Use an improved ion trap design to enhance the analysis ability of the mass spectrometry, especially to improve the sensitivity and resolution for complex samples, which is crucial for accurately identifying trace VOCs in exhaled breath.

[0041] Microfluidics technology integration:

[0042] Microfluidic-based flow path integration technology: Integrate microfluidics technology into the device to achieve precise control of gas and liquid samples. This technology helps to optimize the use of reagents and samples, reduce waste, and improve the repeatability and reliability of detection.

[0043] Deep learning algorithm application:

[0044] In this project, a deep learning algorithm based on the Transformer model will be developed and applied to process and analyze the data obtained from the GC-MS device. The Transformer model is specifically designed to identify and analyze complex patterns in mass spectrometry data, achieving high-precision identification of specific volatile organic compound (VOCs) patterns of respiratory pathogens. In addition, to improve the accuracy and generalization ability of the algorithm, we will combine the deep learning algorithm with traditional mass spectrometry data processing methods.

[0045] Intelligent operating system development:

[0046] Develop a user-friendly intelligent operating system: Design a simple and easy-to-use operation interface so that non-professionals can easily operate the device and support rapid diagnosis and data processing.

[0047] Integrated system integration and testing:

[0048] The technical components of the entire system, including deep learning algorithms, microfluidic technology, low heat capacity chromatographic columns and other related technologies, will be integrated into a complete portable detection system. Finally, comprehensive performance tests will be conducted on the entire system to verify its performance indicators such as detection speed, sensitivity, specificity, stability and reliability, ensuring that the system can meet the requirements of rapid on-site detection.

[0049] Detection speed: Traditional COVID-19 detection methods (such as PCR and antigen tests) usually take a long time to provide results, which poses a limitation in rapid screening and epidemic management. By developing a portable gas chromatography-mass spectrometry (GC-MS) device, the project aims to significantly shorten the detection time and enable rapid on-site detection.

[0050] The beneficial effects of the development are as follows:

[0051] Detection cost: The high detection cost limits the feasibility of large-scale screening. The project is committed to reducing the overall detection cost by independently developing low-cost detection reagents and equipment and reducing dependence on expensive consumables.

[0052] Operational convenience: Detection devices with complex operations usually require technicians with professional training to operate. The system developed in this project includes a user-friendly intelligent simplified Chinese operating system, enabling non-professionals to easily use it and thus simplifying the operation process.

[0053] Sensitivity and specificity: Improving the sensitivity and specificity of COVID-19 detection is crucial in public health responses. Through precise chromatographic techniques and mass analysis, as well as efficient data processing algorithms, the project hopes to achieve extremely high accuracy and reduce false negatives and false positives.

[0054] The above embodiments are only exemplary embodiments of this application and are not used to limit this application. The protection scope of this application is defined by the claims. Those skilled in the art can make various modifications or equivalent replacements within the essence and protection scope of this application, and such modifications or equivalent replacements should also be regarded as falling within the protection scope of this application.

Claims

1. A portable gas chromatography-mass spectrometry detection method, characterized in that, The method includes: sample collection, sample transportation, gas chromatography-mass spectrometry (GC-MS) detection, data processing and interpretation, and result display and output.

2. The portable gas chromatography-mass spectrometry detection method according to claim 1, wherein In the sample collection, the patient directly exhales a gas sample through a dedicated breathing mask. The dedicated breathing mask can obtain the breathing gas sample, and the obtained breathing gas sample in the dedicated breathing mask is detected by GC-MS. In the sample transportation, the exhaled gas sample obtained by the sample volume weight is transported to the analysis part of the device through a closed system.

3. A portable gas chromatography-mass spectrometry detection method according to claim 1, characterized in that, The GC-MS detection includes gas chromatography separation and mass spectrometry analysis. In gas chromatography separation, various chemical substances in the exhaled gas sample are separated. Among the separated compounds, there are volatile organic compounds (VOCs) in the exhaled breath. The compounds associated with specific diseases in the VOCs are identified by high-resolution mass spectrometry technology to determine potential biomarkers. The high-resolution mass spectrometry technology is a low-heat-capacity small chromatographic column, which is used to improve the response speed and sample processing speed of the device. The low-heat-capacity small chromatographic column can be quickly heated and cooled, thereby reducing the residence time of the sample in the chromatographic column and accelerating the entire analysis process.

4. The portable gas chromatography-mass spectrometry combined detection method according to claim 3, characterized in that, The mass spectrometry analysis includes the integration of an asymmetric linear ion trap mass analyzer and microfluidic technology. The asymmetric linear ion trap mass analyzer enhances the analysis ability of the mass spectrometry through an improved ion trap design, improves the sensitivity and resolution for complex samples, and accurately identifies the compounds associated with specific diseases in the trace VOCs in the exhaled breath. The integration function of the microfluidic technology is to achieve precise manipulation and processing of the detection sample.

5. A portable gas chromatography-mass spectrometry detection method according to claim 1, characterized in that, After the exhaled gas sample is detected by GC-MS, data is output. In the data processing and interpretation, a deep learning algorithm of the Transformer model is used to process and analyze the data output after GC-MS detection. The key of the Transformer model lies in the self-attention mechanism, which can be expressed as: Among them, Q represents the query, K represents the key, and V represents the value matrix. The specific values of Q, K, and V are obtained by converting the input data through different weight matrices, and d k is the dimension of the key, which is used to adjust the scaling ratio to help the stability of the model.

6. The portable gas chromatography-mass spectrometry detection method according to claim 5, characterized in that, In the exhaled breath analysis, the Transformer model will be trained to identify disease-specific VOC patterns from the data generated by the portable GC-MS detection method. Through the training of a large number of known samples, the model learns how to associate specific VOC configurations with specific diseases.

7. A portable gas chromatography - mass spectrometry combined system, characterized in that, The system includes a deep learning algorithm, microfluidic technology, and an intelligent operating system for a low-heat-capacity chromatographic column. The deep learning algorithm, microfluidic technology, and intelligent operating system for a low-heat-capacity chromatographic column are integrated into a complete portable detection system, and the system can run any one of the portable GC-MS detection methods in claims 1-6.

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