Portable detection device and method for oral exhaled air

Abstract

The present application belongs to the field of gas detection technology, and relates to a portable detection device and method for oral exhaled gas; comprising a gas collection end, a transmission pipeline, and a micro-integrated gas sensor; the gas collection end is connected to one end of the transmission pipeline, and the other end of the transmission pipeline outputs a gas sample, and the output gas sample is used for mass spectrometry detection; a plurality of the micro-integrated gas sensors are installed in the transmission pipeline, wherein the micro-integrated gas sensor is used to detect a target exhaled breath marker; at least one micro-integrated gas sensor is correspondingly provided for a target exhaled breath marker; by combining mass spectrometry detection with the sensor, the mass spectrum provides more abundant data information in multiple dimensions, and combined with the detection information of the sensor, it can effectively solve the problems of data interference and signal background noise, avoid interference from other gas components, and thereby improve the accuracy of recognition; and compared with traditional oral cancer screening methods, the present invention is faster and more efficient.

Description

Technical Field

[0001] The present application belongs to the field of gas detection technology, and more specifically, relates to a portable detection device and method for oral exhaled gas. Background Art

[0002] Current diagnostic methods for oral cancer primarily include clinical examination, imaging, pathology, and molecular testing. Early clinical examinations, due to the atypical symptoms of oral cancer and the lack of objective diagnostic criteria, can result in false-positive and false-negative results. Further imaging and invasive biopsies, requiring specialized equipment such as X-rays, CT scans, and MRIs, as well as physicians, can lead to long testing cycles and high costs, potentially delaying diagnosis and treatment and increasing the financial burden on patients.

[0003] Compared to the high cost, invasiveness, and low accessibility of biopsies and imaging, exhaled breath medical testing has made significant progress in recent years as a non-invasive diagnostic method. Because changes in a patient's metabolism and disease state can lead to variations in the composition and concentration of volatile organic compounds (VOCs) in exhaled breath, exhaled breath testing technology can screen for oral cancer by detecting the types and concentrations of characteristic VOC markers in exhaled breath.

[0004] The development of sensors for breath detection faces many challenges. Although common metal oxide semiconductor (MOS), surface acoustic wave (SAW), electrochemical, and NDIR sensors have the advantages of high sensitivity and fast response, they are easily interfered with by other gas components in complex gas environments, resulting in reduced recognition accuracy. For example, exhaled breath samples are affected by multiple factors such as diet and drug environment, resulting in complex background noise and severe data interference. Summary of the Invention

[0005] The present invention provides a portable detection device and method for oral exhaled air, which aims to solve the current technical problem that the detection is easily interfered by other gas components in a complex gas environment, thereby reducing the recognition accuracy.

[0006] A portable detection device for oral exhaled gas, comprising a gas collection end, a transmission pipeline, and a micro-integrated gas sensor;

[0007] The gas collection end is connected to one end of the transmission pipeline, and the other end of the transmission pipeline outputs a gas sample, which is used for mass spectrometry detection; a plurality of the micro-integrated gas sensors are installed in the transmission pipeline, wherein the micro-integrated gas sensors are used to detect target exhalation markers; and at least one micro-integrated gas sensor is correspondingly provided for each target exhalation marker.

[0008] In the present invention, by combining mass spectrometry detection with sensors, the mass spectrometer provides richer multi-dimensional data information, and combined with the detection information of the sensor, it can effectively solve the problems of data interference and signal background noise, avoid interference from other gas components, and thus improve the accuracy of recognition; and compared with traditional oral cancer screening methods, the present invention is faster and more efficient.

[0009] Furthermore, one end of the transmission pipeline outputting the gas sample is connected to an online mass spectrometer or a gas collection bag.

[0010] Furthermore, the transmission pipeline includes a heating section and a micro-integrated gas sensor installation section; wherein the heating section is provided with a heating device for heating the inside of the entire transmission pipeline.

[0011] Furthermore, the heating temperature of the heating section is at least 60 degrees Celsius.

[0012] Furthermore, the micro-integrated gas sensor installation section includes a detection chamber, in which a plurality of micro-integrated gas sensors are installed, and the detection chamber is provided with a gas input end, a gas output end, a power interface and a data interface, and the gas input end of the detection chamber is connected to the gas output end of the heating section;

[0013] The power interface is used to supply power to the micro-integrated gas sensor, and the data interface transmits the detection information output by the micro-integrated gas sensor.

[0014] In another aspect, the present invention provides a portable method for detecting oral exhaled breath, using a portable device for detecting oral exhaled breath according to the present invention, comprising the following steps:

[0015] Mass spectrometry data processing: Collect mass spectrometry data and convert ion flight time into mass-to-charge ratio through fast Fourier transform to generate a three-dimensional mass spectrometry data matrix containing timestamp, mass-to-charge ratio and signal intensity;

[0016] Sensor data processing: Based on the micro-integrated gas sensor, sample data is collected, dynamic resistance values are recorded, and ambient temperature and humidity are simultaneously recorded as compensation parameters to generate three-dimensional sensor data containing dynamic resistance values, temperature, and humidity parameters;

[0017] Mass spectrometry analysis: The characteristic peaks of the target object are identified through the characteristic peak matching algorithm, and the concentration of the target object is calculated. Quantitative analysis is performed using the calibrated response coefficient. At the same time, all mass spectrum peaks are detected in real time through mass spectrum data. The self-built proton transfer reaction method standard substance database is used for matching, potential interferences are identified, and a dynamic interference whitelist is established;

[0018] Dynamic compensation of sensor data: An environmental compensation algorithm is used to correct the resistance signal by taking temperature and humidity factors into account. The sliding baseline subtraction method is then used to remove the influence of environmental drift to obtain a purified sensor signal. The target and interfering substance concentration information provided by the mass spectrometry data is then used to establish a physical constraint model for cross-sensitivity compensation. During the compensation process, the recursive least squares algorithm is used to adjust the sensor's sensitivity parameters to the target and interfering substances in real time.

[0019] Data fusion: Mass spectrometry data and sensor data are weighted and fused based on their confidence levels to calculate the true concentration of the target. The confidence level of mass spectrometry data is assessed based on the target match and signal-to-noise ratio. The confidence level of sensor data is calculated by the error between the predicted value and the actual measured value.

[0020] Output results: Calculate the interference suppression ratio based on the calculated true concentration of the target, and output the true concentration of the target and the interference suppression ratio at the same time.

[0021] Preferably, the mass spectrometry analysis comprises the following steps:

[0022] Mass spectrometry data preprocessing: Perform polynomial fitting on the original signal within each mass spectrometry cycle, subtract the baseline signal based on the fitting result to obtain a corrected signal; then, based on the corrected signal, perform mass-to-charge ratio calibration using a known internal standard peak, and update the calibration coefficient for each cycle using a quadratic function model to obtain preprocessed mass spectrometry data;

[0023] Dynamic extraction of characteristic peaks: In the preprocessed mass spectrometry data, the most dramatic signal changes are found. The rising and falling points of the signal are identified through the first-order derivative, and the starting and ending positions of the peak are determined. Continuous wavelet transform is then used to enhance the peak shape of the signal. Peaks with a signal-to-noise ratio greater than 10 are screened based on a set intensity threshold to obtain the characteristic peaks and the corresponding m / z values.

[0024] Target matching: Select a target and match it by searching for its characteristic peaks. Based on the isotope peaks of the target, the Pearson correlation coefficient is used to verify the degree of consistency between the actual data and the theoretical isotope pattern. Successfully matched targets are screened based on the confidence threshold to obtain the matching results and confidence levels.

[0025] Quantitative concentration calculation: Based on temperature, pressure and laboratory calibration conditions as parameters, the response factor is dynamically corrected, and the concentration of the target is calculated based on the corrected response factor and the characteristic peak intensity data obtained by matching the target;

[0026] Interferor whitelist generation: All detected mass spectrometry peaks are grouped based on m / z values, ensuring that the peak spacing within each group is less than 0.1 Da; adjacent peaks with a peak intensity ratio greater than 1:10 are merged; the number of peaks in each group is greater than 3, and the total intensity of the group is greater than 1000 counts, based on this, a candidate peak list of interferors is obtained;

[0027] Based on the candidate peak list of interfering substances and their intensity distribution, a hash table is used to quickly query the peak characteristics of known compounds in the self-built proton transfer reaction method reference material database for matching, and the interfering substances and their types that match the candidate peaks are obtained. The interfering substances are classified according to the degree of influence, and the list of interfering substances in the white list is obtained. represents the concentration of the jth interfering substance; n represents the number of types of interfering substances;

[0028] The response coefficient and cross-sensitivity coefficient are optimized by recursive least squares method:

[0029] Preferably, the physical constraint model is as follows: Where: represents the sensor response data predicted based on the target concentration and the interferent concentration; represents the response coefficient; represents the cross-sensitivity coefficient of the jth interferent; represents the concentration of the jth interferent; n represents the number of interferent types;

[0030] The response coefficient and cross-sensitivity coefficient are optimized by recursive least squares method: Where: Represents the updated parameters; represents the parameters before updating; K represents the Kalman gain; Represents the actual response data of the sensor;

[0031] By using the physical constraint model Remove the influence of the interference from the original signal to obtain the net signal after compensation .

[0032] Preferably, the data fusion comprises the following steps:

[0033] Mass spectrum confidence calculation: Combine the characteristic peak matching and signal-to-noise ratio to calculate the confidence of the mass spectrum: Where: represents the signal-to-noise ratio of the main peak; Indicates the degree of characteristic peak matching, based on the comparison between the theoretical characteristic peak of the target and the matching peak in the actual mass spectrum data; The function is used to compress the calculation result to the range of 0 to 1; represents the confidence level of mass spectrum;

[0034] Sensor confidence calculation: Calculate the sensor confidence based on the ratio of the residual error to the dynamic error threshold: ;

[0035] Where: represents the residual; represents the dynamic error threshold; represents the confidence of the sensor; where the residual is calculated based on the following formula: ;

[0036] Where: represents the net signal after compensation; represents the sensor response data predicted based on the target concentration and the interferent concentration;

[0037] Based on the calculated confidence level, the weights of the mass spectrometry data and sensor data are calculated respectively, and the calculated weights are updated using the sliding window method. Based on the updated weights, the target concentration detected by mass spectrometry and the compensated net signal are weightedly fused to obtain the fused target concentration.

[0038] The beneficial effects of the present invention include: in the present invention, by combining mass spectrometry detection with a sensor, the mass spectrometer provides richer multi-dimensional data information, and combined with the detection information of the sensor, it can effectively solve the problems of data interference and signal background noise, avoid interference from other gas components, and thus improve the accuracy of recognition; and compared with traditional oral cancer screening methods, the present invention is faster and more efficient.

[0039] On the other hand, by establishing a mass spectrometry-guided dynamic compensation model, the cross-sensitivity effect of the sensor is effectively suppressed. By introducing a physical constraint model and a recursive least squares parameter update mechanism, combined with the real-time identification of the white list of interferents by mass spectrometry, the sensor response can be dynamically corrected, making the final concentration estimation more stable and accurate. By integrating the advantages of mass spectrometry and sensors, the error caused by a single data source is reduced, and the accuracy of concentration prediction is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0041] Figure 1 A schematic structural diagram of an implementation scheme provided for an embodiment of the present invention.

[0042] Figure 2A schematic structural diagram of another implementation scheme provided for an embodiment of the present invention.

[0043] Figure 3 This is a schematic diagram of the specific structure of the installation section of the micro-integrated gas sensor provided by an embodiment of the present invention.

[0044] Figure 4 A flowchart of a method provided by an embodiment of the present invention.

[0045] Figure 5 A block diagram of the specific steps of mass spectrometry analysis provided in an embodiment of the present invention.

[0046] Explanation of the accompanying drawings: 1. Micro-integrated gas sensor installation section; 2. Heating section; 3. Gas collection bag; 4. Heating device; 5. Online mass spectrometer; 6. Micro-integrated gas sensor; 7. Airtight joint; 8. Sealing rubber ring; 9. Power interface and data interface. DETAILED DESCRIPTION

[0047] In order to make the technical problems, technical solutions and beneficial effects to be solved by this application more clearly understood, this application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0048] See attached Figures 1 to 3 As shown, a portable detection device for oral exhaled gas includes a gas collection end, a transmission pipeline, and a micro-integrated gas sensor 6;

[0049] The gas collection end is connected to one end of the transmission pipeline, and the other end of the transmission pipeline outputs a gas sample, which is used for mass spectrometry detection; a plurality of the micro-integrated gas sensors 6 are installed in the transmission pipeline, wherein the micro-integrated gas sensor 6 is used to detect the target exhalation marker; and at least one micro-integrated gas sensor 6 is correspondingly provided for one target exhalation marker.

[0050] See attached Figure 1 and attached Figure 2 As a possible implementation of this embodiment, one end of the transmission pipeline outputting the gas sample is connected to an online mass spectrometer 5; real-time detection is achieved by connecting to the online mass spectrometer 5; the collected sample is transmitted to a miniaturized high-sensitivity proton transfer reaction mass spectrometer (such as an online proton transfer reaction mass spectrometer or an online ultraviolet photoionization mass spectrometer). This instrument uses H3O⁺ primary ions generated by a hollow cathode discharge ion source to undergo proton transfer reactions with VOCs in the sample to generate corresponding ions. After separation by a miniaturized mass analyzer, the ions enter a high-performance detector to obtain mass spectrum data.

[0051] As a possible implementation method of this embodiment, one end of the transmission pipeline that outputs the gas sample is connected to a gas collection bag 3; by setting up the gas collection bag 3, the gas sample is collected by the gas collection bag 3, and then the collected gas sample is input into the mass spectrometer for detection, based on this, various different needs can be met.

[0052] In this embodiment, a miniaturized high-sensitivity proton transfer reaction-time-of-flight mass spectrometer is used to achieve direct sampling and detection of exhaled breath samples without pretreatment. First, primary reagent ions are generated by a hollow cathode discharge ion source. These ions undergo a proton transfer reaction with the VOCs to be measured in the reaction chamber, thereby forming specific VOCs ions. The ions are separated at high resolution by a precisely designed miniaturized mass analyzer and then transferred to a high-performance detector for signal acquisition and processing. The entire device has a compact structure and weighs less than 10kg. At the same time, with its excellent performance of a detection limit of less than 0.5ppb, it can achieve high-precision detection of trace VOCs.

[0053] As a possible implementation of this embodiment, the transmission pipeline includes a heating section 2 and a micro-integrated gas sensor installation section 1; wherein the heating section 2 is provided with a heating device 4 for heating the inside of the pipe.

[0054] It should be noted that Figure 1 and Figure 2 The schematic diagrams in the figure all show that the heating section 2 is arranged at the front and rear ends of the micro-integrated sensor installation section 1, but this is not a limitation of the present invention. The optimal technical solution of the present invention can be that the heating section 2 runs through the entire transmission pipeline, that is, a heating device 4 is arranged outside the micro-integrated sensor installation section 1 (for example, electromagnetic heating is used); the temperature requirements of the entire process are fully guaranteed, and wall adsorption losses are effectively avoided.

[0055] See also Figure 1 and Figure 2 As shown, the micro-integrated gas sensor mounting section 1 is arranged in the middle, that is, the gas input end and the gas output end of the micro-integrated gas sensor mounting section 1 are respectively connected to a heating section 2; this is only one implementation method of the embodiment, and a heating section 2 can also be used, that is, it can be arranged before the gas input end or after the gas output end of the micro-integrated gas sensor mounting section 1.

[0056] The heating temperature of the heating section 2 is at least 60 degrees Celsius.

[0057] In this embodiment, by providing the heating section 2 and keeping the heating temperature at at least 60 degrees Celsius, the wall adsorption loss of VOCs can be effectively reduced.

[0058] See also Figure 3As shown, as a possible implementation of this embodiment, the micro-integrated gas sensor installation section 1 includes a detection chamber, in which a plurality of micro-integrated gas sensors 6 are installed, and the detection chamber is provided with a gas input end, a gas output end, a power interface and a data interface 9, and the gas input end of the detection chamber is connected to the gas output end of the heating section 2;

[0059] The power interface is used to supply power to the micro-integrated gas sensor 6 , and the data interface transmits detection information output by the micro-integrated gas sensor 6 .

[0060] The gas input end and the gas output end of the detection chamber are both installed with airtight joints 7, and the exemplary airtight joints 7 are peek joints.

[0061] The detection chamber is formed by assembling two box-lid-shaped structures, wherein the contact surfaces of the two box-lid-shaped structures are provided with a sealing rubber ring 8, for example, see Figure 3 A plurality of bolt mounting holes are provided on the two box cover-shaped structures, and the two box cover-shaped structures are assembled together by means of bolts and nuts.

[0062] In this embodiment, the micro-integrated gas sensor 6 operates at temperatures ranging from room temperature to 350 degrees Celsius. The temperature of the gas-sensing active area is controlled by an internal heating layer. This layer generates heat under the action of a heating voltage, thereby adjusting the sensor's optimal operating temperature. This enhances its gas detection sensitivity and selectivity and prevents signal drift caused by ambient temperature fluctuations. Furthermore, a sealing rubber ring 8 and an airtight joint 7 prevent gas sample leakage, which could reduce detection accuracy.

[0063] It should be noted here that since the operating temperature of the micro-integrated gas sensor 6 is from room temperature to 350 degrees Celsius, it may be thought that the temperature of our heating section 2 and the micro-integrated gas sensor 6 will have a mutual influence. It should be noted that the heating of the micro-integrated gas sensor 6 is achieved through the internal heating layer. The heating layer generates heat under the action of the heating voltage, thereby adjusting the optimal operating temperature of the micro-integrated gas sensor 6 to enhance its gas detection sensitivity and selectivity, and avoid signal drift caused by changes in ambient temperature; since it is a very small spatial range, there is no possibility of mutual influence.

[0064] As a possible implementation of this embodiment, multiple micro-integrated gas sensors 6 are integrated based on a micro PCB board, where the spacing between sensors is 1-2 mm, thereby avoiding cross interference and ensuring that the gas can evenly contact each sensor; the micro-integrated gas sensor 6 is connected to the conditioning circuit board through a connector to ensure the reliability of signal transmission.

[0065] As a possible implementation of this embodiment, a plurality of the micro-integrated gas sensors 6 for different target exhalation markers constitute a sensor array, wherein the sensor array is provided with at least one;

[0066] or

[0067] At least one of the micro-integrated gas sensors 6 targeting the same target exhalation marker constitutes a sensor array. A plurality of sensor arrays are provided, each sensor array targeting a different target exhalation marker.

[0068] It should be noted that the above-mentioned sensor array arrangement is only exemplary. Based on the contents disclosed in the present invention, conventional transformations of the above-mentioned array are within the scope of protection of the present invention. The best array arrangement of the present invention is that a plurality of micro-integrated gas sensors 6 for different target exhaled breath markers constitute a sensor array, wherein the sensor array is provided with at least one; two sensor arrays can be provided here, so as to ensure the smooth collection of each gas, that is, by setting a set of redundancy to ensure the smooth collection of gas, and also to ensure the quality of subsequent data processing.

[0069] As a possible implementation of this embodiment, the transmission pipeline uses a transmission pipeline made of peek material; the transmission pipeline made of peek material (with an inner diameter of 2 mm) has excellent properties such as low adsorption, chemical resistance, low permeability and high temperature tolerance, and is suitable for efficient transmission of exhaled gas.

[0070] As a possible implementation method of this embodiment, since the entire device can be portable and can be directly carried to carry out outdoor and community screening activities, the problem of carryover contamination needs to be considered; therefore, in this embodiment, by arranging an ultraviolet lamp in the heating section 2, the sterilization function of the ultraviolet lamp can achieve the instrument's carryover contamination rate of less than 0.5%.

[0071] See also Figure 4 As shown, on the other hand, the present invention provides a portable detection method for oral exhaled air, using the portable detection device for oral exhaled air of the present invention, comprising the following steps:

[0072] Mass spectrometry data processing: Collect mass spectrometry data and convert ion flight time into mass-to-charge ratio through fast Fourier transform to generate a three-dimensional mass spectrometry data matrix containing timestamp, mass-to-charge ratio and signal intensity;

[0073] Sensor data processing: Based on the micro-integrated gas sensor, sample data is collected, dynamic resistance values are recorded, and ambient temperature and humidity are simultaneously recorded as compensation parameters to generate three-dimensional sensor data containing dynamic resistance values, temperature, and humidity parameters;

[0074] It should be noted that the mass spectrometry data and the sensor data need to be time synchronized, and how to achieve time synchronization is a conventional technical means in this field, so it will not be described in detail.

[0075] Mass spectrometry analysis: The characteristic peaks of the target object are identified through the characteristic peak matching algorithm, and the concentration of the target object is calculated. Quantitative analysis is performed using the calibrated response coefficient. At the same time, all mass spectrum peaks are detected in real time through mass spectrum data. The self-built proton transfer reaction method standard substance database is used for matching, potential interferences are identified, and a dynamic interference whitelist is established;

[0076] As a possible implementation of this embodiment, see Figure 5 As shown, the mass spectrometry analysis includes the following steps:

[0077] Mass spectrometry data preprocessing: Polynomial fitting is performed on the original signal within each mass spectrometry cycle, where an exemplary fitting function is as follows: ;

[0078] in: represents the baseline intensity obtained by fitting; arrive represents the polynomial fitting coefficient;

[0079] represents the mass-to-charge ratio;

[0080] Based on the fitting results, the baseline signal is subtracted to obtain the corrected signal: ;

[0081] Where: Indicates the original signal strength; It represents the signal intensity after removing the baseline drift; represents the baseline intensity obtained by fitting;

[0082] The corrected signal is smoothed using a Savitzky-Golay filter to remove high-frequency noise from the signal. The Savitzky-Golay filter smoothes the signal using a 2nd-order polynomial with a window size of 7 points to obtain the corrected and smoothed signal data.

[0083] Calibration is performed using a known internal standard (e.g., PFTBA, with a known m / z value) based on mass spectral data with baseline drift and noise removed, assuming the peak of the internal standard is known. , the flight time measured experimentally To fit the calibration coefficient of mass-to-charge ratio, the relationship between flight time and mass-to-charge ratio is: Where: represents the true mass-to-charge ratio; and is the calibration coefficient fitted by the internal standard data;

[0084] A calibration function is fitted based on the mass-to-charge ratio and flight time of the known internal standard. This allows the m / z value in the mass spectrometry data to be adjusted to ensure that the error is within 0.02 Da.

[0085] Dynamic extraction of characteristic peaks: In the preprocessed mass spectrometry data, find the part where the signal changes most dramatically, identify the rising and falling points of the signal through the first-order derivative, and determine the starting and ending positions of the peak. The first-order derivative is used to describe the rate of change of signal intensity: ;in: represents the derivative of the signal, which is the rate of change of intensity; Indicates the intensity of mass spectrometry signal; Indicates the amount of change;

[0086] Then, continuous wavelet transform is used to enhance the peak shape characteristics of the signal. Based on the set intensity threshold (intensity greater than 5% of the maximum intensity), peaks with a signal-to-noise ratio greater than 10 are screened out to obtain characteristic peaks and corresponding m / z values. That is, by screening peaks with intensities greater than the maximum intensity and signal-to-noise ratios (SNR) greater than 10, the corresponding m / z values of these characteristic peaks are extracted. Target matching: Assuming the target is toluene, its characteristic peaks are known. , target identification is performed by searching for matching peaks in mass spectrometry data;

[0087] For the isotope peaks of the target compound, the Pearson correlation coefficient is used to verify the degree of agreement between the actual data and the theoretical isotope pattern, and to further distinguish substances with similar mass-to-charge ratios: Where: represents the Pearson correlation coefficient; Indicates the measured signal strength; Indicates the theoretical signal strength; Indicates the mean of the measured signal strength; Indicates the mean of theoretical signal strength;

[0088] For example, if , it means the target object is matched successfully;

[0089] Quantitative concentration calculation: Based on temperature, pressure and laboratory calibration conditions as parameters, the response factor is dynamically corrected, and the concentration of the target is calculated based on the corrected response factor and the characteristic peak intensity data obtained by matching the target; ;

[0090] Where: It represents the response factor under standard laboratory conditions; Indicates the pressure under current experimental conditions; Indicates the pressure under standard laboratory conditions; represents the temperature coefficient; Indicates the temperature under current experimental conditions; Indicates the temperature under standard laboratory conditions;

[0091] Exemplarily, the concentration of the target substance is calculated as follows:

[0092] Since proton transfer reaction mass spectrometry (PTR-MS) usually has no or few fragment peaks, the concentration calculation should focus on the quasi-molecular ion peak (i.e., the protonated ion peak) of the target compound;

[0093] For example, assuming the target compound is toluene, its quasi-molecular ion peak is m / z 93.14. Therefore, the concentration of the target compound can be directly calculated using the intensity of the quasi-molecular ion peak: ;

[0094] Where: Indicates the concentration of the target substance.

[0095] Interferent whitelist generation: All detected peaks are sorted in ascending m / z order and dynamically grouped based on m / z differences, ensuring that the maximum interval within a group does not exceed 0.1 Da. Next, adjacent peaks with an intensity ratio greater than 1:10 are merged to avoid double counting of fragment peaks. Each group must contain at least three peaks with a total integrated intensity greater than 1000 counts to exclude trace interferents.

[0096] To ensure the accuracy of the whitelist, the system is preloaded with a proton transfer reaction mass spectrometry fingerprint library of 500+ volatile organic compounds (VOCs); each compound stores the m / z values, isotope distribution patterns, and retention indices (RIs) of the first five characteristic peaks; during the matching process, a primary match is first performed using the m / z value of the main peak, allowing a deviation of ±0.02 Da; then, a secondary match is used, requiring at least two fragment peaks to match; based on this, the interferents that match the candidate peaks and their types are obtained; the interferents are then classified according to the degree of influence to obtain a list of interferents in the whitelist.

[0097] Dynamic compensation of sensor data: An environmental compensation algorithm is used to correct the resistance signal by taking temperature and humidity factors into account. The sliding baseline subtraction method is then used to remove the influence of environmental drift to obtain a purified sensor signal. The target and interfering substance concentration information provided by the mass spectrometry data is then used to establish a physical constraint model for cross-sensitivity compensation. During the compensation process, the recursive least squares algorithm is used to adjust the sensor's sensitivity parameters to the target and interfering substances in real time.

[0098] As a possible implementation of this embodiment, an exemplary environment compensation algorithm is as follows: ;

[0099] Where: Represents the sensor response data after compensation; Represents the raw response data of the sensor; Indicates the temperature compensation coefficient; T indicates the current temperature value; Indicates the humidity compensation coefficient; Indicates the current relative humidity value;

[0100] In this embodiment, compensation is performed based on the current temperature and humidity relative to a reference value (25°C and 50% humidity, which are only exemplary and the reference value can be set according to actual conditions), and the original sensor response is adjusted by a compensation coefficient;

[0101] Furthermore, the compensation coefficient can be updated smoothly by reading the temperature and humidity sensor data every 100ms and using linear interpolation to avoid sudden changes;

[0102] The exemplary technical solution for sliding baseline subtraction is as follows: the median of the past 10 days (300 data points) is calculated through a sliding window as a dynamic baseline to remove slow drift: ;

[0103] Where: represents the calculated baseline signal;

[0104] Use baseline subtraction to obtain the net signal after removing slow drift : ;

[0105] Where: represents the net signal after removing the slow drift;

[0106] As a possible implementation of this embodiment, the physical constraint model is as follows: Where: represents the sensor response data predicted based on the target concentration and the interferent concentration; represents the response coefficient; represents the cross-sensitivity coefficient of the jth interferent; represents the concentration of the jth interferent; n represents the number of interferent types; Indicates the concentration of the target substance;

[0107] The response coefficient and cross-sensitivity coefficient are optimized by recursive least squares method: ;

[0108] Where: Represents the updated parameters; represents the parameters before updating; K represents the Kalman gain; Represents the actual response data of the sensor, that is ;

[0109] By using the physical constraint model Remove the influence of the interference from the original signal to obtain the net signal after compensation ,Right now ; ;

[0110] Where: Indicates the target concentration measured by the sensor;

[0111] Based on the above technical solution, equal , but it is actually under ideal conditions, that is, the mass spectrometer can 100% identify all interferents (that is, the white list is complete), and the sensitivity of the sensor Constant, interference cross sensitivity coefficient When the sensor signal is completely known and accurate, and there is no noise in the sensor signal, the concentration after sensor compensation will be equal to the concentration calculated by the mass spectrometer. At this time, data fusion will be unnecessary.

[0112] However, in actual situations, the above ideal state cannot be achieved, so data fusion is very necessary; that is, the residual It is definitely not zero; the residual in the ideal state is zero, but due to problems in real scenarios, such as whitelist integrity and sensor sensitivity, the residual is definitely not zero; therefore, data fusion is required.

[0113] Data fusion: Mass spectrometry data and sensor data are weighted and fused based on their confidence levels to calculate the true concentration of the target. The confidence level of mass spectrometry data is assessed based on the target match and signal-to-noise ratio. The confidence level of sensor data is calculated by the error between the predicted value and the actual measured value.

[0114] As a possible implementation of this embodiment, the data fusion includes the following steps:

[0115] Mass spectrum confidence calculation: Combine the characteristic peak matching and signal-to-noise ratio to calculate the confidence of the mass spectrum: ;

[0116] Where: represents the signal-to-noise ratio of the main peak; Indicates the degree of characteristic peak matching, based on the comparison between the theoretical characteristic peak of the target and the matching peak in the actual mass spectrum data; The function is used to compress the calculation result to the range of 0 to 1; represents the confidence level of mass spectrum;

[0117] Sensor confidence calculation: Calculate the sensor confidence based on the ratio of the residual error to the dynamic error threshold: ;

[0118] Where: represents the residual; represents the dynamic error threshold; Indicates the confidence of the sensor;

[0119] The residual is calculated based on the following formula: ;

[0120] Where: represents the net signal after compensation; represents the sensor response data predicted based on the target concentration and the interferent concentration;

[0121] Based on the calculated confidence level, the weights of the mass spectrometry data and sensor data are calculated respectively, and the calculated weights are updated using the sliding window method. Based on the updated weights, the target concentration detected by mass spectrometry and the compensated net signal are weightedly fused to obtain the fused target concentration. ; Where: represents the mass spectrum fusion weight; represents the sensor fusion weight; ;

[0122] Where: represents the concentration of target after fusion;

[0123] Output results: Calculate the interference suppression ratio based on the calculated true concentration of the target, and output the true concentration of the target and the interference suppression ratio at the same time; the interference suppression ratio is obtained based on the following formula: ;

[0124] Where: represents the sum of all interferent concentrations; represents the interferent suppression ratio.

[0125] Oral cancer screening can be achieved by comparing the concentration of the detected target with the preset threshold. For example, the concentration of target VOCs such as ethyl butyrate is compared with the preset concentration threshold. If it exceeds the threshold, further examination can be guided.

[0126] The above-mentioned use of toluene as the target VOCs concentration is not a limitation of the present invention. It should be noted that obtaining markers (targets) for detecting oral cancer is a conventional technical means in this field, that is, these targets are conventional technical means in the medical field. Therefore, the present invention will not elaborate on this. In the present invention, the main purpose is to make the detected concentration more accurate and more conducive to subsequent judgment through the introduction of mass spectrometry data. When the oral cancer markers are clear and the detection data are accurate, how to determine whether oral cancer is likely to occur is a conventional technical means in the medical field. Therefore, the present invention will not elaborate on this.

[0127] It should be noted that this invention proposes for the first time the application of mass spectrometry data to the detection of oral cancer. By introducing mass spectrometry data to calibrate the sensor, an accurate data detection basis is finally achieved through fusion, thereby guiding subsequent oral cancer screening and judgment.

[0128] In this embodiment, a mass spectrometry-guided dynamic compensation model is established to effectively suppress the cross-sensitivity effect of the sensor. The physical constraint model and the recursive least squares parameter update mechanism are introduced. Combined with the real-time identification of the whitelist of interferents by the mass spectrometer, the sensor response can be dynamically corrected, making the final concentration estimation more stable and accurate. In addition, by integrating the advantages of mass spectrometry and sensors, the error caused by a single data source is reduced, and the accuracy of concentration prediction is improved.

[0129] In addition, the present invention establishes a dynamic whitelist of interferents to promptly identify and compensate for the impact of interferents. Especially in the case of significant interferents (such as ethanol, volatile organic compounds, etc.), the sensor can achieve dynamic compensation by adjusting the cross-sensitivity coefficient, which not only improves the adaptability of the sensor in complex environments, but also effectively reduces the concentration estimation error caused by interferents.

[0130] The above are only preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present application should be included in the scope of protection of the present application.

Claims

1. A method for detecting oral exhaled air, characterized in that: A portable detection device for oral exhaled gas is used, comprising a gas collection end, a transmission pipeline, and a micro-integrated gas sensor; The gas collection end is connected to one end of the transmission pipeline, and the other end of the transmission pipeline outputs a gas sample, and the output gas sample is used for mass spectrometry detection; a plurality of micro-integrated gas sensors are installed in the transmission pipeline, wherein the micro-integrated gas sensors are used to detect target exhaled breath markers; at least one micro-integrated gas sensor is correspondingly provided for each target exhaled breath marker; The detection method comprises the following steps: Mass spectrometry data processing: Collect mass spectrometry data and convert ion flight time into mass-to-charge ratio through fast Fourier transform to generate a three-dimensional mass spectrometry data matrix containing timestamp, mass-to-charge ratio and signal intensity; Sensor data processing: Based on the micro-integrated gas sensor, sample data is collected, dynamic resistance values are recorded, and ambient temperature and humidity are simultaneously recorded as compensation parameters to generate three-dimensional sensor data containing dynamic resistance values, temperature, and humidity parameters; Mass spectrometry analysis: The characteristic peaks of the target object are identified through the characteristic peak matching algorithm, and the concentration of the target object is calculated. Quantitative analysis is performed using the calibrated response coefficient. At the same time, all mass spectrum peaks are detected in real time through mass spectrum data. The self-built proton transfer reaction method standard substance database is used for matching, potential interferences are identified, and a dynamic interference whitelist is established; Dynamic compensation of sensor data: An environmental compensation algorithm is used to correct the resistance signal by taking temperature and humidity factors into account. The sliding baseline subtraction method is then used to remove the influence of environmental drift to obtain a purified sensor signal. The target and interfering substance concentration information provided by the mass spectrometry data is then used to establish a physical constraint model for cross-sensitivity compensation. During the compensation process, the recursive least squares algorithm is used to adjust the sensor's sensitivity parameters to the target and interfering substances in real time. Data fusion: Mass spectrometry data and sensor data are weighted and fused based on their confidence levels to calculate the true concentration of the target. The confidence level of mass spectrometry data is assessed based on the target match and signal-to-noise ratio. The confidence level of sensor data is calculated by the error between the predicted value and the actual measured value. Output results: Calculate the interference suppression ratio based on the calculated true concentration of the target, and output the true concentration of the target and the interference suppression ratio at the same time.

2. A method for detecting oral exhaled air according to claim 1, characterized in that: One end of the transmission pipeline outputting the gas sample is connected to an online mass spectrometer or a gas collection bag.

3. The method for detecting oral exhaled air according to claim 1, wherein: The transmission pipeline includes a heating section and a micro-integrated gas sensor installation section; wherein the heating section is provided with a heating device for heating the inside of the entire transmission pipeline.

4. The method for detecting oral exhaled air according to claim 3, characterized in that: The micro-integrated gas sensor installation section includes a detection chamber, in which a plurality of micro-integrated gas sensors are installed, and the detection chamber is provided with a gas input end, a gas output end, a power interface and a data interface, and the gas input end of the detection chamber is connected to the gas output end of the heating section; The power interface is used to supply power to the micro-integrated gas sensor, and the data interface transmits the detection information output by the micro-integrated gas sensor.

5. The method for detecting oral exhaled air according to claim 4, characterized in that: The gas input end and the gas output end of the detection chamber are both equipped with airtight joints.

6. The method for detecting oral exhaled air according to claim 1, characterized in that: A plurality of the micro-integrated gas sensors are integrated based on a micro PCB board, wherein the spacing between the sensors is 1-2 mm.

7. The method for detecting oral exhaled air according to claim 1, characterized in that: The mass spectrometry analysis comprises the following steps: Mass spectrometry data preprocessing: Perform polynomial fitting on the original signal within each mass spectrometry cycle, subtract the baseline signal based on the fitting result to obtain a corrected signal; then, based on the corrected signal, perform mass-to-charge ratio calibration using a known internal standard peak, and update the calibration coefficient for each cycle using a quadratic function model to obtain preprocessed mass spectrometry data; Dynamic extraction of characteristic peaks: In the preprocessed mass spectrometry data, the most dramatic signal changes are found. The rising and falling points of the signal are identified through the first-order derivative, and the starting and ending positions of the peak are determined. Continuous wavelet transform is then used to enhance the peak shape of the signal. Peaks with a signal-to-noise ratio greater than 10 are screened based on a set intensity threshold to obtain the characteristic peaks and the corresponding m / z values. Target matching: Select a target and match it by searching for its characteristic peaks. Based on the isotope peaks of the target, the Pearson correlation coefficient is used to verify the degree of consistency between the actual data and the theoretical isotope pattern. Successfully matched targets are screened based on the confidence threshold to obtain the matching results and confidence levels. Quantitative concentration calculation: Based on temperature, pressure and laboratory calibration conditions as parameters, the response factor is dynamically corrected, and the concentration of the target is calculated based on the corrected response factor and the characteristic peak intensity data obtained by matching the target; Interferor whitelist generation: All detected mass spectrometry peaks are grouped based on m / z values, ensuring that the peak spacing within each group is less than 0.1 Da; adjacent peaks with a peak intensity ratio greater than 1:10 are merged; the number of peaks in each group is greater than 3, and the total intensity within the group is greater than 1000 counts, based on which a candidate peak list of interferors is obtained; Based on the candidate peak list of interferents and their intensity distribution, a hash table is used to quickly query the peak characteristics of known compounds in the self-built proton transfer reaction method standard substance database for matching, and the interferents and their types that match the candidate peaks are obtained. The interferents are classified according to the degree of influence to obtain a list of interferents in the whitelist.

8. The method for detecting oral exhaled air according to claim 7, characterized in that: The physical constraint model is as follows: Where: represents the sensor response data predicted based on the target concentration and the interferent concentration; represents the response coefficient; represents the cross-sensitivity coefficient of the j-th interferer; represents the concentration of the jth interferent; n represents the number of interferent types; Indicates the concentration of the target substance; The response coefficient and cross-sensitivity coefficient are optimized by recursive least squares method: ; Where: Represents the updated parameters; represents the parameters before updating; K represents the Kalman gain; Represents the actual response data of the sensor; By using the physical constraint model Remove the influence of the interference from the original signal to obtain the net signal after compensation .

9. The method for detecting oral exhaled air according to claim 8, characterized in that: The data fusion comprises the following steps: Mass spectrum confidence calculation: Combine the characteristic peak matching and signal-to-noise ratio to calculate the confidence of the mass spectrum: Where: represents the signal-to-noise ratio of the main peak; Indicates the degree of characteristic peak matching, based on the comparison between the theoretical characteristic peak of the target and the matching peak in the actual mass spectrum data; The function is used to compress the calculation result to the range of 0 to 1; Indicates the confidence level of mass spectrum; Sensor confidence calculation: Calculate the sensor confidence based on the ratio of the residual error to the dynamic error threshold: ; Where: represents the residual; represents the dynamic error threshold; Indicates the confidence of the sensor; The residual is calculated based on the following formula: ; Where: represents the net signal after compensation; represents the sensor response data predicted based on the target concentration and the interferent concentration; Based on the calculated confidence level, the weights of the mass spectrometry data and sensor data are calculated respectively, and the calculated weights are updated using the sliding window method. Based on the updated weights, the target concentration detected by mass spectrometry and the net signal after compensation are weightedly fused to obtain the fused target concentration.

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