Portable detection device and method for oral expired gas

Abstract

The invention belongs to the technical field of gas detection, and relates to a portable detection device and method for oral expired gas. Comprising a gas collection end, a transmission pipeline and a micro-integrated gas sensor, the gas collection end is connected with one end of the transmission pipeline, the other end of the transmission pipeline outputs a gas sample, and the output gas sample is used for mass spectrometry detection; the transmission pipeline is internally provided with a plurality of micro-integrated gas sensors, and the micro-integrated gas sensors are used for detecting a target expiration marker; at least one micro-integrated gas sensor is correspondingly arranged for one target expiration marker; mass spectrum detection is combined with a sensor, multi-dimensional richer data information is provided through a mass spectrum, and detection information of the sensor is combined, so that the problems of data interference and signal background noise can be effectively solved, interference of other gas components is avoided, and the recognition accuracy is further improved; compared with a traditional oral cancer screening method, the oral cancer screening method is higher in speed and more efficient.

Description

Technical Field

[0001] This application belongs to the technical field of gas detection. More specifically, it relates to a portable detection device and method for oral exhaled breath. Background Art

[0002] The existing diagnostic methods for oral cancer mainly include clinical examination, imaging examination, pathological examination, and molecular biological detection, etc. In early clinical examinations, due to the atypical symptoms of oral cancer and mainly relying on the experience of physicians for judgment, lacking objective diagnostic indicators, false positive and false negative results may occur. Further imaging examinations and invasive biopsies, etc., have high demands for professional equipment such as X-rays, CTs, MRIs, etc. and physicians, with a long detection cycle, high cost, patients are prone to delay diagnosis and treatment and may increase the economic burden.

[0003] Compared with the high cost, invasiveness, and low accessibility of biopsies and imaging, exhaled breath medical detection technology, as a non-invasive diagnostic method, has made remarkable developments in recent years. Since the metabolism and disease state changes of patients will lead to changes in the composition and concentration of volatile organic compounds (VOCs) in exhaled breath, exhaled breath detection technology realizes oral cancer screening by detecting the types and concentrations of VOCs characteristic markers in exhaled breath.

[0004] The research and development of sensors for exhaled breath detection face multiple challenges. Common sensors such as metal oxide semiconductor (MOS), surface acoustic wave (SAW), electrochemical, NDIR, etc. Although they have the advantages of high sensitivity and fast response, they are easily interfered by other gas components in a complex gas environment, resulting in a reduction in the recognition accuracy rate; for example, exhaled breath samples are affected by multiple factors such as diet, drug environment, etc., with complex background noise and serious data interference problems. Summary of the Invention

[0005] The present invention provides a portable detection device and method for oral exhaled breath, aiming to solve the technical problem that it is easily interfered by other gas components in a complex gas environment, thereby resulting in a reduction in the recognition accuracy rate.

[0006] A portable detection device for oral exhaled breath includes 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 target exhaled breath markers; at least one micro-integrated gas sensor is correspondingly set for one target exhaled breath marker.

[0007] In the present invention, by connecting mass spectrometry detection with a sensor, multi-dimensional and richer data information is provided by mass spectrometry, and combined with the detection information of the sensor, problems of data interference and signal background noise can be effectively solved, interference from other gas components can be avoided, and thus the accuracy of identification can be improved; moreover, compared with traditional oral cancer screening methods, the present invention is faster and more efficient.

[0008] Further, one end of the transmission pipeline for outputting a gas sample is connected to an on-line mass spectrometer or a gas collection bag.

[0009] Further, 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.

[0010] Further, the heating temperature of the heating section is at least 60 degrees Celsius.

[0011] Further, the micro-integrated gas sensor installation section includes a detection chamber, in which a plurality of the micro-integrated gas sensors are installed, and the detection chamber is provided with a gas input end, a gas output end, a power supply 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; wherein the power supply interface is used to supply power to the micro-integrated gas sensors, and the data interface transmits the detection information output by the micro-integrated gas sensors.

[0012] On the other hand, the present invention provides a portable detection method for oral exhaled breath. Using a portable detection device for oral exhaled breath according to the present invention, the method includes the following steps: Mass spectrometry data processing: Collect mass spectrometry data, and convert the ion flight time into a mass-to-charge ratio through fast Fourier transform to generate a three-dimensional mass spectrometry data matrix including a timestamp, a mass-to-charge ratio, and a signal intensity; Sensor data processing: Based on the micro-integrated gas sensors, collect sample data, record the dynamic resistance value, synchronously record the ambient temperature and humidity as compensation parameters, and generate three-dimensional sensor data including the dynamic resistance value, temperature, and humidity parameters; Mass spectrometry analysis: Identify the characteristic peaks of the corresponding target substances through a characteristic peak matching algorithm of the target substances, calculate the concentration of the target substances, perform quantitative analysis through a calibrated response coefficient, and at the same time, detect all the mass spectrometry peaks in real time through the mass spectrometry data, match them with a self-built proton transfer reaction method standard substance database, identify potential interfering substances, and establish a dynamic interfering substance whitelist; Dynamic compensation of sensor data: Through the environmental compensation algorithm, the temperature and humidity factors are taken into account to correct the resistance signal, and then the sliding baseline subtraction method is used to remove the influence of environmental drift to obtain a purified sensor signal. Then, the target and interferent concentration information provided by the mass spectrometry data is 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 interferent in real time; Data fusion: The mass spectrometry data and sensor data are weighted and fused according to their confidence levels to calculate the true concentration of the target. The confidence level of the mass spectrometry data is evaluated based on the matching degree and signal-to-noise ratio of the target. The confidence level of the 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.

[0013] Preferably, the mass spectrometry analysis comprises the following steps: Mass spectrometry data preprocessing: Perform polynomial fitting on the original signal in each mass spectrometry cycle, deduct the baseline signal based on the fitting result, and obtain the corrected signal; then, based on the corrected signal, use the known internal standard peak to calibrate the mass-to-charge ratio, and update the calibration coefficient of each cycle through the quadratic function model to obtain the preprocessed mass spectrometry data; 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, determine the starting and ending positions of the peak, and then use continuous wavelet transform to enhance the peak shape characteristics of the signal. Based on the set intensity threshold, filter out the peaks with a signal-to-noise ratio greater than 10 to obtain the characteristic peaks and the corresponding m / z values; Target matching: select a target, match it by searching for its characteristic peaks, and use the Pearson correlation coefficient to verify the degree of agreement between the actual data and the theoretical isotope pattern for the target's isotope peaks. Filter out the successfully matched targets based on the confidence threshold to obtain the matching results and confidence levels. Quantitative calculation of concentration: 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; Generation of whitelist of interferents: All detected mass spectrometry peaks are grouped based on m / z values ​​to ensure that the peak spacing within each group is less than 0.1Da; 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 which a candidate peak list of interferents is obtained; Based on the candidate peak list of interferents and their intensity distributions, a hash table is used to quickly query the peak characteristics of known compounds in the self-built standard substance database of proton transfer reaction method for matching, so as to obtain the interferents and their types that match the candidate peaks. The interferents are classified according to the degree of influence to obtain the interferent list in the whitelist. represents the concentration of the j-th interferent; n represents the number of types of interferents; For the response coefficient and the cross-sensitivity coefficient, they are optimized by the recursive least squares method: Preferably, the physical constraint model is as follows: ; In the formula: represents the sensor response data predicted according to the target substance concentration and the interferent concentration; represents the response coefficient; represents the cross-sensitivity coefficient of the j-th interferent; represents the concentration of the j-th interferent; n represents the number of types of interferents; For the response coefficient and the cross-sensitivity coefficient, they are optimized by the recursive least squares method: ; In the formula: represents the updated parameter; represents the parameter before update; K represents the Kalman gain; represents the actual response data of the sensor; By using the obtained from the physical constraint model Remove the influence of the interferent from the original signal to obtain the compensated net signal .

[0014] Preferably, the data fusion includes the following steps: Mass spectrometry confidence calculation: Combining the characteristic peak matching degree and the signal-to-noise ratio, calculate the confidence of the mass spectrometry: ; In the formula: represents the signal-to-noise ratio of the main peak; represents the characteristic peak matching degree, which is based on the comparison between the theoretical characteristic peak of the target substance and the peaks matched in the actual mass spectrometry data; The function is used to compress the calculation result to the interval from 0 to 1; represents the mass spectrometry confidence; Sensor confidence calculation: Calculate the confidence of the sensor according to the ratio of the residual to the dynamic error threshold: ; In the formula: represents the residual; represents the dynamic error threshold; represents the confidence of the sensor; where the residual is calculated based on the following formula: ; In the formula: represents the compensated net signal; Represents the sensor response data predicted based on the target concentration and interferent concentration; Based on the calculated confidence levels, the weights of the mass spectrometry data and the sensor data are calculated respectively, and a sliding window method is used to update the weights of the calculated weights. Based on the updated weights, the target concentration detected by mass spectrometry and the compensated net signal are weighted and fused to obtain the fused target concentration.

[0015] The beneficial effects of the present invention include: In the present invention, by connecting mass spectrometry detection with a sensor, rich data information in multiple dimensions is provided by mass spectrometry, and combined with the detection information of the sensor, the problems of data interference and signal background noise can be effectively solved, avoiding interference from other gas components, and thus improving the accuracy of identification; and compared with traditional oral cancer screening methods, the present invention is faster and more efficient.

[0016] 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 interference white list identified in real time by mass spectrometry, the sensor response can be dynamically corrected, making the final concentration estimation more stable and accurate. And by integrating the advantages of mass spectrometry and the sensor, the error caused by a single data source is reduced, and the accuracy of concentration prediction is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] To more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0018] Figure 1 It is a schematic structural diagram of an implementation manner provided for an embodiment of the present invention.

[0019] Figure 2 It is a schematic structural diagram of another implementation manner provided for an embodiment of the present invention.

[0020] Figure 3 It is a schematic structural diagram of the specific structure of the micro-integrated gas sensor installation section provided for an embodiment of the present invention.

[0021] Figure 4 It is a flowchart of the method provided for an embodiment of the present invention.

[0022] Figure 5 It is a block diagram of the specific steps of mass spectrometry analysis provided for an embodiment of the present invention.

[0023] Description of reference numerals: 1. Installation section of the micro-integrated gas sensor; 2. Heating section; 3. Gas collection bag; 4. Heating device; 5. Online mass spectrometer; 6. Micro-integrated gas sensor; 7. Hermetic joint; 8. Sealing rubber ring; 9. Power supply interface and data interface. Detailed implementation manners

[0024] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present application clearer and more understandable, the present application will be 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 the present application and are not used to limit the present application.

[0025] See the 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; One end of the transmission pipeline is connected to the gas collection end, 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 6 are installed in the transmission pipeline, wherein the micro-integrated gas sensor 6 is used to detect target exhaled biomarkers; at least one micro-integrated gas sensor 6 is correspondingly provided for one target exhaled biomarker.

[0026] See the attached Figure 1 and the attached Figure 2 As a possible implementation manner of this embodiment, one end of the transmission pipeline that outputs the gas sample is connected to an online mass spectrometer 5; real-time detection is realized by connecting 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, an online ultraviolet photoionization mass spectrometer), and the instrument uses H3O⁺ primary ions generated by a hollow cathode discharge ion source to generate corresponding ions through proton transfer reaction with VOCs in the sample, and enters a high-performance detector after being separated by a miniaturized mass analyzer, thereby obtaining mass spectrometry data.

[0027] As a possible implementation manner of this embodiment, one end of the transmission pipeline that outputs the gas sample is connected to a gas collection bag 3; by providing the gas collection bag 3, the gas sample is collected through the gas collection bag 3, and then the collected gas sample is input into the mass spectrometer for detection, and based on this, different requirements in multiple aspects can be realized.

[0028] In this embodiment, a miniaturized high-sensitivity proton transfer reaction-time of flight mass spectrometer is adopted, which can realize direct injection detection of exhaled breath samples without pretreatment. First, primary reagent ions are generated by a hollow cathode discharge ion source. These ions undergo proton transfer reactions with the VOCs to be measured in the reaction chamber, thereby forming specific VOCs ions. The ions are separated with high resolution by a precisely designed miniaturized mass analyzer and then transferred to a high-performance detector for signal acquisition and processing. The whole machine has a compact structure and weighs less than 10 kg. At the same time, with its excellent performance of a detection limit lower than 0.5 ppb, it can achieve high-precision detection of trace VOCs.

[0029] As a possible implementation of this embodiment, the transmission pipeline includes a heating section 2 and a micro-integrated gas sensor installation section 1; a heating device 4 for heating the inside of the pipe is provided in the heating section 2.

[0030] It should be noted that Figure 1 and Figure 2 in the schematic diagrams both 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 may be that the heating section 2 runs through the entire transmission pipeline, that is, a heating device 4 (such as electromagnetic heating) is arranged outside the micro-integrated sensor installation section 1; fully ensuring the temperature requirements of the whole process and effectively avoiding wall adsorption loss.

[0031] See Figure 1 and Figure 2 As shown, the micro-integrated gas sensor installation section 1 is arranged in the middle, that is, a gas input end and a gas output end of the micro-integrated gas sensor installation section 1 are respectively connected to a heating section 2; this is just one implementation of the embodiment, and a heating section 2 can also be adopted, that is, it can be arranged before the gas input end or after the gas output end of the micro-integrated gas sensor installation section 1.

[0032] The heating temperature of the heating section 2 is at least 60 degrees Celsius.

[0033] In this embodiment, by setting the heating section 2 and the heating temperature being at least 60 degrees Celsius, the wall adsorption loss of VOCs can be effectively reduced.

[0034] See Figure 3 As shown, as a possible implementation of this embodiment, the micro-integrated gas sensor installation section 1 includes a detection chamber, a plurality of the micro-integrated gas sensors 6 are installed in the detection chamber, and the detection chamber is provided with a gas input end, a gas output end, a power interface and a data interface 9. The gas input end of the detection chamber is connected to the gas output end of the heating section 2; The power interface is used to supply power to the micro-integrated gas sensor 6, and the data interface transmits the detection information output by the micro-integrated gas sensor 6.

[0035] Airtight connectors 7 are installed at both the gas input end and the gas output end of the detection chamber. Exemplarily, the airtight connector 7 is a peek connector.

[0036] The detection chamber is formed by assembling two lid-shaped structures. A sealing rubber ring 8 is installed on the contact surface of the two lid-shaped structures. Exemplarily, see Figure 3 , and a plurality of bolt mounting holes are provided on the two lid-shaped structures, and the two lid-shaped structures are assembled by matching bolts and nuts.

[0037] In this embodiment, the operating temperature of the micro-integrated gas sensor 6 is from room temperature to 350 °C. The temperature of the gas-sensitive action area is realized by an internal heating layer. The heating layer generates heat under the action of a heating voltage, so as to realize the adjustment of the optimal operating temperature of the sensor, enhance its detection sensitivity and selectivity to gases, and avoid signal drift caused by changes in ambient temperature. And the detection accuracy reduction caused by gas sample leakage is prevented through the sealing rubber ring 8 and the airtight connector 7.

[0038] Here, it needs to be particularly noted that since the operating temperature of the micro-integrated gas sensor 6 is from room temperature to 350 °C, it may be considered that there will be an interaction between the temperature of our heating section 2 and the micro-integrated gas sensor 6. For this, it should be noted that the heating of the micro-integrated gas sensor 6 is realized by an internal heating layer. The heating layer generates heat under the action of a heating voltage, so as to realize the adjustment of the optimal operating temperature of the micro-integrated gas sensor 6, enhance its detection sensitivity and selectivity to gases, and avoid signal drift caused by changes in ambient temperature; since it is a very small space range, there is no possibility of interaction.

[0039] As a possible implementation manner of this embodiment, a plurality of the micro-integrated gas sensors 6 are integrated based on a micro PCB board, and the distance between the sensors is 1-2 mm. Based on this, cross-interference is avoided, and at the same time, it is ensured that the gas can uniformly 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.

[0040] As a possible implementation manner of this embodiment, a plurality of the micro-integrated gas sensors 6 for different target exhaled gas markers form a sensor array, and at least one sensor array is provided; Or At least one of the micro-integrated gas sensors 6 for the same target exhaled gas marker forms a sensor array, and a plurality of sensor arrays are provided, and each sensor array is for a different target exhaled gas marker.

[0041] It should be noted that the above sensor array arrangement is only exemplary. Based on the content disclosed in the present invention, conventional transformations of the above array all fall within the protection scope of the present invention. Among them, the most array of the present invention is set as follows: a plurality of the micro-integrated gas sensors 6 for different target exhaled gas markers form a sensor array, and at least one sensor array is provided. Here, two sensor arrays can be set, so as to ensure the smooth collection of each gas, that is, to ensure the smooth collection of gas by setting a set of redundant methods, and also to ensure the quality of subsequent data processing.

[0042] As a possible implementation manner of this embodiment, the transmission pipeline adopts 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.

[0043] As a possible implementation manner of this embodiment, since the entire device can be portable and can directly carry out screening activities outdoors and in communities, the problem of carry-on pollution needs to be considered. Therefore, in this embodiment, an ultraviolet lamp is set in the heating section 2, and the sterilization function of the ultraviolet lamp enables the carry-on pollution rate of the instrument to be lower than 0.5%.

[0044] See Figure 4 As shown, on the other hand, the present invention provides a portable detection method for oral exhaled gas. Using a portable detection device for oral exhaled gas described in the present invention, it includes the following steps: Mass spectrometry data processing: Collect mass spectrometry data, and convert the ion flight time into mass-to-charge ratio through fast Fourier transform to generate a three-dimensional mass spectrometry data matrix including time stamp, mass-to-charge ratio, and signal intensity. Sensor data processing: Based on the micro-integrated gas sensor, collect sample data, record the dynamic resistance value, and synchronously record the ambient temperature and humidity as compensation parameters to generate three-dimensional sensor data including the dynamic resistance value, temperature, and humidity parameters. It should be noted that time synchronization needs to be performed on the mass spectrometry data and sensor data, and how to achieve time synchronization is a conventional technical means in the art, so it will not be elaborated here.

[0045] Mass spectrometry analysis: Identify the characteristic peaks of the corresponding target substances through the characteristic peak matching algorithm of the target substances, calculate the concentration of the target substances, perform quantitative analysis through the calibrated response coefficient, and at the same time detect all the mass spectrometry peaks in real time through the mass spectrometry data. Use the self-built proton transfer reaction method standard substance database for matching to identify potential interfering substances and establish a dynamic interfering substance white list. As a possible implementation manner of this embodiment, seeFigure 5 As shown, the mass spectrometry analysis includes the following steps: Preprocessing of mass spectrometry data: Perform polynomial fitting on the original signals within each mass spectrometry cycle. An exemplary fitting function is as follows: ; Where: represents the baseline intensity obtained by fitting; to represent the polynomial fitting coefficients;

[0046] represents the mass-to-charge ratio; Deduct the baseline signal based on the fitting result to obtain the corrected signal: ; In the formula: represents the original signal intensity; represents the signal intensity after removing baseline drift; represents the baseline intensity obtained by fitting; Perform smoothing on the corrected signal using a Savitzky-Golay filter to remove high-frequency noise in the signal. The Savitzky-Golay filter performs signal smoothing by applying a window size of 7 points and a 2nd-order polynomial to obtain the calibrated and smoothed signal data; Based on the mass spectrometry data with baseline drift and noise removed, use a known internal standard (such as PFTBA, with known m / z values) for calibration. Assume the known peak of the internal standard , and the flight time measured experimentally to fit the calibration coefficient of the mass-to-charge ratio. The relationship between the flight time and the mass-to-charge ratio is: ; In the formula: represents the true mass-to-charge ratio; and are the calibration coefficients fitted from the internal standard data; Fit the calibration function based on the mass-to-charge ratio and flight time of the known internal standard. Thus, adjust the m / z value in the mass spectrometry data to ensure that its error is controlled within 0.02 Da; Dynamic extraction of characteristic peaks: In the preprocessed mass spectrometry data, find the part where the signal changes most violently. Identify the rising and falling points of the signal through the first derivative to determine the starting and ending positions of the peak. The first derivative is used to describe the rate of change of the signal intensity: ; Where: represents the derivative of the signal, which is the rate of intensity change; represents the intensity of the mass spectrometry signal; represents the change amount; Then, the continuous wavelet transform is used to enhance the peak shape characteristics of the signal. Peaks with a signal-to-noise ratio greater than 10 are selected based on a set intensity threshold (intensity greater than 5% of the maximum intensity), and the characteristic peaks and corresponding m / z values are obtained; that is, by screening peaks with an intensity greater than the maximum intensity and a signal-to-noise ratio (SNR) greater than 10, the corresponding m / z values of these characteristic peaks are extracted; Target matching: Assuming the target is toluene, and its characteristic peaks , target identification is performed by searching for matching peaks in the mass spectrometry data; For the isotope peaks of the target substance, the Pearson correlation coefficient is used to verify the degree of agreement between the actual data and the theoretical isotope pattern, and substances with similar mass-to-charge ratios are further distinguished: ; Where: represents the Pearson correlation coefficient; represents the measured signal intensity; represents the theoretical signal intensity; represents the mean of the measured signal intensities; represents the mean of the theoretical signal intensities; Exemplarily, if , it indicates successful target matching; Concentration quantitative calculation: Based on temperature, pressure, and the calibration conditions of the laboratory as parameters, the response factor is dynamically corrected, and the concentration of the target substance is calculated based on the corrected response factor and the characteristic peak intensity data obtained from target matching; ; Where: represents the response factor under laboratory standard conditions; represents the pressure under the current experimental conditions; represents the pressure under laboratory standard conditions; represents the temperature coefficient; represents the temperature under the current experimental conditions; represents the temperature under laboratory standard conditions; Exemplarily, the concentration of the target substance is calculated as follows: Since proton transfer reaction mass spectrometry (PTR-MS) usually has no or very few fragment peaks, the concentration calculation should focus on the quasi-molecular ion peak (i.e., the protonated ion peak) of the target substance; For example, assuming the target substance is toluene and its quasi-molecular ion peak is m / z 93.14. Therefore, the intensity of this quasi-molecular ion peak can be directly used to calculate the concentration of the target substance: ; Where: represents the concentration of the target substance.

[0047] Interference Whitelist Generation: Arrange all detected peaks in ascending order of m / z and perform dynamic grouping based on the m / z difference to ensure that the maximum interval within a group does not exceed 0.1 Da. Then, merge adjacent peaks with an intensity ratio greater than 1:10 to avoid double-counting fragment peaks. Each group should contain at least 3 peaks and the total integrated intensity should be greater than 1000 counts, excluding trace interferences.

[0048] To ensure the accuracy of the whitelist, the system preloads a proton transfer reaction mass spectrometry fingerprint library of more than 500 volatile organic compounds (VOCs); for each compound, the m / z values of the first 5 characteristic peaks, the isotope distribution pattern, and the retention index (RI) are stored; during the matching process, first perform a primary match based on the m / z value of the main peak, allowing an offset of ±0.02 Da; then, use a secondary match, requiring at least two fragment peaks to match; based on this, obtain the interferences and their types that match the candidate peaks; then classify the interferences according to the degree of influence to obtain the list of interferences in the whitelist.

[0049] Dynamic Compensation of Sensor Data: Through an environmental compensation algorithm, consider temperature and humidity factors to correct the resistance signal, and then use the sliding baseline subtraction method to remove the influence of environmental drift to obtain a purified sensor signal. Then, establish a physical constraint model using the target and interference concentration information provided by the mass spectrometry data for cross-sensitivity compensation. During the compensation process, use the recursive least squares algorithm to adjust the sensitivity parameters of the sensor to the target and interferences in real time; As a possible implementation of this embodiment, an exemplary environmental compensation algorithm is as follows: ; In the formula: represents the compensated sensor response data; represents the original sensor response data; represents the temperature compensation coefficient; T represents the current temperature value; represents the humidity compensation coefficient; represents the current relative humidity value; In this embodiment, compensation is performed according to the current temperature and humidity relative to the reference values (25°C and 50% humidity, which are only exemplary and the reference values can be set according to the actual situation), and the original sensor response is adjusted through the compensation coefficient; Furthermore, the temperature and humidity sensor data can be read every 100 ms, and linear interpolation can be used to avoid sudden changes and smoothly update the compensation coefficient; The exemplary technical solution for sliding baseline subtraction is as follows: Calculate the median of the past 10 (300 data points) as the dynamic baseline for removing slow-varying drift: ; In the formula: represents the calculated baseline signal; The net signal after removing the slow-varying drift is obtained using the baseline subtraction technique : ; In the formula: represents the net signal after removing the slow-varying drift; As a possible implementation of this embodiment, the physical constraint model is as follows: ; In the formula: 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 types of interferents; represents the target concentration; For the response coefficient and the cross-sensitivity coefficient, optimization is performed by the recursive least squares method: ; In the formula: represents the updated parameter; represents the parameter before update; K represents the Kalman gain; represents the actual response data of the sensor, that is ; By subtracting the influence of the interferent obtained from the physical constraint model from the original signal, the compensated net signal is obtained , that is ; ; In the formula: represents the target concentration measured by the sensor; Based on the above technical solution, it can be seen that is equal to , but in fact, it is under the ideal state, that is, the mass spectrometry can identify all interferents 100% (i.e., the whitelist is complete), the sensitivity of the sensor is constant, the cross-sensitivity coefficient of the interferent is completely known and accurate, and when there is no noise in the sensor signal, the concentration after sensor compensation will be equal to the concentration calculated by the mass spectrometry. At this time, data fusion is unnecessary; However, in actual situations, the above ideal state cannot be achieved, so data fusion is very necessary; that is, the residual is necessarily not zero; the residual in the ideal state is zero, but due to problems existing in the real scenario, such as the whitelist integrity, sensor sensitivity, etc., the residual is necessarily not zero; therefore, data fusion is required.

[0050] Data fusion: Weighted fusion is performed according to the confidence levels of mass spectrometry data and sensor data to calculate the true concentration of the target substance. Among them, the confidence level of the mass spectrometry data is evaluated based on the matching degree and signal-to-noise ratio of the target substance, and the confidence level of the sensor data is calculated through the error between the predicted value and the actual measured value. As a possible implementation manner of this embodiment, the data fusion includes the following steps: Calculation of mass spectrometry confidence level: Combining the characteristic peak matching degree and signal-to-noise ratio, calculate the confidence level of the mass spectrometry: ; In the formula: represents the signal-to-noise ratio of the main peak; represents the characteristic peak matching degree, which is based on the comparison between the theoretical characteristic peaks of the target substance and the matching peaks in the actual mass spectrometry data; The function is used to compress the calculation result into the interval of 0 to 1; represents the confidence level of the mass spectrometry; Calculation of sensor confidence level: Calculate the confidence level of the sensor according to the ratio of the residual to the dynamic error threshold: ; In the formula: represents the residual; represents the dynamic error threshold; represents the confidence level of the sensor; Among them, the residual is calculated based on the following formula: ; In the formula: represents the net signal after compensation; represents the sensor response data predicted according to the target substance concentration and the interferent concentration; Based on the calculated confidence levels, calculate the weights of the mass spectrometry data and the sensor data respectively, and use the sliding window method to update the weights of the calculated weights. Based on the updated weights, perform weighted fusion on the target substance concentration detected by the mass spectrometry and the net signal after compensation to obtain the fused target substance concentration. ; ; In the formula: represents the mass spectrometry fusion weight; represents the sensor fusion weight; ; In the formula: represents the fused target substance concentration; Output result: Calculate the interferent suppression ratio based on the calculated true concentration of the target substance, and output the true concentration of the target substance and the interferent suppression ratio at the same time. Among them, the interferent suppression ratio is obtained based on the following formula: ; In the formula: represents the sum of all interferent concentrations; represents the interferent suppression ratio.

[0051] By comparing the concentration of the detected target with a preset threshold value, oral cancer screening can be achieved. For example, the concentration of target VOCs such as ethyl butyrate is compared with the preset concentration threshold value. If it exceeds, further examinations can be guided.

[0052] The use of the concentration of toluene target VOCs above is not a limitation of the present invention. It should be noted that obtaining markers (targets) for detecting oral cancer belongs to 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 related matters. In the present invention, mainly through the introduction of mass spectrometry data, the detected concentration is made more accurate and more conducive to subsequent judgment. And in the case where oral cancer markers are clear and detection data is accurate, how to judge whether there may be oral cancer is a conventional technical means in the medical field. Therefore, the present invention will not elaborate.

[0053] It should be noted that the present invention first proposes to apply mass spectrometry data to the detection of oral cancer. By introducing mass spectrometry data to correct the sensor, and finally through fusion, an accurate data detection basis is achieved, and then the subsequent oral cancer screening judgment is guided.

[0054] In this embodiment, 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 white list of interfering substances identified in real time by mass spectrometry, the sensor response can be dynamically corrected, making the final concentration estimation more stable and accurate. And by integrating the advantages of mass spectrometry and the sensor, the error caused by a single data source is reduced, and the accuracy of concentration prediction is improved.

[0055] And the present invention establishes a dynamic white list of interfering substances to timely identify and compensate for the influence from interfering substances. Especially in the case of significant interfering substances (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 a complex environment but also effectively reduces the concentration estimation error caused by interfering substances.

[0056] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

Claims

1. A portable detection device for oral exhaled air, characterized in that: Including gas collection end, transmission pipeline, 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 sensors are used to detect target exhalation markers; and at least one micro-integrated gas sensor is correspondingly arranged for one target exhalation marker.

2. A portable detection device for 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. A portable detection device for oral exhaled air according to claim 1, characterized in that: The transmission pipeline comprises 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. A portable detection device for oral exhaled air according to claim 3, characterized in that: The micro-integrated gas sensor installation section includes a detection chamber, a plurality of the micro-integrated gas sensors are installed in the detection chamber, 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. A portable detection device for oral exhaled air according to claim 3, characterized in that: The gas input end and the gas output end of the detection chamber are both equipped with airtight joints.

6. A portable detection device for 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. A portable method for detecting oral exhaled air, characterized in that: A portable detection device for oral exhaled air according to any one of claims 1 to 6, comprising 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 mass spectrometry three-dimensional data matrix containing timestamp, mass-to-charge ratio and signal intensity; Sensor data processing: Collect sample data based on micro-integrated gas sensors, record dynamic resistance values, and simultaneously record ambient temperature and humidity 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 corresponding target objects are identified through the characteristic peak matching algorithm of the target objects, and the concentration of the target objects is calculated. The quantitative analysis is performed through the calibrated response coefficient. At the same time, all mass spectrum peaks are detected in real time through mass spectrum data, and the self-built proton transfer reaction method standard substance database is used for matching to identify potential interferences and establish a dynamic interference whitelist; Dynamic compensation of sensor data: Through the environmental compensation algorithm, the temperature and humidity factors are taken into account to correct the resistance signal, and then the sliding baseline subtraction method is used to remove the influence of environmental drift to obtain a purified sensor signal. Then, the target and interferent concentration information provided by the mass spectrometry data is 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 interferent in real time; Data fusion: The mass spectrometry data and sensor data are weighted and fused according to their confidence levels to calculate the true concentration of the target. The confidence level of the mass spectrometry data is evaluated based on the matching degree and signal-to-noise ratio of the target. The confidence level of the 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.

8. A portable method for detecting oral exhaled air according to claim 7, characterized in that: The mass spectrometry analysis comprises the following steps: Mass spectrometry data preprocessing: Perform polynomial fitting on the original signal in each mass spectrometry cycle, deduct the baseline signal based on the fitting result, and obtain the corrected signal; then, based on the corrected signal, use the known internal standard peak to calibrate the mass-to-charge ratio, and update the calibration coefficient of each cycle through the quadratic function model to obtain the preprocessed mass spectrometry data; 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, determine the starting and ending positions of the peak, and then use continuous wavelet transform to enhance the peak shape characteristics of the signal. Based on the set intensity threshold, filter out the peaks with a signal-to-noise ratio greater than 10 to obtain the characteristic peaks and the corresponding m / z values; Target matching: select a target, match it by searching for its characteristic peaks, and use the Pearson correlation coefficient to verify the degree of agreement between the actual data and the theoretical isotope pattern for the target's isotope peaks. Filter out the successfully matched targets based on the confidence threshold to obtain the matching results and confidence levels. Quantitative calculation of concentration: 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; Generation of whitelist of interferents: All detected mass spectrometry peaks are grouped based on m / z values ​​to ensure that the peak spacing within each group is less than 0.1Da; 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 interferents is obtained; Based on the candidate peak list of interferents and their intensity distribution, a hash table is used to quickly query the peak features of known compounds in the self-built proton transfer reaction method standard material database for matching, and the interferents and their types matching the candidate peaks are obtained. The interferents are classified according to the degree of influence to obtain a list of interferents in the whitelist.

9. A portable method for detecting oral exhaled air according to claim 8, 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 jth interferent; represents the concentration of the jth interferent; n represents the number of interferent types; 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 .

10. A portable method for detecting oral exhaled air according to claim 7, 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 matching degree of characteristic peaks, based on the comparison between the theoretical characteristic peaks of the target and the matching peaks 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; 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, the weights of the mass spectrometry data and the 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 the mass spectrometry and the compensated net signal are weighted fused to obtain the fused target concentration.

Images