Identification method for chemical compositions of different exhalation media and application thereof

Abstract

The invention belongs to the technical field of biomedicine in the aspect of human health, and discloses a method for identifying chemical compositions of different exhalation media and application of the method. The method comprises the following steps: firstly, respectively collecting different samples exhaled by a human body, carrying out real-time online analysis on the gas phase chemical composition of exhaled gas by using a high-resolution volatile organic compound detection mass spectrometer, and carrying out exhaled particle phase chemical composition detection by using an aerosol chemical component real-time analysis sampling system; and performing chemical composition detection on exhaled air condensate and saliva by using a high performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometer. And analyzing chemical composition concentration difference values in different exhaled media and a blank control group by combining a single sample Wilcoxon symbol rank test and a Bootstrap confidence interval method, and identifying chemical composition components of different exhaled media. The method is non-invasive detection, can reflect complex physiological and pathological states of an organism, and promotes the development of early diagnosis, illness monitoring and personalized treatment strategies.

Description

Technical Field

[0001] This invention belongs to the field of biomedical technology related to human health, specifically relating to a method for identifying the chemical composition of different exhaled media in the human body and its application. Background Technology

[0002] Different exhaled mediators constitute a non-invasive and easily accessible "chemical window into human exhaled air," reflecting the complex physiological and pathological states of the body. Volatile organic compounds in exhaled air, non-volatile substances in the particulate phase, inflammatory mediators in condensate, and metabolites in saliva collectively carry rich information ranging from pulmonary gas exchange and systemic metabolism to the local oral environment and even microbiome activity. By systematically analyzing these complex chemical components, we can not only reveal specific molecular markers in the development of diseases (such as lung cancer, asthma, diabetes, and infections), thereby promoting early diagnosis, disease monitoring, and personalized treatment strategies; but also gain a deeper understanding of the basic metabolic pathways and immune response mechanisms in both healthy and diseased states, providing new technological means and scientific evidence for precision medicine and health management.

[0003] Therefore, establishing such a component identification system is a crucial bridge connecting basic biological research with clinical translational applications. Furthermore, it reflects air pollution exposure levels to a certain extent. By monitoring changes in the concentration of exogenous compounds (such as volatile organic pollutants, heavy metals, and polycyclic aromatic hydrocarbon metabolites) and their induced endogenous stress markers (such as lipid peroxidation products) in human exhaled breath samples, it is possible to objectively and sensitively reveal the extent to which the human body absorbs and metabolizes surrounding pollutants, and assess the fluctuations in air pollution levels experienced by an individual and their early health impacts. However, existing literature and patents rarely provide methods for analyzing multiple exhaled media in a linked manner. In addition, there is a lack of standardized methods for sample collection, sample testing, and data processing, as well as a lack of component databases for healthy individuals and individuals with different diseases in various exhaled media. This makes it difficult to establish a universal and systematic diagnostic model. There is an urgent need to develop methods that can systematically analyze complex chemical compositions and provide technical support for exploring the connections and differences between them. Summary of the Invention

[0004] The technical problem to be solved by this invention is to overcome the difficulties of the incomplete research on human exhaled media, the lack of systematic sampling, measurement and data screening methods, and to provide a method for identifying the chemical composition of different human exhaled media. This method is non-invasive and highly operable.

[0005] The objective of this invention is achieved through the following technical solution: A method for identifying different chemical compositions of exhaled media includes the following steps: S1. Exhaled gas samples were collected from the subjects using Teflon sampling bags, exhaled condensate samples were collected using an exhaled condensate collector (Bioscreen II), and saliva samples were collected using sterile saliva collection tubes. S2. The chemical composition of exhaled gas was analyzed using a high-resolution volatile organic compound detection mass spectrometer (PTR-TOF-MS 10K), and the chemical composition of the particulate phase was analyzed using CHARON and PTR-TOF-MS coupled. A Teflon gas sampling bag filled with high-purity nitrogen (99.999%) was used as a blank control group for measurement. The chemical composition of exhaled gas condensate and saliva was detected using high-performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry (LC-QTOF-MS). Deionized water with the same pretreated volume as the condensate and saliva samples was used as a blank control group for measurement. S3. Subtract the concentration of each subject's sample from the blank control group to obtain the corrected concentration of a certain chemical component X of the four exhaled mediators for each subject: When the number of subjects is n, the data set consisting of X in their exhaled medium is as follows: ; S4. Evaluate data set C using the one-sample Wilcoxon signed-rank test. 总X According to assessment C 总X The significance P-value is obtained by measuring the degree of difference between the hypothetical value and the value of 0. S5. Evaluate data group C using the Bootstrap confidence interval method. 总X According to C 总X The mean and upper and lower limits of the confidence interval were used to evaluate C. 总X The positive and negative value levels; S6. Combine the significance p-value obtained in step S4 with the C-value obtained in step S5. 总X The positive and negative levels of C 总X The result satisfies the following conditions: P < 0.05 in the one-sample Wilcoxon signed-rank test and C < 0.05 in the Bootstrap test. 总X If the value is positive, X is identified as the chemical composition of the sample.

[0006] Preferably, the sampling bag in step S1 is a closed Teflon bag with a volume of 0.5~2 L, a length of 20~30 cm, and a width of 10~20 cm.

[0007] Preferably, H3O is used in the high-resolution volatile organic compound detection mass spectrometer described in step S2. +As an ion source, the mass-to-charge ratio (m / z) is 18–340, the drift tube pressure is 2.8–3.0 mbar, the drift tube temperature is 80–120 °C, and the E / N ratio is 100–120 Td. In the CHARON system, the drift tube pressure is 2.1–2.4 mbar, the drift tube temperature is 80–150 °C, and the E / N ratio is 120–150 Td, where E is the electric field strength, N is the number density of the neutral gas, and 1 TD = 10^26. -17 V cm 2 .

[0008] Preferably, the specific steps for using the high performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometer in step S2 are as follows: chromatographic grade acetonitrile is added to the sample, shaken vigorously, and allowed to stand. The resulting mixture is centrifuged at 2-6°C, and the supernatant is filtered through a 0.22 μm filter and transferred to an LC-MS sample vial for LC-QTOF-MS analysis. An equal volume of deionized water is treated using the same method to prepare a blank control sample. The experiment employed an LC-QTOF-MS instrument, consisting of an ion source, ion optics, quadrupole, collision cell, ion beam shaper, and flight tube. High-resolution time-of-flight (Q-TOF) mass spectrometry was used to identify unknowns, perform chemical composition analysis, and obtain further information, while ensuring excellent accuracy, speed, and isotope fidelity. Chromatographic conditions were as follows: an Agilent Poroshell 120 EC-C18 column (2.1 mm × 100 mm, 1.9 µm) was used for separation in positive ion mode. A binary gradient mobile phase was used, consisting of an aqueous solution (A) containing 0.1–0.2% (v / v) formic acid and an acetonitrile (B) containing 0.1–0.2% (v / v) formic acid. The mobile phase flow rate was 200 μL / min, the injection volume was 10 μL, and the column temperature was fixed at 30 °C. Condensate gradient elution: 0–1 min, 5 vol% B solution and 95 vol% A solution; 1–9 min, increasing to 45 vol% B solution and 55 vol% A solution; 9–12 min, 45 vol% B solution and 55 vol% A solution; 12–14 min, increasing to 90 vol% B solution and 10 vol% A solution; 14–15 min, 9 vol% B solution and 5 vol% A solution; 15–16 min, decreasing to 4 vol% B solution and 55 vol% A solution; 16–17 min, 45 vol% B solution and 55 vol% A solution; 17–19 min, decreasing to 5 vol% B solution and 95 vol% A solution; 19–24 min, 5 vol% B solution and 95 vol% A solution. Saliva gradient elution: 0–1 min, 5 vol% B solution and 95 vol% A solution; 1–10 min, increase to 30 vol% B solution and 70 vol% A solution; 10–11 min, increase to 95 vol% B solution and 5 vol% A solution; 11–12 min, 95 vol% B solution and 5 vol% A solution; 12–13 min, decrease to 30 vol% B solution and 70 vol% A solution; 13–15 min, decrease to 5 vol% B solution and 95 vol% A solution; 15–20 min, 5 vol% B solution and 95 vol% A solution. Mass spectrometry conditions: pressure of the nebulizer of the electrospray ionization source was 20-40 psi; drying gas flow rate was 6-10 L / min; drying gas temperature was 300-350℃; capillary voltage was 3800-4200 V; mass-to-charge ratio of 50-1200 for full scan in MS mode.

[0009] Furthermore, the evaluation of the data obtained in step S3 using the single-sample Wilcoxon signed-rank test in step S4 specifically includes the following steps: S41. By analyzing data set C 总X Each C in X Normality tests were performed on the kurtosis and skewness of the data distribution. Data group C 总X The data set C is normally distributed, so parametric tests are used. 总X If the distribution is non-normal, a nonparametric test should be used. S42. Define the null hypothesis H0 and the alternative hypothesis H1, where the null hypothesis H0 is the hypothesis of data set C. 总X The median is 0, i.e., C 总X The positive and negative attributes are not significantly different from 0; the alternative hypothesis H1 is that the data group C 总X The median is not equal to 0, i.e., C 总X The positive and negative attributes of are significantly different from 0; S43. Regarding C 总X Each observation X in i Calculate the difference d between it and the assumed median, which is set to 0 here. i =X i – 0; Remove all zero differences from subsequent analyses and reduce the sample size n accordingly; the absolute value of all non-zero differences |d i | Sort them in ascending order to obtain their rank; if multiple differences have the same absolute value, assign an average rank to each absolute difference to obtain its rank. S44. Rank each absolute difference according to its original difference d. iThe sign of the rank is marked as positive or negative; the sum of the ranks corresponding to all positive differences is the positive rank sum, denoted as W. + The sum of the ranks of all negative differences is called the negative rank sum, denoted as W. - Wilcoxon test statistic W is selected. + and W - The smaller value, ; S45. When the sample size n > 30, the statistic approximately follows a normal distribution. The value of the normal random variable Z related to W is obtained, and the P-value is obtained by looking up the standard normal distribution table. S46. Set the significance level P to 0.05. If P < 0.05, then reject the null hypothesis and conclude that C... 总X The positive or negative attribute of C is significantly different from 0. If P ≥ 0.05, then the null hypothesis cannot be rejected, meaning C is considered to be negative. 总X The positive and negative attributes of C are not significantly different from 0, i.e., C 总X The overall value is 0. We need to determine whether the difference is significant.

[0010] Furthermore, the Bootstrap confidence interval method described in step S5 evaluates the data set C obtained in step S4. 总X Specifically, the steps include the following: Set data group C 总X With a confidence level set, the number of Bootstrap samples (n) is set to 500-1500. The mean of each Bootstrap sample is calculated, resulting in a new dataset containing the means. These means are then sorted in ascending order and denoted as . The confidence interval is determined using the percentile method: its lower limit is the a-th percentile ( The upper limit is the bth percentile ( The specific formula is as follows:

[0011] Where B is the number of Bootstrap samplings, and 1-α is C 总X The confidence level, α, is the theoretical probability that the confidence interval does not cover the true value, equal to 1 minus the confidence level, a + b = n, based on the upper and lower limits of the confidence interval and the data set C. 总X The mean of the data set C 总X The sign of the value is used to determine the confidence interval; if the entire confidence interval is to the right of 0, i.e., the lower limit is ≥ 0, then C is determined. 总X If the value is positive and the entire confidence interval lies to the left of 0 (i.e., the upper limit is ≤ 0), then C is determined. 总X It is a positive value.

[0012] The application of the identification method for different chemical compositions of exhaled media in the field of non-invasive detection.

[0013] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention transcends the limitations of single-media analysis. The combined identification and analysis of different media can simultaneously provide physiological information of the lower respiratory tract, upper respiratory tract, and oral cavity local environment. Through this multi-media, cross-validation approach, it can provide more accurate and specific physiological and pathological information than any single-media analysis, and establish dynamic and correlated monitoring of physiological and pathological processes.

[0014] 2. The method for identifying the components of human exhaled gas phase, particulate matter, exhaled gas condensate, and saliva in this invention is a non-invasive detection technology. By revealing the metabolic and pathological state in the body, it has broad application potential in many fields such as disease diagnosis (e.g., lung cancer, asthma, diabetes), treatment monitoring, occupational exposure assessment, and exercise metabolism research, providing some support for promoting the transformation of precision medicine and health monitoring towards non-invasive and real-time directions. Attached Figure Description

[0015] Figure 1 This invention provides a process for identifying the chemical composition of different exhaled media. Detailed Implementation

[0016] The present invention will be further described below with reference to specific embodiments, but these should not be construed as limiting the invention. Unless otherwise specified, the technical means used in the embodiments are conventional means well known to those skilled in the art. Unless otherwise specified, the reagents, methods, and equipment used in the present invention are conventional reagents, methods, and equipment in this technical field.

[0017] Example 1: Identifying the chemical composition of exhaled human gas phase 1. Experimental Procedure S1. Preparations before sampling Graduate students from a certain university were recruited as subjects for sample collection. Subjects were instructed not to eat, smoke, or drink any beverages other than purified water for two hours prior to sampling to minimize the influence of other factors on the breath sample. H3O was selected. + As an ion source for proton transfer reaction time-of-flight mass spectrometry.

[0018] S2. Human body sampling work All Teflon gas collection bags (sealed Teflon bags, 0.5–2 L in volume, 20–30 cm in length, and 10–20 cm in width) are rinsed at least 5 times with high-purity nitrogen (99.999%) before use to ensure cleanliness and usability. During collection, a disposable breathing nozzle is connected to the gas collection bag. The subject's mouth is firmly in contact with the nozzle before opening the gas collection bag valve. The subject exhales air from their lungs into the Teflon gas collection bag multiple times through their mouth. After collection, the gas collection bag valve is closed before the subject removes their mouth from the nozzle to prevent other external gases from entering the gas collection bag. The sample is introduced into the drift tube via a PTR-TOF-MS injection system. The gaseous components of the sample react with H3O generated by ionization from the ion source. + A proton transfer reaction occurs, followed by separation in a mass analyzer and detection by an ion detector, resulting in a mass spectrum that is transmitted to a computer display. The mass-to-charge ratio (m / z) ranges from 18 to 340, the drift tube pressure is 2.90 mbar, the drift tube temperature is 80°C, and the E / N ratio is 110 Td, where E is the electric field strength and N is the number density of the neutral gas, 1 TD = 10 -17 V cm 2 A Teflon gas sampling bag filled with high-purity nitrogen (99.999%) was used as a blank control group for measurement.

[0019] S3. Correction Data The concentration of organic chemical components in each subject's exhaled breath is subtracted from the concentration of organic chemical components in the blank control group. The corrected concentration of a specific organic chemical component X in a person's exhaled breath is: In this embodiment, there are 50 healthy subjects, so the data set C consists of X in the exhaled gas phase of the healthy subjects. 总X =[C 1X C 2X ... C 50X ]; S4. One-sample Wilcoxon signed-rank test By analyzing data set C 总X Each C in X Normality tests were performed on the kurtosis and skewness of the data distribution. Data group C 总X The data set C is normally distributed, so parametric tests are used. 总X If the distribution is non-normal, a nonparametric test is used. After establishing the hypothesis, the rank sum, statistic, and p-value are calculated. A significance level of p is set at 0.05. If p < 0.05, the null hypothesis is rejected, and C is considered normal. 总X The positive or negative attribute of C is significantly different from 0. If P ≥ 0.05, then the null hypothesis cannot be rejected, meaning C is considered to be negative. 总X The positive and negative attributes of C are not significantly different from 0, i.e., C 总X The overall value is 0.

[0020] S5. Bootstrap Confidence Interval Method For the dataset consisting of all corrected organic components X, i.e., dataset C 总X The positive and negative value levels are evaluated using the Bootstrap confidence interval method. Set C. 总X With a confidence level of 95%, Bootstrap sampling was used for 1000 samples. (Data set C) 总X For the population, 1000 samplings with replacement are performed, with n samples drawn each time, resulting in 1000 Bootstrap samples. The percentile method is used to determine the 95% confidence interval: its lower limit is the 2.5th percentile (…). The upper limit is the 97.5th percentile ( The specific formula is as follows:

[0021]

[0022] Where B is the number of Bootstrap samplings (here B=1000), and 1-α is C 总X Confidence level (95% confidence level here, α = 0.05), based on the upper and lower limits of the confidence interval and C 总X The mean of C 总X Evaluate the positive and negative values. If the entire confidence interval lies to the right of 0 (i.e., the lower limit ≥ 0), then determine C. 总X If the value is positive and the entire confidence interval lies to the left of 0 (i.e., upper limit ≤ 0), then C is determined. 总X It is a positive value.

[0023] S6. Screening of chemical components Comparing the results obtained in steps S4 and S5, if both the single-sample Wilcoxon signed-rank test and the Bootstrap confidence interval test are satisfied, then when C... 总X If the sample satisfies the condition that P < 0.05 in the single-sample Wilcoxon signed-rank test and is positive in the Bootstrap test, then X is identified as the chemical composition of the sample. The final chemical composition is then determined. Figure 1 As shown.

[0024] 2. Experimental Results: In this embodiment, 116 organic components were selected for evaluation after screening. 96 organic components had a P < 0.05 result through the single-sample Wilcoxon signed-rank test. 87 organic components had an upper bound > 0 and a lower bound ≥ 0 for the confidence interval assessed by the Bootstrap confidence interval method. The top 20 substances by concentration were selected, and the results are shown in Table 1.

[0025] Table 1. Chemical composition of exhaled gas phase.

[0026] Example 2: Identification of the chemical composition of the particulate phase of human exhaled breath 1. Experimental Procedure The difference from Example 1 is that in step S2, exhaled particulate matter is detected by coupling CHARON with PTR-TOF-MS. The CHARON system concentrates submicron-sized organic particles (aerosols) through a pneumatic lens system. The concentrated aerosols are evaporated, and their composition can be analyzed by the PTR-TOF-MS instrument. The drift tube pressure is 2.3 mbar, the drift tube temperature is 120°C, and the E / N ratio is 140 Td (1Td=10). -17 V cm 2 ).

[0027] 2. Experimental Results: In this embodiment, 108 organic components were selected for evaluation after screening. 32 organic components showed a P < 0.05 in the single-sample Wilcoxon signed-rank test; 8 organic components showed an upper confidence interval > 0 and a lower confidence interval ≥ 0 in the Bootstrap confidence interval method. The results are shown in Table 2. Table 1 shows the gaseous chemical composition of human exhaled air, and Table 2 shows the particulate chemical composition of human exhaled air. As shown in Tables 1 and 2, the particulate components of human exhaled air are far fewer than the gaseous components, and 87 gaseous organic components include 8 of the particulate components. This indicates that exhaled air is mainly composed of volatile or semi-volatile substances, and some substances do not exist in a single phase but are distributed simultaneously in both the gaseous and particulate phases. This helps us identify stable and characteristic metabolic response patterns in the body under conditions such as disease, occupational exposure, or pollution exposure, reducing random interference and enhancing the reliability and specificity of the detection results.

[0028] Table 2 Chemical composition of particulate phase in human exhaled air

[0029] Example 3: Identifying the chemical composition of human exhaled breath condensate 1. Experimental Procedure The difference from Example 1 is that the human sample collected in step S2 is exhaled breath condensate, and the sampling method is different. Exhaled breath condensate is collected using a Bioscreen II exhaled breath condensate collector. The instrument's internal semiconductor cooler cools a metal block, and the centrifuge tube wall is in close contact with the metal block for cooling. Exhaled breath enters the centrifuge tube tangentially through the blowing tube, rotating downwards on the tube wall. Water vapor in the exhaled breath condenses on the tube wall, and aerosols in the exhaled breath dissolve in the condensate during flow. The condensate is carried by the airflow to the bottom of the tube. The condensed gas is discharged from the centrifuge tube through the outlet in the center of the gas-liquid separation cap. Disposable consumables (blowing tube, gas-liquid separation cap, 50 mL centrifuge tube) are used for collection to avoid cross-contamination, ensuring safety and reliability. Before sampling, the subject rests for 10 minutes and rinses their mouth with purified water. Collection begins when the collector temperature drops below 4°C and lasts for 10-15 minutes (3-5 ml). After collection, the sample is stored at -4°C.

[0030] Sample pretreatment was performed before analysis: 2 mL of chromatographic grade acetonitrile was added to 1 mL of sample, shaken vigorously for 30 s, and then allowed to stand for 10 min. The resulting mixture was centrifuged at 10,000 rpm for 15 min at 4 °C, and 1 mL of the supernatant was filtered through a 0.22 μm filter and transferred to an LC-MS sample vial for LC-QTOF-MS analysis. An equal volume of deionized water was treated using the same method as a blank control group.

[0031] The experiment was conducted using an LC-QTOF-MS instrument, which consists of an ion source, ion optics components, a quadrupole, a collision cell, an ion beam shaper, and a flight tube. High-resolution time-of-flight (Q-TOF) mass spectrometry was used to identify unknowns, perform chemical composition analysis, and obtain more information, while ensuring excellent accuracy, speed, and isotope fidelity.

[0032] Chromatographic conditions: An Agilent Poroshell 120 EC-C18 column (2.1 mm × 100 mm, 1.9 µm) was used for chromatographic separation. Analysis was performed in positive ion mode using a binary gradient mobile phase consisting of an aqueous solution (A) containing 0.1% (v / v) formic acid and acetonitrile (B) containing 0.1% (v / v) formic acid. The mobile phase flow rate was 200 μL / min, the injection volume was 10 μL, and the column temperature was fixed at 30 °C. The elution gradient was set as follows: 0–1 min, 5 vol% B solution and 95 vol% A solution; 1–9 min, increased to 45 vol% B solution and 55 vol% A solution; 9–12 min, 45% B solution and 55 vol% A solution; 12–14 min, increased to 90 vol% B solution and 10 vol% A solution; 14–15 min, 95 vol% B solution and 5 vol% A solution; 15–16 min, decreased to 45 vol% B solution and 55 vol% A solution; 16–17 min, 45 vol% B solution and 55 vol% A solution; 17–19 min, decreased to 5 vol% B solution and 95 vol% A solution; 19–24 min, 5 vol% B solution and 95 vol% A solution.

[0033] Mass spectrometry conditions: Electrospray ionization (ESI); nebulizer pressure 20-40 psi; drying gas flow rate 8 L / min; drying gas temperature 350℃; capillary voltage 4000 V in positive ion mode; mass-to-charge ratio (m / z) of 50-1200 for full scan in MS mode.

[0034] 1. Experimental Results In this embodiment, 157 organic components were selected for evaluation after screening. 142 organic components showed a p-value < 0.05 using the single-sample Wilcoxon signed-rank test; 113 organic components showed a confidence interval with an upper bound > 0 and a lower bound ≥ 0 using the Bootstrap confidence interval method. The top 20 substances by concentration were selected, and the results are shown in Table 3. Table 3 shows the chemical composition of human exhaled breath condensate. As can be seen from Table 3, the chemical composition of human exhaled breath condensate is very complex, mainly reflecting the lining fluid components of the lower respiratory tract and alveolar surface. This helps to understand the mechanism by which changes in human metabolism affect the chemical composition of exhaled breath condensate.

[0035] Table 3 Chemical composition of human exhaled breath condensate

[0036] Note: Unidentified chemical compositions are expressed as mass-to-charge ratio (m / z). Example 4: Identifying the chemical composition of human saliva 1. Experimental Procedure The difference from Example 3 is that the human sample collected in step S2 is saliva and the elution gradient is set.

[0037] Saliva was collected from subjects using disposable sterile saliva collection tubes. After rinsing their mouths, subjects held the disposable saliva collection tube and sat quietly for about 1 minute, allowing the saliva to flow out naturally until 3-5 mL was collected. The collected saliva was then stored at -4°C. Saliva gradient elution: 0-1 min, 5 vol% B solution and 95 vol% A solution; 1-10 min, increasing to 30 vol% B solution and 70 vol% A solution; 10-11 min, increasing to 95 vol% B solution and 5 vol% A solution; 11-12 min, 95 vol% B solution and 5 vol% A solution; 12-13 min, decreasing to 30 vol% B solution and 70 vol% A solution; 13-15 min, decreasing to 5% B solution and 95 vol% A solution; 15-20 min, 5 vol% B solution and 95 vol% A solution.

[0038] 2. Experimental Results: In this embodiment, 364 organic components were selected for evaluation after screening. 348 organic components showed a P < 0.05 in the single-sample Wilcoxon signed-rank test; 284 organic components showed a confidence interval with an upper bound > 0 and a lower bound ≥ 0 in the Bootstrap confidence interval method. The top 20 substances by concentration were selected, and the results are shown in Table 4. Table 4 shows the chemical composition of human saliva. As can be seen from Table 4, based on concentration levels, the following are the main components of saliva. Analyzing the properties of these chemical substances helps to understand the mechanism by which changes in human metabolism affect the composition of saliva. Integrating information from the exhaled gas phase, particulate phase, condensate, and saliva helps to achieve comprehensive, multi-dimensional monitoring from the upper respiratory tract to the alveoli. Cross-validation helps to improve the reliability and persuasiveness of the research results.

[0039] Table 4 Chemical composition of human saliva

[0040] Note: Unidentified chemical compositions are expressed as mass-to-charge ratio (m / z). The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. A method for identifying the chemical composition of different exhaled media, characterized in that, Includes the following steps: S1. Collect the subject's exhaled gas sample using a Teflon sampling bag, collect the exhaled condensate sample using an exhaled condensate collector, and collect the saliva sample using a sterile saliva collection tube; S2. The chemical composition of exhaled gas was analyzed using a high-resolution volatile organic compound (VOC) detection mass spectrometer. The chemical composition of the particulate phase was analyzed using CHARON and PTR-TOF-MS coupled. A Teflon gas sampling bag filled with high-purity nitrogen was used as a blank control group for measurement. The chemical composition of exhaled gas condensate and saliva was detected using a high-performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometer. Deionized water with the same pretreated volume as the condensate and saliva samples was used as a blank control group for measurement. S3. Subtract the concentration of each subject's sample from the blank control group to obtain the corrected concentration of a certain chemical component X of the four exhaled mediators for each subject: When the number of subjects is n, the data set consisting of X in their exhaled medium is: C 总X =[C 1X C 2X ... C nX ]; S4. Evaluate data set C using the one-sample Wilcoxon signed-rank test. 总X According to assessment C 总X The significance P-value is obtained by measuring the degree of difference between the hypothetical value and the value of 0. S5. Evaluate data group C using the Bootstrap confidence interval method. 总X According to C 总X The mean and upper and lower limits of the confidence interval were used to evaluate C. 总X The positive and negative value levels; S6. Combine the significance p-value obtained in step S4 with the C-value obtained in step S5. 总X The positive and negative levels of C 总X The result satisfies the following conditions: P < 0.05 in the one-sample Wilcoxon signed-rank test and C < 0.05 in the Bootstrap test. 总X If the value is positive, X is identified as the chemical composition of the sample.

2. The method for identifying different chemical compositions of exhaled media according to claim 1, characterized in that, The sampling bag mentioned in step S1 is a closed Teflon bag with a volume of 0.5~2 L, a length of 20~30 cm, and a width of 10~20 cm.

3. The method for identifying different chemical compositions of exhaled media according to claim 1, characterized in that, The high-resolution volatile organic compound detection mass spectrometer described in step S2 uses H3O + As an ion source, the mass-to-charge ratio is 18~340, the drift tube pressure is 2.8~3.0 mbar, the drift tube temperature is 80~120℃, and the E / N ratio is 100~120 Td; in the CHARON system, the drift tube pressure is 2.1~2.4 mbar, the drift tube temperature is 80~150℃, and the E / N ratio is 120~150 Td.

4. The method for identifying different chemical compositions of exhaled media according to claim 1, characterized in that, The specific steps for using the high performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometer in step S2 are as follows: chromatographic grade acetonitrile is added to the sample, shaken vigorously, and allowed to stand. The resulting mixture is centrifuged at 2-6°C. The supernatant is filtered through a 0.22 μm filter and transferred to an LC-MS sample vial for LC-QTOF-MS analysis. An equal volume of deionized water is treated using the same method to prepare a blank control sample. The chromatographic conditions were as follows: the chromatographic separation process was performed in positive ion mode, using a binary gradient mobile phase consisting of an aqueous solution A containing 0.1–0.2% (v / v) formic acid and an acetonitrile B containing 0.1–0.2% (v / v) formic acid; the mobile phase flow rate was 200 μL / min, the injection volume was 10 μL, and the column temperature was fixed at 30 °C. Condensate gradient elution: 0–1 min, 5 vol% B solution and 95 vol% A solution; 1–9 min, increasing to 45 vol% B solution and 55 vol% A solution; 9–12 min, 45 vol% B solution and 55 vol% A solution; 12–14 min, increasing to 90 vol% B solution and 10 vol% A solution; 14–15 min, 9 vol% B solution and 5 vol% A solution; 15–16 min, decreasing to 4 vol% B solution and 55 vol% A solution; 16–17 min, 45 vol% B solution and 55 vol% A solution; 17–19 min, decreasing to 5 vol% B solution and 95 vol% A solution; 19–24 min, 5 vol% B solution and 95 vol% A solution. Saliva gradient elution: 0–1 min, 5 vol% B solution and 95 vol% A solution; 1–10 min, increase to 30 vol% B solution and 70 vol% A solution; 10–11 min, increase to 95 vol% B solution and 5 vol% A solution; 11–12 min, 95 vol% B solution and 5 vol% A solution; 12–13 min, decrease to 30 vol% B solution and 70 vol% A solution; 13–15 min, decrease to 5 vol% B solution and 95 vol% A solution; 15–20 min, 5 vol% B solution and 95 vol% A solution. Mass spectrometry conditions: pressure of the nebulizer of the electrospray ionization source was 20-40 psi; drying gas flow rate was 6-10 L / min; drying gas temperature was 300-350℃; capillary voltage was 3800-4200 V; mass-to-charge ratio of 50-1200 for full scan in MS mode.

5. The method for identifying different chemical compositions of exhaled media according to claim 1, characterized in that, The evaluation of the data obtained in step S3 using the single-sample Wilcoxon signed-rank test in step S4 specifically includes the following steps: S41. By analyzing data set C 总X Each C in X Normality tests were performed on the kurtosis and skewness of the data distribution. Data group C 总X The data set C is normally distributed, so parametric tests are used. 总X If the distribution is non-normal, a nonparametric test should be used. S42. Define the null hypothesis H0 and the alternative hypothesis H1, where the null hypothesis H0 is the hypothesis of data set C. 总X The median is 0, i.e., C 总X The positive and negative attributes are not significantly different from 0; the alternative hypothesis H1 is that the data group C 总X The median is not equal to 0, i.e., C 总X The positive and negative attributes of are significantly different from 0; S43. Regarding C 总X Each observation X in i Calculate the difference d between it and the assumed median, which is set to 0 here. i =X i -0; remove all zero differences from subsequent analyses and reduce the sample size n accordingly; the absolute value of all non-zero differences |d i | Sort them in ascending order to obtain their rank; if multiple differences have the same absolute value, assign an average rank to each absolute difference to obtain its rank. S44. Rank each absolute difference according to its original difference d. i The sign of the rank is marked as positive or negative; the sum of the ranks corresponding to all positive differences is the positive rank sum, denoted as W. + The sum of the ranks of all negative differences is called the negative rank sum, denoted as W. - ; Wilcoxon test statistic W is selected. + and W - The smaller value, ; S45. When the sample size n > 30, the statistic approximately follows a normal distribution. The value of the normal random variable Z related to W is obtained, and the P-value is obtained by looking up the standard normal distribution table. S46. Set the significance level P to 0.

05. If P < 0.05, then reject the null hypothesis and conclude that C... 总X The positive or negative attribute of C is significantly different from 0. If P ≥ 0.05, then the null hypothesis cannot be rejected, meaning C is considered to be negative. 总X The positive and negative attributes of C are not significantly different from 0, i.e., C 总X The overall value is 0. We need to determine whether the difference is significant.

6. The method for identifying different chemical compositions of exhaled media according to claim 1, characterized in that, The Bootstrap confidence interval method described in step S5 evaluates the data set C obtained in step S4. 总X Specifically, the steps include the following: Set data group C 总X With a confidence level set, the number of Bootstrap samples (n) is set to 500-1500. The mean of each Bootstrap sample is calculated, resulting in a new dataset containing the means. These means are then sorted in ascending order and denoted as . The confidence interval is determined using the percentile method: its lower limit is the a-th percentile ( The upper limit is the bth percentile ( The specific formula is as follows: ； Where B is the number of Bootstrap samplings, and 1-α is C 总X The confidence level, α, is the theoretical probability that the confidence interval does not cover the true value, equal to 1 minus the confidence level, a + b = n, based on the upper and lower limits of the confidence interval and the data set C. 总X The mean of the data set C 总X The sign of the value is used to determine the confidence interval; if the entire confidence interval is to the right of 0, i.e., the lower limit is ≥ 0, then C is determined. 总X If the value is positive and the entire confidence interval lies to the left of 0 (i.e., the upper limit is ≤ 0), then C is determined. 总X It is a positive value.

7. The application of the identification method for different chemical compositions of exhaled media as described in any one of claims 1-6 in the field of non-invasive detection.

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