Method and system for detecting aerosol particle

Abstract

To provide systems and methods for identifying the composition of single aerosol particles, particularly that of bioaerosol particles.SOLUTION: A continuous timing laser coupled tightly to a pulse ionization laser may be used to index aerosol particles, measure particle properties, and trigger a pulse ionization laser when each particle enters a trigger laser beam for emission. Ionized fragments and optionally photons associated with each particle producing by the ionization laser may be analyzed using one or more detectors including a TOF-MS detector and an optical detector. Individual single particle spectra are arrayed before averaging to remove noise.SELECTED DRAWING: Figure 1

Description

Related Applications

[0001] This application is related to and claims the benefit of U.S. Patent Application No. 17 / 507,755, filed October 21, 2021, entitled "Methods and Systems for Detecting Aerosol Particles", which is a continuation-in-part of International Application No. PCT / US2020 / 040023, filed June 27, 2020, which is related to and claims the benefit of U.S. Provisional Patent Application No. 62 / 868,906, filed June 29, 2019, entitled "Methods and Systems for Detecting Aerosol Particles Without Using Complex Organic MALDI Matrices", the disclosure of which is hereby incorporated by reference in its entirety. Federal Sponsored Research and Development

[0002] Not applicable.

Technical Field

[0003] The present disclosure relates to methods and devices that use mass spectrometry and one or more optical detection methods to provide high-precision identification of aerosol analysis target particles. More specifically, the present disclosure relates to methods and devices for identifying biological aerosol analytes using, but not limited to, time-of-flight mass spectrometry (TOF-MS), optical single-particle sensors, and at least one of a data analysis system capable of identifying analyte particles using data fusion of data generated from one or more sensors and machine learning methods.

Background Art

[0004] The threat from aerosolized biological and chemical threat agents remains a significant concern for the U.S. government due to the potentially tragic consequences to life and property that can result from such events. Two major threat scenarios of particular concern are as follows: (1) the release of an agent within an enclosed structure (office building, airport, mass transit facility, etc.) where the HVAC system can effectively disperse the agent throughout the structure, and (2) the widespread area release of an agent across an entire populated area such as a town or city. Exposure to the released aerosolized agent can lead to a large number of casualties. In the case of a widespread area release, it is very difficult to protect citizens from the initial exposure without timely information regarding the type, amount, and location of the contaminants. To take prompt corrective action, methods and devices are needed to identify the composition of the threat agent in real time. Samples of the analyte aerosol in the air can be captured using appropriate means such as filters designed to capture inhalable particles, sampling bags, and other similar receptacles. The particles can also be removed from a liquid sample obtained from a wet-wall cyclone or similar device that has been re-aerosolized. An example of a wet-wall cyclone is the SpinCon II (Innovaprep, Drexel, Missouri). The particles in these aerosols can include, but are not limited to, Bacillus anthracis, Ebola virus, ricin, and botulinum toxin. All of these collection methods require additional processing to extract the biological particles for analysis, resulting in a delay of hours or days to detect and identify the dangerous aerosol.

[0005] The aerosol particles to be analyzed need not be limited to those found in ambient air. The aerosol to be analyzed may include exhaled breath particles (EBP) found in the breath of humans or animals. The amount of air exhaled during the breathing of a healthy adult is typically 1 to 2 liters, which includes a normal tidal volume of about 0.5 liters. Humans generate exhaled breath particles (EBP) during various breathing activities such as normal breathing, coughing, talking, and sneezing. The EBP concentration from a patient wearing a ventilator during normal breathing can be about 0.4 to about 2000 particles / breath or 0.001 to 5 particles / mL [1]. Furthermore, the size of EBP can be less than 5 micrometers, and 80% of them can be in the range of 0.3 to 1.0 micrometers. The exhaled particle size distribution is also reported to be between 0.3 and 2.0 micrometers. The average particle size of EBP can be less than 1 micrometer during normal breathing and 1 to 125 micrometers during coughing. Furthermore, 25% of tuberculosis patients exhale 3 to about 600 CFU (colony-forming units) of Mycobacterium tuberculosis when coughing, and the levels of this pathogen were mainly in the range of 0.6 to 3.3 micrometers. These bacteria are rod-shaped, about 2 to 4 micrometers in length and about 0.2 to 0.5 micrometers in width.

[0006] Solutions for detecting and analyzing aerosol analytes such as biological agents are available, but real-time analysis is not possible. In one solution, microfluidic technology is used to clean up the sample and concentrate the biological analyte. For example, specific antibodies can be used to concentrate and purify the biological analyte. This target-specific solution provides reasonable results if there is sufficient time for analyte cleanup and concentration. Other solutions are target-specific and function only for bacterial analytes at the expense of analyzing viruses, toxins, or particulate chemicals. In this method, for example, a sample from a patient needs to be applied to a bacterial culture plate and incubated for 8 - 24 hours. After the bacterial colonies have grown, individual amplified and purified colonies are collected and measured by whole-cell MALDI TOF mass spectrometry. Many studies have investigated the accuracy of this technique and found accurate identification of over 99% of clinical bacterial analytes. Two commercial systems for rapid clinical bacterial identification have been developed, namely the Bruker Biotyper (sold by Becton Dickinson) and the Shimadzu Vitek MS (sold by bioMerieux). These systems provide excellent diagnostic results compared to the "gold standard" of 16sRNA. However, to achieve these highly reliable clinical results, a culture or extraction step, or both, are required to purify the sample. Therefore, the time from sampling to identification of the bioanalyte is usually from 12 hours to over a day. Such delays are often acceptable in clinical laboratories, but are often unacceptable in other applications such as biodefense where real-time identification of bioanalytes is required. Biodefense, and point-of-care healthcare applications require the ability to simultaneously identify in real-time large biological organic molecules (such as proteins, peptides, lipids, etc.) including not only bacteria but also fungi, viruses, and biotoxins. Furthermore, by shortening the analysis time for clinical applications, it is possible to enable more timely treatment and identification of the best treatment course (e.g., distinguishing between viral and bacterial infections), and to evaluate the effectiveness of the treatment course, thereby improving the quality and outcome of care.

[0007] Real-time aerosol particle detection also has many commercial applications. For example, the headspace of a fermentation tank can be analyzed to examine the potential for contaminants. It is often desirable to know the species differentiation of microorganisms in the air within a food or medical facility. Analyte particles can include microorganisms such as viruses, bacteria, algae, or fungi. Analyte particles may also contain a mixture of microorganisms and proteins and peptides.

[0008] The generation of aerosol single-particle MALDI mass spectrometry signatures is fundamentally different from signatures obtained from conventional MALDI or SELDI mass spectrometry, or other mass spectrometry methods that examine solid bulk samples (Figure 8). In conventional MALDI mass spectrometry, ions are extracted from a bulk sample that typically contains a large number of particles, on the order of 1,000. These particles include potential pathogens such as bacteria and viruses, constituent materials that exhibit the characteristics of the pathogens such as proteins, peptides, and lipids, reagents (such as MALDI matrices), and contaminants (substances and by-products related to humans such as environmental substances, sputum from breath samples or cough samples). As shown in Figure 8, these target particles are dispersed throughout the sample and are mixed with other particles such as environmental contaminants. The spatial distribution of the particles within the bulk sample results in a distribution of the distances and flight times (to the detector) of the ions generated from the sample when the sample is affected by the ionization laser.

[0009] Ion diffusion can be reduced to some extent by the design of the ion source region, for example, by adopting methods such as delayed extraction or two-stage extraction. Further, in order to reduce noise and improve the signal-to-noise ratio, usually multiple laser shots are executed and the spectra from each shot are averaged. Although averaging reduces the noise related to the spectrometer and improves the signal-to-noise ratio of the peak, the variability due to sample inhomogeneity is not reduced. It has been found that better results can be obtained by using the average spectrum in an attempt to remove noise using individual measurements and the average spectrum and align the characteristic peaks (Morris et al.). This is because each bulk sample is composed of particles with various compositions, and in one measurement from these particles, ions related to these different particles are generated. Therefore, it is not beneficial to deconvolve the mass signal components related to individual particles during the conventional mass spectrometry of bulk samples. Similar problems are also seen when deconvolving the signal components related to individual particles from an aerosolized sample.

[0010] There is a need for methods and systems for deconvolving spectra related to complex bulk samples or aerosol samples by measuring the mass spectra of individual particles in the sample. There is also a need for methods and devices for analyzing and identifying aerosol analysis particles such as bacteria, fungi, viruses, toxins, etc. with high precision quickly (or in real time). Summary of the Invention

[0011] An exemplary system for identifying the composition of aerosolized particles is disclosed, the system comprising: an aerosol beam generator for generating a beam of single particles; a continuous timing laser generator for generating a timing laser for indexing each particle in the beam; a pulsed ionization laser generator configured to generate at least one of an IR laser pulse and a UV laser pulse that strikes each indexed particle when each indexed particle enters the ionization region of the pulsed ionization laser, triggered by the continuous timing laser; a guide tube having an exit end disposed between the aerosol beam generator and the ionization region, the guide tube configured to urge particles to flow substantially linearly toward the ionization region of the pulsed laser within the guide tube; and at least one detector for analyzing at least one of the ionization fragments and photons associated with each particle and generating unique spectral data associated with each indexed particle. The size of the ionization region may be about 100 μm to 150 μm. The nominal inner diameter of the guide tube may be about twice the size of the ionization region. The nominal length of the guide tube may be about 1 inch to about 5 inches. The nominal length of the guide tube may be about 2 inches to about 3 inches. The guide tube may be made of stainless steel. The distance between the exit end of the guide tube and the ionization region may be about 0.135 inches. The molecular weight of each ionization fragment may be about 1 kDa to about 150 kDa. The at least one detector may have at least one of a TOF-MS detector, a fluorescence detector, a LIBS detector, and a Raman spectrometer. Each of the continuous timing laser and the pulsed ionization laser is characterized by a center line, and the distance between the center line of the continuous timing laser and the center line of the pulsed ionization laser may be about 50 μm. The system may further comprise a plurality of ion extraction stages having a plurality of electrodes and lenses configured to accelerate the ionization fragments generated in the ionization region toward the detector.It may further include a data analysis system that uses data fusion to compile unique spectral data associated with each particle and generates compiled single-particle spectral data. It may further include a machine learning engine arranged to communicate with the data analysis system.

[0012] An exemplary method for identifying the composition of aerosol particles is disclosed, the method comprising: arranging a continuous timing laser beam and a pulsed ionization laser beam to overlap with each other; an aerosol particle generation step of generating an aerosol particle beam, wherein the aerosol particles flow substantially linearly towards the ionization region of the pulsed ionization laser within a guide tube, the aerosol particle beam generation step; a pulsed ionization laser emission step of emitting a pulsed ionization laser when each particle enters the continuous timing laser beam, wherein when a laser pulse from the pulsed ionization laser hits each particle within the ionization region, ionized fragments of each particle and photons associated with each particle are generated, the pulsed ionization laser emission step; analyzing at least one of the ionized fragments of each particle and the photons associated with each particle using at least one detector; and determining the composition of each particle. The distance between the center line of the continuous timing laser beam and the center line of the pulsed ionization laser beam may be about 50 μm. The exemplary method may further comprise indexing each particle within the aerosol particle beam using the continuous timing laser beam to obtain a plurality of indexed particles. The exemplary method may further comprise measuring at least one characteristic of each indexed particle, including at least one of particle size, particle shape, and fluorescence of each indexed particle, using the continuous timing laser. The exemplary method may further comprise selecting which indexed particles to analyze by triggering the pulsed ionization laser when at least one characteristic of the indexed particles meets a predetermined threshold value of the characteristic. The step of determining the composition comprises: generating a plurality of single particle spectra using a TOF-MS detector; aligning each single particle spectrum; removing the noise of each aligned single particle spectrum; averaging the plurality of aligned and noise-removed single particle spectra; and comparing the averaged spectrum with a reference spectrum.The step of aligning the above single-particle spectra includes: selecting one or more mass ranges based on prior information related to the position of the mass range of interest; a reference spectrum selection step of selecting one spectrum for each mass range as a reference spectrum, where the reference spectrum has a spectrum that is at least one of a pre-selected spectrum, a spectrum existing in a reference data library, and a spectrum developed using a measured single-particle spectrum dataset, the reference spectrum selection step; and a step of shifting the peak window of the spectrum dataset to align it with the corresponding window of the reference spectrum in the time domain. The step of selecting one spectrum as a reference spectrum developed using the measured dataset includes: selecting a plurality of measured single-particle spectra; calculating the Pearson correlation coefficient (PCC) of each spectrum data file by cross-correlation with each other spectrum in the dataset and recording the average PCC score of the file; and selecting the spectrum with the highest PCC score as the reference spectrum. The aligned single-particle spectra may be noise-removed using singular value decomposition technology (SVD). The step of determining the above composition may further include at least one of: predicting the composition by comparing the average spectrum data with a training spectrum dataset knowledge base; updating the training dataset knowledge base; and improving the prediction of the composition over time using a machine learning method. The machine learning method may include a supervised machine learning method.

[0013] Other features and effects of the present disclosure are, in part, described in the following description and the accompanying drawings, different aspects of the present disclosure are explained and shown, and in part, those skilled in the art can understand by considering the following detailed description in conjunction with the accompanying drawings or by implementing the present disclosure. The effects of the present disclosure are realized and achieved by the means and combinations specifically pointed out in the appended claims.

Brief Description of the Drawings

[0014] The above aspects of the present disclosure and many accompanying advantages will become better understood and thus more readily appreciated by referring to the following detailed description in conjunction with the accompanying drawings.

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[0015] All reference numbers, identifiers, and callouts in the figures are hereby incorporated herein by reference as if fully set forth herein. The omission of numbering of the elements of the figures is not intended to waive any rights. References without numbers may also be identified by letters in the figures or appendices.

[0016] The following detailed description includes references to the accompanying drawings which form a part of the detailed description. The drawings illustrate, by way of example, specific embodiments in which the disclosed systems and methods may be implemented. These embodiments, which are to be understood as "examples" or "options," are described in sufficient detail to enable those skilled in the art to practice this invention. Embodiments may be combined, other embodiments may be utilized, or structural or logical changes may be made without departing from the scope of this invention. Accordingly, the following detailed description is not to be construed in a limiting sense, and the scope of this invention is defined by the appended claims and their legal equivalents.

[0017] In the present disclosure, an aerosol generally refers to a suspension phase of particles dispersed in air or gas. "Real-time" analysis of an aerosol generally refers to an analysis method and apparatus for identifying aerosol analytes within several minutes after an aerosol sample to be analyzed is introduced into an analyzer or system. Singular notations (corresponding to the terms "a" or "an" in English) are used to include one or more, and the term "or" is used to refer to a non-exclusive "or" unless otherwise specified. Further, it should be understood that the expressions or terms used herein and not otherwise defined are for illustrative purposes only and not for purposes of limitation. Unless otherwise specified in the present disclosure, for the purpose of interpreting the range of the term "about," the error range related to the disclosed values (such as dimensions, operating conditions, etc.) is ±10% of the values shown in the present disclosure. The error range related to the values disclosed as percentages is ±1% of the indicated percentage. The word "substantially" used before a particular word includes the meaning of "a fairly large part of the specified range" and "most but not all of what is specified." Detailed description

[0018] Specific aspects of the invention are described in considerable detail below for the purpose of explaining the composition, principles, and operation of the disclosed methods and systems. However, various modifications can be made, and the scope of the invention is not limited to the exemplary aspects described.

[0019] In an exemplary system 100 (FIG. 1), aerosol particles, e.g., particles containing biological materials in air, are sent at a speed of thousands of particles per second to a suitable inlet element 101 that removes debris and material from the particles and flow into an aerosol beam generator 102 that collimates the particles into a narrow beam of single particles. The beam generator uses differential pumping to reduce the pressure to a level compatible with the high vacuum within chamber 104. The particles may be indexed using a continuous laser from laser generator 103 (e.g., commercially available laser scattering devices including, but not limited to, IBAC and Polaran systems). Further, a continuous laser can be used to determine particle size, fluorescence (autofluorescence), and polarization (particle shape) and identify particles of particular interest. Next, the particles move into vacuum chamber 104 through a series of focusing lenses. This chamber can accommodate an advanced time-of-flight mass spectrometer (TOF-MS) 106 and, optionally, collection optics 107. When each indexed particle enters the center of chamber 104, it is struck by a high-power laser pulse from laser generator 108. Aerosol mass spectrometry requires an ionization laser 108 for ignition when the aerosol particles enter the region (typically less than 150 microns in diameter) irradiated by the laser. Since ionization laser 108 emits pulses with a duration of less than 5 ns (nanoseconds), advanced knowledge is required to predict when the particle enters the ionization region and triggers laser 108. Multiple lasers are used to measure and track the particles to make the time when the particles enter the laser's field of view predictable. In an exemplary system, at least one of the laser from generator 103 and the laser from laser generator 112 can be used to index and detect the particles when they leave beam generator 102. Since both laser beams 108 and 112 are aligned in close proximity, trigger laser 112 is sufficient to predict the path of a single aerosol particle and trigger ionization laser 108, greatly reducing the complexity of the particle timing hardware. Laser 108 may also be triggered using the laser from generator 103.Laser 108 may be triggered only when at least one of the particle size, shape, and fluorescence meets or exceeds a predetermined threshold of its characteristics. When monitoring the composition of aerosol particles in ambient air at periodic intervals, the selective triggering of laser 108 in this way, and subsequent inspection of the ionized fragments of each particle and analysis of the collected data, may be controlled (or adjusted) to avoid redundant data collection and data management. Timing (or trigger) laser 112 may also be used to measure the optical properties (size, shape, and fluorescence) of the particles. These measurements can be used to select the particles to be ionized and combined with the mass spectral measurements and other optical information obtained during ionization for analysis in data analysis system 110 using data fusion methods. The intensity of the laser pulse from generator 108 can be adjusted so that the particles are decomposed to generate ions from the constituent chemical components. That is, the laser vaporizes and ionizes at least a portion of the analyte molecule, generating ions with a specific mass-to-charge ratio (m / z). These high-information-content ions are accelerated to TOF-MS 106 and analyzed there. Further, when the analyte particles absorb sufficient light energy from the laser beam, they emit characteristic photons when transitioning from a high-energy state to a low-energy state. The luminescence may also be related to transitions between vibrational states. The interaction between the high-power laser pulse generated by generator 108 and the particles can also induce transient optical features such as higher-order fluorescence, laser-induced breakdown spectroscopy (LIBS), Raman spectra, and infrared spectra. Chamber 104 may also include focusing optics 107. The unique spectral data associated with each particle and generated using TOF-MS and optical sensor 109, as well as the particle-specific data from laser devices 103 and 112 (e.g., particle size, shape, fluorescence), may undergo data processing including data fusion in data analysis system 110 to generate compiled spectral data associated with each particle. The compiled spectral data may be compared with a training data set including a knowledge base of known biological substance spectra to predict the composition.System 110 communicates with a machine learning engine 111 to update the knowledge base of the training dataset and enable the improvement of the prediction of the composition moment by moment. The pressure in chamber 104 is reduced to at least 10-5 Torr using vacuum pump 105. In an exemplary system 100, the travel time (or residence time) of the particles from beam generator 102 until they are collided with laser 108 is less than about 1 second.

[0020] In an exemplary method 200 (Figure 2), individual aerosol particles 201 in an aerosol beam or stream may be simultaneously exposed in step 202 to an infrared (IR) laser pulse with a wavelength between about 1.0 micrometer and about 1.2 micrometers and a UV laser pulse with a wavelength between about 250 nm and about 400 nm. The advantage of analyzing bioaerosol particles one particle at a time is that each particle represents a "pure sample" of the constituent proteins and other high molecular weight molecules within the particle. In the case of a single airborne bacterium, it represents a "pure culture" of that one organism. The wavelengths of the IR laser pulse and the UV laser pulse may be about 1.06 micrometers and about 355 nm, respectively. For example, a Nd:YAG laser with a frequency tripled (or quadrupled) can be modified to produce an IR laser pulse with a wavelength between about 1.0 micrometer and about 1.2 micrometers and a UV laser pulse with a wavelength between about 250 nm and about 400 nm. The rapid heating of the aerosol particles when exposed to the IR pulse can efficiently "pop open" or instantaneously rupture each particle, generating a number of molecules 203 (ionized small and large fragments) characteristic of each particle. At a high IR laser output density of about 20 MW / cm 2 to about 150 MW / cm 2 , small ions with a molecular weight of less than about 1 kDa, usually less than 500 Da, are generated by pyrolysis (hard ionization), reducing the information content of the resulting spectrum. The IR laser output density is from about 1 MW / cm 2 to about 20 MW / cm 2Lowering to (soft ionization) reduces the pyrolysis effect and may generate large biomolecule fragments from aerosol particles. The repetition rate of the IR laser (pulse frequency or number of pulses per second) is typically in the range of 1 kHz, and the pulse width (duration of the IR pulse) is between about 1 nanosecond (ns) and about 10 ns. However, it was not effective to ionize these exposed biomolecules using only IR laser pulses. The UV pulse interacts with the intrinsic UV chromophore of the particle (a part of the molecule that absorbs UV light) to generate large bioions with a specific mass-to-charge ratio (m / z) for mass spectrometer analysis. There is no need to add a complex organic matrix-assisted laser desorption / ionization (MALDI) matrix. In the MALDI process, a sample treatment step is required in which another chemical substance (in a solvent) coats the sample before analysis by TOF MS. Method 200 eliminates the need for this complex sample treatment step while still generating large beneficial ions, especially in the case of biological aerosol particles. These ions can be analyzed using a high-mass range TOF-MS in step 204, which can analyze ions having a molecular weight between about 1 kDa and about 150 kDa. The resulting spectrum, when analyzed using data fusion and machine learning methods, improves the accuracy, sensitivity, and specificity related to the identification of analyte particles. Method 200 is particularly suitable when the aerosol of the analyte is composed of UV chromophores or many exogenous compounds in the growth medium that tend to absorb ultraviolet light. UV chromophores include, but are not limited to, molecules such as dipicolinic acid (such as found in bacterial spore coats), amino acids containing phenyl groups (such as tryptophan, tyrosine, and phenylalanine). Method 200 can be implemented using the exemplary system 100.

[0021] Aerosol analyte particles collected from ambient air typically contain significant amounts of water. There is a strong association between water and background atmospheric particles, especially particles containing biopolymers such as proteins and DNA. In bacterial cells, lipopolysaccharides, peptidoglycans, and glycans may only account for about 10% of the dry weight of vegetative cells. Furthermore, many other compounds associated with biological particles contain large amounts of hydroxyl groups that have the same strong laser interaction as water. The water (ambient humidity / moisture) associated with all particles sampled from the atmosphere can potentially be used as a laser absorption matrix for single particle TOF-MS. The presence of water ubiquitous in aerosol particles in the atmosphere provides a mechanism for ion generation over a wide range of masses. As previously explained, in the exemplary system 100, the transit time (or residence time) of the particles until the laser 108 collides with the beam generator 102 is less than about 1 second. This short residence time enables the analysis of IR chromophores in the exemplary method 300. As a result of this short transit time, the particles, either strongly bound already to the surface of the particle 310 as a thin film (e.g., monolayer) or as water or hydroxyl groups contained therein, do not evaporate but are available for strong interaction with the IR laser pulse in step 302. Biological materials typically contain high concentrations of molecules containing infrared-active hydroxyl groups. In fact, all cell interactions in the body involve specific interactions between carbohydrate molecules that decorate the surface and are present throughout the cell material. Furthermore, normal preparations of biological materials are often contaminated with growth media such as agar. These materials (IR chromophores) strongly absorb IR radiation due to their high hydroxyl group content. The IR laser pulse may have a wavelength between about 2.7 micrometers and about 3.3 micrometers. The wavelength of the IR pulse may be about 2.94 micrometers. The repetition rate of the IR laser is typically in the range of 1 kHz, and the pulse width may be between about 40 microseconds and about 100 microseconds. The IR laser output density is from about 1 MW / cm 2 to about 20 MW / cm 2It may be between. The overlap between the infrared absorption of hydroxyl-containing molecules such as water, carbohydrates, and agar and the IR laser line is also shown in FIG. 3. The IR laser pulse can be generated using an Er:YAG laser module sold by Pantec Biosolutions AG (Rugell, Liechtenstein). An Optical Parametric Oscillator (OPO) can also be used. The strong interaction between the laser pulse and the particles generates ions 303 over a wide range of masses. These ions can be measured using a high-mass range TOF-MS in step 304 that can analyze ions having a molecular weight between about 1 kDa and about 150 kDa. The natural binding of a small amount of strongly bound surface water and water molecules contained within the particles, combined with the short residence time in vacuum, provides an opportunity to generate large molecular fragments (1 kDa - 150 kDa molecular weight) and a very useful mass spectrum without the need for other MALDI chemical reagents. Thus, method 300 enables rapid detection of aerosol particles with high (>80%) accuracy, sensitivity, and specificity. Method 300 eliminates the need to freeze the particles using liquid nitrogen or other means to freeze the surface water and use the thin film of frozen water as the matrix for MALDI-TOF-MS. Also, more complex methods such as using a water droplet generator (about 50 microns in diameter) to generate water droplets and introduce them into the vacuum chamber of the TOF-MS, or using an acoustic levitation device to generate droplets of water or a solvent (e.g., 50 vol.-% methanol in an aqueous solution) with a diameter of about 2 mm that result in soft evaporation / ionization are not required [3]. The generation of large molecular fragments may also be improved by treating the aerosol particles using a spray of water or a solvent-water mixture prior to ionization using an IR laser pulse. An organic solvent containing at least one of methanol, ethanol, and isopropanol may be used.

[0022] When the aerosol particles contain non-biological particles and it is desired to identify the chemical composition of the particles, the analysis of these particles may be performed by hard ionization to generate small ions. For this purpose, an IR laser pulse having a laser output density between about 20 MW / cm 2 and about 150 MW / cm 2 may be used. Methods 200 and 300 may be modified to allow switching between hard ionization that generates fragments with a molecular weight of less than 1 kDa, usually less than 500 Da, and soft ionization that generates fragments with a molecular weight usually exceeding 1 kDa.

[0023] In the exemplary methods described above, individual aerosol analyte particles are indexed prior to ionization and tracked using at least one continuous laser. Additionally, a continuous laser can be used to measure particle properties such as size and shape. Individual particles are indexed and tracked, enabling data fusion of the mass spectral data associated with each particle and the optical property data of each particle. These optical properties may include the size, shape, and polarization of the particle. Through indexing, the mass spectral data collected after ionization of each particle can be associated with each particle. Next, a large amount of data associated with each particle in the aerosol beam is filtered and analyzed using the data fusion protocol of the data analysis system 110 to identify the composition and type of the particles in real time with high precision, sensitivity, and specificity. Data fusion is defined as the combination of data from multiple sources and aims to obtain improved information from the perspective of cheaper, higher-quality, or more relevant information. A review of data fusion techniques is provided by Castanedo [2], which is hereby incorporated by reference in its entirety.

[0024] In exemplary methods 200 and 300, in addition to TOF-MS mass spectrometry analysis, one or more optical detection methods may also be used. This is because when analyte aerosol particles absorb sufficient light energy from a laser pulse, they transition from a high-energy state to a low-energy state and emit transient optical photons such as higher-order fluorescence, laser-induced breakdown spectroscopy (LIBS), Raman spectra, and infrared spectra. Therefore, in addition to mass spectrometry, an optical sensor / detector 109 may be used to identify the composition of aerosol particles. The measurement data collected using both TOF-MS and the optical sensor may be processed using data fusion techniques to provide information regarding the composition of the aerosol analyte. By collecting information from various detectors including one or more optical techniques and mass spectrometry, the data associated with each particle may be filtered and analyzed using a data fusion protocol to rapidly (near real-time) identify the composition and type of the particles with high accuracy, sensitivity, and specificity. For each indexed individual aerosol particle, data from each measurement including at least one of TOF-MS, LIBS, Raman spectroscopy, and infrared spectroscopy is transferred to a sensor data fusion engine (data analysis system 110), where artificial intelligence tools such as machine learning and deep learning may be used to fully characterize the particles.

[0025] In LIBS, a laser pulse (e.g., from a high-energy Nd:YAG laser having a wavelength of about 1064 nm) is focused on the particles to ablate a small amount of the particles and generate a plasma. The particles of the object to be analyzed are decomposed (dissociated) into ionic species and atomic species. When the plasma cools, characteristic atomic emission lines of the elements can be observed using an optical detector such as a CCD detector. Another exemplary optical detection tool is Raman spectroscopy. Raman spectroscopy provides information regarding molecular vibrations that can be used for sample identification and quantification. This technique involves focusing a laser beam (e.g., a UV laser source having a wavelength of about 330 to about 360 nm) on the sample and detecting inelastically scattered light. Most of the scattered light is at the same frequency as the excitation source and is known as Rayleigh or elastic scattering. Due to the interaction between the incident electromagnetic wave and the vibrational energy levels of the molecules in the sample, the energy of a very small amount of the scattered light is shifted from the laser frequency. Plotting the intensity and frequency of this "shifted" light yields the Raman spectrum of the sample. In fluorescence spectroscopy, the analyte molecule is excited by irradiation at a specific wavelength and emits radiation at a different wavelength. The emission spectrum provides information for both qualitative and quantitative analysis. When light of an appropriate wavelength is absorbed by the molecule, the electronic state of the molecule changes from the ground state to one of many vibrational levels in an excited electronic state. When the molecule is in this excited state, relaxation can occur via several processes. Fluorescence is one of these processes and emits light. By analyzing the various frequencies and their relative intensities of the light emitted in fluorescence spectroscopy, the chemical structure associated with the various vibrational levels can be determined. Certain amino acids in biological samples such as tryptophan have a high fluorescence quantum efficiency, which is advantageous for the use of fluorescence spectroscopy to identify these amino acids.

[0026] Machine learning (ML) techniques for analyzing spectral data obtained and collected using a machine learning engine 111 provide a significant improvement over manual data processing for analyte identification, which is slow and labor-intensive. Machine learning is generally a subset of artificial intelligence and consists of algorithms whose performance improves with data analysis over time. It is good to use supervised machine learning techniques. Supervised learning consists of the task of learning a function that maps inputs to outputs based on examples of input-output pairs. The function is inferred from labeled training data consisting of a series of training examples. Machine learning also includes deep learning methods, which are unsupervised learning methods that can identify signatures within complex datasets without the need to pre-identify specific features. Unsupervised machine learning methods and semi-supervised (a hybrid of supervised and unsupervised learning) can also be used. Unsupervised learning methods may include types of learning that are useful for finding previously unknown patterns within a dataset without existing labels. Two exemplary methods used in unsupervised learning are principal component analysis and cluster analysis. Cluster analysis is used in unsupervised learning to group or segment a dataset with shared attributes in order to infer relationships between algorithms. Cluster analysis is a branch of machine learning that groups unlabeled, classified, or unclassified data. Cluster analysis identifies commonalities in the data and responds based on the presence or absence of such commonalities for each new piece of data. This approach is useful for detecting abnormal data points. Unsupervised learning methods can be used for anomaly detection. This is useful for identifying risks that were previously unknown. For example, air samples can be analyzed periodically to measure the composition of particles in the air, identify particle characteristics (size, shape, fluorescence, etc.) and the spectra associated with the particles, and obtain baseline data information on particles in "normal" ambient air. Particles in the atmosphere after an event, such as the release of biological threats into the atmosphere, deviate from the baseline data and provide particle characteristic data and spectral data that highlight the anomaly (as evidenced by the abnormal spectrum), providing an opportunity to take corrective measures necessary to mitigate the threat.The compiled spectral data, described previously, can be compared to a training data set that includes a knowledge base of known biological substance spectra to predict the particle composition. System 110 can communicate with the machine learning engine 111 to update the knowledge base of the training data set and enable improving the prediction of the configuration over time. Since the mass spectra of biological substances cover a range that is approximately three orders of magnitude larger than the mass spectra of chemical substances, the application of automated techniques becomes significantly more complex.

[0027] Furthermore, environmental contaminants can reduce signal intensity by competing with the target during the ionization process (competing ionization). This introduces signature components (clutter) that need to be deconvolved with the target signature. Current automated methods are mostly limited to searching for very pure targets in samples where the environment does not get messy. The disclosed exemplary method eliminates competing ionization and signature ambiguity by physically separating the target analyte from the clutter (each event is assumed to be either a target or clutter). An exemplary ML schematic 400 for identifying tuberculosis (TB) biomarkers using high-resolution mass spectrometry is shown in FIG. 4. In step 401, a high-resolution Orbitrap mass spectrometer (ThermoFisher Scientific) was used to acquire (or extract) positive and negative ion signals containing thousands of features. A mass ratio (SNR) exceeding a signal-to-noise ratio of 5:1 was selected. The weighted principal component analysis (PCA), an unsupervised dimensionality reduction algorithm, was used in step 402 to reduce a large set of signals to two components. PCA provided a 2D visualization. This was used to investigate whether the extracted signals revealed the essential differences between two classes of samples (TB and non-TB). FIG. 5 shows the output of the PCA of the signals extracted in positive and negative ion modes from 19 sputum-positive tuberculosis patients and 17 non-tuberculosis patients. Positive and negative ion signals were collected from two groups of samples, i.e., non-tuberculosis patients and tuberculosis patients. The results of the PCA revealed that the samples of each group tended to cluster together. This suggests that the two classes of samples can be distinguished using the extracted signals collected from high-resolution mass spectrometry. Step 402 may also be used to analyze data collected from methods 200 and 300 using TOF-MS.

[0028] In method 400, the Significance Analysis of Microarrays (SAM) technique was also applied to the signals extracted in step 401 in step 403 to identify strongly distinguishable features and select the most powerful features for distinguishing two classes of samples. SAM is a feature selection algorithm designed to process large datasets and identify the most powerful features between two classes of samples. The SAM analysis returned a feature ranking list based on the magnitude of change, statistical significance, and false positive rate. The functions identified by SAM were optimized using a support vector machine (SVM) in step 404. An SVM is a supervised machine learning-based classifier that defines a separating hyperplane using a training dataset so that unknown samples can be classified according to the side of the separating hyperplane. The advantage of an SVM depends on the ability to process high-dimensional data, predict the composition of analytes, and continuously improve the knowledge base contained in the training dataset.

[0029] As an example, SAM-based feature selection using signals extracted from anions is shown in FIG. 6. The data has features classified into three classes: (A) up-regulation, (B) down-regulation, and (C) not important. Signals that are higher in tuberculosis (TB) patients than in non-tuberculosis (non-TB) patients (up-regulation) are represented in region A, and region B is represented by signals that are lower in TB patients (down-regulation). Overall, more than 1500 features (ion signals) extracted from the cation mode and more than 500 features extracted from the anion mode were found to be higher in TB patients. Next, SVM analysis was performed to optimize the number of features. In this analysis, thousands of signals showed the potential to identify TB-related signals compared to a relatively small number of subjects (training dataset). As a classification algorithm, SVM can be used to optimize the features selected by SAM by returning a confusion matrix that calculates the percentages of accuracy, sensitivity, and specificity. As shown in FIG. 7, in the cation mode, the highest performance of SVM-based classification existed when approximately 300 selected features were applied. In the anion mode, the highest performance of SVM-based classification was found when approximately 100 selected features were applied. These methods were able to distinguish between TB patients and non-TB patients with 89% accuracy, 100% sensitivity, and 81% specificity. Using the previous exemplary analysis method, lipid extraction and high-resolution mass spectrometry of patient samples collected in Masiphumelele, South Africa were used to identify multiple tuberculosis biomarkers.

[0030] To identify the presence of biological threat agents in the atmosphere, air samples are collected at predetermined time intervals and analyzed using the exemplary methods disclosed previously to generate a historical dataset (training dataset) of background / baseline information in the data analysis system 110. The analysis may be improved over time using machine learning algorithms executed by the engine 111. Variations in the background information may be modeled to map the normal behavior of the atmosphere in the protected area. If the release of biological, biochemical, or chemical aerosol particles is suspected, sampling the air using the previous exemplary methods yields information that deviates from past background information. The first indication of the presence of such a threat is a sharp deviation from the normal background. At this stage, algorithmic determination can be made regarding the composition of individual particles. Thus, corrective measures can be taken promptly to protect human lives and prevent loss of life.

[0031] The exemplary methods and apparatus disclosed previously may also be used for the analysis of liquid samples. In this case, an aliquot of the sample can be aerosolized using appropriate means. For example, a nebulizer can be used to aerosolize the liquid sample in the air. Analyte particles may also be extracted from a swab or may be in the form of a solid sample that can be dissolved using an appropriate solvent. Next, an aliquot of the sample can be aerosolized using appropriate means. For example, a nebulizer can be used to aerosolize the liquid sample in the air. Using the disclosed exemplary methods and devices, viruses and toxins, in addition to bacteria, can be identified in real time. By analyzing data collected from one or more optical detectors and mass spectrometry, the biological fingerprint of the analyte particles can be obtained in real time.

[0032] The disclosed exemplary methods eliminate the need to use the complex sample processing steps associated with MALDI TOF-MS while still generating large beneficial ions, particularly in the case of biological aerosol particles. Further, the generation of large molecular fragments can also be improved by treating the aerosol particles with a spray of water or a solvent-water mixture prior to ionization using an IR laser pulse (e.g., in method 300). Organic solvents having at least one of methanol, ethanol, and isopropanol can be used. In the MALDI process, a sample processing step is required in which another chemical (usually a complex organic molecule in a solvent) coats the sample prior to analysis by TOF-MS. Methods 200 and 300 can be modified to allow for a MALDI matrix (a simple matrix such as an organic solvent) coating step. Combining the MALDI technique with high mass range time-of-flight (TOF) mass spectrometry enables the direct analysis of large peptide components and intact proteins that allow for the biological identification of "whole cells". Applicant's international application PCT / US2016 / 48395, entitled "Coating of Aerosol Particles Using an Acoustic Coater", describes conventional MALDI TOF mass spectrometry, provides examples of complex organic MALDI matrices, and discloses methods and devices for applying a coating of a MALDI matrix solution to bioaerosol particles prior to analysis with an aerosol time-of-flight mass spectrometer, which is hereby incorporated by reference in its entirety.

[0033] An exemplary system 900 (Figs. 9A - D) is disclosed for detecting and ionizing aerosolized single particles in a complex aerosolized sample and optimizing the resolution and data processing of the single particle mass spectrum. An aerosol mass spectrometer, such as described in "System and Method for Determining the Size and Chemical Composition of Aerosol Particles in Real Time" of U.S. Patent Application Publication No. 2011 / 0116090, discloses placing the outlet of an aerosol beam generator at a position away from an ionization laser beam, whereby the trajectories of the particles are dispersed. The exemplary system 900 disclosed herein addresses these deficiencies and significantly reduces particle loss within the system. Since the aerosolized particles are not attached to a fixed surface such as a sample plate, the spatial distribution of the particles during ionization affects the flight time to the detector 906 (such as a TOF - MS detector) and significantly affects the resolution of the single particle mass spectrum. As shown in Fig. 9A, an aerosol beam generator 902 collimates the particles into a narrow beam of single particles. The particles are indexed using a continuous laser from a laser generator 903 (a commercially available diode laser such as a 532 nm diode - pumped laser, model CPS532 from Thor Labs) as they exit the beam generator 902. Further, the continuous laser 903' from the generator 903 can be used to determine at least one of particle size, fluorescence (auto - fluorescence if a UV laser is used), polarization (particle shape) and to identify particles of particular interest. Further, the continuous laser 903' from the laser generator 903 can be used to trigger the ionization laser generator 908 to generate a laser pulse 908' to ionize a single particle entering the ionization region of the ionization laser. The laser generator 908 is triggered only when at least one of the size, shape, and fluorescence of the particle meets or exceeds a predetermined threshold of its characteristics. When each of the particles or indexed particles enters the ionization region, it is irradiated with a high - power laser pulse 908' from the laser generator 908.In aerosol mass spectrometry, when aerosol particles enter an ionization region irradiated by a pulsed laser (usually with a diameter of less than about 150 microns), aerosol mass spectrometry requires that ionization laser 908 be pulsed on. Since ionization laser 908 emits pulses with a duration of less than 5 nanoseconds (ns), advanced knowledge is required to predict the timing when particles enter the ionization region and trigger the emission of laser 908. The intensity of the laser pulse from generator 908 may be adjusted so that each particle is decomposed and ions are generated from the constituent chemical components. That is, the laser evaporates and ionizes at least a part of the molecule to be analyzed, generating ions having a specific mass-to-charge ratio (m / z). These large and information-rich ions are accelerated to detector 906, where they are analyzed. The flight times of these detected particles and fragments (Figure 9D) also depend on the initial positions of the particles in the ionization region (Figure 9A). By selectively triggering laser 908 in this way to monitor the composition of aerosol particles and further using the guide tube and data analysis method described below, reliable and efficient data analysis that optimizes the generation of ionization fragments of each particle and eliminates the collection of extra data becomes possible. Ionization region 909 (substantially the same as the diameter of pulsed laser beam 908') is usually less than about 150 μm and may be about 100 μm in size, which is significantly larger than the typical thickness of the bulk sample shown in Figure 8. The diameter of the trigger laser beam may range from about 150 μm to about 100 μm. Conventional focusing methods such as two-stage extraction and delayed extraction help correct the variations in the initial energy received by each ion, but these methods cannot correct the variations in the positions of individual particles. This may not be a major problem in conventional MALDI MS analysis (or other types of mass spectrometry), but optimizing the generation of fragments and ions from moving particles in the ionization region regardless of the positions of individual particles in ionization region 909 is an important requirement for aerosol MALDI MS analysis.

[0034] On one side, the guide tube 910 (FIG. 9B) is disposed between the outlet of the aerosol beam generator 902 and the ionization region 909, minimizing the diffusion of particles as they traverse the distance from the outlet of the aerosol beam generator 902 to the ionization region 909. Thus, the particle guide tube 910 serves to reduce the spatial distribution of particles between the aerosol beam generator 902 and the ionization region 909 (FIG. 9B). The nominal inner diameter of the guide tube 910 may be about 300 μm, which is at least twice the size of the ionization region. The nominal length of the guide tube 910 may be from about 1 inch to about 5 inches. The nominal length of the guide tube may be from about 2 inches to about 3 inches. The nominal length of the guide tube 910 may be about 2.7 inches. The guide tube 910 may be made of any conductive material, including but not limited to metals such as stainless steel. Without the guide tube 910, a significant number of particles would bypass the ionization region, reducing the sensitivity of the TOF-MS analysis system. Using the guide tube 910, particle loss can be reduced by about three to five times compared to a system without the guide tube 910. The conductive coating on the inner wall reduces particle adhesion to the wall of the stainless steel tube, further reducing particle loss. The outlet end 911 of the guide tube 910 may be disposed directly above the ionization region 909. The gap between the end 911 of the guide tube and the ionization region may be about 0.135 inches. Alternatively, the outlet end 911 may be inserted into the ionization zone and positioned so that the end 911 does not obstruct the path of the laser beam from the generator 908. When each particle exits the guide tube 910 and enters the trigger beam 903', the trigger laser beam 903' activates the ionization laser generator 908, which collides with the particles within the ionization region 909.

[0035] Furthermore, in other aspects, by minimizing the distance between the center line 913 of the trigger laser beam 903' and the center line 914 of the ionization beam 908' to about 50 μm (Figure 9C), the amount of fragments and ions generated from the particles when the ionization laser beam collides can be improved. In this aspect, the entry point of the particles entering the trigger laser beam 903' (point 912 (Figure 9C)) can be used to trigger the laser generator 908 to generate a pulsed ionization laser beam 908' and collide with each particle entering the ionization region 909. This entry point may also be characterized by the flight time profile of each particle or the rising edge of the trace (Figure 9D). As described above, the ionization region 909 (the diameter of the pulsed laser beam 908') may have a diameter of less than about 150 μm and may be between about 100 μm and about 150 μm. The diameter of the trigger laser beam may also be between about 100 μm and about 150 μm. When the ionization laser beam and the trigger laser beam are tightly coupled as an overlapping beam in this way (Figure 9C), the generation of characteristic fragments and ions from each particle is maximized, the amount of these fragments and ions is improved, and the sensitivity related to the analysis using the TOF-MS detector is enhanced. As described above, the molecular weight of each ionization fragment is from about 100 Da to about 150 kDa. Furthermore, when the particle to be analyzed absorbs sufficient light energy from the laser beam, characteristic photons may be emitted when transitioning from a high energy state to a low energy state and detected using a photomultiplier tube (PMT). The PMT is a very sensitive light detector that realizes a current output proportional to the light intensity. The interaction between the high-power laser pulse 908' and the particles may also induce temporary optical properties such as higher-order fluorescence, laser-induced breakdown spectroscopy (LIBS), Raman spectrum, and infrared spectrum. In other aspects, the beam diameter of the trigger beam may be different from the beam diameter of the ionization beam. The beam diameter of the trigger laser beam may be smaller than the beam diameter of the pulsed ionization beam.In another aspect, the trigger beam and the ionization beam may be arranged as consecutive beams with respect to each other while minimizing the distance between the centerlines of the beams.

[0036] In an exemplary method 1300, the ionization laser generator 908 within the system 900 is triggered using the rising edge of a time-of-flight profile that represents the point 912 at which each particle enters the trigger laser beam (FIG. 9D). As can be seen from the figure, the rising edge of the time-of-flight profile for each case closely matches the rising edge of the elastic particle scattering profile obtained when the ionization beam impinges on each particle within the ionization region 909. Triggering the ionization beam using the rising edge of the file time profile, or the entry point of each particle into the laser beam, enables rapid and reliable collisions of the particles by the pulsed ionization laser beam and the generation of a sufficient amount of fragments and ions, which are characteristics of each particle. The exemplary method 1300 avoids the need to calculate the complex velocity profile of a single particle as it flows between the trigger laser beam and the ionization laser beam, and then performs velocity and time-of-flight calculations to initiate the ionization laser. Since there is no need to wait for the particle to exit the trigger laser beam, the trigger laser 903' and the ionization laser 908' can be more tightly coupled in method 1300. Since similar rising edges are seen for particles sized 1 μm, 2 μm, 3 μm, it can be seen that the use of the rising edge is not limited to a particular particle size.

[0037] In other aspects of the exemplary system 900, multi-stage ion extraction or delayed ion extraction can be utilized to reduce the dependence of the initial ionization energy on the position of particles within the ionization laser. FIG. 9E shows a basic single-stage ion source, where electrode grids V1 and V2 are set to voltages that generate an electric field gradient and accelerate the ions generated in ionization region 909. The electrode grids are made of a conductive material such as a metal like brass or stainless steel and need to provide sufficient electrical conductivity and material integrity in a vacuum. V3 and V4 are steering electrodes and are used to cancel the downward velocity component of the ions. V6, V7, and V8 are lenses that focus the ions as they flow along the ion path towards the detector. When the aerosolized particles exit the exemplary particle guide 910, they enter a trigger laser beam (nominal beam diameter between about 100 μm and about 150 μm) generated by laser generator 903, and the ionization laser is emitted as described above in the exemplary method 1300. The ions generated in ionization region 909 are accelerated between electrodes V1 and V2. Since it is desirable to have a large potential difference (e.g., about 10 kV) between V1 and V2, the equipotential lines within ionization region 909 are dense. As a result, the position of the particles within ionization region 909 when irradiated by the laser has a significant impact on the initial potential and the acceleration of the particles. Ions generated near electrode V2 are less accelerated than those generated near electrode V1. Using multi-stage ion extraction (e.g., two-stage extraction) can reduce the impact of the spread of these initial conditions that cause loss of particle acceleration (FIG. 9F). An intermediate electrode grid (V10) can be inserted between electrodes V1 and V2. Reducing the density of the equipotential lines between V1 and V10 reduces the variation in initial energy caused by fluctuations in the initial position of the particles within the sample when ions are generated in ionization region 909, and the potential difference between V2 and V10 enables higher acceleration required for time-of-flight MS analysis.

[0038] The exemplary system 900 described above can improve the signal-to-noise ratio and signal quality by using at least one of the following: the tight coupling of the guide tube 910, the trigger laser 903, and the ionization laser 908; starting the ionization laser using the rising edge of the time-of-flight profile generated by each particle entering the trigger beam and colliding with each particle; and extracting the ions generated in the ionization region 909 in multiple stages. These exemplary hardware aspects significantly improve the data quality and sensitivity of MS data analysis, while also enabling the correction of initial conditions during ionization (when the particles collide with the ionization laser 908 or other ionization sources) using in-silico (computer) manipulation of individual spectra. An exemplary data analysis method 1100 (FIGS. 11A-D) for processing aerosolized single-particle spectra using the exemplary system 900 is disclosed. Since the emission of each pulsed ionization laser generates the spectral characteristics of individual particles, the exemplary signal processing method 1100 can be used to align and remove noise from individual particle traces in step 1101 before calculating the ensemble average in step 1102. Although multiple noise reduction and alignment methods have been proposed for the analysis of mass spectrometry data, averaging multiple spectra before noise removal and alignment destroys information about the contributions and features provided by individual particles. Analysis at the single-particle level allows each spectrum to be preprocessed before integration and peak identification in steps 1103 and 1104. The alignment of single-particle spectra in step 1101 may have the following steps.

[0039] (a) Select the mass range to be aligned. The mass range can be centered around the position of potential biomarkers or other regions of interest. If there are multiple biomarkers, this process can be repeated for each mass range of interest.

[0040] (b) Select one spectrum as a reference for each specified window. The reference spectrum may be preselected, exist in a reference data library, or be developed using the measurement data set. Create a reference spectrum from the measured MS data set, calculate the Pearson correlation coefficient (PCC) for each spectral data file by comparing it with other spectra in the data set, and record the average PCC of the file. The spectrum with the highest score is selected as the reference spectrum. A flowchart for selecting a reference spectrum from three measured single-particle spectra is shown in Figure 11C. This process can be extended to any number of individual spectra within the measured mass spectrum data set. Additionally, correlation coefficients other than the Pearson correlation coefficient (PCC) can be used.

[0041] (c) Use the one-dimensional fast Fourier transform (FFT) method to align the peak windows of the MS spectrum data set to the peak windows of the reference spectrum by cross-correlation. The alignment or peak shift is performed in the time domain.

[0042] (d) Shift and align each file according to its cross-correlation with the reference file to create an aligned MS data set. Once each spectrum is shifted and aligned with the reference spectrum, the spectral data may be denoised in step 1101. Although multiple denoising methods (such as wavelets) can be used, in the exemplary method 1100, single-value decomposition (SVD) will be a useful tool. SVD is a low-rank approximation rather than a dimensionality reduction like PCA. The SVD denoising step may include the following steps.

[0043] (a) Calculate the SVD of the data within the selected data range.

[0044] (b) Use the low-rank approximation from the SVD to remove the noise from the aligned data set (Figure 11D).

[0045] Noise removal is achieved by reconstructing the signal using the highest SVD values. In FIG. 11D, the noise-removed signal is reconstructed from the five largest SVD singular values. [Example]

[0046] [Example 1: Single Particle Spectrum of B. globigii Using Exemplary System 900]

[0047] The exemplary system 900 was composed of a guide tube 910 disposed at the outlet end of the aerosol beam generator and at a position adjacent to the ionization region of the ionization laser. The guide tube was composed of a 316 stainless steel tube with an inner diameter of about 300 μm and a length of about 2 inches. The outlet of the guide tube 911 is about 0.135 inches away from the centerline of the trigger laser. The trigger laser beam was closely coupled to the ionization laser beam with a distance of about 50 μm between the centerlines of the trigger beam and the ionization beam. The UV pulse ionization laser generator 908 was triggered using the rising edge of the time-of-flight profile created by the particles entering the trigger laser beam from the generator 903 (FIG. 9D), and the particles were irradiated in the ionization region of the pulse ionization laser. As shown in FIG. 10A, multiple single particle detections were observed during the analysis of aerosolized B. globigii (Bg) particles. The UV laser irradiated on the particles resulted in a single scan (line) as shown in FIG. 10A. The horizontal axis indicates the mass-to-charge ratio (m / z), and the vertical axis indicates the particle index. A total of 453 individual particles were measured, and more than 449 particles showed a response consistent with Bg. Further, as shown in FIG. 10B, the average of the 453 single particle scans clearly shows a characteristic Bg TOF-MS signature.

[0048] [Example 2: Data Analysis of Bg Spectrum Using Exemplary Data Analysis Method 1100]

[0049] FIG. 12A shows the individual particle mass spectra of Bg before and after alignment of the mass window from about 1060 m / z to about 1100 m / z centered on the 1080 m / z peak and subsequent noise removal. The alignment was performed for each potential marker position following step 1101. Since the mass (m / z) shifts of each marker are independent, it was necessary to align the regions of interest individually before calculating the peak positions and values. As can be seen, two large peaks (1060 m / z and 1080 m / z) are seen in the selected mass range, and these can be clearly identified in the aligned and noise-removed data. Next, in the integrated spectrum, as shown in FIG. 12B, the signal-to-noise ratio is significantly increased. By excluding data with little correlation to the reference spectrum and low intensity data (trimmed data), the noise floor can be further reduced, revealing a third set of weak peaks centered at about 1095 m / z that was hardly recognizable in the raw data (FIG. 12A). Trimming removes spectra with low Pearson scores from the average, significantly reducing the noise floor. In MALDI MS and other mass spectrometry techniques, prior knowledge of the peak positions is essential to accurately identify the composition of each particle. Furthermore, this method can reduce the uncertainty of the peak positions to increase the observable signal. As described above, machine learning tools and artificial intelligence methods can be used to optimize the exemplary data analysis methods disclosed herein. Exemplary machine learning methods may include steps of predicting the composition by comparing the compiled spectral data to a training spectral data set knowledge base, updating the training data set knowledge base, and using the machine learning method to improve the prediction of the composition over time. Exemplary machine learning methods may have supervised machine learning methods.

[0050] The abstract is provided to enable the reader to quickly ascertain the nature and gist of the technical disclosure in accordance with 37 C.F.R. § 1.72(b). It should not be used to interpret or limit the scope or meaning of the claims.

[0051] Although the present disclosure has been described in connection with preferred forms of carrying it out, those skilled in the art will understand that many modifications can be made to it without departing from the spirit of the present disclosure. Therefore, it is not intended that the scope of the present disclosure be limited by the foregoing description.

[0052] It should also be understood that various changes can be made without departing from the essence of the present disclosure. Such changes are also implicitly included in the description. They are still within the scope of this disclosure. It is to be understood that this disclosure is intended to result in patents covering many aspects of the disclosure, independently and as a whole system, and in both method and apparatus modes.

[0053] Furthermore, each of the various elements of the present disclosure and the claims can be achieved in various ways. This disclosure is to be understood as encompassing any variations in device implementation, method or process implementation, or merely variations of any of these elements.

[0054] In particular, it should be understood that the words of each element can be expressed by equivalent device terms or method terms even if only the function or result is the same. Such equivalent, broader, or more general terms should be considered to be included in the description of each element or operation. Such terms can be replaced when necessary to clarify the implicitly broad scope to which this disclosure is entitled. It is necessary to understand that all operations can be expressed as means for taking that operation or as elements that cause that operation. Similarly, each physical element disclosed should be understood to encompass the disclosure of the operation facilitated by that physical element.

[0055] Furthermore, with respect to each term used, unless its use in this application is inconsistent with such interpretation, common dictionary definitions, such as those contained in at least one of a standard technical dictionary recognized by a person skilled in the art and the latest edition of Random House Webster's Unabridged Dictionary, are to be understood as incorporated herein for each term and all definitions, alternative terms, and synonyms thereof.

[0056] Furthermore, the use of the transitional phrase "comprising" is used to maintain the "open-ended" claims of this specification in accordance with conventional claim interpretation. Thus, unless the context requires otherwise, "comprising" is intended to mean including the recited element or step or group of elements or steps, but not excluding other elements or steps or group of elements or steps. Such terms should be construed in the broadest manner to provide the applicant with the broadest scope legally permissible.

[0057] [References] 1. Wan G-H, Wu C-L, Chen Y-F, Huang S-H, Wang Y-L, et al. (2014), "Particle Size Concentration Distribution and Influences on Exhaled Breath Particles in Mechanically Ventilated Patients," PLoS ONE 9(1): e87088. 2. Castanedo, F., "A Review of Data Fusion Techniques," The Scientific World Journal, 2013. 3. Morris, J.S., Coombes, K.R., Koonen, J., Baggerly, A., and Kobayashi, R., "Feature Extraction and Quantification for Mass Spectrometry in Biomedical Applications using the Mean Spectrum," Bioinformatics, vol. 21 (9), 1764-1775, May 2005. 4. Warschat, C. et al., "Mass Spectrometry of Levitated Droplets by Thermally Unconfined Infrared-Laser Desorption," Anal. Chem. 2015, 87, 8323-8327.

[0058] The following lists the technical features described herein. [Technical Feature 1] In a system for identifying the composition of aerosolized particles, an aerosol beam generator that generates a beam of single particles, a continuous timing laser generator that generates a timing laser for indexing each particle in the beam, a pulsed ionization laser generator configured to generate at least one of an IR laser pulse and a UV laser pulse that strikes each indexed particle when each indexed particle enters the ionization region of the pulsed ionization laser, triggered by the continuous timing laser, a guide tube having an exit end disposed between the aerosol beam generator and the ionization region, the guide tube being configured to urge particles to flow substantially linearly toward the ionization region of the pulsed laser within the guide tube, A system characterized by having at least one detector that analyzes at least one of the ionization fragments and photons associated with each particle and generates unique spectral data associated with each indexed particle. [Technical Feature 2] The system according to Technical Feature 1, wherein the size of the ionization region is about 100 μm to 150 μm. [Technical Feature 3] The system according to Technical Feature 1, wherein the nominal inner diameter of the guide tube is about twice the size of the ionization region. [Technical Feature 4] The system according to Technical Feature 1, wherein the nominal length of the guide tube is about 1 inch to about 5 inches. [Technical Feature 5] The system according to Technical Feature 1, wherein the nominal length of the guide tube is about 2 inches to about 3 inches. [Technical Feature 6] The system according to Technical Feature 1, wherein the guide tube is made of stainless steel. [Technical Feature 7] The system according to Technical Feature 1, wherein the distance between the outlet end of the guide tube and the ionization region is about 0.135 inches. [Technical Feature 8] The system according to Technical Feature 1, wherein the molecular weight of each ionization fragment is about 1 kDa to about 150 kDa. [Technical Feature 9] The system according to Technical Feature 1, wherein the at least one detector has at least one of a TOF-MS detector, a fluorescence detector, a LIBS detector, and a Raman spectrometer. [Technical Feature 10] The system according to Technical Feature 1, wherein each of the continuous timing laser and the pulsed ionization laser is characterized by a center line, and the distance between the center line of the continuous timing laser and the center line of the pulsed ionization laser is about 50 μm. [Technical Feature 11] The system according to technical feature 1, further comprising a plurality of ion extraction stages having a plurality of electrodes and lenses configured to accelerate the ionization fragments generated in the ionization region toward the detector. [Technical feature 12] The system according to technical feature 1, further comprising a data analysis system that uses data fusion to compile unique spectral data associated with each particle and generates compiled single-particle spectral data. [Technical feature 13] The system according to technical feature 1, further comprising a machine learning engine arranged to communicate with the data analysis system. [Technical feature 14] In a method for identifying the composition of aerosol particles, a step of arranging a continuous timing laser beam and a pulsed ionization laser beam so as to overlap each other; an aerosol particle generation step of generating an aerosol particle beam, wherein the aerosol particles flow substantially linearly toward the ionization region of the pulsed ionization laser within a guide tube; a pulsed ionization laser emission step of emitting a pulsed ionization laser when each particle enters the continuous timing laser beam, wherein when a laser pulse from the pulsed ionization laser hits each particle in the ionization region, ionized fragments of each particle and photons associated with each particle are generated; a step of analyzing at least one of the ionized fragments of each particle and the photons associated with each particle using at least one detector; and a step of determining the composition of each particle. [Technical feature 15] The method according to technical feature 14, wherein the distance between the center line of the continuous timing laser beam and the center line of the pulsed ionization laser beam is about 50 μm. [Technical feature 16] The method according to technical feature 14, further comprising the step of indexing each particle in the aerosol particle beam using the continuous timing laser beam to obtain a plurality of indexed particles. [Technical feature 17] The method according to technical feature 16, further comprising the step of measuring at least one characteristic of each indexed particle, including at least one of particle size, particle shape, and fluorescence, of the indexed particles using the continuous timing laser. [Technical feature 18] The method according to technical feature 17, further comprising the step of selecting which indexed particle to analyze by triggering a pulsed ionization laser when at least one characteristic of the indexed particle meets a predetermined threshold value of the characteristic. [Technical feature 19] The step of determining the composition comprises the steps of generating a plurality of single particle spectra using a TOF-MS detector, aligning each single particle spectrum, removing the noise of each aligned single particle spectrum, averaging the plurality of aligned and noise-removed single particle spectra, and comparing the averaged spectrum with a reference spectrum, according to the method described in technical feature 14. [Technical feature 20] The step of aligning each single particle spectrum comprises the step of selecting one or more mass ranges based on prior information related to the position of the mass range of interest, and a reference spectrum selection step of selecting one spectrum as a reference spectrum for each mass range, wherein the reference spectrum is at least one of a spectrum selected in advance, a spectrum existing in a reference data library, and a spectrum developed using a measured single particle spectrum dataset. The method according to technical feature 19 having a step of shifting a peak window of a spectral dataset to align with a corresponding window of a reference spectrum in a time domain. [Technical feature 21] The step of selecting one spectrum as a reference spectrum developed using a measured dataset includes the step of selecting a plurality of measured single particle spectra, the step of calculating a Pearson correlation coefficient (PCC) of each spectral data file by cross-correlation with each other spectrum in the dataset and recording an average PCC score of the file, and the step of selecting the spectrum with the highest PCC score as the reference spectrum, as described in technical feature 20. [Technical feature 22] The method according to technical feature 19, wherein the aligned single particle spectra are noise removed using a singular value decomposition technique (SVD). [Technical feature 23] The step of determining the above composition further includes the step of predicting the composition by comparing the average spectral data with a training spectral dataset knowledge base, the step of updating the above training dataset knowledge base, and the method according to technical feature 19 having at least one of the step of improving the prediction of the composition over time using a machine learning method. [Technical feature 24] The method according to technical feature 23, wherein the above machine learning method includes a supervised machine learning method.

Explanation of reference numerals

[0059] 100 System 101 Inlet element 102 Aerosol beam generator 103 Laser generator 104 Vacuum chamber 105 Vacuum pump 107 Condensing optical component 108 Laser generator 109 Optical sensor 110 Data analysis system 111 Machine learning engine 112 Laser generator

Claims

1. In a system for identifying the composition of aerosolized particles, an aerosol beam generator that generates a beam of single particles, a continuous timing laser generator that generates a timing laser for indexing each particle in the beam, a pulsed ionization laser generator that is triggered by the continuous timing laser and is configured to generate at least one of an IR laser pulse and a UV laser pulse that strikes each indexed particle when each indexed particle enters the ionization region of the pulsed ionization laser, a guide tube having an exit end disposed between the aerosol beam generator and the ionization region, the guide tube being configured to urge the particles to flow substantially linearly toward the ionization region of the pulsed laser within the guide tube, and at least one detector that analyzes at least one of the ionization fragments and photons associated with each particle and generates unique spectral data associated with each indexed particle.

2. The system according to claim 1, wherein the size of the ionization region is about 100 μm to 150 μm.

3. The system according to claim 1, wherein the nominal inner diameter of the guide tube is about twice the size of the ionization region.

4. The system according to claim 1, wherein the nominal length of the guide tube is about 1 inch to about 5 inches.

5. The system according to claim 1, wherein the nominal length of the guide tube is about 2 inches to about 3 inches.

6. The system according to claim 1, wherein the guide tube is made of stainless steel.

7. The system according to claim 1, wherein the distance between the exit end of the guide tube and the ionization region is about 0.135 inches.

8. The system according to claim 1, wherein the molecular weight of each ionization fragment is about 1 kDa to about 150 kDa.

9. The system according to claim 1, wherein the at least one detector includes at least one of a TOF-MS detector, a fluorescence detector, a LIBS detector, and a Raman spectrometer.

10. Each of the continuous timing laser and the pulsed ionization laser is characterized by a center line, and the distance between the center line of the continuous timing laser and the center line of the pulsed ionization laser is about 50 μm. The system according to claim 1.

11. The system according to claim 1, further comprising a plurality of ion extraction stages having a plurality of electrodes and lenses configured to accelerate the ionization fragments generated in the ionization region toward the detector.

12. The system according to claim 1, further comprising a data analysis system that uses data fusion to compile unique spectral data associated with each particle and generates compiled single-particle spectral data.

13. The system according to claim 1, further comprising a machine learning engine arranged to communicate with the data analysis system.

14. In a method for identifying the composition of aerosol particles, arranging a continuous timing laser beam and a pulsed ionization laser beam so as to overlap each other; an aerosol particle generation step of generating an aerosol particle beam, wherein the aerosol particles flow substantially linearly toward the ionization region of the pulsed ionization laser in a guide tube, the aerosol particle beam generation step; a pulsed ionization laser emission step of emitting a pulsed ionization laser when each particle enters the continuous timing laser beam, wherein when a laser pulse from the pulsed ionization laser hits each particle in the ionization region, an ionized fragment of each particle and a photon associated with each particle are generated, the pulsed ionization laser emission step; analyzing at least one of the ionized fragments of each particle and the photons associated with each particle using at least one detector; and determining the composition of each particle. A method characterized by comprising.

15. The method according to claim 14, wherein the distance between the center line of the continuous timing laser beam and the center line of the pulsed ionization laser beam is about 50 μm.

16. The method according to claim 14, further comprising indexing each particle in the aerosol particle beam using the continuous timing laser beam to obtain a plurality of indexed particles.

17. The method according to claim 16, further comprising the step of measuring at least one characteristic of each of the indexed particles, including at least one of particle size, particle shape, and fluorescence, using the continuous timing laser described above.

18. The method according to claim 17, further comprising the step of selecting which indexed particle to analyze by triggering a pulsed ionization laser when at least one characteristic of the indexed particle meets a predetermined threshold value of the characteristic.

19. The step of determining the composition comprises generating a plurality of single particle spectra using a TOF-MS detector; aligning each single particle spectrum; removing the noise of each aligned single particle spectrum; averaging the plurality of aligned and noise-removed single particle spectra; comparing the averaged spectrum with a reference spectrum. The method according to claim 14.

20. The step of aligning each single particle spectrum comprises selecting one or more mass ranges based on prior information related to the position of the mass range of interest; a reference spectrum selection step of selecting one spectrum as a reference spectrum for each mass range, wherein the reference spectrum has at least one of a spectrum selected in advance, a spectrum existing in a reference data library, and a spectrum developed using a measured single particle spectrum dataset. The reference spectrum selection step; shifting the peak window of the spectrum dataset to align with the corresponding window of the reference spectrum in the time domain. The method according to claim 19.

21. The step of selecting one spectrum as a reference spectrum developed using the measured dataset comprises selecting a plurality of measured single particle spectra; calculating the Pearson correlation coefficient (PCC) of each spectrum data file by cross-correlation with each other spectrum in the dataset and recording the average PCC score of the file; selecting the spectrum with the highest PCC score as the reference spectrum. The method according to claim 20.

22. The method according to claim 19, wherein the aligned single-particle spectrum is noise-removed using a singular value decomposition technique (SVD).

23. The step of determining the composition further comprises comparing the average spectral data with a training spectral data set knowledge base to predict the composition, updating the training data set knowledge base, and using a machine learning method to improve the prediction of the composition over time, the method according to claim 19 having at least one of the steps.

24. The method according to claim 23, wherein the machine learning method comprises a supervised machine learning method.

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