Method and system for detecting aerosol particles without using complex organic MALDI matrices

Abstract

To provide a method for identifying a composition of bioaerosol particles without performing pretreatment using a complicated organic MALDI matrix.SOLUTION: A continuous timing laser is used to index aerosol particles, measure particle characteristics, and trigger a pulsed ionization laser. Ionized fragments and optional photons associated with each particle generated by the ionization laser are analyzed using one or more detectors, including a TOF-MS detector and an optical detector. The unique spectral data associated with each indexed particle from each detector may be compiled using data fusion to generate compiled spectral data.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to methods and devices that use mass spectrometry and one or more optical detection methods to provide highly accurate identification of aerosol analyte particles. More specifically, the present disclosure relates to methods and devices for identifying biological aerosol analytes using, but not limited to, at least one of time-of-flight mass spectrometry (TOF-MS), optical single particle sensors, and data analysis systems capable of identifying analyte particles using data fusion and machine learning methods of data generated from one or more sensors. [Background technology]

[0002] Threats from aerosolized biological and chemical threat agents remain a significant concern for the U.S. government due to the potentially disastrous consequences to life and property that can result from such events. Two primary threat scenarios of particular concern are: (1) release of an agent within an enclosed structure (such as an office building, airport, or mass transit facility) where HVAC systems can effectively disperse the agent throughout the structure, and (2) widespread area release of an agent throughout a populated area such as a town or city. Exposure to released aerosolized agents can lead to mass casualties. In widespread area releases, protecting citizens from initial exposure is extremely difficult without timely information on the type, quantity, and location of the contaminant. Rapid corrective action requires methods and devices for identifying the composition of threat agents in real time. Airborne analyte aerosol samples can be captured using appropriate means, such as filters, sampling bags, and other similar containment devices designed to capture respirable particles. Particles may also be extracted from liquid samples obtained from wet-wall cyclones or similar devices, which are then re-aerosolized. An example of a wet-wall cyclone is the SpinCon II (Innovaprep, Drexel, Missouri). Particles in these aerosols may include, but are not limited to, anthrax, Ebola virus, ricin, and botulinum toxin. All of these collection methods require additional processing to extract biological particles for analysis, resulting in delays of hours or days to detect and identify hazardous aerosols.

[0003] The aerosol particles analyzed need not be limited to those found in ambient air. The aerosols analyzed may include exhaled particles (EBP) found in human or animal exhaled breath. The volume of air exhaled during breathing by a healthy adult is typically 1–2 liters, including a normal tidal volume of approximately 0.5 liters. Humans generate exhaled particles (EBP) during various respiratory activities, such as normal breathing, coughing, speaking, and sneezing. EBP concentrations from mechanically ventilated patients during normal breathing can range from approximately 0.4 to approximately 2,000 particles / breath or 0.001–5 particles / mL [1]. Furthermore, EBP can be less than 5 micrometers in size, with 80% of them ranging from 0.3–1.0 micrometers. The particle size distribution of exhaled breath has also been reported to be between 0.3 and 2.0 micrometers. The mean particle size of EBP can be less than 1 micrometer during normal breathing and 1–125 micrometers during coughing. Furthermore, 25% of patients with pulmonary tuberculosis cough up between 3 and approximately 600 colony-forming units (CFU) of M. tuberculosis, with levels of this pathogen primarily ranging from 0.6 to 3.3 micrometers. These bacteria are rod-shaped, approximately 2 to 4 micrometers long and 0.2 to 0.5 micrometers wide.

[0004] Although solutions for detecting and analyzing aerosol analytes, such as biological agents, are available, real-time analysis is not possible. One solution uses microfluidic technology to clean up samples and concentrate biological analytes. For example, specific antibodies can be used to enrich and purify biological analytes. This target-specific solution provides reasonable results, provided sufficient time is available for analyte cleanup and concentration. Other solutions are target-specific and only work for bacterial analytes at the expense of analyzing viruses, toxins, or particulate chemicals. This method requires, for example, applying a sample from a patient to a bacterial culture plate and incubating it for 8–24 hours. After bacterial colony growth, individual amplified and purified colonies are collected and measured by whole-cell MALDI-TOF mass spectrometry. Many studies have investigated the accuracy of this technology and found accurate identification of over 99% of clinical bacterial analytes. Two commercial systems for rapid clinical bacterial identification have been developed: the Bruker Biotyper (sold by Becton Dickinson) and the Shimadzu Vitek MS (sold by bioMerieux). These systems provide superior diagnostic results compared to the "gold standard" of 16sRNA. However, achieving these reliable clinical results requires a culture and / or extraction step to purify the sample. Therefore, the time from sampling to bioanalyte identification typically ranges from 12 hours to over a day. While such delays are often tolerable in clinical laboratories, they are often unacceptable for other applications, such as biodefense, which require real-time identification of bioanalytes. Biodefense and point-of-care healthcare applications require the ability to simultaneously identify not only bacteria but also fungi, viruses, and large bioorganic molecules (e.g., proteins, peptides, lipids), including biotoxins, in real time. Furthermore, reducing analysis time in clinical applications can improve quality and outcomes of care by enabling more timely treatment and identification of the best course of treatment (e.g., distinguishing viral from bacterial infections) and evaluation of the effectiveness of treatment courses.

[0005] Real-time aerosol particle detection also has many commercial applications. For example, the headspace of a fermenter can be analyzed for possible contaminants. It is often desirable to know the speciation of microorganisms in the air within a food or healthcare facility. Analyte particles may include microorganisms such as viruses, bacteria, algae, or fungi. Analyte particles may also include mixtures of microorganisms with proteins and peptides.

[0006] Methods and devices are desired to provide rapid (or real-time) analysis and identification of aerosol analyte particles, including bacteria, fungi, viruses, and toxins, with high accuracy and without pre-treating the particles with complex organic MALDI matrices. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] U.S. Patent No. 5,681,752 Summary of the Invention

[0008] Disclosed herein are methods and apparatus that provide real-time or near-real-time, highly accurate identification of aerosol analyte particles using mass spectrometry and one or more optical detection methods. Additionally, the present disclosure relates to methods and devices for identifying biological aerosol analytes using at least one of time-of-flight mass spectrometry (TOF-MS), optical single particle sensors, and data analysis systems capable of data fusion of data from one or more sensors, and real-time identification of analyte particles using machine learning tools.

[0009] An exemplary method for identifying bioaerosol particles without using a complex organic MALDI matrix is ​​disclosed, which may include generating an aerosol particle beam using an aerosol beam generator; indexing each particle in the beam using a first laser; detecting the position of each indexed particle using a continuous timing laser; triggering an ionizing pulsed laser to simultaneously generate an IR laser pulse and a UV laser pulse when each indexed particle reaches an ionization region of the ionizing laser; generating ionized fragments of each indexed particle and photons associated with each indexed particle, wherein each ionized fragment has a molecular weight between about 1 kDa and about 150 kDa; analyzing at least one of the ionized fragments of each indexed particle and the photons associated with each indexed particle using at least one detector; generating unique spectral data associated with each indexed particle from each detector; compiling the unique spectral data using data fusion to generate compiled spectral data; and determining the composition of each particle. At least one of the first laser and the timing laser can be used to measure at least one characteristic of the indexed particle, including at least one of particle size, particle shape, and fluorescence. The ionization laser can be triggered using at least one of the first laser and the timing laser when at least one characteristic of the indexed particle meets a predetermined threshold for that characteristic. The composition of each particle can be determined by comparing compiled spectral data to a knowledge base of training spectral datasets to predict composition, updating the knowledge base of training datasets, and using machine learning techniques to improve the prediction of composition over time. The machine learning method can include a supervised machine learning method.The composition may use unsupervised machine learning methods to classify unlabeled aerosol particles and identify anomalous aerosol particles. The machine learning methods may include weighted principal component analysis (PCA). The unlabeled aerosol particles may be identified, and the presence of anomalous particles (e.g., the presence of an airborne biothreat agent) may be flagged for corrective action. The IR laser pulses may be characterized by a wavelength between about 1.0 micrometers and about 1.2 micrometers. The UV laser pulses may be characterized by a wavelength between about 250 nm and about 400 nm. The IR pulse wavelength may be about 1.06 micrometers. The UV pulse wavelength may be about 355 nm. The IR laser power density may be between about 1 MW / cm2 and about 20 MW / cm2. The IR laser pulse width may be between about 1 ns and about 10 ns. The IR laser pulse repetition frequency may be about 1 kHz. The detector can include at least one of a TOF-MS detector, a fluorescence detector, a LIBS detector, and a Raman spectrometer. The ionized fragments can have a UV chromophore including at least one of dipicolinic acid, tryptophan, tyrosine, and phenylalanine. The IR and UV laser pulses can be generated using a Nd:YAG laser.

[0010] An exemplary method for identifying bioaerosol particles containing IR chromophores without using complex organic MALDI matrices is disclosed, the method comprising the steps of generating an aerosol particle beam using an aerosol beam generator, indexing each particle in the beam using a first laser, detecting the position of each indexed particle using a continuous timing laser, triggering an ionizing pulsed laser to generate an IR laser pulse when each indexed particle reaches an ionization region of the ionizing laser, generating ionized fragments of each indexed particle and photons associated with each indexed particle, wherein each ionized fragment has a molecular weight between about 1 kDa and about 150 kDa, analyzing at least one of the ionized fragments of each indexed particle and the photons associated with each indexed particle using at least one detector, generating unique spectral data associated with each indexed particle from each detector, compiling the unique spectral data using data fusion to generate compiled spectral data, and determining the composition of each particle. At least one characteristic of the indexed particles, including at least one of particle size, particle shape, and fluorescence, can be measured using at least one of a first laser and a timing laser. An ionization laser can be triggered using at least one of the first laser and the timing laser when at least one property of the indexed particle reaches a predetermined threshold for that property. The IR chromophore can include at least one of water, agar, and carbohydrate. The composition of each particle can be determined by comparing the compiled spectral data to a knowledge base of training spectral datasets to predict composition, updating the knowledge base of training datasets, and using machine learning techniques to improve the prediction of composition over time. The machine learning method can include a supervised machine learning method.The composition of each particle can also be determined by using an unsupervised machine learning method to classify unlabeled aerosol particles and identify anomalous aerosol particles. The machine learning method can include weighted principal component analysis (PCA). The unlabeled aerosol particles can be identified, and the presence of anomalous particles (e.g., the presence of an airborne biological threat agent) can be flagged for corrective action. The travel time of each particle from the aerosol beam generator to the ionization region of the ionizing pulsed laser can be less than about 1 second. The IR laser pulse can be characterized by a wavelength between about 2.7 micrometers and about 3.3 micrometers. The IR laser pulse wavelength can be about 2.94 micrometers. The IR laser power density can be between about 1 MW / cm and about 20 MW / cm. The IR laser pulse width can be between about 40 microseconds and about 100 microseconds. The IR laser pulse repetition frequency can be about 1 kHz. The detector can include at least one of a TOF-MS detector, a fluorescence detector, a LIBS detector, and a Raman spectrometer. The IR and UV laser pulses can be generated using at least one of an Er:YAG laser and an OPO laser.

[0011] An exemplary system for identifying bioaerosol particles without the use of complex organic MALDI matrices is disclosed, the system including: 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 be triggered by the timing laser when a predetermined condition is met and generate at least one of an IR laser pulse and a UV laser pulse to generate ionized fragments of each indexed particle and at least one photon associated with each indexed particle; and at least one detector for analyzing the at least one ionized fragment and photon associated with each particle and generating unique spectral data associated with each indexed particle from each detector. The system can further include a data analysis system for compiling unique spectral data associated with each particle using the data fusion to generate compiled spectral data. The system can further include a machine learning engine in data communication with the data analysis system. The pulsed ionization laser power density can be between about 1 MW / cm and about 20 MW / cm. At least one property of the indexed particles, including at least one of particle size, particle shape, and fluorescence, can be measured using a timing laser. The ionization laser can be triggered using a timing laser when at least one property of the indexed particle reaches a predetermined threshold for that property. Multiple continuous timing laser generators can be used to perform at least one of the functions of indexing each particle in the aerosol beam, triggering the pulsed ionization laser generator, and measuring at least one property of each indexed particle. The at least one detector can include at least one of a TOF-MS detector, a fluorescence detector, a LIBS detector, and a Raman spectrometer.

[0012] Other features and advantages of the present disclosure will be set forth in part in the following description and the accompanying drawings, which describe and illustrate different aspects of the present disclosure, and in part will be learned by those skilled in the art by studying the following detailed description in conjunction with the accompanying drawings or by practice of the present disclosure. The advantages of the present disclosure will be realized and attained by means of the instrumentalities and combinations particularly pointed out in the appended claims. [Brief explanation of the drawings]

[0013] The foregoing aspects and many of the attendant advantages of the present disclosure will be more readily appreciated as the same become better understood by reference to the following detailed description, when taken in conjunction with the accompanying drawings. [Figure 1] FIG. 1 is a schematic diagram of an exemplary system for single particle aerosol analysis. [Figure 2] FIG. 2 is a schematic diagram of an exemplary method for single particle aerosol analysis using simultaneous dual wavelength (IR and UV) particle absorption to generate information-rich biological ions. [Figure 3] FIG. 3 is a schematic diagram of an exemplary method for single particle aerosol analysis using hydroxyl group IR absorption as a specific infrared MALDI matrix to generate information-rich ions. [Figure 4] Figure 4 shows a schematic workflow for the identification of TB biomarkers using machine learning techniques. [Figure 5] Figure 5 shows the weighted principal component analysis (PCA) of the signals acquired from the positive and negative ion modes of TB and non-TB samples. [Figure 6] Figure 6 shows significance analysis of microarray (SAM)-based feature selection using signals extracted from anions of tuberculosis and non-tuberculosis samples. [Figure 7] Figure 7 shows the support vector machine (SVM) analysis for optimal feature selection in positive and negative ion modes for TB and non-TB samples.

[0014] All reference numbers, identifiers, and callouts in the figures are incorporated herein by this reference as if fully set forth herein. Failure to number elements in the figures is not intended as a waiver of any rights. Non-numbered references may also be identified by the alphabetic letter of the figure or appendix.

[0015] The following detailed description includes references to the accompanying drawings, which form a part of the detailed description. The drawings show, by way of illustration, specific embodiments in which the disclosed systems and methods may be practiced. These embodiments, which may be understood as "examples" or "options," are described in sufficient detail to enable one skilled in the art to practice the invention. The embodiments may be combined, other embodiments may be utilized, or structural or logical changes may be made without departing from the scope of the invention. Therefore, the following detailed description is not to be taken in a limiting sense, and the scope of the invention is defined by the appended claims and their legal equivalents.

[0016] In this disclosure, aerosol generally refers to a suspended phase of particles dispersed in air or gas. "Real-time" analysis of aerosols generally refers to analytical methods and devices that identify aerosol analytes within minutes of the aerosol sample being analyzed being introduced into the analytical device or system. The singular (equivalent to the English terms "a" or "an") is used to include one or more, and the term "or" is used to refer to a non-exclusive "or" unless otherwise specified. Furthermore, expressions or terms used herein and not otherwise defined are intended for descriptive purposes only and not for limiting purposes. Unless otherwise specified in this disclosure, for purposes of interpreting the scope of the term "about," the error range associated with a disclosed value (e.g., dimensions, operating conditions, etc.) is ±10% of the value stated in this disclosure. The error range associated with values ​​disclosed as percentages is ±1% of the stated percentage. The word "substantially" used before certain words includes the meanings "a significant portion of what is specified" and "most but not all of what is specified." DETAILED DESCRIPTION OF THE INVENTION

[0017] Specific aspects of the present invention are described in considerable detail below for purposes of explaining the composition, principles, and operation of the disclosed methods and systems. However, various modifications may be made and the scope of the invention is not limited to the exemplary aspects described.

[0018] In an exemplary system 100 (FIG. 1), aerosol particles, e.g., particles containing airborne biological material, are directed at a rate of thousands of particles per second through a suitable inlet element 101, which removes debris and material from the particles, and then flow into an aerosol beam generator 102, which collimates the particles into a narrow beam of single particles. The beam generator utilizes differential pumping to reduce the pressure to a level compatible with high vacuum within a chamber 104. Particles can be indexed using a continuous laser from a laser generator 103 (e.g., commercially available laser scattering devices, including, but not limited to, IBAC and Polaran systems). Additionally, the continuous laser can be used to determine particle size, fluorescence (autofluorescence), and polarization (particle shape) to identify particles of particular interest. The particles then travel through a series of focusing lenses into a vacuum chamber 104, which can house an advanced time-of-flight mass spectrometer (TOF-MS) 106 and, optionally, collection optics 107. As each indexed particle enters the center of chamber 104, it is struck with a high-power laser pulse from laser generator 108. Aerosol mass spectrometry requires ionization laser 108 to ignite when an aerosol particle enters the region illuminated by the laser (typically less than 150 microns in diameter). Because ionization laser 108 emits pulses less than 5 ns (nanoseconds) in duration, advanced knowledge is required to predict when a particle will enter the ionization region and trigger laser 108. Multiple lasers are used to measure and track particles, making it possible to predict when a particle will enter the laser's field of view. In an exemplary system, at least one of the lasers from generator 103 and laser generator 112 can be used to index and detect particles as they leave beam generator 102. Because both laser beams 108 and 112 are closely aligned, trigger laser 112 is sufficient to predict the path of a single aerosol particle and trigger ionization laser 108, significantly reducing the complexity of the particle timing hardware. The laser 108 may also be triggered using a laser from the generator 103 .The laser 108 can be triggered only if at least one of particle size, shape, and fluorescence meets or exceeds a predetermined threshold for that characteristic. When monitoring the composition of aerosol particles in ambient air at periodic intervals, the selective triggering of the laser 108 in this manner, and the subsequent examination of each particle's ionized fragments and analysis of the collected data, can be controlled (or adjusted) to avoid redundant data collection and data management. The timing (or triggering) laser 112 can also be used to measure the particle's optical properties (size, shape, and fluorescence). These measurements can be used to select particles for ionization, and the data can be combined with mass spectrometry measurements and other optical information obtained during ionization for analysis in the data analysis system 110 using data fusion techniques. The intensity of the laser pulse from the generator 108 can be adjusted so that the particles are broken down to produce ions from their constituent biochemical components. That is, the laser vaporizes and ionizes at least a portion of the analyte molecules, producing ions of a specific mass-to-charge ratio (m / z). These information-rich ions are accelerated to the TOF-MS 106, where they are analyzed. Furthermore, when analyte particles absorb sufficient light energy from the laser beam, they emit characteristic photons as they transition from a high-energy state to a low-energy state. Emissions can also be associated with transitions between vibrational states. The interaction of particles with high-power laser pulses generated by the generator 108 can also induce transient optical signatures, such as higher-order fluorescence, laser-induced breakdown spectroscopy (LIBS), Raman spectra, and infrared spectra. The chamber 104 can also include collection optics 107. The unique spectral data associated with each particle and generated using the TOF-MS and optical sensor 109, as well as particle-specific data (e.g., particle size, shape, fluorescence) from the laser devices 103 and 112, can undergo data processing, including data fusion, in the data analysis system 110 to generate compiled spectral data associated with each particle. The compiled spectral data can be compared to a training dataset containing a knowledge base of known biological material spectra to predict composition.The system 110 is in data communication with a machine learning engine 111, allowing it to update its knowledge base of training data sets and improve composition predictions over time. The pressure within the chamber 104 is reduced to at least 10 Torr using a vacuum pump 105. In the exemplary system 100, the travel time (or residence time) of a particle from the beam generator 102 to being struck by the laser 108 is less than about 1 second.

[0019] In an exemplary method 200 (FIG. 2), individual aerosol particles 201 in an aerosol beam or stream can be simultaneously exposed in step 202 to infrared (IR) laser pulses with wavelengths between about 1.0 micrometer and about 1.2 micrometers and UV laser pulses with wavelengths 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, this represents a "pure culture" of that single organism. The wavelengths of the IR and UV laser pulses can be about 1.06 micrometers and about 355 nm, respectively. For example, a frequency-tripled (or quadrupled) Nd:YAG laser can be modified to generate IR laser pulses with wavelengths between about 1.0 micrometer and about 1.2 micrometers and UV laser pulses with wavelengths between about 250 nm and about 400 nm. The rapid heating of aerosol particles upon exposure to IR pulses effectively "pops open" or instantaneously explodes each particle, potentially generating numerous molecules (small ionized and large fragments) characteristic of each particle. At high IR laser power densities, from approximately 20 MW / cm to approximately 150 MW / cm, thermal decomposition produces small ions with molecular weights less than approximately 1 kDa, typically less than 500 Da (hard ionization), reducing the information content of the resulting spectrum. Lowering the IR laser power density from approximately 1 MW / cm to approximately 20 MW / cm (soft ionization) reduces the thermal decomposition effect and potentially generates larger biomolecular fragments from aerosol particles. The repetition rate (pulse frequency or number of pulses per second) of IR lasers is typically in the 1 kHz range, and the pulse width (duration of the IR pulse) is between approximately 1 nanosecond (ns) and approximately 10 ns. However, ionizing these exposed biomolecules using IR laser pulses alone has not been effective. The UV pulse interacts with the particles' inherent UV chromophores (portions of molecules that absorb UV light) to generate large bioions of specific mass-to-charge ratios (m / z) for mass spectrometer analysis, without the need for additional complex organic matrix-assisted laser desorption / ionization (MALDI) matrices.The MALDI process requires a sample processing step in which a separate chemical (in a solvent) coats the sample before analysis by TOF MS. Method 200 eliminates the need for this complex sample processing step while still producing large, informative ions, particularly 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 with molecular weights between approximately 1 kDa and approximately 150 kDa. The resulting spectra can be analyzed with data fusion and machine learning methods to improve the accuracy, sensitivity, and specificity associated with identifying analyte particles. Method 200 is particularly suited when the analyte aerosol is composed of UV chromophores, as well as many exogenous compounds in the growth medium that tend to absorb UV light. UV chromophores include, but are not limited to, molecules such as dipicolinic acid (e.g., as found in bacterial spore coatings) and amino acids containing phenyl groups (e.g., tryptophan, tyrosine, and phenylalanine). Method 200 can be performed using the exemplary system 100.

[0020] 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 those containing biomacromolecules such as proteins and DNA. In bacterial cells, lipopolysaccharides, peptidoglycans, and glycans may account for only about 10% of the dry weight of the vegetative cell. Furthermore, many other compounds associated with biological particles contain significant amounts of hydroxyl groups, which have the same strong laser interactions as water. The water associated with all particles sampled from the atmosphere (ambient humidity / moisture) can potentially be used as a laser-absorbing matrix for single-particle TOF-MS. The ubiquitous presence of water on atmospheric aerosol particles provides a mechanism for ion generation across a wide range of masses. As previously explained, in the exemplary system 100, the particle's transit time (or residence time) from the beam generator 102 to the impact of the laser 108 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, particles 310 are already strongly bound to their surfaces as a thin film (e.g., a monolayer) or as water or hydroxyl groups contained therein, but do not evaporate and are available for strong interaction with the IR laser pulse in step 302. Biological materials typically contain a high concentration of molecules containing infrared-active hydroxyl groups. In fact, all cellular interactions in the body involve specific interactions between carbohydrate molecules that decorate surfaces and are present throughout the cellular material. Furthermore, typical 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 content of hydroxyl groups. The IR laser pulse can have a wavelength between about 2.7 micrometers and about 3.3 micrometers. The wavelength of the IR pulse can be about 2.94 micrometers. The IR laser repetition rate is typically in the 1 kHz range, and the pulse width can be between about 40 microseconds and about 100 microseconds. The IR laser power density can be between about 1 MW / cm² and about 20 MW / cm². 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 Figure 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 across a wide range of masses. These ions can be measured using a high-mass-range TOF-MS in step 304, which can analyze ions with molecular weights between approximately 1 kDa and approximately 150 kDa. The small amount of strongly bound surface water contained within the particles and the natural association of water molecules, combined with the short residence time in vacuum, provides the opportunity to generate large molecular fragments (1 kDa to 150 kDa molecular weight) and highly informative mass spectra without the need for other MALDI chemical reagents. Therefore, 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 a thin film of frozen water as a matrix for MALDI TOF-MS. It also avoids more complex methods such as using a droplet generator (approximately 50 microns in diameter) to generate water droplets and introduce them into the vacuum chamber of the TOF-MS, or using an acoustic levitator to generate droplets of water or solvent (e.g., 50 vol.-% methanol in aqueous solution) approximately 2 mm in diameter, which results in soft evaporation / ionization. [3] The generation of large molecular fragments may 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. Organic solvents, including at least one of methanol, ethanol, and isopropanol, may be used.

[0021] If the aerosol particles include non-biological particles and it is desired to identify the chemical composition of the particles, analysis of these particles can be performed by hard ionization to produce small ions. For this purpose, IR laser pulses having a laser power density of between about 20 MW / cm and about 150 MW / cm can be used. Methods 200 and 300 can be modified to allow switching between hard ionization, which produces fragments with molecular weights less than 1 kDa, typically less than 500 Da, and soft ionization, which produces fragments with molecular weights typically greater than 1 kDa.

[0022] In the exemplary method described above, individual aerosol analyte particles are indexed and tracked using at least one continuous laser prior to ionization. Furthermore, the continuous laser can be used to measure particle characteristics such as size and shape. Individual particles are indexed and tracked to enable data fusion of mass spectral data associated with each particle with each particle's optical properties. These optical properties can include particle size, shape, and polarization. Through indexing, mass spectral data collected after each particle's ionization can be associated with each particle. The large amount of data associated with each particle in the aerosol beam can then be filtered and analyzed using the data fusion protocols of the data analysis system 110 to identify particle composition and type in real time with high accuracy, sensitivity, and specificity. Data fusion is defined as the combination of data from multiple sources to obtain improved information in terms of lower cost, higher quality, or more relevant information. A review of data fusion techniques is provided by Castanedo [2], which is incorporated herein by reference in its entirety.

[0023] In exemplary methods 200 and 300, one or more optical detection methods can be used in addition to TOF-MS mass spectrometry analysis. 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 those detected by higher-order fluorescence, laser-induced breakdown spectroscopy (LIBS), Raman spectroscopy, or infrared spectroscopy. Therefore, in addition to mass spectrometry, an optical sensor / detector 109 can be used to identify the composition of the aerosol particles. Measurement data collected using both the TOF-MS and the optical sensor can be processed using data fusion techniques to provide information about the composition of the aerosol analytes. By collecting information from various detectors, including one or more optical methods and mass spectrometry, data fusion protocols can be used to filter and analyze the data associated with each particle, allowing for rapid (near real-time) identification of particle composition and type with high accuracy, sensitivity, and specificity. For each individual indexed aerosol particle, data from each measurement, including at least one of TOF-MS, LIBS, Raman spectroscopy, and infrared spectroscopy, can be transferred to a sensor data fusion engine (data analysis system 110), where artificial intelligence tools such as machine learning and deep learning (machine learning engine 111) can be used to fully characterize the particle.

[0024] In LIBS, a laser pulse (e.g., from a high-energy Nd:YAG laser with a wavelength of approximately 1064 nm) is focused on a particle to ablate a small fraction of the particles, creating a plasma. The analyte particles are broken down (dissociated) into ionic and atomic species. Once the plasma cools, the element's characteristic atomic emission lines can be observed using an optical detector, such as a CCD detector. Another exemplary optical detection tool is Raman spectroscopy. Raman spectroscopy provides information about molecular vibrations that can be used to identify and quantify a sample. This technique involves focusing a laser beam (e.g., a UV laser source with a wavelength of approximately 330 to 360 nm) on a sample and detecting inelastically scattered light. Most of the scattered light is at the same frequency as the excitation source, known as Rayleigh or elastic scattering. Due to the interaction of the incident electromagnetic wave with the vibrational energy levels of the molecules in the sample, a small amount of the scattered light is energy shifted from the laser frequency. Plotting the intensity versus frequency of this "shifted" light yields the Raman spectrum of the sample. In fluorescence spectroscopy, analyte molecules are excited by irradiation at specific wavelengths and emit radiation of different wavelengths. The emission spectrum provides both qualitative and quantitative information. When light of the appropriate wavelength is absorbed by a molecule, the molecule's electronic state changes from a ground state to one of many vibrational levels in one of the excited electronic states. Once the molecule is in this excited state, relaxation can occur through several processes. Fluorescence is one of these processes, resulting in the emission of light. By analyzing the various frequencies of light emitted in fluorescence spectroscopy and their relative intensities, the chemical structures associated with the various vibrational levels can be determined. Certain amino acids in biological samples, such as tryptophan, have high fluorescence quantum efficiencies, which makes fluorescence spectroscopy advantageous for identifying these amino acids.

[0025] Machine learning (ML) techniques for analyzing acquired and collected spectral data using the 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 over time through data analysis. Supervised machine learning techniques can be used. Supervised learning consists 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 set of training examples. Machine learning also includes deep learning methods, which are unsupervised learning methods that can identify signatures in complex datasets without the need to identify specific features in advance. Unsupervised and semi-supervised (hybrid supervised and unsupervised) machine learning methods can also be used. Unsupervised learning methods can include types of learning that help find previously unknown patterns in datasets 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 datasets with shared attributes in order to infer algorithmic relationships. Cluster analysis is a branch of machine learning that groups data that has not been labeled, classified, or categorized. Cluster analysis identifies commonalities in the data and reacts based on the presence or absence of such commonalities in each new piece of data. This approach is useful for detecting anomalous data points. Unsupervised learning methods can be used for anomaly detection, which can help identify previously unknown hazards. For example, air samples can be periodically analyzed to measure the composition of particles in the air and identify particle characteristics (e.g., size, shape, fluorescence) and their associated spectra to obtain baseline data information for particles in "normal" ambient air. Particles in the air following an event, such as the release of a biological threat agent into the atmosphere, deviate from the baseline data, providing particle characteristic data and spectral data that highlight the anomaly (as evidenced by the anomalous spectrum) and provide an opportunity to take necessary corrective actions to mitigate the threat.The compiled spectral data described above can be compared to a training dataset containing a knowledge base of known biological material spectra to predict particle composition. The system 110 can be in data communication with a machine learning engine 111 to update the training dataset knowledge base and enable improved prediction of composition over time. Mass spectra of biological materials cover a range approximately three orders of magnitude greater than mass spectra of chemicals, significantly complicating the application of automated techniques.

[0026] Additionally, environmental contaminants can reduce signal intensity by competing with the target during the ionization process (competitive ionization). This introduces signature components (clutter) that must be deconvolved with the target signature. Current automated methods are mostly limited to searching for highly pure targets in samples without environmental clutter. The disclosed exemplary method eliminates competitive ionization by physically separating the target analyte from the clutter, eliminating signature ambiguity (each event is assumed to be either the target or clutter). An exemplary ML schematic 400 for identifying tuberculosis (TB) biomarkers using high-resolution mass spectrometry is shown in Figure 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 signal-to-noise ratio (SNR) exceeding 5:1 was selected. Weighted principal component analysis (PCA), an unsupervised dimensionality reduction algorithm, was used in step 402 to reduce the large set of signals to two components. PCA provided 2D visualization, which was used to investigate whether the extracted signals revealed essential differences between the two classes of samples (TB and non-TB). Figure 5 shows the PCA output of the extracted signals in positive and negative ion mode from 19 sputum-positive TB patients and 17 non-TB patients. The positive and negative ion signals were collected from two groups of samples: non-TB patients and TB patients. The PCA results revealed that samples from each group tended to cluster together. This suggests that the extracted signals collected from high-resolution mass spectrometry can be used to distinguish between the two classes of samples. Step 402 can also be used to analyze data collected from methods 200 and 300 using TOF-MS.

[0027] In method 400, Significance Analysis of Microarrays (SAM) technology was also applied to the signals extracted in step 401 in step 403 to identify strongly discriminative features and select the most powerful features for distinguishing between the two classes of samples. SAM is a feature selection algorithm designed to process large datasets and identify the most powerful features between the two classes of samples. SAM analysis returned a feature ranking list based on change in abundance, statistical significance, and false positive rate. The features identified by SAM were optimized in step 404 using a support vector machine (SVM). SVM is a supervised machine learning-based classifier that uses a training dataset to define a separating hyperplane so that unknown samples can be classified according to aspects of the separating hyperplane. The advantage of SVM lies in its ability to process high-dimensional data to predict analyte composition and continuously improve the knowledge base contained in the training dataset.

[0028] As an example, SAM-based feature selection using signals extracted from negative ionization is shown in Figure 6. The data have features classified into three classes: (A) up-regulation, (B) down-regulation, and (C) insignificant. Higher signals (up-regulation) in tuberculosis (TB) patients than in non-TB (non-TB) patients are represented by region A, while lower signals (down-regulation) in tuberculosis patients are represented by region B. Overall, over 1,500 features (ion signals) extracted from positive ionization and over 500 features extracted from negative ionization were found to be higher in tuberculosis patients. Next, an SVM analysis was performed to optimize the number of features. This analysis demonstrated the potential for identifying tuberculosis-related signals using thousands of signals compared to a relatively small number of subjects (training dataset). Using SVM as a classification algorithm, the features selected by SAM can be optimized by returning a confusion matrix that calculates the percentages of accuracy, sensitivity, and specificity. As shown in Figure 7, in positive ionization, the best performance of SVM-based classification was achieved when approximately 300 selected features were applied. In negative ion mode, the best 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 above exemplary analytical method, multiple TB biomarkers were identified using lipid extraction and high-resolution mass spectrometry of patient samples collected in Masiphumelele, South Africa.

[0029] To identify the presence of biological threat agents in the air, air samples can be collected at predetermined time intervals and analyzed using the exemplary methods disclosed above to generate a historical data set (training data set) of background / baseline information in the data analysis system 110. The analysis can be improved over time using machine learning algorithms executed by the engine 111. Variations in the background information can be modeled to map the normal behavior of the air in the protected area. When a biological, biochemical, or chemical aerosol particle release is suspected, sampling the air using the exemplary methods described above can provide information that deviates from the historical background information. The first indication of the presence of such a threat is a sudden deviation from the normal background. At this stage, an algorithmic determination can be made regarding the composition of the individual particles. Therefore, rapid corrective action can be taken to protect and prevent loss of life.

[0030] The exemplary methods and devices disclosed above can 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 a liquid sample in air. Analyte particles can also be extracted from a cotton swab or can be in the form of a solid sample that can be dissolved using an appropriate solvent. An aliquot of the sample can then be aerosolized using appropriate means. For example, a nebulizer can be used to aerosolize a liquid sample in air. The exemplary methods and devices disclosed can be used to identify viruses and toxins in real time, in addition to bacteria. By analyzing data collected from one or more optical detectors and mass spectrometry, a biological fingerprint of the analyte particles can be obtained in real time.

[0031] The disclosed exemplary method eliminates the need for complex sample processing steps associated with MALDI TOF-MS while still producing large, informative ions, particularly in the case of biological aerosol particles. Furthermore, 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 before ionization using an IR laser pulse (e.g., in method 300). Organic solvents, including at least one of methanol, ethanol, and isopropanol, can be used. The MALDI process requires a sample processing step in which another chemical (usually complex organic molecules in a solvent) coats the sample before 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 MALDI technology with high-mass-range time-of-flight (TOF) mass spectrometry enables the direct analysis of large peptide components and even intact proteins, enabling "whole-cell" biological identification. Applicant's international application PCT / US2016 / 48395, entitled "Coating 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 MALDI matrix solution to bioaerosol particles prior to analysis in an aerosol time-of-flight mass spectrometer, which is incorporated herein by reference in its entirety.

[0032] The Abstract is provided to comply with 37 C.FR § 1.72(b) to enable the reader to quickly determine the nature and gist of the technical disclosure from a broad perspective, and should not be used to interpret or limit the scope or meaning of the claims.

[0033] While the present disclosure has been described in connection with preferred modes of practicing it, those skilled in the art will appreciate that many modifications can be made thereto without departing from the spirit of the disclosure. Accordingly, it is not intended that the scope of the present disclosure be limited by the foregoing description.

[0034] It is also understood that various modifications can be made without departing from the essence of this disclosure. Such modifications are implicitly included in the description and remain within the scope of this disclosure. It is understood that this disclosure is intended to result in patents covering many aspects of the disclosure, both individually and as a system as a whole, and in method and apparatus modes.

[0035] Moreover, each of the various elements of this disclosure and claims may also be achieved in a variety of ways, and this disclosure should be understood to encompass each such variation, whether it be any device implementation variation, method or process implementation, or simply a variation of any of these elements.

[0036] In particular, it should be understood that each element term may be expressed by equivalent apparatus or method terms, even if only the function or result is the same. Such equivalent, broader, or more general terms should be considered included in the description of each element or operation. Such terms may be substituted where necessary to make clear the implicitly broader scope to which this disclosure is entitled. It should be understood that all operations may be expressed as a means for taking that operation or as an element that causes that operation. Similarly, each physical element disclosed should be understood to encompass a disclosure of the operation that the physical element facilitates.

[0037] Furthermore, for each term used, unless its usage in this application is inconsistent with such interpretation, the common dictionary definition contained, for example, in at least one standard technical dictionary recognized by artisans and the most recent edition of Random House Webster's Unabridged Dictionary, should be understood to be incorporated herein for each term and all definitions, alternative terms, and synonyms.

[0038] Additionally, the use of the transitional phrase "comprising" is used to maintain the "open-ended" claims herein in accordance with conventional claim interpretation. Thus, unless the context requires otherwise, "comprising" is intended to mean the inclusion of a recited element or step or group of elements or steps, but not the exclusion of other elements or steps or group of elements or steps. Such terms should be interpreted in the broadest manner to afford applicant the broadest scope legally permissible. The technical features described herein are listed below. [Technical feature 1] 1. A method for identifying the composition of bioaerosol particles, comprising: generating an aerosol particle beam using an aerosol beam generator; indexing each particle in the beam using a first laser; detecting the position of each indexed particle using a continuous timing laser; triggering an ionizing pulsed laser to simultaneously generate an IR laser pulse and a UV laser pulse when each indexed particle reaches an ionization region of the ionizing laser; a photon generation step of generating ionized fragments of each indexed particle and photons associated with each indexed particle, wherein each ionized fragment has a molecular weight between about 1 kDa and about 150 kDa; analyzing at least one of the ionized fragments of each indexed particle and the photons associated with each indexed particle using at least one detector; generating unique spectral data associated with each indexed particle from each detector; compiling unique spectral data using data fusion to generate compiled spectral data; and determining the composition of each particle. [Technical feature 2] The method according to technical feature 1, further comprising a step of measuring at least one characteristic of the indexed particles, including at least one of particle size, particle shape, and fluorescence, using the first laser. [Technical feature 3] The method according to technical feature 1, further comprising the step of measuring at least one characteristic of the indexed particles, including at least one of particle size, particle shape, and fluorescence, using a timing laser. [Technical feature 4] The method according to technical feature 1, wherein the step of triggering the ionization laser is initiated using a first laser when at least one characteristic of the indexed particle satisfies a predetermined threshold value for that characteristic. [Technical feature 5] The method according to technical feature 1, wherein the step of triggering the ionization laser is initiated using a timing laser when at least one characteristic of the indexed particle satisfies a predetermined threshold value for that characteristic. [Technical feature 6] The step of determining the composition comprises: comparing the compiled spectral data with a knowledge base of training spectral data sets to predict compositions; updating the knowledge base of said training data set; and using machine learning techniques to improve said prediction of composition over time. The method according to technical feature 1, having at least one of the following: [Technical feature 7] The method according to Technical Feature 6, wherein the machine learning method is a supervised machine learning method. [Technical feature 8] The method according to Technical Feature 1, wherein the step of determining the composition includes a step of classifying unlabeled aerosol particles and identifying anomalous aerosol particles using an unsupervised machine learning method. [Technical feature 9] A method according to Technical Feature 8, wherein the machine learning method comprises weighted principal component analysis (PCA). [Technical feature 10] The method according to Technical Feature 1, wherein the IR laser pulse is characterized by a wavelength between about 1.0 micrometers and about 1.2 micrometers. [Technical feature 11] The method according to Technical Feature 1, wherein the UV laser pulse is characterized by a wavelength between about 250 nm and about 400 nm. [Technical feature 12] The method according to technical feature 1, wherein the wavelength of the IR pulse is about 1.06 micrometers. [Technical feature 13] The method according to Technical Feature 1, wherein the wavelength of the UV pulse is about 355 nm. [Technical feature 14] The power density of the above IR laser is approximately 1MW / cm 2 to approximately 20MW / cm 2 The method according to technical feature 1, [Technical feature 15] The method according to Technical Feature 1, wherein the pulse width of the IR laser is between about 1 ns and about 10 ns. [Technical feature 16] The method according to Technical Feature 1, wherein the pulse repetition frequency of the IR laser is about 1 kHz. [Technical feature 17] The method according to technical feature 1, wherein the at least one detector comprises a TOF-MS detector. [Technical feature 18] The method according to technical feature 1, wherein the at least one detector comprises at least one of a TOF-MS detector, a fluorescence detector, a LIBS detector, and a Raman spectrometer. [Technical feature 19] The method according to Technical Feature 1, wherein the ionized fragments have a UV chromophore comprising at least one of dipicolinic acid, tryptophan, tyrosine, and phenylalanine. [Technical feature 20] The method according to Technical Feature 1, wherein the IR laser pulse and UV laser pulse are generated using a Nd:YAG laser. [Technical feature 21] 1. A method for identifying the composition of bioaerosol particles having IR chromophores, comprising: generating an aerosol particle beam using an aerosol beam generator; indexing each particle in the beam using a first laser; detecting the position of each indexed particle using a continuous timing laser; triggering an ionizing pulsed laser to generate an IR laser pulse when each indexed particle reaches an ionization region of the ionizing laser; a photon generation step of generating ionized fragments of each indexed particle and photons associated with each indexed particle, wherein each ionized fragment has a molecular weight between about 1 kDa and about 150 kDa; analyzing at least one of the ionized fragments of each indexed particle and the photons associated with each indexed particle using at least one detector; generating unique spectral data associated with each indexed particle from each detector; compiling unique spectral data using data fusion to generate compiled spectral data; and determining the composition of each particle. [Technical feature 22] 22. The method according to claim 21, wherein the IR chromophore comprises at least one of water, agar, and carbohydrate. [Technical feature 23] 22. The method of claim 21, further comprising using the first laser to measure at least one characteristic of the indexed particles, including at least one of particle size, particle shape, and fluorescence. [Technical feature 24] 22. The method of claim 21, further comprising measuring at least one characteristic of the indexed particles using a timing laser, the characteristic including at least one of particle size, particle shape, and fluorescence. [Technical feature 25] 24. The method according to claim 23, wherein the step of triggering the ionization laser is initiated using a first laser when at least one characteristic of the indexed particle satisfies a predetermined threshold for that characteristic. [Technical feature 26] 25. The method of claim 24, wherein the step of triggering the ionization laser is initiated using a timing laser when at least one characteristic of the indexed particle meets a predetermined threshold for that characteristic. [Technical feature 27] The step of determining the composition comprises: comparing the compiled spectral data with a knowledge base of training spectral data sets to predict compositions; updating the knowledge base of said training data set; and using machine learning techniques to improve said prediction of composition over time. 22. The method according to claim 21, having at least one of the following technical features: [Technical feature 28] A method according to technical feature 27, wherein the machine learning method is a supervised machine learning method. [Technical feature 29] 22. The method according to claim 21, wherein the step of determining the composition comprises using an unsupervised machine learning method to classify unlabeled aerosol particles and identify anomalous aerosol particles. [Technical feature 30] A method according to technical feature 29, wherein the machine learning method comprises weighted principal component analysis (PCA). [Technical feature 31] 22. The method according to claim 21, wherein the travel time of each particle from the aerosol beam generator to the ionization region of the ionizing pulsed laser is less than about 1 second. [Technical feature 32] 22. The method according to claim 21, wherein the IR laser pulse is characterized by a wavelength between about 2.7 micrometers and about 3.3 micrometers. [Technical feature 33] 22. The method according to claim 21, wherein the IR laser pulse wavelength is about 2.94 micrometers. [Technical feature 34] The IR laser power density is approximately 1MW / cm 2 to approximately 20MW / cm 2 21. The method according to claim 21, wherein [Technical feature 35] 22. The method according to claim 21, wherein the IR laser pulse width is between about 40 microseconds and about 100 microseconds. [Technical feature 36] 22. The method according to claim 21, wherein the IR laser pulses are generated using at least one of the Er:YAG laser and the OPO laser. [Technical feature 37] 22. The method according to technical feature 21, wherein the at least one detector comprises at least one of a TOF-MS detector, a fluorescence detector, a LIBS detector, and a Raman spectrometer. [Technical feature 38] 1. A system for identifying the composition of bioaerosol particles, 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 said beam; a pulsed ionization laser generator triggered by the timing laser and configured to generate at least one of an IR laser pulse and a UV laser pulse to generate an ionized fragment of each indexed particle and at least one photon associated with each indexed particle; 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 from each detector. [Technical feature 39] 39. The system of claim 38, further comprising a data analysis system for compiling the unique spectral data associated with each particle using the data fusion to generate compiled spectral data. [Technical feature 40] 39. The system of claim 39, further comprising a machine learning engine disposed in data communication with the data analysis system. [Technical feature 41] The pulsed ionization laser power density is approximately 1 MW / cm 2 to approximately 20MW / cm 2 38. The system according to claim 38, wherein: [Technical feature 42] 39. The system according to technical feature 38, wherein the at least one detector comprises at least one of a TOF-MS detector, a fluorescence detector, a LIBS detector, and a Raman spectrometer. [Technical feature 43] 1. A method for identifying the composition of bioaerosol particles, comprising: generating an aerosol particle beam using an aerosol beam generator; indexing each particle in the beam; measuring at least one of particle size, particle shape, and fluorescence of each indexed particle to select indexed particles for analysis; triggering an ionizing pulsed laser when each selected indexed particle reaches an ionization region of the ionizing laser; generating ionized fragments of each indexed particle, each ionized fragment having a molecular weight between about 1 kDa and about 150 kDa; analyzing at least one ionized fragment of each indexed particle using a TOF-MS detector to generate unique spectral data associated with each particle; and determining the composition of each particle using the unique spectral data. [Technical feature 44] 44. The method according to technical feature 43, wherein the step of selecting which indexed particles to analyze comprises a step of determining whether at least one of particle size, particle shape, and fluorescence of the indexed particles meets a predetermined threshold. [Technical feature 45] 44. The method according to technical feature 43, wherein each of the indexing step, the measuring step, and the triggering step is performed using one or more laser beams. [Technical feature 46] The step of determining the composition comprises: compiling the unique spectral data for each particle using data fusion to generate compiled spectral data; and comparing the compiled spectral data with a training spectral dataset knowledge base to predict composition. [Technical feature 47] updating said training dataset knowledge base; 47. The method according to claim 46, further comprising using machine learning techniques to improve the prediction of the composition over time. [Technical Features 48] 44. The method of claim 43, wherein the IR laser pulse is characterized by a wavelength between about 1.0 micrometers and about 1.2 micrometers. [Technical feature 49] 44. The method of claim 43, wherein the IR laser pulse is characterized by a wavelength between about 2.7 micrometers and about 3.3 micrometers.

[0039] [References] 1. Wan GH, Wu CL, Chen YF, Huang SH, Wang YL, 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. Warschat, C. et al., "Mass Spectrometry of Levitated Droplets by Thermally Unconfined Infrared-Laser Desorption," Anal. Chem. 2015, 87, 8323-8327. [Explanation of symbols]

[0040] 100 Aerosol Identification System 101 Entrance Elements 102 Aerosol Beam Generator 103 Laser generator for particle identification of interest 104 Vacuum Chamber 105 Vacuum Pump 107 Concentrating optics 108 Ionization Laser Generator 109 Optical Sensor 110 Data Analysis System 111 Machine Learning Engine 112 Trigger Laser Generator

Claims

1. A system (100) for identifying the composition of aerosol particles, comprising: an aerosol beam generator (102) for generating a beam of single particles; A continuous timing laser generator (103), generating a timing laser to index each particle in said beam; optically characterizing each indexed particle for particle size, particle shape based on polarization, and fluorescence to generate optical data; the continuously timed laser generator (103) configured to select particles to be ionized from among the indexed particles; an ionizing pulse laser generator triggered by the timing laser and configured to generate an IR laser pulse (302) to ionize fragments associated with each selected indexed particle when the selected indexed particle reaches an ionization region of the IR laser pulse (302) to generate ionized fragments; a TOFM detector (106) that analyzes the ionized fragments to generate unique mass spectral data associated with each selected indexed particle; A data analysis system (110), comprising: generating a compiled data set combining the optical data with the unique mass spectral data associated with the selected indexed particles using a data fusion process; and the data analysis system configured to predict the composition of the aerosol particles by comparing them with a training dataset comprised of a knowledge base of known biological materials.

2. The system described in claim 1, wherein the aerosol particles include one or more non-biological aerosol particles or biological aerosol particles having water bound to their surface.

3. 10. The system of claim 1, wherein the IR laser pulse width is between about 40 μsec and about 100 μsec.

4. 10. The system of claim 1, wherein the IR laser pulses have a pulse repetition frequency of about 1 kHz.

5. The system of claim 1, wherein the diameter of the ionization region is less than about 150 μm.

6. 10. The system of claim 1, wherein the travel time of each particle from the aerosol beam generator to the ionization region of the ionizing pulsed laser is less than about 1 second.

7. 10. The system of claim 1, wherein the IR laser pulse is characterized by a wavelength between about 2.7 micrometers and about 3.3 micrometers.

8. 10. The system of claim 1, wherein the IR laser pulse has a wavelength of about 2.94 micrometers.

9. The system of claim 1 , wherein the IR laser pulses are generated using at least one of an Er:YAG laser and an OPO laser.

10. 10. The system of claim 1, further comprising a machine learning engine disposed in data communication with the data analysis system, the data analysis system configured to update the training dataset knowledge base and use machine learning methods to improve composition predictions over time.

11. The ionizing laser power density of the IR laser pulse is about 1 MW / cm ２ to about 20 MW / cm ２ The system of claim 1 , wherein:

12. The system of claim 1, further comprising at least one of a fluorescence detector, a LIBS detector, and a Raman spectrometer for analyzing photons associated with each selected indexed particle as it reaches the ionization region.

13. A method for identifying the composition of aerosol particles, comprising: generating a beam of aerosol particles using an aerosol beam generator; Using a single, continuous timing laser, indexing each particle in said beam; optically characterizing particle size, particle shape based on polarization, and fluorescence of each indexed particle to generate optical data; selecting particles from the indexed particles to be ionized; using the continuous timing laser to trigger an ionizing pulsed laser generator to generate an IR laser pulse when each selected indexed particle reaches an ionization region of the ionizing pulsed laser generator to ionize fragments associated with each selected indexed particle to produce ionized fragments; analyzing the ionized fragments using a TOF-MS detector to generate unique mass spectral data associated with each indexed particle; Using a data analysis system, generating a compiled data set by combining the optical data with the unique mass spectral data associated with selected indexed particles; and predicting the composition of the aerosol particles by comparing the aerosol particles to a training dataset comprised of a knowledge base of known biological materials.

14. 14. The method of claim 13, wherein selecting which indexed particles to analyze comprises determining whether the particle size, particle shape, and fluorescence of the indexed particles meet predetermined thresholds. Implemented using a machine learning engine disposed in data communication with the data analysis system.

14. The method of claim 13, further comprising using machine learning techniques to improve prediction of composition over time.

16. 14. The method of claim 13, wherein triggering of the ionizing pulsed laser step is initiated using a continuous laser when at least one measured property of the indexed particle meets a predetermined threshold for that property.

17. 14. The method of claim 13, further comprising detecting the position of each indexed particle using a continuous timing laser.

18. The method of claim 13, further comprising the step of analyzing photons associated with each selected indexed particle when the selected indexed particle reaches the ionization region using at least one of a fluorescence detector, a LIBS detector, and a Raman spectrometer.

19. The method of claim 13, wherein the aerosol particles comprise one or more of non-biological particles, biological particles, and biological aerosol particles containing water on the surface of the particles.

20. The method of claim 13, further comprising the step of treating the aerosol particles with one or more sprays of water, organic solvents, and mixtures containing water prior to ionization.

21. The method of claim 20, wherein the organic solvent comprises one or more of methanol, ethanol, or isopropanol.

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