Patent Publication Number: US-2022223235-A1

Title: Spectral classification systems and methods

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims priority to and the benefit of U.S. Provisional Patent Application No. 63/137,094 filed Jan. 13, 2021 and entitled “SPECTRAL CLASSIFICATION SYSTEMS AND METHODS,” which is incorporated herein by reference in its entirety. 
    
    
     TECHNICAL FIELD 
     Embodiments of the present disclosure relate generally to chemical detection systems and methods and, more particularly for example, to systems and methods for classification and/or analysis of chemical sensor data in mobile devices. 
     BACKGROUND 
     Field-deployable chemical sensing devices are often limited by size, weight, power and cost (SWaP-C) constraints. For example, a chemical sensor may be configured to couple gas chromatography with mass spectrometry (GC-MS) for chemical identification and quantification of complex vapor mixtures. A vacuum system is often required for mass spectrometers, which drives much of the SWAP-C requirements of GC-MS systems. Ion Mobility Spectrometry (IMS) operated at atmospheric pressure provides a cheaper alternative to GC-MS, but conventional systems come with a trade-off of increased false alarms and limited specificity. Advancements in IMS technology such as Differential Mobility Spectrometry (DMS, also known as high-Field Asymmetric Ion Mobility Spectrometry, (FAIMS)) utilize smaller ion separation regions, higher electric fields and electric field manipulation to take advantage of the dependence of ion mobility and thermal decomposition on electric field strength. While these advances have improved the specificity of IMS, classification of target responses remain highly empirical and subject to environmental conditions. 
     In view of the foregoing, there is a continued need for improved chemical sensor systems and methods, including IMS systems that are field-deployable for use in chemical detection, classification and/or quantification of complex vapor mixtures. 
     SUMMARY 
     Systems and methods are provided for improved spectral classification and analysis of chemical spectra. Chemical classification systems and methods, including systems and methods for training, validating and selecting models for chemical classification are disclosed herein. In one or more embodiments, a training dataset is defined and chemical features (such as analyte features) are generated and used to train a plurality of models (such as a convolutional neural network (CNN)) for chemical detection and classification. A chemical classification training dataset is generated to train one or more models, the training results are validated for each model using a separate validation dataset, and a model analysis engine analyzes informative metrics and performance results to modify the datasets, features, models, parameters and other data to optimize the models during a next iteration. 
     The scope of the invention is defined by the claims, which are incorporated into this section by reference. A more complete understanding of embodiments of the invention will be afforded to those skilled in the art, as well as a realization of additional advantages thereof, by a consideration of the following detailed description of one or more embodiments. Reference will be made to the appended sheets of drawings that will first be described briefly. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1A  illustrates a chemical classification training and validation system, in accordance with various embodiments of the present disclosure. 
         FIG. 1B  is a flow diagram illustrating an example operation of the chemical classification training and validation system of  FIG. 1A , in accordance with various embodiments of the present disclosure. 
         FIG. 1C  is a chart illustrating example IMS spectra, in accordance with various embodiments of the present disclosure. 
         FIG. 2  illustrates an example chemical classification training and validation system, in accordance with various embodiments of the present disclosure. 
         FIG. 3  illustrates an example chemical classification mobile system, in accordance with various embodiments of the present disclosure. 
         FIG. 4A  illustrates an example neural network training process for classifying chemicals, in accordance with various embodiments of the present disclosure. 
         FIG. 4B  illustrates a model validation process for the neural network of  FIG. 4A , in accordance with various embodiments of the present disclosure. 
         FIG. 5  illustrates an example process for generating training data and chemical classification models, in accordance with various embodiments of the present disclosure. 
         FIG. 6  illustrates a chemical classification system, in accordance with various embodiments of the present disclosure. 
     
    
    
     Embodiments of the disclosure and their advantages are best understood by referring to the detailed description that follows. It should be appreciated that like reference numerals are used to identify like elements illustrated in one or more of the figures. 
     DETAILED DESCRIPTION 
     Ion mobility spectrometry (IMS), and its evolutions such as high-Field Asymmetric Waveform Ion Mobility Spectrometry (FAIMS) and Rapid Thermal Modulation Ion Spectrometry (RTMIS), produce data that is more complex than many technologies deployed in mobile chemical sensing systems, such as mass spectrometry. Unlike mass spectrometry data which is typically represented as a graph with a single independent variable, IMS, FAIMS and RTMIS responses to chemical compounds are non-linear with respect to environmental conditions and system configuration. Thus, it is often more challenging, time-consuming and often highly empirical to develop chemical detection and classification systems that can reliably classify chemical compounds from IMS spectra data in real-world implementations. 
     In the present disclosure, novel machine learning approaches are used to classify compounds based on the unique chemical spectrum produced by a FAIMS, RTMIS and/or similar chemical sensing technology to decrease the development time, decrease the amount of data that is needed to develop classifiers, increase the efficiency of the data collection process, reduce the number of false positives generated by trained models, and/or provide more flexibility to incorporate emerging chemical libraries into trained models. 
     In various embodiments of the present disclosure, the use of statistical classification and machine learning algorithms for two-dimensional spectra (e.g., IMS spectra data) is used to provide a fast, flexible and accurate alternative to empirical development of classifiers for chemical sensors. Prediction models based on statistical algorithms can identify differences in spectral responses between varying analytes, ultimately increasing the specificity of the analytical instrumentation. The algorithms disclosed herein may include and support, but are not limited to, Decision Trees, Support Vector Machines, Logistic Regression, KNeighbors, Naïve Bayes, Ensemble methods (e.g. Random Forests), and Neural Networks. 
     This disclosure further describes a set of software tools and methods that are used to process chemical data (e.g., IMS spectra data), define unique features to differentiate the detection instrument response based on the chemical target, and develop a predictive model to classify an instrument response in the field. To develop these classification models, a dataset of chemical spectra that represents targets of interest, mixtures, interferents and environmental sampling conditions is collected and used to train a plurality of models. In some embodiments, the dataset includes rows of observations with each row containing features that include unique characteristics that differentiate one analyte from other analytes in the dataset. Examples of features may include peak height and peak location in the 2-D IMS spectrum. Other features may be identified by determining which features have the most value for classification, such as be using supervised and/or unsupervised statistical methods. 
     After features are identified, a dataset is created and split into training and validation subsets. The training dataset will be used to develop one or more classification models, while the validation dataset will be used to evaluate the model for accuracy. In some embodiments, a variety of classification models may be evaluated, optimized and compared to determine model(s) that provide the best performance for a desired application. The present disclosure describes methods for multiple models to be trained and validated on the same datasets, so that direct comparisons can be made. These methods allow the incorporation of new data, features and model parameters to iteratively tune the models and expand chemical libraries. 
     Referring to  FIG. 1A , various embodiments of a system for training, validating, evaluating and selecting one or more models for classifying one or more chemicals will be described. A system  50  generates a training dataset and validated models in an iterative process that yields high performance models for chemical classification. The trained models are optimized to classify one or more analytes and the system  50  provides flexibility to incorporate additional target analytes as needed for particular implementations. In the illustrated embodiment, the system  50  includes a model analysis engine  70  configured to operate with a training dataset  56  (e.g., a set of labeled chemical data) for training different machine learning models (e.g., a neural network) in a training process  58 . The training process  58  applies one or more subsets of training data from the training dataset  56  to produce trained classification models  60  and also generates model and/or training dataset metrics during the training process. Each of the trained classification models  60  is validated using a validation dataset  62 , which may be selected as a subset of the training data that is not used for training the model. 
     In various embodiments, the training dataset  56  includes a plurality of labeled chemical data samples, and the validation dataset is a subset of the training dataset  56  that has not been used for training the models. The validation dataset  62  is input to the trained models  60  to classify each sample and the output classification is compared against the corresponding label to measure of the performance of a trained model  60 . The training datasets  56  may include a variety of chemical samples representing a range of real world use cases for training and validating the models. The real-world data samples may be captured, for example, using a chemical sampling apparatus configured to collect, store and/or analyze samples. Atmospheric samples, for example, may be collected by the sampling device and include atmospheric gasses like oxygen and nitrogen, that contain materials to be analyzed, including potentially harmful chemical contaminants or pollutants, biological materials (e.g., anthrax spores), and radioisotopes. The training data may represent the response of one or more sampling devices that may include a FAIMS detector, a Photo Ionization Detector, a Metal Oxide Detector, or other detector to detect the presence of chemicals in the atmosphere. The trained models  60  may be configured to receive and analyze atmospheric samples for detection and classification of one or more desired chemicals. The materials collected by the sampling device may be referred to as analytes. 
     The model/dataset performance results (which may include, for example, chemical classification errors) are provided to a model analysis engine  70 . The model analysis engine  70  may include classification training and optimization algorithms, statistical analysis algorithms for identifying features/attributes associated with a target analyte, optimization algorithms for simplifying the number of variables, model selection algorithms for comparing trained models, validating trained models and selecting features and training dataset parameters for each model, preprocessing algorithms such as preprocessing and normalization algorithms, and other algorithms consistent with the teachings of the present disclosure. In various embodiments, the model analysis engine  70  may incorporate and/or be based on a generalized machine learning platform such Scikit-learn and/or TensorFlow. 
     The model analysis engine  70  receives informative metrics compiled during the training process  58  and validation process or the trained models  60 , and configuration parameters  64  that define a scope of use for the trained models  60  (e.g., user identification of an end-user sampling device, chemical targets and use cases). The model analysis engine  70  may then analyze the received data to modify the training dataset samples used for training the models by identifying samples to retain (e.g., samples that contribute to proper classification), drop from (e.g., samples that do not contributed to proper classification) and/or add to the training dataset  56 . In one or more embodiments, the model analysis engine  70  receives the informative metrics and performance results, analyzes the available data in view of the configuration parameters  64 , and updates the training dataset  56  to train a model with improved results. 
     In various embodiments, the model analysis engine  70  includes various tools including a feature analyzer  72 , a dataset generator  74 , and an assembler/interface  76 . The feature analyzer  72  receives the informative metrics and performance results, extracts features for further processing, and analyzes the relative performance of one or more samples from the training dataset  56  that was used for training one or more models. Metrics may include, for example, extracted features, data indicating changes in neural network parameters, data from previous iterations, and other data captured during training. Analysis of extracted features from the training data  56  may include analysis of analyte features of chemicals of interest from acquired samples that uniquely identify the chemicals, such as peak height and peak location data. In some embodiments, the feature analyzer  72  ranks the features based on performance results and optimizes the features to be used in the next iteration. 
     In various embodiments, the feature analyzer  72  may extract informative metrics and/or performance results into various categories for further analysis, including compiling data based on different classification labels of the chemical samples from the training dataset, data based on performance/underperformance, sample characteristics (e.g., features extracted), and other groupings as may be appropriate. The feature analyzer  72  may analyze IMS spectra data to identify features such as edge of peaks, gaussian peak heights, peak locations, etc. 
     The dataset generator  74  analyzes the training samples from the training dataset  56  based on the performance results and/or the effect the sample had on the training of the model. The dataset generator  74  generates parameters for a new training dataset  56  that may include a subset of current training dataset  56  samples and parameters defining new training datasets to be generated for the next training dataset. The assembler/interface  76  provides a user interface to communicate user defined configuration parameters to the model analysis engine and provide feedback to the user on model analysis results, including data, rankings and options regarding features, data samples and models. In some embodiments, the process continues iteratively until the final training datasets and models  80  that meet certain performance criteria, such as a percentage of correctly classified chemical samples during the validation process, performance for various chemical types/sampling conditions, cost validation and/or other criteria, is generated. The trained models may then be used, for example, in end-user devices to detected and classify one or more chemicals. 
     In one or more embodiments, the dataset generator  74  includes one or more algorithms, neural networks, and/or other machine learning processes that receive the informative metrics and performance results and determines modifications of the training dataset to improve performance. The configuration parameters  64  define one or more goals of the classification models, such as parameters defining labels, chemicals, and environments to be used in the training dataset. For example, the configuration parameters  64  can be used to determine what chemicals the neural network should classify and environments in which the chemicals should appear. 
     In various embodiments, model analysis engine  70  and/or other components for generating the training dataset may include a synthetic sample generator that receives instructions/parameters to create new training samples. Synthetic sample generation may include construction of defined and/or random synthetic samples, informed by configuration parameters  64  and an identification of desirable and undesirable parameters as defined by the dataset generator  74 . For example, the trained models  60  may be configured to label certain chemicals in a variety of real world environments, and the current training dataset may be producing unacceptable results classifying chemicals in certain of the environments. The synthetic sample generator may be instructed to create sample data of a certain chemical classification having a range of features in particular environments in accordance with the received parameters. For example, by modifying existing data samples to create new data samples representing a desired environment. 
     In some embodiments, the dataset generator  74  determines a subset of samples from the training dataset  56  to maintain in the training dataset and defines new samples to be selected and/or generated. In some embodiments, samples from the training dataset  56  may be ranked on performance results by ranking each sample&#39;s impact based on overall performance. For example, the dataset generator  74  may keep a number of top ranked samples for each chemical classification, keep samples that contribute above an identified performance threshold, and/or keep a certain number of top ranked samples overall. The dataset generator  74  may also remove samples from the training dataset  56  that are lowest ranked and/or contribute negatively or below an identified performance threshold. 
     In operation, the system  50  iteratively trains and validates each model and produces performance data (e.g., a table of results) that identifies the relatively accuracy of each model. The performance data may include an identification of the useful features and contributions of the training data samples to the various models. For example, a user may select a set of models and features to test, and the performance data provides a list of tested models, the accuracy of each model and the set of relevant features identified during the test. In some embodiments, the system  50  iteratively splits the dataset into training dataset and a validation dataset, for example, by randomly selecting data for the training and validation datasets. In some embodiments, the accuracy of a feature, contribution of a data element or contribution of other parameters may be determined by running the models under various scenarios that include, exclude or modify the tested feature, data element and/or parameter, and comparing the performance results. After running various scenarios, the importance of each feature, data element and parameter can be determined and used to optimize the models in a next iteration of the process. 
     An example operation of the system  50  for training, validating and selecting one or more models for classifying a chemical will now be described in further detail with reference to  FIG. 1B . A training and validation method  100  includes a data collection step  110  in which chemical samples are collected and processed to create training and validation datasets for one or more chemical targets. In various embodiment, the chemical data can include real-world samples collected by a chemical sensor in the field in various environments, chemical data collected in controlled laboratory environments, and/or synthetic/modified chemical data generated to produce a more robust training dataset. In various embodiments, the data is collected in the form of IMS spectra data, such as represented by the graph  190  in  FIG. 1C . Generally, IMS spectra represents the intensity of ion peaks versus the drift time of ions through an ion mobility spectrometer. FAIMS, for example, separates ions at atmospheric pressure based on the difference between the mobility of an ion in strong and weak electric fields. The field-dependent mobility of an ion is measured with respect to the compensation voltage (V) at which the ion is transmitted through the FAIMS at an applied asymmetric waveform dispersion voltage. Each analyte has a unique two-dimensional spectrum that is used to classify the analyte. 
     After collection of the data, the training dataset is constructed and verified (step  120 ) and pre-processing steps  130  are performed (e.g., feature extraction) to generate input data for one or more machine learning models. The training data and features are processed and refined using peak fitting and/or other statistical approaches to measure and optimize performance (step  140 ). For example, in some embodiments, the mean zero air is subtracted from the chemical data to calculate a mean for an analyte. A Gaussian peak fitting and refinement process smooths the chemical spectra. In some embodiments, peak selection algorithms, peak fitting algorithms, cluster analysis algorithms and/or other statistical algorithms made be used to identify a set of features. The process analyzes the relative importance of each feature and further iterations can be performed to optimize the feature set (e.g., identify additional features and/or select a subset of features) and feature parameters to refine the model. In some embodiments, a feature set is selected that will work across a range of target chemicals for a particular implementation. 
     Next, the models are optimized including pre-processing in step  150  (e.g., feature extraction), determination of best fit/performance (step  160 ) and verification of the trained models (step  170 ). Features may include, for example, compensation values of peaks having values representative of the height of each peak. In some embodiments, the features may be normalized to accommodate data having different ranges/values. The data input to the model may include an array of n observations/samples (rows) and m features/compensation values (columns) and a vector ray of n labels. The labels may represent the chemical compound observed in each sample. In various embodiments, model types may include decision trees, support vector machines, logistic regression, k-Nearest-Neighbors, Naïve Bayes classifiers, and other model types. The models are fit to the training dataset, which may include 10,000 or more labeled training samples. In some embodiments, the training dataset is randomized for use in the model training process. In various embodiments, the training dataset is adapted to minimize under-fitting and over-fitting. The models are validated using a separate validation dataset and various analytics are produced, for example, an estimation of the relative importance of each feature (e.g., relative importance of various compensation voltages to the model). 
     In some embodiments, a model is trained for multi-class classification of multiple analytes, for example, dimethyl methylphosphonate, 2-chloroethyl ethyl sulfide, methyl salicylate and amyl acetate. The training could include, for example, samples of each analyte from multiple instruments under multiple environmental conditions, to generate a trained model configured to predict a probability of each classification for an input data sample. The trained model can then be implemented in a mobile chemical detection system for target detection. The trained machine learning models can provide improved classification that reduce minimum alarm levels and increase probability of detection, more efficiency in classifier development and more flexibility with expanding threat libraries. 
     Referring to  FIG. 2 , various embodiments of a chemical classification system  200  will be described. The chemical classification system  200  may be implemented on one or more servers such as an application server that performs data processing and/or other software execution operations for generating, storing, classifying and retrieving data samples. In some embodiments, the components of the chemical classification system  200  may be distributed across a communications network, such as network  222 . The communications network  222  may include one or more local networks such as a wireless local area network (WLAN), wide area networks such as the Internet, and other wired or wireless communications paths suitable for facilitating communications between components as described herein. The chemical classification system  200  includes communications components  214  operable to facilitate communications with one or more network devices  220  over the communications network  222 . 
     In various embodiments, the chemical classification system  200  may operate as a general-purpose chemical classification system, such as a cloud-based system providing classification to a plurality of network devices (e.g., network device  220 ), or may be configured to operate in a dedicated system that identifies and classifies samples using a database  202 . The chemical classification system  200  may be configured to receive one or more chemical data samples from one or more network devices  220  and process associated chemical identification/classification requests. 
     As illustrated, the chemical classification system  200  includes one or more processors  204  that perform data processing and/or other software execution operations for the chemical classification system  200 . The processor  204  may include logic devices, microcontrollers, processors, application specific integrated circuits (ASICs), or other devices that may be used by the chemical classification system  200  to execute appropriate instructions, such as software instructions stored in memory  206  including dataset generation component  208 , model training and analysis component  210 , and trained chemical classification models  212  (e.g., a neural network trained by the training dataset), and/or other applications. The memory  206  may be implemented in one or more memory devices (e.g., memory components) that store executable instructions, data and information, including image data, video data, audio data, network information. The memory devices may include various types of memory for information storage including volatile and non-volatile memory devices, such as RAM (Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically-Erasable Read-Only Memory), flash memory, a disk drive, and other types of memory described herein. 
     Each network device  220  may be implemented as a computing device such as a portable chemical sampling device, computer or network server, a mobile computing device such as a mobile phone, tablet, laptop computer or other computing device having communications circuitry (e.g., wireless communications circuitry or wired communications circuitry) for connecting with other devices in chemical classification system  200 . 
     The communications components  214  may include circuitry for communicating with other devices using various communications protocols. In various embodiments, communications components  214  may be configured to communicate over a wired communication link (e.g., through a network router, switch, hub, or other network devices) for wired communication purposes. For example, a wired link may be implemented with a power-line cable, a coaxial cable, a fiber-optic cable, or other appropriate cables or wires that support corresponding wired network technologies. Communications components  214  may be further configured to interface with a wired network and/or device via a wired communication component such as an Ethernet interface, a power-line modem, a Digital Subscriber Line (DSL) modem, a Public Switched Telephone Network (PSTN) modem, a cable modem, and/or other appropriate components for wired communication. Proprietary wired communication protocols and interfaces may also be supported by communications components  214 . 
     In various embodiments, a trained chemical classification system may be implemented in a real-time environment, as illustrated in  FIG. 3 . The mobile chemical classification system  250  may include a chemical sampling unit  252  and chemical detection components  254 , configured to acquire and detect one or more chemicals of interest using, for example, a FAIMS sensor or other device or system configured to receive and/or generate ion mobility spectra data. In the illustrated embodiment, the mobile chemical classification system  250  includes a processor and memory  260 , operable to store a trained chemical classification model  270  as described herein to classify the sampled analyte. 
     Referring to  FIG. 4A , an embodiment of a chemical classification training process will now be described. In one embodiment, the chemical classification model  300  is a convolutional neural network (CNN) that receives labeled chemical spectra data from training dataset  302  and outputs a chemical classification. The training dataset may include chemical data acquired using a chemical sensor during various real-world conditions. In one embodiment, the training starts with a forward pass through the neural network including chemical spectra feature extraction  304  in a plurality of convolution layers  306  and pooling layers  308 , followed by chemical classification  310  in a plurality of fully connected layers  312  and an output layer  314 . Next, a backward pass through the neural network may be used to update the CNN parameters in view of errors produced in the forward pass (e.g., misclassified chemicals). In various embodiments, other neural network processes may be used in accordance with the present disclosure. 
     An embodiment for validating the trained chemical classification model is illustrated in  FIG. 4B . A validation dataset  320  representing chemical sensor data is fed into the trained neural network  322 . The validation dataset  320  represents a variety of chemicals, sensor readings and environments to classify chemicals. In some embodiments, a database of labeled chemical spectra data is constructed and a first subset of labeled chemical spectra data is used for training and a second subset of labeled chemical spectra data is used for the validation dataset. Detected errors (e.g., chemical misclassification) may be analyzed and fed back to the training dataset/model evaluation system  324  to optimize the training dataset, features, and trained chemical classification models  326 . In various embodiments, detected errors may be corrected by adding more examples of sensor data for a chemical, removing sensor data from the training dataset, adjusting feature extraction criteria, and other adjustments to the training dataset and model generation process. In some embodiments, the system is configured to generate trained chemical classifications models representing a variety of scenarios, which are compared during the optimization processing. 
     Referring to  FIG. 5 , embodiments of a process for generating training data for chemical detection will now be described. In step  402 , an operator defines the parameters for the training dataset including an identification of the chemicals to be detected and classified, the chemical sensors and data to be modeled, associated features to be extracted from the data, and use cases/environments in which the samples will be captured. In step  404 , a training dataset including 2D spectral classification data is defined to model the use case/environments. Next, modeling and dataset scenarios are determined, in step  406 . For each model, an inference model is generated to detect at least one chemical, in step  408 . In step  410 , each model is validated using a corresponding validation dataset. In step  412 , each model is stored in a database with data identifying the chemical classification scenario and validation results. In step  414 , one or more trained models are selected for deployment based on the validation results, and in step  416 , the selected trained models are downloaded or otherwise transferred to one or more mobile devices for chemical detection. 
     Referring to  FIG. 6 , various embodiments of systems for sampling and detecting chemicals will be described. A chemical classification system  500  may include a sampler  501  for collecting samples and capturing detected chemical data and processing components  510  for classifying one or more chemicals from the captured data. In one implementation, the sampler  501  and processing components  510  are embodied in mobile chemical sampler, such as a small, lightweight, battery operated device that can easily be transported to an area of possible contamination. 
     The sampler  501  may include chemical sample collection components  530  configured to acquire a sample for testing and chemical data capture components  536  configured to capture chemical data from a collected sample. The sampler  501  may include components to enable operators to analyze gas, liquid, or solid samples. In some embodiments, the chemical sample collection components  530  include one or more sampling components (e.g., syringe, cartridge, sample probe, etc.) that is used to sample the matter for analysis by the sampler  501 . The sampler  501  may include an electronic interface, inlet and outlet ports, and/or other features as applicable for a particular implementation. The chemical sample and collection components  530  may further include a sample pump to pull air through the cartridge via an inlet and a flow/volume sensor to measure the sample volume, and a filter to filter debris and other solid or liquid particulates as desired. The chemical sample collection components  530  may have one or multiple sample flow paths to allow for sampling of sequential or simultaneous sampling of multiple samples. The intake system may be adapted to draw in, for example, gasses bearing solid or liquid particulates, liquids, or colloidal suspensions. 
     In some embodiments, the chemical sample collection components  530  include an aerosol or chemical agent detector, which may be a hand-held mobile device, platform-mounted mobile device or a standalone device in a laboratory. The chemical sample collection components  530  and chemical data capture components  536  may be configured for use with rapid thermal modulation ion spectrometry (RTMIS). RTMIS provides various advantages over IMS and FAIMS, including lower ion residence times and quicker scanning. 
     In various embodiments, the sampler  501  (and/or the processing component  510 ) may be configured to record information pertinent to the collected sample including GPS location when sampled, volume of sample collected, date/time stamp, voice data, and image data for use when the sample is analyzed. In some embodiments, the sampler  501  may include a FAIMS detector, a photo ionization detector, or a metal oxide detector to detect the presence of chemicals to alert the user to obtain a sample. The chemical data capture components  536  include components configured to perform a chemical analysis on the analytes in the sample. The chemical data capture components  536  may be any instrument for performing chemical analysis and generating a spectra data as described herein. In some embodiments, the chemical data capture components  536  may include a chemical separation device, such as, e.g., a gas-chromatograph (GC), a combination GC/MS, GC/electron capture detector (ECD), GC/FID, or other device. For example, chemical data capture components may include a gas chromatograph that separates the sample into individual targets and an ion mobility spectrometer analyzes each target to produce sample spectra for further analysis. The ion mobility spectrometer may operate, for example, by separating ions in an electric field based on their mobilities in a carrier buffer gas (e.g., using components such as an ionizer  536   a ) and driving the separated ions to a detector  536   b  through a drift tube. The detector  536   b  measures the separated ions in order of arrival, and the resulting chemical spectra provides a chemical fingerprint for the underlying target. 
     The chemical spectra data is provided to the processing component  510  for further analysis. In various configurations, the chemical classification system  500  may be configured to detect threats such as explosives and chemical and biological warfare agents, illegal drugs or other chemicals of interest. Example targets may include trinitrotoluene (TNT), C-4, pentaerythritol tetranitrate (PETN), RDX, ethylene glycol dinitrate (EGDN), hexamethylene triperoxide diamine (HMTD), triacetone triperoxide (TATP), urea nitrate, ammonium nitrate and other chemicals. 
     The processing component  510  may include, for example, a microprocessor, a single-core processor, a multi-core processor, a microcontroller, a logic device (e.g., a programmable logic device configured to perform processing operations), a digital signal processing (DSP) device, one or more memories for storing executable instructions (e.g., software, firmware, or other instructions), and/or any other appropriate combination of processing device and/or memory to execute instructions to perform any of the various operations described herein. Processing component  510  is adapted to interface and communicate with components the sampler  501  and components  520 ,  540 ,  550  and  552  to perform method and processing steps as described herein. Processing component  510  is also adapted to detect and classify chemicals in the chemical data captured by the sampler  501  through sample processing module  580  and one or more trained chemical classification modules  584 . 
     It should be appreciated that processing operations and/or instructions may be integrated in software and/or hardware as part of processing component  510 , or code (e.g., software or configuration data) which may be stored in memory component  520 . Embodiments of processing operations and/or instructions disclosed herein may be stored by a machine-readable medium in a non-transitory manner (e.g., a memory, a hard drive, or a flash memory) to be executed by a computer (e.g., logic or processor-based system) to perform various methods disclosed herein. 
     Memory component  520  includes, in one embodiment, one or more memory devices (e.g., one or more memories) to store data and information. The one or more memory devices may include various types of memory including volatile and non-volatile memory devices, such as RAM (Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically-Erasable Read-Only Memory), flash memory, or other types of memory. In one embodiment, processing component  510  is adapted to execute software stored in memory component  520  and/or a machine-readable medium to perform various methods, processes, and operations in a manner as described herein. Processing component  510  may be adapted to receive chemical data from the sampler  501 , process and/or store the chemical data, and/or retrieve stored chemical data from memory component  520 . Processing component  510  may further be adapted to classify one or more chemicals using trained chemical classification models  584  as described herein. 
     Display component  540  may include an image display device (e.g., a liquid crystal display (LCD)) or various other types of generally known video displays or monitors. The display component  540  may be used to display information related to operation of the sampler  501  as well as other information about the sample, sample cartridge, or the environment. 
     Control component  550  may include, in various embodiments, a user input and/or interface device, such as a keyboard, a control panel unit, a graphical user interface, or other user input/output. Control component  550  may be adapted to be integrated as part of display component  540  to operate as both a user input device and a display device, such as, for example, a touch screen device adapted to receive input signals from a user touching different parts of the display screen. In one or more embodiments, the control component  550  may be used to select an operation mode or to enter data about the sample or sample cartridge. Different operation modes may be selected that operate the apparatus according to varying parameters. For example, an operation mode may be selected that operates a sample pump for a predetermined length of time. Another operation mode may be selected that operates a sample pump until a predetermined volume of gas has passed through a flow meter. Various operation modes may be programmed into the memory of the processing component  510  by a user, as unique operation modes are developed. 
     Communication component  552  may be implemented as a network interface component adapted for communication with a network including other devices in the network and may include one or more wired or wireless communication components. In various embodiments, a network  554  may be implemented as a single network or a combination of multiple networks, and may include a wired or wireless network, including a wireless local area network, a wide area network, the Internet, a cloud network service, and/or other appropriate types of communication networks. 
     In various embodiments, chemical classification system  500  provides a capability, in real time, to detect and classify chemicals in a sample. Chemical data from a sample may be received from the sampler  501  by processing component  510  and stored in memory component  520 . The sample processing module  580  may process the chemical data for use by the trained chemical classification modules  584 , for transmission to a remote device (e.g., chemical classification host system  556 ) or for other uses depending on the configuration of the chemical classification system  500 . The trained chemical classification module  584  detects and classifies one or more chemicals in the sample data and stores the result in the memory component  520 , an object database or other memory storage in accordance with system preferences. In some embodiments, chemical classification system  500  may send sample data or classification results over network  554  (e.g., the Internet or the cloud) to a server system, such as chemical classification host system  556  for further processing. In some embodiment, the processing components  510  are configured to trigger a notification or alarm to the user (e.g., through the control component  550  or display component  540 ) when a chemical of interest is detected in the environment and should be sampled. 
     Where applicable, various embodiments provided by the present disclosure can be implemented using hardware, software, or combinations of hardware and software. Also, where applicable, the various hardware components and/or software components set forth herein can be combined into composite components comprising software, hardware, and/or both without departing from the spirit of the present disclosure. Where applicable, the various hardware components and/or software components set forth herein can be separated into sub-components comprising software, hardware, or both without departing from the spirit of the present disclosure. 
     Software in accordance with the present disclosure, such as non-transitory instructions, program code, and/or data, can be stored on one or more non-transitory machine-readable mediums. It is also contemplated that software identified herein can be implemented using one or more general purpose or specific purpose computers and/or computer systems, networked and/or otherwise. Where applicable, the ordering of various steps described herein can be changed, combined into composite steps, and/or separated into sub-steps to provide features described herein. 
     Embodiments described above illustrate but do not limit the invention. It should also be understood that numerous modifications and variations are possible in accordance with the principles of the invention. Accordingly, the scope of the invention is defined only by the following claims.