Techniques for sample analysis using product ion collision cross section information

Abstract

This invention describes techniques and apparatus for determining the atomic-level configuration of product ions. In some embodiments, product ion CCS information can be used to determine the substructural configuration of the product ion, such as isomers. For example, in one embodiment, the apparatus may include at least one memory and logic coupled to the at least one memory to: receive resolution information of a plurality of product ions, the resolution information including product ion collision cross-section (CCS) information; and for at least one of the plurality of product ions, determine the variance value of the product ion CCS information. Other embodiments are described in this invention.

Description

[0001] Cross-reference to related applications

[0002] This application claims priority and benefit to UK Patent Application No. 2001249.8, filed on 29 January 2020, the entire contents of which are incorporated herein by reference. Technical Field

[0003] The implementation scheme described herein relates to the overall quality analysis of samples, and more specifically, to the determination of product ions obtained through fragmentation of one or more product ions based on collision cross section (CCS) information of product ions. Background Technology

[0004] Conventional analytical techniques, such as mass spectrometry (MS), cannot effectively and accurately resolve low-level structural identification of components in complex samples. For example, MS spectroscopy can be used to determine the presence of specific metabolites of pharmaceutical compounds, but it is insufficient to identify the presence and / or concentration of specific isomers or other low-level characteristics of metabolites. This challenge is particularly severe for product ions, as processed quality analysis data for precursor ion fragments are severely inadequate or even nonexistent, especially for most classes of molecules. Therefore, techniques for resolving low-level structural characteristics of product ions in complex samples are desired. Attached Figure Description

[0005] Figure 1 An implementation scheme for the first operating environment is shown.

[0006] Figure 2 An implementation scheme for the second operating environment is shown.

[0007] Figure 3A It shows that a structure C6H can be generated. 12 The precursor ion of the product ion of N (m / z = 98.096).

[0008] Figure 3B The following are shown: a structure with C6H according to some embodiments. 12 A graph of CCS versus m / z for the product ions of N (m / z = 98.096).

[0009] Figure 3C It shows a structure with C6H 12 CCS information for the product ions of N (m / z = 98.096).

[0010] Figure 3D CCS information for the product ion with the structure C7H6NO (m / z = 120.0443) is shown.

[0011] Figure 3ECCS information for the product ion with structure C7H8N (m / z = 105.0335) is shown.

[0012] Figure 4A A graph showing the CCS versus m / z of the product ion having the structure C7H6NO (m / z = 120.0443) according to some embodiments is shown.

[0013] Figure 4B The following are shown: a structure with C6H according to some embodiments. 12 A graph of CCS versus m / z for the product ions of N (m / z = 98.096).

[0014] Figure 4C A graph showing the CCS versus m / z of product ions having the structure C7H8N (m / z = 105.0335 and 106.0651) according to some embodiments is presented.

[0015] Figure 4D A graph showing the CCS versus m / z of the product ion having the structure C7H8N (m / z = 106.0651) according to some embodiments is presented.

[0016] Figure 5A The precursor ion that can produce product ions with the structure C8H9O is shown.

[0017] Figures 5B-5D The analytical information of co-eluting product ions with the structure C8H9O according to some embodiments is shown.

[0018] Figure 6 An embodiment of a two-dimensional (2D) product ion spectrum is shown.

[0019] Figure 7 A three-function experimental design based on some implementation schemes is shown.

[0020] Figure 8 MS and MS / MS results of three-function experiments according to some implementation schemes are shown.

[0021] Figure 9 2D IMS precursor / product ion CCS distribution mappings of terfenadine according to some implementation schemes are depicted.

[0022] Figure 10 Empirical and predictive CCS information for terfenadine based on some implementation schemes is described.

[0023] Figure 11 2D IMS precursor / product ion CCS distribution mappings of leucine enkephalin (LeuEnk) according to some embodiments were depicted.

[0024] Figures 12-19 The analysis of known-unknown substances using liquid chromatography-ion mobility-mass spectrometry (LC-IM-MS) according to some implementation schemes is described.

[0025] Figure 20 An implementation scheme for the computing architecture is shown. Detailed Implementation

[0026] Various embodiments can be directed to systems, methods, and / or apparatuses for determining the properties of a sample and / or its components of interest using collision cross section (CCS) or (Ω) information. In some embodiments, the components may include product ions generated by the fragmentation of precursor ions in the sample. In various embodiments, the CCS information of the product ions can be determined and processed according to a product ion analysis process. In some embodiments, the product ion analysis process can use the product ion CCS information to determine the atomic-level properties of the product ions. In some embodiments, substructural properties may include the configuration of the product ions, which may originally have the same or substantially similar physicochemical properties, such as mass-to-charge (m / z) ratio. Non-limiting examples of atomic-level configurations may include isomer configurations, proto-component configurations, etc. Therefore, a product ion analysis process according to some embodiments can be used to combine product ion CCS information with other mass analysis information (such as m / z, drift time (DT or t)). d Retention time (RT or t) r (e.g., isomeric fragments) to determine the substructure configuration of the product ions in the sample.

[0027] Conventional techniques, such as MS methods, cannot distinguish certain structural changes within a compound. For example, mass spectrometry (MS) can be used to determine the m / z of a compound, but it cannot identify different isomers of the compound, where each isomer has the same m / z, especially for product ions. Therefore, additional physicochemical information is needed to differentiate isomers. Consequently, in some embodiments, a product ion CCS process can use product ion CCS information to identify different physicochemical configurations of product ions that may not have been determined using conventional methods.

[0028] Mass spectrometer systems (such as those described in more detail below) Figure 2 System 205 can be used to determine the CCS information of product ions. However, the understanding and definition of product ion CCS in analytical chemistry is still poor. Therefore, the use of product ion CCS information is challenging and limited, making it unlikely to make a meaningful contribution to MS analysis tools. Furthermore, conventional techniques cannot effectively and accurately process product ion CCS data, especially when using certain methods, such as capture / transfer (TAP; Time-Aligned Parallel) fragmentation acquisition schemes.

[0029] For example, using current software tools, there is a lack of efficient methods to link product ions to their precursors. Alternative methods have been used that generate MS data (e.g., broadband DIA, ion mobility-assisted DIA, etc.) and use DT correlation to determine the product ions of all relevant components (e.g., pharmaceuticals), which can be correlated with a lookup table. Fragmented information can be captured and separated into different components, for example, effectively componentized as MS1 or precursor data channels (compared to MS2 or product ion data channels). The observed product ions can be correlated with their CCS values ​​and ions in the lookup table. However, even this approach is time-consuming, inefficient, and requires specialized software tools that are not typically part of an end-user's MS system.

[0030] Therefore, some embodiments can provide several technical advantages over existing technology systems. Furthermore, various embodiments can provide improvements in computational techniques and technical features, for example, by providing a more efficient and effective process for processing product ion CCS information to determine, among other things, the properties of product ions and / or their precursors (e.g., isomer configurations). Among non-limiting technical advantages, the product ion CCS analysis process according to some embodiments can characterize unknowns (e.g., metabolites, pharmaceuticals, natural products, biomarkers, contaminants, etc.) to reduce the number of possible candidates for product ions in a sample (in one non-limiting example, via a library and / or lookup table of common product ions and their CCS values). In another non-limiting technical advantage, the product ion CCS analysis process according to some embodiments can provide improvements over conventional methods (e.g., particularly regarding three-dimensional (3D) descriptors) in CCS modeling and prediction, including but not limited to CCS modeling and / or prediction using artificial intelligence (AI) and / or machine learning (ML) techniques. Among the additional non-limiting advantages, the product ion CCS analysis procedure according to some embodiments can provide improved specificity for product ion determination (e.g., using two-dimensional (2D) product ion spectra). In further non-limiting advantages, the product ion CCS analysis procedure according to some embodiments may be able to distinguish substructurally isomeric compounds (e.g., designer pharmaceuticals) using CCS-m / z fingerprint analysis (2D) and / or CCS-m / z intensity (3D). Furthermore, non-limiting advantages may include a product ion CCS analysis procedure according to some embodiments that can use product ion CCS values ​​in a priori structure determination to reduce the number of possible candidates for the product ion (e.g., for natural products, extractables, leachates, contaminants, lipids, proteins, pharmaceuticals, etc.). The embodiments are not limited to this context. The embodiments provide other technical advantages.

[0031] While some implementations may use pharmaceuticals as examples, the implementations are not limited thereto, as the described processes can be used to identify components in other experimental disciplines. Non-limiting examples may include pharmaceutical impurity characterization, food type / storage analysis, chemical and (biological) pharmaceutical fingerprinting, biomedical research experiments, water / groundwater testing, soil testing, etc.

[0032] Additional illustrative and non-limiting examples of the use of product ion CCS analysis procedures according to some implementation schemes may include food and environmental applications, certification, profiling, speciation, food aging / storage / processing characteristics, pharmaceuticals (e.g., determining pharmaceutical fingerprints, profiling the fingerprints of counterfeit products, product purity, comparison with expected chemical fingerprints), biotransformation products, forensic toxicology, etc. Implementation schemes are not limited to this context.

[0033] This description may include many specific details, such as component and system configurations, to provide a more thorough understanding of the described embodiments. However, those skilled in the art will understand that the described embodiments can be practiced without such specific details. Furthermore, some well-known structures, components, and other features have not been shown in detail to avoid unnecessarily obscuring the described embodiments.

[0034] In the following description, references to “an embodiment,” “an embodiment,” “an exemplary embodiment,” “various embodiments,” etc., indicate that an embodiment of the described technology may include a particular feature, structure, or characteristic. However, more than one embodiment may include that particular feature, structure, or characteristic, and not every embodiment must include that particular feature, structure, or characteristic. Furthermore, some embodiments may have some, all, or none of the features described for other embodiments.

[0035] As used in this specification and claims, unless otherwise stated, the use of ordinal adjectives such as “first,” “second,” “third,” etc., to describe elements indicates only a specific instance of the referenced element or a different instance of a similar element, and does not imply that the elements so described must be in a particular order in time, space, sequence, or any other way.

[0036] Figure 1 An example of an operating environment 100, which may represent some implementation schemes, is shown. For example... Figure 1As shown, the operating environment 100 may include an analytical system 105 for managing the analytical data associated with the analytical system 160. In some embodiments, the analytical system 160 may include one or more analytical instrument systems 162a to 162n for performing quality analysis and / or other analytical processes on the sample. In various embodiments, the analytical instrument systems 162a to 162n may be or may include a chromatography system, a liquid chromatography (LC) system, a gas chromatography (GC) system, a mass analyzer system, a mass spectrometer (MS) system, an ion mobility spectrometry (IMS) system, a high-resolution mass spectrometer (HDMS) system, a time-of-flight (ToF) MS system, an MS system operating in data-related (DDA) mode, an MS system operating in data-independent (DIA) mode, an MS system operating in broadband DIA and / or ion mobility-assisted DIA mode, a high-performance liquid chromatography (HPLC) system, or an ultra-high-performance liquid chromatography (UHPLC) system. Systems, ultra-high performance liquid chromatography (UHPLC) systems, solid-phase extraction systems, sample preparation systems, combinations thereof, components thereof, variations thereof, etc. In some embodiments, any of the analytical instrument systems 162a to 162n can be combined for operation. In an exemplary embodiment, analytical system 160 may be or may include systems with... Figure 1 The analytical system 105 described herein is the same as or substantially similar to the analytical system. Although the examples in this specific embodiment use LC, MS, LC-MS, MS / MS (tandem MS), IMS-MS, and HDMS. e However, the implementation scheme is not limited to this, as this paper envisions other analytical instruments and / or operating modes that can be operated according to some implementation schemes.

[0037] In some implementations, the resolving instrument systems 162a to 162n can be used for analysis. For example, for an LC-MS system, resolving instrument systems 162a and 162b can be used to separate samples and perform mass analysis on the separated samples to generate resolving information 136, which may include, for example, spectral information, retention time, drift time, ion mobility information, CCS information 138, product ion CCS information 138, etc. In another example, for an LC-MS-IMS system, resolving instruments 162a to 162c can be used to separate samples and perform mass analysis and ion mobility analysis on the samples to generate resolving information 136, which may include, for example, spectral information, retention time, drift time, ion mobility information, CCS information 138, product ion CCS information 138, etc. r CCS information (including product ion CCS information) 138, t dInformation 136, such as parsing information. In some embodiments, parsing information 136 may include data from historical analysis or database analysis, such as spectral databases, peptide libraries, protein libraries, standard reference material data, drug databases (e.g., Food and Drug Administration (FDA) databases), drug interaction databases, metabolic databases, proteomics databases, etc. Embodiments are not limited to this context.

[0038] In some embodiments, CCS information 138 may include predicted CCS information and / or modeled CCS information. In some embodiments, CCS information 138, including product ion CCS information, may be combined with machine learning (ML) techniques, including but not limited to artificial intelligence (AI) processes, neural networks, etc. For example, product ion CCS information can be used in ML / AI applications to analyze, predict, model, or otherwise determine product ion properties, such as isomer structures of product ions obtained during experiments. Product ion CCS information may be or may include product ion CCS computational models (e.g., ML processes, AI processes, neural networks (NN), convolutional neural networks (CNN), etc.). Embodiments are not limited to this context. In some embodiments, predicting precursor CCS values ​​and / or modeling precursor CCS values ​​can be used to reduce the number of candidates for viewing, annotation, identification, etc.

[0039] In various embodiments, the analysis system 105 may include a computing device 120 communicatively coupled to one or more of the analysis system 162, analysis instrument systems 162a to 162n, and / or otherwise configured to receive and store analysis information 136. For example, analysis instrument 162b may be used to provide analysis data to a location on a network 150 (e.g., a cloud computing environment or analysis instrument management platform) accessible to the computing device 120. In some embodiments, the computing device 120 may be used to control, monitor, manage, or otherwise process various operational functions of the analysis system 160 and / or its systems 162a to 162n. For example, in various embodiments, the computing device 120 may execute an analysis instrument application 132 for controlling various functions of one or more of the analysis instrument systems 162a to 162n. For example, the analysis instrument application 132 may serve as a control interface for analyzing samples on the analysis instrument systems 162a to 162n, receiving and / or processing analysis information from the analysis instrument systems 162a to 162n, etc. Non-limiting examples of the analytical instrument application 132 may include chromatography data software (CDS), mass spectrometry software, laboratory management software, LC-MS data analysis software, and databases (e.g., mass spectrometry databases, proteomics databases, protein recognition databases, etc.). Further illustrative and non-limiting examples of the analytical instrument application 132 may include Empower. TM(For example, Empower) TM 3) CDS, MassLynx TM Mass spectrometry software, Progenesis TM QI LC-MS data analysis software and UNIFI developed by Waters Corporation (Milford, Massachusetts, United States). TM Scientific information systems and / or variations or portions thereof. In some implementations, software development kits (SDKs) and / or application programming interfaces (APIs) may be used by the software platform to access the analytical instrument application 132 and / or its associated analytical data. Implementations are not limited to this context.

[0040] In some embodiments, computing device 120 may be or may include a standalone computing device, such as a personal computer (PC), server, tablet computer, cloud computing device, etc. In various embodiments, computing device 120 and / or parts or components thereof may be components of one or more of the analytical instrument systems 162a to 162n.

[0041] like Figure 1 As shown, computing device 120 may include processing circuitry 120, memory unit 130, and transceiver 140. Processing circuitry 120 may be communicatively coupled to memory unit 130 and / or transceiver 140. Processing circuitry 120 may include and / or access various logics for performing processing according to some embodiments. For example, processing circuitry 120 may include and / or access product ion CCS logic 122. Processing circuitry 120 and / or product ion CCS logic 122, or portions thereof, may be implemented in hardware, software, or a combination thereof. As used herein, the terms “logic,” “component,” “layer,” “system,” “circuit,” “decoder,” “encoder,” and / or “module” are intended to refer to computer-related entities, which may be hardware, a combination of hardware and software, software, or software in execution, examples of which are provided by exemplary computing architecture 2000. For example, logic, circuitry, or layer may be and / or include, but is not limited to, processes running on a processor, processors, hard disk drives, multiple storage drives (optical and / or magnetic storage media), objects, executable programs, execution threads, programs, computers, hardware circuits, integrated circuits, application-specific integrated circuits (ASICs), programmable logic devices (PLDs), digital signal processors (DSPs), field-programmable gate arrays (FPGAs), systems-on-a-chip (SoCs), memory cells, logic gates, registers, semiconductor devices, chips, microchips, chipsets, software components, programs, application programs, firmware, software modules, computer code, and any combination of the foregoing.

[0042] although Figure 1 The product ion CCS logic 122 is depicted as being within the processing circuitry 120, but the implementation is not limited thereto. For example, the product ion CCS logic 122 may be located within an accelerator, processor core, interface, separate processor chip, or implemented entirely as a software application (e.g., product ion CCS application 134), etc.

[0043] Memory cell 130 may include various types of computer-readable storage media and / or systems in the form of one or more higher-speed memory cells, such as read-only memory (ROM), random access memory (RAM), dynamic RAM (DRAM), dual data rate DRAM (DDRAM), synchronous DRAM (SDRAM), static RAM (SRAM), programmable ROM (PROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, polymer memory (such as ferroelectric polymer memory, bidirectional memory, phase-change or ferroelectric memory), silicon-oxide-nitride-oxide-silicon (SONOS) memory, magnetic cards or optical cards, device arrays (such as redundant array of independent disks (RAID) drives), solid-state storage devices (such as USB storage devices, solid-state drives (SSDs), and any other type of storage media suitable for storing information. Additionally, memory cell 130 may include various types of computer-readable storage media in the form of one or more lower-speed memory cells, including internal (or external) hard disk drives (HDDs), floppy disk drives (FDDs), and optical disk drives (such as CD-ROMs or DVDs) for reading or writing removable optical disks, solid-state drives (SSDs), etc.

[0044] Memory unit 130 may store product ion CCS application 134, which, when executed by computing device 120, may operate independently or in combination with product ion CCS logic 122 to perform various processes according to some embodiments. For example, product ion CCS application 134 may generate CCS information 138, including product ion CCS information locally stored on nodes 152a to 152n of network 150, database 154, and / or network 150. Although product ion CCS application 134 and analytical instrument application 132 are... Figure 1 The application is described as a separate application (and / or logic), but the implementation is not limited to this. For example, the product ion CCS application 134 may be a module or component of the analytical instrument application 132, and vice versa.

[0045] Generally, CCS information 138 can represent the effective area of ​​interaction between a single ion and the neutral gas it travels through. CCS can represent a type of physicochemical property and is related to the chemical (sub)structure and three-dimensional conformation of the ion. In IMS, ions can be separated by their gas-phase transport in an electric field and will have different CCS values ​​depending on their shape-to-charge ratio. In some embodiments, product ion CCS application 134 can use product ion CCS information to determine sample composition, exclude potential candidates, and / or determine the substructural characteristics of sample components (e.g., isomer configuration).

[0046] Figure 2 An example of an operating environment 200, which may represent some implementation schemes, is shown. For example... Figure 2 As shown, the operating environment 200 may include an analysis system 205. In some embodiments, the analysis system 205 may be a configuration of the analysis system 160.

[0047] The analysis system 205 may include a mass spectrometer 210 suitable for use with various embodiments. A sample may be injected into the mass spectrometer 210 at an injection inlet 212. The sample may be ejected from a needle into an ionization chamber 214. Ionization of the sample may occur to form sample ions. The ionized sample may exit the ionization chamber 214, and the ions may flow toward a first vacuum region 216. The sample may be transferred through the first vacuum region to a step-wave ion guide 218. The step-wave ion guide 218 may then initially guide ions along the ion guide in a large cross-sectional area 220, and may then focus the ions into a smaller cross-section in an off-axis portion 222 of the guide. The ions may then be transferred to another ion guide 224, where the ions may pass through a quadrupole mass filter 226.

[0048] The quadrupole mass filter 226 can be used in transport mode, allowing all ions to pass through the filter and enter the three-wave chamber 228. Once the ions are transported into the three-wave chamber 228, they can be collected in groups within the trapping unit 230 of the three-wave chamber 228. A group of ions in the trapping unit 230 can then be released through the helium unit 232 and enter the ion mobility separator 234. The ions can then be temporally separated according to their ion mobility within the ion mobility separator 234, and as they leave the separator, they can enter the transfer unit 236, where ions with a small range of ion mobility are collected in groups, and pass through the transfer unit 236, several lenses 238, and into the ToF actuator region 240. Each group of ions with a small mobility range can then be pulsed from the ToF actuator region 240 to the flight tube 242 and the reflector 244, where they can be reflected back to the detection system 246, where the time of flight and the small range of ion mobility are recorded.

[0049] A second sequential analysis can then be performed on a similar basis, except that after the ions have been separated into groups with small ranges of ionic mobility in separator 234, energy can be supplied to the ions in transfer unit 236 to induce fragmentation of the ions in each group to provide product or fragment ions. Depending on the mobility of the precursor ion, the product ions can be retained in the group and can be transferred to the ToF actuator region 240. Similarly, each group of product ions from the precursor or precursor ion with a small range of mobility can then be pulsed from the ToF actuator region to flight tube 242 and reflector 244, where they are reflected back to detection system 246, where the time of flight of these fragment ions and the small range of mobility of the precursor ions that produced the product ions are recorded.

[0050] In some embodiments, fragmentation may be performed within the capture unit 230. In various embodiments, fragmentation may be performed within the transfer unit 236. In some embodiments, fragmentation may be performed within both the capture unit 230 and the transfer unit (e.g., TAP (capture / transfer) fragmentation). In exemplary embodiments, IMS may be performed on the product ions to generate product ion CCS information. For example, in some embodiments, fragmentation may be performed within the capture unit 230, and IMS may be performed on the resulting product ions.

[0051] According to some embodiments, IMS can be performed via analysis system 205 (or analysis system 160) under various conditions, such as gas type (e.g., nitrogen, helium, etc.), gas pressure, wave height, wave velocity, and / or other characteristics. In some embodiments, IMS conditions can be varied to optimize CCS measurements and / or increase the variance between different conformations (e.g., isomers) of the compound of interest. For example, a first gas type (or other conditions) may be optimal for identifying isomers of compound AD, while a second gas type may be optimal for identifying isomers of compound EH. In another instance, the CCS values ​​for a particular compound may differ depending on the gas type and / or other IMS conditions.

[0052] Figure 3A It shows that a structure C6H can be generated. 12 The precursor ion of the product ion has a N (m / z = 98.096) value. For example, the binding region of the product ion can be fragmented to produce a C6H structure. 12 The product ions of N.

[0053] Figures 3B-3E Experimental results were described to determine that product ions with the same elemental composition can have different structures and CCS information (i.e., CCS values). Figures 3B-3D The fragmentation of the experiments described in the paper occurs in the capture unit (e.g., Figure 2 The capture will take place within unit 230. (See reference) Figure 3B , which depicts, for example, by Figure 3A Figure 310 shows the CCS variants of the C6H12N product ions produced by the fragmentation of the compound depicted in the figure. Figure 3C Depicting through analysis and Figure 3B Figures 315 and 320 are generated from the associated CCS information. For example, Figure 315 depicts the natural fracture analysis indicating two populations (e.g., Jenks natural fracture optimization). Referring to Figure 320, statistical analysis of the two populations indicates that differences may exist, e.g., probability (p) < 0.05. The variation within the group is equal to 0.4%, which is in good agreement with the accuracy of precursor ion CCS measurements (e.g., providing comparative analysis and library search / screening).

[0054] Figure 3D Figure 330 and CCS information 332 show the product ion with the structure C7H6NO (m / z = 120.0443), and... Figure 3E Figure 340 and CCS information 342 show the product ion with the structure C7H8N (m / z = 105.0335). Figure 3D and Figure 3E As shown, spontaneous fracture can be determined from the CCS information of C7H6NO and C7H8N product ions.

[0055] In some implementations, the CCS variance for various products at different m / z values ​​can be determined. In some implementations, the variance can be determined based on CCS values ​​determined for multiple experiments, compared with historical information, computer (in silico) information, ML / AI, etc. In some implementations, the variance value can be determined for a specific product ion. Typically, the variance can be a percentage of the CCS variance, a standard deviation, or a minimum / maximum variance threshold. For example, the variance for one product ion could be 1.13% or 1.13%, while the variance for another product ion could be approximately 0.36% or 0.36%.

[0056] In some embodiments, variance exceeding a predetermined variance threshold can be used to indicate that the variance is due to structural differences in the product ions (e.g., product ions include isomers or other structurally different configurations that affect CCS). For example, variance below the threshold can be statistically insignificant (e.g., due to experimental variance rather than structural differences). For example, the variance threshold could be 0.5%. Thus, a product ion CCS process according to some embodiments can determine that product ions may have structurally different products (e.g., isomers), while other product ions do not have structurally different products.

[0057] In some embodiments, product ion CCS values ​​can be determined and placed in a product ion CCS library. Therefore, in some embodiments, when product ion CCS values ​​are obtained (e.g., for a particular product ion structure or m / z value), the product ion CCS process according to some embodiments can determine a specific structural form (e.g., isomer) based on the product ion CCS values. For example, the CCS value of a meta-form product ion can be determined as X, while the CCS value of an ortho-form product ion can be determined as Y. Alternatively, in some embodiments, product ion CCS values ​​can be used to exclude or filter certain product ions and / or structural forms. For example, m / z can indicate the structure C6H. 12 The product ion of N is present, but the CCS value of the product ion is outside the range of form A. Therefore, the detected product ion is C6H. 12 N can be of form B, form C, etc.

[0058] Figures 4A-4D Experimental results of repeatability analysis of a group of compounds are described (e.g., based on...). Figures 3A-3E (Select from the analytical results described in the text). Figures 4A-4DEach of these figures depicts charts 405, 415, 425, and 435, along with associated CCS information, which shows repeat analyses (n=6) of a group of compounds mixed together based on the commonality of shared product ions. Results are presented in box-and-whisker format to demonstrate variations in product ion CCS measurements. The mean / median accuracy is 0.1%–0.2%, but typically <0.5%. Figures 4A-4D Each of the data points is presented in tabular form (410, 420, 430, 440) showing the observations (left table) and the mean, median, standard deviation, and CV (right table). Regarding CV, outliers are typically caused by measurements associated with undesirable ion statistics and are not removed from the analysis. Figures 4A-4D In Tables 410, 420, 430, and 440, the lower left shows exemplary comparative analyses (p-values ​​from a standard Student's T-test) used for binary comparisons, where the distinction between possible and impossible is based on the observed product ion CCS values. In some examples, the number of detections can be greater than the number of injections; this can be, for example, the result of multiple detections of the same target analyte across the same chromatographic peak (often the so-called "shoulder"), which are considered independent observations.

[0059] Figure 4A Figure 405 shows the CCS versus m / z of the product ion having the structure C7H6NO (m / z = 120.0443) according to some embodiments, and the corresponding information table 410. Figure 4B The following are shown: a structure with C6H according to some embodiments. 12 Figure 415 shows the CCS and m / z of the product ion of N (m / z = 98.096), and the corresponding information table 420. Figure 4C Figure 425 shows the CCS versus m / z of product ions having the structure C7H8N (m / z = 105.0335 and 106.0651) according to some embodiments, and the corresponding information table 430. Figure 4D Figure 435 shows the CCS versus m / z of the product ion having the structure C7H8N (m / z = 106.0651) according to some embodiments, and the corresponding information table 440.

[0060] Figures 5A-5D The analysis, based on some implementation schemes, includes LC separation. For example, Figures 5B-5D Described Figure 5A The analysis results of a group of (near) co-eluting components selected from the structure and common fragment ions in relation to the elemental composition of compound 505 described in the paper (i.e., the product ion having the structure C8H9O (m / z = 121.0648)). Figure 5BCharts 512 and 514, according to some embodiments, are shown for co-eluting product ions having m / z = 121.0648, along with corresponding analytical information 520. For example, Figure 5B The target analysis, along with the overall overview in data table 520, depicts the analysis of a repeat experiment with reconstructed chromatogram 512 and product ion CCS value distribution 514. For example, box-and-whisker plot 514 indicates the presence of 2 or 3 different product ion CCS distributions. Figure 5C Figures 530 and 532 depict the selection of two compounds for early elution, and summary table 540 presents the average / median retention times and %CV values. Here, differentiation based on product ion CCS is feasible. Figure 5D Similar results for the two later-eluting compounds are depicted in Figures 550 and 552, together with summary table 560. Here, separation based on the CV values ​​typically used in experiments would be impossible; however, statistical analysis of the box-and-whisker plot results (550, 552) indicates that separation may be feasible.

[0061] In some implementations, various types of resolution information can be used to analyze sample components. Previous results are obtained by utilizing fragmentation performed in the capture region of the resolution system (e.g., capture unit 230 of system 260), thereby providing MS / MS data annotated with available product ion CCS values, such as... Figure 6 As shown. However, the geometry of the instrument (e.g., system 260) can be within the capture area of ​​the instrument in the experiment (e.g., Figure 2 230) and transfer areas (e.g., Figure 2 Fragmentation is provided in (see example 236) Figure 8 This allows for the provision of precursor and product ion CCS data in a single experiment (see, for example...). Figures 9-11 ).

[0062] For example, Figure 6 The embodiments of product ion spectra from experiments according to some implementation schemes are shown. For example... Figure 6 As shown, a graph 605 of the peak vertices versus m / z for compound 602 can be generated, for example, via analytical system 160 or 205. In another example, a graph 610 of the product ions CCS versus m / z can be generated for compound 602. Resolution information (such as the information depicted in graphs 605 and / or 610) can be used to generate a 2D CCS fingerprint of the compound, such as fragmented product ions according to some embodiments. In other embodiments, resolution information (such as CCS, m / z, and intensity information) can be used to determine a 3D fingerprint of the compound. In various embodiments, the product ion spectra according to some embodiments can be used, among other things, to improve the specificity of quality analysis experiments compared to conventional methods. The embodiments are not limited to this context.

[0063] Figure 7 Trifunctional experimental designs according to some embodiments are illustrated. In some embodiments, the product ion CCS process can use a trifunctional or three-channel TAP fragmentation process. For example, as... Figure 7 As shown, the following three-channel experiments can be performed: Function 1: Conventional low-energy trace (e.g., low-energy capture / transfer (without fragmentation); Function 2: HDMSe-like high-energy trace with transfer fragmentation (e.g., high-energy transfer (fragmentation in 236); product ions share the same CCS value as the precursor); Function 3: High-energy trace with capture fragmentation (e.g., high-energy capture (fragmentation in capture unit 230); product ion CCS). The product ion CCS process can be used to cross-correlate the three-channel information according to the following: channels 1 and 2 are used to obtain the product ion based on the shared drift time; and cross-correlation is performed between channels 2 and 3 to derive the CCS value of the product ion.

[0064] Figure 8 The experimental data for three functions according to some embodiments are shown to be processed using development software (e.g., ApexRT) to provide MS and MS / MS spectra that can be annotated with CCS information. More specifically, Figure 805 depicts the results for function 1 (LE precursor CCS), Figure 810 depicts the results for function 2 (TAP-transferred HE), and Figure 815 depicts the results for function 3 (TAP-captured HE product ions CCS). Exemplary annotations are provided in... Figure 9 and Figure 11 The LC-MS analysis of the standard mixture is shown, with combined / summed MS and MS / MS data / spectrums at the top (905 and 1005) and 2D precursor / product ion CCS mappings at the bottom (910 and 1010).

[0065] Figure 10 Empirical and predicted CCS information for terfenadine is described according to several implementation schemes. In some implementation schemes, ML / AI is used to determine... Figure 10 The predicted product ion CCS values ​​can be used in a variety of applications and offer several technical advantages over conventional techniques. One non-limiting advantage is that the predicted product ion CCS values ​​can facilitate the development of 2D CCS mappings in the absence of empirical data (e.g., Figure 9 and Figure 11 ).

[0066] In some implementations, liquid chromatography-ion mobility spectrometry (LC / IM / MS) can be used to determine the analysis of known unknowns. LC / IM / MS has been used to analyze unknowns in passion fruit complement in studies. However, the implementations are not limited in this respect, as the described process can be applied to a variety of other compounds.

[0067] C-glycoside flavonoids can be used as markers for the quality control of passion fruit medicinal plants. Several studies have focused on using LC-MS for fingerprinting, quantification, or identification of flavonoids in passion fruit, demonstrating the principle of using the combinatorial specificity of LC-IM-MS to analyze “known-unknown” isomers in passion fruit species. However, structural elucidation of the identified flavonoids responsible for promoting phytochemical activities remains. Therefore, the application of LC-MS methods in the analysis of flavonoid markers has increased significantly. Here, passion fruit extracts are analyzed to generate “known-unknown” speciation distributions. This method can be combined with historical analysis of product ion identification. Furthermore, the experimental information is combined with CCS predictions to elucidate the retention-time-independent properties of known flavonoids. More detailed application background information is provided in the following literature: McCullagh et al., “Use of ion mobility mass spectrometry to enhance cumulative analytical specificity and separation to profile 6-C / 8-C-glycosylflavone critical isomer pairs and known-unknowns in medicinal plants,” Phytochem Anal. 2019 Jul; 30(4): 424-436 (“McCullagh”) and Pereira et al., “Distinction of the C-glycosylflavone isomer pairs orientin / isoorientin and vitexin / isovitexin using HPLC-MS exact mass measurement and in-source CID,” Phytochem Anal. 2005 Sep-Oct; 16(5): 295-301 (“Pereira”).

[0068] experiment:

[0069] Samples were prepared and LC-IM-MS data were collected as previously described in McCullagh and Pereira. Briefly, voucher samples of Passiflora incarnata, Passiflora edulis, Passiflora caerulea, and Passiflora alata were used in this study. The materials were dried at 35°C for 48 hours, pulverized, and ground. Leaf materials were mixed with ethanol:water and flavonoids extracted via SPE. LC separation of the filtered and diluted extracts was performed using a UPLC system operating under standard reversed-phase chromatographic conditions. IM-MS data were collected on a hybrid orthogonal accelerated quadrupole time-of-flight (Q-IM-oaTof) mass spectrometer supporting ion mobility. Peak detection and lock-in mass correction were performed on the LC-IM-MS data in multiple dimensions using developed software. After alignment and co-detection, a matrix feature library was created and re-imported into the analysis software. The analysis was performed using an internally developed model. TW CCS N2 Forecast. More details are available in McCullagh and Pereira.

[0070] Informatics. A customized version of the UNIFI Scientific Information System (Waters Corporation) is used to analyze data across multiple dimensions (t... r t d Peak detection and lock-in quality correction were performed on the LC-IM-MS data in terms of m / z and intensity. Default processing parameters were used, and the dataset was exported in native UNIFI format. Next, the data were further analyzed in Progenesis QI (Nonlinear Dynamics, Newcastle upon Tyne, UK) using cross-sample alignment and co-detection, with default processing parameters except for the detection threshold, to match UNIFI peak detection, which was set to the lowest level of zero, thus providing a data matrix to construct a "known unknowns" library. The latter was achieved by exporting the peak detection data as a fragment database and a so-called additional attribute table, which included retention time in MSP and CSV formats, respectively. TW CCS N2 Value. Using development software. 21 The obtained MSP and CSV tables were converted and merged into a single worksheet, which was then imported into the scientific database of the UNIFI scientific information system. The characteristics of product ions detected only in at least two of the three technical replicates were retained in the variant-specific library.

[0071] Machine learning programs. Obtaining results through models trained using machine learning. TWCCS N2 Prediction. This method is similar to that in the following literature: Zhou et al., “Large-scale prediction of collision cross-section values ​​for metabolites in ion mobility-mass spectrometry,” Anal. Chem. (2016), but uses… TW CCS N2 Train the model to fit a suitable model. TW CCS N2 The data was obtained internally using IMS-Q-oaToF and Q-IMS-oaToF geometries and covers a wide range of polarities and a large number of chemical categories. For each compound, 196 chemical descriptors were extracted (see, for example, Bouwmeester et al., “Comprehensive and Empirical Evaluation of Machine Learning Algorithms for Small Molecule LC Retention Time Prediction,” Anal. Chem. (2019), and Landrum, G., “The RDKit Documentation—The RDKit 2016.09.1 ​​documentation,” (2016)), and the model was trained using a gradient boosting algorithm (see, for example, Chen, T. and Guestrin, C., “XGBoost,” Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining—KDD'16 (2016)). The compounds described in this paper were obtained using a nested 10x cross-validation strategy. TW CCS N2 Prediction (see, for example, Bouwmeester et al. and Wessels, LFA et al., “A protocol for building and evaluating predictors of disease state based on microarray data,” Bioinformatics (2005)). This means compounds and their TW CCS N2 The predictions are not part of the training set used to optimize model parameters and hyperparameters.

[0072] Results and discussion:

[0073] Known unknowns. Construct a "known unknowns" library as described in the experiments above. r t d The query tolerances for m / z were determined by creating a subset library of n-1 datasets (excluding the variants of interest) for each individual passionflower variant, and the remaining datasets were used to determine appropriate tolerance settings. A single standard deviation of the search result error across all dimensions was used as a measure of the search tolerance, which for t r t d The m / z dimensions are equal to 0.2 minutes, 1% and 4 ppm, respectively, which are in good agreement with the tolerances commonly used for library-based screening applications (see, for example, Bauer et al., “Evaluation and validation of an ionmobility quadrupole time-of-flight mass spectrometry pesticide screening approach,” J. Sep. Sci. (2018)). Figure 12 The unfiltered wide search tolerances m / z and t are shown in the figure. r and t d An example of the distribution, which shows the empirically derived tolerance / error width of the "known unknowns" query as a dashed line, shows that using tolerances of 1 minute, 10%, and 10 ppm, the number of detected features increases by only 27.6%, demonstrating the high specificity provided by combining the three query dimensions.

[0074] Figure 13 The results shown illustrate an exemplary “known unknown” identification of passion fruit (P. edulis) using the aforementioned tolerances of 0.2 min, 1% and 4 ppm. To investigate how many variant-specific features could be detected, as a single search, data from one variant was used to query the libraries of three other passion fruit variants of interest. The chromatographic search space was further reduced to analyte elution windows ranging from 1 min to 15 min, as... Figure 14 As shown, injection gaps and gradient flushing detections were excluded from the data analysis. Furthermore, a variant-specific abundance threshold, derived from the analysis of data on technique repetitions, was applied.

[0075] The results of the analysis experiment are summarized in Table 1 below:

[0076] Table 1

[0077] Table 2. Summary of detection and screening results for the Passiflora incarnata variants studied with retention times (features / “known unknown” compounds detected between 1 and 15 minutes) and area peak detection limits (threshold = minimum abundance of detected features / variants) Table 2 Table 2 .

[0078]

[0079] Table 1 provides an overview of the number and fraction of features detected in at least two of the three technical replicates, followed by co-detection across replicates and samples, and the detection coverage of “known unknowns” for each of the individual passionflower variants using the search strategy described above. Note that a greater number of features were detected compared to the previously discussed MVA-based profiling experiments, a direct result of using a lower detection threshold designed to increase detection coverage. The detection replicates of the technical features found are similar to, or even slightly more favorable than, previously reported data and results, a direct result of setting multiple feature detection limits during peak detection, thus intentionally limiting the detection of very low abundance non-reproducible features, with an average replicate value of approximately 80% for each of the individual variants. However, it is concluded that the feature number limitation does not affect the analysis, as otherwise the replicate value would have compromised the substantially higher replicate value. The variant-unique detection range is approximately 30% to 50%, indicating a high degree of similarity in chemical composition among the four passionflower variants. More importantly, this example illustrates that because it is not necessary to consider "known unknowns" for analysis, the detection and identification of non-variant-specific compounds can be significantly reduced. This can benefit not only natural product analysis but also a variety of applications, including food and environmental applications such as certification, profiling, speciation, food processing, aging and storage, nutritional formulations, pharmaceutical applications including, for example, drug metabolism, drug fingerprinting, biotransformation product analysis, and clinical applications such as the detection of novel compounds in forensic toxicology. Implementation methods are not limited to this context.

[0080] CCS prediction aids identification. Several common, well-characterized passionflower flavonoids were targeted to illustrate “known-unknown” complements in LC-IM-MS data, and once a compound is confidently identified, it can be annotated as a known-known. To assist the identification process, prediction... TW CCS N2 Values ​​(see, for example, Bouwmeester et al., “Predicting ionmobility collision cross sections by combining conventional and data-driven modelling,” ASMS Proc. MP 366 (2019)) are used to reduce the number of possible isomers and the number of spectra that must be examined. A list of target flavonoid compounds and the resulting average results are summarized in Table 2 below:

[0081] Figure 15

[0082] Table 3. Tentative identification of endogenous passionflower flavonoids reported in the literature and machine learning-based TW CCS N2 pre Figure 16 .

[0083]

[0084]

[0085] ◇ Used for training machine learning TW CCS N2 The predicted compound (adduct specificity) of the model; *Root mean square error (RMSE) for repeated injections and passion fruit variant samples; **By the predicted TW CCS N2 Fragmentation was detected by CID MSn interpretation;

[0086] Δ Fuzzy detection; #By using 5ppm, 10% TW CCS N2 and 10 minutes t r Screening tolerance determination for (experimental) isomers; ≠ Mean absolute error (MAE) in parentheses and observed TW CCS N2 -- = Not detected.

[0087] Figure 17 and Figure 18 Exemplary chemical structures and C6 / C8 glycoside fragmentation pathways are provided respectively. Some selected experimental identification examples are in Figure 19 and Figure 17 The mass (observational and theoretical) of the target compound group is shown in the figure. TW CCS N2 (Observation and Prediction) The measurement error distribution and the root mean square standard error (RMSE) value are shown in the figure. Figure 17 In both cases, most compounds were detected within the expected error distribution range. Some predicted outliers were observed, with some O-linked species, namely β-glucopyranoside, interestingly showing the largest errors, i.e., compounds of (bio)chemical categories that are not adequately represented in the applied predictive model. However, in general, similar errors were observed on variants in terms of abundance and orientation, thus increasing the confidence in the identification assignment of deconvolutioned MS spectra. The utility of predictive CCS-assisted identification independent of retention time and known flavonoid elucidation is discussed in more detail in the following sections, describing how the confirmation of identification is performed based on observed product ion spectra, thereby enabling the metrology of machine learning predictions. TW CCS N2 Output the value.

[0088] The process of annotating known substances (flavonoids) with experimental product ion spectra. Natural product standard flavonoids may not always be available for IM-MS characterization and can be expensive to obtain. In this paper, TW CCS N2 Prediction has been explored as an alternative option and used as an effective identification parameter to reduce the number of isotopic and isomer identifications of flavonoids reported in passion fruit species. Data processing independent of retention time was applied after peak detection, with a 5 ppm precision mass measurement tolerance and a 10% difference between prediction and experimental values. ΔCCS tolerance was used to identify isotopic / isomeric species in extracts from four Passiflora species, thereby enabling the determination of retention time and experimental results. TW CCS N2 value.

[0089] The number of isomeric species varied significantly with the passionflower species and the target flavonoid. For example, for isoharbitis 2”-O-β-glucopyranoside, between 13 and 21 isomeric isotopes were observed, and for isovitexin 2”-O-β-glucopyranoside, the range was 13 to 27. IM also facilitates deconvolution of spectral complexity in the analysis of medicinal plant / herbal extracts and provides access to highly specific identification information. For isoharbitis 2”-O-β-glucopyranoside identified in passionflower, Figure 20 The product ion spectrum, showing retention and drift time alignment, exhibits a negative mode product ion abundance ratio at m / z 284 / 285 and m / z 297 / 298 / 299, characteristic of 6-C disaccharides. This is also the case with isovitexin 2”-O-β-glucopyranoside identified in passionflower, which is also... Figure 20 It is shown in the image. Isovitaminoxanthin ( TW CCS N2 ) and Vitexin ( TW CCS N2 ) is heterogeneous (C 21 H 20 O 10 (The elemental composition of the samples); therefore, they were also observed at m / z 431. For m / z 431, the number of retention time-independent isotopic / isomeric species ranged between 10 and 28 in the four extracts.

[0090] Luteolin-6-C-fucoside prediction TW CCS N2 Value This is similar to those predicted for 6-C glycosides isovitexin and isovitexin. However, as with isovitexin, luteolin 6-C fucoidan has a hydroxyl group at C25 / C26 (isovitexin equivalent C26 / C27), which differs from isovitexin / vitinidin and provides a distinction. At m / z 297 [MH-150] - The cleavage of the glucosyl group forming the product ion is characteristic of 6-C / 8-C glycosides. The abundance ratio of the product ions observed at m / z 284 / 285 and m / z 297 / 298 / 299 confirms the recognition of 6-C glycosides. However, the observed CID spectrum differs from that for isocarboxylic acid; however, due to the absence of hydroxylation at the C7 position, luteolin 6-C-fucoside [MH] is observed at the precursor ion at m / z 431. - .

[0091] Due to fragmentation of the disaccharide moiety, isocytisine-"-O-glucoside m / z 443[MH]-180.0 was observed. - The product ions were identified, and additionally, product ions indicating 6-C glycosides at m / z 297 / 298 / 299 were observed, providing further identification confirmation. The number of isotopic species observed in Passiflora extracts ranged from 2 to 10. At t r [MH] at 8.11 and 8.33 minutes - The isomer peaks at m / z 563 indicate non-differential CID spectra attributable to schaferoside and isoxchaferoside, respectively. Additionally, using predicted... TW CCS N2 Or the measured values ​​may not be able to distinguish these isomers from each other, but only from other observed isomers independent of retention time. However, differentiation can be determined from the obtained CID spectra. The two compounds form product ions at m / z 503.0, which is typical for asymmetric di-C-glycosides. Isoxaflutole and saxoside can be distinguished by ion m / z 473 [MH]-90. - and m / z 443[MH]-120.0] - The relative intensities of the product ions are used for characterization. Isoxaflutole is characterized by m / z 473[MH]-90. - <m / z 443[MH]-120] - The intensity was characterized by sharfotoside, and sharfotoside was characterized by m / z 473[MH]-90. - >m / z 443[MH]-120] - The intensity is used to characterize it.

[0092] Regarding isocytisine-O-glucoside, due to fragmentation of the disaccharide moiety, m / z 443[MH]-180.0 was observed. - Product ions. Furthermore, CID spectroscopy revealed product ions at m / z 297 / 298 / 299, indicating 6-C glycosides, thus providing further identification confirmation. The number of isotopic isobarbiturin-O-glucoside species observed in Passiflora extracts ranged between 2 and 10. Here, the predicted... TW CCS N2 value This differs from the experimentally observed value by 6.2%. However, despite this relatively large difference in this specific example, as previously explained, the predicted value... TW CCS N2 The value has the potential to distinguish isomorphic species from other isomorphic and isomorphic position isomorphs without requiring analytical standards and retention time information.

[0093] Figure 20 An embodiment of an exemplary computing architecture 2000 suitable for implementing the various embodiments described above is shown. In various embodiments, the computing architecture 2000 may include or be implemented as part of an electronic device. In some embodiments, the computing architecture 2000 may represent, for example, systems 205 and / or 305. The embodiments are not limited to this context.

[0094] As used in this application, the terms "system," "component," and "module" are intended to refer to computer-related entities, which may be hardware, a combination of hardware and software, software, or software in execution, examples of which are provided by the exemplary computing architecture 2000. For example, a component can be, but is not limited to, a process running on a processor, a processor, a hard disk drive, multiple storage drives (optical and / or magnetic storage media), an object, an executable file, an execution thread, a program, and / or a computer. For example, both an application running on a server and the server itself can be components. One or more components may reside within a process and / or an execution thread, and components may be located on a single computer and / or distributed across two or more computers. Furthermore, components may communicatively couple with each other to coordinate operation via various types of communication media. Coordination may involve one-way or two-way information exchange. For example, a component may convey information in the form of signals communicated through a communication medium. This information may be implemented as signals assigned to various signal lines. In such assignments, each message is a signal. However, alternative embodiments may use data messages. Such data messages can be sent via various connections. Exemplary connections include parallel interfaces, serial interfaces, and bus interfaces.

[0095] The Computing Architecture 2000 includes various general-purpose computing elements, such as one or more processors, multi-core processors, coprocessors, memory units, chipsets, controllers, peripherals, interfaces, oscillators, timing devices, video cards, audio cards, multimedia input / output (I / O) components, power supplies, etc. However, implementation schemes are not limited to those of the Computing Architecture 2000.

[0096] like ​ As shown, the computing architecture 2000 includes a processing unit 2004, a system memory 2006, and a system bus 20020. The processing unit 2004 can be any of a variety of commercially available processors, including but not limited to: and processor; Application, embedded, and security processors; and and Processor; IBM Know Cell processor; Core(2) Know Processors; and similar processors. Dual microprocessors, multi-core processors, and other multiprocessor architectures can also be used as processing units.

[0097] System bus 20020 provides interfaces for system components, including but not limited to interfaces for connecting system memory 2006 to processing unit 2004. System bus 20020 can be any of several types of bus architectures, which can be further interconnected to memory bus (with or without a memory controller), peripheral bus, and local bus using any of a variety of commercially available bus architectures. Interface adapters can be connected to system bus 2020 via slot architectures. Exemplary slot architectures can include, but are not limited to, Accelerated Graphics Port (AGP), Card Bus, (Extended) Industry Standard Architecture ((E)ISA), Micro Channel Architecture (MCA), NuBus, Peripheral Component Interconnect (Extended) (PCI(X)), PCI Express, PCMCIA, etc.

[0098] System memory 2006 may include various types of computer-readable storage media in the form of one or more high-speed memory cells, such as read-only memory (ROM), random access memory (RAM), dynamic RAM (DRAM), dual data rate DRAM (DDRAM), synchronous DRAM (SDRAM), static RAM (SRAM), programmable ROM (PROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, polymer memory (such as ferroelectric polymer memory, bidirectional memory, phase-change or ferroelectric memory), silicon-oxide-nitride-oxide-silicon (SONOS) memory, magnetic cards or optical cards, device arrays (such as redundant array of independent disks (RAID) drives), solid-state storage devices (e.g., USB storage, solid-state drives (SSDs), and any other type of storage media suitable for storing information. ​ In the illustrated embodiment, system memory 2006 may include non-volatile memory 20202010 and / or volatile memory 2012. The basic input / output system (BIOS) may be stored in non-volatile memory 20202010.

[0099] Computer 2002 may include various types of computer-readable storage media in the form of one or more low-speed memory units, including internal (or external) hard disk drive (HDD) 2014, magnetic floppy disk drive (FDD) 2016 for reading or writing removable disk 2019, and optical disc drive 2020 for reading or writing removable optical disc 2022 (e.g., CD-ROM or DVD). HDD 2014, FDD 2016, and optical disc drive 2020 may be connected to system bus 20020 via HDD interface 2024, FDD interface 2026, and optical disc drive interface 2020, respectively. HDD interface 2024 for external drive implementations may include at least one or both of Universal Serial Bus (USB) and IEEE 1374 interface technologies.

[0100] Drives and associated computer-readable media provide volatile and / or non-volatile storage of data, data structures, computer-executable instructions, etc. For example, multiple program modules may be stored in drive and memory units 20202010, 2012, including an operating system 2030, one or more application programs 2032, other program modules 2034, and program data 2036. In one embodiment, one or more application programs 2032, other program modules 2034, and program data 2036 may include, for example, various application programs and / or components according to some embodiments.

[0101] Users can input commands and information into computer 2002 through one or more wired / wireless input devices (e.g., keyboard 2038 and clicking devices such as mouse 2040). Other input devices may include microphones, infrared (IR) remote controls, radio frequency (RF) remote controls, game controllers, styluses, card readers, dongles, fingerprint card readers, gloves, graphics tablets, joysticks, keyboards, retinal readers, touchscreens (e.g., capacitive, resistive, etc.), trackballs, touchpads, sensors, styluses, etc. These and other input devices are typically connected to processing unit 2004 via input device interface 2042 coupled to system bus 2008, but can be connected via other interfaces such as parallel ports, IEEE 1394 serial ports, game ports, USB ports, IR interfaces, etc.

[0102] Monitor 2044 or other types of display devices are also connected to system bus 20020 via an interface such as video adapter 2046. Monitor 2044 can be internal or external to computer 2002. In addition to monitor 2044, computer typically includes other peripheral output devices such as speakers, printers, etc.

[0103] Computer 2002 can operate in a networked environment via wired and / or wireless communication to one or more remote computers, such as remote computer 2048, using logical connections. Remote computer 2002 can be a workstation, server computer, router, personal computer, portable computer, microprocessor-based entertainment device, peer-to-peer device, or other public network node, and typically includes many or all of the elements described relative to computer 2002, but for simplicity, only memory / storage device 2050 is shown. The depicted logical connections include wired / wireless connections to a local area network (LAN) 2052 and / or a larger network such as a wide area network (WAN) 2054. Such LAN and WAN network environments are common in offices and companies and facilitate enterprise-wide computer networks, such as intranets, all of which can connect to global communication networks, such as the Internet.

[0104] When used in a LAN network environment, computer 2002 connects to LAN 2052 via a wired and / or wireless communication network interface or adapter 2056. Adapter 2056 facilitates wired and / or wireless communication to LAN 2052 and may also include a wireless access point disposed thereon for communication with the wireless functionality of adapter 2056.

[0105] When used in a WAN network environment, computer 2002 may include modem 20520, or a communication server connected to WAN 2054, or have other means for establishing communication via WAN 2054, such as via the Internet. Modem 20520 may be an internal or external, wired and / or wireless device connected to system bus 20020 via input device interface 2042. In a networked environment, program modules or portions thereof depicted relative to computer 2002 may be stored in remote memory / storage device 2050. It should be understood that the network connections shown are exemplary, and other means of establishing communication links between computers may be used.

[0106] Computer 2002 is operable to communicate with wired and wireless devices or wired and wireless entities (such as wireless devices operatively configured in wireless communication) using IEEE 802 series standards (e.g., IEEE 802.16 air modulation techniques). This includes at least Wi-Fi (or Wireless Fidelity), WiMax, and Bluetooth. TM Wireless technologies, etc. Therefore, communication can be a predefined structure like a regular network, or simply self-organized communication between at least two devices. Wi-Fi networks use radio technology known as IEEE 802.11x (a, b, g, n, etc.) to provide secure, reliable, and fast wireless connectivity. Wi-Fi networks can be used to connect computers to each other, connect to the Internet, and connect to wired networks (using IEEE 802.3 related media and functions).

[0107] Many specific details have been set forth herein to provide a thorough understanding of the implementation scheme. However, those skilled in the art will understand that the implementation scheme can be practiced without these specific details. In other instances, well-known operations, components, and circuits have not been described in detail to avoid obscuring the implementation scheme. It is understood that the specific structural and functional details disclosed herein are representative and do not necessarily limit the scope of the implementation scheme.

[0108] The terms “coupled” and “connected”, as well as their derivatives, may be used to describe some implementations. These terms are not intended to be synonymous with each other. For example, the terms “connected” and / or “coupled” may be used to describe some implementations to indicate that two or more elements are in direct physical or electrical contact with each other. However, the term “coupled” may also mean that two or more elements are not in direct contact with each other, but still cooperate or interact with each other.

[0109] Unless otherwise expressly stated, terms such as “processing,” “computing,” “operation,” and “determining” refer to the operation and / or process of a computer or computing system or similar electronic computing device that processes and / or converts data represented as physical quantities (e.g., electrons) within the registers and / or memory of the computing system into physical quantities similarly represented within the memory, registers, or other such information storage, transmission, or display devices of the computing system. Implementations are not limited to this context.

[0110] It should be noted that the methods described herein need not be performed in the order described or in any particular order. Furthermore, the various activities described with respect to the methods identified herein can be performed in a series or in parallel.

[0111] While specific embodiments have been illustrated and described herein, it should be understood that any arrangement intended to achieve the same purpose may be substituted for the specific embodiments shown. This disclosure is intended to cover any and all adaptations or variations of the various embodiments. It should be understood that the above description is illustrative and not restrictive. After reading the above description, combinations of the above embodiments and other embodiments not specifically described herein will be apparent to those skilled in the art. Therefore, the scope of the various embodiments includes any other application in which the above compositions, structures, and methods are used.

[0112] Although the subject matter has been described in language specific to structural features and / or methodological actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, specific features and actions are disclosed as exemplary forms for implementing the claims.

Claims

1. An apparatus comprising: At least one memory; as well as Logic, the logic being coupled to the at least one memory, the logic being used for: Receive analysis information for multiple product ions, the analysis information including product ion collision cross section (CCS) information. For at least one of the plurality of product ions, determine the variance of the CCS information of the product ion; and The determination of the analytical information includes information on one or more of the multiple substructure configurations or multiple isomers of the at least one product ion in response to the variance value exceeding the variance threshold, to distinguish isomers of the at least one product ion or to exclude one or more of the possible candidates of the at least one product ion.

2. The apparatus according to claim 1, wherein the logic is used to exclude candidate ions of the at least one product ion based on the product ion CCS information.

3. The apparatus according to claim 1, wherein the analytical information includes a 2D fingerprint of the product ion, the 2D fingerprint including product ion CCS information and mass-to-charge ratio (m / z) information.

4. The apparatus of claim 3, wherein the logic is used to use the 2D fingerprint to distinguish isomers of the plurality of product ions.

5. The apparatus according to claim 1, wherein the analytical information includes a 3D fingerprint of the product ions, the 3D fingerprint including product ion CCS information, intensity information and mass-to-charge ratio (m / z) information.

6. The apparatus of claim 5, wherein the logic is used to use the 3D fingerprint to distinguish isomers of the plurality of product ions.

7. The apparatus of claim 1, wherein the logic is used for: Receive historical product ion CCS information based on prior structure determination, and Based on the product ion CCS information and the historical product ion CCS information, at least one possible candidate product ion is excluded.

8. A sample analysis method, the method comprising: Receive analysis information for multiple product ions, the analysis information including product ion collision cross section (CCS) information; For at least one of the plurality of product ions, determine the variance value of the CCS information of the product ion. as well as The determination of the analytical information includes information on one or more of the multiple substructure configurations or multiple isomers of the at least one product ion in response to the variance value exceeding the variance threshold, to distinguish isomers of the at least one product ion or to exclude one or more of the possible candidates of the at least one product ion.

9. The method of claim 8, further comprising excluding candidate ions of the at least one product ion based on the product ion CCS information.

10. The method according to claim 8, wherein the analytical information includes a 2D fingerprint of the product ion, the 2D fingerprint including product ion CCS information and mass-to-charge ratio (m / z) information.

11. The method of claim 10, further comprising using the 2D fingerprint to distinguish isomers of the plurality of product ions.

12. The method according to claim 8, wherein the analytical information includes a 3D fingerprint of the product ion, the 3D fingerprint including product ion CCS information, intensity information and mass-to-charge ratio (m / z) information.

13. The method of claim 12, further comprising using the 3D fingerprint to distinguish isomers of the plurality of product ions.

14. The method of claim 8, comprising: Receive historical product ion CCS information based on prior structure determination; as well as Based on the product ion CCS information and the historical product ion CCS information, at least one possible candidate product ion is excluded.

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