Estimating similarity between spectral datasets using spectral decomposition curves

Abstract

Evaluate the similarity between spectral datasets. [Solution] The system comprises a memory for storing computer executable components and a processor for executing them, which includes an identification component for identifying a set of first spectral decomposition data for a first compound, an identification component corresponding to a first ion activation energy including a target ion activation energy omitted from conventional spectroscopic measurements of the first compound, an identification component for further identifying a set of second spectral decomposition data for a second compound, and a comparison component that performs a comparison between the set of first spectral decomposition data and the set of second spectral decomposition data at the target ion activation energy, and as a result obtains a target similarity value that defines the similarity between the set of first spectral decomposition data and the set of second spectral decomposition data at the target ion activation energy.

Description

Technical Field

[0001] Cross - Reference to Related Applications This application claims the benefit of U.S. Non - Provisional Application No. 18 / 947,531, filed on November 11, 2024, the entire disclosure of which is incorporated herein by reference in its entirety.

Background Art

[0002] Comparison of spectral data obtained from one or more chemical structure measuring devices is based on data obtained for multiple ion activation energies, retention times, repeated measurements, etc., which can be a complex and time - consuming task. As a result, it may occupy the chemical structure measuring device or lead to a situation where the user's available time cannot be allocated to other tasks. Similarly, when comparing data obtained from the same device and / or multiple devices, by inputting data obtained at various ion activation energy values, it is possible to at least partially obtain a comprehensive understanding of the spectral data and fragmentation data of the target compound.

Summary of the Invention

[0003] The following presents a summary for providing a basic understanding of one or more exemplary embodiments described herein. This summary is not intended to identify key or critical elements or to delineate the scope of particular embodiments or the scope of the claims. Its sole purpose is to present concepts in a simplified form as a prelude to the more detailed description that follows. In one or more exemplary embodiments, the systems, computer - implemented methods, devices, and / or computer program products described herein include a plug - and - play process that uses data generated by a measuring device (also referred to herein as a measurement device) to efficiently and automatically calibrate, normalize, and / or compare the output data of the measurement device.

[0004] According to one embodiment, the system may include a memory for storing computer-executable components and a processor for executing these computer-executable components. The computer-executable components may include an identification component that identifies a set of spectral resolution data for a first compound. This identification component identifies a set of spectral resolution data corresponding to a first ion activation energy, including a target ion activation energy that is omitted in conventional mass spectrometry measurements for the first compound. Furthermore, this identification component identifies a second set of spectral resolution data for a second compound, corresponding to a second ion activation energy. A comparison component compares the first and second spectral resolution data at the target ion activation energy, resulting in a similarity value that defines the similarity between the first and second spectral resolution data at the target ion activation energy.

[0005] According to another embodiment, the computer implementation method includes a system operablely connected to a processor identifying a first set of spectrally decomposed data corresponding to the first ion activation energy for a first compound (which includes a target ion activation energy that was omitted in conventional spectroscopic measurements of the first compound), identifying a second set of spectrally decomposed data corresponding to the second ion activation energy for a second compound, and further comparing the first and second sets of spectrally decomposed data in terms of ion activation energy, thereby calculating a similarity value indicating the similarity between the first and second sets of spectrally decomposed data in terms of the target ion activation energy.

[0006] In yet another embodiment, the computer program product facilitates the process of evaluating the similarity between spectral data sets, and when the instructions of this program are executed by the processor, they instruct the processor to perform the following actions: The processor identifies a first spectral decomposition data set for a first compound. This first spectral decomposition data set corresponds to the first ion activation energy, which includes a target ion activation energy that was not covered by conventional mass spectrometry measurements of the first compound. Next, the processor identifies a second spectral decomposition data set for a second compound. This second spectral decomposition data set corresponds to the second ion activation energy. The processor then performs a comparison of the first and second spectral decomposition data sets at the target ion activation energy and calculates a target similarity value representing the similarity between the first and second spectral decomposition data sets at the target ion activation energy.

[0007] One or more embodiments described herein can be implemented by being incorporated into, connected to, or coupled with a chemical structure measuring device, such as a scientific measuring instrument.

[0008] One or more exemplary embodiments disclosed herein can be applied in a plug-and-play manner to measuring devices, multiple measuring devices, identical measuring devices using multiple interchangeable parts, etc., for calibration, normalization, and / or comparison of output data with unknown, known, and / or standard data. The framework described herein can be performed in a time-efficient and at least partially automated manner, thereby increasing the uptime of the device and reducing interaction with user entities in pre-test and / or post-test processes.

[0009] One or more embodiments described herein can be used to approximate spectral data corresponding to ion activation energies not specifically used during a spectroscopic analysis operation. This can be achieved by classifying curve data corresponding to one or more fragment ions generated by a compound under different ion activation energies using an approximation function. In this way, comparisons between resolution curve data under various conditions, such as different compounds, different devices, or the same compound but different devices, become possible within a specific range of ion activation energies without directly acquiring spectral data over the entire range of ion activation energies. As a result, one or more embodiments described herein reduce the number of fragmentation runs required to acquire initial mass spectrometry data in the measuring instrument, and generate resolution data obtained from the mass spectrometry data.

[0010] Furthermore, using approximate spectral decomposition data obtained by approximating the decomposition curve data allows for a more comprehensive understanding of the decomposition data across the range of ion activation energies compared to existing methods. This makes it possible to identify local minimums or maximums in the quantified similarity data between two or more spectral decomposition datasets, where at least one dataset is obtained by the aforementioned approximation process. Optionally, all spectral decomposition datasets used for comparison may also be obtained by the aforementioned approximation process.

[0011] The embodiments will be readily apparent from the following detailed description in conjunction with the accompanying drawings. For the sake of this description, similar reference numerals indicate similar structural elements. The embodiments are shown in the figures of the accompanying drawings as examples, not as limitations. [Brief explanation of the drawing]

[0012] [Figure 1] A block diagram of an exemplary and non-limiting system that can facilitate a measurement device data comparison process according to one or more embodiments described herein is shown. [Figure 2] A block diagram of another exemplary, non-limiting system that facilitates the measurement device data comparison process is shown, according to one or more embodiments described herein. [Figure 3] An exemplary graph of exemplary decomposition curve data, following one or more examples described herein, is shown. [Figure 4] In accordance with one or more examples described herein, an exemplary set of graphs showing exemplary approximations of the exemplary decomposition curve data in Figure 3 is presented. [Figure 5] According to one or more examples described herein, Figure 3 shows a graph illustrating an example of approximate spectral decomposition data and the target spectrum generated based on the approximate spectral decomposition data. [Figure 6] The following is an example graph of comparative data obtained by comparing a set of spectrally decomposed data, including the spectrally decomposed data shown in Figure 5, for a single fragmented ion, according to one or more examples described herein. [Figure 7] The following is an example graph of comparative data obtained by comparing sets of spectrally decomposed data (including the spectrally decomposed data in Figure 5) for multiple fragmented ions according to one or more examples described herein. [Figure 8] A series of graphs schematically illustrating the normalization of ion activation energy and the conversion of spectral resolution data from different measuring instruments, according to one or more examples described herein, are shown. [Figure 9] An example of a node-based graph that visually connects nodes as a set of edges using total spectral similarity is shown in Figure 2, along with an overview of the process performed by the non-limiting system, which follows one or more embodiments described herein. [Figure 10] The non-limiting system shown in Figure 1 illustrates a flowchart of one or more processes that can be performed according to one or more embodiments described herein. [Figure 11]Figure 2 shows another flowchart of one or more processes that can be performed by the non-limiting system according to one or more embodiments described herein. [Figure 12] Figure 11 shows a continuation of the flowchart. The non-limiting system shown in Figure 2 represents one or more processes that can be performed according to one or more embodiments described herein. [Figure 13] Figure 12 shows a continuation of the flowchart. The non-limiting system shown in Figure 2 is one or more processes that can be executed according to one or more embodiments described herein. [Figure 14] This block diagram shows an example of an operating environment in which embodiments of the subject matter described herein can be incorporated. [Figure 15] This specification shows an illustrative block diagram of a computing environment in which the subject matter described herein may interact and / or be at least partially implemented. [Modes for carrying out the invention]

[0013] The following detailed description is illustrative and not intended to limit the embodiments and / or applications or uses of the present invention. Furthermore, it is not intended to be bound by any expressions or implied information presented in the preceding sections on the summary of the invention or the modes for carrying out the invention.

[0014] First, regarding chemical structure measurement devices in general, these include, but are not limited to, spectrometers, chromatographs, and devices that combine spectroscopy and chromatography. The data output from these devices can be measurements of the analyte, the intensity of fragment ions generated from the compound during analysis, and the mass-to-charge ratio. An example of such measurement data is mass spectrometry data obtained by operating a mass spectrometer. It can be advantageous to collect multiple sub-datasets at different ion activation energies in order to compare spectral data obtained from multiple compounds and / or instruments, and / or with one or more known and / or standard datasets. However, this is cumbersome, insufficient, and / or a waste of time. Even with such extensive data collection, there remains the possibility that data representing local minimums or maximums of similarity between datasets may be missed or missing, or that errors or outliers may be included.

[0015] Therefore, to address one or more of these shortcomings, one or more embodiments described herein can provide a process for converting initial mass spectrometry data (e.g., initial spectral data) into decomposition curve data, and further using the approximated spectral decomposition data to evaluate the similarity between spectral datasets, or the similarity of compounds corresponding to spectral datasets. As a result, it becomes possible to comprehensively understand the similarity trends at various values ​​of ion activation energy, and to identify local minimum or maximum values ​​corresponding to quantitatively calculated similarity values. Based on this, it becomes possible, but is not limited to, further evaluation of the activation energy of individual ions, creation and updating of a similarity database of entire spectra, comparison of output data from multiple measuring devices for the same or different compounds, or comparison of different data outputs of the same or different compounds obtained from a single measuring device. These comprehensive evaluation functions enable efficient and automated calibration, data evaluation, and reduction of the number of re-executions of tasks.

[0016] Regardless of the presence or absence of post - processing evaluation, one or more embodiments described herein can compare spectral data (e.g., quantified similarity data) using approximate spectral decomposition data, and as a result, it is possible to efficiently and automatically obtain comparison data while significantly reducing time, labor, and the amount of initial data input compared to existing systems.

[0017] As used herein, the phrase "based on" should be understood to mean "at least partially based on" unless otherwise specified.

[0018] As used herein, the term "compound" can refer to a single material, multiple materials, compositions, samples, solutions, products, etc.

[0019] As used herein, the term "data" can include metadata.

[0020] As used herein, the terms "entity", "claim entity", and "user entity" can refer to machines, devices, components, hardware, software, smart devices, stakeholders, organizations, individuals, and / or humans.

[0021] Next, one or more exemplary embodiments will be described with reference to the drawings, and like reference numerals are used throughout to indicate like drawing elements. In the following description, many specific details are set forth for the purpose of providing a more thorough understanding of one or more exemplary embodiments. However, it is clear that one or more exemplary embodiments can be practiced without these specific details in various cases.

[0022] Furthermore, it should be understood that the embodiments shown in one or more drawings described herein are for illustrative purposes only, and therefore the architecture of the embodiments is not limited to the systems, devices, and / or components shown therein, nor is it limited to any particular order, connection, and / or combination of the systems, devices, and / or components shown therein.

[0023] Next, referring to Figures 1 and 2, in one or more exemplary embodiments, the non-limiting systems 100 and / or 200 illustrated in Figures 1 and 2, and / or their systems, may further comprise one or more computers and / or computing-based elements described herein with reference to a computing environment (e.g., computing environment 1500 shown in Figure 15). In one or more described embodiments, the computers and / or computing-based elements may be used in reference to one or more implementations of the shown and / or described systems, devices, components, and / or computer implementation operations in reference to Figures 1 and / or 2, and / or other drawings described herein.

[0024] Referring first to Figure 1, this figure shows a block diagram of an exemplary, non-limiting system 100, which may comprise a spectral data comparison system 102 and a library data store (DS) 135. Optionally, the non-limiting system 100 may comprise a measuring device 149 (e.g., a spectral analyzer or other scientific measuring device). In one or more other embodiments, the measuring device 149 and / or the library data storage 135 may be located outside the spectral data comparison system 102. Alternatively, the spectral data comparison system 102 may be communicatively coupled to the measuring device 149 and / or the library data storage 135.

[0025] In this specification, both above and below, the term “spectral data” generally refers to one or more of the mass spectrometry data 250, the resolved curve data 251, and / or the spectral resolved data 154, 254.

[0026] It should be noted that the spectral data comparison system 102 is described only briefly in order to provide an introduction to a more complex and / or more extended spectral data comparison system 202, as shown in Figure 2. That is, further details regarding the processes that can be performed by one or more exemplary embodiments described herein are provided below with respect to the non-limiting system 200 in Figure 2.

[0027] Referring to Figure 1, the spectral data comparison system 102 can facilitate the generation and / or comparison of spectral decomposition data 154 approximated using one or more approximation functions, thereby enabling efficient identification of local minimum and / or maximum values ​​of comparison data generated based on the spectral decomposition data 154. This comparison is possible between spectral decomposition datasets 154A and 154B obtained from the same or different compounds, and / or from the same or different measuring instruments, and / or when one or more spectral decomposition datasets are obtained from a standardized library, such as data store 135.

[0028] In other words, the spectral data comparison system 102 can generally facilitate the process of generating and / or comparing spectrally resolved data 154 based on a target ion activation energy 160T that was not used in the spectroscopic analysis.

[0029] The spectral data comparison system 102 may comprise at least a memory 104, a bus 105, a processor 106, an identification component 110, and / or a comparison component 116. The processor 106 may be identical to, included in, or different from, the processor 1504 (Figure 15). The memory 104 may be identical to, included in, or different from, the system memory 1506 (Figure 15).

[0030] Using the components described above, the spectral data comparison system 102 can perform one or more comparison processes 170 on the spectral decomposition data 154, thereby generating one or more target similarity values ​​172T that quantitatively represent the similarity between different sets 154A and 154B of the spectral decomposition data 154.

[0031] Generally, the identification component 110 can identify the first spectral resolution data set 154A for the first compound 130A, which corresponds to the first ion activation energy 160A. This first ion activation energy 160A includes the target ion activation energy 160T, which was excluded in conventional mass spectrometry measurements of the first compound 130A. In other words, in conventional spectroscopic analysis, a portion of the first ion activation energy 160A is available, but the target ion activation energy 160T is not included. The identification component 110 can further identify the set of second spectral resolution data 154B, which corresponds to the second ion activation energy 160B for the second compound 130B.

[0032] It should be noted that the first compound 130A and the second compound 130B may be the same compound or different compounds.

[0033] It should be noted that the first ion activation energy 160A may include the same value as the second ion activation energy 160B, or it may include a different value.

[0034] In some cases, the comparison processing component 116 or processor 106 determines whether sufficiently augmented (e.g., approximate) data is provided to facilitate the comparison processing 170. Criteria for this determination may include whether the error is within an acceptable range or below a predetermined threshold, or whether it is based on the number of outliers in the spectral decomposition data 154.

[0035] The comparison component 116 typically performs a comparison process 172 between the first spectral decomposition data group 154A and the second spectral decomposition data group 154B at the target ion activation energy 160T, resulting in a target similarity value 172T that indicates the similarity between the first spectral decomposition data group 154A and the second spectral decomposition data group 154B at the target ion activation energy 160T.

[0036] The identification component 110 and / or the comparison processing component 116 can be operably coupled to a processor 106 which can be operably coupled to memory 104. Bus 105 can provide the operable coupling. The processor 106 can facilitate the execution of the identification component 110 and / or the comparison processing component 116. The identification component 110 and / or the comparison processing component 116 can be stored in memory 104.

[0037] Generally, the non-limiting system 100 provides a function for communication between the spectral data comparison system 102 and equipment associated with the user (e.g., measuring device 149 such as a spectroscopic analyzer) using any suitable communication method (e.g., electronic, telecommunicative, internet, infrared, fiber, etc.).

[0038] It should be noted that one or more additional measuring instruments may also be able to communicate with or be incorporated into the non-limiting system 100. For example, the first measuring instrument 149 may perform spectroscopic analysis of the first compound 130A, and the second measuring instrument 149 may perform spectroscopic analysis of the second compound 130B. Alternatively, the same measuring instrument 149 could be used to perform spectroscopic analysis of both compounds 130A and 130B.

[0039] As an overview of the components and their functions described above, a flowchart of a non-limiting method 1000 is shown with reference to Figure 10, as an example of a process that facilitates the creation and / or comparison of spectral resolution data based on the activation energies of target ions not used in spectroscopic analysis, according to embodiments of the present invention (e.g., system 100 shown in Figure 1, but not limited to). Although the non-limiting method 1000 is described in relation to the non-limiting system 100 shown in Figure 1, this non-limiting method 1000 is also applicable to other systems described herein, such as the non-limiting system 200 shown in Figure 2. Descriptions of similar elements and / or repetitions of processes used in each embodiment are omitted for brevity.

[0040] In step 1002, the non-limiting method 1000 includes the process of identifying a first set of spectrally decomposed data (e.g., first spectrally decomposed data 154A) for a first compound (e.g., first compound 130A) by a system (e.g., identification component 110) operably connected to a processor (e.g., processor 106). This first set of spectrally decomposed data corresponds to a first ion activation energy (e.g., first ion activation energy 160A) that includes a target ion activation energy (e.g., target ion activation energy 160T) that was not considered in conventional spectroscopic measurements of the first compound (e.g., first compound 130A). The method also includes the process of identifying a second set of spectrally decomposed data (e.g., second spectrally decomposed data 154B) for a second compound (e.g., compound 130B). This second set of spectrally decomposed data corresponds to a second ion activation energy (e.g., second ion activation energy 160B).

[0041] In step 1004, the non-limiting method 1000 may include a process to determine whether the system (e.g., comparison component 116) has been provided with data (e.g., spectral decomposition data 154) that is sufficiently augmented to facilitate the comparison (e.g., comparison process 172). This determination may be based on whether the data is within an acceptable error range, meets a specific error threshold, or the number of outliers in the spectral decomposition data (e.g., spectral decomposition data 154). If the answer is "yes," the non-limiting method 1000 may proceed to step 1006. Otherwise, the non-limiting method may return to step 1002 and perform additional specific processing on the spectral decomposition data.

[0042] In step 1006, the non-limiting method 1000 may include a process in which the system (e.g., comparison component 116) compares a first spectral decomposition data set (e.g., first spectral decomposition data set 154A) and a second spectral decomposition data set (e.g., second spectral decomposition data set 154B) at a target ion activation energy (e.g., target ion activation energy 160T), and as a result calculates a target similarity value (e.g., target similarity value 172T) that indicates the similarity between the first spectral decomposition data set (e.g., first spectral decomposition data set 154A) and the second spectral decomposition data set (e.g., second spectral decomposition data set 154B) at the target ion activation energy (e.g., target ion activation energy 160T).

[0043] Referring next to Figure 2, a non-limiting system 200 is shown, which may include a spectral data comparison system 202 and a library data store (DS) 235. Descriptions of similar elements and / or repetitions of processes used in each embodiment are omitted for brevity. The description of the embodiment in Figure 1 may be applicable to the embodiment in Figure 2. Similarly, the description of the embodiment in Figure 2 may be applicable to the embodiment in Figure 1.

[0044] In one or more embodiments, the measuring device 249, such as a spectroscopic analyzer, can be configured to be physically separated from the non-limiting system 200 but connectable via communication.

[0045] Furthermore, one or more additional measuring instruments may also be able to communicate with or be included in the non-limiting system 200. For example, the first measuring instrument 249 may perform spectroscopic analysis of the first compound 230A, and the second measuring instrument 249 may perform spectroscopic analysis of the second compound 230B. Alternatively, the same measuring instrument 249 could be used to perform spectroscopic analysis of both compounds 230A and 230B.

[0046] In one or more embodiments, the library data store 235 exists independently of the non-limiting system 200 but is connectable to the non-limiting system 200 in a communicative manner.

[0047] In one or more embodiments, compounds 230A and 230B may be the same compound or different compounds.

[0048] Generally, the spectral data comparison system 202 facilitates the generation and / or comparison of spectral decomposed data 254 that have been approximated (e.g., fitted) based on one or more approximation functions 252, thereby enabling efficient understanding of the local minimum and / or maximum values ​​of comparison data 272 generated based on spectral decomposed data 254. The comparison process 270 can be performed between spectral decomposed data 254A and 254B obtained from the same or different compounds 230A, 230B, and / or the same or different measuring instruments 249, where one or more of the spectral decomposed data 254A, 254B originate from a standardized library, such as a library data store 235.

[0049] In other words, the spectral data comparison system 202 can generally facilitate the process of generating and / or comparing spectrally resolved data 254 based on a target ion activation energy 260T that was not used in the spectroscopic analysis.

[0050] One or more communications between one or more components of the non-limiting system 200 may be provided by wired and / or wireless means, including, but not limited to, using a cellular network, a wide area network (WAN) (e.g., the Internet), and / or a local area network (LAN). Suitable wired or wireless technologies to support communications include, but are not limited to, Wireless Fidelity (Wi-Fi), Global System for Mobile Communications (GSM), Universal Mobile Telecommunications System (UMTS), Worldwide Interoperability for Microwave Access (WiMAX), Enhanced General Packet Radio Services (ENhANCED GPRS), 3G Partnership Project (3GPP) Long-Term Evolution (LTE), 3G Partnership Project 2 (3GPP2) Ultra Mobile Broadband (UMB), High-Speed ​​Packet Access (HSPA), ZiGBEE and other 802.XX wireless technologies and / or legacy telecommunications technologies, BLUETOOTH®, Session Initiation Protocol (SIP), ZiGBEE®, RF4CE protocol, WirELESSHARTHART protocol, 6LOWPAN (IPv6 Over Low Power WirELESS ArEA). This may include NETwOrkS, Z-WAvE, ultra-wideband (UWB) standard protocols, and / or other proprietary and / or non-proprietary communication protocols.

[0051] The spectral data comparison system 202 can be associated with a cloud computing environment (for example, the cloud computing environment 1400 in Figure 14) and accessed through it, for example.

[0052] The spectral data comparison system 202 may include multiple components. These components may include a memory 204, a processor 206, a bus 205, an identification component 210, a similarity component 212, a generation component 214, a comparison component 216, an output component 218, an evaluation component 220, a notification component 222, and / or a graphing component 224. Using these components, the spectral data comparison system 202 can facilitate the process of generating one or more target similarity values ​​272T necessary for determining the similarity of spectral decomposition data 254.

[0053] Next, we move the discussion to the processor 206, memory 204, and bus 205 of the spectral data comparison system 202. For example, in one or more exemplary embodiments, the spectral data comparison system 202 may comprise a processor 206 (e.g., a computer processing unit, a microprocessor, a classical processor, a quantum processor, and / or a similar processor). In one or more exemplary embodiments, the components associated with the spectral data comparison system 202 may comprise one or more computer and / or machine-readable, writable, and / or executable components, and / or instructions that can be executed by the processor 206, as described herein with reference to or without reference to one or more drawings of the one or more exemplary embodiments, and may provide the execution of one or more processes defined by such components and / or instructions. In one or more exemplary embodiments, the processor 206 may comprise an identification component 210, a similarity component 212, a generation component 214, a comparison component 216, an output component 218, an evaluation component 220, a notification component 222, and / or a graphing component 224.

[0054] In one or more exemplary embodiments, the spectral data comparison system 202 may include a computer-readable memory 204 that can be operably connected to a processor 206. The memory 204 can store computer-executable instructions that, when executed by the processor 206, cause the processor 206 and / or one or more other components of the spectral data comparison system 202 (e.g., an identification component 210, an approximation component 212, a generation component 214, a comparison component 216, an output component 218, an evaluation component 220, an alert component 222, and / or a graphing component 224) to perform one or more actions. In one or more exemplary embodiments, the memory 204 can store computer-executable components (e.g., an identification component 210, an approximation component 212, a generation component 214, a comparison component 216, an output component 218, an evaluation component 220, an alert component 222, and / or a graphing component 224).

[0055] The spectral data comparison system 202 and / or its components described herein can be coupled to each other electrically, operably, optically, and / or otherwise via a bus 205 so as to be communicative with each other. The bus 205 may comprise one or more of the following types of buses: a memory bus, a memory controller, a peripheral bus, an external bus, a local bus, a quantum bus, and / or one or more bus architectures. One or more examples of these buses 205 may be used.

[0056] In one or more embodiments, the spectral data comparison system 202 may be connected (e.g., via a network or the like) to one or more external systems (e.g., an electrical output generation system not shown, one or more output targets, and / or output target control devices), information sources, and / or equipment (e.g., classical and / or quantum computing equipment, communication equipment, and other similar equipment) (e.g., via communication, electrical, operational, optical, or other similar functional means). In one or more exemplary embodiments, one or more components of the spectral data comparison system 202 and / or a non-exclusive system 200 may reside in the cloud and / or locally in a local computing environment (e.g., a specified location).

[0057] In addition to the processor 206 and / or memory 204 described above, the spectral data comparison system 202 may include one or more computer and / or machine-readable, writable, and / or executable components and / or instructions, which, when executed by the processor 206, can provide the execution of one or more operations defined by such components and / or instructions.

[0058] Next, additional components of the spectral data comparison system 202 (e.g., identification component 210, approximation component 212, generation component 214, comparison component 216, output component 218, evaluation component 220, notification component 222, and / or graphing component 224) will be described. As mentioned above, generally, the spectral data comparison system 202 can facilitate the process of generating and / or comparing spectral decomposition data 254 based on a target ion activation energy 260T that was not used in the spectroscopic analysis (e.g., analysis by measuring device 249).

[0059] This process can be broken down into a series of processes, including but not limited to, the process of obtaining and approximating resolved curve data 251 based on mass spectrometry data 250, the process of comparing the spectral resolved data 254 group obtained by the approximation process 450 of resolved curve data 251 (see Figure 4) 270, and the evaluation of the comparison data 272 obtained as a result of the comparison process 270.

[0060] First, in one or more exemplary embodiments, the identification component 210, approximation component 212, generation component 214, comparison component 216, output component 218, evaluation component 220, notification component 222, and / or graphing component 224 can be implemented individually without one or more other components among them. Furthermore, or alternatively, the identification component 210, approximation component 212, generation component 214, comparison component 216, output component 218, evaluation component 220, notification component 222, and / or graphing component 224 can also be comprised of a higher-level analysis component 203. Furthermore, one or more of the functions of the identification component 210, approximation component 212, generation component 214, comparison component 216, output component 218, evaluation component 220, notification component 222, and / or graphing component 224 can also be performed by the higher-level analysis component 203. Alternatively, these components can be omitted by having the higher-level analysis component 203 perform one or more of the functions of the omitted components (identification component 210, approximation component 212, generation component 214, comparison component 216, output component 218, evaluation component 220, notification component 222, and / or graphing component 224).

[0061] As mentioned above, the first step of the series of processing procedures allows for the acquisition and approximation of decomposition curve data 251 based on mass spectrometry data 250.

[0062] First, regarding the identification component 210, this component generally acquires the first group of mass spectrometry data 250A (e.g., acquisition, residency, identification, request, download, etc.). In some cases, the identification component 210 may also acquire a second group of mass spectrometry data 250B. However, in other cases, the identification component 210 may later acquire a second group 254 of spectrally decomposed data already generated from the mass spectrometry data 250B in a different manner.

[0063] In other words, the spectral data comparison system 202 can easily generate the first spectrally resolved dataset 254A from the first group of the mass spectrometry dataset 250A, and generate the second spectrally resolved dataset 254B from the second group of the mass spectrometry dataset 250B. Alternatively, the spectral data comparison system 202 can be limited to easily generating only the first spectrally resolved dataset 254A from the first group of the mass spectrometry dataset 250A.

[0064] Using the acquired mass spectrometry data 250 (for example, a first mass spectrometry dataset 250A, and optionally a second mass spectrometry dataset 250B), the approximation component 212 can generate a first decomposition curve 251A, and optionally a second decomposition curve 251B.

[0065] For example, referring briefly to Figure 3, and also to Figure 2, Figure 3 shows graph 300, which illustrates an example of decomposition curve data 251A.

[0066] Generally, graph 300 consists of a series of resolution curves 251A generated from mass spectrometry data 250.

[0067] By using a series of resolution curves (BDCs), mass spectrometry (MS) spectra in the ion activation energy range (e.g., MS) can be obtained. nBDC makes it possible to describe the same precursor (e.g., MS) in detail. n [M+H] of the spectrum + This can be constructed from a series of experiments measured under different energy conditions (CID 10, CID 20, CID 30, CID 40, CID 50, CID 60, CID 70, CID 80, CID 90, CID 100) for the same ion activation conditions (e.g., ion activation energy such as collision-induced dissociation (CID)).

[0068] It should be noted that it is not possible to create a single BDC curve by combining different precursor molecules or ion activation types (such as CID, high-energy collision decomposition (HCD), and ultraviolet photodecomposition (UVPD)). In other words, different ion activation types with different mechanisms cannot be mixed into one group of BDC curves. Thus, Figure 3 does not contain different ion activation types. Rather, it shows the intensity dependence of a single ion activation type on ion activation energy. A different figure is used for each ion activation type.

[0069] Note that here, M represents the mass of the non-ionized compound (the entire molecule) being analyzed, and H indicates that a hydrogen cation is bonded to the molecule. The resulting ion is denoted as [M+H]+.

[0070] For example, the approximation component 212 can identify sets of individual m / z ions from the mass spectrometry data 250, and for each m / z ion, collect the ionic intensity at each ion activation energy when the mass spectrometry data 250 was acquired, thereby creating a BDC curve for each m / z ion. As a result, the BDC can be a set of diffusion diagrams for all the individual ions.

[0071] Note that depending on the detector used, the unit representing ion intensity may be amperes (e.g., picoamperes or nanoamperes) or counts per second. In other embodiments, the unit representing ion intensity may not be displayed on the graph. In other words, a common method is to use relative intensity, setting the intensity of the highest peak as 100%.

[0072] Note that the BDC curve graph also displays unfragmented ions (precursor ions in the case of the MS2 node, such as [M+H]+ or [MH]-). For example, in Figure 5, the curve labeled m / z=155 represents these precursors. At the lowest energy levels, the abundance of these unfragmented ions is highest. As the energy increases, their abundance decreases, while the number of ions produced by fragmentation increases.

[0073] In other embodiments, the BDC data 251 can be obtained directly (for example, by the identification element 210) without going through the generation process of the BDC 251 by the approximation element 212. Note that the obtained BDC data 251 may not have undergone approximation processing (for example, if it has not undergone scaling, fitting, interpolation, etc.).

[0074] BDCs can be used to identify and distinguish structurally similar compounds as positional isomers, and are not limited to being a tool for verifying the consistency of fragmentation characteristics between sample sources, natural analytes, and / or isotopically labeled compounds in liquid chromatography-mass spectrometry (LC-MS / MS) analysis. Existing frameworks only perform visual analysis of BDCs, and do not employ general mathematical methods for their processing, particularly approximation.

[0075] Referring further to Figure 3, it can be seen that the ion activation energy shown on the x-axis of Graph 300 is identified as normalized collision energy (NCE). In other words, ion activation energy is a general term expressed in units of volts. NCE is available for specific ion activation techniques such as CID (collision-induced dissociation) and HCD (high-energy collision dissociation). Ion activation energy can be an absolute value (in volts) or it can be normalized. Spectra can store both absolute and normalized values. The relationships between these values ​​may be defined by the software that manages, uses, or configures the measuring instrument (e.g., a spectroscopic analyzer).

[0076] In other words, each line in Graph 300 may represent a different fragmentation pattern, such as fragment ions generated from compound 230A, or it may represent the remaining compound 230A after removing the ions generated by fragmentation. Each point on the line represents the ion activation energy versus ion intensity in a single spectrum. This means that the initial mass spectrometry data 250A may have been obtained as a result of mass spectrometry performed using 15 different ion activation energies (NCE values ​​from 10 to 150). The estimated schematic and / or linear lines connecting these points are merely for sequentially connecting the points. In Graph 300, the lines may contain outliers and may not accurately reflect the actual data situation between the data points shown in Graph 300. That is, the decomposed curve data 251 has not yet been expanded (e.g., fitted) and / or interpolated.

[0077] Next, the approximation processing component 212 and Figure 4 will be explained with reference to Figure 2. Generally, the approximation processing component 212 can approximate at least the first decomposition curve 251A representing the mass spectrometry dataset 250A using the approximation function 252. If the second decomposition curve 251B has not yet been approximated, or if the approximated spectral decomposition data 254B has not yet been obtained, the approximation processing component 212 can also approximate the second decomposition curve 252B.

[0078] The approximation function 252 can include functions such as Gaussian distribution, log-normal distribution, skew-Gaussian distribution, Voit distribution, skew-Voit distribution, or other approximation functions, and can be used with or without outlier detection functionality.

[0079] In the approximation process 450, one or more different approximation functions 252 can be used to generally fit the decomposed curve data 251 to the shape of the approximation function. As shown in the eight types of approximation graphs 400A to H in Figure 4 (showing the relationship between ion activation energy and ion intensity), it is possible to use one or more approximation functions 252. In each graph, the shaded region 410 represents the shape of the approximation function 252, and the scattering points 412 represent the decomposed curve data 251.

[0080] As shown in the figure (for example, in graph 400B), one or more of the scattering points 412 may be identified as an outlier 412O. This means that existing methods that do not use approximation methods such as those described herein may not identify these points as outliers.

[0081] Furthermore, by using the approximation process 450, data at other ion activation energies can be obtained, such as when the mass spectrometry data 250 and / or resolved curve data 251 were not acquired by the identification component 210 (for example, when the measuring device 249 did not perform spectroscopic / fragmentation analysis), by interpolating, supplementing, and expanding the data between the scattering points 412.

[0082] It should be noted that the approximation graph 400 is generated by the approximation component 212 for each individual fragmented ion (m / z ion) included in the decomposition curve data 251A shown in Figure 3. Thus, the approximation component 212 can perform the approximation 450 on one or more (e.g., all ions) fragmented ions (m / z ions) included in the decomposition curve data 251A (and / or decomposition curve data 251B). It should be noted that the approximation graph 400 shown in Figure 4 represents only a portion of the complete decomposition curve data 251A for ions at 81.033491 m / z (e.g., a specific range of ion activation energies) for illustrative purposes.

[0083] When multiple approximation functions 252 are used, a user with access to the approximation processing component 212 or the approximation data 450 (for example, a user using a computer device that can communicately connect to the system 200) can determine the best approximation function 252 for each fragment ion. In other words, a different approximation function 252 can be used for different fragmentation ions (for example, decomposition curve data representing lines of different decomposition curves).

[0084] In one or more embodiments, this decision can be based on optimal fit. For example, the optimal approximation function can be found by using appropriate statistical parameters. As an example, the function that minimizes the range of uncertainty can be considered the optimal approximation function. The minimum range of uncertainty can be directly calculated using statistical analysis software. For example, in Figure 4, 400D can be said to be the optimal approximation among the group of approximation graphs 450.

[0085] As shown in Figure 5, using the approximation processing component 450, the respective decomposition curve data 251 corresponding to each decomposition curve 251A was approximated (fitted) by the approximation processing component 212, with reference to Figure 2. As a result, approximate spectral decomposition data 254 is obtained (hereinafter sometimes simply referred to as "spectral decomposition data 254"). The spectral decomposition data 254A is shown in graph 500 of Figure 4 as the relationship between ion activation energy and ion intensity.

[0086] Referring to Figures 2 and 5, the generation component 214 can use the spectral decomposition data 254 to generate target spectral data 510 that defines the target spectrum 512. In particular, by approximating the data between the scattering data points 412, the target spectral data 510 at the target ion activation energy 260T can be obtained from the spectral decomposition data 254. In other words, even if the ion activation energy not used by the measuring device 249, or if mass spectrometry data 250 or decomposition curve data 251 is not available, the approximation process 250 can still obtain the target spectral data 510.

[0087] For example, as shown in Figure 5, the generating component 214 can construct the target spectrum 512 based on the determination of the target spectral data 510 at NCE94 (target ion activation energy).

[0088] However, this is just one example. In practice, the generating component 214 can generate target spectral data 510 that includes or corresponds to multiple data points between the scattering points 412, so that the comparing component 216 can generate and identify a comprehensive dataset that can be used for the comparison process 270. Any appropriate range, frequency, and / or deviation can be used between individual sets of spectral data.

[0089] Next, a second group of processes consisting of one or more processing steps will be described. This group of processes may include performing a comparison process 270 on spectral decomposition data 254 (fitted data in Figure 5) obtained based on decomposition curve data 251 (data obtained as a result of approximation processing 450 in Figure 4).

[0090] In some embodiments, the second set of steps may include the following exemplary set of steps. TIFF2026086388000002.tif39153

[0091] Therefore, referring to Figure 6, and also to Figure 2, the comparison component 216 can perform a comparison process 270 between the first spectral decomposition data set 254A and the second spectral decomposition data set 254B, at least at the target ion activation energy 260T, and as a result, determine a target similarity value 272T that indicates the similarity between the first spectral decomposition data set 254A and the second spectral decomposition data set 254B at the target ion activation energy 260T. Looking at Graph 600 in Figure 6, it can be seen that this comparison can be performed for multiple ion activation energies, such as the range of ion activation energies 260R from which the spectral decomposition data 254A and 254B were generated based on an approximation value 450. Again, appropriate ranges, frequencies, and / or deviations can be arbitrarily set between individual sets of spectral data.

[0092] The comparison processing component 216 can use any appropriate similarity evaluation metric, such as cosine similarity or NIST (National Institute of Standards and Technology) score. As shown in Figure 6, cosine similarity was used as the quantitative similarity evaluation metric for the data corresponding to individual fragment ions.

[0093] In other words, the comparison process 270 yields a comprehensive understanding of local minimums and / or maximums in individual fragment ions or in the compound as a whole (e.g., 230A, 230B). For example, graph 600 shows comparison data 272 containing ion activation energies per quantified similarity for a particular fragmentation ion 602. As shown in the figure, a cosine similarity of 1.0 represents perfect similarity, while values ​​less than 1.0 represent low similarity. Comparison data 272 includes pairs 604 of local minimums (e.g., local minimums) corresponding to different ion activation energy value pairs, which show the ion activation energy values ​​where spectrally decomposed data 254A and 254B differ the most. At local minimums, the similarity is lowest and the dissimilarity is highest. Conversely, at local maximums, the similarity is highest and the dissimilarity is lowest.

[0094] For example, one of the local minimums of 604 can be represented by a target similarity value of 272T, which corresponds to a target ion activation energy of 260T. Such a target similarity value of 272T could not have been generated using existing frameworks.

[0095] Next, referring to Figure 7 in conjunction with Figure 2, as mentioned above, the comparison component 216 can generate comparison data 272 for two or more fragment ions (for example, ions common to spectral resolution data 254A and 254B). In other words, graph 700 shows comparison data 272 for multiple fragmentation ions in the ion activation energy range 260R. In graph 700, each different dotted line represents comparison data for different individual fragmentation ions.

[0096] It is important to note that if an ion is present in the first compound but not in the second compound, this means that the intensity of that ion in the second compound is zero. In this case, when calculating spectral similarity, the intensity value of ions that are not present in the second compound can be set to 0. Ions that are not common to both BDCs may have high informational value and may affect the similarity between spectra.

[0097] In this regard, the output component 218 can also generate overall spectral similarity data 272S, which shows the comparison / similarity between all (or partial) data of spectrally decomposed datasets 254A and 254B, in addition to the similarity value of a single fragment ion. As shown in Figure 7, the comparison processing component 214 can use the overall spectral similarity data 272S to identify local minimum values ​​712 and / or maximum values ​​714 of the overall similarity. For example, the overall similarity can be represented by the area under each curve in graph 700.

[0098] In one embodiment, the total similarity data 272S can be calculated using Equation 1, where E represents the ion activation energy, and the resulting similarity value is in the range of 0 to 1 (<0,1>). A value of 1 means that the two BDCs and the compounds representing them (e.g., combinations of fragmented ions) cannot be distinguished using mass spectrometry spectra.

[0099] Formula 1: JPEG2026086388000003.jpg13150

[0100] Next, referring to Figure 8, in one or more embodiments, when it is required to compare mass spectrometry data 250, decomposition curve data 251, and / or approximate spectral decomposition data 254 generated by two different measuring devices 249 (e.g., measuring device 1 and measuring device 2 shown in Figure 8), the comparison component 216 and / or output component 218 perform the process of normalizing the ion activation energies used by the different measuring devices 249 and further transforming at least one dataset of the spectral decomposition data 254 (e.g., 254A, 254B, etc.).

[0101] For example, the comparison component 216 generates full spectral similarity data 272S based on the result of normalizing the ion activation energy range 260R between the first mass spectrometer 249 in which the mass spectrometry measurement of the first compound 230A was performed and the second mass spectrometer (e.g., another measuring device 249) in which the mass spectrometry measurement of the second compound 230B was performed, and the output component 218 can output this data.

[0102] Normalization can be performed using three equations (Equations 2, 3, and 4), where E1 represents the energy from one device and E2 represents the energy from a second device calculated using parameters A and B. Parameters A and B are optimized using Equation 3. Using these equations, it becomes possible to define the energy conversion between two different devices 1 and 2 using the total similarity. For example, when the conversion is in linear form:

[0103] Formula 2: E1=A E2+ B

[0104] Formula 3: JPEG2026086388000004.jpg13150

[0105] Formula 4: JPEG2026086388000005.jpg13150

[0106] After normalizing the ion activation energies 260A and / or 260B relative to each other, the resulting spectral decomposition data 254 is output by the output component 218 after undergoing a transformation process (e.g., a shift process) 810 performed by the comparison component 216. For example, as shown in Figure 8, the spectral decomposition data 254A can be transformed by normalizing the first ion activation energy 260A with the second ion activation energy 260B.

[0107] The next thing to be discussed is a third group of one or more processes, which may include the evaluation of comparison data 272, which is the output of comparison process 270.

[0108] In other words, referring to Figure 2, we will describe the evaluation (e.g., usage) of the comparison data 272 by the evaluation component 220, the notification component 222, and / or the graph display component 224.

[0109] For example, referring again to Figure 7, the evaluation component 220 can identify the output ion activation energy (e.g., output ion activation energy 712) within the range of ion activation energies 260R that corresponds to the maximum difference in the quantified similarity (e.g., minimum similarity value of comparison data 272) between the first spectral decomposition data 254A and the second spectral decomposition data 254B. This output ion activation energy is either the target ion activation energy 260T or any other ion activation energy. For example, a similarity of 1 means that the two groups are identical. A value of 0 means that the two groups are completely different. To find the maximum difference, it is necessary to identify the minimum value (local minimum, e.g., 604).

[0110] Upon receiving such identification results, the notification component 222 can generate a notification 280 containing data instructing the system to re-fragment the initial compound using the output ion activation energy. The notification 280 can be sent to the user or otherwise provided to the user. For example, the notification 280 can be sent to or provided to a computer device associated with the user (a non-limited device capable of communicating with system 200).

[0111] As another example, looking at Figure 9, the graph display component 224 can generate graph data 910 that defines a node-based graph 900, which includes nodes 912 corresponding to the first compound 230A and the second compound 230B, and ends 914 between these nodes 912 (corresponding to the total spectral similarity value 916 of the total spectral similarity data 272S between the first compound 230A and the second compound 230B).

[0112] As an overview of the components and / or functions described above, a summary of process 950 is shown in Figure 9. As shown in the figure, mass spectrometry data 250 is identified by the identification component, and then the approximation processing component 212 generates resolved curve data 251. As previously stated, this overview is merely an example and not limited to the entire process. In some embodiments, the identification component 210 can identify the resolved curve data 251.

[0113] The approximation processing component 212 can generate spectral decomposition data 254 using spectral decomposition curve data 251 by performing approximation processing 450. The generation component 214 can generate target spectral data 510 using spectral decomposition data 254. The comparison processing component 216 can perform comparison processing 270 based on the target spectral data 510 and spectral decomposition data 254 to generate comparison data 272.

[0114] Referring to Figures 11 and 12, which provide another overview of the components and / or functions described above, a flowchart of Method 1100 is shown, illustrating an example of facilitating the output comparison and / or evaluation process of a measuring device according to an embodiment of the present invention (e.g., System 200 shown in Figure 2). While Method 1100 is described in reference to the non-limiting System 200 in Figure 2, Method 1100 may also be applicable to other systems described herein (e.g., the non-limiting System 100 in Figure 1). Descriptions of similar elements and / or process repetitions used in each embodiment are omitted for brevity.

[0115] In step 1102, the non-limiting method 1100 may include a process in which a system (e.g., an identification component 210) connected to a processor (e.g., processor 206) identifies a first dataset of mass spectrometry data (e.g., first mass spectrometry dataset 250A).

[0116] In step 1104, the non-limiting method 1100 may include a process in which the system (e.g., the identification component 210) identifies a first set of spectrally decomposed data for a first compound (e.g., first compound 230A). This first set of spectrally decomposed data corresponds to a first ion activation energy (e.g., first ion activation energy 260A), including a target ion activation energy (e.g., target ion activation energy 260T) that was not considered in conventional mass spectrometry measurements for the first compound. The method 1100 may also include a process in which a second set of spectrally decomposed data (e.g., second set of spectrally decomposed data 254B) for a second compound (e.g., second compound 230B). This second set of spectrally decomposed data corresponds to a second ion activation energy (e.g., second ion activation energy 260B).

[0117] As mentioned above, the first compound 230A may be the same as or different from the second compound 230B, and vice versa. As mentioned above, the first ion activation energy 260A may be the same as or different from the second ion activation energy 260B, and vice versa.

[0118] In step 1106, the non-limiting method 1100 may include a step in which the system (e.g., the approximation processing component 212) approximates a first decomposition curve (e.g., first decomposition curve 251A) representing a first mass spectrometry dataset (e.g., first mass spectrometry dataset 250A) using an approximation function (e.g., approximation function 252).

[0119] In step 1108, the non-limiting method 1100 may include a process in which the system (e.g., the generating component 214) generates target spectral decomposed data (e.g., target spectral decomposed data 254T) from a first spectral decomposed data set (e.g., first spectral decomposed data set 254A) at a target ion activation energy (e.g., target ion activation energy 260T).

[0120] In step 1110, a non-limiting method 1100 may include the system (e.g., generating component 214) generating target spectral data (e.g., target spectral data 510) that defines a target spectrum (e.g., target spectrum 512) at a target ion activation energy (e.g., target ion activation energy 260T) based on the target spectral decomposition data.

[0121] In step 1112, the non-limiting method 1100 includes the process by which the system (e.g., the generating component 214) performs an approximate calculation using an approximation function of the first decomposition curve (e.g., first decomposition curve 251A) that defines the first mass spectrometry data set (e.g., first mass spectrometry data set 250A), and generates a first spectral decomposition data set (e.g., first spectral decomposition data set 254A) for multiple ion activation energy ranges (e.g., ion activation energy range 260R) that include an additional ion activation energy (e.g., additional ion activation energy 260X) that was not covered by the conventional mass spectrometry measurement for the first compound. This process also includes the first ion activation energy (e.g., first ion activation energy 260A) and the target ion activation energy (e.g., target ion activation energy 260T).

[0122] In step 1114, the non-limiting method 1100 may include a process in which the system (e.g., comparison component 216) performs a comparison (e.g., comparison process 270) between a first spectral decomposition data set (e.g., first spectral decomposition data set 254A) and a second spectral decomposition data set (e.g., second spectral decomposition data set 254B) at a target ion activation energy (e.g., target ion activation energy 260T), and as a result calculates a target similarity value (e.g., target similarity value 272T) that indicates the similarity between the first spectral decomposition data set and the second spectral decomposition data set at the target ion activation energy.

[0123] In step 1116, the non-limiting method 1100 includes the system (e.g., output component 218) generating comparison data (e.g., comparison data 272) that includes ion activation energy values ​​for each similarity value for the group of fragmentation ions (e.g., fragmentation ions 232) common to the first and second compounds between the first and second spectral decomposition data, based at least on the comparison results (e.g., data showing the two straight lines in graph 500 in Figure 5). This comparison data also includes the target similarity value.

[0124] In step 1118, the non-limiting method 1100 includes the step of the system (e.g., output component 218) generating comparison data (e.g., comparison data 272) that includes terms for ion activation energy relative to similarity values ​​(e.g., data shown as multiple lines in graph 500 of Figure 5) between the first spectral decomposition data group (e.g., first spectral decomposition data group 254A) and the second spectral decomposition data group (e.g., second spectral decomposition data group 254B), based on the results of comparing the first spectral decomposition data group and the second spectral decomposition data group in an additional comparison (e.g., additional comparison 270X) of additional ion activation energies within the range of ion activation energies (e.g., additional comparison 270X). This comparison data includes target similarity values ​​(e.g., target similarity values ​​272T) for a group of fragment ions (e.g., fragment ions 232) that are commonly fragmented from the first and second compounds.

[0125] In step 1120, the non-limiting method 1100 includes the system (e.g., output component 218) generating comparison data (e.g., comparison data 272) which includes total spectral similarity data (e.g., total spectral similarity data 272S) normalized to the region indicated by a first spectral decomposition curve (e.g., first spectral decomposition curve 251A) and a second spectral decomposition curve (e.g., second spectral decomposition curve 251B) based on the integral value of the analysis function over the range of ion activation energies. Here, across the range of ion activation energies, the first spectral decomposition curve is defined by the first spectral decomposition data set, and the second spectral decomposition curve is defined by the second spectral decomposition data set.

[0126] In step 1122, the non-limiting method 1100 may include a process in which the system (e.g., output component 218) generates whole spectral similarity data (e.g., whole spectral similarity data 272) based on the result of normalizing (e.g., normalization process 808) the ion activation energy range between a first mass spectrometer (e.g., measuring device 249) that conventionally performed mass spectrometry measurements on a first compound and a second mass spectrometer (e.g., another measuring device 249) that performed mass spectrometry measurements on a second compound.

[0127] In step 1124, the non-limiting method 1100 may include processing to determine whether the system (e.g., the evaluation component 220) should proceed with further evaluation of the output comparison data (e.g., comparison data 272). Otherwise, the non-limiting method 1100 proceeds to termination. If so, the non-limiting method 1100 can proceed to step 1122.

[0128] In step 1126, the non-limiting method 1100 includes the system (e.g., graph display component 224) generating a node-based graph (e.g., graph 900) which includes nodes (e.g., node 912) corresponding to the first and second compounds and edges (e.g., edge 914) extending between these nodes, the graph corresponding to the total spectral similarity values ​​(e.g., total spectral similarity values ​​916) based on total spectral similarity data (e.g., total spectral similarity data 272S) between the first and second compounds.

[0129] In step 1128, the non-limiting method 1100 may include the system (e.g., evaluation component 220) identifying a target ion activation energy or other ion activation energy (e.g., output ion activation energy 712) from a range of ion activation energies that corresponds to the maximum difference in quantitative similarity between the first and second groups of spectrally resolved data.

[0130] In step 1130, a non-limiting method 1100 may include the system (e.g., notification component 222) generating a notification (e.g., notification 280) instructing the system to re-fragment the first compound at the output ion activation energy in response to the identification of the output ion activation energy.

[0131] Additional Overview For the sake of simplicity, the computer implementation and non-computer implementation methodologies provided herein are shown and / or described as a series of actions. It should be understood that innovations in the subject matter are not limited by the illustrated actions and / or the order of actions; for example, actions may occur in one or more sequences and / or simultaneously, and may occur together with other actions not presented and described herein. Furthermore, not all shown actions are available for implementing the computer implementation and non-computer implementation methodologies in accordance with the described subject matter. In addition, computer implementation and non-computer implementation methodologies can alternatively be represented as a series of interrelated states via state diagrams or events. Furthermore, the computer implementation methodologies described below and throughout this specification can be stored in a product for carrying and transferring the computer implementation methodologies to a computer. As used herein, the term "production" is intended to encompass computer programs accessible from any computer-readable device or storage medium.

[0132] In this specification, systems and / or devices are described with respect to the interaction between one or more components (and / or further described later). Such systems and / or components may comprise a designated component or subcomponent, one or more designated components and / or subcomponents, and / or additional components. Subcomponents may be implemented not within a parent component, but as components communicatively coupled to other components. One or more components and / or subcomponents may be combined into a single component that provides aggregate functionality. Components may interact with one or more other components that are not specifically described herein for brevity but are known to those skilled in the art.

[0133] In short, one or more systems, computer program products, and / or computer implementations described or disclosed herein relate to evaluating the similarity between spectral datasets. The system may comprise memories 104, 204 for storing computer executable components and processors 106, 206 for executing the computer executable components. The computer executable components include identification components 110, 210 that identify first spectral resolution datasets 154A, 254A corresponding to first ion activation energies 160A, 260A for first compounds 130A, 230A (including target ion activation energies 160T, 260T that were not considered in conventional mass spectrometry measurements of first compounds 130A, 230A). The identification components 110 and 210 further identify the second spectral decomposition datasets 154B and 254B corresponding to the second ion activation energies 160B and 260B for the second compounds 130B and 230B. The comparison components 116 and 216 compare the first spectral decomposition datasets 154A and 254A with the second spectral decomposition datasets 154B and 254B at the target ion activation energies 160T and 260T, and as a result calculate target similarity values ​​172T and 272T, which indicate the similarity between the first spectral decomposition datasets 154A and 254A and the second spectral decomposition datasets 154B and 254B at the target ion activation energies 160T and 260T.

[0134] One or more exemplary embodiments disclosed herein can be applied in a plug-and-play manner to one or more measuring instruments in various configurations, or to the same measuring device using multiple interchangeable parts, for calibration, normalization, and / or comparison of output data with unknown, known, and / or standard data. The framework described herein can be performed in a time-efficient and at least partially automated manner, thereby increasing the uptime of the device and reducing interaction with user entities in pre-test and / or post-test processes.

[0135] One or more exemplary embodiments described herein can be implemented in, in connection with, and / or coupled to, a scientific measuring instrument.

[0136] In fact, considering one or more exemplary embodiments described herein, a practical application example of one or more systems, computer implementations, and / or computer program products described herein is the ability to interpolate spectral data corresponding to ion activation energies not specifically used during a spectroscopic analysis operation. This can be achieved by using an approximation function to decompose data for one or more fragment ions generated from a given compound using two or more different ion activation energy values. In this way, comparisons between decomposition data in different compounds, different devices, or even the same compound but different devices become possible across a range of ion activation energies without directly acquiring spectral data across the entire range of ion activation energies. As a result, one or more embodiments described herein reduce the number of fragmentation runs required to acquire initial mass spectrometry data in the measuring instrument, and generate decomposition data obtained from the mass spectrometry data.

[0137] Compared to existing frameworks that cannot provide this capability, one or more exemplary embodiments described herein can provide novel results that were previously unavailable. Specifically, based on the use of approximate decomposition data, a more comprehensive understanding of the decomposition data can be obtained compared to existing frameworks over a range of ion activation energies. This enables the identification of local minimums or maximums of quantified similarity between and / or between two or more sets of decomposition data, where at least one set contains approximate decomposition data. Optionally, all sets of decomposition data being compared may contain approximate decomposition data.

[0138] These represent useful and practical applications of computers, providing comparisons of enhanced (e.g., improved and / or optimized) spectral data and / or resolved data corresponding to initial spectral data. Overall, such computerized tools have the potential to bring concrete and tangible technological improvements in the field of materials analysis, more specifically in the analysis of the output of scientific measuring instruments, including but not limited to the field of spectroscopic measurements.

[0139] Furthermore, one or more exemplary embodiments described herein can be used in real-world systems based on the disclosed teachings. For example, minimum and / or maximum values ​​of quantified similarity between sets of spectral data can be determined in correspondence with one or more ion activation energies for a given measuring device, multiple designated measuring devices, the same compound, different compounds, etc. Based on the generated quantified similarity data, fragmentation at one or more ion activation energies can be re-evaluated and / or re-executed, and a database can be generated and / or updated using the total similarity value (e.g., quantified similarity across a range of ion activation energies) to link compounds, molecules, and / or ions, which can be used for the purpose of comparing, error correcting, and / or calibration of differences between output data from multiple measuring devices. These can be useful processes for various industries using materials analysis, product manufacturing, quality control, etc. Thus, embodiments disclosed herein can provide improvements to scientific instrument technology (e.g., improvements to computer technology that supports such scientific instruments, among other improvements).

[0140] Furthermore, one or more exemplary embodiments described herein can achieve a certain level of scale of operation. For example, spectral data corresponding to two or more compounds can be evaluated at least partially in parallel with each other for the same and / or different compounds, measuring devices, time periods, and / or fragmented ions.

[0141] In this specification, systems and / or devices are described with respect to the interaction between one or more components (and / or further described later). Such systems and / or components may comprise a designated component or subcomponent, one or more designated components and / or subcomponents, and / or additional components. Subcomponents may be implemented not within a parent component, but as components communicatively coupled to other components. One or more components and / or subcomponents may be combined into a single component that provides aggregate functionality. Components may interact with one or more other components that are not specifically described herein for brevity but are known to those skilled in the art.

[0142] One or more exemplary embodiments described herein can, in one or more exemplary embodiments, be essentially and / or closely linked to computer technology and cannot be implemented outside of a computing environment. For example, one or more processes performed by one or more exemplary embodiments described herein can provide the execution of programs and / or program instructions relating to the output comparison of measuring devices (e.g., the use of measuring devices for material analysis) in a more efficient and feasible manner compared to existing systems and / or methods using molecular network generation and / or visualization. Systems, computer implementations and / or computer program products that provide the performance of these processes are extremely useful in the field of material analysis and cannot be equally practically implemented in a clever manner outside of a computing environment.

[0143] One or more exemplary embodiments described herein can use hardware and / or software to solve problems that are highly technical, not abstract, and that cannot be performed by humans as a set of mental actions. For example, neither a human being, or even thousands of humans, can efficiently, accurately, and / or effectively analyze computer data / metadata (such as spectral data and / or decomposition data) that define fragmentation intensity, mass-to-charge ratio, retention time, etc., of compounds analyzed by one or more measuring devices, nor can they generate digital visualizations of quantified similarity between compared spectral datasets, but one or more embodiments described herein can provide this process. Furthermore, neither the human brain nor a human being using pen and paper can perform one or more of these processes as the one or more exemplary embodiments described herein do.

[0144] In one or more exemplary embodiments, one or more processes described herein may be used to perform defined tasks relating to one or more of the above-described technologies by one or more specialized computers (e.g., specialized processing units, specialized classical computers, specialized quantum computers, specialized hybrid classical / quantum systems, and / or other types of specialized computers). One or more exemplary embodiments and / or components described herein can be used to solve new problems arising from the use of the technological advancements, quantum computing systems, cloud computing systems, computer architectures, and / or other technologies mentioned above.

[0145] One or more exemplary embodiments described herein may be fully operable to perform one or more other functions (e.g., full power-on, full operation, and / or other functions) while also performing one or more operations described herein.

[0146] To provide an additional overview, the following is a list of embodiments and their features.

[0147] A certain system comprising: a memory for storing computer executable components; and a processor for executing the computer executable components stored in the memory, wherein the computer executable component is an identification component for identifying a first set of spectral decomposition data of a first compound, corresponding to a first ion activation energy, including a target ion activation energy omitted from conventional spectroscopic measurements of the first compound.

[0148] Here, the identification component further identifies a set of second spectral decomposition data for the second compound corresponding to the second ion activation energy, and the comparison component performs a comparison between the first spectral decomposition dataset and the second spectral decomposition dataset at the target ion activation energy, resulting in a target similarity value that defines the similarity between the first spectral decomposition dataset and the second spectral decomposition dataset at the target ion activation energy.

[0149] The system described in the preceding paragraph, wherein the computer-executable component further includes an approximation component that approximates a first decomposition curve representing a first set of mass spectrometry data using an approximation function.

[0150] The system described in the preceding paragraph, wherein the computer executable component further includes a generating component that generates target spectral decomposition data of a first set of spectral decomposition data at a target ion activation energy, wherein the generating component generates target spectral data that defines a target spectrum at a target ion activation energy, based on the target spectral decomposition data.

[0151] The system of the preceding paragraph, wherein the computer executable component further includes: a generating component that generates first spectral decomposition data for a range of ion activation energies, including first ion activation energies and additional ion activation energies (including target ion activation energies) that were omitted from conventional spectroscopic measurements of the first compound, based on an approximation using an approximation function of first decomposition curves defining first mass spectrometry data.

[0152] A system as described in any of the preceding paragraphs, wherein the computer executable component further comprises: an output component that generates comparative data, including a target similarity value, for a group of fragmented ions commonly fragmented from a first compound and a second compound, based on comparison, between a set of first spectral decomposition data and a set of second spectral decomposition data; the comparative data includes a term for ion activation energy per similarity value.

[0153] The system described in any of the preceding paragraphs, wherein the computer executable component further includes: an evaluation component that identifies an output ion activation energy, which is a target ion activation energy or another ion activation energy, from a range of ion activation energies corresponding to the maximum difference in quantified similarity between first spectral decomposition data and second spectral decomposition data; and a notification component that, in response to the identification of the output ion activation energy, generates a notification requesting re-fragmentation of the first compound at the output ion activation energy.

[0154] The system of the preceding paragraph, wherein the computer executable component further includes: The output component generates comparison data for groups of fragmented ions commonly fragmented from the first and second compounds, including a term of ion activation energy per similarity value (including a target similarity value), between the first and second spectrally decomposed data and the second spectrally decomposed data, based on a comparison and an additional comparison of the first and second spectrally decomposed data at additional ion activation energies in the ion activation energy range. Here, the comparison data includes total spectral similarity data based on the integral of an analytical function over the ion activation energy range, normalized to the area represented by the first decomposition curve defined by the first spectrally decomposed data and the second decomposition curve defined by the second spectrally decomposed data.

[0155] A system as described in any of the preceding paragraphs, wherein the computer executable component further includes: a graph display component that generates a node-based graph including nodes corresponding to a first compound and a second compound, and edges extending between the nodes, corresponding to all spectral similarity values ​​of all spectral similarity data between the first compound and the second compound.

[0156] The system described in any of the preceding paragraphs, wherein the total spectral similarity data is further based on the normalization of the range of ion activation energies between a first spectrometer in which a conventional spectroscopic measurement of the first compound was performed and a second spectrometer in which a spectroscopic measurement of the second compound was performed.

[0157] A computer implementation method comprising: a system operably coupled to a processor identifies a set of first spectral decomposition data for a first compound; the system identifies a set of second spectral decomposition data corresponding to a second ion activation energy, corresponding to a first ion activation energy identified by the system, which includes a target ion activation energy omitted from conventional spectroscopic measurements of the first compound; the system performs a comparison between the set of first spectral decomposition data and the set of second spectral decomposition data at the target ion activation energy; and as a result, a target similarity value is obtained that defines the similarity between the set of first spectral decomposition data and the set of second spectral decomposition data at the target ion activation energy.

[0158] A computer implementation method described in the preceding paragraph, further comprising the system approximating a first decomposition curve representing a first set of mass spectrometry data using an approximation function. A computer implementation method according to any of the preceding paragraphs, further comprising: the system generating target spectral decomposition data of a first spectral decomposition dataset at a target ion activation energy; and the system generating target spectral data that defines a target vector at a target ion activation energy based on the target spectral decomposition data.

[0159] A computer implementation method according to any of the preceding paragraphs, further comprising the system generating a set of first spectral decomposition data for a range of ion activation energies, including a first ion activation energy and additional ion activation energies (including a target ion activation energy) omitted from conventional spectroscopic measurements of the first compound, based on an approximation using an approximation function of a first decomposition curve that defines a set of first mass spectrometry data.

[0160] A computer implementation method according to any of the preceding paragraphs, further comprising a program instruction that, by the system, generates comparative data including a target similarity value and a term of ion activation energy per similarity value for a group of fragmentation ions commonly fragmented from the first compound and the second compound, based at least on the comparison, between the first set of spectral decomposition data and the second set of spectral decomposition data, wherein the program instruction is further executable to cause the processor to do the following:

[0161] A computer implementation method according to any of the preceding paragraphs, further comprising: the system identifying an output ion activation energy (target ion activation energy or another ion activation energy) corresponding to the maximum difference in quantified similarity between first spectral decomposition data and second spectral decomposition data from a range of ion activation energies; and the system, in response to the identification of the output ion activation energy, generating a notification requesting re-fragmentation of the first compound at the output ion activation energy.

[0162] A computer program product that facilitates the process of evaluating the similarity between spectral datasets, comprising a computer-readable storage medium into which program instructions are incorporated, and program instructions that are executable by a processor and cause the processor to perform the following operations: the processor identifies a set of first spectral decomposition data for a first compound, which corresponds to the first ion activation energy, including the target ion activation energy omitted from conventional spectroscopic measurements of the first compound; the processor identifies a set of second spectral decomposition data for a second compound; the processor performs a comparison between the set of first spectral decomposition data and the set of second spectral decomposition data at the target ion activation energy, and as a result, a target similarity value is calculated that defines the similarity between the set of first spectral decomposition data and the set of second spectral decomposition data at the target ion activation energy.

[0163] A computer program product as described in the preceding paragraph, wherein the program instruction causes the processor to perform the following operations: the system approximates a first decomposition curve representing a first set of mass spectrometry data using an approximation function.

[0164] A computer program product as described in any of the preceding paragraphs, wherein the program instruction further causes the processor to perform the following operations: the system generates target spectral decomposition data of a first spectral decomposition dataset at a target ion activation energy; and the system generates target spectral data that defines a target vector at a target ion activation energy based on the target spectral decomposition data.

[0165] A computer program product as described in any of the preceding paragraphs, wherein the program instruction further causes the processor to perform the following operations: the processor generates a set of first spectral decomposition data for a range of ion activation energies, including a first ion activation energy and additional ion activation energies (including a target ion activation energy) omitted from conventional spectroscopic measurements of the first compound, based on an approximation using an approximation function of a first decomposition curve that defines a set of first mass spectrometry data; and the processor generates comparative data for a group of fragmentation ions commonly fragmented from the first and second compounds, including a term for ion activation energy per similarity value (including a target similarity value) between the set of first spectral decomposition data and the set of second spectral decomposition data, at least based on comparison.

[0166] A computer program product as described in any of the preceding paragraphs, wherein the program instruction further causes the processor to perform the following operations: the processor generates first spectral decomposition data for a range of ion activation energies, including a first ion activation energie and additional ion activation energies omitted from conventional spectroscopic measurements of the first compound, including a target ion activation energie, based on an approximation of first mass spectrometry data using an approximation function, the processor responds to the maximum difference in quantified similarity between the first spectral decomposition data and the second spectral decomposition data; the processor identifies an output ion activation energie, which is the target ion activation energie or another ion activation energie, from the range of ion activation energies; and the processor generates a notification requesting re-fragmentation of the first compound at the output ion activation energie in response to the identification of the output ion activation energie.

[0167] Exemplary operating environment Figure 14 is a schematic block diagram of an operating environment 1400 in which the described subjects can interact. The operating environment 1400 comprises one or more remote components 1410. The remote components 1410 can be hardware and / or software (e.g., threads, processes, computing devices). In one or more exemplary embodiments, the remote components 1410 may be distributed computing systems connected to programs that use local autoscaling components and / or resources of the distributed computing systems via a communication framework 1440. The communication framework 1440 can comprise wired network devices, wireless network devices, mobile devices, wearable devices, wireless access network devices, gateway devices, femtocell devices, servers, and the like.

[0168] The operating environment 1400 also includes one or more local components 1420. These local components 1420 can be hardware and / or software (e.g., threads, processes, computing devices). In one or more exemplary embodiments, the local components 1420 may include programs that communicate with / use autoscaling components and / or remote resources 1410 and 1420, etc., which are connected to a remotely located distributed computing system via a communication framework 1440.

[0169] One possible communication between a remote component(s) 1410 and a local component(s) 1420 may take the form of data packets adapted for transmission between two or more computer processes. Another possible communication between a remote component(s) 1410 and a local component(s) 1420 may take the form of circuit-switched data adapted for transmission between two or more computer processes within a radio time slot. The operating environment 1400 includes a communication framework 1440 that can be used to facilitate communication between the remote component(s) 1410 and the local component(s) 1420, and may include an air interface, such as an interface to a UMTS network over an LTE network. The remote component(s) 1410 may be operably coupled to one or more remote data stores 1450 (e.g., hard drives, solid-state drives, subscriber identification module (SIM) cards, electronic SIMs (ESIMs), device memory) that can be used to store information on the remote component(s) side of the communication framework 1440. Similarly, a local component 1420(or more) can be operably coupled to one or more local data stores 1430 that can be used to store information on the local component 1420(or more) side of the communication framework 1440.

[0170] Exemplary computing environment To provide additional context to the various embodiments described herein, Figure 15 and the following discussion are intended to provide a brief general description of a suitable computing environment 1500 on which various embodiments of the embodiments described herein can be implemented. Although the embodiments have been described above in the general context of computer executable instructions that can be run on one or more computers, those skilled in the art will recognize that the embodiments can further be implemented in combination with other program modules and / or as a combination of hardware and software.

[0171] Generally, a program module includes routines, programs, components, and data structures that perform tasks or implement abstract data types. Furthermore, this method can be implemented in other computer system configurations (including single-processor or multi-processor computer systems, minicomputers, mainframe computers, Internet of Things (IoT) devices, distributed computing systems, and personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics), each of which can be operably coupled to one or more related devices.

[0172] Furthermore, the embodiments described herein can also be implemented in a distributed computing environment in which a particular task is performed by remote processing devices linked via a communication network. In a distributed computing environment, program modules can be located on both local and remote memory storage devices.

[0173] Computing devices typically include a variety of media, which may include computer-readable storage media, machine-readable storage media, and / or communication media, and these two terms are used separately herein as follows: Computer-readable storage media or machine-readable storage media can be any available storage media accessible by a computer, and include both volatile and non-volatile media, removable and non-removable media. Computer-readable storage media or machine-readable storage media can be implemented in relation to any method or technique for storing information (e.g., computer-readable instructions or machine-readable instructions, program modules, structured data, or unstructured data).

[0174] Computer-readable storage media include, but are not limited to, random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital multipurpose disc (DVD), Blu-ray disc (BD) or other optical disc storage devices, magnetic cassettes, magnetic tapes, magnetic disk storage devices or other magnetic storage devices, solid-state drives or other solid-state storage devices, or other tangible and / or non-temporary media that can be used to store desired information. In this regard, the terms “tangible” or “non-temporary” are used as modifiers to exclude the propagating temporary signal itself when applied to storage, memory, or computer-readable media, and do not waive any rights to standard storage devices, memories, or computer-readable media that are not the propagating temporary signal itself.

[0175] A computer-readable recording medium can be accessed by one or more local or remote computing devices, for example, via access requests, queries, or other data retrieval protocols, and various actions can be performed with respect to the information stored on that medium.

[0176] Communication media typically include any information distribution or transmission medium that embodies computer-readable instructions, data structures, program modules, or other structured or unstructured data in data signals, such as modulated data signals, such as carrier waves or other transmission mechanisms. The term “modulated data signal” refers to a signal having one or more characteristics that are set or modified to encode information in one or more signals. By example, but not limited to, communication media include wired media such as wired networks or direct wired connections, as well as wireless media such as acoustic, RF, infrared, and other wireless media.

[0177] Continuing with reference to Figure 15, an exemplary computing environment 1500 capable of implementing one or more exemplary embodiments described herein includes a computer 1502, which includes a processing unit 1504, system memory 1506, and a system bus 1508. The system bus 1508 connects system components (including, but not limited to, system memory 1506) to the processing unit 1504. The processing unit 1504 can be any of a variety of commercially available processors. Dual microprocessors and other multiprocessor architectures can also be used as the processing unit 1504.

[0178] The system bus 1508 can be one of several types of bus structures that can be further interconnected to a memory bus (with or without a memory controller), peripheral bus, and local bus using any of the various commercially available bus architectures. The system memory 1506 includes ROM 1510 and RAM 1512. The basic input / output system (BIOS) can be stored in non-volatile memory (e.g., ROM, erasable programmable read-only memory (EPROM), EEPROM), and the BIOS contains basic routines that help transfer information between elements within the computer 1502, for example, during startup. RAM 1512 may also include high-speed RAM (e.g., static RAM) for caching data.

[0179] Computer 1502 may further include an internal hard disk drive (HDD) 1514 (e.g., EIDE, SATA) and may include one or more external storage devices 1516 (e.g., a magnetic floppy disk drive (FDD) 1516, a memory stick or flash drive reader, a memory card reader, etc.). Although the internal HDD 1514 is shown to be located within computer 1502, the internal HDD 1514 may also be configured for external use within a suitable chassis (not shown). In addition, although not shown in computing environment 1500, a solid-state drive (SSD) may be used in addition to or instead of the HDD 1514.

[0180] Other internal or external storage may include at least one other storage device 1520 having a storage medium 1522 (e.g., a solid-state storage device, a non-volatile memory device, and / or an optical disc drive that can read from and write to removable media (e.g., CD-ROM discs, DVDs, BDs, etc.)). External storage 1516 can be facilitated by a network virtual machine. The HDD 1514, external storage device 1516, and storage device (e.g., drive) 1520 can be connected to the system bus 1508 by an HDD interface 1524, an external storage interface 1526, and a drive interface 1528, respectively.

[0181] Drives and their associated computer-readable storage media provide non-volatile storage such as data, data structures, and computer-executable instructions. In the case of computer 1502, drives and storage media accommodate the storage of arbitrary data in an appropriate digital format. While the above description of computer-readable storage media refers to each type of storage device, other types of computer-readable storage media, whether existing or to be developed in the future, can also be used as examples of operating environments, and furthermore, any such storage media may contain computer-executable instructions for performing the methods described herein.

[0182] Numerous program modules can be stored in the drive and RAM 1512 (including the operating system 1530, one or more application programs 1532, other program modules 1534, and program data 1536). The operating system, applications, modules, and / or data, in whole or in part, can also be cached in RAM 1512. The systems and methods described herein can be implemented using various commercially available operating systems or combinations of operating systems.

[0183] Computer 1502 may optionally include emulation techniques. For example, a hypervisor (not shown) or other intermediary may emulate a hardware environment for operating system 1530, and the emulated hardware may optionally differ from the hardware shown in Figure 15. In such embodiments, operating system 1530 may comprise one VM from a plurality of virtual machines (VMs) hosted on computer 1502. Furthermore, operating system 1530 may provide a runtime environment (e.g., the JAvA runtime environment or the .NET framework) to application 1532. The runtime environment is a consistent execution environment that enables application 1532 to run on any operating system that includes the runtime environment. Similarly, operating system 1530 may support containers, and application 1532 may take the form of a container, which is a lightweight, standalone executable software package containing, for example, code, runtime, system tools, system libraries, and configuration for the application.

[0184] Furthermore, computer 1502 can enable security modules such as Trusted Processing Modules (TPMs). For example, with a TPM, a boot component performs a hash on the next boot component, waits for a match with a secure value, and then loads the next boot component. This process can be performed at any layer of computer 1502's code execution stack, for example, at the application run level or the operating system (OS) kernel level, thereby providing security at any level of code execution.

[0185] User entities can input commands and information to the computer 1502 through one or more wired / wireless input devices, such as a keyboard 1538, a touchscreen 1540, and a pointing device, such as a mouse 1542. Other input devices (not shown) may include microphones, infrared (IR) remotes, radio frequency (RF) remotes, or other remotes, joysticks, virtual reality controllers and / or virtual reality headsets, gamepads, stylus pens, image input devices, such as cameras, gesture sensor input devices, visual-motion sensor input devices, emotion or face detection devices, and biometric input devices, such as fingerprint or iris scanners. These and other input devices are often connected to the processing unit 1504 through an input device interface 1544, which can be coupled to the system bus 1508, but can also be connected through other interfaces such as parallel ports, IEEE 1394 serial ports, game ports, USB ports, IR interfaces, and BLUETOOTH® interfaces.

[0186] Monitor 1546 or other types of display devices can also be connected to the system bus 1508 via an interface such as a video adapter 1548. In addition to the monitor 1546, the computer typically includes other peripheral output devices (not shown), such as speakers and printers.

[0187] Computer 1502 can operate in a network environment by logically connecting to one or more remote computers, such as remote computer 1550, via wired and / or wireless communication. The remote computer 1550 can be a workstation, server computer, router, personal computer, portable computer, microprocessor-based entertainment device, peer device, or other common network node, typically including many or all of the elements described with respect to computer 1502, but for brevity, only the memory / storage device 1552 is shown. The logical connections shown include wired / wireless connections to a local area network (LAN) 1554 and / or larger networks, such as a wide area network (WAN) 1556. Such LAN and WAN networking environments are common in offices and enterprises, facilitating enterprise-scale computer networks such as intranets, all of which can connect to global communication networks such as the Internet.

[0188] When used in a LAN networking environment, computer 1502 can connect to local network 1554 via a wired and / or wireless network interface or adapter 1558. Adapter 1558 can facilitate wired or wireless communication with LAN 1554, and LAN 1554 may also include a wireless access point (AP) placed on it to communicate with adapter 1558 in wireless mode.

[0189] When used in a WAN networking environment, computer 1502 may include a modem 1560 or connect to a communication server on WAN 1556 via other means for establishing communication on WAN 1556, such as the Internet. The modem 1560 may be an internal or external wired or wireless device and may connect to the system bus 1508 via an input device interface 1544. In a network environment, the program module or a portion thereof shown in relation to computer 1502 may be stored in a remote memory / storage device 1552. The network connection shown is an example, and other means for establishing a communication link between computers may also be used.

[0190] When used in either a LAN or WAN network environment, computer 1502 can access a cloud storage system or other network-based storage system in addition to, or instead of, the external storage device 1516 described above. Generally, the connection between computer 1502 and the cloud storage system can be established via LAN 1554 or WAN 1556, for example, by adapter 1558 or modem 1560, respectively. When computer 1502 is connected to the relevant cloud storage system, the external storage interface 1526 can manage the storage provided by the cloud storage system, similar to other types of external storage, with the help of adapter 1558 and / or modem 1560. For example, the external storage interface 1526 can be configured to provide access to the cloud storage sources as if those sources were physically connected to computer 1502.

[0191] Computer 1502 may be capable of communicating with any wireless device or entity configured to operate within a wireless network (e.g., printers, scanners, desktops, and / or portable computers, portable data assistants, communications satellites, any equipment or location associated with wirelessly discoverable tags (e.g., kiosks, newsstands, store shelves, etc.), and telephones). This may include Wireless Fidelity (Wi-Fi) and Bluetooth® wireless technologies. Thus, the communication may be structured as an existing network, or it may be simply ad-hoc communication between at least two devices.

[0192] Additional Information The embodiments described herein may, at any possible level of technical detail of integration, cover one or more systems, methods, apparatus, and / or computer program products. A computer program product may include a computer-readable storage medium (or more media) having computer-readable program instructions for causing a processor to execute aspects of one or more exemplary embodiments described herein. The computer-readable storage medium may be a tangible device capable of holding and storing instructions used by an instruction execution device. The computer-readable storage medium may, for example, be an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a superconducting storage device, and / or any suitable combination thereof. A non-exclusive list of more specific examples of computer-readable storage media may also include: portable computer diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disk read-only memory (CD-ROM), digital multipurpose disks (DVDs), memory sticks, floppy disks, mechanically encoded devices such as punch cards or grooved raised structures having instructions recorded thereon, and / or any suitable combination thereof. Where used herein, computer-readable storage media should not be construed as transient signals themselves, such as radio waves and / or other freely propagating electromagnetic waves, electromagnetic waves propagating in waveguides and / or other transmission media (e.g., light pulses passing through optical fiber cables), and / or electrical signals transmitted through wires.

[0193] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device and / or to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network may include copper transmission cables, optical transmission fibers, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface within each computing / processing device receives computer-readable program instructions from the network and transfers the computer-readable program instructions for storage in a computer-readable storage medium within each computing / processing device. The computer-readable program instructions for performing the operation of one or more exemplary embodiments described herein may include assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, configuration data for integrated circuits, and / or source code and / or object code written in one or more combinations of Smalltalk, C++ or other object-oriented programming languages, and other procedural programming languages ​​such as the "C" programming language and / or similar programming languages. Computer-readable program instructions can be executed entirely on a computer, partially on a computer, as a standalone software package, partially on a computer, partially on a remote computer, or entirely on a remote computer and / or server. In the latter scenario, the remote computer can be connected to the computer through any type of network, including a local area network (LAN) or wide area network (WAN), and / or the connection can be made to an external computer (for example, via the Internet using an Internet service provider).In one or more exemplary embodiments, an electronic circuit (including, for example, a programmable logic circuit, a field-programmable gate array (FPGA), and / or a programmable logic array (PLA)) can execute computer-readable program instructions by personalizing the electronic circuit using state information of computer-readable program instructions in order to perform an aspect of one or more embodiments described herein.

[0194] The aspects of one or more exemplary embodiments described herein are described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to one or more exemplary embodiments described herein. It will be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to the processors of general-purpose computers, dedicated computers, and / or other programmable data processors for producing machines, and instructions executed via the processors of computers or other programmable data processors can create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagrams. These computer-readable program instructions can also be stored in computer-readable storage media that can instruct computers, other programmable data processing devices, and / or other devices to function in a particular manner, and the computer-readable storage media storing the instructions can comprise a product containing instructions that can implement the modes of function / operation specified in one or more blocks of the flowchart and / or block diagrams. Furthermore, computer-readable program instructions can be loaded into a computer, other programmable data processing device, or other device to generate a computer implementation process by having a series of actions executed on the computer, other programmable device, and / or other device, and the instructions executed on the computer, other programmable device, and / or other device implement the functions / actions specified in one or more blocks of a flowchart and / or block diagram.

[0195] The flowcharts and block diagrams in the drawings illustrate the architecture, functionality, and / or operation of possible implementations in a system, a computer, and / or a computer program product, according to one or more exemplary embodiments described herein. In this regard, each block in a flowchart or block diagram may correspond to a module, segment, and / or part of an instruction containing one or more executable instructions for implementing a specified logical function. In one or more alternative embodiments, the functions described within a block may be performed in a different order than shown in the drawings. For example, two consecutively shown blocks may be executed substantially simultaneously, and / or blocks may sometimes be executed in reverse order depending on the functionality involved. It should also be noted that each block in a block diagram and / or flowchart illustration, and / or combinations of blocks in a block diagram and / or flowchart illustration, may be implemented by a dedicated hardware-based system that can perform the specified functions and / or actions, and / or combinations of dedicated hardware and / or computer instructions.

[0196] While the subject matter described herein is presented in the general context of computer executable instructions for computer program products running on a computer, those skilled in the art will recognize that one or more exemplary embodiments described herein can also be implemented at least partially in parallel with one or more other program modules. Generally, a program module includes routines, programs, components, and / or data structures that perform a particular task and / or implement a particular abstract data type. Furthermore, the computer implementation methods described above can be practiced in single-processor computer systems and / or multi-processor computer systems, minicomputing devices, mainframe computers, and other computer system configurations including computers, handheld computing devices (e.g., PDAs, telephones), and / or microprocessor-based or programmable consumer and / or industrial electronic devices. Each illustrated embodiment can also be practiced in a distributed computing environment where tasks are performed by remote processing devices linked via a communication network. However, one or more embodiments, if not all, of the exemplary embodiments described herein can be implemented in a standalone computer. In a distributed computing environment, program modules can be located in both local and remote memory storage devices.

[0197] As used in this application, the terms “component,” “system,” “platform,” and / or “interface” may refer to and / or include computer-related entities or entities relating to operable machines having one or more specific functionalities. Entities described herein may be hardware, a combination of hardware and software, software, or running software. For example, a component may be, but is not limited to, a process running on a processor, a processor, an object, an executable file, an execution thread, a program, and / or a computer. Exemplarily, both an application running on a server and the server itself may be components. One or more components may reside in a process and / or an execution thread, and components may be localized on one computer or distributed across two or more computers. As another example, each component may run from various computer-readable media storing various data structures. Components may communicate via local and / or remote processes, for example, by following signals containing one or more data packets (e.g., data communication between one component in a local system and another, within a distributed system, or with other systems via a network (e.g., the Internet)). As another example, a component can be a device having a specific function provided by mechanical parts operated by electrical or electronic circuits, which are operated by software and / or firmware applications executed by a processor. In such a case, the processor may be inside or / or outside the device and may execute at least part of the software and / or firmware applications.As yet another example, a component may be a device that provides a specific function through electronic components without mechanical parts, and the electronic components may include a processor and / or other means for running software and / or firmware that at least partially grants the functionality of the electronic components. In one embodiment, a component may emulate an electronic component via a virtual machine, for example, within a cloud computing system.

[0198] Furthermore, the term “or” is used to mean an inclusive “or” rather than an exclusive “or.” That is, unless otherwise explicitly stated or evident from the context, “X uses A or B” is used to mean all natural inclusive interpretations. That is, whether X uses A, X uses B, or X uses both A and B, the expression “X uses A or B” applies in all cases. Furthermore, the articles “A” and “AN” used herein and in accompanying drawings should generally be interpreted as meaning “one or more” unless otherwise explicitly stated or evident from the context. Where used herein, the terms “example” and / or “exemplary” are used to mean serving as an example, case, or illustration. To avoid misunderstanding, the subject matter described herein is not limited by such examples. In addition, any embodiment or design described herein as “example” and / or “exemplary” is not necessarily construed to be preferable or advantageous to any other embodiment or design, nor is it intended to exclude equivalent exemplary structures and techniques known to those skilled in the art.

[0199] As used herein, the term “processor” can refer to substantially any computing processing unit and / or device, and can include, but are not limited to, single-core processors, single processors with software multithreading capability, multi-core processors, multi-core processors with software multithreading capability, multi-core processors with hardware multithreading technology, parallel platforms, and / or parallel platforms with distributed shared memory. In addition, a processor can refer to integrated circuits, application-specific integrated circuits (ASICs), digital signal processors (DSPs), field-programmable gate arrays (FPGAs), programmable logic controllers (PLCs), composite programmable logic devices (CPLDs), discrete gate or transistor logic, discrete hardware components, and / or any combination thereof designed to perform the functions described herein. Furthermore, a processor may utilize nanoscale architectures (e.g., molecular and quantum dot-based transistors, switches, and / or gates, but are not limited to) to optimize space utilization and / or enhance the performance of associated equipment. A processor can be implemented as a combination of computing processing units.

[0200] In this specification, terms such as “storage,” “storage device,” “datastore,” “data storage device,” “database,” and substantially any other information storage component relating to the operation and functionality of a component are used to refer to entities embodied in “memory component,” “memory,” or components that contain memory. The memory and / or memory components described herein may be either volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. Non-volatile memory may include, but not limited to, read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM), flash memory, and / or non-volatile random access memory (RAM) (e.g., ferroelectric RAM (FERAM)). Volatile memory may include RAM that can function as external cache memory, for example. For example, and not an exhaustive list, RAM may be available in many forms, such as synchronous RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), sync-link DRAM (SLDRAM), direct RAMBUS RAM (DRRAM), direct RAMBUS dynamic RAM (DRDRAM), and / or RAMBUS dynamic RAM (RDRAM). In addition, the memory components of the systems and / or computer implementations described herein are intended to include, but are not limited to, these and / or any other suitable types of memory.

[0201] The foregoing includes only examples of systems and computer implementations. Naturally, it is impossible to describe every conceivable combination of components and / or computer implementations for the purpose of illustrating one or more exemplary embodiments, but those skilled in the art will recognize that many more combinations and / or rearrangements of one or more exemplary embodiments are possible. Furthermore, to the extent that terms such as “includes,” “has,” and “possesses” are used in the detailed description, claims, appendices, and / or drawings, such terms are intended to be as comprehensive as the term “comprising,” as “comprising” is used as a transitional term in the claims.

[0202] In describing various embodiments, expressions such as “one embodiment,” “various embodiments,” “one or more exemplary embodiments,” and / or “several embodiments” may be used, each of which may refer to one or more embodiments, whether identical or distinct.

[0203] The descriptions of various embodiments are presented for illustrative purposes only and are not intended to be exhaustive or to limit oneself to the embodiments described herein. Many variations and modifications will be apparent to those skilled in the art without departing from the scope and spirit of the embodiments described herein. The terminology used herein has been selected to best describe the principles, practical applications and / or technical improvements to the technologies available on the market of the embodiments and / or to enable those skilled in the art to understand the embodiments described herein.

Claims

1. It is a system, Memory that stores computer executable components, A processor that executes the computer executable components stored in the memory, The computer executable component includes an identification component that identifies a first spectral decomposition dataset of the first compound, which corresponds to a first ion activation energy including a target ion activation energy that has been omitted from conventional spectroscopic measurements of the first compound. The aforementioned identification component further identifies the second spectral decomposition dataset of the second compound corresponding to the second ion activation energy, The computer-executable component includes a comparison component that performs a comparison between the first spectral decomposition dataset and the second spectral decomposition dataset at the target ion activation energy, thereby obtaining a target similarity value that defines the similarity between the first spectral decomposition dataset and the second spectral decomposition dataset at the target ion activation energy. system.

2. The aforementioned computer executable component further, The system according to claim 1, comprising an approximation component that approximates a first decomposition curve representing a set of first mass spectrometry data using an approximation function.

3. The aforementioned computer executable component further, Includes a generating component that generates target spectral decomposition data for the first spectral decomposition dataset at the target ion activation energy, The system according to claim 1, wherein the generating component generates target spectral data that defines the target spectrum at the target ion activation energy, based on the target spectral decomposition data.

4. The aforementioned computer executable component further, The system according to claim 1, comprising a generating component that generates the first spectral decomposition dataset for a range of ion activation energies, including the first ion activation energies and additional ion activation energies omitted from the conventional spectroscopic measurement of the first compound, including the target ion activation energies, based on an approximation using an approximation function of the first decomposition curve that defines the first set of mass spectrometry data.

5. The aforementioned computer executable component further, The system according to claim 4, further comprising an output component that generates comparative data including, for a group of fragmented ions commonly fragmented from the first compound and the second compound, based at least on the comparison, the target similarity value between the first spectrally decomposed dataset and the second spectrally decomposed dataset, and including a term for ion activation energy per similarity value.

6. The aforementioned computer executable component further, An evaluation component that identifies an output ion activation energy, which is the target ion activation energy or another ion activation energy, from a range of ion activation energies corresponding to the maximum difference in quantified similarity between the first spectral decomposition dataset and the second spectral decomposition dataset, A notification component that, in response to the identification of the output ion activation energy, generates a notification requesting the re-fragmentation of the first compound at the output ion activation energy, The system according to claim 4, including the system described in claim 4.

7. The aforementioned computer executable component further, Based on the above comparison and an additional comparison of the first spectral decomposition dataset and the second spectral decomposition dataset at additional ion activation energies within the range of ion activation energies, the output component generates comparative data for a group of fragmented ions commonly fragmented from the first and second compounds, including the target similarity value and a term for ion activation energy per similarity value between the first spectral decomposition dataset and the second spectral decomposition dataset, The comparison data includes total spectral similarity data based on the integral of the analysis function over the range of ion activation energies, normalized to the region represented by the first decomposition curve defined by the first spectral decomposition dataset and the second decomposition curve defined by the second spectral decomposition dataset, over the range of ion activation energies. The system according to claim 4.

8. The aforementioned computer executable component further, The system according to claim 7, comprising a graphing component that generates a node-based graph including nodes corresponding to the first compound and the second compound, and edges extending between the nodes and corresponding to the total spectral similarity values ​​of the total spectral similarity data between the first compound and the second compound.

9. The system according to claim 7, wherein the total spectral similarity data is further based on the normalization of the range of ion activation energies between a first spectrometer used to perform the conventional spectroscopic measurement of the first compound and a second spectrometer used to perform the spectroscopic measurement of the second compound.

10. A computer implementation method, A system operablely connected to the processor identifies a first spectral resolution dataset of the first compound corresponding to the first ion activation energy, including the target ion activation energy omitted from conventional spectroscopic measurements of the first compound. The system identifies the second spectral decomposition dataset of the second compound corresponding to the second ion activation energy, The system performs a comparison between the first spectral decomposition dataset and the second spectral decomposition dataset at the target ion activation energy, and as a result obtains a target similarity value that defines the similarity between the first spectral decomposition dataset and the second spectral decomposition dataset at the target ion activation energy. Computer implementation methods including

11. The computer implementation method according to claim 10, further comprising using an approximation function to approximate a first decomposition curve representing a first set of mass spectrometry data.

12. The system generates target spectral decomposition data for the first spectral decomposition dataset at the target ion activation energy, The system generates target spectral data that defines the target spectrum at the target ion activation energy based on the target spectral decomposition data, The computer implementation method according to claim 11, further comprising:

13. The computer implementation method according to claim 10, further comprising generating the first spectral decomposition dataset for a range of ion activation energies, including a first ion activation energy and additional ion activation energies omitted from the conventional spectroscopic measurement of the first compound, including the target ion activation energy, based on an approximation using an approximation function of a first decomposition curve that defines the first set of mass spectrometry data.

14. The computer implementation method according to claim 13, further comprising, by the system, generating comparative data for a group of fragmented ions commonly fragmented from the first compound and the second compound, based at least on the comparison, between the first spectrally decomposed dataset and the second spectrally decomposed dataset, including the target similarity value, and including a term for ion activation energy per similarity value.

15. The system identifies the output ion activation energy, which is the target ion activation energy or another ion activation energy, from a range of ion activation energies corresponding to the maximum difference in quantified similarity between the first spectral decomposition dataset and the second spectral decomposition dataset. The system generates a notification requesting the re-fragmentation of the first compound at the output ion activation energy in response to the identification of the output ion activation energy. The computer implementation method according to claim 13, further comprising:

16. A computer program product that facilitates the process of evaluating the similarity between spectral datasets, A computer-readable storage medium containing program instructions, and a program instruction executable by a processor, The program instruction is, the processor The processor identifies a first spectral decomposition dataset of the first compound that corresponds to the first ion activation energy, including the target ion activation energy that was omitted from the conventional spectroscopic measurement of the first compound. The aforementioned processor identifies the second spectral decomposition dataset of the second compound corresponding to the second ion activation energy, The processor performs a comparison between the first spectral decomposition dataset and the second spectral decomposition dataset at the target ion activation energy, and as a result obtains a target similarity value that defines the similarity between the first spectral decomposition dataset and the second spectral decomposition dataset at the target ion activation energy. A computer program product that is executable by the processor to perform the following.

17. The computer program product according to claim 16, wherein the program instruction is further executable by the processor to approximate a first decomposition curve representing a first set of mass spectrometry data using an approximation function.

18. The aforementioned program instruction is, The processor generates target spectral decomposition data for the first spectral decomposition dataset at the target ion activation energy, The processor generates target spectral data that defines the target spectrum at the target ion activation energy, based on the target spectral decomposition data. The computer program product according to claim 17, further executable by the processor so that the processor performs the following.

19. The aforementioned program instruction is, The processor generates the first spectral decomposition dataset for a range of ion activation energies, including the first ion activation energies and additional ion activation energies omitted from the conventional spectroscopic measurement of the first compound, including the target ion activation energies, based on an approximation using an approximation function of the first decomposition curve that defines the first set of mass spectrometry data. The processor generates comparative data for a group of fragmented ions commonly fragmented from the first compound and the second compound, including the target similarity value between the first spectrally decomposed dataset and the second spectrally decomposed dataset, and including a term for ion activation energy per similarity value, based at least on the comparison. The computer program product according to claim 16, further executable by the processor so that the processor performs the following.

20. The aforementioned program instruction is, The processor generates the first spectral decomposition dataset for a range of ion activation energies, including the first ion activation energy and additional ion activation energies omitted from the conventional spectroscopic measurement of the first compound, which include the target ion activation energy, based on an approximation of the first mass spectrometry data using an approximation function. The processor identifies the output ion activation energy, which is the target ion activation energy or another ion activation energy, from the range of ion activation energies corresponding to the maximum difference in quantified similarity between the first spectral decomposition dataset and the second spectral decomposition dataset. The processor generates a notification requesting the re-fragmentation of the first compound at the output ion activation energy in response to the identification of the output ion activation energy. The computer program product according to claim 15, further executable by the processor so that the processor performs the following.

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