Monitoring the operational degradation of scientific measurement devices

Abstract

The embodiments described herein relate to a process for monitoring the degradation of the operational status of measuring devices, such as spectral analysis devices. [Solution] The system may include a memory for storing computer executable components and a processor for executing them. The computer executable component may include a normalization component that normalizes the accuracy of a spectral analyzer based on the expected mass-to-charge ratio corresponding to a selected fragment ion, as a result of a normalized accuracy coefficient, and a determination component that, based on the normalized accuracy coefficient, assigns the peaks of the molecular spectral data generated by the spectral analyzer to the expected mass-to-charge ratio corresponding to the selected fragment ion.

Description

[Technical Field]

[0001] This application relates to monitoring the deterioration of the operating status of scientific measuring devices. [Background technology]

[0002] Calibrating chemical structure measurement devices and / or monitoring the degradation of their accuracy during their lifecycle can be complex and time-consuming processes, potentially reducing the device's usage time or freeing up user entities to other tasks. Similarly, comparing calibration data and / or operational monitoring data from multiple devices, multiple identical device components, or multiple samples can be complex and time-consuming processes, potentially reducing usage time or freeing up user entities to other tasks. [Overview of the project]

[0003] The following is an overview to provide a basic understanding of one or more exemplary embodiments described herein. This overview is not intended to identify major or important elements and / or define the scope of a particular embodiment or claim. Its sole purpose is to present the concepts in a simplified form as a prelude to more detailed descriptions presented later. In one or more exemplary embodiments, the systems, computer implementations, apparatus 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 measuring device) and known compound data to automatically and efficiently calibrate, normalize and / or compare the output data of the measuring device.

[0004] According to one embodiment, the system may include a memory for storing computer-executable components and a processor for executing the computer-executable components. The computer-executable components may include a normalization component that normalizes the accuracy of a spectral analyzer based on the expected mass-to-charge ratio corresponding to a selected fragment ion, as a result of a normalized accuracy coefficient, and a determination component that, based on the normalized accuracy coefficient, assigns the peaks of the molecular spectral data output by the spectral analyzer, corresponding to the selected fragment ion, to the expected mass-to-charge ratio corresponding to the selected fragment ion.

[0005] According to another embodiment, the computer implementation method may include normalizing the accuracy of a spectral analyzer based on the expected mass-to-charge ratio corresponding to a selected fragment ion by a system that consequently operably couples a normalized accuracy coefficient to a processor, and assigning the peaks of the molecular spectral data generated by the spectral analyzer, corresponding to the selected fragment ion, to the expected mass-to-charge ratio corresponding to the selected fragment ion, based on the normalized accuracy coefficient.

[0006] In yet another embodiment, the computer program product facilitates the process of monitoring the operating status of the spectral analysis device, and the program instructions can be executed by the processor to cause the processor to normalize the accuracy of the spectral analysis device based on the expected mass-to-charge ratio corresponding to the selected fragment ions, to obtain the result of the normalized accuracy coefficient, and to cause the processor to give the peaks of the molecular spectral data generated by the spectral analysis device, corresponding to the selected fragment ions, to the expected mass-to-charge ratio corresponding to the selected fragment ions.

[0007] One or more exemplary embodiments described herein can be implemented in, in relation to, and / or coupled to a chemical structure measuring device.

[0008] The 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 known and / or standard data. The framework described herein is time-efficient and partially automated, thus increasing device usage time and reducing interaction with user entities in pre-test processes. This also reduces unintended errors caused by manual calibration by user entities.

[0009] In one or more exemplary embodiments described herein, a unitless criterion for precision coefficients can be used, determined based on acquired, estimated, and / or predicted (also referred to here as theoretical) data related to the measuring device and similarly selected fragment ions. The unitless criterion can be used to normalize the output data of the measuring device, for example, by giving peaks based on predicted mass-to-charge ratios. [Brief explanation of the drawing]

[0010] 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. [Figure 1] According to one or more exemplary embodiments described herein, an example of a non-limiting system block diagram for facilitating a monitoring process for the operational degradation of a measuring device is shown. [Figure 2] According to one or more exemplary embodiments described herein, another example of a non-limiting system block diagram for facilitating a monitoring process for the operational degradation of a measuring device is shown. [Figure 3]According to one or more exemplary embodiments described herein, an example of a graph showing the degradation of operational status plotted over time for an example of a measuring device is provided. [Figure 4] According to one or more exemplary embodiments described herein, for an example of a measuring device, another example of a graph showing positive polarity degradation plotted separately from negative degradation and plotted over time is provided. [Figure 5] According to one or more exemplary embodiments described herein, Figure 2 shows a schematic diagram of one or more processes that can be performed by the non-limiting system. [Figure 6] According to one or more exemplary embodiments described herein, a flowchart of one or more processes that can be performed by the non-limiting system shown in Figure 1 is provided. [Figure 7] According to one or more exemplary embodiments described herein, Figure 2 shows a schematic diagram of one or more processes that can be performed by the non-limiting system. [Figure 8] According to two or more exemplary embodiments described herein, Figure 7 continues with a flowchart of one or more processes that can be performed by the non-limiting system of Figure 2. [Figure 9] A block diagram of an exemplary operating environment that can incorporate embodiments of the subject matter described herein is shown. [Figure 10] This schematic block diagram illustrates a computing environment capable of at least partially interacting with and / or implementing the subjects described herein. [Modes for carrying out the invention]

[0011] The following detailed description is illustrative only and is not intended to limit the embodiments and / or applications or uses of the present invention. Furthermore, it is not intended to be bound by the expressions or implied information presented in the above-mentioned summary section of the invention or the section on embodiments for carrying out the invention.

[0012] First, regarding chemical structure measurement devices, such devices generally include, but are not limited to, spectral analyzers and chromatography devices. These devices may experience a degradation in accuracy over time, ranging from several hours to several days or even longer. This degradation may be relative to the actual measured values ​​and / or output results compared to theoretical or expected measurements. The rate of degradation in accuracy varies depending on the measurement device and / or its components. In one or more cases, the identification and / or measurement of some ions (e.g., cathode ions) may degrade more rapidly than the identification and / or measurement of other ions (e.g., cathode ions). Due to this degradation, existing frameworks may require calibration of the measurement device before use, more frequently, or before experiments.

[0013] To address shortcomings of one or more existing frameworks, one or more frameworks described herein can be used to rapidly and / or automatically monitor the degradation of the operational status of a measuring device, for example, based on accuracy coefficients generated by the measuring device for each monitoring. For example, the accuracy coefficients can be used to determine the operational status and / or calibration of the measuring device, or to compare the measuring device, acquired objects, components of the measuring device, etc.

[0014] In general, one or more frameworks described herein can be used to deepen our understanding of the accuracy, calibration, and operating status of a measuring device, for example, based on the prediction, estimation, and acquired accuracy of fragment ions analyzed by the measuring device. 2 MS n) For example, for common fragment ions whose predicted accuracy values are known from the past, the accuracy obtained by prediction, estimation, and / or acquisition can be determined. The output of one or more frameworks described herein may be an accuracy factor (af) without units, and the accuracy factor itself may indicate the overall current accuracy of the example measurement device.

[0015] That is, the use of one or more exemplary embodiments described herein can monitor the degradation of the operating state of a spectral analysis device based on the determined accuracy associated with specific fragment ions measured by the spectral analysis device. One or more embodiments can normalize the accuracy of the spectral analysis device based on specific fragment ions by aggregating measured values of prediction, acquisition, and / or estimation, and can give peaks of spectral data based on the normalized accuracy factor. Also, as an optional embodiment, the delta accuracy of the spectral analysis device can be evaluated using data corresponding to the same specific fragment ions. Stated another way, the accuracy factor provides a reference for the accuracy of the spectral analysis device. This reference is normalized with respect to the predicted mass-to-charge ratio (m / z) of a specific fragment ion and is represented as a multiplier of the estimated accuracy corresponding to this specific fragment ion. As a result, as the output of the measurement device, a spectral peak corresponding to a specific fragment ion is given to the predicted mass-to-charge ratio.

[0016] As a result of one or more accuracy factors and / or the output of fitting, one or more delta accuracies of the spectral analysis device can be measured. By using a data set including one or more accuracy factors and / or delta accuracies, the calibration, understanding, and / or comparison of the accuracy of the measurement device can be performed in a time-efficient manner.

[0017] Various operational status monitoring evaluations can be performed based on the output of unitless precision coefficients and / or the output of two or more unitless precision coefficients from the same measuring device or multiple measuring devices. For example, displaying or comparing time-series data of precision coefficients graphically can help determine whether the calibration of the measuring device is off, whether the overall operational status is deteriorating, or both.

[0018] For example, if two accuracy coefficients obtained within the first given time range are similar, it may indicate a possible calibration misalignment. Conversely, two additional accuracy coefficients obtained in the time range following the first time range may indicate a deterioration in the operational status of the measuring device over time (for example, the two additional accuracy coefficients do not simply have values ​​similar to one or two of the two initial accuracy coefficients).

[0019] As another example, the accuracy coefficients of different measuring devices measured within the same time range can be compared. In this case, the comparison may show that one measuring device is more accurate than the other.

[0020] As another example, the accuracy coefficients corresponding to different fragment ions measured within the same time range and detected by the same measuring device can be compared. In this case, the comparison may show that the accuracy of the measuring device for one fragment ion is higher than the accuracy of the measuring device for the other different fragment ion.

[0021] Despite the examples above, one or more such comparisons can be made based on one or more accuracy measurements from a measuring device. Since the measurements can be obtained automatically, the comparisons can be made more time-efficiently than with existing frameworks.

[0022] Where used herein, the phrase “based on” should be understood to mean “at least partially based on” unless otherwise specified.

[0023] As used herein, the term "data" may include metadata.

[0024] As used herein, the terms “entity,” “request entity,” and “user entity” may refer to machines, devices, components, hardware, software, smart devices, stakeholders, organizations, individuals, and / or human beings.

[0025] In this specification, the term "sample" may refer to a single substance, multiple substances, a compound, a composition, a solution, a product, etc.

[0026] Next, one or more exemplary embodiments will be described with reference to the drawings, where similar reference numbers are used throughout to indicate similar drawing elements. The following description includes many specific details for illustrative purposes to provide a more thorough understanding of one or more exemplary embodiments. However, it is clear that in various cases, one or more exemplary embodiments can be practiced without these specific details.

[0027] 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.

[0028] Next, referring to Figures 1 and 2, in one or more exemplary embodiments, the non-limiting systems 100 and / or 200 shown in Figures 1 and 2, and / or the systems thereon, may further comprise one or more computers and / or computing-based elements described herein with reference to a computing environment (e.g., computing environment 1000 shown in Figure 10). 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.

[0029] Referring first to Figure 1, this figure shows a block diagram of an exemplary, non-limiting system 100, which may include a degraded operating system 102 and a library data store (DS) 135. Optionally, the non-limiting system 100 may include a measuring device 150 (e.g., a spectral analyzer or other scientific measuring device). In one or more other embodiments, the measuring device 150 and / or the library data storage 135 may be located outside the degraded operating system 102. Alternatively, the degraded operating system 102 may be communicatively coupled to the measuring device 150 and / or the library data storage 135.

[0030] It should be noted that the operational degradation monitoring system 102 is only briefly detailed to provide an introduction to a more complex and / or more scalable operational degradation monitoring 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 given below with respect to the non-limiting system 200 in Figure 2.

[0031] Still referring to Figure 1, the operational degradation monitoring system 102 can generally facilitate monitoring of operational degradation of the measuring device 150 based on accuracy determined in relation to selected fragment ions measured by the measuring device 150 (e.g., a spectral analysis device), for example, based on a unitless accuracy coefficient 130 corresponding to a specific fragment ion 148. The operational degradation monitoring system 102 may comprise at least a memory 104, a bus 105, a processor 106, a normalization component 112 and / or a determination component 122. The processor 106 may be identical to, included in, or different from, the processor 1004 (Figure 10). The memory 104 may be identical to, included in, or different from, the system memory 1006 (Figure 10).

[0032] By using the components described above, the operational degradation monitoring system 102 can classify the operational degradation of the measuring device 150 by facilitating the process of determining and using predicted, acquired, and estimated measurements in an aggregated format. As a result, the operational degradation monitoring system 102 can generate one or more values ​​(e.g., a normalized precision coefficient 130 and / or delta precision 160) for determining the operational status of the measuring device 150.

[0033] Generally, the normalization component 112 can normalize the accuracy of the spectral analysis device based on the expected mass-to-charge ratio (m / z) 144 corresponding to the selected fragment ion 148, thereby yielding a generally normalized accuracy coefficient 130. The selected fragment ion 148 may be a known fragment ion, a general fragment ion, a standard fragment ion, etc. Thus, information about the fragment ion 148, such as the expected mass-to-charge ratio 144 (e.g., corresponding to the selected fragment ion 148) and / or the estimated mass-to-charge ratio 140 (e.g., corresponding to the combination of the selected fragment ion 148 and the measuring device 150), can be stored in a library data storage 135 that is communicably coupled to the operational degradation monitoring system 102 and retrieved from there. In other words, as will be explained in more detail below, the precision coefficient (e.g., normalized precision coefficient 130) is normalized to match the mass-to-charge ratio of a particular theoretical fragment ion 148 (e.g., expected mass-to-charge ratio 144) and can provide a criterion for the precision of the spectral analyzer 150, expressed as a multiplier of the estimated precision (e.g., estimated mass-to-charge ratio 140) depending on the combination of the particular fragment ion 148 and the spectral analyzer 150 (e.g., includes or does include).

[0034] As a result, the spectral peak 149 corresponding to a specific fragment ion 148 may be given as the output of the measuring device 250 to the expected mass-to-charge ratio 144. That is, the determination component 122 can generally give the peak 149 of the molecular spectral data generated by the spectral analyzer device 150 corresponding to the selected fragment ion 148 to the expected mass-to-charge ratio 144 corresponding to the selected fragment ion 148, based on the normalized precision coefficient 130. This may include determining the delta precision 160 as a result of the spectral analyzer device 150 based on the change and / or difference between the mass-to-charge ratio of the obtained peak value 149 and the expected mass-to-charge ratio 144.

[0035] If one or more conditions are met, the decision component 122 and / or processor 106 can determine whether an additional understanding of the accuracy of the spectral analysis device 150 is required. This may be based on data and / or other communication inputs from a computing device to which a user entity is communicably coupled to the non-limiting system 100, and / or based on data and / or other communication inputs based on a decision by the decision component 122 (for example, based on a default instruction to determine the delta accuracy 160).

[0036] As a result of these components, the generated data (e.g., normalized precision coefficient 130 and / or delta precision 160) can be stored in a library data center 135 or the like.

[0037] The normalization component 112 and / or the determination component 122 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 normalization component 112 and / or the determination component 122. The normalization component 112 and / or the determination component 122 can be stored in memory 104.

[0038] In general, the non-limiting system 100 can provide communication between the operational degradation monitoring system 102 and / or any devices related to the user entity using any suitable communication method (e.g., electronic, telecommunicative, internet, infrared, fiber, etc.).

[0039] Referring now to Figure 6 for an overview of the above components and their functions, the diagram shows a flowchart of an example of an unlimiting method 600 that facilitates the process of monitoring the degradation of the operational status of a measuring device 150 (e.g., a spectral analysis device) according to one or more exemplary embodiments described herein (e.g., unlimiting system 100 in Figure 1). Although the unlimiting method 600 is described in relation to the unlimiting system 100 in Figure 1, the unlimiting method 600 may also be applicable to other systems described herein (e.g., unlimiting system 200 in Figure 2). Descriptions of similar elements and / or repetitions of processes used in each embodiment are omitted for brevity.

[0040] In 602, an unrestricted method 600 may include normalizing the accuracy of a spectral analysis device (e.g., spectral analysis device 150) based on an expected mass-to-charge ratio (e.g., expected mass-to-charge ratio 144) corresponding to a selected fragment ion (e.g., selected fragment ion 148) by a system (e.g., normalization component 212) operably coupled to a processor (e.g., processor 106), thereby obtaining a normalized accuracy coefficient (normalized accuracy coefficient 130).

[0041] In 604, the non-limiting method 600 may include a system determination (e.g., determining component 122) of whether an additional understanding of the accuracy of the spectral analysis device is needed. This may be based on input of data or other communications from a computing device to which a user entity is communicably coupled to the non-limiting system 100, and / or on input of data or other communications based on a determination by component 122 (e.g., based on a default instruction to determine the delta accuracy 160). If the answer is "yes", the non-limiting method 600 may proceed to step 606. If the answer is "yes", the non-limiting method may proceed to terminate.

[0042] In 606, a non-limiting method 600 may include, by a system (e.g., a decision component 122), assigning peaks (e.g., peak 149) of molecular spectral data (e.g., spectral data 147) generated by a spectral analysis device, corresponding to a selected fragment ion, based on a normalized precision coefficient, to the expected mass-to-charge ratio corresponding to the selected fragment ion.

[0043] As described above for the determination component 122 (and also applicable to the determination component 222), the fitting may include modifications, calibration, and determination of the delta accuracy 260 of one or more fragment ions, including the selected fragment ions 148 and 248.

[0044] Referring next to Figure 2, a non-limiting system 200 is shown, which may include a degraded operational status monitoring 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.

[0045] In general, the operational degradation monitoring system 202 can facilitate monitoring of operational degradation of the measuring device 250 based on a determined precision associated with a specific fragment ion measured by a spectral analysis device (e.g., a unitless precision coefficient 230 corresponding to a selected ion 248). That is, one or more embodiments described with respect to Figure 2 can classify operational degradation of the measuring device 250 by facilitating a process of determination using predicted, acquired, and estimated measurements in an aggregated form.

[0046] In one or more exemplary embodiments, the degraded operational status monitoring system 202 may be at least partially included by an internal, external, and / or associated computing device with respect to the measuring device 250 analyzed by the degraded operational status monitoring system 202. In one or more other embodiments, the degraded operational status monitoring system 202 may be isolated from the measuring device 250 (e.g., located outside the measuring device 250), and / or the measuring device 250 may be isolated from a non-limiting system 200 (e.g., located outside the non-limiting system 200).

[0047] One or more communications between one or more components of a 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 not limited to, Wireless Fidelity (Wi-Fi), Global System for Mobile Communications (GSM), Universal Mobile Telecommunications System (UMTS), Worldwide Interoperability for Microwave Access (WiMAX), and Enhanced General Packet Radio Services (enhanced GPRS), Third Generation Partnership Project (3GPP®) Long-Term Evolution (LTE), Third Generation Partnership Project 2 (3GPP2) Ultra Mobile Broadband (UMB), High-Speed ​​Packet Access (HSPA), Zigbee and other 802.XX radio technologies and / or legacy telecommunications technologies, Bluetooth®, Session Initiation Protocol (SIP), ZIGBEE®, RF4CE protocol, WirelessHART protocol, 6LoWPAN (IPv6) This may include over Low Power Wireless Area Networks, Z-Wave, ultra-wideband (UWB) standard protocols, and / or other proprietary and / or non-proprietary communication protocols.

[0048] The operational status degradation monitoring system 202 can be associated with a cloud computing environment (for example, the cloud computing environment 900 in Figure 9) and can be accessed, for example, through it.

[0049] The operational status degradation monitoring system 202 may comprise multiple components. These components may include a memory 204, a processor 206, a bus 205, an identification component 210, a normalization component 212, an output component 214, an interpretation component 216, a notification component 218, an evaluation component 220, a determination component 222, and / or a comparison component 224. By using these components, the operational status degradation monitoring system 202 can facilitate the process of generating one or more values ​​for determining the operational status of the measuring device 250.

[0050] The discussion then moves to the processor 206, memory 204, and bus 205 of the degraded operational status monitoring system 202. For example, in one or more exemplary embodiments, the degraded operational status monitoring 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 degraded operational status monitoring 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 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 component 206 may comprise an identification component 210, a normalization component 212, an output component 214, an interpretation component 216, a notification component 218, an evaluation component 220, a decision component 222, and / or a comparison component 224.

[0051] In one or more exemplary embodiments, the degraded operational status monitoring 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 degraded operational status monitoring system 202 (e.g., an identification component 210, a normalization component 212, an output component 214, an interpretation component 216, a notification component 218, an evaluation component 220, a decision component 222, and / or a comparison 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, a normalization component 212, an output component 214, an interpretation component 216, a notification component 218, an evaluation component 220, a decision component 222, and / or a comparison component 224).

[0052] The operational degradation monitoring 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 able to communicate 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.

[0053] In one or more exemplary embodiments, the degraded operational status monitoring system 202 can be coupled to one or more external systems (e.g., an electrical output generation system not shown, one or more output targets, and / or output target controllers), external sources, and / or devices (e.g., classical and / or quantum computing devices, communication devices, and / or similar devices) for, for example, via a network (e.g., in a communicative, electrically, operationally, optically, and / or similar functions). In one or more exemplary embodiments, one or more components of the degraded operational status monitoring system 202 and / or a non-exclusive system 200 can reside in the cloud and / or locally in a local computing environment (e.g., a specified location).

[0054] In addition to the processor 206 and / or memory 204 described above, the operational degradation monitoring 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.

[0055] The following discussion moves on to additional components of the operational degradation monitoring system 202 (e.g., identification component 210, normalization component 212, output component, interpretation component 216, notification component 218, evaluation component 220, decision component 222, and / or comparison component 224). Generally, the operational degradation monitoring system 202 can perform a set of processes that can be separated into various steps, including, but are not limited to, normalizing the accuracy of a measuring device 250 that produces a normalized accuracy coefficient 230, assigning peaks to spectral data based on the normalized accuracy coefficient 230, and / or evaluating the operational status of the measuring device 250.

[0056] First, in one or more exemplary embodiments, the identification component 210, the normalization component 212, the output component 214, the interpretation component 216, the notification component 218, the evaluation component 220, the decision component 222, and / or the comparison component 224 can be implemented individually without one or more other components among the identification component 210, the normalization component 212, the output component 214, the interpretation component 216, the notification component 218, the evaluation component 220, the decision component 222, and / or the comparison component 224. In addition and / or separately, the identification component 210, normalization component 212, output component 214, interpretation component 216, notification component 218, evaluation component 220, decision component 222, and / or comparison component 224 may be included in the advanced analysis component 203, and the advanced analysis component 203 is one or more of the identification component 210, normalization component 212, output component 214, interpretation component 216, notification component 218, evaluation component 220, decision component 222, and / or comparison component 224. The advanced analysis component 203 may omit the identification component 210, normalization component 212, output component 214, interpretation component 216, notification component 218, evaluation component 220, decision component 222, and / or comparison component 224, and perform one or more of the functions of the identification component 210, normalization component 212, output component 214, interpretation component 216, notification component 218, evaluation component 220, decision component 222, and / or comparison component 224 as described below.

[0057] As mentioned above, the first set of one or more processes involves known or selected fragment ions 248 (e.g., in-silico This may include normalizing the accuracy of the measuring device 250 using mass-to-charge ratio data corresponding to the selected fragment ions (such as fragment ions) and the selected fragment ions 248.

[0058] First, regarding the identification component 210, this component is generally capable of acquiring (e.g., obtaining, positioning, identifying, requesting, downloading) molecular spectral data 247 of a sample (e.g., element, compound, material, etc.) 252, and the sample is analyzed by a measuring device (e.g., a scientific measuring device such as a spectral analyzer) 250 (e.g., the same measuring device 250 on which operational status monitoring is performed).

[0059] Based on this, the identification component 210 can acquire mass-to-charge ratio (m / z) data of known fragment ions 248 of the sample 252 (e.g., data including the acquired mass-to-charge ratio 246 [e.g., acquired mass-to-charge ratio value] in the acquired mass-to-charge ratio data). The mass-to-charge ratio data acquired by the identification component 210 may include acquired mass-to-charge ratio value data including one or more mass-to-charge ratio values ​​246 for the selected fragment ions 248, based on the current state and / or current operating state of the measuring device 250.

[0060] For example, a known sample 252 can be used with the measuring device 250 to acquire molecular spectral data 247. The known sample 252 analyzed with the measuring device 250 may yield a set of one or more known fragment ions 248. In other cases, in addition to the matching step of identifying spectral data peaks of spectral data 247, the fragment ions 248 of the sample can also be identified by using an unknown sample 252. This matching step can be performed by components and / or systems other than the identification component 210, the processor 206, and / or the operational degradation monitoring system 202.

[0061] In this specification, the term "peak" in spectral data refers to the peak value within the data, and it is not necessary to display the spectral data 247 graphically.

[0062] The spectral data 247 may take any suitable form, may include data and / or metadata, and may be based on and / or contained in spectra or underlying data of spectra.

[0063] If there is one or more, the spectrum is generated by electron ionization mass spectrometry (MS) after the generation of one or more in-silico fragments from the sample 252 by the measuring device 250. 1 ) spectrum or soft ionization mass spectrometry (MS) n ) may include the spectrum.

[0064] In one or more embodiments, the identification component 210 may also acquire (e.g., obtain, locate, identify, request, download) an estimated mass-to-charge ratio 240 for selected fragment ions 248 based on the current state and / or current operating state of the measuring device 250. The estimated mass-to-charge ratio 240 may be generated by a process understandable to a person with normal skills, using analytical criteria of one or more measuring devices, such as resolution, peak height, and ion implantation time.

[0065] Next, and / or at least partially in parallel with the above process, based on the identification of the selected fragment ions 248, the identification component 210 can obtain (e.g., obtain, locate, identify, request, download) the expected mass-to-charge ratio 244 corresponding to the selected fragment ions 248.

[0066] In one or more cases, the expected mass-to-charge ratio 244 may be the default mass-to-charge ratio depending on the measuring device 250, and in other cases, it may be particularly dependent on at least one measuring device 250 whose operational status is being monitored (for example, based on the hardware, software, and / or firmware of the specific measuring device 250 being monitored).

[0067] For example, the identification component 210 can obtain the predicted mass-to-charge ratio 244 as the predicted mass-to-charge ratio value from the library data storage 235. For example, the structure, chemical formula, and / or mass-to-charge ratio value of the sample 252 can be stored in a raw file such as a comma-divided value (CSV) file or in a database system such as the library data storage 235.

[0068] As another example, the identification component 210 may also obtain the expected mass-to-charge ratio 244 as an expected mass-to-charge ratio value from the library data storage 235, or from another specific location having a file containing structural information for sample 252 or a file containing structural information for a particularly selected fragment ion 248.

[0069] As another example, if sample 252 is an unknown sample 252, a reference for the expected mass-to-charge ratio 244 can be generated. This generation is performed by MS n This can be based on the identification of ions 248 that commonly occur in the spectrum (for example, if past and / or known data is included in the library data center 235).

[0070] In addition, and / or separately, with respect to the unknown sample 252, observe the general neutral loss (e.g., loss of H2, O, NH3, CO, CO2, sugar) (MS) 1 Spectrum (for in-source ions) or MS n(What is in the spectrum) can be used. In such cases, the identification component 210 can use a pair of peak values ​​249 corresponding to the selected fragment ion 248, and the mass-to-charge ratio of another selected fragment ion (different from the selected fragment ion 248). 246 values ​​can be obtained. In this regard, the identification component 210 can obtain an expected mass-to-charge ratio 244 which includes the theoretical value of the neutral loss corresponding to the selected fragment ion 248 and another selected fragment ion. In this regard, the identification component 210 can obtain an estimated mass-to-charge ratio 240 which includes the sum of the estimated mass-to-charge ratio 240 values ​​for the peak value 249. That is, in one or more cases, this sum can be obtained or generated by other means. For example, the normalization component 212 can obtain the sum of two estimated mass-to-charge ratios 240 based on the first estimated mass-to-charge ratio 240 value for the first peak value 249 and the second estimated mass-to-charge ratio 240 value for the second peak value 249.

[0071] In addition, and / or separately, with respect to an unknown sample 252, the identification component 210 can identify impurities that commonly occur from spectral data 247 acquired by the measuring device 250. This is MS 1 The spectrum may contain commonly occurring impurities, such as plasticizers, antioxidants like industrial antioxidants, leaches, and extracts. The spectral data 247 can be stored in a library data storage 235, such as an online database accessible by the operational degradation monitoring system.

[0072] In any two or more of the above cases, the data acquired by the identification component 210 can be merged and / or aggregated to obtain the predicted mass-to-charge ratio 244 for the measuring device 250 and / or selected fragment ions 248.

[0073] In any one or more of the above cases, the identification component 210 can store acquired, generated, and / or acquired data, such as data obtained from external data storage, data generated by the unknown sample 252, and / or data acquired by the unknown sample 252. This data is stored in the library data store 235 for future use by the measuring device 250 and / or the operational degradation monitoring system 202. This data may include metadata and / or be stored in an appropriate format.

[0074] In brief summary above, the acquired mass-to-charge ratio 246, the expected mass-to-charge ratio 244, and / or the estimated mass-to-charge ratio 240 can be obtained by the identification component 210.

[0075] The normalized component 212 is the mass-to-charge ratio obtained. Using 246, the predicted mass-to-charge ratio 244, and / or the estimated mass-to-charge ratio 240, the accuracy coefficient (af) of the measuring device 250 can be determined. This af can be output by the output component 214.

[0076] Using Equation 1, the normalized component 212 can generate the absolute difference between the obtained mass-to-charge ratio (m / z) 246 and the expected mass-to-charge ratio (m / z) 240.

[0077] If the result is TIFF2026097729000002.tif181101 or higher, the output of Equation 1 may commonly be referred to as the estimated accuracy of the measuring device 250.

[0078] Through the results of Equation 1 and the use of Equation 2, the normalization component 212 can determine the af based on the accuracy normalization of the measuring device 250 with respect to the selected fragment ions 248, with the estimated accuracy of the measuring device 250 as a multiplier.

[0079]

number

[0080] The output component 214 can output the accuracy coefficient, which is the obtained normalized accuracy coefficient 230, to the user entity (for example, by communicating the normalized accuracy coefficient 230 with a computing device that can be communicably coupled to the operational status degradation monitoring system 202 and associating it with the user entity).

[0081] In one or more embodiments, the output component 214 can group sets of acquired data, such as the acquired mass-to-charge ratio 246 values, normalized precision coefficients 230, and / or delta precision coefficients 260. Grouping can be performed based on appropriate constraints, such as precision coefficients 230 generated for a specific measuring device 250. In one or more cases, the output component 214 can generate visual data 282 that can be displayed on the screen and / or graphical user interface of a computer device communicatively coupled to a non-limiting system 200. In one or more other cases, the output component 214 can request transmission and / or download of such visual data 282. The visual data 282 may include data and / or metadata defining a comparison between precision coefficients and acquisition time points, as shown in Figure 3 (details below).

[0082] Moving to Figure 5, what is shown is a set of af data 500, which is output as an example by output component 214. The af data 500 set may include, but is not limited to, a file name, acquisition / creation date, and / or various precision factors 230. The raw file (e.g., row 1 of Table 502) is shown to contain data for a single known compound with a known chemical structure. The third column shows the mass-to-charge ratio value of the [M+H]+ ion of that compound. The [M+H]+ ion is a positive polar molecular ion formed when a proton is donated to a neutral molecule M, where H represents the added proton.

[0083] The interpretation component 216 can interpret the normalized precision coefficient as a unitless precision criterion passing through a median normalized to a value of 1. That is, one or more embodiments described herein generate a normalized precision coefficient 230, which is a unitless criterion, by the normalization component 212, thereby facilitating the application of af to one or more peaks 249 of one or more sets of spectral data 247 output by the measuring device 250, and / or facilitating the application of af230 to one or more other calibration and / or operational status monitoring formulas. In particular, the interpretation component 216 can use an operational status threshold of a value of 1 for af230, and if the value exceeds 1, it is interpreted that the measuring device 250 has a problem with acquisition. Note that any appropriate operational status threshold other than 1 (e.g., 1.5, 2.0, etc.) may be used. Also note that different thresholds may be appropriate for different types and / or brands of measuring devices (e.g., based on different hardware, firmware and / or software used).

[0084] If it is determined that the normalized precision coefficient value exceeds 1, the notification component 218 can notify the user entity of the problem with the measurement device 250 by, for example, transmitting the normalized precision coefficient 230 to a computing device that can be communicably coupled to the operational degradation monitoring system 202.

[0085] As described above, a second set of one or more processes may include providing peaks in spectral data based on a normalized precision factor 230. For example, the determinant component 222 may apply the normalized precision factor 230 to the values ​​of peaks 249 (e.g., selected fragment ions 248) in a set of spectral data 247 output by the measuring device 250, thereby normalizing / calibrating the minimum selected fragment ions 248 against the spectral data 247. In one or more cases, this may include improving the precision of the peak values ​​249 using the normalized precision factor 230 and appropriately updating the values ​​in the spectral data 247. In one or more cases, the above processes may be further applied individually to one or more of the other selected fragment ions 248 in the spectral data 247 (e.g., at least partially in parallel with each other).

[0086] If there is one or more, the determination component 222 can identify the aforementioned change in precision as a delta precision 260. This delta precision 260 can be applied only to the selected fragment ions 248 and / or to one or more fragment ions of the spectral data 247, as described above. Similarly, this delta precision 260 may be an output by the output component 214 to the user entity (for example, the normalized precision coefficient 230 is communicated to a computing device that can be communicably coupled to the operational degradation monitoring system 202 and associated with the user entity).

[0087] As described above, another set of processes may include an evaluation of the operational status of the measuring device 250, which may include the use of normalized precision coefficients 230 and / or delta precision coefficients 260. For example, the evaluation component 220 may perform one or more evaluations based on the output of unitless normalized precision coefficients 230 and / or the outputs of two or more unitless precision coefficients 230 for the same measuring device 250 or multiple measuring devices (including measuring device 250 and another measuring device). For example, displaying or comparing the acquired precision coefficient data over time in a graph may help determine whether the measuring device 250 is out of calibration, has a degraded overall operational status, or both.

[0088] Referring again to Figure 3, Figure 3 shows an example graph of the accuracy coefficient 300 of the measuring device 250 based on the accuracy coefficient 230 acquired over 24 hours. The x-axis shows the time and date of data acquisition. The y-axis shows the output accuracy coefficient 230 (e.g., output by the output component 214). The figure shows that, without calibration, the accuracy coefficient 230 of the measuring device 250, and the accuracy of the measuring device 250, with respect to the selected fragment ions 248, generally decreases over 24 hours. As shown above, if the operational threshold is 1, it is met for approximately 12 hours of the 12-hour period, at which point the notification component 218 can output an indication of an acquisition problem with the measuring device 250 by outputting a notification 280 in any appropriate format, for example, based on the output of the interpretation component 216 (e.g., using the operational threshold).

[0089] Referring again to Figure 4, Figure 4 shows an example graph of the accuracy coefficient 400 of the measuring device 250 based on the accuracy coefficient 230 acquired over a three-month period. Based individually on the positive and negative spectral data 247 of the measuring device 250, the acquisition results were twice as good. The x-axis shows the time and date of data acquisition. The y-axis shows the output accuracy coefficient 230 (e.g., output by the output component 214). The figure shows that, with respect to selected fragment ions 248, the accuracy coefficient 230 of the measuring device 250, and the accuracy of the measuring device 250, generally decrease over a three-month period. The positive polarity data 402 does not decrease as much as the negative polarity data 403. This indicates a possible problem with calibration in negative polarity mode due to calibration criteria, software errors, instrument errors and / or failures, etc. As shown above, at approximately 10 days into the three-month period, the operating status threshold of an average value of 1 is met for negative polarity data 403. At this point, the notification component 218 may output an indication of a problem with the acquisition of the measurement device 250 based on the output of the interpretation component 216 (e.g., the use of operational status thresholds).

[0090] As another example, separate from Figures 3 and 4, if two accuracy coefficients measured within the first time range are similar, it may indicate a calibration misalignment. Conversely, two additional accuracy coefficients obtained in the next time range after the first time range may indicate a decline in the operational status of the measuring device over time (for example, the two additional accuracy coefficients simply do not have values ​​similar to one or two of the two initial accuracy coefficients).

[0091] As another example, the accuracy coefficients of different measuring devices measured within the same time range can be compared. In this case, the comparison may show that one measuring device is more accurate than the other.

[0092] As another example, the accuracy coefficients corresponding to different fragment ions measured within the same time range and detected by the same measuring device can be compared. In this case, the comparison may show that the accuracy of the measuring device for one fragment ion is higher than the accuracy of the measuring device for the other different fragment ion.

[0093] In short, the use of one or more exemplary embodiments described herein allows monitoring of the operational degradation of a spectral analyzer based on the accuracy determined by data associated with a specific fragment ion measured by the spectral analyzer. For example, one or more embodiments described herein can classify the operational degradation of a spectral analyzer by facilitating the process of determining and using expected, acquired, and estimated measurements in an aggregated form. This may include generating an accuracy coefficient (e.g., a normalized accuracy coefficient 230) which is a criterion for the accuracy of the spectral analyzer 250 (e.g., including and / or including it) normalized according to the expected mass-to-charge ratio 244 of a particular fragment ion 242 and expressed as a multiplier of the estimated accuracy 240 corresponding to the particular fragment ion 242. As a result, the spectral peak corresponding to a particular fragment ion 242 may be given as an output by the measuring device 250 to the expected mass-to-charge ratio 244.

[0094] Referring to Figures 7 and 8, which serve as another summary of the above components and / or their functions, a flowchart is shown illustrating an exemplary non-limiting method 700 for facilitating monitoring of the operational status of a measuring device, according to one or more exemplary embodiments described herein (e.g., the non-limiting system 200 shown in Figure 2). Although the non-limiting method 700 is described in reference to the non-limiting system 200 in Figure 2, the non-limiting method 700 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 repetitions of processes used in each embodiment are omitted for brevity.

[0095] In 702, the non-limiting method 700 may include obtaining an estimate of accuracy (e.g., estimated mass-to-charge ratio 240) based on two or more combinations of resolution, peak, peak height, mass-to-charge ratio, or ion implantation time (e.g., spectral analysis device 250) by a system (e.g., identification component 210) coupled to a processor (e.g., processor 206).

[0096] In 704, a non-limiting method 700 may include, by system, normalization of the accuracy of a spectral analysis device (e.g., spectral analysis device 212) based on an expected mass-to-charge ratio (e.g., expected mass-to-charge ratio 244) corresponding to a selected fragment ion (e.g., selected fragment ion 248), resulting in a normalized accuracy coefficient (normalized accuracy coefficient 230).

[0097] In 706, the non-restrictive method 700 may include the generation of a normalized precision coefficient, which is a multiplier of the estimated precision of the spectral analysis device, by the system (e.g., the normalization component 212).

[0098] In 708, a non-limiting method 700 may include using the estimated accuracy of a spectral analyzer and, by a system (e.g., a normalization component 212), generating a normalized accuracy based on the difference between the acquired mass-to-charge ratio of the spectral analyzer for a selected fragment ion (e.g., acquired mass-to-charge ratio 246) and the expected mass-to-charge ratio of the spectral analyzer for a selected fragment ion (e.g., expected mass-to-charge ratio 244).

[0099] In the 710, the system (e.g., output component 214) outputs the normalized precision coefficient as a unitless value.

[0100] In 712, the non-restrictive method 700 may include an interpretation of a normalized precision coefficient by the system (e.g., component 216) that includes a precision criterion for the peak passing through a unitless median normalized to a value of 1, where a value greater than 1 indicates an indication of a problem with the acquisition of the spectral analysis device.

[0101] In 713, the non-restrictive method 700 may include determining whether the value of the normalized precision coefficient is greater than 1 by the system (e.g., the interpretation component 216). If the answer is yes, the non-restrictive method 700 may proceed to step 714. Otherwise, the non-restrictive method 700 may bypass step 714 and proceed to step 716.

[0102] In 714, the non-restrictive method 700 may include the generation of a notification (e.g., notification 280) by the system (e.g., notification component 218) if it is determined that the value of the normalized precision coefficient is greater than 1.

[0103] In 716, a non-limiting method 700 may include, by a system (e.g., a decision component 222), assigning the peaks of molecular spectral data generated by a spectral analysis device to the predicted mass-to-charge ratio corresponding to selected fragment ions, based on a normalized precision coefficient.

[0104] As described above regarding the determination component 222, the fitting may include modifications, calibrations, and determinations such as the delta accuracy 260 of one or more fragment ions, including the selected fragment ions 248.

[0105] In 718, the non-restrictive method 700 may include the system (e.g., the decision component 222) using multipliers to determine the peak.

[0106] At 720, the non-limiting method 700 can include applying a precision coefficient normalized to a molecular spectrum that is the spectrum of electron impact ionization mass spectrometry (MS 1 ) or soft ionization mass spectrometry (MS n , where n is 1 or more), determined by the system (e.g., determining component 222).

[0107] At 722, the non-limiting method 700 can include giving peaks by the system (e.g., determining component 222) based on the application of the normalized precision coefficient.

[0108] At 724, the non-limiting method 700 can include outputting a normalized precision coefficient based at least in part on a neutral loss, using the sum of the estimated precision of a spectral analysis device corresponding to a pair of peaks as a result of the difference between a pair of acquired mass-to-charge ratios by a pair of peaks, according to selected fragment ions and fragment ions of another specific compound, by the system (e.g., output component 214).

[0109] Additional summary 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. The present invention is 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 can be used to implement 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.

[0110] In this specification, systems and / or devices are described in relation to the interaction between one or more components (and / or will be described further later). Such systems and / or components may comprise a designated component or subcomponent, one or more of the 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 aggregated 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.

[0111] In short, one or more systems, computer program products, and / or computer implementation methods provided and described herein relate to a process for monitoring the operational degradation of a measuring device. The system may comprise a memory for storage (e.g., memories 104, 204) and a processor (e.g., processors 106, 206) for executing a computer executable component. The computer executable component may comprise a normalization component 112, 212 that normalizes the precision of a spectral analyzer device 150, 250 based on the expected mass-to-charge ratio 144, 244 corresponding to selected fragment ions 148, 248, resulting in normalized precision coefficients 130, 230, and a determination component 122, 222 that, based on the normalized precision coefficients 130, 230, assigns peaks 149, 249 corresponding to the selected fragment ions 148, 248 in molecular spectral data 147, 247 generated by the spectral analyzer device 150, 250 to the expected mass-to-charge ratio 144, 244 corresponding to the selected fragment ions 148, 248.

[0112] In other words, the use of one or more exemplary embodiments described herein allows for monitoring of degradation in the operational status of a spectral analyzer based on the determined accuracy associated with specific fragment ions measured by the spectral analyzer. For example, one or more embodiments described herein can classify degradation in the operational status of a spectral analyzer by facilitating the process of determining and using expected, acquired, and estimated measurements in an aggregated form.

[0113] This monitoring may include normalizing the accuracy of the spectral analyzer based on a specific fragment ion, assigning peaks to spectral data based on the normalized accuracy coefficient, and / or optionally determining the delta accuracy of the spectral analyzer using data corresponding to the same specific fragment ion. Alternatively, the accuracy coefficient is a criterion for the accuracy of the spectral analyzer. This criterion is normalized by the expected mass-to-charge ratio (m / z) of a specific fragment ion and expressed as a multiplier of the estimated accuracy corresponding to this specific fragment ion. As a result, the spectral peak corresponding to a specific fragment ion may be assigned as the output of the measuring device to the expected mass-to-charge ratio.

[0114] One or more precision coefficients and / or fitting outputs can be used to determine one or more delta precisions of a spectral analyzer. Using a dataset containing one or more precision coefficients and / or delta precisions allows for time-efficient calibration, understanding, and / or comparison of the precision of the measuring device. Therefore, the inventors demonstrate that by using a known baseline of selected fragment ions and their associated mass-to-charge ratio, the output of the spectral analyzer can be normalized, resulting in the acquisition of a unitless precision coefficient. Here, the delta precision of the spectral analyzer can be determined using the normalized precision coefficient (e.g., compared to the spectral analyzer's historical precision, or compared to an expected value).

[0115] One or more exemplary embodiments described herein can be implemented in, in relation to, and / or coupled to, a scientific measuring device.

[0116] One or more exemplary embodiments disclosed herein can be applied in a plug-and-play manner to various structures of existing scientific measuring devices. That is, one or more exemplary embodiments described herein can automatically acquire data corresponding to and output from a measuring device. This is useful in determining the operating status of such a measuring device when it cannot access specific hardware and / or software (e.g., agnostic to software and / or hardware).

[0117] In fact, with respect to one or more embodiments described herein, practical applications of one or more systems, computer implementation methods, and / or computer program products described herein are capable of providing the aforementioned precision coefficients and / or delta precision information based on specific / selected fragment ions. Compared with existing frameworks that cannot provide this capability, one or more exemplary embodiments described herein can provide novel results that were not previously available.

[0118] These are convenient and practical applications of computers, and therefore enhance the monitoring of the operational status of measuring devices (e.g., improved and / or optimized). Overall, such computerized tools can bring concrete and tangible technological improvements to the field of materials analysis, particularly in the monitoring of the operational status of measuring devices when performing materials analysis with measuring devices.

[0119] Furthermore, one or more exemplary embodiments described herein can be used in real-world systems based on the disclosed teachings. For example, for a particular measuring device, the precision coefficient and / or delta precision can be determined for a selected fragment ion, for comparison with multiple different ions in one measuring device, for comparison with the same fragment in multiple measuring devices, and / or for long-term operational monitoring of a combination of one measuring device and one fragment ion over time. The molecular structure library data store of molecular structural contents can be identified and its contents evaluated. The current precision of a measuring device can be determined based on at least one precision coefficient, since it is a unitless, normalized value. The degree of precision deviation can be determined with respect to the analysis of a selected fragment ion based on the delta precision. By comparing one or more precision coefficients and / or delta precisions using multiple precision coefficients, time periods, and / or fragment ions of several devices, a deeper understanding of the operational state of the measuring devices can be gained. These can be useful processes for various industries using materials analysis, product manufacturing, quality control, etc. Accordingly, the embodiments disclosed herein can provide improvements to scientific instrument technology (e.g., improvements to computer technology that supports such scientific instruments, among other improvements).

[0120] Furthermore, one or more exemplary embodiments described herein can achieve a certain level of scale of operation. For example, two or more precision factors can be determined, at least partially in parallel, for several devices, time, and / or fragment ions.

[0121] In this specification, systems and / or devices are described in relation to the interaction between one or more components (and / or will be described further later). Such systems and / or components may comprise a designated component or subcomponent, one or more of the 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 aggregated 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.

[0122] 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 monitoring the operational status of a measuring device (e.g., the use of a measuring device on a material) 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 materials analysis and cannot be equally practically implemented in a reasonable manner outside of a computing environment.

[0123] 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 one person nor a thousand or more people can efficiently, accurately, and / or effectively analyze computer data / metadata (e.g., spectral data or mass-to-charge ratio data) to define the accuracy of acquisition, estimation, and / or prediction for one or more measuring devices and one or more selected fragment ions, and / or generate a digital visual representation of delta accuracy, as can be done by the processes of one or more embodiments described herein. Furthermore, neither the human brain nor a person using pen and paper can perform one or more of these processes as the one or more exemplary embodiments described herein do.

[0124] In one or more exemplary embodiments, one or more processes described herein may be configured to perform a defined task relating to one or more of the above-described technologies by one or more dedicated computers (e.g., dedicated processing units, dedicated classical computers, dedicated quantum computers, dedicated hybrid classical / quantum systems, and / or other types of dedicated computers). One or more exemplary embodiments and / or components thereof 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.

[0125] 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.

[0126] To provide an additional overview, the following is a list of embodiments and their features.

[0127] A 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 components include a normalization component that normalizes the accuracy of a spectral analyzer based on a predicted mass-to-charge ratio corresponding to a selected fragment ion, as a result of a normalized accuracy coefficient, and a determination component that, based on the normalized accuracy coefficient, assigns the peaks of molecular spectral data generated by the spectral analyzer corresponding to the selected fragment ion to the predicted mass-to-charge ratio corresponding to the selected fragment ion.

[0128] A system as described in the preceding paragraph, wherein the computer-executable component further comprises an output component that outputs the normalized precision coefficient as a unitless value.

[0129] A system as described in any of the preceding paragraphs, wherein the normalization component generates the normalized accuracy coefficient, which is a multiplier for the estimation accuracy of the spectral analysis device, and the determination component uses the multiplier to determine the peak.

[0130] A system described in any of the preceding paragraphs, wherein the normalized accuracy coefficient is based on the difference between the mass-to-charge ratio obtained by the spectral analyzer for the selected fragment ion and the mass-to-charge ratio predicted by the spectral analyzer for the selected fragment ion, using the estimated accuracy of the spectral analyzer.

[0131] A system described in any of the preceding paragraphs, wherein the estimation accuracy includes estimating an accuracy value based on two or more combinations of resolution, peaks, peak height, mass-to-charge ratio, or ion implantation time with respect to the spectral analysis device.

[0132] A system as described in any of the preceding paragraphs, wherein the computer executable component further comprises an interpreting component that interprets the normalized precision coefficient as a criterion for the precision of a peak passing through a unitless median normalized to a single value (where a value greater than 1 is interpreted as indicating an acquisition problem with the spectral analysis device), and a notification component that generates a notification when it is determined that the value of the normalized precision coefficient is greater than 1.

[0133] A system described in any of the preceding paragraphs, wherein the determination component is electron ionization mass spectrometry (MS). 1 ) spectrum or soft ionization mass spectrometry (MS) n A system that applies a normalized precision coefficient to a molecular spectrum which is the spectrum of (where n is 1 or greater), and the determination component gives the peak based on the application of the normalized precision coefficient by the determination component.

[0134] A system as described in any of the preceding paragraphs, wherein the normalized precision coefficient is at least partially based on neutral loss, and further comprises an output component that outputs the normalized precision coefficient using the sum of the estimated precisions of the spectral analyzer corresponding to the pair of peaks as a result of the difference between the mass-to-charge ratios of a pair of acquisitions by the pair of peaks, depending on the selected fragment ion and the second selected fragment ion of the compound.

[0135] A computer implementation method comprising: normalizing the accuracy of a spectral analysis device based on an expected mass-to-charge ratio corresponding to a selected fragment ion by a system which consequently operably couples a normalized accuracy coefficient to a processor; and assigning, by the system, peaks of molecular spectral data generated by the spectral analysis device corresponding to the selected fragment ion to the expected mass-to-charge ratio corresponding to the selected fragment ion, based on the normalized accuracy coefficient.

[0136] A computer implementation method described in the preceding paragraph, further comprising outputting the normalized precision coefficient as a unitless value by the system.

[0137] A computer implementation method described in any of the preceding paragraphs, further comprising: generating the normalized accuracy coefficient, which is a multiplier for the estimation accuracy of the spectral analysis device, by the system; and providing the peak using the multiplier by the system.

[0138] A computer implementation method described in any of the preceding paragraphs, wherein the normalized accuracy coefficient is based on the difference between the mass-to-charge ratio obtained by the spectral analyzer for the selected fragment ions and the mass-to-charge ratio predicted by the spectral analyzer for the selected fragment ions, using the estimated accuracy of the spectral analyzer.

[0139] A computer implementation method described in any of the preceding paragraphs, further comprising: the system interpreting the normalized precision coefficient as a precision criterion for peaks passing through a unitless median normalized to a single value (where a value greater than 1 is interpreted as indicating an acquisition problem with the spectral analysis device); and the system generating a notification if it determines that the value of the normalized precision coefficient is greater than 1.

[0140] A computer implementation method described in any of the preceding paragraphs, further comprising using the system to determine the normalized precision coefficient by electron impulse ionization mass spectrometry (MS). 1 ) spectrum or soft ionization mass spectrometry (MS) n A computer implementation method comprising applying to the molecular spectrum which is a spectrum of (where n is 1 or greater), and giving peaks based on the application of the normalized precision coefficient by the system.

[0141] A computer program product for facilitating a process for monitoring the operational status of a spectral analysis device, wherein the computer program product includes a computer-readable storage medium having program instructions embodied thereby, the program instructions enabling the processor to: normalize the accuracy of the spectral analysis device based on the expected mass-to-charge ratio corresponding to a selected fragment ion, as a result of the processor normalizing the accuracy coefficient; and cause the processor to assign peaks in the molecular spectral data generated by the spectral analysis device, corresponding to the selected fragment ion, to the expected mass-to-charge ratio corresponding to the selected fragment ion, based on the normalized accuracy coefficient.

[0142] A computer program product as described in the preceding paragraph, wherein the program instruction is further capable of the processor causing the processor to output the normalized precision coefficient as a unitless value.

[0143] A computer program product as described in any of the preceding paragraphs, wherein the program instructions enable the processor to generate a normalized accuracy coefficient which is a multiplier for the estimated accuracy of the spectral analysis device, and to use the multiplier to provide the peak.

[0144] A computer program product as described in any of the preceding paragraphs, wherein the normalized precision coefficient is based on the difference between the mass-to-charge ratio obtained by the spectral analyzer for the selected fragment ions and the mass-to-charge ratio predicted by the spectral analyzer for the selected fragment ions, using the estimated precision of the spectral analyzer.

[0145] A computer program product as described in any of the preceding paragraphs, wherein the program instruction is further capable by the processor of causing the processor to interpret the normalized precision coefficient as a criterion for the precision of a peak passing through a unitless median normalized to a single value (where a value greater than 1 is interpreted as indicating an acquisition problem with the spectral analysis device), and causing the processor to generate a notification if it determines that the value of the normalized precision coefficient is greater than 1.

[0146] A computer program product described in any of the preceding paragraphs, wherein the program instructions are to be performed by the processor on the normalized precision coefficient using electron shock ionization mass spectrometry (MS). 1 )) Spectrum or soft ionization mass spectrometry (MS) n A computer program product that is further capable by the processor of applying to the molecular spectrum which is a spectrum of (where n is 1 or greater), and causing the processor to provide peaks based on the application of the normalized precision coefficient.

[0147] Exemplary operating environment Figure 9 is a schematic block diagram of an operating environment 900 in which the described subjects can interact. The operating environment 900 comprises one or more remote components 910. The remote components 910 may be hardware and / or software (e.g., threads, processes, computing devices). In one or more exemplary embodiments, the remote components 910 may be distributed computing systems that connect to programs that use local autoscaling components and / or resources of the distributed computing systems via a communication framework 940. The communication framework 940 may include wired network devices, wireless network devices, mobile devices, wearable devices, wireless access network devices, gateway devices, femtocell devices, servers, and the like.

[0148] The operating environment 900 also includes one or more local components 920. The local components 920 may be hardware and / or software (e.g., threads, processes, computing devices). In one or more exemplary embodiments, the local components 920 may include programs that communicate with / use auto-scaling components and / or remote resources 910 and 920, etc., which are connected to a remotely located distributed computing system via a communication framework 940.

[0149] One possible communication between the remote component 910 and the local component 920 may take the form of data packets adapted for transmission between two or more computer processes. Another possible communication between the remote component 910 and the local component 920 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 900 includes a communication framework 940 that can be used to facilitate communication between the remote component 910 and the local component 920, and may include an air interface, such as an interface to a UMTS network over an LTE network. The remote component 910 may be operably coupled to one or more remote data stores 950 (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 910 side of the communication framework 940. Similarly, the local component 920 may be operably coupled to one or more local data stores 930 that can be used to store information on the local component 920 side of the communication framework 940.

[0150] Exemplary computing environment To provide additional context to the various embodiments described herein, Figure 10 and the following discussion are intended to provide a brief general description of a preferred computing environment 1900 on which various embodiments of the embodiments described herein can be implemented. Although the embodiments are 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.

[0151] 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.

[0152] 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.

[0153] 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. By example, but not by limitation, 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).

[0154] 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” and “non-temporary” are used, when applied to storage, memory, or computer-readable media, to exclude the propagating temporary signal itself, and do not waive any rights to standard storage devices, memories, or computer-readable media that are not the propagating temporary signal itself.

[0155] 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 operations can be performed on the information stored on that medium.

[0156] 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” means a signal having one or more characteristics that are set or modified to encode information in one or more signals. Communication media include, but are not limited to, wired media such as wired networks or direct wired connections, as well as wireless media such as acoustic, RF, infrared, and other wireless media.

[0157] Continuing with reference to Figure 10, an exemplary computing environment 1000, which can implement one or more exemplary embodiments described herein, includes a computer 1002, the computer 1002 includes a processing unit 1004, system memory 1006, and a system bus 1008. The system bus 1008 connects system components, including but not limited to the system memory 1006, to the processing unit 1004. The processing unit 1004 can be any of a variety of commercially available processors. Dual microprocessors and other multiprocessor architectures can also be used as the processing unit 1004.

[0158] The system bus 1008 can be one of several types of bus structures that can further interconnect with memory buses, peripheral buses, and local buses (with or without a memory controller) using any of various commercially available bus architectures. The system memory 1006 includes ROM 1010 and RAM 1012. The basic input / output system (BIOS) can be stored in non-volatile memory such as ROM, erasable programmable read-only memory (EPROM), or EEPROM, and the BIOS includes basic routines that help transfer information between elements within the computer 1002, such as during startup. RAM 1012 may also include high-speed RAM such as static RAM for caching data.

[0159] Computer 1002 may further include an internal hard disk drive (HDD) 1014 (e.g., EIDE, SATA) and one or more external storage devices 1016 (e.g., a magnetic floppy disk drive (FDD) 1016, a memory stick or flash drive reader, a memory card reader, etc.). Although the internal HDD 1014 is shown as being located inside computer 1002, the internal HDD 1014 may also be configured for external use in a suitable chassis (not shown). In addition, although not shown in computing environment 1000, a solid-state drive (SSD) may be used in addition to or instead of the HDD 1014.

[0160] Other internal or external storage devices may include at least one other storage device 1020 having a storage medium 1022 (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.)). The external storage device 1016 can be facilitated by a network virtual machine. The HDD 1014, the external storage device 1016, and the storage device (e.g., a drive) 1020 can be connected to the system bus 1008 by the HDD interface 1024, the external storage device interface 1026, and the drive interface 1028, respectively.

[0161] 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 1002, drives and storage media correspond to the storage of any data in an appropriate digital format. Although 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.

[0162] The drive and RAM 1012 can store many program modules, including the operating system 1030, one or more application programs 1032, other program modules 1034, and program data 1036. The operating system, applications, modules, and / or data, in whole or in part, can also be cached in RAM 1012. The systems and methods described herein can be implemented using various commercially available operating systems or combinations of operating systems.

[0163] Computer 1002 may optionally include emulation techniques. For example, a hypervisor (not shown) or other intermediary may emulate the hardware environment of operating system 1030, and the emulated hardware may optionally differ from the hardware shown in Figure 10. In such embodiments, operating system 1030 may include one virtual machine among a plurality of virtual machines (VMs) hosted on computer 1002. Furthermore, operating system 1030 may provide application 1032 with a runtime environment such as a Java runtime environment or a .NET framework. The runtime environment is a consistent execution environment that enables application 1032 to run on any operating system that includes the runtime environment. Similarly, operating system 1030 may support containers, and application 1032 may take the form of a container, which is a lightweight, standalone, executable package of software including, for example, code, runtime, system tools, system libraries, and application configuration.

[0164] Furthermore, computer 1002 can enable security modules (e.g., 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 applied at any layer in computer 1002's code execution stack, for example, at the application execution level or the operating system (OS) kernel level, thereby enabling security at any level of code execution.

[0165] User entities can input commands and information to the computer 1002 through one or more wired / wireless input devices (e.g., keyboard 1038, touchscreen 1040) and pointing devices (e.g., mouse 1042). 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-motor 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 1004 through an input device interface 1044 which can be coupled to the system bus 1008, but can also be connected through other interfaces (e.g., parallel ports, IEEE 1394 serial ports, game ports, USB ports, IR interfaces, BLUETOOTH® interfaces).

[0166] Monitor 1046 or other types of display devices can also be connected to the system bus 1008 via an interface such as a video adapter 1048. In addition to monitor 1046, a computer typically also includes other peripheral output devices (not shown), such as speakers and printers.

[0167] Computer 1002 can operate in a network environment using wired and / or wireless logical connections to one or more remote computers (e.g., remote computer 1050). The remote computer 1050 may be a workstation, server computer, router, personal computer, portable computer, microprocessor-based entertainment appliance, peer device, or other common network node, typically including many or all of the elements described for computer 1002, but for brevity only, only the memory / storage device 1052 is shown. The logical connections shown include wired / wireless connections to a local area network (LAN) 1054 and / or larger networks, such as a wide area network (WAN) 1056. Such LAN and WAN networking environments are common in offices and businesses, facilitating enterprise-scale computer networks such as intranets, all of which can connect to global communication networks such as the Internet.

[0168] When used in a LAN network environment, computer 1002 can connect to local network 1054 via a wired and / or wireless network interface or adapter 1058. Adapter 1058 facilitates wired or wireless communication to LAN 1054, and LAN may also include a wireless access point (AP) placed on it to communicate with adapter 1058 in wireless mode.

[0169] When used in a WAN networking environment, computer 1002 may include a modem 1060 or connect to a communication server on WAN 1056 via other means for establishing communication on WAN 1056, such as the Internet. The modem 1060 may be an internal or external device and a wired or wireless device, and may connect to the system bus 1008 via an input device interface 1044. In a network environment, program modules shown with respect to computer 1002 or a part thereof may be stored in a remote memory / storage device 1052. The network connections shown are examples, and other means for establishing communication links between computers may also be used.

[0170] When used in either a LAN or WAN network environment, computer 1002 can access a cloud storage system or other network-based storage system in addition to, or instead of, the external storage device 1016 described above. Generally, the connection between computer 1002 and the cloud storage system can be established via LAN 1054 or WAN 1056, for example, by an adapter 1058 or modem 1060, respectively. When computer 1002 is connected to the relevant cloud storage system, the external storage interface 1026 can manage the storage provided by the cloud storage system, similar to other types of external storage, with the help of the adapter 1058 and / or modem 1060. For example, the external storage interface 1026 can be configured to provide access to cloud storage sources as if those sources were physically connected to computer 1002.

[0171] Computer 1002 may be capable of communicating with any wireless device or entity configured to operate within a wireless communication network, such as a printer, scanner, desktop and / or portable computer, portable data assistant, communications satellite, any equipment or location associated with a wirelessly discoverable tag (e.g., a kiosk, newsstand, store shelf, etc.), and a telephone. 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.

[0172] 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 be, for example, 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., optical pulses passing through optical fiber cables), and / or electrical signals transmitted through wires.

[0173] 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 the computer-readable program instructions in order to perform an aspect of one or more embodiments described herein.

[0174] The aspects of one or more exemplary embodiments described herein are described with reference to flowchart illustrations and / or block diagrams of the 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 executing a series of actions 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 / operations specified in one or more blocks of a flowchart and / or block diagram.

[0175] 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, in accordance with 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.

[0176] While the subject matter described herein is in the general context of computer executable instructions for computer program products executed 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 implemented 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 equipment. 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.

[0177] 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 means of 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 may 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 located inside or outside the device and may execute at least a portion 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.

[0178] Furthermore, the term “or” is used to mean an inclusive “or,” not 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 aspect or design described herein as “example” and / or “exemplary” is not necessarily construed as being preferable or advantageous to other aspects or designs, nor is it intended to exclude equivalent exemplary structures and techniques known to those skilled in the art.

[0179] As used herein, the term “processor” can refer to substantially any computing processing unit and / or device, and includes, but is 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 is 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.

[0180] 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 is 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.

[0181] 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.

[0182] 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 identical or different embodiments.

[0183] The descriptions of various embodiments are presented for illustrative purposes only and are not intended to be exhaustive or to limit the embodiments described herein. Many modifications and variations 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, The system comprises a processor that executes the computer executable component stored in the memory, and the computer executable component is A normalization component that, as a result of the normalized precision coefficient, normalizes the precision of the spectral analysis device based on the expected mass-to-charge ratio corresponding to the selected fragment ions, A system comprising: a determination component that, based on the normalized precision coefficient, assigns the peaks of molecular spectral data generated by the spectral analysis device corresponding to the selected fragment ions to the predicted mass-to-charge ratio corresponding to the selected fragment ions.

2. The system according to claim 1, wherein the computer executable component further, A system comprising an output component that outputs the normalized precision coefficient as a unitless value.

3. The system according to claim 1, The normalization component generates the normalized accuracy coefficient, which is a multiplier for the estimation accuracy of the spectral analysis device. The aforementioned decision component uses this multiplier to give a peak in the system.

4. The system according to claim 3, wherein the normalized accuracy coefficient is based on the difference between the mass-to-charge ratio obtained by the spectral analyzer for the selected fragment ion and the mass-to-charge ratio predicted by the spectral analyzer for the selected fragment ion, using the estimated accuracy of the spectral analyzer.

5. The system according to claim 3, wherein the estimation accuracy includes estimating an accuracy value based on two or more combinations of resolution, peak, peak height, mass-to-charge ratio, or ion implantation time with respect to the spectral analysis device.

6. The system according to claim 1, wherein the computer executable component further, An interpretation component that interprets the normalized precision coefficient to include a unitless precision criterion of peaks per center normalized to 1 (where a value greater than 1 is interpreted as indicating an acquisition problem with the spectral analysis device), A system comprising a notification component that generates a notification when it is determined that the value of the normalized precision coefficient exceeds 1.

7. The system according to claim 1, wherein the determination component is electron shock ionization mass spectrometry (MS １ ) spectrum or soft ionization mass spectrometry (MS) n The normalized precision coefficient is applied to the molecular spectrum, which is the spectrum of (where n is 1 or greater), A system in which the decision component gives a peak based on the application of the normalized precision coefficient by the decision component.

8. The system according to claim 1, The normalized precision coefficient is at least partially based on the neutral loss, and further, A system comprising an output component that outputs the normalized precision coefficient using the sum of the estimated precisions of the spectral analyzer corresponding to the pair of peaks, as a result of the difference between the mass-to-charge ratios of a pair of acquisitions by the pair of peaks, depending on the selected fragment ions and second selected fragment ions of the compound.

9. A computer implementation method, A system operablely coupled to the processor normalizes the accuracy of the spectral analysis device based on the predicted mass-to-charge ratio corresponding to the selected fragment ions, and normalizes the accuracy coefficient. A computer implementation method comprising the system assigning, based on the normalized precision coefficient, peaks of molecular spectral data generated by the spectral analysis device corresponding to the selected fragment ions to the predicted mass-to-charge ratio corresponding to the selected fragment ions.

10. A computer implementation method according to claim 9, further, A computer implementation method comprising outputting the normalized precision coefficient as a unitless value by the system.

11. A computer implementation method according to claim 9, further, The system generates the normalized accuracy coefficient, which is a multiplier for the estimation accuracy of the spectral analysis device, A computer implementation method comprising the system providing a peak using the multiplier.

12. A computer implementation method according to claim 11, wherein the normalized accuracy coefficient is based on the difference between the mass-to-charge ratio obtained by the spectral analyzer for the selected fragment ions and the mass-to-charge ratio predicted by the spectral analyzer for the selected fragment ions, using the estimated accuracy of the spectral analyzer.

13. A computer implementation method according to claim 9, further, The system interprets the normalized precision coefficient to include a unitless precision criterion of the peak per center normalized to a single value (where a value greater than 1 is interpreted as indicating an acquisition problem with the spectral analysis device), A computer implementation method comprising generating a notification when the system determines that the value of the normalized precision coefficient exceeds 1.

14. A computer implementation method according to claim 9, further, The aforementioned system enables electron ionization mass spectrometry (MS). １ ) spectrum or soft ionization mass spectrometry (MS) ｎ Applying the normalized precision coefficient to the molecular spectrum, which is the spectrum of (where n is 1 or greater), A computer implementation method comprising the system providing the peak based on the application of the normalized precision coefficient.

15. A computer program for facilitating a process for monitoring the operational status of a spectral analysis device, wherein the computer program includes a computer-readable storage medium having program instructions embodied therein, and the program instructions are transmitted to a processor, The aforementioned processor normalizes the accuracy of the spectral analysis device based on the predicted mass-to-charge ratio corresponding to the selected fragment ions, and normalizes the accuracy coefficient. A computer program in which the processor can perform the following actions: based on the normalized precision coefficient, cause the peaks of the molecular spectral data generated by the spectral analysis device corresponding to the selected fragment ions to correspond to the predicted mass-to-charge ratio corresponding to the selected fragment ions.

16. A computer program according to claim 15, wherein the program instruction is transmitted to the processor, A computer program in which the processor can further perform the output of the normalized precision coefficient as a unitless value.

17. A computer program according to claim 15, wherein the program instruction is transmitted to the processor, The processor generates the normalized accuracy coefficient, which is a multiplier for the estimation accuracy of the spectral analysis device. A computer program that enables the processor to further perform the operation of generating a peak using the multiplier.

18. A computer program according to claim 16, wherein the normalized accuracy coefficient is based on the difference between the mass-to-charge ratio obtained by the spectral analyzer for the selected fragment ions and the mass-to-charge ratio predicted by the spectral analyzer for the selected fragment ions, using the estimated accuracy of the spectral analyzer.

19. A computer program according to claim 15, wherein the program instruction is transmitted to the processor, The processor causes the normalized precision coefficient to be interpreted to include a unitless precision criterion of one peak per center normalized to one value (where a value greater than 1 is interpreted as indicating an acquisition problem with the spectral analysis device), A computer program that can be further performed by the processor to generate a notification when the processor determines that the value of the normalized precision coefficient exceeds 1.

20. A computer program according to claim 15, wherein the program instruction is transmitted to the processor, The aforementioned processor performs electron impact ionization mass spectrometry (MS). １ ) spectrum or soft ionization mass spectrometry (MS) ｎ Applying the normalized precision coefficient to the molecular spectrum, which is the spectrum of (where n is 1 or greater), A computer program which the processor can further perform by causing the processor to provide the peak based on the application of the normalized precision coefficient.

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