Abnormality verification-based technique for analyzing information

Abstract

Techniques and apparatuses for information evaluation processes are described. In one embodiment, for example, a computer-implemented method for performing an anomaly-by-anomaly review process can include accessing, via one or more processors of a computing device, chromatographic information generated via analysis of a sample using a mass spectrometry system, the chromatographic information including at least one peak and at least one peak attribute of the at least one peak; determining posterior probability information for the chromatographic information; generating an estimated peak model based on the posterior probability information; determining a confidence indicator for the estimated peak model; and generating an anomaly for the at least one peak in response to the confidence indicator exceeding an anomaly threshold. Other embodiments are described.

Description

[0001] Cross-references to related applications

[0002] This application claims the benefit and priority of U.S. Provisional Patent Application No. 62 / 895,751, filed on September 4, 2019, the entire contents of which are incorporated herein by reference. Technical Field

[0003] The implementation scheme described herein generally relates to managing analytical information generated by performing methods using analytical equipment, and more specifically, to the process of viewing the analytical information to verify the operation of the methods and / or analytical equipment. Background Technology

[0004] Analytical instrument performance is continuously monitored to ensure data quality. For example, analysts can perform various quality assurance processes, such as system calibration and / or quality control checks, to verify correct system operation. Quality analytical instruments, such as mass spectrometry (MS) and / or liquid chromatography-mass spectrometry (LC-MS) systems, can provide detailed characterization of complex sample sets, but typically require long turnaround times. Therefore, analysts need to be able to perform quality assurance testing and troubleshoot quality analytical equipment in an efficient and cost-effective manner to maximize throughput.

[0005] Typical quality analysis methods can generate a large amount of analytical information. For example, MS methods may generate hundreds or even thousands of chromatograms. Analytical information for quality assurance samples (e.g., blanks, calibration samples, standards, etc.) must be reviewed to ensure the quality of sample component measurements. However, conventional systems offer inefficient and cumbersome paths to access and review this analytical information. Therefore, quality assurance for MS methods in conventional systems is a major bottleneck negatively impacting productivity. Attached Figure Description

[0006] Figure 1 An implementation scheme for the first operating environment is shown.

[0007] Figure 2 An implementation scheme for the second operating environment is shown.

[0008] Figure 3A and Figure 3B An implementation scheme for a third operating environment is shown.

[0009] Figures 4A to 4C An implementation scheme for the fourth operating environment is shown.

[0010] Figures 5A to 5D An implementation scheme for the fifth operating environment is shown.

[0011] Figures 6A to 6C An implementation scheme for the sixth operating environment is shown.

[0012] Figures 7A to 7E An implementation scheme for the seventh operating environment is shown.

[0013] Figure 8 An implementation scheme for the computing architecture is shown. Detailed Implementation

[0014] Various implementations may generally relate to systems, methods, and / or apparatuses for generating, controlling, processing, operating, or otherwise managing analytical information for an analytical system. In some implementations, a data evaluation process may be used to process analytical information associated with the analytical system, such as analyte or sample analysis, injection, sample lists, batches, analytical methods, runs, experiments, quality control analyses, etc. In some implementations, the data evaluation process may include providing analytical information to a user to facilitate viewing of the analytical information for quality assurance purposes, such as determining whether quality control samples or analytes are within expected limits. In various implementations, the data evaluation process may present a graphical user interface (GUI) object that enables efficient and accurate viewing of analytical information. In an exemplary implementation, the GUI object may enable an anomaly-based (or "anomaly-by-anomaly" quality assurance viewing process.

[0015] In some implementations, the information evaluation process may include data analysis (or information evaluation) methods that involve (in real-time or near real-time) acquiring and / or importing analytical information (e.g., from pre-existing sample analyses or sample lists). The information evaluation process may include selecting an information processing method for processing the analytical information. In various implementations, the information processing method may include various data processing parameters, output information (e.g., graphs, charts, tables, integrals, etc.), quality control limits or thresholds (e.g., including thresholds / limits specific to each particular level), and / or similar information that can be used to process the analytical information, such as information generated from analyzing samples using an analytical system.

[0016] For example, the analytical system may include a quality analysis system, such as a mass spectrometry (MS) or liquid chromatography (LC)-MS (LC-MS) system. Although MS or LC-MS systems are described in some examples, implementations are not limited thereto, as any application capable of operating according to some implementations is contemplated herein. In some implementations, the information assessment process may include a data analysis method that includes acquiring and / or importing analytical information (e.g., MS information) and selecting a data processing method to process the analytical information. In various implementations, data acquisition and / or information assessment may be or may include MS quantification of MS-acquired data. In some implementations, MS-acquired data may be or may include information generated via multiple reaction monitoring (MRM) analysis.

[0017] In various implementations, the information evaluation process can be used to supplement any missing values, such as those based on historical information, values ​​provided in data processing methods, extrapolation, and combinations thereof. In some implementations, data processing methods can be used to identify anomalies in the analytical information. Typically, anomalies can include any value outside a predetermined value or range. For example, the expected value of a quality control (QC) analyte could be... x And if the detection value of the QC analyte exceeds x An anomaly may be generated by adding or subtracting (an anomaly range or percentage) (e.g., 20%). In various implementations, the information evaluation process can be used to present multiple datasets for components of analytical information (analytical components), such as for each sample, analyte, injection, QC analyte, or sample. In an exemplary implementation, a user can, for example, flag anomalies based on visual inspection not automatically triggered by the information evaluation process.

[0018] For example, in some implementations, the information evaluation process can present GUI objects that display various charts, graphs, error bars, etc. for each analyte (see, for example...). Figures 3A to 7E In conventional systems, graphs for different analytical components typically have Y-axis and / or X-axis scaled for a specific range, making visual comparison between different components difficult, if not practically possible. Therefore, in some implementations, all or substantially all graphs may have the same or substantially identical Y-axis and / or X-axis values ​​(e.g., fixed constraints on one or more axes for each graph) to facilitate efficient visual comparison between different analytical components, for example.

[0019] A non-limiting example of a graph for MS analysis information may include the percentage deviation of each analyte from a known data point (e.g., concentration). In various embodiments, each graph may highlight the deviation from the expected value, for example, by presenting the deviation with presentation characteristics different from the expected or non-deviation value via a GUI object. Non-limiting examples of presentation characteristics may include shape, color, size, symbols, etc. Another non-limiting example of a graph for MS analysis information may include a blank chromatogram plotted against a quantitation limit (e.g., LLOQ or a minimum detection standard not excluded by the user). In some embodiments, a blank graph for LLOQ may include an indication of the graph having an integral region for the selected analyte.

[0020] Further non-limiting examples of plots for MS analysis information may include plots of response deviations and / or retention time deviations, indicating, for example, standards, blanks, QC samples, and unknowns with different presentation characteristics. Further non-limiting examples of plots for MS analysis information may include peak integral plots for all injections of the selected analyte.

[0021] In various implementations, the integration settings of analytical components (e.g., specific internal standards) can be modified, for example, to change the graph, resolve anomalies, etc. In various implementations, the Y-axis and / or X-axis of a graph with modified integration settings can be reset, for example, based on modified injection settings. In some implementations, a review trail can be generated for any changes to analytical information and / or its presentation via a GUI object (e.g., changing the integration settings of a standard). In exemplary implementations, an anomaly GUI object (e.g., a toggle button) can be provided to allow a user to select only anomalies for presentation. In some implementations, a display category GUI object can be provided to allow a user to filter to present certain categories of analytical components, such as unknowns, blanks, standards, QC, etc.

[0022] In various implementations, the information evaluation process can be used to determine a confidence (or uncertainty) value or indication for the analyzed information. In some implementations, the confidence (or uncertainty) indication can be or can include a comparison of multiple graphs of the same information using different models (e.g., Gaussian and Bayesian models) and / or error bars. In various implementations, analytical information associated with a certain level of confidence or uncertainty (e.g., below a threshold) can be flagged as anomalies that need to be examined (e.g., as part of an anomaly-by-anomaly examination process).

[0023] Analysts operating in regulated environments who use MS or similar techniques to identify compounds in complex samples are obligated to ensure the satisfactory performance of peak integration algorithms, which typically involves visual inspection of peaks and integration results. This can obviously be very time-consuming for the large amounts of information generated by MS-based analyses. Therefore, various information evaluation processes can include anomaly-by-anomaly review methods configured according to some implementation schemes. In some implementations, the information evaluation process may involve using some peak properties and marking any results where those properties fall outside a certain threshold. Marked elements may be considered anomalous and flagged for review, significantly reducing the quality review required by the analyst.

[0024] Compared to conventional systems and methods, analytical information evaluation processes, according to some implementations, can offer several technical advantages, including improvements in computational techniques. The ability to access analytical information and draw simple conclusions accurately and efficiently from new or pre-existing data is a key aspect of the perceived usability of analytical systems, including MS and LC-MS systems. Conventional systems often provide difficult and complex data paths that hinder users' ability to efficiently and effectively visualize information about their system's functionality or performance (e.g., quality assurance anomalies and how to handle them), leading to frustration and a generally negative impression of such conventional analytical tools. Therefore, some implementations provide analytical information evaluation processes that utilize visualization tools to allow users to obtain efficient and effective visualizations of analytical information associated with the analytical system, methods, analytes, etc. Analytical information evaluation processes, according to some implementations, can allow users to perform data source-independent evaluations and comparisons (e.g., through anomaly-based review processes).

[0025] For example, some implementations allow analysts to use analytical systems to solve problems efficiently and cost-effectively, optimizing throughput, compared to conventional systems and processes. Analysts / viewers can be able to determine the validity of results generated from analysis, for example, by reviewing a large amount of analytical information (e.g., more than 30,000 chromatograms). More specifically, analysts / viewers can be able to identify outliers and unexpected results by performing a visual anomaly-based inspection of processed data / results, which significantly reduces the amount of information that needs to be reviewed. Furthermore, according to some implementations, anomaly-based review processes can be used to provide confidence levels for reported measurements, allowing for anomaly-by-anomaly review workflows based on, for example, confidence levels rather than hard peak attribute thresholds, requiring less expertise from analysts (e.g., analysts do not need to judge the quality of results because the anomaly-by-anomaly review process provides this information), and / or avoiding the need for human intervention (e.g., the process may report results as is, while poor results may be due to poor or insufficient data that cannot be overcome by ad hoc human intervention). Other advantages will be apparent to those skilled in the art based on the descriptions in this disclosure.

[0026] In the following description, references to “an embodiment,” “an embodiment,” “an exemplary embodiment,” “various embodiments,” etc., indicate that an embodiment of the described technology may include a particular feature, structure, or characteristic. However, more than one embodiment may include that particular feature, structure, or characteristic, and not every embodiment must include that particular feature, structure, or characteristic. Furthermore, some embodiments may have some, all, or none of the features described for other embodiments.

[0027] As used in this specification and claims, unless otherwise stated, the use of ordinal adjectives such as “first,” “second,” “third,” etc., to describe an element indicates only a specific instance of the referenced element or a different instance of a similar element, and does not imply that the element described so is in a particular order in time, space, sequence, or any other manner.

[0028] Figure 1 An example of an operating environment 100 that can represent some implementation schemes is shown. For example... Figure 1 As shown, the operating environment 100 may include an analytical system 105 that operates to manage analytical data associated with analytical devices 115a-115n. In some embodiments, analytical devices 115a-115n may be or may include a chromatography system, a liquid chromatography (LC) system, a gas chromatography (GC) system, a mass analyzer system, a mass spectrometer (MS) system, an ion mobility spectrometer (IMS) system, a high-performance liquid chromatography (HPLC) system, or an ultra-high-performance liquid chromatography (UPLC) system. ® Systems such as ultra-high performance liquid chromatography (UHPLC) systems, solid-phase extraction systems, sample preparation systems, heaters (e.g., column heaters), sample managers, solvent managers, in vitro devices (IVD), combinations thereof, components thereof, and variations thereof are included. Although LC, MS, and LC-MS are used in the examples in this detailed description, the implementation is not limited thereto, as other analytical devices capable of operating according to some implementations are contemplated herein.

[0029] In some embodiments, computing device 110 may be communicatively coupled to analysis devices 115a-115n. In other embodiments, computing device 110 may be non-communicatively coupled to analysis devices 115a-115n. Computing device 110 may obtain analysis information 132 directly from data sources 154a-154n and / or directly from analysis devices 115a-115n. In some embodiments, computing device 110 may be or may include a standalone computing device, such as a personal computer (PC), server, tablet computer, cloud computing device, etc. In some embodiments, computing device 110 may be a separate device from analysis devices 115a-115n. In other embodiments, computing device 110 may be part of analysis devices 115a-115n, such as an integrated controller.

[0030] like Figure 1 As shown, computing device 110 may include processing circuitry 120, memory unit 130, and transceiver 160. Processing circuitry 120 may be communicatively coupled to memory unit 130 and / or transceiver 160.

[0031] Processing circuitry 120 may include and / or have access to various logics for performing processing according to some embodiments. For example, processing circuitry 120 may include and / or have access to analysis service logic 122, information evaluation logic 124, and / or GUI logic 126. Processing circuitry and / or analysis service logic 122, information evaluation logic 124, and / or GUI logic 126, or portions thereof, may be implemented in hardware, software, or a combination thereof. As used herein, the terms “logic,” “component,” “layer,” “system,” “circuit,” “decoder,” “encoder,” and / or “module” are intended to refer to computer-related entities, which may be hardware, a combination of hardware and software, software, or software in execution, examples of which are provided by exemplary computing architecture 1000. For example, logic, circuitry, or layer may be and / or include, but is not limited to, processes running on a processor, processors, hard disk drives, multiple storage drives (optical and / or magnetic storage media), objects, executable programs, execution threads, programs, computers, hardware circuits, integrated circuits, application-specific integrated circuits (ASICs), programmable logic devices (PLDs), digital signal processors (DSPs), field-programmable gate arrays (FPGAs), systems-on-a-chip (SoCs), memory cells, logic gates, registers, semiconductor devices, chips, microchips, chipsets, software components, programs, application programs, firmware, software modules, computer code, and any combination of the foregoing.

[0032] although Figure 1 The analysis service logic 122 is depicted as being within the processing circuitry 120, but the implementation is not limited thereto. Furthermore, while the information evaluation logic 124 and GUI logic 126 are depicted as part of the analysis service logic 122, the implementation is not limited thereto, as the information evaluation logic 124 and GUI logic 126 may be separate logic and / or may not be independent logic, but rather part of the analysis service logic 122. For example, the analysis service logic 122 and / or any components thereof may reside within an accelerator, processor core, interface, single processor die, or be fully implemented as a software application (e.g., analysis service application 140), etc.

[0033] Memory cell 130 may include various types of computer-readable storage media and / or systems in the form of one or more higher-speed memory cells, such as read-only memory (ROM), random access memory (RAM), dynamic RAM (DRAM), dual data rate DRAM (DDRAM), synchronous DRAM (SDRAM), static RAM (SRAM), programmable ROM (PROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, polymer memory (such as ferroelectric polymer memory, bidirectional memory, phase-change or ferroelectric memory), silicon-oxide-nitride-oxide-silicon (SONOS) memory, magnetic cards or optical cards, device arrays (such as redundant array of independent disks (RAID) drives), solid-state storage devices (such as USB storage devices, solid-state drives (SSDs), and any other type of storage media suitable for storing information. Additionally, memory cell 130 may include various types of computer-readable storage media in the form of one or more lower-speed memory cells, including internal (or external) hard disk drives (HDDs), floppy disk drives (FDDs), and optical disc drives (such as CD-ROMs or DVDs) for reading or writing removable optical discs, solid-state drives (SSDs), etc.

[0034] The memory unit 130 may store an analysis service application 140, which may operate independently or in combination with analysis service logic 122 to perform various functions according to some implementation schemes.

[0035] In various implementations, the analytics service application 140 may be used to perform, implement, support, or otherwise facilitate an information evaluation process according to some implementations. In some implementations, for example, the analytics service application 140 may provide GUI objects, screens, pages, windows, and / or similar objects for facilitating the information evaluation process (e.g., see...). Figures 2 to 6C ).

[0036] In an exemplary implementation, the analysis service application 140 may allow selection of analysis information 132, such as that associated with an analytical component (e.g., sample analysis and associated quality assurance samples or analytes, such as blanks, standards, QC, etc.). For example, the analysis service application 140 may allow selection of analysis information objects (e.g., files) that include data associated with sample analysis, which may include, for example, quality assurance analytes (or injections) and sample analytes (or injections).

[0037] In some implementations, the analysis information 132 may be or may include objects or other structures, such as data files. Non-limiting examples of analysis information objects (or files) may include raw data files, processed data files, exported package files, combinations thereof, etc. For example, analysis information objects may include comma-separated files (*.csv), Microsoft... ® Excel ® Files (*.xls, *.xlsx, etc.), MS software raw data files (e.g., *.raw MassLynx developed by Waters Corporation in Milford, Massachusetts, USA). ™ Files), UNIFI export package files (*.uep) developed by Waters Corporation, combinations thereof, etc. Implementations are not limited to this context. In some implementations, the analytical information object may include information from pre-existing analyses. In some implementations, the analytical information object may include data streams, such as live or near-live data streams from analytical instruments, servers, networks, etc.

[0038] Figure 2 An example of an operating environment 200, which may represent some implementation schemes, is shown. For example... Figure 2 As shown, the operating environment 200 may include an information processing selection screen 205. In various embodiments, the information processing selection screen 205 may present various GUI objects, such as an analysis information selection object 202 and an information processing method selection object 204. In various embodiments, the analysis information selection object 202 can be used to access pre-existing information, such as analysis information generated during previous analysis.

[0039] In various implementations, the analysis service application 140 may allow selection of an information processing method 134 for processing the analysis information 132 within a selected analysis information object. Typically, the information processing method 134 may include information, parameters, rules, thresholds, integration parameters, etc., for processing the analysis information 132 of the selected analysis information object. In some implementations, the information processing method 134 may include quality control ranges, limits, thresholds, expected values / ranges, tolerances, etc.

[0040] Analysis service application 140 can process analysis information 132 of selected analysis information objects according to selected information processing method 134 to generate processed information 138. In various embodiments, processed information 138 may include analysis values ​​(such as raw values) determined via analysis (such as concentration, mass-to-charge ratio, drift time, voltage (or other electrical signals that can be used to determine values)) and information generated during processing, such as percentage of deviation, anomalies, etc. In some embodiments, analysis information may include gaps, missing values, etc. For example, concentration information may be available at segments (e.g., time, level, etc.) 1-3 and segments 6-10, but not at segments 4 and 5. Therefore, the concentration information at segments 4 and 5 can be determined to be missing values. In various embodiments, processed information 138 may include information generated to provide missing values ​​(gap information) through, for example, estimation, extrapolation, statistical analysis, etc.

[0041] In various implementations, the processed information 138 can be used to generate a GUI object 136 for presentation to a user. In some implementations, the GUI object 136 allows a user to efficiently and effectively visualize analytical information 132 associated with analytical components, such as sample runs. For example, the GUI object 136 can highlight anomalies for the user, such as indicating quality control analytes outside the range specified in the selected information processing method. In another example, the GUI object 136 can highlight gap information, allowing the user to distinguish between actual values ​​and gap information.

[0042] refer to Figure 2 Selecting object 212 allows the analysis service application 140 to process the selected analysis information object based on the chosen information processing method. The results of processing the analysis information object using the information processing method can be presented via various GUI objects, such as... Figures 3A to 6C The screen and associated GUI objects depicted in the image.

[0043] Figure 3A and Figure 3B An example of an operating environment 300 that can represent some implementation schemes is shown. For example... Figure 3A As shown, the operating environment 300 may include an information evaluation screen or page 305A. Figure 3A In some implementations, the information evaluation screen 305A may include a calibration and QC screen depicting calibration and QC information for various analytes 304a-304c (e.g., as an information GUI object). In an exemplary implementation, the information evaluation screen 305A may include a navigation object 302 operable to allow selection of other information evaluation screens associated with a specified evaluation category, such as... Figures 4A to 6CThe screens depicted in the text. Non-limiting examples of evaluation categories may include blank screens, internal standard screens, peak integral screens, etc.

[0044] According to some implementations, the information evaluation process can enable an anomaly-based viewing process, for example, by presenting and highlighting anomalies of expected results in the processed analytical information. In this way, users can focus on checking and / or resolving anomalies, allowing for a more efficient and focused viewing of analytical information compared to existing systems. Therefore, in some implementations, the information evaluation screen 305A can present category-level anomalies 322 indicating anomalies of a specific category. In various implementations, the information evaluation screen 305A can present analyte-level anomalies 320 indicating anomalies of a specific analyte. In some implementations, evaluation-level anomalies 324 and 326 can be presented, indicating anomalies in specific evaluations 306, 308, and 310 associated with the analyte. Non-limiting examples of evaluations may include calibration curves 306, residual plots 308, and quality controls 310. In some implementations, one or more evaluations can be presented as graphs, plots, charts, curves, etc. For example, residual plots 308 and quality controls 310 can be presented as or substantially similar to Levy-Jennings charts with threshold or limit indicators (dashed lines). Values ​​within the threshold (e.g., normal or expected values) may have one or more presentation features, such as shape, color, symbol, etc. Anomalies 324 and 326, or values ​​outside the threshold, may have one or more presentation features to distinguish between anomalies and expected values. In some implementations, threshold limits may be fixed in the information processing method (e.g., to comply with regulations, standard operating procedures, etc.). In various implementations, certain threshold limits can be configured. For example, the threshold limit for residual plot 308 may be approximately 20% (e.g., the information processing method may have a default value of approximately 20%), which can be changed to different ranges by the user.

[0045] like Figure 3A and Figure 3BAs shown, all or substantially all graphs may have the same or substantially the same Y-axis and / or X-axis values ​​(e.g., fixed constraints on one or more axes for each graph) to facilitate efficient visual comparison between different analytical components. For example, the Y-axis and X-axis of the residual graph 308 for analytes 304a-304g are the same. Therefore, some values ​​may be outside the range of one or more axes. In this case, anomaly indicators 326 and 330 may include presentation features to indicate that the actual value is outside the range of one or more axes. For example, anomaly indicators 326 and 330 are in the form of arrows. In some embodiments, the selection of anomaly indicators 326 and 330 may allow visualization of information associated with the actual value, for example, by presenting the actual value, extending one or more axes, etc. For example, the selection of anomaly indicator 330 may cause the presentation of out-of-range value objects 332 that provide value information.

[0046] In various implementation schemes, users can access information about various analytes by navigating or "paging" analytes 304a-304g. For example, Figure 3A Analytes 304a-304c can be displayed, and the activation of the navigation event can allow the user to move to the next set of analytes, for example, by displaying the next set of analytes 304d-304g on screen 305B. Figure 3A and Figure 3B All analytes are depicted, including those with anomalies and those without. In some implementations, the user can select or switch to view only analytes with anomalies, such as analytes 304a, 304c, 304d, and 304f. Depending on some implementations, other selection or switching options may be available, such as switching to view only analytes with calibration anomalies, residual anomalies, QC anomalies, and / or anomalies within a certain range (e.g., only anomalies deviating above a certain threshold), combinations thereof, etc.

[0047] In various implementations, users can initiate workflows to resolve any anomalies presented via information evaluation screens such as screens 305A and 305B. For example, the selection of evaluation objects 306, 308, 310 and / or portions thereof (e.g., specific data points) can allow for evaluation modifications, including but not limited to modifying thresholds, curves, integration parameters, removing data points, etc. In various implementations, evaluation modifications and any associated data can be logged, for example, in a review trail.

[0048] like Figure 3A and Figure 3B As shown, the information evaluation process according to some implementation schemes allows users to efficiently step through the analytes of analytical methods to check certain key quality assurance checks, such as calibrators, residuals, QC, etc., and skillfully make certain modifications to resolve any permissible anomalies.

[0049] Figures 4A to 4C An example of an operating environment 400 that can represent some implementation schemes is shown. For example... Figures 4A to 4C As shown, the operating environment 400 can present information evaluation screens 405A-405C in the form of a blank screen. For example, blank screen 405A can depict graphs 402 and curves 410 for a specific blank analyte. In various embodiments, blank screen 405 can present a list of blank analytes 414, which may include anomaly indicators 404 for any blank analytes associated with anomalies. Thus, a user can browse the list of analytes and view blank evaluation objects (e.g., graphs, curves, etc.) associated with a specific analyte, including, for example, analytes associated with anomalies.

[0050] refer to Figure 4B In some implementations, the information evaluation process allows users to compare the information shown in the blank with the LLOQ (e.g., which may indicate contamination, residue, etc.). For example, blank curve 402 may include an LLOQ plot or curve 414 (e.g., as a background or "phantom" peak) that may be associated with LLOQ information 430. In some implementations, the LLOQ information may be configurable by the user.

[0051] In some implementations, an anomaly may be generated if the integral region within the blank area exceeds a threshold percentage (e.g., 20%) of the LLOQ curve. (See reference) Figure 4C The diagram depicts a blank screen 405C presented in response to the selection of the analyte “pentachlorosylamine” from the analyte list, which is associated with an anomaly indicator 406. The selection of graph 440 or its components can result in the presentation of LLOQ information, such as indicating that the blank is out of range (e.g., the LLOQ response percentage is 23.9%, exceeding the 20% threshold). In various embodiments, the selection of graph 440 or its components can result in the presentation of an anomaly management screen or other objects to allow the user to manage anomalies (e.g., flagging anomalies, changing LLOQ parameters, selecting to ignore anomalies, combinations thereof, etc.). In various embodiments, the user can choose to view only analytes with anomalies. For example, an anomaly toggle GUI object can restrict the analyte list 414 to only list analytes and / or present an active selection of analytes associated with anomalies (e.g., graying out non-anomaly analytes).

[0052] Figures 5A to 5D An example of an operating environment 500 that can represent some implementation schemes is shown. For example... Figures 5A to 5D As shown, the operating environment 500 can present information evaluation screens 505A-505D in the form of standard or internal standard screens. (Reference) Figure 5AThe internal standard screen 505A may contain an analyte list 514, listing analytes, for example, those associated with the selected analysis information and any associated anomaly indicators 516. In various embodiments, the internal standard screen 505A may present the selected internal standard (e.g., Figure 5A The response deviation diagram 502 and / or retention time deviation diagram 504 of D3 albendazole.

[0053] In exemplary embodiments, data point 518 may have various presentation characteristics to indicate certain information, such as the type of data point (e.g., blank, internal standard, QC, sample, or unknown), anomalies, etc. Therefore, users can efficiently visualize which category of analyte, QC, etc., might be associated with an anomaly. In some embodiments, graphs 502 and 504 may include threshold information (dashed lines) used to indicate a threshold associated with, for example, the displayed analytical information that can be used as a basis for determining an anomaly. Figure 5B As described, the selection of data points may result in data point information objects 520, 522, and 524 that present data point information such as data file information, data processing method information, data point type, and deviation percentage.

[0054] In some implementations, the information assessment process can be used to facilitate anomaly resolution via internal standard screens 505A-505D. (Reference) Figure 5C Selecting data points with abnormalities may result in the rendering of a chromatogram screen or window 540 displaying the integration object 542, integration settings 544, and data point information 546 (such as deviation percentage). Users can modify, for example, one or more integration settings in integration settings 544, such as... Figure 5C As shown, the "peak endpoint (%)" value has been changed to 10, which causes the integral object 542 to change, and therefore the deviation percentage (which is now below the 20% threshold) also changes.

[0055] Figures 6A to 6C An example of an operating environment 600, representing some implementation schemes, is shown. For example... Figures 6A to 6C As shown, the operating environment 600 can present information evaluation screens 605A-605C in the form of integral or peak integral screens. (Reference) Figure 6AThe peak integration screen 605A may include an analyte list 614 and integration settings for the selected analytes. Integration plots 602 can be presented for various types of analytes, such as blanks, standards, QC, samples, or unknowns. In some embodiments, a filter 630 can be used to filter the display of integration plots for the selected type of analyte (e.g., a user can select filter 630 to display only the integration plot 602 for blanks). In various embodiments, the user can select to display only integration plots 602 associated with anomalies (e.g., abnormal / unexpected ion ratios, abnormal peak shapes, out-of-tolerance peak shapes, etc.). In exemplary embodiments, the peak integration screen 605A may depict integration plots 602 with the same or substantially the same Y-axis and / or X-axis, for example, to facilitate comparison and / or evaluation of peak integrations.

[0056] refer to Figure 6B An analyte peak integration screen 605B (e.g., avermectin (B1a)). For example, an integration plot 602 can depict a quantitative (or qualitative) trace 650 and an analyte or actual plot or trace 652. Selection of an integration plot 602 and / or a portion thereof can depict plot information associated with the quantitative trace 650 and / or the analyte plot 652. In some embodiments, a deviation exceeding a threshold amount between the quantitative trace 650 and the analyte plot 652 can trigger an anomaly, such as an ion ratio anomaly. In some embodiments, the user can use integration settings 622 to attempt to resolve any anomalies. For example, refer to... Figure 6C The peak integration screen 605C displays a graph 602, where the "peak endpoint (%)" integration setting 622 has been set to 80 and applied to all graphs. Therefore, in some embodiments, the x-axis for multiple graphs can be set or resent based on modified integration and / or injection settings.

[0057] As discussed in this disclosure, conventional peak processing techniques suffer from several inefficiencies and other limitations. For example, conventional integration algorithms fail to provide accurate, useful error indicators or confidence levels for measured peak properties. Therefore, information evaluation processes (including anomaly-by-anomaly reviews) may use thresholds to determine whether a peak has been accurately measured or not, thus not playing a direct role in anomaly reviews. In another example, integration results may be highly sensitive to noise. Therefore, for instance, results may be fundamentally altered due to lower confidence levels than those obtained with more noisy data, as the peak baseline may be improperly placed. Therefore, according to various embodiments, some implementations may use probabilistic analysis processes to achieve anomaly reviews.

[0058] In some implementations, probability-based analytical processes can utilize Bayesian probability analysis of analytical information such as chromatographic data. For example, to extract meaningful measurements from raw data, Bayesian data analysis can use probabilistic concepts to derive an expression for the (posterior) probability that a theoretical model is the correct interpretation of the chromatographic data. For instance, according to some implementations, theoretical models frequently used in chromatography, such as exponentially modified Gaussian peaks, can be used. Figure 7A It describes the analytical information associated with different models. For example... Figure 7A As shown, Figure 705 illustrates the original MRM chromatograms with two different exponentially corrected Gaussian peaks, 710 and 712, based on different models. The models have one or more distinct features, including but not limited to position, number, peak width, and / or peak tailing value. In general, Bayesian theory allows for the expression of the probability that one of several different models is the correct (or most correct or best) interpretation of the data for a given dataset. Although MRM and / or chromatogram information is used as an example in this disclosure, the implementation is not limited thereto. For example, procedures described according to some embodiments (including anomaly-by-anomaly review procedures, probability-based analysis procedures, etc.) can be applied to a wide variety of data types.

[0059] like Figure 7A As shown, one model has a higher probability of correctly interpreting the data than another model (i.e., model 710, which is closer to the data curve in graph 714). Therefore, there may be multiple latent models, each with a certain associated (posterior) probability. The total probability of the models (the sum must be one) is shared among all latent models, resulting in a probability distribution over a set of peak parameters. For a given model, the posterior probability can depend on how well the model matches the data; this can be referred to as the likelihood or probability that the data gives the model (without confusion with the probability that the model gives the data). Furthermore, in some implementations, known information can also be a fundamental input to the process and, for example, can be represented as a "prior probability." For example, referring to graph 705, the probability of a peak occurring at 3 minutes is zero.

[0060] Therefore, in some implementations, probability-based analytical procedures may use latent probability distributions to extract meaningful measurements from the data and determine confidence levels for these measurements. In various implementations, among others, the extraction of meaningful measurements and / or the determination of associated confidence levels may be based on determining and / or evaluating samples from probability distributions. In some implementations, Markov Chain Monte Carlo (MCMC) and / or nested sampling may be used to determine and / or evaluate samples, for example, the same or similar methods described in Skilling's "Nested Sampling for General Bayesian Computation" in Bayesian Analysis, Vol. 4, pp. 833-860 (2006). For example, for chromatographic data, some implementations may use nested sampling and / or Markov Chain Monte Carlo to achieve chromatographic peak detection and integration.

[0061] Typically, Markov chain Monte Carlo (MCM) is a computational technique that uses random numbers to generate samples from an unknown probability distribution. Nested sampling uses MCM to obtain (e.g., weighted) samples from a posterior distribution. For example, some implementations can use peak position, width, asymmetry, and number of repeated samples or measurements (e.g., once weighted) based on the probability that a given sample (e.g., a peak model) is the correct (or most correct or best) interpretation of the data.

[0062] Figure 7B The posterior probability samples of the analyzed information are depicted. (Reference) Figure 7B The graph 715 depicts the posterior probability values ​​of chromatographic information on peak attributes such as retention time 720, number 722, width 724, and asymmetry 726. In various embodiments, the distribution values ​​can be used to provide estimates of each peak attribute, as well as error bars for those estimates.

[0063] The post-hoc sample can represent known information about the presence of a peak in the data and the properties of that peak. In some embodiments, the probability-based analysis process can be general and can allow the discovery of multiple peaks. In other embodiments, the probability-based analysis process can involve a “targeted” method or application that involves anticipated information (e.g., the presence of a single eluting compound, i.e., a peak), as part of prior input.

[0064] In some implementations, using a targeted approach to summarize the data by averaging samples of each sampled attribute can be relatively straightforward. Such estimates can provide an acceptable peak model as an interpretation of the data. However, in reality, multiple peak measurements may exist, and it is precisely the spread or dispersion of these measurements that can provide some indication of their accuracy.

[0065] In some implementations, the standard error of the mean (or other processes used to determine error, standard deviation, etc.) can be used to provide an error bar, for example, to provide an indication of the accuracy of the associated measurement results. Figure 7C A graph depicts analytical information about measurement results associated with error bars configured according to some implementation schemes. (Reference) Figure 7C Figure 725 depicts a comparison of area results (MSRQ Quan axis) for various compounds obtained using certain quantitative / integrative techniques with results obtained via a probability-based analytical procedure (MCMC Quan axis) according to some implementation schemes. Figure 7C In this context, the MCMC results are correlated with the error bars (for logarithmic axis adjustments), for example, measurement result A 730 and measurement result B 732.

[0066] like Figure 7C As shown, compared to the error bar 733 associated with measurement result B 732, the error (error bar, divergence, uncertainty, etc.) associated with measurement result A 730, indicated by the associated error bar 731, provides a result showing a wide range of different levels of uncertainty. Therefore, in some embodiments, measurement result A 730 may be marked as an anomaly for review during anomaly-by-anomaly inspection, while measurement result B 732 may not be marked as an anomaly. In various embodiments, the error, error bar, uncertainty, or "confidence indicator" may be compared to an anomaly threshold. If the confidence indicator exceeds (or falls below, depending on the value scheme) or otherwise exceeds the anomaly threshold, the measurement result (or peak) may be marked as an anomaly.

[0067] Figure 7D Depicting the corresponding Figure 7C The integral result of measurement result A, and Figure 7E Depicting the corresponding Figure 7C The integral result of measurement result B. (Reference) Figure 7D Figure 735 shows the estimated peak model 740 corresponding to measurement result A 730 and the conventional result 742 (e.g., filled peak) (i.e., the area at 3.60e+3 versus the area at 2.54e+3). Figure 7E Figure 745 depicts the estimated peak model 750 corresponding to measurement result B 732 and the conventional result 752 (e.g., a filled peak) (i.e., the area at 3.36e+3 versus the area at 3.04e+3). In some embodiments, the difference between the MCMC value (i.e., the probability-based value) and the MSRQ value (the conventional quantitative / integral method value) can be used as a confidence indicator (e.g., as the probability-conventional difference). Reference Figure 7D and Figure 7EThe confidence indicators in Figure 735 ((probability-based value) - (normal value) or the ratio of probability-based value to normal value) may be sufficient to raise anomalies, while the confidence indicators in Figure 745 may not be sufficient to raise anomalies (i.e., there is a sufficient level of confidence in measurement result B 732).

[0068] In various implementations, error bars and / or probability-normal variance (or other relationships, such as ratios) can be used to generate confidence indicators that indicate the confidence level (or conversely, uncertainty) in the data. In various implementations, the anomaly-by-anomaly review process may include one or more thresholds for labeling results for review based on confidence factors (e.g., probability-normal variance (or ratio) greater than X can be flagged as anomalies requiring review). Implementations are not limited to this context.

[0069] Figure 8 An embodiment of an exemplary computing architecture 800 suitable for implementing the various embodiments described above is shown. In various embodiments, the computing architecture 800 may include or be implemented as part of an electronic device. In some embodiments, the computing architecture 800 may represent, for example, computing device 110. The embodiments are not limited to this context.

[0070] As used in this application, the terms "system," "component," and "module" are intended to refer to computer-related entities, which may be hardware, a combination of hardware and software, software, or software in execution, examples of which are provided by the exemplary computing architecture 800. For example, a component can be, but is not limited to, a process running on a processor, a processor, a hard disk drive, multiple storage drives (optical and / or magnetic storage media), an object, an executable file, an execution thread, a program, and / or a computer. For example, both an application running on a server and the server itself can be components. One or more components may reside within a process and / or an execution thread, and components may be located on a single computer and / or distributed across two or more computers. Furthermore, components may be communicatively coupled to each other to coordinate operation via various types of communication media. Coordination may involve one-way or two-way information exchange. For example, a component may convey information in the form of signals communicated through a communication medium. This information may be implemented as signals assigned to various signal lines. In such assignments, each message is a signal. However, alternative embodiments may use data messages. Such data messages can be sent via various connections. Exemplary connections include parallel interfaces, serial interfaces, and bus interfaces.

[0071] The computing architecture 800 includes various general-purpose computing elements, such as one or more processors, multi-core processors, coprocessors, memory units, chipsets, controllers, peripherals, interfaces, oscillators, timing devices, video cards, audio cards, multimedia input / output (I / O) components, power supplies, etc. However, implementations are not limited to the implementation of the computing architecture 800.

[0072] like Figure 8 As shown, the computing architecture 800 includes a processing unit 804, a system memory 806, and a system bus 808. The processing unit 804 can be any of a variety of commercially available processors, including but not limited to: AMD. ® Athlon ® Duron ® And Opteron ® Processor; ARM ® Application, embedded, and security processors; IBM ® and Motorola ® DragonBall ® and PowerPC ® Processors; IBM and Sony ® Cell processor; Intel ® Celeron ® Core(2) Duo ® Itanium ® Pentium ® Xeon ® and XScale ® Processors; and similar processors. Dual microprocessors, multi-core processors, and other multiprocessor architectures can also be used as processing units 804.

[0073] System bus 808 provides interfaces for system components, including but not limited to interfaces connecting system memory 806 to processing unit 804. System bus 808 can be any of several types of bus architectures, which can be further interconnected to memory bus (with or without memory controller), peripheral bus, and local bus using any of a variety of commercially available bus architectures. Interface adapters can be connected to system bus 808 via slot architectures. Exemplary slot architectures can include, but are not limited to, Accelerated Graphics Port (AGP), Card Bus, (Extended) Industry Standard Architecture ((E)ISA), Micro Channel Architecture (MCA), NuBus, Peripheral Component Interconnect (Extended) (PCI(X)), PCI Express, PCMCIA, etc.

[0074] System memory 806 may include various types of computer-readable storage media in the form of one or more high-speed memory cells, such as read-only memory (ROM), random access memory (RAM), dynamic RAM (DRAM), dual data rate DRAM (DDRAM), synchronous DRAM (SDRAM), static RAM (SRAM), programmable ROM (PROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, polymer memory (such as ferroelectric polymer memory, bidirectional memory, phase-change or ferroelectric memory), silicon-oxide-nitride-oxide-silicon (SONOS) memory, magnetic cards or optical cards, device arrays (such as redundant array of independent disks (RAID) drives), solid-state storage devices (e.g., USB storage, solid-state drives (SSDs), and any other type of storage media suitable for storing information. Figure 8 In the illustrated embodiment, system memory 806 may include non-volatile memory 810 and / or volatile memory 812. The basic input / output system (BIOS) may be stored in non-volatile memory 810.

[0075] Computer 802 may include various types of computer-readable storage media in the form of one or more low-speed memory cells, including internal (or external) hard disk drive (HDD) 814, magnetic floppy disk drive (FDD) 816 for reading or writing to removable disk 818, and optical disc drive 820 (e.g., CD-ROM or DVD) for reading or writing to removable optical disc 822. HDD 814, FDD 816, and optical disc drive 820 may be connected to system bus 808 via HDD interface 824, FDD interface 826, and optical disc drive interface 820, respectively. HDD interface 824 for external drive implementations may include at least one or both of Universal Serial Bus (USB) and IEEE 1384 interface technologies.

[0076] Drives and associated computer-readable media provide volatile and / or non-volatile storage of data, data structures, computer-executable instructions, etc. For example, multiple program modules may be stored in drive and memory units 810, 812, including an operating system 830, one or more application programs 832, other program modules 834, and program data 836. In one embodiment, one or more application programs 832, other program modules 834, and program data 836 may include, for example, various application programs and / or components of computing device 110.

[0077] Users can input commands and information into computer 802 through one or more wired / wireless input devices (e.g., keyboard 838 and clicking devices such as mouse 840). Other input devices may include microphones, infrared (IR) remote controls, radio frequency (RF) remote controls, game controllers, styluses, card readers, dongles, fingerprint card readers, gloves, graphics tablets, joysticks, keyboards, retina readers, touchscreens (e.g., capacitive, resistive, etc.), trackballs, touchpads, sensors, styluses, etc. These and other input devices are typically connected to processing unit 804 via input device interface 842 coupled to system bus 808, but can be connected via other interfaces such as parallel ports, IEEE 1394 serial ports, game ports, USB ports, IR interfaces, etc.

[0078] A monitor 844 or other type of display device is also connected to the system bus 808 via an interface such as a video adapter 846. The monitor 844 can be internal or external to the computer 802. In addition to the monitor 844, the computer typically includes other peripheral output devices such as speakers, printers, etc.

[0079] Computer 802 can operate in a networked environment via logical connections to one or more remote computers, such as remote computer 848, using wired and / or wireless communications. Remote computer 848 may be a workstation, server computer, router, personal computer, portable computer, microprocessor-based entertainment device, peer-to-peer device, or other public network node, and typically includes many or all of the elements described relative to computer 802; however, for simplicity, only memory / storage device 850 is shown. The depicted logical connections include wired / wireless connections to a local area network (LAN) 852 and / or a larger network such as a wide area network (WAN) 854. Such LAN and WAN network environments are common in offices and companies and facilitate enterprise-wide computer networks, such as intranets, all of which can connect to global communication networks, such as the Internet.

[0080] When used in a LAN network environment, computer 802 is connected to LAN 852 via a wired and / or wireless communication network interface or adapter 856. Adapter 856 facilitates wired and / or wireless communication to LAN 852, which may also include a wireless access point configured thereon for communication with the wireless functionality of adapter 856.

[0081] When used in a WAN networking environment, computer 802 may include modem 858, or a communication server connected to WAN 854, or other means for establishing communication via WAN 854 such as via the Internet. Modem 858 may be an internal or external, wired and / or wireless device connected to system bus 808 via input device interface 842. In a networking environment, program modules or portions thereof depicted relative to computer 802 may be stored in remote memory / storage device 850. It should be understood that the network connections shown are exemplary, and other means of establishing communication links between computers may be used.

[0082] Computer 802 is used to communicate with wired and wireless devices or entities, such as wireless devices operatively configured in wireless communication (e.g., IEEE 802.16 air modulation techniques), using the IEEE 802 series of standards. This includes at least Wi-Fi (or Wireless Fidelity), WiMax, and Bluetooth. ™ Wireless technologies, etc. Therefore, communication can be a predefined structure like a regular network, or simply self-organized communication between at least two devices. Wi-Fi networks use radio technology known as IEEE 802.11x (a, b, g, n, etc.) to provide secure, reliable, and fast wireless connectivity. Wi-Fi networks can be used to connect computers to each other, connect to the Internet, and connect to wired networks (using IEEE 802.3 related media and functions).

[0083] Many specific details have been set forth herein to provide a thorough understanding of the implementation scheme. However, those skilled in the art will understand that the implementation scheme can be practiced without these specific details. In other instances, well-known operations, components, and circuits have not been described in detail to avoid obscuring the implementation scheme. It is understood that the specific structural and functional details disclosed herein are representative and do not necessarily limit the scope of the implementation scheme.

[0084] The terms “coupled” and “connected”, as well as their derivatives, may be used to describe some implementations. These terms are not intended to be synonymous with each other. For example, the terms “connected” and / or “coupled” may be used to describe some implementations to indicate that two or more elements are in direct physical or electrical contact with each other. However, the term “coupled” may also mean that two or more elements are not in direct contact with each other, but still cooperate or interact with each other.

[0085] Unless otherwise expressly stated, terms such as “processing,” “computing,” “operation,” and “determining” refer to the operation and / or process of a computer or computing system or similar electronic computing device that processes and / or converts data represented as physical quantities (e.g., electrons) within the registers and / or memory of the computing system into physical quantities similarly represented within the memory, registers, or other such information storage, transmission, or display devices of the computing system. Implementations are not limited to this context.

[0086] It should be noted that the methods described herein need not be performed in the order described or in any particular order. Furthermore, the various activities described with respect to the methods identified herein can be performed in a series or in parallel.

[0087] While specific embodiments have been illustrated and described herein, it should be understood that any arrangement intended to achieve the same purpose may be substituted for the specific embodiments shown. This disclosure is intended to cover any and all adaptations or variations of the various embodiments. It should be understood that the above description is illustrative and not restrictive. After reading the above description, combinations of the above embodiments and other embodiments not specifically described herein will be apparent to those skilled in the art. Therefore, the scope of the various embodiments includes any other application in which the above compositions, structures, and methods are used.

[0088] Although the subject matter has been described in language specific to structural features and / or methodological actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, specific features and actions are disclosed as exemplary forms for implementing the claims.

Claims

1. An apparatus comprising: Memory; One or more processors; and Instructions, stored in the memory and configured to be executed by the one or more processors, to perform an anomaly-by-anomaly inspection process, the process being used to: Access to chromatographic information generated by analyzing a sample using a mass spectrometry system, the chromatographic information including at least one peak and at least one peak attribute of the at least one peak. Determine the posterior probability information of at least one peak attribute of the at least one peak in the analyzed sample, the determination including: The at least one peak is modeled using multiple different peak models. This represents the probability that the peak model selected from the plurality of different peak models is the correct model for the at least one peak, and Define the posterior probability information of the selected model, which depends on the degree of matching between the selected model and the at least one peak. An estimated peak model is generated based on the posterior probability information. A confidence indicator for the chromatographic information is determined based on the difference between the at least one peak and the estimated peak model, and An anomaly is generated for the at least one peak in response to the confidence indicator exceeding the anomaly threshold.

2. The apparatus of claim 1, wherein the instructions, when executed by the one or more processors, perform an anomaly-by-anomaly review process, the process being used to generate at least one information evaluation screen to present at least one graphical user interface (GUI) object to visually highlight the anomaly.

3. The apparatus of claim 1, wherein the estimated peak model is determined using Markov chain Monte Carlo and nested sampling.

4. The apparatus according to claim 1, wherein the posterior probability information is generated via Bayesian probability analysis.

5. The apparatus according to claim 1, wherein the posterior probability information includes a posterior probability sample of the at least one peak attribute, the at least one peak attribute including at least one of retention time, number, width, or asymmetry.

6. The apparatus of claim 1, wherein the confidence indicator indicates the confidence level of the estimated peak model in modeling the chromatographic information.

7. The apparatus of claim 1, wherein the confidence indicator comprises at least one error bar.

8. A computer-implemented method for performing an anomaly-by-anomaly inspection process, the method comprising, via one or more processors of a computing device: Access to chromatographic information generated by analyzing a sample using a mass spectrometry system, the chromatographic information including at least one peak and at least one peak attribute of the at least one peak; Determine the posterior probability information of at least one peak attribute of the at least one peak in the analyzed sample, the determination including: The at least one peak is modeled using multiple different peak models. This represents the probability that the peak model selected from the plurality of different peak models is the correct model for the at least one peak, and Define the posterior probability information of the selected model, the posterior probability information depending on the degree of matching between the selected model and the at least one peak; An estimated peak model is generated based on the posterior probability information; A confidence indicator for the chromatographic information is determined based on the difference between the at least one peak and the estimated peak model; and An anomaly is generated for the at least one peak in response to the confidence indicator exceeding the anomaly threshold.

9. The method of claim 8, further comprising generating at least one information evaluation screen to present at least one graphical user interface (GUI) object to visually highlight the anomaly.

10. The method of claim 8, wherein the estimated peak model is determined using Markov chain Monte Carlo and nested sampling.

11. The method of claim 8, wherein the posterior probability information is generated via Bayesian probability analysis.

12. The method of claim 8, wherein the posterior probability information comprises a posterior probability sample of the at least one peak attribute, the at least one peak attribute comprising at least one of retention time, number, width, or asymmetry.

13. The method of claim 8, wherein the confidence indicator indicates the confidence level of the estimated peak model in modeling the chromatographic information.

14. The method of claim 8, wherein the confidence indicator comprises at least one error bar.

15. A computer-readable storage medium comprising instructions that, when executed, cause the system to: Access to chromatographic information generated by analyzing a sample using a mass spectrometry system, the chromatographic information including at least one peak and at least one peak attribute of the at least one peak; Determine the posterior probability information of at least one peak attribute of the at least one peak in the analyzed sample, the determination including: The at least one peak is modeled using multiple different peak models. This represents the probability that the peak model selected from the plurality of different peak models is the correct model for the at least one peak, and Define the posterior probability information of the selected model, the posterior probability information depending on the degree of matching between the selected model and the at least one peak; An estimated peak model is generated based on the posterior probability information; A confidence indicator for the chromatographic information is determined based on the difference between the at least one peak and the estimated peak model; and An anomaly is generated for the at least one peak in response to the confidence indicator exceeding the anomaly threshold.

16. The computer-readable storage medium of claim 15, wherein the instructions, when executed, cause the system to generate at least one information evaluation screen to present at least one graphical user interface (GUI) object to visually highlight the anomaly.

17. The computer-readable storage medium of claim 15, wherein the estimated peak model is determined using Markov chain Monte Carlo and nested sampling.

18. The computer-readable storage medium of claim 15, wherein the posterior probability information is generated via Bayesian probability analysis.

19. The computer-readable storage medium of claim 15, wherein the posterior probability information comprises a posterior probability sample of the at least one peak attribute, the at least one peak attribute comprising at least one of retention time, number, width, or asymmetry.

20. The computer-readable storage medium of claim 15, wherein the confidence indicator indicates the confidence level of the estimated peak model in modeling the chromatographic information.

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