Using assessment score as a filter for additional library search processing

Abstract

In one aspect, a method of processing a mass spectrum is disclosed, which includes receiving a measured mass spectrum, determining an assessment score for the measured mass spectrum based on a comparison of a plurality of mass peaks in the measure mass spectrum with library mass peaks associated with a set of one or more library mass spectra of one or more known compounds, and analyzing the measured mass spectrum for identification of at least one compound associated with the measured mass spectrum only when the assessment score is greater than the threshold.

Description

4277-0420W001USING ASSESSMENT SCORE AS A FILTER FOR ADDITIONAL LIBRARY SEARCH PROCESSINGRELATED APPLICATIONS

[0001] This application claims priority to, and the benefit of, U.S. Provisional Application No. 63 / 756,456, filed on February 10, 2025, the disclosure of which is incorporated herein by reference in its entirety.TECHNICAL FIELD

[0002] The present disclosure relates generally to mass spectrometry and in particular to methods and systems for data analysis in mass spectrometry.BACKGROUND

[0003] Mass spectrometry (MS) is an analytical technique for determining the structure of chemical substances with both qualitative and quantitative applications. MS can be useful for identifying unknown compounds, determining the composition of atomic elements in a molecule, determining the structure of a compound by observing its fragmentation, and quantifying the amount of a particular chemical compound in a mixed sample. Mass spectrometers detect chemical entities as ions such that a conversion of the analytes to charged ions must occur.

[0004] A variety of techniques and workflows are employed in mass spectrometry. The analysis of data generated via such techniques and workflows can pose certain challenges. For example, tandem mass spectrometry (MS / MS) can generate a wealth of mass spectral data via dissociation of precursor ions into fragment ions, where the mass spectral data in some cases can be highly multiplexed. The analysis of such data, for example, for identification of one or more compounds in a sample under analysis can require the use of complex algorithms and can be time consuming.

[0005] Accordingly, there is a need for enhanced methods and systems that allow a more efficient analysis of mass spectral data.14939-0334-0173, v. 14277-0420W001SUMMARY

[0006] In one aspect, a method of processing a mass spectrum is disclosed, which includes receiving a measured mass spectrum, determining an assessment score for the measured mass spectrum based on a comparison of a plurality of mass peaks in the measured mass spectrum with library mass peaks associated with a set of one or more library mass spectra of one or more known compounds, and analyzing the measured spectrum for identifying at least one compound associated with the measured mass spectrum only when the assessment score is greater than the threshold. In various embodiments, the above method can be performed using a computer. For example, a computer having a digital data processor can receive the measured mass spectrum and can be programmed to process the measured mass spectrum to determine an assessment score associated with that mass spectrum. Further, the computer can be programmed to compare the assessment score with a threshold and to process the measured mass spectrum for identification of at least one compound associated with the mass spectrum only when the assessment score is greater than the threshold.

[0007] As discussed in more detail below, in various embodiments, the assessment score is indicative of how well the library mass peaks match the plurality of mass peaks in the measured mass spectrum. For example, the determination of an assessment score can include the computation of a parameter that is indicative of similarity between the library mass peaks (or a subset of library mass peaks) and corresponding mass peaks in the measured mass spectrum. In various embodiments, the determination of the assessment score can be based on the likelihood that the measured mass spectrum will lead to identification of at least one compound represented by at least a subset of the library mass peaks. In other words, in various embodiments, the assessment score can be utilized to determine whether it would be worthwhile to subject the measured mass peaks to further processing so as to obtain a more robust identification of one or more compounds of interest in a sample under investigation. By way of example, in various embodiments, if the score indicates that the likelihood is negligible that one or more compounds of interest are present in the sample under investigation, further processing of the measured mass spectrum is not undertaken.24939-0334-0173, v. 14277-0420W001

[0008] In various embodiments, a measured mass spectrum can be subjected to some processing that is not computationally intensive (e.g., a processing that is less computationally demanding than a deconvolution method) prior to the determination of an assessment score. By way of example, a measured mass spectrum can be processed to generate a background-subtracted mass spectrum and the assessment score can be computed for the background-subtracted mass spectrum. In other embodiments, the assessment score can be computed for an unprocessed measured mass spectrum.

[0009] In some embodiments, in addition to the assessment score, a reverse assessment score can be calculated that indicates how well the measured mass spectrum fits the library spectrum, ignoring all library mass peaks that are not assigned to respective mass peaks in the measured mass spectrum. A purity score combines the assessment score and the reverse assessment score in a single score.

[0010] As discussed in more detail below, the assessment score can be determined in a variety of different ways. By way of example, in some embodiments, the assessment score can be defined as the percentage of at least a subset of the library mass peaks for which corresponding mass peaks can be found in the measured mass spectrum. In other embodiments, the assessment score can also take into account the intensities of the measured mass peaks relative to the intensities of the respective library mass peaks. Further, an assessment score can be defined as a combination of assessment scores defined in different ways.

[0011] In various embodiments in which the assessment score is greater than the aforementioned threshold, but the purity score is lower than an acceptable threshold, further analysis of the measured mass spectrum can be performed. While in some cases such further analysis does not rely on information gleaned during the initial analysis of the measured mass spectrum to determine the assessment score, in other cases, the information gleaned during the determination of the assessment score can be leveraged in further analysis of the measured mass spectrum.34939-0334-0173, v. 14277-0420W001

[0012] By way of example, further analysis of the measured mass spectrum for the identification of the at least one compound associated with the measured mass spectrum can include applying a deconvolution algorithm to the measured mass spectrum to generate one or more deconvoluted mass spectra regardless of whether those deconvoluted mass spectra contain any mass peaks corresponding to those identified in the library database during the initial analysis of the measured mass spectrum and matching the deconvoluted mass spectra to at least a subset of the library mass peaks to identify at least one target compound. By way of example, in some cases, such a deconvolution algorithm, such as PCVG, can be utilized without leveraging any information regarding the library mass peaks that were relied on for the determination of the assessment score. Alternatively, in other cases, the information gained during the step of determining the assessment score can be leveraged together with a suitable deconvolution algorithm to identify one or more of the target compounds. By way of example, further analysis can include deconvolving only those mass spectra that contain at least one mass peak corresponding to a mass peak in the library database.

[0013] By way of example, and without limitation, the deconvolution algorithm can be Principal Component Analysis with Variable Grouping (PCVG).

[0014] In various embodiments, the measured mass spectrum can be acquired using targeted or untargeted data acquisition techniques. By way of example, and without limitation, in various embodiments data independent acquisition (DIA) methods, such as Sequential Windowed Acquisition of all Theoretical Mass Spectra (SWATH-MS) can be employed. By way of another example, in some embodiments, MRMHR (Multiple Reaction Monitoring High Resolution) data acquisition technique can be employed.

[0015] In various embodiments, the method can further include receiving additional data for the ions associated with the measured mass spectrum along a measurement dimension other than the m / z dimension.

[0016] In a related aspect, a method for performing mass spectrometry is disclosed, which includes measuring a mass spectrum associated with a sample, determining an assessment score44939-0334-0173, v. 14277-0420W001for said measured mass spectrum based on a comparison of a plurality of mass peaks in said measured mass spectrum with library mass peaks associated with a set of one or more library mass spectra of one or more known compounds, and classifying said mass spectrum as suitable or unsuitable for analysis to identify one or more compounds in the sample based on a comparison of said assessment score with a threshold. In various embodiments, the mass spectrum can be classified as suitable for analysis when the determined assessment score is equal to or greater than the threshold and is classified as unsuitable for analysis when the assessment score is less than the threshold.

[0017] In a related aspect, a computer program product is disclosed, which includes a non-transitory and tangible computer readable storage medium storing a program with instructions for execution on a processor so as to perform a method of performing mass spectrometry. The method includes receiving a measured mass spectrum, determining an assessment score for the measured mass spectrum based on a comparison of a plurality of mass peaks in the measured mass spectrum with library mass peaks associated with a set of one or more library mass spectra of one or more known compounds, and analyzing the measured mass spectrum for the identification of at least one compound associated with the measured mass spectrum only when the assessment score is greater than the threshold.

[0018] In various embodiments of the above computer program product, the assessment score utilized by the method stored in the storage medium can be indicative of how well the library mass peaks match the plurality of mass peaks in the measured spectrum. By way of example, the step of determining the assessment score can be indicative of similarity between the library mass peaks and corresponding mass peaks in the measured mass spectrum.

[0019] In various embodiments, the step of analyzing the measured mass spectrum for the identification of the at least one compound associated with the measured mass spectrum includes calculating a group of related mass peaks of the measured mass spectrum relative to mass peaks of each of the set of one or more of the library mass spectra of the one or more compounds. For each spectrum of the set, recalculating the group of related peaks by applying a deconvolution algorithm to the measured mass spectrum. The mass peaks of the recalculated group are54939-0334-0173, v. 14277-0420W001compared to the set using a purity score and at least one spectrum of the set with highest purity score is identified. The at least one compound with the highest purity score and associated with the measured mass spectrum can be identified as the known compound. By way of example, the deconvolution algorithm can include PCVG. The measured mass spectrum can be acquired using a variety of techniques. By way of example, the mass spectrum can be acquired using a targeted or an untargeted data acquisition technique. By way of example, a data independent acquisition (DIA) method, such as SWATH-MS may be employed. By way of another example, MRMHR data acquisition technique may be utilized.

[0020] In various embodiments, the method associated with the computer program product includes receiving additional data for the ions associated with the measured mass spectrum along a measurement dimension other than the m / z dimension.

[0021] In a related aspect, a mass spectrometer is disclosed, which includes an ion source for ionizing a sample to generate a plurality of precursor ions, a mass filter configured to allow passage of at least one subset of the plurality of precursor ions having one or more m / z ratios within an m / z window of the mass filter therethrough, a device for receiving the at least one subset of the precursor ions and generating one or more fragment ions of said precursor ions,

[0022] and a mass analyzer configured to generate one or more ion detection signals corresponding to said fragment ions. The mass spectrometer can further include a data analysis module that is configured to: receive said one or more ion detection signals and generate a mass spectrum, determine an assessment score for the measured mass spectrum, compare the determined assessment score with a threshold, and analyze the measured mass spectrum for identification of at least one compound associated with the measured mass spectrum only when the assessment score is greater than said threshold.

[0023] In various embodiments, the mass filter can be configured to allow acquisition of mass spectral data in a targeted or untargeted acquisition mode.

[0024] Further understanding of various aspects of the present teachings can be obtained with reference to the following detailed description in conjunction with the associated drawings, which are described briefly below.64939-0334-0173, v. 14277-0420W001BRIEF DESCRIPTION OF THE DRAWINGS

[0025] FIG. 1 is a flow chart depicting various steps in a method for processing a mass spectrum according to an embodiment of the present teachings,

[0026] FIG. 2 schematically depicts a computer configured to perform methods according to the present teachings,

[0027] FIG. 3 schematically depicts a mass spectrometer according to an embodiment of the present teachings, and

[0028] FIG. 4 depicts a measured MS / MS mass spectrum acquired using SWATH acquisition method and processed for identification of phenylalanine.DETAILED DESCRIPTION

[0029] It will be appreciated that for clarity, the following discussion will explicate various aspects of embodiments of the applicant’s teachings, while omitting certain specific details wherever convenient or appropriate to do so. For example, discussion of like or analogous features in alternative embodiments may be somewhat abbreviated. Well-known ideas or concepts may also for brevity not be discussed in any great detail. The skilled person will recognize that some embodiments of the applicant’s teachings may not require certain of the specifically described details in every implementation, which are set forth herein only to provide a thorough understanding of the embodiments. Similarly, it will be apparent that the described embodiments may be susceptible to alteration or variation according to common general knowledge without departing from the scope of the disclosure. The following detailed description of embodiments is not to be regarded as limiting the scope of the applicant’s teachings in any manner.

[0030] As used herein, the terms "about" and "substantially equal" refer to variations in a numerical quantity that can occur, for example, through measuring or handling procedures in the real world; through inadvertent error in these procedures; through differences in the manufacture, source, or purity of compositions or reagents; and the like. Typically, the terms "about" and74939-0334-0173, v. 14277-0420W001"substantially" as used herein mean 10% greater or less than the value or range of values stated or the complete condition or state. For instance, a concentration value of about 30% or substantially equal to 30% can mean a concentration between 27% and 33%. The terms also refer to variations that would be recognized by one skilled in the art as being equivalent so long as such variations do not encompass known values practiced by the prior art.

[0031] As used herein the term "and / or" includes any and all combinations of one or more of the associated listed items and may be abbreviated as

[0032] The term “initial assessment score,” or its abbreviation “assessment score,” as used herein, refers to a numerical parameter that is indicative of the degree in which one or more mass peaks in a library database, e.g., corresponding to a known compound, match one or more peaks in a measured mass spectrum of a sample. An assessment score can be in a range of 0 to 1. By way of example, an assessment score can be indicative of the percentage of the library mass peaks that match respective mass peaks in the measured mass spectrum. In some cases, the assessment score can also take into account the relative intensities of the mass peaks in the library database relative to the respective mass peaks in the measured mass spectrum. Further, in some cases, an assessment score can be computed as a combination, e.g., a weighted average, of a plurality of assessment scores defined in different ways.

[0033] As noted above, the analysis of data generated by modern mass spectrometry techniques can be challenging. For example, identification of compounds in a sample via DIA can require deconvolution of a multitude of mass spectral peaks and searches of library databases to identify compounds corresponding to the observed spectral peaks.

[0034] By way of example, in some conventional data processing techniques for analysis of complex liquid chromatography (LC) / MS data, a library database search can be performed to match the spectral peaks in a measured mass spectrum with those corresponding to known compounds in the library database. In some cases, such a database search for identification of unknown compounds can be evaluated based on two scores, one of which (herein referred to as assessment score) is a measure of how well the library spectral data matches the spectral peaks in84939-0334-0173, v. 14277-0420W001the unknown spectrum and the other (herein referred to as purity score) is a measure of how well the spectral peaks in the unknown spectrum match the library spectral data, e.g., how many of the mass spectral peaks of the unknown spectrum can be identified in the library spectral data. While the assessment score does not take into account the spectral peaks that are present in the unknown spectrum but are absent from the library spectral data, the purity score takes into account all the spectral mass peaks in the unknown spectrum.

[0035] By way of example, the unprocessed DIA spectrum of a sample containing a known compound includes spectral peaks corresponding to fragment ions associated with that compound as well as those corresponding to interfering compounds. Thus, for such a sample, the assessment score will be high, but the purity score may be low due to the interfering mass peaks. In some such cases, additional processing of the measured mass spectrum, such as background subtraction or deconvolution, can improve the purity score. For example, an LC deconvolution technique commonly known as Principal Component with Variable Grouping (PCVG) can be applied to the mass spectrum to facilitate the identification of co-eluting compounds. PCVG utilizes Principal Component Analysis (PCA) to reduce the dimensionality of the LC-MS data and the technique of Variable Grouping (VG) groups variables (e.g., m / z values and / or retention times) based on their similarity across samples. Additional information regarding an assessment score and LC deconvolution technique can be found in published PCT application (WO 2023 / 148663), which is herein incorporated by reference in its entirety.

[0036] While a library search against an unprocessed or a background- subtracted measured spectrum can be performed rapidly, subsequent processing of such a spectrum to improve its purity score, e.g., via PCVG, can be time consuming. For example, a typical screen of a sample for certain target compounds (e.g., pesticides), may not discover many of such compounds within the sample and hence can lead to additional processing of the spectral peaks, e.g., via PCVG, for those compounds. For non-target screening, it is also possible not to find library matches for many measured spectral features. In such cases, the additional PCVG processing will be unproductive.94939-0334-0173, v. 14277-0420W001

[0037] In various embodiments, the above shortcomings of conventional mass spectral analysis can be addressed by assigning an initial assessment score to a measured mass spectrum and utilizing that assessment score to determine whether additional processing of the measured mass spectrum to identify one or more compounds corresponding to the spectrum is worth pursuing. In various embodiments, the methods for mass spectral analysis in accordance with the present teachings are based on the realization that in various cases if an assessment score computed for a mass spectrum is less than a particular threshold, the purity score will also be low, and it is typically not feasible to improve the purity score to reach an acceptable value with time-consuming deconvolution of the mass spectral data.

[0038] With reference to the flow chart of FIG. 1, an embodiment of a method for processing a mass spectrum according to the present teachings includes receiving a measured mass spectrum (step 102), determining an assessment score for the measured mass spectrum based on a comparison of a plurality of mass peaks in the measured mass spectrum with library mass peaks associated with a set of one or more library mass spectra of one or more known compounds (step 104), and analyzing the measured mass spectrum for identification of at least one compound associated with the measured mass spectrum only when the assessment score is greater than the threshold (step 106). In other words, in such an embodiment, when the assessment score is less than the threshold, additional processing of the mass spectrum for identification of one or more compounds in the sample is not undertaken.

[0039] In various embodiments of the above method, a computer comprising a digital data processor and other components, e.g., the computer schematically depicted in FIG.2, can receive the measured mass spectrum and can be programmed in accordance with the present teachings to determine the assessment score and compare the assessment score to the predefined threshold. The computer can also be programmed to analyze the measured mass spectrum for the identification of the at least one compound when the comparison of the assessment score with the threshold shows that the assessment score exceeds the threshold.

[0040] In addition to the assessment score, in various embodiments, a purity score is also determined. In such embodiments, when the assessment score is greater than the threshold and104939-0334-0173, v. 14277-0420W001the purity score is below a particular threshold (which can be different from the threshold assigned to the assessment score), the mass spectrum can be subjected to additional processing, e.g., PCVG, to facilitate the identification of one or more compounds within the sample under investigation.

[0041] In various embodiments, prior to the determination of the assessment score, an unprocessed measured mass spectrum can be processed to generate a background-subtracted mass spectrum and the assessment score can be determined based on the background-subtracted mass spectrum.

[0042] As noted above, in general, the assessment score is indicative of similarity between the library mass peaks and corresponding mass peaks in the measured mass spectrum. More specifically, in various embodiments, the assessment score is calculated based on the library spectrum and it ignores peaks that are present only in the unknown spectrum. In other words, the assessment score describes how well the library spectrum is represented in the unknown spectrum.

[0043] Another indicator, herein referred to as a reverse assessment score, indicates how well the measured mass spectrum fits the library spectrum, ignoring all library mass peaks that are not assigned to respective mass peaks in the measured mass spectrum. A purity score combines the assessment score and the reverse assessment score in a single score.

[0044] A high assessment score with a low purity score indicates that the unknown spectrum contains the library compounds, but it is likely impure.

[0045] In various embodiments, the assessment score can range from 0 to 1, where 1 indicates the peaks in the library spectrum are found in the measured spectrum. A low assessment score (e.g., an assessment score close to 0) indicates that peaks in the library spectrum are missing from the measured spectrum.

[0046] In various embodiments, the threshold for the assessment score can be set, e.g., based on the specific method employed for computing it, the specific assay in which it is employed,114939-0334-0173, v. 14277-0420W001and a tolerance for false positive and false negative results. For example, if the threshold selected for the assessment score is too high, the analysis of the measured spectrum even for those compounds that could be identified is not undertaken. On the other hand, if the threshold selected for the assessment score is too low, a large number of unproductive analyses of the measured mass peaks may be undertaken.

[0047] Generally, an assessment score can be computed based on presence or absence of peaks in a measured mass spectrum corresponding to mass peaks in at least one subset of mass peaks in library database (typically within an acceptable tolerance of the respective m / z ratios). Alternatively, the intensity pattern of the library and the measured mass peaks can also be taken into account in computing the assessment score. For example, the assessment score can be based on the percentage of mass peaks of at least one subset of mass peaks in the library database that are present in the measured mass spectrum regardless of the intensities of measured mass peaks. In some cases, such a percentage can be computed only with respect to those measured mass peaks that exhibit an intensity above a certain threshold. The computed value for the assessment score can then be utilized to determine whether additional processing of the measured mass spectrum for identification of one or more compounds of interest is warranted. For example, as noted above, in various embodiments, when the assessment score is less than a certain threshold, further processing of the measured mass spectrum is not undertaken.

[0048] By way of example, in various embodiments, the threshold for the assessment score can be in a range of about 0.1 to about 0.2 such that only when the assessment score is above such a threshold, the process of analysis of the measured mass spectrum for the identification of compound(s) associated with the mass spectrum is undertaken. In other words, when the assessment score is at or below such a threshold, the mass spectrum is not processed any further and it is assumed that additional processing of the mass spectrum, will not lead to a robust identification of compounds associated with the mass spectrum. On the contrary, in various embodiments, when the computed assessment score exceeds the threshold, the measured mass spectrum is subjected to additional processing. By way of example, and without limitation, such processing may include any suitable deconvolution technique, such as PCVG.124939-0334-0173, v. 14277-0420W001

[0049] Such an approach can provide several advantages. For example, certain target compounds may typically be not present in certain screened unknown samples, such as a panel of target pesticides in a typical food sample. In such cases, the assessment score computed based on the mass spectral data can be low as the library peaks corresponding to those target compounds will not be found in the mass spectral data of the unknown sample. In such a case, discontinuing further analysis of the measured mass spectral data when the assessment score is below a threshold can lead to considerable time saving because otherwise, for each of those target compounds (e.g., each of the panel of the pesticides), additional analysis of the mass spectral data (e.g., PCVG) would be initiated.

[0050] A method for processing a mass spectrum, for example, for screening an unknown sample, in accordance with various embodiments of the present teachings can be implemented using software, firmware, and hardware in a manner known in the art and as informed by the present teachings. By way of example, FIG. 2 schematically depicts an example of implementation of such a computer 200 having a digital processing unit 202 (e.g., a microprocessor) that can communicate via one or more communications buses 204 with a random-access memory (RAM) module 206 and a permanent memory module 208. The illustrated computer 200 further includes a communications module 210 that can communicate with other devices of a mass spectrometric system, e.g., via wired connections or via any suitable wireless communication protocol. By way of example, the communications module 210 can communicate with an ion detector of a mass spectrometric system to receive data corresponding to measured mass spectra.

[0051] By way of example, various instructions for performing methods according to various embodiments, such as the determination of an assessment score of a measured mass spectrum, comparison of the assessment score with a pre-stored threshold, and determining whether additional processing of the mass spectrum is warranted for identification of one or more compounds associated with the measured mass spectrum can be stored in the permanent memory module and can be accessed during runtime via the digital processing unit 202 to perform methods according to various embodiments.134939-0334-0173, v. 14277-0420W001

[0052] The term “computer-readable medium” as used herein refers to any media that can participate in providing instructions to processor 202 for execution. Such a medium may take many forms, including but not limited to, non-volatile media, volatile media, and transmission media. Non-volatile media include, for example, optical or magnetic disks, such as storage device 208. Volatile media includes dynamic memory, such as memory 206. Transmission media includes coaxial cables, copper wire, and fiber optics, including the wires that comprise bus 204.

[0053] Common forms of computer-readable media include, for example, a flexible disk, hard disk, magnetic tape, or any other magnetic medium, a CD-ROM, any other optical medium, a RAM, PROM, and EPROM, a FLASH-EPROM, any other memory chip or cartridge, or any other tangible medium from which a computer can read.

[0054] The methods according to various embodiments of the present teachings can be applied to mass spectral data acquired via a variety of mass spectrometric systems. By way of example, the present methods can be utilized in mass spectrometric systems that employ the SWATH data acquisition method, though, as noted above, the practice of the present teachings is not limited to SWATH data acquisition method. By way of example, FIG. 3 schematically depicts such a mass spectrometric system 300 according to an embodiment that includes an LC column 302 that can receive a sample and separate a plurality of analytes in the sample based on their elution times from the LC column. The mass spectrometer 300 further includes an ion source 304 that receives an eluate exiting the LC column and ionizes one or more analytes contained in the eluate to generate a plurality of precursor ions.

[0055] In many implementations, one or more ion guides 306 receive the precursor ions and provide focusing of the ions to generate an ion beam that is received by a mass filter 308. By way of example, the ion guide(s) can include a plurality of rods arranged in a quadrupole configuration to which RF and DC voltages generated by an RF voltage source 310 and a DC voltage source 312 can be applied in a manner known in the art to provide radial confinement of the received ions.144939-0334-0173, v. 14277-0420W001

[0056] In other embodiments, an ion mobility spectrometer (IMS), such as a differential mobility spectrometer (DMS), can be utilized as a separation device to separate ions based on their mobility with the ions exiting the IMS being received by the one or more ion guides 306.

[0057] The mass filter 308 provides an ion transmission window that allows transmission of ions having m / z values within an m / z range through the mass filter. By way of example, the mass filter 308 can include a plurality of rods arranged in a quadrupole configuration to which RF voltages as well as a discriminating DC voltage can be applied via the RF and the DC voltage sources, respectively, to generate an ion transmission window. In this implementation, the ions passing through the mass filter 308 are received by an ion fragmentation device 314 that causes fragmentation of the precursor ions to generate a plurality of product ions. The product ions are received by a mass analyzer 316, which generates mass signal data associated with the product ions.

[0058] When operating in the SWATH data acquisition mode, a controller 318 can control the operation of the RF and the DC voltage sources to scan the ion transmission window of the mass filter 308 over an m / z range so as to generate a plurality of overlapping ion transmission windows. The ions passing through the mass filter 308 are received by the ion fragmentation device 314 in which they undergo fragmentation to generate a plurality of fragment ions, which are received by the mass analyzer 316, which generates ion detection data. By way of example, and without limitation, the mass analyzer can be a time-of-flight (TOF) mass analyzer.

[0059] An analysis module 320 (herein also referred to as an analysis unit) that is in communication with the mass analyzer 316 can receive the ion detection data generated by the mass analyzer and process the ion detection data to generate a mass spectrum. The analysis module 320 can be further configured to operate on the mass spectrum in accordance with the present teachings to determine an assessment score and to compare the determined assessment score with a predefined threshold stored on the analysis module to determine whether further processing of the measured mass spectrum is warranted. In addition, in some embodiments, the analysis module can be configured to determine a purity score of the measured mass spectrum and to apply a deconvolution algorithm to the measured mass spectrum when the assessment154939-0334-0173, v. 14277-0420W001score is equal to or greater than an acceptable threshold but the purity score is below the threshold.

[0060] The following examples are provided for further elucidation of various aspects of the present teachings and are not intended to provide necessarily an optimal way of practicing the present teachings and / or optimal results that may be obtained.

[0061] Example

[0062] MS / MS mass spectrum of a metabolomics sample was acquired using a QTOF system operating in a SWATH acquisition mode and phenylalanine was identified in the sample by processing the MS / MS spectrum. FIG. 4 shows the mass spectrum and identified mass peaks corresponding to fragment ions associated with phenylalanine.

[0063] A library database search of the unprocessed mass spectrum resulted in a poor library match score (<10%). Applying LC and QI (mass filter) deconvolution resulted in a high purity score of 99% for a phenylalanine match. Processing every MS / MS peak via LC+Q1 deconvolution required 7.67 minutes of processing time. In contrast, utilizing an assessment score according to the present teachings for filtering the MS / MS peaks reduced the processing time to 2.17 minutes while allowing positive identification of phenylalanine.

[0064] The above descriptions of various implementations of the present teachings have been presented for purposes of illustration and description. It is not exhaustive and does not limit the present teachings to the precise form disclosed. Modifications and variations are possible in light of the above teachings or may be acquired from practicing of the present teachings. Additionally, the described implementation includes software but the present teachings may be implemented as a combination of hardware and software or in hardware alone. The present teachings may be implemented with both object-oriented and non-object-oriented programming systems.

[0065] Depending on certain implementation requirements, embodiments of the present teachings, the controller can be implemented in hardware, firmware and / or in software.

[0066] In some embodiments, the instructions for operating the optical system can be stored using a non-transitory storage medium such as a digital storage medium, for example a DVD, a Blu-Ray, a CD, a ROM, a PROM, and EPROM, an EEPROM or a FLASH memory, having164939-0334-0173, v. 14277-0420W001electronically readable control signals stored thereon, which cooperate (or are capable of cooperating) with a programmable computer system such that the respective method is performed. Therefore, the digital storage medium may be computer readable.

[0067] While various embodiments have been illustrated and described in detail in the drawings and foregoing description, such illustration and description are to be considered illustrative or exemplary and not restrictive; embodiments of the present disclosure are not limited to the disclosed embodiments. Other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing embodiments of the present disclosure, from a study of the drawings, the disclosure, and the appended claims.

[0068] In the claims, the word “comprising” does not exclude other elements or steps, and the indefinite article “a” or “an” does not exclude a plurality. A single processor or other processing unit may fulfill the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measured cannot be used to advantage. Any reference signs in the claims should not be construed as limiting the scope.

[0069] Those having ordinary skill in the art will appreciate that various changes can be made to the above embodiments without departing from the scope of the present teachings.174939-0334-0173, v. 1

Claims

4277-0420W001What is claimed is:

1. A method of processing a mass spectrum, comprising:receiving a measured mass spectrum,determining an assessment score for said measured mass spectrum based on comparison of a plurality of mass peaks in said measured mass spectrum with library mass peaks associated with a set of one or more library mass spectra of one or more known compounds, andanalyzing the measured mass spectrum for identifying at least one compound associated with said measured mass spectrum only when the assessment score is greater than a threshold.

2. The method of Claim 1, wherein the assessment score is indicative of how closely said library mass peaks match corresponding mass peaks in the measured mass spectrum.

3. The method of Claim 1, wherein the step of analyzing the measured mass spectrum for identification of said at least one compound associated with said measured mass spectrum comprises:applying a deconvolution algorithm to said measured mass spectrum to generate at least one deconvoluted mass spectrum and matching said at least one deconvoluted mass spectrum to at least a subset of said library mass peaks to identify said at least one target compound.

4. The method of Claim 1, wherein the step of analyzing the measured mass spectrum for identification of said at least one compound associated with said measured mass spectrum comprises:utilizing information regarding one or more library mass peaks identified as corresponding to one or more mass peaks in the measured spectrum in the step of determining the assessment score in combination with a deconvolution algorithm to identify said at least one target compound.184939-0334-0173, v. 14277-0420W0015. The method of Claim 3, wherein the deconvolution algorithm comprises Principal Component Analysis with Variable Grouping (PCVG).

6. The method of Claim 1, wherein said measured mass spectrum is acquired using any of a targeted and an untargeted data acquisition method, and wherein, optionally, said targeted data acquisition method comprises MRMHR.

7. The method of Claim 6, wherein said untargeted data acquisition method comprises a data independent acquisition (DIA) method, and wherein optionally the DIA method is a SWATH data acquisition method.

8. The method of Claim 1, further comprising receiving additional data for the ions associated with the measured mass spectrum along a measurement dimension other than m / z dimension.

9. The method of Claim 1, further comprising processing, prior to said step of determining said assessment score, said measured mass spectrum to generate a background-subtracted mass spectrum and utilizing said background-subtracted mass spectrum as the measured mass spectrum to determine said assessment score.

10. The method of Claim 1, further comprising discontinuing processing of said measured mass spectrum when the assessment score is less than said threshold.

11. A computer program product, comprising a non-transitory and tangible computer readable storage medium storing a program with instructions for execution on a processor so as to perform a method of performing mass spectrometry, the method comprising: receiving a measured mass spectrum,determining an assessment score for said measured mass spectrum based on comparison of a plurality of mass peaks in said measured mass spectrum with library mass peaks associated with a set of one or more library mass spectra of one or more known compounds, and194939-0334-0173, v. 14277-0420W001analyzing said measured mass spectrum for identification of at least one compound associated with said measured mass spectrum only when the assessment score is above a threshold.

12. The computer program product of Claim 11, wherein the assessment score is indicative of how closely said library mass peaks match corresponding mass peaks in the measured mass spectrum.

13. The computer program product of Claim 11, wherein the step of analyzing said measured mass spectrum for identification of said at least one compound associated with said measured mass spectrum comprises:(1) applying a deconvolution algorithm to said measured mass spectrum to generate at least one deconvoluted mass spectrum and matching said at least one deconvoluted mass spectrum to at least a subset of said library mass peaks to identify said at least one target compound, or(2) utilizing information regarding one or more library mass peaks identified as corresponding to one or more mass peaks in the measured spectrum in the step of determining the assessment score in combination with a deconvolution algorithm to identify said at least one target compound.

14. The computer program product of Claim 13, wherein the deconvolution algorithm comprises Principal Component Analysis with Variable Grouping (PCVG).

15. The computer program product of Claim 13, wherein said measured mass spectrum is acquired using any of a targeted and an untargeted data acquisition method, and wherein, optionally, said targeted data acquisition method comprises MRMHR.

16. The computer program product of Claim 15, wherein said untargeted data acquisition method comprises a data independent acquisition (DIA) method, and wherein, optionally, the DIA method is a SWATH data acquisition method.204939-0334-0173, v. 14277-0420W00117. The computer program product of Claim 13, wherein the method further comprises receiving additional data for the ions associated with the measured mass spectrum along a measurement dimension other than m / z dimension.

18. The computer program product of Claim 13, where the method further comprises discontinuing processing of said measured mass spectrum when the assessment score is less than the threshold.

19. A mass spectrometer, comprising:an ion source for ionizing a sample to generate a plurality of precursor ions, a mass filter configured to allow passage of at least one subset of the plurality of precursor ions having one or more m / z ratios within an m / z window of the mass filter therethrough,a device for receiving said at least one subset of the precursor ions and generating one or more fragment ions of said precursor ions,a mass analyzer configured to generate one or more ion detection signals corresponding to said fragment ions,a data analysis module configured to:receive said one or more ion detection signals and generate a mass spectrum,determine an assessment score for the measured mass spectrum, and compare the determined assessment score with a threshold, and analyze the measured mass spectrum for identification of at least one compound associated with the measured mass spectrum only when the assessment score is above said threshold.

20. The mass spectrometer of Claim 19, wherein the mass filter is configured to allow acquisition of mass spectral data in a data independent acquisition mode.214939-0334-0173, v. 1

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