Reinjection in response to indeterminate results in mass spectrometric screening.

Abstract

A system / method is provided that facilitates non-deterministic triggered re-injection for mass spectrometry screening. [Solution] Systems / methods are provided that facilitate non-deterministically triggered re-injection for mass spectrometry screening. In various embodiments, the system may access a screening list that specifies multiple target chemicals. In various aspects, the system may classify each of the multiple target chemicals as present or absent in a sample based on having a chromatograph and mass spectrometer perform an iterative injection protocol on the sample. Subsequent iterations of the iterative injection protocol are progressively compound-selective and are triggered by the non-deterministic present or absent classification from the preceding iteration.

Description

[Technical Field]

[0001] In the field of mass spectrometry, it is possible to screen samples for specific chemicals. Existing techniques that allow such screening suffer from various drawbacks. Summary of the Invention

[0002] The following presents a summary to provide a basic understanding of one or more embodiments. This summary is not intended to identify key or critical elements or to delineate the scope of particular embodiments or the claims. Its sole purpose is to present concepts in a simplified form as a prelude to the more detailed description that is presented later. One or more embodiments described herein disclose an apparatus, system, computer-implemented method, or computer program product that enables reinjection in response to indeterminate results in mass spectrometry screening.

[0003] According to one or more embodiments, a scientific instrument is provided. The scientific instrument may include a chromatograph coupled to a mass spectrometer. In various aspects, the scientific instrument may further include a processor capable of executing computer-executable components stored in a non-transitory computer-readable memory. In various cases, the computer-executable components may include an accessing component capable of accessing a screening list defining a plurality of target chemicals. In various cases, the computer-executable components may include a screening component. The screening component may classify each of a plurality of target chemicals as present or absent in a sample based on causing the chromatograph and mass spectrometer to perform an iterative injection protocol on the sample, triggered by an indeterminate present or absent classification in a preceding iteration, with subsequent iterations becoming increasingly compound-selective.

[0004] According to one or more embodiments, a computer-implemented method is provided. In various embodiments, the computer-implemented method can include a device operatively connected to a processor accessing a screening list defining a plurality of target chemicals. In various aspects, the computer-implemented method can include the device classifying each of the plurality of target chemicals as present or absent in the sample based on causing a chromatograph and mass spectrometer to perform an iterative injection protocol on the sample, triggered by an indeterminate present or absent classification in a preceding iteration, with subsequent iterations becoming increasingly compound-selective.

[0005] According to one or more embodiments, a computer program product is provided that enables re-injection in response to an indeterminate result in a mass spectrometry screening. In various embodiments, the computer program product may include a non-transitory computer-readable memory having program instructions embedded therein. In various aspects, the program instructions are executable by a processor and enable the processor to establish electronic communication with a gas chromatograph and a mass spectrometer, where the gas chromatograph and mass spectrometer have been loaded with a sample for analysis. In various cases, the program instructions may be further executable by the processor to enable the processor to obtain a screening list defining one or more hazardous chemicals that are not permitted to be present in the sample for analysis. In various cases, the program instructions may be further executable to enable the processor to classify each of the one or more hazardous chemicals as present or absent in the sample based on causing the gas chromatograph and mass spectrometer to perform a repeat injection protocol on the sample for analysis. Subsequent iterations of the repeat injection protocol are triggered by the classification of indeterminate presence or absence in the preceding iteration, becoming increasingly compound selective. [Brief explanation of the drawings]

[0006] Various embodiments will be readily understood from the following detailed description taken in conjunction with the accompanying drawings, in which: To facilitate this description, like reference numerals refer to like structural elements; Embodiments are illustrated in the drawings by way of example, and not by way of limitation; and the drawings are not necessarily drawn to scale. [Figure 1] FIG. 1 illustrates a block diagram of an exemplary, non-limiting scientific instrument module according to various embodiments described herein. [Figure 2] FIG. 2 illustrates a flow diagram of an exemplary, non-limiting computer-implemented method according to various embodiments described herein. [Figure 3] FIG. 3 illustrates a block diagram of an exemplary, non-limiting scientific instrument module that facilitates non-deterministically triggered re-injection for mass spectrometry screening in accordance with one or more embodiments described herein. [Figure 4] FIG. 4 shows a block diagram of an exemplary, non-limiting scientific instrument module including a repeat injection protocol that facilitates non-deterministically triggered re-injections for mass spectrometry screening, in accordance with one or more embodiments described herein. [Figure 5] 1 shows a block diagram of an exemplary, non-limiting scientific instrument module illustrating how it facilitates repeat injection protocols in accordance with one or more embodiments described herein. [Figure 6] 1 shows a block diagram of an exemplary, non-limiting scientific instrument module illustrating how it facilitates repeat injection protocols in accordance with one or more embodiments described herein. [Figure 7] 1 shows a block diagram of an exemplary, non-limiting scientific instrument module illustrating how it facilitates repeat injection protocols in accordance with one or more embodiments described herein. [Figure 8] 1 shows a block diagram of an exemplary, non-limiting scientific instrument module illustrating how it facilitates repeat injection protocols in accordance with one or more embodiments described herein. [Figure 9]1 shows a block diagram of an exemplary, non-limiting scientific instrument module illustrating how it facilitates repeat injection protocols in accordance with one or more embodiments described herein. [Figure 10] 1 shows a block diagram of an exemplary, non-limiting scientific instrument module illustrating how it facilitates repeat injection protocols in accordance with one or more embodiments described herein. [Figure 11] 1 shows a block diagram of an exemplary, non-limiting scientific instrument module illustrating how it facilitates repeat injection protocols in accordance with one or more embodiments described herein. [Figure 12] 1 shows a block diagram of an exemplary, non-limiting scientific instrument module illustrating how it facilitates repeat injection protocols in accordance with one or more embodiments described herein. [Figure 13] 1 shows a flow diagram of an exemplary, non-limiting, computer-implemented method for facilitating ambiguous triggered reinjection for mass spectrometry screening, in accordance with one or more embodiments described herein. [Figure 14] 1 shows a flow diagram of an exemplary, non-limiting, computer-implemented method for facilitating ambiguous triggered reinjection for mass spectrometry screening, in accordance with one or more embodiments described herein. [Figure 15] 1 shows a flow diagram of an exemplary, non-limiting, computer-implemented method for facilitating ambiguous triggered reinjection for mass spectrometry screening, in accordance with one or more embodiments described herein. [Figure 16] 1 shows a flow diagram of an exemplary, non-limiting, computer-implemented method for facilitating ambiguous triggered reinjection for mass spectrometry screening, in accordance with one or more embodiments described herein. [Figure 17] 1 shows a flow diagram of an exemplary, non-limiting, computer-implemented method for facilitating ambiguous triggered reinjection for mass spectrometry screening, in accordance with one or more embodiments described herein. [Figure 18]1 illustrates a block diagram of an exemplary non-limiting operating environment in which one or more embodiments described herein may be implemented. [Figure 19] 1 illustrates an exemplary network environment in which various implementations described herein can be implemented. DETAILED DESCRIPTION OF THE INVENTION

[0007] The following detailed description is exemplary only and is not intended to limit the embodiments and their application / uses, nor is it intended to be limited by any express or implied information presented in the preceding Background or Summary sections or in the Detailed Description section.

[0008] One or more embodiments will now be described with reference to the drawings, wherein like reference numerals are used to refer to like elements throughout. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a more thorough understanding of one or more embodiments. It will be apparent, however, that in various instances, one or more embodiments may be practiced without these specific details.

[0009] Various operations may be described sequentially as multiple separate operations to best aid in understanding the subject matter disclosed herein. However, the order of description should not be construed to imply that these operations are necessarily order dependent. In particular, these operations may be performed in an order different from that presented. The operations described may be performed in an order different from that described in the embodiment. Various additional operations may be performed, or the operations described may be omitted in additional embodiments.

[0010] Some elements may be referred to in the singular (e.g., "processing device"), and appropriate elements may be represented by multiple instances of that element, and vice versa. For example, a series of operations described as being performed by a processing device may be implemented with different operations being performed by different processing devices. As used herein, the phrase "based on" should be understood to mean "based at least in part on," unless otherwise specified.

[0011] A mass spectrometer coupled to a chromatograph may be considered a type of scientific instrument that may be deployed in scientific, laboratory, research, or clinical operational contexts or settings to determine the chemical composition or makeup of an unknown sample. To facilitate such chemical composition determination, a mass spectrometer or chromatograph may include a complex configuration consisting of operable components (e.g., ion source, ion lenses, heaters, coolers, columns, ovens, injectors, mass analyzers, fluid valves, fluid pumps, circuit switches), sensors (e.g., ion detectors, voltmeters, thermistors, potentiometers, pressure gauges), and consumables (e.g., carrier fluids, calibrants, filters).

[0012] It may be desirable to screen any sample for specific or designated chemicals. Such screening can be readily performed using a mass spectrometer and a chromatograph. In other words, by injecting a portion of the sample into a mass spectrometer and a chromatograph, mass spectrometry data (e.g., a chromatogram, a mass spectrum) characterizing the sample is generated, and the mass spectrometry data can be compared with reference mass spectrometry data known to correspond to the specific or designated chemicals. In this way, it can be determined whether each of the specific or designated chemicals is present in the sample.

[0013] Such screening (suspect, targeted, and non-targeted screening) may be useful in a variety of industries and operational environments. For example, various jurisdictions require the screening of food (e.g., beverages, meat, vegetables, and fruits), food packaging (e.g., food paper, plastic food bags or surfaces), pharmaceuticals (e.g., oral medications, aerosols, and topical medications), medical devices (e.g., bandages and surgical instrument packaging), environmental samples (e.g., air, soil, river / lake / seawater, and animal feces), or industrial samples (e.g., battery solutions or mixtures, semiconductor solutions or mixtures, and manufacturing or processing raw materials) for the presence or absence of regulated, emerging, or other chemical substances of interest (e.g., known or anticipated toxicants; known or anticipated additives; known or anticipated contaminants; known or anticipated impurities; and known or anticipated by-products).

[0014] As the inventors of various embodiments described herein have recognized, existing technologies that enable such screening suffer from various drawbacks. In particular, the inventors have recognized that, when implemented on a sample, existing technologies cause the mass spectrometer and chromatograph to generate mass spectrometry data for the sample according to any scan protocol selected by the user or technician (e.g., a full scan protocol, a selected ion monitoring (SIM) protocol), without knowledge of the specific chemicals to be screened for in the sample. Once the mass spectrometry data is generated, the user or technician can evaluate the data (based on their own expertise) to determine which specific chemicals are present and which are absent in the sample. Unfortunately, as the inventors have recognized, such existing technologies may generate mass spectrometry data that does not allow for a definitive presence or absence determination of one or more specific chemicals (e.g., some of the mass spectrometry data may be noisy or chemically ambiguous). In such situations, a user or technician may rerun the sample on a mass spectrometer and chromatograph using a different or modified scan protocol to obtain new mass spectrometry data (e.g., it may be desirable for the new mass spectrometry data to be less noisy and less chemically ambiguous) in the hopes of enabling a definitive presence / absence determination for one or more specific chemicals. After the new mass spectrometry data is generated, the user or technician may re-evaluate the data. However, the new mass spectrometry data may still not provide a definitive presence / absence determination for some specific chemicals, necessitating further reruns and re-evaluations by the user or technician. Unfortunately, such existing techniques can be very time-consuming. Indeed, even experienced users and technicians may require several hours to evaluate the data generated by a mass spectrometer and chromatograph, and the time required to perform multiple evaluations (e.g., evaluations taking an hour per scan) can quickly add up.These time-consuming and potentially error-prone aspects can be exacerbated by the fact that many industrial or environmental samples must be analyzed in extremely large volumes and at high throughput rates. Additionally, some samples (e.g., biological samples containing unstable or rapidly degrading metabolites) may have a very short shelf life (e.g., minutes to hours), even after derivatization. Therefore, when existing techniques are applied to such samples, the samples may be degraded or altered before a user or technician can conclusively determine the presence or absence of each specific chemical (which may require repeated evaluation of mass spectrometry data equivalent to a single scan, potentially requiring several hours). For at least these reasons, conventional techniques present various technical challenges.

[0015] Therefore, a system or method that can improve upon such technical challenges would be desirable.

[0016] Various embodiments described herein may address one or more of these technical problems. One or more embodiments described herein may include a system, computer-implemented method, apparatus, or computer program product that enables reinjection in response to inconclusive results in mass spectrometry screening. In particular, given a sample and a screening list of chemicals to be screened for the sample, various embodiments described herein may include injecting a portion of the sample into a mass spectrometer and chromatograph, performing a scan on the portion of the sample to generate mass spectrometry data, and comparing the mass spectrometry data to reference mass spectrometry data known to correspond to chemicals on the screening list (e.g., by retention index comparison, quantitation-confirmation ion ratio comparison, library score calculation, etc.). Such a comparison may automatically determine that some chemicals on the screening list are present in the sample, while other chemicals are not present in the sample. If any chemicals on the screening list are not determined to be present or absent by the comparison (e.g., due to noise or ambiguity in the mass spectrometry data), various embodiments described herein may automatically control the mass spectrometer and chromatograph to quickly inject another portion of the sample and perform a new scan on that portion to generate new mass spectrometry data, which may then be compared with the reference mass spectrometry data. In some cases, the new scan may achieve greater sensitivity than the previous scan by using a temporally or ionically selective scan protocol for chemicals on the screening list that were previously determined to be present or absent. Such increased sensitivity may enable the new scan to determine the presence or absence of previously uncertain chemicals with a higher likelihood or probability than the previous scan. If one or more uncertain chemicals are still present on the screening list after the new scan, yet another reinjection iteration may be quickly performed.Such reinjection iterations may be repeated in this manner until there are no more uncertain chemicals on the screening list (e.g., until each chemical on the screening list has been determined to be present or absent in the sample). In other words, each reinjection iteration may be performed in response to the presence of at least one uncertain chemical on the screening list, hence the term "injection in response to an uncertain result." At such point, various embodiments described herein may generate or transmit an electronic notification (e.g., to a user or technician) indicating which chemicals on the screening list are present in the sample and which are absent from the sample.

[0017] Thus, the various embodiments described herein may be considered innovative software modes of operation or screening strategies that, when used with mass spectrometers and chromatographs, can prevent inconclusive mass spectrometry data from being output to a user or technician. Thus, the user or technician is no longer required to repeatedly evaluate inconclusive mass spectrometry data for hours, as in conventional approaches. Furthermore, injections in response to inconclusive results as described herein can be completed in just minutes, as opposed to the hours required with existing techniques. Therefore, unlike conventional techniques, the various embodiments described herein can be used to screen samples that are unstable or have a short shelf life before they degrade or become contaminated.

[0018] Various embodiments described herein may be considered as computerized tools (e.g., any suitable combination of computer-executable hardware or computer-executable software) that are electronically installed in or associated with a mass spectrometer equipped with a chromatograph and that enable reinjection in response to indeterminate results in mass spectrometry screening. In various aspects, such computerized tools may include an access component or a screening component.

[0019] In various embodiments, a mass spectrometer with a chromatograph may be considered to include a mass spectrometer functionally coupled to a chromatograph in any suitable manner. In various aspects, the mass spectrometer may include suitable component hardware. By way of non-limiting example, a mass spectrometer may include any suitable ion beam emission source (e.g., a matrix-assisted laser desorption / ionization (MALDI) source, an electrospray ionization (ESI) source, an atmospheric pressure chemical ionization (APCI) source, an atmospheric pressure photoionization (APPI) source, an inductively coupled plasma (ICP) source, an electron ionization source, a chemical ionization source, a photoionization source, a glow discharge ionization source, a thermospray ionization source, a hybrid source), any suitable mass analyzer (e.g., a quadrupole mass filter analyzer, an ion trap analyzer, a quadrupole ion trap analyzer, a time-of-flight (TOF) analyzer, an electrostatic trap (e.g., Orbitrap) mass analyzer, a Fourier transform ion cyclotron resonance (FT-ICR) mass analyzer), any suitable ion detector (e.g., an electron multiplier detector, a microchannel plate detector, an image charge detector, a Faraday cup detector), or any suitable ion optics (e.g., an ion focusing lens, an ion guide, an ion deflector). Similarly, in various cases, a chromatograph can include any suitable component hardware, such as gas chromatography hardware, liquid chromatography hardware, ion chromatography hardware, etc. By way of non-limiting example, a chromatograph can include a suitable sample injector (e.g., a hot injector such as a split, splitless, direct, or gas sampling valve (GSV); a cold injector such as a cold on-column (COC) or programmed temperature looperization (PTV); an injection syringe; an infusion syringe; an evaporator; a nebulizer), a suitable chromatographic column (e.g., comprising a suitable sorbent packing or a suitable capillary with a different stationary phase membrane), a suitable column oven or heater, or a suitable carrier fluid flow control device (e.g., a fluid valve, a fluid pump).In various embodiments, any suitable autosampler or auxiliary sampling device can be combined with the chromatography hardware to prepare and introduce samples (e.g., gas and liquid sampling valves, headspace autosamplers, solid-phase microextraction (SPME), headspace-SPME, in-tube extraction-dynamic headspace (ITEX-DHS), thermal desorption devices (TD), purge-and-trap devices (P&T), pyrolyzers). In various cases, the carrier gas type can include, but is not limited to, helium, hydrogen, nitrogen, argon, methane, or any suitable combination thereof. In various cases, once a sample is provided, it can be injected into a chromatograph, separated into its constituent components by the chromatograph, ionized, and then analyzed by a mass spectrometer (e.g., the mass spectrometer can record the relative abundance of ions in the sample as a function of mass-to-charge ratio). In various embodiments, a sample can be loaded into a mass spectrometer equipped with a chromatograph.

[0020] In various embodiments, a screening list may be provided. In various aspects, the screening list may specify any number of target chemicals that one desires to screen for against the loaded sample. In other words, one may desire to determine which target chemicals in the screening list are present in the loaded sample and which target chemicals are not present in the loaded sample instead. As a non-limiting example, some target chemicals on the screening list may be known toxins or other harmful substances that one desires not to include in the sample, while other target chemicals may be known additives or beneficial substances that one desires to include in the sample.

[0021] In various cases, a user or technician may create or define a screening list in an Excel sheet (or other suitable electronic format) and upload it to a computerized tool for screening. In other cases, a screening list may be selected from any suitable reference mass spectrometry database (e.g., nominal mass format or high-resolution accurate mass format) according to compound class, type, or group prior to data acquisition.

[0022] In various embodiments, the screening list can be matched to reference mass spectrometry data. In various cases, for each target chemical in the screening list, the reference mass spectrometry data can include a reference retention index, a reference quantitative / confirmatory ion ratio, or a reference mass spectrum corresponding to that target chemical.

[0023] In various embodiments, an access component of a computerized tool may electronically access a mass spectrometer equipped with a chromatograph. That is, the access component may electronically interface with or communicate with a mass spectrometer equipped with a chromatograph, thereby allowing other components of the computerized tool to electronically interact with the mass spectrometer (e.g., send electronic commands, read electronic signals, etc.). Similarly, in various aspects, the access component may electronically access a screening list or reference mass spectrometry data. That is, the access component may electronically receive or retrieve the screening list or reference mass spectrometry data from any suitable database (e.g., a relational database, a graph database, a hybrid database), such that the access component may be considered a conduit through which other components of the computerized tool electronically interact with (e.g., read, write, edit, copy, manipulate) the screening list or reference mass spectrometry data.

[0024] In various embodiments, the screening component of the computerized tool may electronically classify each target chemical in the screening list as present in the loaded sample and classify the remaining target chemicals in the screening list as absent in the loaded sample. In various cases, the screening component may achieve such classification based on having a mass spectrometer equipped with a chromatograph perform an iterative injection protocol on the loaded sample. In various cases, iterations of the iterative injection protocol become increasingly compound selective and may be triggered in response to a classification of indeterminate presence or indeterminate absence in a previous iteration. In particular, a current iteration of the iterative injection protocol may proceed as follows:

[0025] During the current iteration, the screening component may obtain a present run list, an absent run list, and an uncertain run list.

[0026] If the current iteration is the first of an iterative injection protocol, the Present Run List may be an empty or null set. However, if the current iteration is not the first, first, or initial iteration, the Present List may be considered a set containing target chemicals in the Screening List that were classified as present in the samples loaded in any of the previous iterations.

[0027] Similarly, if the current iteration is the first of an iterative injection protocol, the absent list may be an empty or null set, but if the current iteration is not the first, the absent list may be considered a set containing target chemicals in the screening list that were classified as not present in the samples loaded in any of the previous iterations.

[0028] In contrast, if the current iteration is the first of an iterative injection protocol, the indeterminate list may be equivalent to the screening list. However, if the current iteration is not the first, initial, or initial iteration of an iterative injection protocol, the indeterminate run list may be considered to be the set obtained by subtracting the present list and the absent list from the screening list. That is, the indeterminate list may include target chemicals in the screening list that have not been classified as present or absent in any prior iteration.

[0029] Now, during the current iteration, the screening component may inject a portion of the loaded sample into a mass spectrometer equipped with a chromatograph. The screening component may then accordingly scan the injected portion with the mass spectrometer equipped with a chromatograph, thereby obtaining measured mass spectrometry data. In various embodiments, the scan protocol performed by the mass spectrometer equipped with a chromatograph may vary depending on the index of the current iteration. If the current iteration is the first, the mass spectrometer equipped with a chromatograph may use an unrestricted full scan protocol to scan the injected sample portion. If the current iteration is not the first, the mass spectrometer equipped with a chromatograph may scan the injected sample portion using a time-selective or ion-selective scan protocol for each target chemical in the uncertainty list. In some cases, the time-selective scan protocol may be a truncated full scan protocol to include only retention times known or expected to be associated with the target chemicals in the uncertainty list (e.g., as indicated or derived from reference mass spectrometry data). In some cases, the ion-selective scan protocol may be a SIM protocol that monitors only ions known to be quantitative or confirmatory ions for target chemicals in the uncertainty list (e.g., as indicated or derived from reference mass spectrometry data). In some situations, the reference mass spectrometry data may indicate multiple different quantitative ions or multiple different confirmatory ions for any target chemical in the uncertainty list. If a SIM protocol is to be performed in the current iteration and no SIM protocol has been performed in any previous iteration, the screening component may instruct the mass spectrometer with chromatograph to use a SIM protocol that monitors all of the multiple different quantitative ions and multiple different confirmatory ions in the current iteration.However, if a SIM protocol is to be performed in the current iteration and a SIM protocol has been performed in any of the previous iterations, the screening component may use a SIM protocol in the current iteration that monitors a mass spectrometer equipped with a chromatograph, excluding at least one ion from the plurality of different quantitation ions and the plurality of different confirmation ions monitored in the immediately preceding SIM protocol. In other words, one or more of the quantitation ions or confirmation ions monitored in the immediately preceding SIM protocol may be ignored in the SIM protocol in the current iteration. In various cases, the screening component may select which of the plurality of different quantitation ions or the plurality of different confirmation ions to ignore based on any appropriate method (e.g., a priority order such as from least abundant to most abundant or from lowest to highest mass).

[0030] During the current iteration, the screening component may determine which target chemicals (if any) should be moved from the indeterminate list to the present list and which target chemicals (if any) should instead be moved from the indeterminate list to the absent list. In various embodiments, the screening component may make such determinations based on a comparison of the measured mass spectrometry data with reference mass spectrometry data.

[0031] In particular, the screening component can select any target chemical from the indeterminate list, and the reference mass spectrometry data can specify a reference retention time, a reference quantification-to-confirmation ion ratio, or a reference mass spectrum for the selected target chemical. The measured mass spectrometry data can then include a chromatogram whose peaks (e.g., processed by deconvolution combined with blank / matrix injection and background subtraction) can be considered to represent each unknown chemical in the loaded sample. Each peak in the chromatogram can have a corresponding measured retention index and measured mass spectrum. In various embodiments, if no peak in the chromatogram has a measured retention index within an appropriate threshold margin of the reference retention index of the selected target chemical, the screening component can move the selected target chemical from the indeterminate list to an absent list (e.g., can definitively classify the selected target chemical as absent in the loaded sample). On the other hand, if one or more peaks in the chromatogram have measured retention indices within a threshold margin of the reference retention index of the selected target chemical, these one or more peaks can be considered to represent unknown chemicals present in the loaded sample that may match the selected target chemical.

[0032] In some cases, the screening component may determine the degree of similarity between the reference mass spectrum and the measured mass spectra corresponding to those potential matching peaks. If at least one of these measured mass spectra exceeds any suitable upper level of similarity with the reference mass spectrum, the screening component may move the selected target chemical from the indeterminate list to the present list (e.g., may definitively classify the selected target chemical as present in the loaded sample). In contrast, if all of the measured mass spectra fall below an appropriate lower limit of similarity with the reference mass spectrum, the screening component may move the selected target chemical from the indeterminate list to the absent list (e.g., may definitively classify the selected target chemical as absent in the loaded sample). In further contrast, if all of the measured mass spectra fall below an upper limit of similarity with the reference mass spectrum and at least one of them exceeds a lower limit, the screening component may retain the selected target chemical in the indeterminate list (e.g., may not definitively classify whether the selected target chemical is present in the loaded sample).

[0033] By way of non-limiting example, the screening component may be quantitative versus confirmatory. This similarity comparison can be easily performed based on (QC) ion ratios. Specifically, the screening component can calculate respective QC ion ratios for one or more potentially matching peaks (e.g., a reference QC ion ratio designates a specific quantification ion and a specific confirmation ion; the intensities or abundances of these ions can be read from the corresponding measured mass spectrum and appropriately rearranged as fractions to calculate the QC ion ratio). Furthermore, each measured mass spectrum can have a mass tolerance for each quantification ion and confirmation ion. In this case, if there is at least one candidate matching peak whose calculated QC ion ratio is within a threshold percentage difference of the reference QC ion ratio and whose mass tolerance is below an appropriate threshold, the screening component can conclude that the peak represents the selected target chemical (e.g., determine that the measured mass spectrum exceeds an upper limit of similarity to the reference mass spectrum). Next, if all candidate match peaks are outside the threshold percent difference of the reference QC ion ratio and the mass tolerance exceeds the threshold tolerance, the screening component can conclude that none of the peaks represent the selected target chemical (e.g., it can determine that all measured mass spectra are below a lower limit in similarity to the reference mass spectrum). Finally, if neither of the above two conditions is met (e.g., there are candidate match peaks that are within the threshold percent difference of the reference QC ion ratio but their mass tolerance exceeds the threshold tolerance), the screening component cannot conclude about the presence or absence of the selected target chemical (e.g., it can determine that the measured mass spectrum is above a lower limit but below an upper limit in similarity to the reference mass spectrum).

[0034] As another non-limiting example, the screening component may perform this similarity comparison based on the calculation of a library score. Specifically, the screening component may calculate a respective library score (e.g., a reverse score or a forward score) for each potential matching peak. This score is a scalar value whose magnitude indicates the degree of similarity between the measured mass spectrum and the reference spectrum. The screening component may calculate the library score using any suitable technique, such as cosine similarity, Euclidean distance, or peak matching. If there is at least one candidate matching peak whose library score exceeds an upper threshold score, the screening component may conclude that at least one of the candidate matching peaks represents the selected target chemical (e.g., at least one of the measured mass spectra is above an upper level of similarity with the reference mass spectrum). Next, if all candidate matching peaks have library scores below a lower threshold score, the screening component may conclude that none of the candidate matching peaks represents the selected target chemical (e.g., all of the measured mass spectra are below a lower level of similarity with the reference mass spectrum). Finally, if neither of the above two conditions is met (e.g., the highest library score is above the lower threshold but below the upper threshold), the screening component cannot conclude about the presence or absence of the selected target chemical (e.g., the measured mass spectrum is above the lower limit of similarity to the reference mass spectrum but not the upper limit). For example, according to the National Institute of Standards and Technology (NIST), two spectra that are a perfect match can have a library score of 999, while two spectra that have no peaks in common can have a library score of 0.Therefore, as a general guideline, a library score of 900 or above indicates a very good match, a score in the range of 800-900 indicates a good match, a score of 700-800 indicates a moderate match, a score of 600-700 indicates an uncertain match, and a score below 600 indicates a poor match.

[0035] In either case, the screening component may classify each target chemical in the uncertainty list as present, absent, or neither during the current iteration.

[0036] In various cases, the screening component may move a target chemical classified as "present" during the current iteration from the indeterminate list to the present list. Similarly, the screening component may move a target chemical classified as "absent" during the current iteration from the indeterminate list to the absent list.

[0037] In various aspects, the screening component may determine whether the uncertain run list is currently empty during the current iteration. If the uncertain list is not yet empty, the next or subsequent iteration of the iterative injection protocol may be initiated (e.g., by injecting a new portion of the loaded sample and having the chromatograph mass spectrometer scan the new portion using a time- or ion-selective scan protocol for the target chemicals remaining on the uncertain list to obtain new measured mass spectrometry data. The screening component may then classify the target chemicals in the uncertain list as “present,” “absent,” or neither based on the new measured mass spectrometry data, as described above). On the other hand, if the uncertain run list is currently empty, each target chemical in the screening list may be considered to have been finally classified as either “present” or “absent” in the loaded sample. In particular, the present run list may be considered to include all target chemicals classified as present, and the absent run list may be considered to include all target chemicals classified as absent. The union of the present run list and the absent run list may then be considered to be equivalent to the screening list. In other words, no inconclusive results remain. Thus, in some cases, the screening component may communicate the screening results to a user or technician associated with the chromatographic mass spectrometer by transmitting a list of present or absent runs to any suitable computing device or displaying it on any suitable electronic display.

[0038] Various embodiments described herein may be implemented using hardware or software to solve problems that are highly technical in nature (e.g., the problem of enabling re-injection due to uncertain triggers in mass spectrometry screening). These problems are not abstract and cannot be performed by a human as a series of mental operations. Furthermore, certain processing associated with mass spectrometry may be performed by specially configured computers (e.g., a chromatograph and mass spectrometer capable of injecting and scanning a portion of a sample).

[0039] For example, such specific processing may include: accessing, with the device operatively connected to the processor, a screening list specifying a plurality of target chemical compounds; and causing the device to subject the chromatograph and mass spectrometer to an iterative injection protocol on the sample, with subsequent iterations being increasingly more compound selective, if a previous iteration results in an inconclusive present or absent classification, and classifying each of the plurality of target chemical compounds as present or absent in the sample based on the protocol.

[0040] In various embodiments, such specific processing may include, during a current iteration of a repetitive injection protocol, accessing by the device a first run list, the first run list including only those target chemicals classified as present in the sample during any prior iteration of the repetitive injection protocol; accessing by the device a second run list, the second run list including only those target chemicals classified as absent in the sample during any prior iteration of the repetitive injection protocol; and accessing by the device a third run list, the third run list including only those target chemicals not classified as present or absent in the sample during any prior iteration of the repetitive injection protocol.

[0041] In various cases, such specific processing may include, during a current iteration, causing the device to inject a portion of the sample into a chromatograph and mass spectrometer and selectively scan that portion for each target chemical in the third run list, while ignoring each target chemical in the first or second run list, thereby obtaining current mass spectrometry data with increased sensitivity to the target chemicals in the third run list. In some cases, the chromatograph and mass spectrometer may perform selective scans by: selected ion monitoring for quantitation and confirmation ions associated with any target chemical on the third run list; or a time-truncated full scan based on the retention index associated with any target chemical on the third run list.

[0042] In various embodiments, such specific processing may include, during a current iteration, classifying each target chemical in the third run list as either present in the sample, absent, or neither present nor absent based on comparison of the current mass spectrometry data with reference mass spectrometry data corresponding to the screening list by the device; moving target chemicals classified as present in the sample from the third run list to the first run list by the device; moving target chemicals classified as absent in the sample from the third run list to the second run list by the device; and retaining target chemicals not classified as present or absent in the third run list by the device.

[0043] In various embodiments, the current mass spectrometry data includes a chromatogram generated by a chromatograph and mass spectra generated by a mass spectrometer corresponding to peaks in the chromatogram, and classifying the target chemicals in the third run list can include: calculating, by the device, retention indices of the peaks in the chromatogram; selecting, by the device, a target chemical from the third run list; classifying, by the device, the target chemical as absent in the sample if none of the peaks in the chromatogram have a retention index within a threshold margin of a reference retention index associated with the selected target chemical; accessing, by the device, one or more first mass spectra generated by the mass spectrometer for one or more first peaks in the chromatogram if the one or more first peaks have a retention index within a threshold margin of the reference retention index; If at least one of the one or more first mass spectra meets a presence classification criterion relative to the reference mass spectrum, the device classifies the target chemical as present in the sample; if all of the one or more first mass spectra meet an absence classification criterion relative to the reference mass spectrum, the device classifies the target chemical as absent in the sample; and if all of the one or more first mass spectra do not meet the presence criterion and at least one does not meet the absence criterion, the device classifies the target chemical as neither present nor absent in the sample. In various cases, the presence or absence classification criterion can be based on: a comparison of quantitative to confirmatory ion ratios; or calculation of a library score.

[0044] Such specific processing is essentially performed by a computer. In fact, chromatographs and mass spectrometers are highly technical computerized devices equipped with specific computerized hardware, such as temperature sensors, pressure sensors, voltage sensors, ion beam generators, ion focusing lenses, mass analyzers, and ion detectors. A chromatograph-mass spectrometer and the operations it performs cannot be implemented in a reasonable or practical way using human thought and pen and paper without the aid of a computer. For example, it is impossible to inject or pass a portion of a sample through a chromatograph-mass spectrometer equipped with an oven-heated column, ionizer, mass analyzer, or detector using human thought and pen and paper.

[0045] Furthermore, various embodiments described herein may integrate various findings regarding indeterministic triggered reinjections for mass spectrometry screening into practical applications. As described above, it may be desirable to screen a sample for specific chemicals (e.g., known toxins or contaminants) by utilizing a mass spectrometer equipped with a chromatograph. In conventional techniques, the chromatograph-equipped mass spectrometer injects the sample and scans it using any scan protocol selected by the user or technician. The user or technician then evaluates the resulting mass spectrometry data to determine which specified chemicals are present and which are absent in the sample. Due to noise or chemical ambiguity, the resulting mass spectrometry data may be indeterministic about one or more specific chemicals (e.g., the user or technician may not be able to conclusively determine the presence or absence of the one or more specified compounds). Therefore, the user or technician may reinject the sample into the chromatograph-equipped mass spectrometer, obtain new resulting mass spectrometry data, and evaluate the new data to resolve the previous ambiguity. Because the resulting mass spectrometry data can require a user or technician several hours to evaluate, conventional techniques are considered to be extremely time-consuming (e.g., conventional techniques can require several days to process a single screening list). This excessive time consumption is not only undesirable in itself, but also makes conventional techniques unsuitable for screening samples with short shelf lives or unstable samples (e.g., biological samples containing unstable metabolites). Ultimately, by the time a user or technician finally realizes that the mass spectrometry data obtained for an unstable or short-shelf-life sample is inconclusive for a particular chemical, the unstable or short-shelf-life sample may already be degraded or altered, making it futile for the user to re-inject the sample. For at least these reasons, conventional techniques are considered to have various technical challenges.

[0046] The various embodiments described herein may help alleviate this technical challenge by enabling indeterminately triggered re-injections in mass spectrometry screening. In particular, the various embodiments described herein may be considered a new or innovative software mode of operation for a chromatograph-equipped mass spectrometer, which may utilize an iterative injection protocol that prevents the output of indeterminate or ambiguous final mass spectrometry data to a user or technician regarding one or more designated screening compounds. Specifically, assume that it is desired to screen a sample against a given list of chemicals. Then, each iteration of the iterative injection protocol may include: Injecting a portion of the sample into a mass spectrometer equipped with a chromatograph, scanning the injected portion using a time-selective scan protocol (e.g., a truncated full scan protocol) or an ion-selective scan protocol (e.g., a SIM protocol) for chemicals on a given list that were not classified as present or absent in any previous iteration, thereby obtaining measured mass spectrometry data, and classifying the chemicals that were not classified as present or absent as present, absent, or neither based on comparing the measured mass spectrometry data with reference mass spectrometry data (e.g., by calculating QC ion ratios or library scores). Each iteration may be considered to have a narrower monitoring window in time or ion than the previous iteration. Therefore, each iteration may be considered to have a higher chemical sensitivity than the previous iteration. In other words, the possibility or probability that any given iteration will produce an indeterminate or ambiguous presence or absence result may be lower than the previous iteration. In various embodiments, such iterations may be repeated until no chemicals in the given list remain that have not been classified as present or absent. In other words, such iterations may be repeated until there are no more indeterminate chemicals in a given list. Thus, each subsequent, or non-initial, iteration of a repeat injection protocol may be considered a "trigger in response to an indeterminate result."

[0047] It should be noted that the various embodiments described herein differ from existing techniques and may avoid or ameliorate various technical problems plaguing existing techniques. Indeed, the iterative injection protocols described herein may be considered a particular innovative mode of operation or screening strategy that can be implemented by a chromatograph-equipped mass spectrometer for any sample and screening list. As described herein, such a mode of operation or screening strategy involves informing the chromatograph-equipped mass spectrometer of the specific chemicals contained in the screening list. In contrast, in conventional techniques, the chromatograph-equipped mass spectrometer is not informed of the specific chemicals contained in the screening list. Having access to or being informed of the specific chemicals on the screening list allows the chromatograph-equipped mass spectrometer to automatically customize, adjust, or narrow the scan protocol used for chemicals on the screening list that are not classified as "neither present nor absent" in each iteration (e.g., by gradually shortening the time range of a full scan protocol or gradually narrowing the ion selection range of a SIM protocol). This reduces the likelihood of obtaining inconclusive results compared to previous iterations. In contrast, existing technologies do not provide a mechanism for such automatic customization, adjustment, or refinement of scan protocols for each iteration. Furthermore, as described herein, any iteration of an iterative injection protocol can be automatically triggered in response to uncertainty detected during the previous iteration (such uncertainty can be measured by QC ion ratios or library scores). In contrast, existing technologies do not provide such an automatic uncertainty triggering mechanism. Furthermore, the iterative injection protocols described herein can be considered to prevent a user or technician from receiving indeterminate or ambiguous final mass spectrometry data for any chemical on the screening list. Thus, unlike existing technologies, a user or technician need not waste hours of evaluation time before ultimately arriving at an indeterminate result. Finally, the iterative injection protocols described herein can consume a total time on the order of tens of minutes.In contrast, existing techniques, as discussed above, often require hours to days. Thus, while various embodiments may be utilized to reliably or accurately screen unstable or short-shelf-life samples, these samples often degrade or deteriorate before existing techniques can complete such screening. In other words, various embodiments described herein enable computerized devices (e.g., mass spectrometers equipped with chromatographs) to do what was previously impossible (e.g., comprehensively screening unstable or short-shelf-life samples before the samples deteriorate or deteriorate).

[0048] For at least these reasons, the various embodiments described herein may be considered specific and tangible technological advances in the field of mass spectrometry, and thus certainly qualify as useful and practical applications of computers.

[0049] Additionally, various embodiments described herein may control real-world tangible devices based on the disclosed teachings. For example, various embodiments described herein may electronically activate, deactivate, or otherwise drive real-world hardware (e.g., sample injectors, ion beam emission sources, ion focusing lenses, carrier fluid valves / pumps) of real-world scientific instruments (e.g., chromatographs, mass spectrometers, autosamplers).

[0050] FIG. 1 illustrates an exemplary, non-limiting block diagram of a scientific instrument module 102 according to various embodiments described herein.

[0051] The scientific instrument module 102 may be implemented by circuitry (e.g., including electrical and / or optical components) such as a programmed computing device. The logic of the scientific instrument module 102 may be contained in a single computing device or may be distributed across multiple computing devices that communicate with each other as needed. Examples of computing devices in which the scientific instrument module 102 may be implemented, singly or in combination, are discussed herein with reference to FIG. 18, and examples of systems or networks of interconnected computing devices in which the scientific instrument module 102 may be implemented across one or more computing devices are discussed herein with reference to FIG. 19.

[0052] The scientific instrument module 102 may include first logic 104 and second logic 106. As used herein, the term "logic" may include an apparatus that performs a sequence of operations associated with the logic. For example, any of the logic elements included in the scientific instrument module 102 may be implemented by one or more computing devices programmed with instructions that cause one or more processing devices of the computing devices to perform a sequence of associated operations. In particular embodiments, a logic element may include one or more non-transitory computer-readable media having instructions thereon that, when executed by one or more processing devices of the one or more computing devices, cause the one or more computing devices to perform the associated sequence of operations. As used herein, the term "module" may refer to a collection of one or more logic elements that together perform the functionality associated with the module. Different logic elements within a module may take the same form or different forms. For example, some logic within a module may be implemented by a programmed general-purpose processing device, while other logic within the module may be implemented by an application-specific integrated circuit (ASIC). In another example, different logic elements within a module may be associated with different sets of instructions executed by one or more processing devices. A module may omit one or more of the logic elements shown in an associated figure. For example, a module may include a subset of the logic elements shown in an associated figure if that module performs a subset of the operations described herein with reference to that module.

[0053] In various embodiments, there may be a scientific instrument corresponding to the scientific instrument module 102. In various aspects, the scientific instrument may be any suitable computerized device capable of electronically measuring scientifically relevant, clinically relevant, or research-related characteristics, properties, or attributes of an analytical sample (e.g., a known or unknown mixture, compound, or collection of substances). As a non-limiting example, the scientific instrument may be a mass spectrometer coupled to a chromatograph. In this case, the scientific instrument may measure or determine an ion spectrum (e.g., relative ion abundance as a function of mass-to-charge ratio) for the analytical sample.

[0054] In various embodiments, the first logic 104 may access, via a device operatively coupled to the processor, a screening list specifying a plurality of target chemicals.

[0055] In various embodiments, the second logic 106 may classify each of a plurality of chemicals as present or absent in a sample based on having a scientific instrument (e.g., a chromatograph and mass spectrometer) perform an iterative injection protocol on the sample. Subsequent iterations of the iterative injection protocol are progressively more compound-selective and are triggered by an indeterminate present or absent classification in a previous iteration. In particular, during a current iteration of the iterative injection protocol, the second logic 106 may perform the following operations: inject a portion of the sample into the scientific instrument and scan the injected portion using a scan protocol (e.g., a time-truncated full scan or a customized SIM protocol) that is selective for target chemicals not classified as present or absent in any previous iteration, thereby obtaining measured mass spectrometry data; and compare the measured mass spectrometry data with reference mass spectrometry data (e.g., comparing measured QC ion ratios to reference QC ion ratios or calculating a library score) to classify target chemicals not classified as present or absent in any previous iteration as present, absent, or neither. Furthermore, if there is a target chemical that is classified as neither present nor absent in the current iteration, the subsequent iteration proceeds. In this manner, each iteration of the iterative injection protocol may be considered to have been triggered or executed in response to the uncertainty of the preceding iteration. Furthermore, the iterative injection protocol may be completed within minutes, preventing the scientific instrument from outputting final mass spectrometry data containing inconclusive screening results to the user or technician.

[0056] Thus, the scientific instrument module 102 can facilitate non-deterministic triggered re-injections for mass spectrometry screening.

[0057] Figure 2 illustrates an example, non-limiting flow diagram of a computer-implemented method 200 in accordance with various embodiments described herein. The operations of computer-implemented method 200 may be used in any suitable environment to perform appropriate operations (e.g., in conjunction with the various modules, computing devices, and GUIs described in connection with Figures 1, 18, and 19). Although the operations are illustrated in Figure 2 once each and in a particular order, the operations may be reordered or repeated as desired and appropriate (e.g., different operations performed may be performed in parallel when appropriate).

[0058] In various embodiments, operation 202 may include a first operation of accessing, by a device operatively coupled to the processor, a screening list specifying a plurality of target chemicals. In various cases, first logic 104 may perform or otherwise facilitate operation 202.

[0059] In various cases, operation 204 may include a second operation of classifying, by the device, each of a plurality of target chemicals as present or absent in the sample. This classification is based on having the chromatograph and mass spectrometer perform an iterative injection protocol on the sample, with subsequent iterations of this iterative injection protocol becoming increasingly compound-selective and triggered when a previous iteration's determination of presence or absence was inconclusive. In various cases, second logic 106 may perform or otherwise facilitate operation 204.

[0060] Thus, the computer-implemented method 200 can facilitate non-deterministic triggered re-injection for mass spectrometry screening.

[0061] FIG. 3 shows a block diagram of an exemplary, non-limiting system that can facilitate non-deterministically triggered re-injection for mass spectrometry screening, according to one or more embodiments described herein.

[0062] In various embodiments, it may include a mass spectrometer with a chromatograph 302. In various aspects, the mass spectrometer with a chromatograph 302, as the name suggests, may include a mass spectrometer operatively coupled to a chromatograph.

[0063] In various embodiments, the chromatograph of the chromatograph-equipped mass spectrometer 302 can be any suitable chromatograph, such as a gas chromatograph or a liquid chromatograph. In various aspects, the chromatograph can include any suitable component hardware for separating an analytical sample into two or more components. By way of non-limiting example, components can include an injector, an oven-heated column, and a carrier fluid valve or pump. In various aspects, a carrier fluid valve or pump allows a carrier fluid (e.g., an inert gas or a water-organic solvent mixture) to flow through the chromatograph. In various cases, an injector can inject an analytical sample (e.g., a compound to be measured or analyzed) into the flowing carrier fluid. In various cases, the injected analytical sample can be carried by the carrier fluid through an oven-heated column containing any suitable adsorbent packing or stationary phase film. In various embodiments, different constituent portions of an analytical sample (e.g., different elements or molecules) may exhibit different or specific interactions with the adsorbent packing or stationary phase film, causing different constituent portions of the analytical sample to flow through the column at different rates, and these flow rate differences may be considered to physically separate the different constituent portions from one another.

[0064] In various embodiments, the mass spectrometer of the chromatograph-equipped mass spectrometer 302 can be any suitable mass spectrometer. In various cases, the mass spectrometer can include appropriate component hardware for measuring an ion spectrum of an analytical sample. By way of non-limiting example, the component hardware can include an ion beam source, ion optics, a mass analyzer, and an ion detector. In various cases, the ion beam emitter can receive a portion of the analytical sample components from the chromatograph and ionize them into an ion beam. The ion beam emitter can perform this process using a suitable ionization technique, such as electron ionization, chemical ionization, matrix-assisted laser desorption / ionization (MALDI), electrospray ionization, photoionization, or inductively coupled plasma ionization, all of which can be performed in vacuum or at atmospheric pressure. In various embodiments, the ion optics can guide the ion beam generated by the ion beam emitter to the mass analyzer and then to the ion detector. Non-limiting examples of such ion optics include an ion focusing lens, an ion guide, or an ion deflector. In various cases, a mass analyzer may separate or select any ions present in an ion beam according to their mass-to-charge ratio. Non-limiting examples of mass analyzers include quadrupole mass analyzers, time-of-flight mass analyzers (TOF), magnetic sector mass analyzers, electrostatic sector mass analyzers, quadrupole ion trap mass analyzers, ion cyclotron resonance mass analyzers, etc. In various cases, an ion detector may electronically detect or measure the relative abundance of impinging ions. Non-limiting examples of ion detectors include electron multiplier ion detectors and Faraday cup ion detectors.

[0065] In various embodiments, the chromatographic mass spectrometer 302 may currently be loaded with a sample 304. In other words, the sample 304 physically resides in any suitable injector or autosampler of the chromatographic mass spectrometer 302, such that the chromatographic mass spectrometer 302 may inject a portion of the sample 304 for analysis or scanning. In various aspects, the sample 304 may be any suitable mixture, solution, or colloid for which analysis by mass spectrometry is desired. As a non-limiting example, the sample 304 may be a food or beverage mixture, solution, or colloid. As another non-limiting example, the sample 304 may be a pharmaceutical or pharmaceutical mixture, solution, or colloid. As yet another non-limiting example, the sample 304 may be a mixture, solution, or colloid from soil, water, air, animal waste, or other environmental sources. In various cases, the sample 304 may have or exhibit any suitable stability or shelf life. Indeed, in some cases, sample 304 may be a stable mixture, solution, or colloid with a shelf life of days, weeks, months, or years, while in other instances, sample 304 may be an unstable mixture, solution, or colloid with a shelf life of only a few hours or minutes.

[0066] In various embodiments, a screening list 306 may be present. In various aspects, the screening list 306 may be any suitable electronic data having a suitable format, size, or dimensionality (e.g., one or more scalar values, vectors, matrices, tensors, strings, or a suitable combination thereof) that indicates, identifies, or otherwise represents the plurality of target chemicals 308. In various cases, the plurality of target chemicals 308 may include n>1 compounds, for any suitable positive integer n: i.e., ranging from target chemical 308(1) to target chemical 308(n). In various cases, each of the plurality of target chemicals 308 may be any suitable chemical or molecule that is desired to be screened or detected from the sample 304. As a non-limiting example, any of the plurality of target chemicals 308 may be any suitable known toxin, poison, or other dangerous or harmful molecule that is desired to be absent from the sample 304. As yet another non-limiting example, any of the plurality of target chemicals 308 may be any suitable known organic or inorganic contaminant that is expected to be absent from the sample 304. As yet another non-limiting example, any of the plurality of target chemicals 308 may be any suitable chemical additive or enhancer that is desired or expected to be present in the sample 304 .

[0067] In either case, it may be desirable to screen the sample 304 according to the screening list 306. In other words, it may be desirable to conclusively determine which of a plurality of target chemicals 308 are present in the sample 304 and which are absent from the sample 304. As described herein, the system 310 may assist in or perform such screening in an automated and time-efficient manner.

[0068] In various aspects, the system 310 may include a processor 312 (e.g., a computer processing unit, a microprocessor) and non-transitory computer-readable memory 314 operably or communicatively coupled to the processor 312. The non-transitory computer-readable memory 314 may store computer-executable instructions that, when executed by the processor 312, cause the processor 312 or other components of the system 310 (e.g., the access component 316, the screening component 318) to perform one or more operations. In various embodiments, the non-transitory computer-readable memory 314 may store, and the processor 312 may execute, a computer-executable component (e.g., the access component 316, the screening component 318).

[0069] In various embodiments, the system 310 may include an access component 316. In various aspects, the access component 316 may electronically access the chromatographic mass spectrometer 302. That is, the access component 316 may electronically communicate with or interact with the chromatographic mass spectrometer 302 (e.g., send electronic commands or instructions or receive electronic data). Thus, the access component 316 may be considered a proxy or conduit through which other components of the system 310 may interact, communicate with, or otherwise manipulate the chromatographic mass spectrometer 302. In various cases, the access component 316 may electronically access the screening list 306. That is, the access component 316 may electronically receive, retrieve, or otherwise obtain the screening list 306 from any suitable electronic source or database (which may be, for example, from a computerized workstation associated with the chromatographic mass spectrometer 302). In either case, the access component 316 may be considered a proxy or conduit through which other components of the system 310 may interact with, control, or otherwise manipulate the screening list 306 .

[0070] In various embodiments, the system 310 can include a screening component 318. In various aspects, the screening component 318 can individually classify each of a plurality of target chemicals 308 as present or absent in the sample 304 by causing the chromatograph-equipped mass spectrometer 302 to perform an iterative injection protocol on the sample 304, as described herein. The iterations of the iterative injection protocol are progressively compound-selective and uncertainty-triggered.

[0071] It should be noted that in various cases, the access component 316 and the screening component 318 may be collectively considered as one or more software components 315 of the system 310. It should be understood that, in various aspects, for ease of explanation and illustration, one or more software components 315 are primarily described herein as consisting of two components (e.g., the access component 316 and the screening component 318). However, there is no limitation that one or more software components 315 must always be implemented as such two components. Indeed, in some embodiments, the functionality described herein with respect to these two components may be integrated in any suitable manner and implemented with fewer than two components (e.g., in some cases, a single component may perform all of the functionality associated with the access component 316 and the screening component 318). In other embodiments, the functionality described herein with respect to these two components may instead be distributed, separated, divided, or fragmented in any suitable manner and implemented with more than two components (e.g., two or more components may facilitate the functionality performable by the access component 316, and two or more components may facilitate the functionality performable by the screening component 318).

[0072] FIG. 4 shows a block diagram of an exemplary, non-limiting system including a repeat injection protocol that allows for re-injection in response to indeterminate results for mass spectrometry screening, according to one or more embodiments described herein.

[0073] In various embodiments, the screening component 318 may electronically identify a set of present classifications 402 and a set of absent classifications 404. In various aspects, the set of present classifications 402 may include any suitable number of classification labels indicating that one of the plurality of target chemicals 308 has been determined or concluded to be present in the sample 304. Similarly, in various cases, the set of absent classifications 404 may include any suitable number of classification labels indicating that one of the plurality of target chemicals 308 has been determined or concluded to be absent in the sample 304. In various cases, the set of present classifications 402 is disjoint (e.g., non-overlapping) and incompatible with the set of absent classifications 404. In other words, no target chemical may be classified as either present or absent in the sample 304. In various cases, the union of the set of present classifications 402 and the set of absent classifications 404 may be equivalent to the screening list 306. In other words, no target chemical may ultimately be determined or concluded to be either present or absent in the sample 304. In various embodiments, the screening component 318 can identify a set of present classifications 402 and a set of absent classifications 404 by having the chromatograph-equipped mass spectrometer 302 execute a repeat injection protocol 406. Non-limiting aspects of the repeat injection protocol 406 are described with reference to FIGS.

[0074] 5-12 illustrate exemplary, non-limiting block diagrams illustrating how a repeat injection protocol 406 may be facilitated in accordance with one or more embodiments described herein.

[0075] Referring first to Figure 5, in various embodiments, as shown, the screening list 306 may be considered to correspond to reference mass spectrometry data 502. In various aspects, the reference mass spectrometry data 502 may include any suitable attributes, properties, or characteristics exhibited by or known to be possessed by each of the plurality of target chemicals 308. In practice, the reference mass spectrometry data 502 may include a plurality of reference retention indices 504, a plurality of reference quantification / confirmation ion ratios 506 (hereinafter "reference QC ion ratios 506"), or a plurality of reference mass spectra 508.

[0076] In various embodiments, the plurality of reference retention indices 504 may each correspond to a plurality of target chemicals 308. Thus, because the plurality of target chemicals 308 may include n compounds, the plurality of reference retention indices 504 may similarly include n indices: reference retention indices 504(1) through 504(n). In various cases, each of the plurality of reference retention indices 504 may be or indicate a known retention index for one of the plurality of target chemicals 308. As a non-limiting example, reference retention index 504(1) may correspond to target chemical 308(1). Thus, reference retention index 504(1) is a scalar value whose magnitude or value represents or indicates how the retention time of target chemical 308(1) (e.g., the time that target chemical 308(1) spends in a given chromatographic stationary phase or column) is known to relate to the retention time of a standard or reference chemical (e.g., an n-alkane) under any given chromatographic conditions. As another non-limiting example, a reference retention index 504(n) can correspond to a target chemical 308(n). Thus, the reference retention index 504(n) is a scalar value whose magnitude or value indicates how the retention time of the target chemical 308(n) relates to the retention time of a standard or reference compound under particular chromatographic conditions.

[0077] Note that retention time may vary depending on chromatographic conditions (e.g., different column stationary phases, different column dimensions such as length, inner diameter, or film thickness, different oven-programmed data acquisition temperatures). On the other hand, retention index does not vary depending on chromatographic conditions. In fact, retention index may be considered much more reproducible than retention time because it does not change as long as the column stationary phase is the same. Therefore, if the retention times of the standard or reference chemicals are known under specific chromatographic conditions, the retention times of the multiple target chemicals 308 may be inferred or calculated by applying (e.g., multiplying) the multiple reference retention indices 504 to the retention times of the standard or reference chemicals, respectively. As a non-limiting example, assume that the multiple reference retention indices 504 are defined based on a standard or baseline n-alkane solution. In such cases, the standard or reference n-alkane solution may be injected into chromatographic mass spectrometer 302, and the specific retention time of the standard or reference n-alkane solution under specific chromatographic conditions associated with chromatographic mass spectrometer 302 may be measured (e.g., derived from a chromatogram generated by chromatographic mass spectrometer 302 in response to injection of the standard or reference n-alkane solution). In such cases, the retention time of target chemical 308(1) when injected into chromatographic mass spectrometer 302 may be equal to or based on the multiplicative product of the measured specific retention time of the standard or baseline n-alkane solution and reference retention index 504(1). Similarly, the retention time of target chemical 308(n) when injected into chromatographic mass spectrometer 302 may be equal to or based on the product of the measured specific retention time of the standard or baseline n-alkane solution and reference retention index 504(n).

[0078] In various embodiments, the plurality of reference QC ion ratios 506 can each correspond to a plurality of target chemicals 308. Thus, because the plurality of target chemicals 308 can include n compounds, the plurality of reference QC ion ratios 506 can similarly include n ratios, from reference QC ion ratio 506(1) to reference QC ion ratio(n). In various cases, each of the plurality of reference QC ion ratios 506 can represent or be a known ratio or relationship between the abundance or intensity of a specified quantitation ion and a specified confirmation ion in each target chemical 308. As a non-limiting example, reference QC ion ratio 506(1) can correspond to target chemical 308(1). Thus, a target chemical 308(1) (e.g., toluene) can be considered to have a designated quantitation ion (e.g., C7H7+) and a designated confirmation ion (e.g., C7H8+), and reference QC ion ratio 506(1) is a scalar value having a magnitude or value that represents or indicates how the abundance or intensity of that designated quantitation ion is known to relate to the abundance or intensity of that designated confirmation ion (or vice versa) under any given chromatographic conditions. As another non-limiting example, reference QC ion ratio 506(n) can correspond to target chemical 308(n). Thus, a target chemical 308(n) (e.g., acetaminophen) can be considered to have a designated quantitation ion (e.g., C8H9NO2+) and a designated confirmatory ion (e.g., C6H7NO+), and the reference QC ion ratio 506(n) is a scalar value having a value or magnitude that represents or indicates how the abundance or intensity of the designated quantitation ion relates in a known way to the abundance or intensity of the designated confirmatory ion under particular chromatographic conditions.

[0079] It should be noted that any two of the plurality of target chemicals 308 may have the same or different quantification or confirmation ions. In other words, any ion may be designated as: one or more quantification ions of the plurality of target chemicals 308, or one or more confirmation ions of the plurality of target chemicals 308.

[0080] It should also be noted that, in some cases, any target chemical in the screening list 306 may have more than one designated quantitation ion or more than one designated confirmation ion. In such a situation, depending on the number of different combinations of quantitation ions and confirmation ions that exist for that target chemical, that target chemical may correspond to more than one reference QC ion ratio. As a non-limiting example, assume that a target chemical has two different quantitation ions and three different confirmation ions. In this case, that target chemical can be considered to have a total of six different QC ion ratios (because, in this example, there are two possible combinations of quantitation ions and three possible confirmation ions). In various embodiments, all six of these different QC ion ratios may be represented in the reference mass spectrometry data 502.

[0081] In various cases, the plurality of reference mass spectra 508 may each correspond to a plurality of target chemicals 308. Thus, because the plurality of target chemicals 308 may include n compounds, the plurality of reference mass spectra 508 may similarly be composed of n spectra: reference mass spectrum 508(1) through reference mass spectrum 508(n). In various cases, each of the plurality of reference mass spectra 508 may correspond to a respective one of the plurality of target chemicals 308 and may be a known plot of ion abundance or intensity versus mass-to-charge ratio for that compound. As a non-limiting example, reference mass spectrum 508(1) may correspond to target chemical 308(1). Thus, reference mass spectrum 508(1) may be considered to indicate which mass-to-charge ratios (e.g., which particular ions) are known to be relatively abundant or rare in target chemical 308(1). As another non-limiting example, reference mass spectrum 508(n) may correspond to target chemical 308(n). Thus, the reference mass spectrum 508(n) can be viewed as indicating which mass-to-charge ratios are known to be relatively abundant or rare in the target chemical 308(n). In various embodiments, each iteration of the iterative injection protocol 406 may include classifying each of a plurality of target chemicals 308 as "present," "absent," or "indeterminate" in the sample by utilizing or otherwise utilizing the reference mass spectrometry data 502. Non-limiting details regarding how the jth iteration of the iterative injection protocol 406 may be performed, for any suitable positive integer j, are described with reference to Figures 6-11.

[0082] See Figure 6. In various embodiments, the screening component 318 may electronically receive, electronically obtain, electronically acquire, or otherwise electronically access the present run list 602, the absent run list 604, or the indeterminate run list 606 during the jth iteration of the repeated injection protocol 406.

[0083] In various embodiments, the presence run list 602 is an electronic list that may be updated with each iteration of the repeat injection protocol 406 (hence the term "run") and may indicate which of the multiple target chemicals 308 have already been determined or classified as present in the sample 304 (hence the term "presence"). In particular, In the case of TIFF2026042754000002.tif531 (e.g., in the case of the very first, initial, or first iteration of the iterative injection protocol 406), the present run list 602 may be empty. In contrast, in the case of j>1 (e.g., in the case of any subsequent or non-first iteration of the iterative injection protocol 406), the present run list 602 may include any of multiple target chemicals 308 classified as present in the sample 304 by the screening component 318 in any previous iteration of the iterative injection protocol 406.

[0084] Similarly, in various circumstances, the absent list 604 can be an electronic list that can be updated with each iteration of the iterative injection protocol 406 to indicate which of the plurality of target chemicals 308 have already been determined or classified as "not present" in the sample 304 (hence the term "absent"). Notably, when j=1 (e.g., for the very first, initial, or first iteration of the iterative injection protocol 406), the absent run list 604 can be empty. In contrast, when j>1 (e.g., for any subsequent or non-first iteration of the iterative injection protocol 406), the absent run list 604 can include any of the plurality of target chemicals 308 that were classified as absent from the sample 304 by the screening component 318 in any prior iteration of the iterative injection protocol 406.

[0085] In various cases, the indeterminate run list 606 is an electronic list that may be updated with each iteration of the repeated injection protocol 406 (hence the term "run") and may indicate which of the plurality of target chemicals 308 have not been determined or classified as present or absent in the sample 304. In other words, the indeterminate run list 606 may indicate any of the plurality of target chemicals 308 for which the screening component 318 has thus far been unable to make a conclusive presence or absence determination or classification (hence the term "indeterminate"). In particular, when j=1 (e.g., for the very first, initial, or first iteration of the repeated injection protocol 406), the indeterminate run list 606 may be equivalent to the screening list. That is, when j=1, the indeterminate run list 606 does not omit or exclude any of the plurality of target chemicals 308. In contrast, when j=1 (e.g., for any subsequent or non-first iteration of the iterative injection protocol 406), the indeterminate run list 606 may exclude or omit any of the multiple target chemicals 308 that are in the present run list 602 or that are in the absent run list 604. That is, at the jth iteration, the indeterminate run list 606 is obtained by subtracting: the screening list 306 minus the union of the present run list 602 and the absent list 604.

[0086] In other words, the present run list 602 can be considered to track any target chemicals that have been conclusively classified as present in the sample 304, the absent run list 604 can be considered to track any target chemicals that have been conclusively classified as absent from the sample 304, and the indeterminate run list 606 can be considered to track any remaining target chemicals (e.g., any target chemicals that have been determined to meet neither of the criteria defining their presence in the sample 304 nor to meet any of the criteria defining their absence in the sample 304).

[0087] 7. During the jth iteration, the screening component 318 may electronically command, instruct, or otherwise electronically control the chromatograph-equipped mass spectrometer 302 to inject a portion of the sample 304 and perform a scan protocol on the injected portion that may depend on a prior iteration of the iterative injection protocol 406. Specifically, when j=1 (e.g., for the very first, initial, or first iteration of the iterative injection protocol 406), the chromatograph-equipped mass spectrometer 302 may perform a full scan protocol on the injected portion. In other words, when j=1 (e.g., for any subsequent or non-first iteration of the iterative injection protocol 406), the chromatograph-equipped mass spectrometer 302 may perform a scan protocol on the injected portion with increased sensitivity to any target chemical in the indeterminate run list 606. In contrast, for j1 (e.g., a second or subsequent repetition of the iterative injection protocol 406 or a repetition other than the first), the chromatograph-mounted mass spectrometer 302 may perform a scan protocol on the injected sample that increases sensitivity to any target chemicals included in the uncertain run list 606.

[0088] In some cases, such increased sensitivity may be achieved by shortening the duration of the full scan protocol. In particular, for any given target chemical in the indeterminate run list 606, the reference mass spectrometry data 502 may indicate a respective reference retention index (e.g., one of 504) for that given target chemical. As noted above, this reference retention index (in combination with an injection of a standard or baseline compound, which may be performed on the chromatograph mass spectrometer 302 prior to the start of the repeat injection protocol 406) may be used to estimate or calculate what retention time the target chemical would exhibit if injected into the chromatograph mass spectrometer 302. In this manner, a respective retention time may be calculated or estimated for each target chemical in the indeterminate run list 606. In various embodiments, the screening component 318 may instruct the chromatograph mass spectrometer 302 to perform a shortened full scan protocol such that the full scan protocol is performed only if: A full scan protocol is run at the calculated retention time of a target chemical in the uncertainty run list 606, within any suitable threshold margin or window before such calculated retention time, or within any suitable threshold margin or window after such calculated retention time. As a non-limiting example, consider the following example: any suitable positive real number t A Any suitable positive real number t A Any suitable positive real number t B >t A For t B Assume that only target chemical B, having a retention time of t, is included in the uncertain run list 606. Assume further that the threshold margin or tolerance window in such a case is w seconds, represented by an appropriate positive real number w. Therefore, the time-reduced full scan protocol is executed only at all times t that satisfy the following condition:

[0089]

number

[0090] In another example, such increased sensitivity may be achieved by a SIM protocol having a monitoring window that includes quantitation or confirmation ions of target chemicals in the indeterminate run list 606. In particular, for any given target chemical in the indeterminate run list 606, the reference mass spectrometry data 502 (e.g., via 506) may indicate one or more quantitation ions or one or more confirmation ions that are designated or otherwise known to be associated with the given target chemical. In various embodiments, the screening component 318 may instruct, command, or control the mass spectrometer with chromatograph 302 to execute a SIM protocol having a monitoring window narrowed to include only quantitation or confirmation ions of target chemicals in the indeterminate run list 606. As a non-limiting example, consider the following scenario: Assume that target chemical A has quantitation ion QA and confirmation ion CA; and target chemical B has quantitation ion QB and confirmation ion CB. Thus, a SIM protocol may be run with a narrowed monitoring window to include only mass-to-charge ratios (m / z) that are: equal to the known mass-to-charge ratio of QA, CA, QB, or CB; or within or near an appropriate threshold margin relative to the known mass-to-charge ratio of QA, CA, QB, or CB. In this case, the chromatograph-equipped mass spectrometer 302 may ignore, not actively detect, measure, or otherwise attend to, any ions impinging on its detector that have a mass-to-charge ratio outside of such a narrowed window. This may, in some cases, enhance or increase the sensitivity of the chromatograph-equipped mass spectrometer 302 near the known mass-to-charge ratios of quantitation or confirmation ions specified for target chemicals in the indeterminate run list 606, compared to its sensitivity in a full-scan protocol. In other words, ionically narrowing the monitoring window of a SIM protocol in this manner may increase the selectivity or sensitivity of the mass spectrometer 302 for target chemicals in the indeterminate run list 606. In some cases, this may be referred to as "ion selectivity."

[0091] It should be noted that in some aspects of implementing the SIM protocol as described above, any given target chemical may correspond to multiple quantitation ions or multiple confirmation ions. In such situations, the sensitivity of the SIM protocol to any given target chemical may be increased by narrowing the monitoring window of the SIM protocol to include fewer than all of the multiple quantitation ions or fewer than all of the multiple confirmation ions. Indeed, in a situation where a given target chemical is included in the indeterminate run list 606 over multiple consecutive iterations, the first of such multiple consecutive iterations may include a SIM protocol having a monitoring window that includes all of the multiple quantitation ions and all of the multiple confirmation ions; the second of such multiple consecutive iterations may include a SIM protocol having a monitoring window that includes one fewer of the multiple quantitation ions (but still including at least one quantitation ion) or one fewer of the multiple confirmation ions (but still including at least one confirmation ion); the third of such multiple consecutive iterations may include a SIM protocol having a monitoring window that includes two fewer of the multiple quantitation ions (but still including at least one quantitation ion) or two fewer of the multiple confirmation ions (but still including at least one confirmation ion), and so on. In other words, the sensitivity of a SIM protocol to a particular target chemical may be progressively increased by progressively excluding the quantitation or confirmation ions designated for that target chemical from the monitoring window.

[0092] As a non-limiting example, the indeterminate run list 606 may include the first quantitation ion Q A1 , the second quantitative ion Q A2 , First confirmed ion C A1 , the second confirmed ion C A2 , and the third confirmation ion C A3During the j-th iteration of the iterative injection protocol 406, the mass spectrometer with chromatograph 302 produces only a target chemical A having Q A1 , Q A2 , C A1 , C A2 and C. A3 A SIM protocol may be run with a monitoring window that includes only mass-to-charge ratios at or near Q. If target chemical A still remains in the indeterminate run list 606 during the (j+1)th iteration, the monitoring window of the SIM protocol may be incrementally narrowed to include one less quantification or confirmation ion of target chemical A. For example, during the (j+1)th iteration, the chromatograph-equipped mass spectrometer 302 may detect a mass-to-charge ratio of Q A1 and C A1 , C A2 Similarly, if target chemical A still remains on the indeterminate run list 606 during the (j+2)th iteration, the monitoring window of the SIM protocol may again be incrementally narrowed to include one less confirmatory ion for target chemical A (no more quantitation ions for target chemical A should be removed, since it may be beneficial to always include at least one quantitation ion and at least one confirmatory ion for each target chemical being screened). For example, during the (j+2)th iteration, the chromatograph-equipped mass spectrometer 302 may detect a mass-to-charge ratio of Q A2 and C A1 , C A2 A SIM protocol may be performed with a monitoring window that includes only values ​​of mass-to-charge ratio at or near .

[0093] It should be understood that such progressive narrowing of the monitoring window of a SIM protocol can be accomplished in any suitable order of priority. For example, such progressive narrowing can be performed in random ion order (e.g., randomly selecting which quantitation or confirmation ions to ignore for a particular target chemical). As another example, such progressive narrowing can be performed in ascending or descending order of ion mass-to-charge ratio (e.g., ignoring the quantitation or confirmation ions for any given target chemical having the highest or lowest mass-to-charge ratio). As yet another example, such narrowing can be performed in ascending or descending order of ion abundance or intensity (e.g., ignoring the quantitation or confirmation ions with the highest or lowest relative abundance or intensity for a particular target chemical).

[0094] In yet other examples, any suitable combination of temporal selectivity or ion selectivity may be implemented to increase or enhance scan sensitivity to target chemicals in the indeterminate run list 606 .

[0095] In either case, the chromatograph-equipped mass spectrometer 302 can perform a scan protocol on the injected portion of the sample 304 during the jth iteration of the iterative injection protocol 406, which can result in or give rise to measured mass spectrometry data 702. As shown, the measured mass spectrometry data 702 can include, in some examples, a chromatogram 704, a plurality of measured retention indices 708, or a plurality of measured mass spectra 710.

[0096] In various cases, the chromatograph of the chromatograph-mass spectrometer 302 may electronically generate or output the chromatogram 704. In various cases, the chromatogram 704 may be a plot of abundance or intensity versus retention time. In various embodiments, the chromatogram 704 may include a plurality of peaks 706. In various cases, the plurality of peaks 706 may include m>1 peaks, i.e., peak 706(1) through peak 706(m), for any suitable positive integer m. In various cases, each of the plurality of peaks 706 may be considered as a distinct abundance or intensity spike that may represent a respective unknown chemical present in the injected portion of the sample 304. In various embodiments, each of the plurality of peaks 706 may be considered to occur at (e.g., be centered at) a respective retention time (e.g., position on the horizontal axis) of the chromatogram 704.

[0097] In various cases, the plurality of measured retention indices 708 may each correspond to a plurality of peaks 706. Because the plurality of peaks 706 may include m peaks, the plurality of measured retention indices 708 may similarly include m indices, including measured retention indices 708(1) through 708(m). In various cases, each value of the measured retention indices 708 may be derived based on the retention time of a respective peak 706. As a non-limiting example, measured retention index 708(1) may correspond to peak 706(1) (e.g., may correspond to the first unknown chemical in the injected portion of sample 304). Thus, measured retention index 708(1) is a scalar value whose magnitude or value represents or indicates how the retention time of peak 706(1) relates to the retention time of a standard or reference chemical (e.g., as described above, the retention time of a standard or reference chemical, such as an n-alkane solution, may have been determined by mass spectrometer with chromatograph 302 prior to the start of repetitive injection protocol 406). As another non-limiting example, measured retention index 708(m) may correspond to peak 706(m) (e.g., may correspond to the mth unknown chemical present in the injected portion of sample 304). Thus, measured retention index 708(m) may be a scalar value whose magnitude or value represents or indicates how the retention time of peak 706(m) relates to the retention time of a standard or reference chemical.

[0098] In various embodiments, the mass spectrometer of the chromatograph-mass spectrometer 302 may electronically generate or output a plurality of measured mass spectra 710. In various cases, the plurality of measured mass spectra 710 may each correspond to a plurality of peaks 706. Thus, if the plurality of peaks 706 includes m peaks, the plurality of measured mass spectra 710 may likewise include m spectra, including measured mass spectrum 710(1) through measured mass spectrum 710(m). In various cases, each of the plurality of measured mass spectra 710 may be a plot of ion abundance or intensity versus mass-to-charge ratio for one of the plurality of peaks 706. As a non-limiting example, measured mass spectrum 710(1) may correspond to peak 706(1). Measured mass spectrum 710(1) may therefore be considered to indicate which mass-to-charge ratios (e.g., particular ions) were more or less detected when peak 706(1) was observed by chromatograph-mass spectrometer 302. As another non-limiting example, measured mass spectrum 710(m) may correspond to peak 706(m). Measured mass spectrum 710(m) may therefore be considered to indicate which mass-to-charge ratios were more or less detected when peak 706(m) was observed by chromatograph-mass spectrometer 302.

[0099] It should be understood and appreciated that the screening component 318 applies any suitable deconvolution technique, in combination with any suitable blank / matrix injection and background subtraction technique, to the raw data output from the chromatographic mass spectrometer 302 to obtain a plurality of peaks 706, a plurality of measured retention indices 708, or a plurality of measured mass spectra 710. Indeed, as described above, the chromatographic mass spectrometer 302 can have performed an n-alkane injection protocol prior to the initiation of the repetitive injection protocol 406, thereby allowing the calculation of retention times or retention indices during the repetitive injection protocol 406. Similarly, the chromatographic mass spectrometer 302 can have performed a blank solution injection protocol prior to the initiation of the repetitive injection protocol 406, thereby facilitating background subtraction or other preprocessing of the chromatogram 704 or measured mass spectra 710 during the repetitive injection protocol 406.

[0100] In some cases, chromatographic deconvolution may be performed (e.g., by screening component 318) in real time immediately after or in response to an injection of a portion of sample 304, and such chromatographic deconvolution may be performed continuously or continuously during data acquisition (perhaps even while screening component 318 is classifying compounds on the indeterminate run list 606 as present, absent, or still indeterminate).

[0101] Referring now to FIG. 8 , during the jth iteration of the iterative injection protocol 406, the screening component 318 may electronically consider any target chemical in the indeterminate run list 606. Such target chemical may be referred to as a selected target chemical 802. In various embodiments, the plurality of reference retention indices 504 corresponding to the selected target chemical 802 may be referred to as the reference retention indices 804. In various cases, the plurality of reference QC ion ratios 506 corresponding to the selected target chemical 802 may be referred to as the reference QC ion ratios 806. In various cases, the plurality of reference mass spectra 508 corresponding to the selected target chemical 802 may be referred to as the reference mass spectrum 808. In various cases, the reference retention indices 804, the reference QC ion ratios 806, and the reference mass spectrum 808 may be collectively referred to as reference mass spectrometry data 803.

[0102] In various embodiments, the screening component 318 may search through the plurality of measured retention indices 708 to identify a set 810 of measured retention indices that are within any suitable threshold margin or vicinity of the reference retention index 804. In various cases, the set 810 of measured retention indices may include p indices, for any suitable positive integer p, where p is measured retention index 810(1) through measured retention index 810(p). In various cases, the measured retention index 810(1) may be considered the first one of the plurality of measured retention indices 708 that are within the threshold margin or vicinity of the reference retention index 804. Similarly, the measured retention index 810(p) may be considered the pth one of the plurality of measured retention indices 708 that are within the threshold margin or vicinity of the reference retention index 804.

[0103] In various embodiments, any of the plurality of peaks 706 corresponding to the set of measured retention indices 810 may be considered to represent an unknown chemical within the sample 304 and to be a potential match for the selected target chemical 802. In particular, any one of the plurality of peaks 706 corresponding to a measured retention index 810(1) may be referred to as a potential match peak 812(1). Similarly, any one of the peaks 706 corresponding to a measured retention index 810(p) may be referred to as a potential match peak 812(p). In various cases, the potential match peaks 812(1) through 812(p) may be collectively referred to as a set of potential match peaks 812. In various cases, any one of the plurality of measured mass spectra 710 corresponding to the potential match peak 812(1) may be referred to as a measured mass spectrum 814(1). Similarly, any one of the measured mass spectra 710 corresponding to the potential match peak 812(p) may be referred to as a measured mass spectrum 814(p). In various cases, measured mass spectrum 814(1) through measured mass spectrum 814(p) may be collectively referred to as set of measured mass spectra 814.

[0104] In various embodiments, the screening component 318 may electronically generate a classification label 816 for a selected target chemical 802 based on a comparison of a reference QC ion ratio 806 or a reference mass spectrum 808 with the set of measured mass spectra 814. In various cases, the classification label 816 may be any suitable electronic data (e.g., one or more scalar values, one or more vectors, one or more matrices, one or more tensors, one or more strings, or any combination or combinations thereof) that indicates, specifies, or can otherwise represent how the screening component 318 classifies the presence or absence of the selected target chemical 802 for the sample 304. As a non-limiting example, the classification label 816 may take on any of the following three states, classifications, or categories: a "present" state / classification / category; an "absent" state / classification / category; or an "uncertain" state / classification / category. If the classification label 816 indicates a "present" state, class, or category, this may be interpreted to mean that the screening component 318 has concluded that the selected target chemical 802 is present in the sample 304. If the classification label 816 indicates an "absent" state, class, or category, this may be interpreted to mean that the screening component 318 has concluded that the selected target chemical 802 is absent from the sample 304. If the classification label 816 indicates an "indeterminate" state, class, or category, this may be interpreted to mean that the screening component 318 has not concluded that the selected target chemical 802 is either present or absent in the sample 304.

[0105] In various embodiments, in response to determining that at least one of the set of measured mass spectra 814 meets any suitable presence classification criteria relative to the reference QC ion ratio 806 or the reference mass spectrum 808, the screening component 318 may indicate or assign a "present" state, class, or category to the classification label 816. In various cases, in response to determining that all of the set of measured mass spectra 814 meet any suitable absence classification criteria relative to the reference QC ion ratio 806 or the reference mass spectrum 808, the screening component 318 may indicate or assign an "absent" state, class, or category to the classification label 816. In various cases, in response to determining that both all of the set of measured mass spectra 814 do not meet the presence classification criteria and at least one of the set of measured mass spectra 814 does not meet the absence classification criteria, the screening component 318 may indicate or assign an "uncertain" state, class, or category to the classification label 816.

[0106] In some cases, such a determination may be made based on a comparison of QC ion ratios and mass tolerances, as described in connection with Figure 9. In other cases, such a determination may be made based on a calculation of a library score, as described in connection with Figure 10.

[0107] See FIG. 9 . In various embodiments, the screening component 318 can electronically identify sets of QC ion ratios 902 corresponding to each set of measured mass spectra 814. If the set of measured mass spectra 814 includes p spectra, then the set of QC ion ratios 902 can include p ratios, where the ratios are QC ion ratios 902(1) through QC ion ratios 902(n). In various embodiments, each of the sets of QC ion ratios 902 can be based on any ion represented in the reference QC ion ratios 806. As a non-limiting example, assume that the reference QC ion ratio 806 is the ratio of the abundance or intensity of quantitation ion X divided by the abundance or intensity of confirmation ion Y. In this case, the first abundance or intensity of quantitation ion X is represented by measured mass spectrum 814(1), and the first abundance or intensity of confirmation ion Y can also be represented by measured mass spectrum 814(1). QC ion ratio 902(1) may then be equal to or based on the ratio of the first abundances or intensities of quantitation ion X and confirmation ion Y. Also, in such a case, the pth abundance or intensity of quantitation ion X may be indicated by measured mass spectrum 814(p), the pth abundance or intensity of confirmation ion Y may be indicated by measured mass spectrum 814(p), and QC ion ratio 902(p) may be equal to or based on the ratio between the pth abundance or intensity of that quantitation ion X and the pth abundance or intensity of that confirmation ion Y.

[0108] In various embodiments, screening component 318 may electronically calculate a set of ratio errors 904 by comparing each of the set of QC ion ratios 902 to reference QC ion ratio 806. As a non-limiting example, ratio error 904(1) may be a scalar value equal to or based on an appropriate difference or percentage difference between reference QC ion ratio 806 and QC ion ratio 902(1). As a non-limiting example, ratio error 904(p) may also be a scalar value based on an appropriate difference or percentage difference between reference QC ion ratio 806 and QC ion ratio 902(p). In various cases, ratio errors 904(1) through 904(p) may be collectively considered a set of ratio errors 904.

[0109] Additionally, in various cases, the screening component 318 may electronically identify a set of mass tolerance pairs 906 that correspond to the set of measured mass spectra 814 and thus the set of QC ion ratios 902, respectively.

[0110] As a non-limiting example, mass tolerance pair 906(1) can be a pair of scalar values ​​having magnitudes or values ​​representing the respective measurement tolerances exhibited by measured mass spectrum 814(1) for the quantitation ions and confirmation ions that make up QC ion ratio 902(1). Continuing with the above example (including quantitation ion X and confirmation ion Y), mass tolerance pair 906(1) can include a first scalar value representing the mass tolerance exhibited by measured mass spectrum 814(1) for quantitation ion X and a second scalar value representing the mass tolerance exhibited by measured mass spectrum 814(1) for confirmation ion Y. Mass tolerance pair 906(1) can also include a second scalar value representing the mass tolerance exhibited by measured mass spectrum 814(1) for confirmation ion Y. As another non-limiting example, mass tolerance pair 906(p) can be a pair of scalar values ​​having magnitudes or values ​​representing the respective measurement tolerances exhibited by measured mass spectrum 814(p) for the quantitation ions and confirmation ions that make up QC ion ratio 902(p). Continuing again with the above example involving quantitation ion X and confirmation ion Y, mass tolerance pair 906(p) may include a first scalar value representing the mass tolerance that measured mass spectrum 814(p) exhibits for quantitation ion X, and mass tolerance pair 906(p) may also include a second scalar value representing the mass tolerance that measured mass spectrum 814(p) exhibits for confirmation ion Y. In various cases, mass tolerance pairs 906(1) through 906(p) may be collectively considered a set of mass tolerance pairs 906. It should be understood that a lower mass tolerance value or magnitude may indicate better resolution.

[0111] Note that a set of potentially matching peaks 812 may be considered to correspond to both a set of ratio errors 904 and a set of mass tolerance pairs 906, respectively (e.g., potentially matching peak 812(1) may correspond to ratio error 904(1) and mass tolerance pair 906(1); potentially matching peak 812(p) may correspond to ratio error 904(p) and mass tolerance pair 906(p).

[0112] In various embodiments, the "presence" criteria for a selected target chemical 802 is that at least one of the set of potentially matching peaks 812 satisfies the following: the ratio error is below any suitable threshold ratio error value (e.g., below a 15% ratio error threshold), and both scalar values ​​of the mass tolerance pair are below any suitable mass tolerance threshold (e.g., 5 ppm). Thus, if such a potentially matching peak exists, the screening component 318 may indicate a "presence" state, class, or category in the classification label 816. On the other hand, if such a potentially matching peak does not exist, the screening component 318 may prevent the classification label 816 from indicating a "presence" state, class, or category.

[0113] In various cases, the absence criteria for a selected target chemical 802 may be that each of the set of potential matching peaks 812 has the following: a ratio error above a threshold ratio error value; and both scalar values ​​of a mass tolerance pair below the mass tolerance threshold. Thus, if all potential matching peaks meet this, the screening component 318 may indicate or assign an "absent" state, class, or category to the classification label 816. On the other hand, if all potential matching peaks do not meet this condition, the screening component 318 may not cause the classification label 816 to indicate an "absent" state, class, or category.

[0114] In various cases, the uncertainty criteria for the selected target chemical 802 may be the set complement of the presence and absence criteria. Thus, if the screening component 318 determines that neither the presence nor absence criteria are met, the screening component 318 may indicate or assign an uncertainty state, class, or category to the classification label 816.

[0115] It should be appreciated and understood that the ratio error relationships and mass tolerance relationships mentioned in the several paragraphs above are merely non-limiting examples of presence and absence criteria. In various other embodiments, the presence and absence criteria may be defined by or based on other suitable ratio error relationships or mass tolerance relationships.

[0116] Referring now to FIG. 10 , in various embodiments, instead of generating a set of ratio errors 904 or a set of mass tolerance pairs 906, screening component 318 may electronically calculate a set of library scores 1002. In various aspects, a set of library scores 1002 may each correspond to a set of measured mass spectra 814. Thus, because set of measured mass spectra 814 may include p spectra, set of library scores 1002 may also include p scores, from library score 1002(1) to library score 1002(p). In various cases, each score in library score 1002 is a positive real scalar value whose magnitude indicates the degree of geometric or shape-based similarity between reference mass spectrum 808 and each spectrum in measured mass spectra 814. For example, library score 1002(1) may indicate the degree of similarity between measured mass spectrum 814(1) and reference mass spectrum 808. As another example, library score 1002(p) may indicate the degree of similarity between the measured mass spectrum 814(p) and the reference mass spectrum 808. In various embodiments, the screening component 318 may calculate the set of library scores 1002 by implementing or utilizing a suitable library score calculation technique in the field of mass spectrometry. As a non-limiting example, the screening component 318 may calculate the library score using a vectorization of the spectra and a dot product, Euclidean distance, or cosine similarity (e.g., the library score 1002(1) may be based on the dot product, Euclidean distance, or cosine similarity after vectorizing the measured mass spectrum 814(1) and the reference mass spectrum 808, respectively). As another non-limiting example, the screening component 318 may calculate the library score using a Pearson correlation coefficient calculation (e.g., the library score 1002(1) may be equal to or based on the Pearson correlation coefficient between the measured mass spectrum 814(1) and the reference mass spectrum 808). As yet another non-limiting example, screening component 318 may calculate the library score using a technique that utilizes the degree of agreement of peak numbers and peak intensities (e.g., library score 1002(1) may be based on how well the number and heights of peaks in measured mass spectrum 814(1) match those in reference mass spectrum 808).

[0117] Note that the sets of potential matching peaks 812 can each be considered to correspond to a set of library scores 1002 (e.g., potential matching peak 812(1) can correspond to library score 1002(1); potential matching peak 812(p) can correspond to library score 1002(p)).

[0118] In various embodiments, the presence criterion for a selected target chemical 802 may be that at least one of the set of potential matching peaks 812 has a library score above any suitable upper library score threshold value (e.g., an 850 or 85% threshold value). Thus, if such a potential matching peak is present, the screening component 318 may indicate a "present" state, class, or category in the classification label 816. On the other hand, if such a potential matching peak is not present, the screening component 318 may prevent the classification label 816 from indicating a "present" state, class, or category.

[0119] In various cases, the absence criterion for the selected target chemical 802 may be that each of the set of potential matching peaks 812 has a library score below any suitable lower library score threshold value (e.g., a threshold value of 600 or 60%). Thus, if all potential matching peaks meet this, the screening component 318 may indicate or assign an "absent" state, class, or category to the classification label 816. On the other hand, if all potential matching peaks do not meet this condition, the screening component 318 may not cause the classification label 816 to indicate an "absent" state, class, or category.

[0120] As discussed above, in various cases, the uncertainty criteria for a selected target chemical 802 may be the set complement of the presence and absence criteria. Thus, if the screening component 318 determines that neither the presence nor absence criteria are met by the set of measured mass spectra 814, the screening component 318 may indicate or assign an "uncertain" status, class, or category to the classification label 816.

[0121] 11. In various embodiments, rather than relying on comparing QC ion ratios or calculating a library score, the screening component 318 may use artificial intelligence to generate the classification labels 816. Indeed, in various aspects, the screening component 318 may electronically store, maintain, control, or access the machine learning classifier 1102. In various cases, the machine learning classifier 1102 may have any suitable internal architecture.

[0122] In some cases, the machine learning classifier 1102 may have the internal architecture of an appropriate deep learning neural network. Indeed, in various cases, the machine learning classifier 1102 may include an input layer, one or more hidden layers, and an output layer. In various cases, these layers may be connected by appropriate inter-neuron or inter-layer connections, such as forward propagation connections, skip connections, or recurrent connections. Furthermore, in various cases, such layers may be appropriate types of neural network layers with appropriate learnable or trainable internal parameters. For example, any of such input layer, one or more hidden layers, or output layer may be a convolutional layer, and its learnable or trainable parameters may be convolution kernels. As another example, any of such input layer, one or more hidden layers, or output layer may be a fully connected layer, and its learnable or trainable parameters may be a weight matrix or a bias value. As yet another example, any of such input layer, one or more hidden layers, or output layer may be a batch normalization layer, and its learnable or trainable parameters may be a shift factor or a scale factor. As yet another example, any of such input layers, one or more hidden layers, or output layers may be long short-term memory (LSTM) layers, and the learnable or trainable parameters may be input state weight matrices or hidden state weight matrices. As yet another example, any of such input layers, one or more hidden layers, or output layers may be Transformer layers, and the learnable or trainable parameters may be single-head or multi-head attention blocks or other weight matrices. Furthermore, in various cases, any of such layers may be any suitable type of neural network layer, having any suitable fixed or non-trainable internal parameters. For example, any of such input layers, one or more hidden layers, or output layers may be nonlinear layers, padding layers, pooling layers, or concatenation layers.

[0123] In other examples, the machine learning classifier 1102 may have any other suitable internal architecture. As a non-limiting example, the machine learning classifier 1102 may, in some aspects, have the internal architecture of a support vector machine. As another non-limiting example, the machine learning classifier 1102 may, in some aspects, have the internal architecture of a naive Bayes. As yet another non-limiting example, the machine learning classifier 1102 may, in some aspects, have the internal architecture of a decision tree or a random forest.

[0124] In some cases, the internal architecture of the machine learning classifier 1102 may be any suitable combination of any of the above.

[0125] Regardless of its particular internal architecture (e.g., the particular number, type, or configuration of layers), the machine learning classifier 1102 can be configured or trained to classify a given chemical as present, absent, or neither for a given sample based on input measured mass spectrometry data associated with the given sample and input reference mass spectrometry data associated with the given chemical. Thus, in various embodiments, the screening component 318 can electronically utilize the machine learning classifier 1102 to generate the classification label 816.

[0126] In particular, in various embodiments, the screening component 318 may electronically execute the machine learning classifier 1102 on the reference mass spectrometry data 803 and the measured mass spectrometry data 702. In various aspects, such execution may cause the machine learning classifier 1102 to generate a classification label 816. As a non-limiting example, the screening component 318 may concatenate the reference mass spectrometry data 803 and the measured mass spectrometry data 702 and input the concatenated results into an input layer of the machine learning classifier 1102. In various cases, the concatenation may complete a forward propagation through one or more hidden layers of the machine learning classifier 1102, and the output layer of the machine learning classifier 1102 may calculate or compute the classification label 816 based on the activation map or feature map generated by the one or more hidden layers of the machine learning classifier 1102. In other words, the machine learning classifier 1102 may be considered to determine, infer, or predict whether the selected target chemical 802 is present in the sample 304, absent from the sample 304, or whether it is present or absent in the sample 304 cannot yet be conclusively determined.

[0127] In order for the machine learning classifier 1102 to accurately, correctly, or reliably generate the classification labels 816, the machine learning classifier 1102 may first undergo training. One non-limiting example of such training is described in connection with FIG. 12.

[0128] FIG. 12 is a block diagram of an example illustrating how the machine learning classifier 1102 can be trained in accordance with one or more embodiments described herein.

[0129] In various aspects, before training begins, the trainable internal parameters of the machine learning classifier 1102 (e.g., convolution kernels, weight matrices, bias values) may be initialized in any suitable manner (e.g., by random initialization).

[0130] In various embodiments, there may be training measurement mass spectrometry data 1202, training reference mass spectrometry data 1204, or ground truth classification labels 1206. In various aspects, the training measurement mass spectrometry data 1202 may be any suitable mass spectrometry data having the same format, size, or dimensions as the measurement mass spectrometry data 702, for any suitable sample, generated by a mass spectrometer equipped with any suitable chromatograph. In various cases, the training reference mass spectrometry data 1204 may be any suitable mass spectrometry data (e.g., having the same format, size, or dimensions as the reference mass spectrometry data 803) that is known or deemed to be indicative of a given chemical for which the training measurement mass spectrometry data 1202 is to be screened. In various cases, the ground truth classification labels 1206 may be correct or accurate present classifications, absent classifications, or indeterminate classifications that are known or deemed to correspond to the training measurement mass spectrometry data 1202 and the training reference mass spectrometry data 1204.

[0131] In either case, the machine learning classifier 1102 may be run on the training measurement mass spectrometry data 1202 and the training reference mass spectrometry data 1204, and such running may cause the machine learning classifier 1102 to generate an output 1208. For example, in some cases, the training measurement mass spectrometry data 1202 and the training reference mass spectrometry data 1204 may be concatenated; the concatenation may be fed or routed to an input layer of the machine learning classifier 1102; the concatenation may complete a forward propagation through one or more hidden layers of the machine learning classifier 1102; and the output layer of the machine learning classifier 1102 may calculate the output 1208 based on the activation maps or feature maps provided by the one or more hidden layers of the machine learning classifier 1102.

[0132] It should be noted that the format, size, or dimensionality of output 1208 may be determined by the number, arrangement, size, or other characteristics of neurons, convolution kernels, LSTM layers, or other internal parameters of the output layer (or other layers) of machine learning classifier 1102. Thus, by adding, removing, or adjusting characteristics of the output layer (or other layers) of machine learning classifier 1102, output 1208 may be tailored to a desired format, size, or dimensionality. In particular, output 1208 may have the same format, size, or dimensionality as classification labels 816. Thus, output 1208 may be viewed as predicted or inferred presence, absence, or uncertainty classification labels that machine learning classifier 1102 believes correspond to training measurement mass spectrometry data 1202 and training reference mass spectrometry data 1204. In contrast, the ground truth classification labels 1206 may be correct or accurate present, absent, or uncertain classification labels that are known or assumed to correspond to the training measurement mass spectrometry data 1202 and the training reference mass spectrometry data 1204. Note that if the machine learning classifier 1102 has had little or no prior training, the output 1208 may be highly inaccurate. In other words, the output 1208 may differ significantly from the ground truth classification labels 1206.

[0133] In various embodiments, a loss 1210 (e.g., mean absolute error, mean squared error, cross-entropy error) between the output 1208 and the ground truth classification labels 1206 may be calculated. In various cases, trainable internal parameters of the machine learning classifier 1102 may be incrementally updated via backpropagation (e.g., stochastic gradient descent) based on the loss 1210.

[0134] In various cases, this run-and-update procedure may be repeated for any suitable number of training iterations (epochs). This may ultimately allow the trainable internal parameters of the machine learning classifier 1102 to be iteratively optimized to accurately classify presence, absence, or uncertainty based on the input measured mass spectrometry data and reference mass spectrometry data. In various embodiments, appropriate batch sizes, error functions / loss functions, or training termination criteria may be used during such training.

[0135] Although this disclosure primarily describes the machine learning classifier 1102 as being trained in a supervised manner, this is merely a non-limiting example for ease of explanation and illustration. In various embodiments, the machine learning classifier 1102 may be trained using any other suitable training paradigm, such as unsupervised training or reinforcement learning, either of which may be federated or non-federated.

[0136] It should be appreciated that the screening component 318 may generate the classification label 816 by any suitable combination of comparing QC ion ratios as described above, calculating a library score, or implementing a machine learning classifier.

[0137] Note that it is possible that no potential matching peaks exist (e.g., none of the measured retention indices 708 may be within a threshold margin or vicinity of the reference retention indices 804). In such situations, the screening component 318 may indicate or assign an absent state, class, or category to the classification label 816.

[0138] In either case, the screening component 318 can electronically generate a classification label 816 indicating either: that the selected target chemical 802 is present in the sample 304; that the selected target chemical 802 is absent from the sample 304; or that it cannot yet be conclusively determined whether the selected target chemical 802 is present or absent in the sample 304.

[0139] In response to the classification label 816 indicating a "present" state, class, or category, the screening component 318 may move the selected target chemical 802 from the indeterminate running list 606 to the present running list 602 (e.g., so that the selected target chemical 802 is no longer present in the indeterminate running list 606).

[0140] In response to the classification label 816 indicating an absent state, class, or category, the screening component 318 may move the selected target chemical 802 from the indeterminate run list 606 to the absent run list 604 (e.g., so that the selected target chemical 802 is no longer present in the indeterminate run list 606).

[0141] In response to the classification label 816 indicating an indeterminate state, class, or category, the screening component 318 may retain or maintain the selected target chemical 802 in the indeterminate running list 606 .

[0142] In various embodiments, the screening component 318 may move or retain each target chemical in the indeterminate run list 606 in this manner during the jth iteration of the iterative injection protocol 406. After all target chemicals that were in the indeterminate run list 606 at the start of the jth iteration have been moved or retained, the screening component 318 may check whether the indeterminate run list 606 is now empty. If it is not already empty, the screening component 318 may proceed to the (j+1)th iteration of the iterative injection protocol 406. On the other hand, if it is empty, the iterative injection protocol 406 may now be considered complete. In such a case, the present run list 602 may be considered a set of present classifications 402 (e.g., indicating which of the multiple target chemicals 308 have been conclusively determined to be present in the sample 304), and the absent run list 604 may be considered a set of absent classifications 404 (e.g., indicating which of the multiple target chemicals 308 have been conclusively determined to be absent from the sample 304).

[0143] 13-17 show flow diagrams of exemplary, non-limiting computer-implemented methods 1300, 1400, 1500, 1600, and 1700 that can facilitate non-deterministically triggered reinjection for mass spectrometry screening, in accordance with one or more embodiments described herein. In various cases, system 310 can perform or facilitate computer-implemented methods 1300, 1400, 1500, 1600, and 1700.

[0144] Referring first to Figure 13, in various embodiments, operation 1302 may include accessing, by a device (e.g., via 316) operatively coupled to a processor (e.g., 312), a mass spectrometer (e.g., 302) equipped with a chromatograph loaded with a sample (e.g., 304).

[0145] In various embodiments, operation 1304 may include the device (e.g., via 316) accessing a screening list (e.g., 306) that specifies the chemicals (e.g., 308) against which the sample is to be screened.

[0146] In various cases, operation 1306 may include accessing, by the device (e.g., via 316), reference retention indices (e.g., 504) or reference mass spectra (e.g., 508, from which 506 may be derived) corresponding to each chemical in the screening list.

[0147] In various cases, operation 1308 may include causing the device (e.g., via 318) to execute an n-alkane injection protocol and a blank injection protocol on a mass spectrometer equipped with a chromatograph.

[0148] In various embodiments, operation 1310 may include creating, by the device (e.g., via 318), a present run list (e.g., 602), an absent run list (e.g., 604), and an indeterminate run list (e.g., 606), where the present run list may be initially empty, the absent run list may be initially empty, and the indeterminate run list may initially contain all chemicals in the screening list.

[0149] In various cases, operation 1312 may include causing the device (e.g., via 318) to inject a portion of the sample into a mass spectrometer equipped with a chromatograph. In various cases, the computer-implemented method 1300 may proceed to operation 1402 of the computer-implemented method 1400.

[0150] 14. In various embodiments, operation 1402 may include the device (e.g., via 318) determining whether the sample has yet to be scanned (e.g., equivalent to determining whether this is the first or first iteration of the iterative injection protocol 406). If the sample has not yet been scanned (e.g., if this is the first or first iteration), the computer-implemented method 1400 may proceed to operation 1404. If the sample has already been scanned (e.g., if this is a non-first time or a subsequent iteration), the computer-implemented method 1400 may instead proceed to operation 1406.

[0151] In various embodiments, operation 1404 may include causing the device (e.g., via 318) to cause a mass spectrometer with a chromatograph to perform an unrestricted full scan on a portion of the sample, thereby generating measurement data (e.g., 702) including a chromatogram (e.g., 704) and a respective mass spectrum (e.g., one of 710) for each peak (e.g., one of 706) in the chromatogram. In various cases, such measurement data may be obtained by applying any suitable deconvolution or background subtraction technique to the raw data output by the mass spectrometer with a chromatograph, which may be based on the n-alkane injection protocol or blank injection protocol described above. In various cases, the computer-implemented method 1400 may proceed to operation 1408.

[0152] In various cases, operation 1406 may include causing the device (e.g., via 318) to cause a mass spectrometer with a chromatograph to perform a time- or ion-selective scan of each chemical in the indeterminate run list on the portion of the sample, thereby generating measurement data (e.g., 702) including a chromatogram (e.g., 704) and a respective mass spectrum (e.g., one of 710) for each peak (e.g., one of 706) in the chromatogram. As described above, such measurement data may be obtained by applying any suitable deconvolution or background subtraction technique to the raw data output by the mass spectrometer with a chromatograph, which may be based on the n-alkane injection protocol or blank injection protocol described above. In various cases, the computer-implemented method 1400 may proceed to operation 1408.

[0153] In various embodiments, operation 1408 may include determining, by the device (e.g., via 318), whether measurement data has yet to be analyzed for each chemical in the indeterminate run list. If not (e.g., there is at least one compound in the indeterminate run list for which measurement data has not yet been analyzed), computer-implemented method 1400 may proceed to operation 1502 of computer-implemented method 1500. If not (e.g., there are no compounds with measurement data that have not yet been analyzed), computer-implemented method 1400 may proceed to operation 1702 of computer-implemented method 1700.

[0154] Referring now to Figure 15, in various embodiments, operation 1502 may include selecting a compound (e.g., 802) by the device (e.g., via 318) from an indeterminate run list for which measurement data has not yet been analyzed.

[0155] In various embodiments, operation 1504 may include determining, by the device (e.g., via 318), whether one or more peaks (e.g., 812) in the chromatogram have a retention index (e.g., 810) that is within a threshold margin of the reference retention index (e.g., 804) of the selected chemical. If not, the computer-implemented method 1500 may proceed to operation 1506. In that case, the computer-implemented method 1500 may proceed to operation 1508.

[0156] In various cases, operation 1506 may include the device moving the selected chemical from an indefinite run list to an unscheduled run list. In various cases, computer-implemented method 1500 may return to operation 1408 of computer-implemented method 1400.

[0157] In various embodiments, operation 1508 may include determining, by the device (e.g., via 318), whether all of the one or more peaks have mass spectra (e.g., 814) that meet the absence classification criteria relative to a reference mass spectrum (e.g., 808 or 806) of the selected chemical. In various examples, the determination may be based on a ratio comparison of quantitation ions to confirmation ions (e.g., as described in connection with FIG. 9) or a calculation of a library score (e.g., as described in connection with FIG. 10). If not (e.g., if at least one peak has a mass spectrum that does not meet the absence classification criteria), computer-implemented method 1500 may proceed to operation 1602 of computer-implemented method 1600. If not (e.g., if all peaks have mass spectra that meet the absence classification criteria), computer-implemented method 1500 may proceed to operation 1510.

[0158] In various cases, operation 1510 may include moving the selected chemical from an indefinite run list to an unscheduled run list by the device (e.g., via 318). In various cases, computer-implemented method 1500 may return to operation 1408 of computer-implemented method 1400.

[0159] Referring now to Figure 16, in various embodiments, operation 1602 may include determining, by an apparatus (e.g., via 318), whether any of the one or more peaks have a mass spectrum that meets presence classification criteria with respect to the reference mass spectrum. If so, the computer-implemented method 1600 may proceed to operation 1604. If not, the computer-implemented method 1600 may instead proceed to operation 1606.

[0160] In various embodiments, operation 1604 may include moving the selected chemical from an uncertain run list to an existing run list by the device (e.g., via 318). In various cases, the computer-implemented method 1600 may proceed to operation 1408 of the computer-implemented method 1400.

[0161] In various cases, operation 1606 may include maintaining the selected chemical in an indeterminate run list by the device (e.g., via 318). In various cases, computer-implemented method 1600 may proceed to operation 1408 of computer-implemented method 1400.

[0162] Finally, reference is made to Figure 17. In various embodiments, operation 1702 may include the device (e.g., via 318) determining whether the indeterminate execution list is currently empty. If not, computer-implemented method 1700 may proceed to operation 1704. In that case, computer-implemented method 1700 may instead proceed to operation 1706.

[0163] In various aspects, operation 1704 may include returning by the device (eg, via 318) to operation 1312 of the computer-implemented method 1300.

[0164] In various cases, operation 1706 may include transmitting or rendering, by the device (e.g., via 318), the present execution list and the absent execution list to a computing device or electronic display.

[0165] In various cases, machine learning algorithms or models may be implemented in any suitable manner to facilitate appropriate aspects described herein. To facilitate some of the above machine learning aspects of various embodiments, consider the following discussion of artificial intelligence (AI). Various embodiments described herein may employ artificial intelligence to facilitate automation of one or more features or functions. Components may employ various AI-based schemes to implement various embodiments / examples disclosed herein. To provide or support many of the decisions described herein (e.g., measuring, ascertaining, inferring, calculating, predicting, predicting, deriving, foreseeing, detecting, calculating), components described herein may examine all or a subset of the data to which they are permitted access and may infer or determine the state of a system or environment from a series of observations obtained through events or data. Decisions may be used to identify specific situations or actions, such as generating a probability distribution between states. Decisions may be probabilistic; that is, calculating a probability distribution for a state of interest based on a consideration of data and events. Decisions may also refer to techniques used to compose higher-level events from a series of events or data.

[0166] Such determinations may result in the construction of new events or actions from a series of observed events or stored event data, regardless of whether the events are closely correlated in time, and regardless of whether the events and data come from one or multiple event and data sources. The components disclosed herein may employ a variety of classification (e.g., explicit training with training data, as well as implicit training by observing behaviors, preferences, historical information, receiving external information, etc.) schemes or systems (e.g., support vector machines, neural networks, expert systems, Bayesian belief networks, fuzzy logic, data fusion engines, etc.) in connection with performing automated or determined actions in connection with the claimed subject matter. Thus, classification schemes or systems may be used to automatically learn and perform many functions, evaluations, or decisions.

[0167] The classifier takes an input attribute vector, z(z1, z2, z3, z4, z n ) may be mapped to a confidence that the input belongs to a class, such as f(z)confidence(class). Such classification may use probabilistic or statistical analysis (e.g., considerations of analytical utility and cost) to determine the action to be taken automatically. A support vector machine (SVM) may be an example of a classifier that may be used. SVMs operate by finding a hypersurface in the space of possible inputs, which hypersurface attempts to separate triggering criteria from non-triggering events. Intuitively, this correctly classifies test data that is similar but not identical to training data. Other directed and undirected model classification approaches include, for example, Naive Bayes, Bayesian networks, decision trees, neural networks, fuzzy logic models, or probabilistic classification models that provide different independence patterns, any of which may be employed. Classification, as used herein, also includes statistical regression used to develop priority models.

[0168] To provide additional context for the various embodiments described herein, Figure 18 and the following discussion are intended to provide a brief, general description of a suitable computing environment 1800 in which various embodiments described herein may be implemented. Although the embodiments are described above in the general context of computer-executable instructions that may be executed on one or more computers, those skilled in the art will recognize that the embodiments may also be implemented in combination with other program modules, or as a combination of hardware and software.

[0169] Generally, program modules include routines, programs, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Furthermore, those skilled in the art will appreciate that the methods of the present invention can be practiced with other computer system configurations, such as single-processor or multi-processor computer systems, minicomputers, mainframe computers, Internet of Things (IoT) devices, distributed computing systems, and personal computers, portable computing devices, microprocessor-based or programmable consumer electronics, each of which can be operatively coupled to one or more associated devices.

[0170] The illustrated embodiments herein may also be practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

[0171] A computing device typically includes a variety of media, which may include computer-readable storage media, machine-readable storage media, or communication media. These two terms are used interchangeably herein as follows. A computer-readable storage medium or machine-readable storage medium may be any available storage medium that can be accessed by a computer, including both volatile and nonvolatile media, removable and non-removable media. By way of example and not limitation, a computer-readable storage medium or machine-readable storage medium may be implemented in connection with any method or technology for storing information, such as computer-readable or machine-readable instructions, program modules, structured data, or unstructured data.

[0172] A computer-readable storage medium may include, but is not limited to, random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CDROM), digital versatile disc (DVD), Blu-ray disc (BD) or other optical disc storage device, magnetic cassette, magnetic tape, magnetic disc storage device or other magnetic storage device, solid-state drive or other solid-state storage device, or other tangible or non-transitory medium that may be used to store the desired information. In this regard, the terms "tangible" or "non-transitory" as applied to storage, memory, or computer-readable medium herein are understood to exclude, as modifiers, only transitory signals that propagate themselves, and do not waive the right to all standard storage, memory, or computer-readable medium that are not transitory signals that propagate themselves.

[0173] The computer-readable storage medium may be accessed by one or more local or remote computing devices for various operations on the information stored by the medium, for example, via an access request, query, or other data retrieval protocol.

[0174] Communication media typically embodies computer-readable instructions, data structures, program modules, or other structured or unstructured data in a data signal, such as a modulated data signal, such as a carrier wave or other transport mechanism, and includes any information delivery or transmission media. The term "modulated data signal" means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media.

[0175] 18, an exemplary environment 1800 for implementing various embodiments described herein includes a computer 1802, which includes a processing unit 1804, a system memory 1806, and a system bus 1808. The system bus 1808 couples system components including, but not limited to, the system memory 1806 to the processing unit 1804. The processing unit 1804 may be any of a variety of commercially available processors. Dual microprocessors and other multi-processor architectures may also be employed as the processing unit 1804.

[0176] The system bus 1808 may be any of several types of bus structures, which may further connect to a memory bus (with or without a memory controller), a peripheral bus, and a local bus using any of a variety of commercially available bus architectures. The system memory 1806 includes ROM 1810 and RAM 1812. The basic input / output system (BIOS) may be stored in non-volatile memory such as ROM, erasable programmable read-only memory (EPROM), or EEPROM, and contains the basic routines that help to transfer information between elements within the computer 1802, such as during start-up. The RAM 1812 may also include high-speed RAM, such as static RAM, for caching data.

[0177] Computer 1802 further includes an internal hard disk drive (HDD) 1814 (e.g., EIDE, SATA), one or more external storage devices 1816 (e.g., a magnetic floppy disk drive (FDD) 1816, a memory stick or flash drive reader, a memory card reader, etc.). It also includes a drive 1820, such as a solid-state drive or optical disk drive, which may read from or write to a disk 1822, such as a CD-ROM disk, DVD, BD, etc. Alternatively, where a solid-state drive is involved, disk 1822 is not included unless it is separate. While internal HDD 1814 is illustrated as being located within computer 1802, it may be configured for external use within a suitable chassis (not shown). Additionally, although not illustrated in environment 1800, a solid-state drive (SSD) may be used in addition to or in place of HDD 1814. HDD 1814, external storage 1816, and drive 1820 may be connected to system bus 1808 by HDD interface 1824, external storage interface 1826, and drive interface 1828, respectively. Interface 1824 for external drive implementations may include at least one or both of Universal Serial Bus (USB) and Institute of Electrical and Electronics Engineers (IEEE) 1394 interface technologies. Other external drive connection technologies are also contemplated by the embodiments described herein.

[0178] The drives and their associated computer-readable storage media provide non-volatile storage of data, data structures, computer-executable instructions, and the like. In computer 1802, the drives and storage media accommodate the storage of any data in a suitable digital format. Those skilled in the art will appreciate that while the above description refers to particular types of storage devices, other types of computer-readable storage media, whether currently existing or developed in the future, may be used in the exemplary operating environment. It should also be understood that any such storage media may include computer-executable instructions for performing the methods described herein.

[0179] The drives and RAM 1812 may store a number of program modules, including an operating system 1830, one or more application programs 1832, other program modules 1834, and program data 1836. All or portions of the operating system, applications, modules, or data may be cached in RAM 1812. The systems and methods described herein may be implemented using various commercially available operating systems or combinations of operating systems.

[0180] Computer 1802 may optionally include emulation technology. For example, a hypervisor (not shown) or other intermediary may emulate a hardware environment for operating system 1830, and the emulated hardware may optionally differ from the hardware illustrated in FIG. 18 . In such an embodiment, operating system 1830 may comprise one virtual machine (VM) of multiple virtual machines hosted on computer 1802. Additionally, operating system 1830 may provide a runtime environment, such as the Java Runtime Environment or the .NET Framework, for application 1832. A runtime environment is a consistent execution environment that allows application 1832 to run on any operating system that includes the runtime environment. Similarly, operating system 1830 may support containers, and application 1832 may be in the form of a container, which is a lightweight, standalone, executable software package that includes, for example, code, runtime, system tools, system libraries, and application settings.

[0181] Additionally, computer 1802 may be enabled with a security module, such as a Trusted Platform Module (TPM). For example, when using a TPM, a boot component hashes the next loaded boot component and waits for the result to match a secure value before loading the next boot component. This process can occur at any layer of the code execution stack of computer 1802, such as applied at the application execution level or the operating system (OS) kernel level, thereby enabling security at every level of code execution.

[0182] A user may enter commands and information into the computer 1802 through one or more wired / wireless input devices, such as a keyboard 1838, a touch screen 1840, and a pointing device such as a mouse 1842. Other input devices (not shown) may include a microphone, an infrared (IR) remote control, a radio frequency (RF) remote control, or other remote control, a joystick, a virtual reality controller or headset, a game pad, a stylus pen, an image input device (e.g., a camera), a gesture sensor input device, a visual movement sensor input device, an emotion or face detection device, a biometric input device (e.g., a fingerprint or iris scanner), etc. These and other input devices are often connected to the processing unit 1804 through an input device interface 1844 connectable to the system bus 1808, but may also be connected through other interfaces, such as a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, a BLUETOOTH interface, etc.

[0183] A monitor 1846 or other type of display device may also be connected to the system bus 1808 via an interface, such as a video adapter 1848. In addition to the monitor 1846, a computer typically includes other peripheral output devices (not shown), such as speakers and printers.

[0184] The computer 1802 may operate in a networked environment using logical connections via wired or wireless communications to one or more remote computers, such as a remote computer 1850. The remote computer 1850 may be a workstation, a server computer, a router, a personal computer, a portable computer, a microprocessor-based entertainment appliance, a peer device, or other common network node, and typically includes many or all of the elements described relative to the computer 1802, although for simplicity, only the memory / storage device 1852 is illustrated. The logical connections shown include wired / wireless connections to a local area network (LAN) 1854 or a larger network (e.g., a wide area network (WAN) 1856). Such LAN and WAN networking environments are commonplace in offices and businesses and facilitate enterprise-wide computer networks, such as intranets, all of which may connect to a global communications network, such as the Internet.

[0185] When used in a LAN networking environment, the computer 1802 may be connected to the local network 1854 through a wired or wireless communication network interface or adapter 1858. The adapter 1858 may facilitate wired or wireless communication to the LAN 1854, and the LAN may also include a wireless access point (AP) disposed thereon for communicating with the adapter 1858 in a wireless mode.

[0186] When used in a WAN networking environment, the computer 1802 may include a modem 1860 or may be connected to a communications server on the WAN 1856 via other means for establishing communications over the WAN 1856, such as via the Internet. The modem 1860 may be internal or external, a wired or wireless device, and may be connected to the system bus 1808 via the input device interface 1844. In a networked environment, program modules depicted associated with or as part of the computer 1802 may be stored in the remote memory / storage device 1852. It will be understood that the network connections shown are exemplary and other means of establishing a communications link between computers may be used.

[0187] When used in either a LAN or WAN networking environment, computer 1802 can access cloud storage systems or other network-based storage systems, including, but not limited to, networked virtual machines that provide one or more aspects of information storage or processing, in addition to or in place of the external storage device 1816 described above. Generally, the connection between computer 1802 and the cloud storage system may be established via LAN 1854 or WAN 1856, via adapter 1858 or modem 1860, respectively. Once computer 1802 is connected to an associated cloud storage system, external storage interface 1826, with the aid of adapter 1858 or modem 1860, can manage the storage provided by the cloud storage system in the same way as other types of external storage. For example, external storage interface 1826 may be configured to provide access to cloud storage sources as if those sources were physically connected to computer 1802.

[0188] The computer 1802 may be capable of communicating with any wireless device or entity operatively arranged for wireless communication, such as a printer, a scanner, a desktop or portable computer, a portable data assistant, a communications satellite, any equipment or location associated with a radio-detectable tag (e.g., a kiosk, a newspaper stand, a store shelf, etc.), and a telephone. This may include Wireless Fidelity (Wi-Fi) and Bluetooth® wireless technologies. Thus, communication may be in a predefined structure similar to a traditional network, or simply ad-hoc communication between at least two devices.

[0189] FIG. 19 is a schematic block diagram of an exemplary computing environment 1900 with which the disclosed subject matter can interact. The sample computing environment 1900 includes one or more client(s) 1910. The client(s) 1910 can be hardware or software (e.g., threads, processes, computing devices). The sample computing environment 1900 also includes one or more server(s) 1930. The server(s) 1930 can also be hardware or software (e.g., threads, processes, computing devices). The server(s) 1930 can house threads for performing transformations, for example, by employing one or more embodiments described herein. One possible communication between the client(s) 1910 and the server(s) 1930 can be in the form of a data packet adapted to be transmitted between two or more computer processes. The sample computing environment 1900 includes a communication framework 1950 that can be employed to facilitate communications between the client(s) 1910 and the server(s) 1930. The client(s) 1910 are operably connected to one or more client data store(s) 1920 that can be employed to store information local to the client(s) 1910. Similarly, the server(s) 1930 are operatively connected to one or more server data store(s) 1940 that can be employed to store information local to the servers 1930 .

[0190] Various embodiments may be systems, methods, apparatus, or computer program products in any possible level of technical detail. A computer program product may include a computer-readable storage medium (or media) having computer-readable program instructions thereon for causing a processor to perform aspects of various embodiments. A computer-readable storage medium may be a tangible device that can hold and store instructions for use by an instruction-execution device. A computer-readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. A non-exhaustive list of more specific examples of computer-readable recording media also includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), and an erasable programmable read-only memory. (EPROM or flash memory), static random access memory (SRAM), portable compact disc read only memory (CD-ROM), digital versatile disc (DVD), memory stick, floppy disk, mechanically encoded devices such as punch cards or raised structures in grooves with instructions recorded on them, and any suitable combination of the above. As used herein, computer readable storage medium should not be construed as a transitory signal per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., light pulses passing through a fiber optic cable), or electrical signals transmitted over wires.

[0191] The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to each computing / processing device, or may be downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, or a wireless network. The network may include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, or edge servers. A network adapter card or network interface within each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium within the respective computing / processing device. The computer-readable program instructions for performing the operations of various embodiments may be assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, integrated circuit configuration data, or the like. The computer-readable program instructions may be either source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, and procedural programming languages ​​such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuits, including, for example, programmable logic circuits, field programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), may execute the computer-readable program instructions by utilizing state information of the computer-readable program instructions to personalize the electronic circuit to perform various aspects.

[0192] Various aspects are described herein with reference to flowchart illustrations or block diagrams of methods, apparatus (systems), and computer program products according to various embodiments. It should be understood that each block of the flowchart illustrations or block diagrams, and combinations of blocks in the flowchart illustrations or block diagrams, can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, whereby the instructions, executed by the processor of the computer or other programmable data processing apparatus, create a machine that creates means for implementing the functions / acts specified in the flowchart or block diagram blocks. These computer-readable program instructions can also be stored on a computer-readable storage medium that causes a computer, programmable data processing apparatus, or other device to function in a particular manner, such that a computer-readable storage medium having instructions stored thereon includes a product containing instructions that implement aspects of the functions / acts specified in one or more blocks of the flowchart or block diagram. These computer-readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device and cause the computer, other programmable apparatus, or other device to perform a series of operations to generate a computer-implemented process, whereby the instructions executing on the computer, other programmable apparatus, or other device implement the functions / operations specified in the flowchart or block diagram blocks.

[0193] The flowcharts and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, or computer program products according to various embodiments. In this regard, each block in the flowcharts or block diagrams may represent: A block may represent a module, segment, or portion of an instruction, and may comprise one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures.

[0194] For example, two blocks shown in succession may in fact be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending on the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustrations, and combinations of blocks in the block diagrams or flowchart illustrations, may be implemented by a dedicated hardware-based system that performs the specified functions or operations, or a system that executes a combination of dedicated hardware and computer instructions.

[0195] Although the subject matter of the present invention has been described above in the general context of computer-executable instructions for a computer program product executed on one or more computers, those skilled in the art will recognize that the present disclosure may also be implemented in combination with other program modules. Generally, program modules include routines, programs, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Moreover, those skilled in the art will appreciate that various aspects may be implemented in conjunction with single-processor or multi-processor computer systems, minicomputing devices, mainframe computers, as well as computers, portable computing devices (e.g., It will be appreciated that other computer system configurations may be practiced, such as PDAs, telephones, microprocessor-based or programmable consumer or industrial electronic devices. The illustrated aspects may also be practiced in distributed computing environments where tasks are performed by remote processing devices linked through a communications network. However, some, if not all, aspects of the disclosure may be practiced on stand-alone computers. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

[0196] As used herein, the terms "component," "system," "platform," "interface," and similar terms may refer to or include computer-related entities or entities associated with an operable machine having one or more specific functionalities. The entities disclosed herein may be hardware, a combination of hardware and software, software, or software in execution. For example, a component may be, but is not limited to, a process running on a processor, a processor, an object, an executable file, a thread of execution, a program, or a computer. By way of example, both an application running on a server and the server may be a component. One or more components may reside within a process or thread of execution, and a component may be localized on one computer or distributed across two or more computers. As another example, each component may execute from various computer-readable media having various data structures stored thereon. Components may communicate via local or remote processes according to signals containing one or more data packets (e.g., data from one component interacting with another system via signals over a local system, a distributed system, or a network such as the Internet). As another example, a component may be a device having a particular functionality provided by mechanical parts operated by electrical or electronic circuitry operated by a software or firmware application executed by a processor. In such cases, the processor may be internal or external to the device and may execute at least a portion of the software or firmware application. As yet another example, a component may be a device that provides a particular functionality without mechanical parts through electronic components, which may include a processor or other means for executing software or firmware that at least partially imparts the functionality of the electronic component. In one aspect, a component may emulate an electronic component via a virtual machine, for example, in a cloud computing system.

[0197] Furthermore, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or." That is, unless otherwise specified or clear from the context, "X uses A or B" refers to all natural inclusive permutations. That is, if X uses A; if X uses B; or if X uses both A and B, then "X uses A or B" is satisfied in each of the foregoing cases. As used herein, the term "and / or" is intended to have the same meaning as "or." Furthermore, the articles "a" and "an" used in this specification and the accompanying drawings should generally be interpreted to mean "one or more" unless otherwise specified or clear from the context as indicating the singular form. As used herein, the terms "example" or "exemplary" are utilized to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter described herein is not limited by such examples. Furthermore, any aspect or design described herein as "example" or "exemplary" is not necessarily to be construed as preferred or advantageous over other aspects or designs, and is not intended to exclude equivalent exemplary structures and techniques known to those skilled in the art.

[0198] The disclosure herein describes non-limiting examples. For ease of explanation, various parts of this disclosure use the terms "each," "all," or "all" when discussing various examples. The use of the terms "each," "respective," or "all" is not limiting. In other words, when this disclosure provides a description that applies to "each," "all," or "all" of a particular object or component, it should be understood that this is a non-limiting example, and further, it should be understood that in various other examples, such a description may apply to less than "each," "all," or "all" of that particular object or component.

[0199] As used herein, the term "processor" may refer to virtually any computing processing unit or device, including, but not limited to, a single-core processor; a single processor with software multithreading execution capabilities; a multi-core processor; a multi-core processor with software multithreading execution capabilities; a multi-core processor with hardware multithreading technology; a parallel platform; and a parallel platform with distributed shared memory. Furthermore, a processor may refer to an integrated circuit, an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a field-programmable gate array (FPGA), a programmable logic controller (PLC), a complex programmable logic device (CPLD), discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. Furthermore, a processor may utilize nanoscale architectures, such as, but not limited to, molecular and quantum dot-based transistors, switches, and gates, to optimize space utilization or improve the performance of user equipment. A processor may be implemented as a combination of computing processing units. As used herein, terms such as "store," "memory," "data store," "data storage," "database," and substantially any other information storage component associated with the operation and functionality of a component are utilized to refer to a "memory component," an entity embodied in a "memory," or a component that includes a memory. It should be understood that a memory or memory component described herein may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. By way of example and not limitation, nonvolatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM), flash memory, or nonvolatile random access memory (RAM) (e.g., ferroelectric RAM (FeRAM)).Volatile memory may include, for example, RAM, which may act as external cache memory. By way of example, and not limitation, RAM is available in many forms, including synchronous RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), SyncLink DRAM (SLDRAM), direct Rambus RAM (DRRAM), direct Rambus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM). Additionally, the memory component of the systems or computer-implemented methods described herein is intended to comprise, without being limited to, these and any other suitable types of memory.

[0200] The foregoing are merely examples of systems and computer-implemented methods. Of course, for purposes of describing this disclosure, it is not possible to describe every conceivable combination of components or computer-implemented method, although many more combinations and permutations of the present disclosure are possible. Furthermore, to the extent that terms such as "includes," "has," "possesses," and the like are used in the detailed description, claims, appendices, and drawings, such terms are intended to be inclusive, similar to the term "comprising" when used as a transitional term in the claims.

[0201] The description of various embodiments is presented for purposes of illustration and is not intended to be exhaustive or limited to the embodiments disclosed herein. It will be apparent that many modifications and variations are possible without departing from the scope and spirit of the described embodiments. The terminology employed herein has been selected to best explain the principles of the embodiments, practical applications or technical improvements to technology found in the market, or to enable those skilled in the art to understand the embodiments disclosed herein.

[0202] Various non-limiting aspects are illustrated in the following examples.

[0203] Example 1: A system may include a chromatograph coupled to a mass spectrometer and a processor capable of executing computer-executable components stored in non-transitory computer-readable memory, where the computer-executable components may include: an accessing component capable of accessing a screening list specifying a plurality of target chemicals; and a screening component capable of classifying the presence or absence in a sample of each of the plurality of target chemicals based on causing the chromatograph and mass spectrometer to perform an iterative injection protocol on the sample, with subsequent iterations of this iterative injection protocol being progressively more compound-selective and triggered by an indeterminate present or absent classification from a preceding iteration.

[0204] Example 2: The system of any preceding example may be implemented such that, during a current iteration of a repetitive injection protocol, the screening component can: access a first run list containing only those of the plurality of target chemicals that were classified as present in the sample during any preceding iteration of the repetitive injection protocol; access a second run list containing only those of the plurality of target chemicals that were classified as absent in the sample during any preceding iteration of the repetitive injection protocol; and access a third run list containing only those of the plurality of target chemicals that were not classified as either present or absent in the sample during any preceding iteration of the repetitive injection protocol.

[0205] Example 3: The system of any preceding example may be configured, during a current iteration, to cause the chromatograph and mass spectrometer to inject a portion of the sample, selectively scan the portion for each target chemical in the third run list, and ignore each target chemical in the first run list or the second run list, thereby obtaining current mass spectrometry data with increased sensitivity to the target chemicals in the third run list.

[0206] Example 4: The system of any preceding example may be implemented such that the chromatograph and mass spectrometer may perform selective scans via: selected ion monitoring for quantification and confirmation ions associated with any target chemical on the third run list; or a time-truncated full scan based on the retention index associated with any target chemical on the third run list.

[0207] Example 5: The system of any preceding example may be configured to perform the following during the current iteration: classifying each target chemical in the third run list as either present in the sample, absent in the sample, or not classified as either present or absent in the sample based on comparing the current mass spectrometry data to reference mass spectrometry data corresponding to the screening list; moving any target chemical classified as present in the sample from the third run list to the first run list; moving any target chemical classified as absent in the sample from the third run list to the second run list, and maintaining any target chemical classified as neither present nor absent in the sample in the third run list.

[0208] Example 6: The system of any preceding example, wherein the current mass spectrometry data may include a chromatogram generated by the chromatograph and mass spectra generated by the mass spectrometer corresponding to peaks in the chromatogram, and the screening component may be implemented to classify target chemicals in the third run list based on: calculating retention indices for the peaks in the chromatogram; selecting target chemicals from the third run list; classifying the selected target chemical as absent in the sample in response to none of the peaks in the chromatogram having a retention index within a threshold margin of a reference retention index associated with the selected target chemical; accessing one or more first mass spectra generated by the mass spectrometer for the one or more first peaks, respectively, in response to one or more first peaks in the chromatogram having a retention index within a threshold margin of the reference retention index; classifying the selected target chemical as present in the sample in response to at least one of the one or more first mass spectra meeting a presence classification criterion relative to a reference mass spectrum associated with the selected target chemical; classifying the selected target chemical as absent in the sample in response to all of the one or more first mass spectra meeting an absence classification criterion relative to the reference mass spectrum; and classifying the selected target chemical as neither present nor absent in the sample in response to all of the one or more first mass spectra not meeting the presence classification criterion and at least one of the one or more first mass spectra not meeting the absence classification criterion.

[0209] Example 7: The system of any preceding example may be implemented such that the presence or absence classification criteria may be based on: a comparison of quantitative versus confirmatory ion ratios; or calculation of a library score.

[0210] Example 8: The system of any preceding example may be implemented such that the screening component may calculate a retention index for one or more first peaks based on an n-alkane injection protocol performed by the chromatograph and mass spectrometer prior to the replicate injection protocol.

[0211] Example 9: The system of any preceding example may be implemented such that the screening component is capable of: Deconvoluting one or more first peaks before calculating retention indices.

[0212] In various embodiments, any combination or combinations of Examples 1-9 may be implemented.

[0213] Example 10: A computer-implemented method may include accessing, by a device operatively coupled to a processor, a screening list specifying a plurality of target chemicals; and classifying, by the device, the presence or absence of each of the plurality of target chemicals in the sample based on causing a chromatograph and a mass spectrometer to perform an iterative injection protocol on the sample, wherein subsequent iterations of the iterative injection protocol are progressively compound-selective and are triggered by an indeterminate present or absent classification from a preceding iteration.

[0214] Example 11: The computer-implemented method according to any of the preceding examples may further include, during a current iteration of the iterative injection protocol, accessing by the device a first run list, the first run list including only those target chemicals classified as present in the sample during any previous iteration of the iterative injection protocol; accessing by the device a second run list, the second run list including only those target chemicals classified as absent in the sample during any previous iteration of the iterative injection protocol; and accessing by the device a third run list, the third run list including only those target chemicals not classified as present or absent in the sample during any previous iteration of the iterative injection protocol.

[0215] Example 12: The computer-implemented method according to any of the preceding examples may further include, during the current iteration, causing the device to inject a portion of the sample into a chromatograph and a mass spectrometer and selectively scan the portion for each target chemical in a third run list, while ignoring each target chemical included in the first run list or the second run list, to obtain current mass spectrometry data with increased sensitivity to the target chemicals in the third run list.

[0216] Example 13: The computer-implemented method according to any of the preceding examples may be implemented such that the chromatograph and mass spectrometer may perform selective scans via: selected ion monitoring for quantification and confirmation ions associated with any target chemical on the third run list; or a time-truncated full scan based on the retention index associated with any target chemical on the third run list.

[0217] Example 14: The computer-implemented method according to any of the preceding examples, further including, during the current iteration, classifying, by the device, each target chemical in a third run list as either present in the sample, absent, or neither present nor absent based on comparing the current mass spectrometry data with reference mass spectrometry data corresponding to a screening list; moving, by the device, target chemicals classified as present in the sample from the third run list to the first run list; moving, by the device, target chemicals classified as absent in the sample from the third run list to the second run list; and retaining, by the device, target chemicals not classified as present or absent in the third run list.

[0218] Example 15: The computer-implemented method according to any of the preceding examples may be implemented such that the current mass spectrometry data may include a chromatogram generated by the chromatograph and mass spectra generated by the mass spectrometer and corresponding to peaks in the chromatogram, and the classification of the target chemicals in the third run list may include: calculating, by the device, retention indices of the peaks in the chromatogram; selecting, by the device, a target chemical from the third run list; in response to none of the peaks in the chromatogram having a retention index within a threshold margin of a reference retention index associated with the selected target chemical, classifying, by the device, the selected target chemical as absent in the sample; in response to one or more first peaks in the chromatogram having a retention index within a threshold margin of the reference retention index, accessing, by the device, one or more first mass spectra respectively generated by the mass spectrometer for the one or more first peaks; classifying at least one of the one or more first mass spectra. In response to one satisfying the presence classification criteria relative to a reference mass spectrum associated with the selected target chemical, the device classifies the selected target chemical as present in the sample; in response to all of the one or more first mass spectra satisfying the absence classification criteria relative to the reference mass spectrum, the device classifies the selected target chemical as absent in the sample; and in response to all of the one or more first mass spectra not satisfying the presence classification criteria and at least one of the one or more first mass spectra not satisfying the absence classification criteria, the device classifies the selected target chemical as neither present nor absent in the sample.

[0219] Example 16: A computer-implemented method according to any of the previous examples may be implemented such that the presence or absence classification criteria may be based on: a comparison of quantitative versus confirmatory ion ratios; or calculation of a library score.

[0220] Example 17: The computer-implemented method according to any of the preceding examples may be implemented such that the device can calculate retention indices of one or more first peaks based on an n-alkane injection protocol performed by the chromatograph and mass spectrometer prior to the replicate injection protocol.

[0221] Example 18: The computer-implemented method according to any of the preceding examples, further comprising: deconvoluting the one or more first peaks before calculating the retention index.

[0222] In various embodiments, any combination or combinations of Examples 10 to 18 may be implemented.

[0223] Example 19: A computer program product facilitating indeterminately triggered re-injection for mass spectrometry screening may include a non-transitory computer-readable memory having program instructions embodied therein. In various embodiments, the program instructions are executable by a processor and may cause the processor to: establish electronic communication with a gas chromatograph and mass spectrometer, the gas chromatograph and mass spectrometer being loaded with an analytical sample; obtain a screening list specifying one or more hazardous chemicals that are not permitted to be included in the analytical sample; and classify the one or more hazardous chemicals as present or absent, respectively, in the analytical sample based on causing the gas chromatograph and mass spectrometer to perform a replicate injection protocol on the analytical sample, wherein subsequent iterations of the replicate injection protocol are progressively more compound-selective and are triggered by an indeterminate present or absent classification from a preceding iteration.

[0224] Example 20: The computer program product of any of the preceding examples may be implemented such that during a current iteration of an iterative injection protocol, the gas chromatograph and mass spectrometer may perform compound-selective scans in a stepwise manner via: selected ion monitoring directed at quantitation and confirmation ions associated with any of the one or more hazardous chemicals not classified as present or absent in any preceding iteration; or a time-truncated full scan based on retention indices associated with any of the one or more hazardous chemicals not classified as present or absent in any preceding iteration.

[0225] In various embodiments, any combination or combinations of Examples 19-20 may be implemented.

[0226] In various embodiments, any combination or combinations of Examples 1-20 may be implemented.

Claims

1. a chromatograph coupled to a mass spectrometer; a processor that executes computer-executable components stored in a non-transitory computer-readable memory; The computer-executable components include: an access component that accesses a screening list specifying a plurality of target chemicals; a screening component that classifies each of a plurality of target chemicals as present or absent in a sample based on causing the chromatograph and mass spectrometer to perform a replicate injection protocol on the sample; Subsequent iterations of the replicate injection protocol are stepwise compound selective and are triggered by an indeterminate present or absent classification from the preceding iteration. system.

2. During the current iteration of the replicate injection protocol, the screening component: accessing a first run list that includes only those of the plurality of target chemicals that were classified as present in the sample during any previous iteration of a replicate injection protocol; accessing a second run list including only those of the plurality of target chemicals that were classified as absent in the sample during any preceding iteration of the replicate injection protocol; accessing a third run list including only those of the plurality of target chemicals that were not classified as either present or absent in the sample during any prior iteration of the replicate injection protocol; The system of claim 1 .

3. During the current iteration, the screening component: causing the chromatograph and mass spectrometer to inject a portion of the sample, selectively scan the portion for each target chemical in the third run list, and ignore each target chemical included in either the first run list or the second run list, thereby obtaining current mass spectrometry data that is sensitive to the target chemicals in the third run list; The system of claim 2 .

4. Chromatograph and mass spectrometer Selected ion monitoring for quantification and confirmation ions associated with any target chemical on the third run list; or a full scan truncated in time based on the retention index associated with any target chemical on the third run list; Perform selective scans via The system of claim 3.

5. During the current iteration, the screening component: classifying each target chemical in the third running list as present in the sample, absent in the sample, or neither present nor absent in the sample based on comparing the current mass spectrometry data with reference mass spectrometry data corresponding to the screening list; moving any target chemicals classified as present in the sample from the third run list to the first run list; moving any target chemicals classified as absent in the sample from the third run list to the second run list; and maintaining in a third running list any target chemicals that are not classified as present or absent in the sample; Run The system of claim 3 .

6. The current mass spectrometry data comprises a chromatogram produced by the chromatograph and mass spectra produced by the mass spectrometer, each corresponding to a peak in the chromatogram, and the screening component identifies the target chemicals in the third run list by: calculating retention indices of the peaks in the chromatogram; selecting a target chemical from the third running list; classifying the selected target chemical as absent in the sample in response to none of the peaks in the chromatogram having a retention index that is within a threshold margin of a reference retention index associated with the selected target chemical; accessing one or more first mass spectra respectively generated by the mass spectrometer for the one or more first peaks in response to the one or more first peaks of the chromatogram having retention indices that are within a threshold margin of the reference retention index; classifying a selected target chemical as present in the sample if at least one of the one or more first mass spectra satisfies a presence classification criterion relative to a reference mass spectrum associated with the target chemical; classifying the selected target chemical as absent in the sample in response to all of the one or more first mass spectra meeting an absent classification criterion relative to the reference mass spectrum; and classifying the selected target chemical as neither present nor absent in the sample in response to all of the one or more first mass spectra not meeting the presence classification criteria and at least one of the one or more first mass spectra not meeting the absence classification criteria; configured to classify based on The system of claim 5.

7. 7. The system of claim 6, wherein the presence or absence classification criteria is based on a comparison of quantitative versus confirmatory ion ratios; or calculation of a library score.

8. 7. The system of claim 6, wherein the screening component calculates a retention index for the one or more first peaks based on an n-alkane injection protocol performed by the chromatograph and mass spectrometer prior to the replicate injection protocol.

9. The screening component performing deconvolution of the one or more first peaks before calculating the retention index; The system of claim 6.

10. accessing, by a device operatively coupled to the processor, a screening list specifying a plurality of target chemicals; and classifying each of a plurality of target chemicals as present or absent in the sample based on causing the device to cause a chromatograph and a mass spectrometer to perform a repetitive injection protocol on the sample, wherein subsequent iterations of the repetitive injection protocol are progressively compound selective and are triggered by an indeterminate present or absent classification from a preceding iteration, classifying each of a plurality of target chemicals as present or absent in the sample. Computer-implemented methods.

11. The computer-implemented method may further include, during a current iteration of the iterative injection protocol: accessing, by the device, a first run list including only those of the plurality of target chemicals that were classified as present in the sample during any preceding iteration of the iterative injection protocol; accessing, by the device, a second run list including only those of the plurality of target chemicals that were classified as absent in the sample during any preceding iteration of the replicate injection protocol; and accessing, by the device, a third run list including only those of the plurality of target chemicals that were not classified as present or absent in the sample during any prior iteration of the repeated injection protocol; The computer-implemented method of claim 10.

12. The computer-implemented method may further comprise, during the current iteration: causing the device to inject a portion of the sample into a chromatograph and mass spectrometer and selectively scan the portion for each target chemical in the third run list, while ignoring each target chemical in the first run list or the second run list, thereby obtaining current mass spectrometry data with increased sensitivity to the target chemicals in the third run list.

12. The computer-implemented method of claim 11.

13. Chromatograph and mass spectrometer Selected ion monitoring for quantification and confirmation ions associated with any target chemical on the third run list; or a full scan truncated in time based on the retention index associated with any target chemical on the third run list; Perform selective scans via 13. The computer-implemented method of claim 12.

14. The computer-implemented method may further comprise, during the current iteration: classifying, by the device, each target chemical in the third running list as present in the sample, absent in the sample, or not classified as either present or absent in the sample based on comparing the current mass spectrometry data with reference mass spectrometry data corresponding to the screening list; moving, by the device, any target chemicals classified as being present in the sample from the third execution list to the first execution list; moving any target chemicals classified by the device as absent in the sample from the third run list to the second run list; and maintaining in a third running list any target chemicals that were not classified by the device as present or absent in the sample; 13. The computer-implemented method of claim 12.

15. the current mass spectrometry data includes a chromatogram produced by the chromatograph and a mass spectrum produced by the mass spectrometer, each corresponding to a peak in the chromatogram; and the classification of the target chemicals in the third run list is: calculating, by the device, retention indices of the peaks in the chromatogram; selecting, by the device, a target chemical from the third execution list; classifying, by the device, the selected target chemical as absent in the sample in response to none of the peaks in the chromatogram having a retention index that is within a threshold margin of a reference retention index associated with the selected target chemical; accessing, by the device, one or more first mass spectra respectively generated by the mass spectrometer for the one or more first peaks of the chromatogram in response to the one or more first peaks having retention indices that are within a threshold margin of the reference retention index; classifying, by the device, the selected target chemical as present in the sample in response to at least one of the one or more first mass spectra meeting a presence classification criterion relative to a reference mass spectrum associated with the selected target chemical; classifying, by the device, the selected target chemical as absent in the sample in response to all of the one or more first mass spectra meeting an absent classification criterion relative to the reference mass spectrum; and classifying, by the device, the selected target chemical as neither present nor absent in the sample in response to all of the one or more first mass spectra not meeting the presence classification criteria and at least one of the one or more first mass spectra not meeting the absence classification criteria.

15. The computer-implemented method of claim 14.

16. 16. The computer-implemented method of claim 15, wherein the presence or absence classification criteria is based on a comparison of quantitative versus confirmatory ion ratios or calculation of a library score.

17. 16. The computer-implemented method of claim 15, wherein the device calculates the retention index of the one or more first peaks based on an n-alkane injection protocol performed by the chromatograph and mass spectrometer prior to the replicate injection protocol.

18. The computer-implemented method further comprises: deconvoluting the one or more first peaks before calculating the retention index; 16. The computer-implemented method of claim 15.

19. 1. A computer program product for facilitating non-deterministic triggered re-injection for mass spectrometry screening, comprising: a non-transitory computer-readable memory having program instructions embodied therein, the program instructions being executable by a processor, the computer program product comprising: establishing electronic communication with a gas chromatograph and a mass spectrometer, the gas chromatograph and mass spectrometer being loaded with a sample for analysis; Obtaining a screening list specifying one or more hazardous chemicals that are not permitted to be present in the analytical sample; and classifying each of one or more hazardous chemicals as present or absent in the analytical sample based on causing a gas chromatograph and a mass spectrometer to perform a replicate injection protocol on the analytical sample, wherein subsequent iterations of the replicate injection protocol are progressively compound selective and are triggered by an indeterminate present or absent classification from a preceding iteration; Execute Computer program products.

20. During the current iteration of the replicate injection protocol, the gas chromatograph and mass spectrometer: Selected ion monitoring directed at quantitation and confirmation ions associated with any of the one or more hazardous chemicals that were not classified as present or absent in any prior iteration; or a time-truncated full scan based on retention indices associated with any of the one or more hazardous chemicals that were not classified as present or absent in any prior iteration; configured to perform a compound-selective scan in a stepwise manner via 20. The computer program product of claim 19.

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