Patent Description:
<CIT> discloses chemical identification using a chromatography retention index. <CIT> discloses a screening analysis for unknown substance, especially non-metal automobile components, uses gas or liquid chromatography with mass spectrometry and databases of known retention indexes and mass spectra and substances.

Optional features are included in the dependent claims. The present disclosure provides systems and techniques for sample detection using, for example, gas chromatography-mass spectroscopy (GC-MS) systems are described. For instance, a GC-MS system includes a gas chromatograph configured to determine experimental chromatographic data for a calibration sample and an unknown sample, where the experimental chromatographic data includes a first retention time associated with the calibration sample and a second retention time associated with the unknown sample. The GC-MS system also includes a mass spectrometer configured to determine experimental mass spectral data for the unknown sample, where the experimental mass spectral data includes a mass spectrum associated with the unknown sample. The mass spectrometer is configured as a toroidal ion trap, and produces in many instances a non-classical mass spectrum.

A processor is communicatively coupled with the gas chromatograph and the mass spectrometer for receiving the experimental chromatographic data and the experimental mass spectral data. The processor is configured to determine a retention index for the unknown sample based upon the first retention time associated with the calibration sample and the second retention time associated with the unknown sample. The processor is also configured to identify a subset of database entries in an electronic database of reference mass spectral data using the retention index. In implementations, the electronic database includes spectra measured primarily using a classical detection technique, such as a quadrupole mass spectrometer (QMS). The system compares the experimental mass spectral data to the subset of database entries to identify the unknown sample.

In implementations, the system compares the experimental mass spectral data to the subset of database entries to identify the unknown sample using one or more comparison metrics, such as a percent fragment match between the experimental mass spectral data and the subset of database entries and/or a variance match between the experimental mass spectral data and the subset of database entries between the experimental mass spectral data and the subset of database entries. In some instances, a score can be determined using one metric, a combination of two or more metrics, and/or a weighted combination of two or more metrics. The score can be used to determine a match between the unknown sample and an electronic database entry, and the sample can be identified accordingly.

Whereas these algorithms fundamentally use a like versus like method for identification. A difference in the mass spectrum generated by a mass analyzer that produces a non-classical spectrum, such as an Ion Trap mass spectrometer, is that it hinders accurate identification. The pre-search and identification methods described here present an alternate approach that allows accurate identification when searching ion trap mass spectra against a database of more classical mass spectra generated by a quadrupole mass analyzer, for example.

In the figures, the left-most digit(s) of a reference number identify the figure in which the reference number first appears. The use of the same reference number in different instances in the description and the figures may indicate similar or identical items.

Gas chromatography-mass spectroscopy (GC-MS) combines a gas chromatograph with a mass spectrometer. Generally, a gas chromatograph uses a capillary column having particular dimensions (e.g., length, diameter, and film thickness) and phase properties. Differences in chemical properties between different molecules in a sample cause the molecules to separate as the sample travels the length of the column. For instance, an adsorbent can be used to adsorb analytes in a chromatography column. The molecules of the analytes are retained by the column and then elute from the column at different times (referred to as retention times). A mass spectrometer downstream from a gas chromatograph can then detect the ionized molecules separately, e.g., by breaking a molecule into ionized fragments and detecting the fragments using mass-to-charge ratios. Thus, the mass spectrometer can be used to ionize the analytes, separate the resulting ions according to their mass-to-charge ratios, detect the ions, generate signals based upon the detected ions, and process the resulting detected ion signals into mass spectra, from which the analytes can be identified.

A quadrupole mass spectrometer (QMS) is a mass analyzer that uses four (<NUM>) parallel rods to filter sample ions based upon their mass-to-charge ratios. Oscillating electric fields are applied to the parallel rods, and ions travelling between the rods are separated based upon the stability of their trajectories in the electric fields. For example, as ions travel longitudinally through the quadrupole, ions having a particular mass-to-charge ratio will reach a detector at the end of the quadrupole, while other ions having different mass-to-charge ratios will have unstable trajectories and will collide with the rods. In this manner, particular ions can be detected by controlling the operating characteristics of a quadrupole (e.g., by controlling voltages applied to pairs of opposing rods of the quadrupole).

A quadrupole ion trap uses the same physical principles as a QMS, but traps and sequentially ejects ions rather than causing them to collide with the instrumentation. For example, ions can be trapped by a three-dimensional (<NUM>-D) quadrupole field in a space defined by a ring electrode positioned between two end-cap electrodes. One technique for mass-to-charge ratio separation and/or isolation of trapped ions uses mass instability, where the orbits of trapped ions with greater masses remain stable, while the orbits of other trapped ions with less mass become unstable and the ions are ejected (e.g., onto a detector). Another technique for separating and/or isolating trapped ions uses resonance excitation, where various trapped ions are brought into a resonance condition in order of their mass-to-charge ratios. A linear quadrupole ion trap traps ions in a two-dimensional (<NUM>-D) quadrupole field as opposed to a <NUM>-D quadrupole field. A toroidal ion trap can be described as a linear quadrupole trap having a ring-like structure connected at both ends, and can store large volumes of ions throughout its toroid trap structure. This configuration can be used to provide miniaturized ion trap mass analyzers. Further, because the ions are all stored in a common field in a toroidal ion trap, detection can be simplified as ions are ejected from the field together, e.g., as opposed to a configuration that requires an array of detectors.

However, reference mass spectral data used for analyte identification is typically compiled using classical detection techniques, such as QMS detection. When a non-classical technique is employed, such as detection performed using a toroidal ion trap, there can be discrepancies between experimental and reference mass spectral data. This applies to all types of ion traps. For example, a National Institute of Standards and Technology (NIST) database can be used as a reference library for sample identification. Such a database often includes spectra measured primarily on QMS systems. Use of, for instance, a toroidal trap configuration can potentially lead to mass spectral differences when the data is analyzed using a QMS database. These differences can include relative intensity variations, the creation of additional fragments based upon ion chemistry, and so forth.

Techniques are described for identifying unknown samples using a system including a gas chromatograph configured to determine experimental chromatographic data including retention times associated with samples, and a mass spectrometer configured to determine experimental mass spectral data associated with the samples. The mass spectrometer can include a quadrupole field ion trap that uses a non-classical detection technique. The system determines a retention index for an unknown sample based upon retention times for a calibration sample and the unknown sample, and identifies reference mass spectral data using the retention index as a fundamental part of the pre-search algorithm. The reference mass spectral data can include spectra measured using a classical detection technique. The system can compare the experimental mass spectral data to the reference mass spectral data using one or more comparison metrics, such as a percent fragment match and/or a variance match. A score can be determined to identify the unknown sample using one or more of the metrics.

Referring to <FIG>, a gas chromatography-mass spectroscopy (GC-MS) system <NUM> includes GC-MS instrumentation <NUM>, such as a gas chromatograph <NUM> and a mass spectrometer <NUM>. The gas chromatograph <NUM> is configured to determine experimental chromatographic data for a sample injection (e.g., for a calibration sample, an unknown sample, and so forth), where the experimental chromatographic data comprises a retention time associated with each compound in the sample. The mass spectrometer <NUM> is configured to determine experimental mass spectral data for the sample injection, where the experimental mass spectral data comprises a mass spectrum associated with each compound in the sample. The mass spectrometer <NUM> employs a non-classical detection technique. The mass spectrometer <NUM> uses a toroidal ion trap <NUM> for sample detection. In implementations, a controller <NUM> is operatively coupled with the gas chromatograph <NUM> and the mass spectrometer <NUM> for receiving the experimental chromatographic data and the experimental mass spectral data from the GC-MS instrumentation <NUM>. The GC-MS system <NUM> is configured to identify samples introduced to the GC-MS instrumentation <NUM> (e.g., using the controller <NUM>).

In implementations, a GC-MS system <NUM>, including some or all of its components, can operate under computer control. For example, a processor can be included with or in a GC-MS system <NUM> to control the components and functions of GC-MS systems <NUM> described herein using software, firmware, hardware (e.g., fixed logic circuitry), manual processing, or a combination thereof. The terms "controller" "functionality," "service," and "logic" as used herein generally represent software, firmware, hardware, or a combination of software, firmware, or hardware in conjunction with controlling the GC-MS systems <NUM>. In the case of a software implementation, the module, functionality, or logic represents program code that performs specified tasks when executed on a processor (e.g., CPU or CPUs). The program code may be stored in one or more computer-readable memory devices (e.g., internal memory and/or one or more tangible media), and so on. The structures, functions, approaches, and techniques described herein can be implemented on a variety of commercial computing platforms having a variety of processors.

As illustrated in <FIG>, the GC-MS instrumentation <NUM> may be coupled with the controller <NUM> for controlling the gas chromatograph <NUM> and the mass spectrometer <NUM>. The controller <NUM> may include a processor <NUM>, a communications interface <NUM>, and a memory <NUM>. The processor <NUM> provides processing functionality for the controller <NUM> and may include any number of processors, micro-controllers, or other processing systems, and resident or external memory for storing data and other information accessed or generated by the controller <NUM>. The processor <NUM> may execute one or more software programs that implement techniques described herein. The processor <NUM> is not limited by the materials from which it is formed or the processing mechanisms employed therein and, as such, may be implemented via semiconductor(s) and/or transistors (e.g., using electronic integrated circuit (IC) components), and so forth.

The communications interface <NUM> is operatively configured to communicate with components of the GC-MS system <NUM>. For example, the communications interface <NUM> can be configured to transmit data for storage in the GC-MS system <NUM>, retrieve data from storage in the GC-MS system <NUM>, and so forth. The communications interface <NUM> is also communicatively coupled with the processor <NUM> to facilitate data transfer between components of the GC-MS system <NUM> and the processor <NUM> (e.g., for communicating inputs from the GC-MS instrumentation <NUM> to the processor <NUM>). The communications interface <NUM> and/or the processor <NUM> can also be configured to communicate with a variety of different networks, including, but not necessarily limited to: the Internet, a cellular telephone network, a local area network (LAN), a wide area network (WAN), a wireless network, a public telephone network, an intranet, and so on. In <FIG>, the communications interface <NUM> is illustrated as a component of the GC-MS system <NUM>. However, one or more components of the communications interface <NUM> can be implemented as external components communicatively coupled to the GC-MS system <NUM> via a wired and/or wireless connection. The GC-MS system <NUM> can also comprise and/or connect to one or more input/output (I/O) devices (e.g., via the communications interface <NUM>), including, but not necessarily limited to: a display, a mouse, a touchpad, a keyboard, and so on.

The memory <NUM> is an example of tangible computer-readable storage medium that provides storage functionality to store various data associated with operation of the controller <NUM>, such as software programs and/or code segments, or other data to instruct the processor <NUM> and possibly other components of the controller <NUM> to perform the steps described herein. Thus, the memory <NUM> can store data, such as a program of instructions for operating the GC-MS system <NUM> (including its components), spectral data, and so on. The memory <NUM> can include an electronic database <NUM> comprising reference mass spectral data for identifying samples provided to the GC-MS system <NUM>. In a particular instance, the electronic database <NUM> can be a NIST database including spectra measured on a QMS system. Although a single memory <NUM> is shown, a wide variety of types and combinations of memory (e.g., tangible, non-transitory memory) may be employed. The memory <NUM> may be integral with the processor <NUM>, may comprise stand-alone memory, or may be a combination of both.

The memory <NUM> may include, but is not necessarily limited to: removable and non-removable memory components, such as random access memory (RAM), read-only memory (ROM), flash memory (e.g., a secure digital (SD) memory card, a mini-SD memory card, and/or a micro-SD memory card), magnetic memory, optical memory, universal serial bus (USB) memory devices, hard disk memory, external memory, and so forth. In implementations, the GC-MS instrumentation <NUM> and/or memory <NUM> may include removable integrated circuit card (ICC) memory, such as memory provided by a subscriber identity module (SIM) card, a universal subscriber identity module (USIM) card, a universal integrated circuit card (VICC), and so on.

In implementations, a variety of analytical devices can make use of the structures, techniques, approaches, and so on described herein. Thus, although GC-MS systems <NUM> are described herein, a variety of analytical instruments may make use of the described techniques, approaches, structures, and so on. These devices may be configured with limited functionality (e.g., thin devices) or with robust functionality (e.g., thick devices). Thus, a device's functionality may relate to the device's software or hardware resources, e.g., processing power, memory (e.g., data storage capability), analytical ability, and so on.

The following discussion describes procedures that may be implemented utilizing the previously described GC-MS system <NUM> components, techniques, approaches, and modules. Aspects of each of the procedures may be implemented in hardware, software, or a combination thereof. The procedures are shown as a set of blocks that specify operations performed by one or more devices (e.g., GC-MS instrumentation, a computer system controlling GC-MS instrumentation or GC-MS components) and are not necessarily limited to the order shown for performing the operations by the respective blocks. In portions of the following discussion, reference will be made to the GC-MS systems <NUM> of <FIG>.

<FIG> depicts a procedure <NUM> in an example implementation in which experimental chromatographic data is received for a calibration sample (Block <NUM>). For example, with reference to <FIG>, the gas chromatograph <NUM> can be used to determine experimental chromatographic data including a retention time for a calibration sample injection, and the experimental chromatographic data can be supplied to the controller <NUM>. Then, experimental chromatographic data is received for an unknown sample (Block <NUM>). For instance, with continuing reference to <FIG>, the gas chromatograph <NUM> can be used to determine experimental chromatographic data including a retention time for an unknown sample injection, and the experimental chromatographic data can be supplied to the controller <NUM>. Next, a retention index is determined for the unknown sample (Block <NUM>). For example, with continuing reference to <FIG>, the controller <NUM> can be configured to determine a retention index for the unknown sample based upon the retention time for the calibration sample and the retention time for the unknown sample. In implementations, the retention index can be one or more retention times and/or a range of retention times.

Then, a subset of database entries in an electronic database comprising reference mass spectral data is identified using the retention index (Block <NUM>). For instance, with continuing reference to <FIG>, the controller <NUM> can identify one or more database entries in the electronic database <NUM> stored in the memory <NUM> of the controller <NUM> based upon the retention index for the unknown sample. In a particular instance, the electronic database <NUM> is a NIST database in which each library entry comprises a retention index and an allowed deviation. The retention indices in the NIST database can be compared to the retention index for the unknown sample (e.g., using a retention index window comprising multiple retention times and/or a range of retention times) and used to filter the database, reducing the number of potential candidates from the electronic database <NUM>. In some instances, the retention index for the unknown sample can be retained (e.g., in the memory <NUM>) and used to correlate a probability to how well the retention indices selected from the electronic database <NUM> correlate to the retention index determined for the unknown sample, e.g., after sample identification.

Next, experimental mass spectral data is received for the unknown sample (Block <NUM>). For example, with continuing reference to <FIG>, the mass spectrometer <NUM> can be used to determine experimental mass spectral data including a mass spectrum for the unknown sample, and the experimental mass spectral data can be supplied to the controller <NUM>. Then, the experimental mass spectral data is compared to the subset of database entries to identify the unknown sample (Block <NUM>). For instance, with continuing reference to <FIG>, one or more database entries in the electronic database <NUM> stored in the memory <NUM> can be compared to the experimental mass spectral data to identify the unknown sample (e.g., as described below with reference to Blocks <NUM> through <NUM> of <FIG>). In embodiments of the disclosure, the controller <NUM> is configured to initiate an alert when the identification of the unknown sample is associated with a sample of interest (Block <NUM>). For example, with continuing reference to <FIG>, the controller <NUM> is configured to detect the presence of explosive and/or chemical agents and provide a warning or indication of such agents. An alert can be initiated at an indicator <NUM>, an alarm <NUM>, the indicator <NUM> and the alarm <NUM>, and so forth. In some embodiments, the indicator <NUM> comprises an electronic display, one or more indicator lights, and so on. Further, the alarm <NUM> can furnish an audible alarm, a visual alarm (e.g., an indicator light), a tactile alarm, a signal transmitted to a remote monitoring authority, and so forth. However, these alerts are provided by way of example only and are not meant to limit the present disclosure. In other embodiments, different or additional alerts are initiated by the controller <NUM> (e.g., using the indicator <NUM>, the alarm <NUM>, or another alert mechanism). For example, an alert is initiated in the form of an electronic message, such as an email message, a text message, and so on.

Referring now to <FIG>, an example procedure <NUM> is described in which a mass spectrum is received for an unknown sample (Block <NUM>). The mass spectrum can be received from a mass spectrometer, such as the mass spectrometer <NUM> of <FIG> (e.g., as described above with reference to Block <NUM> of <FIG>). The mass spectrum may be represented using a peak table (e.g., as illustrated by the "Unknown Spectrum" graph of <FIG>). In implementations, the mass spectrum includes a list of mass fragments and their relative abundances.

Then, the mass spectrum for the unknown sample is compared to an electronic database entry including mass spectral data (Block <NUM>). For example, with continuing reference to <FIG>, an initial comparison can be performed for the unknown sample using one or more of the database entries in the electronic database <NUM> stored in the memory <NUM> (e.g., as identified above with reference to Block <NUM> of <FIG> and illustrated by the "Library Spectrum" graph of <FIG>). In a particular instance, an initial comparison is performed by counting the number of mass fragments common to both the unknown sample and the electronic database entry. A temporary score can be calculated using the mean percentage of mass fragments matched as follows: <MAT> where ncommon represents the number of common mass fragments, nunk represents the total number of mass fragments in the unknown mass spectrum, and nlib represents the total number of mass fragments in the electronic database spectrum.

In some instances, mass fragments below a mass of forty-three (<NUM>) and above a mass of five hundred (<NUM>) are excluded from the comparison (e.g., when these mass fragments are not measured by the mass spectrometer). Further, mass fragments having an intensity of less than five percent (<NUM>%) of the base peak can be ignored (e.g., to account for a signal-to-noise ratio). As described herein, elimination of mass fragments will not penalize the hit quality when the mass fragments are absent from the electronic database spectrum. In this particular implementation, an electronic database entry must have at least one mass fragment in common with the unknown spectrum, otherwise the hit score fragMatch will be zero (<NUM>).

However, comparing mass fragments between a mass of forty-three (<NUM>) and above a mass of five hundred (<NUM>) is provided by way of example only and is not meant to be restrictive of the present disclosure. Thus, in other instances, more or fewer mass fragments can be compared. For example, in a particular instance, one peak, two peaks, or more than two peaks (e.g., three peaks) can be used to filter or pre-filter the electronic database entries used for the comparison. In some instances, the peaks chosen for the comparison can be based upon the heaviest mass fragments. In other instances, the peaks chosen for the comparison can be the most intense peaks. Further, one or more analytic techniques, such as a rule of transient data fusion, can be used to determine which peaks may be most unique and/or best suited for the comparison.

Since a toroidal ion trap is used, ion chemistry is observed in mass spectra from the trap system. The most common types of ion chemistry are the formation of an M+<NUM> mass fragment, a <NUM> ,<NUM>+<NUM> mass fragment in dimers, and the combination of other mass fragments with a neutral molecule. An M+<NUM> mass fragment is equal to the molecular weight of the chemical plus a value of one (<NUM>). As illustrated in <FIG>, the occurrence of ion chemistry can explain some of the differences seen in the mass spectra for <NUM>-ethoxy-ethanol. For instance, knowing <NUM>-ethoxy-ethanol has a molecular weight of ninety (<NUM>), two of the higher mass fragments present in the unknown can be explained. The mass fragment at mass ninety-one (<NUM>) represents the M+<NUM> mass fragment. The mass fragment at mass one hundred and seventeen (<NUM>) results from a combination of the neutral mass ninety (<NUM>) with the lower ion at mass twenty-seven (<NUM>).

A percent fragment match can be determined between the mass spectrum for the unknown sample and the electronic database entry (Block <NUM>). For example, after the initial search for common mass fragments, the mass spectrum for the unknown sample is compared to the electronic database entry by searching for ion chemistry. In implementations, the term ncommon in the above equation can be replaced by the terms nfoundUnk and nfoundLib as follows: <MAT> In this manner, using the known molecular weight for a specific electronic database entry, a search for the presence of ion chemistry mass fragments such as M+<NUM> and <NUM>+<NUM> in the unknown spectrum is performed. If detected, the number of found unknown mass fragments, nfoundUnk, will increase by a value of one (<NUM>) (or two (<NUM>) when both are present). If the electronic database spectrum contains a mass fragment at M, the entry's molecular weight, it will also be considered a match, but only if M+<NUM> or <NUM>+<NUM> is found in the unknown mass spectrum.

The molecular weight of the electronic database entry is then added to all of the electronic database mass fragments (including fragments with mass less than the forty-three (<NUM>) cutoff in some implementations). These adjusted electronic database mass fragments are then used for comparison in a final pass through the unknown mass fragments. If there are any new matches, nfoundUnk will be increased. It should be noted that in some instances (e.g., when it is not necessarily desirable to count ion chemistry hits as true matches), the increment could be less than a value of one (<NUM>). The term nfoundLib may also be increased, e.g., if the original mass fragment met the initial restrictions. Otherwise, it may not be accounted for in the denominator, nlib. With reference to <FIG>, the percent mass fragment factor for <NUM>-ethoxy-ethanol can be calculated as follows: <MAT> where nfoundUnk is equal to six (<NUM>), nUnk is equal to nine (<NUM>), nfoundLib is equal to four (<NUM>), and nLib is equal to four (<NUM>).

In addition to the mean percent mass fragment match after allowing for ion chemistry, two other criteria can be combined to generate a final hit score. Because heavier mass fragments are more distinctive, spectra can be scaled to emphasize the larger mass fragments in the calculations of the next two factors. The intensities can be scaled based upon the following equation: <MAT> where x(i) represents the original intensity of the ith mass fragment, m(i) is the mass-to-charge ratio of the ith mass fragment, and w(i) is the scaled intensity for the ith mass fragment. The mass spectra of the remaining identified electronic database candidates can also be scaled.

A variance match can be determined between the mass spectrum for the unknown sample and the electronic database entry (Block <NUM>). For instance, a second metric can be determined in a manner similar to the percent mass fragment match described above, but counting variance instead of mass fragments. Different from the percent mass fragment match, the variance match can be a product of the two spectra's contributions, given as follows: <MAT> where the terms vartotUnk and varioiLib represent the total variance of the mass-scaled unknown spectrum and electronic database spectrum, respectively, calculated as follows: <MAT> where w(i) squared represents the variance contribution of a single mass fragment. The found variance terms, varfoundUnk and varfoundLib, are determined as follows: <MAT> where a(i) is equal to a value of one (<NUM>) to indicate a matched mass fragment and equal to a value of zero (<NUM>) if the ith mass fragment is not found. It should be noted that in some instances, it may not necessarily be desirable to weight ion chemistry matches as much as true matches, and, as such, an a(i) value between zero (<NUM>) and one (<NUM>) can be used.

A dot product can be determined between the mass spectrum for the unknown sample and the electronic database entry (Block <NUM>). For instance, a third metric can be a weighted dot product between the scaled unknown mass spectrum and the electronic database spectra. The dot product can be calculated as follows: <MAT> where the denominator normalizes each spectrum to unit vector length. As a result, the dot product is restricted between a value of zero (<NUM>) and a value of one (<NUM>). In this instance, a dot product having a value of one (<NUM>) indicates the spectra are identical, while a dot product having a value of zero (<NUM>) indicates the spectra share no similarity. As seen, the numerator will increase when the ith mass fragment is present in both the unknown mass spectrum and the electronic database entry. In some instances, an allowance for ion chemistry can be incorporated into the algorithm (e.g., as previously described).

Then, a score can be determined using the comparison of the unknown mass spectrum to the electronic database entry (Block <NUM>). For example, a final hit score can be determined using a metric determined as previously described, a combination of two or more metrics, and/or a weighted combination of two or more metrics. In a particular instance, a score can be calculated using the percent mass fragment match (fragMatch), the variance match (varMatch), and the weighted dot product (wDP) as follows: <MAT> where the sum of the coefficients (a, b, c) is equal to a value of (<NUM>), which restricts the range of score values between values of zero (<NUM>) and one (<NUM>). In this particular instance, a higher score equates to a better match between the unknown sample and an electronic database entry, and the sample can be identified accordingly.

Claim 1:
A gas chromatography-mass spectroscopy (GC-MS) system (<NUM>) for identifying an unknown sample, the GC-MS system (<NUM>) comprising:
a gas chromatograph (<NUM>) configured to determine experimental chromatographic data for a calibration sample and an unknown sample, the experimental chromatographic data comprising a first retention time associated with the calibration sample and a second retention time associated with the unknown sample;
an ion trap mass spectrometer (<NUM>) configured to determine experimental mass spectral data for the unknown sample, wherein the ion trap mass spectrometer (<NUM>) comprises a toroidal ion trap (<NUM>), the experimental mass spectral data comprising a mass spectrum associated with the unknown sample;
a processor (<NUM>) communicatively coupled with the gas chromatograph (<NUM>) and the mass spectrometer (<NUM>) for receiving the experimental chromatographic data and the experimental mass spectral data; and
a memory (<NUM>) having computer executable instructions stored thereon, the computer executable instructions configured for execution by the processor (<NUM>) to:
determine a retention index for the unknown sample based upon the first retention time associated with the calibration sample and the second retention time associated with the unknown sample,
identify, using the retention index determined for the unknown sample, a subset of a plurality of database entries included in an electronic database (<NUM>) comprising reference mass spectral data measured using a quadrupole mass spectrometer,
compare the experimental mass spectral data to the subset of the plurality of database entries to identify the unknown sample; and
initiate an alert when the identification of the unknown sample is associated with a sample of interest.