Systems and methods for injection monitoring and diagnostics for gas chromatography

Abstract

To provide systems and methods for monitoring injections into an inlet of a gas chromatography (GC) system, and diagnosing unsuccessful injections.SOLUTION: A system for gas chromatography includes an inlet configured to receive a sample by injection, a column having a stationary phase, a flow control system, and an injection monitoring system. The flow control system is configured to regulate, based on a flow control parameter, a flow of a mobile phase through the inlet and the column. The injection monitoring system is configured to obtain flow control data representative of a measurement of the flow control parameter over time during a time period encompassing an injection of the sample into the inlet; determine, based on the flow control data, that the injection was unsuccessful; and perform, based on the determination that the injection was unsuccessful, a mitigation operation to mitigate the unsuccessful injection of the sample.SELECTED DRAWING: Figure 3

Description

Technical Field

[0001] Gas chromatography (GC) is an analytical technique used to separate and analyze (e.g., detect, identify, and / or quantify) the chemical components of a sample mixture. GC is performed by injecting the sample into its inlet by inserting a syringe needle through the septum of the inlet (also called the injector) of the gas chromatograph. The injected sample is vaporized at the inlet, and the mobile phase (typically called the carrier gas) flows through the inlet and carries the vaporized sample through a column (an elongated tube) having a stationary phase. The mobile phase may be an inert gas or non-reactive gas such as helium, argon, nitrogen, hydrogen, or argon / methane. The components of the sample are differentially retained in the column by the stationary phase based on the various chemical and physical properties of the components and elute from the column at different times. The eluted components are carried by the mobile phase to a detector, and the detector can detect the components and generate a signal representing the detected components.

[0002] When a sample is injected into the inlet, various problems can occur. For example, air bubbles may be drawn into the syringe when the sample is aspirated from the vial, and as a result, the air bubbles are injected into the inlet. In some cases, the level of the sample in the vial may be below the tip of the syringe needle, so that the sample is not drawn into the syringe and, as a result, the sample is not injected into the inlet. In other cases, when the syringe needle punctures the septum of the inlet, the syringe needle may become clogged, thus preventing the sample from being injected into the inlet. In some cases, the syringe needle and / or syringe plunger may be bent, thus preventing complete injection of the sample.

[0003] However, these problems may not be obvious to the user when they occur, or may not be detectable or diagnosable by the user. For example, when a sample is injected and passes through the stationary phase, any problems with injection, such as an insufficient sample volume aspirated by the syringe, injection of air bubbles, and / or injection of less than the total amount of the sample, may no longer be detectable. The low volume of some injections, which can be on the order of a fraction of a milliliter (mL), can make it virtually impossible for the user to detect an incomplete injection. If no signal is detected for the injection, the user may not be able to diagnose the cause or determine whether the problem occurred at the inlet, detector, or some other location (e.g., within the column).

[0004] Conventional techniques for monitoring and detecting incomplete injections include doping the sample with an internal standard and comparing the signal representing the internal standard to the expected signal of the internal standard. However, the use of an internal standard increases the cost, complexity, and time required to perform a GC experiment. While an internal standard can help detect an incomplete injection, its use does not help diagnose the cause of a poor injection. SUMMARY OF THE INVENTION

[0005] The following description presents a simplified summary of one or more aspects of the methods and systems described herein to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, nor is it intended to identify key or critical elements of all aspects or to delineate the scope of any or all aspects. Its sole purpose is to present some concepts of one or more aspects of the methods and systems described herein in a simplified form as a prelude to the more detailed description presented below.

[0006] In some illustrative examples, a system for gas chromatography comprises an inlet configured to receive a sample by injection, a column containing a stationary phase, a flow control system configured to regulate the flow of a mobile phase through the inlet and the column based on flow control parameters, and an injection monitoring system configured to execute a process, the process including obtaining flow control data representing measured values of the flow control parameters over time during a period encompassing the injection of the sample into the inlet, determining based on the flow control data that the injection was unsuccessful, and performing a mitigation operation based on the determination that the injection was unsuccessful.

[0007] In some illustrative examples, an injection monitoring system for a gas chromatography system, when executed by one or more processors, causes a computing device to obtain flow control data from a flow control system included in the gas chromatography system and configured to regulate the flow of a fluid through an inlet of the gas chromatography system based on flow control parameters, the flow control data representing measured values of the flow control parameters over time during a period encompassing the injection of the sample into the inlet, determine based on the flow control data that the injection was unsuccessful, and instruct the gas chromatography system to perform a mitigation operation to mitigate an unsuccessful injection of the sample, the injection monitoring system comprising memory storing executable instructions to execute a process including the above.

[0008] In some exemplary examples, a non-transitory computer-readable medium storing instructions that, when executed, cause at least one processor of a computing device for a gas chromatography system to obtain flow control data from a flow control system configured to adjust the flow of fluid through an inlet of the gas chromatography system based on flow control parameters, wherein the flow control data represents measured values of the flow control parameters over time during a period including injection of a sample into the inlet, and based on the flow control data, determine that the injection was unsuccessful, and based on the determination that the injection was unsuccessful, perform a mitigation operation.

Brief Description of the Drawings

[0009] The accompanying drawings illustrate various embodiments and are a part of this specification. The illustrated embodiments are merely examples and do not limit the scope of the present disclosure. Throughout the drawings, the same or similar reference numerals indicate the same or similar elements.

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DETAILED DESCRIPTION OF THE INVENTION

[0010] A system and method for monitoring an injection into an inlet of a gas chromatography (GC) system and diagnosing an unsuccessful injection are described herein. For example, an injection monitoring system may obtain flow control data from a flow control system configured to regulate the flow of fluid through the inlet of the GC system based on flow control parameters. The flow control data may represent measurements of flow control parameters over time during a period encompassing an injection of a sample into the inlet. Based on the flow control data, the injection monitoring system may determine that the injection was unsuccessful. Based on a determination that the injection was not successful, the injection monitoring system may instruct the GC system to perform a mitigation operation to mitigate an unsuccessful injection of the sample.

[0011] In some examples, the flow control parameter is a pulse width modulation (PWM) valve drive signal for a valve of the flow control system, a pressure signal output by a pressure sensor of the flow control system, or a flow rate signal output by a flow rate sensor of the flow control system.

[0012] The systems and methods described herein improve GC systems and GC methods by detecting an unsuccessful injection when it occurs, even when a user is unable to otherwise identify any problems with the injection. The systems and methods described herein also improve GC systems and GC methods by reducing unsuccessful injections, such as by discarding data obtained based on other unsuccessful injections, performing a diagnostic process to identify the cause of an unsuccessful injection, and / or providing a warning. In some examples, the systems and methods described herein can quantify the volume of sample injected for each injection, including partial injections, and use that information to appropriately scale the GC data obtained (if necessary). The systems and methods described herein eliminate the need to use costly, complex, and time-consuming internal standards to monitor injection quality. The systems and methods described herein can be implemented across a wide range of instruments and experimental conditions with little or no additional effort on the part of the user. The systems and methods described herein can also be implemented on legacy GC systems without the need to install new hardware.

[0013] Reference is now made to the figures to describe various embodiments in more detail. The systems and methods described herein may provide one or more of the advantages described above, and / or various additional and / or alternative advantages made apparent herein.

[0014] Next, with reference to an exemplary gas chromatography (GC) system, exemplary systems and methods for monitoring an injection, diagnosing an unsuccessful injection, and reducing an unsuccessful injection will be described. The GC system described is exemplary and non-limiting.

[0015] Figure 1 shows a functional diagram of an exemplary GC system 100 capable of split mode and splitless mode injection. The GC system 100 includes an inlet 102, a column 104, an input path 106, a column path 108, a detector 109, a split path 110, a purge path 112, a flow control system 115, and a GC controller 117. The GC system 100 can include additional or alternative components useful in certain implementations, such as a charcoal trap (not shown) for capturing contaminants, an oven, and / or an autosampler.

[0016] The inlet 102 (which may also be referred to as an injector) includes a septum 114 that seals over the inlet 102. The septum 114 may be formed of a self-sealing material such as silicone. Alternatively, the septum 114 may be a mechanical spring-assisted device that opens and closes as a needle is inserted. The inlet 102 receives a sample by injection when a syringe needle (not shown) pierces or opens the septum 114 and injects the sample from the syringe into the inlet 102. The sample can include one or more analytes of interest dissolved in a solvent. Exemplary solvents include, but are not limited to, methanol, acetone, pentane, hexane, isooctane, and the like. The inlet 102 receives a mobile phase (e.g., a carrier gas) via the input path 106. The sample is mixed with the mobile phase at the inlet 102, and a portion of the fluid mixture exits the inlet 102 via the column path 108 and passes through the column 104. The column 104 includes a stationary phase that may be solid or liquid. The column 104 separates the components within the injected sample based on their interaction with the stationary phase. The detector 109 is coupled to the output end of the column 104 and detects the components of the sample as they elute from the column 104. In some examples, the detector 109 is a gas chromatography detector such as a flame ionization detector or a thermal conductivity detector. In other examples, the detector 109 is a mass spectrometer. Data generated by the detector 109 can be output to the GC controller 117.

[0017] A small portion of the fluid exits the inlet 102 via a purge path 112. The purge path 112 provides a flow path for expelling a portion of the fluid from the inlet 102 to purge contaminants that may be introduced into the inlet 102 by the septum 114 when the septum 114 is pierced by a syringe needle. The purge path 112 expels the fluid before any contaminants from the septum 114 mix with the injected sample.

[0018] The inlet 102 may be a split / splitless (SSL) inlet or a programmable temperature vaporizer. The PTV inlet may be a polarized temperature vaporizing (PTV) inlet. The PTV inlet may also be operable in split mode and / or splitless mode. In split mode, a portion of the fluid exits the inlet 102 via split path 110. The split path 110 provides a flow path for the fluid to exit the inlet 102. The ratio of the flow rate of the fluid exiting the inlet 102 via split path 110 to the flow rate of the fluid exiting the inlet 102 via the column 104 is referred to as the "split ratio." Any suitable split ratio may be used, such as, but not limited to, 10:1, 20:1, 50:1, 100:1, etc. In splitless mode, no fluid exits the inlet 102 via split path 110. In both split and splitless modes, the flow rate of the carrier gas entering the inlet 102 is equal to the sum of the flow rates of the fluid exiting the inlet 102 (assuming the volume of the injected sample is negligible).

[0019] The GC controller 117 is communicably coupled to the GC system 100 and configured to control the operation of its mass spectrometer. The GC controller 117 can include any suitable hardware (e.g., a processor, a circuit, etc.) and / or software configured to control the operation of various components of the GC system 100 (e.g., the detector 109, the flow control system 115, the oven, the autosampler, etc.) and / or interface with those components. The GC controller 117 can receive the data output by the detector 109, process the data (e.g., generate a chromatogram, generate a mass spectrum, analyze the data, transmit the data to another computing system, etc.), and / or store the data (e.g., in a memory). The GC controller 117 can also include and / or provide a user interface configured to enable interaction between the user and the GC controller 117. The user can interact with the GC controller 117 via a user interface for tactile, visual, auditory, and / or other sensory communication. For example, the user interface can include a display device (e.g., a liquid crystal display (LCD) display screen, a touch screen, etc.) for displaying information (e.g., a chromatogram, a mass spectrum, a notification, etc.) to the user. The user interface can also include an input device (e.g., a keyboard, a mouse, a touch screen device, etc.) that enables the user to provide input to the GC controller 117. In other examples, the display device and / or the input device can be separate from the GC controller 117 but communicably coupled. For example, the display device and the input device can be included in a computer (e.g., a desktop computer, a laptop computer, etc.) communicably connected to the GC controller 117 via a wired connection (e.g., by one or more cables) and / or a wireless connection.FIG. 1 shows the GC controller 117 being included within the GC system 100, although the GC controller 117 can alternatively be implemented completely or partially separately from the GC system 100 by a computing device or the like communicatively coupled to the GC system 100 via a wired connection (e.g., a cable) and / or a network (e.g., a local area network, a wireless network (e.g., Wi-Fi), a wide area network, the Internet, a cellular data network, etc.).

[0020] The flow control system 115 is configured to regulate the flow of fluid entering and exiting the inlet 102. The flow control system 115 includes a set of valves, a set of pressure sensors, and a flow controller. The flow control system 115 may include any additional or alternative components so as to be suitable for a particular implementation. The valve 116 on the input path 106 regulates the flow of carrier gas to the inlet 102, the valve 118 on the split path 110 regulates the flow of fluid exiting the inlet 102 via the split path 110, and the valve 120 on the purge path 112 regulates the flow of fluid exiting the inlet 102 via the purge path 112. The valves 116, 118, and 120 can include any suitable valves such as proportional valves. The pressure sensor 122 downstream of the valve 116 measures the head pressure of the inlet 102 / column 104, the pressure sensor 124 upstream of the valve 118 measures the pressure at the valve 118, and the pressure sensor 126 upstream of the valve 120 measures the pressure at the valve 120. The pressure sensors 122, 124, and 126 can sample the inlet pressure at any suitable sampling rate. In some examples, this sampling rate ranges from 0.5 Hz to 500 Hz or 1 kHz. In further examples, the sampling rate ranges from 1 Hz to 100 Hz.

[0021] The flow controller 128 is configured to control (e.g., open and close) the valves 116, 118, and / or 120 to regulate the flow of fluid entering and exiting the inlet 102. For example, in the flow control mode, the flow controller 128 can regulate the flow of fluid through any one or more of the input path 106, the split path 110, and / or the purge path 112 to maintain a target flow rate through the column path 108 and the column 104 and / or to maintain a target split ratio. In the pressure control mode, the flow controller 128 can regulate the flow of fluid through any one or more of the input path 106, the split path 110, and / or the purge path 112 to maintain a target pressure within the inlet 102.

[0022] In some examples, the flow controller 128 uses feedback control to regulate the flow of fluid entering and exiting the inlet 102. For this purpose, the flow controller 128 is communicatively coupled to the valves 116, 118, and 120 and the pressure sensors 122, 124, and 126. The flow controller 128 receives a pressure signal output by any one or more of the pressure sensors 122, 124, and 126 and generates a valve drive signal for one or more of the valves 116, 118, and 120 based on the pressure signal. In some examples, this valve drive signal is a pulse width modulation (PWM) valve drive signal (e.g., voltage, current, or digital value) that specifies the on / off duty cycle of the valve. For example, a PWM valve drive signal with a 50% duty cycle is on for half the time and off for half the time, while a PWM valve drive signal with a 30% duty cycle is on for 30% of the time and off for 70% of the time. The flow controller 128 outputs a valve drive signal to the valves 116, 118, and / or 120 to regulate the flow of fluid through the input path 106, the column path 108, the split path 110, and / or the purge path 112. Since the conductance of the column 104 is known or can be determined based on the characteristics and features of the device, the measured pressure signal can be easily correlated to the flow rate.

[0023] In some examples, the GC system 100 operates using forward pressure regulation where the flow controller 128 generates a PWM valve drive signal for any one or more of the valves 116, 118, and 120 based on a pressure signal output by any one or more of the pressure sensors 122, 124, and 126. In some examples, the inlet pressure is forward pressure regulated while the split path 110 and / or the purge path 112 are either reverse pressure regulated or forward pressure regulated.

[0024] The flow controller 128 can include any suitable hardware (e.g., a processor, circuitry, etc.) and / or software configured to control and / or interface with the valves 116, 118, and 120 and the pressure sensors 122, 124, and 126. FIG. 1 shows the flow controller 128 as being separate from the GC controller 117, but the flow controller 128 can alternatively be implemented in whole or in part by the GC controller 117.

[0025] It will be recognized that the GC system 100 is merely exemplary and may be modified to suit a particular implementation. For example, the GC system 100 can include a flow sensor that measures the flow rate of the input path 106, the column path 108, the split path 110, and / or the purge path 112 in addition to or instead of the pressure sensors 122, 124, and / or 126. The flow sensor can be any suitable type of sensor configured to measure flow rate, such as, for example, a mass flow sensor or a combination of a pressure sensor and a flow restrictor. In some examples, a flow sensor is used instead of a pressure sensor.

[0026] In some examples, the flow control system 115 does not adjust or change the operation of the valves 116, 118, and 120, but rather maintains the duty cycle of the valve drive signal even if a change in pressure is detected. The pressure may be regulated, for example, using reverse pressure regulation or forward pressure regulation.

[0027] When the sample is injected into inlet 102, the sample vaporizes under the high temperature at the inlet. The increased vapor volume sample instantaneously increases the pressure within inlet 102. The flow control system 115 detects the increased pressure (or change in flow rate) and responds to the increased pressure by adjusting the PWM valve drive signals of valves 116, 118, and / or 120 to decrease the pressure within inlet 102.

[0028] Figure 2 shows an exemplary graph 200 of the PWM valve drive signal (e.g., for valve 116) over time during a period encompassing the injection of the sample into inlet 102. The PWM valve drive signal can be extracted from raw time data acquired or generated by the flow control system 115 (e.g., by valves 116, 118, and / or 120, by pressure sensors 122, 124, and / or 126, and / or by flow controller 128). In the example of Figure 2, the PWM valve drive signal is a voltage signal supplied to the valve (e.g., valve 116). However, in other examples, the PWM valve drive signal may be a current signal or a digital signal representing the voltage or current supplied to the valve. Curve 202 shows the average voltage (V) of the PWM valve drive signal as a function of time during the period. As can be seen, the PWM valve drive signal is in a steady state prior to time t0, indicating that the pressure within inlet 102 is in a steady state. A certain volume of sample is injected into inlet 102 at time t0, increasing the pressure within inlet 102. Curve 202 includes a perturbation 204 showing the response of flow controller 128 to return the pressure within inlet 102 to the steady state. As indicated by the waveform of perturbation 204, the average PWM valve drive signal first decreases (e.g., closes the valve and decreases the pressure within inlet 102). The feedback control of the PWM valve drive signal continues until the system returns to the steady state at time t1 (about 85 seconds after injection at time t0). The attenuation of the flow control system 115 may be adjusted to change the time to return to the steady state.

[0029] Although not shown, measurements of the inlet pressure over time (such as those measured by one or more pressure sensors such as pressure sensors 122, 124, and / or 126) during the period including injection can also have a similar waveform with perturbations, similar to the case of FIG. 2. Similarly, measurements of the flow rate over time (such as those measured by one or more flow sensors) during the period including injection can also have a similar waveform with perturbations, similar to the case of FIG. 2. However, the waveform of the measured inlet pressure or flow rate is likely to be perturbed in the opposite direction to the PWM valve drive signal. For example, the measured pressure or flow measurement increases when the PWM valve drive signal decreases, and vice versa.

[0030] The PWM valve drive signal, inlet pressure, and flow rate are flow control parameters that are extracted from the raw time data and used alone or in combination, as described in more detail below, to detect and / or diagnose unsuccessful injections. Measurements of the flow control parameters over time during the period including injection can be characterized by one or more characterization metrics. Exemplary characterization metrics can include, but are not limited to, the following. a. The baseline (e.g., steady state) measurement of the flow control parameter (e.g., about 7V in FIG. 2), b. The maximum amplitude of the perturbation waveform measured as the difference between the maximum value of the flow control parameter and the baseline (e.g., the maximum change in the valve drive signal duty cycle or the maximum change in the PWM valve drive signal, the maximum change in the inlet pressure, the maximum change in the flow rate through the input path 106, etc.), c. The integrated change of the perturbation waveform calculated as the sum of the differences between each value and the baseline (e.g., the integrated change of the PWM valve drive signal during the period, the integrated inlet pressure during the period, or the integrated flow rate during the period), d. The absolute value of the integrated change of the perturbation waveform calculated as the sum of the differences between each value and the baseline (e.g., the absolute value of the integrated change of the PWM valve drive signal during the period, the absolute value of the integrated inlet pressure during the period, or the absolute value of the integrated flow rate during the period), e. The root mean square (RMS) error of the perturbation waveform, calculated as the square root of the sum of the squares of the differences between each value and the baseline. f. The duration of the perturbation waveform, calculated as the difference in time until a steady state is reached again after sample injection. g. The period of oscillation of the perturbation waveform, and h. The sign of the first peak of the perturbation waveform (e.g., positive or negative) (e.g., the initial direction of the perturbation waveform).

[0031] It will be appreciated that other characterization metrics may be extracted from the raw time data to suit a particular implementation. Any one or more of the characterization metrics may be used alone or in combination by the injection monitoring system to detect unsuccessful injections and, in some cases, diagnose the cause of unsuccessful injections.

[0032] FIG. 3 shows a functional diagram of an exemplary injection monitoring system 300 (“system 300”). System 300 may be implemented in whole or in part by GC system 100 (e.g., by GC controller 117, by flow controller 128, and / or by some other computing system included in GC system 100). Alternatively, system 300 may be implemented wholly or partly separately from GC system 100 (e.g., a remote computing device, system, and / or server is separate from but communicatively coupled to GC controller 117 or flow controller 128).

[0033] System 300 can include, but is not limited to, a memory 302 and a processor 304 that are selectively and communicatively coupled to each other. The memory 302 and the processor 304 can each include or be implemented by hardware and / or software components (e.g., a processor, a memory, a communication interface, instructions stored in the memory for execution by the processor, etc.). In some examples, the memory 302 and the processor 304 can be distributed among multiple devices and / or multiple locations to be useful in a particular implementation.

[0034] The memory 302 can maintain (e.g., store) executable data used by the processor 304 to perform any of the operations described herein. For example, the memory 302 can store instructions 306 that can be executed by the processor 304 to perform any of the operations described herein. The instructions 306 can be implemented by any suitable application, software, code, and / or other executable data instance.

[0035] The memory 302 can also maintain any data obtained, received, generated, managed, used, and / or transmitted by the processor 304. For example, the memory 302 can maintain and / or store a cross-correlation algorithm, an injection classification model, and / or a vapor volume estimation model as described below.

[0036] Processor 304 may be configured to perform various processing operations described herein (e.g., to execute instructions 306 stored in memory 302 for execution). It will be recognized that the operations and examples described herein are merely illustrative of many different types of operations that may be performed by processor 304. In the description herein, any reference to an operation performed by system 300 may also be understood to be performed by processor 304 of system 300. Further, in the description herein, any operation performed by system 300 may be understood to include system 300 instructing or commanding another system or device to perform the operation.

[0037] FIG. 4 shows an exemplary method 400 for detecting an unsuccessful injection. FIG. 4 shows exemplary operations according to one embodiment, although other embodiments may omit, add, reorder, and / or modify any of the operations shown in FIG. 4.

[0038] In operation 402, system 300 obtains flow control data from a flow control system (e.g., flow control system 115) that regulates the flow of fluid through an inlet (e.g., inlet 102) of a GC system (e.g., GC system 100) based on flow control parameters. The flow control data represents measurements of flow control parameters over time during a period that includes an injection of a sample into the inlet. The flow control data includes raw time data and / or data representing one or more characterization metrics extracted from the raw time data. In some examples, system 300 obtains flow control data in response to an injection of a sample. For example, system 300 may determine that an injection has been performed and, in response to the determination that an injection has been performed, obtain flow control data. System 300 may determine that an injection has been performed in any suitable manner, such as based on injection data transmitted by an autosampler and / or based on an injection schedule. In other examples, system 300 obtains flow control data continuously or periodically, regardless of the execution of any injection.

[0039] In some examples, the flow control parameter is a PWM valve drive signal for a valve of the flow control system (e.g., valves 116, 118, and / or 120). In other examples, the flow control parameter is a pressure signal output by a pressure sensor of the flow control system (e.g., pressure sensors 122, 124, and / or 126). The pressure signal output by the pressure sensor represents the pressure in the inlet 102, the split path 110, or the purge path 112. In another further example, the flow control parameter is a flow rate signal output by a flow sensor of the flow control system (e.g., a flow rate sensor on the input path 106).

[0040] The period of the flow control data can have any suitable duration. In some examples, this period is set in advance (e.g., prior to injection or prior to performing a GC experiment) and is set to include the expected perturbation in the measured value of the flow control parameter, the period before the expected perturbation (e.g., while the measured value of the flow control parameter is in a steady state), and the period after the expected perturbation (e.g., while the measured value of the flow control parameter is in a steady state). The duration of the perturbation can depend on various factors such as the damping of the flow control system (e.g., overdamping, underdamping, critical damping), the time constant for feedback control by the flow control system, the volume of the injection, the volume of the inlet capacitance, and the type of the inlet (e.g., SSL, PTV, etc.). In some examples, the duration of the period ranges from 10 seconds to 120 seconds. In a further example, the duration of the period ranges from 15 seconds to 60 seconds. In some examples, the period includes a preset period before the perturbation (e.g., 2 - 5 seconds) and a period after the perturbation (e.g., 2 - 5 seconds). The flow control data can have any suitable sampling rate such as 0.5 Hz to 1 kHz, 5 Hz to 500 Hz, 10 Hz to 100 Hz, or any other suitable sampling rate.

[0041] In operation 404, system 300 determines that an injection was unsuccessful based on the flow control data. An unsuccessful injection (also referred to herein as an "incomplete" injection) is any injection in which less than a threshold amount of sample is injected into the inlet. The threshold amount may be the total volume of the sample expected or specified to be injected into the inlet or a percentage (e.g., 95%) of the total volume. For example, the method parameters for a GC experiment can specify a particular amount of sample (e.g., 2.0 μL, 1.0 μL, 0.5 μL) to be injected into the inlet. Thus, the autosampler can draw a specified amount of sample from the sample vial into the syringe and then inject the contents of the syringe into the inlet. An injection of less than the specified amount (e.g., due to aspiration from the sample vial of less than the specified amount, injection of air bubbles, needle clogging, needle bending, etc.) is an unsuccessful injection. Unsuccessful injections include partial injections (where at least some sample is injected) and dry injections where no sample is injected.

[0042] In some examples where system 300 continuously or periodically obtains flow control data regardless of any injection, system 300 may determine that an injection was unsuccessful in response to the injection of a sample. For example, system 300 may determine that an injection was performed and, in response to that determination, determine whether the injection was unsuccessful. System 300 may determine that an injection was performed in any of the ways described herein. In a further example, system 300 may determine that an injection was performed based on the flow control data (e.g., based on detection of a perturbation in the measured value of the flow control data).

[0043] System 300 determines that an injection was unsuccessful if the measured value of a flow control parameter (e.g., a perturbation) does not behave as expected in response to the injection. System 300 may determine that the flow control parameter does not behave as expected in a variety of different ways.

[0044] In some examples, system 300 compares the flow control data to reference flow control data and, based on that comparison, determines that the flow control parameters are not behaving as expected by determining that the flow control data has changed from the reference flow control data. The reference flow control data represents an expected measurement of the flow control parameter (e.g., PWM valve drive signal, inlet pressure, or flow rate) over time during a period that includes a reference injection. In some examples, the reference flow control data represents measurements of the flow control parameter during a previous period that includes a previous injection of a sample into the inlet during the current GC experiment. In these examples, a previous successful injection during the same GC experiment can be used as the reference injection for monitoring subsequent injections. In further examples, the reference flow control data represents measurements of the flow control parameter during a plurality of previous periods that include a plurality of previous injections of a sample into the inlet during the current GC experiment. In these examples, the flow control data associated with the plurality of previous successful injections is aggregated (e.g., averaged or otherwise statistically processed) to generate the reference flow control data for the reference injection.

[0045] In other examples, the reference flow control data is generated prior to the current GC experiment based on one or more previous injections. In some examples, the reference flow control data can be updated when subsequent injections (by the current GC experiment and / or any other GC experiment by the same or different GC systems) are successfully performed.

[0046] System 300 can determine, in any suitable manner using any suitable technique, that the flow control data has changed from the reference flow control data. In some examples, System 300 cross-correlates the flow control data associated with the current injection with the reference flow control data. Any suitable cross-correlation algorithm or technique can be used. The flow control data and the reference flow control data are time-aligned with respect to the injection. The cross-correlation can compare any one or more characterization metrics derived or extracted from the flow control data and the reference flow control data. In conventional cross-correlation techniques, time-series data is normalized to a reference value along the y-axis. However, in an example where the characterization metric includes the amplitude of the perturbation, and the amplitude of the perturbation indicates the vapor volume of the injected sample and is thus used in the cross-correlation, System 300 does not normalize the time-series data.

[0047] System 300 can determine, in any suitable manner, such as when the result of the cross-correlation is less than a threshold value, that the flow control data has changed from the reference flow control data. For example, if the correlation coefficient of the cross-correlation ranges from +1.0 (perfect positive correlation) to -1.0 (perfect negative correlation) and 0 does not indicate a linear relationship, the threshold value may be +0.97, +0.95, +0.90, or any other suitable value.

[0048] In addition to, or as an alternative to, using the reference flow control data, the system 300 determines that the flow control parameters do not behave as expected based on an injection classification model trained such that the flow control parameters classify an injection as either successful or unsuccessful. For example, the system 300 may extract data representing one or more characterization metrics from the flow control data (e.g., from measurements of the flow control parameters over time during a period associated with the injection), and apply (e.g., input) the extracted characterization metric data to the injection classification model. In some examples, the characterization metric data applied to the injection classification model represents, as described above, the maximum amplitude of the perturbation waveform, the integral change of the perturbation waveform, and / or the absolute value of the integral change of the perturbation waveform. In some examples, data representing one or more other experimental condition parameters may also be applied as input to the injection classification model, including, but not limited to, split ratio (when operating in split mode), inlet type, inlet volume, inlet temperature, pressure within the inlet, sample solvent type, and carrier gas type.

[0049] Based on the input to the injection classification model, the injection classification model classifies the injection as either successful or unsuccessful. The injection classification model, and an exemplary method for training the injection classification model, are described in more detail below.

[0050] In another further example, the system 300 determines that the flow control parameters do not behave as expected by determining the theoretical vapor volume of the injection, estimating the actual vapor volume of the injection, and comparing the estimated vapor volume of the injection to the theoretical vapor volume of the injection.

[0051] The system 300 can calculate the theoretical vapor volume of the injection using the ideal gas law according to Equation (1).

[0052]

Number

[0053]

Number

[0054] System 300 can estimate the actual vapor volume of an injection based on a vapor volume estimation model trained to estimate the actual vapor volume of an injected sample based on flow control data. This method is based on the fact that the characteristics of the perturbation of the flow control parameters are at least partially based on the actual vapor volume of the injected sample. Thus, system 300 may extract data representing one or more characterization metrics from raw time data (e.g., from measurements of flow control parameters over time during a period associated with an injection) and input the extracted characterization metric data into a volume estimation model. One or more other experimental condition parameters may also be input into the injection classification model, including, but not limited to, split ratio (when operating in split mode), inlet type, inlet volume, inlet temperature, pressure within the inlet, solvent type of the sample, and type of carrier gas.

[0055] Based on the input to the vapor volume estimation model, the vapor volume estimation model estimates the actual vapor volume V e (g) of the sample injected into the inlet. The vapor volume estimation model and an exemplary method for training the vapor volume estimation model will be described in more detail below.

[0056] System 300 can compare the estimated actual vapor volume V e (g) of the injected sample with the theoretical vapor volume V t (g) of the injected sample. System 300 can determine that the injection was unsuccessful based on a determination that the estimated actual vapor volume V e (g) exceeds a threshold volume amount (e.g., 0.05 μL, 0.1 μL, etc.) or a threshold percentage (e.g., 5%, 10%, etc.) of the theoretical vapor volume V t (g), such that it varies from the theoretical vapor volume V t (g) by more than the threshold amount (e.g., in response thereto). The rate of change of the estimated actual vapor volume V e (g) can be represented, for example, by the following equation (3).

[0057]

Equation

[0058] Referring again to FIG. 4, in operation 406, based on (e.g., in response to) the determination that the injection was unsuccessful, the system 300 instructs the GC system (e.g., GC system 100) to perform a relaxation operation to mitigate the unsuccessful injection of the sample. In some examples, the relaxation operation includes providing a notification that the injection was unsuccessful. This notification may be provided via a display screen associated with (e.g., included in or communicatively coupled to) the system 300 or the GC system. In additional or alternative examples, the relaxation operation includes discarding data associated with the unsuccessful injection. For example, the GC system (e.g., detector 109 or GC controller 117) can discard any data obtained by GC analysis of the sample that was unsuccessfully injected. In another further example, the relaxation operation includes a diagnostic process to determine the cause of the unsuccessful injection. An exemplary diagnostic process will be described with reference to FIG. 5.

[0059] FIG. 5 shows an exemplary method 500 for performing a GC experiment using injection monitoring and diagnostics. FIG. 5 shows exemplary operations according to one embodiment, although other embodiments may omit, add, reorder, and / or modify any of the operations shown in FIG. 5.

[0060] In operation 502, a sample is injected into the inlet of the gas chromatograph. This injection can be performed in any manner described herein.

[0061] In operation 504, the system 300 checks whether the injection was successful. The system 300 can perform operation 504 in any manner described herein. If the system 300 determines that the injection was successful, the process returns to operation 502 for the next injection. If the system 300 determines that the injection was unsuccessful, the system 300 performs a diagnostic process to determine the cause of the unsuccessful injection.

[0062] This diagnostic process starts with operations 506 and 508, which are performed to check whether the syringe needle is damaged (e.g., bent) so that the needle cannot penetrate the septum. In operation 506, the system 300 instructs the GC system (e.g., GC system 100) to perform a "dry injection" by inserting the needle through the septum into the inlet while the inlet pressure is relatively high but no sample is injected (or by delaying the injection of the sample long enough for any response by the flow control system to return to a steady state). In some cases, the GC system may need to increase the pressure inside the inlet for a dry injection. For example, the pressure for a dry injection may be above a threshold pressure level of 20 psig, 25 psig, 30 psig, 40 psig, or even 45 psig. Piercing or opening the septum while the inlet is under high pressure typically results in a temporary decrease in pressure when the carrier gas escapes through the hole in the septum (in contrast to the temporary increase in pressure when the sample is injected and vaporized). This temporary decrease in pressure at the inlet results in a characteristic perturbation in the measured values of the flow control parameters (e.g., the PWM valve drive signal, the inlet pressure, and / or the flow rate).

[0063] In operation 508, the system 300 determines whether the inlet is leaking slightly as expected. Operation 508 may be performed using any of the methods described above with reference to method 400. For example, the system 300 can obtain flow control data representing the measured values of the flow control parameters over time during a period that includes a dry injection. Then, based on the flow control data, the system 300 can determine whether the measured values of the flow control parameters did not behave as expected in response to the dry injection (e.g., whether the measured values of the flow control parameters include the perturbation characteristics of a slight leak through the septum).

[0064] If the system 300 determines that the measured values of the flow control parameters do not behave as expected, the system 300 determines that the inlet did not leak slightly as expected, and thus determines that the needle is damaged (e.g., bent). Next, the process of method 500 proceeds to operation 510. In operation 510, the system 300 determines that the needle is damaged and instructs the GC system to perform a needle correction operation. This needle correction operation can include providing a notification (e.g., by a display screen associated with the GC controller 117 and / or a display screen associated with the system 300) that the needle may be damaged and / or should be inspected. Additionally, or alternatively, the needle correction operation may include instructing the autosampler of the GC system to discard the syringe and use a replacement syringe. Next, the process returns to operation 502 to perform the next injection or repeat the injection.

[0065] However, if the system 300 determines that the measured values of the flow control parameters behave as expected, the system 300 determines that the inlet leaked slightly as expected, and thus determines that the needle is not damaged. Then, the process of method 500 proceeds to operation 512 for the next step in the diagnostic process (operations 512 and 514) to check for a vial error (e.g., whether the sample vial is empty or too low).

[0066] In operation 512, the system 300 instructs the GC system to perform an additional sample injection using an additional sample drawn from a different vial (e.g., a sample vial or a wash vial). The additional injection may be performed in any manner described herein.

[0067] In operation 514, system 300 obtains additional flow control data representing measured values of flow control parameters over a period including additional injections, and checks whether the additional injections performed in operation 512 were successful based on the additional flow control data. System 300 can perform operation 514 in any manner described herein. If system 300 determines that the additional injections performed in operation 512 were successful, system 300 determines that the vial used for injection in operation 502 is likely to be empty and proceeds to operation 516.

[0068] In operation 516, system 300 instructs the GC system to perform a vial correction operation. In some examples, the vial correction operation includes providing a notification (e.g., by a display screen associated with GC controller 117 and / or a display screen associated with system 300) that the vial used in operation 502 is likely to be empty or too low. Additionally, or alternatively, the vial correction operation may include discarding any data obtained in operation 502. The processing of method 500 then returns to operation 502 for the next injection. In some examples, operation 516 may be omitted, and as a result, the processing of method 500 returns to operation 502 in response to a determination that the injection performed in operation 512 was successful.

[0069] Referring again to operation 514, if the system 300 determines that the additional injection was unsuccessful, the system 300 proceeds to operation 518. In operation 518, the system 300 determines that there is a problem with the syringe (e.g., the needle is likely to be clogged, or the syringe plunger is bent), and instructs the GC system to perform a syringe correction operation. The syringe correction operation can include providing a notification (e.g., by a display screen associated with the GC controller 117 and / or a display screen associated with the system 300) that there is likely a problem with the syringe and / or that the syringe should be inspected. Additionally, or alternatively, the syringe correction operation may include instructing the autosampler of the GC system to discard the syringe and use a replacement syringe. Additionally, or alternatively, the syringe correction operation may include discarding any data obtained in operation 502. Next, the process returns to operation 502 to perform the next injection or repeat the injection.

[0070] Next, an exemplary method for training the injection classification model and the vapor volume estimation model will be described. FIG. 6 shows a block diagram of an exemplary training stage 600 in which a training module 602 uses training data 606 and an evaluation unit 608 to train a machine learning model 604 to classify an injection or estimate the actual vapor volume of an injected sample. When trained as described herein, the machine learning model 604 can implement the injection classification model or the vapor volume estimation model used in method 400 or method 500.

[0071] The training module 602 may execute any suitable heuristic, process, and / or operation configured to train the machine learning model 604. In some examples, the training module 602 may be implemented by hardware and / or software components (e.g., a processor, memory, communication interface, instructions stored in memory for execution by the processor, etc.). In some examples, the training module 602 may be implemented by the system 300, or any component or implementation thereof. For example, the training module 602 may be implemented by the GC controller 117. Alternatively, the training module 602 may be implemented by a computing system (e.g., a personal computer or remote server) that is separate from but communicatively coupled to the GC system 100.

[0072] In some examples, the machine learning model 604 is implemented using one or more supervised and / or unsupervised learning algorithms. In some examples where the trained machine learning model 604 implements an injection classification model, the machine learning model 604 is implemented by a classification algorithm such as, but not limited to, an AdaBoost classifier, a gradient boosting classifier, a random forest classifier, or a support vector classifier. In some examples where the trained machine learning model 604 implements a vapor volume estimation model, the machine learning model 604 is implemented by a neural network (e.g., a convolutional neural network (CNN)) having an input layer, one or more hidden layers, and an output layer. In some examples, the CNN includes a long short-term memory (LSTM) network. In other examples where the trained machine learning model 604 implements a vapor volume estimation model, the machine learning model 604 is implemented by a multiple regression model such as, but not limited to, a LASSO regression model, a ridge regression model, a boosted decision tree regression model, a decision forest regression model, a fast forest quantile regression model, or an ordinal regression model.

[0073] The training data 606 includes a set of training examples 610 (e.g., training examples 610-1 to 610-N). The training data 606 can be generated in any suitable manner. In some examples, the training data 606 is generated based on a series of injections performed over time. Each training example 610 corresponds to a specific injection and includes input data 612 and target output data 614.

[0074] The input data 612 includes flow control data that represents raw time data associated with each injection and / or data representing one or more characterization metrics derived from the raw time data. This characterization metric can include any of the above-described characterization metrics that characterize measured values of flow control parameters over time during the period encompassing the injection. In some examples, the input data 612 also includes data representing experimental condition parameters such as, but not limited to, split ratio (when the injection is performed in split mode), inlet type, inlet volume, inlet temperature, pressure within the inlet, solvent type of the sample, and type of carrier gas.

[0075] The target output data 614 is a known desired output from the machine learning model 604 and can be used to evaluate the output of the machine learning model 604. When the trained machine learning model 604 implements an injection classification model, the target output data 614 represents the classification of the injection, e.g., "success" or "unsuccessful" (or other similar or suitable classifications). When the trained machine learning model 604 implements a vapor volume estimation model, the target output data 614 represents the vapor volume of the injected sample. The vapor volume of the injected sample may be determined in any suitable manner, including empirical and / or theoretical, as described above.

[0076] As shown in FIG. 6, the input data 612 of training example 610-1 is provided as an input vector to the machine learning model 604, and the machine learning model 604 is trained to provide processed output data 616 (e.g., injection classification or estimated vapor volume). The target output data 614 can be provided as an input to an evaluation unit 608 configured to determine (e.g., calculate) an evaluation value provided to the machine learning model 604 based on the processed output data 616 and the input data 612 output from the machine learning model 604. Based on the evaluation value, the training module 602 can adjust one or more model parameters of the machine learning model 604. The machine learning model 604 can then be trained on the next training example (e.g., training example 610-2), and the training can proceed through all of the training examples 610. The training for the training example 610 can be repeated.

[0077] In some examples, the training data 606 is split into two subsets of data such that a first subset of the training data 606 is used to train the machine learning model 604 and a second subset of the training data is used to score the machine learning model 604. For example, the training data 606 can be split such that a first percentage (e.g., 75%) of the training examples 610 is used as a training set to train the machine learning model 604 and a second percentage (e.g., 25%) of the training examples 610 is used as a scoring set to generate an accuracy score for the machine learning model 604.

[0078] FIG. 7 shows an exemplary method 700 that can be performed to train the machine learning model 604 to classify sample injections or estimate the vapor volume of an injected sample. FIG. 7 shows exemplary operations according to one embodiment, although other embodiments can omit, add, reorder, and / or modify one or more of the operations of the method 700 shown in FIG. 7. Each operation of the method 700 shown in FIG. 7 can be performed in any manner described herein.

[0079] In operation 702, the training module 602 obtains flow control data associated with a plurality of injections. The flow control data associated with each injection can obtain flow control data representing measured values of flow control parameters over time during a period including the associated injection.

[0080] In operation 704, the training module 602 generates training data 606 including a plurality of training examples 610 based on the flow control data. Each training example 610 includes input data 612 and target output data 614. The input data 612 includes data representing one or more characterization metrics derived from the flow control data associated with each injection. The target output data 614 is a known desired output from the machine learning model 604. When the machine learning model 604 is trained to classify injections, the target output data 614 represents the classification of the injection, for example, "success" or "unsuccessful" (or other similar or suitable classification). When the machine learning model 604 is trained to estimate the vapor volume of the injected sample, the target output data 614 represents the vapor volume of the injected sample. The vapor volume of the injected sample may be determined in any suitable manner including empirical and / or theoretical as described above.

[0081] In operation 706, the training module 602 uses the training data 606 to train the machine learning model 604 to provide processed output data 616 that classifies the injection and / or estimates the vapor volume of the injection. Once trained, the machine learning model 604 can be used in method 400 and / or method 500 to determine whether the injection was successful.

[0082] FIG. 8 shows an exemplary method 800 for training the machine learning model 604 using the training example 610 during the training stage 600 of FIG. 6. FIG. 8 shows exemplary operations according to one embodiment, although other embodiments may omit, add, reorder, and / or modify one or more operations of method 800. Each operation of method 800 shown in FIG. 8 may be performed in any manner described herein.

[0083] In operation 802, the training module 602 uses the machine learning model 604 to generate processed output data 616 (e.g., injection classification data or estimated sample vapor volume data) based on the input data 612 in the training example 610-1 (e.g., flow control data such as characterization metric data derived from raw time data).

[0084] In operation 804, the training module 602 determines an evaluation value based on the target output data 614 and the processed output data 616 in the training example 610-1. For example, as shown in FIG. 6, the training module 602 can provide the processed output data 616 generated by the machine learning model 604 and the target output data 614 in the training example 610-1 to the evaluation unit 608. The evaluation unit 608 can determine an evaluation value based on the target output data 614 and the processed output data 616. The evaluation value can be any value representing a comparison between the target output data 614 and the processed output data 616, such as the mean squared error. Other implementations for determining the evaluation value are also possible and contemplated.

[0085] In operation 806, the training module 602 adjusts one or more model parameters of the machine learning model 604 based on the determined evaluation value. For example, as shown in FIG. 6, the training module 602 backpropagates the evaluation value determined by the evaluation unit 608 to the machine learning model 604 and can adjust the model parameters (e.g., weight values assigned to various data elements in the training example 610) of the machine learning model 604 based on the evaluation value.

[0086] In some embodiments, the training module 602 may determine whether the model parameters of the machine learning model 604 have been sufficiently adjusted. For example, the training module 602 may determine that the machine learning model 604 has undergone a predetermined number of training cycles and has thus been trained with a predetermined number of training examples. Additionally, or alternatively, the training module 602 may determine that the evaluation value meets a predetermined evaluation value threshold over a threshold number of training cycles, and thus may determine that the model parameters of the machine learning model 604 have been sufficiently adjusted. Additionally, or alternatively, the training module 602 may determine that the evaluation value remains substantially unchanged over a predetermined number of training cycles (e.g., the difference between the evaluation values calculated in successive training cycles meets a difference threshold), and thus may determine that the model parameters of the machine learning model 604 have been sufficiently adjusted.

[0087] In some embodiments, in response to determining that the model parameters of the machine learning model 604 have been sufficiently adjusted, the training module 602 may determine that the training phase of the machine learning model 604 is complete and may select the current values of the model parameters to be the values of the model parameters within the trained machine learning model 604. The trained machine learning model 604 may, in some cases, implement an injection classification model or a vapor volume estimation model.

[0088] In some examples, the machine learning model 604 is trained based on training data 606 obtained during a plurality of different experiments performed under different sets of experimental conditions. As a result, the trained machine learning model 604 can be used over a wide range of experimental conditions. The experimental conditions include, but are not limited to, split ratio (when operating in split mode), inlet type, inlet volume, inlet temperature, pressure within the inlet, sample solvent type, and carrier gas type. The machine learning model 604 can be trained in any suitable manner for use under a wide range of experimental conditions.

[0089] In some examples, the training data 606 includes a plurality of subsets of the training data. Each subset of the training data is obtained based on a distinct set of experimental conditions. The training module 602 can continuously train the machine learning model 604 on each individual subset of the training data in a plurality of training stages. For example, the training module 602 can train the machine learning model 604 on a first subset of the training data 606 in a first training stage. When the training using the first subset is complete, the training module 602 can train the machine learning model 604 on a second subset of the training data 606 in a second training stage. When the training using the second subset is complete, the training module 602 can train the machine learning model 604 on a third subset of the training data 606 in a third training stage, and so on.

[0090] Alternatively, data from a plurality of different subsets of the training data can be mixed such that the machine learning model 604 is trained on different subsets of the training data in one training stage. For example, training examples from various different subsets of the training data can be mixed (e.g., randomly) to form the training data 606.

[0091] In some examples, the machine learning model 604 is trained based on the training data 606 configured for specific experimental conditions. In such examples, the trained machine learning model 604 can then be used only for subsequent iterations of that specific experiment.

[0092] In some examples, the machine learning model 604 may be refined or further trained in real time during the analysis experiment. In some embodiments, the training module 602 may continue to collect training examples 610 and, over time during the experiment, train the machine learning model 604 using the collected training examples 610. For example, if the training module 602 collects one or more additional training examples from one or more data sources, the training module 602 updates the plurality of training examples to include both the existing training examples and the additional training examples, and can train the machine learning model 604 with the updated plurality of training examples according to the training process described herein. Additionally, or alternatively, the training module 602 may periodically collect additional training examples from one or more other data sources, update the plurality of training examples to include both the existing training examples and the additional training examples, and train the machine learning model 604 with the updated plurality of training examples at predetermined intervals.

[0093] The trained machine learning model 604 may also be scored and / or updated (e.g., retrained) in real time during the analysis experiment based on data obtained during the analysis experiment (e.g., based on an analysis acquisition or scan). Throughout the analysis experiment at various times, the system 300 may perform an evaluation to assess the performance of the trained machine learning model 604 using the analysis data already acquired up to that point. The evaluation may be performed at any suitable time, such as periodically (e.g., every nth acquisition), randomly, or in response to a trigger event (e.g., detection of a coalescence or peak spread exceeding a threshold amount). Each evaluation can assess the quality of the trained machine learning model 604. If the system 300 determines that an error condition is met during the evaluation, the system 300 can retrain and / or update the machine learning model 604 using the acquired experimental data.

[0094] Various modifications can be made to the methods, apparatuses, and systems described herein. For example, although the methods described herein are described as being executed in real time during a GC experiment, the methods may be executed at any other time (e.g., after completion of the GC experiment) to determine whether the acquired data is reliable. If it is determined that the data was acquired with an incomplete injection, the data may be discarded.

[0095] The various examples above are described as using flow control data (e.g., a measured inlet pressure) based on the output of pressure sensor 122 to determine whether an injection was unsuccessful. In other examples, flow control data based on the output of pressure sensor 124 (on split path 110) or pressure sensor 126 (on purge path 112) can be used to determine whether an injection was unsuccessful. Pressure sensors 124 and 126 may not be as sensitive as pressure sensor 122 (which measures the inlet pressure), but may still exhibit perturbations in the flow control data. In further examples, flow control data based on the output of any two or three pressure sensors may be used (e.g., pressure sensors 122 and 124, pressure sensors 122 and 126, pressure sensors 124 and 126, or pressure sensors 122, 124, and 126). Similarly, flow control data based on the output of any one or more flow sensors can be used to determine whether an injection was unsuccessful.

[0096] The various examples described above have been described as using a single flow control parameter (e.g., a PWM valve drive signal, an inlet pressure, or a flow rate) to determine whether an injection was unsuccessful. In other examples, any two or more flow control parameters may be used. For example, system 300 can apply both the PWM valve drive signal and the inlet pressure to an injection classification model or a vapor volume estimation model to determine whether an injection was unsuccessful. Similarly, the injection classification model and / or the vapor volume estimation model may be trained based on two or more flow control parameters. For example, in FIG. 6, input data 612 can include flow control data for each of two different flow control parameters (e.g., a PWM valve drive signal and an inlet pressure). In fact, the injection classification model and / or the vapor volume estimation model can be trained for any set of characterization metrics for any set of flow control parameters associated with any combination of sources of flow control data (e.g., any one or more valves, any one or more pressure sensors, and / or any one or more flow sensors).

[0097] In some examples, system 300 may be configured to classify an injection as successful (or complete), partial, or empty, and these different classifications may be used to diagnose an incomplete injection. For example, system 300 may determine that an injection is partial and thus may omit operations 506, 508, and 510 of method 500 (based on the assumption that a partial injection suggests that the needle is not damaged). Similarly, system 300 may determine that the needle is damaged based on the classification of the injection as an empty injection.

[0098] In addition, or alternatively, system 300 can adjust the acquired GC data by determining the vapor volume of a partial injection based on the flow control data associated with the partial injection as described above, and then scaling the GC data based on the estimated vapor volume and the theoretical vapor volume of the injection.

[0099] In certain examples, one or more of the systems, components, and / or processes described herein can be implemented and / or executed by one or more appropriately configured computing devices. For this purpose, one or more of the systems and / or components described above can include or be implemented by any computer hardware and / or computer-implemented instructions (e.g., software) embodied on at least one non-transitory computer-readable medium configured to execute one or more of the processes described herein. Specifically, system components may be implemented on one physical computing device or on two or more physical computing devices. Thus, system components may include any number of computing devices and may employ any of several computer operating systems.

[0100] In certain examples, one or more of the processes described herein can be at least partially executed as instructions embodied in a non-transitory computer-readable medium and executable by one or more computing devices. Generally, a processor (e.g., a microprocessor) receives instructions from a non-transitory computer-readable medium (e.g., memory, etc.) and executes those instructions, thereby executing one or more processes including one or more of the processes described herein. Such instructions can be stored and / or transmitted using any of a variety of known computer-readable media.

[0101] A computer-readable medium (also referred to as a processor-readable medium) includes any non-transitory medium that participates in providing data (e.g., instructions) that can be read by a computer (e.g., by a processor of the computer). Such a medium can take many forms, including but not limited to non-volatile media and / or volatile media. Non-volatile media can include, for example, optical or magnetic disks, and other permanent memories. Volatile media can include, for example, dynamic random access memory (“DRAM”), which typically constitutes main memory. Common forms of computer-readable media include, for example, disks, hard disks, magnetic tape, any other magnetic media, compact disc read-only memory (“CD-ROM”), digital video disc (“DVD”), any other optical media, random access memory (“RAM”), programmable read-only memory (“PROM”), electrically erasable programmable read-only memory (“EPROM”), FLASH-EEPROM, any other memory chip or cartridge, or any other tangible medium that can be read by a computer.

[0102] FIG. 9 shows an exemplary computing device 900 that can be specifically configured to execute one or more of the processes described herein. As shown in FIG. 9, the computing device 900 may include a communication interface 902, a processor 904, a memory device 906, and an input / output (I / O) module 908 that are communicatively connected to each other via a communication infrastructure 910. Although an exemplary computing device 900 is shown in FIG. 9, the components illustrated in FIG. 9 are not intended to be limiting. In other embodiments, additional or alternative components may be used. Next, the components of the computing device 900 shown in FIG. 9 will be described in more detail.

[0103] The communication interface 902 can be configured to communicate with one or more computing devices. Examples of the communication interface 902 include, but are not limited to, a wired network interface (such as a network interface card), a wireless network interface (such as a wireless network interface card), a modem, an audio / video connection, and any other suitable interface.

[0104] The processor 904 generally represents any type or form of processing unit that enables data to be processed and / or one or more of the instructions, processes, and / or operations described herein to be interpreted, executed, and / or directed for their execution. The processor 904 can perform operations by executing computer-executable instructions 912 (such as applications, software, code, and / or other executable data instances) stored in the memory device 906.

[0105] The memory device 906 can include one or more data storage media, devices, or configurations, and can employ any type, form, and combination of data storage media and / or devices. For example, the memory device 906 can include, but is not limited to, any combination of the non-volatile media and / or volatile media described herein. The electronic data including the data described herein can be stored temporarily and / or permanently within the memory device 906. For example, data representing computer-executable instructions 912 configured to instruct the processor 904 to execute any of the operations described herein may be stored within the memory device 906. In some examples, the data may be arranged in one or more databases existing within the memory device 906.

[0106] The I / O module 908 can include one or more I / O modules configured to receive user input and provide user output. One or more I / O modules can be used to receive input for a single virtual experience. The I / O module 908 can include any hardware, firmware, software, or combination thereof that supports input and output capabilities. For example, the I / O module 908 can include, but is not limited to, a keyboard or keypad, a touch screen component (e.g., a touch screen display), a receiver (e.g., an RF or infrared receiver), a motion sensor, and / or one or more input buttons, and may include hardware and / or software for capturing user input.

[0107] The I / O module 908 can include one or more devices for presenting outputs to a user, including, but not limited to, a graphics engine, a display (e.g., a display screen), one or more output drivers (e.g., a display driver), one or more audio speakers, and one or more audio drivers. In certain embodiments, the I / O module 908 is configured to provide graphical data to a display for presentation to a user. The graphical data can represent one or more graphical user interfaces and / or any other graphical content, as may be useful for a particular implementation.

[0108] In some examples, any of the systems, computing devices, and / or other components described herein can be implemented by the computing device 900. For example, the memory 302 may be implemented by the storage device 906, and the processor 304 may be implemented by the processor 904.

[0109] On the other hand, it will be recognized by those skilled in the art that, in the foregoing description, various exemplary embodiments have been described with reference to the accompanying drawings. However, it will be apparent that various modifications and changes may be made thereto without departing from the scope of the invention as set forth in the following claims, and additional embodiments may be implemented. For example, certain features of one embodiment described herein may be combined with or substituted for features of another embodiment described herein. Accordingly, the present specification and drawings are to be considered in an illustrative, rather than a limiting, sense.

[0110] The advantages and features of the present disclosure can be further illustrated by the following examples.

[0111] Example 1. A system for gas chromatography, comprising an inlet configured to receive a sample by injection, a column containing a stationary phase, a flow control system configured to regulate the flow of a mobile phase through the inlet and the column based on flow control parameters, and an injection monitoring system configured to execute a process, the process including obtaining flow control data representing measured values of the flow control parameters over time during a period in which the process includes the injection of a sample into the inlet, determining that the injection was unsuccessful based on the flow control data, and executing a relaxation operation based on the determination that the injection was unsuccessful. The system further comprises the injection monitoring system.

[0112] Example 2. The system according to Example 1, wherein the flow control system comprises a pressure sensor configured to measure an inlet pressure, a valve configured to regulate the flow of the mobile phase into the inlet, and a flow controller configured to receive a pressure signal output by the pressure sensor and output a pulse width modulation (PWM) valve drive signal to the valve based on the pressure signal.

[0113] Example 3. The system according to Example 2, wherein the flow control parameter includes an inlet pressure.

[0114] Example 4. The system according to Example 2, wherein the flow control parameter includes a PWM valve drive signal.

[0115] Example 5. The system according to Example 1, wherein the flow control system comprises a flow sensor configured to measure the flow rate of the mobile phase into the inlet, and the flow control parameter includes the flow rate of the mobile phase.

[0116] Example 6. The system according to Example 1, wherein the flow control data indicates a perturbation in the measured values of the flow control parameters, and determining that the injection was unsuccessful includes determining that the perturbation does not behave as expected.

[0117] Example 7. The system according to Example 6, wherein determining that the perturbation does not behave as expected is based on one or more characterization metrics that characterize the perturbation.

[0118] Example 8. The system of Example 7, wherein one or more characterization metrics include at least one of a maximum amplitude of a perturbation, an integral change of a perturbation, or an absolute value of an integral change of a perturbation.

[0119] Example 9. An injection monitoring system for a gas chromatography system, the injection monitoring system comprising one or more processors and a memory storing executable instructions, the executable instructions, when executed by the one or more processors, causing a computing device to obtain flow control data from a flow control system included in the gas chromatography system and configured to regulate a flow of fluid through an inlet of the gas chromatography system based on a flow control parameter, the flow control data representing measured values of the flow control parameter over time during a period including an injection of a sample into the inlet, determine that the injection was unsuccessful based on the flow control data, and instruct the gas chromatography system to perform a mitigation operation to mitigate an unsuccessful injection of the sample based on the determination that the injection was unsuccessful.

[0120] Example 10. The injection monitoring system of Example 9, wherein the flow control system comprises a valve and the flow control parameter includes a pulse width modulation (PWM) valve drive signal for the valve.

[0121] Example 11. The injection monitoring system of Example 10, wherein determining that the injection was unsuccessful is based on at least one of a maximum change of the PWM valve drive signal during the period, an integral change of the PWM valve drive signal during the period, or an absolute value of an integral change of the PWM valve drive signal during the period.

[0122] Example 12. The injection monitoring system of Example 9, wherein the flow control system comprises a pressure sensor that measures an inlet pressure and the flow control parameter includes a pressure signal output by the pressure sensor.

[0123] Example 13. The injection monitoring system according to Example 12, wherein determining that an injection was unsuccessful is based on at least one of a maximum change in inlet pressure during a period, an integrated inlet pressure during the period, or an absolute value of the integrated inlet pressure during the period.

[0124] Example 14. The injection monitoring system according to Example 9, wherein the flow control system includes a flow sensor in the inlet, and the flow control parameter includes a flow rate signal output by the flow sensor.

[0125] Example 15. The injection monitoring system according to Example 9, wherein determining that an injection was unsuccessful includes applying flow control data to an injection classification model trained to classify an injection as successful or unsuccessful based on the flow control data.

[0126] Example 16. The injection monitoring system according to Example 9, wherein determining that an injection was unsuccessful includes determining a theoretical vapor volume of the injection, estimating an actual vapor volume of the injection, and comparing the estimated actual vapor volume of the injection with the theoretical vapor volume of the injection.

[0127] Example 17. The injection monitoring system according to Example 16, wherein estimating the actual vapor volume of the injection includes applying flow control data to a vapor volume estimation model trained to estimate the actual vapor volume of the injection based on the flow control data.

[0128] Example 18. The injection monitoring system according to Example 9, wherein determining that an injection was unsuccessful includes obtaining reference flow control data representing an expected measured value of a flow control parameter over time during a period including the injection of a sample, and determining that the flow control data has changed from the reference flow control data.

[0129] Example 19. The injection monitoring system according to Example 18, wherein determining that the flow control data has changed from the reference flow control data includes cross-correlating the flow control data with the reference flow control data.

[0130] Example 20. The injection monitoring system according to Example 9, wherein the relaxation action includes providing a notification that the injection was unsuccessful.

[0131] Example 21. The injection monitoring system according to Example 9, wherein the relaxation action includes a diagnostic process, the diagnostic process includes performing a dry injection into the inlet while the pressure inside the inlet is above a threshold pressure level, and detecting, based on the dry injection, that a measured value of a flow control parameter did not behave as expected in response to the dry injection.

[0132] Example 22. The injection monitoring system according to Example 21, further including instructing an autosampler to perform an additional injection of a sample using a replacement syringe.

[0133] Example 23. The injected sample is drawn from a first vial, the relaxation action includes a diagnostic process, the diagnostic process includes performing an additional injection using an additional sample drawn from a second vial different from the first vial, and acquiring additional flow control data from a flow control system, the additional flow control data representing measured values of flow control parameters over time during a period including the additional injection, and determining whether the additional injection was successful or unsuccessful based on the additional flow control data. The injection monitoring system according to Example 9.

[0134] A non-transitory computer-readable medium storing instructions that, when executed, cause at least one processor of a computing device for a gas chromatography system to obtain flow control data from a flow control system configured to adjust the flow of fluid through an inlet of the gas chromatography system based on flow control parameters, the flow control data representing measured values of flow control parameters over time during a period including the injection of a sample into the inlet, determine that the injection was unsuccessful based on the flow control data, and perform a relaxation action based on the determination that the injection was unsuccessful.

[0135] Example 25. The computer-readable medium according to Example 24, comprising applying flow control data to an injection classification model trained to classify an injection as successful or unsuccessful based on the flow control data, where it is determined that the injection was unsuccessful.

[0136] Example 26. The computer-readable medium according to Example 24, comprising determining that an injection was unsuccessful, which includes determining a theoretical vapor volume of the injection, estimating an actual vapor volume of the injection, and comparing the estimated actual vapor volume of the injection with the theoretical vapor volume of the injection.

[0137] Example 27. The computer-readable medium according to Example 26, comprising estimating an actual vapor volume of an injection, which includes applying flow control data to a vapor volume estimation model trained to estimate the actual vapor volume of the injection based on the flow control data.

[0138] Example 28. The computer-readable medium according to Example 24, comprising determining that an injection was unsuccessful, which includes obtaining reference flow control data representing expected measured values of flow control parameters over time during a period including a reference injection, and determining that the flow control data has changed from the reference flow control data based on a cross-correlation between the flow control data and the reference flow control data.

[0139] Example 29. The computer-readable medium according to Example 24, where the flow control parameter includes at least one of a pulse width modulation (PWM) valve drive signal for a valve of the flow control system, an inlet pressure measured by a pressure sensor of the flow control system, or a flow rate of the mobile phase measured by a flow sensor of the flow control system.

[0140] Example 30. The computer-readable medium according to Example 24, where the flow control data indicates perturbations in measured values of the flow control parameter, and determining that an injection was unsuccessful includes determining that the perturbations do not behave as expected.

[0141] Example 31. The computer-readable medium according to Example 30, wherein determining that the perturbation does not behave as expected is based on one or more characterization metrics that characterize the perturbation.

[0142] Example 32. The computer-readable medium according to Example 31, wherein the one or more characterization metrics include at least one of a maximum amplitude of the perturbation, an integral change of the perturbation, or an absolute value of the integral change of the perturbation.

Claims

1. 1. A system for gas chromatography, comprising: an inlet configured to receive a sample by injection; a column including a stationary phase; a flow control system configured to adjust a flow of a mobile phase through the inlet and through the column based on a flow control parameter; 1. An infusion monitoring system configured to execute a process, the process comprising: acquiring flow control data representative of measurements of said flow control parameters over time during a period encompassing injection of said sample into said inlet; determining that the injection was unsuccessful based on the flow control data; and and performing mitigating action based on a determination that the injection was unsuccessful.

2. The flow control system comprises: a pressure sensor for measuring an inlet pressure; a valve to regulate the flow of the mobile phase to the inlet; 10. The system of claim 1, further comprising: a flow controller that receives a pressure signal output by the pressure sensor and outputs a pulse width modulated (PWM) valve drive signal to the valve based on the pressure signal.

3. The system of claim 2 , wherein the flow control parameter comprises the inlet pressure or the PWM valve drive signal.

4. the flow control system comprising a flow sensor for measuring a flow rate of a mobile phase to the inlet; The system of claim 1 , wherein the flow control parameter comprises the flow rate of the mobile phase.

5. the flow control data indicative of perturbations in the measurements of the flow control parameters; The system of claim 1 , wherein determining that the injection was unsuccessful comprises determining that the perturbation does not behave as expected.

6. determining that the perturbation does not behave as expected is based on one or more characterization metrics that characterize the perturbation; The system of claim 5 , wherein the one or more characterization metrics include at least one of a maximum amplitude of the perturbation, an integral change of the perturbation, or an absolute value of an integral change of the perturbation.

7. 1. An injection monitoring system for a gas chromatography system, the injection monitoring system comprising: one or more processors; and a memory storing executable instructions that, when executed by the one or more processors, cause the computing device to: acquiring flow control data from a flow control system included in the gas chromatography system and configured to adjust a flow of fluid through an inlet of the gas chromatography system based on a flow control parameter, the flow control data representing measurements of the flow control parameter over time during a period encompassing an injection of a sample into the inlet; determining that the injection was unsuccessful based on the flow control data; and and based on a determination that the injection was unsuccessful, instructing the gas chromatography system to perform mitigating actions to mitigate the unsuccessful injection of the sample.

8. the flow control system comprises a valve; The infusion monitoring system of claim 7 , wherein the flow control parameter comprises a pulse width modulated (PWM) valve drive signal for the valve.

9. 9. The injection monitoring system of claim 8, wherein the determining that the injection was unsuccessful is based on at least one of a maximum change in the PWM valve drive signal during the period, an integral change in the PWM valve drive signal during the period, or an absolute value of the integral change in the PWM valve drive signal during the period.

10. the flow control system includes a pressure sensor for measuring an inlet pressure; The infusion monitoring system of claim 7 , wherein the flow control parameter comprises a pressure signal output by the pressure sensor.

11. 11. The injection monitoring system of claim 10, wherein the determining that the injection was unsuccessful is based on at least one of a maximum change in the inlet pressure during the period, an integrated inlet pressure during the period, or an absolute value of the integrated inlet pressure during the period.

12. the flow control system comprising a flow sensor in the inlet; The infusion monitoring system of claim 7 , wherein the flow control parameter comprises a flow rate signal output by the flow sensor.

13. 8. The infusion monitoring system of claim 7, wherein determining that the infusion was unsuccessful comprises applying the flow control data to an infusion classification model trained to classify the infusion as successful or unsuccessful based on the flow control data.

14. determining that the injection was unsuccessful, determining a theoretical vapor volume for said injection; estimating an actual steam volume of the injection; and comparing the estimated actual steam volume of the injection with the theoretical steam volume of the injection.

15. determining that the injection was unsuccessful, obtaining baseline flow control data representative of expected measurements of said flow control parameter over time during a period encompassing the injection of a sample; and determining that the flow control data has changed from the baseline flow control data.

16. The mitigation action includes a diagnostic process, the diagnostic process comprising: performing a dull injection into the inlet while pressure in the inlet is equal to or greater than a threshold pressure level; and detecting, based on the blank injection, that the measurement of the flow control parameter did not behave as expected in response to the blank injection.

17. the injected sample is drawn from a first vial; The mitigation action includes a diagnostic process, the diagnostic process comprising: performing an additional injection using an additional sample drawn from a second vial different from the first vial; acquiring additional flow control data from the flow control system, the additional flow control data representing measurements of the flow control parameter over time during a period encompassing the additional injection; and determining whether the additional injection was successful or unsuccessful based on the additional flow control data.

18. 1. A non-transitory computer-readable medium storing instructions that, when executed, direct at least one processor of a computing device for a gas chromatography system to: acquiring flow control data from a flow control system configured to adjust a flow of a fluid through an inlet of a gas chromatography system based on a flow control parameter, the flow control data representing measurements of the flow control parameter over time during a period encompassing an injection of a sample into the inlet; determining that the injection was unsuccessful based on the flow control data; and and performing mitigating actions based on a determination that the injection was unsuccessful.

19. 20. The computer-readable medium of claim 18, wherein determining that the infusion was unsuccessful comprises applying the flow control data to an infusion classification model trained to classify the infusion as successful or unsuccessful based on the flow control data.

20. determining that the injection was unsuccessful, determining a theoretical vapor volume for said injection; estimating an actual steam volume of the injection; and comparing the estimated actual vapor volume of the injection to the theoretical vapor volume of the injection.

21. determining that the injection was unsuccessful, obtaining baseline flow control data representative of expected measurements of said flow control parameter over time during a period encompassing a baseline injection; and determining that the flow control data has changed from the reference flow control data based on a cross-correlation of the flow control data with the reference flow control data.

22. 20. The computer-readable medium of claim 18, wherein the flow control parameters include at least one of a pulse width modulated (PWM) valve drive signal for a valve of the flow control system, an inlet pressure measured by a pressure sensor of the flow control system, or a flow rate of a mobile phase measured by a flow sensor of the flow control system.

23. the flow control data indicative of perturbations in the measurements of the flow control parameters; 20. The computer-readable medium of claim 18, wherein determining that the injection was unsuccessful comprises determining that the perturbation does not behave as expected.

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