Patent Grant
11942314
This application is a National Stage of International Application No. PCT/JP2019/022464 filed on Jun. 6, 2019.
The present invention relates to a mass spectrometer and a mass spectrometry method, and more particularly, to a mass spectrometer and a mass spectrometry method having a function of adjusting device parameters to optimal or nearly optical states based on actual measurement results.
In general, in order to perform highly accurate and highly sensitive measurement using an analyzer, it is necessary to appropriately set device parameters which determine analysis conditions in the analyzer. When a compound in a sample liquid eluted from a column of a liquid chromatograph unit is ionized in a liquid chromatograph mass spectrometer (LC-MS), an ion source by, for example, an electrospray ionization (ESI) method, an atmospheric pressure chemical ionization (APCI) method, or the like is used. In such an ion source, there are various device parameters such as the temperature of each component, an applied voltage, and a gas flowrate of a nebulizing gas or the like.
When the values of these parameters are changed in the LC-MS, ionization efficiency in the ion source, collection efficiency of ions generated in the ion source, and the like change, and signal intensity output from the ion detector also changes. Therefore, a conventional general LC-MS repeatedly measures a sample containing a target compound while changing the values of a plurality of device parameters one by one, and examines a change in signal intensity. The device parameters are adjusted so that, with each of the parameters, the signal intensity becomes as large as possible, that is, the detection sensitivity becomes as high as possible (see Patent Literature 1, for example).
In order to adjust the device parameters so that the detection sensitivity is maximized by the method as described above, it is necessary to set the change width of the value of each parameter as small as possible and to perform the exhaustive measurement in which the measurement is repeated while all the parameters are comprehensively changed. However, with such an exhaustive measurement, the total number of measurements is so large that it takes long time to complete all measurements. In particular, a parameter whose physical quantity is temperature is different from a parameter whose physical quantity is voltage or gas flowrate, since it takes time to change the temperature from one value to the next value and stabilize there. Therefore, a waiting time between measurements is long, and a total measurement time tends to be long. For example, in the LC-MS, if the exhaustive measurements are performed in order to fully adjust the device parameters, measurements may be repeated for a period of time exceeding one day. When the number of measurements increases and the total measurement time becomes longer as described above, the following problems arise.
In the exhaustive measurement by the LC-MS, when the value of a certain parameter among a plurality of device parameters is changed, it is assumed that other parameters and the state of the device do not change (or the change is negligibly small). However, when measurement is performed for a long time, the measured signal intensity may change due to factors other the device parameters, such as a change in a component of a mobile phase used in a liquid chromatograph (LC) or deterioration in a sample. As described above, when there is a temporal change in the signal intensity due to a factor other than the parameter to be changed, the accuracy of adjustment of the device parameters based on the actual measurement result decreases, and there is a possibility that measurement with high sensitivity cannot be performed.
The above problem persists not only in a case where the device parameters are determined on the basis of the result of exhaustive measurements, but also in a case where the device parameters are optimized using data measured in advance as prior knowledge. As disclosed in Non-Patent Literature 1, the present applicant has proposed a method using a multi-task Bayesian optimization method as a method for efficiently automatically adjusting device parameters. However, in the multi-task Bayesian optimization method, a similar model for estimating a model posterior distribution is required as prior knowledge. This similar model is a sensitivity model representing the relationship between the values of a plurality of parameters and sensitivity. In order to create a sensitivity model, however, it is necessary to perform many measurements while changing parameter conditions, which causes a problem due to an increase in measurement time.
The present invention has been made to solve the above problems and an object is to provide a mass spectrometer and a mass spectrometry method which can perform highly accurate parameter adjustment, even when repetition of measurements accompanied by a change in parameter value takes a long time and the temporal change in signal intensity due to various factors cannot be ignored, by reducing or substantially eliminating the influence of such a temporal change in signal intensity.
One aspect of a mass spectrometer according to the present invention is a mass spectrometer including an ionization unit, a mass separation unit, and a detection unit, the mass spectrometer including:
One aspect of a mass spectrometry method according to the present invention is a mass spectrometry method uses a mass spectrometer including an ionization unit, a mass separation unit, and a detection unit, the mass spectrometry method including:
In this case, “not less than two time points before the start, after the end, or in the middle of repetition of the first measurement” can include two time points before the start and after the end, two time points before the start and in the middle, two time points in the middle and after the end, and two time points in the middle that are different from each other.
The term “mass spectrometry” as used in this case includes MS/MS analysis and MSn analysis with n being 3 or more.
In the mass spectrometer according to an aspect of the present invention, under the control of the second measurement control unit, the value of each parameter is always set to a reference value, and measurements for the same target sample are executed. Therefore, a temporal change of a factor or factors other than the each parameter included in the device parameters affects the measurement result. Accordingly, the correction processing unit uses the results of the second measurements to perform correction for removing the influence of the temporal change in the measurement results obtained by repeating the first measurement. Based on the corrected measurement results, the device parameter-related information acquisition unit, for example, determines the device parameters or obtains reference information for determining the device parameters. The reference information is, for example, a sensitivity model used when device parameters are adjusted using the above-described multi-task Bayesian optimization method.
According to the mass spectrometer and the mass spectrometry method according to one aspect of the present invention, even in a case where the first measurement is repeated for a long time and a temporal change in signal intensity due to various factors cannot be ignored, it is possible to reduce or substantially eliminate the influence of such a temporal change and obtain device parameters that allows highly sensitive measurement. In addition, according to the mass spectrometer and the mass spectrometry method according to one aspect of the present invention, since the accuracy of the reference information for determining the device parameters can be improved, the number of repetitions of measurement can be reduced when determining the device parameters by repetition of measurement based on the reference information. That is, it is possible to efficiently obtain device parameters allowing highly sensitive measurement.
An LC-MS which is an embodiment of a mass spectrometer according to the present invention will be described with reference to the accompanying drawings.
Referring to
Although not illustrated, the liquid chromatograph unit 2 includes a liquid feeding pump, an injector, and a column, injects a predetermined amount of sample from the injector into the mobile phase fed by the liquid feeding pump, and feeds the sample into the column on the flow of the mobile phase. Various components (compounds) in the sample are temporally separated while passing through the column, eluted from the column outlet, and introduced into the mass spectrometry unit 3. In the mass spectrometry unit 3, the ion source 31 ionizes components in the eluate from the column, and the mass separation unit 32 separates various generated ions according to the mass-to-charge ratio m/z. The detection unit 33 detects ions separated according to the mass-to-charge ratio and generates a detection signal according to the amount of ions.
A control unit 4 controls the operation of the measurement unit 1, and includes functional blocks such as a first measurement control unit 41, a second measurement control unit 42, a reference value search time measurement control unit 43, a device parameter automatic adjustment time measurement control unit 44, a device parameter storage unit 45, and a third measurement control unit 46. A data processing unit 5 receives data obtained by the measurement unit 1 and performs various data processing. The data processing unit 5 includes functional blocks such as a data storage unit 51, a peak detection unit 52, a data correction processing unit 53, a sensitivity model creation unit 54, a device parameter determination unit 55, and a reference value determination unit 56.
Usually, most of the functional blocks of the control unit 4 and the data processing unit 5 can be implemented by using a personal computer as a hardware resource and executing dedicated control/processing programs installed in the computer on the computer.
The ion generating operation of the ion source 31 will be briefly described.
When the eluate containing a sample component reaches the vicinity of the distal end of the capillary 3121, a biased charge is applied to the eluate by a DC electric field formed by a high voltage (about several kV at the maximum) applied from the high-voltage power supply 3125 to the capillary 3121. The charged eluate is nebulized as fine droplets (charged droplets) into the ionization chamber 311 with the aid of the nebulizing gas ejected from the nebulizing gas tube 3122. The nebulized droplets come into contact with gas molecules in the ionization chamber 311 and are split and refined. Since the temperature inside the ionization chamber 311 is high, the solvent in the droplets is vaporized. In addition, since the heating gas ejected from the heating gas tube 3123 flows so as to surround the nebulizing flow, the vaporization of the solvent from the droplets is promoted, and the spread of the nebulizing flow is suppressed. In the process of vaporization of the solvent from the droplet, the component molecules in the droplet have charges and jump out from the droplet to become gas ions.
Since there is a pressure difference between both opening ends of the desolvation tube 313, a gas flow sucked into the desolvation tube 313 from the inside of the ionization chamber 311 is formed. The charged droplets in which the ions and the solvent derived from the sample component generated from the nebulizing flow from the distal end of the capillary 3121 are not completely vaporized are carried by the gas flow and sucked into the desolvation tube 313. Since a dry gas is ejected from the dry gas tube 314 around the inlet opening of the desolvation tube 313, the solvent from the charged droplets further vaporizes by being exposed to the dry gas. Furthermore, since the desolvation tube 313 is heated to a high temperature by the heater 315, the vaporization of the solvent from the charged droplets also proceeds in the desolvation tube 313. As a result, ions derived from the sample component are efficiently generated and sent to the intermediate vacuum chamber on the next stage.
The ion source 31 has the following seven parameters as device parameters that influence ionization efficiency and ion collection efficiency.
When the values of the seven parameters are changed, the ionization efficiency and/or the ion collection efficiency changes, the amount of ions provided for mass spectrometry changes, and the detection sensitivity (signal intensity) in the detection unit 33 also changes. Since the degree of change in detection sensitivity and the direction of change depend on the component (compound), it is necessary to optimize the parameter value for each compound in order to perform highly sensitive measurement.
A device parameter adjustment method and procedure in the LC-MS according to the present embodiment will be described next.
The LC-MS according to the present embodiment has a function of automatically adjusting device parameters including the above-described seven parameters. For automatic adjustment of device parameters, a technique using a multi-task Bayesian optimization method as disclosed in Non-Patent Literature 1 is used. In order to optimize a device parameter by the multi-task Bayesian optimization method, a sensitivity model indicating the relationship between parameter values and detection sensitivity is required. The higher the accuracy of the sensitivity model, the smaller the number of times to search for an optimal device parameter, that is, the number of times of repetition of measurement at the time of automatic parameter adjustment. In order to create a highly accurate sensitivity model, it is necessary to repeatedly perform measurement on a target compound while exhaustively changing all of the seven parameters to obtain signal intensity. Such exhaustive measurement requires a long time, but in the LC-MS according to the present embodiment, the above-described problem associated with a long total measurement time is solved by the characteristic measuring operation and processing described below.
A reference measurement is a measurement executed on a target compound after the values of the seven parameters are respectively set to predetermined reference values. That is, since a plurality of reference measurements are executed under exactly the same conditions for the seven parameters, if conditions other than the seven parameters and the state of the device are exactly the same, ideally, the measurement results should be the same except for errors due to, for example, restrictions on the accuracy of the device. On the other hand, when there is a difference between measurement results in a plurality of reference measurements, it can be estimated that the main factor is a variation in conditions other than the seven parameters and the state of the device. More specifically, main factors considered as such factors are temporal changes in components of the mobile phase used in the liquid chromatograph unit 2, deterioration in the sample, and the like.
Accordingly, based on the measurement results on a plurality of reference measurements for the same target compound, specifically, the change (difference) in signal intensity in the mass-to-charge ratio corresponding to the target compound, data correction is performed to reduce errors due to variations in conditions other than the seven parameters and the state of the device included in the signal intensity data in the mass-to-charge ratio corresponding to the target compound acquired in the measurement for data collection.
<Method for Correcting Signal Intensity Data>
Referring to
An error caused by a variation in condition other than the seven parameters or the state of the device can be regarded as monotonically increasing (or decreasing) with respect to a time change. Accordingly, a correction equation formula in the form of equation (1) given below is used.
Ycorrect=Yn×(Yref/Yncor)=Yn×[NYref/{(N−n)Y0+nYN}] (1)
In this case, as illustrated in
That is, the signal intensity obtained in the N−1 times of data collection measurement is corrected according to the correction formula in the form of equation (1) using the signal intensity obtained in the reference measurement performed immediately before and immediately after the N−1 times of repetition of the data collection measurement. With this correction, it is possible to reduce errors due to variations in conditions other than the seven parameters and the state of the device.
<Method for Determining Reference Condition During Reference Measurement>
The values of the seven parameters at the time of the reference measurement, that is, reference conditions can be determined by the following procedure.
Step 1: Parameters whose physical quantities are temperatures, specifically, three parameters, namely the temperature of the interface heater 3124, the temperature of the block heater 316, and the temperature of the desolvation tube heater 315, are monotonously changed from low to high or from high to low within a settable range, and a signal intensity at a mass-to-charge ratio corresponding to the target compound is acquired under a combination of different temperatures. A parameter other than the parameters whose physical quantities are temperatures may be a predetermined default value. In addition, the three parameters related to the temperatures do not need to be changed in very small steps and may be changed in large steps that divide the settable range into five. In addition, since the three parameters related to temperatures have a positive correlation with each other, it is not necessary to exhaustively change the values of the parameters, and it is sufficient to acquire signal intensity for a combination of temperatures of about five stages with the three parameters related to the temperatures as one set.
Step 2: Among the signal intensities desired to be acquired in step 1, a combination of values of three parameters related to the temperatures at which the signal intensity is maximum is selected, and is determined as a reference value in the three parameters.
Step 3: When it is desired to obtain higher detection sensitivity, a parameter regarding the voltage applied to the capillary 3121 is monotonously changed from low to high or from high to low within a settable range, and signal intensity for each voltage is acquired. The value of the parameter related to the temperature at this time may be the reference value determined in step 2. The values of the other parameters may be default values. In general, it is not necessary to adjust parameters related to the voltage applied to the capillary 3121.
Step 4: The value of the parameter of the applied voltage which has the maximum signal intensity is selected from the signal intensities desired to be acquired in step 3, and the selected value is determined as the reference value in the parameter.
Step 5: Default values are used as values of the three parameters related to the gas flowrate, and the parameter values determined in steps 2 and 4 are used as reference values. When steps 3 and 4 are omitted, the parameter value of the applied voltage may also be a default value.
The reason for adopting the procedure as described above is that parameters that greatly contribute to ionization efficiency are parameters related to temperature and parameters of the voltage applied to the capillary 3121. If a parameter related to a temperature is not adjusted, the influence of the measurement condition in the measurement performed immediately before reference measurement greatly appears, and the measurement under the reference condition becomes unstable.
The following will describe the comparison result of a change in the signal intensity in repeated measurement in a case where the parameters related to temperature is not adjusted as a reference condition and a case where the parameters related to the temperature is adjusted as a reference condition as described above.
(1) Case where Adjustment of Parameters Related to Temperature is not Performed as Reference Condition
The reference conditions at the time of reference measurement are fixed as follows:
DL=250° C.,HB=400° C.,IFT=300° C.,IFV=3.4 kV(for positive ion measurement; −3.4 kV for negative ion measurement),Neb=2.6 L/min,HeaGas=10.0 L/min, and DryGas=10.0 L/min.
On the other hand, parameters at the time of measurement for data collection are as follows.
The three parameters related to temperature are presented in the form of the following five sets:
In addition, IFV, Neb, HeaGas, and DryGas are fixed to default values within settable ranges of the device. For example, IFV and Neb are respectively fixed to 5.0 kV and 3.0 L/min, and both HeaGas and DryGas are fixed to 10.0 L/min.
(2) Case where Adjustment of Parameters Related to Temperature is Performed as Reference Condition
The above results reveal the importance of appropriately determining parameters related to temperature as a reference condition for reference measurement.
An operation in adjusting a device parameter in the LC-MS according to the present embodiment will be described next. The sensitivity model used for automatic adjustment of device parameters is created as follows.
First, under the control of the reference value search time measurement control unit 43, the measurement unit 1 performs measurement on a sample containing a target compound under the conditions described in step 1 (and step 3). In the data processing unit 5, the peak detection unit 52 detects a peak corresponding to the target compound on a chromatogram created based on the obtained data. Then, the height or the area of the peak is calculated and used as a signal intensity value. The reference value determination unit 56 compares a plurality of signal intensity values obtained under different conditions and determines a parameter value having the maximum signal intensity as a reference value.
Thereafter, the measurement unit 1 repeatedly executes data collection measurement on a sample containing the target compound under the control of the first measurement control unit 41. In addition, under the control of the second measurement control unit 42, the measurement unit 1 executes reference measurement on a sample containing a target compound at an appropriate time point before, after, or in the middle of the repetition of measurement for data collection. Data obtained by the device data collection measurement and the reference measurement is stored in the data storage unit 51.
The peak detection unit 52 detects a peak corresponding to a target compound on a chromatogram created based on data obtained for each measurement and calculates the height or area of the peak to obtain a signal intensity value. The data correction processing unit 53 executes the data correction described above on the signal intensity value obtained by the data collection measurement using the signal intensity value obtained by the reference measurement and obtains a corrected signal intensity value. This data correction reduces the influence of changes in the state of the device other than the device parameters.
The sensitivity model creation unit 54 creates a sensitivity model based on the corrected signal intensity value measured while the value of the parameter is variously changed. As described above, the multi-task Bayesian optimization method is used when device parameters are automatically adjusted. In the multi-task Bayesian optimization method, the posterior distribution of a model function of a system to be optimized is generally estimated based on reference observation data and target observation data. The target observation data is data including the observation value obtained in the system to be optimized, whereas the reference observation data is data including the observation value obtained in a reference system different from but similar to the system to be optimized. The sensitivity model corresponds to this reference observation data and indicates the relationship between the value of each parameter and signal intensity (detection sensitivity) as will be exemplified later. The sensitivity model created by the sensitivity model creation unit 54 is transferred to the control unit 4 and stored in the device parameter automatic adjustment time measurement control unit 44.
In the multi-task Bayesian optimization method adopted in the LC-MS according to the present embodiment, the posterior distribution of the model function is estimated under the assumption that the model function of the system follows a certain stochastic process. The stochastic process at the time of creating a sensitivity model may be a Gaussian process regression so that a secondary effect of suppressing the influence of observation noise can be obtained.
When the automatic adjustment of the device parameters is actually executed, the device parameter automatic adjustment time measurement control unit 44 repeats the measurement on the sample containing the target compound while controlling the measurement unit 1 to automatically change the device parameter in the measurement to be performed next according to the algorithm of the multi-task Bayesian optimization method using the sensitivity model. The multi-task Bayesian optimization method is described in detail in Non-Patent Literature 2 and the like, and the algorithm itself is not the gist of the present invention. Accordingly, a description of this algorithm will be omitted. Using the multi-task Bayesian optimization method makes it possible to search for a device parameter in a nearly optimal state with a small number of times of measurement. In addition, as described above, since the accuracy of data used when creating the sensitivity model is high (the influence of changes in measurement conditions other than the device parameters and the device state is reduced), the accuracy of the sensitivity model itself is also high. Therefore, the number of times of measurement at the time of searching for a device parameter based on the multi-task Bayesian optimization method that refers to a sensitivity model is also reduced accordingly.
The device parameter determination unit 55 determines, as a device parameter, each parameter value when a predetermined condition is satisfied and the repetition of measurement ends. The determined device parameter is stored in the device parameter storage unit 45 of the control unit 4. At the time of subsequent measurement of the target compound, highly sensitive measurement can be performed by using the device parameter. That is, the third measurement control unit 46 reads out a device parameter from the device parameter storage unit 45 and controls the measurement unit 1 according to the parameter to execute measurement.
[Effect of Signal Intensity Data Correction]
In order to confirm the effect of correcting the signal intensity data obtained in the data collection measurement using the signal intensity data obtained in the reference measurement, the amount of variation in signal intensity when three measurements were performed was examined for six compounds. The six compounds were Reserpine, Acetaminophen, Naproxen, Warfarin, Carbamazepine, and Estrone, but they were treated as different compounds since Warfarin is ionizable in both positive and negative ion modes.
Table 1 indicates the calculation result of the variation amount of the signal intensity value in the presence or absence of the signal intensity data correction.
From Table 1, it can be confirmed that a change in signal intensity over time is sufficiently reduced by the correction of the signal intensity data.
In addition, in order to confirm the effect of correcting signal intensity data in automatically adjusting device parameters, the following comparison experiment was performed.
More specifically, sensitivity models were created for a case where the signal intensity data was corrected and a case where the signal intensity data was not corrected (prior art), and the device parameters were adjusted by the multi-task Bayesian optimization method using the sensitivity models as reference information.
Other parameters may be default values.
When the sensitivity models in
After three initial points are randomly determined on the sensitivity model, the maximum signal intensities and the numbers of times of measurement obtained when 19 points are searched are compared.
As can be seen from
From the above results, it has been confirmed that the correction of the signal intensity data using the signal intensity obtained by the reference measurement is effective in shortening the time required for the automatic adjustment of the device parameters. In addition, reducing the number of times of measurement at the time of automatic adjustment of the device parameters is also useful for reducing the injection amount of the sample and the consumption amount of the mobile phase, various gases, and the like.
The LC-MS according to the above embodiment uses the ESI ion source as an ion source, but may be a mass spectrometer using an ion source based on other ionization methods such as an atmospheric pressure chemical ionization (APCI) method, an atmospheric pressure photoionization (APPI) method, a probe electrospray ionization (PEST) method, and an ionization method in a real-time direct analysis (DART) method. In addition, the mass spectrometer is not limited to a single type mass spectrometer such as a quadrupole mass spectrometer. Obviously, the present invention can be applied to a triple quadrupole mass spectrometer, a quadrupole-time-of-flight mass spectrometer, an ion trap time-of-flight mass spectrometer, and the like.
Furthermore, the above embodiments and modifications are merely examples of the present invention, and it is a matter of course that changes, modifications, additions, and the like appropriately made within the scope of the gist of the present invention are included in the claims of the present application.
The embodiments of the present invention have been described above with reference to the accompanying drawings. Finally, various aspects of the present invention will be described.
A mass spectrometer according to the first aspect of the present invention is a mass spectrometer including an ionization unit, a mass separation unit, and a detection unit, the mass spectrometer including:
A mass spectrometry method according to the first aspect of the present invention is a mass spectrometry method uses a mass spectrometer including an ionization unit, a mass separation unit, and a detection unit, the mass spectrometry method including:
According to the mass spectrometer and the mass spectrometry method according to the first aspect of the present invention, even in a case where the first measurement is repeated for a long time and a temporal change in signal intensity due to various factors cannot be ignored, it is possible to reduce or substantially eliminate the influence of such a temporal change and obtain device parameters that allows highly sensitive measurement. Alternatively, since the accuracy of the reference information for determining the device parameter can be improved, the number of repetitions of measurement can be reduced when determining the device parameters by repetition of measurement based on the reference information. That is, it is possible to efficiently obtain device parameters allowing highly sensitive measurement.
In a mass spectrometer according to the second aspect of the present invention, a result of the first measurement corrected by the correction processing unit in the mass spectrometer according to the first aspect can be the signal intensity obtained from the height or area of a peak on a chromatogram.
The term “chromatogram” as used herein is a graph reflecting a temporal change in ionic strength and includes graphs indicating temporal changes in ionic strength obtained in not only a case where a sample is introduced from a chromatograph to a mass spectrometer but also a case where a sample is introduced to a mass spectrometer by a flow injection analysis (FIA) method and a case where the same sample is repeatedly introduced to a mass spectrometer like an ion source by a probe electrospray ionization method.
A mass spectrometer according to the third aspect of the present invention includes, in the mass spectrometer according to the first aspect, a reference value search time measurement control unit configured to control the ionization unit, the mass separation unit, and the detection unit so as to repeatedly execute measurement on a target sample while changing a value of one parameter or values of a plurality of parameters that influence ionization efficiency in the ionization unit among the plurality of parameters, and a reference value determination unit configured to determine the reference value based on results of the measurements.
In a mass spectrometer according to the fourth aspect of the present invention, one parameter or a plurality of parameters that influence the ionization efficiency in the ionization unit in the mass spectrometer according to the third aspect can include a parameter whose physical quantity is temperature.
According to the mass spectrometers according to the third and fourth aspects of the present invention, since device parameters at the time of reference measurement are appropriately determined, the reproducibility and stability of the signal intensity of the reference measurement itself are improved, and the accuracy of the correction processing based on the result of the reference measurement is improved. As a result, it is possible to search for device parameters with which higher detection sensitivity can be obtained, or it is possible to efficiently search for device parameters with which higher detection sensitivity can be obtained.
In a mass spectrometer according to the fifth aspect of the present invention, the device parameter-related information acquisition unit in the mass spectrometer according to the first aspect uses the measurement results corrected by the correction processing unit to create, as the reference information, a sensitivity model indicating a relationship between values of a plurality of parameters and detection sensitivity.
In a mass spectrometer according to the sixth aspect of the present invention, the sensitivity model in the mass spectrometer according to the fifth aspect can be a model that is referred to when an optimal or nearly optimal device parameter is searched for using an algorithm of a multi-task Bayesian optimization method.
In a mass spectrometer according to the seventh aspect of the present invention, the device parameter-related information acquisition unit in the mass spectrometer according to the sixth aspect can create the sensitivity model by a Gaussian process regression based on the measurement results corrected by the correction processing unit.
In the mass spectrometers according to the fifth to seventh aspects of the present invention, an optimal or nearly optimal device parameter is searched for by an algorithm of a multi-task Bayesian optimization method referring to a highly accurate sensitivity model. This makes it possible to find an optimal or nearly optimal device parameter with a small number of searches, thereby improving the measurement efficiency and suppressing the injection amount of sample. This leads to saving consumption materials such as a mobile phase and a gas.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/JP2019/022464 | 6/6/2019 | WO |
| Publishing Document | Publishing Date | Country | Kind |
|---|---|---|---|
| WO2020/245964 | 12/10/2020 | WO | A |
| Number | Name | Date | Kind |
|---|---|---|---|
| 8754361 | Rafferty | Jun 2014 | B1 |
| 20110256551 | Linder | Oct 2011 | A1 |
| 20190002977 | Hess | Jan 2019 | A1 |
| Number | Date | Country |
|---|---|---|
| 2018-156879 | Oct 2018 | JP |
| Entry |
|---|
| Yusuke Tagawa et al., “LC-MS Interface Parameter Optimization for High Sensitivity Measurement”, Shimadzu Review, 2019, pp. 131-135, vol. 75, No. 3, 4. |
| Kevin Swersky et al., “Multi-Task Bayesian Optimization”, NIPS Proceedings, Harvard University, 2013, 9 pages. |
| International Search Report of PCT/JP2019/022464 dated Sep. 3, 2019 [PCT/ISA/210]. |
| Written Opinion of PCT/JP2019/022464 dated Sep. 3, 2019 [PCT/ISA/237]. |
| Number | Date | Country | |
|---|---|---|---|
| 20220216042 A1 | Jul 2022 | US |
