Signal processing device, signal processing system, and signal processing method

Abstract

To provide a signal processing device that improves an S / N ratio without performing filter design for each sample.SOLUTION: A signal processing device performs signal processing on one or more pieces of analysis data acquired using an analysis device to generate a spectrum. The signal processing device comprises a memory that stores the one or more pieces of analysis data, and a processor that performs signal processing on the one or more pieces of analysis data. Each of the one or more pieces of analysis data includes a plurality of data points. The signal processing device takes, for each of the plurality of data points, a first moving average, which is a moving average of a first number of data points, takes a second moving average, which is a moving average of a second number of data points greater than the first number, calculates a difference between the second moving average and the first moving average, and determines the data point as a signal when the difference is greater than a predetermined threshold. The signal processing device generates a first spectrum including the data points determined as the signals for each of the one or more pieces of analysis data.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present disclosure relates to a signal processing device, a signal processing system, and a signal processing method, and more particularly to a technique for generating a spectrum by performing signal processing on analytical data. [Background technology]

[0002] In a signal processing device that generates a spectrum from analytical data acquired using an analytical device, an improvement in the S / N ratio is desired. Non-Patent Document 1 discloses the results of applying various filters to signals obtained from a TOF-MS (Time-of-Flight Mass Spectrometer). [Prior art documents] [Non-patent literature]

[0003] [Non-Patent Document 1] "Resampling and deconvolution of linear time-of-flight records for enhanced protein profiling", Malyarenko, Rapid Commun Mass Spectrom, 20(11), 1670-1678, 2006, doi: 10.1002 / rcm.2496 Summary of the Invention [Problem to be solved by the invention]

[0004] However, to properly remove noise from analytical data, it is necessary to design an appropriate filter. For example, in TOF-MS, noise characteristics may vary depending on the charge state and / or mass of the target ions. Therefore, to design a filter, it is necessary to perform a pre-analysis under the same conditions as the main analysis and calculate the filter coefficients based on the results of that pre-analysis, which is time-consuming for the user.

[0005] The present disclosure has been made to solve such problems, and aims to provide a signal processing device that improves the S / N ratio without designing a filter for each sample. [Means for solving the problem]

[0006] A signal processing device according to one embodiment of the present disclosure generates a spectrum by signal processing one or more analytical data acquired using an analytical device. The signal processing device includes a memory for storing the one or more analytical data and a processor for signal processing the one or more analytical data. Each of the one or more analytical data includes a plurality of data points. The signal processing device calculates, for each of the plurality of data points, a first moving average that is a moving average of a first number of data points, a second moving average that is a moving average of a second number of data points greater than the first number, calculates a difference between the second moving average and the first moving average, and determines a signal if the difference is higher than a predetermined threshold. The signal processing device generates, for each of the one or more analytical data, a first spectrum including the data points determined to be a signal.

[0007] A signal processing method according to another aspect of the present disclosure generates a spectrum by signal processing one or more analytical data acquired using an analytical device. Each of the one or more analytical data includes a plurality of data points. The signal processing method includes the steps of: calculating a first moving average for each of the plurality of data points, the first moving average being a moving average of a first number of data points; calculating a second moving average for a second number of data points greater than the first number; calculating a difference between the second moving average and the first moving average; and determining a signal if the difference is higher than a predetermined threshold. The signal processing method further includes the step of generating a spectrum including the data points determined to be a signal for each of the one or more analytical data. [Effects of the Invention]

[0008] According to the present disclosure, it is possible to provide a signal processing device that improves the S / N ratio without designing a filter for each sample. [Brief explanation of the drawings]

[0009] [Figure 1] 1 is a schematic configuration diagram of a signal processing system according to an embodiment. [Figure 2] 4 is a flowchart showing signal processing according to the first embodiment. [Figure 3] 10 is a flowchart showing signal processing according to the second embodiment. [Figure 4] FIG. 10 is a diagram showing the difference in moving averages in an example. [Figure 5] FIG. 10 is a diagram showing a first spectrum in an example. [Figure 6] FIG. 10 is a diagram showing a second spectrum in an example. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings. In the drawings, the same or corresponding parts are designated by the same reference numerals, and description thereof will not be repeated.

[0011] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings. In the drawings, the same or corresponding parts are designated by the same reference numerals, and description thereof will not be repeated.

[0012] [Configuration of signal processing device and signal processing system] 1 is a schematic configuration diagram of a signal processing system 100 according to an embodiment. The signal processing system 100 includes a signal processing device 1 according to an embodiment and an analysis device 2.

[0013] The signal processing device 1 generates a spectrum by performing signal processing on one or more pieces of analytical data acquired using the analysis device 2. The spectrum is represented, for example, as a graph in which a first axis (e.g., the horizontal axis) represents a physical quantity corresponding to a component contained in a sample or a numerical value correlated to the physical quantity, and a second axis (e.g., the vertical axis) represents a signal intensity corresponding to the value on the horizontal axis. The signal processing device 1 includes a processor 11, a memory 12, an input / output interface (I / F) 13, a display 14, and an input device 15.

[0014] The processor 11 performs signal processing on one or more pieces of analytical data acquired using the analysis device 2 (details will be described later). The processor 11 is typically an arithmetic processing unit such as a CPU (Central Processing Unit) or an MPU (Micro Processing Unit). The processor 11 reads and executes programs stored in the memory 12 to perform various processes.

[0015] The memory 12 stores one or more pieces of analytical data acquired using the analysis device 2. The memory 12 is realized by a storage device such as a read-only memory (ROM), a random access memory (RAM), and a hard disk drive (HDD). The ROM can store programs executed by the processor 11. The RAM can temporarily store data used during execution of a program by the processor 11 and can function as a temporary data memory used as a working area. The HDD is a non-volatile storage device. A semiconductor storage device such as a flash memory may be used in addition to or instead of the HDD. The programs and / or data may be stored in an external storage device accessible by the processor 11.

[0016] The input / output I / F 13 is an interface for exchanging various types of data between the processor 11 and external devices connected to the input / output I / F 13. The external devices include a display 14, an input device 15, and an analysis device 2. The display 14 displays, for example, the processing results of the processor 11. The input device 15 is typically configured with a touch panel, a keyboard, a mouse, etc. The input device 15 accepts input operations by the user to the processor 11.

[0017] In one embodiment, the signal processing device 1 acquires one or more pieces of analytical data acquired using the analysis device 2 via the input / output I / F 13. In another embodiment, the signal processing device 1 may acquire the analytical data via a storage medium on which the analytical data is stored, or via a communication interface (I / F) not shown.

[0018] The analytical device 2 performs an analysis to obtain one or more analytical data for generating a spectrum. The analytical device 2 includes, for example, an introduction unit 20 that introduces a sample, an analytical unit 22 that analyzes the sample, an A / D converter 24 that performs analog-to-digital (A / D) conversion of the detection signal obtained by the analytical unit 22, and a control unit 26 that controls the entire analytical device 2.

[0019] In one embodiment, the analysis device 2 is a time-of-flight mass spectrometer (TOF-MS). The following describes the configuration when the analysis device 2 is a time-of-flight mass spectrometer (TOF-MS).

[0020] The control unit 26 controls various electrical components, including electrodes, arranged in the analysis unit 22, and the power supply unit 23. The power supply unit 23 applies a predetermined voltage to each of the various electrical components in the analysis unit 22 based on commands from the control unit 26. The control unit 26 is configured, for example, by a microcomputer.

[0021] The introduction unit 20 includes an electrospray ionization (ESI) source 201. The ESI source 201 sprays a liquid sample into an ionization chamber 202 while imparting an electric charge to the sample. This ionizes the compounds in the sample. Typically, the same sample is introduced into the ESI source 201 in multiple batches. This allows the same number of analytical data to be acquired as the number of times the sample is introduced.

[0022] However, the method for ionizing compounds is not limited to this. For example, a method using another ion source, such as an atmospheric pressure chemical ion source, may be used. An ion source that ionizes a gas sample or a solid sample, rather than a liquid sample, may also be used.

[0023] The ions generated in the injection section 20 are analyzed in the analysis section 22 as follows: In the analysis section 22, the ions are moved along the ion optical axis C1 as follows.

[0024] Ions in the ionization chamber 202 are first sent to a first vacuum chamber 222 through a desolvation tube 221 and focused by an ion guide 223. The ions focused by the ion guide 223 are sent from the first vacuum chamber 222 to a second vacuum chamber 225 via a skimmer 224. The ions sent to the second vacuum chamber 225 are focused by an ion guide 226. The ions focused by the ion guide 226 are sent from the second vacuum chamber 225 to a third vacuum chamber 227.

[0025] A quadrupole mass filter 228 and a collision cell 229 are arranged in the third vacuum chamber 227. A multipole ion guide 230, an entrance lens electrode 231, and an exit lens electrode 232 are arranged in the collision cell 229. The collision cell 229 functions as an ion trap that accumulates ions. The signal processing system 100 according to this embodiment repeats the accumulation of ions in the ion trap and the ejection of ions from the ion trap for each sequence.

[0026] The ions sent to the third vacuum chamber 227 are introduced into the quadrupole mass filter 228. A voltage corresponding to the ions to be analyzed is applied to the quadrupole mass filter 228.

[0027] Therefore, of the ions introduced into the quadrupole mass filter 228, only ions having a specific mass-to-charge ratio (m / z) corresponding to the applied voltage pass through the quadrupole mass filter 228. Ions that pass through the quadrupole mass filter 228 are called "precursor ions." The precursor ions are introduced into the collision cell 229. A CID (Collision Induced Dissociation) gas inlet 233 supplies CID gas to the collision cell 229. The precursor ions introduced into the collision cell 229 collide with the CID gas and are dissociated. As a result, various product ions are generated within the collision cell 229.

[0028] The product ions are temporarily accumulated in the collision cell 229 by the action of an ion trap formed by an ion guide 230 , an entrance lens electrode 231 , and an exit lens electrode 232 .

[0029] The ions accumulated in the collision cell 229 are emitted as a group of ion packets from the collision cell 229 toward the fourth vacuum chamber 234. An ion transport optical system 235 consisting of a plurality of electrodes is disposed between the third vacuum chamber 227 and the fourth vacuum chamber 234. The ions emitted from the collision cell 229 are guided by the ion transport optical system 235 and introduced into the fourth vacuum chamber 234.

[0030] The fourth vacuum chamber 234 is provided with an orthogonal accelerator 236, a flight tube 237, a reflector 238, and a detector 239. The reflector 238 includes a reflectron 2381 and a backplate 2382. The flight tube 237 defines a flight space 2371 in which ions fly.

[0031] Ions introduced into the orthogonal acceleration unit 236 in the X-axis direction are accelerated in the Z-axis direction and enter a flight space 2371. An electric field that causes the ions to fly back along path C2 is formed in the flight space 2371. Ions that fly from the orthogonal acceleration unit 236 toward the reflector 238 make a U-turn due to the action of the reflected electric field formed by the reflector 238 and enter the flight space 2371 again. The ions then reach the detector 239.

[0032] In the orthogonal acceleration unit 236, ions with smaller m / z are accelerated at a higher speed. Therefore, the time of flight (TOF) of the ions from the ion trap through the orthogonal acceleration unit 236 to the detector 239 varies depending on the m / z of the ions. As a result, the ions are separated according to their m / z. The detector 239 detects the ions in order of m / z. As a result, the detector 239 generates an ion intensity signal as a detection signal. The detector 239 is connected to the A / D converter 24.

[0033] The A / D converter 24 performs A / D conversion on the ion intensity signal received from the detector 239 of the analytical section 22 and transmits the signal to the signal processing device 1 .

[0034] The signal processing device 1 generates "one-shot data" indicating the relationship between ion intensity and time of flight (TOF) using the ion intensity signal digitized by the A / D converter 24. The one-shot data corresponds to an example of "analysis data."

[0035] The signal processing system 100 repeatedly executes a sequence of generating ions in the inlet 20, ejecting the ions accumulated in the collision cell 229, and detecting the ions according to m / z by the detector 239. This generates the same number of "one-shot data" as the number of executed sequences. The TOF range and sampling interval are the same for each sequence. The multiple one-shot data correspond to one example of "multiple analytical data obtained by performing multiple analyses on the same sample."

[0036] The signal processing device 1 generates a TOF spectrum using the generated multiple one-shot data. The TOF spectrum corresponds to one example of a "spectrum." The TOF spectrum is represented by a graph in which the first axis (e.g., the horizontal axis) represents TOF correlated with m / z of ions derived from the sample, and the second axis (e.g., the vertical axis) represents signal intensity corresponding to TOF.

[0037] More specifically, the signal processing device 1 generates a spectrum with an improved S / N ratio by a signal processing method according to an embodiment described below. In this specification, the term "signal" refers to a signal component that reflects the component to be detected, and the term "noise" refers to a signal component that does not reflect the component to be detected (for example, a signal component generated in the signal processing from the detector 239 to the signal processing device 1). Those skilled in the art may also refer to the "signal" and "noise" as "signal component" and "noise component," respectively.

[0038] In another embodiment, the analyzer 2 is a mass spectrometer other than a TOF-MS, in which case the signal processor 1 generates a mass spectrum other than a TOF spectrum.

[0039] In yet another embodiment, the analysis device 2 includes a chromatograph and / or a spectrometer, in which case the signal processing device 1 generates a spectrum according to the type of analysis device 2.

[0040] [Conventional signal processing device] Conventionally, in signal processing of analytical data acquired using an analytical device such as a TOF-MS, various measures have been taken to improve the S / N ratio, such as lengthening the accumulation time of the detection signal received from the A / D converter and performing filtering to remove noise.

[0041] The filtering process generally requires preprocessing such as distinguishing between signal and noise, determining filter coefficients, etc., on the assumption that the detected signal is an impulse response signal. For example, in the filtering process of TOF-MS, it is necessary to distinguish between signal and noise and determine filter coefficients on the assumption that the ion intensity signal of one-shot data is an impulse response signal.

[0042] Common causes of noise in analytical data include ringing from the electrical system of the analytical instrument, detector characteristics, and quantization errors during A / D conversion. In addition, in cases where noise characteristics can vary depending on the characteristics of the components being detected, such as in TOF-MS, the effects of this must also be considered. For example, the noise characteristics of TOF-MS one-shot data may vary depending on the charge state and / or mass of the ions being detected.

[0043] Therefore, when applying a filter to analytical data acquired using an analytical instrument such as TOF-MS, it is necessary to perform a pre-analysis under the same conditions as the main analysis and set the filter coefficients based on the results of the pre-analysis. Conversely, if filter coefficients are used that are not based on the results of the pre-analysis, it may not be possible to reduce noise in the main analysis data, or the signal intensity may be reduced.

[0044] Furthermore, when noise ranges over a wide range from low to high frequencies relative to the sampling rate of the A / D converter, it may not be possible to determine filter coefficients that can effectively remove or reduce noise.

[0045] As described above, the method of applying a filter to analytical data acquired using an analytical device such as a TOF-MS has problems such as the time-consuming task of setting the filter coefficients in advance and the fact that the filter coefficients may not be determined.

[0046] In view of the above circumstances, the signal processing according to the embodiment improves the S / N ratio of the spectrum without designing a filter for each sample.

[0047] [Embodiment 1] The signal processing according to the first embodiment is used when one spectrum is generated from one analytical data.

[0048] Fig. 2 is a flowchart showing signal processing according to embodiment 1. Each step (hereinafter simply referred to as "S") in Fig. 2 is performed by the processor 11 of the signal processing device 1. The processing in Fig. 2 will be described below with reference to some examples (Figs. 4 and 5) in which the signal processing according to embodiment 1 is applied to TOF-MS.

[0049] In S02, the processor 11 acquires one or more analytical data (see "Analysis Data" in FIG. 4). Each of the one or more analytical data includes a plurality of data points.

[0050] In one embodiment of S02, the processor 11 acquires one or more one-shot data acquired using a TOF-MS. Each of the one or more one-shot data includes a plurality of data points. Each of the plurality of data points is defined by a predetermined TOF and an ion intensity detected in the TOF.

[0051] In S04, processor 11 calculates a first moving average, which is a moving average of a first number of data points, for each of the plurality of data points (see "First Moving Average" in FIG. 4).

[0052] In S06, processor 11 calculates a second moving average, which is a moving average of a second number of data points, for each of the multiple data points (see "Second Moving Average" in FIG. 4). The second number is larger than the first number. Although not limited thereto, the second number is, for example, between 2 and 20 times the first number, and more specifically, between 5 and 15 times the first number. In the embodiment described below, the second number is 10 times the first number.

[0053] In S08, the processor 11 calculates the difference between the second moving average and the first moving average for each of the plurality of data points (see "Difference" in FIG. 4).

[0054] In S10, the processor 11 determines whether the difference is equal to or greater than a predetermined threshold. In one embodiment, the predetermined threshold is calculated based on the average value of a predetermined number of data points obtained after the start of analysis and the standard deviation of the predetermined number of data points. For example, the predetermined threshold is the average value plus several times the standard deviation. The predetermined number after the start of analysis is the number of data points obtained during a predetermined period after the start of analysis when a sample has not yet been detected. The predetermined number of data points obtained during this predetermined period are considered to include only the base value of the analysis data (the baseline after the start of analysis) and noise, without any signal derived from the sample. Therefore, a value that takes into account the variation in the base value and noise can be used as the predetermined threshold. As a result, if the difference is different from the base value by more than the variation in noise, the data point can be determined to be a signal.

[0055] If the difference is equal to or greater than a predetermined threshold for each of the plurality of data points (YES in S10), the processor 11 determines it as a signal in S12 (see FIG. 5).

[0056] If the difference is less than a predetermined threshold for each of the multiple data points (NO in S10), the processor 11 determines it to be noise in S14.

[0057] In S16, the processor 11 generates a first spectrum including data points determined to be signals for each of the one or more pieces of analytical data, and ends the process.

[0058] 2, the first number and the second number are determined according to the characteristics of the signal and the characteristics of the noise in the analysis data of the signal processing system 100. The first number and the second number are determined in advance for each signal processing system 100, for example.

[0059] The first number is determined to be a value that reduces (smoothes) the influence of high-frequency components by averaging the analysis data, for example. More specifically, the first number is determined to be equal to or greater than the number of data points corresponding to one wavelength of the high-frequency components. The high-frequency components are considered to reflect, for example, high-frequency noise that occurs in signal processing from the detector 239 to the signal processing device 1. With this configuration, the influence of high-frequency noise can be mitigated in S04.

[0060] The value of the first number is determined based on the characteristics of the signal processing system 100, but is, for example, 10 in the embodiment of Figure 4. In other words, in the embodiment of Figure 4, the first moving average is a moving average of 10 data points.

[0061] The second number is set to take the average over a wider range than the first number. By setting the second number in this way, a local baseline can be calculated using the second moving average. Therefore, the signal is emphasized in a graph that shows the difference between the first and second moving averages.

[0062] More specifically, the second number is set in consideration of the balance with the first number. For example, in S08, the difference between the first and second moving averages is determined to be a value that allows detection of peaks (signals) derived from the target component. For example, if the second number is similar to the first number, the first and second moving averages will be nearly identical, making it difficult to detect signals in the difference. On the other hand, if the second number is set too large compared to the first number, the second moving average will approach the average signal intensity of the entire analysis data, making it impossible to detect signals smaller than the average. Furthermore, this method cannot handle changes in the baseline of the analysis data. The value of the second number is determined based on the characteristics of the signal processing system 100. In the example of FIG. 4, the second number is 10.0 times the first number, or 100. In other words, in the example of FIG. 4, the second moving average is a moving average of 100 data points.

[0063] In S10 to S14, if the difference between the first moving average and the second moving average is equal to or greater than a predetermined threshold, the corresponding data point is determined to be a signal, and the remaining data points are determined to be noise, thereby leaving the data points determined to be signals and removing the data points determined to be noise from the analysis data. Specifically, for example, processor 11 generates a spectrum in which the signal intensity of data points determined to be signals remains the same, while the signal intensity of data points determined to be noise is set to 0. This makes it possible to improve the S / N ratio of the first spectrum, which is the corrected data, compared to the S / N ratio of the analysis data, which is the data before correction.

[0064] [Embodiment 2] The signal processing according to the second embodiment is mainly used when generating one spectrum from multiple pieces of analytical data obtained by analyzing the same sample multiple times.

[0065] Fig. 3 is a flowchart showing signal processing according to embodiment 2. Each step in Fig. 5 is performed by the processor 11 of the signal processing device 1. The processing in Fig. 3 will be described below with reference to an example (Fig. 6) in which embodiment 1 is applied to TOF-MS.

[0066] 3, the processor 11 acquires a plurality of analytical data sets obtained by performing multiple analyses on the same sample, each of which includes a plurality of data points.

[0067] In one embodiment of S02, the processor 11 acquires a plurality of one-shot data sets obtained by performing a plurality of TOF-MS analyses on the same sample.

[0068] The processing from S04 to S14 in FIG. 3 corresponds to the processing from S02 to S14 in FIG. In S16, processor 11 generates a second spectrum by integrating data points determined to be signals from each of the plurality of analytical data. In other words, the second spectrum is an integrated spectrum obtained by integrating signals from the plurality of analytical data (see "Second spectrum" in FIG. 6).

[0069] The signal processing device 1 may perform S02 to S14 each time a plurality of analytical data are obtained, or may perform S02 to S14 for each analytical data after all of the plurality of analytical data have been obtained. In either case, the processing of Fig. 3 does not require user operation and is performed only by internal processing of the signal processing device 1.

[0070] 3, only the signal intensities of data points determined to be signals in each of the plurality of analytical data are integrated, and the signal intensities of data points determined to be noise are not integrated. Specifically, for example, processor 11 leaves the signal intensities of data points determined to be signals unchanged and sets the signal intensities of data points determined to be noise to 0 in each of the plurality of analytical data, and then integrates the signal intensities of signals in each of the plurality of analytical data to generate a second spectrum. Therefore, the second spectrum has an improved S / N ratio compared to a simply integrated spectrum obtained by simply integrating each of the plurality of analytical data (see FIG. 6).

[0071] [Example] 4 to 6 are diagrams showing stepwise results of signal processing according to the embodiment in an example.

[0072] In the examples, the signal processing according to the embodiment was applied to analytical data obtained as a result of analyzing myoglobin (molecular weight 17,000 Da) in a TOF-MS using the ESI method for ionization.

[0073] FIG. 4 is a diagram showing the difference between moving averages in an example. In the example of FIG. 4, a moving average of 10 points of data was taken as the first moving average. Then, a moving average of 100 points of data was taken as the second moving average. Referring to FIG. 4, in the difference between the first and second moving averages, high frequency components superimposed on the main peaks (peaks indicated by symbols P1 and P2) of the one-shot data, which is the data before correction, have been removed.

[0074] FIG. 5 shows a first spectrum in an example. In the example of FIG. 5, the threshold value used was the sum of the average value of the first 100 points of one-shot data and 10 times the standard deviation of the first 100 points of one-shot data. Using this threshold value, the horizontal axis range where the difference value was equal to or greater than the threshold was determined to be the signal range, and the horizontal axis range outside the signal range was determined to be the noise range. A first spectrum was generated in which the signal values ​​of the one-shot data corresponding to the signal range were left unchanged and the signal values ​​of the one-shot data corresponding to the noise range were replaced with 0. Referring to FIG. 5, the first spectrum retained the signal intensities of peaks that were considered to correspond to signals and exhibited significantly higher signal intensities than the surrounding signal intensities, while peaks that were considered to correspond to noise and exhibited slightly higher signal intensities than the surrounding signal intensities were removed.

[0075] FIG. 6 shows a second spectrum in an example. The second spectrum in FIG. 6 (solid line data in FIG. 6 ) was generated by integrating multiple first spectra, each corrected from multiple one-shot data obtained by repeatedly analyzing the same sample. Compared to a conventional simply integrated spectrum (dotted line data in FIG. 6 ), in which all one-shot data were simply integrated without correction, the value of the valley between peaks corresponding to noise in the second spectrum was reduced. Meanwhile, the signal values ​​in the signal range of the one-shot data were maintained in the corrected first spectrum. Therefore, the second spectrum obtained by integrating the first spectra was almost the same as the conventional simply integrated spectrum. As described above, the signal processing according to this embodiment reduces noise while maintaining signal intensity, and the S / N ratio of the second spectrum was improved by approximately 1.7 times compared to the S / N ratio of the simply integrated spectrum. Note that in the example of FIG. 6 , the S / N ratio was calculated as the ratio of the signal intensity of the maximum peak in each spectrum to the signal intensity of the valley adjacent to the maximum peak.

[0076] As described above, according to the signal processing device 1 and the signal processing method according to each of the first and second embodiments, it is possible to provide a signal processing device that improves the S / N ratio without designing a filter for each sample.

[0077] In particular, when the analytical device 2 is a TOF-MS, noise can affect the charge and / or mass of the ions to be detected, but it is possible to obtain a TOF spectrum by removing noise from one-shot data and accumulating only the signal, without the need for pre-analysis and the effort of designing filter coefficients.

[0078] Even when the analysis device 2 is a mass spectrometer other than a TOF-MS, a chromatograph, and / or a spectrometer, various spectra with improved S / N ratios can be obtained without the need for filter design.

[0079] [Aspect] It will be appreciated by those skilled in the art that the exemplary embodiments described above are examples of the following aspects.

[0080] (Item 1) A signal processing device according to one embodiment generates a spectrum by signal processing one or more pieces of analytical data acquired using an analytical device. The signal processing device includes a memory for storing the one or more pieces of analytical data and a processor for signal processing the one or more pieces of analytical data. Each of the one or more pieces of analytical data includes a plurality of data points. The signal processing device calculates, for each of the plurality of data points, a first moving average that is a moving average of a first number of data points, a second moving average that is a moving average of a second number of data points greater than the first number, calculates the difference between the second moving average and the first moving average, and determines that a signal exists if the difference is higher than a predetermined threshold. The signal processing device generates, for each of the one or more pieces of analytical data, a first spectrum including the data points determined to be a signal.

[0081] According to the signal processing device described in paragraph 1, it is possible to provide a signal processing device that improves the S / N ratio without designing a filter for each sample.

[0082] (Item 2) In the signal processing device described in item 1, the one or more analytical data include a plurality of analytical data. The plurality of analytical data are obtained by performing multiple analyses on the same sample. The signal processing device generates a second spectrum by integrating data points determined to be signals among the plurality of analytical data.

[0083] According to the signal processing device described in paragraph 2, the second spectrum can be generated by integrating the signal intensities of the signals in each of the plurality of analytical data. Therefore, the second spectrum has an improved S / N ratio compared to a simply integrated spectrum obtained by simply integrating each of the plurality of analytical data.

[0084] (Item 3) In the signal processing device described in item 1 or 2, the predetermined threshold is calculated based on the average value of a predetermined number of data points among the multiple data points after the start of analysis and the standard deviation of the predetermined number of data points.

[0085] According to the signal processing device described in paragraph 3, a value that is the base of the analysis data and that takes into account noise variations can be adopted as the predetermined threshold value.

[0086] (4) In the signal processing device according to any one of paragraphs 1 to 3, the second number is between 2 and 20 times the first number.

[0087] According to the signal processing device described in paragraph 4, the second number can be set based on the first number. (Item 5) In the signal processing device according to any one of items 1 to 4, the analysis device includes a TOF-MS (Time-of-Flight Mass Spectrometer).

[0088] According to the signal processing device described in paragraph 5, noise can affect the charge and / or mass of the ions to be detected, but it is possible to obtain a TOF spectrum by removing noise from one-shot data and accumulating only the signal, without the need for pre-analysis and the effort of designing filter coefficients.

[0089] (Item 6) In the signal processing device according to any one of items 1 to 4, the analysis device includes a mass spectrometer other than a TOF-MS, a chromatograph, and / or a spectroscope.

[0090] The signal processing device described in paragraph 6 can also obtain various spectra with improved S / N ratios without the need for filter design.

[0091] (Item 7) A signal processing system comprising an analyzer and the signal processing device according to any one of items 1 to 6.

[0092] According to the signal processing system described in paragraph 8, it is possible to provide a signal processing device that improves the S / N ratio without designing a filter for each sample.

[0093] (Item 8) A signal processing method according to another aspect generates a spectrum by signal processing one or more analytical data acquired using an analytical device. Each of the one or more analytical data includes a plurality of data points. The signal processing method includes the steps of: calculating a first moving average for each of the plurality of data points, the first moving average being a moving average of a first number of data points; calculating a second moving average for a second number of data points greater than the first number; calculating the difference between the second moving average and the first moving average; and determining a signal if the difference is higher than a predetermined threshold. The signal processing method further includes the step of generating a spectrum including the data points determined to be a signal for each of the one or more analytical data.

[0094] According to the signal processing device described in paragraph 8, it is possible to provide a signal processing device that improves the S / N ratio without designing a filter for each sample.

[0095] The embodiments disclosed herein should be considered to be illustrative in all respects and not restrictive. The scope of the present invention is defined by the claims, not by the above description, and is intended to include all modifications within the meaning and scope of the claims. [Explanation of symbols]

[0096] 1 signal processing device, 2 analyzer, 11 processor, 12 memory, 13 input / output interface (I / F), 14 display, 15 input device, 20 introduction section, 22 analysis section, 23 power supply section, 24 A / D converter, 26 control section, 100 signal processing system, 201 electrospray ion (ESI) source, 202 ionization chamber, 221 desolvation tube, 222 first vacuum chamber, 223 ion guide, 224 skimmer, 225 second vacuum chamber, 226 ion guide, 227 third vacuum chamber, 228 quadrupole mass filter, 229 collision cell, 230 ion guide, 231 entrance lens electrode, 232 exit lens electrode, 233 CID gas introduction section, 234 fourth vacuum chamber, 235 ion transport optics, 236 orthogonal acceleration section, 237 flight tube, 238 reflector, 239 Detector, 2371 flight space, 2381 reflectron, 2382 backplate, C1 ion optical axis, C2 path.

Claims

1. A signal processing device that performs signal processing on one or more pieces of analysis data acquired using an analysis device to generate a spectrum, the signal processing device includes a memory that stores the one or more pieces of analytical data, and a processor that performs signal processing on the one or more pieces of analytical data; each of the one or more analytical data includes a plurality of data points; The signal processor calculates, for each of the plurality of data points: Taking a first moving average, which is a moving average of a first number of data points; taking a second moving average that is a moving average of a second number of data points that is greater than the first number; Calculating a difference between the second moving average and the first moving average; If the difference is higher than a predetermined threshold, it is determined to be a signal; The signal processing device generates a first spectrum including data points determined to be the signal for each of the one or more analysis data.

2. the one or more analytical data comprises a plurality of analytical data; The plurality of analytical data are obtained by performing a plurality of analyses on the same sample, The signal processing device includes: The signal processing device according to claim 1 , wherein the second spectrum is generated by integrating data points determined to be the signal among the plurality of analysis data.

3. 3. The signal processing device according to claim 1, wherein the predetermined threshold is calculated based on an average value of a predetermined number of data points among the plurality of data points after the start of analysis and a standard deviation of the predetermined number of data points.

4. The signal processing device according to claim 1 , wherein the second number is equal to or greater than two times and equal to or less than twenty times the first number.

5. 3. The signal processing device according to claim 1, wherein the analysis device includes a time-of-flight mass spectrometer (TOF-MS).

6. 3. The signal processing device according to claim 1, wherein the analysis device includes a mass spectrometer other than a TOF-MS, a chromatograph, and / or a spectrometer.

7. A signal processing system comprising the analysis device and the signal processing device according to claim 1 .

8. A signal processing method for generating a spectrum by signal processing one or more pieces of analysis data acquired using an analysis device, comprising: each of the one or more analytical data includes a plurality of data points; The signal processing method includes, for each of the plurality of data points: taking a first moving average, the first moving average being a moving average of a first number of data points; taking a second moving average that is a moving average of a second number of data points that is greater than the first number; calculating a difference between the second moving average and the first moving average; and determining the difference as a signal if the difference is higher than a predetermined threshold value; The signal processing method further comprises the step of generating a spectrum including data points determined to be the signal for each of the one or more analysis data.

Images