JP2015200532A - Signal waveform data processing device - Google Patents

Abstract

PROBLEM TO BE SOLVED: To calculate a base line with high precision and improve the accuracy of a peak height, etc., even in cases where signal characteristics abruptly change on a raw spectral waveform.SOLUTION: A bottom point detection unit 21 detects all of bottom points in a trough between peaks on a spectral waveform that is an object to be processed, and a specific section extraction unit 22 extracts a section, among sections between two bottom points adjacent on m/z axis, whose section length is less than a prescribed section length. An intra-section interpolation unit 23 replaces a signal waveform within the section with a straight line in which the intensity value of bottom points at both ends of each extracted section is linearly interpolated. A termination determination unit 24 determines whether the gentleness of the waveform due to the processed data smoothed by such an interpolation has reached a prescribed permissible state, and, if the permissible state is not reached, performs processing over again from the bottom point detection. Such processing is repeated, and the processed data at the time the waveform reached the permissible state is outputted as a base line.

Description

  The present invention relates to a data processing apparatus that processes signal waveform data obtained by an analyzer, and more particularly to a data processing apparatus that obtains a baseline of a signal waveform such as a mass spectrum or a chromatogram.

  Raw spectrum (profile spectrum) data obtained by a mass spectrometer and raw chromatogram data obtained by a chromatograph include electrical noise, noise from various reagents used during sample preparation, sample preparation, etc. A background noise including the above is superimposed and appears as a baseline in a signal waveform such as a spectrum or a chromatogram. Therefore, in order to accurately calculate the peak height and area, it is necessary to perform a baseline removal process in which a baseline is accurately obtained and subtracted from a spectrum, a chromatogram, or the like. Conventionally, various methods have been proposed as such a baseline removal method.

  Non-Patent Document 1 is an overview paper on a data processing algorithm of a mass spectrometer, in which a plurality of existing baseline removal methods are described in the paragraph “Baseline Correction”. In the main method described here, a window region having a predetermined width is set in the horizontal axis direction (that is, the mass-to-charge ratio direction) for the raw mass spectrum to be processed, and this window region is set in the horizontal axis direction. While moving in a certain step, a baseline value calculation process is executed for each window area to obtain a baseline value (intensity value) for each window area. Then, the baseline value obtained in each window area is regarded as the intensity value in the mass-to-charge ratio value in the center of the window area, and the baseline is obtained by interpolating between the intensity values in the adjacent window areas. . Various methods such as a method using an average value, a method using a minimum value, and a method using a median value are conceivable as a method for calculating a baseline value in each window region.

  Non-Patent Document 2 describes the specification of the mass spectrum baseline removal function “msbackadj” in a well-known commercially available numerical analysis software (MatLab). This algorithm is based on the base described in Non-Patent Document 1 above. It is a typical example of a line calculation method. FIG. 4 is a spectrum showing an example of baseline calculation using this software “msbackadj” and the result of baseline removal. (A) is a raw spectrum waveform and a baseline obtained from the waveform, and (b) is a spectrum waveform subjected to baseline removal. A good baseline can be obtained when the change in signal characteristics on the spectrum waveform is relatively small as in this example. Note that, for such spectral waveform data, not only the method described in Non-Patent Document 2, but also any method described in Non-Patent Document 1, there is no significant difference in the baseline calculation results.

  However, in the above-described existing baseline calculation method, an appropriate baseline is not always obtained for a spectrum in which the signal characteristics on the signal waveform change greatly and suddenly. FIG. 5 shows the raw spectral data obtained by a mass spectrometer combining a matrix-assisted laser desorption / ionization (MALDI) method and in-source decay (ISD). FIG. 11 is a diagram illustrating a result of performing baseline calculation and baseline removal using the “msbackadj”. As can be seen from FIG. 5A, in the region where the signal characteristics on the signal waveform change suddenly, the baseline waveform followability is poor. Then, as shown in FIG. 5B, the peak height in the spectrum after the removal of the baseline becomes inaccurate in such a region where the waveform followability of the baseline is poor.

In “msbackadj” described above, several parameters including the size of the window area can be adjusted when calculating the baseline. Therefore, FIG. 6 shows the result of calculating the baseline by performing parameter adjustment on the same spectrum data as shown in FIG. 5A and removing the baseline. In this example, the waveform followability of the baseline is improved in a region where the signal characteristics on the signal waveform change suddenly (A portion in FIG. 6A). However, the shape of the baseline is not appropriate for the spectrum waveform in the other region (B portion in FIG. 6A), and the peak height becomes inaccurate as a result of removing the baseline. (Part indicated by B ′ in FIG. 6B).
As described above, if the peak height becomes inaccurate as a result of the baseline removal, the quantitative analysis accuracy using the peak height and area is lowered. For this reason, even if a sudden change in the signal characteristics on the signal waveform as described above exists in the spectrum, a baseline calculation algorithm that can accurately determine the baseline with high waveform followability is required. is there.

  In addition, the above-mentioned “msbackadj” has been devised in various ways in the existing algorithm, and although the baseline calculation accuracy is relatively high, the number of parameters that can be adjusted is large (specifically, 6). Adjustment of parameters is cumbersome and not easy. That is, even if there is dissatisfaction with the result obtained under a certain parameter, it is quite difficult for the user to determine how much to increase or decrease which parameter, and after all, trial and error However, there is no choice but to try to increase or decrease each parameter and evaluate the baseline calculation results.

C. Yang and one other, “Comparison of public peak detection algorithms for MALDI mass spectrometry data analysis” ”BMC Bioinformatics, 2009, 10: 4, [Search March 20, 2014], Internet <URL: http://www.biomedcentral.com/1471-2105/10/4> "Mathworks Documentation Center msbackadj", Massworks Inc. (Mathworks), [March 20, 2014 search], Internet <URL: http://www.mathworks.com/help/bioinfo/ref/msbackadj.html> Shigeo Minami, “Waveform Data Processing for Scientific Measurement, Utilization Technology of Microcomputer / PC in Measurement System”, CQ Publisher, April 1986

  The present invention has been made to solve the above-described problems, and the object of the present invention is to perform complicated parameter adjustment even when the signal characteristics change suddenly on the signal waveform. An object of the present invention is to provide a signal waveform data processing apparatus capable of calculating a highly accurate baseline and thereby obtaining a peak waveform having an accurate height and area.

The present invention made to solve the above problems is a signal waveform data processing device for processing signal waveform data obtained by an analyzer,
a) A processing target data input unit for inputting signal waveform data to be processed as an initial value of the processing data;
b) a bottom point detection unit for detecting a bottom point of a valley between peaks with respect to the waveform according to the processing data;
c) Over the entire range of the signal waveform data to be processed, a signal waveform in a section in which the section length is less than a predetermined section length upper limit value is defined as one section between two adjacent bottom points. A section interpolation unit between the bottom points that replaces with a straight line or a curve interpolated using the value of the bottom point that is the end point of the section;
d) It is determined whether or not the gentleness of the waveform shape of the processed data after correction by the inter-bottom interval interpolating section has reached a predetermined allowable state, and processing is performed if the predetermined allowable state has been reached. On the other hand, if the predetermined allowable state has not been reached, a processing end determination unit that continues processing as new processing data to be processed by the bottom point detection unit after the correction has been performed,
It is characterized by having.

  Here, the signal waveform data to be processed includes, for example, spectrum data obtained by a mass spectrometer, chromatogram data obtained by a chromatograph device, and the like.

  In the signal waveform data processing apparatus according to the present invention, the bottom point detection of the valley by the bottom point detection unit can use various conventionally known methods. In addition, since the bottom point detection and the peak detection are substantially the same in terms of waveform processing, the bottom point can be detected using various conventionally known peak detection methods. Specifically, for example, the slope of the signal waveform is sequentially examined, a point where the downward slope becomes zero and the point is turned upward may be searched, and the bottom point may be determined. This corresponds to searching for a zero value when the first derivative of the signal waveform changes from a negative value to a positive value. In addition, in order to find the bottom of the valley that does not show a clear concave shape because another peak is superimposed on the slope of one peak, the second and third derivative values of the signal waveform are used. You may make it utilize.

  Now, assuming that the signal waveform data to be processed is spectrum data obtained by the mass spectrometer, in the signal waveform data processing apparatus according to the present invention, the bottom point detection unit is first input by the processing target data input unit. For the spectral data over a predetermined mass-to-charge ratio range, the bottom points of all valleys are detected. Next, the inter-bottom interval interpolating unit sets the interval length to less than a predetermined interval length upper limit for all the detected bottom points, with one interval between two adjacent bottom points on the mass-to-charge ratio axis. A section is extracted, and the signal waveform in the extracted section is replaced with a straight line or a curve obtained by interpolation processing using the values of the bottom points that are both end points of the section. The simplest interpolation processing is linear interpolation, but interpolation processing using a higher order curve may be used. The predetermined section length upper limit value may be determined in advance by the manufacturer of the apparatus or may be set appropriately by the user. In any case, a relatively narrow peak waveform in which the distance between adjacent bottom points is less than the upper limit of the section length is replaced with a straight line or at least a gentler curve than the peak waveform. At least a part of the signal waveform of the machining data is equalized by the interpolation by the section interpolation unit as compared with that before the processing.

  The processing end determination unit determines whether or not the gentleness of the waveform shape of the processed data obtained by leveling the convex shape waveform has reached a predetermined allowable state, and if the predetermined allowable state has been reached, Then, the process ends and the final machining data is determined as a baseline. On the other hand, if the predetermined allowable state has not yet been reached, the processing data after the correction is performed again as new processing data to be processed by the bottom point detection unit. Here, the predetermined permissible state is, for example, a state in which the interval to be interpolated by the interval interpolation unit between the bottom points has disappeared. Further, a limit may be set for the number of repetitions and time of the interpolation process, and if the limit is reached, it may be considered that a predetermined allowable state has been reached. Generally, a good base corresponding to the original spectrum waveform is obtained by repeating the processes of the bottom point detection unit and the base point interval interpolation unit several times until the waveform shape of the processed data reaches a predetermined allowable state. You can ask for a line.

  In the existing baseline calculation method described above, the signal waveform data is divided into a plurality of narrow window areas from the beginning, and the representative points are obtained by calculation in each window area. Therefore, if the width of the window area is too large, it will not be possible to follow a sudden change in signal characteristics on the signal waveform, resulting in a delayed area. Conversely, if the width of the window area is too small, an appropriate representative point will be created. There is a shortage of information for obtaining the signal, and it tends to be too sensitive to a sudden change in signal characteristics on the signal waveform. On the other hand, in the signal waveform data processing apparatus according to the present invention, the window area is not set, and the width is averaged by the interpolation process in order from the relatively small peak in the entire signal waveform, and finally the predetermined value is obtained. The result is that all peaks having a width less than the upper limit value of the section length are averaged. As a result, it is possible to obtain a baseline having a shape that traces the bottom of the peak in a signal waveform such as a spectrum.

  As described above, the predetermined section length upper limit value may be a fixed value determined in advance by the manufacturer of this apparatus, but this section length upper limit value is an important parameter that determines the shape of the calculated baseline. Yes, it is desirable to change depending on the purpose of analysis and the type of sample to be analyzed. Therefore, as a preferred configuration of the signal waveform data processing apparatus according to the present invention, it is preferable to further include an upper limit value designating unit for an analyst to input the section length upper limit value.

  According to this configuration, if the analyst feels dissatisfied with the obtained baseline, the analyst may adjust the section length upper limit value manually by hand. In other words, if it seems that the peak on the original signal waveform is not sufficiently leveled in the obtained baseline, the upper limit of the section length can be set to be larger than the width of the peak remaining on the baseline waveform. For example, the result of leveling the peak can be obtained. Conversely, if it is found that a peak that should not be leveled is leveled, reset the section length upper limit value to less than the width of the peak that has disappeared, and the peak will not be leveled (not interpolated) ) Results are obtained. As described above, in the signal waveform data processing apparatus according to the present invention, the section length upper limit value, which is the parameter value to be adjusted, and the baseline calculation result are directly connected, so the analyst can intuitively perform parameter adjustment. Parameter adjustment work is simple.

  In many cases, the horizontal axis when displaying the mass spectrum waveform is the mass-to-charge ratio. Therefore, in the apparatus for the mass spectrometer, the upper limit value specifying unit allows the parameter value to be specified and input in the same mass-to-charge ratio unit as the horizontal axis of the waveform display. It may be configured to convert the number of data points according to the sampling interval of the data points. As a result, the analyst can easily adjust the input parameter value while checking the displayed waveform.

  In addition, in the present invention, if the bottom point of the valley of the given signal waveform cannot be detected properly, specifically, if there is a bottom point detection failure, the accuracy of the calculated baseline shape decreases. In particular, when multiple peaks overlap, it may be difficult to detect the bottom of the valley. Even in such a case, to detect the bottom without omission as much as possible, not only one algorithm but also several different algorithms. It is better to detect the bottom point based on. Therefore, in the signal waveform data processing apparatus according to the present invention, it is more preferable that the bottom point detection unit is configured to detect the bottom point by using a plurality of different calculation methods.

  According to the signal waveform data processing device of the present invention, it is possible to obtain an accurate baseline without complicated and troublesome parameter adjustment even for a signal waveform having a shape whose signal characteristics change suddenly. . As a result, the spectrum obtained by subtracting the baseline and the height and area of the peak on the chromatogram can be accurately obtained. For example, the accuracy of quantitative analysis using the peak height and area can be improved. Further, in the signal waveform data processing apparatus according to the present invention, the number of parameters that influence the shape of the baseline is small, and the correspondence between the parameter values and the baseline calculation results is clear. Even when the adjustment is performed, it can be easily performed. Thereby, the baseline of the shape desired by the analyst can be calculated efficiently.

BRIEF DESCRIPTION OF THE DRAWINGS The schematic block diagram of the spectrum data processing apparatus for mass spectrometry which is one Example of this invention. The flowchart which shows an example of the baseline calculation method in the spectrum-data processing apparatus for mass spectrometry of a present Example. Explanatory drawing of the baseline calculation method in the spectrum data processor for mass spectrometry of a present Example. Baseline calculation example using conventional software "msbackadj" and a spectrum showing the result of baseline removal (when signal waveform characteristics change is not sudden). Baseline calculation example using conventional software "msbackadj" and a spectrum showing the result of baseline removal (when signal waveform characteristics change suddenly). Baseline calculation example using conventional software “msbackadj” and spectrum showing baseline removal results (when parameters are adjusted appropriately). The spectrum which shows the example of calculation of the baseline by the spectrum data processor for mass spectrometry of a present Example, and a baseline removal result. The schematic block diagram of the spectrum data processing apparatus for mass spectrometry which is the other Example of this invention. The spectrum which shows the difference of a baseline when changing the section length upper limit in the calculation method of the baseline in the spectrum data processor for mass spectrometry of other examples.

  Hereinafter, a spectrum data processing apparatus for mass spectrometry which is an embodiment of a signal waveform data processing apparatus according to the present invention will be described in detail with reference to the accompanying drawings.

FIG. 1 is a schematic configuration diagram of a spectral data processing apparatus for mass spectrometry according to the present embodiment.
The spectrum data processing apparatus for mass spectrometry of the present embodiment stores a spectrum for storing raw spectrum data (profile data) over a predetermined mass-to-charge ratio range obtained when performing mass analysis in a mass spectrometer (not shown). A data storage unit 1, a baseline calculation unit 2 for obtaining a baseline based on the raw spectrum data, and a baseline subtraction for obtaining spectrum data obtained by subtracting the data indicating the baseline from the original spectrum data and removing the baseline Part 3.

  The baseline calculation unit 2 includes a processing target data input unit 20 that reads spectral data that is a processing target from the spectral data storage unit 1, and a bottom inspection that detects a bottom point of a valley between peaks for given processing data. A specific section extracting section 22 that extracts a section whose section length is less than the section length upper limit value from the section 21 between two base points adjacent on the mass-to-charge ratio axis, and a signal in the extracted section Interpolation section 23 that replaces the waveform (peak waveform) with a straight line (or curve) interpolated using the intensity values of the bottom points located at both ends of the section, and baseline calculation processing based on the interpolated processing data and the like And an end determination unit 24 that determines whether it is time to end the process as a functional block.

  All the functional blocks of the data processing apparatus shown in FIG. 1 can be realized by using a personal computer as a hardware resource and executing dedicated data processing software installed in the personal computer.

  FIG. 2 is a flowchart showing an example of a baseline calculation method in the mass spectrometry spectral data processing apparatus of the present embodiment, and FIG. 3 is an explanatory diagram of this baseline calculation method. The baseline calculation process in the spectrum data processing apparatus for mass spectrometry of the present embodiment will be described with reference to FIGS.

  When the processing is started, first, the processing target data input unit 20 reads spectral data over a predetermined mass-to-charge ratio range from the spectral data storage unit 1, and inputs this into the bottom point detection unit 21 as an initial value of the processing data ( Step S1). The bottom point detection unit 21 detects all bottom points of valleys between peaks in the mass-to-charge ratio direction for the given processing data (step S2). Here, the bottom point is typically a local minimum value between two adjacent peaks, as indicated by P1, P2, P3, and P4 in FIG. Various known methods can be used as the bottom point detection method in the bottom point detection unit 21. Further, as apparent from FIG. 3, since bottom point detection is substantially the same as peak detection, various known peak detection methods can be used for bottom point detection.

  A well-known bottom point detection method calculates a slope of a signal waveform, that is, a primary differential value in the mass-to-charge ratio direction, and the sign of the primary differential value is a negative value (that is, the slope of the signal waveform). This is a method in which the state once becomes zero from the state where the slope is downward slope and then changes to a positive value (that is, the slope of the signal waveform becomes upward slope), and the position of the zero value is the bottom point. Actually, smoothing differentiation combined with smoothing processing is often used in order to find the original valley while removing the influence of a very small uneven waveform caused by noise or the like (for example, Non-Patent Document 3). Etc.)

When a peak is present in isolation on the signal waveform, the bottom point can be detected with high accuracy by the above-described method using the primary differential value. On the other hand, when the skirts of adjacent peaks are largely overlapped, the bottom point may not be detected from only the first derivative value. For such a case, there is also a bottom point detection method using a second derivative value and a third derivative value of a signal waveform. Specifically, a position on the mass-to-charge ratio where the third-order differential value of the signal waveform is zero (positive / negative turning point) and the second-order differential value has a positive maximum value may be set as the bottom point. According to this bottom point detection method, the bottom points between a plurality of peaks that are overlapped and cannot be sufficiently separated can also be detected.
Of course, in step S2, one of various known bottom point detection methods may be used, but a plurality of bottom point detection methods using different algorithms are used in combination, and the plurality of bottom point detection methods are used. All the bottom points detected by any of the methods may be extracted as bottom points. This can reduce the detection of the bottom point.

  If all the bottom points are detected in step S2, then the specific section extraction unit 22 regards the two bottom points adjacent on the mass-to-charge ratio axis as one section, and the two bottom points. The length (mass-to-charge ratio width), that is, the section length is compared with a predetermined section length upper limit value. Then, all the sections whose section length is less than the section length upper limit value are extracted (step S3). In the example of FIG. 3, a section [1] between the bottom point P1 and the bottom point P2, a section [2] between the bottom point P2 and the bottom point P3, and a section [3] between the bottom point P3 and the bottom point P4. However, if the length of the two sections [1] and [3] is less than the section length upper limit and the section length of section [2] is greater than or equal to the section length upper limit In step S3, section [1] and section [3] are extracted, and section [2] is not extracted.

  The intra-interval interpolation unit 23 is a straight line (or curve) obtained by interpolating, for example, the intensity values at both ends of each section, that is, the intensity values of the two bottom points, for all the sections extracted in step S3. The signal waveform in the section is replaced (step S4). The interpolation method is not particularly limited, but usually sufficient results can be obtained by simple linear interpolation. Of course, higher order interpolation methods may be used. In the example of FIG. 3, as shown in FIG. 3B, the peak waveform shown by the dotted line is replaced with a straight line connecting the bottom point P1 and the bottom point P2 for the section [1], and the dotted line is shown for the section [3]. The peak waveform is replaced with a straight line connecting the bottom point P3 and the bottom point P4. On the other hand, since the interpolation is not performed for the interval [2], the original signal waveform remains as it is.

  In this way, in step S4, for all the sections extracted in step S3, the peaks in the sections are averaged and converted into a gentle signal waveform. Thereafter, the end determination unit 24 determines whether or not the gentleness of the waveform shape of the processed data after the process of step S4 has reached a predetermined allowable state (step S5). The judgment as to whether the gentleness of the waveform shape has reached a predetermined permissible state can be made, for example, by confirming whether there are still sections to be subjected to interpolation processing. That is, the specific section extraction unit 22 obtains the number of sections extracted as the specific section, and notifies the end determination unit 24 to that effect when the number of sections is zero. The fact that the number of sections is zero means that all peaks having a width less than the upper limit value of the section length have been leveled, so that it can be considered that the waveform by the processed data has become sufficiently smooth. Therefore, when the above notification is received from the specific section extraction unit 22, the end determination unit 24 may determine that the signal waveform based on the processed data has been sufficiently leveled and has reached a predetermined allowable state.

  If it is determined in step S5 that the gentleness of the waveform based on the processing data at that time has reached a predetermined allowable state, the processing is terminated, and the processing data obtained at that time is the base for the processing target spectrum. The line calculation result is output (step S6). On the other hand, if it is determined in step S5 that the gentleness of the waveform based on the processing data at that time has not reached the predetermined allowable state, the processing data obtained at that time is processed as the next processing target. The data is set and the process returns to step S2. Thereby, the process of step S2, S3, S4 mentioned above is performed again. By repeating the processes in steps S2 to S5 until it is determined that the smoothness of the waveform based on the processed data has reached a predetermined allowable state, the narrow peaks are sequentially smoothed and changed to a waveform with few peaks. Finally, a baseline is derived in which only peaks having a width larger than the width determined by the section length upper limit value are observed.

  Of course, for example, when there is a restriction on the processing time or when there is some problem in the processing, there is a case where the processing time is limited or the number of repetitions is not set, and Yes in step S5. However, the processing may be terminated halfway.

  FIG. 7 shows the result of performing baseline removal and baseline removal obtained by executing the baseline calculation method according to the present embodiment described above on the same spectrum data as shown in FIG. FIG. From FIG. 7A, it can be seen that a baseline is obtained in which the steep rising portion and the tail portion of the peak following the right side of the raw spectral waveform are reliably traced. In the baseline removal result shown in FIG. 7B, an accurate peak height is obtained in any region of the mass to charge ratio range.

  As can be seen from the above description, in the baseline calculation method in the present embodiment, the section length upper limit value for determining the section length between the bottom points is the only important parameter that determines the followability of the baseline to the signal waveform. It is. In other words, if the section length upper limit value is increased, relatively large peaks are leveled, so that the baseline is flattened, and if the section length upper limit value is decreased, the width is relatively increased. Since only small peaks are leveled, irregularities are likely to appear in the base line. The shape of the baseline suitable for a certain signal waveform is not necessarily uniquely determined, and varies depending on the analysis purpose, the type of sample to be analyzed, and the like. Therefore, the section length upper limit value is not fixed to the apparatus, but may be changed by the user (analyzer) as appropriate.

  FIG. 8 is a schematic configuration diagram of a spectral data processing apparatus for mass spectrometry according to another embodiment of the present invention. The section length upper limit input unit 4 is added to the configuration of the above embodiment, and the analyst changes the section length upper limit value by the section length upper limit input unit 4 as necessary. Since the changed section length upper limit value is used in the processing of step 3, the section to be extracted, that is, the section to be interpolated, changes when the analyst changes this value. Therefore, the analyst usually confirms the baseline calculated under a certain section length upper limit value, for example, on a monitor screen, and if it is determined that this baseline is not appropriate, the section length upper limit value input unit 4 Then, change the upper limit of the section length appropriately and instruct the baseline calculation process again. Since the baseline calculated based on this indication is different from the previous one, the analyst will evaluate it again.

  FIG. 9 is a diagram illustrating an example of a baseline calculation result when the section length upper limit value is changed. FIG. 9A shows an example when the section length upper limit value is 250 [Da], and FIG. 9B shows an example when the section length upper limit value is smaller than 40 [Da].

  In FIG. 9 (a), the baseline is calculated on the assumption that the entire peak including the large peak that is not the target and the target peak for quantification superimposed on the skirt is a section where one large peak exists. On the other hand, if you want to calculate the net height or area of the target peak for quantification by removing the effect of the tail of the large non-target peak, you can specify a smaller value as the upper limit for the section length. As long as it is a little larger than the width of these small peaks (around 15 [Da]). For this reason, in FIG. 9B, the section length upper limit value is set to 40 [Da], and thereby the baseline can be calculated so that the influence of the large peak tail portion is removed from the quantitative target peak. I understand that.

  As described above, in the spectral data processing apparatus for mass spectrometry of the present embodiment, the analyst sets the partition length upper limit value according to the width of the peak for which the height or area is desired to be obtained accurately while looking at the spectrum waveform displayed on the monitor screen. It can be set, adjustment of such parameters is easy, and mistakes are unlikely to occur.

  In addition, although the said Example applied this invention with respect to the spectrum data obtained with the mass spectrometer, various peaks etc. in which a peak appears and it is necessary to obtain | require a baseline, such as a chromatogram obtained with a chromatograph apparatus. The baseline calculation method according to the present invention can be applied to the signal waveform.

  Further, since the above embodiment is only one embodiment of the present invention, it is included in the scope of the claims of the present application even if appropriate modifications, corrections, and additions are made within the scope of the present invention for points other than those described above. Of course.

DESCRIPTION OF SYMBOLS 1 ... Spectral data storage part 2 ... Baseline calculation part 20 ... Processing object data input part 21 ... Bottom point detection part 22 ... Specific area extraction part 23 ... Inter-section interpolation part 24 ... End determination part 3 ... Baseline subtraction part 4 ... Section length upper limit input section

Claims (3)

A signal waveform data processing device for processing signal waveform data obtained by an analyzer,
a) A processing target data input unit for inputting signal waveform data to be processed as an initial value of the processing data;
b) a bottom point detection unit for detecting a bottom point of a valley between peaks with respect to the waveform according to the processing data;
c) Over the entire range of the signal waveform data to be processed, a signal waveform in a section in which the section length is less than a predetermined section length upper limit value is defined as one section between two adjacent bottom points. A section interpolation unit between the bottom points that replaces with a straight line or a curve interpolated using the value of the bottom point that is the end point of the section;
d) It is determined whether or not the gentleness of the waveform shape of the processed data after correction by the inter-bottom interval interpolating section has reached a predetermined allowable state, and processing is performed if the predetermined allowable state has been reached. On the other hand, if the predetermined allowable state has not been reached, a processing end determination unit that continues processing as new processing data to be processed by the bottom point detection unit after the correction has been performed,
A signal waveform data processing apparatus comprising: The signal waveform data processing apparatus according to claim 1,
An apparatus for processing signal waveform data, further comprising a section length upper limit designating unit for an analyst to input the section length upper limit. The signal waveform data processing device according to claim 1 or 2,
The signal point data processor according to claim 1, wherein the bottom point detection unit detects a bottom point by using a plurality of different calculation methods.

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