Data processing method, information processing device, recording medium, and computer program product

Abstract

The invention provides a data processing method, an information processing apparatus, a recording medium, and a computer program product. The data processing method is executed by a computer to correct a measured value of a predetermined component obtained by measurement by an analysis device. The data processing method includes: a step (S14) for receiving a measurement value; and steps (S20) and (S22) for calculating a first reference value and a second reference value using the measured values. The data processing method includes a step (S24) in which the measurement value is corrected using the first reference value and the second reference value.

Description

Technical Field

[0001] The present disclosure relates to a data processing method, an information processing device, a recording medium, and a computer program product, and more particularly to data processing for correcting measurement values ​​acquired by an analysis device. Background Art

[0002] The measurement values ​​obtained by the analysis device are sometimes affected by the temperature, humidity, state of the analysis device, and mechanical differences of the analysis device during the measurement. For example, in a mass spectrometry analysis device, dirt in the mass spectrometry analysis unit sometimes affects the measurement sensitivity. Therefore, even when the same sample is measured using the same mass spectrometry analysis device, the measurement values ​​obtained before and after the mass spectrometry analysis unit is cleaned are sometimes different. Therefore, the measurement values ​​obtained by the analysis device may include measurement errors. Measurement errors are, for example, errors caused by different timings when the sample is measured, errors caused by different environments in which the sample is measured, and errors caused by different equipment used to analyze the sample.

[0003] As a method for correcting this measurement error, Dunn, Warwick B., et al. "Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry." Nature protocols 6.7 (2011): 1060-1083. discloses a technique for correcting the measurement value of a sample based on the measurement value of a pooled QC sample obtained by mixing all samples in equal amounts. In Dunn, Warwick B., et al. "Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry." Nature protocols 6.7 (2011): 1060-1083., the pooled QC sample is measured between sample measurements. After the measurement, LOESS smoothing is performed using only the measurement value of the pooled QC sample to calculate an approximate curve. The measurement value of each sample is corrected based on the calculated approximate curve. This method is called QC-based robust LOESS signal correction (QC-RLSC: quality control-based robust locally weighted regression signal correction) method.

[0004] A mixed QC sample is created by mixing all samples in equal amounts. By mixing all samples in equal amounts, the amount of each component contained in the mixed QC sample is the average of all samples for that component. This means that the measured value of the mixed QC sample approaches the average of all sample values, preventing extreme differences between the measured values ​​of the mixed QC sample and those of the individual samples. Furthermore, by mixing all samples to create the mixed QC sample, the components contained in at least one sample are included in the mixed QC sample. Therefore, even if the component to be calibrated is unknown at the start of the measurement, calibration can be performed on the measured value of that component after the measurement. Summary of the Invention

[0005] In the method disclosed in Dunn, Warwick B., et al. “Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry.” Nature protocols 6.7 (2011): 1060-1083., all samples are mixed in equal amounts to prepare a pooled QC sample. Therefore, for example, when the sample is solid, it is sometimes difficult to prepare a pooled QC sample. Thus, a data processing method capable of correcting the measured values of samples by an equivalent calibration method without preparing a pooled QC sample is sought.

[0006] The present disclosure has been made in view of such circumstances, and an object thereof is to provide a technique for correcting measured values obtained by an analytical device without using a pooled QC sample.

[0007] The data processing method according to the first aspect of the present disclosure is executed by a computer to correct the Nth measured value of a specified component obtained by measuring a plurality of samples by an analytical device, where N is an integer satisfying 3 ≤ N. The computer includes a processor and an interface. The data processing method includes the following steps: 1) receiving, through the interface, a plurality of measured values obtained by the analytical device; 2) estimating, by the processor, each reference value obtained when measuring a standard sample obtained by mixing each of the plurality of samples in at least two measurements including the Mth measurement and the Qth measurement, where M and Q are natural numbers satisfying M < N < Q; and 3) correcting, by the processor, the Nth measured value using each reference value. The step of estimating each reference value includes the following steps: a) estimating, by the processor, the Qth reference value obtained when measuring the standard sample at the Qth measurement using the Tth to Qth measured values, where T is a natural number satisfying T < Q; and b) estimating, by the processor, the Mth reference value obtained when measuring the standard sample at the Mth measurement using the Lth to Mth measured values, where L is a natural number satisfying L < M.

[0008] An information processing apparatus according to a second aspect of the present disclosure includes: at least one or more processors; and a memory accessible by the one or more processors. The memory stores one or more commands executed by the processor. The processor performs the following processing by executing the one or more commands: receiving measurement values of a specified component obtained by measuring a plurality of samples by an analysis device; estimating respective reference values obtained when measuring a standard sample obtained by mixing each of the plurality of samples in at least two measurements including the M-th measurement and the Q-th measurement, where M and Q are natural numbers satisfying M < Q; and using the respective reference values to correct the N-th measurement value, where N is a natural number satisfying M < N < Q. When estimating the respective reference values, the processor uses the T-th to Q-th measurement values to estimate the Q-th reference value obtained when measuring the standard sample in the Q-th measurement, where T is a natural number satisfying T < Q, and uses the L-th to M-th measurement values to estimate the M-th reference value obtained when measuring the standard sample in the M-th measurement, where L is a natural number satisfying L < M.

[0009] A recording medium according to a third aspect of the present disclosure is a recording medium storing a program executed by a processor mounted on a computer. The program causes the computer to perform the following processing: receiving measurement values of a specified component obtained by measuring a plurality of samples by an analysis device; estimating respective reference values obtained when measuring a standard sample obtained by mixing each of the plurality of samples in at least two measurements including the M-th measurement and the Q-th measurement, where M and Q are natural numbers satisfying M < Q; and using the respective reference values to correct the N-th measurement value, where N is a natural number satisfying M < N < Q. The program causes the computer to perform the following processing when estimating the respective reference values: using the T-th to Q-th measurement values to estimate the Q-th reference value obtained when measuring the standard sample in the Q-th measurement, where T is a natural number satisfying T < Q; and using the L-th to M-th measurement values to estimate the M-th reference value obtained when measuring the standard sample in the M-th measurement, where L is a natural number satisfying L < M.

[0010] A computer program product according to a fourth aspect of the present disclosure is a computer program product including a program executed by a processor mounted on a computer. The program causes the computer to perform the following processes: receiving measurement values of a specified component obtained by measuring a plurality of samples by an analysis device; estimating respective reference values obtained when measuring a standard sample obtained by mixing each of the plurality of samples in two or more measurements including at least the M-th measurement and the Q-th measurement, where M and Q are natural numbers satisfying M < Q; and using each reference value to correct the N-th measurement value, where N is a natural number satisfying M < N < Q. The program causes the computer to perform the following processes when estimating each reference value: using the T-th to Q-th measurement values to estimate the Q-th reference value obtained when measuring the standard sample at the Q-th measurement, where T is a natural number satisfying T < Q; and using the L-th to M-th measurement values to estimate the M-th reference value obtained when measuring the standard sample at the M-th measurement, where L is a natural number satisfying L < M.

[0011] The above and other objects, features, aspects, and advantages of the present invention will become clear from the following detailed description of the present invention understood in association with the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 is a schematic diagram of an analysis system according to an embodiment.

[0013] Figure 2 is a diagram for explaining the process of producing a mixed QC sample.

[0014] Figure 3 is a diagram for explaining the measurement order of the mixed QC sample and the sample.

[0015] Figure 4 is a diagram for explaining a method of correcting the measurement value of a sample based on the measurement value of a mixed QC sample in a comparative example.

[0016] Figure 5 is a diagram for explaining a data processing method according to an embodiment.

[0017] Figure 6 is a diagram for explaining a data processing method when analysis data is added.

[0018] Figure 7 is a flowchart showing data processing according to an embodiment.

[0019] Figure 8 is showing Figure 7 a flowchart of a subroutine of step S20 shown. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] Hereinafter, the embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. Hereinafter, a mass spectrometer is exemplified as an analysis device, but the present invention is not limited thereto and can be generally applied to analysis devices. In addition, the same or corresponding parts in the figures are marked with the same reference numerals and their descriptions are not repeated.

[0021] [Overall structure of the analysis system]

[0022] Figure 1 1 is a block diagram showing the structure of the analysis system 100 according to the embodiment. Figure 1 The analysis system 100 includes a processing device 10, an input device 20, a display device 30, and a mass spectrometer 40. The analysis system 100 corrects measurement errors in the sample values ​​obtained by the mass spectrometer 40. Measurement errors include, for example, errors caused by differences in the timing of sample measurement, errors caused by differences in the sample measurement environment, and errors caused by differences in the equipment used to analyze the sample. Furthermore, the processing device 10 may also be integrated with the mass spectrometer 40.

[0023] The processing device 10 includes a processor 11, a memory 12, and an input / output interface (I / F) 13. These components are connected to each other via a bus so as to be able to communicate with each other.

[0024] Processor 11 is an example of a circuit that controls the operation of processing device 10 by executing a given program. The program executed by processor 11 may be stored in memory 12 or in a storage device (not shown) external to processing device 10. Processor 11 is, for example, a CPU (Central Processing Unit).

[0025] The memory 12 is capable of non-transitory storage of the program executed by the processor 11, the mass spectrum data produced by the mass spectrometer 40, and the measured values ​​of the components. The measured values ​​of the components are calculated, for example, based on the area value of the peak in the mass spectrum and the height of the peak in the mass spectrum. The program stored in the memory 12 includes a correction program 121. The memory 12 includes a volatile memory (for example, RAM (Random Access Memory)) and a non-volatile memory (for example, ROM (Read Only Memory), a hard disk drive, and a solid state drive). In addition, the above-mentioned program can also be stored in an external storage device that can be accessed by the processor 11.

[0026] The input / output I / F 13 is an interface for exchanging various data between the processor 11 and devices connected to the input / output I / F 13. The input / output I / F 13 is connected to the input device 20, the display device 30, and the mass spectrometer 40. The input / output I / F 13 is implemented, for example, by a terminal block, a connector, and a network adapter. Data exchange via the input / output I / F 13 can be performed wirelessly using Bluetooth (registered trademark) or a wireless LAN, or by wired communication using a USB (Universal Serial Bus). The processing device 10 can receive measurement values ​​obtained by devices other than the mass spectrometer 40 via the input / output I / F 13 and perform data processing based on these measurement values.

[0027] The input device 20 receives information input from the user to the processing device 10. This information includes, for example, the total number of samples, the type of samples, and the components to be measured. The input device 20 is composed of, for example, a touch panel, a mouse, and a keyboard.

[0028] The display device 30 displays information according to instructions from the processing device 10. This information includes, for example, the mass spectrum of the sample, the measured values ​​of the predetermined component before correction, and the measured values ​​of the predetermined component after correction. The display device 30 is constituted by, for example, a liquid crystal display capable of displaying images.

[0029] The mass spectrometer 40 analyzes the sample to generate mass spectral data representing the mass distribution of ions derived from components contained in the sample. The generated mass spectral data is transmitted to the processing device 10. The processing device 10 calculates the measured values ​​of each component based on the mass spectral data. Examples of the mass spectrometer 40 include a liquid chromatograph-mass spectrometer, a gas chromatograph-mass spectrometer, a high-speed liquid chromatograph-tandem mass spectrometer, and a gas chromatograph-tandem mass spectrometer.

[0030] [Comparative Example]

[0031] During measurements using a mass spectrometer, contamination may form in the mass spectrometer during repeated measurements, affecting the analysis results. For example, even for the same sample, measurements taken on different days may yield inconsistent values. In other words, the values ​​obtained by a mass spectrometer may contain measurement errors.

[0032] As a method for correcting measurement errors, a technique is known in which the measured values ​​of a sample are corrected based on the measured values ​​of a standard sample called a mixed QC sample, as described in Dunn, Warwick B., et al. “Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry.” Nature protocols 6.7 (2011): 1060-1083. Figure 2 This is a diagram for explaining the process of preparing mixed QC samples. Assume that there are N samples to be measured. Figure 2 As shown, all samples are mixed in equal amounts to produce a mixed QC sample. By mixing all samples in equal amounts, the amount of each component contained in the mixed QC sample is the average of all samples of the component. That is, the measured value of the mixed QC sample is close to the average value of the measured values ​​of the samples, so that the measured value of the mixed QC sample can be prevented from being extremely different from the measured value of the samples. In addition, by mixing all samples to produce a mixed QC sample, the components contained in at least any one sample are included in the mixed QC sample. Therefore, even if the component to be corrected is unknown at the beginning of the measurement, a correction process can be performed on the measured value of the component after the measurement. The prepared mixed QC sample is dispensed into appropriate quantities. In Figure 2 In the above example, the prepared mixed QC samples are divided into M pieces.

[0033] The mass spectrometer measures the mixed QC sample before and after measuring the sample, and sequentially obtains the measurement value of the sample and the measurement value of the mixed QC sample. Figure 3 This figure is used to explain the measurement sequence of mixed QC samples and samples. Figure 3 In the diagram, the white container represents the sample to be measured, and the gray container represents the mixed QC sample. The operator takes out an equal amount of solution from each of the 36 samples to make a mixed QC sample. The prepared mixed QC sample is divided into appropriate quantities and provided for measurement at appropriate times before and after the sample is measured. For example, Figure 3 As shown in , the mass spectrometer measures the mixed QC sample twice before starting to measure a sample, and then measures the mixed QC sample once every four samples. After the last sample is measured, the mixed QC sample is measured twice.

[0034] By performing a series of measurements as described above, the measured value of the sample and the measured value of the mixed QC sample are sequentially acquired. The processing device 10 corrects the measured value of the sample based on the measured value of the mixed QC sample. Figure 4 This is a diagram for explaining a method for correcting the measured value of a sample based on the measured value of a mixed QC sample. Figure 4 The graph shown in the upper portion of the diagram shows the measured values ​​obtained by measuring the sample and then the mixed QC sample. White circles represent the measured values ​​of the sample, and gray circles represent the measured values ​​of the mixed QC sample. For example, the measured value of the tenth measured sample, represented by point P1, is greater than the measured value of the twenty-first measured sample, represented by point P2.

[0035] Ideally, the measured values ​​of the mixed QC sample should be equal regardless of when they are measured. Therefore, in order to correct the measured values ​​of the samples, LOESS smoothing is performed using only the measured values ​​of the mixed QC sample to calculate the approximate curve L1. The measured values ​​of each sample are corrected based on the calculated approximate curve L1. Figure 4 The relative measurement values ​​of the sample after correction are shown in the graph shown in the lower part of . In addition, the relative measurement values ​​in this graph are normalized values ​​when the value shown by the approximate curve L1 is set to 1. Figure 4 In the graph shown in the lower portion of , the measured values ​​of the mixed QC sample are calibrated to be substantially constant.

[0036] Point P3 represents the corrected value of the tenth sample, and point P4 represents the corrected value of the twenty-first sample. Before correction, point P1 was larger than point P2, but after correction, point P3 is smaller than point P4. Using the QC-RLSC method, measurement errors can be corrected, improving measurement accuracy.

[0037] However, there are several problems in the calibration method using mixed QC samples. For example, (1) when the sample is solid, it is sometimes difficult to prepare a mixed QC sample. In addition, even if a mixed QC sample can be prepared, (2) all samples are required to prepare the mixed QC sample, so the measurement cannot be started before the last sample is obtained. Due to the delay in starting the measurement, the quality of the obtained sample sometimes changes. (3) In the preparation of the mixed QC sample, there is a task of dividing all samples into equal amounts, and the user's working time sometimes becomes longer. (4) When the amount of each sample is small, the amount of sample provided for analysis is sometimes insufficient due to the preparation of the mixed QC sample. In addition, (5) the content of the component contained in a part of the sample in the mixed QC sample becomes less, so the measurement value of the component may not be obtained in the measurement of the mixed QC sample. Moreover, (6) the mixed QC sample needs to be measured during the measurement of the sample, and the number of samples analyzed per unit time decreases.

[0038] [Data processing method according to the embodiment]

[0039] Therefore, in the data processing method according to this embodiment, the processing device 10 estimates, based on the measured values ​​of the sample, the values ​​that would be obtained if a mixed QC sample were prepared and then measured during the measurement of the sample. The processing device 10 then treats the estimated values ​​as the measured values ​​of the mixed QC sample and corrects the measured values ​​of the sample. By using the data processing method according to this embodiment, the processing device 10 can correct the measured values ​​of the sample using a calibration method equivalent to the QC-RLSC method, without using the actual measured values ​​of the mixed QC sample.

[0040] The data processing method according to this embodiment eliminates the need to actually prepare a mixed QC sample, enabling calibration of measurement values ​​even when the sample is solid. Furthermore, since there is no need to prepare a mixed QC sample before starting sample measurements, measurements can begin before all samples have been acquired. Furthermore, the user's workload associated with preparing the mixed QC sample can be reduced.

[0041] Figure 5 This is a diagram for explaining a method of correcting the measurement value of a sample obtained by the mass spectrometer 40 using the data processing method according to this embodiment.

[0042] Reference Figure 5 First, the mass spectrometer 40 analyzes the sample. The processing device 10 receives the measured values ​​of the specified components from the mass spectrometer 40. In addition, the processing device 10 can call the measured values ​​of the sample stored in the memory 12, and can also receive the measured values ​​measured by a device other than the mass spectrometer 40. The mass spectrometer 40 measures the sample in sequence, so Figure 5 As shown in "Actual Analysis" of , the measured values ​​can be arranged in the order in which they were measured.

[0043] Next, the measured value of the mixed QC sample is estimated based on the "sample measurement data." This estimation is performed on a computer, not through actual analysis, and is therefore called "in silico analysis." The value estimated by "computer analysis" is called the "computer mixed QC sample" value. The computer mixed QC sample value is also called the reference value. The reference value is estimated to be the value obtained when a standard sample, i.e., a mixed QC sample, obtained by mixing the samples, is measured at the time the sample is measured.

[0044] In the calibration process shown in the comparative example, the mass spectrometer 40 needs to actually measure the mixed QC sample. Therefore, it is impossible to physically measure the mixed QC sample and the sample at the same time. On the other hand, in the computer analysis, the mass spectrometer 40 does not actually measure the mixed QC sample, so it can be assumed that the mixed QC sample is measured at the same time as the sample is measured to calculate the reference value. Figure 5 For example, assume that a computer-mixed QC sample is measured at the time of the first and fourth sample measurements. In this case, a reference value corresponding to the first measurement and a reference value corresponding to the fourth measurement are calculated. The reference value corresponding to the fourth measurement is the second reference value, but for convenience, it is referred to as the fourth reference value. Furthermore, reference values ​​can be calculated for all measurement values ​​or selectively for some measurement values.

[0045] Specifically, the kth (k is a natural number) reference value I(k) is calculated by the following formula (1).

[0046]

Number 1

[0047]

[0048] In formula (1), P(i) represents the i-th measured value. In formula (1), when calculating the k-th reference value, the values ​​measured from the r-th (r is a natural number satisfying r < k) to the k-th are used. This is because the reference value being calculated is affected by measurements preceding that reference value.

[0049] In addition, in formula (1), the measured value is multiplied by a coefficient corresponding to the measurement order. This is because the reference value is more affected by the measurement near the measurement being calculated. Therefore, the closer the measurement value is to the kth measurement value, the larger the coefficient. The weighting function used to calculate the reference value is not limited to the exp function, and can also be an inverse square function, a logistic function, and a hinge function.

[0050] In addition, the k-th reference value I(k) can also be calculated by the following formula (2).

[0051]

Number 2

[0052]

[0053] In formula (2), Pmean represents the average value of the first to Qth measured values.

[0054] In the formula (1) and the formula (2), r is preferably 1. When r is 1, the number of measurement values ​​used to calculate the k-th reference value is maximized, and the accuracy of the reference value calculation can be improved.

[0055] The processing device 10 performs LOESS smoothing on the benchmark values ​​calculated as described above and derives an approximate curve. The processing device 10 normalizes the sample measurement values ​​so that the derived approximate curve value is 1 at the time of measurement of each sample. Calculating the approximate curve requires calculating at least two benchmark values.

[0056] Furthermore, in a measurement using a mass spectrometer, multiple components contained in a sample can be quantified in a single measurement. In such a case, the measured value is calibrated for each component.

[0057] Furthermore, in chromatograms generated by mass spectrometry, even for the same component, retention times can sometimes fluctuate randomly during measurement. In such cases, the accuracy of the resulting measured values ​​can fluctuate during the measurement period. Therefore, in the aforementioned calibration, a confidence interval can be calculated and added to the corrected measured value.

[0058] In the method described in this embodiment, not all samples need to be recovered before starting measurement. Therefore, according to this data processing method, measurement values ​​of samples included in different batches can be corrected, that is, so-called inter-batch correction can be performed. Figure 6 This is a diagram for explaining a data processing method when experimental data is added. Figure 6 As shown in box 6A of , suppose that the user performs experiment 1 and obtains twelve samples. The user measures the samples and corrects the obtained measurement values ​​through computer analysis. In this case, the measurement of the twelve samples is called a batch. Afterwards, the user performs experiment 2 and obtains twelve new samples that are different from the samples obtained by experiment 1. The measurement of the twelve samples obtained by experiment 2 is a different batch from that of experiment 1. When the correction method shown in the comparative example is used to correct the measurement values ​​of the samples obtained by experiment 2, the user needs to mix the samples obtained by experiment 1 and the samples obtained by experiment 2 to produce a mixed QC sample, and then measure the samples obtained by experiment 1 again. On the other hand, when the method shown in this embodiment is used to correct the measurement values ​​of the samples obtained by experiment 2, as shown in box 6B, the measurement values ​​of the samples obtained by experiment 2 can be corrected together with the measurement values ​​of the samples obtained by the completed experiment 1. Therefore, according to the method shown in this embodiment, even when correcting the measurement values ​​of samples included in different batches, the measurement values ​​can be corrected without re-measuring the measured samples.

[0059] [Processing of measured values]

[0060] Before correcting the measured values ​​as described above, it is also possible to perform processing on the measured values ​​acquired by the mass spectrometer 40. The processing includes, for example, detecting and eliminating abnormal values ​​and supplementing missing values.

[0061] Outlier detection and elimination is the process of detecting and eliminating measured values ​​that significantly deviate from other measured values ​​due to measurement errors. Outlier detection can be performed, for example, using cluster analysis using score plots and detection methods using boxplots from PCA of all samples.

[0062] Missing value filling involves using statistical methods to fill in missing values. Missing values ​​refer to measurement values ​​corresponding to samples that were excluded as outliers in the measurement data, as well as measurement values ​​corresponding to samples for which no measurement values ​​were recorded. Statistical methods for filling in missing values ​​include single imputation and multiple imputation.

[0063] By processing the measured values ​​as described above, it is possible to prevent abnormal values ​​and missing values ​​from affecting the calculation of the reference value. As a result, it is possible to prevent the reference value from becoming an abnormal value and affecting the approximate curve, thereby improving the correction accuracy of the measured values.

[0064] [Data processing flow]

[0065] Figure 7 This is a diagram showing a flowchart of an example of processing of measurement data acquired by measuring a sample by the mass spectrometer 40. In one implementation example, when the processor of the processing device 10 executes the calibration program 121, the main routine is called Figure 7 The processing device 10 can be said to be an example of an information processing device.

[0066] exist Figure 7 In the embodiment, it is assumed that the Nth (N is an integer greater than or equal to 3) measurement value obtained among the measurement values ​​of a predetermined component obtained by measuring a plurality of samples is corrected.

[0067] In step S10 , the processing device 10 receives chromatograms of each of the plurality of samples acquired by the mass spectrometer 40 .

[0068] In step S12 , the processing device 10 receives X target components to be corrected from the user.

[0069] In step S14 , the processing device 10 extracts the measurement value of one target component among the X target components from the chromatogram received in step S10 , and receives the extracted plurality of measurement values.

[0070] In step S16 , the processing device 10 detects abnormal values ​​from the plurality of measurement values ​​received in step S12 and excludes the detected abnormal values.

[0071] In step S18 , the processing device 10 supplements missing values ​​included in the plurality of measurement values ​​received in step S12 .

[0072] In step S20, the processing device 10 estimates each reference value obtained when a standard sample obtained by mixing each of the plurality of samples is measured at two or more times. Figure 8 2 shows a subroutine for estimating the reference value in step S20.

[0073] Reference Figure 8 In step S32, the processing device 10 estimates a Q-th reference value (Q is an integer satisfying N < Q). The Q-th reference value is calculated, for example, by equation (1) or equation (2).

[0074] In step S34, the processing device 10 estimates the Mth (M is a natural number satisfying M<Q) reference value. The Mth reference value is calculated, for example, by equation (1) or equation (2). In step S20, the processing device 10 estimates two or more reference values ​​including the Qth reference value and the Mth reference value. Thereafter, the processing device 10 ends the reference value estimation processing subroutine and returns the control to Figure 7 .

[0075] Reference Figure 7 In step S22 , the processing device 10 calculates an approximate curve using two or more reference values ​​including the Q-th reference value and the M-th reference value, and corrects the N-th measurement value.

[0076] In step S24 , the processing device 10 calculates a confidence interval of the measurement value corrected in step S22 .

[0077] In step S26 , the processing device 10 displays the corrected measurement value calculated in step S22 and the confidence interval calculated in step S24 on the display device 30 .

[0078] In step S28, the processing device 10 determines whether the processing of the X target components received in step S12 has been completed. If the processing of all target components has been completed ("YES" in step S28), the processing device 10 ends the data processing subroutine and returns the processing to the main routine. Otherwise ("NO" in step S28), the processing device 10 returns the processing to step S14.

[0079] In the above data processing, the processing device 10 receives the target component from the user in step S12 . However, the processing device 10 may execute the data processing for all peaks in the chromatogram received in step S10 without receiving the target component from the user.

[0080] Furthermore, the two or more reference values ​​used for calculation of the approximate curve need to include at least a reference value before the measured value to be corrected and a reference value after the measured value. Figure 8 As described in , the reference values ​​used for calculating the approximate curve need to include the Mth reference value and the Qth reference value. The approximate curve may be calculated using only these two reference values, or the approximate curve may be calculated using reference values ​​corresponding to all measurements.

[0081] The above data processing eliminates the need for users to prepare mixed QC samples. Therefore, this data processing can be applied to analyses of samples for which mixed QC samples are difficult to prepare. Examples of such samples include solid materials, such as silicone rubber.

[0082] In addition, by using this data processing, it is possible to start analyzing samples before all samples are recovered. Thus, the time from sample recovery to analysis completion can be shortened. In addition, this data processing can also be applied to the determination of substances that are likely to cause modification and aggregation due to repeated freezing and thawing. When making mixed QC samples, it is necessary to store the first recovered sample without measurement until the last sample is recovered. Generally speaking, samples derived from organisms are frozen and stored. However, sometimes the sample contains substances that are likely to cause modification and aggregation due to repeated freezing and thawing, and it is preferable to avoid freezing and thawing such samples. Since this data processing can start measuring samples before all samples are recovered, it can reduce the number of freezing and thawing cycles of the sample. Thus, this data processing can also be applied to the analysis of substances that are likely to cause modification and aggregation due to repeated freezing and thawing. In addition, substances that are likely to cause modification and aggregation due to repeated freezing and thawing are, for example, insulin and mucin.

[0083] In the above data processing, there is no need to prepare a mixed QC sample, thus reducing the user's work time involved in preparing the mixed QC sample. In addition, in the above data processing, there is no need to aliquot a portion of the acquired sample to prepare the mixed QC sample, thus allowing the entire acquired sample to be used for measurement.

[0084] Furthermore, in the above-described data processing, each measurement value can be corrected for a component contained only in a part of the sample based on the obtained measurement value.

[0085] Moreover, in the above data processing, it is not necessary to measure the mixed QC samples, so the number of samples that can be analyzed per unit time can be increased.

[0086] According to the data processing method involved in the present disclosure, the computer can use the value of the computer mixed QC sample calculated using the measured values of the samples to be measured to perform the calculation related to the correction of the measured value of the sample without obtaining the measured value of the mixed QC sample used in the calculation for correcting the measurement error of the measured value obtained by the analysis device. Thus, even if the computer cannot obtain the measured value of the mixed QC sample, the calculation related to the correction process of the measured value of the sample can be performed.

[0087] [Mode]

[0088] Those skilled in the art understand that the above-mentioned multiple exemplary embodiments are specific examples of the following modes.

[0089] (First item) The data processing method involved in one mode may also be a data processing method executed by a computer to correct the Nth measured value of a specified component obtained by measuring multiple samples through an analysis device, where N is an integer satisfying 3 ≤ N. The computer includes a processor and an interface, and the data processing method includes the following steps: receiving, through the interface, multiple measured values obtained by the analysis device; estimating, by the processor, each reference value obtained when measuring a standard sample obtained by mixing each of the multiple samples in at least two measurements including the Mth measurement and the Qth measurement, where M and Q are natural numbers satisfying M < N < Q; and correcting, by the processor, the Nth measured value using each reference value. The step of estimating each reference value includes the following steps: estimating, by the processor, the Qth reference value obtained when measuring the standard sample at the Qth measurement using the Tth to Qth measured values, where T is a natural number satisfying T < Q; and estimating, by the processor, the Mth reference value obtained when measuring the standard sample at the Mth measurement using the Lth to Mth measured values, where L is a natural number satisfying L < M.

[0090] According to the data processing method described in the first item, it is possible to correct the measured value obtained by the analysis device without using a mixed QC sample.

[0091] (Second item) In the data processing method described in the first item, the step of estimating the Qth reference value may include the following steps: weighting the measurement value obtained when the measurement order is close to the Qth measurement by multiplying it by a large coefficient, and the step of estimating the Mth reference value may include the following steps: weighting the measurement value obtained when the measurement order is close to the Mth measurement by multiplying it by a large coefficient.

[0092] The data processing method described in the second item allows for correction of measurement values ​​obtained by the analyzer without the use of mixed QC samples. The reference value used in this case is estimated by weighting the measurement values ​​obtained in measurements close in order to the reference value being measured by multiplying them by a large coefficient.

[0093] (Item 3) In the data processing method described in Item 1, the step of estimating each of the reference values ​​may further include the following steps: calculating an average value which is the average of the multiple measurement values, and using the average value in the step of estimating the Qth reference value and the step of estimating the Mth reference value.

[0094] According to the data processing method described in Item 3, the measurement value obtained by the analyzer can be corrected without using a mixed QC sample. The reference value used in this case is the average value of a plurality of measurement values.

[0095] (Item 4) In the data processing method described in Item 1 or Item 2, the step of estimating the Qth reference value may include the following steps: assuming the kth measured value to be P(k) and the kth reference value to be I(k), assuming r to be T and k to be Q, and calculating the Qth reference value using Formula (3):

[0096] [Number 3]

[0097]

[0098] The step of estimating the Mth reference value includes the following steps: setting r to L, setting k to M, and calculating the Mth reference value by formula (3).

[0099] According to the data processing method described in the fourth item, the measurement value obtained by the analyzer can be corrected without using a mixed QC sample. The reference value used in this case is calculated by formula (3).

[0100] (Item 5) In the data processing method described in Item 1 or Item 3, the step of estimating the Qth reference value may include the following steps: assuming the kth measured value to be P(k) and the kth reference value to be I(k), assuming the average value of the plurality of measured values ​​to be Pmean, assuming r to be T, and assuming k to be Q, and calculating the Qth reference value using Formula (4),

[0101] [Number 4]

[0102]

[0103] The step of estimating the Mth reference value includes the following steps: setting r to L, setting k to M, and calculating the Mth reference value by formula (4).

[0104] According to the data processing method described in Item 5, the measured value obtained by the analyzer can be corrected without using a mixed QC sample. The reference value used in this case is calculated by Formula (4).

[0105] (Item 6) In the data processing method described in any one of Items 1 to 5, T and L may be 1.

[0106] According to the data processing method described in the sixth aspect, the first measurement value to the measurement values ​​in the predetermined order are used to calculate the reference value in the predetermined order. As a result, the accuracy of the reference value calculation can be improved.

[0107] (Item 7) In the data processing method described in any one of Items 1 to 6, it may be that, in the step of correcting the Nth measurement value, the Nth measurement value is corrected based on an approximate curve calculated by performing local weighted regression smoothing, i.e., LOESS smoothing, using the Qth reference value and the Mth reference value.

[0108] According to the data processing method described in the seventh item, the measured value is corrected based on the approximate curve calculated by LOESS smoothing.

[0109] (Item 8) In the data processing method described in any one of Items 1 to 7, the method may further include the step of detecting an abnormal value in the measured value and excluding the abnormal value.

[0110] According to the data processing method described in the eighth aspect, it is possible to improve the correction accuracy of the measurement values ​​by excluding abnormal values ​​included in the measurement values.

[0111] (Item 9) In the data processing method described in Item 8, the abnormal value may be detected from principal component analysis (PCA) of the measurement value using either a cluster analysis method using a score graph or a detection method using a box plot.

[0112] According to the data processing method described in item 9, by using a cluster analysis method using a score graph and a detection method using a box plot, outliers included in the measured values ​​are detected from the PCA of the measured values ​​and the outliers are excluded, thereby improving the correction accuracy of the measured values.

[0113] (Item 10) The data processing method according to any one of Items 1 to 9 may further include the step of supplementing missing values ​​in the measured values.

[0114] According to the data processing method described in the tenth item, by supplementing missing values ​​included in the measurement values, the correction accuracy of the measurement values ​​can be improved.

[0115] (Item 11) In the data processing method described in Item 10, in the step of filling in the missing values, the missing values ​​may be filled in using either a single imputation method or a multiple imputation method.

[0116] According to the data processing method described in the eleventh item, by using either a single imputation method or a multiple imputation method to fill in missing values ​​included in the measurement values, the correction accuracy of the measurement values ​​can be improved.

[0117] (Item 12) In the data processing method described in any one of Items 1 to 11, the method may further include the step of deriving a confidence interval of the value corrected in the step of correcting the Nth measured value.

[0118] According to the data processing method described in the twelfth item, the user can recognize the confidence interval of the corrected value and can easily determine whether to use the data in the analysis.

[0119] (Item 13) In the data processing method described in any one of Items 1 to 12, the analysis device may be a mass spectrometry device.

[0120] According to the data processing method described in the thirteenth item, it is possible to correct the measurement value obtained by the mass spectrometer without using a mixed QC sample.

[0121] (Item 14) The information processing apparatus involved in one aspect may also include: at least one or more processors; and a memory accessible by the one or more processors, wherein the memory stores one or more commands executed by the processors, and the processors perform the following processing by executing the one or more commands: receiving the measured values of a specified component obtained by analyzing a plurality of samples by an analysis device; estimating respective reference values obtained when measuring a standard sample obtained by mixing each of the plurality of samples in two or more measurements including at least the M-th measurement and the Q-th measurement, where M and Q are natural numbers satisfying M < Q; and using the respective reference values to correct the N-th measured value, where N is a natural number satisfying M < N < Q, wherein when estimating the respective reference values, the T-th to Q-th measured values are used to estimate the Q-th reference value obtained when measuring the standard sample at the Q-th measurement, where T is a natural number satisfying T < Q, and the L-th to M-th measured values are used to estimate the M-th reference value obtained when measuring the standard sample at the M-th measurement, where L is a natural number satisfying L < M.

[0122] The information processing apparatus according to Item 14 can correct the measured values obtained by the analysis device without using mixed QC samples.

[0123] (Item 15) The program involved in one aspect may also be executed by a processor incorporated in a computer to cause the computer to perform the following processing: receiving the measured values of a specified component obtained by analyzing a plurality of samples by an analysis device; estimating respective reference values obtained when measuring a standard sample obtained by mixing each of the plurality of samples in two or more measurements including at least the M-th measurement and the Q-th measurement, where M and Q are natural numbers satisfying M < Q; and using the respective reference values to correct the N-th measured value, where N is a natural number satisfying M < N < Q, wherein when estimating the respective reference values, the following processing is performed: the T-th to Q-th measured values are used to estimate the Q-th reference value obtained when measuring the standard sample at the Q-th measurement, where T is a natural number satisfying T < Q, and the L-th to M-th measured values are used to estimate the M-th reference value obtained when measuring the standard sample at the M-th measurement, where L is a natural number satisfying L < M.

[0124] The program according to Item 15 can correct the measured values obtained by the analysis device without using mixed QC samples.

[0125] While the embodiments of the present invention have been described, the embodiments disclosed herein are to be considered in all respects as illustrative and non-restrictive. The scope of the present invention is indicated by the claims, and all modifications within the meaning and scope equivalent to the claims are intended to be included.

Claims

1. A data processing method executed by a computer for correcting an Nth measured value of a predetermined component obtained by measuring a plurality of samples using an analyzer, wherein: N is an integer satisfying 3 ≤ N, The computer has a processor and an interface, The data processing method includes the following steps: Receiving, through the interface, a plurality of measurement values obtained by the analysis device; The processor estimates respective reference values obtained when measuring a standard sample obtained by mixing each of the plurality of samples in the case of measuring at least two measurements including the Mth measurement and the Qth measurement, where M and Q are natural numbers satisfying M < N < Q; and The processor uses the respective reference values to correct the Nth measurement value, Among them, the step of estimating the respective reference values includes the following steps: The processor uses the Tth to Qth measurement values to estimate the Qth reference value obtained when measuring the standard sample at the Qth measurement, where T is a natural number satisfying T < Q; and The processor uses the Lth to Mth measurement values to estimate the Mth reference value obtained when measuring the standard sample at the Mth measurement, where L is a natural number satisfying L < M.

2. The data processing method according to claim 1, wherein, The step of estimating the Qth reference value includes the following steps: weighting by multiplying a larger coefficient to the measurement values obtained at the measurements whose order of measurement is closer to the Qth measurement, The step of estimating the Mth reference value includes the following steps: weighting by multiplying a larger coefficient to the measurement values obtained at the measurements whose order of measurement is closer to the Mth measurement.

3. The data processing method according to claim 1, wherein, The step of estimating the respective reference values further includes the following steps: calculating an average value that is the average of the plurality of measurement values, In the step of estimating the Qth reference value and the step of estimating the Mth reference value, the average value is used.

4. The data processing method according to claim 1 or 2, wherein, The step of estimating the Qth reference value includes the following steps: in the case of setting the kth measurement value as P(k) and the kth reference value as I(k), setting r as T, setting k as Q, and calculating the Qth reference value by formula (1), [Equation 1] The step of estimating the Mth reference value includes the following steps: setting r as L, setting k as M, and calculating the Mth reference value by formula (1).

5. The data processing method according to claim 1 or 3, wherein, The step of estimating the Qth reference value includes the following steps: in the case of setting the kth measurement value as P(k) and the kth reference value as I(k), setting the average value of the plurality of measurement values as Pmean, setting r as T, setting k as Q, and calculating the Qth reference value by formula (2), [Equation 2] The step of estimating the Mth reference value includes the following steps: setting r as L, setting k as M, and calculating the Mth reference value by formula (2).

6. The data processing method according to any one of claims 1 to 3, wherein, The T and the L are 1.

7. The data processing method according to any one of claims 1 to 3, wherein, In the step of correcting the Nth measured value, the Nth measured value is corrected based on an approximate curve calculated by performing locally weighted regression smoothing, i.e., LOESS smoothing, using the Qth reference value and the Mth reference value.

8. The data processing method according to any one of claims 1 to 3, wherein it further includes the following steps: detecting an outlier in the measured values and excluding the outlier.

9. The data processing method according to claim 8, wherein any one of a clustering analysis method using a score plot and a detection method using a box plot is used to detect the outlier from the principal component analysis, i.e., PCA, of the measured values.

10. The data processing method according to any one of claims 1 to 3, wherein it further includes the following steps: supplementing missing values in the measured values.

11. The data processing method according to claim 10, wherein in the step of supplementing the missing values, any one of a single imputation method and a multiple imputation method is used to supplement the missing values.

12. The data processing method according to any one of claims 1 to 3, wherein it further includes the following steps: deriving a confidence interval of the value corrected in the step of correcting the Nth measured value.

13. The data processing method according to any one of claims 1 to 3, wherein the analysis device is a mass spectrometry device.

14. An information processing apparatus, comprising: at least one or more processors; and a memory accessible by the one or more processors, in, wherein the memory stores one or more commands executed by the processor, and the processor performs the following processing by executing the one or more commands: receiving measured values of a specified component obtained by measuring a plurality of samples by an analysis device; estimating respective reference values obtained when measuring a standard sample obtained by mixing each of the plurality of samples in at least two measurements including the Mth measurement and the Qth measurement, where M and Q are natural numbers satisfying M < Q; and correcting the Nth measured value using the respective reference values, where N is a natural number satisfying M < N < Q, wherein, when estimating the respective reference values, the Qth reference value obtained when measuring the standard sample in the Qth measurement is estimated using the Tth to Qth measured values, where T is a natural number satisfying T < Q, the Mth reference value obtained when measuring the standard sample in the Mth measurement is estimated using the Lth to Mth measured values, where L is a natural number satisfying L < M.

15. A recording medium that stores a program which, when executed by a processor of a computer, causes the computer to perform the following processing: receiving measured values of a specified component obtained by measuring a plurality of samples by an analysis device; Each reference value obtained when a standard sample obtained by mixing each of the plurality of samples is measured during two or more measurements including at least the Mth measurement and the Qth measurement is estimated, wherein M and Q are natural numbers satisfying M < Q; and correcting the Nth measured value using the respective reference values, where N is a natural number satisfying M < N < Q, wherein, when estimating the respective reference values, the following processing is performed: Estimate the Qth reference value obtained when the standard sample is measured at the Qth measurement using the Tth to Qth measurement values, where T is a natural number satisfying T < Q; and Estimate the Mth reference value obtained when the standard sample is measured at the Mth measurement using the Lth to Mth measurement values, where L is a natural number satisfying L < M.

16. A computer program product comprising a program that, when executed by a processor of a computer, causes the computer to perform the following processing: Accept measurement values of a specified component obtained by measuring a plurality of samples by an analysis device; Each reference value obtained when a standard sample obtained by mixing each of the plurality of samples is measured during two or more measurements including at least the Mth measurement and the Qth measurement is estimated, wherein M and Q are natural numbers satisfying M < Q; and Correct the Nth measurement value using each of the reference values, where N is a natural number satisfying M < N < Q, where, when estimating each of the reference values, the following processing is performed: Estimate the Qth reference value obtained when the standard sample is measured at the Qth measurement using the Tth to Qth measurement values, where T is a natural number satisfying T < Q; and Estimate the Mth reference value obtained when the standard sample is measured at the Mth measurement using the Lth to Mth measurement values, where L is a natural number satisfying L < M.

Images