JP3130139B2 - Chromatogram analysis method and chromatographic device - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to the analysis of a chromatogram obtained from a chromatograph.

[0002]

2. Description of the Related Art Conventionally, when the area and height of each peak are determined from the overlap of incompletely separated peaks in a chromatogram, the peak is divided vertically from the valley (minimal point) between the peaks with respect to the time axis. Alternatively, the peak is divided so as to cut off a peak secondary to the tailing peak and the leading peak by a straight line. Although this method is simple, it lacks the accuracy of peak quantification and often deviates by ± 50% from the true value.

As a method of improving this, Gaussi
an (G function) or Exponentially mod
a technique of fitting overlapping peaks using a non-linear least squares method using an analytical function such as an enhanced Gaussian (EMG function);
When performing peak fitting in a multiplicative method, a method using three-dimensional chromatogram data having three axes of time, wavelength, and absorbance (intensity) of a chromatogram is proposed as the data.

[0004]

However, when the above-mentioned least squares method is used, the optimum parameters used for the nonlinear least squares method cannot be obtained as expected, and the solution does not converge. In the case of using a three-dimensional chromatogram as in the latter case, it is troublesome to obtain three-dimensional information, and further, if there is no difference in wavelength spectrum between peaks, it is quite difficult to perform fitting.

The present invention has been made in view of the above points, and a first object of the present invention is to obtain information on two-dimensional chromatograms of a time axis and an intensity axis, and even if the patterns of overlapping peaks are various, A chromatogram analysis method and a chromatographic apparatus that can accurately capture characteristics (area ratio, peak height, retention time, etc.), suppress the quantitative error of each peak to about several percent, and perform high-speed data processing Is to provide.

A second object of the present invention is to accurately determine a baseline of each chromatogram in response to a case where a base line and a peak overlap in various patterns. An object of the present invention is to provide a chromatogram analysis method and a chromatographic apparatus that enable peak quantification.

[0007]

In order to achieve the first object, the present invention proposes the following chromatogram analysis method.

That is, using the shape function of the peaks, the overlapping peaks of various patterns in which the area, height, retention time, etc. of the overlapping peaks are changed are simulated in advance,
Based on these simulated data, a process constant for the black box that can be derived as a solution for the area, height or retention time of each overlapping peak is established, and this process constant is determined by the overlap of the unknown chromatogram input to the black box. The present invention is applied to peak data analysis (this is referred to as “1-1st problem solving means”).

Alternatively, instead of this, the peak shape of a chromatogram of a known sample such as a standard sample is obtained, and an unknown sample (where the unknown sample is a general sample comprising the same components as the standard sample and other components). Is obtained, the peak shape of the unknown sample is overlapped, and the peak area S1 (or peak waveform P1) and height h1 of the standard sample and the total of the overlapped peaks of the unknown sample are obtained. From the area S2 (or the overlapping waveform P2) and the peak height h2 of the same component as the standard sample, the peak area S3 of the other component of the unknown sample (or the peak waveform P3)
From S3 = S2-S1 · h2 / h1 or P3 = P2-
A method of calculating from the relational expression of P1 · h2 / h1 is proposed (this is referred to as a 1-2nd problem solving means).

In order to attain the second object, various patterns obtained by synthesizing a base line and a peak are simulated in advance, and a process processing constant of a black box which can derive a form of a base line from these simulated data as a solution. Is applied, and this process processing constant is applied to the data analysis of the baseline determination of the unknown chromatogram input to the black box (this is referred to as a second means for solving the problem).

The process constants in the first and second problem solving means are typically a coupling constant by a neural network.
This is described in detail in Examples.

[0012]

Operation of the 1-1 Problem Solving Means: Chromatograms of overlapping peaks of various patterns are simulated in advance using a peak shape function. In this case, the optimum pattern desired by the data creator can be selected by trial and error with respect to the overlapping pattern of the chromatogram to be simulated, by substituting the parameters to be substituted into the peak shape function. As a result, various peak overlapping chromatogram patterns (simulated data) in which any of the peak area, peak height, retention time, and the like are known are obtained. This peak shape function uses a G function, an EMG function, etc., but does not use it for peak fitting of an actual chromatogram as in the prior art, but for simulated data for establishing a process processing constant described below. Used as creation.

When the data (here, the chromatogram of the overlapping peaks) is input to the black box as the data processing means, the process processing constant is a solution of the data (here, the area, height, and retention time of the overlapping peaks). To the output side.

By using simulation data whose solution is known in advance, a process processing constant value at which a known solution is output with respect to the input simulation data can be obtained by the learning function. The more diverse the data is, the more various process processing constants adapted thereto can be established. The process processing information obtained in this way is stored. When an actual unknown chromatogram is input to the black box, the process processing constant of the simulated data close to the chromatogram is applied, and the data analysis of the overlapping peaks (for example, the area ratio between peaks, the peak height, Retention time, etc.), and quantitative calculation of each peak becomes possible.

In this case, since the process analysis constant of the simulated data closest to the actual data analysis of the chromatogram is used, no large error occurs in the data analysis.
Moreover, it can be applied to data analysis of various patterns.

Operation of the 1-2 means for solving the problem: A case where the peak area of the standard sample is S1 and the height is h1, while the total area of overlapping peaks of the unknown sample is S2 and the peak height of the same component as the standard sample is h2. , S1 / h2 / h1 correspond to the peak areas of the same components as the standard sample in the unknown sample. Therefore, from the total area S2 of the peaks of the unknown sample, S1 · h2 / h
Subtracting 1 gives the peak area S of the remaining components of the unknown sample.
3 can be obtained simply and with a reduced error. In this method, the peak area S1 is replaced with the peak waveform P1, the total area S2 of the overlapping peaks is replaced with the overlapping peak waveform P2, and the peak area S3 is replaced with the peak waveform P3, thereby similarly enabling quantitative calculation of each peak of the unknown sample. .

Operation of the Second Means for Solving the Problem: In this method,
The data analysis target was used as the peak baseline instead of the overlapping of the peaks of the first problem solving means. In the case of the present invention, since the simulated chromatogram has a known baseline as a solution, the process constant of a black box for deriving the solution is also obtained by inputting the simulated data into the black box and learning it. be able to.

By preparing various types of simulated data of the baseline and the peak, it is possible to prepare various types of process processing constants corresponding thereto.

When an actual unknown chromatogram is input to the black box, a process processing constant of simulated data close to the chromatogram is applied, the baseline of the chromatogram is determined with high accuracy, and the quantification of each peak is performed. Calculations can be made accurately.

[0020]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS An embodiment of the present invention will be described with reference to FIGS.

First, the configuration of a chromatographic apparatus to which the present invention is applied will be described with reference to FIG. FIG. 4 is a system configuration diagram of a liquid chromatograph for analyzing glycohemoglobin by ion exchange chromatography.

The eluent feed pump 40 is provided with an eluent A48, an eluent B49, and an eluent C for performing stepwise elution.
50 for 1.9, 1.0 and 0.4 minutes respectively.
The solution is switched by a 3 minute cycle. A sampler 43 having a movable needle is a standard sample 5 of hemoglobin (Hb).
5 μl of 2 or unknown sample 51 is aspirated, diluted by hemolysis,
The hemolyzed diluted sample is sent to the separation column 41 together with the eluent A48, and the contained components are separated and developed, and are detected by the visible absorbance detector 42. The chromatogram as the detection data is stored in the data analysis unit 45.

The data analysis unit 45 has a data analysis function for identifying and quantitatively calculating peaks in the chromatogram in accordance with the flowchart of FIG. 1 described later. As shown in FIG. , A peak detection function 62, a baseline determination function 63, a peak identification function 64, a differentiation calculation function 65, a chromatogram normalization function 66, and a quantitative calculation function 67. These functions will be described in the flowchart of FIG.

FIG. 2 shows an example of a chromatogram obtained by the separation column 41. When the peaks of 1-Alc (unstable hemoglobin) and s-Alc (stable hemoglobin) overlap each other, as shown in FIG. It is difficult to accurately measure each peak area. In the present embodiment, each peak area is calculated using a neural network in order to ensure the accuracy of the measurement.

Here, the chromatogram analysis performed by the data analysis unit 45 will be described with reference to the flowchart of FIG.

This flow starts when a digitized chromatogram is obtained (step 1). In step 2, peak detection of the chromatogram is performed. In the peak detection, the first point at which the rising increase amount of each peak exceeds the amount equivalent to five times the noise value three times by the peak detection function 62 is set as the peak start point, and vice versa. In step 3, the baseline determination function 63 draws a straight line so that the three points of the start point of the first peak of the chromatogram, the start point of the last peak up to 2.15 min, and the end point of the last peak do not exceed the chromatogram. tie. For example, as shown in FIG. 2, three points, a start point of Ala, an end point of s-Alc, and an end point of Ao, are connected by a straight line. In step 4, peak identification is performed.
In the peak identification, if there is a retention time (time of the peak maximum point) in a time window (permissible time width of peak identification) prepared by the peak identification function 64 in advance, it is fixed to a component of the time window. If there is a maximum point (maximum point of each peak) on the chromatogram, peak identification is completed here.

It should be noted that as shown in FIG.
In the case where -Alc overlaps as a shoulder peak, identification is not possible by the local maximum point identification method. In this case, the peak identification function 64 makes the differential calculation function 65 perform peak identification when l-Alc is not identified. To order. The peak identification function 64 detects an inflection point (a point where the second derivative is 0 and the first derivative is convex downward) on the rising side (left side of the peak) of s-Alc from the peak maximum point to the left. Search for it. If there is, when any inflection point is within the time window, it is identified as 1-Alc. If not, do not identify,
After step 5, the process proceeds to step 17. Here s-
Alc is determined as a single peak, and the quantitative calculation function 67 determines the peak area from the peak area.

If the identification of l-Alc and s-Alc can be confirmed in step 5, the peaks Ala, Alb, F, l-Alc, s-Alc, A
Calculate quantitatively by finding the peak area of o. In this quantitative calculation, the correction factor is multiplied by each peak area, and the area percentage of each peak is quantified by the quantitative calculation function 67.

The following description focuses on the determination of l-Alc and s-Alc.

In step 6, when performing peak separation, a. By vertical division, b. The selection is made by using a neural network, and this selection is made in advance by the operator.

If the vertical division is selected, it is checked in step 7 whether there is a valley (minimum point) behind l-Alc. If there is, the peak area is divided vertically from the valley in step 8 with respect to the time axis, then quantitative calculation is performed in step 10, and the process is terminated in step 18.

Next, the case where the quantitative mode by the neural network is selected in step 6 will be described. Step 11 of the neural network flow
To step 15.

In step 11, a valley (minimum point) is formed in a section from the peak of l-Alc to the end of s-Alc.
Check if there is only one. If two or more, step 1
At 6, a message such as “Neuro quantification is not possible” is output from the CRT 46 and the printer 47 as quantification is impossible by the neural network, and the process shifts to the vertical division processing system after step 6. 0
In this case, the first derivative of l-Alc is an inflection point convex downward, and this inflection point is s-Alc from the peak start point of l-Alc.
It is checked whether there is one in the section up to the peak maximum point of c. If there is one valley or inflection point convex downward, the process proceeds to step 12.

In step 12, the chromatogram normalizing function 12 uses both the vertical and horizontal axes in order to quantify the overlapped peak of l-Alc and s-Alc by a neural network.

In

Normalization is performed within the interval of [0,1]. That is, FIG.
As shown in (a), the actual chromatogram of l-Al
start point A of c, end point B of s-Alc, l-Alc or s-A
Project the maximum point C of lc onto the normalized chromatogram. A
Is set to (0.0, 0.0), B to (1.0, 0.0), and C to 1.0 on the vertical axis. When the point A rises from the base line as shown in FIG. 3, the projection is performed by subtracting the hatched portion. Convert this normalized chromatogram to CRT
46 and a printer 47.

Here, in step 13, whether the shape of the peak is suitable for the neural network is determined by determining whether the total peak area of l-Alc and s-Alc in the normalized chromatogram is 0.2 or more and 0.8 or less. Confirm from that. If it is less than 0.2, the peak is sufficiently sharp and the peak is completely separated. Therefore, neural quantification is unnecessary in step 16, and if it exceeds 0.8, it is abnormally broad. The output is output as network quantification impossible, and the process proceeds to step 7 to enter the mode of quantitative calculation of vertical division. 0.2 total peak area
If it is in the range of ０．８0.8, the process proceeds to step S14.

In step 14, the neural network type peak area distribution function 61 uses the l of the normalized chromatogram.
Calculate what percentage of the area ratio of -Alc and s-Alc is. In step 15, the area ratio of the obtained chromatogram is distributed according to the obtained area ratio and quantitatively calculated. That is, when the point A rises from the baseline as shown in FIG. 3, for convenience, the total peak area above the baseline is distributed at the area ratio determined by the neural network. The area ratio obtained here is output to the CRT 46 and the printer 47. After that, the area percentage is calculated using the peak areas of Ala to Ao in the same manner as in the normal processing, and this flow ends in step 9.

Here, the neural network type peak area distribution function 61 will be described in detail with reference to FIG.

As shown in FIG. 5, this neural network is of the Rumelhart type, and a neural network type peak area distribution function 61 as a black box.
Has a hierarchical structure consisting of an input layer, an intermediate layer, and an output layer, and adopts a supervised learning method by back-progression, whereby learning data (supervised data) whose answer is known in advance is input to the input layer. And a coupling constant group (process processing constant) for the intermediate layer to guide the answer to the output layer by the learning function while calculating sequentially. The output is [0,
[1] includes a sigmoid function S (x)
= 1 / (1 + exp (-x)).

In this neural network, a group of coupling constants connecting the input layer and the intermediate layer and the coupling layer connecting the intermediate layer and the output layer are sequentially corrected using learning simulation data as shown in FIG. 3 (b). .

A specific learning example for calculating the coupling constant group will be described with reference to FIG.

The learning simulation data is a chromatogram with overlapping peaks, which can be created by simulating using, for example, a peak shape function. Can be created using

[0043]

(Equation 1)

[0044]

(Equation 2)

That is, 1 × 1 of the normalized chromatogram
T _R (retention time) for each peak of l-Alc and s-Alc in the square of
(Peak width), τ (tailing peak), peak height h
3 (parameters used in the above equation 1 or equation 2) are randomly varied within an appropriate section, and FIG.
For example, as shown in (b), the peak area ratio is 10: 9.
Dozens of types of learning simulation data for various overlapping patterns such as 0, 20:80, 30:70, 40:60 are created. However, since 1-Alc and s-Alc are close to each other in the actual chromatogram, σ and τ can be approximated by common values. In addition, the peak start point and the peak end point are determined at a height of 0.01 on the normalized intensity axis for convenience.

These dozens of types of learning simulation data are interpolated and input to the input layer as teacher data. This input is performed by dividing the section from A to B of the simulation data into 16 parts with respect to the time axis coordinate and giving 16 points of intensity at the time of this division. In the output layer, the area ratio (answer) of s-Alc corresponding to the teacher data is set in step 1 of FIG.
3, corresponding to a value of 0.2 to 0.8. By providing such supervised data, the intermediate layer is established while the learning layer successively corrects the coupling constant group for deriving the answer for each input data by learning.

The simulation data for learning on which the above-mentioned coupling constant is calculated can be simulated other than the analytic function. For example, first, the peak shape of s-Alc of the standard sample is extracted from the actual chromatogram as a function f (t). This is 1-
Alc is hardly contained. The retention time t _R and the peak width w are adopted as characteristic parameters of this function. For example, w is defined as Equation 3 using the peak area A and the peak height h.

[0048]

(Equation 3)

This function form is normalized to area 1 and t _R and w
Is represented as f (t) having a constant as Gaussian G (t) having t _R and σ as constants. Thereafter, simulation can be performed by expanding and contracting the time scale and the intensity scale using the function f (t) of the actual peak shape in the manner described above.

When the parameters σ, τ, and w relating to the peak width described above are simulated more precisely, they are treated as a function using the retention time of the peak as a variable, for example, σ (t _R ) = at _R + b. There is a need.

Through such learning, the coupling constants of the neural network can be used for the data analysis unit 4 of the chromatograph.
5 is stored in advance. Such a learning simulation and calculation of the coupling constant may be performed before collection at the factory of the chromatographic apparatus manufacturing, or may be performed by the user as needed. In addition, parallel distributed processing (PDP; Parallel Distributed) is applied to the chromatographic apparatus.
If a dedicated computer for processing is installed, when the first standard sample of each analysis is measured, the training data for learning is simulated using the function f (t) of the actual peak shape, and this is simulated. If the input is made to the neural network type peak area distribution function 61, the coupling constant can be renewed each time.

Such a group of coupling constants is stored in the data analyzer 45.
When a chromatogram of an unknown peak overlap is input to the input layer, the peak area ratio in step 14 in FIG. 1 can be obtained from a group of coupling constants close to the input.

Here, the normalization method of the normalization function 66 will be supplementarily described. As described above, in the embodiment, the starting point and the ending point of the overlapping peaks are linearly transformed into (0, 0) and (1, 0) on the horizontal axis, and the maximum point of the overlapping peaks is normalized to the height of the vertical axis 1. . This is done for processing by a neural network, but other normalization methods can be used. That is, the horizontal axis is linearly converted in the same manner as described above, and the vertical axis is normalized so that the total area of the overlapping peaks is 1. In this case, the height of the normalized peak serves as one index, and can be used to determine whether or not neural network quantification is necessary.

For other normalization methods, the actual time axis scale is used as it is on the horizontal axis, and the peak area or height is set to 1
Can also be normalized to This has the advantage that there is no need to perform interpolation processing, but it is necessary to slightly diversify the teacher data. In addition, there is a method in which both the total area and the maximum point of the peak are normalized to 1 to make the horizontal axis flexible. This is characterized in that it is not necessary to determine ambiguous points that are difficult to define mathematically, such as the starting point and the ending point of the peak.

In the above-described neural network type peak area distribution function 61, a pattern of 16 points has been input to the input layer. However, there is also a method of inputting a feature amount of the pattern.
That is, from the normalized chromatogram of FIG. 3, the maximum point, the maximum point, the minimum point (valley) and the coordinates (x,
y) can also be input numerically. This method is characterized in that the number of input layer and intermediate layer units can be reduced to a minimum.

Further, as described above, it is possible to quantify by a neural network of a set of coupling constants. However, a neural network having chromatogram patterns classified into classes and having a coupling constant dedicated to each class is provided. Can be more efficiently and accurately determined. For example, classify into classes with valleys and classes without valleys, or classify by area of the normalized chromatogram,
Class 1 is 0.2 to less than 0.4, and 0.4 to 0.4.
Classes less than 6 are class 2 and classes greater than or equal to 0.6 and less than 0.8 are class 3 and are processed exclusively for each class. In a general chromatogram, this classification method is effective for quantification of a shoulder peak, quantification when a small peak overlaps with a tailing peak, or processing at a leading peak.

In FIG. 4, reference numeral 44 denotes a control unit for controlling the entire chromatograph, 53 denotes an analysis condition determination unit, and the analysis condition determination unit 53 sets the switching time of the eluent when the chromatogram cannot be quantified by the above method. Is instructed to the control unit 44, and feedback for improving the separation is enabled.

In the above embodiment, not only the area ratio as described above but also a feature quantity describing a chromatogram such as a peak height and a holding time can be set as an output item.

According to the present embodiment, the following effects are obtained.
In the separation of 1-Alc and s-Alc in glycohemoglobin analysis, it took 7 minutes to obtain accurate quantification by the conventional vertical peak splitting method, but in the present example, it took about half in 3.3 minutes. Can be quantified.

In addition, in the 3.3-minute analysis, the quantification accuracy of the conventional vertical resolution method was 20 to 30 for s-Alc.
% And l-Alc contained an error of about 50%. However, in the quantitative method using this neural network, s-Alc
The error was suppressed to 3 to 5% for c and about 10% for l-Alc.

Next, another embodiment of the present invention will be described with reference to FIG.

In this embodiment, the area ratio of the overlapping peaks in the chromatogram is obtained by using the actual s-Alc table pattern of the standard sample without employing the neural network method.

That is, first, as shown in FIG. 7A, an actual standard sample s-Alc is analyzed by a chromatograph, and the chromatogram is stored as a table pattern. H1
And the area S1 are calculated. Next, as shown in FIG. 7B, the chromatogram of the unknown sample is obtained, and the total area S2 of the peak h2 of s-Alc and the overlapping peak (overlapping peak of s-Alc and 1-Alc) is calculated. . When the peak of s-Alc is small and does not become the peak, the height of the inflection point is set to h2. Next, in order to make the peak area S1 of s-Alc of the standard sample correspond to the actual area S1 'of s-Alc of the unknown sample, the ratio h2 / h of the peak heights h2 and h1.
h1 is multiplied by the area S1 (S1 ′ = S1 × h2 / h1),
Thus, the peak area S1 'of s-Alc of the unknown sample can be determined, and the peak area of 1-Alc of the unknown sample can be determined from S-S1'.

The above is summarized by the following equation.

[0065]

S3 = S2−S1 · h2 / h1 Specifically, as shown in FIG. 7, the numerical values are expressed in terms of electric quantity, where h1 is 340 μV and S1 is 1,700,000 μm.
V · s, h2 is 470 μV, S2 is 2,800,000
Assuming that μV · s, by substituting into Equation 4, S3 becomes 4
50,000 μV · s.

This method obtains the area ratio of the overlapping peaks by numerical operation, but can be developed to a method of subtracting the waveform of the chromatogram. That is, the waveform of the standard sample multiplied by a certain constant is subtracted from the waveform of the general sample, and l
The area values of -Alc and s-Alc can be determined.

This can be expressed by the following equation.

[0068]

Where P3 is the peak waveform of the standard sample, P2 is the overlapping peak waveform of the general sample, and P3 is the peak waveform of a component of the general sample other than the same component as the standard sample. It is.

In this case, the constant h2 / h1 is almost appropriate, but a slight adjustment may be made so as to satisfy the following condition.

(1) Even a part of the subtracted waveform does not fall below the base line. (2) The subtracted waveform has one vertex and one inflection point on each of the left and right of the vertex (No. Finally, application examples other than the glycohemoglobin analysis, which have been described in the examples, will be described.

In a catecholamine analysis, when a biological fluid such as plasma or urine is used as a sample, an interference component often overlaps a target component. In this case, the quantification based on the peak height is less susceptible to interference. Nevertheless, if the overlap is very large, as in DA (dopamine) in FIG. 8 (c), the quantitative component will be large. Also in this case, the necessary components and the interference components are simulated using the peak shape function as in the first embodiment, and the normalized chromatogram pattern is input to the input layer and the output A method of associating the true value of the peak height is effective. Even in amino acid analysis, a small peak may overlap a shoulder peak and a tailing peak. This can also be accurately quantified by calculating the peak area ratio using the above-described neural network.

The amino acid analysis has a characteristic baseline fluctuation. Cy as shown in FIGS. 8B and 8C
There is a plateau that rises near NH ₃ (ammonia) due to the eluent switching shock just before s (cysteine). This type of baseline significantly changes in size and shape in each chromatogram, which affects how the baseline is drawn and causes errors in peak quantification. For this purpose, it is effective to use a method in which a neural network learns some patterns and draws a baseline according to teacher data.

That is, various patterns obtained by synthesizing the baseline and the peak are simulated in advance, and from these simulated data, a group of coupling constants that can be derived as a solution of the baseline form using a neural network is established. Apply to data analysis of baseline determination of unknown chromatograms entered using.

[0074]

According to the present invention, as described above, the first
In the first and second problem solving means, the time axis and the intensity axis
From the data of the dimensional chromatogram, and even if the patterns of the overlapping peaks are various, the characteristics of each peak can be captured with high accuracy, the quantification error of each peak can be minimized, and accurate quantification can be performed by high-speed data processing. .

According to the second means for solving the problems, even for a chromatogram including various baselines, the baselines are determined with high accuracy, and accurate peak quantification is enabled.

[Brief description of the drawings]

FIG. 1 is a flowchart of a chromatogram analysis according to a first embodiment of the present invention.

FIG. 2 shows an example of a chromatogram to be quantified.

FIG. 3 is an explanatory diagram of processing of a neural network.

FIG. 4 is a configuration diagram of a chromatograph used in the first embodiment.

FIG. 5 is a system configuration diagram of a data analysis unit used in FIG. 4;

FIG. 6 is a diagram showing an example of a shoulder peak.

FIG. 7 is an explanatory diagram showing a chromatogram analysis process according to a second embodiment of the present invention.

FIG. 8 is an explanatory diagram showing a chromatogram analysis process according to a third embodiment of the present invention.

[Explanation of symbols]

40: liquid sending pump, 41: separation column, 42: detector,
43: sampler, 44: control unit, 45: data analysis unit
61: Neural network type distribution function.

Continued on the front page (72) Inventor Hiroshi Satake 882 Ma, Katsuta-shi, Ibaraki Pref.Measurement Equipment Division, Hitachi, Ltd. (72) Inventor Mitsuo Ito 882 Mao, Katsuta-shi, Ibaraki Pref. Within the business division (72) Inventor Yoshiaki Seki 832-2 Nagakubo, Horiguchi, Katsuta-shi, Ibaraki Prefecture Within Hitachi Measurement Engineering Co., Ltd. (58) Field surveyed (Int. Cl. ⁷ , DB name) G01N 30/86

Claims (8)

(57) [Claims] 1. Using a shape function of a peak, simulate in advance overlapping peaks of various patterns in which the area, height, retention time, etc. of overlapping peaks are changed, and calculate each overlapping peak from these simulated data. The method is characterized in that a black box process constant that can be derived as a solution for the area, height or retention time of the black box is established, and this process constant is applied to data analysis of overlapping peaks of unknown chromatograms input to the black box. Chromatogram analysis method. 2. The method according to claim 1, wherein the shape function of the peak is Gaussian, Exponentially.
A chromatogram analysis method characterized by an analytical function such as modified Gaussian (EMG) or a peak shape function obtained from an actual chromatogram. 3. A method in which various patterns obtained by combining a baseline and a peak are simulated in advance, and a black box process processing constant from which a baseline mode can be derived as a solution is established from the simulated data. A chromatogram analysis method, which is applied to data analysis for determining a baseline of an unknown chromatogram input to a black box. 4. The chromatogram analysis method according to claim 1, wherein the processing constants are a group of coupling constants calculated using a neural network. 5. Obtaining a peak shape of a chromatogram of a known sample such as a standard sample, and a chromatogram of an unknown sample (where the unknown sample is a general sample composed of the same components as the standard sample and other components). When the peak of the unknown sample overlaps, the peak area S1 and height h1 of the standard sample and the total area S2 of the overlapping peak of the unknown sample and the peak height of the same component as the standard sample are obtained. A chromatogram analysis method, wherein a peak area S3 of another component of the unknown sample is calculated from h2 using a relational expression of S3 = S2−S1 · h2 / h1. 6. A chromatogram of a known sample such as a standard sample, and a chromatogram of an unknown sample (where the unknown sample is a general sample composed of the same components as the standard sample and other components). When the peak shape of the unknown sample is overlapped, the peak waveform P1 and height h1 of the standard sample and the overlapping peak waveform P2 of the unknown sample and the peak height of the same component as the standard sample are obtained. h2, the peak waveform P of the other components of the unknown sample
3 is calculated from the relational expression of P3 = P2−P1 · h2 / h1. 7. A chromatographic apparatus comprising a function of performing a quantitative calculation using the chromatogram analysis method according to claim 1. Description: 8. The chromatographic apparatus according to claim 7, further comprising a parallel distributed processing system.