JPH07119721B2 - Method for qualitatively quantifying composition components of mixture and method for preparing standard data used in the method - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION Object of the Invention (Industrial field of application) The present invention relates to standard data of a sample substance (which refers to a chemical substance registered in a database as standard spectral data), to a sample substance mixture (multiple The peak intensity value for the chemical shift value of ^13).
The present invention relates to a method for qualitatively quantifying composition components of a mixture for accurately and efficiently identifying a sample substance in a sample substance mixture from measurement of a C-NMR spectrum and a method for preparing standard data used in the method.

(Prior Art) Usually, composition analysis of a mixture is carried out by column chromatography, gas chromatography, high performance liquid chromatography or the like when the respective components can be separated. However, in the case of simultaneous analysis of objects in which it is difficult to separate each component or substances with greatly different structures, these methods cannot be applied, and therefore other methods must be used.

Therefore, recently, researches and developments such as nuclear magnetic resonance (NMR) and electron paramagnetic resonance (ESR), which are effective for analysis of material structure using magnetic resonance, have been advanced in various fields. These are based on the principle that, when the energy levels of particles with a magnetic moment are separated in a static magnetic field, resonance occurs with an oscillating magnetic field or electromagnetic wave having a frequency (frequency) corresponding to the separation interval. It is a thing. However, due to the nature of the detection level, nuclear magnetic resonance is more commonly used as a magnetic sensor, and the others are used to standardize more microscopic atomic-level magnetic quantities.

Here, ¹³ C-NMR in which nuclear magnetic resonance was applied to chemical analysis
The ¹ H-NMR apparatus will be described based on the Fourier transform nuclear magnetic resonance (FT-NMR) apparatus shown in FIG. This FT
First, the NMR apparatus synchronizes the frequency signal a from the high frequency oscillator 1 and the rectangular pulse signal b from the pulse generator 3 with a predetermined period at the gate 2 in order to obtain the variable synchronizing signal c. The CPU 9 variably controls the frequencies of the high frequency oscillator 1 and the pulse generator 3, and the synchronization signal c is transmitted to the sample W as an amplified synchronization signal d by the power amplifier 4. Sample W
Is set in a solubilized state such that ¹³ C or ¹ H is contained in a static magnetic field by the magnetic poles N and S in a natural abundance ratio, and when an amplified signal d including a predetermined frequency is received to cause resonance, The resonance signal d'is emitted and transmitted to the detector 5.

In the detector 5, the relationship between the chemical shift value with respect to the sample substance and the integral value for each spectral region for obtaining the structural parameter from the distribution of the peak intensity due to nuclear magnetic resonance containing ¹³ C or ¹ H in advance. Obtain information on the spectrum of nuclear magnetic resonance specific to the sample substance, and exchange the sample W with the CPU9.
It can be configured to perform search and identification as measurement of ¹³ C-NMR spectrum and ¹ H-NMR spectrum. However, when ¹ H-NMR is used, the spectrum line becomes complicated and it is difficult to apply it to the composition analysis of the mixture.
On the other hand, in the case of using ¹³ C-NMR, for example, one carbon line in an organic compound corresponds to one spectral line, which facilitates analysis and is effective as a composition analysis of a mixture.
Here, ¹³ C-NMR is used.

Further, since the resonance waveform e detected by the detector 5 is minute, it is amplified by the amplifier 6 to be amplified resonance waveform f.
After removing the noise component through the filter 7, the detected resonance waveform g is obtained. In addition, the detected resonance waveform g is the digital detection resonance waveform h because of the calculation processing by the CPU 9.
Is quantized by the A / D converter 8 so as to be transmitted as.
In this way, the digital detection resonance waveform h can be input to the CPU 9, but the detection resonance waveform g that is the original analog signal
In order to reconstruct the signal, the Fourier transform (F
T) is performed. This Fourier transform (FT) is the sample W
In order to obtain the ¹³ C-NMR spectrum of, the CPU 9 obtains the continuous frequency component of the time function related to the digitally detected resonance waveform h from the nuclear magnetic resonance spectrum corresponding to the chemical shift value. Thus, from the D / A converter 10, the detected resonance waveform g of the sample W based on the predetermined frequency signal a is
It is detected as a peak intensity signal of a chemical shift value based on a spectrum line including the resonance frequency, and this is recorded on the recorder 11 while being displayed on the display 12, so that the components of the sample W (a single chemical substance or a plurality of chemical substances) can be recorded. The distribution of peak intensities peculiar to the mixture of substances) is obtained depending on the chemical shift value.

With such an FT-NMR apparatus, it is easy to measure ¹³ C-NMR spectra by introducing a chemical shift value into the resonance spectrum analysis result of chemical substances (substances that can be measured by ¹³ C-NMR containing carbon nuclei). A standard data of a sample substance is created in advance for a part of the sample substance mixture, thereby realizing a method of performing a composition analysis from a search based on the standard data using a computer (CPU9). still,
When quantitative analysis is performed from the measurement of the ¹³ C-NMR spectrum, a method of mixing an internal standard substance as a measurement solvent is used.

(Problems to be Solved by the Invention) In the case of the method of measuring the ¹³ C-NMR spectrum of a mixture to analyze the composition based on the attribution based on the standard data created in advance as described above, it is inevitably related to the component chemical substances. The quality of the database becomes important. When qualitative and quantitative analysis is performed from the measurement of this ¹³ C-NMR spectrum, a method of mixing a measurement sample and an internal standard substance is used. The quality of the database is improved by standardizing the measurement data using the data of the internal standard substance.

However, conventionally, ¹³ C-NMR spectrum is measured using an internal standard substance consisting of one type of chemical substance, and even if measured under the same conditions, the peak intensity value of the internal standard substance may vary depending on the type of sample. It changed unnecessarily and could not be accurately standardized. That is, FIG. 17 (A) shows a sample
It is shown that the peak intensity values when the internal standard substance S _b is mixed with _W1 are P _W1 and P _S1 . In the figure (B), the internal standard substance S _b is mixed with the sample W2. It is shown that the peak intensity values at this time are P _W2 and P _S2 . Under the same conditions, the peak intensity values P _S1 and P _S2 of the internal standard substance S _b are originally the same value, and the peak intensity values P _W1 and P _{W2 with} respect to the peak intensity value P _S1 = P _S2 . Is standardized as standard data.
And when measured under different conditions, internal standard substance
Since the peak intensity value of S _b also fluctuates to, for example, P _S1 ′, the peak intensity value for each sample at this time is standardized using this peak intensity value P _S1 ′ as standard data. For example, if P _S1 / P _S1 ′ = 0.98, the measured peak intensity value P _W3 ′ for sample W3 is P _W3 = P _W3 ′ × (P _S1 / P _S1 ′) = 0.98 ×
It is standardized to P _W3 ′.

However, it suffices that the peak intensity value of the internal standard substance S _b always fluctuates in the same manner under the same conditions, but a slight difference in the measurement environment, or the influence of drift of the measuring device, etc. has different in appearance variation by the structure difference of the low-molecular, etc., the peak intensity value of the internal standard substance S _b even when measured on the same conditions varies, further to have a larger variation is measured under different conditions , I couldn't do accurate standardization. This is because there are many kinds of general sample substances and they have various structures, whereas the internal standard substance has a unique structure consisting of one chemical substance,
Depending sample material not obtained accurately performs normalization of the measured data of ¹³ C-NMR spectrum, chemical correlation shift value and the peak intensity values not ascertained, the composition analysis of the mixture ¹³ C-NMR measurement spectrum It was difficult to apply.

In addition, in the general measurement of the NMR spectrum of the measurement sample, the state of the resonance absorption line varies depending on the bonding state of each atomic nucleus of the measurement sample, the arrangement state of magnetic moments, the influence of environmental conditions, and the like. However, when measuring ¹³ C-NMR spectra for sample substances or sample substance mixtures,
Regarding the chemical shift value, each sample substance that makes up the sample mixture has its own peculiarity, but the peak intensity of the measured data and standard data for them is unstable. The advent of a data processing method for various composition analyzes has been desired.

Furthermore, when the mixture component is searched and identified from the measurement of the ¹³ C-NMR spectrum based on the previously prepared standard data as described above, selection from the standard data for the sample substance as the component becomes complicated. The standard data basically stores the measurement results of ¹³ C-NMR spectra for various sample substances, and has peak intensity values and chemical shift values of each sample substance.

However, if each sample substance that makes up the sample substance mixture exhibits a unique single peak intensity distribution, it is not so difficult to identify it, but in reality, the peak intensity of multiple sample substances is Since the value is contained in each peak intensity value of the sample substance mixture, and a plurality of candidates are selected for attribution from the standard data, identification of the composition for it becomes a problem. When targeting a sample substance mixture that contains sample substances that have similar structures, rather than an arbitrary sample substance, assigning all peaks and performing an accurate composition calculation by the least squares method results in a huge calculation. It will be impossible to calculate at the personal computer level. Therefore, a selection method for composition analysis that accurately and quickly calculates sample substance mixtures consisting of more general types of sample substances and sample substance mixtures consisting of complex sample substances with different structures Has not been realized.

The invention of the present application has been made under the circumstances as described above, and the object of one of the inventions is to always use a standard internal substance even if the sample substance mixture is composed of different complex chemical substances. In order to ensure that the composition analysis of the sample substance mixture based on the data can be carried out, it is necessary to prepare standardized standard data for various kinds of sample substances.
Another object of the present invention is to pay attention to the distribution of peak intensity with respect to the chemical shift value presented in the ¹³ C-NMR spectrum, pretreat this according to a predetermined rule, shape the waveform of the spectral data, and convert the standard data into standard data. By making it or using it as the measurement data for the measurement sample, it is possible to reliably perform the composition analysis based on the ¹³ C-NMR spectrum, and it is also possible to prepare a mixture of sample substances consisting of many kinds of sample substances or a complex chemical composition with different structures. For sample substance mixtures containing various sample substances consisting of substances, in order to facilitate assignment from standard data to the measurement results of ¹³ C-NMR spectra,
Before starting the final composition calculation, pretreatment is performed only on the part where the peak attribution is certain, the subsequent final composition calculation is performed quickly even at the PC level, and the composition ratio calculation of the sample substance is extremely reliable. Another object of the present invention is to provide a method for qualitatively quantifying composition components of a mixture that can be performed.

Structure of the Invention: (Means for Solving the Problem) The above object of the invention relating to the method of creating standard data is to produce an internal standard substance by mixing two or more chemical substances with a sample substance, and The composition is set so that the peak intensity distributions with respect to the chemical shift values presented in the measurement of the ¹³ C-NMR spectrum for a substance are single and do not overlap, and the ¹³ C-NMR spectrum data for a large number of sample substances are stored in the internal The ¹³ C-NMR spectrum of the standard substance was used for normalization, and the distribution of peak intensities with respect to the chemical shift values presented by the ¹³ C-NMR spectrum was measured with a first interval in units of a predetermined interval of the chemical shift value, and A second interval that is narrower than the first interval is set, and when the neighboring mutual intervals in the peak intensity distribution are equal to or greater than the first interval, no processing is performed, and when the second interval is equal to or greater than the second interval. If it is less than the interval, addition processing is performed, and if less than the second interval, processing other than maximum peak intensity is deleted so that the entire peak intensity distribution is a single peak intensity of the first interval or more, This is achieved by using the chemical shift value after each of the above treatments. Further, the above object of the invention relating to the qualitative quantification method of the composition components of the mixture is to obtain the measurement data of the ¹³ C-NMR spectrum for the sample substance mixture by using the standard data prepared as described above, and to obtain the sample substance mixture. Based on the chemical shift value specific to each of the sample substances presented by the ¹³ C-NMR spectrum with respect to the distribution of the peak intensities, the compositional components of the sample substance mixture cannot be easily assigned from the standard data at regular intervals. Excluding a group of consecutive peaks, only the peaks of possible parts are assigned, and the components of the sample mixture are calculated by least squares calculation from the multiple regression model based on the value of the composition ratio that temporarily sets the model formula formed from the assigned components. A multiple regression method in which the composition calculation for identification is performed without any restriction so that the obtained composition ratio can take any of positive, negative and zero values, or The composition calculations for performing component identification of the sample mixture by the least square operation from the regression model,
Least squares method (modified multiple regression method) that limits the calculation so that the obtained composition ratio can be either positive or zero
When the value of each composition ratio is not a positive value in the multiple regression method and is a component close to 0 in the least squares method as a result, the component substance corresponding to the corresponding composition ratio is deleted, Based on the model formula after this deletion, the values of the respective composition ratios are sequentially changed until the corresponding component disappears, and repeated calculation is performed by the multiple regression method or the least squares method. It is achieved by obtaining the composition ratio when the above condition is no longer satisfied.

(Action) In order to analyze the composition of the mixture with the chemical shift value and its peak intensity, which is the ¹³ C-NMR spectrum of nuclear magnetic resonance peculiar to the constituent sample material, The system should be configured so that the chemical shift values are matched correctly to the distribution that the peak intensities exhibit. In this case, in order to create the standard data in advance and to search and identify the component chemical substances of the mixture, the method of creating the standard data and the calculation of the composition components are extremely important.

In the invention related to the method of creating standard data, even for a mixture containing a plurality of chemical substances having different structures as components, ¹³ C-N
To ensure that the composition components can be identified from the measurement of MR spectra, the internal standard substances, which are the basic constituents, are used.
Produced as a mixture of more than one chemical (preferably containing high molecular weight and low molecular weight structures). This is because, in the measurement of ¹³ C-NMR spectrum, first, the universal intrinsic standard value for the internal standard substance is obtained by using the sum or average of the peak intensity values for the chemical shift value of the internal standard substance, and the intrinsic standard value On the other hand, the peak intensity value of the chemical substance that is the measurement sample is standardized and used as standard data. The internal standard substance is generated by mixing two or more kinds of chemical substances, and the specific reference value is calculated based on the sum or average of the peak intensity values. This is also a unique value that can be converted, and accurate standard data and measurement data can be created.

Further, in the present invention, pretreatment data processing is performed at the time of creating standard data or at the time of composition analysis so that composition analysis can be performed reliably from the measurement results of ¹³ C-NMR spectra for sample substances and sample substance mixtures. Each of the above data is simplified. That is, of the distribution of peak intensity with respect to the chemical shift value presented in the measurement of ¹³ C-NMR spectrum,
The peaks that are close to each other are to be subjected to the data processing, and at least the magnitude 2 corresponding to the predetermined chemical shift value interval is used.
A first interval and a second interval of the seed are set. Then, first, by selecting the adjacent peaks in the distribution of peak intensities based on the first interval and obtaining the target for data processing,
The non-processing, the addition processing, and the deletion processing are made to correspond to the three kinds of signals generated from the interval and the section of the second interval in accordance with the predetermined rule, and further, the data intervals regarding the peak intensities after the respective processes are all equal to or more than the first interval. I have to. In this way, by performing a pre-processing when the standard data creation or composition analysis, with respect to the sample material or sample material mixture ¹³
The measurement process of the C-NMR spectrum is facilitated, and the composition analysis is highly accurate.

Furthermore, in the invention relating to the qualitative quantification method,
^{Based on the 13} C-NMR spectrum measurement results, the multiple regression model was set by assigning the sample substance from the unique chemical shift value of each sample substance that constitutes the mixture, and based on the peak intensity value of each sample substance. The sample substances constituting the sample substance mixture are selected from the standard data to identify the composition of the sample substance mixture. That is, first, from the ¹³ C-NMR spectrum measurement results of the sample substance mixture, paying attention to the distribution of the peak intensity unique to each sample substance, and based on the chemical shift value unique to each of these sample substances, the sample substance was easily prepared from the standard data. Except for a group of peaks that are consecutive at regular intervals that cannot be assigned to, only the peaks of the possible portion are assigned. In the standard data, peak intensity values and various chemical shift values for various sample substances are stored. Here, it is possible to exclude peak groups consecutive at regular intervals that cannot be easily attributed once based on the chemical shift values. Only partial peaks are assigned. This assignment does not have a large effect on the component selection even if it is rough, so only the assignment of the chemical shift values of the sample substance mixture, which is relatively easy to assign, is wide,
At this stage, the portion having a narrow chemical shift interval is not performed. After that, the above-mentioned multiple regression method or least squares method was used to calculate the composition of the attribution component attributed only to the peaks of possible portions, excluding the group of peaks that are consecutive at regular intervals that cannot be easily attributed from the standard data as described above. Do.
That is, as far as the multiple regression model, which is the attribution component of the sample substance mixture set based on the chemical shift value of each sample substance, it is the peak intensity value of each sample substance that is considered to be the most probable without error, the standard The composition of the sample substance mixture is calculated based on the peak intensity values of the plurality of sample substances assigned from the data. The composition ratio can be calculated by determining the correlation coefficient of regression. That is, since it is a composition analysis targeting a sample substance mixture, confirmation processing regarding the presence or absence of overlapping components and redesignated components obtained by recombining sample substances is performed by changing the setting of the multiple regression model. This also calculates the regression coefficient by the least-squares calculation. That is, due to the nature of composition ratio calculation by multiple regression calculation or least square calculation, 0 or a negative component may actually be calculated. In such a case, it may be regarded as a non-corresponding component and automatically deleted, but that alone is insufficient for analysis. Therefore, if a sample substance that is not a positive value is included, check the redundancy with a multiple regression model that targets the selected sample substance, and if a sample substance that is not a positive value is included, see Check the respecified components in the regression model.

That is, the multiple regression model is set as follows. When the intensity of a certain peak of the sample substance mixture is M _i , the composition ratio of the sample substance including the peaks attributed to the above-mentioned peaks of the sample substance mixture among the assigned sample substances (here, the three components are assumed) Unknown) as variables A, B, C, and peak intensities (known) of each sample substance as a _i , b _i , c _i , the following equation M _i = A · a for the peak of any sample substance mixture _i + B · b _i + C · c _i (1) is obtained. Usually, the number of peaks of the sample substance mixture is larger than the number of assigned sample substances, and a multiple regression model is set. Such a multiple regression model can be set for all peaks in the sample substance mixture, except for the peaks which are ignored due to narrow shift intervals. The composition ratio (A, B, C) of the sample substance mixture can be obtained by solving the equation (1). By the way, there is a case where 0 or a negative component is present in the composition ratio obtained by the formula (1), and there is a high possibility that this component is not actually contained in the mixture. However, here, for the sake of confirmation, the composition ratio of the negative component in the obtained composition ratio is set to 0, and the peak intensity of each peak of the sample substance mixture is stored as the component in the standard data. The peak intensity is used for the simulation, and the composition ratio is further varied, that is, the sample substance having a composition ratio of 0 is also varied to the positive side for the simulation. The simulation is performed until the difference between the simulation result and the actually measured peak intensity is minimized. This is judged by the sum of squares of all peak intensity differences.

When the composition ratio 0 or a component close to 0 obtained from the above simulation matches 0 and the negative component of the composition ratio initially obtained, this component was detected by attribution, but in reality, the sample substance mixture It is determined that the component is not contained in and deleted from the composition. After that, the multiple regression model is set and the simulation is performed again using the remaining composition components, and the components that are not actually contained are deleted again. The above process is repeated several times to complete the component selection. The judgment of termination is made when all the composition ratio values to be compared have a positive value in either one of the multiple regression method or the least squares method, or when one of the values of the multiple regression method or the least squares method is If it is positive, the comparison is performed when the other value becomes 0. Next, by using the above components, the peaks of the attributed components are assigned to almost all the peaks in the sample substance mixture, and the composition is calculated to obtain the final result.

(Examples) Hereinafter, the present invention will be described in detail with reference to Examples.

FIG. 1 is a flow chart showing the overall flow of the invention relating to the qualitative quantification method, and standard data is prepared in advance for a large number of sample substances. A method of creating standard data used in this invention will be described later. After that, measurement data of ¹³ C-NMR spectrum for the sample substance mixture is obtained (step S1), pretreatment of the measurement data as described later is performed (step S2), and the chemistry according to the sample name which is the name of the sample substance mixture is obtained. The shift value and peak intensity are determined (step S3), and the input data is checked (step S).
Four). The sample name is input so that the origin of the sample substance mixture can be confirmed during the analysis. Also,
The input data is checked by selecting peaks whose peak interval is narrower than the preset peak interval L in the measurement data of the sample substance mixture, and whether there are, for example, 5 or more continuous peak groups among the selected peaks. It is a process of checking whether or not it exists, and if it exists, correcting it to a peak in which peaks are appropriately overlapped and eliminating a group of 5 or more continuous peaks. When the measurement data of the sample substance mixture is, for example, peaks a to k as shown in FIG. 2 (A), groups G1 to G3 having two or more peaks in a range narrower than the peak interval L are obtained, and the peaks among them are determined. Select the group G2 of the peak group with 5 or more consecutive peaks. Then, any peak in group G, for example peak e
And f are added to form a peak l,
The number of G2 peaks is 4 as shown in FIG. 2 (B). As a result, there is no group in which five or more peaks are continuous at a predetermined interval. Such processing must be performed in order to process the attribution of the peak of the sample substance selected by the search at the personal computer level. After checking the input data as described above with respect to the measurement data, the standard data is referred to and the search is performed within the range of the allowable shift value (step S5). Although the search will be described later, the search results are checked after the search to determine whether or not there is a sample substance that may never be contained as a component (step S6). The multiple regression method based on the remaining candidate components (composition calculation for identifying the components of the sample mixture by least squares calculation from the multiple regression model is performed, and the calculated composition ratio is either positive, negative or 0). A method that does not limit the calculation so that it can take a value) or a least squares method (a composition calculation for identifying the components of the sample mixture by a least squares operation from a multiple regression model) is performed. The calculation is performed by limiting the calculation so that only one of the values can be taken) (step S7), and the attribution table consisting of standard data is displayed and checked (step S7). Step S8). In the standard data, there are components with code "S" mark and "N" mark as described later, and if there is an unidentified N peak in a certain candidate substance, this is Usually, it is determined that a significant change in chemical shift has occurred, and the chemical shift value of the corresponding peak in the standard data is corrected so as to match the peak in the corresponding unknown sample (step S9). That is, among the peaks of the standard data, the peaks in which the chemical shift is likely to move in the mixture are ignored during the search, but they must be assigned to the peaks in the mixture during the subsequent simple composition calculation. However, if the deviation of the chemical shift is large, it cannot be assigned automatically, so it is necessary to correct the standard data. However, this correction is done only with the data on the memory of the personal computer, not the standard data that has been saved. A simple composition calculation is first performed from the attribution of the standard data (step S10), and the composition is tentatively determined after the primary determination of the composition component (step S11) and the redesignation of the component (step S11). . After that, peak reassignment and composition calculation are performed (step S13) to quantitatively obtain final results of composition components (step S14).

Here, the standardization method for uniquely obtaining the measurement data of the ¹³ C-NMR spectrum will be described.

Figure 3 (A) ~ (D), relative to the assay mixture was mixed respectively to different compounds a~d an internal standard, ¹³
4 of the composition analysis results obtained by measuring the C-NMR spectrum
Two examples are shown. However, for actual sample compounds
^From the measurement of ¹³ C-NMR spectrum, multiple peaks appear in the constitutional unit of each component in the vicinity of a predetermined point on the spectrum line, but here, for the sake of convenience, they are regarded as data ignoring them and a single peak is simplified. The waveform is shown.

The sample compounds a to d in FIGS. 3 (A) to (D) are four kinds each exhibiting different peak intensity values a _p , b _p , c _p , d _p , and the internal standard substance has, for example, a high molecular weight. The compound α and the low molecular weight compound β are mixed and produced. Specifically, compound α is methylpolysiloxane having a viscosity in the range of 10 to 300 CPS, and compound β is cyclic silicon having a 3 to 10 membered ring, and these are mixed at a mixing ratio of about 7: 3 to 3: 7, respectively. The silicon polymer mixed and produced in step 1 is used as an internal standard substance, and the occupancy ratio of the sample compounds a to d is set to 10 to 20%. Such a setting enables the measurement of ¹³ C-NMR spectrum to be applied to a sample substance mixture containing compounds having different structures from each other. That is, the peak intensity distributions with respect to the shift values are single and do not overlap.

With such a configuration, four types of measurement mixtures [a containing the internal standard substance (α + β) and the sample compounds a to d were mixed [a]
+ (Α + β)], [b + (α + β)], [c + (α +
[beta]]] and [d + ([alpha] + [beta])] for ¹³ C-NMR spectra are shown in FIGS. 3 (A) to (D). Here, in the measurement of the ¹³ C-NMR spectrum, the chemical shift value X from the zero point in each figure presented depending on the frequency S [Hz] giving the wavelength of the spectrum line.
As far as [ppm] is concerned, even if repeated measurement is carried out for the sample compound a in FIG. 3 (A), the values are almost the same under the same conditions. This also applies to the other sample compounds b to d in FIGS. 3 (B) to (C). On the other hand, the peak intensity value corresponding to the vertical axis in each figure is easily affected by an external factor and is unstable, and it is different each time by repeated measurement of the same compound.

Therefore, in the invention relating to the method for creating standard data, each peak intensity value of the ¹³ C-NMR spectrum of the internal standard substance (α + β) is added to obtain a unique reference value and normalize the sample. FIGS. 4 (A) and (B) show ¹³ C-NMR spectra of the sample compounds e and f and the internal standard substance (α + β), and the normalization of the data will be described with reference to these spectra.

As shown in FIGS. 4 (A) and (B), with respect to the distribution of the peak intensities of the sample compound e and the internal standard substances α and β, the chemical shift value X has an extremely small relative variation under each condition, Even under the same conditions, the peak intensity value is (α _p , α _p ′),
There is a non-negligible relative variation between (β _p , β _p ′).
However, the internal standard substances α and β are designed to fluctuate inversely with respect to the sample compounds e and f, respectively, and the sum or average of the peak intensity values α _p , β _p and α _p ′, β _p ′ is obtained. The value is always constant. That is, assuming that the peak intensity values of the internal standard substances α and β are the standard values α ₀ and β ₀ for the medium molecular weight compound, the peak intensity value α _p for the high molecular weight compound e is smaller than the standard value α ₀ . Also becomes larger, and the peak intensity value β _p becomes smaller than the standard value β ₀ . On the other hand, the peak intensity value α _p ′ for the low molecular weight compound f is smaller than the standard value α ₀ , and the peak intensity value β _p ′ is larger than the standard value β ₀ . Therefore, FIG. 4 (A)
For the peak intensity value e _p of the sample e shown in (1), the sum of the peak intensity values α _p and β _p for the internal standard substances α and β (α
_p + β _p ) is standardized as an intrinsic reference value, and the peak intensity values f _p of the sample f shown in (B) of the figure are compared with the peak intensity values α _p ′ and β _p ′ of the internal standard substances α and β. The sum (α _p ′ + β _p ′) is standardized as a unique reference value. The data normalized in this way is used as the standard data of the compounds e and f.

Here, since the peak intensity value of the sample compound is affected by the mixing weight ratio of the internal standard substance (α + β), it is necessary to make the mixing weight ratio constant when normalizing the peak intensity value. Then, in the data processing, the measurement results of the peak intensity values of each sample compound with respect to the sum of the peak intensity values of the internal standard substances α and β (α _p + β _p ) were standardized by standardizing them as a database. Standard data can be created. For example, usually the weight of the sample compound and the internal standard (α + β)
When measuring with the weight ratio of 1: 9, when measuring with the weight ratio of 1.02: 8.98, it is sufficient to convert each peak intensity value to 1: 9 and then perform the above standardization.

In this way, similarly standardized standard data is created for the sample compounds a to d as shown in FIGS. 3 (A) to (D). That is, the compound a in FIG.
Is the sum of (α _pa + β _pa ) as the proper standard value, and for compound b in the same figure (B) is the sum (α _pb + β _pb ) as the proper reference value, and the compound c in the same figure (C) is used. Is the sum (α _pc +
β _pc ) is used as a proper reference value, and the sum (α _pd + β _pd ) is standardized as a proper reference value for the compound d in FIG. FIG. 5 shows standardized standard data of various compounds prepared in this way so that composition analysis of the sample substance mixture can be performed. That is, this standard data serves as a material for preparing a attribution table for the composition analysis of the sample substance mixture. Among the measurement data, those standardized as described above and less than 1% of this strength are marked with a code "S" mark. Further, for a group of peaks adjacent to each other at an interval of 0.1 ppm, for example, the smaller peak is marked with "N". This N peak designation is arbitrarily performed even for peaks in which the chemical shift is likely to change in the mixture.

Next, the preprocessing (step S2) of the measurement data at the time of creating the standard data and at the time of measuring the sample substance mixture will be described.

FIG. 6 is a flow chart showing data processing for chemical shift values in ¹³ C-NMR spectrum measurement when standard data is created or when a sample is searched. Here, in order to apply data processing to the actually measured chemical shift value of the sample compound, first, a predetermined interval of the chemical shift value is set as a unit. For example, if there are two predetermined intervals, the first interval L1
Is set to 0.10 [ppm] and the second interval L2 is set to 0.05 [ppm]. From such two set intervals, three kinds of chemical shift value divisions are established, and in this example, predetermined data processing (including no processing) is performed according to the three divisions of chemical shift values, The distribution of peak intensity is standardized. The set value of the interval is slightly different depending on the measurement conditions and the measuring equipment, and is not limited to the above value.

Next, ¹³ C-NMR concerning the actually measured chemical shift value of the sample compound
It is determined whether or not each peak interval is within the first interval L1 for each peak of the peak intensity measured by the spectrum (step S20). If the first interval L1 or more, the peak interval is wide and noise is large. It is not processed as it is because it is not equal. If the peak interval is within the first interval L1, it is subsequently determined whether it is within the second interval L2 (step S21). In this judgment, the peak interval is the second interval L2.
If it is determined to be within the range, there are multiple peak intensities in a very narrow range, so the peak with the highest peak intensity is left and the other peaks are deleted to form a single waveform (step S22). . Furthermore, when the peak interval is not within the second interval L2, that is, when the peak interval is equal to or greater than the second interval L2 and less than the first interval L1, the peak intensities are added to form a single waveform (step S23).

If the peak interval after each processing is within the first interval L1 again for the data processed as described above (step S24), and it is within the first interval L1 Returns to the step S21, and if it is not within the first interval L1, the peak data is set as the chemical shift value for input (step S25).

And FIG. 7 (A)-(C) shows each processing waveform by the said method with respect to distribution of the peak intensity of the measured data of ¹³ C-NMR spectrum. In each figure, the set first interval L1 and second interval L2 are shown corresponding to the chemical shift value, and the peak interval is Δx _{1 in} the same figure (A) and Δx _{2 in the} same figure (B). In the same figure (C), in the case of Δx ₃ ,
The states of no processing, addition processing, and deletion processing are shown before and after each processing. Incidentally, FIG. 7 (A) to (C)
Is the chemical shift value X [pp of each peak intensity corresponding to the spectral line S [Hz] indicating the resonance frequency of each peak intensity distribution.
m] is converted into a chemical shift value X '[ppm] of a single peak with respect to the spectrum line S' [Hz] after each treatment except the case of FIG.

In the example of FIG. 7A, since the peak interval Δx ₁ is larger than the first interval L1 (Δx ₁ ≧ L1), no processing is performed in this case,
Without changing the chemical shift values X and X ', the spectral line S
And S'are the same. However, since the peak interval Δx ₂ is smaller than the first interval L1 and larger than the second interval L2 in the example of FIG. 6B (L2 ≦ Δx ₂ <L1), in this case, each peak is added to obtain a single value. The chemical shift value X presented at each peak is changed as shown by X'as its average value. Therefore, the spectral line S is also changed to S 'accordingly. Further, in the example of FIG. 7 (C), the peak interval Δx ₃ is smaller than the second interval L2 (ΔX ₃ <L2),
In this case, each peak is left with the maximum peak and the other is deleted, and the chemical shift value shown in each is changed to the value shown with X ′ leaving only the maximum peak. Along with this, change to S '.

As described above, the above-mentioned treatments significantly reduce the number of peaks in the distribution of the peak intensities measured by the ¹³ C-NMR spectrum for the sample compound, thereby simplifying the data processing. However, in order to obtain more completeness, it is necessary to determine again whether or not the peak interval after the above-mentioned processing is within the first interval L1 (step S24). This is because each peak interval whose chemical shift value has been changed may be within the first interval L1 after the change. In that case, whether or not it is within the first interval L1. After the judgment of (step S20), a loop is made to return. As a result, all peak intervals between neighboring peaks can be made equal to or greater than the first interval L1 with respect to the entire peak intensity distribution.

The modified chemical shift value thus obtained is used as the input chemical shift value, and the corresponding reference value of the spectrum data is obtained to complete the whole process. By performing such processing on various sample compounds, standard data will be simplified, and it will be possible to search and identify sample substances that are components even for sample substance mixtures composed of complex chemical substances with different structures. It is possible to create various standard data. Further, although an example of the data processing for the standard data has been described above, the simplified standard data can be utilized by processing the actual search measurement data in the same manner.

The first interval L1 is set in the vicinity of a critical value such that the components of the sample substance mixture in the ¹³ C-NMR spectrum measuring system can be identified without error, and the second interval L2 is set.
Is set near the analysis resolution of the search device. Further, in the above description, data processing is performed with two types of intervals L1 and L2, but it is also possible to set more intervals and perform data processing.

On the other hand, in the search in step S5 of FIG. 1, as shown in FIG. 8, first, the chemical shift value of each peak of the measurement data is made to correspond in order with the chemical shift value of the standard data (step S3
0), the standard data number i is set to i = 1 (step S3
After 1), for example, a substance having a measurement data peak at a chemical shift value within a range of ± 0.2 ppm is picked up (step S32). Then, step S is performed until the number of peaks reaches n.
By repeating 32, the correspondence relationship for all substances in the standard data can be obtained (steps S33, S34). That is, for each peak in the mixture, check how many standard data peaks the peak is composed of, and if a peak of the mixture is composed of peaks in one standard data, The rank 1 is set, and the case where the standard data is composed of two pieces of standard data is set to the rank 2, and the same processing is performed in the same manner. After that, the ranking table is created by rearranging the picked-up standard data in order from the smallest one based on the one that includes at least one rank "1" (step S35). This ranking table is created for peaks of standard data that are not marked with "N" or "S" marks, and the created ranking table is tentatively designated for the components in the next process (step S
It is used for 7), but the one with the lower ranking in the ranking table is deleted as a search component because it is highly likely that it does not exist in the sample substance mixture.

After that, simple composition from step S7 to S10 (qualitative calculation)
The operation will be described below.

FIG. 9 is a flow chart showing an operation processing example of the method of compositionally analyzing the sample substance mixture based on the sample substance assigned from the standard data, with respect to the measurement result of the ¹³ C-NMR spectrum. First, when the measurement of ¹³ C-NMR spectrum is performed on a sample substance mixture, the peak intensity value for each sample substance appears uniquely on the chemical shift value, so after performing the pretreatment necessary for signal processing, the composition The calculation component A is designated according to the ranking table obtained as described above (step
S40). Next, based on the chemical shift value unique to each sample substance, a group of continuous peaks at regular intervals that cannot be easily attributed to the sample substance from the standard data is excluded from the standard data, and only peaks of possible portions are assigned (step S4
1). A simple composition calculation by the multiple regression method (step S42) and a simple composition calculation by the least square method (step S43) are executed in parallel for the assigned sample substance. Then, the sample substance B whose composition ratio becomes 0 or negative by the multiple regression method and the sample substance C whose composition ratio becomes close to 0 by the least squares method are combined (step S44). The overlapping components are checked (step S45), and the overlapping sample substance D is deleted (step S46). This sample substance D is judged not to be involved in the composition of the sample substance mixture even if it is retrieved from the standard data. After that, the remaining sample substance (A-B) by the multiple regression method, the remaining sample substance (B + C-D) after the overlapping component check, and the remaining sample substance (A-C) by the least squares method are used. Respecify sample material A '
(= A-D) is performed (step S47). For this redesignated sample substance A ′, it is checked whether D = 0, that is, whether or not there is an overlapping component (step S48), and if there is an overlapping component (D ≠ 0), the process returns to the first steps S42 and S43. Then, the composition ratio is sequentially changed and the above-described arithmetic processing is repeated. Then, when there is no overlapping component, that is, when D = 0, when all the composition ratio values to be contrasted are either positive values of the multiple regression method or the least squares method, Alternatively, if one of the values of the multiple regression method or the least squares method is positive and the other value to be compared is 0, the routine proceeds to the final composition (quantitative) calculation routine by reassigning peaks.

FIG. 10 (A) shows the peak intensity value and chemical shift value which are the measurement results of ¹³ C-NMR spectrum for the sample substance mixture, and FIGS. 10 (B) to (F) correspond to this chemical shift value. Substances stored in standard data α ₀ , β ₁ , α ₃ ,
The example of the spectrum data of (alpha) ₂ , (alpha) ₁ , (alpha) ₄ is shown.
Here, from the measurement results of ¹³ C-NMR spectrum shown in FIG. 10 (A), if this sample substance mixture is formed from four kinds of sample substances A, B, C and D, ¹³ C-NMR spectrum , The sample substances A, B, C, D exhibit independent peak intensity values Y on their respective chemical shift values X [ppm].
Therefore, from the peak intensity values Ap, Bp, Cp, Dp of each sample substance of the sample substance mixture, its unique chemical shift value A _S , B _S , C _S ,
Based on D _S , it is possible to easily assign the sample substance from standard data prepared in advance as shown in FIG. The sample substances (α ₀ , α ₁ , ...), (β ₀ ,
beta _1, ...), the candidate examples of _{(γ 0, γ 1, ...} ) is only small portion, actually is further stored chemical shift value and the peak intensity values for a number species material. Therefore, each chemical shift value A _S , of each sample substance A, B, C, D shown in FIG.
Regarding the attribution of the sample substance from the standard data based on B _S , C _S , D _S , for example, the substances as shown in the same figures (E) and (B) for the chemical shift values As and Ds of the sample substances A and D, respectively. α
₂ , α ₀ are assigned to correspond to a single value, and the chemical shift values B _S of the sample substance B are shown in FIGS. (C) and (F), and the chemical shift value C _S of the sample substance C is Sample materials β ₁ , α ₁ and α ₃ , α ₄ as shown in FIGS.

Due to such attribution of sample substances, when the ¹³ C-NMR measurement result for the sample substance mixture M consists of four types of sample substances as shown in FIG. Regarding each peak M _pi (known) of the sample substance mixture M based on (A _pi , B _pi , C _pi , D _pi ) (known) and the composition ratio (b, c, d, e) (unknown) of the sample substance The multiple regression model is set as follows: _Mpi = a + b * _Api + c * _Bpi + d * _Cpi + e * _Dpi ... (2). However, although the error term a is provided here, the setting of the multiple regression model can be arbitrarily performed and is not limited to this. Basically, there are a plurality of attribution components for each sample substance, and a least squares operation is performed to calculate each regression coefficient a, b, c, d, e based on this attribution component. For example, assuming that the error of the peak intensity value M _pi of the sample substance mixture M is Z _pi , Z _pi = M _pi − (a + b · A _pi + c · B _pi + d · C _pi + e · D _pi ) ... (3) Assuming the sum of squares S of the errors in order to obtain the regression coefficients which are these unknown constants, The composition ratio is calculated by obtaining each regression coefficient a, b, c, d, e that minimizes. As an initial condition, (∂S / ∂a)
= 0, (∂S / ∂b) = 0, (∂S / ∂c) = 0, (∂S / ∂d) =
Solving five normal equations satisfying 0, (∂S / ∂e) = 0 gives a unique solution, but in reality, it is complicated, so the explanation is omitted here. A PC is used to calculate the regression coefficient of the multiple regression model. By such a procedure, the composition of the sample substance mixture M can be identified.

However, the composition ratio of the sample substance mixture calculated from the attribution component of each sample substance may be determined to be 0 or a negative component that does not correspond to the composition ratio, depending on the calculation result of each regression coefficient by the least squares calculation. In that case, it can be considered that there is a high possibility that the selected component for the attribution component is not included in the composition of the sample substance mixture M.

For each sample substance corresponding to the constituents left in this way, the setting of the multiple regression model is changed again, and the sample substance mixture M
Select all of the sample substances that make up In the case of the sample substance mixture M of FIG. 10 (A), iterative operation is performed while sequentially changing the settings of the multiple regression model so that a, b, c, d are given as positive values. That is, the selection of the sample substance is completed by obtaining the set multiple regression model so that the regression coefficient for all the sample substances of the sample substance mixture M can be obtained as a positive value.
An identification of the composition of the sample substance mixture is obtained. Gaus
The multiple regression method of s-elimination is shown, but in addition, Dampin
g Gauss-Newton method, Marquardt method or the like may be used.

With respect to the component composition shown in FIG. 11, the simple composition calculation result by the multiple regression method is as shown in FIG. 12, and the simple composition calculation result by the least squares method is as shown in FIG.

After the simple composition calculation (step S10) is performed as described above,
Determine the final compositional component (quantitative), but
Description will be made with reference to the flowcharts of FIGS. 15 (A) and 15 (B). In FIG. 15 (A), the peaks of the search components are assigned to the peaks of the mixture existing at intervals smaller than the predetermined interval which were ignored in the simple composition calculation. The calculation used for this is by the multiple regression method. After that, the final composition calculation shown in FIG. 15 (B) is performed by the least square method to obtain the final composition as shown in FIG.

That is, first, the list of groups (groups G1 to G3 in the example of FIG. 2B) created in the check of input data (step S4) is called (step S50), and the data of a certain group and the attribute of that group are assigned. The standard data of the sample substance including the peak is called from each sample substance whose composition has been identified (step S51), and one sample substance whose peak assignment is changed is selected from the sample substances and assigned to one peak in the group. Other sample substances also belong to the peaks in the group, and the composition is calculated by the multiple regression method (step S5
2). Then, it is determined whether the selected sample substance has been calculated by assigning it to all peaks in the group (step
S53) If not already calculated, the peak attribution of the selected sample substance is changed (step S57), and the process returns to step S52 to repeat the above operation. If it has already been calculated, it is judged whether calculation has been performed for all identified sample substances (step S54), and if there is a sample substance to be calculated, change to the next sample substance (step S58). The process returns to step S52. After calculating for all the sample substances, it is judged whether calculation for all groups has been completed (step S55), and if the calculation has not been completed for all groups, the group is changed (step S5).
9) Return to step S51 and repeat the above operation. When the calculation is completed for all groups, the attribution of the peak having a high correlation coefficient is selected as a result of the calculation, and the attribution of the final composition calculation is determined (step S56). Then, a temporary composition is set using all the assigned peaks of the sample substance mixture and the peak data of each finally identified sample substance, and the sum of squares of the differences between the calculated peak intensity and the measured value is obtained. (Step S61). The composition value is increased for one of the temporary component compositions (step S62), the sum of squares of the difference between the calculated peak intensity based on the temporary composition and the measured value is calculated (step S63), and the previous calculation is performed. It is determined whether the sum is smaller than the sum of squares (step S64), and if smaller, the process returns to step S62. If the sum of squares calculated in the previous step S64 is greater than or equal to the sum of squares calculated for the same component (step S65), the sum of squares of the difference between the calculated peak intensity based on the tentative composition and the measured value is calculated. It is determined (step S66), and it is determined whether it is smaller than the sum of squares calculated last time (step S67). If it is smaller than the previous sum of squares, the process returns to step S65, and if it is equal to or larger than the previous sum of squares, it is determined whether or not all components have been calculated (step S68). Changes to the next component (step S71) and step S62
Return to. When calculated for all components, it is determined whether or not the composition matches the previously obtained composition (step S69), and if they do not match, the change amount is reduced (step S72) and the process returns to step S62. In this case, the amount of change in the composition value is initially set to 10% of the composition value, and thereafter, the amount is changed in small steps by an appropriate amount. If the composition obtained in step S69 is the same as the composition obtained last time, the composition component is determined as the final composition (step S70).

EFFECTS OF THE INVENTION As described above, according to the present invention, it is possible to measure a ¹³ C-NMR spectrum even for a mixture containing a compound having a different structure from each other. By creating standardized data in which each component compound is standardized, it is possible to create a standardized data creation method that enables highly reliable composition analysis of ¹³ C-NMR spectra of sample substance mixtures. Has been done.
Further, by stabilizing the quality of the database, the composition analysis can be more easily applied to qualitative / quantitative analysis, and the component compounds of the mixture, which have been regarded as difficult, can be analyzed.

In addition, when processing standard data and measurement data related to ¹³ C-NMR spectrum measurement, uniform processing is performed on the distribution of peak intensities to realize a method that enables simplified data processing. . This also results in improved and streamlined the quality standard data and measurement data, ¹³ C-N
It is intended to provide a data processing method for component compounds, which enables high-speed and accurate search and identification to be performed on a sample substance mixture composed of compounds having different structures for MR spectrum measurement. In addition, ¹³ C-NMR for the sample substance mixture
Easily assign sample substances corresponding to each sample substance from the standard measurement data to the spectrum measurement results, and introduce a mathematical least squares calculation from the attributed components to obtain a highly reliable composition identification method at the PC level. It is realized in. ¹³ C
-When NMR spectra were measured on a mixture, it has been a problem in the past because the sample materials of various types and different structures would complicate selectivity from standard data. Since it is done in a divided manner,
Even for a variety of mixtures, rapid composition analysis is possible with a PC-level system, and qualitative and quantitative analysis can be easily realized.

[Brief description of drawings]

FIG. 1 is a flowchart showing the overall flow of the invention relating to the qualitative quantification method, FIGS. 2 (A) and 2 (B) are diagrams for explaining the check of measurement data, and FIGS. 3 (A) to (D). )
4A and 4B are diagrams for explaining the standardization of data, FIG. 5 is a diagram showing an example of standard data, FIG. 6 is a flow chart showing processing of measurement data, and FIG. (A) to (C) are flowcharts for explaining the pre-processing, FIG. 8 is a flowchart showing an operation example of the search, and FIG.
The figure is a flow chart showing the method for composition analysis of the sample substance mixture based on the attribution component of the sample substance assigned from the standard data for the measurement result of ¹³ C-NMR spectrum of the present invention, and FIG. 10 (A) shows the sample The figure which shows as an example the distribution of the peak intensity regarding each sample substance which is a component from the measurement of the ¹³ C-NMR spectrum of the substance mixture, the same figure (B)-(F).
Shows the chemical shift value and peak intensity corresponding to each sample substance, FIGS. 11 to 14 show the relationship between the actual composition components and the calculation results, and FIGS. 15 (A) and 15 (B) show the final results. 16 is a flow chart showing an example of the operation for determining the composition component, FIG. 16 is a diagram showing an example of a nuclear magnetic resonance apparatus for Fourier transform, and FIGS. 17 (A) and (B) explain that the measurement data must be standardized. FIG. 1 ... High-frequency oscillator, 2 ... Gate, 3 ... Pulse generator, 4 ... Power amplifier, 5 ... Detector, 6 ... Amplifier,
7 ... Filter, 8 ... A / D converter, 9 ... CPU, 10 ...
D / A converter, 11 …… Recorder, 12 …… Display, X
... chemical shift value, Y ... peak intensity value, M ... sample substance mixture.

Claims (5)

[Claims] 1. An internal standard substance is mixed and produced with two or more kinds of chemical substances for a sample substance, and
¹³ C-NMR distribution of peak intensity to the chemical shift value is presented to the spectrum is a single, and then the composition set so as not to overlap, the ¹³ C-NMR spectral data for a number of sample material of the internal standard ¹³ The first interval in units of a predetermined interval of the chemical shift value and the first interval with respect to the distribution of the peak intensity with respect to the chemical shift value presented by the measurement of the ¹³ C-NMR spectrum are standardized by the C-NMR spectrum. A second interval narrower than the interval is set, and when the neighboring mutual intervals of the peak intensity distribution are the first interval or more, no processing is performed, and when the second interval is the second interval or more and less than the first interval, When the addition processing is performed, and when it is less than the second interval, the peak intensity other than the maximum peak intensity is deleted so that the entire peak intensity distribution becomes a single peak intensity of the first interval or more, and after each of the processes. A standard data creation method, characterized in that standard data is created using the chemical shift value of. 2. Using the standard data according to claim 1, obtains the measurement data of the ¹³ C-NMR spectrum for the sample substance mixtures, the ¹³ C-NMR spectrum relative to the distribution of the peak intensity of the sample material mixture Based on the chemical shift value specific to each sample substance presented by the measurement, the sample that constitutes the sample substance mixture from the standard data and the ranking table in which the most likely composition components in the standard data are rearranged in order. Exclude a group of consecutive peaks at regular intervals where substances cannot be easily attributed, assign only peaks of possible parts, and multiple regression based on the value of the composition ratio that temporarily sets the model formula formed from the above-mentioned sample substances Method or the least squares method, and as a result, when the value of each composition ratio is not a positive value in the multiple regression method and is a component close to 0 in the least squares method, The sample substance corresponding to the corresponding composition ratio is deleted, and based on the model formula after this deletion, the value of each composition ratio is sequentially changed until the corresponding component disappears, and the iterative calculation is performed by the multiple regression method or the least squares method. Qualitative quantification of the composition components of the mixture characterized by performing the composition identification of the sample substance mixture by obtaining the composition ratio when the values of all the composition ratios to be compared do not satisfy the above conditions. Method. 3. The ranking table assigns only peaks of possible portions except for a group of consecutive peaks at regular intervals that cannot be easily attributed to the peak of the standard data of claim 1 to the peak of the sample substance mixture. The number of standard data attributed to each peak of the sample substance mixture was measured, and the obtained ranks were rearranged in order from the predetermined standard. The method for qualitatively quantifying composition components of a mixture according to claim 2, wherein the components having a high content are automatically listed. 4. After determining the composition of a sample substance mixture, in order to determine the assignment of each peak of the sample substance mixture, the assignment of peaks of only one sample substance is changed while the assignment of peaks of other sample substances is fixed. A multiple regression calculation is repeated for all identified sample substances, and the assignment of the peak of the sample substance mixture having a high correlation coefficient as a result of the calculation is selected to determine the peak assignment of the final component. The method for qualitatively quantifying the mixture according to 2. 5. Further, a multiple regression calculation is performed using all peaks to obtain the composition of the components, the composition value is assumed based on the obtained composition, and the sum of squares of the difference between the calculated value of the peak intensity and the measured value is calculated. 5. The method of claim 4, wherein the corresponding composition and composition value are determined as the final composition component and composition value when the sum of squares is minimized.
A method for qualitatively quantifying the constituent components of the mixture according to.