KR20200133083A - Method and apparatus for robust raman spectrum discrimination against measurement environmental conditions - Google Patents

Abstract

According to one embodiment of the present invention, provided are a method and an apparatus for increasing a chemical determination performance through an efficient analysis on a Raman spectrum. According to the embodiment of the present invention, the method for determining a robust Raman spectrum against measurement environmental conditions comprises: i) a step of measuring the Raman spectrum on each chemical sample in advance, collecting a library spectrum, and forming a library database; ii) a step of performing a Raman spectroscopy on the subject to be measured including at least one chemical sample, and collecting an input spectrum including at least one Raman spectrum; iii) a step of controlling the resolution of the input spectrum, and removing noise; iv) a step of scanning the input spectrum by using a moving window, measuring a peak value of the input spectrum, and determining a peak group area of the input spectrum; v) a step of dividing the peak group area of the input spectrum into at least one partial spectrum; vi) a step of analyzing the similarity between the partial spectrum and the library spectrum; and vii) a step of drawing chemicals included in the subject to be measured.

Description

Raman spectrum discrimination method and apparatus robust to measurement environmental conditions {METHOD AND APPARATUS FOR ROBUST RAMAN SPECTRUM DISCRIMINATION AGAINST MEASUREMENT ENVIRONMENTAL CONDITIONS}

The present invention relates to a method and apparatus for discriminating a Raman spectrum that is robust to environmental conditions for measurement, and more particularly, to a method and apparatus for increasing chemical substance discrimination performance through efficient analysis of a Raman spectrum.

When a material is irradiated with visible light or ultraviolet light of a short wavelength, the polarization rate changes during molecular vibration, so the incident light is scattered by the change in the wavelength, and the Raman spectrum is obtained by displaying this scattering intensity against the change in wavelength. Spectrum is unique to a substance and is therefore used in the analysis of chemical substances.

The Raman spectrum correction method includes a spectrum correction method using SRM. This method assumes that there is a transfer function that generates spectral differences between Raman devices, and after obtaining it, performs the intensity correction of the spectrum, and once the transfer function is obtained, it can be determined using a simple algorithm such as a correlation coefficient. . However, an additional process of obtaining a transfer function is required, and it is difficult to cope with other environmental variables such as measured temperature.

There is a SHQI method as a method of discriminating the Raman spectrum. Since this method divides the entire spectral region and determines the degree of correlation in each partial spectral region, it is a discrimination algorithm due to the intensity difference characteristic, and has the advantage of high classification efficiency of the Raman spectrum. However, since the peak distribution characteristic is not reflected, there is a disadvantage that the correlation value is uneven, and there is a problem that a value less than 0.95, which is a general critical correlation, often occurs.

And, in the case of the existing algorithm, when selecting a partial region from the entire spectral region, a fixed window parameter was used, and thus there is a problem in that it is not possible to model the peak distribution characteristics of the spectrum.

In Korean Patent Application Laid-Open No. 10-2018-0127939 (name of the invention: a quantitative molecular sensing device and method using a Raman peak point variation), an illumination optical system including a light source for irradiating excitation light to a subject; A detection optical system including a photodetector for detecting light scattered from the subject; And a signal processor for analyzing physical properties of a subject using an output signal from a detection optical system. A quantitative molecular sensing device using Raman peak point variation is disclosed.

Republic of Korea Patent Publication No. 10-2018-0127939

An object of the present invention for solving the above problems is to increase chemical substance discrimination performance through efficient analysis of the Raman spectrum.

In addition, it is an object of the present invention to maintain the chemical substance discrimination performance even if a difference in peak intensity of the Raman spectrum occurs according to ambient temperature conditions.

The technical problem to be achieved by the present invention is not limited to the technical problems mentioned above, and other technical problems that are not mentioned can be clearly understood by those of ordinary skill in the technical field to which the present invention belongs from the following description. There will be.

The configuration of the present invention for achieving the above object includes: i) collecting a library spectrum by measuring a Raman spectrum for each chemical sample in advance and forming a library database; ii) collecting an input spectrum including at least one Raman spectrum by performing Raman spectroscopy on a measurement object including at least one chemical sample; iii) adjusting the resolution of the input spectrum and removing noise; iv) determining a peak cluster region of the input spectrum by scanning the input spectrum using a moving window and measuring a peak value of the input spectrum; v) dividing the peak cluster region of the input spectrum into at least one partial spectrum; vi) analyzing the similarity between each of the partial spectra and the library spectra; And vii) deriving a chemical substance included in the measurement object.

In an embodiment of the present invention, in step v), the partial spectrum may be a spectrum obtained by dividing a peak cluster region of the input spectrum by a preset threshold value.

In an embodiment of the present invention, in step vi), a degree of similarity between the selection spectrum and the library spectrum is derived by performing a cross-correlation diagram for obtaining a correlation using peak region information of each of the partial spectrum and the library spectrum. can do.

In an embodiment of the present invention, in step vi), after normalizing the i-th partial spectrum, which is any one of a plurality (n) of the partial spectra, a partial correlation is derived, and a partial correlation is obtained. Can be used to derive the overall correlation.

In an embodiment of the present invention, the total correlation for the plurality of partial spectra may be an average value of the partial correlation according to the total number of the partial spectra.

In an embodiment of the present invention, the overall correlation for a part of the library spectrum may be an average value of the partial correlation by the number of divisions of a part of the library spectrum.

In an embodiment of the present invention, the final correlation may be a sum of all correlations for a plurality of the selection spectra and all correlations for some of the library spectra.

In an embodiment of the present invention, in step vii), the library spectrum having the largest final correlation with the input spectrum may be determined as a Raman spectrum for the chemical substance.

The configuration of the present invention for achieving the above object includes a signal acquisition unit for collecting an input spectrum including at least one Raman spectrum by performing Raman spectroscopy on a measurement object including at least one chemical sample; It is connected to the signal acquisition unit, adjusts the resolution of the input spectrum and removes noise, scans the input spectrum using a moving window, and measures a peak value of the input spectrum to determine a peak cluster region of the input spectrum. A signal processing unit; A signal discriminating unit connected to the signal processing unit, dividing the peak cluster region of the input spectrum into at least one partial spectrum, and analyzing a similarity between each partial spectrum and the library spectrum; And an output unit connected to the signal discrimination unit and for deriving a chemical substance contained in the measurement object and displaying it on a display screen.

The effect of the present invention according to the above configuration is that it is possible to enhance the discrimination performance through more detailed and efficient correlation evaluation through adaptive window modeling that models the peak distribution of the Raman spectrum.

In addition, the effect of the present invention is that chemical substance identification efficiency can be improved even under conditions such as temperature change of the surrounding environment by determining the chemical substance through correlation analysis between the measured partial spectrum of the Raman spectrum and the library spectrum.

The effects of the present invention are not limited to the above effects, and should be understood to include all effects that can be inferred from the configuration of the invention described in the detailed description or claims of the present invention.

1 is a graph of an input spectrum according to an embodiment of the present invention.
2 is a graph showing a process of forming a partial spectrum according to an embodiment of the present invention.
3 is a graph for comparison between a library spectrum and an input spectrum according to an embodiment of the present invention.
4 is a graph for comparison between a library spectrum and a partial spectrum according to an embodiment of the present invention.
5 is a block diagram of an apparatus for determining a Raman spectrum according to an embodiment of the present invention.
6 is a graph of a correlation distribution when performing a method for discriminating a plurality of chemical substances including a method for discriminating a Raman spectrum of the present invention.

Hereinafter, the present invention will be described with reference to the accompanying drawings. However, the present invention may be implemented in a number of different forms, and therefore is not limited to the exemplary embodiments described herein. In the drawings, parts irrelevant to the description are omitted in order to clearly describe the present invention, and similar reference numerals are assigned to similar parts throughout the specification.

Throughout the specification, when a part is said to be "connected (connected, contacted, bonded)" with another part, it is not only "directly connected", but also "indirectly connected" with another member in the middle. "Including the case. In addition, when a part "includes" a certain component, it means that other components may be further provided, rather than excluding other components unless specifically stated to the contrary.

The terms used in this specification are used only to describe specific embodiments, and are not intended to limit the present invention. Singular expressions include plural expressions unless the context clearly indicates otherwise. In this specification, terms such as "comprise" or "have" are intended to designate the presence of features, numbers, steps, actions, components, parts, or a combination thereof described in the specification, but one or more other features. It is to be understood that the presence or addition of elements or numbers, steps, actions, components, parts, or combinations thereof, does not preclude in advance.

Hereinafter, a method for determining a Raman spectrum of the present invention will be described. 1 is a graph of an input spectrum according to an embodiment of the present invention, and FIG. 2 is a graph of a process of forming a partial spectrum according to an embodiment of the present invention. A moving box in FIG. 1 may represent a moving window.

In the first step, the library spectrum can be collected by measuring the Raman spectrum for each chemical sample in advance and a library database can be formed. Here, first, the resolution of the Raman spectrum of each chemical sample is adjusted, noise is removed, and then the Raman spectrum of the chemical sample is scanned using a moving window and the peak value of the spectrum of the chemical sample is measured to divide the spectrum of the chemical sample. Then, the library spectrum can be obtained by selection. The matters for this are also used for processing the input spectrum, and specific matters will be described in detail in the following related matters. Then, the library spectrum can be converted into data to form a library database.

In the second step, an input spectrum including at least one Raman spectrum may be collected by performing Raman spectroscopy on a measurement object including at least one chemical sample. The second step can be performed by conventional Raman spectroscopy.

In the third step, the resolution of the input spectrum can be adjusted and noise can be removed. Here, resampling of the input spectrum is performed to adjust the resolution, and additive noise and background noise of the input spectrum may be removed. This is a prior art and a detailed description thereof will be omitted.

In the fourth step, as shown in FIG. 1, the input spectrum is scanned using the moving window and the peak value of the input spectrum is measured to determine the peak cluster region of the input spectrum.

First, a threshold value is set. Since the cluster distribution of peak values of the input spectrum must be known, a meaningful threshold value may be required. Specifically, the largest peak in the input spectrum may be equally divided, and a threshold value may be determined as the largest value among noises. However, it is not limited thereto. Further, the length of the moving window and the length of the moving window may be arbitrarily determined.

When searching using a moving window, if the largest peak value in the moving window is greater than a set threshold value, the corresponding region is regarded as a peak region, and if it is smaller than the threshold value, it may be determined as a region without peak.

The same process as in the fourth step may be performed to determine a region in which the peaks are clustered in the input spectrum.

In the fifth step, as shown in FIG. 2, the peak cluster region of the input spectrum may be divided into at least one partial spectrum. Here, the partial spectrum may be a spectrum obtained by dividing the peak cluster region of the input spectrum by a preset threshold value.

In the fourth step, when the peak cluster region in the input spectrum is determined, the partial spectrum may be collected by dividing the peak cluster region in the input spectrum by a threshold dimension that is a predetermined region size. Here, division may not be performed on an area having a size less than or equal to the critical dimension, but division may be performed on an area having a size exceeding the critical dimension. Specifically, as shown in the division by boxes in Figs. 2A and 2B, in the area (A) of Fig. 2, the length of the peak cluster region in the input spectrum exceeds the length of the critical dimension, so that the critical dimension is The division is performed based on the reference, and in the region (B) of FIG. 2, the length of the peak cluster region in the input spectrum is formed to be less than or equal to the length of the critical dimension, so that division based on the critical dimension may not be performed.

In the sixth step, the degree of similarity between each partial spectrum and the library spectrum can be analyzed. Hereinafter, a process of analyzing the similarity of both spectra will be described.

In the sixth step, a cross-correlation diagram for obtaining a correlation may be performed using peak region information of each of the partial spectrum and the library spectrum.

First, after normalizing the i-th partial spectrum, which is any one of a plurality of (n) partial spectra, a partial correlation can be derived, and an overall correlation can be derived using the partial correlation.

The partial correlation of the i-th partial spectrum can be derived by the following [Equation 1].

[Equation 1]

Here, S _i is an i-th partial spectral partial correlation value, and x _i and y _i are data obtained by normalizing the i-th partial spectral region.

), 동일한 2개의 데이터끼리의 내적을 구하면 1이되므로, 상관도의 최대값은 1이고 최소값은 0일 수 있다. 즉, 상관도가 1이면 완전 상관을 의미할 수 있다.)('ㆍ' in [Equation 1] means the dot product, so in the case of a normalized vector (for example, normalizing vector a=(a1, a2), normr(a)=

), since the dot product between two identical pieces of data is 1, the maximum value of the correlation may be 1 and the minimum value may be 0. That is, if the correlation is 1, it may mean complete correlation.)

In addition, the total correlation for the plurality of partial spectra may be an average value of the partial correlation according to the total number of partial spectra. Therefore, the overall correlation for a plurality of partial spectra can be derived by [Equation 2].

[Equation 2]

Here, S _i is the i-th partial spectrum partial correlation value, and n is the number of partial spectrums.

In addition, the overall correlation for a part of the library spectrum may be an average value of the partial correlation according to the number of divisions of a part of the library spectrum. Therefore, the overall correlation for a plurality of library spectra can be derived by [Equation 3].

[Equation 3]

Here, S _i is the i-th partial spectral partial correlation value, and m is the number of library spectra.

The final correlation may be a sum of the total correlation for a plurality of partial spectra and the total correlation for a part of the library spectrum, as shown in [Equation 4] below.

[Equation 4]

Hereinafter, for a visual understanding of the similarity analysis, a description will be given of a process of determining the library spectrum of Acetonitrile and the Acetonitrile measured by inVia-785.0nm equipment.

3 is a graph of a comparison between a library spectrum and an input spectrum according to an embodiment of the present invention, and FIG. 4 is a graph of a comparison between a library spectrum and a partial spectrum according to an embodiment of the present invention.

In the graph (1) of FIG. 3, graph a represents a library spectrum, and graph b represents an input spectrum. In the (2) graph of FIG. 3, the graphs (a) to (d) are graphs comparing the library spectrum and the input spectrum in each of the partial regions (a) to (d) in the graph (1) of FIG. 3 Can be

In the graph (1) of FIG. 4, graph a represents a library spectrum, and graph b represents a partial spectrum. And, in the (2) graph of FIG. 4, the graphs (a) to (d) are graphs comparing the library spectrum and the partial spectrum in each partial region of the (a) to (d) region in the graph (1) of FIG. 4 Can be

As shown in FIG. 3, it can be seen that the library spectrum and the input spectrum have a significantly different peak area, so that a low correlation value is obtained. On the other hand, there are regions that are somewhat inconsistent in the comparison between the library spectrum and the partial spectrum, but the size of the peak area of the library spectrum and the partial spectrum is similar to each other. Since a relatively high correlation is obtained by the process, it can be seen that the overall correlation between the library spectrum and the partial spectrum is higher than that of the library spectrum and the input spectrum.

In the seventh step, chemical substances included in the measurement object can be derived. Here, the input spectrum and the library spectrum having the largest final correlation can be determined as a Raman spectrum for a chemical substance. In addition, such information may be displayed on the display screen.

Hereinafter, the Raman spectrum determination apparatus of the present invention will be described. 5 is a block diagram of an apparatus for determining a Raman spectrum according to an embodiment of the present invention.

As shown in FIG. 5, the Raman spectrum determination apparatus of the present invention includes a signal acquisition unit for collecting an input spectrum including at least one Raman spectrum by performing Raman spectroscopy on a measurement object including at least one chemical sample ( 100); Signal that is connected to the signal acquisition unit 100, adjusts the resolution of the input spectrum and removes noise, scans the input spectrum using a moving window, and measures the peak value of the input spectrum to determine the peak cluster area of the input spectrum. Processing unit 200; A signal discriminating unit 300 connected to the signal processing unit 200, dividing the peak cluster region of the input spectrum into at least one partial spectrum, and analyzing the similarity between each partial spectrum and the library spectrum; And an output unit 400 connected to the signal discrimination unit 300 and deriving a chemical substance included in the measurement target and displaying it on a display screen. In addition, the signal determination unit 300 may be connected to a data unit 500 storing a library database, and data on the library Raman spectrum may be transmitted from the data unit 500 to the signal determination unit 300.

Here, the signal acquisition unit 100 may be a Raman spectrometer. And, the detailed description of each configuration is the same as the description of the corresponding matters in the Raman spectrum determination method of the present invention described above.

Hereinafter, an experiment using the Raman spectrum determination method of the present invention will be described.

The experiment consisted of two things. First, a test data set was prepared that measured 10 kinds of chemical substances in three different equipments (Renishaw 2000 514.5nm, inVia 632.8nm, inVia 785.0nm, and the main experiment for recognizing this in the library spectrum and prior to this In order to verify the validity of the Raman spectrum discrimination method, a preliminary experiment to recognize each other within the test data set was performed [Table 1] below shows the mechanical specifications of the experimental equipment.

Method Rank Results for Test data set from Instrument 1 and Instrument 2 Mean score Segmental HQI One 0.977 0.946 0.931 0.986 0.960 0.951 0.977 0.974 0.965 0.963 0.963 2 0.524 0.597 0.431 0.593 0.625 0.806 0.777 0.726 0.312 0.589 0.598 Adaptive HQI One 0.978 0.973 0.963 0.988 0.977 0.955 0.968 0.978 0.981 0.970 0.970 2 0.458 0.502 0.496 0.553 0.576 0.634 0.595 0.583 0.519 0.585 0.549

Here, the 10 chemical substances are Acetone, Acetonitrile, Benzene, Cyclohexane, Ethyl Alcohol, Ethylene Glycol, Hexane, Nitromethane, 2-Nitrotoluene, and Toluene. And, in order to perform the Raman spectrum determination method (Adaptive HQI, AHQI) of the present invention, the threshold value is determined as the largest value among noises, the moving window length value is set to 100, and the moving length value of the moving window is The experiment was performed by setting it to 50 (half of the moving window length value).

(1) Validity test between experimental data

First, a validity test was performed within the set based on the experimental data of each of 10 substances measured by three types of instruments, and the final correlation results of the substances that ranked in the top 1 and 2 are shown in [Table 2]. To [Table 4]. In the following, adaptive HQI may refer to a Raman spectrum discrimination method of the present invention, and segmental HQI may refer to a conventional chemical substance discrimination method.

Here, [Table 2] is the experimental data for the correlation experiment for the spectrum measured in Instruments 1 and 2, [Table 3] is the experimental data for the correlation experiment for the spectrum measured in Instruments 1 and 3, [Table 4] is the experimental data for the correlation experiment for the spectrum measured in Instruments 2 and 3.

Method Rank Results for Test data set from Instrument 1 and Instrument 2 Mean score Segmental HQI One 0.977 0.946 0.931 0.986 0.960 0.951 0.977 0.974 0.965 0.963 0.963 2 0.524 0.597 0.431 0.593 0.625 0.806 0.777 0.726 0.312 0.589 0.598 Adaptive HQI One 0.978 0.973 0.963 0.988 0.977 0.955 0.968 0.978 0.981 0.970 0.970 2 0.458 0.502 0.496 0.553 0.576 0.634 0.595 0.583 0.519 0.585 0.549

Method Rank Results for Test data set from Instrument 1 and Instrument 3 Mean score Segmental HQI One 0.830 0.901 0.853 0.887 0.924 0.937 0.964 0.954 0.927 0.880 0.906 2 0.594 0.518 0.447 0.443 0.665 0.824 0.763 0.730 0.353 0.570 0.591 Adaptive HQI One 0.957 0.970 0.977 0.989 0.976 0.953 0.976 0.967 0.984 0.952 0.973 2 0.492 0.501 0.505 0.433 0.564 0.614 0.616 0.600 0.464 0.706 0.550

Method Rank Results for Test data set from Instrument 2 and Instrument 3 Mean score Segmental HQI One 0.861 0.920 0.882 0.911 0.957 0.970 0.986 0.979 0.940 0.908 0.931 2 0.610 0.535 0.333 0.431 0.659 0.792 0.735 0.696 0.332 0.574 0.570 Adaptive HQI One 0.973 0.962 0.955 0.994 0.991 0.988 0.987 0.987 0.979 0.980 0.980 2 0.516 0.494 0.453 0.434 0.522 0.599 0.609 0.604 0.458 0.651 0.534

As a result of the experiment, 100% of the discriminant results were shown in both the conventional chemical identification method (Segmental HQI, SHQI) and the Raman spectrum identification method (Adaptive HQI, AHQI) of the present invention. However, in the case of the existing chemical substance identification method (SHQI), it can be confirmed that a low value of correlation is shown. In particular, a look at [Table 3] shows a correlation of 0.834, and such a low correlation can be directly linked to a problem of reliability for the discrimination system.

In contrast, the Raman spectrum identification method (AHQI) of the present invention shows a high correlation result of 0.95 or more under three experimental conditions. On the other hand, when looking at the correlation of the substances ranked second, not the substances ranked first, 22 out of 30 spectra were determined to have a lower correlation compared to the existing chemical substance identification method (SHQI). It can be seen that the Raman spectrum determination method (AHQI) of the present invention does not simply increase the correlation score, but provides a strong discrimination power that increases the correlation score of the target substance and decreases the score for the remaining substances.

(2) Experimental results of the correlation method between the experimental data and the library target substance

[Table 5] shows the results of the discrimination experiment using 14033 kinds of library spectra in the library database for the data of the input spectrum. Looking at the experimental results, it can be seen that the correlation with the target substance has generally increased.In particular, the input spectrum measured at inVia 785.0nm, which showed a marked difference in the library spectrum and the peak intensity, was used to discriminate the existing chemical substance. The method (SHQI) showed an average correlation of 0.9295, while the Raman spectrum identification method (ASHQI) of the present invention showed a correlation of 0.9720.

measuring equipment Experimental substance HQI SHQI(HW) ASHQI(TW) Hit# Score Hit# Score Hit# Score inVia
785.0nm Acetone 59 0.5413 One 0.9210 One 0.9669 Acetonitrile 4 0.6387 One 0.8849 One 0.9763 Benzene 13 0.7468 One 0.9188 One 0.9933 Cyclohexane 17 0.5964 One 0.9595 One 0.9530 Ethyl Alcohol 2 0.7958 One 0.9576 One 0.9624 Ethylene Glycol One 0.9532 One 0.9822 One 0.9861 Hexane 222 0.8030 One 0.9726 One 0.9711 Nitromethane One 0.7832 One 0.9426 One 0.9724 2-Nitrotoluene One 0.9088 One 0.8544 One 0.9584 Toluene 118 0.6034 One 0.9013 One 0.9797 Mean correlation 0.7371 0.9295 0.9720 Standard Deviation 0.1311 0.0389 0.0118 Renishaw 2000 514.5nm Acetone One 0.9738 One 0.9925 One 0.9973 Acetonitrile One 0.9726 One 0.9844 One 0.9932 Benzene One 0.8840 One 0.9906 One 0.9974 Cyclohexane 4 0.9754 One 0.9803 One 0.9812 Ethyl Alcohol 1585 0.8622 One 0.9909 One 0.9970 Ethylene Glycol 4763 0.5660 One 0.9781 One 0.9828 Hexane One 0.9916 One 0.9765 One 0.9930 Nitromethane One 0.8605 One 0.9865 One 0.9820 2-Nitrotoluene One 0.9492 One 0.9781 One 0.9883 Toluene One 0.9278 One 0.9843 One 0.9741 Mean correlation 0.8963 0.9842 0.9886 Standard Deviation 0.1194 0.0056 0.0078 inVia
632.8nm Acetone One 0.9812 One 0.9607 One 0.9680 Acetonitrile One 0.9783 One 0.9410 One 0.9515 Benzene One 0.9677 One 0.9898 One 0.9923 Cyclohexane One 0.9905 One 0.9835 One 0.9565 Ethyl Alcohol One 0.9804 One 0.9533 One 0.9611 Ethylene Glycol One 0.9580 One 0.9786 One 0.9646 Hexane One 0.9933 One 0.9815 One 0.9806 Nitromethane One 0.9748 One 0.9755 One 0.9786 2-Nitrotoluene One 0.9673 One 0.9604 One 0.9762 Toluene One 0.9257 One 0.9495 One 0.9483 Average correlation 0.9717 0.9674 0.9678 Standard Deviation 0.0184 0.0157 0.0133

As described above, in the case of using the Raman spectrum identification method (ASHQI) of the present invention, the discrimination power of the target material and the rest of the material is further strengthened, and is generally considered to be a reliable correlation in the case of verifying the authenticity of the spectrum. A consistent correlation close to 0.95 is shown.

6 is a graph of a correlation distribution when performing a method for discriminating a plurality of chemical substances including a method for discriminating a Raman spectrum of the present invention. Figure 6 (a) is a graph showing the distribution of the correlation of each chemical substance identification method in each equipment, Figure 6 (b) is a graph showing the overall correlation of each chemical substance identification method in each equipment to be.

6 shows the distribution of correlation values according to each method. As shown in Figure 6, compared to the conventional chemical substance identification method (HQI, SHQI), the Raman spectrum identification method (ASHQI) of the present invention, the distribution of the correlation value in the chemical identification using each equipment is significantly higher, It can be seen that the degree of scoring variance is not large.

The above description of the present invention is for illustrative purposes only, and those of ordinary skill in the art to which the present invention pertains will be able to understand that it can be easily modified into other specific forms without changing the technical spirit or essential features of the present invention. will be. Therefore, it should be understood that the embodiments described above are illustrative and non-limiting in all respects. For example, each component described as a single type may be implemented in a distributed manner, and similarly, components described as being distributed may also be implemented in a combined form.

The scope of the present invention is indicated by the claims to be described later, and all changes or modified forms derived from the meaning and scope of the claims and their equivalent concepts should be interpreted as being included in the scope of the present invention.

100: signal acquisition unit
200: signal processing unit
300: signal discrimination unit
400: output
500: data part

Claims (10)

i) collecting a library spectrum by measuring a Raman spectrum for each chemical sample in advance and forming a library database;
ii) collecting an input spectrum including at least one Raman spectrum by performing Raman spectroscopy on a measurement object including at least one chemical sample;
iii) adjusting the resolution of the input spectrum and removing noise;
iv) determining a peak cluster region of the input spectrum by scanning the input spectrum using a moving window and measuring a peak value of the input spectrum;
v) dividing the peak cluster region of the input spectrum into at least one partial spectrum;
vi) analyzing the similarity between each of the partial spectra and the library spectra; And
vii) deriving a chemical substance contained in the measurement target; Raman spectrum determination method robust to environmental conditions for measurement comprising a.
The method according to claim 1,
In step v), the partial spectrum is a spectrum obtained by dividing the peak cluster region of the input spectrum by a preset threshold value.
The method according to claim 1,
In step vi), a degree of similarity between the selection spectrum and the library spectrum is derived by performing a cross-correlation diagram for obtaining a correlation using peak region information of each of the partial spectrum and the library spectrum. Raman spectrum discrimination method robust to.
The method of claim 3,
In step vi), after normalizing the i-th partial spectrum, which is any one of the plurality (n) of the partial spectra, a partial correlation is derived, and an overall correlation is derived using the partial correlation. Raman spectrum discrimination method robust to measurement environmental conditions, characterized in that.
The method of claim 4,
The partial correlation degree of the i-th partial spectrum is derived by the following polynomial.

Here, S _i is an i-th partial spectral partial correlation value, and x _i and y _i are data obtained by normalizing the i-th partial spectral region.
The method of claim 5,
A Raman spectrum discrimination method robust to measurement environmental conditions, wherein the total correlation degree for the plurality of partial spectra is an average value of the partial correlation degree by the total number of the partial spectrum.
The method of claim 6,
The total correlation degree for a part of the library spectrum is an average value of the partial correlation degree by the number of divisions of the part of the library spectrum.
The method of claim 7,
The final correlation is a sum of the total correlations for the plurality of selected spectra and the total correlations for some of the library spectra.
The method of claim 8,
In step vii), the library spectrum having the largest final correlation with the input spectrum is determined as a Raman spectrum for the chemical substance.
A signal acquisition unit for collecting an input spectrum including at least one Raman spectrum by performing Raman spectroscopy on a measurement object including at least one chemical sample;
It is connected to the signal acquisition unit, adjusts the resolution of the input spectrum and removes noise, scans the input spectrum using a moving window, and measures a peak value of the input spectrum to determine a peak cluster region of the input spectrum. A signal processing unit;
A signal discriminating unit connected to the signal processing unit, dividing the peak cluster region of the input spectrum into at least one partial spectrum, and analyzing a similarity between each partial spectrum and the library spectrum; And
And an output unit connected to the signal discrimination unit to derive the chemical substances contained in the measurement target and display them on a display screen.

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