CN116026808A - Raman spectrum discrimination method and system - Google Patents
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Abstract
The invention discloses a Raman spectrum distinguishing method and system, and relates to the field of Raman spectrum analysis. The method comprises the following steps: preprocessing a sample signal to be detected to obtain a first Raman scattering signal of the sample signal to be detected; calculating a correlation coefficient according to the first Raman scattering signal and a preset spectrum database; according to the scheme, the correlation coefficient is calculated by combining the first Raman scattering signal with a preset spectrum database, and the matching spectrum type of the sample to be detected is obtained according to the correlation coefficient.
Description
Technical Field
The invention relates to the field of Raman spectrum analysis, in particular to a Raman spectrum distinguishing method and system.
Background
After the raman scattering signal of the sample to be detected is obtained by using the raman spectrometer, the obtained spectrum signal needs to be subjected to result matching, and the raman signal has fingerprint property, namely uniqueness, so that the corresponding result can be matched by only performing one comparison with a library file in a database. Assuming that 100 database files exist in the database, the database file closest to the sample to be detected can be found only by calculating the similarity between the acquired spectrum signal and each database file of the 100 database files, and then the Raman spectrum result matching is completed.
However, in general, the dimension of one raman spectrum data is 2048 data, the file size of a common database is more than 2000, some large databases are more than 10000 spectrum data, if the sampling signal and the spectrum of the database are subjected to similarity calculation, the calculation amount is large, the calculation time is long, and the online detection is very unfavorable.
Disclosure of Invention
The invention aims to solve the technical problem of providing a Raman spectrum distinguishing method and a Raman spectrum distinguishing system aiming at the defects of the prior art.
The technical scheme for solving the technical problems is as follows:
a raman spectrum discriminating method comprising:
preprocessing a sample signal to be detected to obtain a first Raman scattering signal of the sample signal to be detected;
calculating a correlation coefficient according to the first Raman scattering signal and a preset spectrum database;
and obtaining the matching spectrum type of the sample to be detected according to the correlation coefficient.
The beneficial effects of the invention are as follows: according to the scheme, the correlation coefficient is calculated by combining the first Raman scattering signal with the preset spectrum database, and the matching spectrum type of the sample to be detected is obtained according to the correlation coefficient.
Further, the method further comprises the following steps:
extracting the spectral peak of each spectrum in the preset spectrum database, and grouping the preset spectrum database according to the spectral peak of each spectrum to obtain a plurality of sub-spectrum databases;
the calculating the correlation coefficient according to the first raman scattering signal combined with a preset spectrum database specifically includes:
selecting a matched first sub-spectrum database from a plurality of sub-spectrum databases according to the first Raman scattering signal;
and calculating a correlation coefficient between the first Raman scattering signal and each spectrum in the first sub-spectrum database according to the first Raman scattering signal and the first sub-spectrum database.
The beneficial effects of adopting the further scheme are as follows: according to the scheme, the database is classified, the algorithm can adaptively select the type of the database, the calculated amount is reduced, and the online detection is facilitated.
Further, the selecting a matched first sub-spectrum database from a plurality of sub-spectrum databases according to the first raman scattering signal specifically includes:
calculating the signal-to-noise ratio of the first Raman scattering signal;
extracting the number of spectral peaks of the first raman scattering signal;
and selecting the first sub-spectrum database according to the signal-to-noise ratio and the number of spectrum peaks.
Further, the preprocessing of the sample signal to be detected specifically includes:
and performing spectral baseline removal and spectral denoising treatment on the sample signal to be detected.
Further, the extracting the spectrum peak of each spectrum in the preset spectrum database specifically includes:
and extracting the spectrum peak of each spectrum in the preset spectrum database by adopting a second-order difference algorithm.
The other technical scheme for solving the technical problems is as follows:
a raman spectrum discrimination system comprising: the device comprises a preprocessing module, an associated parameter calculation module and a type discrimination module;
the pretreatment module is used for carrying out pretreatment on a sample signal to be detected to obtain a first Raman scattering signal of the sample signal to be detected;
the correlation parameter calculation module is used for calculating a correlation coefficient according to the first Raman scattering signal combined with a preset spectrum database;
the type discrimination module is used for obtaining the matching spectrum type of the sample to be detected according to the correlation coefficient.
The beneficial effects of the invention are as follows: according to the scheme, the correlation coefficient is calculated by combining the first Raman scattering signal with the preset spectrum database, and the matching spectrum type of the sample to be detected is obtained according to the correlation coefficient.
Further, the method further comprises the following steps: the data block grouping module is used for extracting the spectrum peak of each spectrum in the preset spectrum database, and grouping the preset spectrum database according to the spectrum peak of each spectrum to obtain a plurality of sub-spectrum databases;
the association parameter calculation module is specifically configured to select a matched first sub-spectrum database from a plurality of sub-spectrum databases according to the first raman scattering signal;
and calculating a correlation coefficient between the first Raman scattering signal and each spectrum in the first sub-spectrum database according to the first Raman scattering signal and the first sub-spectrum database.
The beneficial effects of adopting the further scheme are as follows: according to the scheme, the database is classified, the algorithm can adaptively select the type of the database, the calculated amount is reduced, and the online detection is facilitated.
Further, the correlation parameter calculation module is specifically configured to calculate a signal-to-noise ratio of the first raman scattering signal;
extracting the number of spectral peaks of the first raman scattering signal;
and selecting the first sub-spectrum database according to the signal-to-noise ratio and the number of spectrum peaks.
Further, the preprocessing module is used for performing spectrum baseline removal and spectrum denoising processing on the sample signal to be detected.
Further, the data block grouping module is specifically configured to extract a spectral peak of each spectrum in the preset spectrum database by using a second-order difference algorithm.
Additional aspects of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
Fig. 1 is a schematic flow chart of a raman spectrum distinguishing method according to an embodiment of the present invention;
FIG. 2 is a structural frame diagram of a Raman spectrum discrimination system according to an embodiment of the present invention;
FIG. 3 is an original spectrum of losartan potassium provided by other embodiments of the present invention;
FIG. 4 is a schematic diagram of spectral de-baselines provided by other embodiments of the present invention;
FIG. 5 is a schematic diagram of spectral denoising according to other embodiments of the present invention;
FIG. 6 is a schematic diagram of database processing logic according to other embodiments of the present invention;
FIG. 7 is a schematic diagram of database design logic provided by other embodiments of the present invention;
FIG. 8 is a schematic diagram of database selection logic according to other embodiments of the present invention;
FIG. 9 is a schematic diagram of spectrum peak searching principle according to other embodiments of the present invention;
FIG. 10 is a schematic diagram showing the effect of identifying peaks of ammonium nitrate spectra according to other embodiments of the present invention;
FIG. 11 is a schematic diagram of an actual measurement spectrum of ammonium nitrate according to other embodiments of the present invention;
fig. 12 is a schematic structural diagram of a raman spectrometer according to another embodiment of the present invention.
Detailed Description
The principles and features of the present invention are described below with reference to the drawings, the illustrated embodiments are provided for illustration only and are not intended to limit the scope of the present invention.
As shown in fig. 1, a raman spectrum distinguishing method provided by an embodiment of the present invention includes:
s1, preprocessing a sample signal to be detected to obtain a first Raman scattering signal of the sample signal to be detected;
it should be noted that, in a certain embodiment, the preprocessing may include spectral baseline removal, spectral denoising, and so on, and there are many methods for removing baseline interference, and this patent uses an automatic baseline estimation method (Automated Basedline Estimation, ABE) proposed by h.georg Schulze as an example, and the method uses a cyclic sliding window averaging method to smooth a spectrum, and uses a position in an original spectrum that is greater than the smoothed spectrum as a spectral peak, and removes the spectral peak. The spectral denoising algorithm adopts wavelet denoising, and uses Daubechies wavelet function, wherein the Daubechies wavelet is constructed by a world-known wavelet analysis student Ingrid Daubechies, and is generally abbreviated as dbN, N is the order of the wavelet, and the patent N takes 4, namely db4.
S2, calculating a correlation coefficient according to the first Raman scattering signal combined with a preset spectrum database;
in one embodiment, calculating the correlation coefficient may include:
the correlation coefficient used is Pearson correlation coefficient (Pearson correlation coefficient), also called Pearson product-moment correlation coefficient, which is commonly used to measure the correlation between two variables X and Y, with values between-1 and 1, the closer to 1, indicating a linear correlation between X and Y, commonly denoted as r, calculated as follows:
wherein X and Y represent two variables, in this patent can represent measured spectrum and database spectrum, X i The i-th element of the spectrum X is represented,
represents the average value of X and n represents the dimension of the spectrum.
S3, obtaining the matching spectrum type of the sample to be detected according to the correlation coefficient.
According to the scheme, the correlation coefficient is calculated by combining the first Raman scattering signal with the preset spectrum database, and the matching spectrum type of the sample to be detected is obtained according to the correlation coefficient.
Optionally, in some embodiments, further comprising:
extracting the spectral peak of each spectrum in the preset spectrum database, and grouping the preset spectrum database according to the spectral peak of each spectrum to obtain a plurality of sub-spectrum databases;
the calculating the correlation coefficient according to the first raman scattering signal combined with a preset spectrum database specifically includes:
selecting a matched first sub-spectrum database from a plurality of sub-spectrum databases according to the first Raman scattering signal;
and calculating a correlation coefficient between the first Raman scattering signal and each spectrum in the first sub-spectrum database according to the first Raman scattering signal and the first sub-spectrum database.
In one embodiment, as shown in fig. 6, the main peaks are grouped at intervals d, wherein the main peaks are the spectral peaks with the greatest spectral intensity, and the sub-peaks are the spectral peaks except the main peaks, which are arranged in descending order of spectral intensity. d is related to the spectral resolution of the equipment used, typically 10cm, for example, a handheld Raman spectrometer CR2000 from Peking Hua Tainuo Ann detection technologies Co., ltd -1 The d can be 10, and the main peaks of the two are respectively positioned at 1048cm -1 And 1050cm -1 According to the logic shown in FIG. 6, it should be grouped into a group, i.e., database 1 is at intervals of 10cm -1 If the sample to be measured is ammonium nitrate, the spectrum to be measured only carries out correlation coefficient calculation with the data spectrum in a certain group in the database 1, thus greatly reducing the operation amount.
According to the scheme, the database is classified, the algorithm can adaptively select the type of the database, the calculated amount is reduced, and the online detection is facilitated.
Optionally, in some embodiments, the selecting a matched first sub-spectrum database from a plurality of sub-spectrum databases according to the first raman scattering signal specifically includes:
calculating the signal-to-noise ratio of the first Raman scattering signal;
extracting the number of spectral peaks of the first raman scattering signal;
and selecting the first sub-spectrum database according to the signal-to-noise ratio and the number of spectrum peaks.
In a certain embodiment, after the raman spectrometer collects the spectrum of the sample to be measured, the signal-to-noise ratio of the measured spectrum is firstly judged, because the spectrum pretreatment and the subsequent database selection are highly related to the signal-to-noise ratio, and because the signal-to-noise ratio performance of the spectrum is not uniform, the patent adopts a more general calculation method in the industry, as follows:
I peak representing the intensity of the main peak, e.g. 1046cm of the main peak of ammonium nitrate -1 Strength, sigma N Representing the standard deviation of the noise.
The signal to noise ratio is low, which can lead to inaccurate spectral peak identification and the existence of 'false peaks', so that the conditions of a database for spectral peak identification are required to be relaxed, if the signal to noise ratio of the actually measured spectrum is high, the spectrum is considered to be less influenced by noise, the identified spectral peaks are accurate, and the matched database conditions can be strict.
The signal-to-noise ratio threshold is determined by the performance of the device used, and is typically obtained by collecting a batch of data statistics, which can be continuously optimized during algorithm iteration.
The patent can also actively select the database type, a user can actively select the database 1, the database 2 and the like through own expertise or experience, and the database type can also be adaptively selected by an algorithm. Since the principle analysis of the database design shows that the database is not classified, namely the whole database is matched, the calculation amount is large and the time is long, but the accuracy is not interfered by the classification of the database, so the accuracy is highest, the database 1, the database 2 and the database 3 are selected for matching, the calculation amount and the time are sequentially reduced, and the accuracy is correspondingly reduced step by step.
Optionally, in some embodiments, the preprocessing of the sample signal to be tested specifically includes:
and performing spectral baseline removal and spectral denoising treatment on the sample signal to be detected.
Optionally, in some embodiments, the extracting a spectral peak of each spectrum in the preset spectrum database specifically includes:
and extracting the spectrum peak of each spectrum in the preset spectrum database by adopting a second-order difference algorithm.
In an embodiment, after a raman spectrometer acquires a spectrum signal of a sample to be detected, taking losartan potassium as an example, a handheld raman spectrometer CR2000 of the northkyoto tenor detection technology limited company is adopted to acquire a raman spectrum of the sample, the integration time is 1 second, the laser power is set to 120mW, and the acquired losartan potassium original spectrum is shown in fig. 3:
the real raman spectrum should include three parts, namely useful raman signals, baseline interference in the measured spectrum, including fluorescent and phosphorescent backgrounds of the sample itself, sample containers, etc., blackbody radiation of the sample and surrounding environment, and various noises including shot noise, dark current noise and readout noise of the CCD detector, emission noise and cosmic rays introduced by the excitation light source, etc., as follows:
S 1 ＝S 0 +B+N，
wherein S is 1 Representing the measured Raman signal S 0 Representing the real raman signal, B representing the baseline interference, and N being the various noise combinations. Because of the well-established algorithm of spectral baseline removal and spectral denoising, and the focus of the patent is on the establishment of Raman spectral data, the algorithm of spectral baseline removal, spectral noise removal and the like is not subjected to important analysis, and the patent removes the spectral baseline by an automatic baseline estimation method (Automated Basedline Estimation, ABE) proposed by H.Georg Schulze, which adopts a cyclic sliding window averaging methodAnd smoothing the spectrum, taking the position, which is larger than the smoothed spectrum, in the original spectrum as a spectrum peak, and removing the spectrum peak. The spectral denoising algorithm adopts wavelet denoising, and uses Daubechies wavelet function, wherein the Daubechies wavelet is constructed by a world-known wavelet analysis student Ingrid Daubechies, and is generally abbreviated as dbN, N is the order of the wavelet, and the patent N takes 4, namely db4. A baseline schematic of the degranulation using ABE is shown in figure 4. The dashed line in the figure is the spectrum after the original spectrum baseline is removed, and the solid line in the figure is the spectrum baseline. Spectral denoising is also performed after the spectral baseline is removed, and the spectrum after spectral denoising is shown in fig. 5. After the baseline and noise of the original spectrum are removed, the Raman scattering signal S which is the sample to be detected is reserved 0 Will S 0 And calculating a correlation coefficient with spectrum traversal in a database (the spectrum in the database consists of x and y coordinates, x represents wave number and y represents spectrum intensity, the spectrum in the database is a spectrum acquired by large equipment or a spectrum acquired and compared by the same-level equipment for a plurality of times, and the spectrum contains Raman scattering signals of substances), so that substances closest to a sample to be detected in the database can be obtained. The correlation coefficient adopted by the patent is Pearson correlation coefficient (Pearson correlation coefficient), also called Pearson product-moment correlation coefficient, which is commonly used for measuring the correlation between two variables X and Y, wherein the closer to 1, the more linear correlation between X and Y is indicated, and the more commonly used r is represented as follows:
wherein X and Y represent two variables, which in this patent can represent the spectrum S after pretreatment of the measured spectrum 0 And database spectrum, X i The i-th element of the spectrum X is represented,
represents the average value of X and n represents the dimension of the spectrum.
In one embodiment, to reduce the amount of computation and shorten the time for the algorithm to run, the database is processed as follows:
as shown in fig. 6, the main peaks are grouped at intervals d, and the main peaks are the spectral peaks having the greatest spectral intensity, and the sub-peaks are the spectral peaks other than the main peaks arranged in descending order of spectral peak intensity. d is related to the spectral resolution of the equipment used, typically 10cm, for example, a handheld Raman spectrometer CR2000 from Peking Hua Tainuo Ann detection technologies Co., ltd -1 The d can be 10, and the main peaks of the two are respectively positioned at 1048cm -1 And 1050cm -1 According to the logic shown in FIG. 6, it should be grouped into a group, i.e., database 1 is at intervals of 10cm -1 If the sample to be measured is ammonium nitrate, the spectrum to be measured only carries out correlation coefficient calculation with the data spectrum in a certain group in the database 1, thus greatly reducing the operation amount.
Similarly, the database 2 and the data 3 are obtained by arranging the spectrum peak intensities in descending order, and the data 2 and the data 3 are based on the main peak grouping, so that the operation amount is further reduced. The database setup principle is as follows:
FIG. 7 illustrates the principle of database design of the present patent, wherein A is a portion of database 1, B is a portion of database 2, and C is a portion of database 3, and it should be noted that the main peak 1, the secondary peak 1_1 and the tertiary peak 1_1 do not represent a specific position, but a range, and the width is 10cm -1 。
However, some substances have fewer spectral peaks or a poor test environment, and the obtained spectral peaks have too few, such as potassium nitrate, at 200cm -1 ～3000cm -1 Within a range of only 1056cm -1 And 1353cm- 1 The two stronger spectral peaks are obviously unsuitable for matching calculation with the data 2 and the data 3, so that the signal to noise ratio of the measured spectrum needs to be comprehensively considered, and then the class of the database is determined.
In one embodiment, the database selection logic is as shown in FIG. 8: after the Raman spectrometer collects the spectrum of the sample to be measured, the signal-to-noise ratio of the measured spectrum is judged first, because the spectrum pretreatment and the subsequent database selection are highly related to the signal-to-noise ratio, and because the signal-to-noise ratio performance of the spectrum does not have a uniform calculation method, the patent adopts a more general calculation method in the industry, as follows:
I peak representing the intensity of the main peak, e.g. 1046cm of the main peak of ammonium nitrate -1 Strength, sigma N Representing the standard deviation of the noise.
The spectrum pretreatment comprises spectrum baseline removal, spectrum denoising and the like, and a plurality of methods for removing baseline interference are adopted, and an automatic baseline estimation method (Automated Basedline Estimation, ABE) proposed by H.Georg Schulze is taken as an example, and the method adopts a cyclic sliding window averaging method to smooth a spectrum, takes the position of an original spectrum which is larger than the smoothed spectrum as a spectrum peak, and removes the position. The spectral denoising algorithm adopts wavelet denoising, and uses Daubechies wavelet function, wherein the Daubechies wavelet is constructed by a world-known wavelet analysis student Ingrid Daubechies, and is generally abbreviated as dbN, N is the order of the wavelet, and the patent N takes 4, namely db4. The algorithm effect is shown in fig. 4 and 5, respectively.
In a certain embodiment, after the actual measurement spectrum pretreatment, the spectrum peak of the pretreated spectrum can be extracted, and the algorithm for extracting the spectrum peak is many.
After the spectral peak is extracted, there is a comparison to the global signal-to-noise ratio of the spectrum, because if the signal-to-noise ratio is too low, the spectrum peak searching identifies a plurality of 'pseudo peaks', which are not raman signals of the sample, but are spectral noise, as shown in fig. 11, which is the actual measurement spectrum of ammonium nitrate, and it is obvious that the spectral signal-to-noise ratio is very low, which is caused by the defocusing of the probe during sampling, and the reasons for the low spectral signal-to-noise ratio are mainly two, namely, the acquisition environment interference, including the defocusing of the probe and the strong interference of background light, and the lower raman activity of the sample itself to be measured. The signal to noise ratio is low, which can lead to inaccurate spectral peak identification and the existence of 'false peaks', so that the conditions of a database for spectral peak identification are required to be relaxed, if the signal to noise ratio of the actually measured spectrum is high, the spectrum is considered to be less influenced by noise, the identified spectral peaks are accurate, and the matched database conditions can be strict.
The signal-to-noise ratio threshold is determined by the performance of the device used, and is typically obtained by collecting a batch of data statistics, which can be continuously optimized during algorithm iteration.
The patent can also actively select the database type, a user can actively select the database 1, the database 2 and the like through own expertise or experience, and the database type can also be adaptively selected by an algorithm. Since the principle analysis of the database design shows that the database is not classified, namely the whole database is matched, the calculation amount is large and the time is long, but the accuracy is not interfered by the classification of the database, so the accuracy is highest, the database 1, the database 2 and the database 3 are selected for matching, the calculation amount and the time are sequentially reduced, and the accuracy is correspondingly reduced step by step.
In one embodiment, as shown in fig. 2, a raman spectrum discrimination system includes: a preprocessing module 1101, an associated parameter calculation module 1102, and a type discrimination module 1103;
the preprocessing module 1101 is configured to preprocess a sample signal to be detected, and obtain a first raman scattering signal of the sample signal to be detected;
the correlation parameter calculation module 1102 is configured to calculate a correlation coefficient according to the first raman scattering signal in combination with a preset spectrum database;
the type discrimination module 1103 is configured to obtain a matching spectrum type of the sample to be detected according to the correlation coefficient.
According to the scheme, the correlation coefficient is calculated by combining the first Raman scattering signal with the preset spectrum database, and the matching spectrum type of the sample to be detected is obtained according to the correlation coefficient.
Optionally, in some embodiments, further comprising: the data block grouping module is used for extracting the spectrum peak of each spectrum in the preset spectrum database, and grouping the preset spectrum database according to the spectrum peak of each spectrum to obtain a plurality of sub-spectrum databases;
the association parameter calculation module is specifically configured to select a matched first sub-spectrum database from a plurality of sub-spectrum databases according to the first raman scattering signal;
and calculating a correlation coefficient between the first Raman scattering signal and each spectrum in the first sub-spectrum database according to the first Raman scattering signal and the first sub-spectrum database.
According to the scheme, the database is classified, the algorithm can adaptively select the type of the database, the calculated amount is reduced, and the online detection is facilitated.
Optionally, in some embodiments, the correlation parameter calculation module 1102 is specifically configured to calculate a signal-to-noise ratio of the first raman scattering signal;
extracting the number of spectral peaks of the first raman scattering signal;
and selecting the first sub-spectrum database according to the signal-to-noise ratio and the number of spectrum peaks.
Optionally, in some embodiments, the preprocessing module 1101 is configured to perform spectral baseline removal and spectral denoising processing on the sample signal to be measured.
Optionally, in some embodiments, the data block grouping module is specifically configured to extract a spectral peak of each spectrum in the preset spectrum database by using a second-order difference algorithm.
It is to be understood that in some embodiments, some or all of the alternatives described in the various embodiments above may be included.
It should be noted that, the foregoing embodiments are product embodiments corresponding to the previous method embodiments, and the description of each optional implementation manner in the product embodiments may refer to the corresponding description in the foregoing method embodiments, which is not repeated herein.
In some embodiments, the light irradiates the substance with elastic scattering and inelastic scattering, the elastic scattering light is the same component as the wavelength of the excitation light, the inelastic scattering light has a component longer and shorter than the wavelength of the excitation light, collectively referred to as the raman effect, and the raman scattering light carries structural information of the substance due to the modulation of the substance, so that the raman spectrum of the substance can be used to study the structure of the substance. Among the numerous spectroscopic analysis techniques, raman spectroscopic analysis techniques have their unique advantages:
the degree of freedom of the excitation light selection is large. The raman scattering mechanism shows that the frequency shift of the raman spectrum is not limited by the frequency of the light source, and the excitation light sources with different wavelengths can be selected according to the characteristics of different samples.
The detection range is wide. Since both polar and nonpolar molecules can generate raman spectra, the detection objects of raman spectra are very wide, including inorganic substances, organic substances, polymers, ores, animal and plant tissues, catalysts, and the like.
Preparation of the sample is hardly required. Since raman spectrum is a scattering spectrum, measurement of raman spectrum does not require preparation of a sample as does infrared absorption spectrum.
Non-contact non-destructive measurement can be achieved. Because the Raman spectrum does not need preparation of a sample, the Raman spectrum can directly measure the object to be measured without damaging the object to be measured, and therefore, the Raman spectrum analysis technology can realize complete non-contact nondestructive detection.
The substances in the aqueous solution and the glassware can be directly measured. Since raman scattering of water and glass is small, it is very important in industrial production to directly detect solutes in glass containers or aqueous solutions.
Because of the numerous advantages of raman spectroscopy, its application has been widely penetrated into various fields of people's production and life. The use of raman spectroscopy can be briefly summarized in two points:
acquiring a Raman spectrum;
matching Raman spectrum results;
raman spectrum acquisition raman spectrum signals of a sample to be measured are acquired, i.e. using a raman spectrometer. Raman spectrometers typically consist of an excitation light source, a raman probe, a spectral scattering and data processing system, as shown in fig. 12.
L0 is the light source department of Raman spectrometer, R1 represents the laser instrument, and the laser that the laser instrument sent is reflected by speculum M1 to in the D0 Raman probe, through the beam splitting through sending high reflection to converging lens L1, finally gather to sample S0 that awaits measuring by converging lens, S0 time excitation light excitation produces Raman scattering signal and collimates by L1, passes through F1 high pass to Raman filter F2, filters the excitation light to let Raman scattering signal high pass. The raman scattering signals are converged to a D2 spectrum scattering and data processing system through an L2, are coupled to a concave reflecting mirror M2 through a slit S spatial filtering, the spectrum signals are collimated and irradiated to a grating G through an M2, the grating G is used for dispersing and splitting the spectrum signals according to different wavelengths, after the G dispersing and splitting, the spectrum signals with different wavelengths are finally converged to a sensor D through an imaging mirror M3, the sensor D is essentially a photoelectric conversion element such as a Charge-coupled Device (CCD) or a complementary metal oxide semiconductor (Complementary Metal Oxide Semiconductor), and the optical signals are converted into electric signals, and the data processing system performs data analysis on the electric signals.
After the raman scattering signal of the sample to be detected is obtained by using the raman spectrometer, the obtained spectrum signal needs to be subjected to result matching, and the raman signal has fingerprint property, namely uniqueness, so that the corresponding result can be matched by only performing one comparison with a library file in a database. Assuming that 100 database files exist in the database, the database file closest to the sample to be detected can be found only by calculating the similarity between the acquired spectrum signal and each database file of the 100 database files, and then the Raman spectrum result matching is completed.
However, in general, the dimension of one raman spectrum data is 2048 data, the file size of a common database is more than 2000, some large databases are more than 10000 spectrum data, if the sampling signal and the spectrum of the database are subjected to similarity calculation, the calculation amount is large, the calculation time is long, and the online detection is very unfavorable.
Therefore, the invention provides the rapid database matching method which has the advantages of less calculation amount, less calculation time and simple operation.
The reader will appreciate that in the description of this specification, a description of terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the method embodiments described above are merely illustrative, e.g., the division of steps is merely a logical function division, and there may be additional divisions of actual implementation, e.g., multiple steps may be combined or integrated into another step, or some features may be omitted or not performed.
The above-described method, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present invention is essentially or a part contributing to the prior art, or all or part of the technical solution may be embodied in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-only memory (ROM), a random access memory (RAM, randomAccessMemory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The present invention is not limited to the above embodiments, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the present invention, and these modifications and substitutions are intended to be included in the scope of the present invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (10)
1. A raman spectrum discriminating method comprising:
preprocessing a sample signal to be detected to obtain a first Raman scattering signal of the sample signal to be detected;
calculating a correlation coefficient according to the first Raman scattering signal and a preset spectrum database;
and obtaining the matching spectrum type of the sample to be detected according to the correlation coefficient.
2. A raman spectrum discriminating method according to claim 1 further comprising:
extracting the spectral peak of each spectrum in the preset spectrum database, and grouping the preset spectrum database according to the spectral peak of each spectrum to obtain a plurality of sub-spectrum databases;
the calculating the correlation coefficient according to the first raman scattering signal combined with a preset spectrum database specifically includes:
selecting a matched first sub-spectrum database from a plurality of sub-spectrum databases according to the first Raman scattering signal;
and calculating a correlation coefficient between the first Raman scattering signal and each spectrum in the first sub-spectrum database according to the first Raman scattering signal and the first sub-spectrum database.
3. A raman spectrum discriminating method according to claim 2 wherein said selecting a matching first sub-spectrum database from a plurality of sub-spectrum databases based on said first raman scattering signal, comprises:
calculating the signal-to-noise ratio of the first Raman scattering signal;
extracting the number of spectral peaks of the first raman scattering signal;
and selecting the first sub-spectrum database according to the signal-to-noise ratio and the number of spectrum peaks.
4. The raman spectrum distinguishing method according to claim 1, wherein the preprocessing of the sample signal to be detected specifically comprises:
and performing spectral baseline removal and spectral denoising treatment on the sample signal to be detected.
5. The method for distinguishing raman spectra according to claim 2, wherein the extracting a spectral peak of each spectrum in the preset spectrum database specifically comprises:
and extracting the spectrum peak of each spectrum in the preset spectrum database by adopting a second-order difference algorithm.
6. A raman spectrum discrimination system comprising: the device comprises a preprocessing module, an associated parameter calculation module and a type discrimination module;
the pretreatment module is used for carrying out pretreatment on a sample signal to be detected to obtain a first Raman scattering signal of the sample signal to be detected;
the correlation parameter calculation module is used for calculating a correlation coefficient according to the first Raman scattering signal combined with a preset spectrum database;
the type discrimination module is used for obtaining the matching spectrum type of the sample to be detected according to the correlation coefficient.
7. A raman spectrum discriminating system according to claim 6 further comprising: the data block grouping module is used for extracting the spectrum peak of each spectrum in the preset spectrum database, and grouping the preset spectrum database according to the spectrum peak of each spectrum to obtain a plurality of sub-spectrum databases;
the association parameter calculation module is specifically configured to select a matched first sub-spectrum database from a plurality of sub-spectrum databases according to the first raman scattering signal;
and calculating a correlation coefficient between the first Raman scattering signal and each spectrum in the first sub-spectrum database according to the first Raman scattering signal and the first sub-spectrum database.
8. The raman spectrum discrimination system according to claim 7, wherein said correlation parameter calculation module is specifically configured to calculate a signal-to-noise ratio of said first raman scattering signal;
extracting the number of spectral peaks of the first raman scattering signal;
and selecting the first sub-spectrum database according to the signal-to-noise ratio and the number of spectrum peaks.
9. The raman spectrum discrimination system according to claim 6, wherein the preprocessing module is configured to perform spectral baseline removal and spectral denoising processing on the sample signal to be detected.
10. The raman spectrum discrimination system according to claim 7, wherein the data block grouping module is specifically configured to extract a spectral peak of each spectrum in the preset spectrum database by using a second-order difference algorithm.
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| CN117454123B (en) * | 2023-12-26 | 2024-03-12 | 奥谱天成（厦门）光电有限公司 | Raman spectrum pure matter matching method based on filtering, system and medium thereof |
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