CN112213295A - Method and device for separating Raman spectrum of sample containing doped spectrum - Google Patents
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Abstract
The invention provides a method and a device for separating a Raman spectrum of a sample containing a doping spectrum, wherein the method comprises the following steps: s1: acquiring a Raman spectrum S and a plurality of doping spectrums of a sample to be detected; s2: normalizing the plurality of doping spectra to obtain a reference spectrum g; s3: decomposing the Raman spectrum S and the reference spectrum g by adopting rapid wavelet transformation, and setting the approximate coefficient of the nth layer of the two decomposed spectrums as zero; n is the number of decomposition layers; s4: performing wavelet reconstruction on the two spectrums processed in the step S3 to obtain a first reconstructed spectrum S1 and a second reconstructed spectrum g 1; s5: performing linear fitting on the first reconstruction spectrum S1 and the second reconstruction spectrum g1 to obtain a superposition coefficient c corresponding to the doping spectrum; s6: calculating by adopting the following formula to obtain a spectrum Y without a doping spectrum: y-c g. When the method is used, a sample spectrum without a substrate does not need to be obtained in advance for reference, and the difficulty in the actual operation process is effectively reduced.
Description
Technical Field
The invention relates to the field of sample Raman spectrum processing, in particular to a method and a device for separating a sample Raman spectrum containing a doping spectrum.
Background
The spontaneous Raman spectrum is widely applied to the field of medical diagnosis, the Raman spectrum based on the molecular vibration characteristic can detect the change of molecular components in the canceration process of cells, and the accurate and quick identification of cancer cells can be realized through statistical analysis. Compared with surface enhanced Raman, spontaneous Raman spectroscopy has higher repeatability and is more suitable for establishing a cancer diagnosis standard library. In addition, the spontaneous Raman spectrum has the advantages of no label, no damage, low cost and the like, and has important clinical application value.
However, the raman spectrum of clinical biological samples usually contains irrelevant information such as fluorescence spectrum, substrate raman spectrum, etc., which makes the raman spectrum for measuring biomolecules more difficult to identify and has a significant impact on the subsequent statistical analysis. The measured spectrum is usually considered as a linear superposition of the biological raman spectrum and the base spectrum. Since the common substrate spectrum (e.g. glass, quartz, etc.) is doped with a narrow spectral component similar to the raman spectrum, it becomes very difficult to separate the raman spectrum from the doped spectrum.
The methods adopted at present mainly comprise a method for solving multivariate minimum values and an Extended Multivariate Signal Correction (EMSC) algorithm. In both methods, the measured base spectrum is used as a reference, and then the superposition coefficient of the base reference spectrum and the polynomial fluorescence background is estimated. And therefore the result is usually affected by the initial coefficients and the order of the predetermined polynomial, which seriously affects the speed and accuracy of the calculation. The method for solving the multivariate minimum value needs to preset a polynomial to simulate the fluorescence background, the selection of the polynomial order can greatly influence the result, and the method for solving the multivariate minimum value is a method for iteratively searching the optimal solution, and the setting of the initial value can greatly influence the calculation speed and the result. The extended multivariate signal correction algorithm is a linear fitting method, which needs to preset a polynomial background spectrum, needs to measure the spectral line of a biological sample to be measured under the influence of no substrate spectrum in advance, and then performs linear fitting on the polynomial background spectral line, the biological spectral line without the substrate and the substrate reference spectral line, so as to estimate the superposition coefficient of each spectral line in the biological Raman spectrum containing the substrate, and finally realizes the linear separation of the Raman spectrum and the doping spectrum. The detection of the biological sample to be detected without the substrate spectrum causes the method to need to establish a huge reference database, and the complicated process causes the method to be not beneficial to clinical popularization and application.
Disclosure of Invention
The present invention is directed to provide a technical solution for separating a raman spectrum of a sample containing a dopant spectrum, so as to solve the problems of complicated steps and low accuracy of the existing separation method.
The object of the invention can be achieved by the following technical measures:
in a first aspect, the present invention provides a method of separating a raman spectrum of a sample containing a dopant spectrum, the method comprising the steps of:
s1: acquiring a Raman spectrum S and a plurality of doping spectrums of a sample to be detected;
s2: normalizing the plurality of doping spectra to obtain a reference spectrum g;
s3: decomposing the Raman spectrum S and the reference spectrum g by adopting rapid wavelet transformation, and setting the approximate coefficient of the nth layer of the two decomposed spectrums as zero; n is the number of decomposition layers;
s4: performing wavelet reconstruction on the two spectrums processed in the step S3 to obtain a first reconstructed spectrum S1 and a second reconstructed spectrum g 1;
s5: performing linear fitting on the first reconstruction spectrum S1 and the second reconstruction spectrum g1 to obtain a superposition coefficient c corresponding to the doping spectrum;
s6: calculating by adopting the following formula to obtain a spectrum Y without a doping spectrum: y-c g.
Further, the doping spectrum is a substrate spectrum corresponding to a bearing substrate, and the bearing substrate is used for bearing the sample to be tested.
Further, obtaining a plurality of doping spectra comprises: substrate spectra are recorded for a plurality of locations of the carrier substrate.
Further, step S2 includes:
and calculating the mean value of the spectrum of the substrate at a plurality of positions of the bearing substrate, and taking the calculated mean value as a reference spectrum g.
Further, the wavelet basis of the wavelet transform in step S3 is "Sym 11", and the number n of decomposition layers is 8.
Further, after step S1, the method further includes: and denoising the Raman spectrum S and each doped spectrum.
Further, the sample to be detected is a biological sample.
In a second aspect, the present invention provides a separation apparatus for raman spectroscopy of a sample containing a dopant spectrum, the apparatus comprising means for performing a method according to the first aspect of the present invention.
In distinction to the prior art, the present invention provides a method and apparatus for separating a raman spectrum of a sample containing a dopant spectrum, the method comprising the steps of: s1: acquiring a Raman spectrum S and a plurality of doping spectrums of a sample to be detected; s2: normalizing the plurality of doping spectra to obtain a reference spectrum g; s3: decomposing the Raman spectrum S and the reference spectrum g by adopting rapid wavelet transformation, and setting the approximate coefficient of the nth layer of the two decomposed spectrums as zero; n is the number of decomposition layers; s4: performing wavelet reconstruction on the two spectrums processed in the step S3 to obtain a first reconstructed spectrum S1 and a second reconstructed spectrum g 1; s5: performing linear fitting on the first reconstruction spectrum S1 and the second reconstruction spectrum g1 to obtain a superposition coefficient c corresponding to the doping spectrum; s6: calculating by adopting the following formula to obtain a spectrum Y without a doping spectrum: y-c g. The method can effectively avoid the influence of fluorescence spectrum in the clinical biological sample, and greatly improves the performability and the accuracy of the method. Meanwhile, compared with an Extended Multivariate Signal Correction (EMSC) algorithm, the method does not need to obtain a sample spectrum without a substrate in advance for reference, so that the difficulty in the actual operation process is greatly reduced, the time is greatly saved, and the application and popularization of the Raman spectrum in clinical rapid diagnosis are facilitated.
Drawings
FIG. 1 is a flow chart of a method of separating a Raman spectrum of a sample containing a dopant spectrum according to the present invention;
FIG. 2 is a flow chart of a method of separating fluorescence spectra according to the present invention;
FIG. 3 is a diagram of the difference in scale of the base spectrum, Raman spectrum, fluorescence spectrum and their relationship to the wavelet scale involved in the present invention;
fig. 4 is a schematic overall flow chart of the separation of the substrate spectrum, the raman spectrum and the fluorescence spectrum according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In order to make the description of the present disclosure more complete and complete, the following description is given for illustrative purposes with respect to the embodiments and examples of the present invention; it is not intended to be the only form in which the embodiments of the invention may be practiced or utilized. The embodiments are intended to cover the features of the various embodiments as well as the method steps and sequences for constructing and operating the embodiments. However, other embodiments may be utilized to achieve the same or equivalent functions and step sequences.
At present, the raman spectrum S of a medically common biological sample mainly includes the following parts: the true raman fingerprint spectrum R, the bioluminescence spectrum F and the substrate spectrum G of the biological component are not transformed due to the uniform stability of the substrate, and thus the substrate spectrum can be further expressed as the product of the reference spectrum G of the substrate and a scaling factor c. Specifically, it can be expressed by formula (1):
s = R + F + c g formula (1)
According to the spectral characteristics, the glass signal not only contains a broad spectrum signal close to a fluorescent background, but also contains narrow spectrum information of a Raman signal. It is therefore very difficult to decompose the three unknown spectra simultaneously, the best method being to convert the problem into a superposition of the two spectra by reducing the variables. For this reason, we introduce fast wavelet transform based on multi-resolution analysis, which is a common method for removing fluorescence background in Raman spectrum without substrate. The difference in scale of the three spectra (base spectrum, raman spectrum, fluorescence spectrum) and their relationship to the wavelet scale are shown in fig. 3. According to the scale difference between the Raman spectrum and the fluorescence background, when the number n of the decomposition layers reaches a certain value, the last layer of wavelet approximation coefficient is set to zero to obtain a Raman spectrum line with almost eliminated fluorescence. The one-dimensional wavelet of the spectrum can be represented by equation (2):
in the formula (2), t is a horizontal coordinate of the spectrum, i.e., a Raman frequency shift, k represents a position coordinate of the function in the transverse direction, j determines the width of the function, j0Representing any starting scale, aj0(k) Is an approximation or scale coefficient, dj(k) Detail or wavelet coefficients, ψ is a basic wavelet or mother wavelet,
is a parent wavelet or scale function.
When we apply this method directly to the clinical sample raman line, not only will the fluorescence background be eliminated but also part of the substrate signal will be affected due to the influence of part of the substrate signal that is close to the fluorescence background scale. The remaining spectrum only comprises the real Raman fingerprint spectrum R and partial glass Raman spectrum signal G of the biological componentpThe two signals satisfy a linear superposition relationship, as shown in formula (3):
in equation (3), subscripts R and pg represent the true raman spectrum and the partial glass raman spectrum, respectively. It can be seen that we can estimate the coefficient c of the glass spectrum by decomposing the original raman spectrum and the reference spectrum on a specific n layer, extracting all the spectral components with the number of decomposed layers smaller than n, and then performing linear fitting on the two extracted spectra.
To this end, as shown in fig. 1, the present application provides a method for separating a raman spectrum of a sample containing a dopant spectrum, the method comprising the steps of:
the process first proceeds to step S1: and acquiring a Raman spectrum S and a plurality of doping spectrums of the sample to be detected.
The doping spectrum refers to all stable interference spectral lines which are not influenced by the sample to be detected and doped in the Raman spectrum of the sample. Further, in this embodiment, the doping spectrum is a substrate spectrum corresponding to a carrying substrate, and the carrying substrate is used for carrying the sample to be tested. The material of the bearing substrate can be glass, quartz and the like. Acquiring a plurality of doping spectra comprises: substrate spectra are recorded for a plurality of locations of the carrier substrate.
Then, the process proceeds to step S2: and normalizing the plurality of doping spectra to obtain a reference spectrum g.
Taking the doping spectrum as the base spectrum as an example, step S2 includes: and calculating the mean value of the spectrum of the substrate at a plurality of positions of the bearing substrate, and taking the calculated mean value as a reference spectrum g. In certain embodiments, step S2 may further include the steps of: 1. randomly collecting a plurality of (three or more) doping spectrums (such as substrate spectrums) at different positions and carrying out denoising treatment on the collected doping spectrums; 2. normalizing the denoised multiple doped spectra (such as the substrate spectrum), wherein the specific formula of normalization is as follows: (current spectral value-spectral minimum)/(spectral maximum-spectral minimum) to convert all spectral intensity values into the range of (0, 1) interval; 3. the normalized plurality of spectra are averaged to obtain a unique reference spectrum g.
Then, the process proceeds to step S3: decomposing the Raman spectrum S and the reference spectrum g by adopting rapid wavelet transformation, and setting the approximate coefficient of the nth layer of the two decomposed spectrums as zero; and n is the number of decomposition layers.
In the present embodiment, the wavelet basis of the wavelet transform is "Sym 11", and the number of decomposition layers n is 8. Of course, in other embodiments, the wavelet basis and the number n of decomposition layers used in the wavelet transform may be adjusted according to actual needs.
Then, the process proceeds to step S4: wavelet reconstruction is performed on the two spectra processed in step S3, resulting in a first reconstructed spectrum S1 and a second reconstructed spectrum g 1. The first reconstructed spectrum S1 is a spectrum obtained by decomposing and then reconstructing the raman spectrum S, and the second reconstructed spectrum g1 is a spectrum obtained by decomposing and then reconstructing the reference spectrum g.
Then, the process proceeds to step S5: and performing linear fitting on the first reconstruction spectrum S1 and the second reconstruction spectrum g1 to obtain a superposition coefficient c corresponding to the doping spectrum. Specifically, the calculation of the superposition coefficient c may be calculated with reference to formula (3).
Then, the process proceeds to step S6: calculating by adopting the following formula to obtain a spectrum Y without a doping spectrum: y-c g. Specifically, the spectrum Y without the substrate spectrum can be obtained by subtracting the reference spectrum g with the coefficient c from the original raman spectrum S.
In certain embodiments, after step S1, the method further includes: and denoising the Raman spectrum S and each doped spectrum. The denoising process may be wavelet denoising, which is assisted by removing the noise in the raman spectrum S and each of the doped spectra (e.g., the base spectrum), so as to facilitate the subsequent processing of the two spectra.
The second aspect of the present application also provides a separation apparatus for raman spectroscopy of a sample containing a dopant spectrum, the apparatus comprising means for performing a method as described in the first aspect of the present application. Preferably, the apparatus comprises a computer storage medium having stored thereon a readable computer program which, when executed by a processor, implements the method according to the first aspect of the application. The computer storage medium is an electronic component with a data storage function, and includes but is not limited to: RAM, ROM, magnetic disk, magnetic tape, optical disk, flash memory, U disk, removable hard disk, memory card, memory stick, etc.
The spectrum Y from which the substrate spectrum is removed includes only the fluorescence spectrum and the raman spectrum of the biological sample. An automated smoothing algorithm is also proposed in the present application in order to remove the fluorescence spectrum. The method is based on a zero-order SG filter (namely a Savitzky-Golay filter), and the width required to be set when the zero-order SG filter is smoothed is estimated through multiple times of spline curve interpolation fitting, specifically, as shown in figure 2, the method for separating the fluorescence spectrum comprises the following steps:
the process first proceeds to step S7: extracting sample points from the spectrum Y at equal intervals according to a set sampling interval; the initial value of the sampling interval i is set to 2. Spectrum Y is spectrum Y with the base spectrum removed.
Then, the process proceeds to step S8: interpolation fitting is carried out on each sample point by adopting a spline curve, and a spectral line Y after fittingiThe length is consistent with spectrum Y. Preferably, in this embodiment, a cubic spline curve is used to perform interpolation fitting on each sample point.
Then, the process proceeds to step S9: will YiAnd Yi-1Each corresponding data point is compared if YiIs not equal to Yi-1Then the minimum value of the two is taken to form a new YiAnd the sampling interval is set to i + 1; wherein, Y1Y; the data points include sample points and interpolation points. The interpolation point is a point interpolated between adjacent sample points in step S8.
Re-executing steps S7 to S9 until YiAnd Yi-1If all the data points are equal, the process proceeds to step S10: when Y isi＝Yi-1At that time, the sampling interval i and the fitted line Y at that time are recordedi. The sampling interval i obtained at this time is the maximum interval between local minima.
Fitted spectral line YiIs to determine the termination window width of the SG filter by fitting an intermediate product with multiple (e.g. cubic) spline curves to obtain the final YiAlso as a result of fluorescence estimation, and a result of estimation using SG filtersClosely, but in the processing of biological samples, it is generally considered that the results obtained with SG filters are smoother, closer to true level, and therefore more suitable for raman spectroscopy processing of biological samples.
Step S11: smoothing the spectrum Y by adopting an SG filter to obtain the spectrum Yj(ii) a The SG filter has an order of 0 and a width of 2 × j +1, where j is initially 2. The spectrum Y refers to a spectrum obtained after removing the base spectrum from the original spectrum.
Step S12: will YjAnd Yj-1Comparing the data points at the corresponding positions, if YjIs not equal to Yj-1Then the minimum value of the two is taken to form a new YjAnd sequentially increasing the size of j, specifically: the original j is assigned as j + 1.
Repeating the steps S11, S12, wherein the spectrum processed by SG filter in step S11 is changed to Y at this timejUntil step S13: when the size of j reaches the set value, recording Y at the momentj. In this embodiment, the set magnitude is i/2, where i is the maximum interval between local minima.
S13: and obtaining a final Raman spectrum R of the sample to be detected by adopting the following formula: r ═ Y-Yj。
Through the calculation of the steps, the real Raman fingerprint spectrum R, the biological fluorescence spectrum F and the substrate spectrum G of the biological components in the Raman spectrum of the clinical biological sample can be quickly separated. The calculation flow of the whole process is shown in fig. 3.
The application provides a special scale analysis method (such as a method shown in figure 1) based on wavelet transformation, which realizes the extraction and separation of a substrate spectrum in spontaneous Raman spectrum of a common biological sample in clinic by simultaneously carrying out scale extraction and comparison on an original sample Raman spectrum and a reference spectrum. Meanwhile, a method for acquiring the width parameter 'frame' when the zero-order SG filter is used for fluorescence spectrum background estimation is also provided (such as the method shown in figure 2).
The beneficial effect of this application is as follows:
1. the method adopting the scale analysis can effectively avoid the influence of the fluorescence spectrum in the clinical biological sample, so that a polynomial does not need to be set to replace the fluorescence background to reduce parameters when the superposition problem of three spectra is processed. Therefore, the performability and the accuracy of the method are greatly improved; compared with an Extended Multivariate Signal Correction (EMSC) algorithm, the method does not need to obtain a sample spectrum without a substrate in advance for reference, greatly reduces the difficulty in the actual operation process, greatly saves the time, and is favorable for the application and popularization of the Raman spectrum in clinical rapid diagnosis.
2. The fluorescence spectrum removing method with the zero-order SG filter realizes automation of a traditional manual method, the method combines a common SG smoothing filter with a manual point-finding fitting method, the fluorescence background can be quickly obtained from the Raman spectrum, and compared with a traditional polynomial algorithm and wavelet transformation fluorescence removing method, the method can obtain the fluorescence background more accurately.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (8)
1. A method of separating a raman spectrum of a sample containing a dopant spectrum, the method comprising the steps of:
s1: acquiring a Raman spectrum S and a plurality of doping spectrums of a sample to be detected;
s2: normalizing the plurality of doping spectra to obtain a reference spectrum g;
s3: decomposing the Raman spectrum S and the reference spectrum g by adopting rapid wavelet transformation, and setting the approximate coefficient of the nth layer of the two decomposed spectrums as zero; n is the number of decomposition layers;
s4: performing wavelet reconstruction on the two spectrums processed in the step S3 to obtain a first reconstructed spectrum S1 and a second reconstructed spectrum g 1;
s5: performing linear fitting on the first reconstruction spectrum S1 and the second reconstruction spectrum g1 to obtain a superposition coefficient c corresponding to the doping spectrum;
s6: calculating by adopting the following formula to obtain a spectrum Y without a doping spectrum: y-c g.
2. The method for separating the raman spectrum of the sample containing the dopant spectrum according to claim 1, wherein the dopant spectrum is a base spectrum corresponding to a supporting substrate for supporting the sample to be measured.
3. The method for separating a raman spectrum of a sample containing dopant spectra of claim 2, wherein obtaining a plurality of dopant spectra comprises: substrate spectra are recorded for a plurality of locations of the carrier substrate.
4. The method for separating a raman spectrum of a sample containing a dopant spectrum according to claim 3, wherein the step S2 includes:
and calculating the mean value of the spectrum of the substrate at a plurality of positions of the bearing substrate, and taking the calculated mean value as a reference spectrum g.
5. The method for separating a raman spectrum of a sample containing a dopant spectrum according to claim 1, wherein the wavelet basis of the wavelet transform in step S3 is "Sym 11" and the number n of decomposition layers is 8.
6. The method for separating a raman spectrum of a sample containing a dopant spectrum according to claim 1, further comprising, after step S1: and denoising the Raman spectrum S and each doped spectrum.
7. The method for separating a raman spectrum of a sample containing a dopant spectrum according to claim 1, wherein the sample to be measured is a biological sample.
8. A separation device for raman spectroscopy of a sample containing a dopant spectrum, the device comprising means for performing the method of any one of claims 1 to 7.
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