CN109596558B - Spectrogram basic dimension correction and differential analysis method based on moving least square method - Google Patents
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Abstract
The invention belongs to the field of metrology, and particularly relates to a spectrogram basic dimension correction and differential analysis method based on a moving least square method. The method comprises the following steps: carrying out basic dimension correction on the obtained spectrogram of a known standard substance (series) by using a moving least square method; and performing differential analysis by using the corrected spectrogram, specifically: circularly carrying out difference making in a plurality of spectrograms obtained from a known standard substance (series) to obtain the maximum safety threshold of the system; performing multi-spectrum cyclic difference making between the known non-standard substance (series) and the known standard substance (series) to obtain the minimum warning threshold of the system; and circularly differentiating the spectrogram of the unknown sample with the spectrogram (series) of the known standard substance, and comparing the spectrogram with the maximum safety threshold and the minimum safety threshold to obtain a spectrogram analysis result.
Description
Technical Field
The invention belongs to the field of metrology, and particularly relates to a spectrogram basic dimension correction and differential analysis method based on a moving least square method.
Background
Spectrogram analysis is an indispensable technology in modern scientific research. In the basic subject-chemistry, a series of spectrogram characterization methods using four spectrums, namely mass spectrum, nuclear magnetic resonance, vibration spectrum such as infrared spectrum, electron transition such as ultraviolet visible, and combined spectrum such as thermal analysis-infrared, gas chromatography-mass spectrum, and the like as advanced methods are gradually developed.
The traditional spectrogram analysis method is used for capturing and analyzing local information in chemical discipline, such as splitting peak coupling analysis in nuclear magnetic resonance, derivative spectrum peak analysis in ultraviolet-visible, principal component analysis in infrared spectrum, and the like. Such methods require researchers (1) to have certain chemical literacy to belong to chemical structures, properties and spectrogram representations; (2) different analysis software may be switched when different spectrograms are analyzed, such as Mesta nova commonly used for nuclear magnetism, EZ OMIC commonly used for infrared and ultraviolet; (3) the time consumption of single spectrum analysis is long, and manual discrimination, analysis and comparison are needed when a database is called for comparison; (4) for samples with non-single components and non-constant composition ratios, the analysis period is longer, the workload is larger and the effect is not good enough.
With the development of subjects such as computer science, statistics, control science and the like, deeper and deeper subject theories and wider application examples are developed, which lays a good foundation for the development of the fields of metrology, particularly chemometrics and spectrograms. The conventional spectrogram analyzing means usually depends on methods such as peak area calculation and peak type attribution embedded in some software, or qualitative analysis of outflow time of a sample, spectrogram attribution analysis after noise reduction by high-order advantages, and the like. In fact, as the structure, composition and proportion of the research object become more and more complex, the detected information of the instrument becomes more and more, and the spectrogram information becomes more and more abundant and difficult to analyze. When it is difficult to fully analyze the attribution of each peak or the meaning represented by each data point in a spectrogram, a part of natural extracts, especially animal and plant extracts containing dozens or even hundreds of components, have certain use limitations and operation difficulties by using the traditional spectrogram analysis method. Therefore, when the sample identification and quality control are carried out, establishing a set of simple and complete standard control technology and flow thereof, which can still be intuitively understood and easily implemented by theoretical technicians not in the field, becomes a task with challenges and great practical significance.
Disclosure of Invention
In view of the above defects or improvement requirements of the prior art, the present invention provides a method for spectrogram basic dimension correction and differential analysis based on a moving least square method, which aims to perform normalization and correction on the basic dimension of a spectrogram, and then fully utilize the advantages of computer operation and the strategy for establishing a data (library) standard, thereby preferably performing differential spectrogram analysis, establishment and management of a quality (series) library, and identification report generation of a sample.
In order to achieve the above object, according to an aspect of the present invention, there is provided a spectrogram-based dimension correction method based on a moving least square method, including the steps of:
step 2, fitting the original data of the imported target spectrum segment by using a moving least square method to obtain a fitting function with the aim of minimizing the in-situ fitting error;
and 3, substituting the corrected independent variable coordinates of the target spectrum segment into the fitting function for calculation to obtain the corrected dependent variable coordinates of the target spectrum segment.
According to another aspect of the present invention, there is provided a differential analyte identification method based on the fundamental dimension correction method, comprising the steps of:
step 2, performing pairwise difference on the calibration spectrograms of the known standard substances, specifically comprising the following steps: aligning the independent variable coordinate series of the spectrogram corrected by any two known standard substances in the known standard substances, then performing difference on the corrected dependent variable coordinates of the two known standard substances corresponding to each corrected independent variable coordinate, taking the absolute difference value, and summing the obtained difference values of all corrected dependent variable coordinates to obtain a differential value; carrying out subtraction on a plurality of known standard substance calibration spectrograms, solving corresponding differential values of the spectrograms, and taking the maximum value of the obtained differential values as the maximum safety threshold of the system;
step 3, basic dimension correction is carried out on the spectrograms of the selected known non-standard substances by adopting the basic dimension correction method respectively, so that the spectrograms of the plurality of non-standard substances have the same correction independent variable coordinate series after correction, and then the correction spectrograms of the known non-standard substances are obtained;
step 4, performing pairwise difference on the calibration spectrograms of the known standard substance and the non-standard substance, specifically: aligning the corrected spectrogram of any known standard substance with the independent variable coordinate series of the corrected spectrogram of any known non-standard substance, then carrying out pairwise difference on the corrected dependent variable coordinates of the known standard substance and the known non-standard substance corresponding to each corrected independent variable coordinate, and summing the obtained difference values of all pairwise corrected dependent variable coordinates to obtain a difference value; carrying out subtraction on a plurality of known standard substances and a plurality of known non-standard substance calibration spectrograms according to the method, solving corresponding differential values of the known standard substances and the known non-standard substance calibration spectrograms, and taking the minimum value of the obtained sums as the minimum warning threshold of the system; and the minimum alert threshold is numerically greater than the maximum safety threshold;
step 5, performing basic dimension correction on the spectrogram of the unknown sample by using the basic dimension correction method, so that the spectrogram of the unknown sample has the same correction independent variable coordinate series with the known standard substance after correction, and obtaining the correction spectrogram of the unknown sample;
step 6, carrying out pairwise difference on the calibration spectrograms of the unknown sample and the known standard substance, specifically comprising the following steps: aligning the corrected spectrogram of the unknown sample with the independent variable coordinate series of the corrected spectrogram of any known standard substance, then performing difference on the correction dependent variable coordinates of the unknown sample and the known standard substance corresponding to each correction independent variable coordinate, and summing the obtained difference values of all the correction dependent variable coordinates, namely the difference value at the moment; and judging whether the unknown sample is the standard substance according to the differential result.
Preferably, the method for judging whether the unknown sample is a standard substance according to the differential result of the unknown sample comprises the following specific steps:
if the differential result of the unknown sample is less than the maximum safety threshold, the spectrogram of the unknown sample can be determined as a standard spectrogram, and the unknown sample belongs to a standard substance;
if the difference result of the unknown sample is greater than the minimum alarm threshold, the spectrum of the unknown sample is deemed to be a non-standard spectrum and the unknown sample does not belong to the standard substance.
Preferably, the differential result of the unknown sample of step 6 is not between the minimum alarm threshold and the maximum safety threshold.
Preferably, the difference is calculated as a euclidean difference, a manhattan difference, a chebyshev difference, or a weighted difference or a gradient difference of the dimensions.
In general, compared with the prior art, the above technical solution contemplated by the present invention can achieve the following beneficial effects:
(1) the scheme adopts a quick optimization algorithm based on a mobile least square method and deployed in a computer compiler to carry out basic dimension correction. The correction enables the original uncorrected aligned independent variable to be subjected to adaptive regularization after correction and reconstruction, namely, the independent variable has identical properties on a target section of a dimension. The method is realized after the computer is redeployed, not only completely retains the structural characteristics of mathematical operation, but also utilizes powerful computer technology to enable the technology to become automatic and intelligent.
(2) The invention uses the corrected spectrogram in differential full-spectrum analysis and standard establishment. The application of the differential spectrogram analysis method can provide a new idea for the comparison method of the existing standard spectrogram and a reliable new scheme for the reconstruction and the redevelopment of standard spectrogram data (database). Because the differential analysis method is based on the differential comparison method which is carried out after the identity (full alignment) of the data on the basis dimension of the research spectrogram, the adaptability of the scatter spectrum is exerted by the basic dimension correction based on the moving least square method.
(3) The scheme can also be used for judging the success rate of a solid theory that a detector does not have a very professional subject background. The detector can generate an identification report only by solving the difference between the spectrograms and judging the related maximum and minimum values. The method has the advantages that after the same support function and the parameter selection of the same data are carried out, a unique solution can be obtained through comparison, the accuracy rate is high, the characteristics are stable, compared with the traditional principal component analysis, the time consumption is greatly reduced, the method has strong resolving power, and the method is very sensitive to the detail capture of the obtained spectrogram.
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FIG. 1 is a simplified overall flow diagram of the process of the present invention;
FIG. 2 is a schematic diagram of the basic dimension correction method based on the moving least square method in the present invention;
FIG. 3 is a flow chart of a method of differential full-spectrum analysis in an embodiment of the present invention;
FIG. 4 is a flow chart of a method for sample identification using differential full-spectrum analysis in accordance with an embodiment of the present invention;
FIG. 5 is a sample spectrum selection for p-anisidine detection in a BRUKER-VERTEX70 model infrared spectrometer, in accordance with an embodiment of the present invention;
FIG. 6 is the X-axis in-situ fast optimization moving least squares fitting results based on p-anisidine sampling spectra detected in a BRUKER-VERTEX70 type infrared spectrometer in accordance with an embodiment of the present invention.
FIG. 7 shows the results of the parameter-selecting fast-optimizing mobile least-squares fitting based on the spectrum of p-anisidine in a BRUKER-VERTEX70 infrared spectrometer.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and 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 addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
In general, there is a case where "digital misalignment" occurs in a sample spectrum of an infrared spectrum. By "out of place" is meant that the spectra measured by the instrument for the same or same series of samples are sampled at the same wavelength band and the same resolution as the initial experimental settings, but often have some random shifts at specific locations. E.g. also set from 648cm-1To 3500cm-1In 2cm-1For the resolution selection, the first point may be sampled at 648.51cm-1It is also possible to sample at 649.23cm-1. This situation is widely present in the fields of metrology and spectrogram, so that the existing accurate spectrogram analysis can be either retained in principal component analysis of a single spectrogram, or trend line analysis can be given by fitting through linear connected scatter points and the like. The scheme realizes 'spectrogram position correction' to normalize all data points to the selected position of the target section to be researched, so as to realize the identification of the sample by further using the correction methodAnd (3) determining, for example, whether the unknown sample belongs to the same substance as the standard sample.
Conventionally, in the research related to the moving least square method, the algorithm is often used in the aspects of image segmentation, establishment of a fitted curved surface, research on the fitting effect and the like, for example, li shuling and the like in university of Chongqing university (nature science edition) published in the application of moving least square approximation in image segmentation, and weiyi and the like in university of Shanghai transportation science published in the application of improved moving least square method and application thereof in structural reliability analysis. There is no report on the specific targeting of this algorithm to the field of spectrography and its research.
The invention relates to a spectrogram basic dimension correction and differential analysis method based on a moving least square method. The method comprises the following steps: carrying out basic dimension correction on the obtained spectrogram of a known standard substance (series) by using a moving least square method; and performing differential analysis by using the corrected spectrogram, specifically: circularly carrying out difference making in a plurality of spectrograms obtained from a known standard substance (series) to obtain the maximum safety threshold of the system; performing multi-spectrum cyclic difference making between the known non-standard substance (series) and the known standard substance (series) to obtain the minimum warning threshold of the system; and circularly differentiating the spectrogram of the unknown sample with the spectrogram (series) of the known standard substance, and comparing the spectrogram with the maximum safety threshold and the minimum safety threshold to obtain a spectrogram analysis result.
The invention provides a spectrogram basic dimension correction method based on a moving least square method, which comprises the following steps of:
And 2, aiming at minimizing the in-situ fitting error, carrying out in-situ fitting on the introduced original data of the target spectrum segment by using a mobile least square method deployed in a computer compiler, and selecting a tight support weight function of the target spectrum segment to obtain a fitting function. The tight support function is a function with tight support characteristics, and the functions with the tight support characteristics are various and can be called after being deployed in a computer.
And 3, substituting the corrected independent variable coordinates of the target spectrum segment into the tight support weight function and the fitting function for calculation to obtain the corrected dependent variable coordinates of the target spectrum segment.
The following are examples: in this example, we call the moving least squares method to deploy in the python compiler of Anaconda to correct the two-dimensional sampled spectrogram to minimize the fitting error. The fitting function is as follows:
in this embodiment, a linear basis is used, and the mathematical expression is as follows
PT(x)＝[1，x]
B(x)＝[w1(x)p(x1)w2(x)p(x2)...wN(x)p(xN)]
f＝[f(x1)，f(x2)，...，f(xN)]T
In the above formula, xIAs the abscissa of the sampled spectrogram point, f (x)I) Is the ordinate of the sampling spectrogram point, I is the I-th data point on the spectrogram, N is the number of the data points, wI(x) Is a tight support weight function; x is the abscissa of the corrected spectrogram point,
is the ordinate of the corrected spectrogram point.
The above mathematical process can be implemented in a compiler deployed in an office computer based on a variety of computer languages.
And substituting the corrected abscissa of the target spectrum segment into the tight support weight function and the fitting function for calculation to obtain the corrected ordinate of the target spectrum segment. The basic dimensionality correction is carried out on a plurality of measured spectrograms of the same sample or a plurality of spectrograms of a plurality of samples according to the method, so that the abscissa of the spectrograms is the same, namely, the spectrogram position correction is realized, and all data points are normalized to the selected position of the target section which is wanted to be researched.
For example, in one embodiment, the target spectral segments are selected to be paired from 648cm-1To 4000cm-1Scanning 128 times with 4cm resolution-1The spectrogram is subjected to correction parameter setting according to the method. And (4) making a table after the correction is finished, wherein the original sampling spectrum, the in-situ calculation spectrum and the spectrum after the basic dimension correction are listed in the table 1. The test sample was p-anisidine. The in-situ calculated spectrum is the same as the abscissa of the original sampled spectrum, and a fitting function is input to obtain a corresponding ordinate data set.
A visual scatter plot of all data is presented in fig. 5, 6, 7 of the accompanying description. FIG. 5 is a sample spectrum selection for p-anisidine detection in a BRUKER-VERTEX70 model infrared spectrometer, in accordance with an embodiment of the present invention; FIG. 6 is the X-axis in-situ fast optimization moving least squares fitting results based on p-anisidine sampling spectra detected in a BRUKER-VERTEX70 type infrared spectrometer in accordance with an embodiment of the present invention. FIG. 7 shows the results of the parameter-selecting fast-optimizing mobile least-squares fitting based on the spectrum of p-anisidine in a BRUKER-VERTEX70 infrared spectrometer. The horizontal axis in the scattergram is the wavenumber λ in cm-1Is a unit; the vertical axis represents the absorption intensity Abs.
For convenience of presentation, only from 1550cm are shown-1To 1440cm-1Table of spectral data of (a). Wherein the odd columns of the table are the wavenumbers λ in cm-1Is a unit; the even columns in the table are the absorption intensities Abs.
TABLE 1
In one embodiment, a sample to be identified is a commercial product purchased from a merchant, is suspected to be a standard, but is not determined to be the standard, and then the differential analysis of the sample is performed by the method based on the basic dimension calibration method. The method specifically comprises the following steps:
step 2, performing pairwise difference on the calibration spectrogram of the known standard substance, specifically comprising the following steps: aligning the independent variable coordinate series of the spectrogram corrected by any two known standard substances in the known standard substances, then performing difference on the corrected dependent variable coordinates of the two known standard substances corresponding to each corrected independent variable coordinate, and summing the obtained single-point difference values of all the corrected dependent variable coordinates to obtain a difference value. Performing single-point subtraction on a plurality of known standard substance calibration spectrograms, solving difference values of the spectrograms, and taking the maximum value of the difference values of the known standard substances as the maximum safety threshold of the system;
step 3, performing basic dimension correction on the spectrograms of the selected plurality of known standard substances and the selected plurality of known non-standard substances by adopting the basic dimension correction method respectively, so that the spectrograms of the plurality of known standard substances and the plurality of non-standard substances have the same correction independent variable coordinate series after correction, and obtaining correction spectrograms of the known standard substances and the non-standard substances;
step 4, performing pairwise difference on the calibration spectrograms of the known standard substance and the non-standard substance, specifically: aligning the corrected spectrogram of any known standard substance with the independent variable coordinate series of the corrected spectrogram of any known non-standard substance, then carrying out pairwise difference on the corrected dependent variable coordinates of the known standard substance and the known non-standard substance corresponding to each corrected independent variable coordinate, and summing the obtained single-point difference values of all pairwise corrected dependent variable coordinates to obtain a difference value. Performing single-point subtraction on a plurality of known standard substances and a plurality of known non-standard substance calibration spectrograms according to the method, solving difference values of the known standard substances and the known non-standard substance calibration spectrograms, and taking the minimum value of the obtained difference values as the minimum warning threshold of the system; and the minimum alert threshold is numerically greater than the maximum safety threshold;
step 5, performing basic dimension correction on the spectrogram of the unknown sample by using the basic dimension correction method, so that the spectrogram of the unknown sample has the same correction independent variable coordinate series with the known standard substance after correction, and obtaining the correction spectrogram of the unknown sample;
step 6, carrying out pairwise difference on the calibration spectrograms of the unknown sample and the known standard substance, specifically comprising the following steps: aligning the corrected spectrogram of the unknown sample with the independent variable coordinate series of the corrected spectrogram of any known standard substance, then performing difference on the corrected dependent variable coordinates of the unknown sample and the known standard substance corresponding to each corrected independent variable coordinate, and summing the obtained single-point difference values of all the corrected dependent variable coordinates to obtain the differential value of the unknown sample. Obtaining the identification result of whether the standard substance is obtained according to the differential result; the method specifically comprises the following steps: if the differential result of the unknown sample is less than the maximum safety threshold, the spectrogram of the unknown sample can be determined as a standard spectrogram, and the unknown sample belongs to a standard substance; if the difference result of the unknown sample is greater than the minimum alarm threshold, the spectrum of the unknown sample is deemed to be a non-standard spectrum and the unknown sample does not belong to the standard substance.
It is desirable to select the appropriate known standard substance and known non-standard substance such that the differential result between the unknown sample and the known standard substance in step 6 is preferably not between the minimum alarm threshold and the maximum safety threshold. In selecting the types of known standard substances and known non-standard substances, the closer the standard substance type is to the sample to be tested, the higher the detection accuracy, while the selection of the non-standard substance type is relatively less stringent, so that the minimum warning threshold may be much higher than the maximum safety threshold. However, in order to improve the accuracy of identification of an unknown sample to be identified, the class of substances closer to the sample to be identified should be selected as standard or non-standard substances as possible, preferably with the minimum warning threshold and the maximum safety threshold closer together.
In the above identification method, the difference making method may be various, such as calculating euclidean difference, manhattan difference, chebyshev difference, or performing weighted difference or gradient difference on other mathematical characteristics such as dimension.
In this example, several substituted aniline compounds were chosen for spectral scanning, which were: 3, 5-dichloroaniline, o-bromoaniline, m-bromoaniline and p-bromoaniline. The machine adopts a BRUKER-VERTEX70 type infrared spectrometer from 648 to 4000cm-1Average results obtained after 128 scans.
3, 5-dichloroaniline was selected as the standard substance series (hereinafter referred to as "standard acceptable samples"), and o-bromoaniline and p-bromoaniline were selected as the non-standard substance series (hereinafter referred to as "non-standard acceptable samples"). The original mark of the sample to be identified is removed as the known drug, namely the m-bromoaniline, and the sample to be identified is set as the sample to be identified of the suspected halogenated aniline.
In the present example of differential analysis, the infrared spectral data of each substance is corrected and analyzed by first correcting the spectrum of each substance in the target spectrum segment by the above-described method so that their abscissas are aligned. Since the infrared spectrum is a two-dimensional spectrum, i.e., A (absorption) - λ (wave number cm)-1) And a two-dimensional spectrogram is formed between the two parts, so that the functions and parameters selected in the correction are the same as those of the previous correction embodiment.
The data are differentiated and summed between standard qualified samples and between non-standard qualified samples by the following formula to obtain a differential value,
wherein i represents the ith data point, N is the number of data points, AStudy Spectrum iRepresents the corresponding ordinate absorption value, A, of the ith data point of the spectrum under investigationStandard spectrum iExpressing the corresponding ordinate absorption value of the ith data point of the standard spectrum; a difference table is thus obtained, see table 2:
TABLE 2
Wherein the series 1-10 represents the multi-sample measurement result of 3, 5-dichloroaniline (standard qualified sample), the series 1-100-19 represents o-bromoaniline (non-standard sample), the series 1-200-4 represents m-bromoaniline (sample to be identified), and the series 1-300-9 represents p-bromoaniline (non-standard sample). ". 0" and "-star" only serve as tailgating. As can be seen from Table 2, the largest difference between the spectra of the three standards is 0.323498152902554, and the smallest difference between the spectra of the standards and the non-standards is 20.9238814454843. These two values correspond to the maximum safety threshold and the minimum warning threshold in step 4 above, respectively.
The identification of the sample is substituted into the differential full spectrum method in the same way, and the difference is obtained by comparing the sample with the known standard spectrogram. In this example, the difference analysis is performed on the sample to be tested and all spectrograms (i.e., spectrograms of three 3, 5-dichloroaniline, 1-10.0-star-, 1-10-1.0-star-, 1-10-2.0-star-) in the standard qualified sample library, and the obtained differences of scattering points are summed up to 27.89536933, 27.91447388 and 27.90450665, respectively.
Comparing the difference between the measured sample and the standard chart with the minimum alarm threshold and the maximum safety threshold, wherein the sample within the maximum safety threshold can be regarded as the same sample as the standard, and the sample outside the minimum alarm threshold can be regarded as a non-standard sample. So for this example, all the differential results for the sample to be identified are greater than the minimum alarm threshold, and the sample to be identified can be deemed to be a non-standard sample relative to the standard sample (3, 5-dichloroaniline). This result is consistent with its original assignment as m-bromoaniline.
FIG. 1 is a simplified overall flow diagram of the process of the present invention; the method specifically comprises the following steps: the method is specifically divided into two aspects of basic dimension correction and corrected differential analysis based on a moving least square method;
FIG. 2 is a schematic diagram of the basic dimension correction method based on the moving least square method in the present invention; the method specifically comprises the following steps: when the method is used for correction, firstly, the correction coordinate is selected, and then the original spectrogram is corrected and generated, so that a result is obtained.
FIG. 3 is a flow chart of a method of differential full-spectrum analysis in an embodiment of the present invention; firstly, determining the relevant properties of a spectrogram (whether the spectrogram is a usable scatter diagram, the dimension of an independent variable, the dimension of a dependent variable and the like), and then correspondingly correcting the spectrogram; and then, carrying out difference value calculation and full spectrum summation on the dependent variable on each independent variable point in the selected section to obtain a differential value, and finally determining a maximum safety threshold and a minimum warning threshold between the standard samples and the non-standard samples. The spectrogram related by the invention comprises a series of spectrograms which take four spectrums-mass spectrum, nuclear magnetic resonance, vibration spectrum such as infrared spectrum, electron transition such as ultraviolet visible as the center, and combination spectrum such as thermal analysis-infrared, gas chromatography-mass spectrum and the like as advanced methods.
FIG. 4 is a flow chart of a method for sample identification using differential full-spectrum analysis in accordance with an embodiment of the present invention; the method specifically comprises the following steps: firstly, two threshold values are obtained by using the differential full-spectrum analysis method, the same type of spectrum which is analyzed before is obtained for a sample to be identified, and correction is carried out in the same process. Then, the sample to be identified and each standard sample are subjected to differential analysis, respectively. And finally, comparing the obtained difference value with a maximum safety threshold and a minimum warning threshold so as to obtain a confidence result.
The invention provides a new idea of applying the moving least square method to spectrogram basic dimension correction, and the new idea is used for spectrogram differential analysis, so that the spectrogram correction and analysis of a measured object can be rapidly, stably and reliably carried out, and further, whether an unknown sample is a standard substance or a non-standard substance is identified.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (4)
1. A differential analysis substance identification method based on a basic dimension correction method is characterized by comprising the following steps:
step 1, performing basic dimension correction on the spectrograms of a plurality of selected known standard substances by respectively adopting a basic dimension correction method to ensure that the spectrograms of the plurality of known standard substances have the same correction independent variable coordinate series after correction, namely obtaining the correction spectrograms of the known standard substances;
step 2, performing pairwise difference on the calibration spectrogram of the known standard substance, specifically comprising the following steps: aligning the independent variable coordinate series of spectrograms of any two known standard substances in the known standard substances after correction, then carrying out difference on the corrected dependent variable coordinates of the two known standard substances corresponding to each corrected independent variable coordinate, and carrying out total summation on the obtained difference values of all corrected dependent variable coordinates to obtain a differential value; carrying out difference on a plurality of known standard substance calibration spectrograms, solving corresponding difference values, and taking the maximum value in the obtained difference values as the maximum safety threshold of the system;
step 3, performing basic dimension correction on the selected spectrograms of the known non-standard substances by respectively adopting the basic dimension correction method, so that the spectrograms of the known non-standard substances have the same correction independent variable coordinate series after correction, and obtaining correction spectrograms of the known non-standard substances;
step 4, performing pairwise difference on the calibration spectrograms of the known standard substance and the non-standard substance, specifically: aligning the corrected spectrogram of any known standard substance with the independent variable coordinate series of the corrected spectrogram of any known non-standard substance, then carrying out pairwise difference on the corrected dependent variable coordinates of the known standard substance and the known non-standard substance corresponding to each corrected independent variable coordinate, and summing the obtained difference values of all pairwise corrected dependent variable coordinates to obtain a difference value; carrying out subtraction on a plurality of known standard substances and a plurality of known non-standard substance calibration spectrograms according to the method, solving difference values, and taking the minimum value in the obtained difference values as the minimum warning threshold of the system; and the minimum alert threshold is greater than the maximum safety threshold;
step 5, performing basic dimension correction on the spectrogram of the unknown sample by using the basic dimension correction method, so that the spectrogram of the unknown sample has the same correction independent variable coordinate series with the known standard substance after correction, and obtaining the correction spectrogram of the unknown sample;
step 6, carrying out pairwise difference on the calibration spectrograms of the unknown sample and the known standard substance, specifically comprising the following steps: aligning the corrected spectrogram of the unknown sample with the independent variable coordinate series of the corrected spectrogram of any known standard substance, then carrying out difference on the corrected dependent variable coordinates of the unknown sample and the known standard substance corresponding to each corrected independent variable coordinate, and summing the obtained difference values of all the corrected dependent variable coordinates to obtain a difference value; judging whether the unknown sample is a standard substance according to the differential result;
the basic dimension correction method is a spectrogram basic dimension correction method based on a moving least square method, and specifically comprises the following steps:
step 1, selecting a target spectrum segment to be corrected, establishing description of a basic group coordinate on the spectrum segment, and obtaining original data of the target spectrum segment to be corrected, wherein the original data comprises independent variable coordinates and dependent variable coordinates of the target spectrum segment;
step 2, fitting the original data of the imported target spectrum segment by using a moving least square method to obtain a fitting function with the aim of minimizing the in-situ fitting error;
and 3, substituting the corrected independent variable coordinates of the target spectrum segment into the fitting function for calculation to obtain the corrected dependent variable coordinates of the target spectrum segment.
2. The method for identifying a substance according to claim 1, wherein the determination of whether or not the unknown sample is the standard substance is made based on the differential result of the unknown sample by:
if the differential result of the unknown sample is less than the maximum safety threshold, the spectrum of the unknown sample can be deemed as a standard spectrum, and the unknown sample belongs to a standard substance:
if the difference result of the unknown sample is greater than the minimum alarm threshold, the spectrum of the unknown sample is deemed to be a non-standard spectrum and the unknown sample does not belong to the standard substance.
3. The method of identifying a substance as in claim 1 wherein the result of the summation of step 6 is not between the minimum alarm threshold and the maximum safety threshold.
4. The method of identifying a substance as in claim 1 wherein the difference is calculated as a euclidean difference, a manhattan difference, a chebyshev difference, or a weighted difference or a gradient difference over the dimensions.
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