CN112834451A - Sample identification method and device based on infrared spectrum and storage medium - Google Patents
Sample identification method and device based on infrared spectrum and storage medium Download PDFInfo
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- ZPUCINDJVBIVPJ-LJISPDSOSA-N cocaine Chemical compound O([C@H]1C[C@@H]2CC[C@@H](N2C)[C@H]1C(=O)OC)C(=O)C1=CC=CC=C1 ZPUCINDJVBIVPJ-LJISPDSOSA-N 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- BQJCRHHNABKAKU-KBQPJGBKSA-N morphine Chemical compound O([C@H]1[C@H](C=C[C@H]23)O)C4=C5[C@@]12CCN(C)[C@@H]3CC5=CC=C4O BQJCRHHNABKAKU-KBQPJGBKSA-N 0.000 description 2
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- GVGLGOZIDCSQPN-PVHGPHFFSA-N Heroin Chemical compound O([C@H]1[C@H](C=C[C@H]23)OC(C)=O)C4=C5[C@@]12CCN(C)[C@@H]3CC5=CC=C4OC(C)=O GVGLGOZIDCSQPN-PVHGPHFFSA-N 0.000 description 1
- SNIOPGDIGTZGOP-UHFFFAOYSA-N Nitroglycerin Chemical compound [O-][N+](=O)OCC(O[N+]([O-])=O)CO[N+]([O-])=O SNIOPGDIGTZGOP-UHFFFAOYSA-N 0.000 description 1
- 239000000006 Nitroglycerin Substances 0.000 description 1
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Abstract
The invention provides a sample identification method, equipment and a storage medium based on infrared spectroscopy, wherein the sample identification method comprises the steps of establishing a standard spectrum library of substances on the basis of infrared spectroscopy of a series of standard substances, wherein each piece of spectrum data in the standard spectrum library is called as a standard spectrum; constructing a series of test samples, carrying out infrared spectrum scanning on the test samples, and extracting each piece of spectrum data of the test samples as a test spectrum; calculating a weighted correlation coefficient of the test spectrum and the standard spectrum, setting a similar interval according to the weighted correlation coefficient, and establishing an infrared spectrum identification model; performing infrared spectrum scanning on a sample to be detected to obtain spectral data of the sample to be detected; and inputting the spectral data into an infrared spectrum recognition model to recognize the sample to be detected. The sample identification method disclosed by the invention adopts an infrared spectrum technology, and compared with instruments such as a mass spectrum and the like, the sample identification method does not use a chemical reagent, is green and environment-friendly, applies a weighted correlation coefficient in an identification model, and has high identification accuracy and high detection efficiency.
Description
Technical Field
The invention relates to the technical field of infrared spectrum identification, in particular to a sample identification method, sample identification equipment and a storage medium based on infrared spectrum.
Background
The infrared spectrum is mainly divided into absorption spectrum, emission spectrum and scattering spectrum, and is referred to herein as infrared absorption spectrum. The infrared absorption spectrum is derived from a vibrational spectrum caused by vibrational rotation and energy level transition of molecules. The infrared spectrum is generally divided into 3 regions: far infrared region (about 400-10 cm)-1) Middle infrared region (about 4000-400 cm)-1) Near infrared region (14000-4000 cm)-1)。
Although the prior art can carry out matching identification on substances according to the characteristic peak positions of infrared spectra of different substances, the method needs to search peaks by a machine and involves extraction of peak shape characteristics and the like, the process is complex, the subjectivity is strong, and the method is not suitable for online detection, so that the development of a sample identification method suitable for the infrared spectra is urgently needed.
Accordingly, the prior art is yet to be improved and developed.
Disclosure of Invention
The invention mainly aims to solve the technical problems of complex identification process and low efficiency of the existing method for identifying substances by utilizing infrared spectrum.
The invention provides a sample identification method based on infrared spectrum, which comprises the following steps:
establishing a standard spectrum library of substances based on infrared spectra of a series of standard substances, wherein each piece of spectral data in the standard spectrum library is called as a standard spectrum;
constructing a series of test samples, carrying out infrared spectrum scanning on the test samples, and extracting each piece of spectrum data of the test samples to be used as a test spectrum;
calculating the weighted correlation coefficient of the test spectrum and the standard spectrum, setting a similar interval according to the weighted correlation coefficient, and establishing an infrared spectrum identification model;
performing infrared spectrum scanning on a sample to be detected to obtain spectral data of the sample to be detected;
and inputting the spectral data into the infrared spectrum recognition model to recognize the sample to be detected.
Optionally, in the first aspect of the inventionIn a first embodiment of the aspect, in the standard spectrum library, an average value of m data points corresponding to p pieces of spectral data of the same substance measured in different batches is used as the standard spectrum s of the substanceoriginal，
Optionally, in a second implementation manner of the first aspect of the present invention, before the calculating the weighted correlation coefficient between the test spectrum and the standard spectrum, the calculating includes:
filtering the test spectrum and the standard spectrum, and calculating to obtain mathematical expectation and variance of the standard spectrum data after filtering and mathematical expectation and variance of the test spectrum data after filtering;
and standardizing the standard spectrum data after the filtering processing and the test spectrum data after the filtering processing.
Optionally, in a third embodiment of the first aspect of the present invention, the processed standard spectral data is normalized
Wherein s issecondFor the filtered standard spectral data, EsFor mathematical expectation of filtered standard spectral data, σs 2E is the variance of the standard spectral data after filtering processing and is the base number of a natural logarithm function;
standardized test spectral data
Wherein, tsecondFor the filtered test spectral data, EtFor mathematical expectation of filtered standard spectral data, σt 2Is the variance of the filtered standard spectral data.
Optionally, in a fourth embodiment of the first aspect of the present invention, the calculating the weighted correlation coefficient between the test spectrum and the standard spectrum uses the following formula:
weighted correlation coefficient
Wherein s isiIs the ith data point, t, in the normalized standard spectrum dataiTo normalize the ith data point in the processed test spectrum data,
ωiis the weight of the ith data point,
and k is a custom parameter.
Optionally, in a fifth embodiment of the first aspect of the present invention, k is 0.01 or 0.5.
Optionally, in a sixth embodiment of the first aspect of the present invention, the test sample comprises a sample of the same substance in a standard library of spectra and a sample of a substance in a non-standard library of spectra.
Optionally, in a seventh embodiment of the first aspect of the present invention, the sample to be tested includes drugs and explosives.
The present invention provides in a second aspect an infrared spectroscopy-based sample identification apparatus, the sample identification apparatus comprising: a memory having instructions stored therein and at least one processor, the memory and the at least one processor interconnected by a line;
the at least one processor invokes the instructions in the memory to cause the sample identification device to perform the infrared spectroscopy-based sample identification method as in any one of the above.
A third aspect of the invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements a method for infrared spectroscopy-based sample identification as defined in any one of the above.
Has the advantages that: the invention provides a sample identification method, equipment and a storage medium based on infrared spectroscopy, wherein the sample identification method comprises the steps of establishing a standard spectrum library of substances on the basis of infrared spectroscopy of a series of standard substances, wherein each piece of spectrum data in the standard spectrum library is called as a standard spectrum; constructing a series of test samples, carrying out infrared spectrum scanning on the test samples, and extracting each piece of spectrum data of the test samples as a test spectrum; calculating a weighted correlation coefficient of the test spectrum and the standard spectrum, setting a similar interval according to the weighted correlation coefficient, and establishing an infrared spectrum identification model; performing infrared spectrum scanning on a sample to be detected to obtain spectral data of the sample to be detected; and inputting the spectral data into an infrared spectrum recognition model to recognize the sample to be detected. The sample identification method disclosed by the invention adopts an infrared spectrum technology, and compared with instruments such as a mass spectrum and the like, the sample identification method does not use a chemical reagent, is green and environment-friendly, applies a weighted correlation coefficient in an identification model, and has high identification accuracy and high detection efficiency.
Drawings
FIG. 1 is a flow chart of one embodiment of a method for infrared spectroscopy-based sample identification according to the present invention;
FIG. 2 is an infrared scanning raw spectrum of a substance in a standard spectrum library;
FIG. 3 is a material distribution diagram of a standard spectrum to which the highest correlation coefficient between a test spectrum and the standard spectrum belongs;
fig. 4 is a schematic diagram of an embodiment of an infrared spectrum-based sample identification apparatus according to the present invention.
Detailed Description
The embodiment of the invention provides a sample identification method and device based on infrared spectroscopy and a storage medium.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that the embodiments described herein may be practiced otherwise than as specifically illustrated or described herein. Furthermore, the terms "comprises," "comprising," or "having," and any variations thereof, are intended to cover non-exclusive inclusions, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
For ease of understanding, a detailed flow chart of an embodiment of the present invention is described below, with reference to fig. 1, in which a first aspect of the present invention is a method for sample identification based on infrared spectroscopy, the method for sample identification based on infrared spectroscopy comprising:
s100, establishing a standard spectrum library of substances on the basis of infrared spectrums of a series of standard substances, wherein each piece of spectrum data in the standard spectrum library is called as a standard spectrum;
s200, constructing a series of test samples, carrying out infrared spectrum scanning on the test samples, and extracting each piece of spectrum data of the test samples to be used as a test spectrum;
s300, calculating a weighted correlation coefficient of the test spectrum and the standard spectrum, setting a similar interval according to the weighted correlation coefficient, and establishing an infrared spectrum identification model;
s400, performing infrared spectrum scanning on a sample to be detected to obtain spectral data of the sample to be detected;
s500, inputting the spectral data into the infrared spectrum recognition model to recognize the sample to be detected.
The sample identification method based on the infrared spectrum is characterized in that an infrared spectrum identification model is firstly established, the infrared spectrum of a sample is identified by the infrared spectrum identification model, the model is based on the infrared spectrum of a series of standard substances, a standard spectrum library of the substances is established, each piece of spectrum data in the standard spectrum library is called as a standard spectrum, and then an identification matching model is established through a weighted correlation coefficient. According to the invention, the similarity of the infrared spectrum is measured through the weighted correlation coefficient, the weighted correlation coefficient can effectively use the spectral characteristics for similarity calculation, so that the reliability of the result is improved, the test spectrum of the test sample is highly similar to but different from the standard spectrum of the substances in the standard spectrum library, the common characteristics are reflected, and the individual difference is reflected. Therefore, a similar interval of the spectrum similarity is determined, products in the interval are similar substances, and otherwise, the products are non-similar substances or abnormal samples.
In an alternative embodiment of the first aspect of the present invention, in the standard spectrum library, the average value of m data points corresponding to p pieces of spectral data of the same substance measured in different batches is used as the standard spectrum s of the substanceoriginal，
In an alternative embodiment of the first aspect of the present invention, the calculating the weighted correlation coefficients of the test spectrum and the standard spectrum comprises:
filtering the test spectrum and the standard spectrum, and calculating to obtain mathematical expectation and variance of the standard spectrum data after filtering and mathematical expectation and variance of the test spectrum data after filtering;
and standardizing the standard spectrum data after the filtering processing and the test spectrum data after the filtering processing.
In this embodiment, both the test spectrum and the standard spectrum need to be preprocessed, where the preprocessing includes filtering the spectrum data, and further, normalization processing is needed to obtain the finally needed standard spectrum data and test spectrum data.
In an alternative embodiment of the first aspect of the present invention, the processed standard spectral data is normalized
Wherein s issecondFor the filtered standard spectral data, EsFor mathematical expectation of filtered standard spectral data, σs 2After filtering processingE is the base of the natural logarithm function;
standardized test spectral data
Wherein, tsecondFor the filtered test spectral data, EtFor mathematical expectation of filtered standard spectral data, σt 2Is the variance of the filtered standard spectral data.
In an alternative embodiment of the first aspect of the present invention, the calculating the weighted correlation coefficient between the test spectrum and the standard spectrum uses the following formula:
weighted correlation coefficient
Wherein s isiIs the ith data point, t, in the normalized standard spectrum dataiTo normalize the ith data point in the processed test spectrum data,
ωiis the weight of the ith data point,
and k is a custom parameter.
In an alternative embodiment of the first aspect of the present invention, k is 0.01 or 0.5. In the embodiment, the weight formula includes a parameter k that needs to be specified by the user, which is the only parameter to be considered by the algorithm model of the weighted correlation coefficient method of the present invention, when k is 0.5, most data is used for the algorithm model, and when k is 0.01, only few local points participate in the algorithm model.
In an alternative embodiment of the first aspect of the present invention, the test sample comprises a sample of the same substance in a standard library of spectra and a sample of a substance in a non-standard library of spectra. In this embodiment, the test sample is used for determining the weighted correlation coefficient, the weighted correlation coefficient of the test spectrum of the sample of the same substance in the standard spectrum library and the standard spectrum of the substance is the highest, and the highest weighted correlation coefficient of other substances in the standard spectrum library and the substance in the standard spectrum library is lower, so that a threshold value of the weighted correlation coefficient can be determined, wherein the threshold value is higher than the threshold value and represents the substance belonging to the standard spectrum library, and the threshold value is lower than the threshold value and belongs to the substance out of the standard spectrum library.
In an alternative embodiment of the first aspect of the present invention, the sample to be tested includes drugs and explosives. More specifically, the sample to be tested comprises cocaine, heroin, methamphetamine, morphine, tai' an, cannabis and nitroglycerin.
The first aspect of the invention is a specific example of a sample identification method based on infrared spectroscopy, as follows:
The infrared spectrum signal comprises an infrared spectrum in a certain waveband range, specifically an infrared absorbance curve or other spectral parameters derived from formula deformation, and is acquired by a Fourier infrared spectrometer produced by BRUKER company (Germany).
Taking the average value of m data points corresponding to p spectral data of the same substance measured in different batches as the standard spectrum s of the substanceoriginal，
The spectrum comprises a standard spectrum soriginalAnd a test spectrum toriginal(ii) a The preprocessing comprises the steps of filtering the spectral data, further performing standardization processing, and finally obtaining standard spectral data s and test spectral data t.
The spectrum pretreatment comprises the following steps:
3.1 spectral filtering treatment, wherein S-G smoothing filtering is mainly used;
3.2 finding the spectral data s obtained after filteringsecondOr tsecondMathematical expectation of (E)sOr EtSum variance σs 2Or σt 2；
3.3 normalization
Step 4, establishing a weighted correlation coefficient algorithm model
Specifically, calculating a weighted correlation coefficient r of the test spectrum t and a standard spectrum s, wherein s is standard spectrum data finally obtained in the step 3, and s in s isiIs the ith data point in the standard spectral data. t in tiIs the ith data point in the test spectrum data.
ωiIs as followsWeights of i data points.
In the above expression of the weighted correlation coefficient r, the weight ωiThe corresponding expression is as follows:
the above formula contains a parameter k that needs to be specified by the user, which is the only parameter that needs to be considered in the weighted correlation coefficient algorithm model, and k is 0.5 in this embodiment.
Testing the test spectrum of each substance in the standard library for multiple times, and obtaining a correlation coefficient between the test spectrum and each standard spectrum in the standard library; and testing the test spectrum of the non-existing substance in the standard library for multiple times, obtaining the correlation coefficient between the test spectrum and the standard spectrum of each substance in the standard library, and counting the distribution of the correlation coefficient of each test spectrum, thereby setting a reasonable similar interval for each standard spectrum substance in the standard library.
In this embodiment, referring to fig. 2, fig. 2 is an infrared scanning raw spectrum of 10 substances in a standard spectrum library, referring to fig. 3, and sample labels 0, 1, … 9 in the ordinate of fig. 3 represent substance distributions of standard spectra to which the highest correlation coefficients between a test spectrum and 10 standard spectra in the standard spectrum library belong. The test spectra of 10 substances in the standard spectrum library are tested, 20 (200 in total) test spectra of the substances in the standard spectrum library and 200 test spectra of substances outside the standard spectrum library are tested, and the 10 different substances in the standard spectrum library and the substances outside the standard spectrum library are marked by 11 marks with different shapes. And (3) respectively calculating correlation coefficients of all the test spectra t and the standard spectra of 10 substances in the standard spectrum library, and obtaining the highest correlation coefficient between each test spectrum and 10 standard spectra, the distribution of the highest correlation coefficient and the substance distribution of the corresponding standard spectrum, as shown in fig. 3. It can be easily seen that the correlation coefficient between the test spectrum of the sample of the same substance in the standard spectrum library and the standard spectrum of the substance is the highest and is higher than 0.85. The highest correlation coefficient of other substances except the standard spectrum library and 10 substances in the standard spectrum library is lower than 0.85. The threshold value of substantially each substance may be set to approximately 0.85. The similarity interval is [ 0.85, 1 ]. Of course, the threshold value can be fine-tuned specifically according to the correlation coefficient distribution of the substance. And the highest correlation coefficient between all the test spectra and 10 standard spectra forms a highest correlation coefficient matrix, wherein the expression is r, in the embodiment, n is 10, h is 400, and if rjv is in the correlation coefficient similar interval of the jth substance, the substance to which the test spectra belong is judged as the jth substance. Otherwise, judging the sample to belong to other substances outside the standard spectrum library.
Fig. 4 is a schematic structural diagram of an infrared spectrum-based sample identification device according to an embodiment of the present invention, which may have relatively large differences due to different configurations or performances, and may include one or more processors 10 (CPUs) (e.g., one or more processors) and a memory 20, and one or more storage media 30 (e.g., one or more mass storage devices) for storing applications or data. The memory and storage medium may be, among other things, transient or persistent storage. The program stored on the storage medium may include one or more modules (not shown), each of which may include a series of instruction operations in the sample identification device. Still further, the processor may be configured to communicate with a storage medium to execute a series of instruction operations in the storage medium on the answer sheet segmentation apparatus.
The infrared spectroscopy-based sample identification apparatus may also include one or more power supplies 40, one or more wired or wireless network interfaces 50, one or more input-output interfaces 60, and/or one or more operating systems, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art will appreciate that the infrared spectroscopy-based sample identification apparatus configuration shown in fig. 4 does not constitute a limitation of the sample identification apparatus and may include more or fewer components than those shown, or some components in combination, or a different arrangement of components.
The present invention also provides a computer readable storage medium, which may be a non-volatile computer readable storage medium, and which may also be a volatile computer readable storage medium, having stored therein instructions, which, when executed on a computer, cause the computer to perform the steps of the infrared spectroscopy-based sample identification method.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses, and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The integrated unit, 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 may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A sample identification method based on infrared spectroscopy is characterized by comprising the following steps:
establishing a standard spectrum library of substances based on infrared spectra of a series of standard substances, wherein each piece of spectral data in the standard spectrum library is called as a standard spectrum;
constructing a series of test samples, carrying out infrared spectrum scanning on the test samples, and extracting each piece of spectrum data of the test samples to be used as a test spectrum;
calculating the weighted correlation coefficient of the test spectrum and the standard spectrum, setting a similar interval according to the weighted correlation coefficient, and establishing an infrared spectrum identification model;
performing infrared spectrum scanning on a sample to be detected to obtain spectral data of the sample to be detected;
and inputting the spectral data into the infrared spectrum recognition model to recognize the sample to be detected.
2. The method for identifying a sample according to claim 1, wherein the standard spectrum S of a substance is the average value of m data points corresponding to p spectral data of the same substance measured in different batches in the standard spectrum libraryoriginal，
3. The method of claim 2, wherein said calculating a weighted correlation coefficient between said test spectrum and said standard spectrum comprises:
filtering the test spectrum and the standard spectrum, and calculating to obtain mathematical expectation and variance of the standard spectrum data after filtering and mathematical expectation and variance of the test spectrum data after filtering;
and standardizing the standard spectrum data after the filtering processing and the test spectrum data after the filtering processing.
4. The method of claim 3, wherein the normalized standard spectral data is obtained by normalizing the infrared spectrum-based sample identification
Wherein s issecondFor the filtered standard spectral data, EsFor mathematical expectation of filtered standard spectral data, σs 2E is the variance of the standard spectral data after filtering processing and is the base number of a natural logarithm function;
standardized test spectral data
Wherein, tsecondFor the filtered test spectral data, EtFor mathematical expectation of filtered standard spectral data, σt 2Is the variance of the filtered standard spectral data.
5. The method for infrared spectrum based sample identification as claimed in claim 4, wherein the calculation of weighted correlation coefficients for the test spectrum and the standard spectrum uses the following formula:
weighted correlation coefficient
Wherein s isiIs the ith data point, t, in the normalized standard spectrum dataiTo normalize the ith data point in the processed test spectrum data,
ωiis the weight of the ith data point,
and k is a custom parameter.
6. The method of claim 5, wherein k is 0.01 or 0.5.
7. The method for infrared spectrum based sample identification as claimed in claim 1, wherein the test samples comprise samples of the same species in a standard spectral library and samples of species in a non-standard spectral library.
8. The method for identifying a sample according to claim 1, wherein the sample to be tested comprises drugs and explosives.
9. An infrared spectroscopy-based sample identification apparatus, the sample identification apparatus comprising: a memory having instructions stored therein and at least one processor, the memory and the at least one processor interconnected by a line;
the at least one processor invokes the instructions in the memory to cause the sample identification device to perform the infrared spectroscopy-based sample identification method of any one of claims 1-8.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a method for infrared spectroscopy-based sample identification as claimed in any one of claims 1 to 8.
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