CN112712108B - Raman spectrum multivariate data analysis method - Google Patents
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Abstract
The invention relates to a Raman spectrum multivariate data analysis method, which specifically comprises the following steps: collecting original Raman spectrum data of various samples by using a Raman spectrum detection instrument; preprocessing the original data by an autonomous development analysis method; carrying out normalization and mean value centering data processing on the basis of spectrum data preprocessing; selecting and extracting spectral characteristic data by using principal component analysis or least square discriminant analysis aiming at the preprocessed data, and selecting significant characteristic components in the spectral data by using single-factor analysis of variance and cross verification respectively; performing spectrum identification by combining the classification models, and evaluating the reliability of each classification model by using unbiased one-method cross validation; and selecting the rest samples for testing to obtain the accuracy, sensitivity and specificity of sample classification and the identification performance of the tested worker characteristic curve evaluation system of the model. The invention can be widely applied to the field of Raman spectrum information processing and computer recognition of spectrum characteristics.
Description
Technical Field
The invention relates to the field of Raman spectrum information processing and spectrum characteristic computer identification thereof, in particular to a Raman spectrum multivariate data analysis method.
Background
Raman spectra are generated based on interactions of light with chemical bonds within the material, and are a non-destructive analysis technique that can obtain detailed information of chemical structure, phase and morphology, crystallinity, and molecular interactions of the sample. The raman spectrum can be used to transfer the molecular energy spectrum in the infrared region to the visible region for detection. Therefore, the Raman spectrum is used as a supplement of the infrared spectrum and is a powerful weapon for researching the molecular substance structure. With the development and progress of scientific technology, the Raman spectrum technology is applied to multiple fields of petroleum, chemical industry, materials, biology, environmental protection, geology and the like, and provides more information on molecular structure for the development of various industries.
At present, raman spectroscopy technology has been developed as one of the most important technologies in the foundation of analytical science and application science research. Because of its molecular sensitivity, easy implementation, and applicability to water environments, raman spectroscopy techniques have also been widely used in other multidisciplinary research fields. Furthermore, recent developments have combined the chemosensitivity and specificity of raman scattering with the high spatial resolution of confocal microscopy, reconstructing image information that yields the biochemical composition of the sample. However, the widespread use of raman spectroscopic analysis techniques and their related analysis techniques is also limited by a number of technical difficulties. Firstly, raman scattering is a weak optical phenomenon, and the generated spectrum information (i.e., raman spectrum) is very easily interfered by environmental and external factors; secondly, in complex biochemical environments or other systems, different kinds of biological macromolecules contain similar biochemical structures, so that the raman spectrum of the biological macromolecules has the phenomena of overlapping spectrum peak positions, uneven spectrum peak intensity and extending spectrum peak width (half-width).
Based on the background, the method for analyzing the Raman spectrum multivariate data is provided, and on the basis of realizing the original Raman spectrum pretreatment of different types of samples, the characteristic extraction and classification identification multivariate data analysis method is used for realizing the extraction and judgment of the spectrum characteristic information of different materials.
Disclosure of Invention
The invention aims to provide a Raman spectrum multivariate data analysis method and a software system, which are applied to Raman spectrum and spectrum data set pretreatment and multivariate analysis of various organic and inorganic materials. And carrying out feature extraction on the sample spectrum according to the Raman spectrum data set by combining with PCA and PLS-DA algorithm, and then carrying out discriminant analysis on the sample feature by combining with LDA, PLS-DA, SVM and PCA-SVM algorithm.
In order to achieve the above object, the present invention provides the following solutions:
a method of raman spectroscopy multivariate data analysis comprising the steps of:
s1, measuring and obtaining original Raman spectra and spectrum data sets of various organic and inorganic materials by using a Raman spectrum detection instrument;
s2, preprocessing the obtained Raman spectrum data set by using a Raman spectrum multivariate data analysis software system;
s3, carrying out pretreatment on the obtained Raman spectrum data set, and carrying out normalization and mean value centering treatment on the Raman spectrum data;
s4, extracting Raman spectrum characteristic data by adopting a principal component analysis method PCA or a partial least squares-discriminant analysis method PLS-DA, and extracting significant characteristic components in the Raman spectrum data by utilizing single-factor variance analysis and cross verification respectively;
s5, combining the classification models, respectively establishing and utilizing the four classification models for the features extracted in the step S4, and carrying out classification and identification on spectrum information;
s6, cross-verifying by using an unbiased first method, and evaluating the reliability of the classification model;
and S7, selecting residual data for testing to obtain accuracy, sensitivity and specificity of sample classification and a characteristic curve of a tested worker of the classification model, and evaluating performance of the classification model.
Preferably, in the step S2, the preprocessing mainly includes: spectral feature range selection, cosmic ray removal, background fluorescence signal processing based on a polynomial fitting method, and spectral smoothing based on a Savitzky-Golay convolution method.
Preferably, in the step S3, on the basis of the preprocessing, spectral intensity normalization, spectral peak area normalization, peak intensity normalization and mean value centering processing are selected according to the requirements.
Preferably, the main component analysis PCA in step S4 includes:
conversion of a set of linearly related variables into linearly independent variables by direct-to-alternating conversionVariable, reducing the dimension of the spectrum data set, and extracting the obvious characteristic J in the data set; constructing a sample data set X (I X J) according to the observed sample number I and the spectrum characteristic number J, carrying out spectrum peak area normalization and mean value centering treatment on the sample data set, and then obtaining a covariance matrix X T X is a group; singular value decomposition is carried out on the covariance matrix to obtain X=PΔQ T Wherein P is a left singular vector, Q is a right singular vector, and Δ is a diagonal matrix of singular values;
F＝PΔ，F＝PΔ＝PΔQ T q=xq, the matrix Q gives the coefficients used to calculate the linear combination of factor scores, hence the name projection matrix, and multiplying Q by X gives the projection value F of the observed value on the principal component.
Preferably, the four classification models in the step S5 include: based on a linear discriminant analysis method LDA, a partial least squares-discriminant analysis method PLS-DA, a support vector machine SVM and a principal component analysis and support vector machine PCA-SVM algorithm.
Preferably, in the step S7, the remaining data is selected for testing, so as to obtain a characteristic curve ROC of the tested worker of the performance index of each classification model, and the raman spectrum data and the biochemical difference are analyzed in combination with the steps S5 to S7.
Preferably, the ROC curve is a subject working characteristic curve, and can reflect sensitivity and specificity of a spectrum classification model; the ROC curve is prepared by continuously changing the classification threshold value to calculate a series of sensitivity and specificity, and then plotting by taking the sensitivity as an ordinate and the 1-specificity as a horizontal sitting, wherein the larger the area under the curve is, the higher the prediction accuracy of the classification model is.
The beneficial effects of the invention are as follows:
1. the invention has perfect Raman spectrum data set preprocessing function, can select a spectrum characteristic range of an acquired single Raman spectrum or spectrum data set, remove cosmic rays, perform background fluorescence signal processing based on a polynomial fitting method and spectral smoothing processing based on Savitzky-Golay convolution, and perform normalization (spectrum intensity normalization, spectrum peak area normalization, peak intensity normalization) and mean value centering processing functions according to requirement selection;
2. the invention integrates and optimizes a plurality of Raman spectrum multivariate data analysis methods commonly used for various organic materials and inorganic materials: a principal component analysis method (PCA), a partial least squares-discriminant analysis method (PLS-DA), a linear discriminant analysis method (LDA), a Support Vector Machine (SVM), and a principal component analysis combined with the support vector machine (PCA-SVM);
3. the PCA-SVM classification algorithm model combining principal component analysis and a support vector machine improves the classification performance of the model on the basis of SVM;
4. the invention can effectively identify and distinguish the characteristics of samples including various organic and inorganic materials represented by biological tissues and cells, but is not limited to the samples;
5. in the feature extraction part, PCA, PLS-DA and single factor analysis of variance are combined, and features with obvious significance in the data set are selected through cross verification.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of data analysis according to the present invention;
FIG. 2 is a schematic diagram of a Raman spectrum data preprocessing interface according to the present invention;
FIG. 3 is a schematic diagram of a smoothing process performed by removing cosmic rays, removing background noise, and performing a smoothing process according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of the result after the mean value centering process according to the embodiment of the present invention;
FIG. 5 is a schematic diagram of a cross-validation and classification summary interface of a PCA-LDA model in an embodiment of the invention;
FIG. 6 is a schematic diagram of a cross-validation and categorization summary interface for PLS-DA models in accordance with an embodiment of the present invention;
FIG. 7 is a schematic diagram of an SVM model training interface in an embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
FIG. 1 is a flow chart of the data analysis of the present invention;
raman spectrometer, commercially available and self-developed in various types, comprising: a large scientific research-level Raman spectrum detection instrument and a small portable Raman spectrum detection instrument are used for measuring and obtaining Raman spectra and spectrum data sets of various organic and inorganic materials;
preprocessing the acquired Raman spectrum data through a spectrum preprocessing interface shown in fig. 2, wherein the spectrum preprocessing interface comprises spectrum characteristic range selection, cosmic ray removal, background fluorescence signal processing based on a polynomial fitting method and spectrum smoothing processing based on a Savitzky-Golay convolution method (the result and the interface are shown in fig. 2 and 3); normalization and mean centering can be selected according to requirements on the basis of pretreatment (the result is shown in fig. 4);
the normalization processing comprises the following steps: to eliminate the effects of power disturbances and sample non-uniformities, spectral intensity normalization may be selected; to discuss quantitative information of a substance, spectral peak area normalization may be selected; to further highlight certain material content variations in order to eliminate effects due to sample and instrument variations, spectral peak intensity normalization may be chosen.
The feature extraction is carried out on the pretreated spectrum data set, and the invention provides two methods: principal component analysis method (Principal component analysis, abbreviated as PCA), partial least squares-discriminant analysis method (Partial least squares-discriminant analysis, abbreviated as PLS-DA); and (3) selecting any one of the methods to analyze the spectrum data set, and then respectively utilizing single-factor analysis of variance and cross-validation to select the spectrum characteristic component with the most significant meaning.
The main component analysis comprises the following specific steps:
converting a group of linear related variables into linear unrelated variables through positive-negative conversion, so that the dimensionality of a spectrum data set is reduced, and meanwhile, obvious characteristics in the data set are extracted; the sample dataset is X (I J), I is the number of observed samples, and J is the number of spectral features.
Firstly, carrying out normalization and mean value centering treatment on the area of a spectrum peak, and then obtaining a covariance matrix X T X is a group; singular value decomposition is carried out on the covariance matrix to obtain X=PΔQ T Where P is the left singular vector, Q is the right singular vector, and Δ is the diagonal matrix of singular values.
F＝PΔ，F＝PΔ＝PΔQ T Q=xq, the matrix Q gives coefficients for calculating a linear combination of factor scores, and is therefore also called a projection matrix (or loading matrix), and multiplying Q by X gives the projection value F of the observed value on the principal component (F is also called score matrix).
The linear discriminant analysis LDA step is:
(1) The contracted data set comprises two types of samples, and a divergence matrix S between the types is calculated b Sum mu 1 ；
S b ＝(μ 0 -μ 1 )(μ 0 -μ 1 ) T u 0
Projecting the data onto a straight line omega, the projections of the centers of the two types of samples on the straight line are omega respectively T μ 0 And omega T μ 1 ；
(2) Calculating the intra-class divergence matrix S of the sample w
(3) Calculating an inter-class divergence matrix S b And the intra-class divergence matrix S of the same class as the sample w Is Li Shang of the broad sense of (5)Solving a projection direction omega;
(4) Projection straight line, i.e. y=ω T x；
(5) And projecting a new unknown sample onto the straight line, and classifying the category to which the projection point belongs according to the center distance from the projection point to the two types of samples.
FIG. 5 shows a PCA-LDA model cross-validation and classification summary interface in an embodiment of the invention.
The least square discrimination method includes:
(1) Carrying out mean value centering treatment on the data;
(2) Calculating the predicted response value of each sample according to least square regression;
(3) The posterior probability of the sample belonging to each category is calculated according to the probability density function and the Bayesian formula, such as time A and event B:
P(A|B)＝P(B|A)*P(A)/P(B)
(4) The class with the highest probability is selected as the predictive label.
FIG. 6 shows a PLS-DA model cross-validation and classification summary interface;
the support vector machine comprises the following steps:
(1) Convention hyperplane omega T x+b=y; where ω is the normal vector and b is the displacement.
(2) Calculating the distance d from the point to the hyperplane y;
(3) Maximizing the classification interval;
s.t.y i (w T ·Φ(x i )+b)≥1,i＝1,2,…,n
wherein phi (x) i ) Is a feature space conversion function, i.e. a mapping function, s.t. is a constraint.
(4) Introducing a relaxation variable allows some data to be misclassified, preventing overfitting;
the constraint s.t. is:
y i (w·x i +b)≥1-ξ,i＝1,2,…,n
ξ i ≥0,i＝1,2,…,n
an SVM training model interface is shown in fig. 7.
The invention uses a linear discriminant analysis method (Linear discriminant analysis, LDA), a partial least squares-discriminant analysis method (PLS-DA), a support vector machine (Support vector machine, SVM) and a principal component analysis combined with a support vector machine (Principal component analysis combined Support vector machine, PCA-SVM) algorithm to establish four classification models, and the four classification models are used for extracting the characteristics respectively.
The reliability of each classification model is evaluated by using an unbiased one-method cross-validation, so as to prevent the occurrence of the over-fitting phenomenon.
In the above steps, the total sample is N, N is selected t Data as training set, then N ts ＝N-N t The method is used for obtaining the accuracy, sensitivity and specificity of sample classification and the characteristic curve of the tested worker of the model, so as to evaluate the performance of the Raman spectrum multivariate data analysis method on the Raman spectrum identification of the sample (especially the biological tissue sample).
The above embodiments are merely illustrative of the preferred embodiments of the present invention, and the scope of the present invention is not limited thereto, but various modifications and improvements made by those skilled in the art to which the present invention pertains are made without departing from the spirit of the present invention, and all modifications and improvements fall within the scope of the present invention as defined in the appended claims.
Claims (3)
1. The Raman spectrum multivariate data analysis method is characterized by comprising the following steps of:
s1, measuring and obtaining original Raman spectra and spectrum data sets of various organic and inorganic materials by using a Raman spectrum detection instrument;
s2, preprocessing the obtained Raman spectrum data set by using a Raman spectrum multivariate data analysis software system;
the pretreatment mainly comprises the following steps: selecting a spectrum characteristic range, removing cosmic rays, processing a background fluorescence signal based on a polynomial fitting method and performing spectrum smoothing based on a Savitzky-Golay convolution method;
s3, carrying out pretreatment on the obtained Raman spectrum data set, and carrying out normalization and mean value centering treatment on the Raman spectrum data;
s4, extracting Raman spectrum characteristic data by adopting a principal component analysis method PCA or a partial least squares-discriminant analysis method PLS-DA, and extracting significant characteristic components in the Raman spectrum data by utilizing single-factor variance analysis and cross verification respectively;
s5, combining the classification models, respectively establishing and utilizing the four classification models for the features extracted in the step S4, and carrying out classification and identification on spectrum information;
the four classification models include: based on a linear discriminant analysis method LDA, a partial least squares-discriminant analysis method PLS-DA, a support vector machine SVM and a classification model established by combining principal component analysis with a support vector machine PCA-SVM algorithm;
s6, cross-verifying by using an unbiased first method, and evaluating the reliability of the classification model;
s7, selecting residual data for testing to obtain accuracy, sensitivity and specificity of sample classification and a characteristic curve of a tested worker of the classification model, and evaluating performance of the classification model;
selecting residual data for testing to obtain a characteristic curve ROC of a tested worker of the performance indexes of each classification model, and analyzing the Raman spectrum data and biochemical differences by combining the steps S5-S7;
the ROC curve is a working characteristic curve of a subject and can reflect the sensitivity and the specificity of a spectrum classification model; the ROC curve is prepared by continuously changing the classification threshold value to calculate a series of sensitivity and specificity, and then plotting by taking the sensitivity as an ordinate and the 1-specificity as a horizontal sitting, wherein the larger the area under the curve is, the higher the prediction accuracy of the classification model is.
2. The method according to claim 1, wherein in the step S3, based on the preprocessing, spectral intensity normalization, spectral peak area normalization, peak intensity normalization, and mean centering are selected according to the requirements.
3. The method for analyzing the multivariate data of raman spectrum according to claim 1, wherein the principal component analysis PCA in step S4 comprises:
converting a group of linear related variables into linear unrelated variables through positive-negative conversion, reducing the dimension of a spectrum data set, and extracting obvious characteristics J in the data set; constructing a sample data set X (I X J) according to the observed sample number I and the spectrum characteristic number J, carrying out spectrum peak area normalization and mean value centering treatment on the sample data set, and then obtaining a covariance matrix X T X is a group; singular value decomposition is carried out on the covariance matrix to obtain X=PΔQ T Wherein P is a left singular vector, Q is a right singular vector, and Δ is a diagonal matrix of singular values;
F＝PΔ，F＝PΔ＝PΔQ T q=xq, the matrix Q gives the coefficients used to calculate the linear combination of factor scores, hence the name projection matrix, and multiplying Q by X gives the projection value F of the observed value on the principal component.
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| CN113989578B (en) * | 2021-12-27 | 2022-04-26 | 季华实验室 | Method, system, terminal device and medium for analyzing peak position of Raman spectrum |
| CN114295600A (en) * | 2021-12-30 | 2022-04-08 | 西北大学 | Improved Raman spectrum multivariate data analysis and imaging method |
| CN115184336B (en) * | 2022-07-15 | 2024-03-15 | 新疆维吾尔自治区人民医院 | Method for identifying Sjogren syndrome and interstitial lung disease based on serum Raman spectrum |
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