CN114062300A - Trace additive detection technology based on infrared multi-source spectrum - Google Patents
Trace additive detection technology based on infrared multi-source spectrum Download PDFInfo
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- 238000001228 spectrum Methods 0.000 title claims abstract description 34
- 238000001514 detection method Methods 0.000 title claims abstract description 29
- 239000000654 additive Substances 0.000 title claims abstract description 25
- 230000000996 additive effect Effects 0.000 title claims abstract description 19
- 238000005516 engineering process Methods 0.000 title claims abstract description 11
- 230000004927 fusion Effects 0.000 claims abstract description 37
- 238000012937 correction Methods 0.000 claims abstract description 22
- 238000000034 method Methods 0.000 claims abstract description 17
- 239000000446 fuel Substances 0.000 claims abstract description 12
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 8
- 238000007781 pre-processing Methods 0.000 claims abstract description 4
- 239000011159 matrix material Substances 0.000 claims description 45
- 230000003595 spectral effect Effects 0.000 claims description 12
- 238000005259 measurement Methods 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 claims description 6
- 238000012417 linear regression Methods 0.000 claims description 6
- 238000013178 mathematical model Methods 0.000 claims description 6
- 238000010561 standard procedure Methods 0.000 claims description 6
- 230000003044 adaptive effect Effects 0.000 claims description 3
- 238000004458 analytical method Methods 0.000 claims description 3
- 238000012569 chemometric method Methods 0.000 claims description 3
- 230000002860 competitive effect Effects 0.000 claims description 3
- 238000000354 decomposition reaction Methods 0.000 claims description 3
- 230000004069 differentiation Effects 0.000 claims description 3
- 230000008030 elimination Effects 0.000 claims description 3
- 238000003379 elimination reaction Methods 0.000 claims description 3
- 239000000284 extract Substances 0.000 claims description 3
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- 239000000203 mixture Substances 0.000 claims description 3
- 238000010606 normalization Methods 0.000 claims description 3
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- 238000010239 partial least squares discriminant analysis Methods 0.000 claims description 3
- 238000002360 preparation method Methods 0.000 claims description 3
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- 238000007430 reference method Methods 0.000 claims description 3
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- 238000012360 testing method Methods 0.000 claims description 3
- 238000004611 spectroscopical analysis Methods 0.000 claims 6
- 238000011156 evaluation Methods 0.000 claims 1
- 230000009466 transformation Effects 0.000 claims 1
- 239000002699 waste material Substances 0.000 abstract description 3
- 239000002816 fuel additive Substances 0.000 abstract description 2
- 230000006872 improvement Effects 0.000 description 13
- 239000000295 fuel oil Substances 0.000 description 4
- 238000004519 manufacturing process Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
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- 239000003153 chemical reaction reagent Substances 0.000 description 1
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- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/3577—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing liquids, e.g. polluted water
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- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
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Abstract
The invention discloses a trace additive detection technology based on infrared multi-source spectrum, which comprises the following steps: acquiring near-infrared and mid-infrared multi-source spectrum data; preprocessing data, including selecting parameters and extracting characteristics; picking up a data correction model through data fusion, wherein the data fusion comprises low-layer data fusion, middle-layer data fusion and high-layer data fusion; and evaluating sample result data. The invention realizes the accurate detection of trace additives in the fuel by means of near-infrared and mid-infrared multi-source spectrums and through a multi-source spectrum data layer fusion technology and a chemometrics algorithm. When the fuel additive is measured, the method can be completed only by performing near-infrared and intermediate-infrared multi-source spectrum detection, and the content of the original internal trace additive can be quickly, efficiently and accurately obtained without using a method specified by national standards. The waste of detection samples is avoided, and the retrieval efficiency and precision are improved.
Description
Technical Field
The invention relates to the field of detection of trace additives in fuel oil, in particular to a trace additive detection technology based on infrared multi-source spectrum.
Background
The content of additives in the fuel has a significant impact on the fuel quality. Therefore, the detection of the trace additive in the fuel oil has important significance for the quality control of the fuel oil, the improvement of the production process and the control of the production cost.
In the prior art, when the trace additive of the fuel is quantitatively analyzed, a chemical quantitative detection method specified by national standards is often adopted, so that a detection sample and a chemical reagent are wasted, only a single component can be detected in each detection, and the multiple trace additives in the fuel cannot be quickly and efficiently detected.
Therefore, a detection method which is rapid and efficient and does not generate waste of detection samples is needed in the field of detection of trace additives in fuel oil.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, provides a trace additive detection technology based on infrared multi-source spectrum, and adopts the following technical scheme for realizing the purpose:
a trace additive detection technology based on infrared multi-source spectrum comprises the following steps:
s1, acquiring near-infrared and mid-infrared multi-source spectrum data;
s2, preprocessing data, including parameter selection and feature extraction;
s3, establishing a data correction model through data fusion, wherein the data fusion comprises low-layer data fusion, middle-layer data fusion and high-layer data fusion;
s3-1, the low-level fusion is data-level fusion of spectra, data of all data sources are simply connected into a matrix according to the sequence of samples, the number of rows of the matrix is the same as that of analysis samples, the number of columns of the matrix is the same as that of the spectral data sources, and then a single model for final classification or prediction is provided by a chemometric method;
s3-2, extracting relevant features from each data source respectively by the middle layer fusion, combining the relevant features into a matrix, and processing the matrix by a classification or correction method;
s3-3, the high level fusion computes individual classification or regression models from each data source and combines the individual model results to obtain a final decision;
and S4, evaluating sample result data.
As an improvement, the establishment of the correction model in S3 includes the following steps:
a. collecting representative samples, and collecting optical data of the samples by using a research instrument; strictly controlling measurement conditions including measurement parameters such as sample preparation, sample loading, test conditions, instrument parameters and the like;
b. accurately measuring the attribute to be measured, namely a standard reference value, of the sample by using a national standard method;
c. correlating the spectrum data with the detected data by a mathematical method, generally converting the spectrum data, selecting an effective load wave band, performing regression calculation with a standard method measured value, then obtaining a calibration equation, and establishing a mathematical model;
d. when an unknown sample is analyzed, firstly, a sample to be detected is scanned to obtain a spectrum, a proper pre-established mathematical model is called according to spectral characteristics, and the component content or attribute classification of the sample to be detected is calculated by utilizing the established model.
As a refinement, the representative sample in step a is a sample whose composition and range of variation are close to those of the sample to be analyzed.
As an improvement, the step c of converting the spectral data includes normalization, first or second order differentiation, and the like.
As an improvement, the method for extracting the characteristic wavelength comprises the following steps: one or more of partial least squares discriminant analysis (PLS-DA), competitive adaptive re-weighting algorithm (CARS), spaced partial least squares (BiPLS), joint spaced partial least squares (SiPLS), continuous projection algorithm (SPA), invariant information elimination (UVE), and random frog-leap (SFLA).
As improvement, the fuel quality index parameters are analyzed and calculated by adopting multiple regression:
multiple regression (or univariate) calculations, based on the following models:
Y＝β0+β1X1+β2X2+…+βmXm+e
e represents the random error after removing the influence of m independent variables on Y. After the least square method, the estimation is as follows:
thus, the fuel component concentration value can be obtained from the characteristic band spectrum information.
As an improvement, the most commonly adopted quantitative analysis in the data fusion is a partial least square method, the partial least square method simultaneously extracts useful information from the X matrix and the Y matrix one by one, and a linear regression model is established until a certain principal component exists;
the PLS step:
(1) is subjected to matrix decomposition, and the model is
X＝TP+E
Y＝UQ+F
In the formula: a scoring matrix of the T, U-X matrix and the Y matrix;
load (principal component) matrices of the P, Q-X and Y matrices;
e, F-errors introduced when fitting X and Y with a PLS model;
(2) linear regression of T and U
B is a correlation coefficient matrix
U＝TB
B＝T'U(T'T)-1
In prediction, from the matrix X of the unknown sampleIs unknownAnd P obtained by correctionCorrection ofIt finds the T of the X matrix of the unknown sampleIs unknown. Then, the following results were obtained:
Yis unknown＝TIs unknownBQ。
As an improvement, SEC (standard error correction) is used to evaluate the model performance of the correction model:
in the formula:
yi-calibration set sample reference method measurements;
d is the degree of freedom of the correction model, equal to n-k;
n-number of calibration samples;
the number of k-PLS major factors;
if the spectral data and the reference data are subjected to mean centering before the calibration model is established, d is n-k-1.
The invention has the advantages that:
1. the invention realizes the accurate detection of trace additives in the fuel by means of near-infrared and mid-infrared multi-source spectrums and through a multi-source spectrum data layer fusion technology and a chemometrics algorithm.
2. When the fuel additive is measured, the method can be completed only by performing near-infrared and intermediate-infrared multi-source spectrum detection, and the content of the original internal trace additive can be quickly, efficiently and accurately obtained without using a method specified by national standards. The waste of detection samples is avoided, and the retrieval efficiency and precision are improved.
Drawings
FIG. 1 is a flow chart of the present invention.
Detailed Description
The present invention will be described in detail and specifically with reference to the following examples so as to facilitate the understanding of the present invention, but the following examples do not limit the scope of the present invention.
Example 1
The embodiment discloses a trace additive detection technology based on infrared multi-source spectrum, which comprises the following steps:
s1, acquiring near-infrared and mid-infrared multi-source spectrum data;
s2, preprocessing data, including parameter selection and feature extraction;
s3, establishing a data correction model through data fusion, wherein the data fusion comprises low-layer data fusion, middle-layer data fusion and high-layer data fusion;
s3-1, the low-level fusion is data-level fusion of spectra, data of all data sources are simply connected into a matrix according to the sequence of samples, the number of rows of the matrix is the same as that of analysis samples, the number of columns of the matrix is the same as that of the spectral data sources, and then a single model for final classification or prediction is provided by a chemometric method;
s3-2, extracting relevant features from each data source respectively by the middle layer fusion, combining the relevant features into a matrix, and processing the matrix by a classification or correction method;
s3-3, the high level fusion computes individual classification or regression models from each data source and combines the individual model results to obtain a final decision;
and S4, evaluating sample result data.
As an improvement, the establishment of the correction model in S3 includes the following steps:
a. collecting representative samples, and collecting optical data of the samples by using a research instrument; strictly controlling measurement conditions including measurement parameters such as sample preparation, sample loading, test conditions, instrument parameters and the like;
b. accurately measuring the attribute to be measured, namely a standard reference value, of the sample by using a national standard method;
c. correlating the spectrum data with the detected data by a mathematical method, generally converting the spectrum data, selecting an effective load wave band, performing regression calculation with a standard method measured value, then obtaining a calibration equation, and establishing a mathematical model;
d. when an unknown sample is analyzed, firstly, a sample to be detected is scanned to obtain a spectrum, a proper pre-established mathematical model is called according to spectral characteristics, and the component content or attribute classification of the sample to be detected is calculated by utilizing the established model.
As a refinement, the representative sample in step a is a sample whose composition and range of variation are close to those of the sample to be analyzed.
As an improvement, the step c of converting the spectral data includes normalization, first or second order differentiation, and the like.
As an improvement, the method for extracting the characteristic wavelength comprises the following steps: one or more of partial least squares discriminant analysis (PLS-DA), competitive adaptive re-weighting algorithm (CARS), spaced partial least squares (BiPLS), joint spaced partial least squares (SiPLS), continuous projection algorithm (SPA), invariant information elimination (UVE), and random frog-leap (SFLA).
As improvement, the fuel quality index parameters are analyzed and calculated by adopting multiple regression:
multiple regression (or univariate) calculations, based on the following models:
Y＝β0+β1X1+β2X2+…+βmXm+e
e represents the random error after removing the influence of m independent variables on Y. After the least square method, the estimation is as follows:
thus, the fuel component concentration value can be obtained from the characteristic band spectrum information.
As an improvement, the most commonly adopted quantitative analysis in the data fusion is a partial least square method, the partial least square method simultaneously extracts useful information from the X matrix and the Y matrix one by one, and a linear regression model is established until a certain principal component exists;
the PLS step:
(1) is subjected to matrix decomposition, and the model is
X＝TP+E
Y＝UQ+F
In the formula: a scoring matrix of the T, U-X matrix and the Y matrix;
load (principal component) matrices of the P, Q-X and Y matrices;
e, F-errors introduced when fitting X and Y with a PLS model;
(2) linear regression of T and U
B is a correlation coefficient matrix
U＝TB
B＝T'U(T'T)-1
In prediction, from the matrix X of the unknown sampleIs unknownAnd P obtained by correctionCorrection ofIt finds the T of the X matrix of the unknown sampleIs unknown. Then, the following results were obtained:
Yis unknown＝TIs unknownBQ。
As an improvement, SEC (standard error correction) is used to evaluate the model performance of the correction model:
in the formula:
yi-calibration set sample reference method measurements;
d is the degree of freedom of the correction model, equal to n-k;
n-number of calibration samples;
the number of k-PLS major factors;
if the spectral data and the reference data are subjected to mean centering before the calibration model is established, d is n-k-1.
The embodiments of the present invention have been described in detail, but they are merely exemplary, and the present invention is not equivalent to the above-described embodiments. Any equivalent modifications and substitutions to those skilled in the art are also within the scope of the present invention. Accordingly, it is intended that all equivalent alterations and modifications be included within the scope of the invention, without departing from the spirit and scope of the invention.
Claims (8)
1. A trace additive detection technology based on infrared multi-source spectrum is characterized by comprising the following steps:
s1, acquiring near-infrared and mid-infrared multi-source spectrum data;
s2, preprocessing data, including parameter selection and feature extraction;
s3, establishing a data correction model through data fusion, wherein the data fusion comprises low-layer data fusion, middle-layer data fusion and high-layer data fusion;
s3-1, the low-level fusion is data-level fusion of spectra, data of all data sources are simply connected into a matrix according to the sequence of samples, the number of rows of the matrix is the same as that of analysis samples, the number of columns of the matrix is the same as that of the spectral data sources, and then a single model for final classification or prediction is provided by a chemometric method;
s3-2, extracting relevant features from each data source respectively by the middle layer fusion, combining the relevant features into a matrix, and processing the matrix by a classification or correction method;
s3-3, the high level fusion computes individual classification or regression models from each data source and combines the individual model results to obtain a final decision;
and S4, evaluating sample result data.
2. A trace additive detection technique based on infrared multi-source spectroscopy as claimed in claim 1, wherein the establishment of the calibration model in S3 includes the following steps:
a. collecting representative samples, and collecting optical data of the samples by using a research instrument; strictly controlling measurement conditions including measurement parameters such as sample preparation, sample loading, test conditions, instrument parameters and the like;
b. accurately measuring the attribute to be measured, namely a standard reference value, of the sample by using a national standard method;
c. correlating the spectrum data with the detected data by a mathematical method, generally converting the spectrum data, selecting an effective load wave band, performing regression calculation with a standard method measured value, then obtaining a calibration equation, and establishing a mathematical model;
d. when an unknown sample is analyzed, firstly, a sample to be detected is scanned to obtain a spectrum, a proper pre-established mathematical model is called according to spectral characteristics, and the component content or attribute classification of the sample to be detected is calculated by utilizing the established model.
3. A trace additive detection technique based on infrared multisource spectroscopy as claimed in claim 2, wherein the representative sample in step a is a sample whose composition and variation range are close to those of the sample to be analyzed.
4. A trace additive detection technique based on infrared multisource spectroscopy as claimed in claim 2, wherein the transformation of the spectral data in step c comprises normalization, first or second order differentiation, etc.
5. A trace additive detection technique based on infrared multisource spectroscopy as claimed in claim 1, wherein the method for extracting characteristic wavelength is as follows: one or more of partial least squares discriminant analysis (PLS-DA), competitive adaptive re-weighting algorithm (CARS), spaced partial least squares (BiPLS), joint spaced partial least squares (SiPLS), continuous projection algorithm (SPA), invariant information elimination (UVE), and random frog-leap (SFLA).
6. A trace additive detection technique based on infrared multi-source spectroscopy as claimed in claim 1, wherein the fuel quality index parameter is analyzed and calculated using multiple regression:
multiple regression (or univariate) calculations, based on the following models:
Y＝β0+β1X1+β2X2+…+βmXm+e
e represents the random error after removing the influence of m independent variables on Y. After the least square method, the estimation is as follows:
thus, the fuel component concentration value can be obtained from the characteristic band spectrum information.
7. The infrared multi-source spectrum-based trace additive detection technology as claimed in claim 1, wherein the most commonly used quantitative analysis in the data fusion is partial least squares, which extracts useful information from the X matrix and the Y matrix one by one, and builds a linear regression model until a certain principal component;
the PLS step:
(1) is subjected to matrix decomposition, and the model is
X＝TP+E
Y＝UQ+F
In the formula: score matrix of T, U-X matrix and Y matrix
Load (principal component) matrix of P, Q-X matrix and Y matrix
E, F-errors introduced when fitting X and Y with a PLS model
(2) Linear regression of T and U
B is a correlation coefficient matrix
U＝TB
B＝T'U(T'T)-1
In prediction, from the matrix X of the unknown sampleIs unknownAnd P obtained by correctionCorrection ofIt finds the T of the X matrix of the unknown sampleIs unknown. Then, the following results were obtained:
Yis unknown＝TIs unknownBQ。
8. A trace additive detection technique based on infrared multisource spectroscopy as claimed in claim 1, characterized in that the calibration model is subjected to model performance evaluation using SEC (calibration standard error):
in the formula:
yi-calibration set sample reference method measurements;
d is the degree of freedom of the correction model, equal to n-k;
n-number of calibration samples;
the number of k-PLS major factors;
if the spectral data and the reference data are subjected to mean centering before the calibration model is established, d is n-k-1.
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