CN112683816B - Spectrum identification method for spectrum model transmission - Google Patents
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Abstract
The invention provides a spectrum identification method for spectrum model transmission, which comprises the steps of classifying sample spectra of a host and a slave, establishing a spectrum transmission model, carrying out spectrum correction on a host prediction set sample, and then bringing the sample into the slave to obtain sample composition and content results; the method effectively eliminates the difference of different spectrum instruments under different environments, realizes the intercommunication of the host spectrum and the slave, reduces the workload of analyzing and testing and establishing a standard sample spectrum library; compared with the traditional method, the method has the advantages of less required parameters and data amount and high correction accuracy.
Description
Technical Field
The invention belongs to the technical field of spectral analysis, and particularly relates to a spectral identification method for spectral model transfer.
Background
The spectrum analysis technology is developed on the basis of subjects such as chemical detection and computer science, is more and more widely applied to the aspects of food detection, medicine detection, oil product detection and the like, and is called as a hot technology in the field of chemical analysis.
In the practical application of the spectrum instrument, the structure difference exists among the spectrometers of different manufacturers and different models, and even the same instrument has inevitable spectrum difference under the influence of different use environments. Therefore, when the spectrum data of a plurality of spectrum instruments and the sample spectrum library are synchronized, a spectrum model needs to be established, and the intercommunication of the spectrum data and the sample spectrum library is realized. Particularly, the establishment of the sample library with high toxicity, high acquisition difficulty and high library establishment difficulty can be conducted into other spectral instruments from the existing spectral instruments of the sample library in a spectral model transmission mode to complete library establishment.
The spectral model establishment is to perform mathematical analysis on spectral data obtained by measurement of a spectrometer to obtain spectral characteristics of the spectrometer. The spectral data are processed by quantifying the spectral characteristics of each spectral instrument, so that the spectral data are communicated in each spectral instrument.
The spectral model transfer method mainly comprises a standard sample method, such as a shenk's method and a direct calibration method (DS). This method needs to select a certain number of samples to form a reference set (or a conversion set), and the spectrum of the sample is measured on the master and slave machines respectively, so as to find out the functional relationship. Another is a non-standard method, such as Finite Impulse Response (FIR) methods, which do not require a standard set.
At present, the DS method and the PDS method are the most common and effective methods in the standard sample method. The DS method corrects the data of the full spectrum area one by one, the principle is simple, but the processed data is more, and the required standard sample amount is large; the PDS method selects a small spectral interval window for correction, and the selection of the window has a great influence on the correction effect and may generate a certain error.
The existing spectrum model transmission method needs a large number of samples, has large processing capacity on spectrum data, and is not accurate enough in model transmission.
Disclosure of Invention
In view of this, the present invention provides a spectrum identification method by spectrum model transfer, which can enable a spectrum on a master instrument to be applied to multiple slave instruments, eliminate the spectrum instruments due to structural differences, environmental differences, and the like, and reduce the cost of establishing a standard library.
A spectrum identification method of spectrum model transmission comprises the following steps:
step 1, respectively collecting spectral data of the same sample by a host and a slave;
step 2, obtaining a spectrum transfer model through correcting the spectrum drift amount and correcting the spectrum intensity, and specifically comprising the following steps:
step 2a, correcting the spectrum drift amount:
aiming at the spectral data of each sample collected by the host and the slave, determining the corresponding wave number v of each peak value in the host spectral data and the offset epsilon of the peak value in the slave spectral data relative to the wave number v, and obtaining a group of data of the wave number v and the corresponding offset epsilon; traversing the spectrum data of all samples to obtain a spectrum offset set X (v, epsilon); and (3) performing curve fitting on the epsilon value in the set to obtain a corresponding relation function between the wave number v and the offset epsilon: e ═ f (ν);
step 2b, correcting the spectral intensity:
for any sample, its spectral curve function in the host is defined as Y1A(v) corresponding to the spectral curve function in the slave defined as Y1B(v), then:
ΔY1(ν)＝Y1A(ν)-Y1B(ν)
at any wavenumber v, Δ Y1(v) with Y1A(v) linear variation, namely:
ΔY1(ν)＝k(ν)Y1A(ν)+d(ν)
wherein k (v) is a linear coefficient of variation, which is a function of the wavenumber v; d (v) is a fixed variation as a function of the wavenumber v;
analyzing all sample spectral data, wherein the relative intensity value of each sample in the host spectral data at the wave number v is Y1A(ν)，Y2A(ν)，…，YLA(v); the relative intensity value of each sample at the wave number v in the spectral data of the machine is Y1B(ν)，Y2B(ν)，…，YLB(v); then Δ Y is obtained correspondingly1(ν)，ΔY2(ν)，…，ΔYL(v), performing least square normal fitting on the L data to obtain k (v) and d (v):
wherein the content of the first and second substances,
represents Y1A(ν)，Y2A(ν)，…，YLA(v) mean value;
step 3, spectrum correction:
and (3) recording a curve function of certain sample spectrum data needing to be transmitted to the slave in the host as L1S (v), and obtaining the curve function of the slave spectrogram of the sample after spectrum correction as L1T (v):
L1T(ν)＝L1S(ν+f(ν))+ΔY1(ν)；
and 4, storing the corrected spectrogram curve function L1T (v) into a database of the slave computer, acquiring the spectral data of the unknown sample by the slave computer, comparing the spectral data with the spectrum in the database of the slave computer, and obtaining the final result of the content of each component of the sample by the slave computer.
Preferably, the spectrum is any of near infrared, mid infrared, raman or uv-visible.
Preferably, after the spectral data is obtained in step 1, it is pre-processed.
Preferably, the preprocessing includes noise smoothing processing, baseline correction processing and intensity normalization processing.
Preferably, in the step 2b, at least four curve fits are performed when the curve fit is performed on the epsilon value.
The invention has the following beneficial effects:
the invention provides a spectrum identification method for spectrum model transmission, which comprises the steps of classifying sample spectra of a host and a slave, establishing a spectrum transmission model, carrying out spectrum correction on a host prediction set sample, and then bringing the sample into the slave to obtain sample composition and content results; the method effectively eliminates the difference of different spectrum instruments under different environments, realizes the intercommunication of the host spectrum and the slave, reduces the workload of analyzing and testing and establishing a standard sample spectrum library; compared with the traditional method, the method has the advantages of less required parameters and data amount and high correction accuracy.
Drawings
FIG. 1 is a flow chart of an embodiment of the spectral model transfer method of the present invention.
Detailed Description
The invention is described in detail below by way of example with reference to the accompanying drawings.
The invention provides a spectrum identification method for spectrum model transmission, which comprises the following main steps as shown in figure 1:
I. classification of sample spectra
The spectrum analyzer is divided into a host machine and a slave machine, wherein the host machine collects reference values of spectrum data of m samples, and the slave machine collects spectrum data and reference values of L samples in the m samples, wherein m is larger than L. Wherein the sample data in the slave is a subset of the master, and the sample set is called a reference set; the set of samples in the host apart from the reference set is called the prediction set.
II. Pretreatment of sample spectra
And performing baseline correction, spectrogram sampling or interpolation on the spectral data of the host computer and the slave computer to obtain sample spectrums with consistent spectral ranges, consistent resolution and normalized intensity.
III, obtaining a spectrum transfer model by correcting the spectrum drift amount and the spectrum intensity, specifically:
iia, spectral shift correction
The drift amount is defined as the wave number difference amount at the spectral peak, and only the spectral peak shift amount epsilon at the spectral peak position relative to the same substance in the slave can be observed in the spectrogram of a certain substance in the master machine, so that the whole spectral range cannot be covered.
Therefore, the invention provides that the drift amount of the peak value in the spectrogram of all samples in the reference set is recorded to obtain a spectrum offset set X (v, epsilon) of the host machine relative to the slave machine, wherein v is the wave number, and epsilon is the corresponding offset. And (3) performing curve fitting on the values in the set { epsilon } to obtain a corresponding relation function between the wave number v and the offset epsilon: e ═ f (v).
III b spectral intensity correction
Any sample in the reference set whose spectral curve function in the host is defined as Y1A(v) corresponding to the spectral curve function in the slave defined as Y1B(ν)。
ΔY1(ν)＝Y1A(ν)-Y1B(ν)
Generally, at any wavenumber ν, Δ Y1(v) with Y1A(v) linear variation, namely:
ΔY1(ν)＝k(ν)Y1A(ν)+d(ν)
wherein k (v) is a linear coefficient of variation, which is a function of the wavenumber v; d (v) is a fixed variation, also a function of the wavenumber v.
By spectral analysis of all samples in the reference set, different relative intensity values Y are obtained at the wavenumber v1A(ν)，Y2A(ν)，…，YLA(v), and Y1B(ν)，Y2B(ν)，…，YLB(v). Can obtain Delta Y1(ν)，ΔY2(ν)，…，ΔYLAnd (v) performing linear fitting on the L data to obtain k (v) and d (v). The normal fit by least squares is as follows:
IV, spectral correction
The spectrum correction is carried out by utilizing a spectrum transfer model when the predicted concentrated sample spectrum is transferred from the host computer to the slave computer.
And predicting that the curve function of the master spectrogram of the concentrated sample 1 is L1S (v), and obtaining that the curve function of the slave spectrogram of the sample 1 is L1T (v) after spectral correction.
L1T(ν)＝L1S(ν+f(ν))+ΔY1(ν)；
And storing the corrected spectrogram curve function L1T (v) in a database of the slave computer, acquiring the spectral data of the unknown sample by the slave computer, comparing the spectral data with the spectrum in the database of the slave computer, and obtaining the final result of the content of each component of the sample by the slave computer.
The spectrum in the step I can be any one of near infrared spectrum, intermediate infrared spectrum, Raman spectrum or ultraviolet-visible spectrum.
And the spectrum preprocessing in the step II comprises noise smoothing processing, baseline correction processing and intensity normalization processing.
The drift amount curve fitting method in the step III a is four-time curve fitting or higher.
In summary, the above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (5)
1. A spectrum identification method transmitted by a spectrum model is characterized by comprising the following steps:
step 1, respectively collecting spectral data of the same sample by a host and a slave;
step 2, obtaining a spectrum transfer model through correcting the spectrum drift amount and correcting the spectrum intensity, and specifically comprising the following steps:
step 2a, correcting the spectrum drift amount:
determining the wave number v corresponding to each peak value in the host machine spectral data and the offset epsilon of the peak value in the slave machine spectral data relative to the wave number v aiming at the spectral data of each sample collected by the host machine and the slave machine, and obtaining a group of data of the wave number v and the corresponding offset epsilon; traversing the spectral data of all samples to obtain a spectral offset set X (v, epsilon); and (3) performing curve fitting on the epsilon values in the set to obtain a corresponding relation function between the wave number v and the offset epsilon: f (v);
step 2b, correcting the spectral intensity:
for any sample, its spectral curve function in the host is defined as Y1A(v) Corresponding to the spectral curve function in the slave defined as Y1B(v) And then:
ΔY1(v)＝Y1A(v)-Y1B(v)
at any wavenumber v,. DELTA.Y1(v) With Y1A(v) Linear variation, i.e.:
ΔY1(v)＝k(v)Y1A(v)+d(v)
wherein k (v) is a linear coefficient of variation, which is a function of wavenumber v; d (v) is a fixed variation as a function of wavenumber v;
analyzing all sample spectral data, wherein the relative intensity value of each sample in the host spectral data at the wave number v is Y1A(v)，Y2A(v)，…，YLA(v) (ii) a The relative intensity value of each sample at the wave number v in the spectral data of the machine is Y1B(v)，Y2B(v)，…，YLB(v) (ii) a Then Δ Y is obtained correspondingly1(v)，ΔY2(v)，…，ΔYL(v) And performing least squares linear fitting on the L data to obtain k (v) and d (v):
wherein the content of the first and second substances,
represents Y1A(v)，Y2A(v)，…，YLA(v) The mean value of (a);
step 3, spectrum correction:
the curve function of certain sample spectrum data which needs to be transmitted to the slave in the master machine is recorded as L1S(v) Corrected by spectrumThen obtaining the curve function L of the sample from the spectrogram1T(v)：
L1T(v)＝L1S(v+f(v))+ΔY1(v)；
Step 4, curve function L of the corrected spectrogram1T(v) And storing the spectral data of the unknown sample in a database of the slave, comparing the spectral data of the unknown sample with the spectrum in the database of the slave, and obtaining the final result of the content of each component of the sample by the slave.
2. A method of spectral pattern transfer for spectral identification as claimed in claim 1 wherein the spectrum is any of near infrared, mid infrared, raman or uv-vis.
3. A method of spectral signature delivery as claimed in claim 1 wherein the spectral data obtained in step 1 is preprocessed.
4. A method for spectral identification delivered by a spectral model according to claim 3, wherein the preprocessing comprises noise smoothing, baseline correction, and intensity normalization.
5. The method for spectral pattern transfer according to claim 1, wherein in step 2b, the curve fitting is performed at least four times when the epsilon value is curve fitted.
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