CN101666746B - Laser induced spectrum data processing method based on wavelet analysis - Google Patents

Laser induced spectrum data processing method based on wavelet analysis Download PDF

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CN101666746B
CN101666746B CN2009100755782A CN200910075578A CN101666746B CN 101666746 B CN101666746 B CN 101666746B CN 2009100755782 A CN2009100755782 A CN 2009100755782A CN 200910075578 A CN200910075578 A CN 200910075578A CN 101666746 B CN101666746 B CN 101666746B
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spectrum
data
sequence
characteristic background
spectrum measuring
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CN101666746A (en
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贾锁堂
张生俊
尹王保
阎高伟
王红兵
李平柱
罗振红
王学钦
张雷
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TAIYUAN HAITONG AUTOMATION CONTROL CO Ltd
Shanxi University
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TAIYUAN HAITONG AUTOMATION CONTROL CO Ltd
Shanxi University
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Abstract

The invention relates to the technical field of spectral analysis, in particular to a laser induced spectrum data processing method based on wavelet analysis, which can improve the spectral analysis precision and efficiency and solve the problems that the current laser induced spectrum analysis results are influenced by ineffective measurement data, and the like. The laser induced spectrum data treatment method based on wavelet analysis comprises the following steps: (1) acquiring an effective measurement pattern template G<m>, extracting the effective measurement pattern template when a substance standard sample to be measured is calibrated, and carrying out calibration operation with the corresponding measurement data of the effective measurement pattern template to acquire calibration parameters; and (2) processing actual spectrum measurement data of the measured sample, comparing the actually measured spectrum data with the effective measurement pattern template in actual measurement, keeping the effective spectrum measurement data, and calculating the effective measurement spectrum data based on the calibration parameters to obtain the element content information of the measured sample. The invention can effectively improve the measurement precision, and acquire the effective measurement data of required number based on laser excitation measurement of limited number of time, thereby effectively prolonging the service life of a laser measurement system.

Description

Laser induced spectrum data processing method based on wavelet analysis
Technical field
The present invention relates to field of spectral analysis technology, specifically is a kind of laser induced spectrum data processing method based on wavelet analysis that can improve spectral analysis precision and efficient.
Background technology
Laser-induced spectrum (Laser Induced Breakdown Spectroscopy, LIBS) be that a kind of laser excitation material that utilizes produces plasma, by analyzing the Atomic Emission Spectral Analysis technology that luminescence of plasma spectrum obtains material element component content information.But therefore advantages such as the LIBS technology has, and specimen preparation is simple, multielement Synchronization Analysis, the fast remote analysis of analysis speed are widely used in the qualitative or quantitative test of various materials, and great practical value is arranged.
But because the characteristic of laser plasma is subjected to the interference of matrix effect and some objective factors that are difficult to avoid easily, as factors such as laser intensity pulsation, sample surfaces characteristics, laser-induced spectrum exists problems such as randomness is big, poor repeatability, has influenced the degree of accuracy of quantitative test.
Be to eliminate the randomness and the undulatory property of laser induced spectrum measuring, improve the degree of accuracy of laser-induced spectrum quantitative test, employing at present repeatedly excites measuring method, and the direct averaged of measurement result is repeatedly obtained the laser-induced spectroscopic analysis result.But it is conspicuous repeatedly exciting and measuring the problem of bringing: the life-span that at first influences laser instrument, secondly influence the real-time of measuring, the most important thing is can't also include some invalid measured values to the direct averaged of measurement result repeatedly with avoiding, thus the precision that influence is measured and analyzed.
Summary of the invention
The present invention is subjected to problems such as invalid measurement data influences in order to solve present laser-induced spectroscopic analysis result, and a kind of laser induced spectrum data processing method based on wavelet analysis is provided.
The present invention adopts following technical scheme to realize: the laser induced spectrum data processing method based on wavelet analysis comprises the steps:
1) effective measurement pattern class template G mObtaining step;
A, n standard model in the standard of physical sample sets to be measured carried out m time laser induced spectrum measuring respectively, and journal each time spectrum measuring data G corresponding with each standard model I, j, constitute standard model spectrum measuring data set G={G I, j, wherein, i=1,2 ..., n, j=1,2 ..., m, G I, jExpression is carried out the spectrum measuring data that the j time laser induced spectrum measuring obtains to standard model i, and spectrum measuring data G I, jRepresent in the sequence mode: G I, j(k)=[X 1, X 2... X k... X N], N is a sequence length;
B, to each spectrum measuring data sequence G among the standard model spectrum measuring data set G I, j(k) carry out L yardstick one-dimensional discrete stationary wavelet and decompose, 4≤L≤8 obtain respectively and each spectrum measuring data sequence G I, j(k) Dui Ying high frequency coefficient of dissociation
Figure GSB00000168078400021
With the low frequency coefficient of dissociation
Figure GSB00000168078400022
C, with each spectrum measuring data sequence G I, j(k) the corresponding low frequency wavelet coefficient of dissociation of difference
Figure GSB00000168078400023
Carry out spectrum reconstruct, obtain and each spectrum measuring data sequence G I, j(k) characteristic background spectroscopic data one to one
Figure GSB00000168078400024
Constitutive characteristic background spectrum data acquisition
Figure GSB00000168078400025
The characteristic background spectroscopic data
Figure GSB00000168078400026
Represent in the sequence mode equally:
Figure GSB00000168078400027
And sequence length and spectrum measuring data sequence G I, j(k) sequence length is identical;
D, to characteristic background spectroscopic data set G bIn the characteristic background spectroscopic data
Figure GSB00000168078400028
Carry out cluster analysis, with characteristic background spectroscopic data set G bBe divided into several mode class subclass
Figure GSB00000168078400029
Promptly
Figure GSB000001680784000210
Wherein, h=1,2 ..., H, H is for dividing the mode class subclass number that obtains with the set of characteristic background spectroscopic data according to cluster analysis; According to spectrum measuring data G I, jWith the characteristic background spectroscopic data One-to-one relationship and characteristic background spectroscopic data set G bDivision, standard model spectrum measuring data set G is divided into and characteristic background spectroscopic data set G bThe mode class subclass
Figure GSB00000168078400032
Several mode class subclass G one to one h, i.e. G={G 1, G 2..., G h..., G H;
E, to each mode class subclass G among the standard model spectrum measuring data set G hThe spectrum measuring data G that comprises I, jCarry out constituent content calibration computing, obtain and each mode class subclass G hCalibrate parameter beta one to one hWith the calibration operation result;
F, the standard value of selecting to calibrate operation result and test substance standard model differ minimum mode class subclass G hThe pattern that is had is as effective measurement pattern, with this mode class subclass G hCorresponding calibration parameter beta hWhen being surveyed, calculates sample the calibration parameter that constituent content adopts;
G, extraction and this mode class subclass G hCharacteristic of correspondence background spectrum data pattern class subclass
Figure GSB00000168078400033
Select characteristic background spectroscopic data mode class subclass
Figure GSB00000168078400034
In all characteristic background spectroscopic data sequences
Figure GSB00000168078400035
The maximal value of middle position k is as the higher limit of effective measurement pattern class template sequence location k
Figure GSB00000168078400036
The minimum value of position k is as the lower limit of effective measurement pattern class template sequence location k
Figure GSB00000168078400037
Promptly
Figure GSB00000168078400038
Figure GSB00000168078400039
Form effective measurement pattern class template Wherein, 1≤k≤N, E are characteristic background spectroscopic data mode class subclass
Figure GSB000001680784000311
Middle characteristic background spectroscopic data sequence
Figure GSB000001680784000312
Number, N is a characteristic background spectroscopic data sequence
Figure GSB000001680784000313
Sequence length;
2), sample actual spectrum Measurement and Data Processing step;
H, sample is carried out the single laser spectral measurement of inducting, and write down this time spectrum measuring data G j, j=1,2, And represent in the sequence mode: G j(k)=[X 1, X 2..., X k..., X N], N is a sequence length;
I, to laser induced spectrum data sequence G j(k) the one-dimensional discrete stationary wavelet that carries out the L yardstick decomposes, and obtains and laser induced spectrum data sequence G j(k) Dui Ying high frequency coefficient of dissociation
Figure GSB000001680784000314
With the low frequency coefficient of dissociation
Figure GSB000001680784000315
Wherein, the L value is identical with L value among the step b;
J, the low frequency wavelet coefficient of dissociation that obtains with step I Carry out spectrum reconstruct, obtain and laser induced spectrum data sequence G j(k) characteristic of correspondence background spectrum data
Figure GSB00000168078400042
The characteristic background spectroscopic data
Figure GSB00000168078400043
Represent in the sequence mode equally:
Figure GSB00000168078400044
And sequence length and spectrum measuring data sequence G j(k) sequence length is identical;
K, judging characteristic background spectrum data sequence Whether with effective measurement pattern class template
Figure GSB00000168078400046
Coupling, promptly Whether set up, as setting up this characteristic background spectrum
Figure GSB00000168078400048
Belong to effective measurement pattern class, then with this characteristic background spectroscopic data
Figure GSB00000168078400049
Measure spectrum data G jEffectively;
1, to sample repeated execution of steps h to step k, up to the effective measure spectrum data G that obtains more than three times j
M, effective measure spectrum data G to being obtained jAccording to that obtain among the step f and effective measurement pattern class subclass G hCorresponding calibration parameter beta hCarry out constituent content and calculate, with the mean value of result of calculation constituent content analysis result as sample.
The spectrum reconstruct that the L yardstick one-dimensional discrete stationary wavelet used in the described method decomposes, carry out with the low frequency wavelet coefficient of dissociation, the cluster analysis that the characteristic background spectroscopic data is carried out and be known technology in the laser induced spectrum measuring technical field to the constituent content calibration computing that spectrum measuring data carries out, engineering technical personnel are known for this area.
Compared with prior art, the present invention at first when standard of physical sample to be measured is calibrated, extracts effective measurement pattern class template, and calibrates computing with the measurement data of effective measurement pattern class template correspondence, obtains the calibration parameter; Then, when sample is carried out actual measurement, the spectroscopic data that laser excitation obtains is compared with effective measurement pattern class template, the validity of checking spectroscopic data, reject invalid measure spectrum data, keep effective spectroscopic data, effective measure spectrum The data is calculated with the calibration parameter that effectively the measurement pattern class template is corresponding, obtain material composition constituent content information.Because the present invention is a foundation with effective measurement pattern class template in the actual measurement process, screen the validity (weighing promptly whether measurement result is effective each time) of measurement data, therefore, can in measuring, limited number of time laser excitation obtain effective measurement data of required number, need not to carry out laser-induced spectrum a large amount of times excites, can reduce laser-induced spectrum significantly and excite number of times, thereby effectively improve the life-span of laser measurement system; And the results of elemental analyses of sample calculates with effective measurement data, and therefore, measuring accuracy is high, and through check, the results of elemental analyses precision that obtains with the present invention has the raising more than 25%.
Description of drawings
Fig. 1 is the effective measurement pattern class template of the present invention G mThe process flow diagram of obtaining step;
Fig. 2 is the process flow diagram of sample actual spectrum Measurement and Data Processing step of the present invention;
Fig. 3 is for to carry out exciting for 100 times resulting laser-induced spectrum figure to a certain sample;
Fig. 4 is for certain excites corresponding laser-induced spectrum figure among Fig. 3;
Fig. 5 be utilize the reconstruct of low frequency wavelet coefficient of dissociation with Fig. 4 in laser-induced spectrum characteristic of correspondence bias light spectrogram;
Fig. 6 be utilize the reconstruct of low frequency wavelet coefficient of dissociation with Fig. 3 in laser-induced spectrum characteristic of correspondence bias light spectrogram;
Fig. 7 is effective measurement pattern class template G mSynoptic diagram.
Embodiment
Laser induced spectrum data processing method based on wavelet analysis comprises the steps:
1) effective measurement pattern class template G mObtaining step, as shown in Figure 1;
A, n standard model in the standard of physical sample sets to be measured carried out m time laser induced spectrum measuring respectively, and journal each time spectrum measuring data G corresponding with each standard model I, j(as shown in Figure 3), constitute standard model spectrum measuring data set G={G I, j, wherein, i=1,2 ..., n, j=1,2 ..., m, G I, jExpression is carried out the spectrum measuring data that the j time laser induced spectrum measuring obtains to standard model i, and spectrum measuring data G I, jRepresent in the sequence mode: G I, j(k)=[X 1, X 2... X k... X N], N is a sequence length; Wherein, the number of times m that standard of physical sample to be measured is carried out laser induced spectrum measuring generally is greater than and equals 50 times.
B, to each spectrum measuring data sequence G among the standard model spectrum measuring data set G I, j(k) carry out L yardstick one-dimensional discrete stationary wavelet and decompose, 4≤L≤8 obtain respectively and each spectrum measuring data sequence G I, j(k) Dui Ying high frequency coefficient of dissociation
Figure GSB00000168078400061
With the low frequency coefficient of dissociation
Figure GSB00000168078400062
C, with each spectrum measuring data sequence G I, j(k) the corresponding low frequency wavelet coefficient of dissociation of difference
Figure GSB00000168078400063
Carry out spectrum reconstruct, obtain and each spectrum measuring data sequence G I, j(k) characteristic background spectroscopic data one to one
Figure GSB00000168078400064
(as shown in Figure 6), constitutive characteristic background spectrum data acquisition
Figure GSB00000168078400065
The characteristic background spectroscopic data Represent in the sequence mode equally:
Figure GSB00000168078400067
And sequence length and spectrum measuring data sequence G I, j(k) sequence length is identical;
D, to characteristic background spectroscopic data set G bIn the characteristic background spectroscopic data
Figure GSB00000168078400068
Carry out cluster analysis, with characteristic background spectroscopic data set G bBe divided into several mode class subclass
Figure GSB00000168078400069
Promptly
Figure GSB000001680784000610
Wherein, h=1,2 ..., H, H is for dividing the mode class subclass number that obtains with the set of characteristic background spectroscopic data according to cluster analysis; According to spectrum measuring data G I, jWith the characteristic background spectroscopic data One-to-one relationship and characteristic background spectroscopic data set G bDivision, standard model spectrum measuring data set G is divided into and characteristic background spectroscopic data set G bThe mode class subclass
Figure GSB000001680784000612
Several mode class subclass G one to one h, i.e. G={G 1, G 2..., G h..., G H;
E, to each mode class subclass G among the standard model spectrum measuring data set G hThe spectrum measuring data G that comprises I, jCarry out constituent content calibration computing, obtain and each mode class subclass G hCalibrate parameter beta one to one hWith the calibration operation result;
F, the standard value of selecting to calibrate operation result and test substance standard model differ minimum mode class subclass G hThe pattern that is had is as effective measurement pattern, with this mode class subclass G hCorresponding calibration parameter beta hWhen being surveyed, calculates sample the calibration parameter that constituent content adopts;
G, extraction and this mode class subclass G hCharacteristic of correspondence background spectrum data pattern class subclass
Figure GSB00000168078400071
Select characteristic background spectroscopic data mode class subclass
Figure GSB00000168078400072
In all characteristic background spectroscopic data sequences
Figure GSB00000168078400073
The maximal value of middle position k is as the higher limit of effective measurement pattern class template sequence location k
Figure GSB00000168078400074
The minimum value of position k is as the lower limit of effective measurement pattern class template sequence location k
Figure GSB00000168078400075
Promptly
Figure GSB00000168078400077
Form effective measurement pattern class template
Figure GSB00000168078400078
As shown in Figure 7, wherein, 1≤k≤N, E are characteristic background spectroscopic data mode class subclass
Figure GSB00000168078400079
Middle characteristic background spectroscopic data sequence
Figure GSB000001680784000710
Number, N is a characteristic background spectroscopic data sequence
Figure GSB000001680784000711
Sequence length;
2), sample actual spectrum Measurement and Data Processing step, as shown in Figure 2;
H, sample is carried out the single laser spectral measurement of inducting, and write down this time spectrum measuring data G j, as shown in Figure 4, j=1,2, And represent in the sequence mode: G j(k)=[X 1, X 2..., X k..., X N], N is a sequence length;
I, to laser induced spectrum data sequence G j(k) the one-dimensional discrete stationary wavelet that carries out the L yardstick decomposes, and obtains and laser induced spectrum data sequence G j(k) Dui Ying high frequency coefficient of dissociation
Figure GSB000001680784000712
With the low frequency coefficient of dissociation
Figure GSB000001680784000713
Wherein, the L value is identical with L value among the step b;
J, the low frequency wavelet coefficient of dissociation that obtains with step I
Figure GSB000001680784000714
Carry out spectrum reconstruct, obtain and laser induced spectrum data sequence G j(k) characteristic of correspondence background spectrum data
Figure GSB000001680784000715
As shown in Figure 5; The characteristic background spectroscopic data
Figure GSB000001680784000716
Represent in the sequence mode equally: And sequence length and spectrum measuring data sequence G j(k) sequence length is identical;
K, judging characteristic background spectrum data sequence
Figure GSB00000168078400081
Whether with effective measurement pattern class template
Figure GSB00000168078400082
Coupling, promptly Whether set up, as setting up this characteristic background spectrum
Figure GSB00000168078400084
Belong to effective measurement pattern class, then with this characteristic background spectroscopic data Measure spectrum data G jEffectively;
1, to sample repeated execution of steps h to step k, up to the effective measure spectrum data G that obtains more than three times j
M, effective measure spectrum data G to being obtained jAccording to that obtain among the step f and effective measurement pattern class subclass G hCorresponding calibration parameter beta hCarry out constituent content and calculate, with the mean value of result of calculation constituent content analysis result as sample.

Claims (1)

1. the laser induced spectrum data processing method based on wavelet analysis is characterized in that comprising the steps:
1) effective measurement pattern class template G mObtaining step;
A, n standard model in the standard of physical sample sets to be measured carried out m time laser induced spectrum measuring respectively, and journal each time spectrum measuring data G corresponding with each standard model I, j, constitute standard model spectrum measuring data set G={G I, j, wherein, i=1,2 ..., n, j=1,2 ..., m, G I, jExpression is carried out the spectrum measuring data that the j time laser induced spectrum measuring obtains to standard model i, and spectrum measuring data G I, jRepresent in the sequence mode: G I, j(k)=[X 1, X 2... X k... X N], N is a sequence length;
B, to each spectrum measuring data sequence G among the standard model spectrum measuring data set G I, j(k) carry out L yardstick one-dimensional discrete stationary wavelet and decompose, 4≤L≤8 obtain respectively and each spectrum measuring data sequence G I, j(k) Dui Ying high frequency coefficient of dissociation
Figure FSB00000168078300011
With the low frequency coefficient of dissociation
Figure FSB00000168078300012
C, with each spectrum measuring data sequence G I, j(k) the corresponding low frequency wavelet coefficient of dissociation of difference
Figure FSB00000168078300013
Carry out spectrum reconstruct, obtain and each spectrum measuring data sequence G I, j(k) characteristic background spectroscopic data one to one Constitutive characteristic background spectrum data acquisition
Figure FSB00000168078300015
The characteristic background spectroscopic data
Figure FSB00000168078300016
Represent in the sequence mode equally: And sequence length and spectrum measuring data sequence G I, j(k) sequence length is identical;
D, to characteristic background spectroscopic data set G bIn the characteristic background spectroscopic data
Figure FSB00000168078300018
Carry out cluster analysis, with characteristic background spectroscopic data set G bBe divided into several mode class subclass
Figure FSB00000168078300019
Promptly
Figure FSB000001680783000110
Wherein, h=1,2 ..., H, H is for dividing the mode class subclass number that obtains with the set of characteristic background spectroscopic data according to cluster analysis; According to spectrum measuring data G I, jWith the characteristic background spectroscopic data
Figure FSB000001680783000111
One-to-one relationship and characteristic background spectroscopic data set G bDivision, standard model spectrum measuring data set G is divided into and characteristic background spectroscopic data set G bThe mode class subclass Several mode class subclass G one to one h, i.e. G={G 1, G 2..., G h..., G H;
E, to each mode class subclass G among the standard model spectrum measuring data set G hThe spectrum measuring data G that comprises I, jCarry out constituent content calibration computing, obtain and each mode class subclass G hCalibrate parameter beta one to one hWith the calibration operation result;
F, the standard value of selecting to calibrate operation result and test substance standard model differ minimum mode class subclass G hThe pattern that is had is as effective measurement pattern, with this mode class subclass G hCorresponding calibration parameter beta hWhen being surveyed, calculates sample the calibration parameter that constituent content adopts;
G, extraction and this mode class subclass G hCharacteristic of correspondence background spectrum data pattern class subclass
Figure FSB00000168078300022
Select characteristic background spectroscopic data mode class subclass
Figure FSB00000168078300023
In all characteristic background spectroscopic data sequences
Figure FSB00000168078300024
The maximal value of middle position k is as the higher limit of effective measurement pattern class template sequence location k The minimum value of position k is as the lower limit of effective measurement pattern class template sequence location k
Figure FSB00000168078300026
Promptly
Figure FSB00000168078300027
Figure FSB00000168078300028
Form effective measurement pattern class template
Figure FSB00000168078300029
Wherein, 1≤k≤N, E are characteristic background spectroscopic data mode class subclass
Figure FSB000001680783000210
Middle characteristic background spectroscopic data sequence
Figure FSB000001680783000211
Number, N is a characteristic background spectroscopic data sequence
Figure FSB000001680783000212
Sequence length;
2), sample actual spectrum Measurement and Data Processing step;
H, sample is carried out the single laser spectral measurement of inducting, and write down this time spectrum measuring data G j, j=1,2, And represent in the sequence mode: G j(k)=[X 1, X 2..., X k..., X N], N is a sequence length;
I, to laser induced spectrum data sequence G j(k) the one-dimensional discrete stationary wavelet that carries out the L yardstick decomposes, and obtains and laser induced spectrum data sequence G j(k) Dui Ying high frequency coefficient of dissociation
Figure FSB000001680783000213
With the low frequency coefficient of dissociation
Figure FSB000001680783000214
Wherein, the L value is identical with L value among the step b;
J, the low frequency wavelet coefficient of dissociation that obtains with step I
Figure FSB000001680783000215
Carry out spectrum reconstruct, obtain and laser induced spectrum data sequence G j(k) characteristic of correspondence background spectrum data
Figure FSB00000168078300031
The characteristic background spectroscopic data
Figure FSB00000168078300032
Represent in the sequence mode equally: And sequence length and spectrum measuring data sequence G j(k) sequence length is identical;
K, judging characteristic background spectrum data sequence
Figure FSB00000168078300034
Whether with effective measurement pattern class template
Figure FSB00000168078300035
Coupling, promptly Whether set up, as setting up this characteristic background spectrum
Figure FSB00000168078300037
Belong to effective measurement pattern class, then with this characteristic background spectroscopic data
Figure FSB00000168078300038
Measure spectrum data G jEffectively;
1, to sample repeated execution of steps h to step k, up to the effective measure spectrum data G that obtains more than three times j
M, effective measure spectrum data G to being obtained jAccording to that obtain among the step f and effective measurement pattern class subclass G hCorresponding calibration parameter beta hCarry out constituent content and calculate, with the mean value of result of calculation constituent content analysis result as sample.
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Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1880944A (en) * 2005-09-27 2006-12-20 重庆大学 Multi-component analysis method for spectrum

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1880944A (en) * 2005-09-27 2006-12-20 重庆大学 Multi-component analysis method for spectrum

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
尼珍等.近红外光谱分析中光谱预处理方法的作用及其发展.《药物分析杂质》.2008,第28卷(第5期),824-829. *
朱军.小波变换用于FTIR光谱定量分析.《计算机技术与发展》.2008,第18卷(第11期),197-203. *
王志平等.混合光谱中已知组分光谱的自加强方法研究.《光谱学与光谱分析》.2009,第29卷(第7期),1946-1949. *
金伟等.基于PLS建模在近红外光谱分析中的应用展望.《现代农业科学》.2008,第15卷(第11期),10-11. *

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