CN113624745A - Method for improving long-term stability of laser-induced breakdown spectroscopy based on light spots - Google Patents
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Abstract
A method for improving long-term stability of laser-induced breakdown spectroscopy based on light spots comprises the following steps: when laser-induced breakdown spectroscopy is carried out, a beam quality analyzer is used for recording laser spot images, and spot images and spectra of different days are obtained; a spectrum intensity correction model is established by using the light spot image and the corresponding spectrum, and then the spectrum of different days is corrected by using the model, so that the long-term stability of the spectrum is improved. The method is different from the conventional laser-induced breakdown spectroscopy measurement in that the method considers that the laser spot can be naturally changed along with the change of the measurement environment, so that the intensity of the spectrum is changed, a light beam quality analyzer is added into an experimental device, the information of the laser spot is additionally obtained, the influence rule of the spot on the spectrum intensity is obtained by establishing a machine learning model, the model is used for correcting the influence of the spot change on the spectrum intensity, and the long-term stability of the laser-induced breakdown spectroscopy is improved.
Description
Technical Field
The invention relates to a method for improving long-term stability of a laser-induced breakdown spectroscopy based on light spots, and belongs to the technical field of laser-induced breakdown spectroscopy.
Background
Laser-induced Breakdown Spectroscopy (LIBS) can be used for rapidly determining element content, and is a novel atomic emission Spectroscopy technology. The LIBS works according to the following principle: focusing a beam of pulse laser on the surface of a substance to be detected, and ablating and exciting the beam of pulse laser into plasma; the plasma radiates photons during the rapid decay process, and the frequency and number of the photons contain information on the elemental species and concentration of the substance to be measured. The LIBS technology has the advantages of high measurement speed, wide application range, low requirement on sample pretreatment and higher development potential in the fields of process industrial monitoring, environmental monitoring, outer space exploration and the like.
However, the long-term stability of the LIBS signal is currently poor: without artificially changing experimental conditions, the spectral reproducibility measured at different times using the same set of experimental equipment and experimental parameters, the same sample, is poor, making it difficult to achieve high-precision quantitative analysis, where there is no clear definition of the specific time of "long-term" reference, but it is generally believed that differences can be found in spectra for two consecutive days (j.anal.at.spectra., 2018,33, 1564). It is presently believed that the long term instability of laser induced breakdown spectroscopy is primarily due to changes in environmental conditions and drift of the measurement instrument over time. The existing special research aiming at the long-term stability of the laser-induced breakdown spectroscopy is less, and the main reason is that the influence factors of the long-term stability are more and not completely clear, and researchers are difficult to strictly control variables in the experimental process.
Chinese patent document "a laser induced breakdown spectroscopy detection system with beam stability analysis" (CN201811432198.5) describes a detection system for simultaneously monitoring laser energy and beam quality by using a beam splitter, an energy meter and a beam quality analyzer, which is used to improve the stability of the spectrum. However, the technology only uses a parameter of the beam quality factor M2, the effective information of the spot image is not fully utilized, and compared with the spot image, the spot shape cannot be fully described by M2; according to the method, the spectrum stability is improved by rejecting the spectrum with large fluctuation of the energy and beam quality factor M2, rejection has certain randomness, and all spectrum information cannot be effectively utilized.
Disclosure of Invention
The invention aims to provide a method for improving the long-term stability of a laser-induced breakdown spectrum based on light spots, and aims to solve the problem that the long-term stability is poor due to the fact that the intensity of the laser-induced breakdown spectrum changes along with time due to environmental changes, instrument drift and the like, so that the accuracy of quantitative analysis is severely restricted.
The technical scheme of the invention is as follows:
a method for improving long-term stability of laser-induced breakdown spectroscopy based on light spots is characterized by comprising the following steps:
1) a laser emits a beam of pulse laser, the laser is divided into two beams by a spectroscope at an outlet, one beam enters a beam quality analyzer, and the other beam is focused on the surface of a sample by a focusing lens;
2) shooting a laser spot image by a beam quality analyzer, collecting plasma radiation by a light collecting device arranged near the surface of a sample, transmitting the plasma radiation to a spectrometer through an optical fiber, and carrying out dispersion on composite radiation by the spectrometer to generate a spectrum;
3) the light beam quality analyzer and the spectrometer respectively transmit the light spot image and the spectrum to the computer, and the light spot images and the spectrum are equal in quantity and are in one-to-one correspondence;
4) at least 30 light spot images and 30 spectra are obtained every day without changing experimental conditions, the measurement days are at least 10 days, and n light spot images P are obtainediAnd n line intensities Ii；
5) Randomly dividing the n light spot images and the n spectrums into a training set and a testing set, and defining the standard spectrum intensity as the average intensity of the spectrums of any day in the training set or the average intensity I of the spectrums of all the training setsSign board；
6) The light spot image P of the training setTraining deviceAs model input, the relative deviation e of the spectral intensity of the training set from the standard spectral intensity is calculated using equation 1Training deviceE is to beTraining deviceAs model output, a machine learning algorithm is used to establish a light spot-based spectrum correction model:
in the formula 1, eTraining deviceRelative deviation of the training set spectral intensity from the standard spectral intensity;
Itraining deviceCollecting spectral intensity for training; i isSign boardIs the standard spectral intensity;
7) respectively taking the relative deviation of the light spot image and the spectrum intensity of the training set as the input and the output of the model, and establishing a spectrum correction model based on the light spot by using a machine algorithm;
8) all the light spot images PiAs an input substitution model, the relative deviation e of all spectra is obtainediUsing eiCorrecting the intensities of all the spectra to obtain corrected spectral intensity IRepair theAnd finishing the correction of the spectrum:
Irepair the: the corrected spectral intensity;
Ii: all spectral intensities, i ═ 1,2,. n, n are the total number of spectra;
ei: relative deviation of all spectral intensities from the standard spectral intensity.
In the above technical solution, the machine learning algorithm is a linear and nonlinear algorithm, including a partial least squares algorithm, a support vector machine algorithm, or a neural network algorithm.
Compared with the prior art, the invention has the following advantages and prominent technical effects: firstly, the light spot image and the spectrum are utilized to establish a spectrum correction model based on the light spots, the original spectrum intensity is corrected, all data can be effectively utilized, and the discarding and the waste of the original data are avoided; the establishment of the spectrum correction model is based on the truly existing physical process that the shape and the intensity of the plasma are influenced by the shape of the light spot, and further the spectrum intensity is influenced, the model is reliable and effective, the correction effect is good and stable, and the randomness and the instability existing in the prior art are avoided; the invention improves the long-term stability of the spectrum by correcting the original data through the algorithm, so that the environmental conditions are not required to be strictly controlled and kept unchanged during the long-term experiment process, partial cost is saved, and the experiment flow is simplified.
Drawings
Fig. 1 is a schematic structural principle diagram of the method for improving the long-term stability of the laser-induced breakdown spectroscopy based on the light spot provided by the invention.
In fig. 1: 1-a laser; 2-a beam splitting lens; 3-a beam quality analyzer; 4-a focusing lens; 5-a sample to be detected; 6-plasma; 7-a light-receiving device; 8-an optical fiber; 9-a spectrometer; 10-computer.
FIG. 2 is a flowchart of a method for improving long-term stability of laser-induced breakdown spectroscopy based on a light spot, provided by the invention.
FIG. 3 is a graph showing the results of the spectral intensities before and after the correction of example 1, in which the original spectral intensity collected for 11 days and the spectral intensity after the correction by the model are shown.
FIG. 4 is a graph showing the results of the spectral intensities before and after the correction in example 2.
Detailed Description
The invention is further described below with reference to the figures and examples.
As shown in fig. 1, a high-energy-density pulse laser beam emitted by a laser 1 is divided into two beams according to a certain proportion by a beam splitting lens 2, one beam reaches a beam quality analyzer 3 to obtain a laser spot image, and the other beam is focused on the surface of a sample 5 to be measured by a focusing lens 4; a sample to be detected is ablated by laser and generates plasma 6, radiation light emitted by the plasma is collected by a light receiving device 7, then enters a spectrometer 9 through an optical fiber 8, is converted into a spectrum signal and then is transmitted to a computer 10 for data processing; compared with the conventional LIBS system, the method realizes the simultaneous measurement of the spectrum and the laser spot image by arranging the beam splitting lens and the beam quality analyzer in the laser light path.
Fig. 2 is a flowchart of a method for improving long-term stability of a laser-induced breakdown spectroscopy based on a light spot, which specifically includes the following steps:
1) a spectroscope is arranged at the outlet of the laser to divide the emergent laser into two beams, and a beam quality analyzer is arranged behind one beam of laser to obtain a spot image; focusing the other beam of laser to the surface of the sample to obtain a spectrum;
2) at least 30 light spot images and 30 spectra are obtained every day without changing experimental conditions, the measurement days are at least 10 days, and n light spot images P are obtainediAnd n line intensities Ii；
3) Defining the average spectral intensity within any day of the test period or the average spectral intensity over all days of the test period as the standard spectral intensity ISign board；
4) Randomly dividing the n light spot images and the n spectrums into a training set and a testing set; the training set spectral intensity I is calculated using the formulaTraining deviceRelative to the standard spectral intensity ISign boardRelative deviation e ofTraining device(ii) a The light spot image P of the training setTraining deviceAs model input, the relative deviation eTraining deviceAs model output, machine learning algorithms are used (machine learning algorithms are linear and non-linear algorithms, including partial least squares)Algorithm, support vector machine algorithm or various neural network algorithms), establishing a spectrum correction model based on the light spots:
in formula 1, eTraining deviceRelative deviation of the training set spectral intensity from the standard spectral intensity;
Itraining deviceCollecting spectral intensity for training; i isSign boardIs the standard spectral intensity;
5) n light spot images PiAs an input substitution model, the spectral relative deviation e is obtainediUsing eiCorrecting the intensities of all the spectra to obtain corrected spectral intensity IRepair theAnd finishing the correction of the spectrum:
Irepair the: the corrected spectral intensity;
Ii: all spectral intensities, i ═ 1,2,. n, n are the total number of spectra;
ei: the relative deviation of the measured spectral intensity from the standard spectral intensity.
Example 1:
taking a pure silicon sample as an example, the raw spectral signals collected at random for 11 days within one month were compared with the spectral intensities after correction using a spot-based spectral correction model.
The examples used a Nd YAG laser operating at 1064nm, pulse duration 8ns and pulse energy 105 mJ. The focal length of the focusing lens is 15cm, and the focal point is 1mm below the surface of the sample to be measured. The spectrometer starts to collect signals 1 mus after plasma generation with an exposure time of 3 mus. A spectroscope is arranged at the outlet of the laser, so that 60% of laser energy after light splitting reaches the surface of a sample through a focusing lens to obtain a spectrum; 40% of the laser energy reaches the beam quality analyzer, and laser spot images are collected. Over 30 spot images and 30 spectra were collected each day for 11 days of the experiment, for a total of 818 spectra and corresponding spots.
The number of principal components of partial least squares is determined by cross validation of a training set using a partial least squares algorithm built in MATLAB R2021a, in this example the number of principal components is taken as 1. Selecting 267 light spots and 267 spectrums on the 5 th day and the 6 th day as a test set, selecting 551 light spots and 551 spectrums on the other days as a training set, selecting Si 288.15nm as a characteristic spectral line, selecting the average spectral intensity of all the days as standard spectral intensity, calculating the relative deviation of the training set relative to the standard spectral intensity according to formula 1, and establishing a partial least square model of the relative deviation of the light spots and the spectrums; then 818 light spots are used as model input to obtain corresponding standard deviation, and 818 corrected spectrums are obtained according to the formula 2.
In the formula 1, eTraining deviceRelative deviation of the training set spectral intensity from the standard spectral intensity;
Itraining deviceCollecting spectral intensity for training; i isSign boardIs the standard spectral intensity;
Irepair the: the corrected spectral intensity;
Ii: all spectral intensities, i ═ 1,2,. n, n are the total number of spectra;
ei: the relative deviation of the measured spectral intensity from the standard spectral intensity;
the results are shown in FIG. 3. Spectral reproducibility is measured as Relative Standard Deviation (RSD), with lower RSD indicating higher stability. In particular, the smaller the mean intensity RSD value of the spectrum measured on different days, the smaller the fluctuation of the spectrum from day to day, the higher the long-term stability. Experimental results show that after the spectrum correction model based on the light spots is applied, the RSD of the daily average value of the spectrum intensity is reduced from 20.96% to 11.90%, the diurnal fluctuation degree of the spectrum is greatly reduced, and the long-term stability is remarkably improved.
Example 2:
the 818 spots and 818 spectra in example 1 were analyzed by selecting a convolutional neural network as a modeling algorithm, and the average spectral intensity over all days was selected as the standard spectral intensity. The structure of the convolutional neural network is as follows in order: the device comprises a convolution layer, a pooling layer, three convolution layers, a flattening layer and a full-connection layer, wherein in the convolution layer, the sizes of convolution kernels are 5 × 5,3 × 3 and 3 × 3 respectively, the numbers of convolution kernels are 15,10,10, 5 and 5 respectively, the pooling layer adopts maximum pooling, the pooling size is 2 × 2, the step length is 2, and the activation functions after the convolution layer are ReLU functions. Selecting the average spectral intensity of all days as the standard spectral intensity, calculating the relative deviation of the training set relative to the standard spectral intensity according to the formula 1, training the 551 spots and the 551 spectral relative deviations of the training set by using the convolutional neural network, then using 818 spots and spectra as network input to obtain 818 spectral relative deviations, and obtaining 818 corrected spectra according to the formula 2. The result is shown in figure 4, the RSD of the mean value of the spectrum intensity is reduced from 20.96% to 4.61%, the diurnal fluctuation degree of the spectrum is greatly reduced, and the long-term stability is remarkably improved.
Claims (2)
1. A method for improving long-term stability of laser-induced breakdown spectroscopy based on facula is characterized by comprising the following steps:
1) a spectroscope is arranged at the outlet of the laser to divide the emergent laser into two beams, and a beam quality analyzer is arranged behind one beam of laser to obtain a spot image; focusing the other beam of laser to the surface of the sample to obtain a spectrum;
2) at least 30 light spot images and 30 spectra are obtained every day without changing experimental conditions, the measurement days are at least 10 days, and n light spot images P are obtainediAnd n line intensities Ii；
3) Randomly dividing the n light spot images and the n spectrums into a training set and a testing set, and performing the training setThe average spectral intensity in any day or all days of the training set is defined as the standard spectral intensity ISign board；
4) The training set spectral intensity I is calculated using the formulaTraining deviceRelative to the standard spectral intensity ISign boardRelative deviation e ofTraining device(ii) a The light spot image P of the training setTraining deviceAs model input, the relative deviation eTraining deviceAs model output, a machine learning algorithm is used to establish a light spot-based spectrum correction model:
in formula 1, eTraining deviceRelative deviation of the training set spectral intensity from the standard spectral intensity;
Itraining deviceCollecting spectral intensity for training; i isSign boardIs the standard spectral intensity;
5) n light spot images PiAs an input substitution model, the spectral relative deviation e is obtainediUsing eiCorrecting the intensities of all the spectra to obtain corrected spectral intensity IRepair theAnd finishing the correction of the spectrum:
Irepair the: the corrected spectral intensity;
Ii: all spectral intensities, i ═ 1,2,. n, n are the total number of spectra;
ei: the relative deviation of the measured spectral intensity from the standard spectral intensity.
2. The method for improving the long-term stability of the laser-induced breakdown spectroscopy based on the light spots as claimed in claim 1, wherein the machine learning algorithm is linear and nonlinear algorithms, including partial least squares algorithm, support vector machine algorithm or neural network algorithm.
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