Characteristic Spectral Lines Research Articles

Laser dressing process monitoring of superabrasive grinding wheel based on spectroscopy

In order to solve the problems of poor efficiency and low automation in laser dressing of superabrasive grinding wheels, the spectral monitoring research based on the automatic dressing of superabrasive grinding wheels was systematically carried out in this paper. For the first time, the characteristic element spectral lines that could respectively characterize the identity information of grain and bond were established, and by monitoring the characteristic element spectral line waveforms, the profiling/sharpening processing status identification was realized. It was the first to propose using the presence or absence of line spectrum during the laser dressing process as a monitoring indicator, which could achieve qualitative identification of the high-efficiency processing status of the laser beam focus. When the laser beam was in the positive defocus state, the material could be removed more accurately. A greater quantity of material would be eliminated when the laser beam was in the negative defocus state. The mapping relationship between the spectral characteristic waveform and the defocus amount of the laser beam during the laser dressing process was revealed, and a spectral monitoring theory of the defocus amount fluctuation of superabrasive grinding wheels was constructed. A high-efficiency automated laser profiling method for grinding wheels based on spectrum-assisted monitoring and self-adaptive feed was innovatively explored, and experiments had proven that this method could achieve self-adaptive feed for laser beam fast slow compensation, ultimately achieving high efficiency and automatic dressing of superabrasive grinding wheels.

A new code for low-resolution spectral identification of white dwarf binary candidates

Close white dwarf binaries (CWDBs) are considered to be progenitors of several exotic astronomical phenomena (e.g., type Ia supernovae, cataclysmic variables). These violent events are broadly used in studies of general relativity and cosmology. However, obtaining precise stellar parameter measurements for both components of CWDBs is a challenging task given their low luminosities, swift time variation, and complex orbits. High-resolution spectra (R$> 20 000$) are preferred but expensive, resulting in a sample size that is insufficient for robust population study. Recently, studies have shown that the more accessible low-resolution (R$ 2000$) spectra (LRS) may also provide enough information for spectral decomposition. To release the full potential of the less expensive low-resolution spectroscopic surveys, and thus greatly expand the CWDB sample size, it is necessary to develop a robust pipeline for spectra decomposition and analysis. We aim to develop a spectroscopic fitting program for white dwarf binary systems based on photometry, LRS, and stellar evolutionary models. The outputs include stellar parameters of both companions in the binary including effective temperature, surface gravity, mass, radius, and metallicity in the case of MS stars. We used an artificial neural network (ANN) to build spectrum generators for DA/DB white dwarfs and main-sequence stars. Characteristic spectral lines were used to decompose the spectrum of each component. The best-fit stellar parameters were obtained by finding the least $ solution to these feature lines and the continuum simultaneously. Compared to previous studies, our code is innovative in the following aspects: (1) implementing a sophisticated binary decomposition technique in LRS for the first time; (2) using flux-calibrated spectra instead of photometry plus spectral lines, in which the latter requires multi-epoch observations; (3) applying an ANN in binary decomposition, which significantly improves the efficiency and accuracy of generated spectra. We demonstrate the reliability of our code with two well-studied CWDBs, WD 1534+503 and PG 1224+309. We also estimate the stellar parameters of 14 newly identified CWDB candidates, most of which are fitted with double component models for the first time. Our estimates agree with previous results for the common stars and follow the statistical distribution in the literature. We provide a robust program for fitting binary spectra of various resolutions. Its application to a large volume of white dwarf binary candidates will offer important statistic samples to stellar evolution studies and future gravitational wave monitoring.

Rapid and High-Performance Analysis of Total Nitrogen in Coco-Peat Substrate by Coupling Laser-Induced Breakdown Spectroscopy with Multi-Chemometrics

Nitrogen is an important nutrient element for crop growth. Rapid and accurate acquisition of nitrogen content in cultivation substrate is the key to precise fertilization. In this study, laser-induced breakdown spectroscopy (LIBS) was used to detect the total nitrogen (TN) of coco-peat substrate. A LIBS spectrum acquisition system was established to collect the spectral line signal of samples with wavelengths ranging from 200 nm to 860 nm. Synergy interval partial least squares (Si-PLS) algorithm and elimination of uninformative variables (UVE) algorithm were used to select the spectral data of TN characteristic lines in coco-peat substrate. Univariate calibration curve and partial least squares regression (PLSR) were used to build mathematical models for the relationship between the spectral data of univariate characteristic spectral lines, full variables and screened multi-variable characteristic spectral lines of samples and reference measurement values of TN. By comparing the detection performance of calibration curves and multivariate spectral prediction models, it was concluded that UVE was used to simplify the number of spectral input variables for the model and PLSR was applied to construct the simplest multivariate model for the measurement of TN in the substrate samples. The model provided the best measurement performance, with the calibration set determination coefficient () and calibration set root mean square error (RMSEC) values of 0.9944 and 0.0382%, respectively; the prediction set determination coefficient () and prediction set root mean square error (RMSEP) had values of 0.9902 and 0.0513%, respectively. These results indicated that the combination of UVE and PLSR could make full use of the variable information related to TN detection in the LIBS spectrum and realize the rapid and high-performance measurement of TN in coco-peat substrate. It would provide a reference for the rapid and quantitative assessment of nutrient elements in other substrate and soil.

Research on a new multiple-screening method for laser-induced plasma spectroscopy utilizing Lorentz

In the field of Laser Induced Breakdown Spectroscopy (LIBS) research, the screening and extraction of complex spectra play a crucial role in enhancing the accuracy of quantitative analysis. This paper introduces a novel approach for multiple screenings of LIBS spectra using Lorentz Screening and Sensitivity and Volatility Analysis. Initially, Create symmetrical sampling standards for Lorentz fitting. Then the Lorentz fitting is used to uniformly screen the collected spectral information on both sides in order to eliminate adjacent interference peaks. Subsequently, Sensitivity and Volatility Analysis is employed to further remove overlapping peaks and select spectra with low volatility and high sensitivity. Sensitivity and Volatility Analysis is a spectral discrimination method proposed on the premise of intensity's correlation with concentration. It utilizes a Z-score method that incorporates both deviation and standard deviation for effective analysis. Furthermore, it meticulously selects spectral lines with minimal interference and volatility, thereby augmenting the precision of quantitative analysis. The quantitative accuracy (R2) for Chromium (Cr) and Nickel (Ni) elements can reach 0.9919 and 0.9768, respectively. Their average errors can be reduced to 0.0566 % and 0.1024 %. The study demonstrates that Lorentz Screening and Sensitivity and Volatility Analysis can select high-quality characteristic spectral lines to improve the performance of the model.

Particles influence on the direct absorption spectroscopy of TDLAS

Tunable Diode Laser Absorption Spectroscopy (TDLAS) technology is widely used for monitoring the gas pollutants. But in actual measurement environments, there are usually particles, which will affect the TDLAS direct absorption spectroscopy. In this paper, the influence of particles on the direct absorption spectroscopy of TDLAS was analyzed by using TDLAS-DAS technology combined with particle extinction method. According to analysis, it was found that the presence of particles does not affect the gas characteristic absorption spectral line strength and the gas absorption spectroscopy broadening, but it does affect the output laser intensity and absorbance received by the detector. The transmittance of particles can be obtained through the slope of the output laser intensity, which can be used to correct the absorbance to achieve accurate measurement of gas concentration. This paper analyzed the effects of particle size, incident wavelength, and composition on particle extinction coefficient and obtained the extinction coefficient of soot. If the laser wavelength is 1.53 μm and the particle size of soot is 45 μm, the extinction coefficient of soot is 2.1016. The magnitude of laser intensity attenuation at different particle concentrations can be obtained based on the extinction coefficient of particulate matter. When the concentration of soot is greater than 26290 mg/m3 and the optical path is 100 cm, the transmittance is less than 0.1 and the laser intensity attenuation reach over 90 %, which may affect the detection of gas absorption spectroscopy information and the accuracy of measurement. On this basis, this paper established the relationship between gas concentration and corrected absorbance to invert the gas concentration in the presence of particulate matter when the concentration of soot is less than 26290 mg/m3. The inversion gas concentration error obtained by the absorbance peak calibration method is within 3 %, and the inversion gas concentration error obtained by the absorbance integration method is within 1 %. From the inversion results, it can be seen that the accurate measurement of gas concentration in the presence of particulate matter can be achieved by extracting the slope of the output laser intensity to correct absorbance.

Rapid and accurate identification of bacteria utilizing laser-induced breakdown spectroscopy.

Timely and accurate identification of harmful bacterial species in the environment is paramount for preventing the spread of diseases and ensuring food safety. In this study, laser-induced breakdown spectroscopy technology was utilized, combined with four machine learning methods - KNN, PCA-KNN, RF, and SVM, to conduct classification and identification research on 7 different types of bacteria, adhering to various substrate materials. The experimental results showed that despite the nearly identical elemental composition of these bacteria, differences in the intensity of elemental spectral lines provide crucial information for identification of bacteria. Under conditions of high-purity aluminum substrate, the identification rates of the four modeling methods reached 74.91%, 84.05%, 85.36%, and 96.07%, respectively. In contrast, under graphite substrate conditions, the corresponding identification rates reached 96.87%, 98.11%, 98.93%, and 100%. Graphite is found to be more suitable as a substrate material for bacterial classification, attributed to the fact that more characteristic spectral lines are excited in bacteria under graphite substrate conditions. Additionally, the emission spectral lines of graphite itself are relatively scarce, resulting in less interference with other elemental spectral lines of bacteria. Meanwhile, SVM exhibited the highest precision rate and recall rate, reaching up to 1, making it the most effective classification method in this experiment. This study provides a valuable approach for the rapid and accurate identification of bacterial species based on LIBS, as well as substrate selection, enhancing efficient microbial identification capabilities in fields related to social security and military applications.

Optical System Design for Laser-Induced Fluorescence Study of Helium Atoms on the Linear Plasma Device

According to the characteristic spectral lines of helium atoms, an optical system for the laser-induced fluorescence study of helium atoms has been designed. The design includes a helium spectral scheme and a laser injection and fluorescence collection system. The diode laser generates a 667.8-nm laser, and the laser is injected into the linear plasma device through an optical fiber. The fluorescence collection system detects 501.6-nm fluorescence signals. Experiments were carried out on the linear plasma device during helium discharge, simulating the helium ash environment at the boundary of the fusion reactor. The fluorescence collection was realized, and the fluorescence signals showed an increasing trend with laser power. Atomic density calibration and study will be performed based on the collected signals in the next step.

Research on online monitoring of aircraft skin laser paint removal thickness using standard curve method and PCA-SVR based on LIBS.

High-frequency pulse lasers, applied in the form of rapid scanning, act upon the surface of aircraft skin paint layers, thereby removing the paint layers, exhibiting characteristics of efficiency and eco-friendliness. Real-time monitoring of the paint removal effect and prevention of substrate damage necessitates the continuous monitoring of paint removal thickness. Combining Laser-Induced Breakdown Spectroscopy (LIBS) online monitoring technology enables laser-controlled paint removal under multiple effects coupling, meeting the requirements of airworthiness maintenance. This paper, based on a high-frequency nanosecond infrared pulse laser paint removal LIBS monitoring platform, conducts research on laser paint removal thickness LIBS online monitoring of aluminum alloy plates coated with dual-layer paint. Spectra corresponding to the removal thickness of each group are collected and, respectively, paint removal thickness monitoring models based on LIBS spectra are established using the standard curve method and Principal Component Analysis-Support Vector Regression (PCA-SVR) algorithm. When monitoring paint removal thickness using the standard curve method, the intensity of five Ti element characteristic spectral lines selected is correlated with the paint removal thickness, and segmented curve fitting according to the paint layer structure satisfies the segmented curve fitting of topcoat and topcoat + primer. Among them, the average coefficient of the curve fitting of the Ti II 589.088 nm characteristic spectral line is 0.89, and the root mean square error (RMSE) is 12.28 μm. Its performance is superior in the five standard curves; thus, its fitting equation is used as the criterion for paint removal thickness monitoring. To further improve monitoring accuracy, research on paint removal thickness monitoring models based on PCA-SVR is conducted. Compared to the traditional univariate standard curve method, the PCA-SVR model does not require segmented monitoring. After parameter optimization, the average fitting coefficient reaches 0.97, and the RMSE is 2.92 μm. The results indicate that the PCA-SVR-based paint removal thickness monitoring model has higher accuracy, thereby forming the basis for paint removal thickness monitoring. Through comparative research on paint removal thickness monitoring models, two types of paint removal thickness monitoring criteria are obtained, providing model solutions for high-precision monitoring and automation of aircraft skin laser paint removal thickness.

Detection of zinc in pig feed based on the cavities of different shapes combined with LIBS

Considering the serious risks posed by the heavy metals present in pig diets, monitoring and controlling the amount of these metals is crucial. The laser-induced breakdown spectroscopy (LIBS) is a promising technique for performing elemental analysis due to its unique advantages, such as rapid, in situ, nondestructive, and online detection of various minerals. In order to improve the detection accuracy and detection limit (LOD) of elements, the spatial constraint combined with laser-induced breakdown spectroscopy (CC-LIBS) is used to detect the quantity of zinc in pig feed and achieve the detection of samples with lower concentrations. Zn I 480.86 nm is selected as the characteristic spectral line, and the effects of different two-dimensional (cylindrical) and three-dimensional (hemispherical, truncated cone) cavities on the enhancement factor are compared under different time delays. The results show that the optimal conditions include a truncated cone cavity D5H3, a delay time of 2.08, and an enhancement factor of 3.01 associated with the analytical spectral line. The detection limit of zinc in samples under CC-LIBS is 62.67 mg/kg (the recommended quantity of zinc in pig diet mentioned in the safe use of feed additives is 43–80 mg/kg), which is 35.65% lower than that under the LIBS (97.39 mg/kg). Therefore, this work provides a new test basis and idea for the detection of zinc in pig feed.

Solid substrate assisted enhanced laser induced breakdown spectroscopy for metal element analysis in aqueous solution.

Due to plasma quenching caused by the dense water medium, laser-induced breakdown spectroscopy (LIBS) faces challenges such as strong continuous background radiation and weak and broadened characteristic spectral lines when directly detecting metal elements in liquids. In this work, we introduced a simple approach to improve underwater LIBS signals with a solid substrate-assisted method, which requires no sample pre-treatment and simple operation and thus has potential for in situ marine applications. In this method, four submerged solid substrates (Zn, Cu, Ni, and Si) were employed to investigate the breakdown characteristics of underwater LIBS and the mechanism of spectral enhancement by using a CaCl2 solution. The results demonstrated a significant improvement in the detection sensitivity of Ca with these substrates even at a short laser pulse with a relatively low laser energy (10 mJ). Among them, the semiconductor Si substrate exhibited the best enhancement effect, with an enhancement factor of over 75 for the Ca ionic lines at 393.4 nm and 396.8 nm and an enhancement factor of 29 for the Ca atomic line at 422.7 nm, respectively. This is mainly because the presence of substrate decreases the breakdown threshold of the liquid sample, and a higher plasma excitation temperature and electron density are obtained, which, in turn, leads to higher signal intensity. Furthermore, significant plasma emission enhancements for a wide range of elements are also achieved from seawater. These findings can contribute to the development of compact underwater in situ LIBS sensors with low power consumption, while ensuring high detection sensitivity.

3.4 μm mid-infrared pulse train laser based on stepped acousto-optic <i>Q</i>-switched external cavity pumped MgO:PPLN optical parametric oscillator

3.4 μm mid-infrared pulse train laser generates the characteristic spectral lines of absorption peaks of CH<sub>4</sub>, NO<sub>2</sub> and other molecules. Its pulse train working mode can increase the signal-to-noise ratio of laser detection echo signal. It has broad application prospects in radar detection, environmental monitoring, spectral analysis and other fields, which has aroused great interest of scientists. Compared with the traditional intra-cavity control method, the step <i>Q</i>-switching method has the advantages of simple structure, high energy utilization rate and accurate control. Therefore, in this paper, both the theoretical study and the experimental study of a 3.4 μm mid-infrared pulse train laser pumped by a step-type acousto-optic <i>Q</i>-switched external cavity in MgO:PPLN optical parametric oscillator are carried out. The theoretical model of fundamental frequency step acousto-optic <i>Q</i>-switching is established. The change trends of photon number density at different step intervals are simulated. The optimal trigger time of step signal required for step acousto-optic <i>Q</i>-switching is obtained. The possibility of obtaining pulse train laser output by step acousto-optic <i>Q</i>-switching is determined. In the simulation, the single pulse envelope contains 3 sub-pulse train waveforms. The pulse width of the sub-pulse in the pulse envelope gradually increases, the sub-pulse interval is 5 μs, and the pulse envelope repetition frequency is 20 kHz. At the same time, the optimal trigger times for the step signal are obtained to be 16.00, 21.00, 26.00, 50.00 μs. The mid-infrared pulse train laser experiment of step acousto-optic <i>Q</i>-switched external cavity pumped MgO:PPLN optical parametric oscillator is carried out, and the 3.4 μm mid-infrared pulse train laser output containing three sub-pulses in a single pulse envelope is obtained. The sub-pulse interval is 5 μs, the narrowest pulse width is 12.8 ns, and the pulse envelope repetition frequency is 20 kHz, when the maximum average output power is 1.08 W, the maximum optical-to-optical conversion efficiency of 1064 nm fundamental light and 3.4 μm parametric light is 10.05%, and the beam quality factor <inline-formula><tex-math id="Z-20240205221930">\begin{document}$M^2$\end{document}</tex-math><alternatives><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="4-20231348_Z-20240205221930.jpg"/><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="4-20231348_Z-20240205221930.png"/></alternatives></inline-formula> is 2.01. The output position of the sub pulses in each repetition period in the experiment is basically consistent with the simulation result, and it is found in both simulation and experiment that the width of the sub pulses within each pulse envelope gradually increases. The experimental results show that 3.4 μm mid-infrared pulse train laser output can be obtained by using step AOQS combined with OPO. This innovative method provides theoretical and technical support for detecting atmospheric pollutant concentration by using mid-infrared pulse train laser.

Investigating the Relationship between Zinc Deficiency andRenal Failure using Laser-Induced breakdown Spectroscopy(LIBS)

Kidney failure is a common health problem affecting millions of people worldwide, and zinc deficiency is a major contributor to this condition. In this study, the use of a laser-induced breakdown spectroscopy (LIBS) technique for the diagnosis of renal failure in zinc-deficient individuals was validated. The study was conducted by analyzing plasma samples collected from individuals with renal failure and zinc deficiency using the LIBS technique. Then the plasma parameters, such as electronic temperature and electron density, were analyzed and their equations were derived. To identify the characteristic spectral lines of elements in blood samples and compare them with those of healthy individuals. The results showed that the plasma parameters of individuals with renal failure and zinc deficiency were significantly different from those of healthy individuals. This study demonstrates the potential of the LIBS technique as a non-invasive diagnostic tool for renal failure in zinc-deficient individuals.

Density Functional Study of Structural and Vibrational Properties of α-Moganite

α-moganite is a recently discovered polymorph of silica, commonly intergrown with quartz in natural microcrystalline silica samples. An important challenge is finding an effective method for estimating its amount in a sample under study, which is important for its applications, related to the technology of growth of dielectric layers, as well as for fundamental problems, related to the formation of both terrestrial and lunar mineral deposits and biogenic formation. One of these methods is vibrational spectroscopy, with the help of which the presence of a particular compound is determined by the presence of characteristic spectral lines. In this work, the search for such lines is carried out using density functional theory calculations and comparisons of the IR and Raman spectra of α-quartz and α-moganite. With the help of such calculations, the stability of the moganite structure has been proven for the first time, and its spectral characteristics have been determined over the entire range of vibrational frequencies. Several new spectral lines characteristic of α-moganite were discovered in the 65–85 cm−1 region. Moreover, the evolution of spectral peculiarities under hydrostatic pressure was studied.

New Approach for Direct Determination of Manganese Valence State in Ferromanganese Nodules by X-ray Fluorescence Spectrometry

A new X-ray fluorescence technique is proposed as an alternative to the labor-consuming volumetric method for the estimation of manganese valence state in ferromanganese nodules. The approach is based on the measurement of the relative intensities of some X-ray fluorescence spectrum characteristic spectral lines and satellites (MnKβ5 and MnKβ′) preconditioned by electron transfer from the valence shell. Calibration curves were created using manganese oxide samples (MnO, Mn2O3, MnO2) and 12 certified reference materials of ferromanganese nodules, cobalt-bearing ferromanganese crusts, and manganese ores with certified (or determined by approved methods) total and tetravalent manganese content. The presence of high iron content was taken into account. A set of oceanic ferromanganese nodules samples collected in the Magellan Seamounts (Pacific Ocean) were analyzed. Differences between the results of the X-ray fluorescence method and volumetric techniques for tetravalent manganese content were 4.9 rel.%, which is comparable with the accuracy of the volumetric technique (3.6 rel.%).

Quantification of lithium in molten chlorides by optical emission spectrometry using a novel molten-salt-electrode microplasma source

A new molten-salt-electrode microplasma (MSEMP) source for optical emission spectrometry (OES) was constructed, equipped with a molten salt electrode and a metal hollow tube (fed with helium) counter electrode. The use of the MSEMP-OES for constituent analysis of molten salt was primarily evaluated. The acquirement of characteristic spectral lines of some elements in several molten chloride samples verified the good qualitative ability of the proposed MSEMP-OES. Solid copper was dissolved via charge transfer and excited in glow discharge, indicating its potential application in solid sample analysis. The temperature and composition of molten salt were found to have no influence on the lithium response and the accurate quantification of lithium in molten salt was achieved. This work provides new insights for promoting the application of microplasma technology in the in-situ analysis of elements, even in special samples.

A 1083 nm Narrow-Linewidth DFB Semiconductor Laser for Quantum Magnetometry

A 1083 nm laser, corresponding to a characteristic spectral line of 3He 23S1-23P, is the core light source for spin-exchange optical pumping-free technology, and thus has important developmental significance. In this paper, precise wavelength 1083.34 nm semiconductor lasers with 285 mW output power, −144.73 dBc/Hz RIN noise and 30.9952 kHz linewidth have been successfully achieved via reasonable chips design, high-quality epitaxial growth process and ultra-low reflectivity coating fabrication. All the results show the highest output power and ultra-narrow linewidth of the single-frequency 1083 nm DFB semiconductor laser achieved in this paper, which can fully satisfy the requirement of quantum magnetometers.

Automatic background correction method for laser-induced breakdown spectroscopy

In laser-induced breakdown spectroscopy (LIBS), fluctuations in laser energy, fluctuations in laser-sample or laser-plasma interactions, and environmental noise cause the acquired spectral to contain different backgrounds. The background has a substantial impact on the analysis of spectra. The aim of this work is to present a method for automatic estimation of diverse spectral backgrounds. The proposed method utilizes the ideas of window functions and differentiation, combined with a piecewise cubic Hermite interpolating polynomial (Pchip), to achieve automatic removal of spectral backgrounds. In simulation experiments on background correction, it has been observed that this method outperforms asymmetric least squares (ALS) and Model-free background correction in terms of effectiveness. It exhibits the highest signal-to-background ratio (SBR). This method effectively eliminates the baseline that makes the spectrum elevated and some of the white noise. It also demonstrates stability in the background baseline jumps and in the dense region of the characteristic spectral lines. By using this approach to correct the spectral backgrounds of seven different aluminum alloys, there is a notable enhancement in the linear correlation coefficient between spectral intensity and the concentration of Mg element. In the experiment of measuring Mg concentration in aluminum alloys,the linear correlation coefficients between the predicted and actual concentrations of Mg in aluminum alloys were substantially improved after correction of the spectral background. The values of linear correlation coefficients after background correction using asymmetric least squares (ALS) and model-free methods were 0.9913 and 0.9926, respectively. The correlation coefficients between predicted and actual concentrations of Mg in aluminum alloys were improved from 0.9154 to 0.9943 by using the method proposed in this study. In contrast to ALS and Model-free methods, the proposed method showed more significant improvement on this experiment with higher linear correlation and smaller error. Both simulation and quantitative experimental findings affirm that this method achieves high accuracy and can be effectively employed for background correction prior to quantitative analysis.

Greenhouse gas monitoring instrument on the GF-5 satellite-II: on-orbit spectral calibration.

The greenhouse gas monitoring instruments were carried on the Gaofen-5-II satellite, which was launched into orbit from the Taiyuan Satellite Launch Center on September 7, 2021. In order to improve the on-orbit data quantification level, a calibration device based on diffuse reflector system was designed, which can realize on-orbit spectral and radiation calibration. In this paper, the principle of standard spectral line selection is given, and the characteristic spectral lines that can be used for on-orbit spectral calibration are extracted. The wavelength deviation evaluation function is established by using the method of matching the high-resolution reference spectrum after the linear function of the convolution instrument with the on-orbit calibration measurement spectrum, and finally using the Levenberg-Marquardt algorithm to evaluate the function. The optimization solution is the on-orbit wavelength calibration result. According to the above method, the on-orbit calibration data are processed. After calibration, the maximum deviation of the on-orbit spectral offset is changed from 0.133 to 0.009c m -1, and variations in magnitude less than 10% of the spectral resolution for C O 2 (1.57µm) band have been detected.

Identification and classification of metal copper based on laser-induced breakdown spectroscopy

Precious and half-precious metals are widely used in various fields, which makes it of great significance to recycle them, and copper was taken as an example for the investigation in this paper. A system based on laser-induced breakdown spectroscopy combined with machine learning algorithms was developed and employed in the lab to identify and classify several metal devices that contain copper element. According to the obtained emission spectra, 36 characteristic spectral lines of copper element are observed in the spectrogram of high-purity copper, as well as some metallic elements including Zn, Ca, Mg, and Na that also appeared. Moreover, eight types of similar metal devices containing copper element which are common in life (electrode, copper plug, copper tape, carbon brush, wire, circuit board, gasket, and coil) were selected to perform spectral analysis. Rough classification can be achieved by observing the spectra of eight metal devices. The effective classification process of metal devices was implemented by conducting principal component analysis, which built a model to reduce the dimension of spectral data for classification. Several samples are distributed at different positions in the principal component space, which is established based on the three principal components as the coordinate axis. K-nearest neighbors were employed to verify the classification effectiveness, acquiring the final classification accuracy of 99%. The results show that the development system has a broad development prospect for identifying metal copper and classifying metal devices that contain copper element.

Accuracy enhancement of laser-induced breakdown spectroscopy by polarization spectrum fusion

Laser-induced breakdown spectroscopy (LIBS) has been proven to be an effective way for online analysis of coal properties (such as ash content, volatile content, and calorific value), but the quantitative analysis accuracy still needs to be improved. Herein, a polarization spectral data fusion scheme was proposed to obtain more abundant spectral information and improve the performance of quantitative analysis models. First, the polarization effect of laser-induced coal plasma emission was analyzed using the Stokes parameters of the average spectra. Due to the partially polarized phenomenon of emission, fused data of PRLIBS (0°, 0) (horizontal plasma polarization spectra) and PRLIBS (90°, 0) (vertical plasma polarization spectra) was utilized to establish an efficient prediction model to obtain high prediction accuracy. For fusion models, the root mean square error prediction (RMSEP) of ash content, volatile content and calorific values were 1.497%, 0.595%, and 0.597%, respectively, and the coefficients of determination (R2) were 0.980, 0.968, and 0.978, respectively. Comparison with regression models for individual PRLIBS (0°, 0), PRLIBS (90°, 0), and LIBS data, the data fusion scheme significantly improved the performance of the prediction model. The improvement in model performance is due to the fact that there is no perfect correlation between PRLIBS (0°, 0) and PRLIBS (90°, 0), whilst polarization spectral fusion makes it possible to acquire more number of characteristic spectral lines with high importance score. The results demonstrated the feasibility of PRLIBS spectral fusion to provide better analytical results for ash content, volatile content, and calorific values of coal.