QUANTITATIVE-ANALYSIS; PARAMETERS; SAMPLES; LIBS

A calibration method based on homogeneous material for correcting laser-induced breakdown spectroscopy (LIBS) measurement-error bias in the case of dust pollution under laboratory conditions is proposed. The measured plasma spectra of the sample can be corrected by measuring the spectral integral of the homogeneous material. Thus, we can effectively minimize the dust pollution effect on LIBS and guarantee its precision. Results show that the mean absolute errors of CaO, MgO, Fe2O3, Al2O3, and SiO2 in cement samples are decreased notably from 1.02%, 0.06%, 0.15%, 0.57%, and 0.80% to 0.41%, 0.02%, 0.04%, 0.35%, and 0.39%, respectively. Combination of this calibration method with the traditional optical dustproof methods will significantly extend the LIBS equipment maintenance cycle and make preliminary preparations for the next practical industrial application. (C) 2017 Optical Society of America

Laser-induced breakdown spectroscopy; Proximate analysis of coal; Spectral fitting; Support vector machine;LIBS; SYSTEM

Online accurate proximate analysis of coal is vitally important to the optimization of industrial production and reduction in coal consumption. However, due to the ""matrix effect"" caused by the complex and diverse coal species in China, the measurement accuracy needs to be improved by using laser-inducedbreakdown spectroscopy (LIBS). In our experiment, both the spectral pretreatment method and the calibration model for the conversion of laser induced coal plasma spectra to the coal proximate analysis results were optimized. Experimental results showed that, compared with the traditional method, the proposed single or multiple-peak Lorentzian spectral fitting for spectral line intensity calculation reduced the mean RSD from 12. 1% to 9. 7%. For kernel function parameters optimization, the mean absolute error (MAE) of the particle swarm optimization (PSO) was smaller than that of the grid parameter (Grid) and the genetic algorithm (GA). The root mean square error (RMSEP) of support vector machine (SVM) regression model based on PSO parameter optimization was less than that of partial least squares regression (PLS). By combining the single- or multiple-peak Lorentzian spectral fitting method with the PSO based SVM for regression modeling, the average absolute errors (AAE) of predicted proximate analysis results were certified to be: 1. 37% for coal ash content of 16%similar to 30%, 1. 77% for coal ash content of 30% or more, 0. 65 MJ . kg(-1) for calorific value of 9 similar to 24 MJ . kg(-1), 1. 09% for volatile matter of 20% or less, and 1. 02% for volatile matter of 20% or more.

EMISSION-LINES; ALUMINUM; PLASMAS; COAL

A novel self-absorption-free laser-induced breakdown spectroscopy (SAF-LIBS) technique is proposed to directly capture the optically thin spectral line by matching the measured doublet atomic lines intensity ratios with the theoretical one. To realize the experimental SAF-LIBS, the integration time, the fiber collection angle, and the delay time are optimized. The optically thin conditions are validated by comparing the linearity of Boltzmann plots with the traditional self-absorption (SA) correction method and evaluating the SA coefficients. The applicability and limitation of SAF-LIBS on element concentration and laserenergy are also discussed. Univariate quantitative analysis results show that, compared with ordinary LIBS, the average absolute error of aluminum concentration has been reduced by an order of magnitude, which proves that this SAF-LIBS technique is qualified to realize accurate chemical composition measurements. (C) 2017 Optical Society of America

INDUCED BREAKDOWN SPECTROSCOPY; SELF-ABSORPTION; EMISSION-LINES

The temporal evolution of doublet lines intensity ratio, plasma temperature (T) and electron density (N-e) of different radial parts of aluminum plasma as well as the relationship between optically thin condition and plasma characteristics are investigated. With respect to doublet lines intensity ratio, the optimal delay time (t(ot)) when the plasma is optically thin is determined by comparing the experimental intensity ratio of doublet Al(I) lines with the theoretical value. The optimal integration time, fiber collection angle, and delay time corresponding to the optically thin condition are 400 ns, 45 degrees, and 500 ns, respectively, in which case the linearity of the calibration curve reaches 0.98, enabling more accurate composition measurements. The maximum temperature values are reached within 300 ns at 10 degrees, 30 degrees, 45 degrees, and 80 degrees, but after 900 ns at 55 degrees, 60 degrees, and 70 degrees. Combined with the morphologic image of the plasma, we infer that the time at which a radial part of plasma reaches the highest temperature is related to the extent of its expansion. It is generally understood that the faster the radial part of plasma expands, the sooner the temperature reaches its maximum. The radial part of plasma may not be optically thin when the corresponding temperature reaches its maximum. If the highest temperature is obtained earlier, the optically thin condition is likely to appear in the descending stage of the temporal evolution curve of temperature. Otherwise, it may appear in the ascending stage. There seems to be no simple positive correlation between electron density and optically thin condition.

PLASMA SPECTROSCOPY; ELEMENTAL ANALYSIS; LIBS

Fast detection of heavy metals in plant materials is crucial for environmental remediation and ensuring food safety. However, most plant materials contain high moisture content, the influence of which cannot be simply ignored. Hence, we proposed moisture influence reducing method for fast detection of heavy metals using laser-induced breakdown spectroscopy (LIBS). First, we investigated the effect of moisture content on signal intensity, stability, and plasma parameters (temperature and electron density) and determined the main influential factors (experimental parameters F and the change of analyte concentration) on the variations of signal. For chromium content detection, the rice leaves were performed with a quick drying procedure, and two strategies were further used to reduce the effect of moisture content and shot-to-shot fluctuation. An exponential model based on the intensity of background was used to correct the actual element concentration in analyte. Also, the ratio of signal-to-background for univariable calibration and partial least squared regression (PLSR) for multivariable calibration were used to compensate the prediction deviations. The PLSR calibration model obtained the best result, with the correlation coefficient of 0.9669 and root-mean-square error of 4.75 mg/kg in the prediction, set. The preliminary results indicated that the proposed method allowed for the detection of heavy metals in plant materials using LIBS, and it could be possibly used for element mapping in future work.