QUANTITATIVE-ANALYSIS; CARBON CONTENT; LIBS; TEMPERATURE; TRANSFORM; PLASMAS; LINES

To minimize the impact of spectral interference on laser-induced breakdown spectroscopy (LIBS) quantitative analyses, an algorithm based on wavelet transform was developed for simultaneous correction of spectral interference and continuum background. The root-mean-square error of calibration (RMSEC) of the univariate regression model for the element of interest was applied to determine the wavelet function, decomposition level, and scaling factor alpha. When the interference-free analytical lines of the elements of interest cannot be directly obtained from the measured spectra, they can be extracted from the spectra with the developed method for quantitative analysis. This method was applied for LIBS analyses of chromium (Cr), silicon (Si), titanium (Ti), and manganese (Mn) with continuum backgrounds and spectral interference in low alloy steel samples. The root-mean-square errors of cross-validation (RMSECV) of elements Cr, Si, Ti, and Mn were 0.0295, 0.0140, 0.0183, and 0.0558 wt%, respectively. The results demonstrated that the developed algorithm contributed to accuracy improvement for LIBS quantitative analyses with the presence of spectral interference.

IN-SITU; LIBS; STEELS; CLASSIFICATION; EXPLORATION; ELEMENTS; SPECTRA; SAMPLES; SOIL; MN

Stand-off laser-induced breakdown spectroscopy (ST-LIBS) has attracted increasing attention as a valuable method for the remote analysis of materials. In this work, a multi-collector (MC) system combining low cost small lenses imitating the structure and shape of an artificial optical compound eye (AOCE) is presented to enhance the spectral intensity of ST-LIBS. The volume of the MC system is one eighteenth smaller than that of a telescope system and the number of collectors can be flexibly changed according to the requirements. The mechanisms of spectral enhancement are also discussed. In order to illustrate the performance of the MC system, the spectral intensities and the limits of detection (LoDs) of Mn and Cr elements acquired by the telescope system and the MC system were compared at a distance of 2 meters. The results showed that the spectral intensity acquired from the MC system was enhanced by 2.2 times, and the LoDs of Mn and Cr were decreased to 294 mu g g(-1) and 49 mu g g(-1). These results indicate that the MC system has great potential in providing a portable, flexible and effective collection method for remote detection.

Laser-induced breakdown spectroscopy; Characteristic spectral lines of non-metallic elements; Support vector machine; Plastics identification;ARTIFICIAL NEURAL-NETWORKS; INDUCED PLASMA; EMISSION; POLYMERS

Laser-induced breakdown spectroscopy (LIPS) combined with support vector machine (SVM) was adopted to identify 20 kinds of different colored industrial plastics from different manufacturers in open air. The experimental parameters of spectral acquisition were optimized firstly. 100 spectra recorded under optimum conditions were randomly and equally divided into training set and test set. 6 non-metallic characteristic spectral lines were used to avoid the interference with metallic lines. And the training time of SVM model was reduced. The results show that 996 of 1000 test spectra were identified correctly and the average classification accuracy is reached to 99.6%. The classification efficiency is improved with 6 non-metallic characteristic spectral lines. The research demonstrates that, when fewer of major non-metallic characteristic spectral lines are used, laser-induced breakdown spectroscopy technique with support vector machine can identify more kinds of plastics with high accuracy and efficiency.

Laser-induced breakdown spectroscopy; Support vector machine; Principal component analysis; Tissue classification;BIOMEDICAL APPLICATIONS; LIBS; IDENTIFICATION; ELEMENTS; IMPROVEMENT; PROGRESS; PORK; MN

To improve the classification accuracy of fresh meat species using laser. induced breakdown spectroscopy (LIBS), the support vector machine (SVM) and principal component analysis (PCA) were combined to classify fresh meat species (including pork, beef, and chicken). A simple sample preparation to flatten fresh meat by glass slides was proposed. For each meat sample, 150 spectra were recorded and randomly arranged. The first 75 spectra were used to train a model while the others were used for model validation. By analyzing the 49 normalized spectral lines (K, Ca, Na, Mg, Al, H, O, etc.) in the different tissues, the classification model was built. The results showed that the dimensionality of input variables was decreased from 49 to 10 and modeling time was reduced from 89. 11 s to 55. 52 s using PCA, thus improving the modeling efficiency. The mean classification accuracy of 89. 11% was achieved. The method and reference data are provided for further study of fresh meat classification by laser-induced breakdown spectroscopy technique.

ACCURACY IMPROVEMENT; WAVELET TRANSFORM; LIBS; SPECTROMETRY; PEAKS

The complex and serious spectral interference makes it difficult to detect trace elements in soil using laser induced breakdown spectroscopy (LIBS). To address it, LIBS-assisted by laser-induced fluorescence (LIES-LIP) was applied to selectively enhance the spectral intensities of the interfered lines. Utilizing this selective enhancement effect, all the interference lines could be eliminated. As an example, the Pb I 405.78 nm line was enhanced selectively. The results showed that the determination coefficient (R-2) of calibration curve (Pb concentration range = 14-94 ppm), the relative standard deviation (RSD) of spectral intensities, and the limit of detection (LOD) for Pb element were improved from 0.6235 to 0.9802, 10.18% to 4.77%, and 24 ppm to 0.6 ppm using LIBS-LIF, respectively. These demonstrate that LIBS-LIF can eliminate spectral interference effectively and improve the ability of LIBS to detect trace heavy metals in soil.

IMPROVEMENT; CHEMOMETRICS; SPECTROMETRY; CONFINEMENT; CALIBRATION; SELECTION; PLASMA; LIBS; SOIL

In this work, a genetic algorithm (GA) was employed to select the intensity ratios of the spectral lines belonging to the target and domain matrix elements, then these selected line-intensity ratios were taken as inputs to construct an analysis model based on an artificial neural network (ANN) to analyze the elements copper (Cu) and vanadium (V) in steel samples. The results revealed that the root mean square errors of prediction (RMSEPs) for the elements Cu and V can reach 0.0040 wt. % and 0.0039 wt. %, respectively. Compared to 0.0190 wt. % and 0.0201 wt. % of the conventional internal calibration approach, the reduction rates of the RMSEP values reached 78.9% and 80.6%, respectively. These results indicate that the GA combining ANN can excellently execute the quantitative analysis in laser-induced breakdown spectroscopy for steel samples and further improve analytical accuracy. (C) 2017 Optical Society of America

Laser-induced breakdown spectroscopy; Rare earth elements; Aqueous solution; Surface-enhanced;RARE-EARTH-ELEMENTS; QUANTITATIVE-ANALYSIS; ACCURACY IMPROVEMENT; EMISSION; SAMPLES; LIBS; EXTRACTION; LIQUIDS; WATER

Determination of rare earth elements (REEs) plays an important role in the extraction process. In this work, surface-enhanced laser-induced breakdownspectroscopy (SENLIBS) was introduced to detect REEs (lanthanum, cerium, praseodymium, and neodymium elements) in an aqueous solution. The emission lines of La II 394.91 nm, Ce II 418.66 nm, Pr II 422.29 nm, and Nd II 406.10 nm were selected for quantitative analysis by drying the analytical samples on a Zn metal substrate surface and optimizing the experimental parameters. The results showed that the limits of detection (LoDs) for determining La, Ce, Pr, and Nd elements can reach to 0.85, 4.07, 2.97, and 10.98 mu g mL(-1), respectively, which proved that SENLIBS is a feasible method for determining REEs.