MINERALS; ELEMENTS; SAMPLES; WATER; PB

On-stream analysis of the element content in ore slurry has important significance in the control of the flotation process and full use of raw materials. Therefore, techniques that can monitor the chemistry in slurries online are required. Laser-induced breakdown spectroscopy (LIBS) is one of the potential approaches to online measurements due to its capability of in situ and real-time analysis. However, using LIBS for on-stream analysis of slurries is challenging due to the issues such as surface ripples, sample splashing, sedimentation, etc. To address these problems, we developed a slurry circulation system. The effects of slurry flow rate on LIBS spectra were investigated to achieve the optimal detecting surface for better repeatability of LIBS. The coefficient of determination R-2 of the calibration curve for Fe element is 0.982, and the limit of detection of Fe element was estimated to be 0.075 wt. % under the optimized experimental parameters. The results show that this slurry circulation system is applicable to the on-stream slurry analysis. (C) 2017 Optical Society of America

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.

VACUUM-ULTRAVIOLET; INDUCED PLASMA; SPECTROMETRY; OPTIMIZATION; COAL; IMPROVEMENTS; ABSORPTION

Carbon is a key element for steel properties but (a) hard to be determined by laser-induced breakdown spectroscopy (LIBS). Utilizing the combination of carbon in analytes and nitrogen in ambient gas to generate carbon nitrogen (CN) radicals, LIBS assisted with laser-induced radical fluorescence (LIBS-LIRF) was proposed to resonantly excite radicals instead of atoms in plasmas. The CN radicals in the B-2 Sigma-A(2)II band were stimulated by a 421.60 nm laserwavelength and emitted 388.34 nm fluorescence. The results show that the spectral intensity of the CN radicals was enhanced by 2 orders of magnitude using LIBS-LIRF. Then carbon content in steels was accurately and sensitively determined without spectral interference. The limits of detection (LoDs) were 0.039 and 0.013 wt % in air and nitrogen gas, respectively. The limits of quantification (LoQs) were 0.130 and 0.043 wt % in air and nitrogen gas, respectively. This work demonstrated the feasibility of LIES to realize reliable carbon determination in steel industry.

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