Zhibo Cong

Wavelet denoising method for laser-induced breakdown spectroscopy

The wavelet threshold denoising method is an effective noise suppression approach for noisy laser-induced breakdown spectroscopy spectrum. The wavelet threshold denoising method is influenced by several key issues such as the choice of wavelet, the choice of decomposition level, threshold selection, and the choice of thresholding functions. In this paper, the double threshold optimization models of semi-soft thresholding function are established firstly. Next, on the basis of grey relational analysis and Euclid closeness of fuzzy theory, a method of amending the double thresholds of semi-soft thresholding function is put forward. The performance of the proposed method is verified by analysis of both synthetic and observed signals. The limit of detection values are reduced by more than 50% and the signal to noise ratios are improved by a factor of two by using the proposed method.

A Method for Resolving Overlapped Peaks in Laser-Induced Breakdown Spectroscopy (LIBS)

Spectral peak overlapping is a basic problem in analytical data processing of laser-induced breakdown spectroscopy (LIBS). Curve fitting is the typical method of resolving overlapped peaks. For preventing ambiguous fitting, appropriate initial values must be known. The aim of this work was to present a method that could be used to determine appropriate initial values of the curve-fitting method by using fractional differential theory. According to the variation of characteristic points of Lorentzian peaks at different fractional differential orders, parameter estimators were obtained that were used to calculate the initial values of the curve-fitting method. As it is a widely used optimization method, the Levenberg–Marquardt method was used in curve fitting. Simulation and LIBS experimental results proved that the proposed method of the initial value estimation can effectively resolve the overlapped peaks in LIBS data processing.

Selection of spectral data for classification of steels using laser-induced breakdown spectroscopy

Principal component analysis (PCA) combined with artificial neural networks was used to classify the spectra of 27 steel samples acquired using laser-induced breakdown spectroscopy. Three methods of spectral data selection, selecting all the peak lines of the spectra, selecting intensive spectral partitions and the whole spectra, were utilized to compare the influence of different inputs of PCA on the classification of steels. Three intensive partitions were selected based on experience and prior knowledge to compare the classification, as the partitions can obtain the best results compared to all peak lines and the whole spectra. We also used two test data sets, mean spectra after being averaged and raw spectra without any pretreatment, to verify the results of the classification. The results of this comprehensive comparison show that a back propagation network trained using the principal components of appropriate, carefully selected spectral partitions can obtain the best results. A perfect result with 100% classification accuracy can be achieved using the intensive spectral partitions ranging of 357-367 nm.

Application of Stand-off Double-Pulse Laser-Induced Breakdown Spectroscopy in Elemental Analysis of Magnesium Alloy*

In this study, a stand-off and collinear double pulse laser-induced breakdown spectroscopy (DP LIBS) system was designed, and the magnesium alloy samples at a distance of 2.5 m away from the LIBS system were measured. The effect of inter-pulse delay on spectra was studied, and the signal enhancement was observed compared to the single pulse LIBS (SP LIBS). The morphology of the ablated crater on the sample indicated a higher efficiency of surface pretreatment in DP LIBS. The calibration curves of Ytterbium (Y) and Zirconium (Zr) were investigated. The square of the correlation coefficient of the calibration curve of element Y reached up to 0.9998.