The laser induced breakdown spectroscopy (LIBS) technology coupled with a principal component analysis and relevance vector machine (PCA-RVM) was used for rapidly detect the concentration of heavy metal Cr in soil. The PCA-RVM was obtained by optimizing the relevance vector machine (RVM) with PCA.LIBS spectrum of 14 soil samples with different concentration of elements were collected and used for the model training and prediction. 10 of those were selected as training sample sets to build the PCA-RVM model, and the others as test sample sets for model evaluation. Comparing with the prediction results of PCA-RVM model with that obtained using RVM and support vector machines (SVM) model, the analytical accuracy improved by 84.17% to RVM and 92.62% to SVM at the maximum, respectively. Indicating that the PCA-RVM model can effectively improve the detection accuracy and repeatability of LIBS analysis of elemental concentration in soil.
Since the zeroth-order Bessel and Airy function are invariant propagation modes in free space, they can be potentially
used not only in time but also in space. Different from nonlinear solitary wave, Airy-Bessel configuration wave packets
with particle-like nature are a kind of stable linear wave packets without spatio-temporal spread during propagation in
free space because it combine spatial Bessel beams with temporal Airy pulses. In the paper, by studying spatially
induced group velocity dispersion effect during propagation of ultrashort pulsed Bessel beams, we find that Gaussian-
Bessel wave packets can evolve as Airy-Bessel in given propagation conditions. The research results are expected to
open up one new channel to generate stable linear localized wave packets.
A new iterative method for creating a pure phase hologram to diffract light into two arbitrary two-dimensional intensity profiles in two output planes is presented. This new method combines the Gerchberg-Saxton (GS) iterative algorithm and the compensation iterative algorithm. Numerical simulation indicates that the new method outperforms the most frequently used method in accuracy when it is used to generate large size images. A preliminary experiment of optical reconstruction has been taken and used to verify the feasibility of our method.
With the combination of neuro-genetic approach and laser-induced breakdown spectroscopy (LIBS), an improved
method is proposed to predict the concentrations of Ni, Zr and Ba in soil samples. In this method, an artificial neural
network (ANN) based on gradient descent with momentum and adaptive learning rate back propagation (GDMABP)
algorithm is used. Simultaneously, an optimization strategy based on genetic algorithm (GA) is employed for selecting
number of neurons in hidden layer and momentum coefficient in GDMABP ANN and to obtain an optimized network.
Subsequently, the network is used to predict concentration of Ni, Zr and Ba from the tested LIBS data. The approach of
neuro-genetic for LIBS analysis is described in detail. The predicted results are compared with those obtained from
conventional calibration curve method. Overall, the method of combining neuro-genetic approach with LIBS is capable
of predicting elemental concentration.
Calibration-Free Laser-Induced Breakdown Spectroscopy (CF-LIBS) is a promising approach for quantitative analysis
without using certified samples and calibration curves. It can overcome the matrix effects. However this method is based
on the hypothesis that in the actual temporal observation window the plasma is in local thermal equilibrium (LTE). In
this paper, the plasma is generated using a Q-switched Nd:YAG laser hits on certified soil samples in air at atmospheric
pressure. The local values of the parameters that characterize laser induced plasma (temperature, electron density) have
been derived from the recorded spectra with different observation window. The electron density in the plasma at
different time delay after laser firing has been investigated in detail, which can be served as a criterion of the existence of
the LTE. As a result, an appropriate time delay is obtained. By comparing the temperatures deduced from the
spectroscopy line intensity of neutral atom and ion emissions at different gate width, the optimized time duration which
satisfies the LTE is obtained. Finally, we analyze the importance of observation window and its effect on the accuracy
and precision of this method.
Laser-induced breakdown spectroscopy (LIBS) is a promising technique for in situ environmental analysis. The potential
of this technique for accurate quantitative analysis of heavy metals in soil could be greatly improved by optimized the
time delay, laser energy, working distance, et. and by analyzing the results with a procedure which overcomes the
problem related to the physical character of soil sample. A LIBS system for soil analysis is reported here. The optimum
experimental conditions for quantitatively measurement of Sr and other heavy metals in soil are presented. A new data
acquisition and statistical method has been used to analyze the recorded spectra. The precision of this method, in terms
of relative standard deviation (RSD), is of 7 % for Sr I 460.73 nm. The calibration curve for quantitative measurement of
Sr has been built. From the calibration curves, the detection limits of Sr in soil were determined to be 15.0 µg/g, and are
better than the data reported in literature.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.