Laser-induced breakdown spectroscopy quantitative analysis method via adaptive analytical line selection and relevance vector machine regression model

Abstract A new LIBS quantitative analysis method based on analytical line adaptive selection and Relevance Vector Machine (RVM) regression model is proposed. First, a scheme of adaptively selecting analytical line is put forward in order to overcome the drawback of high dependency on a priori knowledge. The candidate analytical lines are automatically selected based on the built-in characteristics of spectral lines, such as spectral intensity, wavelength and width at half height. The analytical lines which will be used as input variables of regression model are determined adaptively according to the samples for both training and testing. Second, an LIBS quantitative analysis method based on RVM is presented. The intensities of analytical lines and the elemental concentrations of certified standard samples are used to train the RVM regression model. The predicted elemental concentration analysis results will be given with a form of confidence interval of probabilistic distribution, which is helpful for evaluating the uncertainness contained in the measured spectra. Chromium concentration analysis experiments of 23 certified standard high-alloy steel samples have been carried out. The multiple correlation coefficient of the prediction was up to 98.85%, and the average relative error of the prediction was 4.01%. The experiment results showed that the proposed LIBS quantitative analysis method achieved better prediction accuracy and better modeling robustness compared with the methods based on partial least squares regression, artificial neural network and standard support vector machine.

A laser induced breakdown spectroscopy quantitative analysis method based on the robust least squares support vector machine regression model

Data fluctuation in multiple measurements of Laser Induced Breakdown Spectroscopy (LIBS) greatly affects the accuracy of quantitative analysis. A new LIBS quantitative analysis method based on the Robust Least Squares Support Vector Machine (RLS-SVM) regression model is proposed. The usual way to enhance the analysis accuracy is to improve the quality and consistency of the emission signal, such as by averaging the spectral signals or spectrum standardization over a number of laser shots. The proposed method focuses more on how to enhance the robustness of the quantitative analysis regression model. The proposed RLS-SVM regression model originates from the Weighted Least Squares Support Vector Machine (WLS-SVM) but has an improved segmented weighting function and residual error calculation according to the statistical distribution of measured spectral data. Through the improved segmented weighting function, the information on the spectral data in the normal distribution will be retained in the regression model while the information on the outliers will be restrained or removed. Copper elemental concentration analysis experiments of 16 certified standard brass samples were carried out. The average value of relative standard deviation obtained from the RLS-SVM model was 3.06% and the root mean square error was 1.537%. The experimental results showed that the proposed method achieved better prediction accuracy and better modeling robustness compared with the quantitative analysis methods based on Partial Least Squares (PLS) regression, standard Support Vector Machine (SVM) and WLS-SVM. It was also demonstrated that the improved weighting function had better comprehensive performance in model robustness and convergence speed, compared with the four known weighting functions.

Interactions between oligoamines and superoxide anion

1- Oligoamines and EDTA inhibited the reduction of cytochrome-C and nitrobule tetrazolium (NBT) induced by the hypoxanthine/xanthine oxidase superoxide anion generating system in the following order of effectiveness: putrescine > diaminopropane > spermidine > EDTA > spermine > cadaverine. 2- Oligoamines and EDTA did not affect the rate of urate formation from the hypoxanthine/xanthine oxidase system. 3- Oligoamines and EDTA inhibited the reduction of cytochrome-C induced by stimulated PMNL's in the same order of effectiveness as mentioned before. 4- Oligoamines and EDTA inhibited luminol dependent stimulated PMNL's chemiluminescence. 5- Oligoamines and EDTA inhibited the aerobic photoreduction of NBT. 6- Oligoamines-copper sulphate complexes inhibited the reduction of cytochrome-C induced by the hypoxanthine/xanthine oxidase system more effectively than oligoamines or copper sulphate individually. 7- Superoxide anion, hydrogen peroxide and hydroxyl radical induced breakdown of isolated intact guinea pig liver lysosomes. 8- Oligoamines and EDTA protected isolated intact guinea pig liver lysosomes from the lytic effect of superoxide anion generated either by the hypoxanthine/xanthine oxidase system or by stimulated PMNL's. 9- Oligoamines and EDTA have no stabilizing effect on isolated intact guinea pig liver lysosomes. 10- The uptake of oligoamines by lysosomes was in the following order: putrescine > spermidine > spermine. 11- Oligoamines were metabolised into aldehyde compounds either by the hypoxanthine/xanthine oxidase system or stimulated PMNL's. 12- Oligoamines and EDTA have no effect on the activities of free lysosomal enzymes (acid phosphatase and -glucosaminidase). 13- Oligoamines and EDTA inhibited lipid peroxidation in guinea pig liver lysosomes induced either by the hypoxanthine/xanthine oxidase or ascorbic acid-ferrous sulphate. 14- Oligoamines and EDTA have no effect on the release of PGE_2 from stimulated peritoneal guinea pig PMNL's. 15- Oligoamines increased the uptake of (^3H)thymidine and (^3H)leucine by stimulated peritoneal guinea pig macrophages in the following order of effectiveness: spermine > spermidine > putrescine > cadaverine. 16- PGE_2, dibutyryl Cyclic AMP, and theophylline inhibited luminol dependent stimulated peritoneal guinea pig PMNL's chemiluminescence.