Cadmium and hexavalent chromium; laser-induced breakdown spectroscopy (LIBS); lead, quantitative analyses; plant materials;PLASMA-MASS-SPECTROMETRY; NEAR-INFRARED SPECTROSCOPY; ARTIFICIAL NEURAL-NETWORK; GANNAN NAVEL ORANGE; POWDER SAMPLES; DOUBLE-PULSE; IN-SITU; MULTIVARIATE-ANALYSIS; ELEMENTAL ANALYSIS; ATOMIC-ABSORPTION

Bearing the merits of rapid, minimally destructive, and simultaneous multi-element analyses, laser-induced breakdown spectroscopy (LIBS) shows its unique advantages in quantitative analyses of lead, cadmium, and hexavalent chromium in plant materials. However, the greatest challenge LIBS must confront is calibration. Various methods for calibration are proposed and put into effect; nevertheless, limits of detection acquired by LIBS are not acceptable when they are compared with the maximum residue limits drawn up by governments, and LIBS's performances in quantitative analyses are to be improved. This review summarizes recent studies of analyzing lead, cadmium, and hexavalent chromium in plant materials quantitatively by LIBS; weighs the strengths and weaknesses of their calibration methods; and recommends the combination of matrix-matched standards based on spiked sample materials and internal standard as well as chemometrics in complicated situations for calibration in LIBS. Selecting the emission line of the analyte, sample enrichment and signal enhancement are measures that this review puts forward to improve the performances of LIBS in calibration. These quantitative analyses of lead, cadmium, and hexavalent chromium in plant materials by LIBS provide an opportunity to be utilized in mapping distributions and remediation for soil and water, as well as supervision for agricultural products safety and pollution treatments.

Laser-induced breakdown spectroscopy; Matcha; Green tea powder; Linear discriminant analysis (LDA);LIBS

The feasibility of using laser-induced breakdown spectroscopy (LIBS) combined with chemometrics methods for fast identification of matcha and green tea powder was investigated in this paper. The main differences between matcha and green tea powder are varieties of tea plants, cultivation management, growth time and processing technology. LIBS spectra between 230 to 880 nm of matcha produced by different manufacturers and green tea powder made with different fixation methods were selected and min-max normalization was the measure for preprocessing. Characteristic wavelengths were selected according to the X-variables loadings on the basis of principal component analysis (PCA), and then linear discriminant analysis (LDA) models were built based on characteristic wavelengths. The results showed that the LDA model based on characteristic wavelengths could identify matcha and green tea powder rapidly. Four characteristic wavelengths belong to C(I) 247. 94 nm, Mg(II) 279. 60 nm, Ca(II) 393. 45 nm and Fe(II) 766. 68 nm. The accuracy of the calibration and the prediction set all reached 100%. Laser-induced breakdown spectroscopy could accurately identify matcha produced by different manufacturers and green tea powder made with different fixation methods.

Laser-induced breakdown spectroscopy; Baseline correction; Asymmetric penalty; Convex optimization;PARTIAL LEAST-SQUARES; WAVELET-TRANSFORM; MODEL; LIBS; COAL

For laser-induced breakdown spectroscopy (LIBS) quantitative analysis technique, baseline correction is an essential part for the LIBS data preprocessing. As the widely existing cases, the phenomenon of baseline drift is generated by the fluctuation of laser energy, inhomogeneity of sample surfaces and the background noise, which has aroused the interest of many researchers. Most of the prevalent algorithms usually need to preset some key parameters, such as the suitable spline function and the fitting order, thus do not have adaptability. Based on the characteristics of LIBS, such as the sparsity of spectral peaks and the low-pass filtered feature of baseline, a novel baseline correction and spectral data denoising method is studied in this paper. The improved technology utilizes convex optimization scheme to form a non-parametric baseline correction model. Meanwhile, asymmetric punish function is conducted to enhance signal-noise ratio (SNR) of the LIBS signal and improve reconstruction precision. Furthermore, an efficient iterative algorithm is applied to the optimization process, so as to ensure the convergence of this algorithm. To validate the proposed method, the concentration analysis of Chromium (Cr),Manganese (Mn) and Nickel (Ni) contained in 23 certified high alloy steel samples is assessed by using quantitative models with Partial Least Squares (PLS) and Support Vector Machine (SVM). Because there is no prior knowledge of sample composition and mathematical hypothesis, compared with other methods, the method proposed in this paper has better accuracy in quantitative analysis, and fully reflects its adaptive ability. (C) 2017 Elsevier B.V. All rights reserved.

Laser-induced breakdown spectroscopy; tissue ablation; soft tissue; classification; principal component analysis; k nearest neighbors; confusion matrix; pork;BIOMEDICAL APPLICATIONS

Classification of pork soft tissues, including skin, fat, loin, tenderloin and ham muscles, was achieved using combination of laser-induced breakdownspectroscopy, principal component analysis and k nearest neighbors classification.