Laser-induced breakdown spectroscopy; Soil; Calibration curve; Chemometrics analysis;LIBS; CALIBRATION

Abundance or deficiency of soil elements is an expression of soil fertility. Rapid detection of elements in soil is a key point of front information acquirement tools in precision agriculture, and it also provides a theoretical basis for pollution prevention of soil heavy metal and sustainable development of agriculture. This research focused on using laser-induced breakdown spectroscopy LIBS) technique combined with calibration curve and chemometrics method to conduct the simultaneous quantitative analysis of multi-elements (Al, Fe, Mg, Ca, Na and K) in soil. First of all, five certified reference materials (CRM) of soil numbered GBW07446, GBW07447, GBW07454, GBW07455, GBW07456 were ablated by a laboratorial LIBS setup in air. 50 LIBS spectra of each type of soil were averaged to reduce the error in experiment process. By integrating the acquired LIBS emission spectra and atomic spectra database from national institute of standards and technology (NIST), analytical spectral lines and corresponding spectral regions were identified. Then, calibration curves of the intensity of a peak and integrated intensity of a peak or several peaks (peak area) coupling with the element content s were fitted. The results indicated that the linear relation from the calibration curves fitted by peak areas and element contents were superior to the calibration curves fitted using intensity of a peak and element contents (except the Fe). Meanwhile, partial least-squares regression (PLSR) was employed to build the quantitative model by using the selected spectral regions and corresponding element contents, which offered a promising result with relatively high RP and showed more advantages than the calibration curve method. The approach revealed that LIES technology combined with chemometrics methods displayed a bight prospect in the field of spectrochemical analysis. The achievements of the research not only provide a guide for detecting soil nutrient spatial distribution and precision fertilization technique, but also lay a theoretical foundation for developing the portable LIBS detector used in the field.

Laser-induced breakdown spectroscopy (LIES); Soil; Heavy metal; Chemometrics methods; Quantitative analysis;NEURAL-NETWORK; LIBS

A large number of farm lands are contaminated by heavy metals in the process of industrialization and urbanization. Precise detection of heavy metals in soil offers valid reference for prevention and recovery of heavy metals in the field. In this research, Laser induced breakdown spectroscopy (LIBS) and chemometrics methods were employed to conduct quantitative analysis of heavy metals Pb and Cd in soil. Based on the pollution extent, soil samples with 15 concentration gradients of Pb and Cd were manually made up. Then, the LIBS emission lines of all soil samples were collected firstly. In order to eliminate errors and noise of spectral data, preprocessing methods called removal of abnormal data and normalization were used. Then, characteristic lines and spectral regions of Pb and Cd were determined based on our LIES spectra and Atomic Spectra Database (ASD) of National Institute of Standards and Technology (NIST). Quantity regression models based on multiple linear regression (MLR), partial least squares regression (PLSR), least squares support vector machine (LS-SVM) and back propagation-artificial neural network (BP-ANN) were set up and their results were compared. As a result, models based on non-linear methods (LS-SVM and BP-ANN) offered a promising results than the linear methods of MLR and PLSR. The probable reason was that non-linear methods had an advantage to deal with matrix effects of soil automatically. The results indicated that LIBS coupled with multiple chemometrics methods provided a brand-new analysis approach for heavy metals accurate detection in soil and it could be considered as an effective theoretical foundation of making protection and recovery decision for soil contaminated by heavy metals.

PLASMA SPECTROSCOPY; ELEMENTAL ANALYSIS; LIBS

Fast detection of heavy metals in plant materials is crucial for environmental remediation and ensuring food safety. However, most plant materials contain high moisture content, the influence of which cannot be simply ignored. Hence, we proposed moisture influence reducing method for fast detection of heavy metals using laser-induced breakdown spectroscopy (LIBS). First, we investigated the effect of moisture content on signal intensity, stability, and plasma parameters (temperature and electron density) and determined the main influential factors (experimental parameters F and the change of analyte concentration) on the variations of signal. For chromium content detection, the rice leaves were performed with a quick drying procedure, and two strategies were further used to reduce the effect of moisture content and shot-to-shot fluctuation. An exponential model based on the intensity of background was used to correct the actual element concentration in analyte. Also, the ratio of signal-to-background for univariable calibration and partial least squared regression (PLSR) for multivariable calibration were used to compensate the prediction deviations. The PLSR calibration model obtained the best result, with the correlation coefficient of 0.9669 and root-mean-square error of 4.75 mg/kg in the prediction, set. The preliminary results indicated that the proposed method allowed for the detection of heavy metals in plant materials using LIBS, and it could be possibly used for element mapping in future work.

Laser induced breakdown spectroscopy; Coffee beans; Caffeine; Partial least squares; BP neural network;LIBS

The feasibility of fast detection of caffeine content in coffee beans based on laser induced breakdown spectroscopy (LIES) combined with chemometrics methods was studied. The coffee beans were grinded. 0.5 g of powerd material was transformed into a disk by manual tableting machine. 60 disks of coffee bean material were prepared for LIBS data acquiring. The samples were pretreated by acid wet digestion, and actual caffeine content of each sample was obtained by automic absorption spectrometer (AAS). Baseline correction was applied on the original spectral data to eliminate the negative values. Wavelet transform (WT) was used to reduce the noise, wavelet basis function is Daubechies 5 (db5) and decomposition level is 10. Normalization were employed to deal with variations caused by matrix effects and experimental conditions. The partial least squares (PLS) model on full data appeared to over-fitting. Regression coefficients and principal component analysis (PCA) were used to select characteristic variables, respectively. PLS models and back propagation (BP) neural network model were built by the variables selected. In the PLS model on the variables selected by regression coefficients, correlation coefficient of calibration set (R-c) was 0.96, correlation coefficient of prediction set (R-p) was 0.91. In the PLS model on the variables selected by PCA, R-c=0.94, R-p=0.90. In the BP neural network model on variables selected by PCA, R-c=0.96, R-p=0.96. The characteristic variables selected by two methods correspond to C, H, O, N, Na, Mg, Ca, Fe, Mn. The PLS models on the variables selected by regression coefficients and PCA both performed well on prediction samples. It demonstrated the certain relationship existing between the elements and caffeine content and the selected variables were effective. But the precise relationship bwtween C, H, O, N, Na, Mg, Ca, Fe, Mn and caffeine content needs further study. The BP neural network model on variables selected by PCA performed better than the PLS model, which demonstrated the selected variables were suitable for different modeling methods. The study showed LIBS could be applied to fast determination of caffeine content within coffee bean combined with chemometrics methods. The method of caffeine content detection presented by this study is innovative.

Laser induced breakdown spectroscopy (LIBS); Laser wavelength; Plasma time evolution; Plasma shielding effect; Coal;INDUCED BREAKDOWN SPECTROSCOPY; ELEMENTAL ANALYSIS; DEPENDENCE; PARAMETERS

In face of the complicated and changeable coal in Chinese power stations, the on-line measurement of coal elemental composition is extremely significant in terms of enhancing the safety and efficiency of boilers as well as lowering the waste emission during operation. In this work, laser induced breakdownspectroscopy (LIBS) was applied to coal quality measurement. Laser wavelength was changed as 355, 532 and 1 064 nm to investigate the influence on coal LIBS features, including plasma time evolution and the spectral lines intensity-time characteristics of different elements. Energy threshold for shielding effect was also tested to verify how it varies with laser wavelengths. Additionally, coal LIBS spectrum was analyzed under different laser wavelengths. It has been proven by experiments that higher intensity of coal LIBS spectrum and energy threshold can be easily achieved when using laser of 532 nm, making it a fantastic energy source for coal LIBS tests. Results of these experiments serve to guide the industrial application of LIBS technology in the field of coal quality measurement.

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.

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.

Chemometrics; Classification; Laser-induced breakdown spectroscopy; Variety; Walnut powder;JUGLANS-REGIA L.; SUPPORT VECTOR MACHINE; CLASSIFICATION; LIBS; CHEMOMETRICS; VARIABILITY; EMISSION; SAMPLE; OILS

Rapid classification of varieties of walnut powder is important to protect consumers against potential fraud because different varieties of walnut powder vary dramatically in quality, taste, and price. Laser-induced breakdown spectroscopy (LIBS) is a promising technology for on-line application that has the advantages of no or little sample preparation, fast response (usually seconds to minutes), and remote detection capability. In this research, LIBS and multi-variable analysis methods were applied to classify varieties of walnut powder for the first time. Before LIBS analysis, a fairly flat surface was provided to reduce fluctuation, and no other sample preparation was needed. After acquiring the LIBS spectra, principal component analysis (PCA) was used to make qualitative discrimination, and classification models were developed with partial least squares discrimination analysis (PLS-DA) and support vector machine (SVM). It was shown that three origins as well as different varieties from the same origin could be differentiated successfully by LIBS and chemometrics, while the discrimination of six different varieties with a single ""global"" model was not very successful. SVM could help to handle complex LIBS dataset, which improved the classified rate. The presented results provide the first proof-of-principle data for on-line application of the LIBS technique for classification of varieties of walnut powder.