MULTIVARIATE-ANALYSIS; WOOD

The black cottonwood poplar (Populus trichocarpa) leaf ionome (inorganic trace elements and mineral nutrients) is an important aspect for determining the physiological and developmental processes contributing to biomass production. A number of techniques are used to measure the ionome, yet characterizing the leaf spatial heterogeneity remains a challenge, especially in solid samples. Laser-induced breakdown spectroscopy (LIBS) has been used to determine the elemental composition of leaves and is able to raster across solid matrixes at 10 pm resolution. Here, we evaluate the use of LIBS for solid sample leaf elemental characterization in relation to neutron activation. In fact, neutron activation analysis is a laboratory-based technique which is used by the National Institute of Standards and Technology (NIST) to certify trace elements in candidate reference materials including plant leaf matrices. Introduction to the techniques used in this research has been presented in this manuscript Neutron activation analysis (NAA) data has been correlated to the LIBS spectra to achieve quantification of the elements or ions present within poplar leaves. The regression coefficients of calibration and validation using multivariate analysis (MVA) methodology for six out of seven elements have been determined and vary between 0.810 and 0.998. LIBS and NM data has been presented for the elements such as, calcium, magnesium, manganese, aluminum, copper, and potassium. Chlorine was also detected but it did not show good correlation between the LIBS and NAA techniques. This research shows that LIBS can be used as a fast, high-spatial resolution technique to quantify elements as part of large-scale field phenotyping projects. (C) 2017 Elsevier B.V. All rights reserved.

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.