TY - JOUR A1 - Zhang Chengjun, A1 - Fan Rong, A1 - Li Jun, A1 - Mischke, Steffen A1 - Dembele, Blaise A1 - Hu Xiaolan, T1 - Carbon and oxygen isotopic compositions - how lacustrine environmental factors respond in northwestern and northeastern China JF - Acta geologica Sinica : english edition N2 - Surface lake sediments, 28 from Hoh Xil, 24 from northeastern China, 99 from Lake Bosten, 31 from Ulungur and 26 from Heihai were collected to determine C-13 and O-18 values. Considering the impact factors, conductivity, alkalinity, pH, TOC, C/N and carbonate-content in the sediments, Cl, P, S, and metal element ratios of Mg/Ca, Sr/Ca, Fe/Mn of bulk sediments as environmental variables enable evaluation of their influences on C-13 and O-18 using principal component analysis (PCA) method. The closure and residence time of lakes can influence the correlation between C-13 and O-18. Lake water will change from fresh to brackish with increasing reduction and eutrophication effects. Mg/Ca in the bulk sediment indicates the characteristic of residence time, Sr/Ca and Fe/Mn infer the salinity of lakes. Carbonate formation processes and types can influence the C-13-O-18 correlation. O-18 will be heavier from Mg-calcite and aragonite formed in a high-salinity water body than calcite formed in freshwater conditions. When carbonate content is less than 30%, there is no relationship with either C-13 or O-18, and also none between C-13 and O-18. More than 30%, carbonate content, however, co-varies highly to C-13 and O-18, and there is also a high correlation between C-13 and O-18. Vegetation conditions and primary productivity of lakes can influence the characteristics of C-13 and O-18, and their co-variance. Total organic matter content (TOC) in the sediments is higher with more terrestrial and submerged plants infilling. In northeastern and northwestern China, when organic matter in the lake sediments comes from endogenous floating organisms and algae, the C-13 value is high. C-13 is in the range of -4%o to 0 parts per thousand when organic matter comes mainly from floating organisms (C/N<6); in the range of -4 parts per thousand to 8 parts per thousand when organic matter comes from diatoms (C/N=6 to 8); and -8 parts per thousand to -4 parts per thousand when organic matter comes from aquatic and terrestrial plants (C/N>8). KW - Limnology KW - isotopic analysis KW - carbonates KW - organic matter KW - PCA KW - Tibet KW - Xinjiang KW - Northeastern China Y1 - 2013 U6 - https://doi.org/10.1111/1755-6724.12133 SN - 1000-9515 SN - 1755-6724 VL - 87 IS - 5 SP - 1344 EP - 1354 PB - Wiley-Blackwell CY - Hoboken ER - TY - JOUR A1 - Rethfeldt, Nina A1 - Brinkmann, Pia A1 - Riebe, Daniel A1 - Beitz, Toralf A1 - Köllner, Nicole A1 - Altenberger, Uwe A1 - Löhmannsröben, Hans-Gerd T1 - Detection of Rare Earth Elements in Minerals and Soils by Laser-Induced Breakdown Spectroscopy (LIBS) Using Interval PLS JF - Minerals N2 - The numerous applications of rare earth elements (REE) has lead to a growing global demand and to the search for new REE deposits. One promising technique for exploration of these deposits is laser-induced breakdown spectroscopy (LIBS). Among a number of advantages of the technique is the possibility to perform on-site measurements without sample preparation. Since the exploration of a deposit is based on the analysis of various geological compartments of the surrounding area, REE-bearing rock and soil samples were analyzed in this work. The field samples are from three European REE deposits in Sweden and Norway. The focus is on the REE cerium, lanthanum, neodymium and yttrium. Two different approaches of data analysis were used for the evaluation. The first approach is univariate regression (UVR). While this approach was successful for the analysis of synthetic REE samples, the quantitative analysis of field samples from different sites was influenced by matrix effects. Principal component analysis (PCA) can be used to determine the origin of the samples from the three deposits. The second approach is based on multivariate regression methods, in particular interval PLS (iPLS) regression. In comparison to UVR, this method is better suited for the determination of REE contents in heterogeneous field samples. View Full-Text KW - LIBS KW - rare earth elements KW - minerals KW - PCA KW - iPLS regression Y1 - 2021 U6 - https://doi.org/10.3390/min11121379 SN - 2075-163X VL - 11 SP - 1 EP - 17 PB - MDPI CY - Basel, Schweiz ER - TY - JOUR A1 - Brinkmann, Pia A1 - Köllner, Nicole A1 - Merk, Sven A1 - Beitz, Toralf A1 - Altenberger, Uwe A1 - Löhmannsröben, Hans-Gerd T1 - Comparison of handheld and echelle spectrometer to assess copper in ores by means of laser-induced breakdown spectroscopy (LIBS) JF - Minerals N2 - Its properties make copper one of the world’s most important functional metals. Numerous megatrends are increasing the demand for copper. This requires the prospection and exploration of new deposits, as well as the monitoring of copper quality in the various production steps. A promising technique to perform these tasks is Laser Induced Breakdown Spectroscopy (LIBS). Its unique feature, among others, is the ability to measure on site without sample collection and preparation. In this work, copper-bearing minerals from two different deposits are studied. The first set of field samples come from a volcanogenic massive sulfide (VMS) deposit, the second part from a stratiform sedimentary copper (SSC) deposit. Different approaches are used to analyze the data. First, univariate regression (UVR) is used. However, due to the strong influence of matrix effects, this is not suitable for the quantitative analysis of copper grades. Second, the multivariate method of partial least squares regression (PLSR) is used, which is more suitable for quantification. In addition, the effects of the surrounding matrices on the LIBS data are characterized by principal component analysis (PCA), alternative regression methods to PLSR are tested and the PLSR calibration is validated using field samples. KW - LIBS KW - copper-bearing minerals KW - UVR KW - PCA KW - PLSR Y1 - 2023 U6 - https://doi.org/10.3390/min13010113 SN - 2075-163X VL - 13 IS - 1 PB - MDPI CY - Basel ER -