A multivariate log-ratio calibration (MLC) model for XRF-core-scanning devices is presented, based on a combination of basic XRF-spectrometry theory and principles of compositional data analysis. The performance of the MLC model is evaluated in comparison with other empirical calibration procedures for XRF core scanner data using two data sets acquired with two different XRF core scanners. The quality of calibration models is assessed by calculating the uncertainties associated with predicted concentrations using cross-validation techniques. Results show that (1) the commonly used direct linear calibration (DLC) methods, which are based on the questionable assumption of a unique linear relation between intensities and concentrations and do not acknowledge the compositional nature of the calibration problem, give poor results; (2) the univariate log-ratio calibration (ULC) model, which is consistent with the compositional nature of the calibration problem but does not fully incorporate absorption and enhancement effects on intensities, and permits estimation of "relative" concentrations only, is markedly better, and (3) the MLC algorithm introduced in this contribution, which incorporates measurement uncertainties, accommodates absorption and enhancement effects on intensities, and exploits the covariance between and among intensities and concentrations, is the best by far. The predictive power of the MLC model may be further increased by employing automatic sample selection based on the multivariate geometry of intensity measurements in log-ratio space. The precision attained by MLC in conjunction with automatic sample selection is comparable to that attained by conventional XRF analysis of heterogenous materials under laboratory conditions. A solution to the long-standing problem of XRF core scanner calibration implies that high-resolution records of sediment composition with associated uncertainties can now be routinely established, which should increase the range of quantitative applications of XRF-core-scanning devices and strengthen inferences based on analysis of geochemical proxies.
We present an early Holocene record from Lake Meerfelder Maar in Germany for in‐depth interpretation of depositional changes in annually laminated lake sediments as proxies for climatic and local environmental changes. We characterized the compositional changes in the sediment record using Ward's clustering analyses of the micro X‐ray fluorescence core scanning data and linked these to microfacies descriptions. The down‐core distribution of the clusters allowed us to define boundaries that represent variations of a comprehensive element assemblage occurring at 11 555, 11 230, 10 650, 10 515 and 9670 varve a BP. Our main results show that during the Early Holocene the long‐term vegetation reorganization and evolution of the lake's catchment played a predominant role for sediment deposition. Abrupt shifts occurred at the Younger Dryas/Holocene and the Preboreal/Boreal biostratigraphical boundaries. We do not observe clear signals corresponding to known short‐term climatic oscillations described in the North Atlantic region such as the Preboreal Oscillation. A unique and intriguing episode in the history of the lake of predominantly organic deposition and very low amounts of allochthonous sediments occurred between 10 515 and 9670 varve a BP and is related to hydrological thresholds.
XRF core scanning has evolved to become a standard analytical technique for the rapid assessment of elemental, density and textural variations in a wide range of sediments and other materials, with applications ranging from palaeoceanography, paleoclimatology, geology, and environmental forensics to environmental protection. In general, scanning provides rapid, non-destructive acquisition of elemental and textural variations at sub-millimetre resolution for a wide range of materials. Numerous procedural adaptations have been developed for the growing number of applications, such as analyses of unconsolidated, water-rich sediments, powdered soil samples, or resin bags. Here, practical expertise and guidance from the Itrax community, gained over 15 years, is presented that should provide insights for new and experienced users.
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