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Hazelnuts are rarely cultivated in Germany, although they are a valuable source for macro- and micronutrients and can thus contribute to a healthy diet. Near the present, 15 varieties were cultivated in Thuringia, Germany, as a pilot study for further research. The aim of our study was to evaluate the micro- and macronutrient composition of representative, randomly mixed samples of the 15 different hazelnut cultivars. Protein, fat, and fiber contents were determined using established methods. Fatty acids, tocopherols, minerals, trace elements, and ultra-trace elements were analyzed using gas chromatography, high-performance liquid chromatography, and inductively coupled plasma triple quadrupole mass-spectrometry, respectively. We found that the different hazelnut varieties contained valuable amounts of fat, protein, dietary fiber, minerals, trace elements, and alpha-tocopherol, however, in different quantities. The variations in nutrient composition were independent of growth conditions, which were identical for all hazelnut varieties. Therefore, each hazelnut cultivar has its specific nutrient profile.
The quantification and identification of aerosols in industry plays a key role in process monitoring and control and lays the foundation for process automation aspired by the industry 4.0 initiative.
However, measuring particulate matter's mass and number concentrations in harsh environments poses great analytical constraints.
The presented approach comprises a comprehensive set of light-and imaging-based techniques, all contactless, in-line, and real-time. It includes, but is not limited to, stroboscopic imaging, laser-induced breakdown spectroscopy (LIBS) and laser-induced incandescence (LII). Stroboscopic imaging confirmed the particles sphericity and was used to measure the particle number density. A phase-selective LIBS setup with low fluence and 500 Hz repetition rate was used to classify each particle with a single-pulse and in real time. Simultaneously, the created plasma was captured by CCD imaging to determine the detection volume and hit rate of the LIBS setup.
Both data sets combined were converted to a particle number density, which was consistent with the particle number density of the stroboscopic measurements. Furthermore, using a photodiode and microphone in parallel to the LIBS setup allowed for the photoacoustic normalization of the spectral line intensity at the laser repetition rate of 500 Hz.
This was done as a partial photoacoustic normalization method with the cut-off based on the coefficient of variation (CV), reducing it by 25%. Aside from that photodiode and microphone were proven to be valuable event counting with the advantage of the less spatially constricted. A second laser setup was used for laser -induced incandescence (LII) making it possible to classify the particles based on their incandescence tendency. Given its larger probing volume, LII could be employed at very low particle number densities.
With respect to the current literature, this is the first approach of using LII as an in-line, real-time analytical technique for the compositional classification of metal-bearing aerosols.
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
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