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The lack of soil data, which are relevant, reliable, affordable, immediately available, and sufficiently detailed, is still a significant challenge in precision agriculture. A promising technology for the spatial assessment of the distribution of chemical elements within fields, without sample preparation is laser-induced breakdown spectroscopy (LIBS). Its advantages are contrasted by a strong matrix dependence of the LIBS signal which necessitates careful data evaluation. In this work, different calibration approaches for soil LIBS data are presented. The data were obtained from 139 soil samples collected on two neighboring agricultural fields in a quaternary landscape of northeast Germany with very variable soils. Reference analysis was carried out by inductively coupled plasma optical emission spectroscopy after wet digestion. The major nutrients Ca and Mg and the minor nutrient Fe were investigated. Three calibration strategies were compared. The first method was based on univariate calibration by standard addition using just one soil sample and applying the derived calibration model to the LIBS data of both fields. The second univariate model derived the calibration from the reference analytics of all samples from one field. The prediction is validated by LIBS data of the second field. The third method is a multivariate calibration approach based on partial least squares regression (PLSR). The LIBS spectra of the first field are used for training. Validation was carried out by 20-fold cross-validation using the LIBS data of the first field and independently on the second field data. The second univariate method yielded better calibration and prediction results compared to the first method, since matrix effects were better accounted for. PLSR did not strongly improve the prediction in comparison to the second univariate method.
The lack of soil data, which are relevant, reliable, affordable, immediately available, and sufficiently detailed, is still a significant challenge in precision agriculture. A promising technology for the spatial assessment of the distribution of chemical elements within fields, without sample preparation is laser-induced breakdown spectroscopy (LIBS). Its advantages are contrasted by a strong matrix dependence of the LIBS signal which necessitates careful data evaluation. In this work, different calibration approaches for soil LIBS data are presented. The data were obtained from 139 soil samples collected on two neighboring agricultural fields in a quaternary landscape of northeast Germany with very variable soils. Reference analysis was carried out by inductively coupled plasma optical emission spectroscopy after wet digestion. The major nutrients Ca and Mg and the minor nutrient Fe were investigated. Three calibration strategies were compared. The first method was based on univariate calibration by standard addition using just one soil sample and applying the derived calibration model to the LIBS data of both fields. The second univariate model derived the calibration from the reference analytics of all samples from one field. The prediction is validated by LIBS data of the second field. The third method is a multivariate calibration approach based on partial least squares regression (PLSR). The LIBS spectra of the first field are used for training. Validation was carried out by 20-fold cross-validation using the LIBS data of the first field and independently on the second field data. The second univariate method yielded better calibration and prediction results compared to the first method, since matrix effects were better accounted for. PLSR did not strongly improve the prediction in comparison to the second univariate method.
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.
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.
Continuous synthesis of pyridocarbazoles and initial photophysical and bioprobe characterization
(2013)
Pyridocarbazoles when ligated to transition metals yield high affinity kinase inhibitors. While batch photocyclizations enable the synthesis of these heterocycles, the non-oxidative Mallory reaction only provides modest yields and difficult to purify mixtures. We demonstrate here that a flow-based Mallory cyclization provides superior results and enables observation of a clear isobestic point. The flow method allowed us to rapidly synthesize ten pyridocarbazoles and for the first time to document their interesting photophysical attributes. Preliminary characterization reveals that these molecules might be a new class of fluorescent bioprobe.
Hemolysis, the rupturing of red blood cells, can result from numerous medical conditions (in vivo) or occur after collecting blood specimen or extracting plasma and serum out of whole blood (in vitro). In clinical laboratory practice, hemolysis can be a serious problem due to its potential to bias detection of various analytes or biomarkers. Here we present the first "mix-and-measure' method to assess the degree of hemolysis in biosamples using luminescence spectroscopy. Luminescent terbium complexes (LTC) were studied in the presence of free hemoglobin (Hb) as indicators for hemolysis in TRIS-buffer, and in fresh human plasma with absorption, excitation and emission measurements. Our findings indicate dynamic as well as resonance energy transfer (FRET) between the LTC and the porphyrin ligand of hemoglobin. This transfer leads to a decrease in luminescence intensity and decay time even at nanomolar hemoglobin concentrations either in buffer or plasma. Luminescent terbium complexes are very sensitive to free hemoglobin in buffer and blood plasma. Due to the instant change in luminescence properties of the LTC in presence of Hb it is possible to access the concentration of hemoglobin via spectroscopic methods without incubation time or further treatment of the sample thus enabling a rapid and sensitive detection of hemolysis in clinical diagnostics.
Hemolysis, the rupturing of red blood cells, can result from numerous medical conditions (in vivo) or occur after collecting blood specimen or extracting plasma and serum out of whole blood (in vitro). In clinical laboratory practice, hemolysis can be a serious problem due to its potential to bias detection of various analytes or biomarkers. Here we present the first ‘‘mix-and-measure’’ method to assess the degree of hemolysis in biosamples using luminescence spectroscopy. Luminescent terbium complexes (LTC) were studied in the presence of free hemoglobin (Hb) as indicators for hemolysis in TRIS-buffer, and in fresh human plasma with absorption, excitation and emission measurements. Our findings indicate dynamic as well as resonance energy transfer (FRET) between the LTC and the porphyrin ligand of hemoglobin. This transfer leads to a decrease in luminescence intensity and decay time even at nanomolar hemoglobin concentrations either in buffer or plasma. Luminescent terbium complexes are very sensitive to free hemoglobin in buffer and blood plasma. Due to the instant change in luminescence properties of the LTC in presence of Hb it is possible to access the concentration of hemoglobin via spectroscopic methods without incubation time or further treatment of the sample thus enabling a rapid and sensitive detection of hemolysis in clinical diagnostics.