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Precision agriculture (PA) strongly relies on spatially differentiated sensor information. Handheld instruments based on laser-induced breakdown spectroscopy (LIBS) are a promising sensor technique for the in-field determination of various soil parameters. In this work, the potential of handheld LIBS for the determination of the total mass fractions of the major nutrients Ca, K, Mg, N, P and the trace nutrients Mn, Fe was evaluated. Additionally, other soil parameters, such as humus content, soil pH value and plant available P content, were determined. Since the quantification of nutrients by LIBS depends strongly on the soil matrix, various multivariate regression methods were used for calibration and prediction. These include partial least squares regression (PLSR), least absolute shrinkage and selection operator regression (Lasso), and Gaussian process regression (GPR). The best prediction results were obtained for Ca, K, Mg and Fe. The coefficients of determination obtained for other nutrients were smaller. This is due to much lower concentrations in the case of Mn, while the low number of lines and very weak intensities are the reason for the deviation of N and P. Soil parameters that are not directly related to one element, such as pH, could also be predicted. Lasso and GPR yielded slightly better results than PLSR. Additionally, several methods of data pretreatment were investigated.
Precision agriculture (PA) strongly relies on spatially differentiated sensor information. Handheld instruments based on laser-induced breakdown spectroscopy (LIBS) are a promising sensor technique for the in-field determination of various soil parameters. In this work, the potential of handheld LIBS for the determination of the total mass fractions of the major nutrients Ca, K, Mg, N, P and the trace nutrients Mn, Fe was evaluated. Additionally, other soil parameters, such as humus content, soil pH value and plant available P content, were determined. Since the quantification of nutrients by LIBS depends strongly on the soil matrix, various multivariate regression methods were used for calibration and prediction. These include partial least squares regression (PLSR), least absolute shrinkage and selection operator regression (Lasso), and Gaussian process regression (GPR). The best prediction results were obtained for Ca, K, Mg and Fe. The coefficients of determination obtained for other nutrients were smaller. This is due to much lower concentrations in the case of Mn, while the low number of lines and very weak intensities are the reason for the deviation of N and P. Soil parameters that are not directly related to one element, such as pH, could also be predicted. Lasso and GPR yielded slightly better results than PLSR. Additionally, several methods of data pretreatment were investigated.
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.
Laser-based ion mobility (IM) spectrometry was used for the detection of neuroleptics and PAH. A gas chromatograph was connected to the IM spectrometer in order to investigate compounds with low vapour pressure. The substances were ionized by resonant two-photon ionization at the wavelengths lambda = 213 and 266 nm and pulse energies between 50 and 300 mu J. Ion mobilities, linear ranges, limits of detection and response factors are reported. Limits of detection for the substances are in the range of 1-50 fmol. Additionally, the mechanism of laser ionization at atmospheric pressure was investigated. First, the primary product ions were determined by a laser-based time-of-flight mass spectrometer with effusive sample introduction. Then, a combination of a laser-based IM spectrometer and an ion trap mass spectrometer was developed and characterized to elucidate secondary ion-molecule reactions that can occur at atmospheric pressure. Some substances, namely naphthalene, anthracene, promazine and thioridazine, could be detected as primary ions (radical cations), while other substances, in particular acridine, phenothiazine and chlorprothixene, are detected as secondary ions (protonated molecules). The results are interpreted on the basis of quantum chemical calculations, and an ionization mechanism is proposed.
In this work, ion mobility (IM) spectra of more than 50 aromatic compounds were recorded with a laser-based IM spectrometer at atmospheric pressure. IM spectra of PAH in the laser desorption experiment show a high complexity resulting from the occurrence of monomeric, dimeric, and oligomeric cluster ions. The mobilities of all compounds were determined in helium as drift gas. This allows the calculation of the diffusion cross sections (Omega(calc)) on the basis of the exact hard sphere scattering model and their comparison with the experimentally determined diffusion cross sections (Omega(exp)). Extended Omega(exp)/Omega(calc) and Omega(exp/)mass correlations were performed in order to gain insight into conformational properties of cationic alkyl benzenes and internal rotation of phenyl rings in aromatic ions. This is demonstrated with some examples, such as the evaluation of the dihedral angle of the ions of 9,10- diphenylanthracene, o- and m-terphenyl, and 1,2,3- and 1,3,5-triphenylbenzene. Furthermore, sandwich and T-structures of dimeric PAH cations are discussed. The analysis was extended to oligomeric ions with up to nine monomer units. Experimental evidence is presented suggesting the formation of pi-stacks with a transition toward modified pi-stacks with increasing cluster size. The distance between monomeric units in dimeric and oligomeric ions was obtained