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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 increasing development of antibiotic resistance in bacteria has been a major problem for years, both in human and veterinary medicine. Prophylactic measures, such as the use of vaccines, are of great importance in reducing the use of antibiotics in livestock. These vaccines are mainly produced based on formaldehyde inactivation. However, the latter damages the recognition elements of the bacterial proteins and thus could reduce the immune response in the animal. An alternative inactivation method developed in this work is based on gentle photodynamic inactivation using carbon nanodots (CNDs) at excitation wavelengths λex > 290 nm. The photodynamic inactivation was characterized on the nonvirulent laboratory strain Escherichia coli K12 using synthesized CNDs. For a gentle inactivation, the CNDs must be absorbed into the cytoplasm of the E. coli cell. Thus, the inactivation through photoinduced formation of reactive oxygen species only takes place inside the bacterium, which means that the outer membrane is neither damaged nor altered. The loading of the CNDs into E. coli was examined using fluorescence microscopy. Complete loading of the bacterial cells could be achieved in less than 10 min. These studies revealed a reversible uptake process allowing the recovery and reuse of the CNDs after irradiation and before the administration of the vaccine. The success of photodynamic inactivation was verified by viability assays on agar. In a homemade flow photoreactor, the fastest successful irradiation of the bacteria could be carried out in 34 s. Therefore, the photodynamic inactivation based on CNDs is very effective. The membrane integrity of the bacteria after irradiation was verified by slide agglutination and atomic force microscopy. The method developed for the laboratory strain E. coli K12 could then be successfully applied to the important avian pathogens Bordetella avium and Ornithobacterium rhinotracheale to aid the development of novel vaccines.
The increasing development of antibiotic resistance in bacteria has been a major problem for years, both in human and veterinary medicine. Prophylactic measures, such as the use of vaccines, are of great importance in reducing the use of antibiotics in livestock. These vaccines are mainly produced based on formaldehyde inactivation. However, the latter damages the recognition elements of the bacterial proteins and thus could reduce the immune response in the animal. An alternative inactivation method developed in this work is based on gentle photodynamic inactivation using carbon nanodots (CNDs) at excitation wavelengths λex > 290 nm. The photodynamic inactivation was characterized on the nonvirulent laboratory strain Escherichia coli K12 using synthesized CNDs. For a gentle inactivation, the CNDs must be absorbed into the cytoplasm of the E. coli cell. Thus, the inactivation through photoinduced formation of reactive oxygen species only takes place inside the bacterium, which means that the outer membrane is neither damaged nor altered. The loading of the CNDs into E. coli was examined using fluorescence microscopy. Complete loading of the bacterial cells could be achieved in less than 10 min. These studies revealed a reversible uptake process allowing the recovery and reuse of the CNDs after irradiation and before the administration of the vaccine. The success of photodynamic inactivation was verified by viability assays on agar. In a homemade flow photoreactor, the fastest successful irradiation of the bacteria could be carried out in 34 s. Therefore, the photodynamic inactivation based on CNDs is very effective. The membrane integrity of the bacteria after irradiation was verified by slide agglutination and atomic force microscopy. The method developed for the laboratory strain E. coli K12 could then be successfully applied to the important avian pathogens Bordetella avium and Ornithobacterium rhinotracheale to aid the development of novel vaccines.
The combination of high-performance liquid chromatography and electrospray ionization ion mobility spectrometry facilitates the two-dimensional separation of complex mixtures in the retention and drift time plane. The ion mobility spectrometer presented here was optimized for flow rates customarily used in high-performance liquid chromatography between 100 and 1500 mu L/min. The characterization of the system with respect to such parameters as the peak capacity of each time dimension and of the 2D spectrum was carried out based on a separation of a pesticide mixture containing 24 substances. While the total ion current chromatogram is coarsely resolved, exhibiting coelutions for a number of compounds, all substances can be separately detected in the 2D plane due to the orthogonality of the separations in retention and drift dimensions. Another major advantage of the ion mobility detector is the identification of substances based on their characteristic mobilities. Electrospray ionization allows the detection of substances lacking a chromophore. As an example, the separation of a mixture of 18 amino acids is presented. A software built upon the free mass spectrometry package OpenMS was developed for processing the extensive 2D data. The different processing steps are implemented as separate modules which can be arranged in a graphic workflow facilitating automated processing of data.
The contamination of barley by molds on the field or in storage leads to the spoilage of grain and the production of mycotoxins, which causes major economic losses in malting facilities and breweries. Therefore, on-site detection of hidden fungus contaminations in grain storages based on the detection of volatile marker compounds is of high interest. In this work, the volatile metabolites of 10 different fungus species are identified by gas chromatography (GC) combined with two complementary mass spectrometric methods, namely, electron impact (EI) and chemical ionization at atmospheric pressure (APCI)-mass spectrometry (MS). The APCI source utilizes soft X-radiation, which enables the selective protonation of the volatile metabolites largely without side reactions. Nearly 80 volatile or semivolatile compounds from different substance classes, namely, alcohols, aldehydes, ketones, carboxylic acids, esters, substituted aromatic compounds, alkenes, terpenes, oxidized terpenes, sesquiterpenes, and oxidized sesquiterpenes, could be identified. The profiles of volatile and semivolatile metabolites of the different fungus species are characteristic of them and allow their safe differentiation. The application of the same GC parameters and APCI source allows a simple method transfer from MS to ion mobility spectrometry (IMS), which permits on-site analyses of grain stores. Characterization of IMS yields limits of detection very similar to those of APCI-MS. Accordingly, more than 90% of the volatile metabolites found by APCI-MS were also detected in IMS. In addition to different fungus genera, different species of one fungus genus could also be differentiated by GC-IMS.
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.
Mold fungi on malting barley grains cause major economic loss in malting and brewery facilities. Possible proxies for their detection are volatile and semivolatile metabolites. Among those substances, characteristic marker compounds have to be identified for a confident detection of mold fungi in varying surroundings. The analytical determination is usually performed through passive sampling with solid phase microextraction, gas chromatographic separation, and detection by electron ionization mass spectrometry (EI-MS), which often does not allow a confident determination due to the absence of molecular ions. An alternative is GC-APCI-MS, generally, allowing the determination of protonated molecular ions. Commercial atmospheric pressure chemical ionization (APCI) sources are based on corona discharges, which are often unspecific due to the occurrence of several side reactions and produce complex product ion spectra. To overcome this issue, an APCI source based on soft X-radiation is used here. This source facilitates a more specific ionization by proton transfer reactions only. In the first part, the APCI source is characterized with representative volatile fungus metabolites. Depending on the proton affinity of the metabolites, the limits of detection are up to 2 orders of magnitude below those of EI-MS. In the second part, the volatile metabolites of the mold fungus species Aspergillus, Alternaria, Fusarium, and Penicillium are investigated. In total, 86 compounds were found with GC-EI/APCI-MS. The metabolites identified belong to the substance classes of alcohols, aldehydes, ketones, carboxylic acids, esters, substituted aromatic compounds, terpenes, and sesquiterpenes. In addition to substances unspecific for the individual fungus species, characteristic patterns of metabolites, allowing their confident discrimination, were found for each of the 4 fungus species. Sixty-seven of the 86 metabolites are detected by X-ray-based APCI-MS alone. The discrimination of the fungus species based on these metabolites alone was possible. Therefore, APCI-MS in combination with collision induced dissociation alone could be used as a supervision method for the detection of mold fungi.
Laser-induced breakdown spectroscopy (LIBS) analysers are becoming increasingly common for material classification purposes. However, to achieve good classification accuracy, mostly noncompact units are used based on their stability and reproducibility. In addition, computational algorithms that require significant hardware resources are commonly applied. For performing measurement campaigns in hard-to-access environments, such as mining sites, there is a need for compact, portable, or even handheld devices capable of reaching high measurement accuracy. The optics and hardware of small (i.e., handheld) devices are limited by space and power consumption and require a compromise of the achievable spectral quality. As long as the size of such a device is a major constraint, the software is the primary field for improvement. In this study, we propose a novel combination of handheld LIBS with non-negative tensor factorisation to investigate its classification capabilities of copper minerals. The proposed approach is based on the extraction of source spectra for each mineral (with the use of tensor methods) and their labelling based on the percentage contribution within the dataset. These latent spectra are then used in a regression model for validation purposes. The application of such an approach leads to an increase in the classification score by approximately 5% compared to that obtained using commonly used classifiers such as support vector machines, linear discriminant analysis, and the k-nearest neighbours algorithm.