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.

The application of electrospray ionization (ESI) ion mobility (IM) spectrometry on the detection end of a high-performance liquid chromatograph has been a subject of study for some time. So far, this method has been limited to low flow rates or has required splitting of the liquid flow. This work presents a novel concept of an ESI source facilitating the stable operation of the spectrometer at flow rates between 10 μL mn(-1) and 1500 μL min(-1) without flow splitting, advancing the T-cylinder design developed by Kurnin and co-workers. Flow rates eight times faster than previously reported were achieved because of a more efficient dispersion of the liquid at increased electrospray voltages combined with nebulization by a sheath gas. Imaging revealed the spray operation to be in a rotationally symmetric multijet mode. The novel ESI-IM spectrometer tolerates high water contents (≤90%) and electrolyte concentrations up to 10mM, meeting another condition required of high-performance liquid chromatography (HPLC) detectors. Limits of detection of 50 nM for promazine in the positive mode and 1 μM for 1,3-dinitrobenzene in the negative mode were established. Three mixtures of reduced complexity (five surfactants, four neuroleptics, and two isomers) were separated in the millisecond regime in stand-alone operation of the spectrometer. Separations of two more complex mixtures (five neuroleptics and 13 pesticides) demonstrate the application of the spectrometer as an HPLC detector. The examples illustrate the advantages of the spectrometer over the established diode array detector, in terms of additional IM separation of substances not fully separated in the retention time domain as well as identification of substances based on their characteristic Ims.

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 μL/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.

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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.