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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 mu L min(-1) and 1500 mu L 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 10 mM, 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 mu M 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 pressure dependence of sheath gas assisted electrospray ionization (ESI) was investigated based on two complementary experimental setups, namely an ESI-ion mobility (IM) spectrometer and an ESI capillary - Faraday plate setup housed in an optically accessible vacuum chamber. The ESI-IM spectrometer is capable of working in the pressure range between 300 and 1000 mbar. Another aim was the assessment of the analytical capabilities of a subambient pressure ESI-IM spectrometer. The pressure dependence of ESI was characterized by imaging the electrospray and recording current-voltage (I-U) curves. Qualitatively different behavior was observed in both setups. While the current rises continuously with the voltage in the capillary-plate setup, a sharp increase of the current was measured in the IM spectrometer above a pressure-dependent threshold voltage. The different character can be attributed to the detection of different species in both experiments. In the capillary-plate experiment, a multitude of charged species are detected while only desolvated ions attribute to the IM spectrometer signal. This finding demonstrates the utility of IM spectrometry for the characterization of ESI, since in contrast to the capillary-plate setup, the release of ions from the electrospray droplets can be observed. The I-U curves change significantly with pressure. An important result is the reduction of the maximum current with decreasing pressure. The connected loss of ionization efficiency can be compensated by a more efficient transfer of ions in the IM spectrometer at increased E/N. Thus, similar limits of detection could be obtained at 500 mbar and 1 bar.
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.
In precision agriculture, the estimation of soil parameters via sensors and the creation of nutrient maps are a prerequisite for farmers to take targeted measures such as spatially resolved fertilization. In this work, 68 soil samples uniformly distributed over a field near Bonn are investigated using laser-induced breakdown spectroscopy (LIBS). These investigations include the determination of the total contents of macro- and micronutrients as well as further soil parameters such as soil pH, soil organic matter (SOM) content, and soil texture. The applied LIBS instruments are a handheld and a platform spectrometer, which potentially allows for the single-point measurement and scanning of whole fields, respectively. Their results are compared with a high-resolution lab spectrometer. The prediction of soil parameters was based on multivariate methods. Different feature selection methods and regression methods like PLS, PCR, SVM, Lasso, and Gaussian processes were tested and compared. While good predictions were obtained for Ca, Mg, P, Mn, Cu, and silt content, excellent predictions were obtained for K, Fe, and clay content. The comparison of the three different spectrometers showed that although the lab spectrometer gives the best results, measurements with both field spectrometers also yield good results. This allows for a method transfer to the in-field measurements.
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.
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 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.