Dokument-ID Dokumenttyp Verfasser/Autoren Herausgeber Haupttitel Abstract Auflage Verlagsort Verlag Erscheinungsjahr Seitenzahl Schriftenreihe Titel Schriftenreihe Bandzahl ISBN Quelle der Hochschulschrift Konferenzname Quelle:Titel Quelle:Jahrgang Quelle:Heftnummer Quelle:Erste Seite Quelle:Letzte Seite URN DOI Abteilungen OPUS4-44007 misc Riebe, Daniel; Erler, Alexander; Brinkmann, Pia; Beitz, Toralf; Löhmannsröben, Hans-Gerd; Gebbers, Robin Comparison of Calibration Approaches in Laser-Induced Breakdown Spectroscopy for Proximal Soil Sensing in Precision Agriculture 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. 2019 16 Postprints der Universität Potsdam Mathematisch-Naturwissenschaftliche Reihe 786 urn:nbn:de:kobv:517-opus4-440079 10.25932/publishup-44007 Institut für Chemie OPUS4-44006 Wissenschaftlicher Artikel Riebe, Daniel; Erler, Alexander; Brinkmann, Pia; Beitz, Toralf; Löhmannsröben, Hans-Gerd; Gebbers, Robin Comparison of Calibration Approaches in Laser-Induced Breakdown Spectroscopy for Proximal Soil Sensing in Precision Agriculture 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. Basel MDPI 2019 16 Sensors 19 23 10.3390/s19235244 Institut für Chemie OPUS4-58292 Wissenschaftlicher Artikel Erler, Alexander; Riebe, Daniel; Beitz, Toralf; Löhmannsröben, Hans-Gerd; Grothusheitkamp, Daniela; Kunz, Thomas; Methner, Frank-Jürgen Characterization of volatile metabolites formed by molds on barley by mass and ion mobility spectrometry 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. Hoboken Wiley 2020 10 Journal of mass spectrometr 55 5 1 10 10.1002/jms.4501 Institut für Physik und Astronomie OPUS4-44417 Wissenschaftlicher Artikel Erler, Alexander; Riebe, Daniel; Beitz, Toralf; Löhmannsröben, Hans-Gerd; Gebbers, Robin Soil Nutrient Detection for Precision Agriculture Using Handheld Laser-Induced Breakdown Spectroscopy (LIBS) and Multivariate Regression Methods (PLSR, Lasso and GPR) 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. Basel MDPI 2020 17 Sensors 20 2 10.3390/s20020418 Institut für Chemie OPUS4-51834 Wissenschaftlicher Artikel Erler, Alexander; Riebe, Daniel; Beitz, Toralf; Löhmannsröben, Hans-Gerd; Grothusheitkamp, Daniela; Kunz, T.; Methner, Frank-Jürgen Detection of volatile organic compounds in the headspace above mold fungi by GC-soft X-radiation-based APCI-MS 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. Hoboken Wiley 2018 10 Journal of mass spectrometr 53 10 911 920 10.1002/jms.4210 Institut für Chemie OPUS4-44418 misc Erler, Alexander; Riebe, Daniel; Beitz, Toralf; Löhmannsröben, Hans-Gerd; Gebbers, Robin Soil Nutrient Detection for Precision Agriculture Using Handheld Laser-Induced Breakdown Spectroscopy (LIBS) and Multivariate Regression Methods (PLSR, Lasso and GPR) 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. 2019 19 Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe 815 urn:nbn:de:kobv:517-opus4-444183 10.25932/publishup-44418 Institut für Chemie OPUS4-47340 Dissertation Erler, Alexander Entwicklung von online-Detektionsverfahren für landwirtschaftlich relevante Analyten Die Entwicklung nachhaltiger Bewirtschaftungs- und Produktionsmethoden ist eine der zentralen Fragestellungen der modernen Agrarwirtschaft. Die vorliegende Arbeit beschäftigt sich mit zwei Forschungsthemen, die das Konzept Nachhaltigkeit beinhalten. In beiden Fällen werden analytische Grundlagen für die Entwicklung entsprechender landwirtschaftlicher Arbeitsmethoden gelegt. Das erste Thema ist an den sogenannten Präzisionsackerbau angelehnt. Bei diesem wird die Bearbeitung von Agrarflächen ortsabhängig ausgeführt. Das heißt, die Ausbringung von Saatgut, Dünger, Bewässerung usw. richtet sich nach den Eigenschaften des jeweiligen Standortes und wird nicht pauschal gleichmäßig über ein ganzes Feld verteilt. Voraussetzung hierfür ist eine genaue Kenntnis der Bodeneigenschaften. In der vorliegenden Arbeit sollten diese Parameter mittels der analytischen Technik der Laser-induzierten Breakdown Spektroskopie (LIBS), die eine Form der Elementaranalyse darstellt, bestimmt werden. Bei den hier gesuchten Bodeneigenschaften handelte es sich um die Gehalte von Nährstoffen sowie einige sekundäre Parameter wie den Humusanteil, den pH-Wert und den pflanzenverfügbaren Anteil einzelner Nährstoffe. Diese Eigenschaften wurden durch etablierte Referenzanalysen bestimmt. Darauf aufbauend wurden die Messergebnissen der LIBS-Untersuchungen durch verschiedene Methoden der sogenannten multivariaten Datenanalyse (MVA) ausgewertet. Daraus sollten Modelle zur Vorhersage der Bodenparameter in zukünftigen LIBS-Messungen erarbeitet werden. Die Ergebnisse dieser Arbeit zeigten, dass mit der Kombination von LIBS und MVA sämtliche Bodenparameter erfolgreich vorhergesagt werden konnten. Dies beinhaltete sowohl die tatsächlich messbaren Elemente als auch die sekundären Eigenschaften, welche durch die MVA mit den Elementgehalten in Zusammenhang gebracht wurden. Das zweite Thema beschäftigt sich mit der Vermeidung von Verlusten durch Schädlingsbefall bei der Getreidelagerung. Hier sollten mittels der Ionenmobilitätsspektrometrie (IMS) Schimmelpilzkontaminationen detektiert werden. Dabei wurde nach den flüchtigen Stoffwechselprodukten der Pilze gesucht. Die durch Referenzmessungen mit Massenspektrometern identifizierten Substanzen konnten durch IMS im Gasvolumen über den Proben, dem sogenannten Headspace, nachgewiesen werden. Dabei wurde nicht nur die Anwesenheit einer Kontamination festgestellt, sondern diese auch charakterisiert. Die freigesetzten Substanzen bildeten spezifische Muster, anhand derer die Pilze identifiziert werden konnten. Hier wurden sowohl verschiedene Gattungen als auch einzelne Arten unterschieden. Die Messungen fanden auf verschiedenen Nährböden statt um den Einfluss dieser auf die Stoffwechselprodukte zu beobachten. Auch die sekundären Stoffwechselprodukte der Schimmelpilze, die Mykotoxine, konnten durch IMS detektiert werden. Beide in dieser Arbeit vorgestellten Forschungsthemen konnten erfolgreich abgeschlossen werden. Sowohl LIBS als auch IMS erwiesen sich für den Nachweis der jeweiligen Analyten als geeignet, und der Einsatz moderner computergestützter Auswertemethoden ermöglichte die genaue Charakterisierung der gesuchten Parameter. Beide Techniken können in Form von mobilen Geräten verwendet werden und zeichnen sich durch eine schnelle und sichere Analyse aus. In Kombination mit entsprechenden Modellen der MVA sind damit alle Voraussetzungen für Vor-Ort-Untersuchungen und damit für den Einsatz in der Landwirtschaft erfüllt. 2020 viii, 123 urn:nbn:de:kobv:517-opus4-473406 10.25932/publishup-47340 Institut für Chemie