Analytische Chemie
Filtern
Dokumenttyp
- Posterpräsentation (5)
- Zeitschriftenartikel (2)
- Vortrag (2)
Schlagworte
- PLSR (9) (entfernen)
Organisationseinheit der BAM
Eingeladener Vortrag
- nein (2)
Near-infrared (NIR) spectroscopy is a promising candidate for low-cost, nondestructive, and high-throughput mass quantification of micro¬plastics in environmental samples. Widespread application of the technique is currently hampered mainly by the low sensitivity of NIR spectroscopy compared to thermo-analytical approaches commonly used for this type of analysis. This study shows how the application of NIR spectroscopy for mass quantification of microplastics can be extended to smaller analyte levels by combining it with a simple and rapid microplastic enrichment protocol. For this purpose, the widely used flotation of microplastics in a NaCl solution, accelerated by centrifugation, was chosen which allowed to remove up to 99 % of the matrix at recovery rates of 83–104 %. The spectroscopic measurements took place directly on the stainless-steel filters used to collect the extracted particles to reduce sample handling to a minimum. Partial least squares regression (PLSR) models were used to identify and quantify the extracted microplastics in the mass range of 1–10 mg. The simple and fast extraction procedure was systematically optimized to meet the requirements for the quantification of microplastics from common PE-, PP-, and PS-based packaging materials with a particle size < 1 mm found in compost or soils with high natural organic matter content (> 10 % determined by loss on ignition). Microplastics could be detected in model samples at a mass fraction of 1 mg g-1. The detectable microplastic mass fraction is about an order of magnitude lower compared to previous studies using NIR spectroscopy without additional enrichment. To emphasize the cost-effectiveness of the method, it was implemented using some of the cheapest and most compact NIR spectrometers available.
In respect of an efficient cultivation of agricultural cropland, a site-specific fertility management is necessary. Therefore, affordable and extensive mapping methods are needed. For this purpose, the research project I4S (intelligence for soil) has the goal to develop an integrated system. This system includes a sensor platform, which contains different sensors, like XRF, VIS-NIR, Gamma and LIBS.
LIBS (laser-induced breakdown spectroscopy) is known as a fast and simultaneous multi-element analysis with little or no sample preparation. The main task of LIBS measurements in this project is the real time determination of the elemental contents of nutrients in soils, like calcium, magnesium, potassium. For this purpose, a special setup has been designed. The sample uptake operates with the help of a rotatable sample plate which circulates with different velocities to simulate the application on the field. To provide a higher intensity and a better reproducibility of the obtained signal, a double-pulse Nd:YAG laser (1064 nm) was used. In order to minimize dust formation from the soil during the operation of the laser, a dust removal by suction has been integrated.[1] When using relative methods such as LIBS, a suitable calibration curve is needed for absolute quantification. The complex matrix of soils, as well as the influence of moisture and grain size in soils makes the absolute quantification by LIBS challenging. To overcome these influences, chemometric methods were used. With the principal component analysis (PCA) a classification of soils into different soil types was performed and a calibration curve based on partial least squares regression (PLSR) was generated. With this calibration model’s elemental distribution maps for different German agricultural fields were created.
As part of the BonaRes research initiative funded by the German Federal Ministry of Education and Research (BMBF), strategies are being developed to use soil as a sustainable resource in the bioeconomy. The interdisciplinary subproject I4S - “Intelligence for soil” - is responsible for the development of an integrated system for site-specific management of soil fertility. For this purpose, a platform is constructed and various sensors are installed. Real-time data will be summarized in models and decision-making algorithms will be used to control fertilization and accordingly improve soil functions. This would allow investigations in close meshed dynamic grid and fast analysis of large areas to generate higher yields. This is important given that the distribution of minor and trace elements varies widely. Aim of the Federal Institute for Materials Research and Testing (BAM) in the frame of I4S is the characterization of an X-ray fluorescence (XRF) based sensor for robust online-analysis of arable land. The non-destructive and contactless XRF is suitable for rapid in-situ analysis on the field due to minimal sample preparation and simultaneous multi-element analysis.
Soils are already considered as a complex matrix due to their wide range of elements in different contents, especially light elements with low atomic numbers (Z<19). Problems by measuring soil samples also arise from heterogeneity of the sample and matrix effects. Large grain size distribution causes strong inhomogeneity and matrix effects occur through physical properties related to high concentration of main components. Matrix-specific calibration strategies for determination of total major and minor plant essential nutrients are particularly important regarding these difficulties. For accurate calibration, data treatment and evaluation must also be considered. Univariate and multivariate data analysis were compared regarding their analytical figures of merit. Using principal component analysis (PCA) it was possible to classify German soils in different groups as sand, clay and silt. Calibration models were obtained by partial least squares regression (PLSR) and the content of macro- and micronutrients in German soils was predicted. Elemental distribution maps for different German arable lands were created and the results compared to reference measurements. The correlation between predicted values and reference values were in good agreement for most major and minor nutrients.
In respect of an efficient cultivation of agricultural cropland, a site-specific fertility management is necessary. Therefore, affordable and extensive mapping methods are needed. The research projects I4S (intelligence for soil) has the goal to develop a system for this purpose. I4S is one of ten interdisciplinary research project associations of the innovation programme called BonaRes, which is funded by the German Federal Ministry of Education and Research (BMBF).
The system includes a sensor platform, which contains different sensors, like XRF, VIS-NIR, Gamma and LIBS. The main task of LIBS measurements in this project is the real-time determination of the elemental contents of major and minor nutrients in soils, like calcium, magnesium, potassium. LIBS (laser-induced breakdown spectroscopy) is known as a fast and simultaneous multi-element analysis with little or no sample preparation. The main task of LIBS measurements in this project is the real-time determination of the elemental contents of nutrients in soils, like calcium, magnesium, potassium. For this purpose, a special setup has been designed. The sample uptake operates with the help of a rotatable sample plate which circulates with different velocities to simulate the application on the field. To provide a higher intensity and a better reproducibility of the obtained signal, a double-pulse Nd:YAG laser (1064 nm)was used. In order to minimize dust formation from the soil during the operation of the laser, a dust removal by suction has been integrated. When using relative methods such as LIBS, a suitable calibration curve is needed for absolute quantification. The complex matrix of soils, as well as the influence of moisture and grain size in soils makes the absolute quantification by LIBS challenging. To overcome these influences, chemometric methods were used. With the principal component analysis (PCA) a classification of soils into different soil types was performed and a calibration curve based on partial least squares regression (PLSR) was generated. With this calibration model’s elemental distribution maps for different German agricultural fields were created.
Aim of the Federal Institute for Materials Research and Testing (BAM) in the frame of I4S - “Intelligence for Soil” is the characterization of an X-ray fluorescence (XRF) based sensor for robust online-application of arable land.Fast soil mapping for agricultural purpose allows the site-specific optimized introduction of plant essential nutrients like S, K, Ca, and Fe. This is important given that the distribution of minor and trace elements varies widely. The non-destructive and contactless XRF is suitable for rapid in-situ analysis on the field due to minimal sample preparation and simultaneous multi-element analysis.
Soils are already considered as a complex matrix due to their wide range of elements in different contents, especially light elements with low atomic numbers (Z<19). Problems by measuring soil samples also arise from heterogeneity of the sample and matrix effects. Large grain size distribution causes strong inhomogeneity and matrix effects occur through physical properties related to high concentration of main components. Matrix-specific calibration strategies for determination of total major and minor plant essential nutrients are particularly important regarding these difficulties. For accurate calibration, data treatment and evaluation must also be considered. Empirical univariate and multivariate data analysis were compared regarding their analytical figures of merit. Using principal component analysis (PCA) it was possible to classify German soils in different groups as sand, clay and silt. A calibration curve was obtained by partial least squares regression (PLSR) and the elemental content of German soils was predicted. Elemental distribution maps for different German arable lands were created and the results compared to reference measurements. The correlation between predicted values and reference values were in good agreement for most major and minor nutrients.
Mit wachsenden Bevölkerungszahlen steigt auch der Rohstoffkonsum, und der nachhaltigere und effizientere Umgang mit knappen Ressourcen wie Wasser und Boden rückt in den Fokus.
Im Rahmen der vom Bundesministerium für Bildung und Forschung (BMBF) geförderten Forschungsinitiative BonaRes werden Strategien entwickelt, um Boden als nachhaltige Ressource in der Bioökonomie zu nutzen. Das interdisziplinäre Teilprojekt I4S – Intelligence for soil – ist dabei für die Entwicklung eines integrierten Systems zum ortsspezifischen Management der Bodenfruchtbarkeit zuständig. Hierfür wird eine Plattform gebaut, auf der verschiedene Sensoren installiert werden sollen, deren in Echtzeit erhaltene Messdaten in Modelle und Entscheidungsalgorithmen zur Steuerung der Düngung und dementsprechend Verbesserung der Bodenfunktionen einfließen sollen.
Einer dieser Sensoren, auf dessen Grundlage ein robustes Online-Verfahren zur Bestimmung der Makro- und Mikronährstoffe, wie Ca oder K in Böden entwickelt werden soll, ist die Röntgenfluoreszenzanalyse (RFA). Um Messungen auf einem Feld zu simulieren, wurde in einen Laboraufbau ein Probenteller installiert, der sich bei verschiedenen Winkelgeschwindigkeiten bewegen lässt und so dynamisches Messen der als Spur aufgetragenen Probe ermöglicht. So kann der Aufwand der Probenvorbereitung minimiert werden.
Gerade bei einer komplexen Matrix wie Boden, die eine breite Elementverteilung aufweist, ist die Datenauswertung ein wichtiger Faktor. Die bisher verwendete univariate Datenanalyse liefert gute Ergebnisse und zeigt, dass durch Kalibrierung der RFA mit 14 Referenzmaterialien große Ackerflächen ausgewertet werden können. Dies funktioniert aber nur, solange die Elementgehalte der Proben mit denen der Referenzmaterialien vergleichbar sind.
Die zusätzlich verwendete multivariate Datenanalyse bietet hingegen die Möglichkeit, Modellrechnungen der Böden durchzuführen und Böden mithilfe der Hauptkomponentenanalyse (PCA) besser zu klassifizieren. Des Weiteren können basierend auf der Partial Least Squares Regression (PLSR) Kalibriermodelle erstellt werden, welche Prognosen zu den Elementgehalten unbekannter Ackerböden ermöglichen. Zusätzlich bietet die multivariate Datenanalyse die Möglichkeit, Störgrößen wie unterschiedliche Korngrößenverteilung und Feuchtigkeitsgrad der Probe in das Modell miteinzubeziehen. Beide Auswertemethoden sollen anhand statisch gemessener Proben verglichen werden und durch Messung einer Vielzahl realer Bodenproben von unterschiedlichen Standorten erweitert werden. Im weiteren Verlauf müssen beide Modelle auf dynamisch bewegte Proben übertragen werden.
Mit wachsenden Bevölkerungszahlen steigt auch der Rohstoffkonsum, und der nachhaltigere und effizientere Umgang mit knappen Ressourcen wie Wasser und Boden rückt in den Fokus.
Im Rahmen der vom Bundesministerium für Bildung und Forschung (BMBF) geförderten Forschungsinitiative BonaRes werden Strategien entwickelt, um Boden als nachhaltige Ressource in der Bioökonomie zu nutzen. Das interdisziplinäre Teilprojekt I4S – Intelligence for soil – ist dabei für die Entwicklung eines integrierten Systems zum ortsspezifischen Management der Bodenfruchtbarkeit zuständig. Hierfür wird eine Plattform gebaut, auf der verschiedene Sensoren installiert werden sollen, deren in Echtzeit erhaltene Messdaten in Modelle und Entscheidungsalgorithmen zur Steuerung der Düngung und dementsprechend Verbesserung der Bodenfunktionen einfließen sollen. So wären Untersuchungen in engmaschigen, dynamischen Rastern und schnelle Analysen großer Flächen möglich, um höhere Erträge zu erwirtschaften.
Einer dieser Sensoren, auf dessen Grundlage ein robustes Online-Verfahren zur Bestimmung der Makro- und Mikronährstoffe, wie Ca, K und Mn in Böden entwickelt werden soll, ist die Röntgenfluoreszenzanalyse (RFA). Die RFA eignet sich vor allem durch ihre schnelle, kontaktlose, simultane Multielementanalyse und wird bereits zur Bestimmung von Schwermetallen in Böden eingesetzt. Ein weiterer Vorteil der RFA ist die geringe Probenvorbereitung. Um Messungen auf einem Feld zu simulieren, wurde in einen Laboraufbau ein Probenteller installiert, der sich bei verschiedenen Winkelgeschwindigkeiten bewegen lässt und so dynamisches Messen der als Spur aufgetragenen Probe ermöglicht.
Gerade bei einer komplexen Matrix wie Boden, die eine breite Elementverteilung aufweist, ist die Datenauswertung ein wichtiger Faktor. Die bisher verwendete univariate Datenanalyse liefert gute Ergebnisse und zeigt, dass durch Kalibrierung der RFA mit 14 Referenzmaterialien große Ackerflächen ausgewertet werden können. Dies funktioniert aber nur, solange die Elementgehalte der Proben mit denen der Referenzmaterialien vergleichbar sind.
Die zusätzlich verwendete multivariate Datenanalyse bietet hingegen die Möglichkeit, Modellrechnungen der Böden durchzuführen und Böden mithilfe der Hauptkomponentenanalyse (PCA) besser zu klassifizieren. Des Weiteren können basierend auf der Partial Least Squares Regression (PLSR) Kalibriermodelle erstellt werden, welche Prognosen zu den Elementgehalten unbekannter Ackerböden ermöglichen. Zusätzlich bietet die multivariate Datenanalyse die Möglichkeit, Störgrößen wie unterschiedliche Korngrößenverteilung und Feuchtigkeitsgrad der Probe in das Modell miteinzubeziehen. Beide Auswertemethoden sollen anhand statisch gemessener Proben verglichen werden und durch Messung einer Vielzahl realer Bodenproben von unterschiedlichen Standorten erweitert werden. Im weiteren Verlauf müssen beide Modelle auf dynamisch bewegte Proben übertragen werden.
Multivariate data analysis is a universal tool for the evaluation of process spectroscopic data. In process analytics, huge amounts of information are produced i) by the many variables contained in one spectrum often exceeding 1000 (wavenumbers, wavelength, shifts…), and ii) the high number of spectra that is generated within the measurement period. Multivariate data analysis, often also called Chemometrics, help to extract the relevant information which is needed to examine and even control processes. Therefore, calibration models must be precise and robust, and moreover, must cover a wide range of variation of factors posing an influence on the process.
In this pre-conference course an introduction to both, explorative data analysis by PCA (Principal Component Analysis), and regression analysis by the most frequently used method, i.e. PLSR (Partial Least Squares Regression) is given. In principal, all optical spectroscopic methods are suited for multivariate evaluation. It will be demonstrated that under certain preconditions, even process NMR spectra can be predicted by PLSR models.
At first, basic principles of multivariate data analysis will be provided. This includes a short introduction into the concept of model building and interpretation of results. Detailed aspects of data pretreatment and calibration & validation strategies for chemometric models will be provided with own data from NIR, Raman and NMR spectroscopy.
In a first example, the development of an online compatible method for the quantification of methanol in biodiesel by PLSR is presented. This also includes the classification of biodiesel feedstocks by PCA and statistical tools which allow for the estimation of a full uncertainty budget.
The Raman spectroscopic prediction of Hydroformylation reaction in a miniplant is used to discuss shortcomings and pitfalls which may occur with the transfer of off-line models to the real processes. Design of experiment and strategies for suitable lab-scale experiments are presented as a possible way to overcome problems.
In a third application the prediction of the reactants of an esterification reaction based on process NMR data is demonstrated.
Abstract. Biodiesel quality control is a relevant issue as biodiesel properties influence diesel engine performance and integrity. Within the European Metrology Research Program (EMRP) ENG09 project “Metrology for Biofuels”, an on-line /at-site suitable near-infrared spectroscopy (NIRS) method has been developed in parallel with an improved EN14110 headspace GC analysis method for methanol in biodiesel. Both methods have been optimized for a methanol content of 0.2 mass% as this represents the maximum limit of methanol content in FAME according to EN 14214:2009. The NIRS method is based on a mobile NIR spectrometer equipped with a fiber-optic coupled probe. Due to the high volatility of methanol, a tailored air-tight adaptor was constructed to prevent methanol evaporation during measurement. The methanol content of biodiesel was determined from evaluation of NIRS spectra by Partial Least Squares Regression (PLS). Both GC analysis and NIRS exhibited a significant dependence on biodiesel feedstock. The NIRS method is applicable to a content range of 0.1 % (m/m) to 0.4 % (m/m) of methanol with uncertainties at around 6% relative for the different feedstocks. A direct comparison of headspace GC and NIRS for samples of FAMEs yielded that the results of both methods are fully compatible within their stated uncertainties.