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Raman-microspectroscopy was used for the non-destructive characterization and differentiation of six different meat spoilage associated microorganisms, namely Brochothrix thermosphacta DSM 20171, Micrococcus luteus, Pseudomonas fluorescens DSM 4358, Escherichia coli Top10 and K12 and Pseudomonas fluorescens DSM 50090. To evaluate and classify the Raman-spectroscopic data at species and strain level an adequate preprocessing and subsequent principal component analysis was used. The same procedure was extended to an independent test data set, which could be successfully assigned to the correct bacterial species and even to the right strain. The evaluation was not only successful in differentiation of gram-positive and gram-negative bacteria but also the discrimination between the different bacterial species and strains was possible. This means that the training data set, the preprocessing method and the evaluation of the data lead to a robust principal component analysis. Even the correct assignment of unknown samples is possible. The results show that Raman-microspectroscopy in combination with an appropriate chemometric treatment can be a good tool for a rapid examination and classification of microbial cultures.
In this paper, the application of established pansharpening algorithms for the fusion of hyperspectral images from Raman Microspectroscopy and panchromatic images from conventional Brightfield Microscopy is investigated. Seven different methods based on Multiresolution Analysis and Component Substitution are applied and evaluated through visual assessment and quantitative quality measures at full and reduced resolution. The obtained results indicate that among the considered concepts, multi-resolution methods are the more promising approaches for a physically and chemically meaningful fusion of the considered modalities. Here, pansharpening based on High-Pass Filtering led to the best results.
Der vorliegende Übersichtsartikel berichtet über Fortschritte in der molekularen Modellierung und Simulation mittels massiv-paralleler Hoch- und Höchstleistungsrechner (HPC). Im SkaSim-Projekt arbeiteten dazu Partner aus der HPC-Community mit Anwendern aus Wissenschaft und Industrie zusammen. Ziel dabei war es mittels HPC-Methoden die Vorhersage von thermodynamischen Stoffdaten in Bezug auf Effizienz, Qualität und Zuverlässigkeit weiter zu optimieren. In diesem Zusammenhang wurden verschiedene Themen bearbeitet: Atomistische Simulation der homogenen Gasblasenbildung, Oberflächenspannung klassischer Fluide und ionischer Flüssigkeiten, multikriterielle Optimierung molekularer Modelle, Weiterentwicklung der Simulationscodes ls1 mardyn und ms2, atomistische Simulation von Gastrennprozessen, molekulare Membran-Strukturgeneratoren, Transportwiderstände und gemischtypenspezifische Bewertung prädiktiver Stoffdatenmodelle.
Polyurethane (PU) coatings were successfully produced using unmodified kraft lignin (KL) as an environmentally benign component in contents of up to 80 wt%. Lignin samples were precipitated from industrial black liquor in aqueous solution working at room temperature and different pH levels (pH 2 to pH 5). Lignins were characterized by UV-Vis, FTIR, pyrolysis-GC/MS, SEC and 31P-NMR. Results show a correlation between pH level, OH number and molecular weight Mw of isolated lignins. Lignin-based polyurethane coatings were prepared in an efficient one step synthesis dissolving lignin in THF and PEG425 in an ultrasonic bath followed by addition of 4,4-diphenylmethanediisocyanate (MDI) and triethylamine (TEA). Crosslinking was achieved under very mild conditions (1 hour at room temperature followed by 3 hours at 35 °C). The resulting coatings were characterized regarding their physical properties including ATR-IR, TGA, optical contact angle, light microscopy, REM-EDX and AFM data. Transparent homogeneous films of high flexibility resulted from lignins isolated at pH 4, possessing a temperature resistance up to 160 °C. Swelling tests revealed a resistance against water. Swelling in DMSO depends on index, pH of precipitation and catalyst utilization for PU preparation. According to AFM studies, surface roughness is between 10 and 28 nm.
The study aimed to detect volatile organic compounds (VOCs) during spoilage of chicken breast filets under modified atmosphere packaging (MAP, (70% O2; 30% CO2)). Storage tests were conducted at 6 °C in a household refrigerator. Measurements were made using untreated chicken breast filets and using filets inoculated with either Pseudomonas fluorescens or Escherichia coli bacteria. The gas space above the sample was adsorbed once a day on Tenax® TA and analyzed using thermal desorption-gas chromatography-mass spectrometry (TD-GC/MS). During storage, 60 volatile organic compounds of hydrocarbons, alcohols, aldehydes, ketones, esters, ethers, and sulfur-containing compounds were detected. It was shown that the presence of most hydrocarbons and aldehydes declined during storage time, whereas most of the alcohols, ketones, sulfur-containing compounds, esters, and ethers increased. Some of these detected VOCs could act as indicators to describe the freshness loss of the product. The best spoilage markers for spoiled chicken breast filets under MAP were 2-methyl-1-propanol, 2-methyl-1-butanol, 3-methyl-1-butanol, 3-hydroxy-2-butanone, ethyl acetate, 2-butanone, and sulfur-containing compounds.
The application of Raman and infrared (IR) microspectroscopy is leading to hyperspectral data containing complementary information concerning the molecular composition of a sample. The classification of hyperspectral data from the individual spectroscopic approaches is already state-of-the-art in several fields of research. However, more complex structured samples and difficult measuring conditions might affect the accuracy of classification results negatively and could make a successful classification of the sample components challenging. This contribution presents a comprehensive comparison in supervised pixel classification of hyperspectral microscopic images, proving that a combined approach of Raman and IR microspectroscopy has a high potential to improve classification rates by a meaningful extension of the feature space. It shows that the complementary information in spatially co-registered hyperspectral images of polymer samples can be accessed using different feature extraction methods and, once fused on the feature-level, is in general more accurately classifiable in a pattern recognition task than the corresponding classification results for data derived from the individual spectroscopic approaches.
