TY - THES A1 - Angenendt, Denis T1 - Erstellung einer Risiko- und Gefahrenanalyse zur Bewertung von allergenen, biologischen, chemischen, bio-chemischen und physikalischen Kontaminanten für die praktische Anwendung in der Lebensmittelindustrie N2 - This bachelor thesis deals with contaminants in the food industry and presents them in a hazard analysis and risk assessment according to the specifications of Sauels Frische Wurst GmbH Fleischwaren & Co. KG. Allergenic, microbiological, chemical, bio-chemical and physical contaminants were considered. The allergenic contaminants include gluten, crustaceans and molluscs, fish, eggs, soy, milk, nuts, peanuts and celery. The microbiological ones are the apathogenic microorganisms yeasts and molds and the pathogenic microorganisms are Bacillus cereus, Clostridium perfingens, Escherichia coli, Yersinia enterocolitica, Campylobacter jejuni, Listeria monocytogenes, Toxoplasma gondii and Staphylococus areus. Hard plastic and metal were evaluated as possible foreign bodies. In the chemistry section, intentional radiation, acrylamide, mercury, bisphenol A, and ethylene oxide were addressed. The bio-chemical contaminant considered was aflatoxin. The following contaminants were evaluated as highly critical. Whilst all allergens pose a risk due to their harmful potential, it is the incorrect labeling which constitutes the risk from the producers perspective. Among microbiological contaminants, Listeria monocytogenes and Escherichia coli are the microorganisms with the highest risk. This is based on the fact that these are typical recontamination bacteria. The physical contaminants described all recieved the same rating in the hazard analysis and risk assessment. The actual nature of a foreign body determines the hazard in each individual case in the assessment. Ethylene oxide has been evaluated as the most critical in the analysis among the chemical contaminants, because of its high probability of occurrence, which is based on the calculated recall rate and its carcinogenicity. The highest risk of the bio-chemical contaminants comes from aflatoxins, as this is the only mycotoxin that is pathogenic. The application of hazard analysis and risk assessment must take into account company-specific factors. New scientific findings, detection methods and other relevant contaminants must be continuously incorporated into the analysis. Y1 - 2022 ER - TY - THES A1 - Kerst, Mario T1 - Validation and optimization of mathematical predictive models for the identification of microorganisms using Raman-spectroscopy N2 - The identification and classification of microorganisms remains a challenge in many areas, especially considering time and cost-efficiency. Fast, reliable and cheap methods are of great interest for research, industry and health care. Raman spectroscopy is a method that fulfills these criteria and, in this work, we aim to show its effectiveness to classify a selection of microorganisms. Using a standardized protocol, eighteen microorganisms were measured using Raman spectroscopy and classified with several types of mathematical classification models, including linear discriminant analysis, support vector machines and convolutional neural networks. To prepare the measured spectral data for classification several steps for the data evaluation and transformation were applied and their effectiveness for removing noise and other interferences evaluated. The resulting predictions outcomes were compared to select the most efficient model, showing that with an overall true positive prediction rate of 86.87 % the convolutional neural network performed the best. Further inspection of the results shows issues with the measurements of selected organisms, indicating that an improved measurement protocol is required to achieve proper predictions. Overall this work shows that Raman spectroscopy in combination with mathematical classification models is a viable and fast method for classification of microorganisms. KW - Raman spectroscopy KW - Microbiology KW - Linear discriminant analysis KW - Support vector machine KW - Convolutional neural network Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:1383-opus4-14820 ER - TY - THES A1 - Hermannsdörfer, Frederic T1 - Mechanical Preparation of External Thermal Insulation Composite Systems N2 - The building industry relies heavily on energy conservation, energy efficiency, and resource efficiency. Thermal insulation composite systems (TICS) have been used to insulate building facades since the 1970s, resulting in significant energy savings. Additionally, as a result of EnEV 2002 implementation, energy consumption for building facades has gradually increased. The initial systems have reached the end of their useful life due to the durability of these composites, and an increasing amount of TICS waste has been generated in recent years. TICS are currently processed solely in waste incineration plants; no other method of recycling these devices exists. Due to technical constraints and the high calorific value of TICS, thermal disposal alone is not feasible. Additionally, because TICS systems are comprised of numerous components, significant resources will be wasted if they are not recycled properly. This study examines actual TICS waste that is technologically processed in order to recycle it based on its composition. KW - WDVS KW - ETICS KW - Mechanical preparation Y1 - 2022 ER -