TY - THES A1 - K C, Manoj T1 - Material uses of nut and other fruit shells N2 - This review paper focused on the potential uses of nut and fruit shells. Based on the information from several kinds of literature, the composition of nut and fruit shells were studied, and the shells demonstrated the possibility to produce a wide range of materials such as bio-adsorbent like activated carbon; construction materials like concrete block, earth block, bricks, cement, etc.; and bio-composites like WPC, bio-plastic, etc. Besides these antimicrobials, drugs, insecticides, nanoparticles, and impact-resistant materials could be prepared from nut and fruit shells. Moreover, the production process for these materials was explained in the later part of the paper. Also, the yield, cost, and performance of materials prepared from nut and fruit shells were discussed and found that some of the products were as good as commercially available materials. The environmental impact being one of the important parts, materials from nut and fruit shells had a beneficial impact on nature. However, the entire life cycle assessment suggested that better techniques are required to attain a lower negative impact on the ecosystem. Since nut and fruit shells were the easy and low-cost raw material, they had a greater chance of replacing other expensive, non-environment-friendly raw materials and their products. Overall, this research shed light on the material use of nut and fruit shells along with its benefits; production process; material yield, cost, and performance; with possible implications and recommendations that can lead to better utilization of biological resources. Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:1383-opus4-8755 ER - TY - THES A1 - Tewes, Thomas T1 - Entwicklung einer Methode zur Identifizierung von Mikroorganismen über Raman-Spektroskopie N2 - Zielstellung: Das Ziel dieser Arbeit war es, eine Methode zur Identifizierung von Mikroorganismen über Mikro-Raman-Spektroskopie zu entwickeln. Dafür musste zunächst eine Methode gefunden werden, um Spektren verschiedener Mikroorganismen mit ausreichender Qualität zu erhalten. Im Anschluss sollte die Datensammlung erfolgen, bei der möglichst viele repräsentative Spektren gesammelt werden. Mit den Daten mussten danach Modelle zur Vorhersage unbekannter Raman-Spektren entwickelt werden, was eine entsprechende Datenvorbehandlung voraussetzte. Um die Praxistauglichkeit der Modelle zu prüfen, sollten diese auf Spektren von Proben angewandt werden, die nicht in den Kalibrationssets enthalten waren. Neben der Entwicklung der Methode und der Vorhersagemodelle, sollten auch verschiedene Einflüsse der Analysebedingungen berücksichtigt werden. Methoden: Neun verschiedene Mikroorganismen, die aus kryokonservierten Reinkulturen stammten, wurden bei 30 °C für 24 Stunden auf Trypton Soja Agar (TSA) bebrütet. Die Spektrenaufzeichnung erfolgte bei einer Anregung von 633 nm über ein konfokales Raman-Mikroskop der Firma Renishaw. Sowohl die Datenvorbehandlung (Basislinienkorrektur, Glättung, Normalisierung, Hauptkomponentenanalyse (PCA)), als auch die Modellentwicklung, erfolgte über die Software MATLAB. Es wurden verschiedene Klassifikationsverfahren angewandt, um ein Modell mit möglichst guter Leistung zu finden. Ergebnisse: Für die Entwicklung der Kalibrationsmodelle wurden insgesamt 2953 Raman-Spektren von neun verschiedenen Mikroorganismen gesammelt. Störungen durch Fluoreszenz wurden durch Vorbestrahlung von bis zu 15 Minuten ausgebrannt. Die Modelle basierend auf der Quadratischen Diskriminanzanalyse (QDA), dem k-Nächster-Nachbar-Algorithmus (kNN) und der Support Vector Maschine (SVM), wurden mit verschiedener Anzahl an Hauptkomponenten und einer fünffachen Kreuzvalidierung erstellt. Die theoretischen Vorhersagegüten belaufen sich bei den genauesten Modellen auf 99,0 % (QDA), 99,1 % (kNN) und 99,7 % (SVM). Für die praktische Validierung wurden für jeden Mikroorganismus 100 weitere „unbekannte“ Spektren gesammelt. Die Vorhersagegenauigkeiten in der Praxis belaufen sich auf 98,1 % (QDA), 97,1 % (kNN) und 97,0 % (SVM). Diskussion: Eine zuverlässige Identifizierung der in dieser Arbeit untersuchten Mikroorganismen konnte über alle verwendeten Klassifikationsverfahren erreicht werden. Auf Stamm-ebene erzielt die SVM in der Praxis die genauesten Ergebnisse, jedoch nicht bei den restlichen Mikroorganismen. Die QDA erzielt in der Praxis etwas genauere Vorhersagen als das kNN-Modell, jedoch basiert das kNN-Modell mit der besten Leistung auf den wenigsten Hauptkomponenten. Dies kann als Vorteil angesehen werden, da weniger Dimensionen zur erfolgreichen Klassifizierung in der Regel robustere Modelle in der Praxis bedeuten. N2 - Objective: The objective of this work was to develop a method for the identification of microorganisms via micro-Raman spectroscopy. First, a method was established to obtain spectra of sufficient quality for various microorganisms. Subsequently, the data collection was performed, in which as many representative spectra as possible were collected. Afterwards, models for the prediction of unknown Raman spectra were developed with the data, which required a corresponding data pre-treatment. To test the practicability of the models, they were applied to spectra of samples that were not included in the calibration sets. In addition to the development of the method and the predictive models, different influences of the analysis conditions should also be considered. Methods: Nine different microorganisms from cryopreserved pure cultures were incubated at 30 °C for 24 hours on tryptone soy agar (TSA). The spectra were obtained at 633 nm excitation via a confocal Raman microscope from the company Renishaw. Both the data pre-treatment (baseline correction, smoothing, normalization, principal component analysis (PCA)) and the model development were carried out using the MATLAB software. Various classification methods were used to determine the model with the best possible performance. Results: For the development of the calibration models 2953 Raman spectra of nine different microorganisms were collected. Disturbances by fluorescence were bleached out by pre-irradiation for up to 15 minutes. The models based on quadratic discriminant analysis (QDA), the k-nearest neighbor algorithm (kNN), and support vector machine (SVM) were created with different numbers of principal components and using a five-fold cross-validation. The theoretical prediction accuracies for the most accurate models are 99.0 % (QDA), 99.1 % (kNN) and 99.7 % (SVM). For practical validation, 100 more "unknown" spectra were collected for each microorganism. The accuracies of the prediction in practice are 98.1 % (QDA), 97.1 % (kNN) and 97.0 % (SVM). Discussion: All classification methods used in this thesis identified reliably the investigated microorganisms. At strain-level, SVM achieves the most accurate results in practice, but not with the remaining microorganisms. In practice, QDA achieves slightly more accurate predictions than the kNN-model. However, the kNN-model with the best performance is based on the lowest number of principal components. This can be considered as an advantage, because using less dimensions for successful classification usually means more robust models in practice. KW - Mikro-Raman-Spektroskopie KW - Mikroorganismen KW - Identifizierung KW - HeNe-Laser 633 nm KW - Fluoreszenz KW - Raman-Spektroskopie KW - Hochschule Rhein-Waal Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:1383-opus4-3212 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 -