Bakterite eristamine fluoromeetri spektrist masinõppe abil

Abstract

In this master thesis, the most suitable machine learning solution is found for the fluorometer device H2B-Spectral developed by LDI Innovation OÜ. The machine learning methods tested in this thesis aim to improve the differentiation of various microorganisms on selected solid surfaces. The device functions as a multi-channel fluorometer, exciting the measured sample surface with three different ultraviolet wavelengths and reading the emitted optical fluorescence signal on three different wavelength channels. Based on the obtained eight number data (one channel provides no information), the sensor's software must classify the measurement point into pre-learned classes. In this study, over thirteen classes of various microorganisms are measured, and different machine learning methods (including decision tree, random forest, KNN, support vector machine, ensemble voting) are compared for their classification performance. The most effective classification method identified in this study will be implemented in the standard machine learning system in the software for H2B-Spectral.