Differentiation and classification of bacterial endotoxins based on surface enhanced Raman scattering and advanced machine learning
Yanjun Yang, Beibei Xu, James Haverstick, Nabil Ibtehaz, Artur Muszyński, Xianyan Chen, Muhammad E. H. Chowdhury, Susu M. Zughaier, Yiping Zhao
Abstract
., and a modified deep learning algorithm, RamanNet, have been applied to differentiate and classify these endotoxins. It has been found that most conventional machine learning algorithms can attain a differentiation accuracy of >99%, while RamanNet can achieve 100% accuracy. Such an approach has the potential for precise classification of endotoxins and could be used for rapid medical diagnoses and therapeutic decisions for pathogenic infections.
Topics & Concepts
Raman scatteringSurface (topology)ScatteringMaterials scienceSimple (philosophy)Raman spectroscopyComputer scienceArtificial intelligenceNanotechnologyPhysicsOpticsMathematicsPhilosophyGeometryEpistemologyBiosensors and Analytical DetectionAdvanced Chemical Sensor TechnologiesSpectroscopy Techniques in Biomedical and Chemical Research