Advances in Spectral Techniques for Detection of Pathogenic Microorganisms

Abstract

The highly contagious viral illness Coronavirus disease 2019, caused by severe acute respiratory syndrome coronavirus-2, has led to nearly 5 million deaths worldwide. The detection of highly infectious pathogens or novel pathogens causing emerging infectious diseases is highly challenging. Encouragingly, spectral detection—including laser-induced fluorescence spectroscopy, infrared absorption spectroscopy, Raman spectroscopy and their combinations—has been broadly used to detect pathogenic microorganisms on the basis of their physical and chemical characteristics. Surface-enhanced Raman spectroscopy with labels can detect organisms at a minimum concentration of 3 cells/mL. The changes in cells’ biochemical reactions before and after polioviral infection can be detected by Fourier transform infrared spectroscopy. However, the sensitivity and specificity of different spectral detection categories differs, owing to their different detection principles. Flexible detection methods require interdisciplinary researchers familiar with both pathogen biology and instruments. This review summarizes the advances in spectral techniques used in detecting pathogenic microorganism.

Comparative Analysis of Traditional and Advanced Clustering Techniques in Bioaerosol Data: Evaluating the Efficacy of K-Means, HCA, and GenieClust with and without Autoencoder Integration

“…Despite these advancements to supervised approaches, there remains a lack of research dedicated to the development of unsupervised algorithms for clustering of ambient bioaerosols. Consequently, the more antiqued algorithms, including K-means and HCA, continue to dominate the field [29][30][31].…”