Optical fast detection, classification and concentration measurement of bio-agents through Laser-Induced Fluorescence Spectroscopy

Pathogens spread is a worrying issue in terms of security and public health. Currently, the time required for microbiological analyses significantly delays the detection and classification, resulting in a potential spread of pathogens among people before proper counteractions are undertaken. Evidences in the literature show that UltraViolet Laser-Induced Fluorescence (UV-LIF) is a reliable technique for fast discriminating the presence of biological agents in a sample. However, although it has been deeply investigated on bacteria over the years, no study has been focused on its applicability to virological analyses. Moreover, the current main weakness of such technique is related to the classification algorithms that work properly when only one species is present in a specific sample. This is a highly unlikely condition in real scenarios, just considering the presence of harmless background material that may interfere with the analysis. With regard to the above, this research aims at demonstrating the capability of UV-LIF to detect and classify biological agents and to measure their concentration almost in real time. Once a short-range UV-LIF system with excitation wavelength of 355 nm was set up, its effectiveness in terms of detection (specificity, sensitivity, accuracy, etc.) was first validated on Bacillus clausii spores and riboflavin. As expected, the correlation among incident intensity, concentration of the sample and exposure time to the laser radiation was demonstrated. Then, the detection capability of the system was also assessed on two more complex biological agents, which are Escherichia coli and Staphylococcus aureus bacteria. In both cases, Principal Component Analysis (PCA) was performed on the datasets in order to preliminary evaluate the performance in discriminating agents with an increasing level of similarity. After that, the work was addressed to demonstrate for the first time the capability of UV-LIF to detect and classify almost in real time viruses of environmental interest with a short-range system with excitation wavelength of 266 nm. In collaboration with the German Aerospace Centre of Lampoldshausen (Hardthausen, Germany), UV-LIF technique was tested for the first time also on mixed samples. Specifically, mixtures of four fluorophores (FAD, NADH, tyrosine and tryptophan) were analysed through a remote sensing UV-LIF system with two excitation wavelengths (266 nm and 355 nm). In both cases, proper supervised machine learning algorithms, especially neural networks-based ones, have been applied to the LIF spectra in order to gain information for classification and concentration measurement. The results obtained have validated the applicability of UV-LIF in environmental virological analyses and its capability to measure the relative concentration of the different fluorophores in each sample. On one hand, the achievement of these goals have ensured a step forward in virological analyses, with the capability to fast detect, classify and measure the concentration of harmful viruses. The development of a fast virological analysis method may revolutionise this field, allowing fast responses to epidemiological events, reducing their risk and consequences, as well as improving the efficiency and frequency of environmental monitoring. On the other hand, the demonstration that it is possible to classify biological agents and measure their relative concentration in mixed samples thanks to suitable supervised machine learning algorithms allows to discriminate harmful biological agents from harmless ones and from environmental background, such as pollen or dust, avoiding false positive results. Least Square Minimisation Method (LSM) was also proposed as an alternative approach to determine the concentration of the different agents that compose a mixture, following UV-LIF measurements. Finally, once the potentialities of UV-LIF have been demonstrated, the last question to be answered was about the convenience of the application of a LIF-based system in real contexts. The case of its installation in air systems of a hospital to avoid the onset of nosocomial infections was taken into account. A systematic review of the economic impact of such infections has established that it is economically advantageous with respect to the costs needed for prolonged cares. In conclusion, it can be affirmed that the main strength of UV-LIF technique is its capability to detect and classify biological agents, including also viruses, in pure or mixed samples with great advantages in terms of time and costs with respect to the traditional methods used. However, future work has to be performed in order to overcome its limitations and to develop ad hoc devices that could be adopted in different fields of application.