Access Type

Open Access Dissertation

Date of Award

January 2011

Degree Type


Degree Name



Physics and Astronomy

First Advisor

Steven J. Rehse


Laser-induced breakdown spectroscopy (LIBS) has gained a reputation as a flexible and convenient technique for rapidly determining the elemental composition of samples with minimal or no sample preparation. In this dissertation, I will describe the benefits of using LIBS for the rapid discrimination and identification of bacteria (both pathogenic and non-pathogenic) based on the relative concentration of trace inorganic elements such as Mg, P, Ca, and Na. The speed, portability, and robustness of the technique suggest that LIBS may be applicable as a rapid point-of-care medical diagnostic technology.

LIBS spectra of multiple genera of bacteria such as Escherichia, Streptococcus, Mycobacterium, and Staphylococcus were acquired and successfully analyzed using a computerized discriminant function analysis (DFA). It was shown that a LIBS-based bacterial identification might be insensitive to a wide range of biological changes that could occur in the bacterial cell due to a variety of environmental stresses that the cell may encounter.

The effect of reducing the number of bacterial cells on the LIBS-based classification was also studied. These results showed that with 2500 bacteria, the identification of bacterial specimens was still possible. Importantly, it was shown that bacteria in mixed samples (more than one type of bacteria being present) were identifiable. The dominant or majority component of a two-component mixture was reliably identified as long as it comprised 70% of the mixture or more.

Finally, to simulate a clinical specimen in a precursor to actual clinical tests, Staphylococcus epidermidis bacteria were collected from urine samples (to simulate a urinary tract infection specimen) and were tested via LIBS without washing. The analysis showed that these bacteria possessed exactly the same spectral fingerprint as control bacteria obtained from sterile deionized water, resulting in a 100% correct classification. This indicates that the presence of other trace background biochemicals from clinical fluids will not adversely disrupt a LIBS-based identification of bacteria.