The Effect of Bacterial Environmental and Metabolic Stresses on a Laser-Induced Breakdown Spectroscopy (LIBS) Based Identification of <I>Escherichia coli</I> and <I>Streptococcus viridans</I>

Abstract

Mohaidat, Q.; and Rehse, Steven J.. (2011). The effect of bacterial environmental and metabolic stresses on a laserinduced breakdown spectroscopy (LIBS) In this paper we investigate the effect that adverse environmental and metabolic stresses have on the laser-induced breakdown spectroscopy (LIBS) identification of bacterial specimens. Single-pulse LIBS spectra were acquired from a non-pathogenic strain of Escherichia coli cultured in two different nutrient media: a trypticase soy agar and a MacConkey agar with a 0.01% concentration of deoxycholate. A chemometric discriminant function analysis showed that the LIBS spectra acquired from bacteria grown in these two media were indistinguishable and easily discriminated from spectra acquired from two other non-pathogenic E. coli strains. LIBS spectra were obtained from specimens of a nonpathogenic E. coli strain and an avirulent derivative of the pathogen Streptococcus viridans in three different metabolic situations: live bacteria reproducing in the log-phase, bacteria inactivated on an abiotic surface by exposure to bactericidal ultraviolet irradiation, and bacteria killed via autoclaving. All bacteria were correctly identified regardless of their metabolic state. This successful identification suggests the possibility of testing specimens that have been rendered safe for handling prior to LIBS identification. This would greatly enhance personnel safety and lower the cost of a LIBS-based diagnostic test. LIBS spectra were obtained from pathogenic and non-pathogenic bacteria that were deprived of nutrition for a period of time ranging from one day to nine days by deposition on an abiotic surface at room temperature. All specimens were successfully classified by species regardless of the duration of nutrient deprivation.

A critical review of recent progress in analytical laser-induced breakdown spectroscopy

“…Successful discrimination of pathogenic from non-pathogenic bacteria has been achieved, including some multi-drug-resistant strains of bacteria including Staphylococcus aureus and other strains causing hospital-acquired infections (HAI) [112,132]. Another advantage of LIBSbased identification of pathogens is that bacterial LIBS spectra have been revealed not to change with time as the culture ages, or on abiotic surfaces, or in killed or inactivated specimens [133]. A support vector machine classification model has been developed for the identification of anthrax spores [134].…”

Laser-induced breakdown spectroscopy (LIBS): an overview of recent progress and future potential for biomedical applications

“…Bacterial LIBS spectra do not change with time as the • bacterial culture ages on an abiotic surface (necessary for accurate identification and detection of surface contamination [70]). Bacterial LIBS spectra can be easily obtained from killed (via • autoclaving) or inactivated (via UV light) specimens, and such treatment (which renders the specimen completely safe for handling) does not decrease identification specificity and does not decrease LIBS spectral intensity [70]. Intensity of the LIBS spectrum is linearly dependent on • cell number, but the specificity is not dependent on cell number [52].…”

A comparison of multivariate analysis techniques and variable selection strategies in a laser-induced breakdown spectroscopy bacterial classification

“…[1][2][3][4] Over the last five years we and others have demonstrated a sensitive and specific identification of live bacterial biospecimens utilizing a discriminant function analysis (DFA) to classify LIBS spectra. [5][6][7][8] The intensities of strong specific elemental atomic emission lines normalized by the total observed spectral power have been utilized as independent variables in this multivariate analysis. [9] The selection of specific spectral lines to serve as independent variables in the multivariate analysis is known as variable down-selection.…”