Félix Rivera, Hilsamar

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    Surface enhanced Raman spectroscopy of vegetative cells and endospores of Bacillus thuringiensis as a model for detection of biothreats
    (2012-05) Félix Rivera, Hilsamar; Hernández Rivera, Samuel P.; College of Arts and Sciences - Sciences; Ríos Velázquez, Carlos; Rivera Portalatin, Nilka M.; Briano Peralta, Julio G.; Department of Chemistry; Ramírez Vick, Jaime
    The development of rapid and efficient techniques for biological detection, identification and classification has a positive impact in fields ranging medical, environmental and industrial microbiology to biological warfare agents countermeasures and national security. Traditional and standoff Raman spectroscopy can be used at near or long distances from the sample to obtain information of the chemical fingerprint of microorganisms. In this research, biochemical components of the bacterial cell wall and endospores of Bacillus thuringiensis (Bt) in aqueous suspensions and as well as aerosols particles were identified by surface enhanced Raman spectroscopy (SERS) using small silver (Ag) nanospheres (NS) excited by a 532 nm as laser source. These nanoparticles were chemically reduced by hydroxylamine and borohydride and capped with citrate. Volume ratio of bacteria to NS, activation of “hot spots”, aggregation, and surface charge modification of the NS were studied and optimized to obtain good Bt signal enhancements by a simple SERS protocol of detection. A volume ratio of 0.125 using a bacterial concentration at the specific OD600 of 1 ± 0.1 and NaCl 0.1 M as blank was used to analyze the vegetative cells and endospores samples. Slight aggregation of the NS using NaCl 0.1M as well as surface charge modification to a more acidic ambient (pH range 5 to 7) was induced using small size (19±3 nm) borohydride reduced NS in the form of metallic suspensions aimed at increasing the Ag NS-Bt interactions. Principal component analyses (PCA) and partial least squares (PLS) regressions of SERS spectra coupled to discriminant analysis were used to classify and discriminate between vegetative cells and endospores components of bacterial samples growth at 5 and 24 h at a 95% confidence level and at different stages of bacterial growth. Bioaerosol detection was the largest challenge in this research. Results show that the optimization was successfully used observing and assigning the characteristic peaks of aerosolized Bt. In this study, the bacterial detection range used was 104 cells. In terms of concentration, it is important because is considered as pathogenic.