Vibrational spectroscopy studies of biomolecular systems: from amino acids to microorganisms

Abstract

Vibrational spectroscopy: infrared and Raman, was applied to the study of two amino acids and ten bacterial strains with the purpose of developing rapid methods of identification and discrimination. Use of spectroscopic techniques in characterization of signatures of biological agents has gained considerable attention in recent years, mainly because of the high sensitivity and selectivity that can be attained through their practice. They can also be easily adapted to portable equipment to obtain fingerprint information of biomolecules and microorganisms in the field. In this research, various spectroscopic studies were conducted, both in the lab and in field applications. In the first study the most stable conformations and orientations of Ltryptophan (L-Trp) on silver (Ag) and gold (Au) nanoparticles (NPs) was determined using Raman spectroscopy. The objective of the work was to determine if L-Trp molecules interact with the Ag/Au NPs through the carboxylate end, through the amino group end, or through both using surface enhanced Raman spectroscopy (SERS). The work also focused on how parameters such as analyte concentration, average nanoparticle size and pH affected the binding of L-Trp to the NPs surfaces. In a second related study Ag/Au NPs were synthetized using a laser ablation technique and SERS activity of prepared NPs was evaluated with L-histidine (L-His). In other studies quantum cascade laser spectroscopy (QCLS) was used to identify biochemical components of bacterial cell wall of various microorganism species from vibrational modes of molecular components in the biosamples. Principal component analysis (PCA) and partial least squares analysis coupled to discriminant analysis (PLSDA) of QCL spectra were used to classify and discriminate between gram-positive and gram-negative bacteria at a 95% confidence level. Results demonstrate that the QCLS techniques used: reflection and transmission, accompanied with powerful multivariate analyses techniques were successful in detecting and classifying the microorganisms studied by means of their characteristic spectral information.