Spectroscopic analysis of bacterial biological warfare simulants and the effects of environmental conditioning on a bacterial spectrum.

The ability to distinguish bacteria from mixed samples is of great interest, especially in the medical and defence arenas. This paper reports a step towards the aim of differentiating pathogenic endospores in situ, to aid any required response for hazard management using infrared spectroscopy combined with multivariate analysis. We describe a proof-of-principle study aimed at discriminating biological warfare simulants from common environmental bacteria. We also report an evaluation of multiple pre-processing techniques and subsequent differences in cross-validation of two pattern recognition models (Support Vector Machines and Principal Component-Linear Discriminant Analysis) for a six-class classification (bacterial classification). These classifications were possible with an average sensitivity of 88.0 and 86.9 %, and an average specificity of 97.6 and 97.5 % for the SVM and the PC-LDA models, respectively. Most spectroscopic models are built upon spectra from bacteria that have been specifically prepared for analysis by a particular method; this paper will comment upon the differences in the bacterial spectrum that occur between specific preparations when the bacteria have spent 30 days in the simulated weather conditions of a hot dry climate.

Short-wave infrared excited spatially offset Raman spectroscopy (SORS) for through-barrier detection.

Spatially offset Raman spectroscopy (SORS) using 1064 nm excitation is demonstrated here to detect chemicals through a physical barrier such as a container. This excitation wavelength overcomes the issue of fluorescence from the target chemical, whilst retaining the benefits of the SORS technique for through-barrier detection. These advantages have a wide range of applications in both civilian and military environments.