Raman spectroscopy is a powerful technique for rapid, non-invasive and reagentless analysis of materials, including biological cells. In many samples of biological origin, laser illumination leads to luminescence in addition to Raman scattering. This luminescence will often dissipate after prolonged laser exposure. A common practice is to allow a sample to "photobleach" prior to acquisition of a high quality Raman spectrum. In an effort to automate data acquisition on such samples we are investigating an automated means of quantifying photobleaching and acquiring Raman spectra after photobleaching. We present results of a comparison between an automated approach to acquiring a spectrum on a sample with dynamic luminescence and a manual approach taken by a trained spectroscopist. This component of smart biomedical sensor technology will help allow high quality spectral data to be acquired reproducibly to potentially aid non-spectroscopists with application of Raman spectroscopic approaches.
Contamination of drinking water with pathogenic microorganisms such as Cryptosporidium has become an increasing concern in recent years. Cryptosporidium oocysts are particularly problematic, as infections caused by this organism can be life threatening in immunocompromised patients. Current methods for monitoring and analyzing water are often laborious and require experts to conduct. In addition, many of the techniques require very specific reagents to be employed. These factors add considerable cost and time to the analytical process. Raman spectroscopy provides specific molecular information on samples, and offers advantages of speed, sensitivity and low cost over current methods of water monitoring.
Raman spectroscopy is an optical method that has demonstrated the capability to identify and differentiate microorganisms at the species and strain levels. In addition, this technique has exhibited sensitivities down to the single organism detection limit. We have employed Raman spectroscopy and Raman Chemical Imaging, in conjunction with chemometric techniques, to detect small numbers of oocysts in the presence of interferents derived from real-world water samples. Our investigations have also indicated that Raman Chemical Imaging may provide chemical and physiological information about an oocyst sample which complements information provided by the traditional methods. This work provides evidence that Raman imaging is a useful technique for consideration in the water quality industry.