Noncontact, Optical Molecular Method for Detection and Identification of Cryptosporidium parvum Oocysts in Drinking WaterEPA Contract Number: EPD04032
Title: Noncontact, Optical Molecular Method for Detection and Identification of Cryptosporidium parvum Oocysts in Drinking Water
Investigators: Stewart, Shona
Current Investigators: Stewart, Shona , Maier, John
Small Business: ChemImage Corporation
EPA Contact: Manager, SBIR Program
Project Period: March 1, 2004 through August 31, 2004
Project Amount: $69,978
RFA: Small Business Innovation Research (SBIR) - Phase I (2004) RFA Text | Recipients Lists
Research Category: Drinking Water , SBIR - Water and Wastewater , Small Business Innovation Research (SBIR)
Contamination of drinking water with pathogenic microorganisms such as Cryptosporidium has become an increasing concern in recent years. Cryptosporidium oocysts particularly are problematic, because infections caused by this organism can be life threatening in immunocompromised patients. Current methods for monitoring and analyzing water often are laborious and require expertise. 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 has demonstrated the capability to identify and differentiate microorganisms at the species and strain levels. In addition, this technique has demonstrated sensitivities down to the single oocyst detection limit.
ChemImage Corporation will employ Raman spectroscopy and imaging to detect and identify Cryptosporidium parvum cysts in drinking water. ChemImage Corporation also will demonstrate that Raman imaging, in combination with chemometric techniques, can identify small numbers of the oocysts and differentiate between oocysts and other interferents present in drinking water. This proof of concept will be a critical first step to implementation of a new, important class of continuous, online detection strategies that will increase the safety of the water supply.