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Noncontact, Optical Molecular Method for Detection and Identificaton of Cryptosporidium Parvaum Oocysts in Drinking Water

Award Information
Agency: Environmental Protection Agency
Branch: N/A
Contract: EP-D-04-032
Agency Tracking Number: EP-D-04-032
Amount: $69,978.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: N/A
Solicitation Number: N/A
Timeline
Solicitation Year: N/A
Award Year: 2004
Award Start Date (Proposal Award Date): N/A
Award End Date (Contract End Date): N/A
Small Business Information
7301 Penn Ave.
Pittsburgh, PA 15208
United States
DUNS: 931897748
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Shona Stewart
 Senior Scientist
 (412) 241-7335
 sstewart@chemimage.com
Business Contact
 John Belechak
Title: Director of Operations & Controller
Phone: (412) 241-7335
Email: belechak@chemimage.com
Research Institution
N/A
Abstract

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.

* Information listed above is at the time of submission. *

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