Cryptosporidium parvum is a challenge for drinking water utilities as it is resistant to common disinfectants and has a very low infectious oocyst dose (in the range of 10-1000 oocysts for healthy persons and as low as 10-100 for immunocompromised individuals). Additionally, Cryptosporidium parvum has been listed by the CDC as a Class B bioterrorism agent.  Current Cryptosporidium detection methods are expensive, slow, and plagued with low and variable recovery rates.  This research couples a ‘sandwich immunoassay' with Surface Enhanced Raman Spectroscopy (SERS) for the capture and quantification of Cryptosporidium parvum oocysts in finished drinking waters. Although common in biological analyses, little work has been done on the employment of sandwich immunoassays for the detection of pathogens in drinking waters. SERS is gaining popularity for low level detection techniques as it can detect picomole (10^-12 M) to femtomole (10^-15 M) concentrations of organic compounds in the presence of gold and silver colloids. Specifically, this assay employs a polycarbonate filter membrane functionalized with anti-Cryptosporidium parvum IgG antibodies.  Poly-L-lysine acts as spacer on the surface of the membrane and activation by glutaraldehyde creates a reactive layer that binds with the antibodies to create an anti-Cryptosporidium surface.  Water samples containing Cryptosporidium parvum oocyst are filtered through the membrane and are captured by the antibodies.  The membrane is exposed to a solution of fluorescent dye-labeled immunogold.  The immunogold consists of 12 nm gold nanoparticles with anti-Cryptosporidium parvum antibodies adsorbed and fluorescent dyes covalently bound to the gold nanoparticle surface.  Excess immunogold is washed away and the membrane filter surface is analyzed with Raman Spectroscopy.  The results of several optimization studies (i.e. varying Raman dyes, varying filter pore sizes, varying specific anti-Cryptosporidium antibodies) will be presented.  Also, the potential for expanding this assay to a multi-pathogen detection method will be addressed.