SAMPLING TECHNIQUES FOR CATFISH
Project Number: 0208-32000-001-32
Start Date: Sep 01, 2009
End Date: Aug 31, 2010
This project will update and complement data that is old and inconclusive on the prevalence of pathogens in catfish farms (fish, water, soil), as well as chemical and drug residues in these samples. It will be a collaborative study between Mississippi State University and University of Arkansas at Pine Bluff, the Mississippi State Chemical Lab, and the USDA-FSIS, with support from the USDA-ARS and other agencies. This project will also help to obtain samples for a parallel study into the cause and incidence of catfish fillet color variation being proposed to the USDA-ARS.
In addition to answering questions about the incidence of human pathogens and chemical residues in catfish, this project will also help to develop a statistically sound field sampling program. Moreover, it will strengthen the relationship between academia, government, and private industry in the goal of providing safe and high quality food to the consumer.
Samples of water, soil (sediment), and catfish from ponds will be collected from 4-5 different farms in different geographical areas during the four seasons of the year to analyze for human pathogens and chemical residues. Farms sampled will be selected by area, size, management, and processing plant target so as to reflect the diversity of the industry as much as possible.
A total of three water and soil samples and 100 fish from each farm will be collected and identified by random numbers. Only one person will know the exact location of the ponds and will randomize sites so as to maintain confidentiality of data. This data will be kept confidential and will only be shared with the farmers through the PI and with the agency. Data to be published will be shared with the stakeholders prior to release.
Samples will be placed in refrigerated containers and taken/shipped to labs for analyses. Established methods will be used to analyze the samples for pathogens and chemical residues. In the case of pathogens, an efficient process for analyzing for multiple pathogens will be optimized.