An official website of the United States government.
The overall objective of this research is to develop rapid, non-destructive sensors and systems capable of assessing quality and safety of biorpcessed products while providing a mechanism for detection of terror based contaminates. <P> Specific objectives include: 1. Development of aroma-based sensing technologies capable of determining the presence of contaminants and foreign materials affecting product safety and quality. 2. Development of optical sensing technologies capable of determining the presence of contaminants and foreign materials affecting product safety and quality. 3. Development of appropriate contaminant pattern recognition models using data generated from gaseous and optical sensing systems.
Non-Technical Summary: <BR>The American public has long been concerned about the safety and quality of their food supply. According to Center for Disease Control and Prevention statistics, food born diseases annually cause approximately 47.8 million illnesses, 127,839 hospitalizations and 3,037 deaths in the U.S. (CDC, 2011). The economic and human cost of this is immense. Unfortunately, we now face the risks associated with the intentional terror-based contamination of our food supply. Food based terror attacks have been documented in Israel since 1978, when the Arab Revolutionary Council contaminated oranges with mercury to damage trade between Israel and Europe. Terror attacks on our domestic food supply have also taken place. These include everything from the 1989 cyanide poisoning of Chilean grapes to the 1984 Dalles, Oregon incident when a religious cult contaminated salad bars in local restaurants with Salmonella typhimurium. It was the cult's hope that they would sicken voters on Election Day, preventing them from voting and allowing the cult's candidates to take control of the municipal government. Now we must not only insure a safe, high quality food supply for the American public, but we must also defend it from attack (Rasco and Bledsoe, 2005). With these quality, safety and defensive concerns in mind, it is important to note that the threats are numerous. Economic losses resulting from both unintentional and intentional food contamination would be devastating. This is evidenced from the 1997 Escherichia coli 0157:H7 contamination event that resulted in the recall of $70 million dollars worth of beef (Rasco and Bledsoe, 2005). While these risks are great, they can be managed. Through the use of improved sensing and sampling technologies, we can significantly increase our ability to manage this risk, increasing the safety and quality of our food supply. Previously conducted research has focused entirely on seafood products. The rising need for quality and safety in bioprocessed products now makes it essential for this project's focus be broadened. No specific product is mentioned below because the availability of extramural funding will be an important deciding factor in the selection of the commodity and safety issue selected for study. <P> Approach: <BR> Previously conducted research has focused entirely on seafood products. The rising need for quality and safety in bioprocessed products now makes it essential for this project's focus be broadened. No specific product is mentioned below because the availability of extramural funding will be an important deciding factor in the selection of the commodity and safety issue selected for study. Fresh samples of bioprocessed products will be collected for gaseous and optical analysis from producers and processors at all points in the supply and production chain. Once collected, the time of initial samplecollection, processing history and storage history will be recorded. Samples will be divided into a series of sub-samples used for conventional defect determination, gaseous analysis and optical analysis. Gaseous analysis will consist of exposing gas vapors emanating from the sample to an electronic nose, and monitoring the individual response of the nose's sensors. Optical analysis will be accomplished with an NIRS6500 monochrometer spectrometer (NIRSystems, Silver Spring, MD) measuring sample reflectance from 400 to 2498 nm in 2 nm increments (1050 spectra). Gaseous and optical data will initially be analyzed as specified by Dodd et al (2004) and Requena (1998). It will also be analyzed with multivariate modeling procedures like MLR, PCR, PCA, neural networks, genetic algorithms, etc. to develop predictive models. Gaseous and optical data will then be combined and re-analyzed to determine if prediction algorithms can be improved though sensor fusion. Following completion of these analyses, new samples will be collected using the previously stated procedure and tested to verify the validity of the prediction algorithms. Comparisons with conventional defect detection procedures will also occur.