Progress 01/01/04 to 12/31/04
Outputs
Further tests have been done for goat meat with E. coli bacteria. Our experiments indicate that the e-nose can detect bacteria but not with 100% accuracy. Since there are a lot of factors affecting the results more experiments need to be done before the accuracy can be predicted. It seems that the e-nose is not able to detect very low concentration of the contaminant. However, 100% identification could not be obtained consistently even with higher concentration or density. The results from the experiments indicated that e-nose has a potential for being used as a tool for rapid detection of contamination, however more experiments need to be done before any conclusion can be drawn
Impacts
The impact of the research findings would be of enormous importance and influence on the rapid detection of E. coli on goat meat surfaces. The technology thus generated could be used for the HACCP program in the meat industries.
Publications
- Huang, Y. Y. Lan and R. Lacey. 2004. Artificial Senses for Characterization of Food Quality. Journal of Bionics Engineering Vol.1 (3): 159-173. Lan, Y., L. Joshee and S. Wang. 2004. Rapid Detection of E. coli O157:H7 in Ground Beef through an Electronic Nose. The Eight World Congress on Biosensors. Granada, Spain. Lan, Y., L. Joshee and S. Wang. 2004. Rapid Detection of E. coli on Goat Meat Surfaces through an Electronic Nose. IFT Annual Conference. Las Vegas, NV.
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Progress 01/01/03 to 12/31/03
Outputs
Goat meat was obtained from the goat meat center at Fort Valley State University (FVSU), goat meat research center. E. coli was grown overnight at 37 degrees C with continuous shaking in a125 ml flask. The optical density (O.D) of this culture was adjusted to give a concentration of about 108 cfu/ml (colony forming units per ml). This culture was used to inoculate the meat pieces. The e-nose was trained with two classes, class 1 was meat with no bacteria (good) and class2 was meat inoculated with bacteria (bad). 6 to 7 exposures were used to get a cross validation of 91 -100%. Meat pieces of about 2x3 cm was prepared and placed in jars of 50ml volume. These were then inoculated with 300μl of the bacterial culture, covered with parafilm to make it air tight and left at room temperature for 2-4 hours to collect the gas produced by the bacteria. The head space thus generated was then sniffed using trained e-nose (Cyranose-320) to detect the presence of the bacteria.
Our experiments indicate that the e-nose can detect bacteria but not with 100% accuracy. The correct identification varied from 18 to 77 % in different batches with 300 μl of the culture and 2 h head space accumulation time. The increase in time to 3 and 4 h did not yield much difference in the results. However, the e-nose once trained could be used for identification without updating the training set for at least 2 weeks. The results from the experiments indicated that e-nose has a potential for being used as a tool for rapid detection of contamination, however more experiments need to be done before any conclusion can be drawn. Since there are a lot of factors affecting the results more experiments need to be done before the accuracy can be predicted. It seems that the e-nose is not able to detect very low concentration of the contaminant. However, 100% identification could not be obtained consistently even with higher concentration or density.
Impacts
The impact of the research findings would be of enormous importance and influence on the rapid detection of E. coli on goat meat surfaces. The technology thus generated could be used for the HACCP program in the meat industries.
Publications
- Lan. Y., Y. Huang and R. Lacey. 2003. Multisensor Data Fusion Application for Improving Determination of Food Quality and Safety. ASAE Paper No. 03-6052. ASAE International Meeting. Las Vegas, NV.
- Lan, Y. and K. Zhu. 2003. Rice Grain Identification Using an Electronic Nose. Proceeding of the International Conference on Crop Harvesting and Processing, Louisville, Kentucky, February 9-11.
- Lan, Y. and K. Zhu. 2003. Rice Sample Classification Using an Electronic Nose. Proceeding of the Pittcon. Orlando, FL, March 9-14.
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Progress 01/01/02 to 12/31/02
Outputs
A Cyanose 320 was used to rapidly detect pathogenic bacteria in chicken carcass and rice sample classification. The Spreeta biosensor was tested for feasibility in rapid detection of bacteria in chicken carcass. The results of this study demonstrated that the SPR biosensor has the potential for rapid and specific pathogen detection. The SPR biosensor is capable of reporting the presence of bacteria in 30 min at levels of 10,000,000 CFU/ml. With further investigation and refinements, there may be more promising uses for electronic nose and biosensor in food safety and control applications such as multisensor data fusion.
Impacts
The impact of the research findings would be of enormous importance and influence on the rapid detection of E. coli on goat meat surfaces. The technology thus generated could be used for the HACCP program in the meat industries.
Publications
- Lan, Y. and J. Zhu. 2002. Classification of Rice Varieties Using Electronic Nose. ASAE GA Section Meeting. Tybee Island, GA May 15-18, 2002.
- Lan, Y. and Y. Huang. 2002. Transducers in Bioinstrumentation in Encyclopedia of Agricultural and Food Engineering. Editor: Dennis R. Heldman. Marcel Dekker (accepted).
- Huang, Y. and Lan, Y. 2002. System Configuration in Process Control Systems in Encyclopedia of Agricultural and Food Engineering. Editor: Dennis R. Heldman. Marcel Dekker (accepted).
- Zhu, J. and Y. Lan. 2002. Rapid Identification of Rice Varieties Using an Electronic Nose. Proceeding of the 29th RTWG Meeting. Little Rock, Arkansas
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