USE OF DIGITAL AROMA TECHNOLOGY AND SPME,GC-MS TO COMPARE VOLATILE COMPOUNDS PRODUCED BY BACTERIA ISOLATED FROM PROCESSED POULTRY

Authors: ARNOLD, JUDY; Senter, Samuel - Sam

Submitted to: Journal of the Science of Food and Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/17/1998
Publication Date: N/A
Citation: N/A

Interpretive Summary: Three methods: digital aroma technology, solid-phase micro extraction (SPME), and gas chromatographic-mass spectral (GC-MS) analysis were used to compare gases produced by bacteria that are important for the food safety of poultry products. The bacteria used in the study are common to biofilms in the poultry processing environment. The instrument for digital aroma technology, called the electronic nose, measured changes caused by volatile gases from the headspace of samples exposed to electronic sensors. Differences or similarities among the samples were shown by graphs that were plotted by the Sammon mapping technique. Artificial neural network software was used to model groups of samples and classify subsequent unknowns. Compounds isolated from the headspace of sealed cultures using polydimethylsiloxane SPME fibers and identified by GC-MS analysis were predominantly alcohols and indole. The patterns for each of the different bacteria were nearly the same when retested and gave similar results each time they were compared with each of the three methods.

Technical Abstract: Digital aroma technology, solid-phase micro extraction (SPME), and gas chromatographic-mass spectral (GC-MS) analysis of the headspace volatile organic compounds (VOC) were used to compare bacterial species important for food safety and common to biofilms in the poultry processing environment. The instrument for digital aroma technology, called the electronic nose, measured changes in resistance of polymer sensors caused by volatile gases from the headspace of samples. Graphical output by the Sammon mapping technique produced patterns of differences or similarities among the samples. Artificial neural network software was used to model groups of samples and classify subsequent unknowns. Compounds isolated from the headspace of sealed cultures using polydimethylsiloxane SPME fibers and identified by GC-MS analysis were predominantly alcohols and indole. These qualitative profiles were repetitive for specific organisms in relation to purity and repeatability of the cultures, differed by species, and were used as objective standards to compare the graphical outputs of the electronic nose.