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In this study a sensor array and pattern recognition routines (an electronic nose) were used to monitor a sausage fermentation in order to follow the changes in emitted volatile compounds during the fermentation process and to compare the electronic nose results with a sensory analysis. From the sensor array responses the fermentation time could be predicted using different methods, where principal component regression and an artificial neural network based on all sensors in the electronic nose performed best. A sensory panel evaluated the final product and these results were compared with the electronic nose measurements in the early stage of the process and on the final sausages. A principal component analysis showed that one of the sausage batches clearly deviated from the other using both the sensory panel data and the electronic nose responses. The deviating batch was different already after 4 h and the difference was consistent during the process. © 1998 SCI.

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