Detection of the bacteria concentration level in pasteurized milk by using two different artificial multisensory methods

The purpose of this paper is to describe the use of an E-nose and E-tongue that were evaluated for the Escherichia coli detection at different concentrations and their ability to discriminate this bacterium from others, such as Klebsiella pneumoniae and Salmonella enterica in pasteurized milk. For data processing, the PCA and LDA methods were applied. Likewise, for the data classification, the SVM and k-NN methods were used. Moreover, each method was applied to the data set obtained by both sensory systems which had different data dimensionality. For detecting and classifying E. coli, S. enterica, and K. pneumoniae in pasteurized milk, it was observed that both systems obtained comparable results with 94.7% and a 92.5% success rate. Thus the devices successfully detected and classified the three bacteria tested, clearly differentiating them from the sterile milk samples. On the other hand, the E-tongue with the gold electrode achieved a 98.7% success rate in the discrimination of decreasing concentrations of E. coli, from 1 x 10(6) CFU/mL to 1 x 10(-2) CFU/mL, in pasteurized milk.