The Electronic tongue (ET) has been used as a diagnostic technique in the medical sector. It is composed of a multisensor array set with high cross-sensitivity and low selectivity characteristics. The research investigated using Astree II Alpha MOS ET to determine the limit of early detection and diagnosis of food-borne human pathogenic bacteria and to recognize unknown bacterial samples relying on pre-stored models. Staphylococcus aureus (ATCC 25923) and Escherichia coli (ATCC25922) were proliferated in nutrient broth (NB) medium with original inoculum (approximately 107*105 CFU/mL). They were diluted up to 10-14 and the dilutions ranging from 10-14 to 10-4 were measured using ET. The partial least square (PLS) regression model detected the limit of detection (LOD) of the concentration that was monitored to grow the bacteria with different incubation periods (from 4 to 24 h). The measured data were analysed by principal component analysis (PCA) and followed by projecting unknown bacterial samples (at specific concentrations and time of incubation) to examine the recognition ability of the ET. Astree II ET was able to track bacterial proliferation and metabolic changes in the media at very low concentrations (between the dilutions 10-11 and 10-10 for both bacteria). S.aureus was detected after 6 h incubation period and between 6 and 8 h for E.coli. After creating the strains' models, ET was also able to classify unknown samples according to their foot-printing characteristics in the media (S.aureus, E.coli or neither of them). The results considered ET a powerful potentiometric tool for the early identification of food-borne microorganisms in their native state within a complex system to save patients' lives.
Keywords: Electronic tongue; food-borne pathogens; multivariate data analysis; partial least squares; principal component analysis.
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