Electronic tongue (ET) is a simple device that may have some applications in the medical field as an alternative tool to traditional diagnostic methods. The aim of this study is to evaluate the potential use and accuracy of ET in the diagnosis of certain bacterial infections.
An alpha-Astree ET was used for the detection of known bacterial strains Staphylococcus aureus (ATCC 25923), Escherichia coli (ATCC 25922), and Pseudomonas aeruginosa (ATCC 27853) which were tested at three-time intervals; 15, 18, and 24 hrs (hr). This was used to build a qualitative and quantitative mathematical model to detect the presence of bacteria at the earliest possible time. When the model is robust, new unknown samples were tested after 15 hrs of bacterial growth. Samples were identified using multivariate data analysis techniques.
Principal Component Analysis (PCA) scores showed that ET can distinguish between the three bacteria at different times 15, 18, and 24 hrs using different sensors. In the PCA scores plots, the discrimination index was 83% at 15 hrs, 88% at 18 hrs, and 96% at 24 hrs, the variances explained by the two principal components were 84%, 99%, and 97% at 15, 18, and 24 hrs, respectively. Fifteen hours was the earliest time at which the bacteria could be detected. Then six samples of E.coli (as unknown samples) were tested after 15 hrs of inoculation, the two discrimination function explained about 100% of the variance (ie, 79.7+22.3%) and all unknown samples were identified as E.coli.
ET could differentiate between types of bacteria in addition to identifying unknown bacterial cultures (E. coli) at times shorter than that required in the current culture-based methods (24-48 hrs), this could be of a great value in early diagnosis of life-threatening infections.