Application of unsupervised clustering algorithm and heat-map analysis for selection of lactic acid bacteria isolated from dairy samples based on desired probiotic properties

Abstract Phenotypic and genotypic characterization of candidate probiotic strains are indispensable steps to the nourishments utilization. Hence, in this study, sixty-three lactic acid bacteria (LAB) isolates of traditional dairy origin already distinguished using pyrosequence-based 16S-rRNA profiling were characterized for their probiotic properties. The unsupervised clustering algorithm and heat-map analysis efficiency for selection of candidate probiotic strains by oppressing their phenotypic characteristics were tested. Results demonstrated that YP9, YP8, TP15, and ShP1 isolates show highest acid and bile tolerance, and Cell surface hydrophobicity. Moreover, CP3, CP12, TP15, TP16, TP17, ShP1, YP8, YP9, and CuP3 showed the highest BSH activity. Interestingly, the inhibitory effects of YP8, ShP1, and YP9 against Yersinia, Listeria, and Yersinia, respectively, were confirmed. Clustering and heat-map analysis effectively selected and distinguished nine Lactobacillus isolates (YP8, YP9, CP3, TP15-17, ShP1, CP12, and CuP3) as candidate probiotic strains. This study opens a new avenue on the selection and characterization of new isolates of bacteria based on their probiotics properties for future application in functional foods.