Analysis of Bacterial Diversity in Street Food and Its Functional Gene Pathways Based on Metagenomic Technology

Street food has become the top choice in people's lives due to its convenience and variety. However, the safety issues of street food are also becoming increasingly prominent. To understand the composition of the microbial community in street food and predict its functional genes, this study randomly selected 70 samples of seven types of food around colleges and universities, including snack foods (fried and baked foods), cooked food products, pastries, cold dishes, beverages, staple foods (rice and flour foods), and salad sushi. These 70 samples were analyzed by 16S rDNA sequencing using mutagenic high - throughput sequencing technology, focusing on the composition and diversity of microbial communities in different street foods and gene functional prediction. The results showed that the highest richness values of Chao1, ACE, and Shannon were observed in snacks (SN), while the lowest were in salad (SA). The highest Simpson richness value was found in snacks (SN), and the lowest in sulla (SU). A total of nine bacterial phyla were identified from the seven food samples, involving 152 bacterial genera, with nine of them being shared genera. The dominant genera in MF, SF, SU, BE, and SA were Pantoea, Weissella, Bacillus, Streptococcus, and Pseudomonas, respectively. KEGG functional gene prediction for the seven food samples indicated that the microorganisms in these samples were mainly associated with six major groups and 35 secondary - level metabolic pathways. These pathways are mainly related to six major functional categories (KEGG level 1), namely metabolism, genetic information processing, information processing, cellular processes, human diseases, and organism systems. The results of this study will not only assist consumers in choosing street food but also provide new ideas for the future safety evaluation of street food.