Disinfection of lettuce using organic acids: an ecological analysis using 16S rRNA sequencing. Issue 30 (14th June 2019) - Wang, Jiayi; Tao, Dongbing; Wang, Shan; Li, Chen; Li, Yanru; Zheng, Fenge; Wu, Zhaoxia
Abstract : Comparing disinfection efficacies of same type sanitizer using a combined microbial reduction/16S rRNA analysis strategy. Abstract : Organic acid disinfection efficacy has been previously estimated by analyzing microbial reduction on fresh produce. However, the effects of organic acids on the fresh produce microbiome are not considered for the evaluation of disinfection efficacy. Here, we studied the effects of seven generally recognized as safe organic acids (lactic, tartaric, acetic, propionic, malic, succinic, and citric acid), on the microbial counts and community on the surface of lettuce. The community was dominated by the following genera: Xanthomonas (24.73%), Sphingomonas (15.85%), Massilia (10.23%), Alkanindiges (9.00%), Acinetobacter (7.57%), and Pseudomonas (6.02%). Organic acid washing did not affect microbial diversity. Lactic acid was the most effective agent causing aerobic plate count reduction of 0.97 log CFU g −1 ; additionally, it increased the Escherichia – Shigella abundance from 0.77% to 3.29%. The relative abundance of Xanthomonas, a plant pathogen, was significantly increased by malic and propionic acid—propionic acid caused an increase from 24.73% to 47.53%. Microbial interaction analyses revealed the co-exclusion of Xanthomonas with the other core taxa, suggesting that the microbial distribution on the lettuce surface after disinfection carries a higher risk of quality loss. Therefore, the difference in disinfection efficacy of sanitizers was reflected in both microbial counts and bacterial community changes. We also propose a potential solution to control fresh produce safety and the rational use of sanitizers by collecting microbial diversity, composition, and count data from planting, transport, minimal processing, shelf and consumer storage, and gut digestion, and then using big data technology to develop a model to provide recommendations for sanitizer selection.