The present study aimed to characterize, through descriptive statistics, data from scientific articles selected in a systematic integrative review that performed a microbiological diagnosis of Salmonella spp. in aquaculture. Data were obtained from research articles published in the BVS, Scielo, Science Direct, Scopus and Web of Science databases. The selected studies were published between 2000 and 2020 on samples of aquaculture animal production (fish, shrimp, bivalve mollusks, and other crustaceans) and environmental samples of aquaculture activity (farming water, soil, and sediments). After applying the exclusion criteria, 80 articles were selected. Data such as country of origin, categories of fish investigated, methods of microbiological diagnosis of Salmonella spp., sample units analyzed and most reported serovars were mined. A textual analysis of the word cloud and by similarity and descending hierarchical classification with the application of Reinert's algorithm was performed using R® and Iramuteq® software. The results showed that a higher percentage of the selected articles came from Asian countries (38.75%). Fish was the most sampled category, and the units of analysis of the culture water, muscle and intestine were more positive. The culture isolation method is the most widespread, supported by more accurate techniques such as PCR. The most prevalent Salmonella serovars reported were S. Typhimurium, S. Weltevreden and S. Newport. The textual analysis showed a strong association of the terms "Salmonella", "fish" and "water", and the highest hierarchical class grouped 25.4% of the associated text segments, such as "aquaculture", "food" and "public health". The information produced characterizes the occurrence of Salmonella spp. in the aquaculture sector, providing an overview of recent years. Future research focusing on strategies for the control and prevention of Salmonella spp. in fish production are necessary and should be encouraged.
Keywords: Reinert’s algorithm; Salmonella; fish farming; food safety; public health; similarity; word cloud.