Visual analytics of arsenic in various foods

Abstract

Arsenic is a naturally occurring toxic metal and its presence in food composites could be a potential risk to the health of both humans and animals. Arseniccontaminated groundwater is often used for food and animal consumption, irrigation of soils, which could potentially lead to arsenic entering the human food chain. Its side effects include multiple organ damage, cancers, heart disease, diabetes mellitus, hypertension, lung disease and peripheral vascular disease. Research investigations, epidemiologic surveys and total diet studies (market baskets) provide datasets, information and knowledge on arsenic content in foods. The determination of the concentration of arsenic in rice varieties is an active area of research. With the increasing capability to measure the concentration of arsenic in foods, there are volumes of varied and continuously generated datasets on arsenic in food groups. Visual analytics, which integrates techniques from information visualization and computational data analysis via interactive visual interfaces, presents an approach to enable data on arsenic concentrations to be visually represented. The goal of this doctoral research in Environmental Science is to address the need to provide visual analytical decision support tools on arsenic content in various foods with special emphasis on rice. The hypothesis of this doctoral thesis research is that software enabled visual representation and user interaction facilitated by visual interfaces will help discover hidden relationships between arsenic content and food categories. The specific objectives investigated were: (1) Provide insightful visual analytic views of compiled data on arsenic in food categories; (2) Categorize table ready foods by arsenic content; (3) Compare arsenic content in rice product categories and (4) Identify informative sentences on arsenic concentrations in rice. The overall research method is secondary data analyses using visual analytics techniques implemented through Tableau Software. Several datasets were utilized to conduct visual analytical representations of data on arsenic concentrations in foods. These consisted of (i) arsenic concentrations in 459 crop samples; (ii) arsenic concentrations in 328 table ready foods from multi-year total diet studies; (iii) estimates of daily inorganic arsenic intake for 49 food groups from multicountry total diet studies; (iv) arsenic content in rice product categories for 193 samples of rice and rice products; (v) 758 sentences extracted from PubMed abstracts on arsenic in rice. Several key insights were made in this doctoral research. The concentration of inorganic arsenic in instant rice was lower than those of other rice types. The concentration of Dimethylarsinic Acid (DMA) in wild rice, an aquatic grass, was notably lower than rice varieties (e.g. 0.0099 ppm versus 0.182 for a long grain white rice). The categorization of 328 table ready foods into 12 categories enhances the communication on arsenic concentrations. Outlier concentration of arsenic in rice were observed in views constructed for integrating data from four total diet studies. The 193 rice samples were grouped into two groups using a cut-off level of 3 mcg of inorganic arsenic per serving. The visual analytics views constructed allow users to specify cut-off levels desired. A total of 86 sentences from 53 PubMed abstracts were identified as informative for arsenic concentrations. The sentences enabled literature curation for arsenic concentration and additional supporting information such as location of the research. An informative sentence provided global “normal” range of 0.08 to 0.20 mg/kg for arsenic in rice. A visual analytics resource developed was a dashboard that facilitates the interaction with text and a connection to the knowledge base of the PubMed literature database. The research reported provides a foundation for additional investigations on visual analytics of data on arsenic concentrations in foods. Considering the massive and complex data associated with contaminants in foods, the development of visual analytics tools are needed to facilitate diverse human cognitive tasks. Visual analytics tools can provide integrated automated analysis; interaction with data; and data visualization critically needed to enhance decision making. Stakeholders that would benefit include consumers; food and health safety personnel; farmers; and food producers. Arsenic content of baby foods warrants attention because of the early life exposures that could have life time adverse health consequences. The action of microorganisms in the soil is associated with availability of arsenic species for uptake by plants. Genomic data on microbial communities presents wealth of data to identify mitigation strategies for arsenic uptake by plants. Arsenic metabolism pathways encoded in microbial genomes warrants further research. Visual analytics tasks could facilitate the discovery of biological processes for mitigating arsenic uptake from soil. The increasing availability of central resources on data from total diet studies and research investigations presents a need for personnel with diverse levels of skills in data management and analysis. Training workshops and courses on the foundations and applications of visual analytics can contribute to global workforce development in food safety and environmental health. Research investigations could determine learning gains accomplished through hardware and software for visual analytics. Finally, there is need to develop and evaluate informatics tools that have visual analytics capabilities in the domain of contaminants in foods.