Dietary fibre has proven to promote healthy body mass and reduce the risk of non-communicable diseases. To date, in Slovenia, there were only a few outdated studies of dietary fibre intake; therefore, we explored the dietary fibre intake using food consumption data collected in the SI.Menu project. Following the EU Menu methodology, data were collected on representative samples of adolescents, adults, and elderlies using a general questionnaire, a food propensity questionnaire, and two 24 h recalls. The results indicate that the intake of dietary fibre in Slovenia is lower than recommended. The proportion of the population with inadequate fibre intakes (<30 g/day) was 90.6% in adolescents, 89.6% in adults, and 83.9% in elderlies, while mean daily fibre intakes were 19.5, 20.9, and 22.4 g, respectively. Significant determinants for inadequate dietary fibre intake were sex in adolescents and adults, and body mass index in adults. The main food groups contributing to dietary fibre intake were bread and other grain products, vegetables and fruits, with significant differences between population groups. Contribution of fruits and vegetables to mean daily dietary fibre intake was highest in elderlies (11.6 g), followed by adults (10.6 g) and adolescents (8.5 g). Public health strategies, such as food reformulation, promoting whole-meal alternatives, consuming whole foods of plant origin, and careful planning of school meals could beneficially contribute to the overall dietary fibre intake in the population.

Besides the numerous studies in the last decade involving food and nutrition data, this domain remains low resourced. Annotated corpuses are very useful tools for researchers and experts of the domain in question, as well as for data scientists for analysis. In this paper, we present the annotation process of food consumption data (recipes) with semantic tags from different semantic resources—Hansard taxonomy, FoodOn ontology, SNOMED CT terminology and the FoodEx2 classification system. FoodBase is an annotated corpus of food entities—recipes—which includes a curated version of 1000 instances, considered a gold standard. In this study, we use the curated version of FoodBase and two different approaches for annotating—the NCBO annotator (for the FoodOn and SNOMED CT annotations) and the semi-automatic StandFood method (for the FoodEx2 annotations). The end result is a new version of the golden standard of the FoodBase corpus, called the CafeteriaFCD (Cafeteria Food Consumption Data) corpus. This corpus contains food consumption data—recipes—annotated with semantic tags from the aforementioned four different external semantic resources. With these annotations, data interoperability is achieved between five semantic resources from different domains. This resource can be further utilized for developing and training different information extraction pipelines using state-of-the-art NLP approaches for tracing knowledge about food safety applications.

Introduction: Contemporary data and knowledge management and exploration are challenging due to regular releases, updates, and different types and formats. In the food and nutrition domain, solutions for integrating such data and knowledge with respect to the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles are still lacking.Methods: To address this issue, we have developed a data and knowledge management system called NutriBase, which supports the compilation of a food composition database and its integration with evidence-based knowledge. This research is a novel contribution because it allows for the interconnection and complementation of food composition data with knowledge and takes what has been done in the past a step further by enabling the integration of knowledge. NutriBase focuses on two important challenges; data (semantic) harmonization by using the existing ontologies, and reducing missing data by semi-automatic data imputation made from conflating with existing databases.Results and discussion: The developed web-based tool is highly modifiable and can be further customized to meet national or international requirements. It can help create and maintain the quality management system needed to assure data quality. Newly generated data and knowledge can continuously be added, as interoperability with other systems is enabled. The tool is intended for use by domain experts, food compilers, and researchers who can add and edit food-relevant data and knowledge. However, the tool is also accessible to food manufacturers, who can regularly update information about their products and thus give consumers access to current data. Moreover, the traceability of the data and knowledge provenance allows the compilation of a trustworthy management system. The system is designed to allow easy integration of data from different sources, which enables data borrowing and reduction of missing data. In this paper, the feasibility of NutriBase is demonstrated on Slovenian food-related data and knowledge, which is further linked with international resources. Outputs such as matched food components and food classifications have been integrated into semantic resources that are currently under development in various international projects.

Tracking food intakes using a mobile app can be an effective approach for some individuals, but it is crucial to consider their personal preferences and health status before adopting this method. Personalized nutrition apps can also serve as an efficient tool for collecting, analysing, and reporting dietary data to support dietary surveys, such as EU Menu by the European Food Safety Authority (EFSA). However, developing an easy-to-use and efficient app requires several essential components. This work presents the insights gained during the development of Eatvisor, a personalized nutrition mobile app. In Slovenia, there was a lack of such tools tailored to national food choices, dietary habits, and recommendations. The app utilizes a food composition database (FCDB) developed in Slovenia; however, the database had many missing compositional data for generic and branded foods. To address this issue, an in-house database management system (DBMS) was developed to support the compilation of different types of data from various sources, such as foreign FCDBs, GS1 Global Data Synchronisation Network (GDSN), database of waste streams, and crowdsourcing, while adhering to the food standards of CEN and respecting the FAIR (Findable, Accessible, Interoperable, Reusable) principles. It is worth emphasizing that compiling a FCDB is a complex task that requires up-to-date knowledge and technical solutions to streamline and expedite the process. Specifically, the DBMS was integrated with semantic resources for organizing knowledge about food (FNS-Harmony ontology linked with FoodOn), as well as food-related regulatory issues. Additionally, methodologies were developed to support missing data imputation in a semi-automated way, and DBMS was designed to manage missing dietary recommendations in a machine-readable format. Moreover, DBMS allows collecting and linking data and knowledge required for personalized advising, including food composition data, biomarker reference intervals, and tailored dietary advice, for both domain experts and machines. Finally, this work evaluates DBMS from the perspective of the data and knowledge required for the development of a personalized nutrition mobile app, such as Eatvisor. The results suggest that DBMS can effectively support the development of a personalized nutrition app, and the methods used can serve as a framework for developing similar apps in other regions.

Food environments have a major influence on consumers’ food choices. The increasing abilities and accessibility of technology have caused food choices to be made in digital environments. Contemporary consumers increasingly select and purchase foods via web-based and mobile applications, where their perceptions and food choices are mediated entirely through user-interface (UI) designs. Therefore, online services offer a great opportunity for choice architects to develop new digital nudging strategies that could promote healthier food choices. At the conference, we present the results of two recently conducted studies to test two digital nudging strategies: the first was conducted within an experimental online grocery store and the second was conducted using a mobile application for food (specifically snacks) tracking. A review of existing digital nudging strategies used on online grocery stores was provided to demonstrate why research on the potential impacts of digital environments on food choices is needed. In addition, two different digital nudging strategies were presented, providing details about the applied human–computer interaction principles. The first nudging strategy, implemented within an experimental online grocery store, is based on the re-positioning of product categories and of food products within those categories based on the content of a specific nutrient (specifically dietary fibre). The second strategy, implemented within a mobile application, exploits the influence of UI, implemented as background images of either healthy or unhealthy snacks, on snack choices. In addition, the mobile app was published as an open-access app; thus, further studies investigating the impact of UI can be conducted and the easy collection of data is facilitated. The results of both studies, performed in different digital settings and in two countries (Slovenia and Australia), were presented. The main findings suggest that further investigation of this research field is warranted, and that strategically implementing digital nudging strategies in online environments may promote healthier food choices. Lastly, this research provides insights (i) for consumers on how UIs can affect food choice decisions, (ii) for choice architects in the health-promotion research area, as it may inform the development of interventions to promote healthy food choices, and (iii) for policy makers regarding whether regulations are needed to protect consumers in online food choice settings.