**1. Introduction**

There is a close relationship between eating habits, nutrition and health [1]. Many efforts have been made to investigate the nutrient composition of foods consumed by the population [2]. Food composition data describe the content in terms of energy, macronutrients and micronutrients, as well as other compounds such as phytochemicals, antinutrients, bioactive compounds or toxic compounds in foods [3]. Generally, food composition data are published via food composition tables (FCT) and more recently as food composition databases (FCDB) [4–7].

**Citation:** Hinojosa-Nogueira, D.; Pérez-Burillo, S.; Navajas-Porras, B.; Ortiz-Viso, B.; de la Cueva, S.P.; Lauria, F.; Fatouros, A.; Priftis, K.N.; González-Vigil, V.; Rufián-Henares, J.Á. Development of an Unified Food Composition Database for the European Project "Stance4Health". *Nutrients* **2021**, *13*, 4206. https:// doi.org/10.3390/nu13124206

Academic Editors: Alessandra Durazzo, Rosa Casas and Massimo Lucarini

Received: 27 October 2021 Accepted: 22 November 2021 Published: 24 November 2021

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The FCT/FCDB provide data of foods and beverages consumed by the largest portion of a population [8–10]. Currently, there are new agents to take into account, such as climate change [11–13] or the loss of biodiversity [12,14]. Add to this the constant change in consumer preferences [15,16], such as the increased consumption of processed products [17], novel foods [12,18], and an increase in global trade [5,19]. Due to these factors, FCDB are increasingly trying to collect a greater number of nutrients, bioactive compounds and foods.

FCDB data can come from: (i) original analytical data; (ii) published or imputed values of a specific or similar food; and (iii) calculated values or data provided by other FCDB [6,9,10]. On the other hand, food composition can be influenced by different factors [5,6,11,15,20–22] as depicted in Figure 1. All these factors can result in a somewhat different food composition between countries, and even between regions from the same country, thus requiring the development of more detailed and higher quality FCDB [14,15].

**Figure 1.** Main factors affecting the nutrient content and food composition. Numbers correspond to appropriate references.

FCT/FCDB are an essential tool in a wide range of areas. For example, in the field of public health [10], health programs and clinical practice [10,23], nutritional epidemiology [24], in research and food safety [18,20,25], in the food industry [20], and in agricultural programs and policies [7,23].

Currently, a growing number of countries are updating their FCT/FCDB, for example, McCance and Widdowson's food composition table [26], the Dutch Food Composition Database (NEVO) [27] and Frida Food Data [28] which include a wide range of foods and compounds, making them a reference at an international level [12]. However, several countries still lack their own data sets [10,15,20,22,29] so they often resort to foreign FCT/FCDB [9,24] such as the Food and Agriculture Organization (FAO) [30]; or the United States Department of Agriculture (USDA) FCDB [31], among others. Nevertheless, most food composition data are based on fresh foods, while information on processed foods, recipes or fortified foods is usually missing or not up to date [9,15,16,29]. Organizations such as the International Network of Food Data Systems (INFOODS) [23,32,33] are making great efforts to provide information about different FCT/FCDB, promoting the reliability and up-to-date nature of the data [34,35].

Therefore, by having a harmonized and standardized FCT/FCDB, comparisons between countries would be possible and nutritional data would be more accurate and comprehensive [10,15,34]. In order to standardize terms between data bases, different ontologies are used in nutritional research [9,36,37]. The most common is LanguaL™ [38,39]. LanguaL™ is based on the concept that any food (or food product) may be described systematically by a combination of characteristics [39]. There are other descriptors such as those developed by the European Food Safety Authority (EFSA), and FoodEx2 [40]. FoodEx2 is a standardized food classification that consists of individual food items aggregated into food groups and food categories in a hierarchical structure [15,37,40–42]. Recently, efforts have been made to map FoodEx2 facet descriptors with LanguaL codes [39,42].

In order to overcome this challenge, in the last decades great progress has been made to develop standards and guidelines focused on the harmonization and standardization of FCDB [10,15,23]. Among the most important is FAO/INFOODS that coordinates food composition activities at the international level [6,32]. FAO/INFOODS has developed different strategies in an attempt to harmonize data and make it comparable across countries [4,6,32,33,43–46].

Additionally, there have also been numerous EU-funded initiatives to standardize and harmonize food compositional data [15] such EUROFOODS, COST99 or NORFOODS [16]. More recently, they continued via the European Food Information Resource Network (EuroFIR), now known as EuroFIR AISBL [2,10,16,47]. The main objective of EuroFIR is to contribute to the harmonization of high-quality food composition data in Europe [47–49]. For this purpose, the EuroFIR project has developed different tools like its own LanguaL™ descriptors; EuroFIR Theasauri or FoodEXplorer. FoodEXplorer is a query tool that includes food composition data across more than 30 countries [20,47] and is updated regularly [2,47,50]. In Europe, these networks allowed the development of large multicenter nutritional studies. For example, the European Prospective Investigation into Cancer and Nutrition (EPIC) [51]. Notably, food composition analysis is very expensive and can be time consuming [22,46]. However, an increasing number of FCDB are introducing as many nutrients and bioactive compounds available as possible [30,52–54]. FooDB (https://www.foodb.ca, accessed on 27 October 2021) represents the most comprehensive effort to integrate food composition data [24] and a large amount of different compounds [55].

Stance4Health (Smart Technologies for personalized Nutrition and Consumer Engagement) (S4H) is a project funded by European Union's Horizon 2020 research and innovation program, aimed at evaluating the benefits of a novel smart personalized nutrition service in a large clinical study [56]. One of the main tasks of the project is to build a nutritional database (with as many foods and nutrients as possible) to complete the national FCDBs from the countries involved in the project. As the FCT/FCDB of the countries is completed, a more accurate approximation of the users' diets will be achieved.

The aim of the present study is to describe all the harmonization efforts and introduce this novel and unified Stance4Health's FCDB (S4H FCDB). This database will be part of the app developed in the framework of the European project, which will be used in different personalized nutritional intervention studies (Trial ID: ISRCTN63745549).
