Home practices can mitigate furan and derivatives in vegetable-based infant meals.

This study assessed the impact of current home practices including reheating, standing, and stirring on mitigation of furan and its derivatives in vegetable-based infant meals. Three vegetable-based infant meals (vegetables alone, with fish, with meat) underwent different home practices including reheating, post-reheating standing (60, 120 and 240 s) and post-reheating stirring (30, 60, 120 and 240 s). Targeted quantification of furan, 2-methylfuran (2-MF) and 3-methylfuran (3-MF) and exploration of additional furan derivatives were undertaken in treated and untreated vegetable-based infant meals using SHS-GC-Q Exactive-Orbitrap-MS. For the three compounds, the quality of the measurements was first validated with suitable linearity, limits of quantification, precision and recoveries. A second step highlighted high concentrations of furan (78.5-103.9 µg/kg), 2-MF (4.8-10.1 µg/kg) and 3-MF (3.4-5.8 µg/kg) in the three vegetable-based infant meals before preparation and the assessment of the cumulative risk related to these three furan compounds confirmed the relevance of studying home mitigation strategies. The third step showed that post-reheating stirring was the most effective home practice for mitigation, with maximum observed reductions of 66.3, 59.9 and 57.7 % for furan, 2-MF and 3-MF, respectively. In a fourth step, a suspect screening approach carried out on SHS-GC-Q Exactive-Orbitrap MS data revealed the presence of 2-ethyl-, 2-ethyl-5-methyl-, 2-butyl- and 2-vinyl-furan in vegetable-based meals and showed a similar mitigation trend of home practices on the relative concentrations of these four additional furan derivatives. Finally, despite a significant mitigation reaching 69 % of the furan concentration, the combined effect of home practices on furan compounds was not sufficient to rule out the risk associated with the consumption of vegetable-based infant foods and additional options are discussed.

Dataset of the nutritional composition of follow-on infant formulas commercialized worldwide in 2021.

The main objective of infant follow-on formulas, consumed from the age of 6 to 12 months, is to mimic the composition of breast milk in order to meet the nutritional needs of infant. In this context, their composition is governed in Europe by a strict regulation that has evolved in 2020 to force manufacturers to improve the nutritional profile of the formulas. The objective of this dataset was to collect the ingredient lists and nutritional values of infant follow-on formulas present on the world market with a focus on the lipid fraction. The data collection was carried out from December 2020 to April 2021 directly on the product packaging or on the websites of the different brands. Only "classic" infant follow-on formulas that are widely consumed were listed. Thus, the ingredient lists and nutritional values of 91 infant formulas were collected. The nutritional values are systematically presented for 100 g of powder, for 100 Kcal and for 100 mL of formula. The sources of fats, emulsifiers and vitamins A and E were also extracted from the ingredient lists. This dataset can be used as a tool for the formulation of infant follow-on formulas or to situate the positioning of products in relation to the market.

Infant food users' perceptions of safety: A web-based analysis approach.

This paper aims to explore consumer beliefs about health hazards in infant foods by analyzing data gathered from the web, focusing on forums for parents in the UK. After selecting a subset of posts and classifying them by topic, according to the food product discussed and the health hazard discussed, two types of analyses were performed. Pearson correlation of term-occurrences highlighted what hazard-product pairs are most prevalent. Ordinary Least Squares (OLS) regression performed on sentiment measures generated from the texts provided significant results indicating positive or negative sentiment, objective or subjective language, and confident or unconfident modality associated with different food products and health hazards. The results allow comparison between perceptions obtained in different countries in Europe and may lead to recommendations concerning information and communication priorities.

Structuring ontologies from natural language for collaborative scenario modeling in agri-food systems.

Prospective studies require discussing and collaborating with the stakeholders to create scenarios of the possible evolution of the studied value-chain. However, stakeholders do not always use the same words when referring to one idea. Constructing an ontology and homogenizing vocabularies is thus crucial to identify key variables, which serve in the construction of the needed scenarios. Nevertheless, it is a very complex and time-consuming task. In this paper we present the method we used to manually build ontologies adapted to the needs of two complementary system-analysis models (namely the "Godet" and the "MyChoice" models), starting from interviews of the agri-food system's stakeholders. The objective of the paper is to explore whether and how prospective studies may have to gain from complementing the methodologies used (here Godet) with formal approaches from other disciplines, such as knowledge engineering (here MyChoice), which is usually not the case currently.

Consumer perception data and scientific arguments about food packaging functionalities for fresh strawberries.

This data article contains data characterizing consumer perception and scientific arguments about food packaging functionalities for fresh strawberries. These data are associated with the article "Choice of environment-friendly food packagings through argumentation systems and preferences" (see Yun et al., 2018). These data are stored in a public repository structured by an ontology. These data could be retrieved through the @Web tool, user-friendly interface to capitalize and query data (Buche et al., 2013; Guillard et al., 2017). The @Web tool is accessible online at http://pfl.grignon.inra.fr/atWeb/.