Establishing a Common Nutritional Vocabulary - From Food Production to Diet

Andrés-Hernández, Liliana; Blumberg, Kai; Walls, Ramona; Dooley, Damion; Mauleon, Ramil; Lange, Matthew; Weber, Magalie; Chan, Lauren; Malik, Adnan; Möller, Anders; Ireland, Jayne; Revilla, Lucía Segovia de la; Zhang, Xuhuiqun; Burton‐Freeman, Britt; Magelli, Paul; Schriever, Andrew; Forester, Shavawn M.; Liu, Lei; King, Graham J.

doi:10.3389/fnut.2022.928837

Cited by 7 publications

(5 citation statements)

References 40 publications

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“…The Compositional Dietary Nutrition Ontology (CDNO) uses OBA to link nutritional components found in food to their human dietary roles which include traits. This allows the integration of nutritional components like “concentration of calcium” (CDNO:0200138) with associated traits, for example, bone strength (OBA:VT0001542) (Andrés-Hernández et al 2022 ). OBA trait classes have been used for the annotation of domestic guinea pig electrophysiology data (Farrell and Bengtson 2019 ).…”

Section: Resultsmentioning

confidence: 99%

The Ontology of Biological Attributes (OBA)—computational traits for the life sciences

et al. 2023

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Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the annotation of genome-wide association studies (GWAS), Quantitative Trait Loci (QTL) mappings or any population-focussed measurable trait data. The integration of trait and biological attribute information with an ever increasing body of chemical, environmental and biological data greatly facilitates computational analyses and it is also highly relevant to biomedical and clinical applications. The Ontology of Biological Attributes (OBA) is a formalised, species-independent collection of interoperable phenotypic trait categories that is intended to fulfil a data integration role. OBA is a standardised representational framework for observable attributes that are characteristics of biological entities, organisms, or parts of organisms. OBA has a modular design which provides several benefits for users and data integrators, including an automated and meaningful classification of trait terms computed on the basis of logical inferences drawn from domain-specific ontologies for cells, anatomical and other relevant entities. The logical axioms in OBA also provide a previously missing bridge that can computationally link Mendelian phenotypes with GWAS and quantitative traits. The term components in OBA provide semantic links and enable knowledge and data integration across specialised research community boundaries, thereby breaking silos.

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Section: Resultsmentioning

confidence: 99%

The Ontology of Biological Attributes (OBA)—computational traits for the life sciences

et al. 2023

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“…CDNO [53], launched in 2020, provides a hierarchy of over 1,000 terms for nutritional attributes -vitamins, carbohydrates, lipids, minerals, proteins, etc. -and matching bioactive chemical concentrations from crops, livestock, and fisheries that contribute to human diet and which are referenced in precision food commodity laboratory analytics.…”

Section: Cdno: Compositional Dietary Nutrition Ontologymentioning

confidence: 99%

OBO Foundry food ontology interconnectivity

Dooley,

Andrés-Hernández,

Bordea

et al. 2024

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Since its creation in 2016, the FoodOn food ontology has become an interconnected partner in various academic and government projects that span agricultural and public health domains. This paper examines recent data interoperability capabilities arising from food-related ontologies belonging to, or compatible with, the encyclopedic Open Biological and Biomedical Ontology Foundry (OBO) ontology platform, and how research organizations and industry might utilize them for their own projects or for data exchange. Projects are seeking standardized vocabulary across many food supply activities ranging from agricultural production, harvesting, preparation, food processing, marketing, distribution and consumption, as well as more indirect health, economic, food security and sustainability analysis and reporting tools. To satisfy this demand for controlled vocabulary requires establishing domain specific ontologies whose curators coordinate closely to produce recommended patterns for food system vocabulary.

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“…The present 'big data' era gives many possibilities, but comparable use of data may be a challenge in international collaboration. Hence, there is a need for transparency and international classi cation 'libraries', perhaps also linked to food and diet ontologies (33) .…”

Section: Conclusion and Recommendationsmentioning

confidence: 99%

Building nutritionally meaningful product groups for loyalty card data: the LoCard Food Classification process

Kanerva

Kinnunen

Nevalainen

et al. 2023

Preprint

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Analysing customer loyalty card data is a novel method for assessing dietary quality and changes in a population’s food consumption. However, prior to its use, the thousands of grocery products available in stores must be reclassified into appropriate categories suitable for the use of nutrition and health research. This paper depicts how such a classification is compiled and how it reflects the nutritional quality of the food classes. Healthfulness was considered the main criterion guiding the reclassification of the 3574 grocery product groups. In addition, the main ingredient of the product group, type of food and purpose of use, and carbon footprint were considered in the reclassification process. The classified food groups were linked with the national food composition database, and the nutrient profile was assessed by calculating the Nutrient Rich Food Index (NRFI) for each product group. Our four-level classification hierarchy had 38 food groups at its broadest level (Class 1). Only 1% (n=38) of the grocery product groups were left unclassified. Standard deviation in NRFI decreased from 0.21 to 0.08 from the broadest to the finest level of classification. We conclude it is possible to assign a great majority of the grocery product groups to classes based on their nutritional quality. However, the challenge is classification of product groups that lack detailed information on their contents or include main ingredients that have opposite health effects, such as products including both plant- and animal-based proteins.

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Establishing a Common Nutritional Vocabulary - From Food Production to Diet

Cited by 7 publications

References 40 publications

The Ontology of Biological Attributes (OBA)—computational traits for the life sciences

The Ontology of Biological Attributes (OBA)—computational traits for the life sciences

OBO Foundry food ontology interconnectivity

Building nutritionally meaningful product groups for loyalty card data: the LoCard Food Classification process

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