Nutritics GB23 database: an enhancement of the McCance Widdowson’s The Composition of Foods Integrated Dataset (CoFID) database
Notice bibliographique
Résumé
Food composition databases (FCDs) play a vital role in nutrition research, providing essential data on the nutritional content of food and drinks, typically obtained through chemical analyses of representative samples. FCDs are used in dietary surveys, clinical practice, research, and policy development(1). Public Health England funds and systematically publishes a UK FCD McCance Widdowson’s The Composition of Foods Integrated Dataset (CoFID). Some nutrients that are not available in the CoFID database including Omega-3 and 6, Folates, Amino Acids, and Vitamin D(2) but are available in sources including the Quadram Institute Food Labelling 2021 dataset(3) and National FCDs(1). To address this limitation, this study aims to enrich the CoFID database with additional nutrients and create a new database known as “GB23”. A reference hierarchy was established, including the CoFID 2021 dataset, the Quadram Institute Food Labelling Dataset 2021, and National Food Composition Databases. Nutrients were gap-filled where (a) data was represented as ‘N’ i.e. the nutrient is present in significant quantities, but the specific amount is unknown or not reliably documented or (b) the nutrient may be present in significant quantities, but the value was not reported. Nutrients were gap-filled, and the GB23 database was cross-checked against the previous CoFID database (GB15), and any outliers were identified. Aggregate nutrient values, nutrient breakdown and variability was analysed to ensure accuracy and consistency. Analysis was completed using Excel version V16.69.1. 2,887 foods and 34 unique nutrients were updated. 7 nutrients were gap-filled due to criteria (a) and 27 were updated due to criteria (b). 43,067 micronutrients values were gap-filled, including Omega 3 (n 84), Omega 6 (n 74), Folate (n 28), folateDFE (n 19), folicAcidFortified (n 19), folateFood (n 6), 21 Amino Acids (n 42,837), and Vitamin D (n 0). 5,553 macronutrients were updated based on re-calculated values from the Food Labelling 2021 dataset on Carbohydrate, Sugar, Fat, Saturated Fat, Fibre, Protein, Salt. Folic acid values (n 19) were taken from manufacturer and supermarket websites, and representative values calculated. 5 FCDs (2006 Norwegian FCD, 2014 German Nutrient DB, 2015 Canadian Nutrient File, 2014 NEVO online, and 2016 New Zealand FoodFiles) were identified as sources for Vitamin D values, but additional quality checks are required. 75 photos were added to new CoFID foods. Correlation between missing nutrients, foods, and food categories was analysed, e.g. 36 products were identified as sources of missing Omega-3 and 6 values. This study enhances the CoFID database and identifies areas for improvement, including limited Vitamin D data, and increases transparency on the Nutritics GB23 database. As FCDs are and will remain central to nutrition, analysis on their limitations and areas for enhancements are key to ensuring robust nutrition analysis, research, and policy development.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,025 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,011 | 0,015 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,004 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,051 | 0,038 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».