The new on-line Czech Food Composition Database

Macháčková, M.; Holasová, M.; Mašková, E.; s.r.o, THINline interactive

doi:10.1016/j.foodchem.2013.01.095

Cited by 5 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…LTD.), which draws upon NUTTAB and AUSNUT databases, utilises INFOODS tagnames. Additionally, a recent reflection on the new Czech FCDB identified that visual descriptors may be an effective and simple means to make a databases written food descriptions more user friendly [ 31 ].…”

Section: Discussionmentioning

confidence: 99%

Food Composition Database Format and Structure: A User Focused Approach

et al. 2015

View full text Add to dashboard Cite

This study aimed to investigate the needs of Australian food composition database user’s regarding database format and relate this to the format of databases available globally. Three semi structured synchronous online focus groups (M = 3, F = 11) and n = 6 female key informant interviews were recorded. Beliefs surrounding the use, training, understanding, benefits and limitations of food composition data and databases were explored. Verbatim transcriptions underwent preliminary coding followed by thematic analysis with NVivo qualitative analysis software to extract the final themes. Schematic analysis was applied to the final themes related to database format. Desktop analysis also examined the format of six key globally available databases. 24 dominant themes were established, of which five related to format; database use, food classification, framework, accessibility and availability, and data derivation. Desktop analysis revealed that food classification systems varied considerably between databases. Microsoft Excel was a common file format used in all databases, and available software varied between countries. User’s also recognised that food composition databases format should ideally be designed specifically for the intended use, have a user-friendly food classification system, incorporate accurate data with clear explanation of data derivation and feature user input. However, such databases are limited by data availability and resources. Further exploration of data sharing options should be considered. Furthermore, user’s understanding of food composition data and databases limitations is inherent to the correct application of non-specific databases. Therefore, further exploration of user FCDB training should also be considered.

show abstract

Section: Discussionmentioning

confidence: 99%

Food Composition Database Format and Structure: A User Focused Approach

et al. 2015

View full text Add to dashboard Cite

show abstract

“…Although the overall nutritional composition of apple fruit is characterized and readily available in the main national and international food composition databases and tables (Souci et al, 2000;USDA, 2015), the aggregate data do not allow an adequate segmentation of apple types or a detailed study of country-specific dietary intakes. Country-specific food composition databases remain an important tool to develop food policies and nutritional recommendations, since cultivars and production conditions affect nutritional composition (Black et al, 2011;Korošec et al, 2013;Machackova et al, 2013). Data on fruit composition have a wide range of uses in clinical practice, epidemiological research, public health and education, and by the food industry (Williamson, 2006).…”

Section: German Society For Horticultural Sciencementioning

confidence: 99%

Physicochemical quality, macronutrients, and dietary fiber in apples from the Protected Geographical Indication ‘Maçã de Alcobaça’, Portugal

Almeida¹,

Gomes²

2017

Europ.J.Hortic.Sci

View full text Add to dashboard Cite

Original article-Thematic Issue Introduction Apple is the largest fruit category in the European market. This fruit is part of the regular purchasing habits of European consumers who have a gross annual per capita apple consumption of ca. 19 kg (Martinez-Palou and Rohner-Tielen, 2008). Dozens of cultivar groups with distinct sensory attributes are available. Market categories are currently defined according to skin color (yellow, green, red, bicolor, and russet), perception of sweetness or acidity (sweet and sour), and texture. In Europe, consumers expect small stores to display 3 to 5 apple types whereas a large supermarket provides up to 8 or 12 apple categories. The visual and taste diversity of apple fruit corresponds to differences in chemical composition with nutritional relevance. However, the level of important bioactive compounds, such as total phenolics Summary Apple is a major fruit in the European diet. The range of cultivar types and origins diversify the nutritional composition of apples. Eight apple cultivars (Malus ×domestica Borkh. 'Casa Nova', 'Fuji', 'Galaxy', 'Golden Delicious', 'Granny Smith', 'Jonagored', 'Reinette', and 'Starking') with the "Maçã de Alcobaça" Protected Geographical Indication (PGI), Portugal, were harvested at commercial maturity, stored for 3 to 3.5 months at 0°C, and characterized at the edible stage for their nutritional composition. Fruit quality was assessed by color, soluble solids content, titratable acidity, pH, Thiault index and firmness. The concentration moisture, dry matter, carbohydrates, protein, fat, ash, and fiber defined the nutritional profile. 'Reinette' had highest soluble solids content, titratable acidity, carbohydrates, protein, ash, energy, dry matter. 'Granny Smith' had the lowest soluble solids and carbohydrates contents, dry matter and energy. 'Casa Nova', a traditional cultivar, had high fiber content. Energy content varied by more than 30%, lowest in 'Granny Smith' and highest in 'Reinette', and is strongly correlated with dry matter and soluble solids content. The cultivarspecific range of nutritional value provides a basis for market segmentation based on nutritional content.

show abstract