Exploring two food composition databases to estimate nutritional components of whole meals
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.22/20415 |
Resumo: | The integration of foodomics data to explain the impact of diet on health requires a precise knowledge of nutrients composition of complex meals. This work assesses the adequacy of two food composition databases (FCDBs) for calculation of nutritional composition of whole meals, compared to the golden standard “lab chemical analyses” and search for predictive models to overcome some limitations of FCDBs. Six meals were designed by integrating healthy foods in a meal based on the “Western diet” pattern. The nutritional composition of each meal was i) chemically determined; ii) retrieved from the Portuguese food composition table (TCAP) and from iii) United States Department of Agriculture database (USDA). Compared to chemical analyses, both FCDBs significantly (p < 0.05) overestimate the amount of Na and vitamin B6; TCAP also overestimate the amount of Ca (p < 0.05), while USDA overestimate energy, fat, available carbohydrates, P, and Fe. Linear regression analyses were used to adjust nutrient values based on TCAP and USDA. Predictive models from both FCDBs were successfully obtained for reliable estimation of protein, PUFA, available carbohydrates, total carbohydrates, sugars, Zn, β-carotene, vitamin E, riboflavin, and niacin in meals with a given uncertainty, which is provided by the respective correction factors. Those predictive models are limited to the range of theoretical values of meals studied. |
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Exploring two food composition databases to estimate nutritional components of whole mealsPortuguese food composition tableUnited States department of agriculture databaseNutritional chemical analysisNutrients biasPredictive regression modelsThe integration of foodomics data to explain the impact of diet on health requires a precise knowledge of nutrients composition of complex meals. This work assesses the adequacy of two food composition databases (FCDBs) for calculation of nutritional composition of whole meals, compared to the golden standard “lab chemical analyses” and search for predictive models to overcome some limitations of FCDBs. Six meals were designed by integrating healthy foods in a meal based on the “Western diet” pattern. The nutritional composition of each meal was i) chemically determined; ii) retrieved from the Portuguese food composition table (TCAP) and from iii) United States Department of Agriculture database (USDA). Compared to chemical analyses, both FCDBs significantly (p < 0.05) overestimate the amount of Na and vitamin B6; TCAP also overestimate the amount of Ca (p < 0.05), while USDA overestimate energy, fat, available carbohydrates, P, and Fe. Linear regression analyses were used to adjust nutrient values based on TCAP and USDA. Predictive models from both FCDBs were successfully obtained for reliable estimation of protein, PUFA, available carbohydrates, total carbohydrates, sugars, Zn, β-carotene, vitamin E, riboflavin, and niacin in meals with a given uncertainty, which is provided by the respective correction factors. Those predictive models are limited to the range of theoretical values of meals studied.ElsevierRepositório Científico do Instituto Politécnico do PortoSilva, MartaRibeiro, MafaldaViegas, OlgaMartins, Zita E.Faria, MiguelCasal, SusanaPinto, EdgarAlmeida, AgostinhoPinho, OlíviaFerreira, Isabel M.P.L.V. O.2022-04-28T09:45:40Z2021-092021-09-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/20415engSilva, M., Ribeiro, M., Viegas, O., Martins, Z. E., Faria, M., Casal, S., Pinto, E., Almeida, A., Pinho, O., & Ferreira, I. M. P. L. V. O. (2021). Exploring two food composition databases to estimate nutritional components of whole meals. Journal of Food Composition and Analysis, 102, 104070. https://doi.org/https://doi.org/10.1016/j.jfca.2021.1040700889-157510.1016/j.jfca.2021.1040701096-0481metadata only accessinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-03-13T13:15:56Zoai:recipp.ipp.pt:10400.22/20415Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:40:29.906450Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Exploring two food composition databases to estimate nutritional components of whole meals |
title |
Exploring two food composition databases to estimate nutritional components of whole meals |
spellingShingle |
Exploring two food composition databases to estimate nutritional components of whole meals Silva, Marta Portuguese food composition table United States department of agriculture database Nutritional chemical analysis Nutrients bias Predictive regression models |
title_short |
Exploring two food composition databases to estimate nutritional components of whole meals |
title_full |
Exploring two food composition databases to estimate nutritional components of whole meals |
title_fullStr |
Exploring two food composition databases to estimate nutritional components of whole meals |
title_full_unstemmed |
Exploring two food composition databases to estimate nutritional components of whole meals |
title_sort |
Exploring two food composition databases to estimate nutritional components of whole meals |
author |
Silva, Marta |
author_facet |
Silva, Marta Ribeiro, Mafalda Viegas, Olga Martins, Zita E. Faria, Miguel Casal, Susana Pinto, Edgar Almeida, Agostinho Pinho, Olívia Ferreira, Isabel M.P.L.V. O. |
author_role |
author |
author2 |
Ribeiro, Mafalda Viegas, Olga Martins, Zita E. Faria, Miguel Casal, Susana Pinto, Edgar Almeida, Agostinho Pinho, Olívia Ferreira, Isabel M.P.L.V. O. |
author2_role |
author author author author author author author author author |
dc.contributor.none.fl_str_mv |
Repositório Científico do Instituto Politécnico do Porto |
dc.contributor.author.fl_str_mv |
Silva, Marta Ribeiro, Mafalda Viegas, Olga Martins, Zita E. Faria, Miguel Casal, Susana Pinto, Edgar Almeida, Agostinho Pinho, Olívia Ferreira, Isabel M.P.L.V. O. |
dc.subject.por.fl_str_mv |
Portuguese food composition table United States department of agriculture database Nutritional chemical analysis Nutrients bias Predictive regression models |
topic |
Portuguese food composition table United States department of agriculture database Nutritional chemical analysis Nutrients bias Predictive regression models |
description |
The integration of foodomics data to explain the impact of diet on health requires a precise knowledge of nutrients composition of complex meals. This work assesses the adequacy of two food composition databases (FCDBs) for calculation of nutritional composition of whole meals, compared to the golden standard “lab chemical analyses” and search for predictive models to overcome some limitations of FCDBs. Six meals were designed by integrating healthy foods in a meal based on the “Western diet” pattern. The nutritional composition of each meal was i) chemically determined; ii) retrieved from the Portuguese food composition table (TCAP) and from iii) United States Department of Agriculture database (USDA). Compared to chemical analyses, both FCDBs significantly (p < 0.05) overestimate the amount of Na and vitamin B6; TCAP also overestimate the amount of Ca (p < 0.05), while USDA overestimate energy, fat, available carbohydrates, P, and Fe. Linear regression analyses were used to adjust nutrient values based on TCAP and USDA. Predictive models from both FCDBs were successfully obtained for reliable estimation of protein, PUFA, available carbohydrates, total carbohydrates, sugars, Zn, β-carotene, vitamin E, riboflavin, and niacin in meals with a given uncertainty, which is provided by the respective correction factors. Those predictive models are limited to the range of theoretical values of meals studied. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-09 2021-09-01T00:00:00Z 2022-04-28T09:45:40Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.22/20415 |
url |
http://hdl.handle.net/10400.22/20415 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Silva, M., Ribeiro, M., Viegas, O., Martins, Z. E., Faria, M., Casal, S., Pinto, E., Almeida, A., Pinho, O., & Ferreira, I. M. P. L. V. O. (2021). Exploring two food composition databases to estimate nutritional components of whole meals. Journal of Food Composition and Analysis, 102, 104070. https://doi.org/https://doi.org/10.1016/j.jfca.2021.104070 0889-1575 10.1016/j.jfca.2021.104070 1096-0481 |
dc.rights.driver.fl_str_mv |
metadata only access info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
metadata only access |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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