T B C A - 7 . 2

Brazilian Food Composition Table.

Coded by OBTZ-Tech

Introduction

As of 2013, the Brazilian Network of Food Data Systems (TBCA) is being developed in an integrated manner between the Brazilian Network of Food Data Systems (BRASILFOODS), the University of São Paulo (USP), and the Food Research Center (FoRC/CEPID/ FAPESP).

The TBCA presents two different databases, one with original analytical data related to foods from Brazilian biodiversity and regional foods, and the other with information on the content of components of the most consumed foods in Brazil, intending to allow the evaluation of the intake of nutrients and facilitate the planning of meal plans.

The information from the TBCA databases was obtained both through directly analyzing foods in laboratories at the Department of Food and Experimental Nutrition of the Faculty of Pharmaceutical Sciences, USP, and by compiling information from analytical data for Brazilian foods from publications, dissertations, theses, internal data from other laboratories, analytical reports made available by food industries, and other tables.

Data inclusion in the TBCA involves several aspects, such as the complete description of the food or product, sampling plan, analytical methods, and quality of the analysis procedures. This information is compiled using a standardized form resulting in detailed documentation about the food and the sampling and analysis procedures.

Top

History

Considering the importance of data on food composition, the United Nations University (UNU) recommended, in 1984, creating the International Network of Food Data Systems (INFOODS) under the coordination of the Food and Agriculture Organization of the United Nations (FAO). This network aims to stimulate and coordinate efforts to improve the availability and quality of food composition data.

Since 1992, INFOODS has been proposing new guidelines and criteria for the area of food composition, publishing guides and manuals with protocols to be used in generating and compiling data to expand communication and exchange of information between laboratories and institutions for national and international research.

In Brazil, the Brazilian Food Composition Data Network (BRASILFOODS) was created in 1984, headquartered at the USP, as part of the Latin American Food Composition Data Network (LATINFOODS), which follows the quality guidelines recommended by INFOODS.

Motivated by INFOODS, especially Dr. Nevin Scrimshaw from UNU and Dr. Ricardo Bressani from LATINFOODS, the Integrated Food Composition Project was created in Brazil in 1989 under the coordination of Prof. Franco Maria Lajolo from the Department of Food and Experimental Nutrition at the Faculty of Pharmaceutical Sciences, USP. The project informally convened two dozen Brazilian researchers and professionals to discuss and conduct studies to improve the quality of data on the chemical compositions of foods. This project was later incorporated by BRASILFOODS. Since 2013, BRASILFOODS has been working collaboratively with the Food Research Center, developing and updating the TBCA, which has become one of the FoRC axes, since the chemical compositions of foods can be directly associated with the health-disease binomial, and it is also necessary for developing various activities.

As a result of the joint efforts of the project and BRASILFOODS, the TBCA was created in 1998 to centralize information on the chemical compositions of Brazilian foods. The TBCA data profile, with each new version, reflects the evolution of the area’s development in the country in both the analytical and compilation aspects.

Version 1.0 (1998) was released with data on the centesimal composition (CC) of 300 foods; Version 3.0 (2001) also had information on resistant starch (RS), dietary fiber (DF), fatty acids and cholesterol, and carotenoids in addition to 696 CC data points. Version 4.0 (2005) had 1,205 CC data points, a food search system (Portuguese, English, scientific name), information on household measures, and relevant publications in the area. Version 5.0 (2008) also presented data on glycemic response and carbohydrate profile in addition to updated data on CC, DF, RS, vitamin A and carotenoids, fatty acids, and cholesterol. Version 6.0 (2017) presented two databases, one with original data related to foods from Brazilian biodiversity (BD-B – Portuguese acronym) and another (Database for the Evaluation of Food Intake – BD-AIN – Portuguese acronym) with information on the contents of 34 components of 1,900 foods. Version 7.0 contains data for over 1,200 foods in BD-B and over 3,400 foods in BD-AIN. Version 7.2 contains data on more than 5,700 foods, with more than 4,000 preparations, of different types of food (preparation with texture alteration, gluten-free, lactose-free, and vegan and vegetarian) to allow the evaluation of the nutrient intake and facilitate the assessment of food consumption, in addition to menu preparation.

Top

General information

The TBCA comprises two databases and complementary tables, as described below.

TBCA – Nutrient Intake Assessment (TBCA BD-AIN – Portuguese acronym): Prepared based on analytical data for food and industrialized products in the Brazilian market, this provides the complete profile of information about its CC, vitamins, and minerals and identifies the addition of salt and sugar in preparations, aiming to evaluate nutrient intake. It includes the foods most consumed by the Brazilian population. These are primarily national data, which can be analytical, calculated, or obtained from other sources, in this case, adjusted for moisture or lipids of the food of origin.

Standard recipes, commonly used by the Brazilian population and available in the literature (Fisberg; Slater-Villar, 2002, Araújo; Guerra, 2008, Pinheiro et al., 2008, Bombem et al., 2012, Magalhães; Rodrigues, 2015), or standards were used (salt, sugar, oil, onion, and garlic, quantity/proportion of ingredients – cereals, vegetables, fish and seafood, meat, eggs, legumes) to estimate the chemical compositions of the preparations (TBCA, 2023). All estimates calculated were based on how foods/preparations are consumed, that is, with oil, seasonings, salt, and sugar.

For the estimates, the conversion factor (or cooking yield factor) was applied to the total preparation, while the nutrient retention factors were used individually for each ingredient, as instructed by Reinivuo et al. (2009). The conversion factors (or cooking yield factors) were based on the literature (Bergström, 1994; Bognár, 2002; Matthews and Garrison, 1975; USDA, 2014) or our own estimates, while the nutrient retention factors (Bognár, 2002; USDA, 2007; Vásquez-Caicedo et al., 2008) were compiled from the literature according to the food type and preparation. The document from the National Health Surveillance Agency (ANVISA) for the use of salt in collective restaurants was used as a reference (ANVISA, 2014) to determine the amount of salt added to the calculated preparations, considering the food group.

Three methods were used for the estimates:

  • Direct method – used as instructed by Greenfield and Southgate (2003). The calculation is performed using national analytical data on foods/ingredients with simple preparation (for example, boiled, grilled, or roasted vegetables) from TACO (2011) and data from other publications, used proportionally, to estimate 100 g of final preparation ready for consumption; for this method, it is unnecessary to apply conversion factors (or cooking yield factor) and nutrient retention (nutrient retention factor). Some examples of its application are cereals, pasta, meat, and legumes, with or without other added ingredients, such as vegetables, sauces, seasonings, and salt (rice with broccoli, pasta with sauce, and meat with potatoes).
  • Indirect method – used as instructed by Machackova et al. (2018). This is used when no information is available about the prepared ingredient; for this method, conversion (or cooking yield factor) and nutrient retention (nutrient retention factor) are applied. The chemical composition estimates are based on the initial and final weights and the ingredients containing the highest amount of moisture. Some examples of its application are in preparations such as cakes, bread, and fried foods.
  • Mixed method – used in situations where preparations use ingredients that undergo more than one preparation method, that is, application of the nutrient retention factor to specific ingredients, such as a sandwich that has some of its ingredients heated and some in natura (bread, cold cuts, and vegetables; Giuntini et al., 2019).
  • Bergström, L. Nutrient losses and gains in the preparation of foods 1994.
  • Bognár, A. Tables on weight yield of food and retention factors of food constituents for the calculation of nutrient composition of cooked foods (dishes). Bundesforschungsanstalt für Ernährung: 2002.
  • FAO. FAO/INFOODS global food composition database for fish and shellfish, version 1.0 - uFiSh1.0. Rome, 2016.
  • Magalhães, S. G.; Rodrigues, E. L. Fichas de preparações e análise do valor nutricional. Rio de Janeiro: Rubio, 2015. 288p.
  • Matthews, R. H.; Garrison, Y.J. Food yields summarized by different stages of preparation. Washington, D.C.: 1975.
  • Pinheiro, A. B. V. et al. Tabela para avaliação de consumo alimentar em medidas caseiras. 5. ed. São Paulo: Atheneu, 2004. 131p.Paulo: Atheneu, 2004. 131 p.
  • USDA. U. S. Departmenr of Agriculture. USDA Table of Nutrient Retention Factors,Release 6. Natl Acad Press 2007:18.
  • USDA. U. S. Department of Agriculture. USDA Table of Cooking Yields for Meat and Poultry, release 2 2014:1–30.
  • Vásquez-Caicedo, A. L.; Bell, S.; Hartmann, B. Report on collection of rules on use of recipe calculation procedures including the use of yield and retention factors for imputing nutriente values for composite foods 2008:184.

TBCA – Biodiversity and Regional Food (TBCA BD-B – Portuguese acronym): This database results from compiling data on the chemical compositions of foods belonging to BD-B and typical dishes from various regions of the country. It presents only original analytical information without data aggregation or attribution. The indicators used for food selection were as follows (FAO, 2016): (i) food described at variety/cultivar/race level; (ii) wild food, according to how the analyzed sample was obtained (food obtained directly from nature); and (iii) food classified as underutilized according to the INFOODS List of Underutilized Species (FAO/INFOODS, 2013).

Complementary Tables: These contain analytical data on specific components available in .pdf format for download (the available file information is only in Portuguese) - Under implementation:

The content of each component shown is expressed per 100 g of the edible portion on a whole basis. Small differences between the nutritional information values on labels may be found, compared with data on the same foods present in the TBCA, due to the formulation and raw material adjustments. Additionally, food labeling follows specific legislation – RDC No. 429, of 10/8/2020 – which determines that the value must be expressed per portion (in grams and home measure) and per 100 g and provides for the respective rounding (IN No. 75, of 10/8/2020).

Several companies have collaborated for the TBCA by sending data on the chemical compositions of foods, accompanied by detailed analytical reports. Some of these foods mentioned may not be available on the market because they are seasonal, regional, or about to be discontinued.

Top

How to Cite

The disclosure of data is encouraged for non-commercial purposes, and it is necessary to cite the source:

Brazilian Food Composition Table (TBCA). University of São Paulo (USP). Food Research Center (FoRC). Version 7.2. São Paulo, 2022. [Access at: xxxx]. Available at http://www.fcf.usp.br/tbca.

For commercial purposes, it is necessary to contact the coordinators.

When data are used to calculate nutritional information, citation of the source is waived.

Total or partial reproduction of the material is prohibited. If material information is used, it is mandatory to cite the source. Marketing is forbidden. Total or even partial alteration of the content is prohibited (Creative Commons NonCommercial-NoDerivatives [CC BY-NC-ND 4.0]).

Top

Credits

Responsible committee

Department of Food and Experimental Nutrition FCF-USP/FoRC Researchers

  • Franco M. Lajolo (Coordinator).
  • Elizabete Wenzel de Menezes (Vice coordinator).
  • Eduardo Purgatto (Academic Coordinator) epurgatt@gmail.com.

Faculty of the Department of Food and Experimental Nutrition FCF-USP/FoRC Researchers

  • Bernadette Dora Gombossy de Melo Franco.
  • Neuza Mariko Aymoto Hassimotto.

Current Employees

  • Eliana Bistriche Giuntini – Researcher at FoRC.
  • Kristy Soraya Coelho – Researcher at FoRC.

Web Design and Platform Development

  • OBTZ-Tech.

Former collaborators

  • Adriana Elisa Canzio – undergraduate student at FCF-USP.
  • Alexandra Tavares de Melo – undergraduate student at the Faculty of Nutrition FSP-USP.
  • Aline de Oliveira Santos – technician at the Laboratory of Chemistry, Biochemistry and Molecular Biology of Food II.
  • Ana Tereza Ratto – graduate student at PRONUT FCF/FEA/FSP-USP.
  • Beatriz Rosana Cordenunsi – professor at the Department of Food and Experimental Nutrition FCF-USP.
  • Eliana Rodrigues Mazzini – graduate student in food science FCF-USP.
  • Érica Oliveira Rocha – undergraduate student at FCF-USP.
  • Eun Yee Hong – undergraduate student at the Faculty of Nutrition FSP-USP.
  • Fábio Augusto R. Gonçalves – undergraduate student at FCF-USP (Webmaster Version 1-3).
  • Flávia Biet – undergraduate student at the Faculty of Nutrition FSP-USP.
  • Fernanda Grande – graduate student at PRONUT FCF/FEA/FSP-USP.
  • Juliana de Almeida Egas Negrini – nutrition undergraduate student at FSP-USP.
  • Juliana Godoy Lucena – undergraduate student at the Faculty of Nutrition FSP-USP.
  • Lúcia Caruso – graduate student at PRONUT FCF/FEA/FSP-USP.
  • Maria Cristina Mendes Matuiama – undergraduate student at the Faculty of Nutrition FSP-USP.
  • Maria Stella Bonin Ito – graduate student in food science FCF-USP.
  • Marilene de Vuono C. Penteado – professor at the Department of Food and Experimental Nutrition FCF-USP.
  • Marsia Dolores Serrano Sansón – nutritionist (Basque University of Quiroga).
  • Milana Cara Tanasov Dan – graduate student in food science FCF-USP.
  • Nelaine Cardoso dos Santos – graduate student at PRONUT FCF/FEA/FSP-USP.
  • Samira Bernardino Ramos do Prado – graduate student in food science FCF-USP.
  • Tássia do Vale Cardoso Lopes – graduate student at PRONUT FCF/FEA/FSP-USP.
  • Tullia M. C. C. Filisetti – professor at the Department of Food and Experimental Nutrition FCF-USP.
  • Ursula M. Lanfer Marquez – professor at the Department of Food and Experimental Nutrition FCF-USP.
  • Yona Hopkins do Carmo Fonseca – undergraduate student at the Faculty of Nutrition FSP-USP.

Thanks

To the Computer Laboratory of the Faculty of Pharmaceutical Sciences of the University of São Paulo (LABINFO)

  • Luís Alberto V. Gioso, Auriluce M. Oliveira, Renato Flamini.

To the following funding bodies: FAO, CAPES, CNPq, FoRC, and FAPESP;

To the food industries and laboratories that collaborated in providing data.

Address

Department of Food and Experimental Nutrition.
Faculty of Pharmaceutical Sciences (University of São Paulo).
Avenida Professor Lineu Prestes, 580 – Block 14.
CEP 05508-900 – University City – São Paulo – SP – Brazil.
Phone: +55 11 3091-2482 – Fax: +55 11 2648-0677.

FoRC – Food Research Center
Rua do Lago, 250 – Ed. Semi Industrial, Block C – Food Engineering Laboratory.
Zip code 05508-080 – Cidade Universitária – São Paulo – SP.

Top

Publications

Main publications related to BRASILFOODS and INFOODS

Coelho KS, Giuntini EB, Grande F, Dias JS, Purgatto E, Franco BDGM, et al. Brazilian Food Composition Table (TBCA): development and functionalities of the online version. J Food Compos Anal. 2019;84:103287(6 p.) doi.org/10.1016/j.jfca.2019.103287..

Grande F, Giuntini EB, Coelho KS, Purgatto E, Franco BDGM, Lajolo FM, Menezes EW. Biodiversity food dataset: Centralizing chemical composition data to allow the promotion of nutrient-rich foods in Brazil. Matern Child Nutr. 2020;16(S3):e13005 (9 p.) doi.org/10.1111/mcn.13005

Giuntini EB, Coelho KS, Grande F, Marchioni DML, De Carli E, Sichieri R, et al. Brazilian Nutrient Intake Evaluation Database: An essential tool for estimating nutrient intake data. J Food Compos Anal. 2019;83:103286 (7 p.) doi.org/10.1016/j.jfca.2019.103286

Grande F, Giuntini EB, Coelho KS, Menezes EW. Elaboration of a standardized dataset for foods fortified with iron and folic acid in Brazil. J Food Compos Anal. 2019;83:103285 (7 p.) doi.org/10.1016/j.jfca.2019.103285

Giuntini EB, Menezes EW. Fibra alimentar. In: ILSI ed. Série de Publicações ILSI Brasil - Funções Plenamente Reconhecidas de Nutrientes. vol. 18. 2a ed. São Paulo: ILSI, 2011. 67 p. [citado 2020 dez 20]. Disponível em: ILSI / Série: Funções Plenamente Reconhecidas de Nutrientes (ilsibrasil.org).

Menezes EW, Grande F, Giuntini EB, Lopes TVC, Dan MCT, Prado SBR, et al. Impact of dietary fiber energy on the calculation of food total energy value in the Brazilian Food Composition Database. Food Chem. 2016;193:128-33. doi.org/10.1016/j.foodchem.2015.01.051

Grande F, Giuntini EB, Lajolo FM, Menezes EW. How do calculation method and food data source affect estimates of vitamin A content in foods and dietary intake? J Food Compos Anal. 2016;46:60-9. doi.org/10.1016/j.jfca.2015.11.006

Menezes EW, Grande F, Giuntini EB, Lopes TVC, Dan MCT, Prado SBR, et al. Impact of dietary fiber energy on the calculation of food total energy value in the Brazilian Food Composition Database. Food Chem. 2016;193:128-33. doi.org/10.1016/j.foodchem.2015.01.051

Prado SBR, Giuntini EB, Grande F, Menezes EW. Techniques to evaluate changes in the nutritional profile of food products. J Food Compos Anal. 2016;53:1-6, doi.org/10.1016/j.jfca.2016.08.007

Lopes TVC, Cyrillo DC, Giuntini EB, Lajolo FM, Menezes EW. Tabela brasileira de composição de alimentos - USP: Compilação de dados a serviço do bem público. Arch Latinoam Nutr. 2015;65:186-92. Disponível em: scielo.org.ve/scielo.php?script=sci_arttext&pid=S0004-06222015000300008

Lopes TVC, Giuntini EB, Dan MCT, Menezes EW. Compilation of mineral data: Feasibility of updating the food composition database. JFood Comp Anal. 2015;39:87-93. doi.org/10.1016/j.jfca.2014.12.002

Blanco AM, Pablo S, Samman N, Ariza JS, Masson L, Nunez L, Menezes EW. LATINFOODS activities and challenges during the period of 2009-2012. Arch Latinoam Nutr. 2014;64: 206-14.

The Food Monitoring Group. Progress with a global branded food composition database. Food Chem. 2013;140:451-7. doi.org/10.1016/j.foodchem.2012.10.065

Menezes EW, Giuntini EB, Dan MCT, Sardá FAH, Lajolo FM. Codex dietary fibre definition - Justification for inclusion of carbohydrates from 3 to 9 degrees of polymerisation. Food Chem. 2013;140:581-5. doi.org/10.1016/j.foodchem.2013.02.075

Menezes EW, Lopes TVC, Mazzini ER, Dan MCT, Godoy C, Giuntini EB. Application of Choices criteria in Brazil: Impact on nutrient intake and adequacy of food products in relation to compounds associated to the risk of non-transmissible chronic diseases. Food Chem. 2013;140:547-52. doi.org/10.1016/j.foodchem.2013.02.031

Dunford E, Webster J, Metzler AB, Czernichow S, Ni Mhurchu C, Wolmarans P, et al. International collaborative project to compare and monitor the nutritional composition of processed foods. Eur J Prev Cardiol. 2012;19:1326-32. dx.doi.org/10.1177/1741826711425777

Giuntini EB, Menezes EW. Fibra alimentar. In: ILSI ed. Série de Publicações ILSI Brasil - Funções Plenamente Reconhecidas de Nutrientes. vol. 18. 1a ed. São Paulo: ILSI, 2011. 23 p. [citado 2020 dez 20]. Disponível em: ILSI / Série: Funções Plenamente Reconhecidas de Nutrientes (ilsibrasil.org)

Menezes EW, Santos NC, Giuntini EB, Dan MCT, Genovese M I, Lajolo FM. Brazilian flavonoid database: Application of quality evaluation system. J Food Compos Anal. 2011;24:629-36. doi.org/10.1016/j.jfca.2010.09.004

Menezes EW, Giuntini EB, Dan MCT, Santos NC, Melo AT, Lajolo FM. Brazilian Network of Food Data Systems and LATINFOODS Regional Technical Compilation Committee: Food composition activities (2006 - 2009). J Food Compos Anal. 2011;24:678-81. doi.org/10.1016/j.jfca.2010.09.003

Menezes EW. 7th International Food Data Conference: Food composition and biodiversity. J Food Compos Anal. 2009;22:359-60. doi.org/10.1016/j.jfca.2009.04.004

Menezes EW, Giuntini EB, Dan, M.; Lajolo FM. New information on carbohydrates in the Brazilian Food Composition Database. J. Food Comp Anal. 2009;22:446-52. doi.org/10.1016/j.jfca.2009.02.001

Menezes EW, Giuntini EB, Lajolo FM, Moron, C. Latinfoods: Food composition activities in Latin America (2004-2006). J Food Compos Anal. 2007;20:704-8. doi.org/10.1016/j.jfca.2006.04.006

Giuntini EB, Lajolo FM, Menezes EW. Composição de alimentos: um pouco de história. Arch Latinoam Nutr. 2006;56(3): 295-303. Disponível em: scielo.org.ve/scielo.php?script=sci_arttext&pid=S0004-06222006000300014

Giuntini EB, Lajolo FM, Menezes EW. Tabela Brasileira de Composição de Alimentos TBCA-USP (Versões 3 e 4) no contexto internacional. Arch Latinoam Nutr. 2006; 56(4):366-74. Disponível em: scielo.org.ve/scielo.php?script=sci_arttext&pid=S0004-06222006000400009

Menezes EW, Ratto AT, Giuntini EB, Lajolo FM. Composição de alimentos: compilação e uniformização de estruturas para intercâmbio de dados. Braz J Food Technol. 2005;8(1):25-33. Disponível em: bj.ital.sp.gov.br/artigos/brazilianjournal/free/p05183.pdf

Menezes EW, Mello AT, Lima GH, Lajolo FM. Measurement of carbohydrates components and their impact on energy value of foods. J. Food Comp Anal. 2004;17(3-4):331-8. doi.org/10.1016/j.jfca.2004.03.018

Arabbi PR, Genovese MI, Lajolo FM. Flavonoids in vegetable foods commonly consumed in Brazil end estimated by the brazilian population. J Agric Food Chem. 2004;52:1124-31. doi.org/10.1021/jf0499525

Menezes EW, Giuntini EB, Lajolo FM. A questão da variabilidade e qualidade de dados de composição de alimentos. NUTRIRE Rev Soc Bras Alim Nutr. 2003;26:63-76.

Giuntini EB, Lajolo FM, Menezes EW. Potencial de fibra alimentar em países ibero-americanos: alimentos, produtos e resíduos. Arch Latinoam Nutr. 2003;53(1):14-20. Disponível em: scielo.org.ve/scielo.php?script=sci_arttext&pid=S0004-06222003000100002

Giuntini EB, Ratto AT, Lajolo FM, Menezes EW. Tabela brasileira de composição de alimentos: TBCA-USP versão 2003. Rev Bras Cienc Farmac. 2003;39(3):137-40.

Ratto AT, Giuntini EB, Lajolo FM, Menezes EW. Formulário para compilação de dados de composição de alimentos - TBCA-USP. Rev Bras Cienc Farmac. 2003;40(3): 127-9.

Menezes EW, Gonçalves, F. A. R.; Giuntini EB, Lajolo FM. Brazilian food composition database: Internet dissemination and other recent developments. J. Food Comp Anal. 2002;15(4):453-64. doi.org/10.1006/jfca.2002.1083

Rosin PMP, Lajolo FM, Menezes EW. Measurement and characterization of dietary starches. J Food Compos Anal. 2002;15(4):367-77. doi.org/10.1006/jfca.2002.1084

Lajolo FM, Saura-Calixto F, Wittig de Penna E, Menezes EW. Fibra dietética en Iberoamérica: Tecnología y salud. Obtención, caracterización, efecto fisiológico y aplicación en alimentos. Projeto CYTED XI.6 "Obtención y caracterización de fibra dietética para su aplicatión en regimenes especiales". CNPq. São Paulo: Editora Varela; 2001. 471 p.

Menezes EW, Lajolo FM. Contenido en fibra dietética y almidón resistente en alimentos y productos iberoamericanos. Projeto CYTED XI.6 "Obtención y caracterización de fibra dietética para su aplicatión en regimenes especiales". CYTED/CNPq. São Paulo: Docuprint; 2000.

Menezes EW, Caruso L, Lajolo FM. An application of criteria to evaluate quality of dietary fibre data in Brazilian foods. J Food Compos Anal. 2000;13(4):455-73. doi.org/10.1006/jfca.2000.0890

Caruso L, Lajolo FM, Menezes EW. Modelos esquemáticos para avaliação da qualidade analítica dos dados nacionais de fibra alimentar. Ciênc Tecnol Aliment. 1999;19(3):406-12. Disponível em: scielo.br/scielo.php?script=sci_arttext&pid=S0101-20611999000300020

Lajolo FM, Menezes EW. Uma análise retrospectiva e contextualização da questão. Grupo de Trabalho: Composição de Alimentos. Bol SBCTA. 1997;31 (2):90-1.

Menezes EW, Caruso L, Lajolo FM. Uniformização internacional de dados brasileiros de composição de alimentos. Bol SBCTA. 1997;31(2):93-104.

Marquez UL, Penteado MVC. Variação e nível de detalhamento de alguns nutrientes. Teor de fenilalanina. Bol SBCTA. 1997;31(2):109-11.

Filisetti TMCC. Estudo colaborativo para análise de fibra alimentar. Bol SBCTA. 1997;31(2):112-3.

Menezes EW, Caruso L, Lajolo FM. Food composition situation in Brazil. 16th Congress of Nutrition, Montreal, Canadá, 1997, PT380.

Penteado MVC. Análise de vitaminas: Importância e desafios. III Vitamina C. Ciência de Alimentos. Avanços e perspectivas na América Latina. Campinas: Ed. DB UNICAMP; 1997. p. 326-9.

Marques UL. Fenilcetonúria: aspectos bioquímicos, nutricionais e importância na alimentação. Cad Nutr. 1996;11:51-68.

Lajolo FM. Grupo de trabalho: Composição de alimentos. Bol SBCTA. 1995;29(1):57-69.

Lajolo FM, Menezes EW, Vannucchi, H. Alimentos básicos e sua adequação nutricional. In: Wheba J. Nutrição da criança. São Paulo: Fundo Editorial Byk; 1991. p. 77-122.

Lajolo FM, Filisetti-Cozzi TMCC, Menezes EW. Carboidratos e fibras. In: Carrazza F. Marcondes E. Nutrição clínica em pediatria. São Paulo: Sarvier; 1991. p. 61-84.

Lajolo FM, Menezes EW, Filisetti-Cozzi TMCC. Considerações sobre carboidratos e fibra. Arch Latinoam Nutr. 1988;38(3):519-42.

Lajolo FM. Efeito do processamento sobre o valor nutricional dos alimentos. Situação na América Latina e Caribe e importância para a elaboração de tabelas de composição. Arch Latinoam Nutr. 1987;37(4):666-72.

Lajolo FM, Vannucchi H. Tabelas de composição de nutrientes em alimentos: Situação no Brasil e necessidades. Arch Latinoam Nutr. 1987;37(4):702-13.

Publications related to the area of food composition

Burlingame B. INFOODS contribution to fulfilling needs and meeting challenges concerning food composition databases. Procedia Food Sci. 2013;2:35-45.

Bell S, Colombani PC, Pakkala H, Christensen T, Møller A, Finglas P. Food composition data: Identifying new uses, approaching new users. J Food Compos Anal. 2011;24:727-31.

Charrondière UR, Burlingame B. Report on the FAO/INFOODS Compilation Tool: A simple system to manage food composition data. J. Food Compos Anal. 2011;24:711-5.

Charrondière UR, Burlingame B, Berman S, Elmadfa I. Food composition study guide. Question & exercises. INFOODS, FAO Food and Agriculture Organization of United Nations (FAO), Rome, 2009. v1 and v2. [citado 2020 dez 20].

Haytowitz DB, Perhrsson PR, Holden JM. The national food and nutrient analysis program: a decade of progress. J Food Compos Anal. 2008;21:94-102.

Gry J, Blach L, Eriksen FD, Pilegaard K, Pumb J, Rhodes M, et al. EuroFIR-BASIS- A combined composition and biological activitive compounds in plant-based foods. Trends Food SciTechnol, 2007;18:434-44.

Burlingame B. Fostering quality data in food composition databases: visions for the future. J. Food Compos Anal. 2004;17:251-8.

Charrondiere UR, Chevassus-Agnes S, Marroni S, Burlingame B. Impact of different macronutrient definitions and energy conversion factors on energy supply estimations. J. Food Compos Anal. 2004;17:339-60.

Greenfield H, Southgate DAT. Datos de composición de alimentos: Obtención, gestión, utilización. 2a ed. Food and Agriculture Organization of United Nations (FAO), Rome, 2003. [citado 2020 dez 20]. Disponível em: ftp://ftp.fao.org/docrep/fao/009/y4705s/y4705s.pdf.

Food and Agricultural Organization (FAO). Food energy - methods of analysis and conversion factors. Food and Nutrition Paper 77. Report of a workshop. Rome, 2002. [citado 2020 dez 20]. Disponível em: fao.org/docrep/006/Y5022E/Y5022E00.HTM

Holden JM, Bhagwat SA, Patterson KY. Development of a multi-nutrient data quality evaluation system. J Food Compos Anal. 2002;15:339-48.

Southgate, D. A. T. Data Quality in sampling, analysis, and compilation. J Food Compos Anal. 2002;15:507-13.

Food and Agricultural Organization (FAO). Carbohydrates in human nutrition. Food and Nutrition Paper 66. Report of a Joint FAO/WHO Expert Consultation. Rome; 1997. [citado 2020 dez 20]. Disponível em: fao.org/docrep/w8079e/w8079e00.htm

Rand WM, Pennington JAT, Murphy SP, Klensin JC. Compiling Data for Food Composition Data Bases. The United Nations University,1991. [citado 2020 dez 20]. Disponível em: ftp://193.43.36.92/es/esn/infoods/Randeal1991CompFCDBases.pdf

Rand WM, Windham CT, Wyse BW, Young VR. Food Composition Data: A User's Perspective. The United Nations University, 1987. [citado 2020 dez 20]. Disponível em: archive.unu.edu/unupress/unupbooks/80633e/80633E00.htm

Related dissertations and theses

Coelho KS. Concepção de uma ferramenta computacional para a geração de planos alimentares personalizados, considerando preferências e necessidades nutricionais [tese]. São Paulo: Universidade de São Paulo, Programa Interunidades PRONUT-FCF/FEA/FSP; 2018. Disponível em: teses.usp.br/teses/disponiveis/89/89131/tde-05112018-162055/pt-br.php

Grande F. Reformulação da base de dados da Tabela Brasileira de Composição de Alimentos (TBCA) [tese]. São Paulo: Universidade de São Paulo, Programa Interunidades PRONUT-FCF/FEA/FSP; 2018. Disponível em:

Prado SBR. Alimentos processados: avaliação comparativa do perfil nutricional e sistematização do processo de categorização de alimentos prioritários para atualização de base de dados [dissertação]. São Paulo: Universidade de São Paulo, Faculdade de Ciências Farmacêuticas São Paulo; 2014. Disponível em: teses.usp.br/teses/disponiveis/9/9132/tde-19012015-143203/en.php

Grande F. Tabela Brasileira de Composição de Alimentos (TBCA-USP): atualização e inclusão de dados de vitaminas. [dissertação]. São Paulo: Universidade de São Paulo, Programa Interunidades PRONUT-FCF/FEA/FSP; 2013. Disponível em: teses.usp.br/teses/disponiveis/89/89131/tde-07062013-150447/pt-br.php

Mazzini ER. Base de dados de informação nutricional, relacionados com doenças crônicas não transmissíveis, de alimentos comercializados no Brasil [dissertação]. São Paulo: Universidade de São Paulo, Faculdade de Ciências Farmacêuticas São Paulo; 2013. Disponível em: teses.usp.br/teses/disponiveis/9/9132/tde-27032014-160716/pt-br.php

Lopes TVC. Inclusão de dados de minerais na Tabela Brasileira de Composição de Alimentos (TBCA-USP). [dissertação]. São Paulo: Universidade de São Paulo, Programa Interunidades PRONUT-FCF/FEA/FSP; 2012. Disponível em: teses.usp.br/teses/disponiveis/89/89131/tde-09092013-112049/pt-br.php

Melo AT. Aprimoramento de ferramentas para compilação de dados: Tabela Brasileira de Composição de Alimentos (TBCA-USP) [dissertação]. São Paulo: Universidade de São Paulo, Programa Interunidades PRONUT-FCF/FEA/FSP; 2010. Disponível em: teses.usp.br/teses/disponiveis/89/89131/tde-31082010-142215/pt-br.php

Santos NC. Tabela Brasileira de Composição de Alimentos (TBCA-USP): Banco de dados de flavonoides [dissertação]. São Paulo: Universidade de São Paulo, Programa Interunidades PRONUT-FCF/FEA/FSP; 2009. Disponível em: teses.usp.br/teses/disponiveis/89/89131/tde-21102009-164855/pt-br.php

Giuntini EB. Tabela Brasileira de Composição de Alimentos TBCA-USP: 2001-2004 [doutorado]. São Paulo: Universidade de São Paulo, Programa Interunidades PRONUT-FCF/FEA/FSP; 2005.

Rato AT. Tabela Brasileira de Composição de Alimentos (TBCA-USP): Apoio para determinar a qualidade das informações nutricionais em rótulos de alimentos [dissertação]. São Paulo: Universidade de São Paulo, Programa Interunidades PRONUT-FCF/FEA/FSP; 2005.

Souza AG. Índice glicêmico e carga glicêmica de frutos brasileiros [dissertação]. São Paulo: Universidade de São Paulo, Programa Interunidades PRONUT-FCF/FEA/FSP; 2005. Disponível em: teses.usp.br/teses/disponiveis/89/89131/tde-21022009-214416/pt-br.php

Ito MSB. Tabela Brasileira de Composição - USP: Banco de dados de alimentos industrializados [dissertação]. São Paulo: Universidade de São Paulo, Faculdade de Ciências Farmacêuticas; 2003. Disponível em: teses.usp.br/teses/disponiveis/9/9131/tde-03052004-092133/pt-br.php

Top

How to query

The data can be searched as shown below:
  • Search for food (food name in Portuguese, food name in English, or scientific name of the food), considering the food groups: All foods will be listed, with available data, considering the chosen database (TBCA – Nutrient Intake Assessment or TBCA – Biodiversity and Regional Food).
  • Search by food group: All foods from the chosen group will be listed, with their specifications, considering the TBCA – Nutrient Intake Assessment or TBCA – Biodiversity and Regional Food.
  • Type of food: All foods in the group and the type of food chosen will be listed, with their specifications, considering the TBCA – Nutrient Intake Assessment.
  • Search by component: All foods will be listed based on the amount of the nutrient, according to the food group to which they belong, considering the TBCA – Nutrient Intake Assessment. The reports for consultation may be presented in ascending or descending order of the component in the food.

Data presentation

TBCA – Nutrient Intake Assessment (TBCA BD-AIN)

The data can be consulted by mean values or by the complete information for each food (number of data points, mean, standard deviation, minimum, maximum, and source of information), with values for each nutrient per 100 g of the edible part of the food. Some components have the abbreviation “NA,” which means “not analyzed”; almost all cases apply to data on components that could not be found in certain foods. Components identified as a “trace” quantity through the abbreviation “tr” were thus assigned in two situations: (I) when also declared as a trace in the original reference used in the data aggregation; (II) when mean values were less than 0.005.

The information source(s) are presented at the end.

The average values of the components are also presented in weight per unit or more usual household measures to ensure they comply with the nutrition labeling legislation and to facilitate user consultation (Pinheiro et al., 2008, Bombem et al., 2012). Regarding utensils, volumes were considered based on Moreira (2002). When in solid form, the food must fill the container to the edges without excess (shallow). For liquid food, the volume was converted into grams according to the product density.

For data in household measurements, volumetric measurements of the following utensils were considered:

  • Cup, with a total capacity of 180 mL;
  • Double cup, with a total capacity of 250 mL;
  • Small size shell, with a total capacity of 80 mL;
  • Medium size shell, with a total capacity of 150 mL;
  • Serving spoon, with a total capacity of 30 mL;
  • Tablespoon, with a total capacity of 11 mL;
  • Dessert spoon, with a total capacity of 9 mL;
  • Teaspoon, with a total capacity of 4 mL;
  • Coffee spoon, with a total capacity of 2 mL (Moreira, 2002).
  • Bombem KCM et al. Manual de medidas caseiras e receitas para cálculos dietéticos São Paulo: MBooks, 2012. 191p.
  • Pinheiro ABV et al. Tabela para avaliação de consumo alimentar em medidas caseiras. 5. ed. São Paulo: Atheneu, 2004. 131p.Paulo: Atheneu, 2004. 131 p.
  • Pacheco M. Tabela de equivalentes, medidas caseiras e composição química dos alimentos. Rio de Janeiro: Rubio, 2006. 654p.
  • Moreira MA. Medidas caseiras no preparo de alimentos. Goiânia: AB Editora, 2002. 144p.
TBCA – Biodiversity and Regional Food (TBCA BD-B)

A component report is presented according to availability in the database (analytical data from the original publication only), with values for each nutrient per 100 g of the edible part of the food. Finally, the sources of information are presented.

Top

Adopted Criteria

Definition of Nutrients

The component content presented in the TBCA was obtained through validated analytical methods widely used by the scientific community. The identifiers (tagnames) recommended by INFOODS and LATINFOODS were adopted (http://www.fao.org/infoods/infoods/standards-guidelines/food-component-identifiers-tagnames/en/) to identify the components according to the method used to standardize and facilitate the exchange of information between analysts, compilers, users, and between databases in different regions of the world. Each identifier comprises an abbreviation of the component name and its best-known name, the unit per 100 g of edible portion; for some components, the analytical and calculation methods are also identified, in addition to general comments (Table 1).

Table 1. Identifiers/analytical methodology adopted for describing each component.
Component Unit Tagname Description
Energy kJ < ENERC > 17 x <CHOAVLDF> + 17 x <PROCNT> + 37 X <FAT> + 8 x <FIBTG> + 29 x <ALC>
Energy kcal < ENERC > 4 x <CHOAVLDF> + 4 x <PROCNT> + 9 X <FAT> + 2 x <FIBTG> + 7 x <ALC>
Moisture g < WATER > Moisture in an oven at 105°C.
Ash g < ASH > -
Total carbohydrates g < CHOCDF > Total carbohydrates calculated by difference (100 g – total grams of moisture, protein, lipids, and ash). Includes the dietary fiber fraction.
Available carbohydrates g < CHOAVLDF > Metabolizable carbohydrates calculated by difference. Excludes the dietary fiber fraction (100 g – total grams of moisture, protein, lipids, ash, and dietary fiber).
Proteins g < PROCNT > Total protein. FAO/73 conversion factors were used to calculate proteins from total nitrogen (Greenfield & Southgate, 1992).
Animal products: meat and fish – 6.25; gelatin – 5.55; milk and derivatives – 6.38; casein – 6.40; human milk – 6.37; egg: whole – 6.25; albumin – 6.32; yolk – 6.12.
Plant products: wheat: whole – 5.83; bran – 6.31; embryo – 5.80; endosperm – 5.70; rice and rice flour – 5.95; rye and rye flour – 5.83; barley and barley flour – 5.83; oats – 5.83; corn – 6.25; beans – 6.25; soy – 5.71; oilseeds: Brazil nuts – 5.46; others – 5.30.
Lipids g < FAT > Total lipids.
< FATCE > Total lipids obtained through continuous extraction (Soxhlet method).
Fiber g < FIBTG > Total dietary fiber determined by the AOAC enzymic-gravimetric or non-enzymic-gravimetric method (for foods with low starch content; Cho et al., 1997; Li & Cardozo, 1992).
Cholesterol mg < CHOLE > Cholesterol determined by enzymatic or chromatographic method.
Total saturated fatty acids. g < FASAT >
Total monounsaturated fatty acids g < FAMS >
Total polyunsaturated fatty acids g < FAPU >
Calcium mg < CA >
Magnesium mg < MG >
Manganese mg < MN >
Phosphor mg < P >
Iron mg < FE >
Sodium mg < NA >
Potassium mg < K >
Copper mg < CU >
Zinc mg < ZN >
Selenium mcg < SE >
Vitamin A mcg < VITA > Expressed in retinol equivalents (RE); being calculated as RE = retinol + 1/6 trans-b-carotene + 1/12 cis-b-carotene + 1/12 a-carotene + 1/12 b-cryptoxanthin (FAO/WHO, 2001).
Vitamin A mcg < VITA RAE > Expressed in retinol activity equivalents (RAE); being calculated as RE = retinol + 1/12 trans-b-carotene + 1/24 cis-b-carotene + 1/24 a-carotene + 1/24 b-cryptoxanthin (IOM, 2001).
Thiamine mg < THIA >
Riboflavin mg < RIBF >
Niacin mg < NIA > Preformed niacin.
Pyridoxine (B6) mg < VITB6A > Total pyridoxine determined by analysis.
Cobalamin (B12) mg < VITB12 >
Vitamin D mg < VITD > Vitamin D calculated as the sum of ergocalciferol (vitamin D2) and colecalciferol (vitamin D3).
Alpha-tocopherol (vitamin E) mg < TOCPHA >
Vitamin C mg < VITC > Calculated as the sum of ascorbic and dehydroascorbic acids.
Folate mg < FOLDFE > Expressed in food folate equivalents. DFE (mg) = dietary folate (mg) + [1.7 × folic acid (mg)].
Added salt g Amount of sodium chloride (table salt) added to preparations or processed foods (ingredients or ready-to-eat) when the ingredient list is available.
Added sugar g Amount of sucrose (refined sugar) added to preparations or processed foods (ingredients or ready-to-eat) when the ingredient list is available.
Added fat g Amount of fat (including oil, olive oil, butter, and margarine) added to preparations or processed foods (ingredients or ready-to-eat) when the ingredient list is available. – Under implementation!
Vegetable protein g Amount of plant-based protein in the food or preparation – Under implementation!
Animal protein g Amount of protein of animal origin in the food or preparation – Under implementation!

  • Cho, S., Devries, J.W., Prosky, L. Dietary fiber analysis and applications. USA: AOAC International, 1997. 202p.
  • FAO/WHO. Human Vitamin and Mineral Requirements, Report 7ª Joint FAO/OMS Expert Consultation. Bangkok: Food and Agriculture Organization of the United Nations; 2001. 303 p.
  • Greenfield, H.; Southgate, D.A.T. Food composition data: production, management and use.London: Chapman & Hal, 1992. 243p.
  • Institute of Medicine (IOM). Food and Nutrition Board. Dietary reference intakes for vitamin A, vitamin K, arsenic, boron, chromium, copper, iodine, iron, manganese, molybdenum, nickel, silicon, vanadium, and zinc. Washington: National Academies; 2001. 773 p.
  • Li, B.W.; Cardozo, M.S. Nonenzymatic-gravimetric determination of total fiber in fruits and vegetables. J. AOAC Int., v.75, n.2, p.372-4, 1992.

Energy calculation

The Atwater general factors were used to calculate the energy values of foods, also considering the energy resulting from the fermentation of DF, in accordance with the FAO recommendation (2003). This calculation was performed by multiplying the contents of proteins, lipids, available carbohydrates, DF, and alcohol by the factors described in Table 2.

Table 2. Factors used to calculate the energy values of foods.
  Conversion factor applied
Component kJ/g kcal/g
Protein 17,0 4,0
Lipid 37,0 9,0
Available carbohydrates 16,0 4,0
Total dietary fiber 8,0 2,0
Alcohol 29,0 6,9
  • FAO - Food and Agriculture Organization. Food energy: methods of analysis and Conversion Factors. Report of a technical workshop. Roma, FAO, Food and Nutrition Paper, 2003;77 [cited 2013 May 25]. Available from: http://www.fao.org.
Description of values presented in TBCA – Nutrient Intake Assessment (BD-AIN)

The following information is presented to describe the content of each nutrient:

  • Nutrient content: can be defined by a single value or by an average of several analytical data points, when available; standard deviation: shows the variability of nutrient content. This must be presented whenever at least three values are used to calculate the average nutrient content.
  • Standard deviation: shows the variability of nutrient content. It must be presented whenever at least three values are used to calculate the average nutrient content;
  • Minimum: lowest value found for the nutrient. This must be presented whenever at least two values are used to calculate the average nutrient content.
  • Maximum: highest value found for the nutrient. This must be presented whenever at least two values are used to calculate the average nutrient content.
  • Number of data points used: This identifies the number of values used to calculate the average nutrient content.
  • References: This field must include all the numbers that identify the references used in calculating the average/individual content of the nutrient.
  • Types of data: These identify the type of information presented; the different types presented are described in the following item.
Types of data available in TBCA – Nutrient Intake Assessment (BD-AIN)

Analytical value: These values are taken from published works, internal laboratory data, or analyses conducted exclusively for preparing the food composition database. The data are carefully documented, allowing location of the information source and the analytical methodology.

Calculated value: These values are calculated from other analytical data (for example, carbohydrates by difference and energy). They can also be values derived from calculating recipes (calculated from the nutrient content of the ingredients and corrected for the necessary factors – e.g., yield and retention factors).

Assigned value: These values are derived from analytical values obtained for a similar or different forms of the same food (for example, cooked or sautéed vegetable), corrected for moisture and lipids. Part of this information was supplemented with data from other tables.

Calculated value/RI: these values were obtained by means of calculation, from analytical data of raw ingredients, which were converted into ready-to-eat foods/preparations.

Calculated value/PI: these values were obtained by means of calculation, based on analytical data of ingredients with simple preparation, which were converted into ready-to-eat preparations.

  • Alcohol = “zero” in products of animal or vegetable origin that have not passed through fermentation processes;
  • Dietary fiber = “zero” in foods of animal origin such as meat, poultry, fish, eggs, and milk;
  • Carbohydrates = “zero” or trace in unprocessed animal foods (meat, poultry, and fish), except viscera (e.g., liver) and mollusks;
  • Cholesterol, retinol, and vitamin B12 = “zero” in plant-based foods (except vitamin B12 in fermented foods, and mushrooms);
  • Vitamin D2 = “zero” in foods of animal origin;
  • Vitamin D3 = “zero” in foods of vegetable origin;
  • Vitamin C = “zero” in oils;
  • Folic acid = “zero” in unfortified foods.

Estimated value: These values are calculated based on analytical data on similar foods/preparations.

Trace value
Components identified as “trace” quantity through the abbreviation “tr” were thus assigned when the mean value was less than 0.006 for most vitamins and less than 0.06 for macronutrients (Greenfield, Southgate, 2003).
Table 3 presents the limits used for assigning the values to be declared as “trace.”
Table 3. Ways of expressing values for users in food composition databases (per 100 g of edible part).
Component Unit Number of significant digits Trace = lass then
Energy kJ (kcal) 3 0,6
Major components g 3 0,06
Fatty acids g
mg		 3
3		 0,01
0,01
Cholesterol mg 3 0,1
Inorganic components mg
µg		 3
1		 0,01
1
Vitamin A Retinol Carotenoids µg 3 0,6
Vitamin D µg 2 0,06
Vitamin E Tocopherols mg 2 0,006
Vitamin K µg 2 0,06
Thiamine mg 2 0,006
Riboflavin mg 2 0,006
Niacin mg 2 0,006
Vitamin B6 mg 2 0,006
Pantothenic acid mg 2 0,006
Biotin mg 2 0,006
Vitamin B12 µg 2 0,006
Folates µg 2 0,06
Source: Adapted from Greenfield, Southgate (2003).

Data are presented with the values of significant digits shown in Table 3 without exceeding the limit of two decimal places.

Not analyzed

A value is declared as “Not Parsed” or “NA” when the component has not been parsed. This usually occurs when a food or similar preparation lacks the component in the food matrix (for example, cholesterol in vegetable oils; vitamin C in meats); although unanalyzed, it is considered “zero” for assessing food consumption.

Not detected

A value is declared as “Not detected” or “ND” when its values are so low in the food that they do not reach the limit of detection of the analytical method employed, but it is not “zero.”

Food description

The foods are identified in detail to ensure a more appropriate choice by the user. Thus, the food is usually accompanied by information about type, processing, and scientific name, among others. The alphanumeric code uniquely identifies the food in the TBCA; the alphabetic character represents the food group and the number a specific food

Food groups
Group       Group description
A – Cereals and derivatives (*)
  • Rice, oats, rye, barley, corn, sorghum, wheat, triticale (raw and cooked, without adding other ingredients);
  • Pasta, bread, biscuits, starch, flakes, flour, bran, and fiber (although it has a high fiber content, it is not indicated for special purposes).
B – Vegetables and derivatives (*)
  • Vegetables in general (vegetables and legumes) and edible parts of plants (raw and cooked, without adding other ingredients): roots and tubers – sweet potatoes, cassava, yams, beets, carrots, turnips, radishes; bulbs and stalks – onions, garlic, leeks; leaves – chard, lettuce, watercress; fruits and flowers – tomatoes, eggplants, squashes; pods and seeds, sprouts – bean sprouts, mushrooms.
C – Fruits and derivatives (*)
  • Edible part;
  • Fruit juice (no other ingredient added).
D – Fats and oils
  • Oils, olive oils, margarines, hydrogenated vegetable fats, butter, lard, and fats in general.
E – Fish and seafood (*)
  • Fish and seafood (fresh, salted, canned, pre-prepared, frozen).
F – Meat and derivates (*)
  • All meats (fresh, salted, canned, pre-prepared, frozen);
  • Offals and viscera, pâtés, and sausages.
G – Milk and derivates (*)
  • Milk from all breeds of animals;
  • Whole, skimmed, semi-skimmed, powdered, condensed, and fermented milk, cream, cheese, yogurt, and dairy drinks.
H – Beverages (*)
  • Alcoholic or non-alcoholic beverages;
  • Coffee and tea.
J – Eggs and derivates (*)
  • All eggs, whole or parts thereof, whether processed or not.
K – Sugar and sweets (*)
  • Sugar, honey, corn glucose, jellies, chocolates, concentrated sweets (guava jam), jams, brown sugar, syrups and toppings for ice cream, and candies.
L – Miscellaneous
  • Powdered coffee, biological and chemical yeast, salt and salt-based seasoning, sauces, and broths.
M – Fast food
  • Ready-to-eat foods available in cafeterias in general.
N – Food for special purposes (*)
  • Bars, tablets, capsules, gym proteins, isotonics, enteral diets, and others indicated for special purposes, as they have certain concentrations of nutrients.
R – Processed foods (unprepared)
  • Powders, mixtures for preparing cakes, pies, and dehydrated products in general (soups, purées, risottos).
T – Legumes and derivatives (*)
  • Beans, soybeans, peas, lentils, chickpeas, lupine, guando, peanuts, and carob (raw and cooked, without adding other ingredients).
U – Nuts and seeds (*)
  • Oilseeds: hazelnuts, almonds, macadamias, nuts, chestnuts;
  • Seeds: sesame, linseed, pine nuts.
(*) Food groups can contain preparations based on the foods comprising them.
Source: Adapted from Greenfield; Southgate (2003).
Types of Food
  • Fresh food – includes fruits, vegetables, cereals, legumes, and raw meat.
  • Simple food preparation – includes food with basic processing (e.g., boiled, roasted, and grilled food) but without added seasoning (oil, onion, garlic, and salt).
  • Processed food (ingredient) – includes processed foods serving as ingredients, such as dry cereals (pasta, flour, and bran), frozen pulp, broth in tablets, powders for preparation, and raw meat derivatives.
  • Ready-to-eat processed food – includes cereal flakes, dried fruits, oils, olive oils, margarines, dairy products, sausages, and other ready-to-eat processed foods.
  • Preparation – includes preparations, multi-ingredients, ready-to-eat, including oil, onion, garlic, and salt.
  • Preparation with texture alteration – includes preparations for special diets intended for people with difficulty chewing or swallowing and infant feeding (breastfeeding).
  • Gluten-free preparation – includes preparations for special diets intended for people with intolerance or allergy to gluten, considering the exclusion of wheat, oats, rye, and barley.
  • Lactose-free preparation – includes preparations for special diets intended for people with intolerance or allergy to lactose.
  • Vegan preparation – includes preparations for special diets intended for people who do not consume food of animal origin.
  • Vegetarian preparation – includes preparations for special diets intended for people who do not consume food of some animal origins, such as meat and derivatives.
  • Diet preparation – includes preparations for special diets, intended for people with specific needs, where the preparation is free of a certain nutrient, such as sugar free.
  • Light preparation – includes preparations for special diets, aimed at people seeking a minimum reduction of up to 25% of a certain nutrient (carbohydrate, lipids, sodium, among others) or energy, when compared to the conventional preparation.
Top

Related Sites

Top

Join Us

Food composition data, available from the TBCA, are freely accessible and available online, in addition to being made available on the TBCA with due credit to the authors.

The following information must be provided to send data:

  • Provide the name of the food in detail, for example, the scientific name, part of the food, and preparation method. In the absence of details, it is understood that it is raw or fresh food.
  • Express the composition of nutrients per 100 g of the edible portion of the food; for liquid food, the volume must be converted into g according to the product density.
  • Specify for each nutrient the mean value, standard deviation or variation (minimum and maximum values), and methodology (indicate the name and complete reference of the method used).
  • Detail the number of samples analyzed and treatment given to the samples, as this information will be important in assessing the data quality.
  • Record in detail the methodology used for analyzing each nutrient.
  • It is important to specify the humidity to facilitate the conversion of the food in different bases.

Send your scientific publication (in .pdf format) or the information produced in your laboratory to tbca.contato@usp.br.

Top

App

Install our applications through QR Codes.

Raised Image
Topo

Book

Purchase our book from the link below.



COPYRIGHT REGISTRATION CERTIFICATE

The Brazilian Book Chamber certifies that the intellectual work described is registered in accordance with the legal terms and regulations of Brazilian Copyright Law No. 9,610/1998. As per legal determination, the work registered here cannot be plagiarized, used, reproduced or disclosed without authorization of its author(s).

Raised Image

Link to the preparation calculation form BRASILFOODS 2023

Topo

Nutri Persona

Coming soon an innovative tool for intelligent solutions in nutrition.

Raised Image
Topo

Work Registration

Check the record of our table data through the QR Code.

Raised Image
Topo

Contact

If you have questions or suggestions, send your message to tbca.contato@usp.br.

Support

FoRC

Raised Image

USP

Raised Image

BRASILFOODS

Raised Image

Fapesp

Raised Image
Top