Suchergebnisse

1 Food Laws and Standards -- Development of Federal Food Laws -- Other FDA Regulations, Guidelines, and Action Levels -- Other Regulatory Agencies -- Operating under the Food Additives Amendment -- Approved Food Additives -- Summary -- Selected References -- 2 Sampling and Proximate Analysis -- Sampling -- Moisture -- Crude Fat -- Crude Protein -- Crude Fiber -- Ash -- Selected References -- 3 Instrumental Methods of Analysis -- Weighing Devices -- Visible and Ultraviolet Spectrophotometry -- Infrared Spectrophotometry -- Fluorometry -- Gas Chromatography -- Atomic Absorption Spectrophotometry -- Column Liquid Chromatography -- Thin-Layer Chromatography -- pH -- Polarography -- X-Ray Fluorescence -- Scanning Electron Microscopy -- Selected References -- 4 Carbohydrates -- General Properties -- Monosaccharides -- Oligosaccharides -- Polysaccharides -- Biosynthesis -- Relationship of Structure to Sweetness -- Nutritional Value of Carbohydrates -- Commercial Sugar and Sugar Products -- Corn Sweeteners -- Qualitative Analysis -- Quantitative Analysis -- Selected References -- 5 Lipids -- Simple Lipids and Their Constituents -- Compound Lipids -- Derived Lipids -- Nutritional Value of Fats and Fat Products -- Commericial Fats and Fat Products -- Deterioration of Fats -- Analysis -- Selected References -- 6 Proteins -- Amino Acids and the Peptide Bond -- Classification of Proteins -- Structure of Proteins -- Molecular Weight and Isoelectric Point of Proteins -- Metabolism of Proteins -- Proteins and Nutrition -- Analysis -- Selected References -- 7 Enzymes -- Kinetics of Enzyme-Catalyzed Reactions -- Structure and Active Site -- Nomenclature and Classification -- Food Enzymes -- Commercial Applications -- Enzyme Assays -- Selected References -- 8 The Vitamins -- Water-Soluble Vitamins -- Fat-Soluble Vitamins -- Analysis -- Selected References -- 9 Flavoring Agents -- Common Spices and Herbs -- Oleoresins -- Flavoring Extracts -- Analysis -- Selected References -- 10 Coloring Agents and Color of Foods -- Nature of Light and Color -- Specification and Measurement of Color -- Coloring Agents -- Qualitative Analysis -- Quantitative Analysis -- Selected References -- 11 Wheat and Wheat Products -- Classification of Wheats -- Grain Standards -- Analysis of Wheat -- Production of Wheat Flour -- Standards for Wheat Flours and Related Products -- Components of Flour -- Analysis of Flour -- End Products of Flour -- Determination of Baking Quality -- Selected References -- 12 Milk and Milk Products -- Composition of Milk -- Grades of Milk -- Classes of Milk -- Some Commercial Milk Products -- Analysis of Fluid Milk Products -- Analysis of Dried Milk Products -- Selected References -- 13 Meat, Poultry, and Fish -- Meat -- Poultry -- Analysis of Poultry and Meat Products -- Seafoods -- Analysis of Seafoods -- Selected References -- 14 Food Deterioration, Preservation, and Contamination -- Food Deterioration -- Food Preservation Methods -- Food Contaminants -- Analysis -- Selected References.

Food composition data is important for stakeholders and users active in the areas of food, nutrition and health. Newchallenges related to the quality of food composition data reflect the dynamic changes in these areas while the emerging technologies create new opportunities. These challenges and the impact on food composition data for the Mediterranean region were reviewed during the NUTRIMAD 2018 congress of the Spanish Society for Community Nutrition. Data harmonization and standardization, data compilation and use, thesauri, food classification and description, and data exchange are some of the areas that require new approaches. Consistency in documentation, linking of information between datasets, food matching and capturing portion size information suggest the need for new automated tools. Research Infrastructures bring together key data and services. The delivery of sustainable networks and Research Infrastructures in food, nutrition and health will help to increase access to and effective use of food composition data. EuroFIR AISBL coordinates experts and national compilers and contributes to worldwide efforts aiming to produce and maintain high quality data and tools. A Mediterranean Network that shares high quality food composition data is vital for the development of ambitious common research and policy initiatives in support of the Mediterranean Diet. ; The authors gratefully acknowledge the contributions of the many members of EuroFIR and RICHFIELDS project (funded by the European Union's Horizon 2020 research and innovation funding programme under grant agreement no. 654280) partners who contributed to the developments referred to in this article ; info:eu-repo/semantics/publishedVersion

Aim: To assess the differences between the old and new Indian food composition databases and to evaluate the impact of the expansion of new food composition information on the existing dietetic practices. Methods: In the comparative analysis of the two food databases, a total of 117 foods were compared for differences in their composition by calculating the percentage change in values of food constituents in the new database as compared to the old database. Prior to the assessment of the differences between the two data bases, the nutritive values for all the foods were computed for 100% dry part to bring about uniformity and overcome the differences in moisture content of these foods between the databases. Results: The updated database differs extensively from the preceding version. Not only is the new database more representative of the various foods consumed across India, but also all the data reported are based on laboratory analysis carried out by the National Institute of Nutrition laboratories. Also, the new database reports values for added micronutrients as well as for bioactive components in Indian foods. Considerable difference in macro and micronutrient content of foods is noted in the new database when compared to the old database. Conclusions: The updated Indian Food Composition Database with accurate and nationally representative data on nutritive and non-nutritive composition of Indian foods will greatly assist food and nutrition professionals in India with improving their estimates of dietary intakes and in turn improving their advice to patients, clients and consumers.

Since 1989, total dietary fiber values in USDA databases were determined by the enzymatic-gravimetric (EGF) method (AOAC 991.43), where "fiber" is the residue remaining after samples are subjected to enzymatic treatments mimicking digestion. In 2009 EGF was modified to recover additional non-digestible components (e.g., galacto/fructo-oligosaccharides, polydextrose, resistant starch) (AOAC 2009.01, 2011.25) (mEGF). Limited mEGF data and high cost create a need to identify suitable foods for analysis. USDA's National Food and Nutrient Analysis Program sampled suitable foods for analysis by EGF and mEGF. No detectable difference between EGF and mEGF was found in almonds, wheat bread, oatmeal cookies, tortilla chips, taco shells, kale, fast food French fries, or cooked dried pulses. mEGF exceeded EGF in uncooked dried pulses (5.4-10.5 g/100 g), raw potatoes (13.7 g/100 g), and cooked plantains (3.1 g/100 g), and slightly higher (0.7-2.2 g/100 g) in hummus, canned refried beans, prepared wild rice mix, and frozen raspberry berries, concentrate, and puree. Statistical power was hindered by high analytical uncertainty, especially for mEGF (up to 33% RSD), likely due to cumulative errors in the multiple steps comprising mEGF. mEGF analyses should focus on foods containing significant levels of fiber components not included in EGF, and reporting individual, particularly metabolically active, fiber components. ; USDA Agricultural Research Service Nutrient Data Laboratory [59-8040-8-005, 60-8040-6-011, 60-8040-8-005]; Virginia Tech (USDA) [59-8040-8-005, 60-8040-6-011, 60-8040-8-005]; Virginia Tech (National Cancer Institute) [59-8040-8-005, 60-8040-6-011, 60-8040-8-005]; Food and Drug AdministrationUnited States Department of Health & Human Services; American Pulse Association [15-1435-01]; National Processed Raspberry Council [8040-05-25 0057704] ; This work was supported by cooperative agreements59-8040-8-005, 60-8040-6-011 and 60-8040-8-005 between the USDA Agricultural Research Service Nutrient Data Laboratory and Virginia Tech (including funds from interagency agreements between USDA and the National Cancer Institute and the Food and Drug Administration, the American Pulse Association (agreement #15-1435-01), and the National Processed Raspberry Council (agreement #8040-05-25 0057704). Amy Rasor, Nancy Conley, and Ryan McGinty assisted with sample preparation. ; Public domain authored by a U.S. government employee

This report describes the activities of two projects that were carried out us-ing the infrastructure of the Nordic Food Analysis Network, i.e. the 'Nordic Food Composition Data for Labelling (NordCoLa)' project carried out between 2018 and 2020, and the preceding project 'Fostering the quality and use of Nordic food composition data', carried out under the Finnish Presidency of the Nordic Council of Ministers in 2016. The primary aim of the NordCoLa project was to evaluate the needs, gaps and synergies of the Nordic food consumption databases in relation to the data to be used to implement the new European nutrient labelling legislation. This was to ensure quality food composition data in the Nordic countries for food producers and other users for nutrient labelling purposes. The results of the projects are summarised in this report.

This report describes the activities of two projects that were carried out us-ing the infrastructure of the Nordic Food Analysis Network, i.e. the 'Nordic Food Composition Data for Labelling (NordCoLa)' project carried out between 2018 and 2020, and the preceding project 'Fostering the quality and use of Nordic food composition data', carried out under the Finnish Presidency of the Nordic Council of Ministers in 2016. The primary aim of the NordCoLa project was to evaluate the needs, gaps and synergies of the Nordic food consumption databases in relation to the data to be used to implement the new European nutrient labelling legislation. This was to ensure quality food composition data in the Nordic countries for food producers and other users for nutrient labelling purposes. The results of the projects are summarised in this report.

Information sharing and management among all the intervenients in food chain is crucial to improve food safety and to promote and protect public health. It optimizes resources usage and provides evidence support to define and promote policies in food and nutrition and to increase the efficiency of food productive systems. Objectives. To share information among Portuguese Food Composition Table users, to identify their needs, in order to define priorities for the update of the National Food Composition Table. Material and Methods. INSA, IP established a partnership with GS1 Portugal, to develop a programme, "Portuguese Food Information Resource" (PortFIR), to implement national networks of knowledge sharing on nutrition and food safety. The program has the collaboration of governmental and private organizations encompassing health, agriculture and economy sectors and includes a food information web portal integrated in a Food Information Network Management System (SGRIA). Two networks were created to date, "Food Composition" and "Food Microbiological Information", acting through working groups (WG), some of which are transversal to both networks. "Users" WG (GTU) developed a questionnaire to evaluate the needs and satisfaction of the users of the Portuguese Food Composition Table (FCT). The questionnaire was implemented on-line from July to October 2011 using the software LimeSurvey®, and FCT users were invited to participate through direct contact by email and/or through professional associations and networks. Results. The analysis of the responses revealed that the preferred format of the FCT is "on-line" (52%) (options: on-line, CD-Rom, book, other) and that 80% of responding users are satisfied with either FCT format. Regarding additional information needs, users refer allergens (50%), food portion (39%), fatty acids n-3 and n-6 (25%), and traditional dishes (23%). Respondents consider the number of foods, micronutrients and composite dishes as the main limitations of the present FCT. Conclusions. The cooperation between data producers, users and compilers is contributing to optimize the usage of National resources and to disseminate the knowledge about nutrition and food safety within the country. The information obtained through "PortFIR – GTU" will allow compilers to establish informed priorities taking into account the expressed needs of users across the food chain.

In order to gain regulatory approval to market a new seed product derived with biotechnology, grain and forage composition data must be collected from field trials, and summaries must be reported to various government agencies. Currently, both tests of differences in composition between a genetically modified organism (GMO) and its control and tests of equivalence of the GMO to conventional genotypes are required by regulatory agencies. Bayesian analyses offer an attractive option for regulatory assessments by expressing results that can be interpreted more easily by a wide audience and by providing more ways to examine various hypotheses of interest. In order to extend Bayesian methodology for application to different compositional analytes, and to take advantage of the information obtained in previous experiments, the use of informative prior distributions for composition studies is proposed. Methods for determining suitable informative prior distributions analytically are shown in four situations: (1) eliciting opinions from an expert, (2) finding the best fit from an overdetermined set of summary statistics from one previous study, (3) performing a meta-analysis of summary statistics from previous studies with an assumed common prior distribution, and (4) performing a different meta-analysis with the prior distribution determined by a mixture of different assumed prior distributions from previous studies. Examples from soybean composition studies are used to illustrate these techniques.

To prevent some food related illnesses, health policies may influence consumers' behaviors through appropriate food labeling. In some countries, like the US, nutrition labeling is mandatory, whereas it is still optional in the EU, except when a nutrition claim is made in the labeling or the advertising of a foodstuff. In fact, the choice between voluntary versus mandatory labeling depends on several factors related, on the demand side, to the potential impacts of nutrition labeling on consumers' health, and on the supply side, to the possible extension of labeling if it only relies on firms' initiatives. Focusing on the supply side, the purpose of this study is to assess the development of voluntary nutrition labeling in France, and to determine the relationship between the presence of nutritional label, the food composition and prices. Analyses are performed on biscuits/pastries data, using detailed data on nutritional characteristics and prices of food items.

International audience ; To prevent some food related illnesses, health policies may influence consumers' behaviors through appropriate food labeling. In some countries, like the US, nutrition labeling is mandatory, whereas it is still optional in the EU, except when a nutrition claim is made in the labeling or the advertising of a foodstuff. In fact, the choice between voluntary versus mandatory labeling depends on several factors related, on the demand side, to the potential impacts of nutrition labeling on consumers' health, and on the supply side, to the possible extension of labeling if it only relies on firms' initiatives. Focusing on the supply side, the purpose of this study is to assess the development of voluntary nutrition labeling in France, and to determine the relationship between the presence of nutritional label, the food composition and prices. Analyses are performed on biscuits/pastries data, using detailed data on nutritional characteristics and prices of food items.

A labelling assessment study of Greek prepacked "quality label" cheeses was conducted with a view to provide an overview of the whole category. In total, 158 prepacked products belonging to 19 "quality label" cheeses were identified in the Greek market. Among them, Feta had the highest share followed by Kasseri, Graviera Kritis, Kefalograviera and Ladotyri Mitilinis with 81, 16, 15, 11 and 9 products found in the market, respectively. For the rest of the 14 cheeses, the share was limited, ranging from 1 to 4. All labelling indications, nutritional information, claims and other labelling data were recorded and analysed in relation to their compliance against European food law requirements. The results of the analysis showed that for only 6 of the 19 cheeses, all products fully complied with EU labelling legislation. Among the 14 mandatory labelling requirements, the lowest overall compliance was observed for allergens declaration (65%). The analysis of the nutritional data showed a remarkable variability between cheeses and products. Differences in the nutritional characteristics were more pronounced among soft, semi-hard, hard and whey cheese. The above data were entered into an archival database. Application of global harmonisation and standardisation guidelines and tools lead to the initialisation of a branded food composition database (BFCD), conceptualising a specialised database for "quality label" foods.