Nutrition/ Agricultural food security/health/ Policies Roundtable Discussion on Food Composition Database education/policies December 17-18, 2015 Research “Development of food composition database with good quality in ASEAN” Food based nutrient intake Product - diseases dietary guidelines development/ Food aid/ Consumer Guidelines for quality evaluation and for checking of FCDB/FCTs information fortification throughout their development process Nutrient requirements Prapasri Puwastien Prapasri.puw@mahidol.ac.th Research in food Nutrient intakes Food balance sheet/ Product formulation/ and nutrition/ Food labelling Breeding/research (dietary assessment) Diet formulation Institute of Nutrition, Mahidol University, Thailand Uses of food composition data Steps in establishing food composition database of good quality Criteria for quality FCDB/FCTs 1. Objective setting 2. Selection and prioritise foods and nutrients to be included in FCDB FCDB/FCTs should be: Representative: represent the composition of commonly consumed foods. A list of foods and nutrients • variability in the composition of the food should be given . by analysis 3. Sampling plan and sampling Of sound analytical quality • Comprehensive coverage of foods Food samples & all characteristics • Published data, or borrowed/imputed/ Comprehensive coverage of nutrients calculated data • 4. Sample preparation: homogenised sample Clear food descriptions: name and description • Instrument 5. A nalysis Std methods Consistent and unambiguous expressed; units, calculation, rounding • Internal QC system External 1. EuroFIR/USDA 6. Analysed Data Documentation at nutrient value level: sources of data, methods, data • guideline for representative Check, recalculate, validate evaluating data quality quality, statistics (data points, min, max…) 7. Archival data Sound analytical Tables/databases clear and easy to use • quality 8. Reference Database 2. FAO/INFOODS guideline for comprehensively Content compatible and conform to international & regional standards • checking data prior to publication checking data and all information cover foods and nutrients Few missing data 9. User database of good quality • Consistently and unambiguously 3.FAO/INFOODS evaluation expressed Ref: Greenfield H, Southgate DAT: Food composition data. Production, Management and Use, Second edition. Food and Agriculture system on the quality of 10. Published FCTs/FCDB Others Organization of the United Nations, Rome, 2003. http://www.fao.org/fileadmin/templates/food_composition/images/FCD.pdf published FCT/FCDB-on process
Categories of quality assessment: EuroFIR and USDA Guidelines for assessing/checking data quality throughout the developing process EuroFIR quality assessment of data USDA quality assessment of data 1. EuroFIR and USDA systems for quality assessment of FCD Categories assessed (7 components) Categories assessed (5 categories) EuroFIR System USDA System 1. Food description: For use in different countries For use in the U.S. - for all type of foods: 12 criteria - for manufactured food: 5 criteria Modified for compatibility with US system 2. Component identification: Designed for data from scientific literature, Designed for data to be included in USDA 3 criteria analytical reports and similar database 3. Sampling plan: 6 criteria • Sampling Plan: 6 criteria Designed for assessment of Fe, Se, carotenoids. Designed for all foods and nutrients 4. Number of analytical samples: • Number of analysed samples: Modified to include flavanoids, vit B2, vit K 5 criteria 1 criteria 7 assessment categories 5 assessment categories • Sample Handling: 7 criteria 5. Sample handling: 2 criteria Food description and component identification More focus on sampling and analytical methods are important for data exchange 6. Analytical method 2 criteria Analytical methods: 7 criteria, • special nutrient analysis Analytical assessments designed to be used by 7. Analytical quality control: 3 criteria Analytical quality control: non-expert users/compilers (with guidelines) Designed by experts in particular nutrients • with varying knowledge and skills 6 criteria 7 categories, 33 – 38 criteria 5 categories, 27 criteria Ref: Mark Roe: presentation at FoodComp 2015, Wageningen, the Netherland EVALUATION PROCESS Data quality assessment: based on EuroFIR Guideline • In each category CRITERIA will be used to assess the level of quality Categories of data quality for consideration Yes NO N/A QI FOOD DESCRIPTION FOR ALL FOODS EuroFIR Guideline • For each criterion, a compiler will give one answer: YES, NO, Is the food group known? (e.g. beverage, dessert, pasta dish) Was the food source of the food or of the main ingredient clearly provided? 7 categories or NOT APPLICABLE (N/A) Was the part of plant or part of animal clearly indicated? If relevent, was the analyzed portion described and is it stated explicitly if the food was analsed with or without the inedible 1. Food description part? “NOT APPLICABLE”: the considered criterion is not relevant for 2. Component identification If relevent, was the extent of heat treatment provided? If the food was cooked, were satisfactory cooking method details provided? 3. Sampling plan the food and nutrient considered Was relevent information on treatment applied provided? 4. Number of analytical samples Was information on preservation method provided? 5. Sample handling Was information on packing medium provided? 6. Analytical method Was information about the origin of food provided? • Then a quality score (Quality index, QI) for each quality category will 7. Analytical performance If relevent, was the month or season of production indicated? be assigned. Was the moisture content of the sample measured and the result given? Food Description QI (Quality Index score) COMPONENT IDENTIFICATION Is the component described unambiguously? EuroFIR Workpackage 1.3, Task Group 4 Guidelines for Quality Index Attribution to Original Data Is the unit unequivocal? from Scientific Literature or Reports for EuroFIR Data Interchange. Is the matrix unit unequivocal? http://www.eurofir.net/sites/default/files/Deliverables/EuroFIR_Quality_Index_Guidelines.pdf Component Identification QI (Quality Index score) Institute of Nutrition, Mahidol University 7 Institute of Nutrition, Mahidol University 8
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