Centre for Advanced Research in Principal Investigator Sciences (CARS) Prof. Nazma Shaheen, PhD Institute of Nutrition and Food Science Co-Principal Investigators Prof. Abu Torab MA Rahim, PhD University of Dhaka Prof. M. Mohiduzzaman Prof. S.M. Mizanur Rahman Dr. Latiful Bari National Consultant Prof. Amir Hussain Khan, PhD International Consultant T. Longvah, PhD Research Assistants Cadi Parvin Banu Avonti Basak Tukun Food Composition Table for Bangladesh Background What is the problem with the existing FCT? New high yielding varieties and non local foods are constantly being introduced in the food production/supply chain With increasing urbanization food consumption behavior is shifting with towards more commercialized foods and processed foods The nutrient value of these foods is yet to be evaluated though sporadic analytical work has been conducted Moreover, existing FCTs contain a number of missing nutrient values Food Composition Table for Bangladesh Methodological Differences Nutrients Existing FCT Updated FCT Dietary fibre Crude fibre Total dietary fibre Vitamin C Titrimetric methods Analyzed by HPLC Beta-carotene Analyzed as total carotene Analyzed as Betacarotene by HPLC Vitamin B1 & B2 Borrowed value Analyzed by HPLC Retinol Borrowed value Analyzed by HPLC Sum of proximate Not within range 95-105 % Food Composition Table for Bangladesh Objectives Identify Key Foods (KFs) and critical nutrients for FCDB Analyze 20 sampled foods under AOAC laboratory procedures from the list of KFs Evaluate existing secondary data for scientific quality and compile all available (new & old) data to construct a food composition database for Bangladesh Estimate a single value for each nutrient of each food from all data records Adapt, estimate, borrow and compile values for missing nutrients for a complete & comprehensive FCDB Food Composition Table for Bangladesh Methodology The KF Identification Approach Key Foods are those foods, that in aggregate, contribute >75% of the nutrient intake for selected nutrients of public health importance from the diet DKF & HKI’s FCT for Food Consumption Data Food Composition Data g consumed each ingredient for all foods Repeat for all nutrients The Key Foods process uses food composition and food consumption data to identify and prioritize foods and nutrients for analysis (Haytowitz, et al., 2000) HIES 2010 & INFS’ NNS 1996 for g consumed X nutrient value of each ingredient Ranked list of % contribution of food to total nutrient intake Top 75% Intake KEY FOODS Food Composition Table for Bangladesh Findings The Key Food List (KFs having >1% of citation are presented) Sl No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Food Item* Rice (6) Tomato (6) Green Chili (6) Egg Plant (5) Banana (5) Onion (5) Tilapia fish (4) Wheat Flour (4) Potato (4) Pond Pangas (4) Silver carp (4) Hen's egg (4) Rooti (4) Lentils (3) Jack fruit (3) Mango (3) % of Total Citation** 7.06 7.06 7.06 5.88 5.88 5.88 4.71 4.71 4.71 4.71 4.71 4.71 4.71 3.53 3.53 3.53 Sl No. 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Food Item* % of Total Citation** Shrimp (2) Rohu (2) Cooking oil (1) Hilsha fish (1) Amaranth stem (1) Pointed gourd (1) Bitter gourd (1) Bean (1) Pumpkin (1) 2.35 2.35 1.18 1.18 1.18 1.18 1.18 1.18 1.18 Indian spinach (1) 1.18 Lady’s finger (10 Puti (1) Mrigal fish (1) 1.18 1.18 1.18 Jute leaves (1) In parentheses: * # appeared in nutrient group; citation of all foods = 87 1.18 ** # of total Food Composition Table for Bangladesh 20 Key Foods Selected for Analysis Sl No. 1. 2. 3. 4. 5. 6. 7. 8 9. 10. Food Item Rice Tomato Green Chili Egg Plant Banana Onion Tilapia fish Wheat Flour Potato Pond Pangas Sl No. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. Food Item Hen's egg Lentils Jack fruit Mango Rohu Bean Cooking oil Chicken Carrot Milk Food Composition Table for Bangladesh Methodology The sampling frame, interestingly, covered all major agroecological zones of Bangladesh Sample frame and sampling protocol Stratified sampling (National Population Census model) Level 1: of List of population regions (7 divisions Bangladesh) Level 2: List of Haats in each division for food collection (rural) List of Wholesale/Retail Markets in each selected city corporation areas for food collection (urban) Level 4: Random sampling from stock lots Level 3: Level 5: Composite sampling for analysis Food Composition Table for Bangladesh Preparation of composite sample Sample collected from seven divisions Composite sample Weighing Dressing Washing Air drying Food Composition Table for Bangladesh Analytical methods I. Methods AOAC and other standard methods of food analysis. II. i. Parameters Proximate analysis: Protein, (by Micro-level digestion-distillation system) vi. Fat, CHO, Water, Ash Macro-minerals: Na, K, Ca, Mg Heavy metals: As, Cd, Pb, Sb Trace elements : Cu, Zn, Fe, Se, Cr, Mo, Mn, V, Ni Amino acid Total Phenol Antioxidant activity: DPPH & ORAC vii. Antinutrients: Phytate & Oxalate i. Fatty acid profile Total dietary fiber (TDF) Total sugar (TS) Total free sugar (TFS) i. ii. iii. iv. v. ii. iii. iv. Retinol vi. β-Carotene vii. Vitamin C, B1, B2, viii. Vitamin B6 v. (by AAS, & FP) (by ICPMS) (by ICPMS) (by AA auto-analyzer) (by Spectrophotometer) (by Spectrophotometry) (by Open column & High performance liquid chromatography ) (by Gas liquid chromatography) (by Enzymatic-gravimetric method) (by titrimetric method) (by titrimetric method) ( High performance liquid chromatography) ( High performance liquid chromatography) ( High performance liquid chromatography) ( Microbial assay) Food Composition Table for Bangladesh QC protocol Quality Assurance Program (QAP) √ √ √ √ Method Standardization Method Validation: Internal standard (IS), External standard (ES), % of recovery Data Quality: Precision (CV), Accuracy (In-house reference material – IHRM, Certified reference material and well documented food), SEM Meticulous Documentation New components in this FCTs 87 components including Total dietary fibre Vitamin B1, B2, B6 Retinol, beta-carotene Amino acids Fatty acids Minerals: Mg, Na, K, P, Zn, Cu Antinutrient: Phytate & Oxalate Total phenol content, antioxidant capacity (DPPH, ORAC) Total sugar Proximate Nutrients Name Water (%) Protein Fat TDF CHO Ash (available) g/100g EP Cereals Pulses Root & tubers Vegetables Fruits Fish Meat Egg Milk Rice Wheat flour Lentil Potato Onion Carrot Bean Brinjal Energy Kcal Green chili Banana Jackfruit Mango Tomato Pangas fish Rohu fish 12.35 12.21 12.16 81.71 83.73 89.71 90.02 91.35 85.51 75.22 76.99 78.44 95.01 70.84 76.25 6.51 10.61 27.73 1.19 1.37 0.92 2.41 1.9 2.77 1.26 1.19 0.79 1.11 15.9 20.56 0.41 1.64 0.79 0.16 0.07 0.26 0.11 0.06 0.13 0.84 0.2 0.41 0.25 10.96 2.55 3.43 4.4 13.2 2.11 1.89 2.55 4.3 4.073 8.371 2.6 7.2 1.56 1.65 NA NA 76.80 70.3 43.2 13.96 12.26 5.96 2.5 1.957 2.179 19.2 13.3 18.04 1.44 0.0 0.0 0.55 0.8 2.92 0.87 0.68 0.60 0.65 0.66 1.04 0.84 1.08 0.76 0.54 0.96 0.90 344.0 347.0 317.38 66.260 58.930 34.960 29.0 24.110 37.710 95.0 74.0 82.130 15.750 162.24 105.19 Tilapia fish Chicken breast Chicken leg Egg Milk 76.21 72.86 71.94 72.31 88.27 20.8 22.29 19.19 14.49 3.10 3.02 1.82 5.69 8.34 3.74 NA NA NA NA NA 0.0 0.0 0.0 0.0 4.30 1.08 1.08 0.96 0.81 0.64 110.38 105.54 127.97 134.62 63.060 NA, Not applicable Qualitative Differences Foods Water (g) Protein (g) Fat (g) Available CHO (g) TDF (g) Crude fiber (g) Ash (g) Energy (kcal) Rice, parboiled 13.3 6.4 0.4 79.0 - 1.9 0.7 356 (345.2) Rice, BR-28, parboiled 12.4 6.5 0.4 76.8 3.4 - 0.5 344 Wheat flour (coarse) 12.2 12.1 1.7 69.4 - 1.9 2.7 341 Wheat flour, white 12.2 10.6 1.6 70.3 4.4 - 0.8 347 Lentil 12.4 25.1 0.7 59.0 - 0.7 2.1 343 12.2 27.7 0.8 43.2 13.2 Lentil Black values – Existing FCT Red values_ updated FCT - 2.9 317 Overestimation of Energy & Protein Energy: Previously used formula CHO = 100-(moisture + protein + fat + ash + crude fiber ) Corrected formula Available CHO= 100-(moisture + protein + fat + ash + TDF + alcohol) Protein: Previously used formula: Protein= Nitrogen x 6.25 Corrected formula: Protein= Nitrogen x Jone’s factor for different food e.g. for rice 5.95 for wheat 5.70 Minerals Content (mg/100g) Heavy metals Name Elements with unknown food toxicity (μg/100 g EP) Sb Ba V Ni Ag Rice 0.519 Wheat flour 0.097 Lentil Potato Onion Carrot Bean Brinjal Green Chili Banana 0.338 0.326 0.106 0.339 0.141 0.176 0.342 0.050 Jackfruit 0.157 Mango Tomato Pangas fish Rohu fish Tilapia fish Potentially toxic elements (μg/100 g EP) Cd As Pb 10.173 39.116 0.081 1.064 5.845 NA 3.271 15.249 0.122 1.957 0.618 2.42 7.823 7.335 6.340 2.800 14.544 5.149 4.004 0.156 90.701 32.288 23.163 04.014 75.695 39.410 82.653 0.838 NA 0.092 0.024 0.028 0.046 0.141 0.026 NA 0.082 1.011 1.598 0.965 0.335 2.532 1.351 0.008 0.405 0.284 0.242 0.250 0.399 0.280 0.207 0.006 NA NA NA NA 2.558 NA NA 0.108 1.056 33.219 0.118 1.366 0.278 0.95 0.142 NA 0.064 0.202 0.071 12.248 394.85 1 17.069 28.303 45.885 348.39 111.97 23.688 19.552 17.045 276.07 7 26.303 16.801 0.667 6.460 17.785 0.292 6.137 0.478 1.974 3.531 6.317 20.972 NA 0.326 1.426 0.009 0.036 NA 0.030 0.003 0.109 1.756 0.015 0.014 0.075 0.275 0.220 2.756 2.750 34.221 20.606 0.056 0.614 0.504 2.140 Chicken breast 0.029 1.913 0.395 0.183 NA 0.008 1.010 NA Chicken leg 0.044 0.491 0.545 0.001 0.022 1.055 0.279 Egg Egg 0.012 0.522 1.647 0.004 0.031 0.328 1.107 Milk NA, Not available Milk 0.014 2.450 132.60 9 33.543 0.529 3.501 0.005 0.03 0.860 0.984 Cereals Pulses Root & tubers Vegetables Fruits Fish Meat Water soluble vitamins (mg/100 g EP) b-Carotene & Retinol Name Retinol β-carotene μg/100 g EP Rice NA NA Wheat flour NA NA Lentil NA 33.984 Potato NA 27.15 Onion NA 22.776 Carrot NA 3945.956 Bean NA 202.592 Brinjal NA 45.438 Green Chili NA 114.828 Banana NA 21.442 Jackfruit NA 28.178 Mango NA 299.543 Tomato NA 103.853 Pangas fish 5.143 NA Rohu fish 3.193 NA Tilapia fish 2.033 NA Chicken breast 25.152 ± 1.5 NA Chicken leg 22.802 ± 1.4 NA Egg Egg 165.246 ± 1.1 NA Milk Milk 30.177 ± 0.2 NA Cereals Pulses Root & tubers Vegetables Fruits Fish Meat NA, Not applicable Anti-nutrient: Oxalate & Phytate Selected nutrient content of three cultured fishes (g/100g EP) 20.8 20.6 25 Rui Telapia Pangas 15.9 20 11 15 2 3.2 2.6 5 3 5.1 10 0 Protein (g) Fat (g) Retinol (mcg) Fatty acid content of three cultured fishes (g/100g EP) Iron rich fishes (selected) Name Name Silver carp, kata chara Taki, kata chara Chital, kata chara Fesha Mrigal, chokh soho Chela, Fulchela Meni Punti, Vadi punti, kata chara Chanda, Ranga, chokh soho Chompa Fe (mg/100g) 1.5 1.5 1.6 1.8 1.8 1.9 1.9 2.0 2.0 2.0 Parshe Shing mach, kata chara Tatkini Fesha, Teli Kachki, bivinno projati Punti, Vadi punti, chokh soho Tengra, bivinno projati Mola, chokh soho Olua Chapila Chela, Narkeli Fe (mg/100 g) 2.1 2.1 2.2 2.3 2.4 2.6 2.8 3.8 4.5 4.8 5.4 Protein content (g%), essential amino acid profile (mg/g protein)and total essential amino acid (mg/g protein) of food samples. Sample Rice, BR-28, parboiled, Protein Trp Thr Val Met Ile Leu Phe His Lys TEAA 6.51 8 34 57 32 35 77 53 23 36 354 Wheat, flour, white 10.6 12 28 42 21 29 65 45 22 26 290 Lentil, dried 27.7 9 37 49 5 38 73 52 23 76 362 Pangas, without bones, 15.9 15 43 48 35 39 72 39 20 79 390 Rohu, without bones 20.6 15 42 48 31 37 70 40 26 77 386 Tilapia, without bones 20.8 14 43 45 32 37 72 39 23 77 383 22.3 13 44 52 36 44 75 38 36 72 411 Chicken leg, without skin 19.2 12 43 51 34 42 77 39 27 73 399 Eggs, chicken, farmed 14.5 15 31 63 31 63 72 85 14 43 417 Milk, cow, whole fat (pasteurised, UHT )* 3.08 11 40 61 22 42 87 44 25 73 406 milled Chicken breast, without skin Chemical score and predicted first- limiting amino acid according to reference Protein (egg) Name Chemical Score Limiting Amino Acid Egg Milk, cow, whole fat (pasteurised, UTH) 100 51 SAA Chicken leg, without skin 67 Ile Chicken breast, without skin 66 AAA Pangas, without bones 62 Ile Rohu, without bones 59 Ile Tilapia, without bones Rice, BR-28, parboiled, milled 58 AAA 50 Trp Wheat, flour, white 46 Ile Lentil, dried 23 SAA Summary of data compilation steps with FAO data compilation tool 1.2.1 Data source Archival record Reference database User database • Collection of compositional data • Compilation of information from data sources • Compilation of archival data records for each food • Selection and compilation of series of values for each food item in database Food Composition Table for Bangladesh Different Stages Employed in Preparing FCDB Single Ingredient Recipe (55) Foods Rice, BR-28, parboiled Water Protein (g) (g) 12.4 6.5 Fat (g) 0.4 Available CHO (g) 76.8 TDF (g) Ash (g) Energy Kcal 3.4 0.5 344 Rice, BR-28, Parboiled, boiled 71.4 2.1 0.1 24.3 1.1 0.2 109 Potato, Diamond, raw 81.7 1.2 0.2 14.0 2.1 0.9 66 Potato, Diamond, raw Boiled (with out salt) 81.5 1.2 0.2 14.2 2.1 0.9 67 Potato, Diamond, raw Boiled (with salt) 77.0 1.4 0.8 16.6 2.5 1.8 84 Multi Ingredient Recipe (11) Foods Water (g) Protein (g) Fat (g) Available CHO (g) TDF (g) Ash (g) Energy Kcal Plain khichuri 65.7 4.1 7.4 17.7 2.5 1.6 163 4.7 7.3 21.0 - - 168 6.18 6.83 20.3 4.21 0.92 176 Analytical value* Analytical value** 65.77 *Some Common Indian Recipes and their Nutritive Value, NIN **Rahim et.al, Institute of Nutrition and Food Science, DU Key Findings *Key foods for Bangladesh have been identified using consumption-composition and consumption frequency database (HIES, 2010). *Nutrient values of mostly consumed KFs (high yielding variety) currently are dominant in production and consumption in Bangladesh. *Some of the nutrients e.g. Amino Acid profile, Fatty Acid profile, vitamin B profile, heavy metals etc. have been analyzed for the first time in FCDB *All the analysis has been done by AOAC and FAO recommended methods and using certified reference material (RM) and in house RM, as appropriate). *A complete archival databank for food composition has been constructed, which contains approximately 2575 entries from all secondary data sources. * A food composition database from the archival databank has been developed using the INFOOD compilation tool 1.2.1. * Secondary data collection, compilation, management and archiving has been done using FAO recommended compilation guideline for the 1st time. * A comprehensive FCT for Bangladesh with least missing nutrient values has been developed. Limitations SW388R7 Data Analysis & Computers II Slide 32 There is a serious lack of secondary data on total dietary fiber, niacin equivalents, phosphorous and folate. Therefore, most of these data were imputed from other sources (e.g. Indian FCT (IND), Thai FCT (TH), Vietnam FCT (VIN), Pakistan (PAK), USDA (US25), UK (UK6), Danish (DK7),FAO/INFOODS analytical Food Composition Database (ADB), FAO/INFOODS and Food Composition Database for Biodiversity (BID). Iodine content of the foods is highly dependent on soil and has regional variation which cannot be captured by composite analysis. Therefore, these values were omitted. Only L-Ascorbic acid was estimated for KFs by HPLC which may not give the total Vitamin C content Calcium content in milk, pasteurized and fresh milk (cow) was noted to be low. This has been confirmed by repeated analysis. Policy Implications Detailed information on nutrient composition of local foods serves as a basic tool for planning and assessment of food, nutrition and health programmes Formulation of national food and nutrition policy through the setting goals for agricultural, aqua cultural, animal and poultry production. Designing guidelines for consumption and particular policies such as trade, assistance, food fortification or supplementation, increased subsidy or promotion of certain foods. Determination of gross per capita nutrient availability to assess gross adequacy or inadequacy of the national food supply/shortfall or excess. Preliminary checking of nutritional label information or claims. Nutritional regulation of food supply and compliance with CODEX standards Recommendations SW388R7 Data Analysis & Computers II Slide 34 Further work is necessary for which allocation of funding is required in order to generate primary analytical data for the rest of the key foods as determined in present project. To develop a comprehensive FCDB in response to long-term change in the food chain, efforts have been made to increase the quality of data by the generation of data of 20 KFs and including as many analytical data of Bangladeshi foods, generated by the food scientists of Bangladesh and aboard. Nutrient values presented with 3rd bracket, [ ] would need to be reconfirmed by re-analysis of the foods. Further revision should include numerous foods of archival database as it was not possible to incorporate these into reference database due to lack of reference values to fill up the missing nutrients. Recommendations (contd.) SW388R7 Data Analysis & Computers II Slide 35 As the reference values become available at the regional level, especially in the case of fish, those foods should be incorporated into the user database. Only selected mixed recipes were included in the current FCT due to time constraints. The future edition of the database should include traditional and frequently consumed recipes. It is necessary to develop a list of all the ingredients, cooking methods, yield factors for the majority of foods and nutrient retention factors. Weights, measures and serving sizes also need to be standardized as part of the recipe calculations and analysis. SW388R7 Data Analysis & Computers II Recommendations (contd.) Slide 36 Since the FCDB has been constructed with rigorous and meticulous analytical and compilation methodology, its wide dissemination should be undertaken. Biodiversity and varietal species of foods other than rice could not be considered in the current due limited funding resources and lack of available data. Future funding should be directed toward adequate generation of food composition data that capture elements of biodiversity and variety. At the same time, adequate training should be made available for food scientists and analysts to generate and manage food composition data according to INFOODS Guidelines. E-learning tools as available from FAO should be widely disseminated for use. We appreciate the active contribution of various academic, research and government organizations as well as authors of published papers, reports, scientific proceedings and theses providing analytical food composition data (contributors’ names have been cited in bibliography)