Download - Bangladesh Food Composition table 2013
Food Composition Table for Bangladesh
Centre for Advanced Research in Sciences (CARS)
Principal Investigator 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
Background What is the problem with the existing FCT?
Food Composition Table for Bangladesh
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
Methodological Differences
Food Composition Table for Bangladesh
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 Beta-carotene by HPLC
Vitamin B1 & B2 Borrowed value Analyzed by HPLC
Retinol Borrowed value Analyzed by HPLC
Sum of proximate Not within range 95-105 %
Objectives
Food Composition Table for Bangladesh
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
Methodology
Food Composition Table for Bangladesh
HIES 2010 & INFS’ NNS 1996 for Food Consumption Data
g consumed each ingredient for all foods
g consumed X nutrient value of each ingredient
DKF & HKI’s FCT for
Food Composition Data
Ranked list of % contribution of food to total nutrient intake
Repeat for all nutrients
Top 75%
Intake KEY FOODS
The KF Identification Approach Key Foods are those foods, that in aggregate, contribute >75% of the n u t r i e n t i n t a k e f o r selected nutrients of public health importance from the diet
The Key Foods process uses food composition and food consumption data to identify and priorit ize foods and nutrients for analysis (Haytowitz, et al., 2000)
Findings
Food Composition Table for Bangladesh
Sl No. Food Item* % of Total Citation** Sl No. Food Item* % of Total
Citation** 1 Rice (6) 7.06 17 Shrimp (2) 2.35 2 Tomato (6) 7.06 18 Rohu (2) 2.35 3 Green Chili (6) 7.06 19 Cooking oil (1) 1.18 4 Egg Plant (5) 5.88 20 Hilsha fish (1) 1.18 5 Banana (5) 5.88 21 Amaranth stem (1) 1.18 6 Onion (5) 5.88 22 Pointed gourd (1) 1.18 7 Tilapia fish (4) 4.71 23 Bitter gourd (1) 1.18 8 Wheat Flour (4) 4.71 24 Bean (1) 1.18 9 Potato (4) 4.71 25 Pumpkin (1) 1.18 10 Pond Pangas (4) 4.71 26 Indian spinach (1) 1.18
11 Silver carp (4) 4.71 27 Lady’s finger (10 1.18 12 Hen's egg (4) 4.71 28 Puti (1) 1.18 13 Rooti (4) 4.71 29 Mrigal fish (1) 1.18 14 Lentils (3) 3.53 30 Jute leaves (1) 1.18
15 Jack fruit (3) 3.53 In parentheses: * # appeared in nutrient group; ** # of total citation of all foods = 87 16 Mango (3) 3.53
The Key Food List (KFs having >1% of citation are presented)
20 Key Foods Selected for Analysis
Food Composition Table for Bangladesh
Sl No. Food Item Sl No. Food Item
1. Rice 11. Hen's egg 2. Tomato 12. Lentils 3. Green Chili 13. Jack fruit 4. Egg Plant 14. Mango 5. Banana 15. Rohu 6. Onion 16. Bean 7. Tilapia fish 17. Cooking oil 8 Wheat Flour 18. Chicken 9. Potato 19. Carrot 10. Pond Pangas 20. Milk
Methodology
Food Composition Table for Bangladesh
Sample frame and sampling protocol
Level 1: List of population regions (7 divisions of Bangladesh)
Level 2: List of Haats in each division for food collection (rural)
Level 4: Random sampling from stock lots
Level 5: Composite sampling for analysis
Level 3: List of Wholesale/Retail Markets in each selected city corporation areas for food collection (urban)
Stratified sampling (National Population Census model)
The sampling frame, interestingly, covered a l l m a j o r a g r o -ecological zones of Bangladesh
Preparation of composite sample
Food Composition Table for Bangladesh
Sample collected from seven divisions Weighing Washing
Air drying Dressing Composite sample
Analytical methods
Food Composition Table for Bangladesh
I. Methods AOAC and other standard methods of food analysis. II. Parameters
i. Proximate analysis: Protein, (by Micro-level digestion-distillation system) Fat, CHO, Water, Ash i. Macro-minerals: Na, K, Ca, Mg (by AAS, & FP) ii. Heavy metals: As, Cd, Pb, Sb (by ICPMS) iii. Trace elements : Cu, Zn, Fe, Se, Cr, Mo, Mn, V, Ni (by ICPMS) iv. Amino acid (by AA auto-analyzer) v. Total Phenol (by Spectrophotometer) vi. Antioxidant activity: DPPH & ORAC (by Spectrophotometry) vii. Antinutrients: Phytate & Oxalate (by Open column & High performance liquid chromatography ) i. Fatty acid profile (by Gas liquid chromatography) ii. Total dietary fiber (TDF) (by Enzymatic-gravimetric method) iii. Total sugar (TS) (by titrimetric method) iv. Total free sugar (TFS) (by titrimetric method) v. Retinol ( High performance liquid chromatography) vi. β-Carotene ( High performance liquid chromatography) vii. Vitamin C, B1, B2, ( High performance liquid chromatography) viii. Vitamin B6 ( Microbial assay)
Quality
Assurance P r o g r a m (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
QC protocol
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 (available) Ash Energy
g/100g EP Kcal
Cereals Rice 12.35 6.51 0.41 3.43 76.80 0.55 344.0 Wheat flour 12.21 10.61 1.64 4.4 70.3 0.8 347.0
Pulses Lentil 12.16 27.73 0.79 13.2 43.2 2.92 317.38
Root & tubers
Potato 81.71 1.19 0.16 2.11 13.96 0.87 66.260 Onion 83.73 1.37 0.07 1.89 12.26 0.68 58.930 Carrot 89.71 0.92 0.26 2.55 5.96 0.60 34.960
Vegetables Bean 90.02 2.41 0.11 4.3 2.5 0.65 29.0 Brinjal 91.35 1.9 0.06 4.073 1.957 0.66 24.110 Green chili 85.51 2.77 0.13 8.371 2.179 1.04 37.710
Fruits
Banana 75.22 1.26 0.84 2.6 19.2 0.84 95.0 Jackfruit 76.99 1.19 0.2 7.2 13.3 1.08 74.0 Mango 78.44 0.79 0.41 1.56 18.04 0.76 82.130 Tomato 95.01 1.11 0.25 1.65 1.44 0.54 15.750
Fish Pangas fish 70.84 15.9 10.96 NA 0.0 0.96 162.24 Rohu fish 76.25 20.56 2.55 NA 0.0 0.90 105.19 Tilapia fish 76.21 20.8 3.02 NA 0.0 1.08 110.38
Meat Chicken breast 72.86 22.29 1.82 NA 0.0 1.08 105.54 Chicken leg 71.94 19.19 5.69 NA 0.0 0.96 127.97
Egg Egg 72.31 14.49 8.34 NA 0.0 0.81 134.62 Milk Milk 88.27 3.10 3.74 NA 4.30 0.64 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
Lentil 12.2
27.7
0.8
43.2
13.2 - 2.9
317
Black values – Existing FCT Red values_ updated FCT
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 Potentially toxic elements
(µg/100 g EP) (µg/100 g EP) Sb Ba V Ni Ag Cd As Pb
Cereals Rice 0.519 12.248 10.173 39.116 0.081 1.064 5.845 NA
Wheat flour 0.097 394.851 3.271 15.249 0.122 1.957 0.618 2.42
Pulses Lentil 0.338 17.069 7.823 90.701 NA 0.082 0.405 NA
Root & tubers Potato 0.326 28.303 7.335 32.288 0.092 1.011 0.284 NA Onion 0.106 45.885 6.340 23.163 0.024 1.598 0.242 NA Carrot 0.339 348.39 2.800 04.014 0.028 0.965 0.250 NA
Vegetables Bean 0.141 111.97 14.544 75.695 0.046 0.335 0.399 2.558 Brinjal 0.176 23.688 5.149 39.410 0.141 2.532 0.280 NA Green Chili 0.342 19.552 4.004 82.653 0.026 1.351 0.207 NA
Fruits
Banana 0.050 17.045 0.156 0.838 NA 0.008 0.006 0.108
Jackfruit 0.157 276.077 1.056 33.219 0.118 1.366 0.278 0.95
Mango 0.142 26.303 0.292 6.317 0.009 0.109 0.275 20.606 Tomato NA 16.801 6.137 20.972 0.036 1.756 0.220 0.056
Fish Pangas fish 0.064 0.667 0.478 NA NA 0.015 2.756 0.614 Rohu fish 0.202 6.460 1.974 0.326 0.030 0.014 2.750 0.504 Tilapia fish 0.071 17.785 3.531 1.426 0.003 0.075 34.221 2.140
Meat Chicken breast 0.029 1.913 0.395 0.183 NA 0.008 1.010 NA
Chicken leg 0.044 2.450 0.491 0.545 0.001 0.022 1.055 0.279
Egg Egg 0.012 132.609 0.522 1.647 0.004 0.031 0.328 1.107
Milk Milk 0.014 33.543 0.529 3.501 0.005 0.03 0.860 0.984 NA, Not available
Water soluble vitamins (mg/100 g EP)
β-Carotene & Retinol
Name Retinol β-carotene
µg/100 g EP
Cereals Rice NA NA
Wheat flour NA NA
Pulses Lentil NA 33.984
Root & tubers
Potato NA 27.15
Onion NA 22.776
Carrot NA 3945.956
Vegetables
Bean NA 202.592
Brinjal NA 45.438
Green Chili NA 114.828
Fruits
Banana NA 21.442
Jackfruit NA 28.178
Mango NA 299.543
Tomato NA 103.853
Fish
Pangas fish 5.143 NA
Rohu fish 3.193 NA
Tilapia fish 2.033 NA
Meat 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 NA, Not applicable
Anti-nutrient: Oxalate & Phytate
Selected nutrient content of three cultured fishes (g/100g EP)
20.6
2.6 3.2
20.8
3
2
15.9
11
5.1
0
5
10
15
20
25
Protein (g) Fat (g) Retinol (mcg)
RuiTelapiaPangas
Fatty acid content of three cultured fishes (g/100g EP)
Iron rich fishes (selected)
Name
Fe (mg/100g)
Silver carp, kata chara 1.5
Taki, kata chara 1.5 Chital, kata chara 1.6 Fesha 1.8 Mrigal, chokh soho 1.8 Chela, Fulchela 1.9 Meni 1.9 Punti, Vadi punti,
kata chara 2.0
Chanda, Ranga, chokh soho 2.0
Chompa 2.0
Name Fe (mg/
100g)
Parshe 2.1 Shing mach, kata
chara 2.1
Tatkini 2.2 Fesha, Teli 2.3 Kachki, bivinno projati 2.4 Punti, Vadi punti,
chokh soho 2.6
Tengra, bivinno projati 2.8
Mola, chokh soho 3.8 Olua 4.5 Chapila 4.8 Chela, Narkeli 5.4
Sample Protein Trp Thr Val Met Ile Leu Phe His Lys TEAA
Rice, BR-28, parboiled,
milled 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
Chicken breast, without
skin 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
Protein content (g%), essen0al amino acid profile (mg/g protein)and total essen0al amino acid (mg/g protein) of food samples.
Name Chemical Score Limiting Amino Acid
Egg 100 Milk, cow, whole fat (pasteurised, UTH) 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 58 AAA Rice, BR-28, parboiled, milled 50 Trp
Wheat, flour, white 46 Ile
Lentil, dried 23 SAA
Chemical score and predicted first-‐ limi0ng amino acid according to reference Protein (egg)
Summary of data compilation steps with FAO data compilation tool 1.2.1
Food Composition Table for Bangladesh
Data source • Collection of compositional data
Archival record
• Compilation of information from data sources
Reference database
• Compilation of archival data records for each food
User database
• Selection and compilation of series of values for each food item in database
Different Stages Employed in Preparing FCDB
Single Ingredient Recipe (55)
Foods Water (g)
Protein (g)
Fat (g)
Available CHO (g)
TDF (g) Ash (g) Energy Kcal
Rice, BR-28, parboiled 12.4 6.5 0.4 76.8 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
Analytical value*
4.7 7.3 21.0 - - 168
Analytical value**
65.77 6.18 6.83 20.3 4.21 0.92 176
*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
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.
SW388R7 Data Analysis & Computers II Slide 32
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
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.
SW388R7 Data Analysis & Computers II Slide 34
Recommendations (contd.)
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 Slide 35
Recommendations (contd.)
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.
SW388R7 Data Analysis & Computers II Slide 36
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)