Food Composition Table for Bangladesh

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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
Institution
Research Team
Food Composition Table for Bangladesh
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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
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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 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

Food Composition Table for Bangladesh
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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
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Methodology
The KF Identification Approach
Key Foods are those foods, that in aggregate,
contribute gt75 of the nutrient intake for
selected nutrients of public health importance
from the diet
The Key Foods process uses food composition and
food consumption data to identify and prioritize
foods and nutrients for analysis (Haytowitz, et
al., 2000)

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Findings
The Key Food List (KFs having gt1 of citation are
presented)
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 Ladys 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 In parentheses appeared in nutrient group of total citation of all foods 87 In parentheses appeared in nutrient group of total citation of all foods 87
16 Mango (3) 3.53 In parentheses appeared in nutrient group of total citation of all foods 87 In parentheses appeared in nutrient group of total citation of all foods 87 In parentheses appeared in nutrient group of total citation of all foods 87

Food Composition Table for Bangladesh
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20 Key Foods Selected for Analysis
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

Food Composition Table for Bangladesh
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Methodology
The sampling frame, interestingly, covered all
major agro-ecological zones of Bangladesh

Food Composition Table for Bangladesh
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Preparation of composite sample
Sample collected from seven divisions
Weighing
Washing
Composite sample
Air drying
Dressing

Food Composition Table for Bangladesh
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Analytical methods
I. Methods AOAC and other standard methods of food analysis.
II. Parameters Proximate analysis Protein, (by Micro-level digestion-distillation system) Fat, CHO, Water, Ash Macro-minerals Na, K, Ca, Mg (by AAS, FP) Heavy metals As, Cd, Pb, Sb (by ICPMS) Trace elements Cu, Zn, Fe, Se, Cr, Mo, Mn, V, Ni (by ICPMS) Amino acid (by AA auto-analyzer) Total Phenol (by Spectrophotometer) Antioxidant activity DPPH ORAC (by Spectrophotometry) Antinutrients Phytate Oxalate (by Open column High performance liquid chromatography ) Fatty acid profile (by Gas liquid chromatography) Total dietary fiber (TDF) (by Enzymatic-gravimetric method) Total sugar (TS) (by titrimetric method) Total free sugar (TFS) (by titrimetric method) Retinol ( High performance liquid chromatography) ß-Carotene ( High performance liquid chromatography) Vitamin C, B1, B2, ( High performance liquid chromatography) Vitamin B6 ( Microbial assay)

Food Composition Table for Bangladesh
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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
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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

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Proximate Nutrients
Name Water () Protein Fat TDF CHO (available) Ash Energy
g/100g EP g/100g EP g/100g EP g/100g EP g/100g EP g/100g EP Kcal
Cereals Rice 12.35 6.51 0.41 3.43 76.80 0.55 344.0
Cereals 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
Root tubers Onion 83.73 1.37 0.07 1.89 12.26 0.68 58.930
Root tubers 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
Vegetables Brinjal 91.35 1.9 0.06 4.073 1.957 0.66 24.110
Vegetables 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
Fruits Jackfruit 76.99 1.19 0.2 7.2 13.3 1.08 74.0
Fruits Mango 78.44 0.79 0.41 1.56 18.04 0.76 82.130
Fruits 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
Fish Rohu fish 76.25 20.56 2.55 NA 0.0 0.90 105.19
Fish 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
Meat 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 NA, Not applicable NA, Not applicable NA, Not applicable NA, Not applicable NA, Not applicable NA, Not applicable NA, Not applicable NA, Not applicable
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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
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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 Jones
  factor for different food e.g. for rice 5.95
• for wheat 5.70

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Minerals Content (mg/100g)
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Heavy metals
Name Elements with unknown food toxicity Elements with unknown food toxicity Elements with unknown food toxicity Elements with unknown food toxicity Elements with unknown food toxicity Potentially toxic elements Potentially toxic elements Potentially toxic elements
Name (µg/100 g EP) (µg/100 g EP) (µg/100 g EP) (µg/100 g EP) (µg/100 g EP) (µg/100 g EP) (µg/100 g EP) (µg/100 g EP)
Name 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
Cereals 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
Root tubers Onion 0.106 45.885 6.340 23.163 0.024 1.598 0.242 NA
Root tubers 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
Vegetables Brinjal 0.176 23.688 5.149 39.410 0.141 2.532 0.280 NA
Vegetables 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
Fruits Jackfruit 0.157 276.077 1.056 33.219 0.118 1.366 0.278 0.95
Fruits Mango 0.142 26.303 0.292 6.317 0.009 0.109 0.275 20.606
Fruits 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
Fish Rohu fish 0.202 6.460 1.974 0.326 0.030 0.014 2.750 0.504
Fish 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
Meat 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 NA, Not available NA, Not available NA, Not available NA, Not available NA, Not available NA, Not available NA, Not available NA, Not available NA, Not available
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Water soluble vitamins (mg/100 g EP)
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b-Carotene Retinol
Name Retinol ß-carotene ß-carotene
µg/100 g EP µg/100 g EP µg/100 g EP
Cereals Rice NA NA NA
Cereals Wheat flour NA NA NA
Pulses Lentil NA NA 33.984
Root tubers Potato NA NA 27.15
Root tubers Onion NA NA 22.776
Root tubers Carrot NA NA 3945.956
Vegetables Bean NA NA 202.592
Vegetables Brinjal NA NA 45.438
Vegetables Green Chili NA NA 114.828
Fruits Banana NA NA 21.442
Fruits Jackfruit NA NA 28.178
Fruits Mango NA NA 299.543
Fruits Tomato NA NA 103.853
Fish Pangas fish 5.143 5.143 NA
Fish Rohu fish 3.193 3.193 NA
Fish Tilapia fish 2.033 2.033 NA
Meat Chicken breast 25.152 1.5 25.152 1.5 NA
Meat Chicken leg 22.802 1.4 22.802 1.4 NA
Egg Egg 165.246 1.1 165.246 1.1 NA
Milk Milk 30.177 0.2 30.177 0.2 NA
NA, Not applicable NA, Not applicable NA, Not applicable NA, Not applicable NA, Not applicable
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Anti-nutrient Oxalate Phytate
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Selected nutrient content of three cultured
fishes (g/100g EP)
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Fatty acid content of three cultured fishes
(g/100g EP)
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Iron rich fishes (selected)
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
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
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Protein content (g), essential amino acid
profile (mg/g protein)and total essential amino
acid (mg/g protein) of food samples.
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
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Chemical score and predicted first- limiting
amino acid according to reference Protein (egg)
Name Chemical Score Limiting Amino Acid
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
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Summary of data compilation steps with FAO data
compilation tool 1.2.1

Food Composition Table for Bangladesh
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Different Stages Employed in Preparing FCDB
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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
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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
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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.
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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.

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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

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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.
•  

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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.

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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.

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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)