2. 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
3. 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
4. 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 %
5. 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
6. 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
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)
7. 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)
8. 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
9. 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
10. Preparation of composite sample
Food Composition Table for Bangladesh
Sample collected from seven divisions Weighing Washing
Air dryingDressingComposite sample
11. 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)
12. 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
13. 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
14. 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
16. 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
20. 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
26. 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)
27. 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
29. 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
30. 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
31. 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.
32. 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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Slide 32
33. 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
34. 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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35. 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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Slide 35
36. 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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Slide 36
37. 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)
