Transcript Food Composition Tables for Bangladesh

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
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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)