a food composition database for bangladesh with special...
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A Food Composition Database for Bangladesh with Special reference to
Selected Ethnic Foods
Final Report PR #11/08
By
Sheikh Nazrul Islam, Principal Investigator
Md. Nazrul Islam Khan, Co-Investigator M. Akhtaruzzaman, Co-Investigator
Institute of Nutrition and Food Science
University of Dhaka
November 2010
This study was carried out with the support of the
National Food Policy Capacity Strengthening Program me
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This study was financed under the Research Grants Scheme (RGS) of the National Food Policy Capacity Strengthening Programme (NFPCSP). The purpose of the RGS was to assist in improving research and dialogue within civil society so as to inform and enrich the implementation of the National Food Policy. The NFPCSP is being implemented by the Food and Agriculture Organization of the United Nations (FAO) and the Food Planning and Monitoring Unit (FPMU), Ministry of Food and Disaster Management with the financial support of EU and USAID.
The designation and presentation of material in this publication do not imply the expression of any opinion whatsoever on the part of FAO nor of the NFPCSP, Government of Bangladesh, EU or USAID and reflects the sole opinions and views of the authors who are fully responsible for the contents, findings and recommendations of this report.
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Executive Summary
A food composition database (FCD) provides essential information on the nutritive value
of foods for which updated data is available. FCD is required for formulating diets,
calculating the nutritive value of diets, quantitatively assessing diets for individuals or
different population groups and for diet therapy and management. FCD can also be used
as a guideline for food analysis in estimating nutrient levels of foods prior to actual
analysis. This is particularly useful in nutrition labeling. On the whole FCD provides the
basis for planning food, nutrition and health related policy tools. Bangladesh is in the
process of revisiting the existing FCD, with the purpose of updating and analyzing the
nutrient composition of general and ethnic foods. Presently, the nutrient values of many
of the foods have been obtained from food composition tables prepared by the Institute of
Nutrition and Food Science (INFS), University of Dhaka and Helen Keller International
(1988), wherein most of the nutrient data is based on the analysis that was car long ago,
and some that was drawn from the FCD of neighbouring countries, notably India. In the
ensuing decades, major changes have occurred in the nature and complexity of the food
chain as also in the environment, soil composition, cropping patterns and intensity. Little
is known about the nutrient composition of most of the new high yielding varieties of rice,
wheat, maize, potatoe s, fruits, vegetables, fish and livestock that have become part of
the nation’s production and consumption systems. Also, the nutrient composition of the
indigenous foods grown and consumed in the Chittagong Hill Tracts (CHT) and other
tribal areas is not known. To prepare dietary guidelines and determine standard dietary
intake, the true nutrient content of these foods needs to be known.
The present study has been undertaken to prepare a FCD with special reference to
general and ethnic foods. The study was designed to (i) conduct a comprehensive food
consumption survey (CFCS) among general and ethnic populations to identify the key
food items and (ii) carry out analysis for nutrient values of key food items. The survey
was conducted on a randomly selected sample of 2015 households covering 1210
general and 805 ethnic households. A total of 75 general and ethnic foods have been
selected for analysis of 22 nutrients and calorie. Validated standard and AOAC methods
have been employed for analysis of the nutrients in the selected 75 key foods. The
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nutrient profiling comprised proximate principles such as protein, fat, carbohydrate,
dietary fiber, phytate, selected micronutrients and related compounds such as total
carotenoids, β-carotene, vitamin C and minerals. Nutrient data obtained have been
compared with reported values published in different articles and books, most of which
are consistent with the reported value. The data has been compared with the FCT and
the Thai FCT. This food composition database would serve as an important primary
source for updating FCT in Bangladesh which is an essential tool in food policy planning
and program.
Keywords: Food Composition Database, General food, Ethnic food, Bangladesh
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Contents
Executive summary 2
Chaper s 4
Tables 6
Figures 7
Abbreviations
8
Chapter 1 Introduction
1 Introduction 10
1.1 Background 10
1.2 Rationale of the study 11
1.3 Objectives and approach 14
Chapter 2 Materials and Methods
2 Materials and methods 16
2.1 Identification of key food items through CFCS 16
2.1.1 Comprehensive Food Consumption Survey (CFCS) 17
2.1.1.1 Sample size determination 18
2.1.1.2 Selection of general households 19
2.1.1.3 Selection of ethnic household 21
2.1.1.4 Questionnaire design, enumerator training and pre-testing 25
2.1.1.5 Comprehensive food consumption survey 26
2.1.1.5.1 Data collection, management and analysis 26
2.1.2 Focus group discussions (FGD) 27
2.1.3 Lifestyle characteristics of the general and ethnic population 28
2.1.4 Selection of key food items 28
2.2 Analysis of nutrients in key foods 35
2.2.1 Food sampling protocol 35
2.2.1.1 General food sampling protocol 37
2.2.1.2 Ethnic food sampling 39
2.2.2 Procedure for food sample collection 40
2.2.3 Identification of collected food samples 40
2.2.4 Sample preparation for analysis 41
2.2.5 Chemicals 43
2.2.6 Methods of nutrient analysis 43
2.2.6.1 Analysis of moisture 45
2.2.6.2 Estimation of protein 45
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2.2.6.3 Estimation of total fat and fatty acids 45
2.2.6.4 Estimation of ash content 45
2.2.6.5 Analysis of crude fibre and dietary fibre 46
2.2.6.6 Analysis of phytic acid 46
2.2.6.7 Calculation of carbohydrate and energy 47
2.2.6.8 Analysis of vitamin C 47
2.2.6.9 Analysis of carotenoids 47
2.2.6.10 Analysis of β-carotene 48
2.2.6.11 Analysis of mineral profile 48
2.2.7 Quality assurance programme (QAP) 48
Chapter 3 Results and Discussion
3 Results and Discussion 50
3.1 Key food identification 51
3.1.1 Comprehensive Food Survey (CFCS) 51
3.1.2 Focus group discussions (FGDs) 60
3.1.3 Lifestyle characteristics of general and ethnic people 66
3.1.4 Identification of key foods 74
3.1.5 Selection of key food 76
3.2 Collection of food sample 82
3.3 Nutrient composition of key foods 85
3.3.1 Proximate Nutrients 86
3.3.2 Water in key Foods 87
3.3.3 Dietary fiber 87
3.3.4 -Phytate content 87
3.3.5 Vitamins and Minerals in key Foods 88
Key Findings 103
Policy Implications and Recommendations 105
Policy Recommendat ions 107
Future Research 109
Conclusion 109
Acknowledgements 110
References 112
Research team 116
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Tables page
Table 2.1 Ethnic household selection representing 70% of ethnic population 22
Table 2.2 Focus group discussions 28
Table 2.3 Food items consumed by only general people (percent frequency ≥ 5%) 31
Table 2.4 Food items consumed by only ethnic households (percent frequency ≥ 5%) 32
Table 2.5 Food items commonly consumed by both General and Ethnic people
(Percent frequency ≥ 5% households)
33
Table 2.6 Ethnic food items listed from ethnic CFCS and FGDs 34
Table 2.7. Nutrients analysed and the analytical techniques employed 44
Table 3.1 Location and descriptive of CFCS the data collection among the native
local/Indigenous population
54
Table 3.2 Location and descriptionof CFCS data collection among the ethnic population 55
Table 3.3 FGDs settings 60
Table 3.4 FGD outcome: Food consumption pattern of the Marma, Chakma, Tanchanga and
Tripura communities
61
Table 3.5 Socioeconomic profile of general households 68
Table 3.6 Food security by households’ type in general population 69
Table 3.7 Morbidity and its treatment by household type in general population 70
Table 3.8 Socioeconomic profile of ethnic households 71
Table 3.9 Food security of ethnic tribes 72
Table 3.10 Morbidity and its treatment by ethnic tribes 73
Table 3.11 Key food list consumed by both the native general and *ethnic people 78
Table 3.12 Exclusive ethnic food list 79
Table 3.13 Proximate nutrient composition of cereals and leafy vegetables 90
Table 3.14 Vitamin C, carotenoids and micromineral composition of cereals and leafy
vegetables
91
Table 3.15 Macromineral composition of cereals and leafy vegetables 92
Table 3.16 Proximate composition of roots & tuber, non-leafy vegetables and fruits 93
Table 3.17 Vitamin C, carotenoids and micromineral composition of roots & tuber, non-leafy vegetables
94
Table 3.18 Macromineral composition of of roots & tuber, non-leafy vegetables 95
Table 3.19 Proximate composition of fish, egg and meat 96
Table 3.20 Micromineral composition of fish, egg and meat 97
Table 3.21 Macromineral composition of fish, egg and meat 98
Table 3.22 β-carotene content in general and ethnic foods 99
Table 3.23 Dietary fiber in key food items 100
Table 3.24 Phytic acid content in key food items 101
Table 3.25 Comparision of protein value in the present FCD with IFCT, DKPM, Thai
FCT
102
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Figures page
Figure 2.1 Sampling plan of general households 20
Figure 2.2 Sampling plan for ethnic households 23
Figure 2.3 Geographical locations of ethnic CFCS 24
Figure 2.4 Multi-regions sampling plan for general food sample 38
Figure 2.5 Multi-regions sampling plan for ethnic food 39
CFCS activities 52
Figure 3.1 Distribution of general and ethnic households 56
Figure 3.2 Distribution of selected general households by division and household type 57
Figure 3.3 Distribution of ethnic households by districts 58
Figure 3.4 Distribution of ethnic households by tribes 59
FGDs activities 62
Figure 3.5 Number of food item consumed by population type 74
Figure 3.6: Distribution of common food item consumed by ≥5% HH 75
Figure 3.7 Distribution of ethnic food of food items consumed by ≥5% HH 75
Figure 3.8 Distribution of general food items consumed by ≥5% HH 75
General key foods 80
Ethnic key food 81
Ethnic food collection activities 82
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Abbreviations
AOAC Association of Official Analytical Chemists
CFCS Comprehensive Food Consumption Survey
CHT Chittagong Hill Tracts
CV Co-efficient of Variance
DAE Department of Agricultural Extension
DKPM
EP
Dhesio Khadder Pustiman
Edible Portion
EU European Union
ES External Standard
FAO Food and Agriculture Organization of the United Nations
FCDB Food Composition Database
FCT Food Composition Tables
FGDs Focus group discussions
HKI Helen Keller International
HYV High Yielding Varieties
IFCT Indian Food Composition Tables
INFS Institute of Nutrition and Food Science
IS Internal Standard
NFCD National Food Composition Database
SRM Standard Reference material
SEM Standard Error of Mean
TAT Technical Advisory Team
TDF Total Dietary Fiber
USAID United States Agency for International Development
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Chapter 1
Introduction
10
1 Introduction
A food composition database (FCDB) provides detailed information on the nutrient
composition of foods. FCDBs provide values for energy and nutrients (e.g. protein,
vitamins and minerals) and other important food components or bioactive compounds
that are important for human nutrition. This includes the nutrient profile of key foods
commonly taken by the population. The key food list comprises the local staples,
cereals, fish, meat, vegetables, fruits, milk and others. The nutritive values are either
based on chemical analysis which are carried out in analytical laboratories or are
estimated from other appropriate data. The earliest known food composition table
was produced in 1818 (Somogyi, 1974). The current knowledge of nutrition is still
incomplete, and studies are still required, often at ever increasing level of
sophistication, into the composition of foods and the role of these components and
their interactions in health diseases (Greenfield and Southgate, 2003a). Food
composition database will serve to address the basic need for nutrient information,
public health problems in the country, the current knowledge in nutrition, and for food
safety and toxicity.
1.1 Background
Food is one of the essential components for human survival. Good health needs a
balanced diet. In order to achieve this, the nutrient composition of most frequently
consumed foods has to be made well-known and available to the mass population.
Food composition database is of great importance in health and nutrition. It is used in
research studies dealing with the effects of diets on health, reproduction and
development. There is a significant relationship between diet and health and
diseases. Lack of proper dietary habits contributes to the development of many
diseases. In this regard, there is a worldwide call for updating or establishing the
Food Composition Database. Many countries, particularly in the developing world,
lack the resources needed for setting up a national food composition programme.
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Some countries are collaborating on food composition analyses among the
institutions in their own country and in the region. Accordingly Bangladesh has
undertaken steps in generating its own FCD.
Bangladesh is an agriculture based country. Agriculture produces around 90% of its
food need including cereals and vegetables (FAO/WFP CFSAM 2008; WFP, 2010). It
has been blessed with high yielding varieties (HYV) of rice, and plenty of vegetables
and fruits. There are 141 varieties of leafy vegetables (commonly known as shak)
and 25 varieties of non-leafy vegetables in Bangladesh (Maksuda, 2010). Among the
leafy vegetables, 97 items are identified as ethnic varieties, and the rest are
consumed by both the general and ethnic people. A good number of shaks grow as
weeds or during cultivation of other crops. Many of the poor and landless people
depend on these indigenous foods (SANFEC, 2005). Several the indigenous fruits
and vegetables are known to be nutritionally rich with vitamins and minerals. The
biologically rich open water bodies include 260-500 species of inland fish, and some
seventy five of these species are regularly consumed by poor communities (Minkin et
al, 1997; Rahman and Minkin, 2003; Rahman, 2005; FAO/CINE, 2009 ). The nutrient
content of these foods should be incorporated into the food composition table as a
valuable source of information on nutrition and food diversity. The nutritive values of
these abundantly produced foods as well as the ethnic foods needs to be analyzed
and incorporated in the Food Composition Database.
1.2 Rationale of the study
The national food intake pattern in Bangladesh is dominated by cereals contributing
up to 74-76% of total dietary energy as against the internationally accepted value 54-
55% for developing countries (WHO/FAO, 2003; WHO/FAO, 2004; Murshid et al.,
2008; Yusuf et al, 2009). Vegetables comprise one-fifth of total diet for rural people.
Protein and micronutrient rich foods account for less than 10 percent of the rural
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person’s diet. Intake of vegetables and fruits has increased considerably. It is still
very low, although their consumption is vital for a diversified and nutritious diet (BBS,
2007). The high intake of cereal based food and low intake of micronutrient rich foods
results in an unmbalanced diet and causes different health disorders. Diets rich in
vegetables and fruits contribute to micronutrients that have specific antioxidant
functions and many of which reduce the risk of many health disorders including
cardiovascular complications, diabetes related damage, cancers (Connealy, 2008;
Liu, 2003; Kaur and Kapoor, 2001), even HIV infection (Oguntibeju, 2009; Baeten et al,
2001). Additionally they provide phytochemicals that have marked health significance.
Therefore, it is important to identify the food sources of various nutrients that are
required for the maintenance of good health.
Over the last decade, food composition activities have increasingly been undertaken
by several agencies and programmes for its ever growing importance. Many national,
regional and international organizations recognize its significance. The food
composition data are used primarily for the planning, assessment and establishment
of human energy and nutrient requirements and intakes. Its importance is versatile.
It is required for nutrition planning and in agriculture, health and nutrition assessment;
formulation of national; institutional and therapeutic diets; nutrition education and
training; formulation of food based dietary guidelines; research on nutrition,
agriculture and epidemiology; product development; nutrition labeling; setting food
standards and establishing food safety regulations.
Until now, data on nutrient values have been obtained from food composition tables
(FCT) prepared for Bangladesh by the Institute of Nutrition and Food Science,
University of Dhaka (INFS, DU, 1986) and Helen Keller International (HKI, 1988).
Most of the nutrient data in these FCT were analyzed long ago with uch of the data
borrowed from neighboring countries. Moreover, the nutrient composition of ethnic
foods is not available in the Bangladesh food composition table. With the increasing
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concern of the relationships between diet, food habits and degenerative lifestyle
diseases, there is increased interest in food composition data. At the same time,
there is a call for attention to the major limitations in the available data and to support
a variety of research activities in this area (Greenfield and Southgate, 2003a),
particularly in food security mapping. This would help to bridge the lack of
information on the nutrient and non-nutrient content of different foodstuffs consumed
by different populations and subgroups including ethnic populations.
Further, changes in the food chain due to emergence of high yielding varieties (HYV)
newer foods and changes in soil composition (due to environmental changes,
increased use of fertilizers and crop intensity) have resulted in possible changes in
the composition of nutrient in the foods now being grown. The food chain of the
country has been modified during the last decades. Nutritive values of these local
food items need to be analyzed and incorporated in the food composition database.
All these facts call for a renewed look and analysis of the most frequently consumed
foods.
It is time to prepare a Food Composition Database with nutrient data through
analysis of general, ethnic and relatively newer foods. Such a Food Composition
Database will help in formulating dietary guidelines for different people to meet their
nutrient requirements. This is also in line with one of the key areas of intervention of
the National Food Policy Plan of Action (2008-2015).
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1.3 Objectives and approach
Considering the importance of having a National Nutrient Database, this study aimed
to prepare a Food Composition Database with reference to general and ethnic
foods. To this end, the study was designed to:
� identify the most frequently consumed foods of the general and ethnic
people of Bangladesh through a comprehensive food consumption
survey(CFCS);
� prepare a key foods list that contributes 75% of any one nutrient need
(key food list);
� analyse macronutrients, micronutrients, and anti-nutrients in the selected
key foods (nutrient value of food);
� develop a comprehensive National Food Composition Database (NFCD)
with the analytical results obtained; and
� provide recommendations for food policy planning and program.
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Chapter 3
Materials and Methods
16
2 Materials and methods
Food composition database gives detailed information on the nutrient composition of
foods providing values of nutrients, energy and other important food components for
each food. Nutrient values can be obtained by chemical analysis of foods in
laboratories (direct method) or can be estimated from published literature,
unpublished laboratory reports (indirect method) or by combining data of direct and
indirect methods containing lab analytical values together with the values taken from
the literature and other database as well as imputed and calculated values.
Therefore, the types of food composition data are of original analytical values (lab
generated analytical, published or unpublished), imputed values (analytical values
obtained for a similar food), calculated values, borrowed values and presumed
values.
This study has aimed to prepare a food composition database with nutrient
composition of general and ethnic foods based on nutrient data generated by
laboratory analysis of key foods. Thus, this study comprised-
• Identification of key food items through Comprehensive Food
Consumption Survey (CFCS) and
• Analysis of nutrients in the selected key foods.
2.1. Identification of key food items through CFCS
The key foods provide 75% of daily nutrient need (Haytowitz et al, 1996; 2000;
2002). Identifying and prioritizing the most significant foods and nutrients for sampling
and analysis is essential in preparation of national food composition database. Key
foods can be listed by data obtained from food consumption surveys that determine a
food's relative nutrient contribution to the diet of a population. Diet has been
implicated in the etiology of chronic diseases in many populations. It is further noted
17
that some food source as those consumed by specific population contribute
substantially to the nutrient of their diets. Therefore, alternative methods of collecting
information, such as small localized surveys and interviews were carried out.
Key food items were indentified through a Comprehensive Food Consumption
Survey (CFCS) among the general and ethnic population of Bangladesh.
General and Ethnic Foods
In this study, general foods are referred to those foods which are consumed by local
general people (Rahman et al, 2001; Rashid et al, 2007) who constitute the majority
of the Bangladeshi population. Ethnic foods are those foods which are consumed by
ethnic tribal people who are the inhabitants of the Chittagong Hill Tracts (CHT) region
and other specific locations in Bangladesh.
The majority of the foods that have been analysed for the nutrient content are
commonly consumed by both the general and ethnic people of Bangladesh. Some
foods which are uncommon in the food consumption list have also been included for
analysis of their nutrient profile.
2.1.1 Comprehensive Food Consumption Survey (CFCS)
Food consumption surveys form the basis for food intake surveys or dietary surveys.
The aim of the CFCS was to collect food consumption data of the general and ethnic
population that included the types and amounts of food intake, frequency of intake
and dietary practices. CFCS was also conducted to prepare a comprehensive
database that would be useful for food safety risk assessmen. It would also provide a
valuable resource for health protection and public health policy planning.
In this study, the comprehensive food consumption survey (CFCS) was conducted to
collect data on the diversity of food items that are most frequently consumed by
general and ethnic people in Bangladesh. The aim of this survey was to obtain a key
18
food list that includes the most frequently and commonly consumed foods by both
groups of population. In addition, the CFCS also collected information on the lifestyle,
socioeconomic information, food security and health related knowledge of the
general and ethnic people.
The CFCS was conducted among a cross-sectional population of adults and older
groups of population of general and ethnic origins (Kuhnlein et al, 2006). A pretested
questionnaire was used to conduct the survey. Pretesting was performed by trained
enumerators in cluster mapping locations.
2.1.1.1 Sample size determination
In the study, households were taken as the sampling unit. This is based on the
principle that in most cases, food is first purchased in the household and then
consumed by the members of the household. To determine the sample size required
the following statistical formula was used:
n = {Z2P(1-P)}/ d 2 where,
n = Minimum sample size
P = Expected proportion of the household consuming the diversified food items
Z = Standard error corresponding to a given confidence level
d = Precision of the estimate which is considered to be 0.05 at 95% confidence level.
Considering the prevalence of diversity in food consumption by the households and
by the individuals at 0.15% and the standard scores of the estimate at 95%
confidence level with precision of 0.05, the above equation gave a value of sample
size of 196 households equivalent to 200 households as minimum sample size from
each of the six divisions of Bangladesh. Thus, it comprised a total of 1200 general
households. It was selected to get the percentage of households consuming the
specific food items throughout the year by the general population in Bangladesh.
19
2.1.1.2 Selection of general households
In selecting the 1200 general representative households, a three stage sampling
technique was used.
Bangladesh, administratively, is divided into six divisions. In selecting the 1200
households, 200 households were selected from each of the six divisions in the first
stage. To select the 200 households from each division in the second stage, two
districts were randomly selected from each of the six divisions and then 100
households were selected from each of the selected 12 districts. Finally in the third
stage, 50 households from urban setting (district city) and 50 households from
multiple rural settings under the same district were randomly selected. The
household sampling plan is presented in the following diagram.
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Figure 2.1: Sampling plan of general households
Chittagong HH # 200
Rajshahi HH # 200
Barisal HH # 200
D1
Dhaka HH # 200
Khulna HH # 200
Sylhet HH # 200
D1 D2 D1 D2 D1 D2 D2 D2 D1 D2 D1
Bangladesh HH # 1200
R U R U R U R U R U R U R U R U R U R U R U R U
21
2.1.1.3 Selection of ethnic households
Twenty eight tribes comprising 2,33,417 number of households have been living in
Bangladesh (BBS survey, 1991). Among them, the tribes which have at least 1.5%
representation in the total ethnic households living in Bangladesh were taken into the
study. This stood at 11 tribes that had ≥5% representation in the total tribal population
living in Bangladesh. These included Marma, Chakma, Tanchanga, Tripura, Bam,
Murang, Monipuri, Khashia, Shaotal, Garo and Hajong, which comprise 1,64,667
households representing 70.54% of total ethnic households living in Bangladesh. Ethnic
people of the 11 tribes live in the four divisions of Bangladesh namely Dhaka (Durgapur
Upazilla under Netrokona districts), Sylhet (Kamalgonj Upazilla under Moulavi Bazar
district), Chittagong (Khagrachari, Rangamati and Bandarban Sadar Upazilla) and
Rajshahi (Godagari Upazilla under Rajshahi district).
On the basis of probability proportions (PPS) to the size, a total of 400 households were
selected from the 11 tribes. In selecting the households on the PPs basis, the
household numbers, in the case of some tribes, were found to be less than 30 in
number. To have the normality in the distribution, the household’s size was increased to
at least 30 in number. In doing this, the total number of households to be selected stood
at 500 households. The 500 households were selected randomly from the 11
representative tribes. The selected ethnic household list by tribes is given in table 2.1
and figure 2.2. They were interviewed using a pretested questionnaire.
Following the presentation of the study’s interim findings at the Workshop in Rangamati,
CHT on 18th March, 2010 a careful review showed that there was need to have an
appropriate inclusion of ethnic foods for nutrient analysis. Suggestions were also made
by some of the CHT ethnic members. It was, therefore, decided to include some more
ethnic food items so as to have the nutrient profile of an adequate number of ethnic
foods. In consultation with FAO Technical Assistance Team (TAT) members, a CFCS
22
was, therefore, conducted on another 300 ethnic households in Khagrachari and
Rangamati districts of CHT. It was undertaken among Marma, Chakma, Tripura and
Tangchaga ethnic community during March and April, 2010. Thus the CFCS on ethnic
households was carried out on a total of 805 households. The selection criteria
employed to recruit the ethnic households are described in table 2.1 and figure 2.2 and
2.3.
Table 2.1: Ethnic household selection representing 70% of ethnic population
Tribe name No. of household in respective tribe
PPS-Households in respective tribe
Projected PPS-Households in respective tribe
Targeted Households selected in the study
Chakma 44730 108 136 238
Marma 29137 71 88 171
Tanchanga 4043 10 12 51
Tripura 15220 37 46 87
Bam 2681 7 8 30
Murong 4273 10 13 30
Monipuri 3559 9 11 25
Khasia 7500 19 23 25
Santal 36406 88 111 89
Garo 12867 31 39 31
Hajong 4251 10 13 28
Total households 1,64,667 (>70%) 400 500 805
23
Figure 2.2: Sampling plan for e thnic household s
24
Figure 2.3: Geographical locations of ethnic CFCS
Moulavi Bazar
Mymensingh
Rajshahi
Khagrachari
Rangamati
Bandarban
25
2.1.1.4 Questionnaire design, enumerator training and pre-testing
Questionnaire design: The major components included in the questionnaire were- types
of food consumed by the households throughout the year, socioeconomic profile, family
food security, nutritional knowledge, knowledge on nutritional deficiency diseases etc of
the projected households. This stand that whole process of collecting information on
food items commonly consumed by the household throughout the year, socioeconomic
condition and other lifestyle factors related to questionnaire. It was conducted through
direct interview to the household’s respondent during the survey. A semi precode
formatted questionnaire was used as the basic data collection tool to get the household
information. Considering the importance of the study in the national context and its
objectives, information on the variable collected were meticulously included in the
questionnaire, discussed with the Technical Advisory Team (TAT) members and
carefully examined so that all the relevant information were taken and recorded during
the comprehensive consumption survey.
The questionnaire was designed in the light of experience achieved from the National
Nutrition Survey and various other large scale surveys conducted in Bangladesh
focusing on the required variables to answer the objectives as well as purpose of the
study. The questionnaire was field tested prior to actual use and was modified on the
basis of the feed-back received from the field tests.
The questionnaire and selection of survey site were finalized and approved in
consultation with Technical Advisory Team (TAT) members of this Programme.
Enumerator recruitment and training: A team consisting of four enumerators with one
supervisor were recruited and trained to conduct the field survey. All the enumerators
recruited were university graduates and postgraduates. In the five member’s team, two
enumerators belonged to general community and three were ethnic who were fluent in
speaking and understanding the general people’s language as well as the tribal people’s
language. More ethnic members were recruited because they were familiar with the
26
difficulties in tribal locations where the ethnic people are mostly concentrated as well as
to facilitate the data collection within the stipulated time. Initially, all field staffs received 7
days’ orientation training consisting of familiarization of the questionnaire through guided
readings and field trials.
Pretesting questionnaire: The enumerator team spent a considerable time in the office
and at the field-testing sites in practicing the techniques of recording types of food
consumed by the household throughout the year and the other related variables included
in the questionnaire as well as the related data collection activity. Fifty households
comprising general and ethnic people were interviewed in pretesting the questionnaire.
2.1.1.5 Comprehensive food consumption survey
To identify the most common food items consumed by the general and ethnic people,
2015 households was selected comprising 1210 general and 805 ethnic households that
were interviewed with a precoded and pretested questionnaire. Though there is
disproportionate distribution of general population in rural and urban locations, in order
to obtain the maximum diversity in consumption of different food items, an equal number
of households were selected from both the urban and rural locations. Further, to get the
factual data on food consumption in the rural and urban population, a weighted food
frequency was calculated giving the actual weighted representation of the rural urban
population proportion in the country.
2.1.1.5.1 Data collection, management and analysis
Data collection: Data were collected from the selected locations and households
through home visits during the period January to May 2009 and during April to May,
2010. To get the information related to food purchase, consumption and other variables,
the household head (male) and the spouse were interviewed. Every day, the collected
information/data was checked, coded and cross checked by the interviewers and finally
27
by the supervisor at the field sites in order to avoid any misreporting. Any confusion
arising out of this matter was settled on the following day during subsequent home visits.
This process of scrutinizing the data was performed during the entire period of CFCS.
Data management and analysis: The questionnaire was edited and entered into
SPSS program. Data entry was done by the computer data entry personnel of INFS, DU
and this was followed by an extensive period of logical checking to identify any error in
data entry, which were then corrected by consulting the original questionnaires.
2.1.2 Focus group discussions (FGDs)
The focus group is a type of group interview (http://www.extension.iastate.edu/publications/
pm1969b.pdf). It provides qualitative approaches to research aiming to obtain in-depth
information on concepts, perceptions and ideas of a group on certain specific topic in
short time at relatively low cost. The FGD supplements the survey data. In case of
health and nutrition, it is primarily done to get information regarding the lifestyle, food
consumption, food security, health and nutrition knowledge of a community. The
activities of conducting a focus group include- identification of the objectives of the focus
group discussions, preparation of questions, selection of participants, selection of
location and facilitator, note-taker and planning of session. It produces high quality data
if it is employed for the right purposes using the right procedures.
The FGD comprises a group of approximately 6-12 participants with key informants such
as community leaders and a critique, and the discussion may last for one hour to one
and half hour (IDRC, http://www.idrc.ca/en/ev-56615-201-1-DO_TOPIC.html). It is an important tool for
acquiring feedback regarding the topic, and it facilitates the enumerators to talk to the
people in a more natural setting than a one-to-one interview. In presence of the critique,
the participants and key informants are directly asked about their perceptions, opinions,
beliefs and attitudes towards a particular topic. Their responses are discussed, criticized
and recorded.
28
It has a high apparent validity - since it is easy to understand, and the results are
believable. FGD is relatively easy to assemble, good for getting rich data in participants'
own words and developing deeper insights, good for obtaining data from children and/or
people with low levels of literacy, identifying factual errors or extreme views. Its
limitations are -the responses of each participant are not independent, a few dominant
focus group members can skew the session. Focus groups require a skilled and
experienced moderator and the data analysis requires expertise and experience.
In the present study, FGD was conducted among the ethnic communities of Marma,
Chakma, Tripura and Tangchaga living in Khagrachari and Rangamati during March and
April, 2010 (table 2.2). It was carried out to obtain information on their food consumption
pattern.
Table 2.2: Focus group discussions
Division District Upazilla Time of visit Location Type of HHs
Chittagong
Khagrachari Khagrachari sadar
31/03/2010 Marma palli Marma
Rangamati Rangamati sadar
03/04/2010 Chakma palli Chakma
Rangamati Rangamati sadar
08/04/2010 Tanchanga para,
Tanchanga
Khagrachari Khagrachari sadar
21/04/2010 Tripura para Tripura
2.1.3 Lifestyle characteristics of the general and ethnic population
Although the primary aim of the CFCS was to obtain the information on the food
consumption pattern of the general and ethnic people, information on their lifestyle such
as socioeconomic profile, food security and morbidity and care taken for it were also
collected, analysed and addressed.
2.1.4 Selection of key food items
It is documented that the key foods contribute up to 80 percent of any nutrient, but the
total nutrient contribution of key foods in a diet accounts for approximately 90 percent of
29
the nutrient contents of the diet. In selecting the key foods, priority was given to those
foods that contribute primarily to the energy anf key nutrients of the diet. In addition,
considerations were given to- the basic need for nutrient composition, public health
problems in the country, current knowledge on nutrition and toxicity, availability of
existing data, existence of adequate analytical methods, and feasibility of analytical
works. Special focus was given to the distribution of nutrients in foods with emphasis on
β-carotene, vitamin C, calcium and iron content. Importance of food trading was also
considered in making the key food list (Greenfield and Southgate, 2003e).
Analysis of CFCS data indicated that food items consumed by the ≥5% households
included a list of 120 foods comprising 20 foods consumed only by the general people
(table 2.2), 46 foods consumed only by ethnic people (table 2.3) and 54 common food
items consumed by both the general and ethnic population (table 2.4).
The study undertook preparation of a database with nutrient composition of 50 key food
items. In preparation of the list of 50 food items out of 120 items, the following criteria
were used:
� food items that were consumed by ≥15% of the households were included in the
key food list.
� some of the ethnic foods were excluded though consumed by >15% households
of the ethnic population on the basis that these are being consumed by a very
minor group of population. The above exclusion criteria condensed the food list
to 70 food items.
� further to make the list to 50 items, the foods containing poor micronutrients (less
or no β-carotene) were excluded.
� thus the key food list included 50 food items.
The 50 key food items were initially selected in consultation with Technical Assistance
Team (TAT) members. Later in compliance with the recommendation made by some
ethnic participants at Rangamati workshop for inclusion of more ethnic tribal foods, a
30
critical review and discussion were made with TAT members, and it was decided to
survey on additional 300 ethnic households to include an adequate number of ethnic
foods for nutrient analysis.
Inclusion of additional ethnic foods made the key food list of 75 food items. This revised
key food list comprised 53 general food items (most of which are consumed by ethnic
people) and 22 ethnic foods.
31
Table 2.3: Food items consumed by only general peop le (percent frequency ≥ 5%)
Sl no English name Bengali name Scientific name Urban Rur al weighted % frequency
Leafy vegetables
1 Spleen Amaranth Data shak Amaranthus dubius 14
2 Jute Pat shak Corchorus capsularis 11
3 Swamp Morning-glory Kalmi shak Ipomoea aquatica 17
4 Coco-yam Sobuj kochu shak Colocasia esculenta 5
Non-Leafy vegetables
5 Spleen Amaranth Data Amaranthus dubius 13
6 Bean Broad Makhon shim Canavalia gladiata 5
7 Drumstick Shajna data Moringa olefera 7
Fruits
8 Apple Apel Pyrus malus 7
9 Bullocks Heart Atafol Annona reticulata 5
10 Water melon Tormuz Citrullus vulgaricus 22
Fish and Meat
11 Sunfish Mola mach Mola mola 14
12 Taki fish Taki mach Channa puncpatus 10
13 Bailla Bele mach Awaous guamensis 17
14 Ganges River Gizzard Shad Chapila mach Gonialosa manmina 6
15 Zig-zag eel/Tire track eel Baim mach Mastacembelus armatus 5
16 Hilsha Fish Ilish mach Tenualosa ilisha 7
17 Chingri mach Shrimp Macrobrachium rosenberghii 29
18 Striped dwarf catfish Tengra Fish (Taja) Mystus vittatus 23
19 Beef Garor mangsha Beef cattle 26
20 Chicken egg (farm) Murgir dim (farm) Gallus bankiva murghi 45
32
Table 2.4: Food items consumed by only ethnic house holds (percent frequency ≥ 5%)
Sl. no English name Bengali/Local name
Scientific name Urban Rural weighted % frequency native
general people
% frequency of Ethnic people food
consumption
CEREALS 1 Rice parboiled (Brri-29) Sidhoy chal Oryza sativa 99 34 2 Lentil (deshi) Masur dal Lens culinaris 78 35
LEAFY VEGETABLES 3 Joseph’s Coat Lalshak Amaranthus gangeticus 84 49 4 Bottle Gourd Lau shak Lagenaria siceraria 47 42 5 Indian spinach Pui shak Basella alba 64 28 6 Radish Mula shak Raphanus sativus 7 Spinach Palong sag Spinacea oleracea 41 17 8 Coco-yam Sobuj kochu shak Colocasia esculenta 18 17 9 Bathua Pigweed Chenopodium album 13 7
ROOTS & TUBERS 10 Potato Gol Alu Solanum tuberosum 93 93 11 Radish Mula Raphanus sativus 44 40 12 Coco-yam Sobuj kochu Colocasia esculenta 33 37
NON-LEAFY VEGETABLES 13 Egg plant Begun Solanum melongena 81 80 14 Bean Shim Dolichos lablab 70 75 15 Cabbage Badha Kopi Brassica oleracea var. capitata 80 58 16 Cauliflower Foolkopi Brassica oleracea var. botrytis 90 74 17 Cow pea Borboti Vigna catjang 38 8 18 Cucumber Shasha Cucumis sativus 20 21 19 Folwal Potol Trichosanthes dioica 49 16 20 Gourd (Ash) Chal kumra Benincasa cerifera 31 21 21 Bitter Gourd Karola Momordica charantia 43 42 22 Sweet pumpkin Misti kumra Cucurbita maxima 40 39 23 Kakrol Kakrol Momordica cochinchinensis 20 8 24 Ladies finger Dherosh Abelmoschus esculentus 43 24 25 Bottle gourd Lau Lagenaria siceraria 68 56 26 Snake gourd Chichinga Trichosanthes anguina 53 19 27 Jackfruit (immature) Kacha kathal Artocarpus heterophyllus 8 23 28 Green papaya Kacha papay Carica papaya 30 27 29 Plantan (green) Kacha kola Musa paradisiaca 12 18 30 Tomato (green) Kacha tomato Lycopersicon lycopersicum 21 33 31 Yam Stem Kachur data/loti Colocasia esculenta 28 12
FRUITS 32 Mango ripe(deshi) Paka Am Mangifera indica 66 56 33 Black berry (deshi) Kalojam Syzygium cumini 17 8 34 Jackfruit (ripe) Paka Kathal Artocarpus heterophyllus 60 56 35 Banana (ripe) Paka kala Musa sapientum 29 17 36 Bitter Plum Boroi Zizyphus mauritiana 38 36 37 Pine Apple (Jaldugi) Anarash (Jaldugi) Ananas comosus 12 5 38 Tomato (ripe) Tomato paka Lycopersicon lycopersicum 61 52
FISH 39 Carp Katol mach Labeo rohita 21 7 40 Tilapia Tilapia mach Anabus testudineus 20 25 41 Dragon Fish Pangash Pangasius pangasius 44 26 42 Fry (very small) Choto puti Puntius ticho 56 27 43 Sunfish Mola mach Mola mola 11 9 44 Shrimp(dry) Chingri (shukna) Heterocarpus ensifer 7 22 45 Rohu Rui Labeo ruhita 45 35 46 Shrimp Chingri Heterocarpus ensifer 30 6
33
Table 2.5: Food items commonly consumed by both Gen eral and Ethnic people (Percent frequency ≥ 5% households)
Sl no
English name
Bengali/
Local name
Scientific name Urban-Rural weighted % frequency native general people
% frequency of Ethnic people food consumption
Sl. no English name
Bengali/
Local name
Scientific name Urban_Rural weighted % frequency native general people
% frequency of Ethnic people food consumption
CEREALS 27 Folwal Potol Trichosanthes dioica 49 16 1 Rice parboiled (Brri-29) Sidhoy chal Oryza sativa 99 34 28 Ash Gourd Chal kumra Benincasa cerifera 31 21 PULSE 29 Bottle Gourd Lau Lagenaria siceraria 68 56 2 Lentil (deshi) Masur dal Lens culinaris 78 35 30 Snake Gourd Chichinga Trichosanthes anguina 53 19 LEAFY VEGETABLES 31 Jackfruit immature Kacha Kathal Artocarpus heterophyllus 8 23 3 Joseph’s Coat Lalshak Amaranthus gangeticus 84 49 FRUITS 4 Bottle Gourd Lau shak Lagenaria siceraria 47 42 32 Mango ripe (deshi) Paka Am Mangifera indica 66 56 5 Indian spinach Poi shak Basella alba 64 28 33 Black berry (deshi) Kalojam Syzygium cumini 17 8 6 Radish Mula shak Raphanus sativus 38 34 34 Jackfruit (ripe) Paka Kathal Artocarpus heterophyllus 60 56 7 Spinach Palong sag Spinacia oleracea 41 17 35 Banana (ripe) Paka kala Musa sapientum 29 17 8 Coco-yam Sobuj kochu shak Colocasia esculenta 18 17 36 Pineapple (jaldogi) Anarosh Ananas comosus 12 5 9 Bathua leaves Batua shak Chenopodium album 13 7 37 Bitter Plum Boroi Zyzyphus mauritiana 38 36
ROOTS & TUBERS 38 Tomato (Ripe) Tomato paka Lycopesicon lycopersicum 61 52 10 Potato Gol Alu Solanum tuberosum 93 93 FISHES 11 Radish Mula Raphanus sativus 44 40 39 Carp (small) Nala Labeo rohita 24 16 12 Coco-yam Sobuj kochu Colocasia esculenta 33 37 40 Ruhi Ruhi Labeo rohita 46 35 13 Coco-yam stem Sobuj kochu Colocasia esculenta 41 Carp Katol mach Catla catla 21 7 Non-LEAFY VEGETABLES 42 Tilapia Tilapia mach Anabus testudineus 20 25 14 Egg plant Begun Solanum melongena 81 80 43 Dragon Fish Pangash Pangasius pangasius 44 26 15 Bitter Gourd Karola Momordica charantia 43 42 44 Sunfish Mola mach Mola mola 11 9 16 Sweet pumpkin Misti kumra Cucurbita maxima 40 39 45 Silver Carp Silver Carp Hypophthalmichthys nobilis 42 13 17 Kakrol Kakrol Momordica cochinchinensis 20 8 46 Taki fish Taki mach Channa puncpatus 10 13 18 Ladies finger Dherosh Abelmoschus esculentus 43 24 47 Painted catfish Tengra (dry) Pseudolaguvis shawi 23 9 19 Green papaya Kacha papay Carica papaya 30 27 48 Fry (very small) Choto puti Puntius ticho 56 27 20 Green tomato Kacha tomato Lycopersicon lycopersicum 21 33 49 Shrimp (dry) Chingri (dry) Heterocarpus ensifer 7 22 21 Green banana Kacha kala Musa sapientum 12 18 50 Shrimp Chingri Heterocarpus ensifer 30 6 22 Bean Shim Lablab purpureus 70 75 51 Puti fish (rotten) Chepa Puntius puntio 12 16 23 Cabbage Badha Kopi Brassica oleracea var capitata 80 58 52 Laitta fish Laitta mach na 7 12 24 Cauliflower Foolkopi Brassica oleracea var. bortrytis 90 74 MEAT 25 Cow pea Borboti Vigna catjang 38 8 53 Chicken (farm) Farm murgi Gallus bankiva 40 33 26 Cucumber Shasha Curcumis sativus 20 21 54 Beef Garor mangsha Beef cattle 26 5
34
Table 2.6: Ethnic food items listed from ethnic CFC S and FGDs Sl no
English name Bengali name Scientific name % household consume (n=805)
Sl no
English name Bengali name Scientific name % household consume (n=805)
CEREALS 33 na Hahnagulu na 1 Rice parboiled (Brri-29) Sidhoy chal Oryza sativa 6 34 Gourd (Ridge) na na 5 2 Rice sunned* Atap chal Oryza sativa 58 35 Plantain Flower na na 14 3 Radish Mula shak Raphanus sativus 8 36 Yam Pan/jhum alu* na 4 Sweet pumpkin Misti kumra shak Cucurbita maxima 5 37 Yam (Elephant) Ole kachu na 42 5 Thankuni Thankuni Pata Centella asiatica 12 38 Plantain Stem Kolar thore na 11 6 Bitter gourd Karala pata* Momordica charantia 18 39 Olekopi Olekopi na 34 7 Rashun Leaves Rashun shak na 5 FRUITS 6 8 Dheki leaves Dheki shak na 39 40 Pamelo (Red) Jambura (Lal) na 9 Jarul Khambang na 13 41 Papaya (ripe) Paka pepey Carica papaya 9 10 Dumurshomi Leaves Dumurshumi shak na 7 42 Pineapple (wild ) Anarash (bonno) na 6 11 Seneya Leaves Seneha shak na 13 43 Wild Melon Sindera* Cumis melo 35 12 Lelom Leaves Lelom shak na 23 44 na Roshko* Syzygium balsameum 40 13 na Sabarang* Ajuga macrosperma 33 45 Bead tree kusumgulu* Elaeocarpus angustifolius 55 14 Roselle Amila pata* Hibiscus sabdariffa 32 FISH, MEAT AND EGG 15 na Lalam pata* Premna obtusifolia 30 46 Ilsha (salted) Ilish mach Tenualosa ilisha 6 16 Indian Ivy-rue Baruna Shak* Xanthoxylum rhetsa 38 47 Kachki Fish Kachki mach Corica soborna 11 17 na Ojan shak* Spilanthes calva 48 48 Poa fish Poa mach Glassogobius giuris 29 18 na Ghanda batali* Paederia foetida 54 49 Lota Fish Lota mach na 10 19 na Orai balai Premna esculenta 28 50 Churi Fish (Dried) Churi mach na 38 20 Purslane Bat slai* Portulaca oleracea 32 51 Prawns whole (dried) Chingri shampurna Heterocarpus ensifer 14 21 Yellow saraca Maytraba Saraca thaipingensis 26 52 Nappi paste Nappi na 56 22 Yellow Flower Holud fool na 9 53 Zhinuk Shell Mollusk shell 7 23 Ginger Flower Ada shak na 5 54 Crabs Kakra Liocarcinus vernalis 24 24 Sime Flower Sime fool na 13 55 Shark Hangar Carcharhinus amblyrhynchos 13 NON -LEAFY VEGETABLES 56 Shark (Dried) Hangar shutki Carcharhinus amblyrhynchos 21 25 Pea eggplant Mistti begun* Solanum spinosa 31 57 Kuchia fish Kuchia Monopterus cuchia 20 26 Solanum Tak begun* Solanum virginianum 35 58 Snails (Small) Shamuk (choto) Helix pomatia 39 27 Sigon data Sigon data* Lasia spinosa 40 59 Snails (Large) Shamuk (Boro) Helix pomati 8 28 Tara (Like Kochu data) Tara data na 19 60 Rat Idur Rattus norvegicus) 6 29 Basher Korol Basher korol na 39 61 Frog Beng Litoria caerulea 33 30 na Banchalta* na 50 62 Egg Dim Gallus bankiva 13 31 na Fakong na 48 63 na Gobar poka na na 32 Wild mushroom Edur kan na na 64 Pork Shukurer mangsha Sus scrofa domestica 54 *ethnic food **raw na: not vailable
35
2.2. Analysis of nutrients in key foods
In generation of nutrient data for food composition database, designing and executing
sampling protocol, preparation of analytical samples and portions, selection of analytical
method, execution of analytical procedures with appropriate number of analytes and
analytical replicates, involvement of skilled lab personnel, evaluation of analytical values
and documentation of data are of utmost important (Greenfield and Southgate, 2003b).
Lapse in any of the process would result in error in the representative nutrient data. The
basic principles of producing quality data should give attention on-
• the collection and preparation of food sample
• the selection of the analytical method and its validation within the
laboratory carrying out the analysis of a particular food
• proper execution of methods, and
• review of the values obtained.
Therefore, adequate and appropriate care and precaution were taken in designing and
addressing these approaches.
2.2.1. Food sampling protocol
A sampling plan is the predetermined procedure for selection, collection, preservation,
transportation and preparation of the analytical portion to be used from a lot as samples.
A sampling plan should be a well organized document for program objectives (Proctor et
al, 2003).
Foods are biological materials and exhibit variation in composition, particularly prone to
variation in water, carbohydrate and vitamin contents. This variation is related to a
number of factors such as cultivation place (cultivated, wild, garden), geographical
location, seasons, state of maturity, cultivar and breed, etc. Therefore, collection of food
sample needs to be specific in terms of timing and frequency to reflect these variations.
36
Food sampling is one of the most important aspects for compositional analysis. It
determines the analytical data quality that needs to provide representative nutrient value
to the users. However, food sampling is a difficult because of its variability and
heterogeneity in composition. The primary objective in food sampling is to collect a
representative food sample and to ensure that changes in nutrient composition do not
occur between collection and analysis (Greenfield and Southgate, 2003c).
Sampling error arises with using a part of total food sample. It is because of
heterogeneity nature of foods. Taking small portions at the primary sampling stage can
lead to sampling error. Sampling error is also associated with poor labeling &
documentation, non-conforming sample use, incorrect mixing, and also inappropriate
storage. In practice, 100-500g represents a convenient sample size. The larger sample
size the more reliable the sampling; however, sample size is limited by time, cost,
sampling methods and logistics of sample handling, analysis and data processing.
Therefore, replicate samples of representative amount must always be taken when
estimating the composition of food.
It is further noted that food samples should be representative of the food “as
consumed”, and as “available for consumption”. Since the database is for mass people
consumption, food samples were collected from the points from where mass people
take it for their consumption (Greenfield and Southgate, 2003b).
To minimize the geographical variation, food samples were collected from wholesale
markets located at four the entry points (figure 2.4) to Dhaka city, where consumable
matured food items come from all over the country for mass people consumption. Two
samples were collected from cultivation fields. Also to avoid sampling error, a large
portion (approximately 2.0kg) of replicate samples for every item was collected from
each collection point. Since there is limited scope to study the seasonal variation in
37
nutrient composion in the food composition database, food items, particularly the
vegetables, fruits and fishes were collected during their peak available period.
2.2.1.1 General food sampling protocol
In this study a multi-regions sampling plan was used to collect representative food
samples. The identified and selected key food items were collected from four different
wholesale markets located at the four entry points to Dhaka city, and from two
cultivation fields (figure 2.4). Every two samples were pooled together to make a single
analyte (test sample), thus made three analytes for each food item, which were then
analyzed for their nutrient profile (figure 2.4). Sampling of general food item was started
at June 2009, particularly cereals and it was continued upto March, 2010.
38
Figure 2.4: Multi-regions sampling plan for general food sample
39
2.2.1.2 Ethnic food sampling
Ethnic food items were collected from local weekly markets at Rangamati and
Khagrachari. Three food samples for each food item were collected from each market.
Every two food samples were pooled together to make three analytes (test sample),
which were analyzed for their nutrient profile. The ethnic food sampling plan is depicted
in figure 2.5. A few ethnic foods were collected during September through December,
2009, but most of the ethnic foods were collected during April-May, 2010.
A C Analyte-I
Rangamati Bnorupa bazar
Sample A Sample B Sample C Sample F Sample E Sample D
D F Analyte-I B E
Analyte-I
Khargrachari Bazar
Figure 2.5: Multi -regions sampling plan for ethnic food
40
2.2.2 Procedure for food sample collection
Representative food samples were collected from the selected wholesale markets
where food items come from all over the country and from cultivation fields. Attempt was
taken to collect tender fresh sample. Collection was made in new clean plastic poly
bags. In case of field sample collection, some water was sprayed on the vegetable
samples during packing into the poly bag, and thus kept it moistened during
transportation from the field to the lab.
For collection of general food items, particularly the vegetable items, replicate samples
of approximately 2.0kg of each food was purchased from each of the four selected
wholesale markets and from the two cultivation fields. These replicate samples were
mixed together to make a single sample for each collection point, and thus made six
samples for six collection sites. Two samples were then pooled to make a single analyte
and thus made three analytes for each food item.
Ethnic food samples were collected from weekly wholesale markets at Rangamati and
Khagrachari. Three samples for each food items of approximately 1.5kg were purchased
from each market. The samples were water sprayed and packed into new clean plastic
poly bags for transportation to the lab.
2.2.3 Identification of collected food samples
Nutrient profile in food composition database needs to be representative of the foods-
“what the mass people consume” and “from where they collect it”? To minimize the
compositional variations that may arised by geographical locations, timing of collection,
sample preparation; the food samples, particularly vegetables, fruits and fishes, were
collected from the wholesale markets where the foods arrive from four geographical
regions of the country. It thus ensured the representative consumable food items of all
geographical locations. Samples were collected at very early morning from the collection
41
points, taken to the lab and immediately processed for analyte preparation at adequate
required lab environment with trained and skilled lab personnel.
Rice, maize and lentil
A market survey was conducted in different wholesale and local rice markets in and
around the Dhaka City to find out the rice varieties which were consumed by the
majority of population. It was then identified and certified by an expert at the Grain
Quality and Nutrition Division, Bangladesh Rice Research Institute, Gazipur. It was the
BRRI -29 variety. The lentil deshi and maize deshi varieties were also indentified and
certified by BRRI.
Vegetables and fruits
The vegetable and fruit items were categorically identified and certified by personnel of
Department of Agricultural Extension (DAE) and the taxonomist of the Department of
Botany, Dhaka University. In case of ethnic foods, food samples were purchased from
the weekly wholesale markets with the help of local ethnic DAE staff, who confirmed its
identity. After taking the food sample to the lab, the taxonomic expert further identified it
for its scientific and English name.
Fish, meat and eggs
The identified fresh fish samples were purchased from wholesale and local markets at
Dhaka city. Meat and egg samples were also purchased from the local market. They
were then rapidly processed for estimation of moisture content. The dried samples were
used for analysis of proximate nutrients and mineral contents.
2.2.4 Sample preparation for analysis
Generation of nutrient values employs a range of analytical procedures and it requires a
number of analytical sample portions. Taking of analytical portions and size depend on
the analytical method to be used. When food samples are used for analysis of a range
of nutrients, it is convenient to store some analytical portions (at least 3 portions) at -40
42
or -70oC (Greenfield and Southgate, 2003c). Care should be taken to separate the
edible portion and inedible portion. When analytical portions are taken repetitively from
stored samples for analysis of different nutrients, it is convenient to store multiple
identical sample units in the freeze.
In this study, the properly collected food items were first rinsed with tape water followed
by washing with distilled water, then gently swabbed with tissue paper and air dried. The
cleaned air-dried sample was diced or cut into small pieces (peeled where needed)
using a cleaned stainless knife on a cleaned plastic cutting surface. Hand gloves were
used throughout the process. The diced food sample was taken to a stainless steel bowl
and mixed with a plastic spatula. Adequate precautions were taken to avoid any metal
contamination. In case of vitamin analysis, these operations were performed very fast in
dim light to avoid any degradation by oxygen and light, and for some food items,
portions of fresh process sample(s) were kept frozen. Where required, the clean air
dried sample was homogenated with a lab blender, and the required portion of the
sample analyte was taken from the homogenated material.
Vegetable and fruit analytical sampling
The vegetables and fruits were subjected to multiple nutrient analyses. Accordingly, they
were processed for analytical samplings and stored in multiple portions as –
(a) 3x5g taken for carotenoid analysis, (b) 3x5g taken for vitamin C analysis, (c) 3x20g for B-
vitamins analysis, (d) 3x10g for sugar analysis (for fruits), (d) 3x10g for dietary fiber analysis, (e)
3x10g for crude fiber analysis, (f) 3x10g taken for nitrogen analysis, (g) 3x10g taken for mineral
analysis, (h) 3x25g taken for moisture analysis, and (i) remaining portion in multiple units frozen
and stored at -20oC & -40oC depending on nutrient to be analysed.
Fish, meat and egg analytical sampling
Fish: Approximately 1.0-1.5kg fish of consuming size of each variety was collected from
3 wholesale and from 3 local markets located at Dhaka and its peripheries from where
43
people purchased fish for their consumption. The fish samples were brought to lab
quickly to avoid any spoilage during transport. Ice box was used during collection of fish
from the peripheral points. Taking the sample in lab, it was cleaned and processed for
edible portion. Small fish was taken as a whole.
Meat: About 20-25 meat cuts at least 2-3 pieces from each of five regions of the
slaughted animal were purchased from butcher shop at two markets and brought in
clean plastic poly bags to the lab, where it was processed for analytical sampling.
Egg: Twelve eggs of each variety were collected from the local markets from where
mass people taken for their consumption. Each 4 eggs were pooled together to process
to make a single analyte, and thus prepared three analytes for each variety.
2.2.5 Chemicals
All chemicals and reagents used in the analysis of the nutrient profile were of analytical
grade and were purchased from Merck (Darmstadt, Germany, BDH (UK), Sigma
Chemical Co (St. Louis, MO, USA). Ascorbic acid, β-carotene, and B-vitamins, were
procured from Sigma Chemical Co. (St. Louis, MO, USA).
2.2.6 Methods of nutrient analysis
Use of appropriate and accurate methods employing skilled analysts can only ensure
reliable data for preparation of a food composition database. However, the choice of
analytical methods is limited to equipment facilities and technical staffs available.
The original project proposition was aimed to analyse 50 food items for their nutrient
profile comprising proximate composition, minerals, vitamin C, total carotenoids,
carotene profile and B-vitamins. Because of the fund constraint and time limitation,
arising out of the inclusion of additional 25 food items in the analysis, the number of
nutrients to be analyzed was reduced to proximate nutrients, minerals, vitamin C and
44
carotenoid for the 75 food items. Analysis of β-carotene was limited to 20 vegetables
and fruits of both general and ethnic origin. The nutrients analysed and the analytical
techniques employed are summarized in the table 2.7.
Table 2.7: Nutrients analysed and the analytical techniqu es employed
Nutrient class Nutrients AOAC and Standards methods
Macronutrients Moisture Drying in Air oven at 100-105oC (AOAC, 1998a)
Protein Micro-Kjeldahl method (AOAC, 1998b)
Fat Soxhlet extraction (Raghuramulu et al, 2003a)
Fatty acids By calculation (Greenfield & Southgate, 2003)
Crude fiber Gravimetric (Raghuramulu et al, 2003b)
Ash Muffle furnace (AOAC, 199c)
Dietary Fiber Sigma Kit (AOAC, 1998d; Sigma TDF-100A)
Carbohydrate By Calculation (Rand et al, 1991)
Micronutrients
Vitamin Carotenoids Spectrophotometry (Roriguez-Amaya and Kimura, 2004;
Rahman et al, 1990)
β-carotene HPLC (Roriguez-Amaya and Kimura, 2004)
Vitamin C Spectrophotometry (AOAC, 1998e)
Mineral Cu, Zn, Fe, Mn, Ca,
Mg, Na, K, P
Atomic Absorption Spectrophotometry (Petersen, 2002)
Antinutrients Phytate Spectrophotometry (Wheefer and Ferral, 1971)
45
2.2.6.1 Analysis of moisture
Moisture content is one of the most variable components, particularly in the plant foods.
This variability affects the food composition as a whole. Therefore, the moisture value
remains as an essential component in food composition database.
The moisture content in the food items was determined by measuring the amount of
water removed from the food (AOAC, 1998a). It was done by direct heating the food in
an Air oven at 100-105oC to constant weight.
2.2.6.2 Estimation of protein
Protein content in the food items was determined by indirect method estimating total
nitrogen in the food. It was calculated by multiplying the total nitrogen using the
respective factor as estimated by Micro-Kjeldahl method (AOAC, 1998b).
2.2.6.3 Estimation of total fat and fatty acids
The most frequently used method for fat estimation in food is the continuous extraction
of fat with petroleum ether or diethyl ether. For some specific foods, mixture of
chloroform and methanol is also used to extract fat.
In this study, dried food was subjected to continuous extraction with petroleum ether in a
Soxhlet extractor (AOAC, 1998c). Chloroform-methanol extraction was also used in
isolation of fat in some particular food items such as meat and eggs (Raghuramulu et al,
2003).
Total fatty acid content in the foods was estimated by calculation and by multiplication of
total fat content by a factor (Greenfield and Southgate, 2003d).
2.2.6.4 Estimation of ash content
In ash estimation, dried food sample is ignited at 600oC to burn out all organic materials.
The inorganic material which is ignited at this temperature is the ash.
46
In this study, ash in the food sample was estimated by heating the dried sample in a
Muffle furnace at 600oC for 3h (AOAC, 1998d). Ash content was calculated from weight
difference.
2.2.6.5 Analysis of crude fibre and dietary fibre
Crude fibre was estimated by gravimetric method as described by Raghuramulu et al
(2003). The dried and fat free food sample was treated with boiling sulphuric acid at
constant volume, cooled, filtered, washed with hot water, made alkaline, boiled, filtered
and washed with water followed by ethanol and ether wash. The residue was then
heated in a Muffle furnace at 600oC for 3h. Crude fibre was finally calculated from the
weight difference.
Dietary fibre was analysed by AOAC method (1998d) using total dietary fibre assay kit
(TDF-100, Sigma Chemical Co., Saint Louis, Missouri, USA). In this method, a
combination of enzymatic and gravimetric techniques was used. Dried fat free sample
was gelatinized with heat stable α-amylase, then enzymatically digested with protease
and amyl glycosidase to remove the protein and starch present in the food sample.
Ethanol was added to precipitate the soluble dietary fibre. The residue was filtered and
washed with ethanol and acetone. After drying, half of the residue was analysed for
protein and half for ash. Total dietary fibre was the weight of the residue minus the
weight of the protein and ash.
2.2.6.6 Analysis of phytatic acid
Phytic acid was determined by spectrophotometric method (Wheeler and Ferrat, 1971).
Phytic acid in the food sample reacting with ferric chloride developed red colour with
potassium thiocyanate. This colour difference was read in the spectrophotometer at
485nm against the water blank. Intensity of the colour is proportional to ferric ion
concentration, which was used in the calculation of phytic acid content in the food
sample.
47
2.2.6.7 Calculation of carbohydrate and energy
The content of available carbohydrate in the food sample was determined by difference.
Carbohydrate was calculated by subtracting the sum percentage of moisture, protein,
fat, ash, crude and dietary fibre (Rand et al, 1991; FAO, 2003).
The energy content in the food sample was calculated by the sum of protein, fat and
carbohydrate using respective Atwater factors (Rand et al, 1991).
2.2.6.8 Analysis of vitamin C
Ascorbic acid in food sample was estimated by spectrophotometric method (AOAC,
1998e). The fresh food sample (vegetable or fruit) was homogenized in a mortar with
pestle using metaphosphoric acid, filtered, treated, and incubated at 60oC for 60 minutes
with 2, 4-dinitrophenyl hydrazine. Adding 85% sulphuric acid, it was read at 520nm in
spectrophotometer (UV-1601, UV-Visible, Shimadzu).
2.2.6.9 Analysis of carotenoids
Carotenoid content in the vegetable or fruit sample was determined by acetone-
petroleum-ether extraction followed by spectrophotometric measurement (Roriguez-
Amaya and Kimura, 2004). Extraction of carotenoid was performed by grinding of
processed food sample in mortar and pestle, filtration through sintered glass filter under
vacuum and separation from acetone to petroleum ether.
When the color of the eluent is orange like, it was read at 450nm in a spectrophotometer
(UV-1601, UV-Visible, Shimadzu) for concentration of total carotenoids; when it was
green color containing chlorophyll, the extract was passed through a column packed with
activated 1:1 alumina and sodium anhydrous to remove the green pigments. The column
eluent was then read at 450nm. All preparative and extractive procedures were
performed in dim light to avaoid phoyosensitive damage.
48
2.2.6.10 Analysis of β-carotene
Reverse phase HPLC (Shimadzu PC based Binary Gradient HPLC Prominence System
with PDA Detector, SPD-M20A; Solvent delivery System, LC-20AT; LC Solution Multi
Workstation Software) was used to determine the β-carotene (Roriguez-Amaya and
Kimura, 2004). The nitrogen dried carotenoid was reconstituted with mobile phase
(acetonitrile: methanol: 2-propanol-) and 50µl reconstituted sample was injected into the
VYDAC reverse phase C18 column (5µm particle size). The column was re-equilibrated
with the mobile phase for at least five minutes before the next injection. β-carotene was
purchased from Sigma Chemical Co. USA and was used as standard analytes.
2.2.6.11 Analysis of mineral profile
Mineral content in the food sample was analysed by Atomic absorption
spectrophotometric method (Petersen, 2002). Dried food sample was subjected to wet
digestion with nitric acid and perchloric acid in an auto- digestor at 325oC. The digested
sample after appropriate dilution was aspirated into the spectrophotometer where it was
burned into atomic components and it was read at their respective wavelength.
Sigma standard elements were used as standard analytes.
2.2.7 Quality assurance programme (QAP)
Method standardization and validation were carried out with internal standard (IS),
external standard (ES), intra and inter lab analysis of particular food and percent
recovery. Data quality was maintained by precision (co-efficient of variance, CV),
accuracy (Standard Reference material, SRM) and well documented foods, standard
error of mean (SEM).
49
Chapter 3
Results and Discussion
50
3 Results and Discussions
Healthy well-nourished people are the outcome of successful social and economic
development and constitute an essential input into the development process. Good
health needs balanced diets, which could be obtained and designed from nutrient
composition of key foods. Therefore, nutrient composition of key foods is to be well-
known and available to the mass population.
Public health nutrition activities, nutrition, agricultural, health and epidemiological
research, food industries and trade decision and government policy planning concerning
nutrition and agriculture, all depend on an accurate knowledge of what is in food. It is the
nutrient composition of food that can provide this information. Currently these data are
not adequate to meet the existing needs of planners, practioners, and professionals in
Bangladesh. Often the data are incomplete, inconsistent and inaccessible.
There is a worldwide call for updating food composition databases. The third world
countries are far behind to address this attempt. Like most of the developing countries,
Bangladesh does not have food composition database. The current food composition
table (FCT) - ‘Deshio Khadder Pustiman’ prepared by the Institute of Nutrition and Food
Science (INFS), University of Dhaka, later edited by Helen Keller International (HKI) in
english version- ‘Tables of Nutrient Composition of Bangladeshi Foods’ was prepared
long back; most of the nutrient data used were analyzed long ago, and some were
assumed to be borrowed from neighboring countries, and did not have the nutrient data
of ethnic foods.
Over the last decade food composition activities have increasingly been addressed by
many agencies. As an effort to contribute to this need, this study has been undertaken
with an aim to prepare a food composition database with reference to general and ethnic
foods of Bangladesh.
51
3.1 Key food identification
Nutrient profiling of a food is expensive in term of its identification, collection, processing
and analysis. Therefore, analyzing every food item for every nutrient and meeting all
user requirements is difficult. Consequently, priorities must be determined. Key foods
have been identified as those foods that contribute up to 75% of any one nutrient to the
dietary intake (Haytowitz et al, 1996; 2000; 2002). Key foods can be documented by
food consumption survey.
In this study, the key foods was indentified through CFCS and FGDs and priorities made
in consultation with TAT members.
3.1.1 Comprehensive Food Consumption Survey (CFCS)
Food consumption survey comprises collection of information about food intake
frequency and amount of food consumed (Brussaard et al, 2002). It is performed by
household survey. The aim of CFCS is to generate food consumption statistics. Food
consumption data and nutrient values help to generate Key Foods list. In identifying the
key foods, nutrient contribution of the food and public health significance of nutrients are
taken into consideration.
The proposal was to conduct CFCS on 1700 households comprising 1200 general
households and 500 ethnic tribal households. Later on as per recommendation received
from the 5th dissemination workshop at Rangamati on the 18th March 2010, more ethnic
households were included in the CFCS to make a total 805 ethnic households
CFCS activities
To select the key food items to be investigated for their nutrient profiling, CFCS was
carried out to collect food consumption data of the general and ethnic tribal population.
Before starting it, survey locations were mapped out, a questionnaire was developed
and pretested and sample size was determined. These activities were finalized and
52
approved in consultation with FAO Technical Advisory Team members. CFCS sampling
plan of the general and ethnic households are described in the table 3.1 and 3.2, and
some CFCS activities in ethnic tribes are highlighted in the photographs.
CFCS Team
Co-Investigator and DAE enumerator with ethnic peo ple
Enumerator taking interview
53
Enumerator with ethnics
PI and enumerator with ethnics taking interview
Co- Investigator and enumerator with ethnics taking interview
54
Table 3.1: Location and description of CFCS data collection among the general population
Division District Upazilla Date of visit Location No of HH interviewed
Type of HH
Dhaka Netrokona Netrokona sadar
31.01.09 to 02.02.09 West chakpara 50 Urban Mohandrapur 54 Rural
Manikgonj Saturia 08.02.09 to 11.02.09 Sawdagar para & Uttarkaunna
50 Urban
12.02.09 to 16.02.09 Char saturia 50 Rural Sylhet Moulavibazar Moulavibazar
Sadar 21.02.09 to 22.02.09 Suvro 52 Urban 23.02.09 to 25.02.09 Kodupur 50 Rural
Habigonj Madhobpur 25.02.09 to 27.02.09 Godampara & Krishnanagar
51 Urban
28.02.09 to 02.03.09 West madhobpur 50 Rural Chittagong Feni Feni Sadar 16.03.09 to 17.03.09 North Charipur 50 Urban
18.03.09 to 19.03.09 Nagarkandi, Mathiara 50 Rural Comilla Comilla Sadar 20.03.09 to 21.03.09 Gabindapur 50 Urban
22.03.09 to 24.03.09 Kashinathpur 50 Rural Rajshahi Natore Natore Sadar 03.04.09 to 04.04.09 Uttar Patua para 51 Urban
Ulupur 49 Rural Rajshahi Rajpara 01.04.09 to 02.04.09 Terkhadia 52 Urban
Kashia danga 52 Rural Khulna Jessore Jessore
Kotoali 04.04.09 to 06.04.09 Shangkarpur 50 Urban
Mubarak Kathi 49 Rural Jhenaidah Kaligonj 08.04.09 to 09.04.09 Arpara Nadir par 50 Urban
Mithapukur 50 Rural Barisal Barisal Barisal Kotoali 11.04.09 to 14.04.09 Ganopara 50 Rural
Rupatoli 50 Urban Jhalokathi Jhalokathi
Sadar 13.04.09 to 14.04.09 Krishnakathi 50 Urban
Rajapremhar 50 Rural Total 1210
55
Table 3.2: Location and description of CFCS data co llection among the ethnic population
Division District Upazilla Time of visit Location No of HH interviewed
Type of
household
Dhaka Netrokona Durgapur 03.02.09 to 07.02.09 Gopalpur, Nolua 28 Hajong
Debdul 31 Garo
Sylhet Moulavi Bazar Kamolgonj 18.02.09 to 20.02.09 Tilokpur 25 Monipuri
Magurchara & Kashiapunji 25 Khasia
Chittagong Khagrachari Khagrachari sadar
31.03.10 to 15.04.10
17.04.10 to 23.04.10
Nilkantipara 70 Marma
Dewanpara 60 Chakma
Soyanundarpara 50 Tripura
Rangamati Rangamati sadar
03.04.10 to 10.04.10 Haja Chara, Diglibak, Shap Chari,
69 Chakma
Naraichari, Vhulu Chari, 21 Tanchanga
Tanchanga para, Banna Chari 30 Marma
Bandarban Bandarban sadar
06.03.09 to 07.03.09 Raicha Senior para 30 Tanchanga
07.03.09 to 08.03.09 Kalaghata 37 Tripura
09.03.09 to 12.03.09 Balaghata Biddopara, Painchara, Parjatan Chakma para, Pain para, Nadir par, Balaghata bazar
109 Chakma
13.03.09 to 14.03.09 Bameri para 30 Murang
Faruk para 30 Bam
Puratan and nutun choroi para 71 Marma
Rajshahi Rajshahi Godagari 29.03.09 to 31.03.09 Nimghat para, Nobai bottala, Dangapara, Nimghatu para
89 Shaotal
Total 805
56
Figure 3.1: Distribution of general and ethnic households by number
1210
805
General Ethnic
57
Figure 3.2: Distribution of selected general househ olds by division and household type
1
21
41
61
81
101
104 100 100 101 99 100
100 103 100 103 100 100
Rural Urban
Number of
households
58
Figure 3.3: Distribution of ethnic households by di strict
0
50
100
150
200
250
300
350
59 50
180
120
307
89
Number of
households
59
60
Figure 3.4: Distribution of ethnic households by tr ibe and number
238
17151
8730
30
25
2589
31
28
Chakma Marma Tanchanga Tripura
Bam Murong Monipuri Khashia
Shantal Garo Hajong
61
3.1.2 Focus group discussions (FGDs)
In the present study, FGDs were conducted to enrich and supplement the CFCS food
consumption data. It carried out among the ethnic community of Marma, Chakma,
Tripura and Tangchaga tribes living in Khagrachari and Rangamati. It was done during
March and April, 2010. The FGD composed of 8-12 community participants, 2 key
informants- one from the community NGO person and one was DAE block supervisor,
and a critique- the agriculture officer. The composition, characteristics and activities of
the FGDs are depicted in the table 3.3 and photographs.
The key question was the type of foods that the ethnic people consume throughout the
year. Their response to this issue was discussed, criticized and recorded carefully. In
the CFCS it is indicated that ethnic people consumed about 46 food items, most of
which are also consumed by the general people; therefore, these are not absolutely
ethnic. To explore the true ethnic foods, the FGDs were conducted among the ethnic
communities. FGDs showed that aboutt 47 foods comprising leafy vegetables, non-
leafy vegetables, fruits, fish and meat of wild origin are consumed by the ethnic
people. The outcome of the FGDs is listed in the table 3.4.
Table 3.3: FGDs settings
FGD community
Objective Location No. of participant s
Duration of discussion
Marma Type of food intake throughout the year
Pankhaiya para, Khagrachari, CHT 12 90 minutes
Chakma South Rangapani, Bidhadhan Chakma Bari, Chakma palli, Rangamati, CHT
8 60 minutes
Tanchanga Tanchaga para, Dharmaraj Babu Bari, Kotoali, Rangamati, CHT
8 75 minutes
Tripura Ghasbhan no 2 project Gram, Jagonnath Mandir, Khagrachari, CHT
10 90 minutes
62
Table 3.4: FGD outcome: Food consumption pattern of the Marma, Chakma, Tanchanga and Tripura communities
na: not available * ethnic food
Sl no English name Bengali name Scientific name Sl no English name Bengali name Scientific name LEAFY VEGETABLE S 25 na Banchalta* na 1 Rashun Leaves Rashun shak na 26 na Fakong na 2 Dheki leaves Dheki shak na 27 na Hahnagulu na 3 Jarul Khambang na 28 Yam Pan/jhum alu* na 4 Dumurshomi Leaves Dumurshumi shak na FRUITS 5 Seneya Leaves Seneha shak na 29 Pamelo (red) Jambura (Lal) na 6 Lelom Leaves Lelom shak na 30 Pineapple (wild ) Anarash (bonno) na 7 na Sabarang* Ajuga macrosperma 31 Wild Melon Sindera* Cumis melo 8 Roselle Amila pata* Hibiscus sabdariffa 32 na Roshko* Syzygium balsameum 9 na Lalam pata* Premna obtusifolia 33 Bead tree kusumgulu* Elaeocarpus angustifolius 10 Indian Ivy-rue Baruna Shak* Xanthoxylum rhetsa FISH AND MEAT 11 na Ojan shak* Spilanthes calva 34 Lota Fish Lota mach Na 12 na Ghanda batali* Paederia foetida 35 Churi Fish (Dried) Churi mach na 13 na Orai balai Premna esculenta 36 Nappi paste Nappi na 14 Purslane Bat slai* Portulaca oleracea 37 Zhinuk Shell Mollusk shell 15 Yellow saraca Maytraba Saraca thaipingensis 38 Crabs Kakra Liocarcinus vernalis 16 Yellow Flower Holud fool na 39 Shark Hangar Carcharhinus amblyrhynchos
17 Ginger Flower Ada shak na 40 Shark (dried) Hangar shutki Carcharhinus amblyrhynchos
18 Sime Flower Sime fool na 41 Kuchia fish Kuchia Monopterus cuchia NON –LEAFY VEGETABLES 42 Snails (small) Shamuk (choto) Helix pomatia
19 Pea eggplant Mistti begun* Solanum spinosa 43 Snails (large) Shamuk (Boro) Helix pomati 20 Solanum Tak begun* Solanum virginianum 44 Rat Idur Rattus norvegicus) 21 Sigon data Sigon data* Lasia spinosa 45 Frog Beng Litoria caerulea 22 Tara (Like Kochu data) Tara data na 46 na Gobar poka na 23 Basher Korol Basher korol na 47 Pork Shukurer mangsha Sus scrofa domestica 24 Wild mushroom Edur kan na
63
FGDs activities
FGD in Marma community in Marma palli, Khagrachari
FGD in Marma community in Marma palli, Khagrachari
64
FGD in Chakma community in Chakma palli, Rangamati
FGD in Chakma community in Chakma palli, Rangamati
65
FGD in Tanchanga community in tanchanga palli in Ra ngamati
FGD in Tanchanga community in tanchanga palli in Ra ngamati
66
FGD in Tripura community in Tripura palli, Khagrach ari
FGD in Tripura community in Tripura palli, Khagrach ari
67
3.1.3 Lifestyle characteristics of general and ethnic people
The key objective of the CFCS was to obtain food consumption information of the
general and ethnic population of Bangladesh. In addition to collecting the food
consumption data, the lifestyle profile of this population was also addressed.
Analysis of socioeconomic data showed that amongst the 1210 general households
(table 3.1) only 65 households were female headed and the rest were the male headed.
The male-headed urban household heads were more educated in numbers than their
counter part in rural locations (tables 3.5). Their main occupation was found to be earth
cutting. It may be because of their low educational level as well as currently running
road and civil works in the rural and semi urban areas. Female headed household
heads were mostly engaged in household works. Mean age of the male headed
household heads were similar in rural and urban areas. Female headed household
heads were comparatively older than the male head. The monthly income and
expenditure of both the urban and rural households were found similar.
Prevalence of illiteracy was high among the Marma and Shaontal while Chakma and
Tripura were more educated, and consequently Chakma and Tripura people were
employed in services (table 3.8). The monthly family income was found to be highest
among the Tripura followed by Chakma, and lowest income was found in the Marma
and Shaontal tribes.
Food security data indicated that almost 3% households frequently experienced food
shortage, while 12% percent reported to have food shortage infrequently (tables 3.6,
3.9). Food insecurity was high in February of the year. Rural (46%) and Urban (54%)
households reported to have infrequent balanced diet. Almost 9% household ate less
than three times a day. In food shortage, adult women had to eat less and it was higher
among the rural than the urban households. Compared to the general population, food
insecurity was high among the ethnic people. It was found higher among Marma and
68
Shaontal while food security was comparatively better among the Tripura, Tanchanga
and Chakma tribes.
In term of morbidity, most of the rural and urban household heads reported the suffering
of their under five children from diarrhea in the last one month (tables 3.7, 3.10). Most of
them did not take any specific care for the treatment of diarrhea. Comparing the
prevalence of diarrhea among the general population, prevalence of diarrhoea among
the ethnic under five children was found too high. It was found to be lowest among the
Tanchanga and highest among the Tripura children.
The lifestyle data reveal that the ethnic people are far behind the general population in
terms of socioeconomic situation, food security and health care access facilities.
Special care should be taken to address these problems.
69
Table 3.5: Socioeconomic profile of general households
Parameters Urban Rural
Frequency Percent Frequency Percent
Type of household (HH) 606 50.1 604 49.9
Gender of household head Male Female
575 31
94.9 5.1
570 34
94.4 5.6
Education of male headed HH head Below primary Below SSC Below HSC HSC to Below BSc BSc to MSc Illiterate can sign only Can read and sign Total
Education of female headed HH head Below primary Below SSC Below HSC Illiterate can sign only Can read and sign Total
106 162 37 39 4
61 75 121 606
1 6 3 3
12 6
31
17.5 26.8 6.1 6.5 0.7
10.1 12.30 20.0 100.0
3.4
17.2 10.3 10.1 39.0 20.0 100.0
129 156 34 32 2
222 28 -
604
7 1 0 12 9 5 34
21.3 25.9 5.7 5.3 0.4 36.7 4.7 -
100.0
20.0 3.3 -
36.7 25.0 15.0
100.0
Occupation of male headed HH head Agri (work) Earth cutting Rickshaw / van driver Others Total
20 574
2 10 606
3.3 94.7 0.4 1.6
100.0
13 583
8 -
604
2.1 96.5 1.4 -
100.0
Occupation of female headed HH head Agri (work) Earth cutting Household work NGO worker Others Total
1 6
18 4 2
31
3.4 17.2 58.6 13.9 6.9
100.0
- 2 30 20 -
34
-
6.7 86.6 6.7 -
100.0 Mean ± Sd Percent Mean ± Sd Percent
Age of male headed HH head (Year) 15-30 30-45 45-60 60-75 Total
26.68±3.40 38.67±4.35 51.90±3.87 66.10±3.53 40.73±1.42
23.3 45.9 25.4 5.4
100.0
27.30 ± 2.95 38.37± 4.28 53.11 ± 4.76 67.15 ± 3.49
41.23 ± 11.64
21.3 48.9 23.9 5.9
100.0 Age of female headed HH head (Year)
15-30 30-45 45-60 60-75 Total
27.25±3.20 40.08 ±3.77 52.78 ±4.24 66.67 ±2.89 45.00 ±11.79
13.8% 44.8% 31.0% 10.3%
100.0%
26.00 ± 0.00 39.23 ± 4.02 55.00 ± 4.88 70.00 ± 0.00
48.20 ± 11.21
3.3 43.3 46.7 6.7
100.0
Monthly total income (Tk.) <5000 5001 – 8000 5001 – 8000 8000 – 11000 11000 – 14000 >14000 Total
4093.82 ± 926.68 6673.87 ± 827.44 9653.40 ± 749.35 12520.00± 699.55
18976.00 ±5949.64 7837.54 ± 4556.45
30.69 36.63 17.00 7.43 8.25 100.0
4058.38 ± 931.65 6714.47 ± 843.55 9493.94 ±745.71
12411.36 ±790.41 20924.00 ±7222.35 8006.99 ± 5075.75
28.64 39.40 16.40 7.28 8.28
100.0
Monthly average total expenditure (Tk.) 6259.28 ± 3148.04 100.0 6169.38 ± 3324.94 100.0
70
Table 3.6: Food security by households’ type in general population
Parameters
Urban Rural
Frequency Percent Frequency Percent
Experience food shortage in family Never ever Some times Often/always Total
517 74 15 606
85.3 12.2 2.5
100.0
522 64 18 604
86.5 10.6 3.0
100.0
Time of food shortage January February Whole year Total
21 64 4
89
23.60 71.91 4.49
100.0
22 58 2
82
26.83 70.73 2.44
100.0 Status of getting balance food
Always Never ever some times Total
242 38 326 606
39.9 6.3 53.8
100.0
282 42 280 604
46.7 7.0 46.4
100.0 HH head ate < 3 times a day
Yes No Total
54 552 606
8.9 91.1
100.0
57 547 604
9.4 90.6
100.0
Children ate <3 times a day Yes No Total
25 581 606
4.1 95.9
100.0
16 588 604
1.7 97.5
100.0 Children starve d whole day
Yes No Total
12 594 606
2.0 98.0
100.0
5
598 604
1.0 99.0
100.0 Adult member starve d whole day
Yes No Total
29 577 606
4.8 95.2
100.0
19 585 604
3.1 96.9
100.0
Weight loss any member Yes No Did not verify Total
8 573 25 606
1.32 94.55 4.13
100.0
8 581 15 604
1.33 96.19 2.48
100.0
Who ate less during food s hortage None response Adult women Adult men Total
500 86 20 606
82.51 14.19 3.30
100.0
456 133 15 604
75.50 22.02 2.48
100.0
71
Table 3.7: Morbidity and its treatment by household type in general population
Response
Urban Rural
Frequency Percent Frequency Percent
Eating adequately but not gainin g weight Yes No Don’t understand Total
23 530
53 606
3.8 87.5
8.7 100.0
19 524
61 604
3.1 86.8 10.1
100.0
Member suffers from stomach ache Yes No Don’t know Total
29
575 2
606
4.8
94.9 0.3
100.0
28
576 -
604
4.6
95.4 -
100.0 Know ledge about reasons of diarrhoea
Answered rightly Answer partly right Wrongly answered Total
446 117
43 606
73.7 19.3
7.1 100.0
397 162
45 604
65.6 26.8 7.5
100.0 Diarr hoea in any <5 children in last month
Didn’t experienced Last week One month ago More than one month ago Total
471
14 24 97
606
77.6
2.3 4.0
16.0 100.0
478
13 33 80
604
79.2 2.2 5.5
13.2 100.0
Measures taken to get rid of diarrhoea Didn’t experienced Fed home prepared saline Fed packet saline Medicine Medicine and oral saline Total
471
14 64 4
53 606
77.8
2.3 10.6
0.7 8.7
100.0
478
10 62
4 50
604
79.1 1.7
10.3 0.7 8.2
100.0 Giving anti helminthes drug regularly to <6y children
Cannot remember Yes No Total
309 245
52 606
51.0 40.5
8.6 100.0
334 223
47 604
55.3 36.9 7.8
100.0
Immuniz ation to child ren Don’t know Complete Incomplete Total
339 260
7 606
55.9 42.9
1.2 100.0
357 236
11 604
59.1 39.1 1.8
100.0
72
Table 3.8: Socioeconomic profile of ethnic households
Parameters CHAKMA MARMA SHAONTAL TRIPURA TANCHANGA OTHERS
Type of household Frequency % Frequency % Frequency % Frequency % Frequency % Frequency % Education Level HH Head
Below primary Below SSC Below HSC HSC and above Illiterate can sign only Can read and sign Total
20 48 26 51 82 9 2
238
8.3
20.4 11.1 21.3 34.3 3.7 0.9
100.0
9 -
18 -
127 7 -
161
5.6 -
11.3 -
78.9 4.2 -
100.0
2
27 4 1
50 4 -
88
2.3 10.2 4.5 1.1 56.8 4.5 -
100.0
-
19 33 9 24 2 -
87
-
21.6 37.8 10.8 27.0 2.7 -
100.0
2 19 5 -
33 12 -
70
3.3 267 6.7 -
46.7 16.7
- 100.0
14 34 15 11 70 23 -
166
8.1 20.3 9.3 6.4 41.9 14.0
- 100.0
Occupation of HH head Agri (work) (1) Earth cutting (2) Rickshaw / van driver (5) Business (7) Jobless (9) Service (11) Others (12) Total
79 2 7
11 -
79 60 238
33.3 0.9 2.8 4.6 -
33.3 25.0 100.0
63 - 7
11 9 -
70 161
39.4
- 3.2 7.0 5.6 -
43.7 100.0
38 - 4 - 5
12 29 88
43.2
- 4.5 -
5.7 13.6 33.0
100.0
- - - 7 -
71 9 87
- - -
8.1 -
81.1 10.8 100.0
19 - 2 9 - 2 37 70
26.7
- 3.3 13.3
- 3.3 53.3
100.0
46 - 4 8 2
20 86 166
27.9
- 2.3 4.7 1.2 12.2 51.7
100.0
n Mean±sd n Mean±sd n Mean±sd n Mean±sd n Mean±sd n Mean±sd Age (y) dist ribution of HH Head
15-30 30-45 45-60 60-75 Total
31 145 53 9
238
26.5±3.01 38.3±3.82 53.1±4.57 69.5±4.20 41.2±10.50
20 63 57 20 161
28.1±2.52 38.5±3.96 53.5±4.27 66.4±3.09 46.2±12.4
14 42 28 4
88
27.6±2.41 37.2±4.27 52.8±3.50 66.0±4.24 42.0±11.1
12 49 16 9 87
27.4±4.22 39.7±3.88 51.6±3.69 66.3±2.99 43.1±11.3
14 33 19 5 70
28.2±1.94 38.4±4.07 52.8±4.98 70.0±1.09 42.3±12.1
18 78 58 13 166
27.8±2.07 38.1±4.30 52.8±4.25 66.5±4.29 44.3±11.3
Family monthly income (taka) <5000 5001 – 8000 8000 – 11000 11000 – 14000 >14000 Total
145 57 18 11 7
238
3090±949 6965±884 9375±694 12600±894 1667±2887 5306±3462
138 20 - - -
161
2780±1088 6444±846
- - -
3265±1635
82 5 1 - -
88
2343±1268 6100±548 10000.0
- -
2643±1704
24 19 24 9 12 87
3640±1418 6875±991 9500±667 12375±478
23200±11344 9510±7292
-
56 7 5 2 70
-
2739±1066 7000±1000 9500±707 14000±210 4034±2997
-
130 25 9 2
166
- 3055±1182 6301±832 9709±923
12000±304 4004±2286
Family monthly expenditure 238 6574±4392 161 3768±1754 88 3125±998 87 10512±1189 70 5345±1979 166 5478±2137
73
Table 3.9: Food security of ethnic tribes
Parameters CHAKMA MARMA SHAONTAL TRIPURA TANCHANGA OTHERS Freq. % Freq. % Freq. % Freq. % Freq. % Freq. %
Experience food sho rtage in family Never ever Some times Often/always Total
163 64 11
238
68.5 26.9 4.6
100.0
98 61 2
161
60.6 38.0 1.4
100.0
30 36 22 88
34.1 40.9 25.0
100.0
73 14
- 87
83.8 16.2
- 100.0
51 12 7
70
73.3 16.7 10.0
100.0
113 36 17
166
68.0 21.5 10.5
100.0 Time of food shortage
January February Whole year Total
162 64 11
237
68.3 26.9 4.8
100.0
98 42 21
161
61.1 25.9 13.0
100.0
31 45 12 88
35.0 51.0 14.0
100.0
85
- 2
87
97.3
- 2.7
100.0
48 17 3
68
68.0 24.0 4.0 78
105 37 19
151
63.0 22.1 11.7 96.8
Status of getting balance food Always Never ever some times Total
95 13
130 238
39.8 5.6
54.6 100.0
41
- 120 161
25.4
- 74.6
100.0
12 3
73 88
14.0 3.0
83.0 100.0
54
- 33 87
62.2
- 37.8
100.0
26 2
42 70
36.7 3.3
60.0 100.0
44 16
105 166
26.7 9.9
63.4 100.0
HH head ate < 3 times a day Yes No Total
68
170 238
28.7 71.3
100.0
25
136 161
15.5 84.5
100.0
46 42 88
52.3 47.7
100.0
-
87 87
-
100.0 100.0
16 54 70
23.3 76.7
100.0
43
123 166
25.7 74.3
100.0 Children ate <3 times a day
Yes No Total
51
187 238
21.3 78.7
100.0
11
150 161
7.0
93.0 100.0
40 48 88
45.5 54.5
100.0
0
87 87
-
100.0 100.0
9
61 70
13.3 86.7
100.0
20
146 166
12.2 87.8
100.0 Children starved whole day
Yes No Total
2
236 238
0.9
99.1 100.0
2
159 161
1.4
98.6 100.0
7
81 88
8.0
92.0 100.0
-
87 87
-
100.0 100.0
-
70 70
-
100.0 100.0
4
162 166
2.3
97.7 100.0
Adult member starved whole day Yes No Total
13
225 238
5.6 94.4
100.0
9
152 161
5.6
94.4 100.0
13 75 88
14.8 85.2
100.0
-
87 87
-
100.0 100.0
5
66 70
6.7
93.3 100.0
14
152 166
8.2
91.8 100.0
Weight loss in any member Not respond Yes No Did not verify Total
82 2
79 75
238
34.3 0.9
33.3 31.5
100.0
36
- 70 54
161
22.5
- 43.7 33.8
100.0
21
- 30 37 88
23.9
- 34.1 42.0
100.0
31
- 56
- 87
35.1
- 64.9
- 100.0
12 2
47 9
70
16.7 3.3
66.7 13.3
100.0
19 5
111 31
166
11.6 2.9
66.9 18.6
100.0 Who ate less during food shortage
Non response Adult women Adult men Others Total
162
7 60 9
238
68.2 2.8
25.2 3.7
100.0
88 40 25 8
161
54.4 24.6 15.8 5.3
100.0
33 19 34 2
88
37.5 21.6 38.6 2.3
100.0
87
- - -
87
100.0
- - -
100.0
49 5
16 -
70
70.0 6.7
23.3 -
100.0
102 22 23 20
166
61.5 13.0 13.7 11.8
100.0
74
Table 3.10: Morbidity and its treatment by ethnic tribes
Parameters CHAKMA MARMA SHAONTAL TRIPURA TANCHANGA OTHERS
Freq. % Freq. % Freq. % Freq. % Freq. % Freq. % Eating adequately but not gaining weight
Not respond Yes No Don’t understand Total
15 15
137 71
238
6.5 6.5 57.4 29.6
100.0
- -
93 68
161
- -
57.7 42.3
100.0
- 1 71 16 88
-
1.1 80.7 18.2
100.0
- 5 80 2 87
-
5.4 91.9 2.7
100.0
- -
65 5 70
- -
93.3 6.7
100.0
1 10
140 15
166
-
0.6 5.8 84.3 9.3
Member suffers from stomach ache Yes No Don’t know Total
9
220 9
238
3.7 92.6 3.7
100.0
2
159 -
161
1.4 98.6
- 100.0
-
88 -
88
-
100.0 -
100.0
-
87 -
87
-
100.0 -
100.0
12 54 5 70
16.7 76.7 6.7
100.0
19
146 1
166
11.6 87.8 0.6
100.0 Knowledge about reasons of diarrhea
Answered rightly Answer partly right Wrongly answered Total
134 71 33
238
56.5 29.6 13.9
100.0
68 73 20
161
42.2 45.1 12.7
100.0
32 43 12 88
36.8 49.4 13.8
100.0
87 - -
87
100.0
- -
100.0
42 12 16 70
60.0 16.7 23.3
100.0
97 52 17
166
58.1 31.4 10.5 100.0
Diarrhea in any ≤5 children in last month Didn’t experienced Last week One month ago More than one month ago Cannot remember Total
117 2 -
86 33
238
49.1 0.9 -
36.1 13.9
100.0
60 3 -
49 49
161
37.1 1.6 -
30.6 30.6
100.0
48 1 3 21 15 88
54.0 1.1 3.4 24.1 17.2
100.0
40 - 2 16 28 87
28 2 5 16 19 70
40.0 3.3 6.7 23.3 26.7
100.0
74 1 18 26 46
100
44.8 0.6 10.9 15.8 27.9
100.0
44.8 0.6 10.9 15.8 27.9 100.0
Measures taken to get relief of diarrhoea Didn’t experienced Fed packet saline Medicine Medicine and oral saline Total
152 48 13 24
238
63.9 20.4 5.6 10.2
100.0
126 12 18 6
161
78.2 7.3 10.9 3.6
100.0
63 15 3 7 88
71.3 17.2 3.4 8.0
100.0
68 9 2 7 87
78.4 10.8 2.7 8.1
100.0
37 28 2 2 70
53.3 40.0 3.3 3.3
100.0
122 19 14 11
166
73.3 11.2 8.7 6.8
100.0 Giving anti helminthics regularly to <6y children
Cannot remember Yes No Total
108 79 51
238
45.4 33.3 21.3
100.0
44 76 41
161
27.1 47.1 25.7
100.0
37 22 29 88
42.0 25.0 33.0
100.0
33 49 5 87
37.8 56.8 5.4
100.0
21 40 9 70
30.0 56.7 13.3
100.0
74 68 24
166
44.8 40.7 14.5 100.0
75
3.1.4 Identification of Key foods
The key food approach is used to identify and select food items for analysis of nutrient
profile. It concentrates to utilize analytical resource on those foods that contribute significant
amounts of nutrients of public health significance to the diet (Haytowitz et al, 1996). It is
done by analysis of CFCS data. The purpose of key food list is to select important foods for
human nutrition and to provide nutrients of public health benefit.
In this study, CFCS and FGDs identified a total of 138 food items comprising- 54 foods
consumed by both the general and ethnic population, 20 foods consumed only by the
general and 64 foods consumed only by the ethnic people (figure 3.5). The distribution of
food groups in the common, general and ethnic foods are depicted in the figures 3.6, 3.7 and
3.8.
Figure 3.5: Number of food item (n=138) consumed by population type
64
20
54
Only Ethnic Only General Both
76
Figure 3.6: Distribution of common food item (n =54) consumed by ≥5% HH
Figure 3.7: Distribution of ethnic food (n=64) of f ood items consumed by ≥5% HH
Figure 3.8: Distribution of general (n=20) food items consumed by ≥5% HH
117
23
16
8
Cereal Pulses Leafy Veg Non-Leafy Veg Fruits Animal
4
3
10
3
Leafy Veg Non-Leafy Animal Fruit
2
22
16
8
16
Cereal Leafy Veg Non-Leafy Veg Fruits Animal Foods
77
3.1.5 Selection of key foods
The CFCS and FGDs identified 138 key food items. From this list, 75 food items were
selected for analysis of their nutrient profile aiming to prepare the food composition
database.
The objective of food composition database is to ensure inclusion of a range of food items
eaten by the population for which the database is being prepared. However, ideally a truly
“comprehensive database” is, in fact, an impossible objective. It is primarily because of very
large number of foods in the human diet. The volume of analytical work required for nutrient
profiling and resource implications also make it difficult. Therefore, a strategy needs to be
developed for establishing priorities in selecting food items for inclusion into the database
(Greenfield and Southgate, 2003e).
In addition to considering the nutrient input of the foods, nutrient contribution of the food to
energy intake should be focused first. Food items of public health significance also need to
be addressed. In Bangladesh, micronutrient deficiency is a public health issue; deficiencies
in vitamin A, iron, and iodine are acute problems (WFP: Micronutrient deficiencies in Bangladesh;
Country summary-Bangladesh). Zinc deficiency is widespread; highly prevalent in children in
developing countries (Zinc Nutritive Initiative: http://www.zinc-crops.org/why_zinc.html). Bangladeshi
children are also suffering from zinc deficiency (Nutrition Country Profiles: Bangladesh;
http://www.tulane.edu/~internut/ Countries/Bangladesh/bangladeshlxx.html). Bangladesh currently exports
certain vegetables. Therefore, importance of food trade also needs to be taken into account
in selection of key foods.
Food grouping is important in the selection of key foods. This ensures that the diet as a
whole is considered and that the focus is not distorted by emphasizing one food group at the
expense of the diet as a whole. Most food composition database have between 10 and 25
food groups, however, it is culturally dependent. It is to be noted that food group should
78
focus food intakes by population rather than food intake by individual (Greenfield and
Southgate, 2003e).
The project proposition was to analyze 50 foods for their nutrient profile including proximate,
vitamin C, carotenoids, carotene profile, B-vitamins, fatty acids, antinutrient phytate, and
minerals. In compliance with the recommendation made at Rangamati workshop for
inclusion of more ethnic tribal foods, the food list was increased to 75 and the nutrient profile
was condensed to concomitantly in analysis of proximates, vitamin C, carotenoids, and β-
carotene, antinutrient phytate and minerals for the 75 food items.
In selecting this 75 key food, priority selection criteria were employed in which- food items
consumed by ≥15% of households were included in the key food list; the ethnic foods
consumed by >15% households but yet by a very minor group of ethnic population, were
excluded; also foods containing less micronutrient (poor health significance, such as foods
contain less or no β-carotene) were excluded. The key food list which are consumed by
both the general and ethnic, and food items consumed by only the ethnic are presented in
tables 3.11 and 3.12.
Because of the limitation to the number of food to be included, priority was given to focus the
food groups in selecting the key foods so that the selected key foods represent a whole diet.
The selected key foods included the most commonly and frequently consumed food groups
such as- cereals, lentil, vegetables, fruits, fishes, eggs and meats.
79
Table 3.11: Key food list consumed by both the native general and *ethnic people
Sl no English name Bengali name Scientific name Sl no English name Bengali name Scientific name Sl
no English name Bengali name Scientific name
CEREAL 26 Yellow saraca Maytraba Saraca thaipingensis 51 Amla Amloki Emblica officinalis
1 Rice parboiled (Brri-29) Sidhoy chal Oryza sativa ROOTS & TUBER 52 Melon (mix) Melon (mix) Bangi/futi
2 Rice sunned** Atap chal Oryza sativa 27 Potato Gol Alu Solanum tuberosum 53 Wood apple Bael Aegle marmelos
3 Maize Vutta Zea mays 28 Sweet potato (red) Misti alu Ipomoea batatas 54 Palm (ripe) Paka tal Borassus flabellifer
PULSE Dal 29 Carrot Gazor Daucus carota 55 Pineapple (jaldogi) Anarosh Ananas comosus
4 Lentil (deshi) Masur dal Lens culinaris non-LEAFY VEGETABLES 56 Monkey jack* Monkey jack* Deuwa*
LEAFY VEGETABLES 30 Egg plant Begun Solanum melongena 57 Burmese grape* Burmese grape* Lotkon*
5 Joseph’s Coat Lalshak Amaranthus gangeticus 31 Bitter Gourd Karola Momordica charantia 58 Wild Melon Sindera* Cumis melo
6 Spleen Amaranth Data shak Amaranthus dubius 32 Sweet pumpkin Misti kumra Cucurbita maxima 59 Roshko* Syzygium balsameum
7 Bottle Gourd Lau shak Lagenaria siceraria 33 Kakrol Kakrol Momordica cochinchinensis 60 Bead tree kusumgulu* Elaeocarpus angustifolius
8 Radish Mula shak Raphanus sativus 34 Ladies finger Dherosh Abelmoschus esculentus FISHES
9 Coco-yam Sobuj kochu shak Colocasia esculenta 35 Green papaya Kacha papay Carica papaya 61 Carp Ruhi Labeo rohita
10 Jute Pat shak Corchorus capsularis 36 Folwal Potol Trichosanthes dioica 62 Tilapia Tilapia mach Anabus testudineus
11 Indian spinach Poi shak Basella alba 37 Green chilli Kacha marich Capsicum frutescens 63 Dragon Fish Pangash Pangasius pangasius
12 Spinach Palong sag Spinacia oleracea 38 Pea eggplant Mistti begun* Solanum spinosa 64 Sunfish Mola mach Mola mola
13 Swamp Morning-glory Kalmi shak Ipomoea aquatica 39 Solanum Tak begun* Solanum virginianum 65 Arguskala Kachki mach Scatophagus argus
14 Thankuni Thankuni Pata Centella asiatica 40 Sigon data Sigon data* Lasia spinosa 66 Taki fish Taki mach Channa puncpatus
15 Coriander Dhane pata Coriandrum sativum 41 Yam Pan/jhum alu* Dioscorea pentaphylla 67 Silver Carp Silver Carp Hypophthalmichthys nobilis
16 Spearmint Pudina pata Mentha viridis 42 Banchalta Banchalta* Dillenia pentagyna 68 Poa fish Poa mach Glassogobius giuris
17 Bitter gourd Karala pata* Momordica charantia 43 Fekong Fakong Alpinia nigra EGGS
18 na Sabarang* Ajuga macrosperma FRUITS 69 Chicken egg (farm) Murgir dim (f) Gallus bankiva murghi
19 Roselle Amila pata* Hibiscus sabdariffa 44 Mango ripe(deshi) Paka Am Mangifera indica 70 Chicken egg (deshi) Murgir dim (d) Gallus bankiva murghi
20 na Lalam pata* Premna obtusifolia 45 Black berry (deshi) Kalojam Syzygium cumini 71 Duck egg Hasher dim Anas platyrhyncha
21 Indian Ivy-rue Baruna Shak* Xanthoxylum rhetsa 46 Jackfruit (ripe) Paka Kathal Artocarpus heterophyllus MEAT
22 na Ojan shak* Spilanthes calva 47 Lichi (deshi)) Lichu Lichi sinensis 72 Chiken (farm) Farm murgi Gallus bankiva murghi
23 na Ghanda batali* Paederia foetida 48 Banana (ripe) Paka kala Musa sapientum 73 Chiken (deshi) Desi murgi Gallus bankiva murghi
24 na Orai balai Premna esculenta 49 Water melon Tormuz Citrullus vulgaricus 74 Beef Garor mangsha Beef cattle
25 Purslane Bat slai* Portulaca oleracea 50 Papaya (ripe) Paka pepey Carica papaya 75 Pork* Shukor Pot bellied pig
*ethnic food ** raw
80
Table 3.12: Exclusive ethnic food list
English name Local name Scientific name
Leafy vegetables
1 Bitter gourd leaves Karala pata Momordica charantia
2 na Sabarang Ajuga macrosperma
3 Roselle Amila pata Hibiscus sabdariffa
4 na Lalam pata Premna obtusifolia
5 Indian Ivy-rue Baruna Shak Xanthoxylum rhetsa
6 na Ojan shak Spilanthes calva
7 na Ghanda batali Paederia foetida
8 na Orai balai Premna esculenta
9 Purslane Bat slai Portulaca oleracea
10 Yellow saraca Maytraba Saraca thaipingensis
non-LEAFY VEGETABLES
11 Pea eggplant Mistti begun Solanum spinosa
12 Solanum Tak begun Solanum virginianum
13 na Sigon data Lasia spinosa
14 Yam Pan/jhum alu Dioscorea pentaphylla
15 na Banchalta Dillenia pentagyna
16 na Fakong Alpinianigra
Fruits
17 Monkey jack Deuwa Artocarpus lakoocha
18 Burmese grape Lotkon Pirardia sapida
19 Wild Melon Sindera Cumis melo
20 na Roshko Syzygium balsameum
21 Bead tree Kusumgulu Elaeocarpus angustifolius
Meat
22 Pork Shukor Pot bellied pig
81
General key foods
Pat shak
Potato Black berry
Deuwa
non -Leafy vegetables
Kalmi shak
Bael Pui shak Lotokon
82
Ethnic key food items
83
3.2 Collection of food sample
Procedure for collection of food sample is important to get reliable representative
nutrient values. Care should be taken to avoid risk of inadvertent moisture loss and
deterioration of nutrients during transport from the collection point to the lab.
The food samples that were collected from distant points such as field samples and
ethnic foods were water sprayed to keep moisten, well packed in clean dark plastic poly
bags to prevent water loss and damage by light, and then transported to lab within
shortest time span. Some activities of ethnic food sampling are shown in following
photographs.
Ethnic food collection
84
Ethnic food collection
Ethnic food collection
Ehtnic food collection
Ehtnic food sorting
85
Team member with DAE personnel
Ethnic food display
86
3.3 Nutrient Compositions of Key Foods
Food composition database needs to be comprehensive. Its aim is to include food items
that are most commonly consumed by mass population for maintenance of their health
and nutrition. It is expected to be the primary source of nutritive information for food
policy program planning, designing dietary guideline, therapeutic diet formulation,
nutrition and agriculture research and training. By prioritizing the food items, the foods
that provide important nutrients for human nutrition as well as foods of public health
significance, are to be selected for analysis of nutrient profile and the analysis of every
sample for its content of all the nutrients is not required (Haytowitz et al, 2000).
Analysis of a range of foods contributing nutrients to the diet of human nutrition and
health is important. It is not truly possible, primarily, because a very large number of
foods form the human diet. The volume of analytical works and resource implications
for this work further make it impossible. Therefore, the strategy of prioritizing in
selecting food items and nutrients to be analysed has to be addressed properly.
In the present study, by prioritizing the consumption frequency and nutrient contribution
to the diet, seventy five key foods were selected for analysis of nutrient profile, which
contribute to human nutrition and which is of public health significance. The nutrient
profile included proximate nutrients and nutrients of health significance such as vitamin
C, carotenoids, β-carotene, and minerals. The results of proximate nutrients and
micronutrients are presented in the tables 3.13 to 3.24.
Foods, being biological materials, have variations in composition; therefore a database
cannot accurately predict the composition of any single sample of a food. It is especially
true for labile nutrients such as vitamin C, folates and carotenoids. As a result, FCD
cannot be used as literatures for comparision with values obtained for the foods
elsewhere. Nutrient values from one country are to be compared with values obtained in
other countries by reference to the original literature. However, FCD can be used as
87
reference when the nutrient values are known to be based on original analytical values
(Greenfield and Southgate, 2003a).
The present study has analysed 75 key foods for their nutrient profile comprising 23
components grouped as proximate nutrients, vitamins, minerals and antinutrient. The
values obtained were reviewed and compared with the values reported in different food
composition databases such as- Dhesio Kgadder Pustiman (Ahmed et al, 1977), HKI –
Food CompositionTable (FCT) (Darnton-Hill et al, 1988), Nutritive values of Indian
Foods (IFCT: Indian Food Composition Tables; Gopalan et al, 2004), Thai FCT
(Puwastien et al, 1999) and with the values reported in literatures. It is to be noted that
some of the nutrients which have been analysed and included in this database are
missing or do not have in the other FCTs.
3.3.1 Proximate nutrients
The principal proximate nutrients are protein, fat and carbohydrate. They are oxidized in
the body to give energy. In addition to providing energy, the primary function of protein
is to supply amino acids for building body proteins. Fats, besides being a concentrated
source of energy, provide essential fatty acids having vitamin like function in the body.
Water is an essential element, with which the proximate principles form the bulk of the
diet. Dietary fibers are indigestible complex molecules, contribute to the bulk and have
some important function in the digestive tract.
The values obtained for proximate nutrients were found to be very much consistent with
those reported in other FCTs. For example, the protein values obtained in the present
study for some key foods were almost similar to those reported in the IFCT, DKPM and
Thai FCT (table 3.19). It is also consistent with literature data (Alam and
Rahman:http://www.cepis.org.pe/bvsacd/arsenico/arsenic/zahangir.pdf). Such as protein
value was 6.96g/100g ep for BRRI-29 rice (table 3.13) which ranges 6.4-7.4g/100g ep in
88
IFCT, DKPM, Thai FCT. Somewhat similar outcomes were also obtained for other
proximate values in most of the key foods analysed.
3.3.2 Water in key foods
Water is an essential constituent in food composition database because water content is
one of the most variable components, particularly in plant foods. This variability affects
the composition of the food as a whole. The water content estimated in the key food
items was shown to be very much matched with those reported in other FCTs. Such as
moisture value in BRRI-29 rice is 12.14g/100 ep (table 3.13) which is almost same as
reported in the IFCT (13.3g/100g ep) and Thai FCT (11.2g/100g ep). Moisture content
in the other tested food items also has the comparable results.
3.3.3 Dietary fibre
Dietary fiber is the indigestible portion of plant foods. It has a number of physiological
functions and benefits including reduced appetite, lower variance in blood sugar levels,
reduced risks of heart disease, metabolic syndromes, diabetes, colorectal cancer and
constipation (http://en.wikipedia.org/wiki/Dietary_fiber). It also facilitates and improves
absorption of minerals. Therefore, information on dietary fiber content in plant foods is
important. This study has estimated dietary fiber content in a number of key foods.
Most of the values were consistent with the reported data; such as dietary fiber contents
in Amaranth leaves, Spinach leaves, Coriander leaves, Mint leaves, Carrot and
Pumpkin (table 3.23) were almost equivalent to those reported in IFCT and literatures
(http://www.dietary-fiber.info/; Punna et al, 2004).
3.3.4 Antinutrient-Phytate content
Phytic acid is a common constituent of many plant foods. It is a phytonutrient and has
antioxidant effect. Phytic acid, by binding with minerals, inhibits their absorption and
consequently induces mineral deficiencies to people who consume diet containing high
89
phytate. Phytic acid as antioxidant is effective in prevention of colon cancer
(http://en.wikipedia.org/wiki/Phytic_acid).
In the present work, phytic acid content has been estimated for 35 key food items. The
data were compared with IFCT and also with literature value. It was indicated that some
values were matched with either IFCT or literature value and some did not. Such as
phytic acid content in Maize and Lentil were estimated to be 959.85 and 516.12mg/100g
ep (table 3.24) respectively which are almost same as reported elsewhere (Hidvegi and
Lasztity, 2002; Dost and Tokul, 2006) for the same foods. Phytic acid level estimated
for vegetables was also somewhat within the range as reported by Udosen and
Ukpanah (1993). Potato contained approximately same amount of phytic acid (16.36 vs
14.00mg/100g ep) as reported in IFCT.
3.3.5 Vitamins and minerals in key foods
Vitamins assist the enzymes that release energy from carbohydrates, proteins and fats.
Minerals are used for shaping of body structure and skeleton. They enhance the
immune system, support normal growth and development, and help cells and organs to
their functions. Vitamins and minerals are widely available from the natural foods we
eat.
Vitamin content Vitamins analysed in the key food items included carotenoid, vitamin C and beta-
carotene. There was a fairly good variation in the values obtained in this study as
compared to the other FCTs, but some of the values were found almost consistent. For
examples- carotenoid values for Sabuj Kochu Sak, Corriendar leaves, Mula Sak and
Mango (ripe) obtained in this study were estimated 8.35, 6.83, 4.22 and 2.56mg/100g
ep (table 3.14, 3.17) against the values 10.278, 6.918, 5.295 and 2.743mg/100g ep
respectively in the IFCT. Similarly the vitamin C values for Sweet potato, Black berry
and Amla in the present study was found to be 23.92, 65.58 and 434.05mg/100g ep
90
respectively (table 3.17) while the values for the same foods were 24.00, 60.00 and
600mg/100g ep respectively in the IFCT. There was a wide variation in beta-carotene
value as compared to other FCTs. However, a few food items tested was found to have
a value of little variation as compared to IFCT value, such as the beta–carotene content
in Sobuj kochu shak was 7146.59µg/100g ep (table 3.23) as against a value
5920µg/100g ed in the IFCT. ,
Mineral content
Minerals are indispensable for normal life processes. They are required for metabolic
activities which are critical for cell differentiation and replication. Minerals, particularly
the trace elements, are essential to form endogenous antioxidant enzymes that are
required for endogenous antioxidant activity and immune modulation (Percival, 1998;
Shankar and Prasad, 1998).
A total of nine minerals –copper, zinc, iron, manganese, calcium, magnesium, sodium,
potassium and phosphorous were estimated in this study. Attempt has been made to
compare the values of these minerals with the data reported in other FCTs and
literatures. Some values are found to be consistent and some are inconsistent. It is
noted that most of the mineral content in rice, maize and lentil were found almost
matched with the data of IFCT and to some extent with Thai values. Calcium,
magnesium and phosphorous; and iron and manganes values for rice obtained in this
study were 12.75, 42.72 and 125.96 mg/100g ep and 990 and 612.45 µg/100g ed (table
3.14, 3.15) while these values are 9.0, 61.00 and 143mg/100g ep; and 1000.00 and 660
µg/100g ep respectively for the same foods in the IFCT. Similarly, copper value for
maize; calcium, magnesium. phosphorous, iron and manganese values for lentil were
also nearly consistent with IFCT values. In case of vegetables, copper value for Dheros
and Mistikumra; zinc value for Mistikumra, Carrot and Bael; iron value for Palong sak,
Begun, Potato, Sweet potato were almost same as compared to the IFCT value for
these foods.
91
Table 3.13: Proximate nutrient composition of cereals and leafy vegetables
*ethnic na: not available
Sl. No. English name Bengali/Local name Scientific name Moisture Protein Fat FA CF Ash CHO Energy
g/100g edible portion Kcal/100g CEREALS
1 Rice parboiled (Brri-29) Sidhoy chal Oryza sativa 12.14±0.03 6.96±0.08 0.31±0.00 0.26 0.24±0.00 0.60±0.01 79.75 349.63 2 Rice sunned* Atap chal Oryza sativa 12.98±0.13 7.74±0.04 0.43±0.00 0.37 0.26±0.00 0.54±0.01 78.11 347.29 3 Maize Vutta Zea mays 11.18±0.02 10.99±0.11 2.89±0.13 1.94 2.53±0.03 1.38±0.03 71.98 357.90
PULSE 4 Lentil (deshi) Masur dal Lens culinaris 11.38±0.13 23.91±0.13 0.73±0.01 0.7 0.69±0.02 2.63±0.01 60.66 344.85
LEAFY VEGETABLES 5 Joseph’s Coat Lalshak Amaranthus gangeticus 90.75±0.11 2.39±0.58 0.19±0.02 0.15 0.9± 0.02 1.42± 0.1 4.35 28.67 6 Spleen Amaranth Data shak Amaranthus dubius 91.40±0.22 2.36±0.55 0.30±0.02 0.24 0.88±0.01 0.93±0.04 4.13 28.66 7 Bottle Gourd Lau shak Lagenaria siceraria 92.82±0.30 2.58±0.70 0.22±0.01 0.18 1.17±0.02 2.19±0.12 1.02 16.38 8 Radish Mula shak Raphanus sativus 95.33±0.86 1.82±0.23 0.25±.02 0.2 0.62±0.01 1.12±0.22 0.85 12.97 9 Coco-yam Sobuj kochu shak Colocasia esculenta 89.29±0.40 2.45±0.92 0.41±0.02 0.33 0.77±0.03 2.14±0.16 4.94 33.25 10 Jute Pat shak Corchorus capsularis 85.70±0.07 5.2±0.95 0.63±0.02 0.50 1.36±0.52 2.31±0.05 8.47 60.35 11 Indian spinach Poi shak Basella alba 93.84±0.02 1.5±0.65 0.22±0.01 0.18 0.54±0.03 0.99±0.04 2.91 19.62 12 Spinach Palong shag Spinacia oleracea 89.93±0.07 2.26±1.11 0.21±0.04 0.17 0.73±0.01 2.12±0.06 4.75 29.93 13 Swamp morning-glory Kalmi shak Ipomoea aquatica 92.32±0.12 1.99±0.80 0.32±0.04 0.26 0.95±0.01 0.63±0.10 3.79 26.00 14 Thankuni Thankuni Pata Centella asiatica 81.84±0.06 2.3±1.30 0.85±0.01 0.68 0.90±0.02 1.70±0.17 12.41 66.49 15 Corriander Dhane pata Coriandrum sativum 88.99±0.33 3.04±1.00 0.23±0.05 0.18 0.99±0.03 2.17±0.18 4.58 32.55 16 Spearmint Pudina pata Mentha viridis 87.16±0.49 3.07±1.02 0.42±0.03 0.34 1.36±0.02 1.23±0.11 6.76 43.10 17 Bitter gourd Karola pata* Momordica charantia 91.57±0.14 2.13±0.11 0.15±0.00 0.12 0.62±0.01 1.70±0.09 3.83 25.19 18 na Sabarang* Ajuga macrosperma 88.63±0.24 2.57±0.06 1.29±0.08 1.03 1.25±0.06 1.7±0.05 4.56 40.13 19 Roselle Amila pata* Hibiscus sabdariffa 90.56±0.21 2.86±0.02 1.53±0.10 1.22 1.20±0.03 0.75±0.04 3.10 37.61 20 na Lalam pata* Premna obtusifolia 86.91±0.08 3.38±0.08 1.30±0.06 1.04 1.79±0.06 2.18±0.06 5.44 42.98 21 Indian Ivy-rue Baruna Shak* Xanthoxylum rhetsa 77.70±0.39 3.17±0.06 1.82±0.08 1.46 2.51±0.09 1.95±0.06 12.85 80.46 22 na Ojan shak* Spilanthes calva 89.03±0.08 3.10±0.03 1.08±0.11 0.86 1.31±0.05 1.92±0.06 3.56 36.36 23 na Ghanda batali* Paederia foetida 82.87±0.52 2.90±0.02 2.84±0.01 2.27 3.41±0.10 1.79±0.07 6.19 61.92 24 na Orai balai Premna esculenta 78.81±1.16 4.22±0.03 2.44±0.01 1.95 3.71±0.05 3.05±0.23 7.77 69.92 25 Purslane Bat slai* Portulaca oleracea 91.68±0.34 1.95 ± 0.03 0.66±0.02 0.53 0.89±0.04 2.12±0.06 2.70 24.54 26 Yellow saraca Maytraba Saraca thaipingensis 78.72±1.12 7.80 ± 0.12 2.79±0.08 2.23 2.70±0.06 2.46±0.11 5.53 78.43
92
Table 3.14: Vitamin C, carotenoids and micromineral composit ion of cereals and leafy vegetables
nd: not done na: not available *ethnic
Sl.No.
English name Bengali/Local name Scientific name Vitamin C Carotenoids Copper Zinc Iron Manganese
µg/100g edible portion CEREALS
1 Rice parboiled (Brri-29) Sidhoy chal Oryza sativa nd nd 510.00±17.13 310.31±19.72 990±10 612.45±19.23 2 Rice sunned* Atap chal Oryza sativa nd nd 490.13±11.31 740.49±10.17 910±10 592.27±31.79 3 Maize Vutta Zea mays nd nd 430.21±13.99 400.90±27.90 1310±20 552.97±18.31
PULSE 4 Lentil (deshi) Masur dal Lens culinaris nd nd 1620.91±80.12 6040.71±99.8 6130.53±50.29 987.83±37.19
LEAFY VEGETABLES 5 Joseph’s Coat Lalshak Amaranthus gangeticus 22.55±0.35 4.31±0.03 444.03±11.01 1128.58±1.82 2368.18±2.02 4995.37±4.72 6 Spleen Amaranth Data shak Amaranthus dubius 26.32±5.68 4.45±0.78 87.55±1.13 977.31±2.99 2897.03±2.97 4205.93±10.04 7 Bottle Gourd Lau shak Lagenaria siceraria 22.2±1.78 3.05±0.02 157.71±2.92 659.5±0.93 2107. 53±2.02 243.73±4.12 8 Radish Mula shak Raphanus sativus 68.85±0.73 4.22±0.16 89.34±1.27 457.85±2.98 904.52±1.00 89.34±2.16 9 Coco-yam Sobuj kochu shak Colocasia esculenta 60.09±5.20 8.35±0.10 226.74±3.85 684.49±1.20 10 05.35±0.01 1155.08±4.00 10 Jute Pat shak Corchorus capsularis 54.43±1.27 9.14±0.14 20.99±1.00 1469.27±1.0 9715.14±2.00 1619.19±1.00 11 Indian spinach Poi shak Basella alba 55.59±3.95 8.17±0.06 49.29±1.91 431.33±1.18 985.83±2.00 739.37±0.82 12 Spinach Palong shag Spinacia oleracea 22.44±2.93 4.35±0.07 60.24±2.05 512.01±2.00 1566.26±3.86 1430.72±2.11 13 Swamp morning-glory Kalmi shak Ipomoea aquatica 41.83±4.90 5.66±0.10 2010.74±0.14 767.46±2.68 1089.79±0.96 414.43±1.99 14 Thankuni Thankuni Pata Centella asiatica 37.77±1.68 7.47±0.21 508.17±2.02 2431.94±1.97 3702.36±2.56 2250.45±1.40 15 Corriander Dhane pata Coriandrum sativum 76.56±4.47 6.83±0.03 1233.48±2.01 1585.9±5.01 4977.97±3.00 462.56±8.08 16 Spearmint Pudina pata Mentha viridis 57.03±5.60 7.61±0.02 183.97±2.00 1760.84±0.03 3968.46±2.02 289.09±2.00 17 Bitter gourd Karola pata* Momordica charantia 107.90±3.40 10.47±0.49 66.45±2.13 865.95±2.99 1348.88±2.01 99.92±5.97 18 na Sabarang* Ajuga macrosperma 12.92±0.03 5.97±0.15 1159.09±0.01 522.62±0.47 2818.18±0.00 1659.09±0.00 19 Roselle Amila pata* Hibiscus sabdariffa 16.08±0.37 4.41±0.08 1026.61±0.41 513.31±0.31 3954.37±0.00 2737.64±0.73 20 na Lalam pata* Premna obtusifolia 18.86±0.09 3.03±0.13 1396.57±0.50 1554.67±0.00 3847.17±0.88 4295.13±0.88 21 Indian Ivy-rue Baruna Shak* Xanthoxylum rhetsa 38.04±1.81 6.11±0.27 312.53±0.52 312.45 0.05 4375.00±0.01 12946.43±0.02 22 na Ojan shak* Spilanthes calva 15.11±0.02 4.61±0.38 351.26±0.00 461.03±0.03 2634.47± 0.40 2678.38±0.06 23 na Ghanda batali* Paederia foetida 7.36±0.02 6.99±0.10 305.02±0.01 135.59±0.01 3423.73±1.00 4779.66±0.29 24 na Orai balai Premna esculenta 22.94±2.82 4.45±0.78 253.69±0.58 1818.19±0.09 3551.79±0.80 175.48±0.21 25 Purslane Bat slai* Portulaca oleracea 3.24±0.05 2.24±0.14 215.77±0.01 414.94±0.04 27 21.99±0.00 2356.86±0.09 26 Yellow saraca Maytraba Saraca thaipingensis 92.6±0.00 13.18±1.15 251.57±0.01 1006.29±0.21 1425.58±0.43 1299.79±0.11
93
Table 3.15: Macromineral composition of cereals and leafy v egetables
Sl.No. English name Bengali/Local name Scientific name Calcium Magnesium Sodium Potassium Phosphorous mg/100gm edible portion
CEREALS 1 Rice parboiled (Brri-29) Sidhoy chal Oryza sativa 12.75±0.61 42.72±0.81 10.97±0.01 109.89±0.06 125.96±0.4 2 Rice sunned* Atap chal Oryza sativa 11.67±0.38 43.29±1.38 5.43±0.01 108.62±0.17 140.67±6.69 3 Maize Vutta Zea mays 21.53±0.56 176.76±5.22 13.79±0.08 248.4±1.58 28 1.99±1.92
PULSE 4 Lentil (deshi) Masur dal Lens culinaris 66.12±2.47 104.16±1.82 33.15±0.05 561.18±0.08 313.26±3.44
LEAFY VEGETABLES 5 Joseph’s Coat Lalshak Amaranthus gangeticus 90.75±0.01 27.94±0.95 83.26±.01 277.52±1.01 41. 63±1.01 6 Spleen Amaranth Data shak Amaranthus dubius 104.89±0.01 25.74±1.87 78.53±0.94 261.78±0.93 34.9±0.91 7 Bottle Gourd Lau shak Lagenaria siceraria 85.66±1.96 18.49±1.20 35.84±1.16 222.22±1.01 26 .89±0.91 8 Radish Mula shak Raphanus sativus 83.92±1.93 14.13±1.00 83.75±1.04 223.34±0.85 22 .33±0.95 9 Coco-yam Sobuj kochu shak Colocasia esculenta 77.75±1.90 26.1±0.91 53.48±0.99 374.33±1.09 42. 78±0.81
10 Jute Pat shak Corchorus capsularis 132.98±1.99 41.83±0.85 59.97±0.99 224.89±0.99 59.97±0.97 11 Indian spinach Poi shak Basella alba 55.14±0 .92 19.1±1.01 104.74±1.03 110.91±1.07 18.48±0.91 12 Spinach Palong sag Spinacia oleracea 47.65±1.90 22.36±0.84 248.49±1.10 173.19±1.33 24.47±1.04 13 Swamp morning-glory Kalmi shak Ipomoea aquatica 34.08±1.99 16.42±0.81 107.44±1.06 207.21±0.77 36.45±1.10 14 Thankuni Thankuni Pata Centella asiatica 147.37±1.89 50.09±1.00 199.64±1.02 508.17±0.99 45.37±0.90 15 Corriander Dhane pata Coriandrum sativum 113.33±1.80 28.19±0.92 121.15±0.99 396.48±0.90 30.29±0.90 16 Spearmint Pudina pata Mentha viridis 110.12±0.98 33.25±0.86 78.84±1.00 354.8±0.90 36 .14±0.96 17 Bitter gourd Karala pata* Momordica charantia 170.94±1.99 22.9±0.99 66.61±0.86 258.12±0.98 22 .9±0.91 18 na Sabarang* Ajuga macrosperma 49.34±0.01 0.0±0.00 0.40±0.02 268.18±1.00 52.2 7±1.0 19 Roselle Amila pata* Hibiscus sabdariffa 30.57±0.50 0.49±0.00 0.31±0.02 144.50±0.50 38 .02±0.00 20 na Lalam pata* Premna obtusifolia 35.84±0.05 0.00±0.0 0.45±0.03 376.81±0.00 44.79±0.00 21 Indian Ivy-rue Baruna Shak* Xanthoxylum rhetsa 84.82±0.10 0.0±0.00 0.67±0.02 348.21±0.20 44.7 9±0.26 22 na Ojan shak* Spilanthes calva 26.23±0.19 0.00±0.00 0.46±0.02 338.08±0.01 50.5 8±0.44 23 na Ghanda batali* Paederia foetida 64.51±0.00 0.07±0.00 0.51±0.04 298.31±0.31 40.85±0.17 24 na Orai balai Premna esculenta 54.41±0.01 0.13±0.00 0.88±0.04 600.42±0.43 43.65±1.37 25 Purslane Bat slai* Portulaca oleracea 20.28±0.20 8.30±0.00 0.50±0.02 285.47±0.70 24.3 4±1.11 26 Yellow saraca Maytraba Saraca thaipingensis 39.83±0.19 2.64±0.01 0.71±0.04 469.60±0.59 109.94±0.93
na: not available *ethnic
94
Table 3.16: Proximate composition of roots & tuber, non-leafy ve getables and fruits Sl.no English name Bengali/Local name Scientific name Moisture Protein Fat FA CF Ash CHO Energy
g/100g edible portion Kcal/100g ROOTS & TUBER
27 Potato Gol Alu Solanum tuberosum 79.65±0.15 2.07±0.03 0.62±0.01 0.5 0.36±0.01 0.76±0.01 16.54 80.02 28 Sweet potato (red) Misti alu Ipomoes batatas 65.05±0.07 1.17±0.03 0.29±0.01 0.23 0.78±0.01 1 .05±0.02 31.66 133.93 29 Carrot Gazor Daucus carota 89.67±0.40 0.81±0.03 1.00±0.03 0.8 0.57±0.02 0.92±0.09 10.33 41.32
NON-LEAFY VEGETABLES 0 Egg plant Begun Solanum melongena 93.42±0.05 1.21±0.81 0.05±0.00 0.04 0.74±0.01 1.14±0.09 3.44 19.05 31 Bitter Gourd Karola Momordica charantia 93.91±0.39 1.11±0.54 0.07±0.01 0.06 1.16±0.07 0 .87± 0.02 2.88 16.59 32 Sweet pumpkin Misti kumra Cucurbita maxima 93.33±0.04 0.59±0.03 0.08±0.00 0.06 0.23±0.06 0 .67±0.07 5.10 23.48 33 Kakrol Kakrol Momordica cochinchinensis 89.33±0.58 1.47±0.22 0.10±0.00 0.08 1.55±0.04 1 .25±0.02 6.30 31.98 34 Ladies finger Dherosh Abelmoschus esculentus 92.65±0.08 1.31±0.22 0.19±0.02 0.15 0.57±0.00 1.19±0.06 4.09 23.31 35 Green papaya Kacha papay Carica papaya 93.85±0.03 0.60±0.20 0.05±0.00 0.04 0.64±0.01 1 .32±0.02 3.54 17.01 36 Folwal Potol Trichosanthes dioica 92.89±0.14 1.31±0.02 0.07 0.00 0.06 1.44±0.02 0.58±0.02 4.29 23.03 37 Green chilli Kacha marich Capsicum frutescens 84.83±0.18 2.86±0.95 0.83±0.04 0.66 4.9±0.14 1.13±0.16 5.45 40.71 38 Pea eggplant Mistti begun* Solanum spinosa 84.43±0.40 2.45±0.03 2.13±0.21 1.70 4.21±0.07 1.12±0.04 5.66 51.61 39 Solanum Tak begun* Solanum virginianum 78.94±0.16 2.70±0.03 5.27±0.35 4.22 6.97±0.06 1. 57±0.03 4.55 76.43 40 Sigon data Sigon data* Lasia spinosa 96.09±0.25 0.66±0.02 0.32±0.01 0.26 0.59±0.05 0.83±0.02 1.51 11.56 41 Yam Pan/jhum alu* Dioscorea pentaphylla 66.05±0.59 2.69±0.04 1.17±0.13 0.14 1.72±0.05 1. 14±0.11 28.23 125.21 42 Banchalta Banchalta* Dillenia pentagyna 89.58±0.30 2.12±0.03 0.63±0.01 0.51 1.22±0.06 1.34±0.10 5.11 34.59 43 Fekong Fakong Alpinia nigra 97.00±0.11 0.44±0.02 0.27±0.01 0.22 0.89±0.04 0. 79±0.03 0.61 6.63
FRUITS 44 Mango ripe (deshi) Paka Am Mangifera indica 86.84±0.28 0.61±0.20 0.63±0.07 0.50 0.73±0.85 0 .35±0.04 10.84 51.47 45 Black berry (deshi) Kalojam Syzygium cumini 86.32±0.04 0.62±0.07 0.27±0.02 0.22 1.08±0.06 1.05±0.20 11.66 51.55 46 Jackfruit (ripe) Paka Kathal Artocarpus heterophyllus 77.88±0.53 1.53±0.07 0.14±0.02 0.11 0.58±0.12 0 .79±0.03 19.08 83.70 47 Lichi (deshi)) Lichu Lichi sinensis 83.70±0.36 1.26±0.09 0.93±0.07 0.74 0.66±0.20 0.8±0.06 13.45 67.21 48 Banana (ripe) Paka kala Musa sapientum 74.56±0.38 1.31±0.07 0.36±0.05 0.29 0.26±0.02 0 .97±0.05 22.54 98.64 49 Water melon Tormuz Citrullus vulgaricus 92.97±0.47 0.73±0.09 0.20±0.00 0.16 0.09±0.03 0.36±0.01 5.65 27.32 50 Papaya (ripe) Paka papay Carica papaya 91.14±0.58 0.61±0.10 0.14±0.01 0.11 0.74±0.03 0 .53±0.04 6.84 31.06 51 Amla Amloki Emblica officinalis 82.52±0.12 0.60±0.19 0.12±0.01 0.10 0.85±0.00 1.18±0.10 14.73 62.40 52 Melon (mix) Bangi/futi Cucumus melo 95.02±0.33 0.19±0.06 0.21±0.00 0.17 0.17±0.45 0 .25±0.03 4.16 19.29 53 Wood apple Bael Aegle marmelos 61.86±0.55 3.55±0.06 2.56±0.07 2.05 1.33±0.01 0.22±0.05 30.48 159.16 54 Palm (ripe) Paka tal Borassus flabellifer 81.21±0.11 0.66±0.08 0.42±0.03 0.34 0.97±0.07 0 .92±0.03 30.48 159.16 55 Pineapple (jaldogi) Anarosh Ananas comosus 85.08±0.18 0.61±0.10 0.58±0.02 0.46 1.06±0.02 0.45±0.01 12.22 56.54 56 Monkey jack* Deuwa* Artocarpus lakoocha 60.74±0.75 1.97±0.01 8.73±0.16 6.70 3.63±0.06 0 .98±0.12 24.31 180.45 57 Burmese grape* Lotkon* Pirardia sapida 90.54± 0.49 1.61±0.20 2.49±0.10 1.99 4.2±0.18 0.52±0.06 0.64 31.41 58 Wild Melon Sindera* Cumis melo 95.88±0.11 0.36±0.03 0.52±0.05 0.42 0.79±0.03 0. 54±0.02 1.91 13.76 59 na Roshko* Syzygium balsameum 87.12±0.25 0.70±0.03 1.54±0.01 1.23 1.32±0.07 1. 33±0.08 7.99 48.62 60 Bead tree kusumgulu* Elaeocarpus angustifolius 92.51±0.08 0.95±0.03 0.94±0.13 0.75 0.88±0.09 0. 81±0.03 3.91 27.90
na: not available *ethnic
95
Table 3.17: Vitamin C, carotenoids and micromineral composition of roots & tuber, non-leafy vegetables Sl.No. English name Bengali/Local name Scientific name Vitamin C Carotenoids Copper Zinc Iron Manganese
mg/100g edible µg/100g edible portion ROOTS & TUBER
27 Potato Gol Alu Solanum tuberosum 8.80±0.84 nd 290.00±10.00 790.00±10.00 400.00±0.00 10.43 ±1.05 28 Sweet potato (red) Misti alu Ipomoea batatas 23.92±3.09 0.25±0.02 100.00±20.00 170.00±30.00 250.00±0.00 12.31±2.51 29 Carrot Gazor Daucus carota 11.17±0.76 8.56±0.49 53.72±0.98 327.39±0.05 638.56±20.33 3.82 ±0.84
NON-LEAFY VEGETABLES 30 Egg plant Begun Solanum melongena 6.66±0.58 nd 184.09±1.01 197.24±3.00 289.28±0.90 65.75 ± 1.8 31 Bitter Gourd Karola Momordica charantia 136.39±10.46 1.79±0.03 182.04±1.99 388.35±0.15 400.49±5.00 254.85 ± 6.07 32 Sweet pumpkin Misti kumra Cucurbita maxima 12.12±0.41 3.81±0.13 40.03±1.00 306.87±3.97 400 .27±2.02 0.13 ± 0.01 33 Kakrol Kakrol Momordica cochinchinensis 119.06±7.01 0.27±0.06 2401.66±4.00 476.19±4.01 538.3±2.00 62.11± 2.08 34 Ladies finger Dherosh Abelmoschus esculentus 10.18±1.10 0.38±0.03 103.93±2.00 430.59±2.01 282.11±3.01 29.7 ± 2.04 35 Green papaya Kacha papay Carica papaya 13.74±0.42 nd 24.59±1.00 258.14±2.00 417.95±3.01 9.83 ± 1.86 36 Folwal Potol Trichosanthes dioica 44.18±1.81 nd 70.39±1.00 239.44±1.22 309.86±1.01 84.51±1.31 37 Green chilli Kacha marich Capsicum frutescens 101.0±12.22 1.01±0.06 1832.06±3.01 1190.84±0.09 4488.55±2.00 183.21± 2.04 38 Pea eggplant Mistti begun* Solanum spinosa 6.99±0.30 3.62±0.18 305.35±0.01 122.14±0.01 213.74±0.29 549.62 ± 0.02 39 Solanum Tak begun* Solanum virginianum 16.66±0.105 4.58±0.24 345.57±0.50 302.38±0.32 1857.45±0.00 734.34 ± 0.33 40 Sigon data Sigon data* Lasia spinosa 2.63±0.06 0.95±0.01 91.22±0.20 224.56±0.50 196 .49±0.00 1340.35 ± 0.35 41 Yam Pan/jhum alu* Dioscorea pentaphylla 19.25±0.24 0.48±0.01 1152.54±0.43 338.98±0.02 1084.75± 0.00 610.17 ± 0.17 42 Banchalta Banchalta* Dillenia pentagyna 31.16±1.11 15.17±0.04 447.28±0.01 575.08±0.89 660.28±0.28 1853.04 ± 0.00 43 Fekong Fakong Alpinia nigra 3.24±0.07 0.13±0.01 89.53±0.01 131.30±0.71 537.15±0.06 1020.60 ± 0.01
FRUITS 44 Mango ripe(deshi) Paka Am Mangifera indica 10.88±1.20 2.56±0.26 173.23±2.03 543.31±1.81 1312.34±2.61 577.43± 0.96 45 Black berry (deshi) Kalojam Syzygium cumini 65.58±7.04 0.39±0.11 116.42±2.00 1090.52±0.30 1758.14±1.96 147.3 ± 1.95 46 Jackfruit (ripe) Paka Kathal Artocarpus heterophyllus 11.08±0.50 0.71±0.13 30.5±1.00 566.45±2.95 915.03±3.00 261.44 ± 4.21 47 Lichi (deshi)) Lichu Lichi sinensis 0.07±0.00 0.07±0.00 251.63±2.16 522.88±3.02 1013.07±2.00 88.35±3.16 48 Banana (ripe) Paka kala Musa sapientum 15.65±4.21 nd 45.92±2.00 714.29±1.19 1122.45±2.95 204.08 ± 0.99 49 Water melon Tormuz Citrullus vulgaricus 3.84±0.65 4.20±0.22 80.81±3.02 176.43±3.66 498. 32±1.82 78.11± 2.20 50 Papaya (ripe) Paka papay Carica papaya 7.48±2.65 2.33±0.27 1431.11±2.00 2933.33±2.00 145.78±1.00 186.67±3.98 51 Amla Amloki Emblica officinalis 434.05±27.31 nd 8146.85±0.95 734.27±1.15 1153.85±2.00 104.9 ± 2.00 52 Melon (mix) Bangi/futi Cucumus melo 3.65±1.33 0.80±0.13 61.87±3.00 62.71±3.94 245.76±0.10 177.97± 2.99 53 Wood apple Bael Aegle marmelos 15.67±2.12 0.15±0.01 2031.41±2.01 432.81±2.03 2233.86±2.00 202.44± 2.08 54 Palm (ripe) Paka tal Borassus flabellifer 35.13±0.21 3.57±0.09 4172.93±3.02 413.53±1.96 1240.6±10.00 150.37±4.23 55 Pineapple (jaldogi) Anarosh Ananas comosus 27.82±3.20 0.71±0.02 240±5.00 601.48±2.31 1600± 10.00 671.16±4.69 56 Monkey jack* Deuwa* Artocarpus lakoocha 11.68±1.65 4.13±0.46 796.18±1.01 3980.89±0.04 5254.78±1.00 549.36± 0.73 57 Burmese grape* Lotkon* Pirardia sapida 12.05 ± 1.60 0.12±0.01 248.14±1.98 903.23±2.00 1488.83±1.93 1091.81± 9.97 58 Wild Melon Sindera* Cumis melo 9.95±0.23 1.84±0.04 32.86±0.37 32.87±0.02 156. 12±0.00 49.30 ± 0.29 59 na Roshko* Syzygium balsameum 13.12±0.07 1.19±0.04 128.58±0.56 0.13±0.01 0.26±0.01 154.04 ± 0.00 60 Bead tree kusumgulu* Elaeocarpus angustifolius 6.08±0.23 0.26±0.01 344.05±0.01 403.89±0.01 1780.10±0.10 299.18 ± 0.09
nd: not done na: not available *ethnic
96
Table 3.18: Macromineral composition of roots and tuber and non- leafy vegetables
Sl.No. English name Bengali/Local name Scientific name Calcium Magnesium Sodium Potassium Phosphorous mg/100gm edible portion
ROOTS & TUBER 27 Potato Gol Alu Solanum tuberosum 9.38±0.68 41.96±1.22 10.42±0.03 403.61±0.41 43. 3±0.14 28 Sweet potato (red) Misti alu Ipomoes batatas 47.09±3.44 33.59±2.03 10.85±0.03 304.0±0.82 38.2±0.14 29 Carrot Gazor Daucus carota 0.23±0.00 5.03±0.00 68.26±0.01 87.46±3.27 1.10±0.25
NON-LEAFY VEGETABLES 30 Egg plant Begun Solanum melongena 7.23± 2.07 11.51±0.95 32.87±1.10 157.79±1.16 19 .72± 0.74 31 Bitter Gourd Karola Momordica charantia 10.92±1.93 14.32±0.98 36.41±1.99 182.04±1.00 19.72±0.90 32 Sweet pumpkin Misti kumra Cucurbita maxima 13.74±2.92 3.54±0.86 26.68±0.95 120.08±0.94 13. 34±0.91 33 Kakrol Kakrol Momordica cochinchinensis 9.83±1.84 19.57±0.98 51.76±1.11 186.34±0.83 25.88±0.89 34 Ladies finger Dherosh Abelmoschus esculentus 45.95±1.96 19.67±0.79 37.12±1.00 178.17±1.00 27 .84±0.86 35 Green papaya Kacha papay Carica papaya 17.76±1.90 13.09±1.00 43.02±1.01 129.07±1.02 15.37±1.07 36 Folwal Potol Trichosanthes dioica 17.32±1.98 15±1.00 28.17±1.02 147.89±0.90 17.61±0.89 37 Green chilli Kacha marich Capsicum frutescens 12.21±2.09 27.94±0..95 76.34±1.00 274.81±1.06 38.17±1.86 38 Pea eggplant Mistti begun* Solanum spinosa 26.81±0.21 0.65±0.02 0.40 ± 0.03 277.86 ±0.10 63.05±1.98 39 Solanum Tak begun* Solanum virginianum 19.27±0.75 4.49±0.40 0.52 ± 0.04 336.96 ±0.00 69.83±0.71 40 Sigon data Sigon data* Lasia spinosa 1.64±0.29 0.49±0.00 0.20 ±0.03 147.32 ± 0.32 19.48±0.53 41 Yam Pan/jhum alu* Dioscorea pentaphylla 1.89±0.40 17.09± 0.01 0.78±0.04 352.54 ±0.0 34. 90±1.00 42 Banchalta Banchalta* Dillenia pentagyna 15.98±0.00 0.64±0.29 0.47±0.02 287.54 ±0.45 40.72±0.26 43 Fekong Fakong Alpinia nigra 1.48±0.08 033±0.00 0.21±0.02 134.89±0.00 19.22±1.32
FRUITS 44 Mango ripe (deshi) Paka Am Mangifera indica 16.08±1.99 6.69±1.01 2.79±0.52 98.48±1.00 7.74±0.82 45 Black berry (deshi) Kalojam Syzygium cumini 26.73±1.84 11.99±1.00 50.57±3.14 106.91±1.01 11. 64±0.86 46 Jackfruit (ripe) Paka Kathal Artocarpus heterophyllus 12.64±1.84 26.8±0.89 87.15±1.05 305.01±1.00 10.89±0.90 47 Lichi (deshi)) Lichu Lichi sinensis 20.83±0.97 5.15±1.10 119.21±5.83 134.8±1.00 15.77±0.89 48 Banana (ripe) Paka kala Musa sapientum 6.38±2.69 26.28±0.90 102.04±1.01 255.1±1.00 19.13±1.00 49 Water melon Tormuz Citrullus vulgaricus 13.47±0.97 4.01±1.73 31.94±3.68 58.92±0.94 6.36± 1.02 50 Papaya (ripe) Paka papay Carica papaya 15.11±1.93 6.62±1.02 11.85±0.93 133.33±1.00 11.02±0.98 51 Amla Amloki Emblica officinalis 13.81±1.85 8.08±0.99 69.93±5.95 174.83±1.05 13. 11±0.99 52 Melon (mix) Bangi/futi Cucumus melo 6.46±1.00 1.02±0.02 2.86±0.67 27.54±1.11 1.46±0.60 53 Wood apple Bael Aegle marmelos 70.68±0.98 16.58±1.27 6.92±0.53 427.57±1.19 23.04±1.03 54 Palm (ripe) Paka tal Borassus flabellifer 7.89±0.01 13.91±1.02 93.98±1.01 375.94±1.05 14.1±0.99 55 Pineapple (jaldogi) Anarosh Ananas comosus 24.82±0.94 12±2.00 41.81±2.97 122.22±1.00 6.82±0 .88 56 Monkey jack* Deuwa* Artocarpus lakoocha 66.68±2.01 23.69±0.90 79.17±6.11 348.33±1.01 22.69±0.94 57 Burmese grape* Lotkon* Pirardia sapida 52.11±2.07 11.29±0.95 7.21±1.07 198.51±1.00 17.12±0.99 58 Wild Melon Sindera* Cumis melo 4.27±0.25 1.13±0.09 0.18±0.01 66.74 ±0.00 28.16±1.86 59 na Roshko* Syzygium balsameum 8.19±0.01 5.96±0.09 0.39±0.03 256.74 ± 0.22 39. 53±1.01 60 Bead tree kusumgulu* Elaeocarpus angustifolius 0.17±0.01 2.69±0.00 0.31±0.03 109.20 ±0.00 14. 66±1.19
nd: not done na: not available *ethnic
97
Table 3.19: Proximate composition of fish, egg and meat
Sl.No. English name Bengali/Local name Scientific name Moisture Protein Fat FA Ash CHO Energy g/100g edible portion Kcal/100g
FISHES 61 Carp Ruhi Labeo rohita 75.63±0.67 15.60±0.38 5.07±0.10 3.55 0.57±0.06 3.13 120.55 62 Tilapia Tilapia mach Anabus testudineus 73.92±0.48 16.87±0.30 5.11±0.08 3.58 0.59±0.01 3.51 127.51 63 Dragon Fish Pangash Pangasius pangasius 71.91±1.64 13.71±0.10 11.95±0.21 8.37 0.47±0.01 1.96 170.23 64 Sunfish Mola mach Mola mola 76.29±0.49 12.96±0.13 6.39±0.04 4.47 1.61±0.06 2.75 120.35 65 Arguskala Kachki mach Scatophagus argus argus 80.73±0.10 12.99±0.07 2.13±0.06 1.49 1.09±0.08 3.06 83.37 66 Taki fish Taki mach Channa puncpatus 79.71±0.05 17.18±0.93 1.47±0.02 1.03 0.60±0.02 1.04 86.11 67 Silver Carp Silver Carp Hypophthalmichthys nobilis 75.45±0.20 14.59±0.17 6.10±0.12 4.27 0.52±0.03 3.34 126.62 68 Poa fish Poa mach Glassogobius giuris 77.69±0.43 15.52±0.26 3.46±0.08 2.42 0.51±0.02 2.82 104.50
EGGS 69 Chicken egg (farm) Murgir dim (farm) Gallus bankiva murghi 75.78±0.50 12.07±0.19 11.37±0.10 9.44 0.77±0.03 0.78 153.73 70 Chicken egg (deshi) Murgir dim (deshi) Gallus bankiva murghi 76.12±1.92 11.33±0.15 11.60±0.07 9.63 0.89±0.03 0.95 153.52 71 Duck egg Hasher dim Anas platyrhyncha 68.39±0.19 15.47±0.37 15.87±0.29 13.17 0.95±0.06 0.27 205.79
MEATS 72 Chiken (farm) Farm murgi Gallus bankiva murghi 74.61±1.88 16.29±0.34 5.65±0.36 5.34 1.13± 0.03 2.32 125.29 73 Chiken (deshi) Desi murgi Gallus bankiva murghi 74.92±0.62 15.61±0.43 3.05±0.51 2.88 0.72± 0.01 5.70 112.69 74 Beef Garor mangsha Beef cattle 75.67±0.53 12.49±0.25 8.64±0.12 8.16 1.03± 0.05 2.17 136.40 75 Pork* Shukor Pot bellied pig 47.96±0.73 11.49±0.30 38.72±0.80 36.05 1.59±0.09 1.83 401.76
*ethnic
98
Table 3.20: Micromineral composition of fish, egg and meat
Sl no. English name Bengali/Local name Scientific name Copper Zinc Iron Manganese
µg/100g edible portion FISHES
61 Carp Ruhi Labeo rohita 1853.66±0.35 1110.73±67.62 1376.15±75.47 30.0 ±1. 99 62 Tilapia Tilapia mach Anabus testudineus 1566 .58±0.63 1403.19±144.17 1311.58±77.01 44.39±0.90 63 Dragon Fish Pangash Pangasius pangasius 1517.86±0.92 646.23±183.67 1277.92±107.33 38.62±0 .94 64 Sunfish Mola mach Mola mola 2508.31±0.31 3431.63±68.42 1338.57±12.05 60.57±1.07 65 Arguskala Kachki mach Scatophagus argus argus 1838.38±2.56 3108.31±49.86 1064.45±66.34 82.05±1.04 66 Taki fish Taki mach Channa puncpatus 1804.88±0.12 757.16±112.10 1173.21±8.18 33.94±1.0 5 67 Silver Carp Silver Carp Hypophthalmichthys nobilis 1679.46±0.50 903.66±186.90 1163.18±175.76 28.89±1 .40 68 Poa fish Poa mach Glassogobius giuris 2584.27±0.23 1188.31±12.66 1576.92±76.80 72.19±0.79
EGGS 69 Chicken (farm) Murgir dim (farm) Gallus bankiva murghi 1980.58±0.99 1171.45±0.35 1539.17±8.09 59.71±1.7 70 Chicken (deshi) Murgir dim (deshi) Gallus bankiva murghi 2383.42±1.02 2034.18±358.18 1653.98±46.18 56.22±1.0 71 Duck egg Hasher dim Anas platyrhyncha 3411.18 ±1.09 1405.57±1.55 2159.02±4.76 87.17±1.0 2
MEAT 72 Chiken (farm) Farm murgi Gallus bankiva murghi 2126.58±0.61 1292.25±69.77 1583.77±109.77 53.92±1 .54 73 Chiken (deshi) Desi murgi Gallus bankiva murghi 2436.55±3.45 1572.20±69.96 1467.14±0.00 62.18±0.9 9 74 Beef Garor mangsha Beef cattle 2776.70±1.13 1839.81±267.22 1385.72±34.87 143.93±0.98 75 Pork* Shukor Pot bellied pig 5738.22±0.07 2380.67±144.21 3412.80±324.96 156.02±1.02
*ethnic
99
Table 3.21: Macromineral composition of fish, egg and meat
Sl.No
. English name Bengali/Local name Scientific name Calcium Magnesium Sodium Potassium Phosphorous
mg/100gm edible portion
FISHES
61 Carp Ruhi Labeo rohita 0.34±0.04 11.82±0.16 133.94±27.63 238.49±28.13 5.81±0.55
62 Tilapia Tilapia mach Anabus testudineus 0.56±0.06 12.85±0.05 128.45±0.04 245.97±11.92 7 .16±0.13
63 Dragon Fish Pangash Pangasius pangasius 0.42±0.02 6.05±4.19 103.47±18.80 169.21±30.58 4 .99±1.04
64 Sunfish Mola mach Mola mola 0.60±0.00 11.95±0.01 110.07±6.80 139.79±3.53 13.17±0.21
65 Arguskala Kachki mach Scatophagus argus argus 0.48±0.00 9.71±0.01 67.69±5.52 92.60±9.71 10.54±0.02
66 Taki fish Taki mach Channa puncpatus 0.49±0.00 10.01±0.04 88.58±0.01 165.93±9.37 5.48±0.26
67 Silver Carp Silver Carp Hypophthalmichthys nobilis 0.49±0.00 11.05±0.04 104.82±6.41 186.35±7.19 6.03±0.03
68 Poa fish Poa mach Glassogobius giuris 0.57±0.10 12.42±1.37 139.89±14.23 270.81±10.87 7.49±1.24
EGGS
69 Chicken (farm) Murgir dim (farm) Gallus bankiva murghi 0.43±0.005 10.07±0.12 126.33±6.82 90.06±4.86 5.25±0.15
70 Chicken (deshi) Murgir dim (deshi) Gallus bankiva murghi 0.48±0.01 10.57±0.12 134.75±7.21 96.80±4.39 6.41±0.44
71 Duck egg Hasher dim Anas platyrhyncha 0.53±0.005 10.66±0.07 133.96±9.14 85.14±5.57 6.00±0.65
MEAT
72 Chiken (farm) Farm murgi Gallus bankiva murghi 0.24±0.03 12.02±0.33 117.53±7.16 200.93±7.28 6.55±0.38
73 Chiken (deshi) Desi murgi Gallus bankiva murghi 0.25±0.01 12.27±0.00 131.90±7.18 234.50±2.94 7. 20±0.17
74 Beef Garor mangsha Beef cattle 0.16±0.02 10.69±0.43 92.04±13.70 145.12±23.69 3 .53±0.50
75 Pork* Shukor Pot bellied pig 0.69±.0.15 17.37±5.42 168.85±88.67 179.07±85.66 6.16±3.09 *ethnic
100
Table 3.22: β-carotene content in general and ethnic foods
English name Local/Bengali/Ethnic Name
Botanical/Scientific name β-carotene µg%
edible portion LEAFY VEGETABLE S
1 Joseph’s Coat Lal shak Amaranthus gangeticus 1256.53
2 Spleen Amaranth Data shak Amaranthus dubius 4904.33
3 Coco-yam Sobuj kochu shak Colocasia esculenta 7146.59
4 Bottle Gourd Lau shak Lagenaria siceraria 2370.64
5 Indian spinach Pui shak Basella alba 1775.10
6 Swamp Morning-glory Kalmi shak Ipomoea aquatica 2383.68 14 Corriander leaves Dhane pata Coriandrum sativum 1470.54
7 na Sabarang* Ajuga Macrosperma 467.28
8 Roselle Amila pata* Hibiscus sabdariffa 1606.83
9 na Baruna Shak* Xanthoxylum rhetsa 1465.49
10 na Ojan shak/Surja kannya* Spilantses calva 1102.88
11 na Orai balai* Premna esculenta 1110.74
12 Yellow saraca Maytraba* Saraca thaipingensis 1486.42
13 na Ghanda batali* Paederia foetida 1708.97
ROOT & TUBERS AND non -LEAFY VEGETABLE S
15 Carrot Gazor Daucus carota 1689.43
16 Sweet pumpkin Misti kumra Cucurbita maxima 51.41
17 Kakrol Kakrol Momordica cochinchinensis 163.00
18 Banchalta* Banchalta* Dillenia pentagyna 55.47
FRUITS
19 Mango ripe(deshi) Paka Am Mangifera indica 356.28
20 Jack frujt (ripe) Paka Kathal Artocarpus heterophyllus 28.86
21 Papaya (ripe) Paka papay Carica papaya 425.77
22 Melon (mix) Bangi/futi Cucumus melo 663.68
23 Water melon Tormuz Citrullus vulgaricus 299.73
24 na Rashko* Syzygium balsameum 8.90
25 Bead tree kusumgulu* Elaeocarpus angustifolius 388.43
*ethnic food na: not available
101
Table 3.23: Dietary fiber in key food items
Sl.No. English name Bengali/Ethnic
name Scientific name g/100g edible LEAFY VEGETABLES
1 Joseph’s Coat Lalshak Amaranthus gangeticus 4.23±0.53
2 Spleen Amaranth Data shak Amaranthus dubius 4.35±0.48
3 Bottle Gourd Lau shak Lagenaria siceraria 4.38±0.61
4 Radish Mula shak Raphanus sativus 2.58±0.18
5 Coco-yam Sobuj kochu shak Colocasia esculenta 2.90±0.75
6 Jute Pat shak Corchorus capsularis 5.75±0.03
7 Indian spinach Poi shak Basella alba 2.18±0.17
8 Spinach Palong shak Spinacia oleracea 2.92±0.21
9 Swamp morning-glory Kalmi shak Ipomoea aquatica 3.71±0.09
10 Thankuni Thankuni Pata Centella asiatica 8.66±1.07 11 Corriander Dhane pata Coriandrum sativum 5.92±0.15
12 Spearmint Pudina pata Mentha viridis 6.91±0.31
13 Bitter gourd Karola pata* Momordica charantia 2.25±0.59
14 Carrot Gazor Daucus carota 3.68±0.57
NON-LEAFY VEGETABLES
15 Egg plant Begun Solanum melongena 2.28±0.34
16 Bitter Gourd Karola Momordica charantia 0.41±0.02
17 Sweet pumpkin Misti kumra Cucurbita maxima 1.14±0.88 18 Kakrol Kakrol Momordica cochinchinensis 0.44±0.03
19 Ladies finger Dherosh Abelmoschus esculentus 3.10±0.41
20 Green papaya Kacha papay Carica papaya 2.71±0.28
21 Green chilli Kacha marich Capsicum frutescens 4.91±0.86
FRUITS
22 Mango ripe(deshi) Paka Am Mangifera indica 3.65±0.30
23 Black berry (deshi) Kalojam Syzygium cumini 7.25±0.82
24 Jackfruit (ripe) Paka Kathal Artocarpus heterophyllus 5.14±1.06
25 Banana (ripe) Paka kala Musa sapientum 1.90±0.16
26 Water melon Tormuz Citrullus vulgaricus 1.61±0.80
27 Papaya (ripe) Paka papay Carica papaya 0.59±0.04
28 Amla Amloki Emblica officinalis
29 Melon (mix) Bangi/futi Cucumus melo 2.15±0.49
30 Wood apple Bael Aegle marmelos 6.96±2.33
31 Monkey jack Deuwa Artocarpus lakoocha 2.11±0.11
102
Table 3.24: Phytic acid content in key food items
English name Bengali/Local name Scientific name Phytic acid mg%
edible portion CEREALS AND LENTIL 1 Rice parboiled (Brri-29) Sidhoy chal Oryza sativa 116.86 ± 5.43 2 Rice sunned** Atap chal Oryza sativa 147.97± 4.44 3 Maize Vutta Zea mays 959.85 ± 2.86 4 Lentil (deshi) Masur dal Lens culinaris 516.12 ± 9.1 LEAFY VEGETABLES 5 Joseph’s Coat Lalshak Amaranthus gangeticus 10.37±0.62 6 Spleen Amaranth Data shak Amaranthus dubius 16.4±1.40 7 Bottle Gourd Lau shak Lagenaria siceraria 3.31±0.04 8 Radish Mula shak Raphanus sativus 1.88±0.16 9 Coco-yam Sobuj kochu shak Colocasia esculenta 11.46±0.15 10 Jute Pat shak Corchorus capsularis 16.4±0.16 11 Swamp Morning-glory Kalmi shak Ipomoea aquatica 2.43±0.09 12 Thankuni Thankuni Pata Centella asiatica 3.41±0.12 13 Bitter gourd Karala pata* Momordica charantia 3.74±0.12 14 Roselle Amila pata* Hibiscus sabdariffa 15.90±0.07 non-LEAFY VEGETABLES 15 Egg plant Begun Solanum melongena 10.88±0.07 16 Bitter Gourd Karola Momordica charantia 8.27±0.29 17 Sweet pumpkin Misti kumra Cucurbita maxima 15.85±0.30
18 Kakrol Kakrol Momordica cochinchinensis
5.25±0.02
19 Ladies finger Dherosh Abelmoschus esculentus 5.98±0.02 20 Green papaya Kacha papay Carica papaya 7.72±0.07 21 Green chilli Kacha marich Capsicum frutescens 13.72±0.85 ROOTS & TUBERS 22 Potato Gol Alu Solanum tuberosum 16.36±0.04 23 Sweet potato (red) Misti alu Ipomoea batatas 20.25±0.15 24 Carrot Gazor Daucus carota 9.28±1.14 FRUITS 25 Mango ripe(deshi) Paka Am Mangifera indica 9.28±1.14 26 Black berry (deshi) Kalojam Syzygium cumini 10.05±0.06 27 Jackfruit (ripe) Paka Kathal Artocarpus heterophyllus 26.42±0.16 28 Banana (ripe) Paka kala Musa sapientum 18.34±2.74 29 Water melon Tormuz Citrullus vulgaricus 9.48±0.06 30 Papaya (ripe) Paka papay Carica papaya 26.83±0.06 31 Amla Amloki Emblica officinalis 8.20±0.49 32 Melon (mix) Bangi/futi Cucumus melo 19.82±0.10 33 Wood apple Bael Aegle marmelos 120.95±2.02 34 Monkey jack* Deuwa* Artocarpus lakoocha 30.9±0.43 35 Burmese grape* Lotkon* Pirardia sapida 13.57±1.27
*ethnic ** raw
103
Table 3.25: Comparision of protein value in the present FCD with I FCT, DKPM, Thai FCT
pFCD: present Food Composition Database IFCT: Indian Food Composition Table DKPM: Deshio Khadder Pustiman Thai_FCT: Thai Food Composition Table
Sl.
no
Food items pFCD IFCT DKPM T-FCT Sl.
no
Food items cFCD IFCT DKPM T-FCT
Cereals Roots & Tubers
1 Rice parboiled 6.96 6.4 6.4 7.4 25 Potato 2.07 1.6 1.6 2.5
2 Rice (Atap) 7.74 na 6.8 na 26 Sweet Potato 1.17 1.2 1.2 0.9
3 Maize 10.99 11.1 11.1 na 27 Carrot 0.81 0.9 1.2 1.6
Pulses Fruits
4 Lentil (Deshi) 23.91 25.1 25.1 na 28 Mango (Ripe) 0.61 0.6 1.0 0.6
Leafy Vegetables 29 Jack fruit 1.53 1.9 1.8 1.7
5 Bottle Gourd 2.58 2.3 2.3 4.5 30 Papaya (Ripe) 0.62 0.6 1.0 0.5
6 Coco-yam 2.45 3.9 3.9 na 31 Black berry 0.61 na 1.9 na
7 Joseph’s Coat 2.39 2.8 3.3 na 32 Pine apple 0.61 0.4 0.9 0.4
8 Swamp Morning-glory 1.99 2.9 1.8 na 33 Banana (Ripe) 1.31 1.2 0.7 1.3
9 Indian spinach 1.5 na 2.2 na 34 Wood Apple 3.55 7.1 2.6 na
10 Jute leaves 5.2 na 2.6 na 35 Amla 0.6 na 0.9 na
11 Corandar leaves 3.04 3.3 3.3 2.3 36 Lichi 1.26 1.1 1.1 1.0
12 Mint leaves 3.07 4.8 2.9 na 37 Melon 0.19 0.3 0.3 na
13 Thankuni leaves 2.3 na 2.6 na 38 Water melon 0.73 0.2 0.2 0.6
14 Spleen Amaranth 2.36 na 1.8 na 39 Palm (Ripe) 0.66 0.7 0.7 0.5
15 Radish Shak 1.82 3.8 1.7 2.2 40 Eggs
16 Spinach 2.26 2 3.3 2.1 41 Chicken egg 12.07 13.3 13.3 12.8
17 Non Leafy Vegetables 42 Duck egg 13.5 13.5 15.47 12.1
18 Green chilli 2.86 na 1.6 1.4 Meat
19 Egg plant 1.21 1.4 1.8 na 43 Beef 12.49 22.6 22.6 na
20 Green papay 0.6 0.7 0.9 0.6 44 Chicken 16.29 26.6 25.9 17.3
21 Kakrol 1.47 na 2.1 na 45 Pork 11.49 18.7* 18.7 20.9
22 Ladies finger 1.31 1.9 1.8 na
23 Folwal 1.31 na 2.4 na
24 Sweet pumpkin 0.59 1.4 1.4 1.4
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Key Findings
Aim of this study was to prepare “A Food Composition Database for Bangladesh with
Special Reference to Selected Ethnic Foods”. In order to have this attempt done, the
study was designed to –identify key food items and analyses the key food for their
nutrient profile aiming at preparation of food composition database.
The key foods were identified through comprehensive food consumption survey (CFCS)
and focus group discussion (FGD). CFCS was conducted amongst 1210 general
households and 805 ethnic tribal households, and FGD was performed among Marma,
Chakma, Tanchanga and Tripura ethnic communities. Through CFCS and FGD 138
food items were identified, from which, 75 key food items were listed for analysis for
their nutrient profile. This key list comprised 53 food items consumed by both the
general and ethnic people; and 22 food items consumed by only the ethnic tribes.
The nutrient profile analysed for the 75 key foods comprised- proximate nutrients,
energy content; phytic acid , vitamin C, carotenoids, beta-carotene and minerals.
Comprehensive food consumption survey (CFCS)
• reveals food consumption pattern of general and ethnic population
• ethnic people consume almost all of the wild foods
• ethnic people consume most of the native general foods
• general people usually do not intake ethnic foods.
Key Foods
• A total of 138 food items comprising general and ethnic foods were identified.
• foods consumed by ≥ 5% households as well as nutrient dense foods of public
health significance were selected to make a list of 75 key foods for analysis of
their nutrient profile.
105
Nutrient profile analysed
The selected seventy-five key foods were analysed for- proximate nutrients:
moisture, protein, total fat, fatty acid, carbohydrate, crude fiber, dietary fiber, ash;
energy content ; antinutrient: phytic acid; vitamins: vitamin C, carotenoids, beta-
carotene; and minerals: copper, zinc, iron, manganese, calcium, magnesium,
sodium, potassium and phosphorous.
106
Policy Implications
Bangladesh has made major strides to meet the food needs of its increasing population
through boosting agricultural production. Agriculture produces above 90% of its food
need including cereals and vegetables, and to some extent fruits. It has been blessed
with high yielding varieties (HYV) of rice, plenty of vegetables and seasonal fruits, and
biologically rich open water fisheries. While cereal production is sufficient in Bangladesh
and certain vegetables and fruits are being exported.
The national food intake pattern in Bangladesh documents that people consume high
amount of cereal based diet and lesser amounts of micronutrient rich vegetable and
fruits. This results in an imbalanced diet habit. The changes in food chain with the
emergence of HYV newer foods as well as change in soil composition (due to
environmental changes, increased fertilizers use and crop intensity) have resulted in
possible changes in the nutrient composition of the foods being grown. Thus, the food
chain of the country has changed during the last decades. In addition, introduction of
western foods in Bangladesh markets has also changed the food habits. All these facts
call for a fresh analysis of the most frequently consumed foods.
Bangladesh does not have its own food composition database. This project is the start
point for development of a food composition database for Bangladesh.
This part food composition database (FCD) will provide the basis for planning food,
nutrition and health related policy tools. It will be the primary source of food composition
information for food and agriculture policy program planning. It will help in designing
• balanced diets
• food based dietary guidelines
• therapeutic diets
• health, nutrition and agriculture research
• nutrition education and training
• food security, safety, and regulations.
107
A food composition database preferably includes all nutrients that are important in
human nutrition. It should consider the basic need for nutrient information, nutrient
contribution to public health nutrition, public health problems, nutrient of public health
significance, and importance of food trade need. In preparation of food composition
database, the improvement of analytical facilities should also be addressed.
It is noted that most of the databases have between 10 and 25 food groups comprising
hundreds to thousands of food items. The present database includes nutrient
composition of 75 food items incorporating 9 food groups. However, it is reference point
for developing and updating the food composition database.
Since Bangladesh is at the advent of preparation of its own food composition database,
consistent financial support should be ensured to have a national food composition
database with at least 500 food items consumed by mass population including ethnic
people.
Funding should also be provided to support lab upgradation for the analysis of
micronutrients of public health significance.
108
Policy Recommendations
1. Updating FCT for Bangladesh
There is lack of up-to-date knowledge on food composition tables (FCT) for Bangladesh.
Given the major changes that have occurred in the complexity of the food chain as also
in the environment, soil composition, cropping patterns and cropping intensity, there is
need to update knowledge on the nutrient composition of most of the new high yielding
varieties of rice, wheat, maize, potatoes, fruits, vegetables, fish and livestock that have
become part of the nation’s production and consumption systems. There is need for
updating and constructing a revised FCT for use as tools in determining standard dietary
intake for different population groups including ethnic groups. The results of this
research can be a pilot contribution which needs to be built on for further work on FCT.
2. Constructing FCT of ethnic foods
Preparation of food composition tables for Bangladesh require research to ascertain the
extent to which the nutrient content of the new varieties of foods including ethnic and
traditional consumed by the tribal population contributes to the diets in Bangladesh. In
particular, the nutrient composition of the indigenous foods grown and consumed in the
Chittagong Hill Tracts (CHT) and other tribal areas is not known. The FCT for
Bangladesh needs to include the nutrient composition of ethnic foods and new FCT will
therefore need to be constructed.
3. Preparation of food based dietary guidelines (FBDGs)
To prepare dietary guidelines and determine standard dietary intake, the true nutrient
content of all foods consumed by the overall population needs to be known especially
ethnic groups. Knowing the profile of ethnic foods being produced and consumed is
critical in food and agriculture planning and in developing dietary guidelines. FCT on
ethnic foods can help inform agriculture, food and health policy on enhancing the supply
and demand for ethnic food sources which can serve as a valuable source of both
109
macro and micronutrients. FBDGs for Bangladesh need to be elaborated and
implemented through a shared consensus and wider dissemination through efforts of
relevant stakeholders.
4. Strengthening collaboration for harmonization
Studies on the analyses of Bangladeshi foods have been carried out in the Institute of
Nutrition and Food Science (INFS), Institute of Food Science and Technology (IFST),
Institute of Public Health and Nutrition (IPHN), Bangladesh Agriculture University (BAU),
ICDDR,B and other research organizations. Collaboration among these institutions and
relevant GoB institutions such as BARC, BARI, BRRI and DAE should be strengthened.
There is an urgent need to consolidate and compile the FCT for Bangladesh through a
harmonization of the food composition analyses carried out over the years in these
institutions.
110
Future Research
This study identifies a large number of food items that are consumed by mass
population including ethnics, but it has analysed only 75 key foods. The nutrient profile
of these 75 foods is not representative food items for a national food composition
database. Therefore, this attempt should be continued to prepare a national database
of at least 500 food items with a comprehensive nutrient profile.
Conclusion
The present report is a part of a wider food composition database. It provids newer
nutrient data of selected key foods. This database is expected to be the primary source
of nutrient values for food and agriculture policy planning, preparing dietary guidelines,
therapeutic diet formulation and research on nutrition, health and agriculture. It will be
useful to the policymakers and professionals who are working towards improving
nutrition and public health in Bangladesh.
This database will motivate future attempts for the analysis of nutrient profile of mass
peoples’ foods to develop a national food composition database.
A separate tabulation of nutrient data of the selected foods is also provided as a
reference output along with this report.
111
Acknowledgements
This study was a collaborative research between the Institute of Nutrition and Food
Science, University of Dhaka and the Department of Pharmaceutical Chemistry, Faculty
of Pharmacy, University of Dhaka; Department of Agricultural Extension, Ministry of
Agriculture; Grain Quality and Nutrition Division, Bangladesh Rice Research Institute
(BRRI), Gazipur; Department of Biochemistry, Sher-e-Bangla Agriculture University; to
some extent- Nutrition Biochemistry Laboratory, ICDDR,B with the participation of fifteen
scientists including a number of post-graduates, M.Phil and PhD students who were
involved in designing, planning and carrying out this work. Thanks are due to the
Laboratory team who were involved in the analysis of nutrient profile of the key foods
and CFCS team who carried out the comprehensive consumption survey.
We are thankful to Professor Dr. Sagarmay Barua, Director, Institute of Nutrition and
Food Science, University of Dhaka for his whole hearted constant appreciation and
cooperation in carrying out and completetion of this work. Thanks are due to the
Director, Center for Excellence, University of Dhaka for providing some lab facilities, and
for allowing us to use the conference room. We are also indebted to Professor Dr. Md.
Aminul Haque Bhuyan of the Institute of Nutrition and Food Science, University of
Dhaka for his untiring suggestions and encouragement in carrying out this work.
We are obliged to the authorities of Dhaka University for enabling a silky-smooth end to
this work.
Special thanks are due to Mr. Paban Kumar Chakma, Agriculture Officer- Rangamati,
CHT and Mr. Gugal Chandra De, Agriculture Officer- Khagrachari, CHT as well as to the
other DAE staff for their sincere assistance in conducting the CFCS and the Focus
Group Discussions among the ethnic communities.
We express our gratefulness to Late Professor Dr. HKM Yusuf, Nutritionist, NFPCSP -
FAO; Dr. Lalita Bhattacharjee, Nutritionist, NFPCSP - FAO and Dr. Mohammad Abdul
Mannan, National Food Utilization and Nutrition Advisor, NFPCSP-FAO, FAO
Representation in Bangladesh for their constant technical support, valuable criticism
and discussion and important suggestions in completion of this work. Additionally,
appreciation is given to Dr. Rezaul Karim Talukder, Socioeconomist, NFPCSP - FAO
for his initial suggestions in the study. Thanks are also due to Dr. Nur Ahamed
Khondaker, Research Grant Administrator, FAO-NFPCSP, FAO Representation in
Bangladesh, for his unswerving administrative support in the completion of this work.
112
We are also appreciative of the Finance Department, FAO Bangladesh for its active help
in the smooth release of funds.
We are grateful to Mr. Ciro Fiorillo, Chief Technical Adviser, NFPCSP - FAO, for his
technical and administrative support in carrying out this work.
A final thanks to the Food and Agriculture Organization of the United Nations (FAO) and
the Food Planning and Monitoring Unit (FPMU), Ministry of Food and Disaster
Management for their support under the National Food Policy Capacity Strengthening
Programme (NFPCSP) with financial assistance from EU and USAID.
113
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RESEARCH TEAM
Principal Investigator Professor Sheikh Nazrul Islam1, PhD Co-Investigators Professor Md. Nazrul Islam Khan1, PhD Professor M. Akhtaruzzaman1, PhD Professor Saiful Huque1, PhD Professor Monira Ahan2, PhD
Laboratory Team Muhammad Ali Siddique3, PhD (BRRI, Gazipu) Ashrafi Hossain4 MSc, (SBAU)
Md. Abdul Jalil5, MSc, PhD student (DAE) Shah Md. Anayet Ullah Siddiqui1, MSc (INFS, DU) Maksuda Khatun6, PhD student (Botany, DU)
Mahbuba Kawser1, MS, M.Phil, PhD student (INFS, DU) Parveen Begum1, MS, M.Phil (INFS, DU) Anjan Kumar Roy7, MSc, M.Phil. student (INFS, DU; ICDDR’B) Sabnam Mustafa1, M. Phil student (INF, DU) Kohinur Begum, MSc (INFS, DU) Abu Bakar Siddique, MSc (INFS, DU Md. Tariqual Islam Sajib4, MSc (SBAU) Dipa Jamal, MSc (INFS, DU)
Tanjina Rahman1, MS (INFS, DU) Farzana Bhuyan1 MSc student (INFS, DU) Mia Sakib Anam1 MSc student (INFS, DU) Syeda Munia Haque, MSc student (INFS, DU)
CFCS Team Nur Mohammad Siddiki, MSc (supervisor) Shafiqul Islam Khan, MSc Rupesh Chakma, MSc Pintu Chakma, MSc Ripan Chakma, MSc
Consultant Professor Sagarmay Barua, PhD (Director, INFS, DU)
1Institute of Nutrition and Food Science, University of Dhaka, Dhaka-1000, Bangladesh; 2Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Dhaka, Dhaka-1000, Bangladesh; 3Grain Quality and Nutrition Division, Bangladesh Rice Research Institute (BRRI), Gazipur-1701, Bangladesh; 4Department of Biochemistry, Sher-e-Bangla Agriculture University, Dhaka-1207, Bangladesh; 5Department of Agricultural Extension, Khamar Bari, Dhaka-1215, Bangladesh; Department of Botany, University of Dhaka, Dhaka-1000, Bangladesh; 7Nutrition Biochemistry Laboratory, ICDDR’B, Mohakhali, Dhaka-1212, Bangladesh