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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 Programme
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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 nations 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, -carotene, vitamin C and minerals. Nutrient data obtained have been
compared with reported values published in different articles and books, most of whichare 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
Chapers 4
Tables6
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-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 Recommendations 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%) 32Table 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, Tanchangaand
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 90Table 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 98Table 3.22 -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 itemconsumed 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 StandardFAO 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
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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 compoundsthat 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 shaksgrow asweeds 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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persons 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 inthe 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
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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 foodsare 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 foodsare 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
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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)}/ d2 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 fromeach 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.
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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 1200households, 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
D1
Dhaka
HH # 200
Khulna
HH # 200
Sylhet
HH # 200
D1 D2 D1 D2D2 D2 D1 D2D1
BangladeshHH # 1200
R U R U R U R U R U R U R U R U R U R
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was, therefore, conducted on another 300 ethnic households in Khagrachari and
Rangamati districts of CHT. It was undertaken among Marma, Chakma, Tripura and
Tangchagaethnic 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
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Figure 2.2: Sampling plan for ethnic households
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Figure 2.3: Geographical locations of ethnic CFCS
Moulavi Bazar
Mymensingh
Rajshahi
Khagrachari
Rangamati
Bandarban
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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 households 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 members team, two
enumerators belonged to general community and three were ethnic who were fluent in
speaking and understanding the general peoples language as well as the tribal peoples
language. More ethnic members were recruited because they were familiar with the
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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
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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.
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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 Tangchagaliving 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 Khagracharisadar
31/03/2010 Marma palli Marma
Rangamati Rangamatisadar
03/04/2010 Chakma palli Chakma
Rangamati Rangamatisadar 08/04/2010 Tanchangapara, TanchangaKhagrachari Khagrachari
sadar21/04/2010 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
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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 focuswas given to the distribution of nutrients in foods with emphasis on
-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 by15% of the households were included in thekey 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-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
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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.
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Table 2.3: Food items consumed by only general people (percent frequency 5%)
Sl no English name Bengali name Scientific name Urban Rural 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 174 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
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Table 2.4: Food items consumed by only ethnic households (percent frequency 5%)
Sl. no English name Bengali/Localname
Scientific name Urban Rural weighted% frequency native
general people
% frequency ofEthnic people food
consumption
CEREALS1 Rice parboiled (Brri-29) Sidhoy chal Oryza sativa 99 342 Lentil (deshi) Masur dal Lens culinaris 78 35
LEAFY VEGETABLES3 Josephs Coat Lalshak Amaranthus gangeticus 84 494 Bottle Gourd Lau shak Lagenaria siceraria 47 425 Indian spinach Pui shak Basella alba 64 286 Radish Mula shak Raphanus sativus7 Spinach Palong sag Spinacea oleracea 41 178 Coco-yam Sobuj kochu shak Colocasia esculenta 18 179 Bathua Pigweed Chenopodium album 13 7
ROOTS & TUBERS10 Potato Gol Alu Solanum tuberosum 93 9311 Radish Mula Raphanus sativus 44 4012 Coco-yam Sobuj kochu Colocasia esculenta 33 37
NON-LEAFY VEGETABLES
13 Egg plant Begun Solanum melongena 81 8014 Bean Shim Dolichos lablab 70 75
15 Cabbage Badha KopiBrassica oleracea var. capitata
80 5816 Cauliflower Foolkopi Brassica oleraceavar. botrytis 90 7417 Cow pea Borboti Vigna catjang 38 818 Cucumber Shasha Cucumis sativus 20 2119 Folwal Potol Trichosanthes dioica 49 1620 Gourd (Ash) Chal kumra Benincasa cerifera 31 2121 Bitter Gourd Karola Momordica charantia 43 4222 Sweet pumpkin Misti kumra Cucurbita maxima 40 3923 Kakrol Kakrol Momordicacochinchinensis 20 824 Ladies finger Dherosh Abelmoschus esculentus 43 2425 Bottle gourd Lau Lagenaria siceraria 68 5626 Snake gourd Chichinga Trichosanthes anguina 53 1927 Jackfruit (immature) Kacha kathal Artocarpus heterophyllus 8 2328 Green papaya Kacha papay Carica papaya 30 2729 Plantan (green) Kacha kola Musa paradisiaca 12 18
30 Tomato (green) Kacha tomato Lycopersicon lycopersicum 21 3331 Yam Stem Kachur data/loti Colocasia esculenta 28 12
FRUITS
32 Mango ripe(deshi) Paka Am Mangifera indica 66 5633 Black berry (deshi) Kalojam Syzygium cumini 17 834 Jackfruit (ripe) Paka Kathal Artocarpus heterophyllus 60 5635 Banana (ripe) Paka kala Musa sapientum 29 1736 Bitter Plum Boroi Zizyphus mauritiana 38 3637 Pine Apple (Jaldugi) Anarash (Jaldugi) Ananas comosus 12 538 Tomato (ripe) Tomato paka Lycopersicon lycopersicum 61 52
FISH
39 Carp Katol mach Labeo rohita 21 740 Tilapia Tilapia mach Anabus testudineus 20 2541 Dragon Fish Pangash Pangasius pangasius 44 2642 Fry (very small) Choto puti Puntius ticho 56 27
43 Sunfish Mola mach Mola mola 11 944 Shrimp(dry) Chingri (shukna) Heterocarpus ensifer 7 2245 Rohu Rui Labeo ruhita 45 3546 Shrimp Chingri Heterocarpus ensifer 30 6
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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.
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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.
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Figure 2.4: Multi-regions sampling plan for general food sample
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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 CAnalyte-I
Rangamati
Bnorupa bazar
Sample A Sample B Sample C Sample FSample ESample D
D FAnalyte-IB E
Analyte-I
Khargrachari
Bazar
Figure 2.5: Multi-regions sampling plan for ethnic food
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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 weremixed 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 plasticpoly 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
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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
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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 metalcontamination. 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
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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, -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 nutrientprofile 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
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carotenoid for the 75 food items. Analysis of -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 techniques 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)
-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)
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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.
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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 -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.
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2.2.6.10 Analysis of-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 -carotene (Roriguez-Amaya and
Kimura, 2004). The nitrogen dried carotenoid was reconstituted with mobile phase
(acetonitrile: methanol: 2-propanol-) and 50l reconstituted sample was injected into the
VYDAC reverse phase C18 column (5m particle size). The column was re-equilibrated
with the mobile phase for at least five minutes before the next injection. -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).
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Chapter 3
Results and Discussion
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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 nutrientcomposition 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 Pustimanprepared 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.
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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 Foodslist. 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
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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 people
Enumerator taking interview
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Enumerator with ethnics
PI and enumerator with ethnics taking interview
Co- Investigator and enumerator with ethnics taking interview
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Table 3.1: Location and description of CFCS data collection among the general po
Division District Upazilla Date of visit Location No of
intervie
Dhaka Netrokona Netrokona
sadar
31.01.09 to 02.02.09 West chakpara 50
Mohandrapur 54Manikgonj Saturia 08.02.09 to 11.02.09 Sawdagar para &
Uttarkaunna
50
12.02.09 to 16.02.09 Char saturia 50
Sylhet Moulavibazar Moulavibazar
Sadar
21.02.09 to 22.02.09 Suvro 52
23.02.09 to 25.02.09 Kodupur 50
Habigonj Madhobpur 25.02.09 to 27.02.09 Godampara &
Krishnanagar
51
28.02.09 to 02.03.09 West madhobpur 50
Chittagong Feni Feni Sadar 16.03.09 to 17.03.09 North Charipur 50
18.03.09 to 19.03.09 Nagarkandi, Mathiara 50
Comilla Comilla Sadar 20.03.09 to 21.03.09 Gabindapur 50
22.03.09 to 24.03.09 Kashinathpur 50
Rajshahi Natore Natore Sadar 03.04.09 to 04.04.09 Uttar Patua para 51Ulupur 49
Rajshahi Rajpara 01.04.09 to 02.04.09 Terkhadia 52
Kashia danga 52
Khulna Jessore Jessore
Kotoali
04.04.09 to 06.04.09 Shangkarpur 50
Mubarak Kathi 49
Jhenaidah Kaligonj 08.04.09 to 09.04.09 Arpara Nadir par 50
Mithapukur 50
Barisal Barisal Barisal Kotoali 11.04.09 to 14.04.09 Ganopara 50
Rupatoli 50
Jhalokathi Jhalokathi
Sadar
13.04.09 to 14.04.09 Krishnakathi 50
Rajapremhar 50
Total 121
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Table 3.2: Location and description of CFCS data collection among the ethnic popula
Division District Upazilla Time of visit Location No o
interv
Dhaka Netrokona Durgapur 03.02.09 to 07.02.09 Gopalpur, Nolua 2
Debdul 3
Sylhet Moulavi Bazar Kamolgonj 18.02.09 to 20.02.09 Tilokpur 2
Magurchara & Kashiapunji 2
Chittagong Khagrachari Khagrachari
sadar
31.03.10 to 15.04.10
17.04.10 to 23.04.10
Nilkantipara 7
Dewanpara 6
Soyanundarpara 5
Rangamati Rangamati
sadar
03.04.10 to 10.04.10 Haja Chara, Diglibak, ShapChari,
6
Naraichari, Vhulu Chari, 2
Tanchanga para, Banna Chari 3
Bandarban Bandarban
sadar
06.03.09 to 07.03.09 Raicha Senior para 3
07.03.09 to 08.03.09 Kalaghata 3
09.03.09 to 12.03.09 Balaghata Biddopara,Painchara, Parjatan Chakmapara, Pain para, Nadir par,Balaghata bazar
1
13.03.09 to 14.03.09 Bameri para 3
Faruk para 3
Puratan and nutun choroi para 7
Rajshahi Rajshahi Godagari 29.03.09 to 31.03.09 Nimghat para, Nobai bottala,Dangapara, Nimghatu para
8
Total 8
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Figure 3.1: Distribution of general and ethnic households by number
1210
805
General Ethnic
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Figure 3.3: Distribution of ethnic households by district
0
50
100
150
200
250
300
350
59 50
180
120
307
89
Number
of
households
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59
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Figure 3.4: Distribution of ethnic households by tribe and number
238
171
51
8730
30
25
2589
31
28
Chakma Marma Tanchanga Tripura
Bam Murong Monipuri Khashia
Shantal Garo Hajong
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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 Tangchagatribes 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
FGDcommunity
Objective Location No. ofparticipants
Duration ofdiscussion
Marma Type of foodintakethroughoutthe year
Pankhaiya para, Khagrachari, CHT 12 90 minutes
Chakma South Rangapani, BidhadhanChakma Bari, Chakma palli,Rangamati, CHT
8 60 minutes
Tanchanga Tanchaga para, Dharmaraj BabuBari, Kotoali, Rangamati, CHT
8 75 minutes
Tripura Ghasbhan no 2 project Gram,
Jagonnath Mandir, Khagrachari, CHT
10 90 minutes
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Table 3.4: FGD outcome: Food consumption pattern of the Marma, Chakma, Tanchangaand Tripura comm
na: not available * ethnic food
Sl no English name Bengali name Scientific name Sl no English name Bengali name Scientific
LEAFY VEGETABLES 25 na Banchalta* na1 Rashun Leaves Rashun shak na 26 na Fakong na2 Dheki leaves Dheki shak na 27 na Hahnagulu na3 Jarul Khambang na 28 Yam Pan/jhum alu* na4 Dumurshomi Leaves Dumurshumi shak na FRUITS5 Seneya Leaves Seneha shak na 29 Pamelo (red) Jambura (Lal) na6 Lelom Leaves Lelom shak na 30 Pineapple (wild ) Anarash (bonno) na7 na Sabarang* Ajuga macrosperma 31 Wild Melon Sindera* Cumis melo8 Roselle Amila pata* Hibiscus sabdariffa 32 na Roshko* Syzygium balsa9 na Lalam pata* Premna obtusifolia 33 Bead tree kusumgulu* Elaeocarpus an10 Indian Ivy-rue Baruna Shak* Xanthoxylum rhetsa FISH AND MEAT11 na Ojan shak* Spilanthes calva 34 Lota Fish Lota mach Na12 na Ghanda batali* Paederia foetida 35 Churi Fish (Dried) Churi mach na13 na Orai balai Premna esculenta 36 Nappi paste Nappi na
14 Purslane Bat slai* Portulaca oleracea 37 Zhinuk Shell Mollusk shell15 Yellow saraca Maytraba Saraca thaipingensis 38 Crabs Kakra Liocarcinus ver16 Yellow Flower Holud fool na 39 Shark Hangar Carcharhinus amb
17 Ginger Flower Ada shak na 40 Shark (dried) Hangar shutki Carcharhinus amb
18 Sime Flower Sime fool na 41 Kuchia fish Kuchia Monopterus cucNON LEAFY VEGETABLES 42 Snails (small) Shamuk (choto) Helix pomatia
19 Pea eggplant Mistti begun* Solanum spinosa 43 Snails (large) Shamuk (Boro) Helix pomati20 Solanum Tak begun* Solanum virginianum 44 Rat Idur Rattus norvegic21 Sigon data Sigon data* Lasia spinosa 45 Frog Beng Litoria caerulea22 Tara (Like Kochu data) Tara data na 46 na Gobar poka na23 Basher Korol Basher korol na 47 Pork Shukurer mangsha Sus scrofa dom24 Wild mushroom Edur kan na
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FGDs activities
FGD in Marma community in Marma palli, Khagrachari
FGD in Marma community in Marma palli, Khagrachari
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FGD in Chakma community in Chakma palli, Rangamati
FGD in Chakma community in Chakma palli, Rangamati
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FGD in Tanchanga community in tanchanga palli in Rangamati
FGD in Tanchanga community in tanchanga palli in Rangamati
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FGD in Tripura community in Tripura palli, Khagrachari
FGD in Tripura community in Tripura palli, Khagrachari
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3.1.3Lifestyle 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 householdheads 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 Shaontalwhile Chakmaand
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 Shaontaltribes.
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 Marmaand
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Shaontal while food security was comparatively better among the Tripura, Tanchanga
and Chakmatribes.
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
Tanchangaand highest among the Tripurachildren.
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.
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Table 3.5: Socioeconomic profile of general households
ParametersUrban Rural
Frequency Percent Frequency Percent
Type of household (HH) 606 50.1 604 49.9
Gender of household headMaleFemale
575
31
94.9
5.1
570
34
94.4
5.6Education of male headed HH head
Below primaryBelow SSCBelow HSCHSC to Below BScBSc to MScIlliteratecan sign onlyCan read and signTotal
Education of female headed HH headBelow primaryBelow SSCBelow HSCIlliteratecan sign onlyCan read and signTotal
10616237394
6175121606
1633
126
31
17.526.86.16.50.7
10.112.3020.0100.0
3.417.210.310.139.020.0100.0
12915634322
22228-
604
710129534
21.325.95.75.30.436.74.7-
100.0
20.03.3-
36.725.015.0
100.0
Occupation of male headed HH headAgri (work)Earth cuttingRickshaw / van driverOthersTotal
205742
10606
3.394.70.41.6
100.0
135838-
604
2.196.51.4-
100.0
Occupation of female headed HH headAgri (work)Earth cuttingHousehold workNGO workerOthersTotal
16
1842
31
3.417.258.613.96.9
100.0
-23020-
34
-6.786.66.7-
100.0Mean Sd Percent Mean Sd Percent
Age of male headed HH head (Year)15-3030-4545-6060-75Total
26.683.4038.674.3551.903.8766.103.5340.731.42
23.345.925.45.4
100.0
27.30 2.9538.37 4.2853.11 4.7667.15 3.49
41.23 11.64
21.348.923.95.9
100.0Age of female headed HH head (Year)
15-3030-4545-6060-75Total
27.253.2040.08 3.7752.78 4.2466.67 2.8945.00 11.79
13.8%44.8%31.0%10.3%
100.0%
26.00 0.0039.23 4.0255.00 4.8870.00 0.00
48.20 11.21
3.343.346.76.7
100.0
Monthly total income (Tk.)14000Total
4093.82 926.686673.87 827.449653.40 749.35
12520.00 699.5518976.00 5949.647837.54 4556.45
30.6936.6317.00
7.438.25100.0
4058.38 931.656714.47 843.559493.94 745.71
12411.36 790.4120924.00 7222.358006.99 5075.75
28.6439.4016.40
7.288.28100.0
Monthly average total expenditure (Tk.)6259.28 3148.04 100.0 6169.38 3324.94 100.0
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Table 3.6: Food security by households type in general population
ParametersUrban Rural
Frequency Percent FrequencyPercent
Experience food shortage in familyNever everSome timesOften/alwaysTotal
5177415
606
85.312.22.5
100.0
5226418
604
86.510.63.0
100.0Time of food shortage
JanuaryFebruaryWhole yearTotal
21644
89
23.6071.914.49
100.0
22582
82
26.8370.732.44
100.0Status of getting balance food
AlwaysNever eversome timesTotal
24238326606
39.96.353.8
100.0
28242280604
46.77.046.4
100.0HH head ate < 3 times a day
YesNoTotal
54552606
8.991.1
100.0
57547604
9.490.6
100.0
Children ate
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Table 3.7: Morbidity and its treatment by household type in general population
ResponseUrban Rural
Frequency Percent Frequency Percent
Eating adequately but not gaining weightYesNoDont understandTotal
2353053
606
3.887.58.7
100.0
1952461
604
3.186.810.1
100.0Member suffers from stomachache
YesNoDont knowTotal
29575
2606
4.894.90.3
100.0
28576
-604
4.695.4
-100.0
Knowledge about reasons of diarrhoeaAnswered rightlyAnswer partly rightWrongly answeredTotal
44611743
606
73.719.37.1
100.0
39716245
604
65.626.87.5
100.0Diarrhoea in any
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Table 3.8: Socioeconomic profile of ethnic households
Parameters CHAKMA MARMA SHAONTAL TRIPURA
Type of household Frequency % Frequency % Frequency % Frequency %Education Level HH Head
Below primaryBelow SSCBelow HSCHSC and aboveIlliteratecan sign onlyCan read and signTotal
204826518292
238
8.320.411.121.334.33.70.9
100.0
9-18-
1277-
161
5.6-11.3
-78.94.2-
100.0
22741
504-
88
2.310.24.51.156.84.5-
100.0
-19339242-
87
-21.637.810.827.02.7-
100.0
Occupation of HH headAgri (work) (1)Earth cutting (2)Rickshaw / van driver (5)Business (7)Jobless (9)Service (11)Others (12)Total
7927
11-
7960238
33.30.92.84.6-
33.325.0100.0
63-7
119-
70161
39.4-
3.27.05.6-
43.7100.0
38-4-5
122988
43.2-
4.5-
5.713.633.0
100.0
---7-
71987
---
8.1-
81.110.8100.0
n Meansd n Meansd n Meansd n Meansd Age (y) distribution of HH Head
15-3030-4545-6060-75Total
31145539
238
26.53.0138.33.8253.14.5769.54.2041.210.50
20635720161
28.12.5238.53.9653.54.2766.43.0946.212.4
1442284
88
27.62.4137.24.2752.83.5066.04.2442.011.1
124916987
27.44.2239.73.8851.63.6966.32.9943.111.3
Family monthly income (taka)14000Total
1455718117
238
309094969658849375694126008941667288753063462
13820---
161
278010886444846
---
32651635
8251--
88
23431268610054810000.0
--
26431704
24192491287
364014186875991950066712375478
23200113495107292
Family monthly expenditure 238 65744392 161 37681754 88 3125998 87 105121189
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Table 3.9: Food security of ethnic tribes
Parameters CHAKMA MARMA SHAONTAL TRIPURA TFreq. % Freq. % Freq. % Freq. % Fr
Experience food shortage in familyNever ever
Some timesOften/alwaysTotal
163
6411
238
68.5
26.94.6
100.0
98
612
161
60.6
38.01.4
100.0
30
362288
34.1
40.925.0
100.0
73
14-
87
83.8
16.2-
100.0Time of food shortage
JanuaryFebruaryWhole yearTotal
1626411
237
68.326.94.8
100.0
984221
161
61.125.913.0
100.0
31451288
35.051.014.0
100.0
85-2
87
97.3-
2.7100.0
Status of getting balance foodAlwaysNever eversome timesTotal
9513
130238
39.85.6
54.6100.0
41-
120161
25.4-
74.6100.0
123
7388
14.03.0
83.0100.0
54-
3387
62.2-
37.8100.0
HH head ate < 3 times a dayYesNoTotal
68170238
28.771.3
100.0
25136161
15.584.5
100.0
464288
52.347.7
100.0
-8787
-100.0100.0
Children ate
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Table 3.10: Morbidity and its treatment by ethnic tribes
Parameters CHAKMA MARMA SHAONTAL TRIPURA
Freq. % Freq. % Freq. % Freq. % Eating adequately but not gaining weight
Not respond
YesNoDont understandTotal
15
1513771
238
6.5
6.557.429.6
100.0
-
-9368
161
-
-57.742.3
100.0
-
1711688
-
1.180.718.2
100.0
-
5802
87
-
5.491.92.7
100.0Member suffers from stomach ache
YesNoDont knowTotal
92209
238
3.792.63.7
100.0
2159
-161
1.498.6
-100.0
-88-
88
-100.0
-100.0
-87-
87
-100.0
-100.0
Knowledge about reasons of diarrheaAnswered rightlyAnswer partly rightWrongly answeredTotal
1347133
238
56.529.613.9
100.0
687320
161
42.245.112.7
100.0
32431288
36.849.413.8
100.0
87--
87
100.0--
100.0Diarrhea in any 5 children in last month
Didnt experiencedLast weekOne month agoMore than one month agoCannot remember
Total
1172-
8633
238
49.10.9-
36.113.9
100.0
603-
4949
161
37.11.6-
30.630.6
100.0
48132115
88
54.01.13.424.117.2
100.0
40-2
1628
87
28251619
70Measures taken to get relief of diarrhoea
Didnt experiencedFed packet salineMedicineMedicine and oral salineTotal
152481324
238
63.920.45.610.2
100.0
12612186
161
78.27.310.93.6
100.0
63153788
71.317.23.48.0
100.0
68927
87
78.410.82.78.1
100.0Giving anti helminthics regularly to
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3.1.4 Identification of Key foods
The key food approach is used t