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FOOD COMPOSITION
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Journal of Food Composition and Analysis 17 (2004) 291–300
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Original Article
Protein, carbohydrate and fibre in cereals from Mali—how to
fit the results in a food composition table and database
I. Barikmoa,*, F. Ouattarab, A. Oshauga
b
a
Akershus University College, Kjeller, Norway
Institut National de Recherche en Sant!e Publique, Bamako, Mali
Received 4 July 2003; received in revised form 14 February 2004; accepted 26 February 2004
Abstract
During the past 5 years, the main staple foods (cereals) used in Mali have been collected to develop a
food composition table and database. We present recent results of protein, carbohydrate and fibre content
for some cereals. Samples were collected from five different regions. To reduce laboratory costs, composite
samples (cs) were made. The cereals analysed were sorghum (Sorghum bicolor) (cs ¼ 142), millet
(Pennisetum glaucum) (cs ¼ 163), maize (Zea mays) (cs ¼ 107), wheat (Triticum aestivum) (cs ¼ 123), rice
(Oryza sativa) (cs ¼ 151) and fonio (Digitaria exilis) (cs ¼ 104). Fonio is an old cereal cultivated across the
dry savannahs in West Africa, and is very popular in Mali. All samples were cleaned and processed (ready
to cook) before analysis. Detailed sampling plans were used. For total nitrogen, Kjeldahl and Dumas
combustion methods were used. Methods used for carbohydrate (sugar and starch) were polarimetric,
spectrophotometric and HPLC, and a gravimetric method was used for fibre. The mean7s.d. content of
protein for 100 g cereal was: in millet 7.971.4 g, sorghum 10.370.7 g, maize 7.671.1 g, rice 6.370.3 g,
wheat 10.671.1 g and fonio 7.270.4 g. The mean7s.d. content of carbohydrate and fibre per 100 g cereal
was: in millet 65.8710.1 and 6.272.3 g, sorghum 73.574.3 and 4.770.1 g, maize 73.0710.2 and
4.671.3 g, rice 83.777.8 and 1.170.0 g, wheat 75.171.8 and 3.070.0 g and fonio 74.370.1 and 2.270.3 g,
respectively. As indicated by the standard deviations there were considerable geographical differences in
nutrient content for the same cereal. There is no apparent explanation for these differences. Until this is
explored further, it is necessary to develop separate tables for different regions.
r 2004 Elsevier Ltd. All rights reserved.
Keywords: Food composition table; Database; Africa; Mali; Cereal; Protein; Carbohydrate; Fibre
*Corresponding author. Akershus University College, Box 423, Lillestr^m, Norway. Tel.: +47-64849182; fax: +4764849002.
E-mail address: [email protected] (I. Barikmo).
0889-1575/$ - see front matter r 2004 Elsevier Ltd. All rights reserved.
doi:10.1016/j.jfca.2004.02.008
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1. Introduction
The composition of nutrients in food can vary considerably between regions within a
country as well as between countries. Such differences can be caused by variation in temperature,
rainfall and access to water, use of fertilizer, nutrient content of the soil, etc. (Greenfield and
Southgate, 1992).
A 5-year initiative has been taken to develop a food composition table and database for
Mali by analysing staple foods and other foods that are frequently eaten, such as dry green
leaves, some vegetables and wild fruits (Barikmo et al., 2002). The staple foods in Mali are
cereals and the most commonly eaten are millet, sorghum, rice, maize, wheat and fonio.
The cereals accounted for 74% of the energy and plant foods provided 74% of the protein
in the Malian diet (FAO/WAICENT, 2003). In the past, protein quality of wild gathered
foods from Mali (Nordeide et al., 1994) and nutrient composition of green leaves (Nordeide et al.,
1996) have been examined, and the results are included in the Malian food composition
table and database. It became clear early in the work that nutrient content varied considerably.
A food composition table and databases with average figures on nutrient content in foods
would therefore have been of limited use. A key question is how to ensure access to representative
results of nutrient content of foods in a national database, taking into account ecological and
seasonal variations. One response is to analyse foods using a sampling plan that captures these
variations.
Mali is a land-locked country in West Africa which covers a surface area of 1,241,248 km2. Its
total population in 2000 was 11.9 million inhabitants with a 3% growth per year (UN, 2003). The
country can be divided into three ecological zones: the Sahara zone which covers 56% of the total
surface and is characterized by less than 200 mm of annual rainfall (regions of Timbuktu, Gao and
Kidal); the Sahel zone which covers 19% of the land mass with an annual rainfall between 200
and 700 mm (region of Mopti and Kayes); and the Sudan zone that receives 700–1400 mm of rain
per year and is covered by significant vegetation such as savannah and forests (regions of Se! gou,
Koulikoro, Bamako and Sikasso) (FAO/WAICENT, 2003). In addition to this ecological
variation Mali has three climatic seasons: the cold season from October to January; the hot season
from February to May/June; and finally the rainy season from June/July to September. The
considerable ecological and climatic variations cause varied soil and growing conditions for plants
and animals that may affect the nutrient content of the local food. Anecdotal data indicate that
use of fertilizer in Mali is very low, and is basically limited to the area where agriculture is less
risky and integrated in the market economy.
The purpose of this article is to present results of protein, carbohydrate and fibre content for
some cereals in five different regions in Mali with considerable ecological variation, and to suggest
how to ensure the correct use of such results.
2. Materials and methods
The collected cereal samples are all produced in Mali, except for the wheat flour. The
contribution from each region to the national cereal production varies a lot. In 1996/1997
the Se! gou region (the Sudan zone) accounted for 35% of the national production, followed by the
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region of Sikasso also in the Sudan zone (about 22%), Koulikoro in the Sudan zone (13%) and
Mopti and Kayes in the Sahel zone (17% and 8%). The proportion from the regions of the Sahara
zone to the total national grain production was 5% from Timbuktu and 1% from Gao (SISEI
Mali, 2003).
With this background, sampling plans were worked out (Barikmo and Ouattara, 1998;
Ouattara and Barikmo, 2002). For the cereals the main collection started in the region of
Se! gou and Mopti in 1999, followed by a new collection of the same food items in the
capital, Bamako, in 2000. In Bamako, most of the cereals on the market came from the regions of
Se! gou, Sikasso and Koulikoro, the last one being located close to the capital. In 2002 the
collection continued, this time in the Timbuktu region. Timbuktu was chosen because of its
special conditions in the desert and the fact that no samples from this region had been analysed
before.
2.1. Sampling plan and methods
All the collections followed a major sampling plan. Because we wanted foods from
different selected regions, stratified sampling1 combined with randomly selected counties
in each region was considered adequate (Greenfield and Southgate, 1992). The main
feature was that in each region, counties were randomly chosen and the samples were
collected from different retailers at different markets in each county (Table 1). How
many counties, markets and retailers to select depended on the population figures,
but as a general rule, 10–12 primary samples of each food item were collected from each
county. In order to minimize the costs and transportation, composite samples2 prepared
from, e.g., 12 different primary samples with multiple composition3 from one county, were mixed
with composite samples from the other counties in the region. Thus all the samples in each region
were mixed.
2.2. Food collection
The food collected was the most commonly produced and used cereals in Mali, namely millet
(Pennisetum glaucum), sorghum (Sorghum bicolor), maize (yellow) (Zea mays), rice (Oryza sativa),
wheat (Triticum aestivum) and fonio (Digitaria exilis). Fonio is probably the oldest African cereal,
and is cultivated across the savannahs in West Africa. It is very popular in Mali (NRC, 1996).
Because the food collection was done from retailers at different markets, no cultivar or variety
names were available.
Table 1 shows that between 163 (millet) and 104 (fonio) primary samples of each cereal were
taken to make reduced composite samples, and samples sent for analyses represented 3–5 cereal
grains. The number of samples varied from region to region. In Kayes, only maize was collected,
but for the other regions the variation in the number of samples was due to the availability of the
different cereals in the area.
1
A sample consisting of portions obtained from identified subparts (strata) of the parent population. Within each
stratum, the samples were taken randomly.
2
A representative part of the primary sample obtained by a division and reduction process.
3
Different cultivars but the same food, e.g., millet from 12 different retailers.
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Table 1
Number of samples of cereals and origin in Mali, collected in 1998–2002
Region
Countiesa
Millet
(Date of collection)
No
csb
Kayes (Nov 1998)
S!egou (Nov 1999)
Mopti (Nov 1999)
Bamako (Nov 2000)
Timbuktu (Jun 2002)
2
4
4
6
4
—
40
39
71
13
Primary samples
Analysed samples
Sorghum
Maize
nc
csb
nc
csb
—
1
1
1
1
—
37
28
67
10
—
1
1
1
1
19
13
4
61
10
163
142
4
Rice
nc
1
1
1
1
1
107
4
Wheat
csb
nc
csb
nc
csb
nc
—
44
24
72
10
—
1
1
1
1
—
27
18
68
10
—
1
1
1
1
—
23
11
70
0
—
1
1
1
0
151
5
Fonio
123
5
104
4
3
a
Counties=number of counties in each region where the primary food samples were collected.
cs=numbers of primary samples used for preparing composite samples.
c
n=numbers of composite samples sent for analysis, made from cs.
b
2.3. Sample handling
After pooling the samples in the field, they were prepared for laboratory analysis in Bamako.
Millet, sorghum, maize and fonio were bought on the local market as whole grain but rice was
bought in its polished form. Wheat was bought as flour in all the regions, except in Timbuktu
where it was bought as whole grain and grounded to flour by a local woman. The wheat was
locally grown in the region of Timbuktu, but in all the other regions it was imported from abroad,
mostly from Europe. The cereals were analysed as ready to cook and different procedures had to
be followed according to type. For millet, maize and sorghum the bran was separated from the
grain. After the separation, the grain was washed, and then grounded to flour by hand or by small
mills. The flour was kept frozen at 20 C until analysis. Fonio and rice grains were cleaned and
kept frozen. The wheat flour was kept dry in a dark place but not frozen. Two hundred grams of
each cleaned cereal were taken for laboratory analyses. The frozen samples were transported to
the laboratories on ice.
2.4. Laboratory analyses
None of the laboratories in Mali was accredited. The analyses were therefore done in South
Africa and Norway. For the analyses of protein, carbohydrate and fibre, three different
laboratories were used. The methods used are shown in Table 2. For the analysis of protein in
maize collected in 1998 (Kayes region) the National Institute of Nutrition and Seafood Research
in Norway was used. For carbohydrate and fibre on samples collected in 1998, and for all the
analyses done on the 1999 (Se! gou and Mopti regions) and 2000 (Bamako) samples, another
Norwegian laboratory, LabNett St^rdal A/S, was used. The last samples collected in Timbuktu in
2002 were analysed in co-operation with the Food and Agricultural Organization of the United
Nations (FAO). The analyses of these samples were performed by the ARC-Irene Analytical
Service in South Africa. For total nitrogen, Kjeldahl and Dumas combustion methods were used.
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Table 2
Analytical methods used for the different parameters and laboratories
Parameters
Description
Protein
Determination of
crude protein
Carbohydrate
Determination of
sugar
Determination of
starch
Analytical methods used, Assay name (Eurofood Codea)
Institute of Nutrition
and Seafood
research, Norway
LabNettac St^rdal
A/S, Norway
ARC-Irene
Analytical Services,
South-Africa
PROT0916 (ME61—
Kjeldahl method)
93/28EØF (ME61—
Kjeldahl method)
ASM 041 Dumas
Combustion
(ME73—protein
from total nitrogen)
HPLC (ME52—
HPLC)
Enzymatic hydrolysis
99/79/EU (ME70—
polarimetry)
(ME85—
spectrophotometry)
Enzymatic hydrolysis
(ME85—
spectrophotometry)
Fibre
Total dietary fibre
AOAC (ME50—
gravimetric method)
a
Guidelines notes for preparing and exporting food composition data according to the common formats of export
files (2000). Vignat J., Ireland J., M^ller A. and Charrondi"ere U.R. (poster).
The methods used for carbohydrate (sugar and starch) were polarimetric, spectrophotometric and
HPLC, and a gravimetric method was used for fibre. All the laboratories were accredited for the
analyses, but as shown in Table 2, they used different methods.
2.5. Conversion factors
The conversion factors for nitrogen to protein were 6.31 for millet, 6.25 for sorghum, maize and
fonio, 5.95 for rice and 5.70 for wheat flour (Greenfield and Southgate, 1992).
2.6. Water content
For making comparisons and averaging values, the water content in the cereals was
standardized by calculation. The water content was assumed to be 12% for millet and fonio,
and 11% for sorghum, maize and rice. Nutrient contents were then calculated from dry weight
values assuming these moisture contents.
2.7. Statistics
Statistical Package for Social Sciences (SPSS for Windows, 2001) was used to estimate the mean
and standard deviations of the samples.
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3. Results
The results revealed large differences in the content of protein, carbohydrate and fibre, and they
occurred both in the different regions and in the different cereals.
Tables 3a and b show differences between the regions in the protein content of millet, maize and
wheat. The mean values were 8.0 g/100 g in millet, 10.4 g/100 g in sorghum, 7.6 g/100 g in maize,
6.1 g/100 g in rice, 10.3 g/100 g in wheat and 7.2 g/100 g in fonio. For millet Mopti was the region
with the lowest values, being 26% lower than the mean and Se! gou had the highest content, with
10% above the mean. The maize in Mopti had the lowest protein value, namely 13% lower than
the mean (7.6 g/100 g cereal) while Timbuktu had 24% higher value than the mean. The protein
content in wheat flour from Timbuktu had the highest value, namely 15% higher than the mean.
The lowest content of protein in wheat flour was in Bamako, with 10% lower than the mean
(10.3 g/100 g cereal).
The mean values for carbohydrate were 65.7 g/100 g in millet, 73.5 g/100 g in sorghum, 73.0 g/
100 g in maize, 83.7 g/100 g in rice, 75.1 g/100 g in wheat and 74.3 g/100 g in fonio. For
carbohydrate the difference was most obvious for millet and maize (Table 3a). For millet, both
minimum and maximum values differed around 15% from the mean, lowest content in Mopti and
highest in Timbuktu. For maize the Mopti region again had the lowest value, namely 19% lower
than mean while Timbuktu had the highest, with 13% above the mean.
The mean values for fibre were 6.2 g/100 g in millet, 4.7 g/100 g in sorghum, 4.6 g/100 g in maize,
1.1 g/100 g in rice, 3.0 g/100 g in wheat and 2.2 g/100 g in fonio. Percentage differences from mean
were from 18% to 25% in millet, maize and fonio and all the values were from cereals from
Mopti. On the other hand, the differences were close to zero in sorghum, rice and wheat (see
Tables 3a and b).
4. Discussion
4.1. Possible reasons for variation in the data
The collection of food items was done in accordance with the sampling plan. As a collection
method, primary food samples from different cultivars, obtained by division and reduction
processes, were used. The disadvantage with this method is that the information on the variation
between the same food in the region is lost, although it permits to reduce the cost of nutrient
analysis. The ample variations as reported here, even though this method was used, were not
expected. It appears that the variation is real; the differences between Se! gou and Mopti regions
are examples of this. The sample collection was done in the same year and season (cold season and
just after the harvest), by the same fieldworkers; the samples were handled in the same way, and
analysed at the same laboratories using the same methods. The collection in Bamako was done
one year later, but in the same season. The collection method was the same as for Se! gou and
Mopti, the same fieldworkers were used together with some new, the handling of the samples was
the same and so were the laboratory and analytical methods. The last collection, in the Timbuktu
region, was done 2–3 years later. It was the hot season and the stocks were almost depleted. This
may have had an influence on the results. Also, another laboratory was chosen and thereby also
(a) Millet, sorghum and maize
Region
Millet, flour
Sorghum, flour
Maize, flour
Protein % Diffa CHO % Diffa Fibre % Diffa Protein % Diffa CHO % Diffa Fibre % Diffa Protein % Diffa CHO % Diffa Fibre % Diffa
—
8.8
5.9
8.5
8.7
8.0
1.4
10.0
26.3
6.3
8.7
n.r.
—
66.5
55.3
n.a.
75.4
65.7
10.1
1.2
15.8
14.8
n.r.
—
7.8
4.6
n.a.
n.a.
6.2
2.3
25.8
25.8
n.r.
—
9.8
10.4
10.0
11.3
10.4
0.7
5.8
0.0
3.8
8.7
n.r.
—
71.5
70.5
n.a.
78.6
73.5
4.4
2.7
4.1
6.9
n.r.
—
4.7
4.6
n.a.
n.a.
4.7
0.1
1.1
1.1
n.r.
7.2
7.1
6.6
7.8
9.4
7.6
1.2
5.3
6.6
13.2
2.6
23.7
n.r.
77.8
72.6
59.0
n.a.
82.5
73.0
11.8
6.6
0.5
19.2
13.0
n.r.
4.7
5.5
3.7
n.a.
n.a.
4.6
1.3
1.4
18.7
20.1
n.r.
(b) Rice, wheat and fonio
Region
Rice
Wheat, flour
Fonio
Protein % Diffa CHO % Diffa Fibre % Diffa Protein % Diffa CHO % Diffa Fibre % Diffa Protein % Diffa CHO % Diffa Fibre % Diffa
Kayes
S!egou
Mopti
Bamako
Timbuktu
Mean
s.d.
—
6.3
6.2
6.6
5.8
6.1
0.3
3.3
1.6
8.2
4.9
n.r.
—
77.1
77.1
n.a.
92.3
83.7
7.8
7.9
7.9
10.3
n.r.
—
1.1
1.1
n.a.
n.a.
1.1
0.0
0.0
0.0
n.r.
—
9.8
10.2
9.3
11.8
10.3
1.1
4.9
1.0
9.7
14.6
n.r.
—
73.7
74.4
n.a.
77.2
75.1
1.8
1.9
0.9
2.8
n.r.
—
3.0
3.0
n.a.
n.a.
3.0
0.0
0.0
0.0
n.r.
—=not collected, n.a.=not analysed, n.r.=not relevant, CHO=carbohydrate.
Numbers in bold shows biggest percentage difference from mean for each cereal and nutrient.
a
% Diff=percent differences from mean.
—
7.4
7.5
6.7
—
7.2
0.4
2.8
4.2
6.9
n.r
—
74.3
74.4
n.a.
—
74.3
0.1
0.0
0.1
n.r.
—
1.9
2.6
n.a.
—
2.2
0.3
13.6
18.2
n.r.
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Kayes
S!egou
Mopti
Bamako
Timbuktu
Mean
s.d.
I. Barikmo et al. / Journal of Food Composition and Analysis 17 (2004) 291–300
Table 3
Content of protein, carbohydrate and fibre (g/100 g) (a) in millet, sorghum and maize; (b) rice, wheat and fonio, from five different regions in Mali
297
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some of the methods were different. For protein it seems that the methods had no influence on the
data, as both methods measure crude protein. For millet the large difference in protein content
was between Se! gou and Mopti regions (analysed with the same method) and there were no large
differences for sorghum and rice (Tables 3a and b). For maize there are results from five regions.
Timbuktu had the highest value and it should be investigated if maize from this region actually
contains more protein than the others and why. The same also applies to wheat flour. Wheat is
cultivated in this region and the protein content in wheat flour was higher than in the imported
flour collected in the other regions. All the retailers were asked if fertilizer was used, but most of
them, in all the regions, did not know. For carbohydrate content the differences in millet and
maize were considerable between Mopti and Timbuktu. Different methods were used but all
samples were analysed by hydrolysis. So these differences may well be real but further research is
needed. The fibre content in the cereals varied for some cereals but not for others. Tables 3a and b
show no values from Timbuktu because crude fibre was analysed by mistake and therefore is not
included.
4.2. How to fit the results into a database
How to handle these differences in a food composition table and database is a crucial
question. There is no agreed upon range of acceptable values above and below the mean.
This is a common problem in food database compilation and for clarity maybe one should
come to an agreement on a standardized cut-off point. Are 20% differences from
the mean a sensible cut-off point when considering including or rejecting values? Removing
outliers and using mean figures is not an adequate approach. Removing the ‘‘trouble protein’’
from millet, maize and wheat to make acceptable mean values (less than 20% difference from the
mean) will eliminate the existing diversity in the country. For carbohydrate all the differences were
less than 20% and the variation would be considered as acceptable according to this cut-off point
but not if the cut-off point was 10%. For fibre the type of food seems to be the most important
factor but the number of analyses are too few to conclude. These analyses point out the
importance of adequate cut-off points. Both mean values and separate tables with figures from
each region showing the variation should be included in food composition databases and printed
tables. The data should be compiled into databases open for general use, preferentially within the
INFOODS system.
4.3. The end users and need for different quality criteria for the data
Food composition tables give detailed information on the nutrient composition of foods. It is
used as a tool in assessing the nutritional quality of diet, food and nutrition security work, food
and nutrition training, education and research, food production, regulations, control and trade,
consumer protection and information, as well as epidemiological research. The end users want as
good quality on the data as possible, to ensure correct use of the data. It should be obvious that
epidemiological research and dietary assessment need representative data of the best quality,
meaning that it is important that the figures in the food database reflect the food composition that
investigated persons have eaten. Using average values from central regions can create problems
since they may not be representative for foods grown locally in Mali, a country where people may
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eat considerable quantities of staple foods; e.g., a survey in 1997 (Diallo et al., 1998) showed that
the average intake of cereals among adults was 650 g per day. Using maize as the staple and the
figures for protein from Table 3a, the average intake of protein would have varied from 61 g per
day by the highest value to 43 by the lowest. The mean value would give 50 g. For the Mopti
population, e.g., where the protein content was 6.6 g/100 g maize, this could lead to incorrect
assessment of a diet; a diet too low in protein would be assessed as adequate just by using the
mean value from the food composition table. This shows the disadvantage of using average values
and can lead to erroneous conclusions and wrong advice, with possible harmful effects, especially
for children.
In epidemiological surveys when researchers try to find a relationship between nutrient intake
and health, relationships can be hidden because the survey under- or overestimates intakes of
different nutrients (Willet and Buzzard, 1998). This can be one of the reasons why it is difficult to
find evidence for nutrient and disease associations.
In other situations, e.g., in teaching about food and nutrition in general the regional
differences are not so important and representative average figures for the nation can
be used. Professionals working on food composition table and database have tried
to inform the epidemiological researchers about this issue for years (Southgate, 2002),
but still it is easily overlooked and it is hard to convince research funders of the necessity
for food collection and analyses in order to improve food composition databases. Food
analyses should be included in plans for epidemiological surveys, even though it will increase the
budget.
5. Conclusion
In the Food Composition Table for Mali, data for different geographical and ecological
areas are included. This will make use of the data for different purposes more relevant
and meet the needs of various users. It would in particular be important when data are
used for dietary advice in different regions, and in local epidemiological research. But
still not enough is known about the nutrient content of the foods in Mali and their large
differences, although local ecological variations may be important. More research and enhanced
co-operation between different experts in nutrition, analytical laboratories, industry, and agencies
in charge of food composition tables and database development are necessary and should be
strengthened.
Acknowledgements
The authors would like to thank the Str^mme Foundation in Norway and Bamako for their
financial support and co-operation. We also acknowledge the Action d’Appui aux Initiatives de
De! veloppement de Bafoulabe! (AIDEB) in Bafoulabe! and the Care Mali and fieldworkers who
have taken part in the collection of the samples. We also acknowledge the support from FAO for
collection of some of the food samples.
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