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Nutrient intake data calculated using food composition tables: factors affecting accuracy

(introductory text)

Materials and methods


Laboratory of Nutritional Biochemistry, Department of Nutrition, Federal University of Pernambuco, Cidade Universitaria, Brazil



Specific nutrient deficiencies continue to be one of the world's major public health problems, especially in underdeveloped countries. In terms of number of individuals affected and geographical distribution, vitamin A and iron deficiencies are among the most prominent [25]. One very serious drawback for the design and implementation of nutrition intervention programmes is the inadequacy of dietary information, almost always plagued by the spectre of inaccuracy [21]. Thus, it is not uncommon to find reports describing high iron intakes in areas where iron-deficiency anaemia appears with undesirably high prevalence [2, 9, 11], or extremely low vitamin A intakes which are not accompanied by a compatibly high prevalence of eye lesions [2, 6, 9, 11]. Similar problems exist when trying to establish correlations between the intake of other nutrients and related clinical or biochemical indicators [3,12, 18, 20, 21].

The accuracy of nutrient information depends on the methods of collecting and handling the data. Some of these have been examined [3, 8,14, 20, 21] but, in general, attempts to reconcile dietary and biochemical or clinical information from nutrition surveys are still needed. The present study was designed to evaluate the relative contribution to the inaccuracy of dietary information of both regional differences in the nutrient composition of foods and the differences between those values obtained by calculation and those obtained by direct analysis of foods as eaten.

Materials and methods

Materials and methods

Three recipes for the dishes most frequently consumed by the population in Northeast Brazil were selected for this study. These recipes are practically standard and appear with very little variation among users [19]; they can thus be considered representative of local alimentary practices. The proportion of raw ingredients and the per cent composition as actually eaten are shown in table 1. The culinary preparations, also in accordance with local practices, were as follows:

Table 1. Composition of regional dishes

Ingredients Raw
(% in serving)
Beans (mulatinho) 39.5 21. 1
Beef,dried 19.8 24.2
Bacon 4.9 10.1
Okra 2.0 2.7
Pumpkin 15.8 21.7
Green herbsa 14.8 18.9
Salt 1.0 -
Pork blood 39 5 39 5
Pork liver 12.5 12.5
Pork heart 12.9 12.9
Chard 11.9 11.8
Green herbsa 21.8 21.7
Salt 1.0 -
Beef 42.6 24.0
Pumpkin 9.1 8.9
Sweet potatoes 9.1 10 9
Potatoes 9.1 91
Plantain 7.3 116
Wild cabbage 3 7 7 4
Green herbsa 18.8 15 0
Cassava flour - 5 1
Salt 1.0 -

a. A mixture, in equal parts, of green pepper, coriander, green onions, tomatoes, and onions.

- Feijoada: the beans (mulatinho type) were soaked in water for one hour, after which the beef and bacon were added. The mixture was boiled for two hours. Then the vegetables were added for a final boiling for 30 minutes.

- Sarapatel de porco: the pork blood and viscera were cooked in salted water, cut in small pieces, and boiled for one hour with the vegetables.

- Cozido: the meat and aromatic herbs were boiled in water for two hours. When these were nearly cooked, the vegetables were added. Before serving, the solids and liquid were separated, the latter to be mixed with cassava flour.

Individual servings of each preparation, in accordance with local uses [19], were duly homogenized with a blender, and appropriate aliquots of the homogenates were taken for the analysis of moisture, ash [10], fibre [24], ether extract [16], protein [15], calcium [4], phosphate [5], iron [10], vitamin A and carotenoids [1], and vitamin C [23]. The minerals were analysed in aliquots of the ashes. Carbohydrates were calculated by difference. The net weight of raw and cooked ingredients and the relative contribution of the latter in individual servings were recorded. All assays were run in duplicate, using appropriate standards. Differences greater than 5 per cent between duplicates were considered unacceptable. All ingredients of the recipes were also analysed individually. The nutrient composition of the recipes was calculated using the values of the Food Composition Table of INCAP-ICCND [17] and those obtained from our analysis of the individual raw ingredients. Hence, two estimates of the nutrient composition of each recipe were made, along with direct analysis of appropriate aliquots of each dish. All reagents were analytical grade.



Table 2 permits comparison of the values of the INCAP table [17] with those obtained at our laboratory for the proximal composition, plus the iron, vitamin A, and vitamin C content of the 22 ingredients of the 3 dishes selected for this study. The values for fibre, calcium, and phosphate were not included in the table, as they add little to the objective of this work. It is readily apparent that agreement within a margin of + 20 per cent was obtained only in about one-third of the total number of analyses performed. Results were below 80 per cent of the table value in 38 per cent of the observations, while 28 per cent presented with differences of more than 20 per cent above the figure in the table.

In the case of iron and vitamin A, the tendency was for the INCAP table values to grossly overestimate the content of the foodstuffs analysed. Especially remarkable was the case of iron, where 90 per cent of our values were well below 80 per cent of the figure presented by the INCAP table. Most of the major differences in protein content were in foodstuffs that are unimportant as sources of this nutrient, like coriander, pepper, and onions.

Table 3 was constructed to show the practical implications of the differences in the nutrient compositions described above. A previous survey [2], carried out in a country village, was used as a source of data to recalculate nutrient intake using the values obtained at our laboratory. Protein consumption shows very little difference, as could be expected from table 2. Vitamin A intake, on the other hand, seems to have been slightly underestimated, and that of iron grossly overestimated, when the INCAP table values were used.

The problem of "foods as eaten" was approached by comparing the results of direct analysis of the dish with the nutrient composition calculated using the values in the INCAP table or the the values obtained by local analyses (table 2). The changes in the relative proportion of the ingredients after cooking were taken into account by directly weighing the ingredients before and after culinary processing.

Figure 1 shows that up to 22-fold differences could be found when comparing the nutrient composition of the food as eaten with that calculated from the composition of the raw ingredients ("recipe calculation"). As could be expected from the degree of "complexity" of the dishes, feijoada was the one containing the largest differences.



It should first be emphasized that our aim was not to construct a Food Composition Table for local foodstuffs but, rather, to evaluate the degree of accuracy that can be expected from nutrient-intake data calculated from a Food Composition Table for Use in Latin America (i.e. recommended for this purpose).

It is apparent that differences of up to one order of magnitude can be found between the results of our local analysis and the values for some nutrients given by the INCAP table (the vitamin C content of green pepper determined here was 20 times higher than that given by the table). Of particular interest was the observation that nearly all the values for iron content were below 50 per cent of the value given by the INCAP table. It can readily be appreciated that the use of the table leads to an overestimation of iron consumption, and to reconciling the dietary data with the high prevalence of iron-deficiency anaemia found in the region [2, 11,22]. It is also interesting to note that no value for the iron content of the 20 foodstuffs analysed here fell within 80-120 per cent of the value in the INCAP table. Equally interesting is the fact that the protein content of the so-called "sources of protein" showed little difference between the two values compared here. This might be the starting-point for suggesting that nutrient composition data could be divided into two categories: those of nutrients that show a high variation - probably attributable to regional differences (soil, climate, season, species) - and those of nutrients in some foodstuffs that show very little variation, probably insignificant for dietary evaluation purposes. Minerals and some vitamins are likely examples of the first category, while protein - being a compulsory component of foodstuffs derived from animal or plant tissues - could be a good example of the second category. Appropriate software for identifying the members of each category could be easily developed, and there are probably enough data available from various food composition tables to be used for this purpose.

Table 2. Nutrient composition of local foodstuffs, Recife

  INCAP table no. Moisture Protein Fat Carbohydrate Ash lron Vitamin A Vitamin C
Foodstuff   (%) (%) (%) (%) (%) (mg/100B) (µg/l00g) (mg/l00g)
Pork blood 549 77 17.53 0.12 3.56 1.79 14.70 - 1.31
Pork liver 552 56 26.98 2.30 13.32 1.40 1.40 4,441 40.88
Pork heart 540 68 20.37 3.92 9.58 2.73 14.00 - 5.06
Coriander 143 90 1.10 2.75 3.14 2.99 11.60 1,291 129.00
Green onions 142 89 1.20 3.55 4.63 0.82 0.31 268 47.81
Green pepper 80 92 0.24 3.33 3.60 0.53 0.22 270 190.50
Tomato 271 95 0.49 0.55 2.92 0.65 0.09 368 16.69
Onion 137 85 0.21 2.41 11.57 0.55 0.20 - 11.62
Corn flour 27 11 8.62 1.76 77.54 0.56 0 26  
Beef, lean 579 63 21.93 6.25 7.61 1.12 0.06 - 2.02
Pumpkin 127 68 0.71 0.14 26.45 2.29 0.27 1,526 16.88
Sweet potato 108 64 1.57 0.10 31.02 0.81 0.04 10 26.63
Sweet potato, yellow 107                
Potatoes 242 86 0.71 0.11 11.63 1.27 0.34 0 32.00
Plantain 438 58 1.16 0.59 43.46 1.25 0.30 88 34.03
Cabbage, wild 149 90 4.63 0.17 3.19 1.32 1.44 - 190.69
Cassava flour 276 7 0.52 0.18 88.68 1.14 0.98 - 13.50
Beans, mulatinho 481 13 25.52 1.78 52.38 3.54 4.97 - 2.26
Beef, dried 582 28 56.80 16.32 17.07 15.95 6.90 - 1.13
Bacon 682 7 0.27 80.04 11.05 1.64 - - 1.16
Anguria - 91 1.35 0.05 4.74 0.41 0.34 - 36.38
Okra 252 83 1.61 0.06 12.85 1.21 0.56 14 27.06

Table 3. Estimated iron and vitamin A intakes using nutrient composition figures from INCAP table and analysisa

Nutrient Table Analysis
Protein g/d 20.9 21.7
Iron, mg/d 10.5 3.0
Vitamin A, µg/d    
(as retinol equivalents) 143.2 174.9

a. Food consumption data are from a survey in Agua Preta, Pernambuco [2].

Vitamin A nutriture constitutes a problem that should be looked at - in our region - from another angle. It is apparent that the difference in the figures for consumption resulting from the use of the INCAP table and our results is not enough to explain a lack of prevalence of severe signs of vitamin A deficiency [2, 6, 7, 12] which is not compatible with the very low vitamin A intakes reported in several surveys [2, 9, 12]. One possible explanation might be that some regional fruits, with a very high carotene and carotenoids content, are consumed by the population but not reported in the surveys. We have observed that the fact that some of these fruits are not actually "bought" may lead the population not to consider them as "foods." Thus, a significant contribution to vitamin A intake may have been overlooked in the past.

The problem of regional differences in nutrient composition - and the difficulties generated by the use of food consumption tables which are, most of the time, inadequate for specific situations - is well known. Our data have only shown what the practical implication of this may be, and one way to reconcile dietary data with other indicators of the nutritional status. Our data on "dish-nutrient composition" (fig. 1) shows another very serious drawback in the analysis of survey data with the aid of food composition tables: the so-called "foods-as-eaten" problem. In theory, there has never been any reason to consider as reliable "recipe composition data," i.e. the compound nutrient composition of a dish, obtained by addition of the contribution of each single (raw) ingredient.

Fig. 1. Dish composition: calculated v. analysed values.

This approach ignores liability to heat of a great many nutrients, and losses that may result from chemical reactions as a consequence of the interaction between ingredients which are "incubated" for variable periods at 100°C or more. From a chemical view, feijoada was the dish to undergo the most drastic treatment (see recipe) and, in keeping with this, losses of 66 to 95 per cent were observed for protein and vitamin A respectively, regardless of the nutrient composition value used for the "recipe calculation." It was beyond the scope of this work to determine the actual causes for these losses, and we are first to admit that that of protein was the most intriguing. But the obvious conclusion is that food composition tables cannot continue to be used, without restriction, for calculating nutrient intakes.

The number of foodstuffs and nutrients analysed here was very modest. One should bear in mind, however, that sweet potatoes, cassava flour, sugar, beans, and a little meat account for more than 70 per cent of the daily food intake of the underprivileged in this region [2], as well as in most of the rest of the country. This is what we consider to be "alimentary monotony." Regarding nutrients, our contention is that emphasis should always be given to those capable of generating, by deficient or excessive intake, public health problems. These nutrients would include protein, vitamin A, and iron; the list would vary according to region, but would certainly be limited in each case.

It is becoming increasingly important to count on reliable sources of accurate data for nutrient intake evaluation in connection with a number of nutrition-related diseases.

Our data show that food composition tables do not meet accuracy requirements when the analysis involves foods that are not consumed raw, and where the presence and amount of nutrients in foodstuffs are very dependent on local conditions, mainly soil composition. This work also shows that dietary data can be reconciled with related clinical and biochemical indicators.



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