Proposal for the Validation of the Italian Food Composition Database

(2000) 13, 511}523 doi:10.1006/jfca.2000.0904 Available online at http://www.idealibrary.com on

JOURNAL OF FOOD COMPOSITION AND ANALYSIS

SHORT COMMUNICATION Proposal for the Validation of the Italian Food Composition Database Maria Parpinel*, Patrizia Gnagnarella-, and Simonetta Salvini? * Epidemiology Unit, Aviano Cancer Centre (CRO), Via Pedemontana Occ.le, 33081 Aviano (PN), Italy; - Division of Epidemiology and Biostatistics, European Institute of Oncology, Via Ripamonti 435, 20141 Milan, Italy; and ? Analytical Epidemiology Section, Epidemiology Unit, Cancer Study and Prevention Centre, A.O. Careggi, Viale Volta 171, 50131 Florence, Italy Received July 7, 1999, and in revised form February 29, 2000

The aim of this project is to validate the nutrient composition data presented in the book &&Food Composition Database for Epidemiological Studies in Italy'' and to con"rm the working methodology, based on the &&combination method'' described by Green"eld and Southgate (1991). This database was compiled deriving 35% of data from the food composition tables of the National Institute of Nutrition, Rome; the remaining data were obtained from other Italian and foreign tables and from scienti"c papers. In addition, some values were calculated or estimated. Food composition data presented in the book will be compared with ad hoc chemical laboratory analyses. A sample of frequently consumed Italian food items and of items whose composition was totally &&borrowed'' from foreign sources will be chemically analysed and the results will be compared with the values included in the database. A speci"c sampling plan will be developed. The following parameters will be measured for each food item: weight, moisture, macronutrients, some vitamins and minerals. Laboratory quality control will be established a priori. Due to the complexity and the cost of the study, details are still open to debate and suggestions.  2000 Academic Press

Key =ords: validation study; food composition database; sampling plan; chemical analyses.

INTRODUCTION Diet plays an important role in the protection from cancer and other chronic diseases. Large epidemiological studies need to have valid tools for transforming consumption data expressed in terms of food items into consumption data expressed in terms of nutrients or food components. All dietary assessment methods have potential sources of error and also the use of incorrect food composition tables may introduce additional imprecision in nutrient intake estimation (Bingham, 1987). A food composition database (DB) should contain all food items representing the real food consumption of the populations to be studied. The existing Italian food composition tables are insu$cient in terms of the information they provide and in terms of the format in which they are o!ered to researchers (printed material). A collaborative project has led to the publication and distribution of a compiled DB &&Food Composition Database for Epidemiological Studies in Italy'' (Salvini  To whom correspondence and reprint requests should be addressed. Tel.:#39-055-5012270. Fax:#39-055-5012280. E-mail: [email protected]. 0889}1575/00/040511#13 $35.00/0

 2000 Academic Press

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PARPINEL, GNAGNARELLA, AND SALVINI

et al., 1998), that is an extension of the original work carried out on a limited number of foods (Salvini et al., 1996). The project was partially funded by the Italian Association for Cancer Research (AIRC) and also supported by COST Action 99 (1994) and by Kellogg's Italia. The origin is a large DB including approximately 3300 food items, developed in the 1980s by the National Institute of Nutrition (Rome) for the analysis of the national nutrition survey that took place in 1980}1984 (Turrini et al., 1991). From this DB, the food items that were most frequently consumed by the participants to the four epidemiological studies involved were selected. The studies are the &&National Nutrition Survey 1980}1984'', National Institute of Nutrition, Rome; the &&Case-Control Study of Breast Cancer, Ovary Cancer and Cancer of the Digestive Tract with Emphasis on the Role of Diet'', Aviano Cancer Centre, Aviano; the &&EPIC Prospective Study'', Italian group (National Institute of Tumours, Milan and Cancer Study and Prevention Centre, Florence); and the &&ONCONUT Project Diet and Cancer'', IRCSS De Bellis, Bari. A compiled DB resulted from this project, where data have been obtained from pre-existing sources and not from ad hoc chemical analyses. Compiled according to a well-de"ned methodology, it contains information on the composition of 788 food items, mainly simple and raw food. Thirty-seven food components plus energy (kcal and kJ) are included, with missing values reduced to a minimum (only 0.43%). Sources of data for each nutrient are recorded and estimated values and calculations are clearly shown. All the composition data included in the DB are published in a book and are easily accessible on the enclosed computer disc. Data were mainly derived from Italian sources: 35% from the food composition table of the National Institute of Nutrition (INN) (Carnovale and Miuccio, 1989) and 10.3% from the food composition table of the University of Perugia (Fidanza and Versiglioni, 1989). For the rest of the data, including information for b-carotene and retinol, vitamins E and D, folates and vitamin B , foreign food composition  tables were used, mainly British (26.8%) and American (15.6%). Other DBs (1.5%) and speci"c scienti"c papers (0.4%) were also used, and 9.9% of the data were estimated. Most food composition DBs, nowadays, are prepared by combining the direct and indirect methods (Green"eld and Southgate, 1992), i.e. joining original analytical values together with values taken from other DBs as well as imputed and calculated values. This is believed to be a cost-e!ective methodology. Some researchers have compared published data from food composition tables with analytical data (Porrini et al., 1986; Boulous et al., 1996; Bedogni et al., 1999), but to our knowledge no studies, to date, have tried to base such comparisons on a speci"c list of foods, selected to represent the most commonly consumed items in a country. The aim of our project is to compare the composition of a group of frequently consumed food items, included in the Italian DB, with the composition obtained by chemical analyses of food items sampled on Italian territory. This would help us to assess if the compilation methods has produced a reliable food composition DB for the Italian population (Holden, 1995). Crucial points of this project are the selection of participating laboratories, the design of the sampling plan and the choice of the analytical methods to be used for the nutrient analysis. The procedure adopted for the selection, purchase, transportation, preservation and preparation of the food items should be well conducted and standardized to ensure the quality of the food matrices to be analysed. The analytical methods should be comparable to those included in the DB and speci"cally chosen for each food component.

VALIDATION OF A FOOD COMPOSITION DATABASE

513

MATERIALS AND METHODS Food ¸ist Two lists of food items were prepared: the "rst includes the most consumed items in a sample of the Italian population, and the second includes items for which no Italian composition data were found. The food items that are included in the nutritional questionnaire of Aviano's Case-Control Study were selected (Franceschi et al., 1996). Using speci"c consumption data based on the same study and published by Favero et al. (1997), we selected items that had been consumed at least once a week by the 2588 women included in the study. Data for males are comparable in terms of relative frequency of consumption (unpublished). The resulting list includes 42 items, mainly simple, raw foods. In addition, we also included 20 items from the DB for which composition data were obtained from non-Italian sources or by calculation or estimate. The validation study will therefore focus on 62 food items, i.e., 8% of the total DB (Table 1). Food Components The food components to be analysed are those included in the DB (Table 2). The determination of proximate composition (moisture, total ash, total nitrogen, total fat, TABLE 1 Food items to be analysed Code

Food item

Code

Food item

1300 419 1003 450085 600050 31 33 1900 3007 312 1091 1098 2006 700012 700796 2006 60092 1800 19061 1713 1714 2017 12987 405 805698 429 331 9600 1603 700429 590

Anchovies Apples Beef, young, lean cuts Biscuits or cookies, plain Bread white, commercially prepared Bread white, type 0, 100 g loaf Bread, wholemeal Butter Carbonated drink, orange #avour Carrots Chicken, breast Chicken, whole Chocolate, milk Co!ee substitute, barley grain, beverage Co!ee, moka, beverage Crackers, salted top Crackers, wholemeal Eggs, chicken, whole Flaky pastry or pu! pastry Gorgonzola cheese Grana cheese Honey Ice cream, fruit Juice, orange, fresh Juice, tomato Kiwi fruits Lettuce Linseed Milk, cow, semi-skimmed Millk, cow, skimmed, UHT Milk, cow, whole

700637 1718 11796 404 50 900018 60091 19010 425 336 9005 382 381 1799 1145 800188 5 3018 666010 2021 9039 666008 888002 390 19072 8020 907 902 702901 4010 1633

Milk, soya Mozzarella cheese Mozzarella cheese, bu!alo Oranges Pasta Pasta, fresh, made with eggs Pasta, wholemeal Pastilles or candies, fruit Peaches Peppers Polenta Potato crisps, plain Potatoes Processed cheese, sliced, plain Prosciutto crudo (pork meat, salted, raw) Pudding, chocolate Rice, white, polished Soup cube Soup cube, vegetable Sugar Sweetcorn, canned, drained Sweetener, saccharin tofu tomatoes, ripe Valerian (valerianella olitoria) Veal, fat and lean meat Vegetable oil, corn Vegetable oil, mixed seeds Vegetable oil, olive, extra virgin Wine, red Yogurt, low fat, fruit

 Food items for which foreign data were used.

514

PARPINEL, GNAGNARELLA, AND SALVINI TABLE 2 Food components to be analysed

Water Ash Total nitrogen Lipids Total saturated fatty acids Oleic acid Total monounsaturated fatty acids Linoleic acid Linolenic acid Other polyunsaturated fatty acids Total polyunsaturated fatty acids Cholesterol Soluble carbohydrates Starch Fibre Alcohol

Sodium Potassium Iron Calcium Phosphorus Zinc Thiamine Ribo#avin Niacin Vitamin B  Folic acid Vitamin C Total retinol b-carotene equivalent Vitamin E Vitamin D

available carbohydrates) is essential to characterize a food item. Moreover, depending on the estimated costs of the chemical analyses, the validation study will focus on all the nutrients and food components of the DB, or on selected food components that will vary from food to food. In particular, one possibility is to concentrate chemical analyses on nutrients that were borrowed from non-Italian sources. Sampling Plan The design of the sampling plan will consider the following points: (1) collection of food items representative of those consumed by the study population; (2) collection of information on variations in the composition of food; (3) prevention of losses, contamination or degradation of the material during collection, handling, storage and analysis; and (4) representative food portions. The sampling plan will be set out for each food item and planned in di!erent geographical areas of Italy (north, centre and south). In order to outline the sampling procedure, the following information will be considered: distribution of the Italian population (ISTAT, 1997), regional consumption patterns, production and import data, distribution of goods and information on wholesales (Horwitz, 1990; Green"eld and Southgate, 1992; ISMEA, 1999). Analyses of single replicate food samples would be required to obtain detailed information on variation within food items. Although it is generally suggested to perform separate analyses on single food items (Rand, 1992; Stewart, 1995), to limit the costs of this project we decided to analyse composite samples, since this is a coste!ective procedure to obtain reliable data (Green"eld and Southgate, 1992). Moreover, we will compare results with our database, where most of the data are derived from food tables that do not give any details on the variation of the nutrient in the food of interest. For each food item, pooling various samples of standard weight will create a single composite sample. The number of samples needed to build a composite sample will be determined through a statistical equation (Cochran, 1977), based on the assumption of a normal distribution of nutrients in food (Schubert et al., 1987) and considering the variability of food composition as in previously published studies (Holden, 1995). The

515

VALIDATION OF A FOOD COMPOSITION DATABASE

following formula, adapted from Cochran and from Holden (Cochran, 1977; Holden, 1995) was used: (t) n5 CV. (r) where t is the abscissa of the normal curve that cuts o! an area a at the tails of the distribution, indicating the desired con"dence level. The precision of the estimate is indicated by r, i.e., the proximity of the estimate to the true mean (e.g., within 10%) and CV shows the coe$cient of variation, that substitutes the standard error of the estimate divided by the sample mean, shown in the original formula. Table 3 shows the number of food samples needed, based on food composition variability (the coe$cient of variation) and on the desired level of precision of the chemical analysis, for a signi"cance level of 0.05 (t was set equal to 1.96 in the formula). The required number of samples varies from 1 (for a nutrient with 10% coe$cient of variation and 20% precision), to 1537 (for a nutrient with 100% coe$cient of variation and 5% precision). With increasing variability of the nutrient of interest and for greater precision of the estimate, the number of samples increases. The shaded area of the table shows, for 10 and 15% precision, the number of samples needed for the most frequent coe$cients of variation. In order to compute the exact number of samples, information on the variability of each food component within each food group will be examined. To select the sampling centres, we looked at the distribution of the Italian population by residence (ISTAT, 1997). Data are shown in Table 4. In northern Italy, 40% of the population lives in centres with 10 000 inhabitants or less, 37% in centres with TABLE 3 Examples of sample size calculations for food items with varying coe$cients of variation and for di!erent levels of precision. The level of signi"cance is set equal to 0.05 CV

r"5%

r"10%

r"15%

r"20%

5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

4 15 35 61 96 138 188 246 311 384 465 553 649 753 864 983 1110 1245 1387 1537

1 4 9 15 24 35 47 61 78 96 116 138 162 188 216 246 278 311 347 384

0 2 4 7 11 15 21 27 35 43 52 61 72 84 96 109 123 138 154 171

0 1 2 4 6 9 12 15 19 24 29 35 41 47 54 61 69 78 87 96

 The shaded area of the table shows, for 10 and 15% precision, the number of samples needed for the most frequent coe$cients of variation.  CV"Coe$cient of variation.  r"precision of the estimate.

516

PARPINEL, GNAGNARELLA, AND SALVINI TABLE 4

Percentage distribution of the Italian population according to number of inhabitants of the town of residence. Data are from the Italian Bureau of Statistics (ISTAT, 1997) Area of Italy

Number of inhabitants (in thousands) of the town of residence (2 2}10 10}50 50}100 100}500 '500

Total inhabitants (%)

North Centre South

8.1 3.6 5.4

31.9 18.9 24.7

28.6 31.2 35.5

8.1 12.3 15.8

12.1 9.9 10.4

11.3 24.0 8.3

25, 518, 434 (44.4) 11, 019, 359 (19.2) 20, 923, 184 (36.4)

Italy

6.2

26.8

31.6

11.7

1.1

12.6

57, 460, 977 (100.0)

10 000}100 000 inhabitants, and 23% in centres with more than 100 000 residents. The corresponding distribution according to the size of the town of residence is 22, 44 and 34% in the centre of Italy and 30, 51 and 19% in the south, respectively. Sampling will therefore be planned also according to the above information. Considering our resources and the capabilities of the laboratories, we are hypothesizing collection of an average of 20 samples of each food. The sampling procedure will be balanced according to the distribution of the Italian population in the north (44.4%), in the centre (19.2%) and in the south (35.5%) (ISTAT, 1997). Samples will be collected from di!erent retail outlets, chosen by volume of sale (supermarkets, hard discount stores, grocery shops and street markets) in the three areas of the country. Samples will be placed on ice in a portable cooler or in styrofoam boxes, and transported or mailed to the laboratory where the analysis will be carried out. For each product, a carefully homogenized composite sample of 2000 g will be prepared. The size and number of total samples needed will depend on packages or form in which samples are found on the market. Chemical Analyses The analytical methods that were used as a reference in the compilation of the DB will be employed, as appropriate, for each nutrient or food component, in order to produce data that can be compared with those included in the DB. The choice of the most suitable analytical method for the production of data for a nutritional DB has to take some important factors into consideration. Since the primary objective of a food composition DB is nutritional, the method of analysis chosen for a nutrient should be the one which most closely re#ects the nutritive value of the food (Green"eld and Southgate, 1992). Moreover, it should consider the distribution of nutrients in food matrices, the e!ect of processing and preparation and the expected range of concentration of each nutrient. A draft protocol of the validation study has been submitted to some laboratories. In order to maximize data quality, participants were requested to provide us with details concerning their experience, the equipment used, quality control procedures and methods of handling, processing and storing food samples (Prosky et al., 1985; Hollman and Katan, 1987; Southgate, 1987; Prosky et al., 1988; Hollman and Wagsta!e, 1990). Following the considerations by Horwitz et al. (1978) and by Egan (1974), we developed a grid of suggested analytical methods, for each food component or group of components. The suggested analytical method for each component is listed in Table 5, together with a de"nition of the components and the units in which they are expressed in the DB.

TABLE 5 Chemical analyses Food components and de"nition

Unit (for 100 g of food)

Type of method

References

g

Dry ashing

Osborne and Voogt (1978) Egan (1981)

MOISTURE OR WATER Water content of the item

g

Air oven At 100}1053C (heat stable food) At 1303C (grains, -ours, pastas) Vacuum oven At 603C (heat sensible food, or rich in sugars and fats) Freeze drying

AOAC (1980) (and updates)

Dean & Stark distillation ( fat food, food rich in volatile substances, alcoholic beverages)

Makower and Nielsen (1948), Thung (1964) AOAC (1980)

PROTEIN Determination of total nitrogen (N)

g

Kjeldhal procedure (manual or automatic, according to availability) Biuret reaction (colorimetry) Dye-binding procedures (colorimetry) IR spectrophotometer (IR absorption or NIR re#ectance transmittance)

AOAC (1980) (and updates), Egan et al. (1981) (automatic)

TOTAL FAT Sum of triglycerides, phospholipids, sterols and related compounds

g

Solvent extraction Ether Chloroform/methanol Acid hydrolysis (Schmidt) Alkaline hydrolysis (Roose}Gottlieb)

AOAC (1980), Folch et al. (1957)

Extraction Esteri"cation GLC

Folch et al. (1957) MetCalf et al. (1961) IUPAC (1979)

g

517

FATTY ACIDS Total saturated fatty acids Oleic acid (C : )   Total monounsaturated fatty acids Linoleic acid (C : )   Linolenic acid (C : )   Other polyunsaturated fatty acids Total polyunsaturated fatty acids

AOAC (1980) AOAC (1980)

VALIDATION OF A FOOD COMPOSITION DATABASE

ASH Material derived from the incomplete combustion of the food item

518

TABLE 5 (Continued) Food components and de"nition CHOLESTEROL Sum of sterols of animal origin

Unit (for 100 g of food) mg

References

GLC

AOAC (1980), Adams (1986), Kovacs et al. (1979) Kageyama (1971) (enzymatic kit, Boehringer Mannheim) Shen et al. (1982)

Enzyme kit

SUGARS (SOLUBLE SUGARS) Sum of monosaccharides (glucose, galactose, fructose) and disaccharides (saccharose and lactose) expressed as monosaccharides

g

HPLC

AOAC (1980), Southgate et al. (1978), Dean (1978)

Speci"c enzymatic procedure

Boehringer enzyme kit (Egan et al., 1981), Bergmeyer (1974) Southgate (1976)

Colorimetry STARCH Expressed as monosaccharide

FIBRE Sum of cellulose, hemicellulose, pectin, gums and lignin

g

g

Enzymatic hydrolysis, with spectophotometric measurement of glucose Polarimetry HPLC (after enzymatic hydrolysis)

AOAC (1980), Dean (1978) Egan et al. (1981) Willis et al. (1980)

Gravimetric-enzymatic (total dietary ,bre)

AOAC (1980), Prosky et al. (1985, 1988)

Acid hydrolysis after starch removal; GLC measurement of monomers (total ,bre as sum of non-starch polysaccharides)

Englyst and Cummings (1984, 1988); Englyst et al. (1982)

Colorimetry (total ,bre as sum of non-available carbohydrates)

Southgate (1969)

PARPINEL, GNAGNARELLA, AND SALVINI

Type of method

TABLE 5 (Continued) Food components and de"nition ALCOHOL Concentration of ethanol

g/100 mL (beverages) g/100 g (solids)

mg

Type of method

References

GLC

Enzymatic kit (for alcoholic beverages) Distillation (except for food rich in volatile substances)

Bergmeyer (1974) Standard Inland Revenue distillation method

Atomic absorption spectrophotometry (AAS) with #ame or graphite furnace

AOAC (1984)

ICPOES inductively coupled plasma optical emission spectrophotometry

AOAC (1990)

Phosphorus (P)

mg

Colorimetry

Fiske and Subbarow, (1925)

VITAMINS Thiamine (B ) 

mg

Fluorimetry HPLC

AOAC (1984) Fellman et al. (1982) Wimalasiri and Willis (1985) Bell (1974)

Ribo#avin (B ) 

mg

Niacin Sum of nicotinic acid and nicotinamide

mg

Microbiological

Vitamin C Sum of ascorbic and dehydroascorbic acid

mg

Fluorimetry HPLC

Microbiological

AOAC (1984)

HPLC

Toma and Tabekhia (1979) Yoshida et al. (1982)

Colorimetry

Roe and Kuether (1943)

HPLC

Morawski (1984), Keating and Haddad (1982), Speek et al. (1984)

519

Microbiological

AOAC (1984) Fellman et al. (1982) Wimalasiri and Willis (1985) AOAC (1984)

VALIDATION OF A FOOD COMPOSITION DATABASE

MINERALS: Sodium (Na), Potassium (K), iron (Fe), calcium (Ca), zinc (Zn)

Unit (for 100 g of food)

520

TABLE 5 (Continued) Food components and de"nition VITAMINS Vitamin B  Sum of pyridoxal, pyridoxine, pyridoxamine

Type of method

References

mg

Microbiological

Osborne and Voogt (1978)

lg

HPLC Microbiological

Gregory and Feldstein (1985) AOAC (1984)

HPLC

Gregory et al. (1982)

Retinol Sum of all-trans retinol and other retinoids with vitamin activity

lg

HPLC

Stancher and Zonta (1982) Khachik et al. (1991)

b-Carotene equivalents Sum of carotenoids with vitamin activity (b-carotene, a-carotene, a-cryptoxanthine and b-cryptoxanthine)

lg

HPLC

Stancher and Zonta (1982) Khachik et al. (1991)

Vitamin D Sum of the compounds with vitamin activity (ergocalcipherol, vit D  and colecalcipherol, vit D )  Vitamin E Sum of tocopherols and tocotrienols with vitamin activity (a-tocopherol, b-tocopherol, c-tocopherol, d-tocopherol; a-tocotrienol, b-tocotrienol, c-tocotrienol) and expressed as a-tocopherol equivalent

lg

HPLC

Thompson et al. (1982)

mg

HPLC

Barnett et al. (1980)

 References are from Carnovale and Marletta (1997). All other references are from Green"eld and Southgate (1992).

PARPINEL, GNAGNARELLA, AND SALVINI

Folic acid Sum of total folates

Unit (for 100 g of food)

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521

We also considered the issue of nutrients present in very small amounts, so called traces. This indicates that the nutrient is present but cannot be measured, since it falls below the detection limit of the method or of the instrument, or that below a certain level the nutrient has no nutritional meaning. There are therefore limits in the expression of a nutrient that can strictly depend on the method (as for "bre or water), or on the nutritional importance of the nutrient or on its bioavailability (as for vitamins). Suggestions for appropriate modes of expression of traces and limits for the various nutrients or food components are given by Green"eld and Southgate (1992). Statistical Analyses As a measure of validity, intra-class correlation coe$cients between the analytical values and the compiled values will be computed. Moreover, we will compare the overall correlation between the analytical data and both Italian and foreign data in the DB. Correlation coe$cients within food groups will also be computed. Finally, we will evaluate di!erences emerging for nutrients and/or groups of nutrients (minerals, vitamins, macronutrients) and for groups of food. As observed in other settings (e.g., in nutritional epidemiology), measurement error, or natural variability of the measure of interest, could attenuate the correlation between the measured nutrients and the compiled data from the DB; de-attenuated correlation coe$cients could be obtained by taking into account the variance of the laboratory measures (Rosner and Willett, 1988). This procedure could be applied to the present study if separate analyses were performed for the sampled food items, or if replicate measurements were available for the composite samples. We expect to obtain correlation coe$cients close to 0.6: this would permit us to conclude that borrowed data from foreign DBs, tables or scienti"c papers are appropriate for the creation of DBs, when resources are limited. DISCUSSION Several researchers have attempted to compare food composition data estimated by calculation from food composition tables, with direct chemical analyses. Boulous et al. (1996) and Porrini et al. (1986) reported a good agreement between calculation and chemical analysis, when comparing the composition of di!erent dishes and food items consumed in their countries. Porrini reported reliable data for macronutrients, but less reliable values for vitamins. Both authors attributed the reason for the slight discrepancies to variations in food composition and to possible nutrient modi"cations which occurred during the cooking process. Bedogni et al. (1999) compared the analytical composition of the meals of Italian soldiers in two di!erent weeks, with the results computed by means of food composition tables: di!erences were negligible for energy, ranging from 0.4 to 0.7% in the 2 weeks. The largest di!erence was found for "bre, where the di!erence ranged from 0 to 32%. Ribeiro et al. (1995) have compared the nutritional composition of meals collected from catering services, with direct chemical analysis: correlation coe$cients ranged from 0.81 to 0.92 for energy, from 0.51 to 0.90 for lipids, from 0.84 to 0.94 for moisture and from 0.85 to 0.88 for carbohydrates. Correlation coe$cients were lower for micronutrients as for example for iron, where results ranged from 0.15 to 0.27. A study targeted to examine the accuracy of the food composition table in comparison with sophisticated instrumental measurement of minerals (inductively coupled plasma mass spectrometry, ICP-MS) was carried out in six areas of Asia (Zhang et al., 1999; Shimbo et al., 1999), where 24-h food duplicate samples were measured. The estimated

522

PARPINEL, GNAGNARELLA, AND SALVINI

versus measured ratio ranged from 75 to 114% for sodium, from 91 to 120% for potassium and from 69 to 165% for calcium; larger di!erences were seen for phosphorous (113}306%) and for iron (124}368%). Finally, in a review article, Schakel and colleagues reported the results of several studies on the topic, showing various degrees of variation between computed and analytical data. In general, data were more similar for macro- than for micronutrients (Schakel et al., 1997). The present study will concentrate on single food items, rather than on dishes or meals, and agreement of results will be reported for food, for food groups and for food components. CONCLUSIONS The present study was set up to assess whether data included in the &&Food Composition Database for Epidemiological Studies in Italy'' are comparable with data obtained analytically on food items consumed in our country and sampled on the Italian territory. Since the use of pooled samples determines the loss of information on betweensamples variation, further studies conducted on individual samples would be helpful to explore variability of composition in Italian food products. The validation of the methodology used in the compilation process would represent a "rst attempt to demonstrate the appropriateness of a compiled DB in Italy. This would also strengthen the Italian DB in view of collaborative studies within Europe, where harmonization of nutrition-related activities becomes more and more necessary. We are grateful to the Italian Association for Cancer Research (AIRC), to Kellogg's Italia, and to the COST 99 Action that have "nancially supported and encouraged our work. We are indebted to Maura Mezzetti for helpful comments on designing sampling plan and statistical analysis.

REFERENCES Bedogni, G., Bernini Carri, E., Gatti, G., Severi, S., Poli, M., Ferrari, F., and Battistini, N. (1999). Comparison of food composition tables and direct chemicals analysis for the assessment of macronutrient intake in a military community. Int. J. Food Sci. Nutr. 50, 73}79. Bingham, S. A. (1987). The dietary assessment of individuals; methods, accuracy, new techniques and recommendations. Nut. Abs. Rev. (Ser. A) 57, 705}742. Boulous, C., Kanellou, A., Trichopoulou, A., and the Foods and Nutrients Working Group (1996). Computer and chemically determined nutrient content of foods in Greece. Int. J. Food Sci. Nutr. 47, 507}511. Carnovale, E. and Marletta, L. (1997). ¹abelle di Composizione degli Alimenti. Istituto Nazionale della Nutrizione, Roma. Carnovale, E. and Miuccio, F. (1989). ¹abelle di Composizione degli Alimenti. Istituto Nazionale della Nutrizione, Roma. Cochran, W. G. (1977). Sampling ¹echniques, 3rd edn, Wiley, New York. pp. 1}78. COST Action 99 (Eurofoods) (1994). Food Consumption and Composition Data (MoU COST/277/94). Egan, H. (1974). Report of the Government Chemist, 1973. HMSO, London. Favero, A., Salvini, S., Russo, A., Parpinel, M., Negri, E., Decarli, A., La Vecchia, C., Giacosa, A., and Franceschi, S. (1997). Sources of macro- and micronutrients in Italian Women: results from a food frequency questionnaire for cancer studies. Eur. J. Can. Prev. 6, 277}278. Fidanza, F., and Versiglioni, N. (1989). ¹abelle di composizione degli alimenti. Istituto di Scienza dell' Alimentazione, Universita` degli Studi di Perugia, Idelson, Napoli.

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