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Carotenoid content of fruits and vegetables: An evaluation of analytic data

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Abstract

The test of the association between dietary intake of specific carotenoids and disease incidence requires the availability of accurate and current food composition data for individual carotenoids. To generate a carotenoid database, an artificial intelligence system was developed to evaluate data for carotenoid content of food in five general categories, namely, number of samples, analytic method, sample handling, sampling plan, and analytic quality control. Within these categories, criteria have been created to rate analytic data for β-carotene, α-carotene, lutein, lycopene, and β-cryptoxanthin in fruits and vegetables. These carotenoids are also found in human blood. Following the evaluation of data, acceptable values for each carotenoid in the foods were combined to generate a database of 120 foods. The database includes the food description; median, minimum, and maximum values for the specific carotenoids in each food; the number of acceptable values and their references; and a confidence code, which is an indicator of the reliability of a specific carotenoid value for a food. The carotenoid database can be used to estimate the intake of specific carotenoids in order to examine the association between dietary carotenoids and disease incidence.

Section snippets

METHODS

A system was developed for the evaluation of analytic data for levels of five carotenoids in foods: β-carotene, α-carotene, lutein + zeaxanthin, lycopene, and β-cryptoxanthin. This system was based on those previously described for the evaluation of selenium and copper data 20., 21., 22., but was modified to accommodate carotenoid data evaluation. Objective evaluation was aided by use of an artificial intelligence system that incorporated standardized questions and decision pathways (23).

RESULTS

Table 3, Table 4 provide information on the carotenoid content of fruits and vegetables, the most important food sources of carotenoids. These tables include a median for each food as well as minimum and maximum values where more than one acceptable study was reported. A confidence code, based on both the quality and quantity of existing data, is associated with each carotenoid value. All values in Table 3, Table 4 derive from analytic data.

Table 5 includes the number of acceptable values and a

DISCUSSION

The preceding tables represent the most comprehensive estimates of individual carotenoids in fruits and vegetables. These estimates are derived from critically evaluated published and unpublished sources. Users can review the specific criteria to better understand the rating process and the meaning of the confidence codes assigned to the data. Heinonen and others 18., 19., 27., 31., 32. have published limited data for meats, grains, dairy products, fats and oils, and other foods that suggest

IMPLICATIONS

A primary objective for critically evaluating food composition data is the identification of food items for future laboratory analysis. Foods believed to contain notable amounts of a carotenoid, but that had a confidence code of “C” or had no reliable data, are a priority for additional analyses. Information about those foods can be used as the basis for development of a sampling strategy to obtain statistically representative food samples for carotenoid analysis as has been done for selenium

Acknowledgements

The authors thank Carol S. Davis for creating the tables and figures, Doug Bigwood for computer programming, Frederick Khachik for insightful discussions, and Larry Douglass and Will Potts for statistical advice.

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    Partial support by the National Cancer Institute through reimbursable agreement YO1-CN-30609 is acknowledged.

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