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Analysis of Carotenoids in Nutritional Studies

  • Frederick Khachik
Part of the Carotenoids book series (CAROT, volume 5)

Abstract

The ability to establish a statistically sound relationship between dietary intake of carotenoids and the incidence of chronic disease requires detailed knowledge of the qualitative and quantitative distribution of these compounds in the food supply as well as in human blood, major organs, and tissues. The occurrence and distribution of nutritionally important carotenoids in foods, especially fruits and vegetables, is surveyed in Chapter 3 and the distribution of carotenoids in human blood, organs and tissues is covered in Chapter 7. In the past two decades, technological advances in high-performance liquid chromatography (HPLC) have provided analysts with powerful, sensitive tools to separate carotenoids and low levels of their metabolites and analyse them quantitatively with great precision. For HPLC analysis to be of value, it must be based on rigorous identification of the compounds under study. Even in laboratories with good HPLC facilities, the traditional, classical methods thinlayer and column chromatography (TLC and CC) are still widely used, for rapid preliminary screening of extracts, for isolating and purifying carotenoids for further study, for comparison of samples with standards, and for monitoring the course of reactions. This Chapter evaluates HPLC methods, describes quantitative analysis of carotenoids in extracts from foods and human samples and discusses a systematic approach to separation and identification. The procedures described and illustrated are ones that have been used extensively in the author’s laboratory for many years, but they are not the only ones. Many other procedures have been described and widely used by other researchers.

Keywords

Geometrical Isomer Nutritional Study HPLC Condition HPLC Profile HPLC Retention Time 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Birkhäuser Verlag Basel 2009

Authors and Affiliations

  • Frederick Khachik

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