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18. Chapter Seven - Condensed tannins--Their content in plant foods, changes during processing, antioxidant and biological activities.

Author

Amarowicz, Ryszard and Pegg, Ronald B.

Abstract

Condensed tannins are considered nutritionally undesirable, because they precipitate proteins, inhibit digestive enzymes, and can affect the absorption of vitamins and minerals. From the consumer's point of view, they impart astringency to foods. Yet, they are viewed as a double-edged sword, since they possess antioxidant and anti-inflammatory activities. Intake of a small quantity of the right kind of tannins may in fact be beneficial to human health. This chapter reports on the chemical structure of condensed tannins, their content in plants and food of plant origin, how they are extracted, and methods for their determination. A description of the effects of processing on condensed tannins is discussed and includes soaking, dehulling, thermal processing (i.e., cooking, boiling, autoclaving, extrusion), and germination. The astringency of condensed tannins is described in relation to their interactions with proteins. Finally, details about the biological properties of condensed tannins, including their antimicrobial, anti-inflammatory, anticancer, anti-diabetic, and anti-obesity activities, are reviewed. [ABSTRACT FROM AUTHOR]

Published
2024
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22. Vitamin Analysis

Author

Pegg, Ronald B., Eitenmiller, Ronald R., Heldman, Dennis R., Series Editor, and Nielsen, S. Suzanne, editor

Published
2017
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50. Metabolomic analysis combined with machine learning algorithms enables the evaluation of postharvest pecan color stability.

Author

Kang, Min Jeong, Pegg, Ronald B., Kerr, William L., Wells, M. Lenny, Conner, Patrick J., and Suh, Joon Hyuk

Subjects
*MACHINE learning, *COLORIMETRY, *FLAVONOIDS, *ANTHOCYANIDINS, *PECAN, *ANTHOCYANINS
Abstract

Nut kernel color is a crucial quality indicator affecting the consumers first impression of the product. While growing evidence suggests that plant phenolics and their derivatives are linked to nut kernel color, the compounds (biomarkers) responsible for kernel color stability during storage remain elusive. Here, pathway-based metabolomics with machine learning algorithms were employed to identify key metabolites of postharvest pecan color stability. Metabolites in phenylpropanoid, flavonoid, and anthocyanin biosynthetic pathways were analyzed in the testa of nine pecan cultivars using liquid chromatography–mass spectrometry. With color measurements, different machine learning models were compared to find relevant biomarkers of pecan color phenotypes. Results revealed potential marker compounds that included flavonoid precursors and anthocyanidins as well as anthocyanins (e.g., peonidin, delphinidin-3- O -glucoside). Our findings provide a foundation for future research in the area, and will help select genes/proteins for the breeding of pecans with stable and desirable kernel color. [Display omitted] • Metabolomics with machine learning (ML) identified markers for pecan color stability. • Lasso regression and SVM-RFE were the most effective ML models for marker selection. • Selected markers included anthocyanidins, anthocyanins, and flavonoid precursors. • Collected information will help pecan breeding with desirable kernel color. [ABSTRACT FROM AUTHOR]

Published
2024
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