JOURNAL ARTICLE
Insights into the digestion of chestnut (Castanea sativa) shells bioactive extracts—ultrasound vs. microwave‐assisted extraction.
Published In: International Journal of Food Science & Technology, 2024, v. 59, n. 7. P. 5128 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Pinto, Diana; Moreira, Manuela M.; Vallverdú‐Queralt, Anna; Delerue‐Matos, Cristina; Rodrigues, Francisca 3 of 3
Abstract
This article focuses on evaluating the impact of in vitro gastrointestinal digestion on the bioaccessibility and bioactivity of polyphenol-rich extracts from chestnut (Castanea sativa) shells (CSS) obtained by two eco-friendly extraction methods: microwave-assisted extraction (MAE) and ultrasound-assisted extraction (UAE). The study found that total phenolic and flavonoid contents, antioxidant/antiradical activities, and reactive oxygen/nitrogen species scavenging capacities of both extracts decreased during digestion, with bioaccessibility around 25–26%. Key phenolic compounds such as gallic acid, caftaric acid, and catechin were identified as the most bioaccessible, and the extracts exhibited notable hypoglycaemic and neuroprotective enzyme inhibition after digestion. Overall, MAE extracts showed slightly higher bioaccessibility post-digestion, and the findings support the potential use of CSS extracts as sustainable nutraceutical ingredients with antioxidant, anti-ageing, hypoglycaemic, and neuroprotective properties.
Additional Information
- Source:International Journal of Food Science & Technology. 2024/07, Vol. 59, Issue 7, p5128
- Document Type:Article
- Subject Area:Agriculture and Agribusiness
- Publication Date:2024
- ISSN:0950-5423
- DOI:10.1111/ijfs.17253
- Accession Number:177904246
- Copyright Statement:Copyright of International Journal of Food Science & Technology is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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