JOURNAL ARTICLE
Optimization of fermentation conditions for enhancing the antioxidant activity of medicine and food homologous materials using Lactobacillus plantarum.
Published In: Letters in Applied Microbiology, 2025, v. 78, n. 4. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Wang, Yikai; Chen, Qiming; Lu, Zhengrong; Dong, Quanling; Wang, Jiaxu; Hu, Yuanlong; Tang, Tiantian; Liu, Zhanmin 3 of 3
Abstract
This article focuses on optimizing the fermentation of a mixture of medicine and food homologous (MFH) materials—Codonopsis radix, Astragalus membranaceus, Platycodon grandiflorus, and Tiger milk mushroom—using Lactobacillus plantarum to enhance their antioxidant activity and bioavailability. Key fermentation parameters, including material-to-water ratio, sucrose concentration, and inoculum size, were identified and optimized through single-factor experiments, Pareto analysis, response surface methodology (RSM), and artificial neural networks (ANN), with ANN providing superior predictive accuracy. Under optimized conditions, fermentation significantly increased the levels of bioactive compounds such as polysaccharides, polyphenols, flavonoids, and saponins, resulting in enhanced antioxidant capacities measured by DPPH, ABTS, and ferric ion reducing assays. Fourier-transform infrared (FTIR) analysis confirmed structural changes in active compounds post-fermentation, supporting improved bioactivity. This study offers a methodological framework for developing MFH-based probiotic fermented functional foods with potential applications in dietary supplements and nutrition.
Additional Information
- Source:Letters in Applied Microbiology. 2025/04, Vol. 78, Issue 4, p1
- Document Type:Article
- Subject Area:Science
- Publication Date:2025
- ISSN:0266-8254
- DOI:10.1093/lambio/ovaf051
- Accession Number:185320828
- Copyright Statement:Copyright of Letters in Applied Microbiology 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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