Static Magnetic Field-Assisted Fermentation of Ginkgo biloba Leaves by Lacticaseibacillus paracasei: Process Optimization for Total Flavonoids, Nutritional Components, and Hypoglycemic Activity Analysis
A static magnetic field (SMF)-assisted liquid-state fermentation strategy using Lacticaseibacillus paracasei (L. paracasei) is proposed to improve the comprehensive utilization value of Ginkgo biloba leaves. Taking total flavonoid extraction yield as the objective, Box-Behnken response surface methodology was adopted to optimize the fermentation process. Meanwhile, the nutritional components and hypoglycemic activity of Ginkgo biloba leaves in vitro before and after fermentation were compared and analyzed. The optimal fermentation conditions were determined as follows: 3-fold-diluted De Man, Rogosa, and Sharpe (MRS) medium, an SMF intensity of 4.2 mT, an SMF duration of 2.9 h, and an inoculum size of 6.5%. Under these conditions, the total flavonoid extraction yield reached (3.41 ± 0.11)%, which was 3.33 times that of unfermented raw material. Analysis of the nutritional components revealed that after SMF-assisted fermentation, crude protein and crude fat contents increased by 59.58% and 13.72%, while ash and crude fiber contents decreased by 24.84% and 35.66%, respectively. SMF application also significantly affected the crude fiber content (p < 0.05). Meanwhile, the contents of phytic acid, tannins, phenolic acids, and ginkgolic acids in the SMF-assisted group declined by 29.56%, 43.10%, 23.42%, and 50.37%, respectively, and the SMF further promoted the degradation of anti-nutritional factors. Hypoglycemic activity analysis indicated that fermentation markedly enhanced the inhibitory effect of Ginkgo biloba leaf-derived flavonoids against α-glucosidase and α-amylase, and that SMF stimulation may also contribute to strengthening this inhibitory activity. This study demonstrates that SMF-coupled probiotic fermentation is an effective and environmentally friendly approach for processing Ginkgo biloba leaves, providing experimental support for high-value utilization of plant-based resources through physical-field-assisted microbial fermentation.
Rehmannia Radix is an important medicinal and edible plant resource in China. However, the production of paocai by fermenting Rehmannia Radix has rarely been reported. This study employed response surface methodology to optimize fermentation conditions, and investigated the changes in physicochemical properties, bioact...
This study evaluates the impact of ultrasound-assisted extraction (UAE) on starch and polyphenol recovery from cassava rhizomes (Manihot esculenta Crantz). Using a Box–Behnken design, key parameters—i.e., temperature, time, and solid-to-liquid ratio—were optimized for maximum yield. The extracted starch and polyphenols...
Vigna Nivetha Chandrasekaran, J. Antih, Charlotte Silvestre et al.· Applied Sciences· 0 citations
This study developed a green and sustainable extraction method for anthocyanins from
Solanum betaceum
peel using choline chloride–based natural deep eutectic solvents (NADES). Among seven tested systems, the choline chloride−1,4-butanediol (ChCl–BD) NADES exhibited the highest extraction yield (26.8 mg/g), signific...
Ya-Qian Yang, Rui-Qi Liu, Yu-Lei Qin et al.· Frontiers in Sustainable Foo...· 0 citations
A green efficient deep eutectic solvent (DES)-based system was developed for total alkaloid extraction from Eupatorium lindleyanum DC. and evaluated for postharvest grape preservation. Among 18 DESs, choline chloride–citric acid (2:1, molar ratio) showed the highest extraction efficiency. Box–Behnken design and respons...
Zhi-Meng Xu, Han-Bing Yuan, Ling Li et al.· Molecules· 0 citations
The broader application of Lingzhi (Ganoderma lucidum) in functional foods is fundamentally restricted by its intense, triterpenoid-driven bitterness. This study investigated the biochemical changes in Lingzhi mushroom mycelium (LMM) over a 10-day controlled fermentation with Lactiplantibacillus plantarum. To evaluate...
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.