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Yongbin Han
Researcher at Nanjing Agricultural University
Publications - 71
Citations - 1740
Yongbin Han is an academic researcher from Nanjing Agricultural University. The author has contributed to research in topics: Chemistry & Medicine. The author has an hindex of 18, co-authored 52 publications receiving 1006 citations.
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Optimization of ultrasound-assisted extraction of melanin from Auricularia auricula fruit bodies
TL;DR: In this paper, an ultrasound-assisted extraction method was applied to melanin extraction from A. auricula fruit bodies to improve the extraction efficiency, and two extractions sufficiently reached the maximal melanin yield (120.05 µmg/100 µg).
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Optimization of carotenoids extraction from Rhodobacter sphaeroides
TL;DR: In this paper, the authors compared ultrasonic assisting, grinding and HCl assisting on carotenoids extraction yield from Rhodobacter sphaeroides (R. scaeroides) and found that the HCl-assisted extraction was the most effective.
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Fermentation of blueberry and blackberry juices using Lactobacillus plantarum, Streptococcus thermophilus and Bifidobacterium bifidum: Growth of probiotics, metabolism of phenolics, antioxidant capacity in vitro and sensory evaluation.
TL;DR: In this article, three potential probiotic strains were selected to ferment blueberry and blackberry juices, and the viable cell counts of selected strains were increased by 0.4-0.7 log CFU/mL in berry juices environments after 48-h fermentation.
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Composition and colour stability of anthocyanins extracted from fermented purple sweet potato culture
TL;DR: In this paper, the components and colour stability of anthocyanins in purple sweet potato were investigated, and the results showed that five major Anthocyanin components were detected by high performance liquid chromatography (HPLC).
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Combining various wall materials for encapsulation of blueberry anthocyanin extracts: Optimization by artificial neural network and genetic algorithm and a comprehensive analysis of anthocyanin powder properties
TL;DR: In this paper, Artificial Neural Network (ANN) combined with genetic algorithm (GA) was successfully applied to model the influences of formulation composition on encapsulation productivity and encapsulation efficiency, as well as obtain optimum formulations.