To improve the utilization value of raspberry leaves, the extraction and purification conditions of phenolic compounds from raspberry leaves were optimized, and the contents of phenolic compounds and the biological activities of extracts were studied. After steam explosion pretreatment at 115 °C for 15 min, raspberry leaf extract with a total phenolic content (TPC) of 136.30~140.51 mg GAE/g was obtained via homogenization and ultrasound-assisted extraction. In addition, the adsorption relationship between raspberry leaf polyphenols and middle polar XDA-6 macroporous resin was best described by the Langmuir model, and tended to be monolayer adsorption. Its adsorption kinetics best resembled the pseudo second-order kinetic model, and it was speculated that this was influenced by multiple factors. According to the optimal integrated extraction-purification process, the TPC of the extracts increased to 738.98 mg GAE/g after one application of purification and 905.27 mg GAE/g after two applications of purification. Moreover, the latter case showed the highest antioxidant activity and α-glucosidase inhibition activity, and the content of the most typical compound, quercetin-3-glucuronide, reached 199.69 mg/g. SE has a double-edged effect, and is more conducive to the release of active substances as a pre-treatment method. This study provides a theoretical basis for the efficient use of raspberry leaves, further improving their medicinal and economic value.
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http://dx.doi.org/10.3390/molecules28176321 | DOI Listing |
Plant Dis
January 2025
Biotechnology, plant protection, Nongsheng Group C735, Zijin Campus, Zhejiang University, Hangzhou, Zhejiang, China, 310058;
To meet the need of crop leaf disease detection in complex scenarios, this study designs a method based on the computing power of mobile devices that ensures both detection accuracy and real-time efficiency, offering significant practical application value. Based on a comparison with existing mainstream detection models, this paper proposes a target detection and recognition algorithm, TG_YOLOv5, which utilizes multi-dimensional data fusion on the YOLOv5 model. The triplet attention mechanism and C3CBAM module are incorporated into the network structure to capture connections between spatial and channel dimensions of input feature maps, thereby enhancing the model's feature extraction capabilities without significantly increasing the parameter count.
View Article and Find Full Text PDFFront Plant Sci
November 2024
Division of Biotechnology and Plant Health, Norwegian Institute of Bioeconomy Research, Ås, Norway.
Raspberry ( L.) is susceptible to aphid-borne viruses. We studied the incidence of four of them - black raspberry necrosis virus (BRNV), raspberry leaf mottle virus (RLMV), raspberry vein chlorosis virus (RVCV), and Rubus yellow net virus (RYNV) - in raspberry plants and aphids in and around Norwegian raspberry crops for three years (2019, 2021, and 2022).
View Article and Find Full Text PDFJ Agric Food Chem
November 2024
School of Pharmacy, Guangxi University of Chinese Medicine, No. 13, Wuhe Avenue, Nanning, Guangxi 530200, China.
Rubusoside─a high-sweetened, nonsugar sweetener─is mainly extracted from var. (S. Lee) L.
View Article and Find Full Text PDFJ Agric Food Chem
November 2024
School of Life Science, Anhui Agricultural University, Hefei 230036, Anhui, China.
Nat Prod Res
October 2024
Laboratory of Natural Compounds Chemistry and Bioanalytics, Core Facility Center "Arktika", M.V. Lomonosov Northern (Arctic) Federal University, Arkhangelsk, Russia.
Cloudberry (.) is a plant rich in various biologically active compounds. In this work, the composition and concentrations of pentacyclic triterpenoids (PCTs) and phytosterols in various parts of cloudberries were studied using high-performance liquid chromatography-tandem mass spectrometry by multiple reaction monitoring mode (MRM, targeted analysis) and precursor ion scan mode (non-targeted analysis and semi-quantitative determination).
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