It is significantly crucial to develop a robust pretreatment for the quantitative analysis of herbs. However, the traditional strategies are time-consuming, tedious, and not eco-friendly. In this work, cloud point extraction (CPE) is engineered for the simultaneous separation and enrichment of ferulic acid (FA), chlorogenic acid (CLA), and caffeic acid (CA) from dandelion prior to its determination by high-performance liquid chromatography (HPLC). A famous nonionic surfactant of Triton X-114 was selected as an extractant of CPE, and parameters affecting the extraction, such as surfactant concentration, salt content, pH value, temperature, and incubation time, were investigated carefully. Furthermore, the well-designed CPE with ultrasonic assistance combined with HPLC was developed for the detection of the target analytes in dandelion. The established method having a good linearity in the range of 0.15-26.2 mg L with more than 0.9979 and the spiked recoveries ranging from 81 to 96% was applied to test real samples of dandelion. The contents of CA in samples were consistent with those assayed by the method (Chinese Pharmacopoeia 2015). The proposed method afforded good analytical performances, shorter pretreatment time (65 min), and less organic solvent consumption (less than 1.0 mL). It was proved that the developed method presented a facile, inexpensive, efficient, and environment-friendly pretreatment and can be used for the quantitative analysis of CLA, CA, and FA in dandelion. As expected, the proposed method would be a promising potential for the quality analysis of herbal medicines.
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http://dx.doi.org/10.1021/acsomega.1c01768 | DOI Listing |
J Phys Chem B
December 2024
Department of Chemistry, The Pennsylvania State University, University Park, Pennsylvania 16802, United States.
The cloud point temperatures of aqueous poly(-isopropylacrylamide) (PNIPAM) and poly(ethylene) oxide (PEO) solutions were measured from pH 1.0 to pH 13.0 at a constant ionic strength of 100 mM.
View Article and Find Full Text PDFBiomed Tech (Berl)
December 2024
Institute for Artificial Intelligence in Medicine (IKIM), University Hospital Essen (AöR), Essen, Germany.
Objectives: The shape is commonly used to describe the objects. State-of-the-art algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from the growing popularity of ShapeNet (51,300 models) and Princeton ModelNet (127,915 models).
View Article and Find Full Text PDFSci Data
December 2024
National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan, 430070, P. R. China.
Point cloud analysis is a crucial task in computer vision. Despite significant advances over the past decade, the developments in agricultural domain have faced challenges due to a scarcity of datasets. To facilitate 3D point cloud research in agriculture community, we introduce Crops3D, the diverse real-world dataset derived from authentic agricultural scenarios.
View Article and Find Full Text PDFJ Imaging
December 2024
European Commission, Joint Research Centre (JRC), Via Enrico Fermi 2749, 21027 Ispra, Italy.
In this paper, we face the point-cloud segmentation problem for spinning laser sensors from a deep-learning (DL) perspective. Since the sensors natively provide their measurements in a 2D grid, we directly use state-of-the-art models designed for visual information for the segmentation task and then exploit the range information to ensure 3D accuracy. This allows us to effectively address the main challenges of applying DL techniques to point clouds, i.
View Article and Find Full Text PDFJ Imaging
December 2024
Faculty of Sustainable Design Engineering, University of Prince Edward Island, Charlottetown, PE C1A 4P3, Canada.
This study introduced a novel approach to 3D image segmentation utilizing a neural network framework applied to 2D depth map imagery, with Z axis values visualized through color gradation. This research involved comprehensive data collection from mechanically harvested wild blueberries to populate 3D and red-green-blue (RGB) images of filled totes through time-of-flight and RGB cameras, respectively. Advanced neural network models from the YOLOv8 and Detectron2 frameworks were assessed for their segmentation capabilities.
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